Volume to Price Ratio (Confirmed Strength Index) For personal peeps only
Results are if you only had $250 and did x25 leveraging ( yes that's x4 within a week)
Search in scripts for "volume"
888 BOT #backtest█ 888 BOT #backtest
This is an Expert Advisor 'EA' or Automated trading script for ‘longs’ and ‘shorts’, which uses only a Take Profit or, in the worst case, a Stop Loss to close the trade.
It's a much improved version of the previous ‘Repanocha’. It doesn`t use 'Trailing Stop' or 'security()' functions (although using a security function doesn`t mean that the script repaints) and all signals are confirmed, therefore the script doesn`t repaint in alert mode and is accurate in backtest mode.
Apart from the previous indicators, some more and other functions have been added for Stop-Loss, re-entry and leverage.
It uses 8 indicators, (many of you already know what they are, but in case there is someone new), these are the following:
1. Jurik Moving Average
It's a moving average created by Mark Jurik for professionals which eliminates the 'lag' or delay of the signal. It's better than other moving averages like EMA, DEMA, AMA or T3.
There are two ways to decrease noise using JMA. Increasing the 'LENGTH' parameter will cause JMA to move more slowly and therefore reduce noise at the expense of adding 'lag'
The 'JMA LENGTH', 'PHASE' and 'POWER' parameters offer a way to select the optimal balance between 'lag' and over boost.
Green: Bullish, Red: Bearish.
2. Range filter
Created by Donovan Wall, its function is to filter or eliminate noise and to better determine the price trend in the short term.
First, a uniform average price range 'SAMPLING PERIOD' is calculated for the filter base and multiplied by a specific quantity 'RANGE MULTIPLIER'.
The filter is then calculated by adjusting price movements that do not exceed the specified range.
Finally, the target ranges are plotted to show the prices that will trigger the filter movement.
Green: Bullish, Red: Bearish.
3. Average Directional Index (ADX Classic) and (ADX Masanakamura)
It's an indicator designed by Welles Wilder to measure the strength and direction of the market trend. The price movement is strong when the ADX has a positive slope and is above a certain minimum level 'ADX THRESHOLD' and for a given period 'ADX LENGTH'.
The green color of the bars indicates that the trend is bullish and that the ADX is above the level established by the threshold.
The red color of the bars indicates that the trend is down and that the ADX is above the threshold level.
The orange color of the bars indicates that the price is not strong and will surely lateralize.
You can choose between the classic option and the one created by a certain 'Masanakamura'. The main difference between the two is that in the first it uses RMA () and in the second SMA () in its calculation.
4. Parabolic SAR
This indicator, also created by Welles Wilder, places points that help define a trend. The Parabolic SAR can follow the price above or below, the peculiarity that it offers is that when the price touches the indicator, it jumps to the other side of the price (if the Parabolic SAR was below the price it jumps up and vice versa) to a distance predetermined by the indicator. At this time the indicator continues to follow the price, reducing the distance with each candle until it is finally touched again by the price and the process starts again. This procedure explains the name of the indicator: the Parabolic SAR follows the price generating a characteristic parabolic shape, when the price touches it, stops and turns (SAR is the acronym for 'stop and reverse'), giving rise to a new cycle. When the points are below the price, the trend is up, while the points above the price indicate a downward trend.
5. RSI with Volume
This indicator was created by LazyBear from the popular RSI.
The RSI is an oscillator-type indicator used in technical analysis and also created by Welles Wilder that shows the strength of the price by comparing individual movements up or down in successive closing prices.
LazyBear added a volume parameter that makes it more accurate to the market movement.
A good way to use RSI is by considering the 50 'RSI CENTER LINE' centerline. When the oscillator is above, the trend is bullish and when it is below, the trend is bearish.
6. Moving Average Convergence Divergence (MACD) and (MAC-Z)
It was created by Gerald Appel. Subsequently, the histogram was added to anticipate the crossing of MA. Broadly speaking, we can say that the MACD is an oscillator consisting of two moving averages that rotate around the zero line. The MACD line is the difference between a short moving average 'MACD FAST MA LENGTH' and a long moving average 'MACD SLOW MA LENGTH'. It's an indicator that allows us to have a reference on the trend of the asset on which it is operating, thus generating market entry and exit signals.
We can talk about a bull market when the MACD histogram is above the zero line, along with the signal line, while we are talking about a bear market when the MACD histogram is below the zero line.
There is the option of using the MAC-Z indicator created by LazyBear, which according to its author is more effective, by using the parameter VWAP (volume weighted average price) 'Z-VWAP LENGTH' together with a standard deviation 'STDEV LENGTH' in its calculation.
7. Volume Condition
Volume indicates the number of participants in this war between bulls and bears, the more volume the more likely the price will move in favor of the trend. A low trading volume indicates a lower number of participants and interest in the instrument in question. Low volumes may reveal weakness behind a price movement.
With this condition, those signals whose volume is less than the volume SMA for a period 'SMA VOLUME LENGTH' multiplied by a factor 'VOLUME FACTOR' are filtered. In addition, it determines the leverage used, the more volume, the more participants, the more probability that the price will move in our favor, that is, we can use more leverage. The leverage in this script is determined by how many times the volume is above the SMA line.
The maximum leverage is 8.
8. Bollinger Bands
This indicator was created by John Bollinger and consists of three bands that are drawn superimposed on the price evolution graph.
The central band is a moving average, normally a simple moving average calculated with 20 periods is used. ('BB LENGTH' Number of periods of the moving average)
The upper band is calculated by adding the value of the simple moving average X times the standard deviation of the moving average. ('BB MULTIPLIER' Number of times the standard deviation of the moving average)
The lower band is calculated by subtracting the simple moving average X times the standard deviation of the moving average.
the band between the upper and lower bands contains, statistically, almost 90% of the possible price variations, which means that any movement of the price outside the bands has special relevance.
In practical terms, Bollinger bands behave as if they were an elastic band so that, if the price touches them, it has a high probability of bouncing.
Sometimes, after the entry order is filled, the price is returned to the opposite side. If price touch the Bollinger band in the same previous conditions, another order is filled in the same direction of the position to improve the average entry price, (% MINIMUM BETTER PRICE ': Minimum price for the re-entry to be executed and that is better than the price of the previous position in a given %) in this way we give the trade a chance that the Take Profit is executed before. The downside is that the position is doubled in size. 'ACTIVATE DIVIDE TP': Divide the size of the TP in half. More probability of the trade closing but less profit.
█ STOP LOSS and RISK MANAGEMENT.
A good risk management is what can make your equity go up or be liquidated.
The % risk is the percentage of our capital that we are willing to lose by operation. This is recommended to be between 1-5%.
% Risk: (% Stop Loss x % Equity per trade x Leverage) / 100
First the strategy is calculated with Stop Loss, then the risk per operation is determined and from there, the amount per operation is calculated and not vice versa.
In this script you can use a normal Stop Loss or one according to the ATR. Also activate the option to trigger it earlier if the risk percentage is reached. '% RISK ALLOWED'
'STOP LOSS CONFIRMED': The Stop Loss is only activated if the closing of the previous bar is in the loss limit condition. It's useful to prevent the SL from triggering when they do a ‘pump’ to sweep Stops and then return the price to the previous state.
█ BACKTEST
The objective of the Backtest is to evaluate the effectiveness of our strategy. A good Backtest is determined by some parameters such as:
- RECOVERY FACTOR: It consists of dividing the 'net profit' by the 'drawdown’. An excellent trading system has a recovery factor of 10 or more; that is, it generates 10 times more net profit than drawdown.
- PROFIT FACTOR: The ‘Profit Factor’ is another popular measure of system performance. It's as simple as dividing what win trades earn by what loser trades lose. If the strategy is profitable then by definition the 'Profit Factor' is going to be greater than 1. Strategies that are not profitable produce profit factors less than one. A good system has a profit factor of 2 or more. The good thing about the ‘Profit Factor’ is that it tells us what we are going to earn for each dollar we lose. A profit factor of 2.5 tells us that for every dollar we lose operating we will earn 2.5.
- SHARPE: (Return system - Return without risk) / Deviation of returns.
When the variations of gains and losses are very high, the deviation is very high and that leads to a very poor ‘Sharpe’ ratio. If the operations are very close to the average (little deviation) the result is a fairly high 'Sharpe' ratio. If a strategy has a 'Sharpe' ratio greater than 1 it is a good strategy. If it has a 'Sharpe' ratio greater than 2, it is excellent. If it has a ‘Sharpe’ ratio less than 1 then we don't know if it is good or bad, we have to look at other parameters.
- MATHEMATICAL EXPECTATION: (% winning trades X average profit) + (% losing trades X average loss).
To earn money with a Trading system, it is not necessary to win all the operations, what is really important is the final result of the operation. A Trading system has to have positive mathematical expectation as is the case with this script: ME = (0.87 x 30.74$) - (0.13 x 56.16$) = (26.74 - 7.30) = 19.44$ > 0
The game of roulette, for example, has negative mathematical expectation for the player, it can have positive winning streaks, but in the long term, if you continue playing you will end up losing, and casinos know this very well.
PARAMETERS
'BACKTEST DAYS': Number of days back of historical data for the calculation of the Backtest.
'ENTRY TYPE': For '% EQUITY' if you have $ 10,000 of capital and select 7.5%, for example, your entry would be $ 750 without leverage. If you select CONTRACTS for the 'BTCUSDT' pair, for example, it would be the amount in 'Bitcoins' and if you select 'CASH' it would be the amount in $ dollars.
'QUANTITY (LEVERAGE 1X)': The amount for an entry with X1 leverage according to the previous section.
'MAXIMUM LEVERAGE': It's the maximum allowed multiplier of the quantity entered in the previous section according to the volume condition.
The settings are for Bitcoin at Binance Futures (BTC: USDTPERP) in 30 minutes.
For other pairs and other timeframes, the settings have to be adjusted again. And within a month, the settings will be different because we all know the market and the trend are changing.
█ 888 BOT (SPANISH)
Este es un Expert Advisor 'EA' o script de trading automatizado para ‘longs’ y ‘shorts’, el cual, utiliza solo un Take Profit o, en el peor de los casos, un Stop Loss para cerrar el trade.
Es una versión muy mejorada del anterior ‘Repanocha’. No utiliza ‘Trailing Stop’, ni funciones ‘security()’ (aunque usar una función security no significa que el script repinte) y todas las señales son confirmadas, por consiguiente, el script no repinta en modo alertas y es preciso en en el modo backtest.
Aparte de los anteriores indicadores se han añadido algunos más y otras funciones para Stop-Loss, de re-entrada y apalancamiento.
Utiliza 8 indicadores, (muchos ya sabéis sobradamente lo que son, pero por si hay alguien nuevo), son los siguientes:
1. Jurik Moving Average
Es una media móvil creada por Mark Jurik para profesionales la cual elimina el ‘lag’ o retardo de la señal. Es mejor que otras medias móviles como la EMA, DEMA, AMA o T3.
Hay dos formas de disminuir el ruido utilizando JMA. El aumento del parámetro 'LENGTH' hará que JMA se mueva más lentamente y, por lo tanto, reducirá el ruido a expensas de añadir ‘lag’
Los parámetros 'JMA LENGTH', 'PHASE' y 'POWER' ofrecen una forma de seleccionar el equilibrio óptimo entre ‘lag’ y sobre impulso.
Verde : Alcista, Rojo: Bajista.
2. Range filter
Creado por Donovan Wall, su función es la de filtrar o eliminar el ruido y poder determinar mejor la tendencia del precio a corto plazo.
Primero, se calcula un rango de precio promedio uniforme 'SAMPLING PERIOD' para la base del filtro y se multiplica por una cantidad específica 'RANGE MULTIPLIER'.
A continuación, el filtro se calcula ajustando los movimientos de precios que no exceden el rango especificado.
Por último, los rangos objetivo se trazan para mostrar los precios que activarán el movimiento del filtro.
Verde : Alcista, Rojo: Bajista.
3. Average Directional Index (ADX Classic) y (ADX Masanakamura)
Es un indicador diseñado por Welles Wilder para medir la fuerza y dirección de la tendencia del mercado. El movimiento del precio tiene fuerza cuando el ADX tiene pendiente positiva y está por encima de cierto nivel mínimo 'ADX THRESHOLD' y para un periodo dado 'ADX LENGTH'.
El color verde de las barras indica que la tendencia es alcista y que el ADX está por encima del nivel establecido por el threshold.
El color Rojo de las barras indica que la tendencia es bajista y que el ADX está por encima del nivel de threshold.
El color naranja de las barras indica que el precio no tiene fuerza y seguramente lateralizará.
Se puede elegir entre la opción clásica y la creada por un tal 'Masanakamura'. La diferencia principal entre los dos es que en el primero utiliza RMA() y en el segundo SMA() en su cálculo.
4. Parabolic SAR
Este indicador, creado también por Welles Wilder, coloca puntos que ayudan a definir una tendencia. El Parabolic SAR puede seguir al precio por encima o por debajo, la particularidad que ofrece es que cuando el precio toca al indicador, este salta al otro lado del precio (si el Parabolic SAR estaba por debajo del precio salta arriba y viceversa) a una distancia predeterminada por el indicador. En este momento el indicador vuelve a seguir al precio, reduciendo la distancia con cada vela hasta que finalmente es tocado otra vez por el precio y se vuelve a iniciar el proceso. Este procedimiento explica el nombre del indicador: el Parabolic SAR va siguiendo al precio generando una característica forma parabólica, cuando el precio lo toca, se para y da la vuelta (SAR son las siglas en inglés de ‘stop and reverse’), dando lugar a un nuevo ciclo. Cuando los puntos están por debajo del precio, la tendencia es alcista, mientras que los puntos por encima del precio indica una tendencia bajista.
5. RSI with Volume
Este indicador lo creo un tal LazyBear de TV a partir del popular RSI.
El RSI es un indicador tipo oscilador utilizado en análisis técnico y creado también por Welles Wilder que muestra la fuerza del precio mediante la comparación de los movimientos individuales al alza o a la baja de los sucesivos precios de cierre.
LazyBear le añadió un parámetro de volumen que lo hace más preciso al movimiento del mercado.
Una buena forma de usar el RSI es teniendo en cuenta la línea central de 50 'RSI CENTER LINE'. Cuando el oscilador está por encima, la tendencia es alcista y cuando está por debajo la tendencia es bajista.
6. Moving Average Convergence Divergence (MACD) y (MAC-Z)
Fue creado por Gerald Appel. Posteriormente se añadió el histograma para anticipar el cruce de medias. A grandes rasgos podemos decir que el MACD es un oscilador consistente en dos medias móviles que van girando en torno a la línea de cero. La línea del MACD no es más que la diferencia entre una media móvil corta 'MACD FAST MA LENGTH' y una media móvil larga 'MACD SLOW MA LENGTH'. Es un indicador que nos permite tener una referencia sobre la tendencia del activo sobre el cual se está operando, generando de este modo señales de entrada y salida del mercado.
Podemos hablar de mercado alcista cuando el histograma del MACD se sitúe por encima de la línea cero, junto con la línea de señal, mientras que hablaremos de mercado bajista cuando el histograma MACD se situará por debajo de la línea cero.
Está la opción de utilizar el indicador MAC-Z creado por LazyBear que según su autor es más eficaz, por utilizar el parámetro VWAP (precio medio ponderado por volumen) 'Z-VWAP LENGTH' junto con una desviación standard 'STDEV LENGTH' en su cálculo.
7. Volume Condition
El volumen indica el número de participantes en esta guerra entre toros y osos, cuanto más volumen más probabilidad de que se mueva el precio a favor de la tendencia. Un volumen bajo de negociación indica un menor número de participantes e interés por el instrumento en cuestión. Los bajos volúmenes pueden revelar debilidad detrás de un movimiento de precios.
Con esta condición se filtran aquellas señales cuyo volumen es inferior a la SMA de volumen para un periodo 'SMA VOLUME LENGTH' multiplicado por un factor 'VOLUME FACTOR'. Además, determina el apalancamiento utilizado, a más volumen, más participantes, más probabilidad de que se mueva el precio a nuestro favor, es decir, podemos utilizar más apalancamiento. El apalancamiento en este script lo determina las veces que está el volumen por encima de la línea de la SMA.
El apalancamiento máximo es de 8.
8. Bollinger Bands
Este indicador fue creado por John Bollinger y consiste en tres bandas que se dibujan superpuestas al gráfico de evolución del precio.
La banda central es una media móvil, normalmente se emplea una media móvil simple calculada con 20 períodos. ('BB LENGTH' Número de periodos de la media móvil)
La banda superior se calcula sumando al valor de la media móvil simple X veces la desviación típica de la media móvil. ('BB MULTIPLIER' Número de veces la desviación típica de la media móvil)
La banda inferior de calcula restando a la media móvil simple X veces la desviación típica de la media móvil.
la franja comprendida entre las bandas superior e inferior contiene, estadísticamente, casi un 90% de las posibles variaciones del precio, lo que significa que cualquier movimiento del precio fuera de las bandas tiene especial relevancia.
En términos prácticos, las bandas de Bollinger se comporta como si de una banda elástica se tratara de manera que, si el precio las toca, éste tiene mucha probabilidad de rebotar.
En ocasiones, después de rellenarse la orden de entrada, el precio se devuelve hacia el lado contrario. Si toca la banda de Bollinger se rellena otra orden en la misma dirección de la posición para mejorar el precio medio de entrada, (% MINIMUM BETTER PRICE': Precio mínimo para que se ejecute la re-entrada y que sea mejor que el precio de la posición anterior en un % dado) de esta manera damos una oportunidad al trade de que el Take Profit se ejecute antes. La desventaja es que se dobla el tamaño de la posición. 'ACTIVATE DIVIDE TP': Divide el tamaño del TP a la mitad. Más probabilidad de que se cierre el trade pero menos ganancias.
█ STOP LOSS y RISK MANAGEMENT.
Una buena gestión de las pérdidas o gestión del riesgo es lo que puede hacer que tu cuenta suba o se liquide en poco tiempo.
El % de riesgo es el porcentaje de nuestro capital que estamos dispuestos a perder por operación. Este se aconseja que debe estar comprendido entre un 1-5%.
% Risk = (% Stop Loss x % Equity per trade x Leverage) / 100
Primero se calcula la estrategia con Stop Loss, después se determina el riesgo por operación y a partir de ahí se calcula el monto por operación y no al revés.
En este script puedes usar un Stop Loss normal o uno según el ATR. También activar la opción de que salte antes si se alcanza el porcentaje de riesgo. '% RISK ALLOWED'
'STOP LOSS CONFIRMED': Solamente se activa el Stop Loss si el cierre de la barra anterior se encuentra en la condición de límite de pérdidas. Es útil para evitar que se dispare el SL cuando hacen un ‘pump’ para barrer Stops y luego se devuelve el precio a la normalidad.
█ BACKTEST
El objetivo del Backtest es evaluar la eficacia de nuestra estrategia. Un buen Backtest lo determinan algunos parámetros como son:
- RECOVERY FACTOR: Consiste en dividir el ‘beneficio neto’ entre el ‘drawdown’. Un excelente sistema de trading tiene un recovery factor de 10 o más; es decir, genera 10 veces más beneficio neto que drawdown.
- PROFIT FACTOR: El ‘Profit Factor’ es otra medida popular del rendimiento de un sistema. Es algo tan simple como dividir lo que ganan las operaciones con ganancias entre lo que pierden las operaciones con pérdidas. Si la estrategia es rentable entonces por definición el ‘Profit Factor’ va a ser mayor que 1. Las estrategias que no son rentables producen factores de beneficio menores que uno. Un buen sistema tiene un profit factor de 2 o más. Lo bueno del ‘Profit Factor’ es que nos dice lo que vamos a ganar por cada dolar que perdemos. Un profit factor de 2.5 nos dice que por cada dolar que perdamos operando vamos a ganar 2.5.
- SHARPE: (Retorno sistema – Retorno sin riesgo) / Desviación de los retornos.
Cuando las variaciones de ganancias y pérdidas son muy altas, la desviación es muy elevada y eso conlleva un ratio de ‘Sharpe’ muy pobre. Si las operaciones están muy cerca de la media (poca desviación) el resultado es un ratio de ‘Sharpe’ bastante elevado. Si una estrategia tiene un ratio de ‘Sharpe’ mayor que 1 es una buena estrategia. Si tiene un ratio de ‘Sharpe’ mayor que 2, es excelente. Si tiene un ratio de ‘Sharpe’ menor que 1 entonces no sabemos si es buena o mala, hay que mirar otros parámetros.
- MATHEMATICAL EXPECTATION:(% operaciones ganadoras X ganancia media) + (% operaciones perdedoras X pérdida media).
Para ganar dinero con un sistema de Trading, no es necesario ganar todas las operaciones, lo verdaderamente importante es el resultado final de la operativa. Un sistema de Trading tiene que tener esperanza matemática positiva como es el caso de este script.
El juego de la ruleta, por ejemplo, tiene esperanza matemática negativa para el jugador, puede tener rachas positivas de ganancias, pero a la larga, si se sigue jugando se acabará perdiendo, y esto los casinos lo saben muy bien.
PARAMETROS
'BACKTEST DAYS': Número de días atrás de datos históricos para el calculo del Backtest.
'ENTRY TYPE': Para % EQUITY si tienes 10000$ de capital y seleccionas 7.5% tu entrada sería de 750$ sin apalancamiento. Si seleccionas CONTRACTS para el par BTCUSDT sería la cantidad en Bitcoins y si seleccionas CASH sería la cantidad en dólares.
'QUANTITY (LEVERAGE 1X)': La cantidad para una entrada con apalancamiento X! según el apartado anterior.
'MAXIMUM LEVERAGE': Es el máximo multiplicador permitido de la cantidad introducida en el apartado anterior según la condición de volumen.
Los settings son para Bitcoin en Binance Futures (BTC:USDTPERP) en 30 minutos.
Para otro pares y otras temporalidades se tienen que ajustar las opciones de nuevo. Además para dentro de un mes, los ajustes serán otros distintos ya que el mercado y la tendencia es cambiante.
Autonomous 5-Minute RobotKey Components of the Strategy:
Trend Detection:
A 50-period simple moving average (SMA) is used to define the market trend. If the current close is above the SMA, the market is considered to be in an uptrend (bullish), and if it's below, it's considered a downtrend (bearish).
The strategy also looks at the trend over the last 30 minutes (6 candles in a 5-minute chart). The strategy compares the previous close with the current close to detect an uptrend or downtrend.
Volume Analysis:
The strategy calculates buyVolume and sellVolume based on price movement within each candle.
The condition for entering a long position is when the market is in an uptrend, and the buy volume is greater than the sell volume.
The condition for entering a short position is when the market is in a downtrend, and the sell volume is greater than the buy volume.
Trade Execution:
The strategy enters a long position when the trend is up and the buy volume is higher than the sell volume.
The strategy enters a short position when the trend is down and the sell volume is higher than the buy volume.
Positions are closed based on stop-loss and take-profit conditions.
Stop-loss is set at 3% below the entry price.
Take-profit is set at 29% above the entry price.
Exit Conditions:
Long trades will be closed if the price falls 3% below the entry price or rises 29% above the entry price.
Short trades will be closed if the price rises 3% above the entry price or falls 29% below the entry price.
Visuals:
The SMA (50-period) is plotted on the chart to show the trend.
Buy and sell signals are marked with labels on the chart for easy identification.
With this being said this algo is still being worked on to be autonomous
Analyze the Market Direction: Determine whether the market is in an uptrend or downtrend over the past 30 minutes (using the last 6 candles in a 5-minute chart).
Use Trend Indicators and Volume: Implement trend-following indicators like moving averages or the SMA/EMA crossover and consider volume to decide when to enter or exit a trade.
Enter and Exit Trades: The robot will enter long positions when the trend is up and short positions when the trend is down. Additionally, it will close positions based on volume signals and price action (e.g., volume spikes, price reversals).
GKD-BT Full Giga Kaleidoscope Backtest [Loxx]Giga Kaleidoscope GKD-BT Full Giga Kaleidoscope Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Full Giga Kaleidoscope Backtest
The Full Giga Kaleidoscope Backtest module enables users to backtest Full GKD Long and Short signals, allowing the creation of a comprehensive NNFX trading system consisting of two confirmation indicators, a baseline, a measure of volatility/volume, and continuations.
This module offers two types of backtests: Trading and Full. The Trading backtest allows users to evaluate individual Long and Short trades one by one. On the other hand, the Full backtest enables the analysis of Longs or Shorts separately by toggling between them in the settings, providing insights into the results for each signal type. The Trading backtest simulates actual trading conditions, while the Full backtest evaluates all signals regardless of their Long or Short nature.
Additionally, the backtest module allows testing with 1 to 3 take profits and 1 stop loss. The Trading backtest supports 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also includes a trailing take profit feature.
Regarding the percentage of trade removed at each take profit, the backtest module incorporates the following predefined values:
Take profit 1: 50% of the trade is removed.
Take profit 2: 25% of the trade is removed.
Take profit 3: 25% of the trade is removed.
Stop loss: 100% of the trade is removed.
After achieving each take profit, the stop loss level is adjusted accordingly. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into effect after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest module also provides the option to restrict testing to a specific date range, allowing for simulated forward testing using past data. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. Historical take profit and stop loss levels are displayed as overlaid horizontal lines on the chart for reference.
To utilize this strategy, follow these steps:
1. GKD-B Baseline Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-B Baseline."
2. GKD-V Volatility/Volume Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-V Volatility/Volume."
3. Adjust the "Confirmation 1 Type" in the GKD-C Confirmation Indicator to "GKD New."
4. GKD-C Confirmation 1 Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-C Confirmation 1."
5. Adjust the "Confirmation 2 Type" in the GKD-C Confirmation 2 Indicator to "GKD New."
6. GKD-C Confirmation 2 Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 2 module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-C Confirmation 2."
7. Adjust the "Confirmation Type" in the GKD-C Continuation Indicator to "GKD New."
8. GKD-C Continuation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-C Confirmation."
The GKD system utilizes volatility-based take profits and stop losses, where each take profit and stop loss is calculated as a multiple of volatility. Users have the flexibility to adjust the multiplier values in the settings to suit their preferences.
In a future update, the Full Giga Kaleidoscope Backtest module will include the option to incorporate a GKD-E Exit indicator, completing the full trading strategy.
█ Full Giga Kaleidoscope Backtest Entries
Within this module, there are ten distinct types of entries available, which are outlined below:
Standard Entry
1-Candle Standard Entry
Baseline Entry
1-Candle Baseline Entry
Volatility/Volume Entry
1-Candle Volatility/Volume Entry
Confirmation 2 Entry
1-Candle Confirmation 2 Entry
PullBack Entry
Continuation Entry
Each of these entry types can generate either long or short signals, resulting in a total of 20 signal variations. The user has the flexibility to enable or disable specific entry types and choose which qualifying rules within each entry type are applied to price to determine the final long or short signal.
The following section provides an overview of the various entry types and their corresponding qualifying rules:
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Volatility Types Included
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Full Giga Kaleidoscope Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Vorext as shown on the chart above
Confirmation 2: Coppock Curve as shown on the chart above
Continuation: Fisher Transform as shown on the chart above
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
GKD-BT Solo Confirmation Super Complex Backtest [Loxx]Giga Kaleidoscope GKD-BT Solo Confirmation Super Complex Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Solo Confirmation Super Complex Backtest
The Solo Confirmation Super Complex Backtest module allows users to perform backtesting on Full GKD Long and Short signals using GKD-C confirmation indicators. These signals are further refined by GKD-B Baseline and GKD-V Volatility/Volume indicators and augmented by an additional GKD-C Confirmation indicator acting as a Continuation indicator. This module serves as a comprehensive tool that falls just below a Full GKD trading system. The key difference is that the GKD-BT Solo Confirmation Super Complex utilizes a single GKD-C Confirmation indicator, while the Full GKD system employs two GKD-C Confirmation indicators. Both the Solo Confirmation Super Complex and the Full GKD systems incorporate an extra GKD-C Confirmation indicator to identify Continuation signals, which provide both longs and shorts on developing trends following an initial trend change.
This module encompasses two types of backtests: Trading and Full. The Trading backtest permits users to evaluate individual trades, whether Long or Short, one at a time. Conversely, the Full backtest allows users to analyze either Longs or Shorts separately by toggling between them in the settings, enabling the examination of results for each signal type. The Trading backtest emulates actual trading conditions, while the Full backtest assesses all signals, regardless of being Long or Short.
Additionally, this backtest module provides the option to test the core GKD-C Confirmation and GKD-C Continuation indicators with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed.
Take profit 2: 25% of the trade is removed.
Take profit 3: 25% of the trade is removed.
Stop loss: 100% of the trade is removed.
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest module also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
To utilize this strategy, follow these steps:
1. GKD-B Baseline Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-B Baseline."
2. GKD-V Volatility/Volume Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-V Volatility/Volume."
3. Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."
4. GKD-C Confirmation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-C Confirmation."
5. Adjust the "Confirmation Type" in the GKD-C Continuation Indicator to "GKD New."
6. GKD-C Continuation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-C Continuation."
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
In a future update, the option to include a GKD-E Exit indicator will be added to this module to complete a full trading strategy.
█ Solo Confirmation Super Complex Backtest Entries
Within this module, there are eight distinct types of entries available, which are outlined below:
Standard Entry
1-Candle Standard Entry
Baseline Entry
1-Candle Baseline Entry
Volatility/Volume Entry
1-Candle Volatility/Volume Entry
PullBack Entry
Continuation Entry
Each of these entry types can generate either long or short signals, resulting in a total of 16 signal variations. The user has the flexibility to enable or disable specific entry types and choose which qualifying rules within each entry type are applied to price to determine the final long or short signal. You'll notice that these signals are different form the core GKD signals mentioned towards the end of this description. Signals from the GKD-BT Solo Confirmation Super Complex Backtest are modifided to add additional qualifications to make your finalized trading strategy more dynamic and robust.
The following section provides an overview of the various entry types and their corresponding qualifying rules:
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle:
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Basline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle:
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Baseline agrees
6. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle:
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
█ Volatility Types Included
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Solo Confirmation Complex Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Vortex as shown on the chart above
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
Strategy Rsi-Long Short by SebasVentuIt is the popular RSI indicator with VWAP as the source instead of the close.
What is Volume Weighted Average Price (VWAP)?
The VWAP is calculated by adding the dollars traded for each trade (price multiplied by the number of shares traded) and then dividing it by the total shares traded. That is, volume.
Es el popular indicador RSI con VWAP como fuente en lugar de cierre.
¿Qué es el Precio Promedio Ponderado por Volumen (VWAP)?
El VWAP se calcula sumando los dólares negociados por cada transacción (precio multiplicado por el número de acciones negociadas) y luego dividiéndolo por el total de acciones negociadas. Es decir, volumen.
GKD-BT Baseline Backtest [Loxx]The Giga Kaleidoscope GKD-BT Baseline Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ GKD-BT Baseline Backtest
The GKD-BT Baseline Backtest allows traders to backtest the Regular and Stepped baselines used in the GKD trading system. This module includes 65+ moving averages and 15+ types of volatility to choose from.
Additionally, this backtest module provides the option to test the GKD-B indicator with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed
Take profit 2: 25% of the trade is removed
Take profit 3: 25% of the trade is removed
Stop loss: 100% of the trade is removed
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
This backtest also includes an optional GKD-E Exit indicator that can be used to test early exits.
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
To utilize this strategy, follow these steps:
1. (Required) Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline indicator into the GKD-BT Baseline Backtest field "Import GKD-B Baseline"
2. (Optional) Import the value "Input into NEW GKD-BT Backtest" from the GKD-E Exit indicator into the GKD-BT Baseline Backtest field "Import GKD-E Exit". You can toggle the Exit on or off using the "Activate GKD-E Exit" option.
Baselines that are compatible with this backtest module:
GKD-B Baseline
GKD-B Stepped Baseline
Volatility Types Included
17 types of volatility are included in this indicator
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: GKD-BT Baseline Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent
Confirmation 1: Sherif's HiLo
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Fisher Transform as shown on the chart above
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
GKD-BT Giga Stacks Backtest [Loxx]Giga Kaleidoscope GKD-BT Giga Stacks Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Giga Stacks Backtest
The Giga Stacks Backtest module allows users to perform backtesting on Long and Short signals from the confluence of GKD-B Baseline, GKD-C Confirmation, and GKD-V Volatility/Volume indicators. This module encompasses two types of backtests: Trading and Full. The Trading backtest permits users to evaluate individual trades, whether Long or Short, one at a time. Conversely, the Full backtest allows users to analyze either Longs or Shorts separately by toggling between them in the settings, enabling the examination of results for each signal type. The Trading backtest emulates actual trading conditions, while the Full backtest assesses all signals, regardless of being Long or Short.
Additionally, this backtest module provides the option to test using indicators with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed.
Take profit 2: 25% of the trade is removed.
Take profit 3: 25% of the trade is removed.
Stop loss: 100% of the trade is removed.
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest module also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
To utilize this strategy, follow these steps (where "Stack XX" denotes the number of the Stack):
GKD-B Baseline Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline module into the GKD-BT Giga Stacks Backtest module setting named "Stack XX: Import GKD-C, GKD-B, or GKD-V."
GKD-V Volatility/Volume Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume module into the GKD-BT Giga Stacks Backtest module setting named "Stack XX: Import GKD-C, GKD-B, or GKD-V."
GKD-C Confirmation Import: 1) Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."; 2) Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation module into the GKD-BT Giga Stacks Backtest module setting named "Stack XX: Import GKD-C, GKD-B, or GKD."
█ Giga Stacks Backtest Entries
Entries are generated form the confluence of up to six GKD-B Baseline, GKD-C Confirmation, and GKD-V Volatility/Volume indicators. Signals are generated when all Stacks reach uptrend or downtrend together.
Here's how this works. Assume we have the following Stacks and their respective trend on the current candle:
Stack 1 indicator is in uptreend
Stack 2 indicator is in downtrend
Stack 3 indicator is in uptreend
Stack 4 indicator is in uptreend
All stacks are in uptrend except for Stack 2. If Stack 2 reaches uptrend while Stacks 1, 3, and 4 stay in uptrend, then a long signal is generated. The last Stack to align with all other Stacks will generate a long or short signal.
█ Volatility Types Included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Stacks Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Vorext
Confirmation 2: Coppock Curve
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
GKD-BT Solo Confirmation Complex Backtest [Loxx]Giga Kaleidoscope GKD-BT Solo Confirmation Complex Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Solo Confirmation Complex Backtest
The Solo Confirmation Complex Backtest module enables users to perform backtesting on Standard Long and Short signals from GKD-C confirmation indicators, filtered by GKD-B Baseline and GKD-V Volatility/Volume indicators. This module represents a complex form of the Solo Confirmation Backtest in the GKD trading system. It includes two types of backtests: Trading and Full. The Trading backtest allows users to test individual trades, both Long and Short, one at a time. On the other hand, the Full backtest allows users to test either Longs or Shorts by toggling between them in the settings to view the results for each signal type. The Trading backtest simulates real trading, while the Full backtest tests all signals, whether Long or Short.
Additionally, this backtest module provides the option to test the GKD-C Confirmation indicator with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed.
Take profit 2: 25% of the trade is removed.
Take profit 3: 25% of the trade is removed.
Stop loss: 100% of the trade is removed.
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest module also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
To utilize this strategy, follow these steps:
1. GKD-B Baseline Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline module into the GKD-BT Solo Confirmation Complex Backtest module setting named "Import GKD-B Baseline indicator."
Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."
2. GKD-C Confirmation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation module into the GKD-BT Solo Confirmation Complex Backtest module setting named "Import GKD-C Confirmation indicator."
3. GKD-V Volatility/Volume Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume module into the GKD-BT Solo Confirmation Complex Backtest module setting named "Import GKD-V Volatility/Volume indicator."
4. The Solo Confirmation Complex Backtest module exclusively supports Standard Entries, both Long and Short. However, please note that this module uses a modified version of the Standard Entry. In this modified version, long and short signals are directly imported from the Confirmation indicator, and then baseline and volatility filtering is applied.
The GKD-B Baseline filter ensures that only trades aligning with the GKD-B Baseline's current trend are accepted. This filter takes into consideration the Goldie Locks Zone, which allows trades where the closing price of the last candle has moved within a minimum XX volatility and a maximum YY volatility range. The GKD-V Volatility/Volume filter allows only trades that meet a minimum threshold of ZZ GKD-V Volatility/Volume, which varies based on the specific GKD-V Volatility/Volume indicator used.
The Solo Confirmation Complex Backtest execution engine determines whether signals from the GKD-C Confirmation indicator are accepted or rejected based on two criteria:
1. The GKD-C Confirmation signal must be qualified by the direction of the GKD-B Baseline trend and the GKD-B Baseline's sweet-spot Goldie Locks Zone.
2. Sufficient Volatility/Volume, as indicated by the GKD-V Volatility/Volume indicator, must be present to execute a trade.
The purpose of the Solo Confirmation Complex Backtest is to test a GKD-C Confirmation indicator in the presence of macro trend and volatility/volume filtering.
Volatility Types Included
17 types of volatility are included in this indicator
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Solo Confirmation Complex Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Volatility-Adaptive Rapid RSI T3
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
GKD-BT Solo Confirmation Simple Backtest [Loxx]Giga Kaleidoscope GKD-BT Solo Confirmation Simple Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Solo Confirmation Simple Backtest
The Solo Confirmation Simple Backtest module enables users to perform Standard Long and Short signals on GKD-C confirmation indicators. This module represents the simplest form of Backtest in the GKD trading system. It includes two types of backtests: Trading and Full. The Trading backtest allows users to test individual trades, both long and short, one at a time. On the other hand, the Full backtest allows users to test either longs or shorts by toggling between them in the settings to view the results for each signal type. The Trading backtest simulates real trading, while the Full backtest tests all signals, whether long or short.
Additionally, this backtest module provides the option to test the GKD-C indicator with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed
Take profit 2: 25% of the trade is removed
Take profit 3: 25% of the trade is removed
Stop loss: 100% of the trade is removed
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
To utilize this strategy, follow these steps:
1. Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."
2. Import the value "Input into NEW GKD-BT Backtest" into the GKD-BT Solo Confirmation Simple Backtest module (this strategy backtest).
**The GKD-BT Solo Confirmation Simple Backtest module exclusively supports Standard Entries, both Long and Short. However, please note that this module uses a modified version of the standard entry, where long and short signals are directly imported from the Confirmation indicator without any baseline or volatility filtering applied.**
Volatility Types Included
17 types of volatility are included in this indicator
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Solo Confirmation Simple Backtest as shown on the chart above
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Volatility-Adaptive Rapid RSI T3
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
ORB Fusion Adaptive🎯 ORB Fusion Adaptive Strategy
Professional Opening Range Breakout Trading System
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The first fully-automated, multi-instrument ORB strategy on TradingView.
💡 WHAT IT DOES:
Automatically trades Opening Range breakouts and failed breakout reversals with intelligent position sizing, professional risk management, and complete trade lifecycle tracking. Built for serious backtesting and live trading.
⚡ KEY FEATURES:
✓ Automated Entry & Exit (breakouts + reversals)
✓ Adaptive Position Sizing (3 modes: Fixed, Risk-Based, Risk-Based Initial)
✓ Multi-Instrument Support (Futures, Forex, Crypto, Stocks)
✓ Advanced Risk Management (daily limits, drawdown protection)
✓ ML-Powered Breakout Filtering (pContinue/pFail scoring)
✓ Failed Breakout Detection (high-probability reversals)
✓ Native Trailing Stops (Pine Script v5)
✓ Multi-Target Management (T1, T2, T3)
✓ Real-Time Performance Dashboard
✓ Comprehensive Backtesting (5+ years tested)
🎯 STRATEGY LOGIC:
Entry Signals
The strategy enters trades on two high-probability setups:
1. ORB Breakouts
• Detects when price breaks above/below Opening Range
• Confirms with volume (configurable threshold)
• ML filter scores probability of continuation
• Enters within 3 bars of breakout (no late entries)
• Stop placed at ORB midpoint or ATR-based
• Targets at Fibonacci extensions (1.0x, 1.618x, 2.5x ORB range)
2. Failed Breakout Reversals
• Monitors for breakouts that fail and reverse
• Confirms failure after price returns inside ORB
• Automatic reversal entry with tight stops
• Three profit targets (0.5x, 1.0x, 1.5x ORB range)
• Historical 65-75% win rate on reversals
Exit Management
• Stop Loss: Multiple methods (ATR, ORB Mid, ORB Opposite, Hybrid)
• Native Trailing Stop: Activates after configurable R-multiple profit
• Profit Targets: Single target or scaled exits at T1/T2/T3
• Daily Stop: Halts trading after max daily loss
• Drawdown Protection: Circuit breaker at max drawdown threshold
🔧 ADAPTIVE POSITION SIZING:
The strategy's most powerful feature - intelligent position sizing that adapts to your instrument and account:
Mode 1: Fixed
• Simple contract/lot size
• Best for: Testing, stable position sizing
• Use case: "Always trade 2 contracts"
Mode 2: Risk-Based (Current Equity)
• Size = (Current Equity × Risk%) / (Stop Distance × Point Value)
• Compounds with wins, reduces with losses
• Best for: Aggressive compounding
• Use case: Maximize geometric growth
Mode 3: Risk-Based (Initial Capital)
• Size = (Initial Capital × Risk%) / (Stop Distance × Point Value)
• No compounding - consistent risk exposure
• Best for: Professional risk management
• Use case: Live trading with stable risk
Multi-Instrument Intelligence
Auto-detects and calculates correctly for:
• Futures: Uses contract point value (ES $50/pt, NQ $20/pt, MES $5/pt, MNQ $2/pt)
• Forex: Uses pip value per lot (Standard/Mini/Micro/Nano)
• Crypto: Uses contract multiplier (1.0 BTC, 0.001 BTC, etc)
• Stocks: Uses $1 per share
Manual override available if auto-detect fails.
📊 RISK MANAGEMENT:
Multi-Layer Protection
• Per-Trade Risk: 0.5-2% of capital (configurable)
• Daily Risk Limit: Max 4% loss per day (halts trading)
• Max Drawdown Pause: Circuit breaker at 12% drawdown
• Position Size Limits: Min/max contracts enforced
• Max Stop Distance: ATR-based hard limit (prevents catastrophic losses)
Trailing Stop System
• Uses Pine Script's native trail_price and trail_offset
• Activates after configurable R-multiple profit (default 0.5R)
• Trail distance: ATR-based (tight 0.5 ATR to loose 2.0 ATR)
• Works reliably in backtesting AND live trading
• No manual stop management needed
🌍 GLOBAL MARKET SUPPORT:
Configurable Sessions
Unlike basic ORB indicators, this strategy works globally:
• US Equities: 9:30 AM ET default
• Forex London: Custom session 08:00-08:30 GMT
• Forex NY: Custom session 13:30-14:00 GMT
• Asian Markets: Custom session with Tokyo timezone
• Crypto: 24/7 support with custom ORB windows
Timezone support includes:
America/New_York, Europe/London, Asia/Tokyo, Asia/Hong_Kong, UTC, and more.
Session ORBs
• Asian Session ORB (Tokyo open)
• London Session ORB (Forex primary)
• NY Session ORB (US market hours)
Critical for Forex traders working multiple sessions.
📈 BACKTESTING CAPABILITIES:
Realistic Simulation
• Commission: Configurable (default $1 per order)
• Slippage: 2 ticks default (adjustable)
• Volume Confirmation: Prevents unrealistic fills
• RTH Filtering: Optional Regular Trading Hours only
• Bar Magnifier: Improved intrabar execution
Performance Metrics Dashboard
Real-time statistics displayed:
• Total Trades & Win Rate
• Net P&L & Profit Factor
• Current Drawdown
• Daily P&L tracking
• Position details (if in trade)
• Position sizing mode & current size
Historical Testing
• Supports 5000+ bars of history
• Test across multiple market conditions
• Bull markets, bear markets, range-bound periods
• Optimize by day type (trend vs rotation)
🎛️ CUSTOMIZATION OPTIONS:
ORB Settings
• Timeframe: 5, 15, 30, or 60 minutes
• Confirmation: Close, Wick, or Body
• Volume: On/off with multiplier threshold
• LTF Precision: Sub-minute high/low detection
• RTH Filter: Regular Trading Hours only option
Breakout Detection
• ML Filtering: Enable/disable with thresholds
• Failed Breakout: Sensitivity (2-10 bars)
• Failure Buffer: ATR-based confirmation
• Entry Window: Bars after signal (prevents late entries)
Stop Methods
• ATR: Tight dynamic stops (recommended)
• ORB Mid: Structural stop at midpoint
• ORB Opposite: Wide stop at opposite boundary
• Hybrid: Best of ATR and structural
Target Methods
• Single: One target, full exit
• Scaled: Partial exits at T1/T2/T3 (recommended)
• Trail Only: No fixed targets, trail to exit
🔬 OPTIMIZATION GUIDE:
For Futures (ES, NQ, MNQ, MES)
• ORB: 30 min
• Confirmation: Close
• Volume: ON (1.5x)
• Stop Method: ATR (1.0x multiplier)
• Position Mode: Risk-Based (Initial)
• Risk Per Trade: 1.5%
• Failed Breakouts: ENABLE
For Forex Majors
• ORB: 60 min (or 15 min at London open)
• Confirmation: Close
• Volume: OFF (tick volume unreliable)
• Stop Method: ATR (1.5x multiplier)
• Position Mode: Risk-Based (Initial)
• Risk Per Trade: 1.0%
• Custom Session: 0800-0900 GMT
• Timezone: Europe/London
For Crypto (BTC, ETH)
• ORB: 60 min
• Confirmation: Close
• Volume: OFF or ON (1.2x)
• Stop Method: ATR (2.0x wider stops)
• Position Mode: Fixed or Risk-Based
• Risk Per Trade: 2.0% (higher volatility)
• Custom Session: Define your preferred window
For Stocks/ETF
• ORB: 15-30 min
• Confirmation: Body (most conservative)
• Volume: ON (2.0x threshold)
• Stop Method: Hybrid
• Position Mode: Risk-Based (Initial)
• Risk Per Trade: 1.0%
• RTH Only: ENABLED
• Gap Analysis: ENABLED
💎 ADVANCED FEATURES:
Initial Balance Analysis
• First hour range tracking (A + B periods)
• IB extensions at 0.5x, 1.0x, 1.5x, 2.0x
• Day type classification (Trend/Normal/Rotation)
• Adjusts strategy behavior by day type
ORB Extensions
• Fibonacci targets: 1.272x, 1.5x, 1.618x, 2.0x, 2.618x, 3.0x
• Dynamic monitoring for take-profit zones
• Extension tracking in statistics
VWAP Integration
• Institutional benchmark reference
• Standard deviation bands (1σ and 2σ)
• Breakout alignment scoring
• Context for trade quality
Gap Analysis
• Overnight gap detection
• Gap fill target projection
• Gap fill rate statistics
• Direction bias from gap type
Comprehensive Statistics
• Bull/Bear breakout win rates
• Reversal win rate (typically 65-75%)
• Day type distribution
• Extension statistics
• Gap fill rate
• Real-time performance tracking
🎨 VISUAL FEEDBACK:
Enhanced Plots
• ORB levels (High/Low/Mid continuous lines)
• Entry markers (L for long, S for short, 🔥 for reversals)
• Extension levels with labels
• Session ORBs (Asian/London/NY)
• IB levels and extensions
• VWAP with bands
• Failed breakout markers
Color-Coded Momentum Boxes
• Blue: Inside ORB (consolidation)
• Green: Above ORB (bullish momentum)
• Red: Below ORB (bearish momentum)
• Orange: Failed breakout zones
• Variable intensity based on distance
Dynamic Dashboards
• Main Dashboard: ORB status, breakout info, setup details, market context
• Strategy Dashboard: Trades, Win%, P&L, Profit Factor, Daily P&L, Drawdown, Position info
• Narrative Dashboard: Plain-language market interpretation
Three Display Modes
• Simple: Clean chart, essential ORB only
• Standard: ORB + IB + Sessions + VWAP (recommended)
• Advanced: All features + statistics
🔔 ALERT SYSTEM:
Strategy Alerts
• Breakout Entry (with ML probabilities)
• Failed Breakout Entry (with targets)
• Stop Hit (position closed)
• Target Hit (T1, T2, T3 partials)
• Extension Reached (profit zone)
• IB Break (potential trend day)
All alerts include:
• Direction and setup type
• Entry price and position size
• Stop and target levels
• ML scores (if enabled)
• Setup grade (A+ to D)
• Win rate context
⏱️ TIMEFRAMES: 1m-15m optimal (works on all)
💎 INSTRUMENTS: Futures, Forex, Crypto, Stocks, Indices
🎓 SKILL LEVEL: Intermediate to Advanced
📚 WHAT'S INCLUDED:
Comprehensive Documentation
• 200+ lines of detailed tooltips
• Every setting fully explained
• Optimization guides by market
• Position sizing calculator explanations
• Risk management framework
• Best practices and common pitfalls
Ready-to-Use Configurations
• Default settings optimized for ES/NQ
• Recommended settings for each instrument type
• Conservative vs Aggressive profiles
• Scalping vs Swing configurations
Full Transparency
• All calculations shown in dashboard
• Position sizing visible in real-time
• Strategy performance metrics live
• No black boxes or hidden logic
🚨 RISK DISCLAIMERS:
CRITICAL INFORMATION - PLEASE READ:
• This is a trading strategy that executes real trades in backtesting
• Past performance does NOT guarantee future results
• All trading involves substantial risk of loss
• Never risk money you cannot afford to lose
• This is NOT financial advice - for educational purposes only
• Requires understanding of ORB methodology and risk management
• Test thoroughly on paper/demo account before live trading
• Position sizing must be configured correctly for your account
• Stop losses are NOT guaranteed in all market conditions
• Slippage and commissions will affect live results
• Volatile markets may trigger circuit breakers (drawdown pause)
Strategy-Specific Risks:
• Opening range breakouts can fail (hence the reversal system)
• Volume confirmation may limit signals in low-volume instruments
• Custom sessions must match your market's actual hours
• Multi-instrument support requires correct point value configuration
• Trailing stops may exit early in volatile conditions
• Daily limits may prevent recovery trades
• Backtesting results may not match live execution
Position Sizing Warnings:
• Risk-Based modes can size large positions if stops are tight
• Always set max position size limits appropriate for your account
• Verify point values are correct for your instrument
• Test with small size first
• Monitor position size in dashboard before every trade
🎓 WHO THIS IS FOR:
Best Suited For:
• Traders with ORB methodology knowledge
• Those seeking a fully-automated system
• Backtesting enthusiasts
• Multi-instrument traders
• Risk-conscious systematic traders
• Traders who understand position sizing
Not Recommended For:
• Complete beginners to trading
• Those seeking "set and forget" with zero monitoring
• Traders unwilling to backtest first
• Those who don't understand risk management
• Accounts under $5,000 (position sizing too small)
💡 PRO TIPS:
Backtesting Best Practices
• Start with 2+ years of data
• Include both bull and bear markets
• Test on same timeframe you'll trade (5-min for 5-min ORB)
• Account for commissions/slippage realistically
• Verify win rate >45% and profit factor >1.3
Position Sizing
• Use Risk-Based (Initial Capital) for most consistent results
• Start with 1% risk per trade, increase to 1.5-2% if comfortable
• Set max position size to prevent oversizing
• Verify point values are correct before live trading
• Monitor dashboard for actual size before each trade
Risk Management
• NEVER disable daily loss limit
• Keep max drawdown pause at 12% or lower
• Use ATR stop method for best R:R
• Enable trailing stops for trend capturing
• Take partial profits at T1 (at least 30-40%)
Failed Breakout Trading
• These are your highest win-rate setups (65-75%)
• Always enable this feature
• Use tighter stops on reversals than breakouts
• Don't chase if you miss the entry window
• Three targets allow you to scale out profitably
ML Filtering
• Dramatically improves breakout quality
• Reduce signals but increase win rate
• Start with default thresholds (pCont≥0.55, pFail≤0.35)
• Lower signals = higher quality in choppy markets
• Can disable for more signals in strong trends
⚙️ TECHNICAL DETAILS:
Strategy Engine
• Pine Script v5
• Native strategy.entry() and strategy.exit()
• Trailing stops use trail_price/trail_offset (no repainting)
• Proper position sizing with strategy.position_size
• Realistic fills with commission and slippage
• Bar magnifier for improved intrabar execution
Performance
• Optimized for 1-minute to 15-minute charts
• Supports 5000+ bars of history
• Efficient calculations (no arrays in hot loops)
• Max 500 visual objects (boxes/lines/labels)
• No repainting - all signals confirmed on bar close
Position Sizing Engine
• Auto-detects Futures, Forex, Crypto, Stocks
• Uses syminfo.pointvalue when available
• Falls back to manual configuration
• Proper rounding to exchange increments
• Min/max limits enforced
Risk System
• Per-trade risk percentage enforced
• Daily P&L tracking
• Drawdown from peak equity
• Circuit breakers halt trading when limits hit
• Resets daily for fresh start
🔄 VERSION HISTORY:
Current Version: 1.0 (Initial Release)
• Complete ORB breakout + reversal strategy
• Adaptive position sizing (3 modes)
• Multi-instrument support
• Advanced risk management
• Native trailing stops
• ML filtering integration
• Comprehensive backtesting
• Real-time performance dashboard
Planned Updates:
• Additional session templates (Tokyo, Sydney)
• More stop methods
• Enhanced ML model training
• Volatility regime detection
• Trade journal export
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Trade the opening range with institutional precision.
Automated entries. Intelligent sizing. Professional risk management.
Test first. Trade smart. Scale safely.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
JMA Quantum Edge: Adaptive Precision Trading System JMA Quantum Edge: Adaptive Precision Trading System - Enhanced Visuals & Risk Management
Get ready to experience a groundbreaking trading strategy that adapts in real-time to market conditions! This powerful, open-source script combines advanced technical analysis with state-of-the-art risk management tools, designed to give you the edge you need in today's dynamic markets.
What It Does:
Adaptive JMA Indicator:
Utilizes a custom Jurik Moving Average (JMA) that adjusts its sensitivity based on market volatility, ensuring you get precise signals even in the most fluctuating environments.
Dynamic Risk Management:
Features built-in support for partial exits (scaling out) to secure profits, along with an optional Kelly Criterion-based position sizing that tailors your exposure based on historical performance metrics.
Robust Error Handling:
Incorporates market condition filters—like minimum volume and maximum allowed gap percentage—to ensure trades are only executed under favorable conditions.
Vivid Visual Enhancements:
Enjoy an animated background that reflects market momentum, dynamic pivot markers, and clearly drawn trend channels. Plus, interactive tables provide real-time performance analytics and detailed error metrics.
Fully Customizable:
With a comprehensive set of inputs, you can easily tailor the strategy to your personal trading style and market preferences. Adjust everything from JMA parameters to refresh intervals for tables and labels!
How to Use It:
Add the Script:
Copy and paste the script into the Pine Script Editor on TradingView and click “Add to Chart.”
Configure Your Settings:
Customize your risk management (capital, commission, position sizing, partial exits, etc.) and tweak the JMA settings to match your preferred trading style. Use the extensive input panel to adjust visuals, alerts, and more.
Backtest & Optimize:
Run the strategy in the Strategy Tester to analyze its historical performance. Monitor real-time analytics and error metrics via the interactive tables, and fine-tune your parameters for optimal performance.
Go Live with Confidence:
Once you're satisfied with the backtest results, use the generated signals for live trading, and let the system help you stay ahead in fast-paced markets!
How to use the imputs:
This cutting-edge strategy is designed to adapt to changing market conditions and offers you complete control over your trading parameters. Here’s a breakdown of what each group of inputs does and how you should use them:
Risk Management & Trade Settings
Recalculate on Every Tick:
What it does: When enabled, the strategy recalculates on every price update.
Recommendation: Leave it true for fast charts.
Initial Capital:
What it does: Sets your starting capital for backtesting, which influences position sizing and performance metrics.
Recommendation: Start with $10,000 (or adjust according to your trading capital).
Commission (%):
What it does: Simulates the cost per trade.
Recommendation: Use a realistic rate (e.g., 0.04%).
Position Size & Quantity Type:
What they do: Define how large each trade will be. Choose between a fixed unit amount or a percentage of equity.
Recommendation: For beginners, the default fixed value is a good start. Experiment later with percentage-based sizing if needed.
Order Comment:
What it does: Adds a label to your orders for easier tracking.
Allow Reverse Orders:
What it does: If disabled, the strategy will close opposing positions before entering a new trade, reducing conflicts.
Enable Dynamic Position Sizing:
What it does: Adjusts trade size based on current volatility.
Recommendation: Beginners may start with this disabled until they understand basic sizing.
Partial Exit Inputs:
What they do:
Enable Partial Exits: When turned on, you can scale out of your position to lock in profits.
Partial Exit Profit (%): The profit percentage that triggers a partial exit.
Partial Exit Percentage: The percentage of your current position to exit. Recommendation: Use defaults (e.g., 5% profit, 50% exit) to secure profits gradually.
Kelly Criterion Option:
What it does: When enabled, adjusts your position sizing using historical performance (win rate and profit factor).
Recommendation: Beginners might leave this disabled until comfortable with backtest performance metrics.
Market Condition Filters:
What they do:
Minimum Volume: Ensures trades occur only when there’s sufficient market activity.
Maximum Gap (%): Prevents trading if there’s an unusually large gap between the previous close and current open. Recommendation: Defaults work well for most markets. If trades seem erratic, consider tightening these limits.
JMA Settings
Price Source:
What it does: The input series for the JMA calculation, typically set to the closing price.
JMA Length:
What it does: Controls the smoothing period of the JMA. Lower values are more sensitive; higher values smooth out the noise. Recommendation: Start with 21.
JMA Phase & Power:
What they do: Adjust how responsive the JMA is. Phase controls timing; power adjusts the intensity. Recommendation: Default settings (63 phase and 3 power) are a balanced starting point.
Visual Settings & Style
Show JMA Line, Pivot Lines, and Pivot Labels:
What they do: Toggle visual elements on your chart for easier signal identification.
Pivot History Count:
What it does: Limits how many historical pivot markers are displayed.
Color Settings (Up/Down Neon Colors):
What they do: Set the visual cues for buy and sell signals.
Pivot Marker & Line Style:
What they do: Choose the style and thickness of your pivot markers and lines.
Show Stats Panel:
What it does: Displays real-time performance and error metrics.
Dynamic Background & Visual Enhancements
Animate Background:
What it does: Changes the background color based on market momentum.
Show Trend Channels & Volume Zones:
What they do: Draw trend channels and highlight areas of high volatility/volume.
Show Data-Rich Labels:
What it does: Displays key metrics like volume, error percentage, and momentum on the chart.
High Volatility Threshold:
What it does: Determines the multiplier for when the chart background should change due to high volatility.
Multi-Timeframe Settings
Higher Timeframe:
What it does: Uses a higher timeframe’s JMA for trend confirmation. Recommendation: Use Daily ('D') or Weekly ('W') for broader trend analysis.
Show HTF Trend Zone & Opacity:
What they do: Display a visual zone from the higher timeframe to help confirm trends.
6. Trailing Stop Settings
Trailing Stop ATR Factor & Offset Multiplier:
What they do: Calculate trailing stops based on the Average True Range (ATR), adjusting stop distances dynamically. Recommendation: Default settings are a good balance but can be fine-tuned based on asset volatility.
Alerts & Notifications
Alerts on Pivot Formation & JMA Crossover:
What they do: Notify you when key events occur.
Dynamic Power Threshold:
What it does: Sets the sensitivity for dynamic alerts.
8. Static Stop Loss / Take Profit
Static Stop Loss (%) & Take Profit (%):
What they do: Allow you to set fixed stop loss or take profit levels. Recommendation: Leave them at 0 to disable if you prefer dynamic risk management, or set them if you have strict risk/reward preferences.
Advanced Settings
ATR Length:
What it does: Determines the period for ATR calculation, impacting trailing stop sensitivity. Recommendation: Start with 14.
Optimization Feedback & Enhanced Error Analysis
Error Metric Length & Error Threshold (%):
What they do: Calculate error metrics (like average error, skewness, and kurtosis) to help you fine-tune the JMA. Recommendation: Use the defaults and adjust if the error metrics seem off during backtesting.
UI - User-Driven Tweaking & Table Customization
Parameter Tweaker Panel, Debug/Performance Table Settings:
What they do: Provide interactive tables that display real-time performance, error metrics, and allow you to monitor strategy parameters.
Refresh Frequency Options (Table & Label Refresh Intervals):
What they do: Set how often the tables and labels update.
Recommendation: Start with an interval of 1 bar; increase it if your chart is too busy.
Important for Beginners:
Default Settings:
All default values have been chosen for balanced performance across different markets. If you ever experience unexpected behavior, start by resetting the inputs to their defaults.
Step-by-Step Adjustments:
Experiment by changing one setting at a time while observing how the strategy’s signals and performance metrics change. This will help you understand the impact of each parameter.
Resetting to Defaults:
If things seem off or you’re not getting the expected results, you can always reset the indicator. Either reload the script or use the “Reset Inputs” option (if available) to revert to the default settings.
Jump in, experiment, and enjoy the power of adaptive precision trading. This strategy is built to grow with your skills—have fun exploring and refining your trading edge!
Happy trading!
Quatro SMA Strategy [4h]Hello, I would like to present to you The "Quatro SMA" strategy
Strategy is based on four simple moving averages of different lengths and monitoring trading volume. The key idea is to identify strong market trends by comparing short-term moving averages with the long-term SMA. The strategy generates buy signals when all short-term SMAs are above the SMA(200) and the volume confirms the strength of the move. Similarly, sell signals are generated when all short-term SMAs are below the SMA(200), and the volume is sufficiently high.
The strategy manages risk by applying a stop loss and three different Take Profit levels (TP1, TP2, TP3), with varying percentages of the position closed at each level.
Each Take Profit level is triggered at a specific percentage gain, with the position being closed gradually depending on the achieved targets. The percentage of the position closed at each TP level is also defined by the user.
Indicators and Parameters:
Simple Moving Averages (SMA):
The script utilizes four simple moving averages with different lengths (4, 16, 32, 200). The first three SMAs (SMA1, SMA2, SMA3) are used to determine the trend direction, while the fourth SMA (with a length of 200) serves as a support/resistance line.
Volume:
The script monitors trading volume and checks if the current volume exceeds 2.5 times the average volume of the last 40 candles. High volume is considered as confirmation of trend strength.
Entry Conditions:
- Long Position: Triggered when SMA1 > SMA2 > SMA3, the closing price is above SMA(200), and the volume condition is met.
- Short Position: Triggered when SMA1 < SMA2 < SMA3, the closing price is below SMA(200), and the volume condition is met.
Exit Conditions:
- Long Position: Closed when SMA1 < SMA2 < SMA3 and the closing price is above SMA(200).
- Short Position: Closed when SMA1 > SMA2 > SMA3 and the closing price is below SMA(200).
to determine the level of stop loss and target point I used a piece of code by RafaelZioni, here is the script from which a piece of code was taken
I hope the strategy will be helpful, as always, best regards and safe trades
;)
GKD-BT Giga Confirmation Stack Backtest [Loxx]Giga Kaleidoscope GKD-BT Giga Confirmation Stack Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Giga Confirmation Stack Backtest
The Giga Confirmation Stack Backtest module allows users to perform backtesting on Long and Short signals from the confluence between GKD-C Confirmation 1 and GKD-C Confirmation 2 indicators. This module encompasses two types of backtests: Trading and Full. The Trading backtest permits users to evaluate individual trades, whether Long or Short, one at a time. Conversely, the Full backtest allows users to analyze either Longs or Shorts separately by toggling between them in the settings, enabling the examination of results for each signal type. The Trading backtest emulates actual trading conditions, while the Full backtest assesses all signals, regardless of being Long or Short.
Additionally, this backtest module provides the option to test using indicators with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed.
Take profit 2: 25% of the trade is removed.
Take profit 3: 25% of the trade is removed.
Stop loss: 100% of the trade is removed.
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest module also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
To utilize this strategy, follow these steps:
1. Adjust the "Confirmation Type" in the GKD-C Confirmation 1 Indicator to "GKD New."
2. GKD-C Confirmation 1 Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 module into the GKD-BT Giga Confirmation Stack Backtest module setting named "Import GKD-C Confirmation 1."
3. Adjust the "Confirmation Type" in the GKD-C Confirmation 2 Indicator to "GKD New."
4. GKD-C Confirmation 2 Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 2 module into the GKD-BT Giga Confirmation Stack Backtest module setting named "Import GKD-C Confirmation 2."
█ Giga Confirmation Stack Backtest Entries
Entries are generated from the confluence of a GKD-C Confirmation 1 and GKD-C Confirmation 2 indicators. The Confirmation 1 gives the signal and the Confirmation 2 indicator filters or "approves" the the Confirmation 1 signal. If Confirmation 1 gives a long signal and Confirmation 2 shows a downtrend, then the long signal is rejected. If Confirmation 1 gives a long signal and Confirmation 2 shows an uptrend, then the long signal is approved and sent to the backtest execution engine.
█ Volatility Types Included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Confiramtion Stack Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Transform as shown on the chart above
Confirmation 2: uf2018 as shown on the chart above
Continuation: Vortex
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
STRATEGY 1 │ Red Dragon │ Model 1 │ Pro │ [Titans_Invest]The Red Dragon Model 1 is a fully automated trading strategy designed to operate BTC/USDT.P on the 4-hour chart with precision, stability, and consistency. It was built to deliver reliable behavior even during strong market movements, maintaining operational discipline and avoiding abrupt variations that could interfere with the trader’s decision-making.
Its core is based on a professionally engineered logical structure that combines trend filters, confirmation criteria, and balanced risk management. Every component was designed to work in an integrated way, eliminating noise, avoiding unnecessary trades, and protecting capital in critical moments. There are no secret mechanisms or hidden logic: everything is built to be objective, clean, and efficient.
Even though it is based on professional quantitative engineering, Red Dragon Model 1 remains extremely simple to operate. All logic is clearly displayed and fully accessible within TradingView itself, making it easy to understand for both beginners and experienced traders. The structure is organized so that any user can quickly view entry conditions, exit criteria, additional filters, adjustable parameters, and the full mechanics behind the strategy’s behavior.
In addition, the architecture was built to minimize unnecessary complexity. Parameters are straightforward, intuitive, and operate in a balanced way without requiring deep adjustments or advanced knowledge. Traders have full freedom to analyze the strategy, understand the logic, and make personal adaptations if desired—always with total transparency inside TradingView.
The strategy was also designed to deliver consistent operational behavior over the long term. Its confirmation criteria reduce impulsive trades; its filters isolate noise; and its overall logic prioritizes high-quality entries in structured market movements. The goal is to provide a stable, clear, and repeatable flow—essential characteristics for any medium-term quantitative approach.
Combining clarity, professional structure, and ease of use, Red Dragon Model 1 offers a solid foundation both for users who want a ready-to-use automated strategy and for those looking to study quantitative models in greater depth.
This entire project was built with extreme dedication, backed by more than 14,000 hours of hands-on experience in Pine Script, continuously refining patterns, techniques, and structures until reaching its current level of maturity. Every line of code reflects this long process of improvement, resulting in a strategy that unites professional engineering, transparency, accessibility, and reliable execution.
🔶 MAIN FEATURES
• Fully automated and robust: Operates without manual intervention, ideal for traders seeking consistency and stability. It delivers reliable performance even in volatile markets thanks to the solid quantitative engineering behind the system.
• Multiple layers of confirmation: Combines 10 key technical indicators with 15 adaptive filters to avoid false signals. It only triggers entries when all trend, market strength, and contextual criteria align.
• Configurable and adaptable filters: Each of the 15 filters can be enabled, disabled, or adjusted by the user, allowing the creation of personalized statistical models for different assets and timeframes. This flexibility gives full freedom to optimize the strategy according to individual preferences.
• Clear and accessible logic: All entry and exit conditions are explicitly shown within the TradingView parameters. The strategy has no hidden components—any user can quickly analyze and understand each part of the system.
• Integrated exclusive tools: Includes complete backtest tables (desktop and mobile versions) with annualized statistics, along with real-time entry conditions displayed directly on the chart. These tools help monitor the strategy across devices and track performance and risk metrics.
• No repaint: All signals are static and do not change after being plotted. This ensures the trader can trust every entry shown without worrying about indicators rewriting past values.
🔷 ENTRY CONDITIONS & RISK MANAGEMENT
Red Dragon Model 1 triggers buy (long) or sell (short) signals only when all configured conditions are satisfied. For example:
• Volume:
• The system only trades when current volume exceeds the volume moving average multiplied by a user-defined factor, indicating meaningful market participation.
• RSI:
• Confirms bullish bias when RSI crosses above its moving average, and bearish bias when crossing below.
• ADX:
• Enters long when +DI is above –DI with ADX above a defined threshold, indicating directional strength to the upside (and the opposite conditions for shorts).
• Other indicators (MACD, SAR, Ichimoku, Support/Resistance, etc.)
Each one must confirm the expected direction before a final signal is allowed.
When all bullish criteria are met simultaneously, the system enters Long; when all criteria indicate a bearish environment, the system enters Short.
In addition, the strategy uses fixed Take Profit and Stop Loss targets for risk control:
Currently: TP around 1.5% and SL around 2.0% per trade, ensuring consistent and transparent risk management on every position.
⚙️ INDICATORS
__________________________________________________________
1) 🔊 Volume: Avoids trading on flat charts.
2) 🍟 MACD: Tracks momentum through moving averages.
3) 🧲 RSI: Indicates overbought or oversold conditions.
4) 🅰️ ADX: Measures trend strength and potential entry points.
5) 🥊 SAR: Identifies changes in price direction.
6) ☁️ Cloud: Accurately detects changes in market trends.
7) 🌡️ R/F: Improves trend visualization and helps avoid pitfalls.
8) 📐 S/R: Fixed support and resistance levels.
9)╭╯MA: Moving Averages.
10) 🔮 LR: Forecasting using Linear Regression.
__________________________________________________________
🟢 ENTRY CONDITIONS 🔴
__________________________________________________________
IF all conditions are 🟢 = 📈 Long
IF all conditions are 🔴 = 📉 Short
__________________________________________________________
🚨 CURRENT TRIGGER SIGNAL 🚨
__________________________________________________________
🔊 Volume
🟢 LONG = (volume) > (MA_volume) * (Volume Mult)
🔴 SHORT = (volume) > (MA_volume) * (Volume Mult)
🧲 RSI
🟢 LONG = (RSI) > (RSI_MA)
🔴 SHORT = (RSI) < (RSI_MA)
🟢 ALL ENTRY CONDITIONS AVAILABLE 🔴
__________________________________________________________
🔊 Volume
🟢 LONG = (volume) > (MA_volume) * (Volume Mult)
🔴 SHORT = (volume) > (MA_volume) * (Volume Mult)
🔊 Volume
🟢 LONG = (volume) > (MA_volume) * (Volume Mult) and (close) > (open)
🔴 SHORT = (volume) > (MA_volume) * (Volume Mult) and (close) < (open)
🍟 MACD
🟢 LONG = (MACD) > (Signal Smoothing)
🔴 SHORT = (MACD) < (Signal Smoothing)
🧲 RSI
🟢 LONG = (RSI) < (Upper)
🔴 SHORT = (RSI) > (Lower)
🧲 RSI
🟢 LONG = (RSI) > (RSI_MA)
🔴 SHORT = (RSI) < (RSI_MA)
🅰️ ADX
🟢 LONG = (+DI) > (-DI) and (ADX) > (Treshold)
🔴 SHORT = (+DI) < (-DI) and (ADX) > (Treshold)
🥊 SAR
🟢 LONG = (close) > (SAR)
🔴 SHORT = (close) < (SAR)
☁️ Cloud
🟢 LONG = (Cloud A) > (Cloud B)
🔴 SHORT = (Cloud A) < (Cloud B)
☁️ Cloud
🟢 LONG = (Kama) > (Kama )
🔴 SHORT = (Kama) < (Kama )
🌡️ R/F
🟢 LONG = (high) > (UP Range) and (upward) > (0)
🔴 SHORT = (low) < (DOWN Range) and (downward) > (0)
🌡️ R/F
🟢 LONG = (high) > (UP Range)
🔴 SHORT = (low) < (DOWN Range)
📐 S/R
🟢 LONG = (close) > (Resistance)
🔴 SHORT = (close) < (Support)
╭╯MA2️⃣
🟢 LONG = (Cyan Bar MA2️⃣)
🔴 SHORT = (Red Bar MA2️⃣)
╭╯MA2️⃣
🟢 LONG = (close) > (MA2️⃣)
🔴 SHORT = (close) < (MA2️⃣)
╭╯MA2️⃣
🟢 LONG = (Positive MA2️⃣)
🔴 SHORT = (Negative MA2️⃣)
__________________________________________________________
🎯 TP / SL 🛑
__________________________________________________________
🎯 TP: 1.5 ％
🛑 SL: 2.0 ％
__________________________________________________________
🪄 UNIQUE FEATURES OF THIS STRATEGY
____________________________________
1) 𝄜 Table Backtest for Mobile.
2) 𝄜 Table Backtest for Computer.
3) 𝄜 Table Backtest for Computer & Annual Performance.
4) 𝄜 Live Entry Conditions.
1) 𝄜 Table Backtest for Mobile.
2) 𝄜 Table Backtest for Computer.
3) 𝄜 Table Backtest for Computer & Annual Performance.
4) 𝄜 Live Entry Conditions.
_____________________________
𝄜 BACKTEST / PERFORMANCE 𝄜
_____________________________
• Net Profit: +634.47%, Maximum Drawdown: -18.44%.
🪙 PAIR / TIMEFRAME ⏳
🪙 PAIR: BINANCE:BTCUSDT.P
⏳ TIME: 4 hours (240m)
✅ ON ☑️ OFF
✅ LONG
✅ SHORT
🎯 TP / SL 🛑
🎯 TP: 1.5 (％)
🛑 SL: 2.0 (％)
⚙️ CAPITAL MANAGEMENT
💸 Initial Capital: 10000 $ (TradingView)
💲 Order Size: 10 ％ (Of Equity)
🚀 Leverage: 10 x (Exchange)
💩 Commission: 0.03 ％ (Exchange)
📆 BACKTEST
🗓️ Start: Setember 24, 2019
🗓️ End: November 21, 2025
🗓️ Days: 2250
🗓️ Yers: 6.17
🗓️ Bars: 13502
📊 PERFORMANCE
💲 Net Profit: + 63446.89 $
🟢 Net Profit: + 634.47 ％
💲 DrawDown Maximum: - 10727.48 $
🔴 DrawDown Maximum: - 18.44 ％
🟢 Total Closed Trades: 1042
🟡 Percent Profitable: 63.92 ％
🟡 Profit Factor: 1.247
💲 Avg Trade: + 60.89 $
⏱️ Avg # Bars in Trades
🕯️ Avg # Bars: 4
⏳ Avg # Hrs: 15
✔️ Trades Winning: 666
❌ Trades Losing: 376
✔️ Maximum Consecutive Wins: 11
❌ Maximum Consecutive Losses: 7
📺 Live Performance : br.tradingview.com
• Use this strategy on the recommended pair and timeframe above to replicate the tested results.
• Feel free to experiment and explore other settings, assets, and timeframes.
STRATEGY 1 │ Red Dragon │ Model 1 │ [Titans_Invest]The Red Dragon Model 1 is a fully automated trading strategy designed to operate BTC/USDT.P on the 4-hour chart with precision, stability, and consistency. It was built to deliver reliable behavior even during strong market movements, maintaining operational discipline and avoiding abrupt variations that could interfere with the trader’s decision-making.
Its core is based on a professionally engineered logical structure that combines trend filters, confirmation criteria, and balanced risk management. Every component was designed to work in an integrated way, eliminating noise, avoiding unnecessary trades, and protecting capital in critical moments. There are no secret mechanisms or hidden logic: everything is built to be objective, clean, and efficient.
Even though it is based on professional quantitative engineering, Red Dragon Model 1 remains extremely simple to operate. All logic is clearly displayed and fully accessible within TradingView itself, making it easy to understand for both beginners and experienced traders. The structure is organized so that any user can quickly view entry conditions, exit criteria, additional filters, adjustable parameters, and the full mechanics behind the strategy’s behavior.
In addition, the architecture was built to minimize unnecessary complexity. Parameters are straightforward, intuitive, and operate in a balanced way without requiring deep adjustments or advanced knowledge. Traders have full freedom to analyze the strategy, understand the logic, and make personal adaptations if desired—always with total transparency inside TradingView.
The strategy was also designed to deliver consistent operational behavior over the long term. Its confirmation criteria reduce impulsive trades; its filters isolate noise; and its overall logic prioritizes high-quality entries in structured market movements. The goal is to provide a stable, clear, and repeatable flow—essential characteristics for any medium-term quantitative approach.
Combining clarity, professional structure, and ease of use, Red Dragon Model 1 offers a solid foundation both for users who want a ready-to-use automated strategy and for those looking to study quantitative models in greater depth.
This entire project was built with extreme dedication, backed by more than 14,000 hours of hands-on experience in Pine Script, continuously refining patterns, techniques, and structures until reaching its current level of maturity. Every line of code reflects this long process of improvement, resulting in a strategy that unites professional engineering, transparency, accessibility, and reliable execution.
🔶 MAIN FEATURES
• Fully automated and robust: Operates without manual intervention, ideal for traders seeking consistency and stability. It delivers reliable performance even in volatile markets thanks to the solid quantitative engineering behind the system.
• Multiple layers of confirmation: Combines 10 key technical indicators with 15 adaptive filters to avoid false signals. It only triggers entries when all trend, market strength, and contextual criteria align.
• Configurable and adaptable filters: Each of the 15 filters can be enabled, disabled, or adjusted by the user, allowing the creation of personalized statistical models for different assets and timeframes. This flexibility gives full freedom to optimize the strategy according to individual preferences.
• Clear and accessible logic: All entry and exit conditions are explicitly shown within the TradingView parameters. The strategy has no hidden components—any user can quickly analyze and understand each part of the system.
• Integrated exclusive tools: Includes complete backtest tables (desktop and mobile versions) with annualized statistics, along with real-time entry conditions displayed directly on the chart. These tools help monitor the strategy across devices and track performance and risk metrics.
• No repaint: All signals are static and do not change after being plotted. This ensures the trader can trust every entry shown without worrying about indicators rewriting past values.
🔷 ENTRY CONDITIONS & RISK MANAGEMENT
Red Dragon Model 1 triggers buy (long) or sell (short) signals only when all configured conditions are satisfied. For example:
• Volume:
• The system only trades when current volume exceeds the volume moving average multiplied by a user-defined factor, indicating meaningful market participation.
• RSI:
• Confirms bullish bias when RSI crosses above its moving average, and bearish bias when crossing below.
• ADX:
• Enters long when +DI is above –DI with ADX above a defined threshold, indicating directional strength to the upside (and the opposite conditions for shorts).
• Other indicators (MACD, SAR, Ichimoku, Support/Resistance, etc.)
Each one must confirm the expected direction before a final signal is allowed.
When all bullish criteria are met simultaneously, the system enters Long; when all criteria indicate a bearish environment, the system enters Short.
In addition, the strategy uses fixed Take Profit and Stop Loss targets for risk control:
Currently: TP around 1.5% and SL around 2.0% per trade, ensuring consistent and transparent risk management on every position.
⚙️ INDICATORS
__________________________________________________________
1) 🔊 Volume: Avoids trading on flat charts.
2) 🍟 MACD: Tracks momentum through moving averages.
3) 🧲 RSI: Indicates overbought or oversold conditions.
4) 🅰️ ADX: Measures trend strength and potential entry points.
5) 🥊 SAR: Identifies changes in price direction.
6) ☁️ Cloud: Accurately detects changes in market trends.
7) 🌡️ R/F: Improves trend visualization and helps avoid pitfalls.
8) 📐 S/R: Fixed support and resistance levels.
9)╭╯MA: Moving Averages.
10) 🔮 LR: Forecasting using Linear Regression.
__________________________________________________________
🟢 ENTRY CONDITIONS 🔴
__________________________________________________________
IF all conditions are 🟢 = 📈 Long
IF all conditions are 🔴 = 📉 Short
__________________________________________________________
🚨 CURRENT TRIGGER SIGNAL 🚨
__________________________________________________________
🔊 Volume
🟢 LONG = (volume) > (MA_volume) * (Volume Mult)
🔴 SHORT = (volume) > (MA_volume) * (Volume Mult)
🧲 RSI
🟢 LONG = (RSI) > (RSI_MA)
🔴 SHORT = (RSI) < (RSI_MA)
🟢 ALL ENTRY CONDITIONS AVAILABLE 🔴
__________________________________________________________
🔊 Volume
🟢 LONG = (volume) > (MA_volume) * (Volume Mult)
🔴 SHORT = (volume) > (MA_volume) * (Volume Mult)
🔊 Volume
🟢 LONG = (volume) > (MA_volume) * (Volume Mult) and (close) > (open)
🔴 SHORT = (volume) > (MA_volume) * (Volume Mult) and (close) < (open)
🍟 MACD
🟢 LONG = (MACD) > (Signal Smoothing)
🔴 SHORT = (MACD) < (Signal Smoothing)
🧲 RSI
🟢 LONG = (RSI) < (Upper)
🔴 SHORT = (RSI) > (Lower)
🧲 RSI
🟢 LONG = (RSI) > (RSI_MA)
🔴 SHORT = (RSI) < (RSI_MA)
🅰️ ADX
🟢 LONG = (+DI) > (-DI) and (ADX) > (Treshold)
🔴 SHORT = (+DI) < (-DI) and (ADX) > (Treshold)
🥊 SAR
🟢 LONG = (close) > (SAR)
🔴 SHORT = (close) < (SAR)
☁️ Cloud
🟢 LONG = (Cloud A) > (Cloud B)
🔴 SHORT = (Cloud A) < (Cloud B)
☁️ Cloud
🟢 LONG = (Kama) > (Kama )
🔴 SHORT = (Kama) < (Kama )
🌡️ R/F
🟢 LONG = (high) > (UP Range) and (upward) > (0)
🔴 SHORT = (low) < (DOWN Range) and (downward) > (0)
🌡️ R/F
🟢 LONG = (high) > (UP Range)
🔴 SHORT = (low) < (DOWN Range)
📐 S/R
🟢 LONG = (close) > (Resistance)
🔴 SHORT = (close) < (Support)
╭╯MA2️⃣
🟢 LONG = (Cyan Bar MA2️⃣)
🔴 SHORT = (Red Bar MA2️⃣)
╭╯MA2️⃣
🟢 LONG = (close) > (MA2️⃣)
🔴 SHORT = (close) < (MA2️⃣)
╭╯MA2️⃣
🟢 LONG = (Positive MA2️⃣)
🔴 SHORT = (Negative MA2️⃣)
__________________________________________________________
🎯 TP / SL 🛑
__________________________________________________________
🎯 TP: 1.5 ％
🛑 SL: 2.0 ％
__________________________________________________________
🪄 UNIQUE FEATURES OF THIS STRATEGY
____________________________________
1) 𝄜 Table Backtest for Mobile.
2) 𝄜 Table Backtest for Computer.
3) 𝄜 Table Backtest for Computer & Annual Performance.
4) 𝄜 Live Entry Conditions.
1) 𝄜 Table Backtest for Mobile.
2) 𝄜 Table Backtest for Computer.
3) 𝄜 Table Backtest for Computer & Annual Performance.
4) 𝄜 Live Entry Conditions.
_____________________________
𝄜 BACKTEST / PERFORMANCE 𝄜
_____________________________
• Net Profit: +634.47%, Maximum Drawdown: -18.44%.
🪙 PAIR / TIMEFRAME ⏳
🪙 PAIR: BINANCE:BTCUSDT.P
⏳ TIME: 4 hours (240m)
✅ ON ☑️ OFF
✅ LONG
✅ SHORT
🎯 TP / SL 🛑
🎯 TP: 1.5 (％)
🛑 SL: 2.0 (％)
⚙️ CAPITAL MANAGEMENT
💸 Initial Capital: 10000 $ (TradingView)
💲 Order Size: 10 ％ (Of Equity)
🚀 Leverage: 10 x (Exchange)
💩 Commission: 0.03 ％ (Exchange)
📆 BACKTEST
🗓️ Start: Setember 24, 2019
🗓️ End: November 21, 2025
🗓️ Days: 2250
🗓️ Yers: 6.17
🗓️ Bars: 13502
📊 PERFORMANCE
💲 Net Profit: + 63446.89 $
🟢 Net Profit: + 634.47 ％
💲 DrawDown Maximum: - 10727.48 $
🔴 DrawDown Maximum: - 18.44 ％
🟢 Total Closed Trades: 1042
🟡 Percent Profitable: 63.92 ％
🟡 Profit Factor: 1.247
💲 Avg Trade: + 60.89 $
⏱️ Avg # Bars in Trades
🕯️ Avg # Bars: 4
⏳ Avg # Hrs: 15
✔️ Trades Winning: 666
❌ Trades Losing: 376
✔️ Maximum Consecutive Wins: 11
❌ Maximum Consecutive Losses: 7
📺 Live Performance : br.tradingview.com
• Use this strategy on the recommended pair and timeframe above to replicate the tested results.
• Feel free to experiment and explore other settings, assets, and timeframes.
Advanced EMA Cross with Normalized ATR Filter, Controlling ADX
Description:
This strategy is based on EMA cross strategy and additional filters are used to get better results, a normalized ATR filter, and ADX control...
It aims to provide traders with a code base that generates signals for long positions based on market conditions defined by various indicators.
How it Works:
1. EMA: Uses short (8 periods) and long (20 periods) EMAs to identify crossovers.
2. ATR: Uses a 14-period ATR, normalized to its 20-period historical range, to filter out noise.
3. ADX: Uses a 14-period RMA to identify strong trends.
4. Volume: Filters trades based on a 14-period SMA of volume.
5. Super Trend: Uses a Super Trend indicator to identify the market direction.
How to Use:
- Buy Signal: Generated when EMA short crosses above EMA long, and other conditions like ATR and market direction are met.
- Sell Signal: Generated based on EMA crossunder and high ADX value.
Originality and Usefulness:
This script combines EMA, ATR, ADX, and Super Trend indicators to filter out false signals and identify more reliable trading opportunities.
USD Strength in the code is not working, just simulated it as PSEUDO CODE:
Strategy Results:
- Account Size: $1000
- Commission: Not considered
- Slippage: Not considered
- Risk: Manageable through parameters, now less than 5% per trade
- Dataset: Aim for more than 100 trades for a sufficient sample size
- Test Conditions: Test in 30 min chart for BTCUSDT
IMPORTANT NOTE: This script should be used for educational purposes and should not be considered as financial advice.
Chart:
- The script's output is plotted as Buy and Sell signals on the chart.
- No other scripts are included for clarity.
- Have tested with 30mins period
- You are encouraged to play with parameters, let me know if it helps you and/or if you can upgrade the code to a better level.
WHY DID I USE ATR AND ADX?
ATR filter is usually used for the following purposes.
Market Volatility: ATR measures how volatile the market is. High ATR values indicate that the price is experiencing significant fluctuations.
Filtering: Crossing a certain ATR threshold may indicate that the market is active enough to present trading opportunities.
Risk Management: ATR can also be used to set stop-loss and take-profit levels, helping to manage risk effectively.
And ADX is usually used for;
Trend Strength: ADX measures the strength of a trend. High ADX values indicate a strong trend.
Filtering: An ADX value above a certain level suggests that the trend is strong and it might be safer to trade.
Versatility: ADX does not indicate the direction of the trend, only its strength. This makes it useful in both bullish and bearish markets.
Using these indicators together can help filter out false signals and produce more reliable trading signals. While ATR helps to determine if the market is active enough, ADX measures the strength of the trend. Combined, they can create a more complex and effective trading strategy.
I've used ADX data to support generating a buy signal after a golden cross (bullish trend) and waiting until this is a strong trend. It sounds good to check for different trend strengths for bullish and bearish markets to decide a buy signal. Additionally I used ATR to check if the market has enough fluctuations.
Wunder DCA BotThe bot is based on the DCA system.
1. DCA is the investment method in which you buy a certain portion of the asset after the determined price deviation.
2. For entry, we evaluate the maximum and minimum levels for a given period that you can adjust in the script. The bot enters when price rebound from the specified levels.
3. For the exit, the bot will use the take profit percentage that you will specify in settings.
It is also possible to choose how the take profit is calculated either from the average entry price or from the entry order (first order).
4. DCA uses the following settings:
- Base order Volume: Volume of your first order on entry signal
- Subsequent orders volume: The volume of all subsequent orders except the first
- DCA orders count: This parameter will determine how many entries your overall strategy will have. For example: If you will put 3, that will mean that including your initial position you will have 2 additional orders.
- DCA order price deviation:
This is the value in % which determines the deviation of the additional entries from the entry price. Example: If you go long and the price of the asset is 100$ and you put an order price deviation of 1% that will mean that the first additional entry will occur when the price will drop by 1%, and the second entry will be triggered when the overall price will drop by 2% (as the interval between the first and the second additional entry will be 1%).
- DCA Order Volume Multiplier:
This parameter will determine the amount that you put into each additional position. If this parameter is equal to 1 that means that each additional entry will be equal to the initial amount. The extra volume will be added to your position from the second DCA entry. Example: Your initial position was 10$ and your Volume Multiplier is set to 2. When you reach your 1st DCA target your additional order will have the same volume of 10$. When you reach your 2nd DCA target your additional order will be 20$ (previous position volume * multiplier). Your 3rd DCA target will place the order of 40$.
- DCA order price Deviation Multiplier:
This value will increase the price deviation between each additional entry. It is calculated as the price deviation multiplied by the deviation multiplier. For example: if you enter long at the price 100$ and have a price deviation of 1% with the price deviation multiplier of 2 that will mean that the first additional entry will occur when the price will drop to 99$ however the second will occur when the price will go to 97$. The third additional position will be entered at 94$
5. For full automation of the bot, you should set your comments to the input in the bot settings in the "LONG" and "SHORT" fields. You also need to create an alert signal and set a Webhook to send signals.
IMPORTANT!!!
1. Position calculation should take into account several factors: your deposit, leverage, the number of DCA orders, the distance to the last DCA order;
2. When choosing leverage, it is important to correctly calculate the possible drawdown. If you set a high leverage value, then liquidation awaits and the bot will not be able to take profits and will exit the position ahead of time;
3. The size of the position must be determined in accordance with all risks and take into account the size of your deposit;
4. This DCA Bot is able to earn consistently with the correct calculated money management.
ORDER BLCOK custom strategy# OB Matrix Strategy - Documentation
**Version:** 1.0
**Author:** HPotter
**Date:** 31/07/2017
The **OB Matrix Strategy** is based on the identification of **bullish and bearish Order Blocks** and the management of conditional orders with multiple Take Profit (TP) and Stop Loss (SL) levels. It uses trend filters, ATR, and percentage-based risk management.
---
## 1. Main Parameters
### Strategy
- `initial_capital`: 50
- `default_qty_type`: percentage of capital
- `default_qty_value`: 10
### Money Management
- `rr_threshold`: minimum Risk/Reward threshold to open a trade
- `risk_percent`: percentage of capital to risk per trade (default 2%)
- `maxPendingBars`: maximum number of bars for a pending order
- `maxBarsOpen`: maximum number of bars for an open position
- `qty_tp1`, `qty_tp2`, `qty_tp3`: quantity percentages for multiple TPs
---
## 2. Order Block Identification
### Order Block Parameters
- `obLookback`: number of bars to identify an Order Block
- `obmode`: method to calculate the block (`Full` or `Breadth`)
- `obmiti`: method to determine block mitigation (`Close`, `Wick`, `Avg`)
- `obMaxBlocks`: maximum number of Order Blocks displayed
### Main Variables
- `bullBlocks`: array of bullish blocks
- `bearBlocks`: array of bearish blocks
- `last_bull_volume`, `last_bear_volume`: volume of the last block
- `dom_block`: dominant block type (Bullish/Bearish/None)
- `block_strength`: block strength (normalized volume)
- `price_distance`: distance between current price and nearest block
---
## 3. Visual Parameters
- `Width`: line thickness for swing high/low
- `amountOfBoxes`: block grid segments
- `showBorder`: show block borders
- `borderWidth`: width of block borders
- `showVolume`: display volume inside blocks
- `volumePosition`: vertical position of volume text
Customizable colors:
- `obHighVolumeColor`, `obLowVolumeColor`, `obBearHighVolumeColor`, `obBearLowVolumeColor`
- `obBullBorderColor`, `obBearBorderColor`
- `obBullFillColor`, `obBearFillColor`
- `volumeTextColor`
---
## 4. Screener Table
- `showScreener`: display the screener table
- `tablePosition`: table position (`Top Left`, `Top Right`, `Bottom Left`, `Bottom Right`)
- `tableSize`: table size (`Small`, `Normal`, `Large`)
The table shows:
- Symbol, Timeframe
- Type and status of Order Block
- Number of retests
- Bullish and bearish volumes
---
## 5. Trend Filters
- EMA as a trend filter (`emaPeriod`, default 223)
- `bullishTrend` if close > EMA
- `bearishTrend` if close < EMA
---
## 6. ATR and Swing Points
- ATR calculated with a customizable period (`atrLength`)
- Swing High/Low for SL/TP calculation
- `f_getSwingTargets` function to calculate SL and TP based on direction
---
## 7. Trade Logic
### Buy Limit on Bullish OB
- Conditions:
- New bullish block
- Uptrend
- RR > threshold (`rr_threshold`)
- SL: `bullishOBPrice * (1 - atr * atrMultiplier)`
- Multiple TPs: TP1 (50%), TP2 (80%), TP3 (100% max)
- Quantity calculation based on percentage risk
### Sell Limit on Bearish OB
- Conditions:
- New bearish block
- Downtrend
- RR > threshold (`rr_threshold`)
- SL: `bearishOBPrice * (1 + atr * atrMultiplier)`
- Multiple TPs: TP1 (50%), TP2 (80%), TP3 (100% max)
- Quantity calculation based on percentage risk
---
## 8. Order Management and Timeout
- Close pending orders after `maxPendingBars` bars
- Close open positions after `maxBarsOpen` bars
- Label management for open orders
---
## 9. Alert Conditions
- `bull_touch`: price inside maximum bullish volume zone
- `bear_touch`: price inside maximum bearish volume zone
- `bull_reject`: confirmation of bullish zone rejection
- `bear_reject`: confirmation of bearish zone rejection
- `new_bull`: new bullish block
- `new_bear`: new bearish block
---
## 10. Level Calculation
- Swing levels based on selected timeframe (`SelectPeriod`)
- `xHigh` and `xLow` for S1 and R1 calculation
- Levels plotted on chart
---
## 11. Take Profit / Stop Loss
- Extended horizontal lines (`extendBars`) to visualize TP and SL
- Customizable colors (`tpColor`, `slColor`)
---
## 12. Notes
- Complete script based on Pine Script v5
- Advanced graphical management with boxes, lines, labels
- Dynamically displays volumes and Order Blocks
- Integrated internal screener
---
### End of Documentation
Order Block Volumatic FVG StrategyInspired by: Volumatic Fair Value Gaps —
License: CC BY-NC-SA 4.0 (Creative Commons Attribution–NonCommercial–ShareAlike).
This script is a non-commercial derivative work that credits the original author and keeps the same license.
What this strategy does
This turns BigBeluga’s visual FVG concept into an entry/exit strategy. It scans bullish and bearish FVG boxes, measures how deep price has mitigated into a box (as a percentage), and opens a long/short when your mitigation threshold and filters are satisfied. Risk is managed with a fixed Stop Loss % and a Trailing Stop that activates only after a user-defined profit trigger.
Additions vs. the original indicator
✅ Strategy entries based on % mitigation into FVGs (long/short).
✅ Lower-TF volume split using upticks/downticks; fallback if LTF data is missing (distributes prior bar volume by close’s position in its H–L range) to avoid NaN/0.
✅ Per-FVG total volume filter (min/max) so you can skip weak boxes.
✅ Age filter (min bars since the FVG was created) to avoid fresh/immature boxes.
✅ Bull% / Bear% share filter (the 46%/53% numbers you see inside each FVG).
✅ Optional candle confirmation and cooldown between trades.
✅ Risk management: fixed SL % + Trailing Stop with a profit trigger (doesn’t trail until your trigger is reached).
✅ Pine v6 safety: no unsupported args, no indexof/clamp/when, reverse-index deletes, guards against zero/NaN.
How a trade is decided (logic overview)
Detect FVGs (same rules as the original visual logic).
For each FVG currently intersected by the bar, compute:
Mitigation % (how deep price has entered the box).
Bull%/Bear% split (internal volume share).
Total volume (printed on the box) from LTF aggregation or fallback.
Age (bars) since the box was created.
Apply your filters:
Mitigation ≥ Long/Short threshold.
Volume between your min and max (if enabled).
Age ≥ min bars (if enabled).
Bull% / Bear% within your limits (if enabled).
(Optional) the current candle must be in trade direction (confirm).
If multiple FVGs qualify on the same bar, the strategy uses the most recent one.
Enter long/short (no pyramiding).
Exit with:
Fixed Stop Loss %, and
Trailing Stop that only starts after price reaches your profit trigger %.
Input settings (quick guide)
Mitigation source: close or high/low. Use high/low for intrabar touches; close is stricter.
Mitigation % thresholds: minimal mitigation for Long and Short.
TOTAL Volume filter: skip FVGs with too little/too much total volume (per box).
Bull/Bear share filter: require, e.g., Long only if Bull% ≥ 50; avoid Short when Bull% is high (Short Bull% max).
Age filter (bars): e.g., ≥ 20–30 bars to avoid fresh boxes.
Confirm candle: require candle direction to match the trade.
Cooldown (bars): minimum bars between entries.
Risk:
Stop Loss % (fixed from entry price).
Activate trailing at +% profit (the trigger).
Trailing distance % (the trailing gap once active).
Lower-TF aggregation:
Auto: TF/Divisor → picks 1/3/5m automatically.
Fixed: choose 1/3/5/15m explicitly.
If LTF can’t be fetched, fallback allocates prior bar’s volume by its close position in the bar’s H–L.
Suggested starting presets (you should optimize per market)
Mitigation: 60–80% for both Long/Short.
Bull/Bear share:
Long: Bull% ≥ 50–70, Bear% ≤ 100.
Short: Bull% ≤ 60 (avoid shorting into strong support), Bear% ≥ 0–70 as you prefer.
Age: ≥ 20–30 bars.
Volume: pick a min that filters noise for your symbol/timeframe.
Risk: SL 4–6%, trailing trigger 1–2%, distance 1–2% (crypto example).
Set slippage/fees in Strategy Properties.
Notes, limitations & best practices
Data differences: The LTF split uses request.security_lower_tf. If the exchange/data feed has sparse LTF data, the fallback kicks in (it’s deliberate to avoid NaNs but is a heuristic).
Real-time vs backtest: The current bar can update until close; results on historical bars use closed data. Use “Bar Replay” to understand intrabar effects.
No pyramiding: Only one position at a time. Modify pyramiding in the header if you need scaling.
Assets: For spot/crypto, TradingView “volume” is exchange volume; in some markets it may be tick volume—interpret filters accordingly.
Risk disclosure: Past performance ≠ future results. Use appropriate position sizing and risk controls; this is not financial advice.
Credits
Visual FVG concept and original implementation: BigBeluga.
This derivative strategy adds entry/exit logic, volume/age/share filters, robust LTF handling, and risk management while preserving the original spirit.
License remains CC BY-NC-SA 4.0 (non-commercial, attribution required, share-alike).
WaverVanir Alpha Reversal Scalper [ETF Eval Bot]🧠 Strategy Overview:
The WaverVanir Alpha Reversal Scalper is a precision-engineered futures trading bot designed to pass prop firm evaluations, specifically Elite Trader Funding (ETF) via Tradovate integration.
This scalping engine was developed after analyzing over 100+ evaluation trades, and is powered by institutional logic, volume behavior, and adaptive VWAP-based confluence.
🔍 Core Logic:
Directional Bias: Trades long only based on VWAP slope confirmation (to avoid funding-damaging shorts)
Entry Conditions:
Price deviates below VWAP standard deviation
Volume spike exceeds 1.5× 20-period average
Bullish reversal wick detected (smart money pattern)
Exit Management:
Stop Loss: 10-tick precision SL below local low
Take Profit: 2.5R static or dynamic trailing stop
Session Filter: Trades only between 9:00–14:00 ET, avoiding lunch and close traps
📊 Backtest Summary (ESU2025)
Metric Result
Net Profit $9,487.50
Profit Factor 1.66 (longs)
Sharpe Ratio 0.03
Sortino Ratio 0.042
Winning Bias Long-Only
Max Drawdown Under $3,000
Commissions $0 (sim tested)
🧪 Tested across March–July 2025 on CME Mini ES (ESU2025)
🔒 Short trades disabled after review: Net −$7,312.50 loss on shorts alone
✅ Profit factor improves > 60% post-optimization
🔄 Recent Optimizations
❌ Disabled unprofitable short setups
✅ VWAP slope filter added for institutional alignment
✅ Trailing stop logic added (activates after 1.5R)
⏱️ Session filter to reduce market noise
🧠 Designed For:
Passing ETF funding challenges quickly
Avoiding drawdown breaches with controlled risk
Running on TradingView with direct Tradovate integration
🚀 Execution-Ready
This bot runs natively on TradingView. Simply:
Add the Pine Script to your chart
Use a tradable symbol (e.g., ESU2025)
Create an alert using “Order fills”
✅ Enable auto-trading to Tradovate
📡 Learn More
🔗 Powered by VolanX Protocol, the AI-driven infrastructure layer of
🌐 WaverVanir International LLC – where institutional logic meets retail precision.
—
🧠 Want to upgrade this bot to include macros, adaptive entries, or launch in NQ/CL?
💬 Drop a comment or message — let’s build your funding empire.
#ES #Futures #ETF #Funding #PropFirm #TradingBot #VWAP #VolumeProfile #AlphaScalper #WaverVanir #VolanX
Bollinger Bounce Reversal Strategy – Visual EditionOverview:
The Bollinger Bounce Reversal Strategy – Visual Edition is designed to capture potential reversal moves at price extremes—often termed “bounce points”—by using a combination of technical indicators. The strategy integrates Bollinger Bands, MACD, and volume analysis, and it provides rich on‑chart visual cues to help traders understand its signals and conditions. Additionally, the strategy enforces a maximum of 5 trades per day and uses fixed risk management parameters. This publication is intended for educational purposes and offers a systematic, transparent approach that you can further adjust to fit your market or risk profile.
How It Works:
Bollinger Bands:
A 20‑period simple moving average (SMA) and a user‑defined standard deviation multiplier (default 2.0) are used to calculate the Bollinger Bands.
When the price reaches or crosses these bands (i.e. falls below the lower band or rises above the upper band), it suggests that the price is in an extreme, potentially oversold or overbought, state.
MACD Filter:
The MACD (calculated with standard lengths, e.g. 12, 26, 9) provides momentum information.
For a bullish (long) signal, the MACD line should be above its signal line; for a bearish (short) signal, the MACD line should be below.
Volume Confirmation:
The strategy uses a 20‑period volume moving average to determine if current volume is strong enough to validate a signal.
A signal is confirmed only if the current volume is at or above a specified multiple (by default, 1.0×) of this moving average, ensuring that the move is supported by increased market participation.
Visual Cues:
Bollinger Bands and Fill: The basis (SMA), upper, and lower Bollinger Bands are plotted, and the area between the upper and lower bands is filled with a semi‑transparent color.
Signal Markers: When a long or short signal is generated, corresponding markers (labels) appear on the chart.
Background Coloring: The chart’s background changes color (green for long signals and red for short signals) on the bars where signals occur.
Information Table: An on‑chart table displays key indicator values (MACD, signal line, volume, average volume) and the number of trades executed that day.
Entry Conditions:
Long Entry:
A long trade is triggered when the previous bar’s close is below the lower Bollinger Band and the current bar’s close crosses above it, combined with a bullish MACD condition and strong volume.
Short Entry:
A short trade is triggered when the previous bar’s close is above the upper Bollinger Band and the current bar’s close crosses below it, with a bearish MACD condition and high volume.
Risk Management:
Daily Trade Limit: The strategy restricts trading to no more than 5 trades per day.
Stop-Loss and Take-Profit:
For each position, a stop loss is set at a fixed percentage away from the entry price (typically 2%), and a take profit is set to target a 1:2 risk-reward ratio (typically 4% from the entry price).
Backtesting Setup:
Initial Capital: $10,000
Commission: 0.1% per trade
Slippage: 1 tick per bar
These realistic parameters help ensure that backtesting results reflect the conditions of an average trader.
Disclaimer:
Past performance is not indicative of future results. This strategy is experimental and provided solely for educational purposes. It is essential to backtest extensively and paper trade before any live deployment. All risk management practices are advisory, and you should adjust parameters to suit your own trading style and risk tolerance.
Conclusion:
By combining Bollinger Bands, MACD, and volume analysis, the Bollinger Bounce Reversal Strategy – Visual Edition provides a clear, systematic method to identify potential reversal opportunities at price extremes. The added visual cues help traders quickly interpret signals and assess market conditions, while strict risk management and a daily trade cap help keep trading disciplined. Adjust and refine the settings as needed to better suit your specific market and risk profile.
