Hull AMA SignalsThis script is a comprehensive trading indicator named "Hull AMA Signals", which combines AMA and HSO by LuxAlgo and ther video based strategy techniques to provide buy (long) and sell (short) signals. It overlays directly on the price chart, offering a dynamic and visually intuitive trading aid. The core components of this indicator are Adaptive Moving Averages (AMA), Hull Moving Average (HMA), and a unique Hull squeeze oscillator (HSO), each configured with customizable parameters for flexibility and adaptability to various market conditions.
Features and Components
Adaptive Moving Averages (AMA): This indicator employs two sets of AMAs, each with distinct lengths, multipliers, lags, and overshoot parameters. The AMAs are designed to adapt their sensitivity based on the market's volatility, making them more responsive during significant price movements and less prone to false signals during periods of consolidation.
Hull Moving Average (HMA): The HMA is calculated using a sophisticated algorithm that aims to reduce the lag commonly associated with traditional moving averages. It provides a smoother and more responsive moving average line, which helps in identifying the prevailing market trend more accurately.
Hull Squeeze Oscillator (HSO): A novel component of this indicator, the HSO, is designed to identify potential market breakouts. It does so by comparing the Hull Moving Average's direction and momentum against a dynamically calculated mean, generating bullish or bearish signals based on the crossover and divergence from this mean.
Buy (Long) and Sell (Short) Signals: The script intelligently combines signals from the AMA crossovers and the Hull squeeze oscillator to pinpoint potential buy and sell opportunities. Bullish signals are generated when there's a positive crossover in the AMAs accompanied by a bullish dot from the HSO, whereas bearish signals are indicated by a negative crossover in the AMAs along with a bearish dot from the HSO.
Customization and Style Options: Users have the ability to adjust various parameters such as the length of the moving averages, multipliers, and source data, enabling customization for different trading strategies and asset classes. Additionally, color-coded visual elements like gradients and shapes enhance the readability and instant recognition of trading signals.
Use Cases
Trend Identification: By analyzing the direction and position of the AMAs and HMA, traders can easily discern the prevailing market trend, helping them to align their trades with the market momentum.
Signal Confirmation: The combination of AMA crossovers and HSO signals provides a robust framework for confirming trade entries and exits, potentially increasing the reliability of the trading signals.
Volatility Adaptation: The adaptive nature of the AMAs and the dynamic calculation of the HSO mean allow this indicator to adjust to changing market volatility, making it suitable for a wide range of market environments.
This indicator is suitable for traders looking for a comprehensive and dynamic technical analysis tool that combines trend analysis with signal generation, offering both visual appeal and practical trading utility.
Moving Averages
Candle Colours and EMA Colours [LuciTech]this indicator assigns a colour to each candle based on the relationship between the price and the EMAs, The indicator first checks whether the close price is above or below the first EMA, If the close price is above the first EMA the candle is coloured green. If the close price inbetween both EMAs the candle is colored gray. If the close price is below the second EMA, the candle is coloured red.
the indicator also colours the EMAs based on the closed price, if closed price is above the EMAs its coloured green and if price is closed below the EMA is coloured red.
The colours of the candles and EMAs can be changed in "style" and the periods of the EMAs can be changed in inputs.
Momentum Ghost Machine [ChartPrime]Momentum Ghost Machine (ChartPrime) is designed to be the next generation in momentum/rate of change analysis. This indicator utilizes the properties of one of our favorite filters to create a more accurate and stable momentum oscillator by using a high quality filtered delayed signal to do the momentum comparison.
Traditional momentum/roc uses the raw price data to compare current price to previous price to generate a directional oscillator. This leaves the oscillator prone to false readings and noisy outputs that leave traders unsure of the real likelihood of a future movement. One way to mitigate this issue would be to use some sort of moving average. Unfortunately, this can only go so far because simple moving average algorithms result in a poor reconstruction of the actual shape of the underlying signal.
The windowed sinc low pass filter is a linear phase filter, meaning that it doesn't change the shape or size of the original signal when applied. This results in a faithful reconstruction of the original signal, but without the "high frequency noise". Just like any filter, the process of applying it requires that we have "future" samples resulting in a time delay for real time applications. Fortunately this is a great thing in the context of a momentum oscillator because we need some representation of past price data to compare the current price data to. By using an ideal low pass filter to generate this delayed signal we can super charge the momentum oscillator and fix the majority of issues its predecessors had.
This indicator has a few extra features that other momentum/roc indicators dont have. One major yet simple improvement is the inclusion of a moving average to help gauge the rate of change of this indicator. Since we included a moving average, we thought it would only be appropriate to add a histogram to help visualize the relationship between the signal and its average. To go further with this we have also included linear extrapolation to further help you predict the momentum and direction of this oscillator. Included with this extrapolation we have also added the histogram in the extrapolation to further enhance its visual interpretation. Finally, the inclusion of a candle coloring feature really drives how the utility of the Momentum Machine .
There are three distinct options when using the candle coloring feature: Direct, MA, and Both. With direct the candles will be colored based on the indicators direction and polarity. When it is above zero and moving up, it displays a green color. When it is above zero and moving down it will display a light green color. Conversely, when the indicator is below zero and moving down it displays a red color, and when it it moving up and below zero it will display a light red color. MA coloring will color the candles just like a MACD. If the signal is above its MA and moving up it will display a green color, and when it is above its MA and moving down it will display a light green color.
When the signal is below its MA and moving down it will display a red color, and when its below its ma and moving up it will display a light red color. Both combines the two into a single color scheme providing you with the best of both worlds. If the indicator is above zero it will display the MA colors with a slight twist. When the indicator is moving down and is below its MA it will display a lighter color than before, and when it is below zero and is above its MA it will display a darker color color.
Length of 50 with a smoothing of 100
Length of 50 with a smoothing of 25
By default, the indicator is set to a momentum length of 50, with a post smoothing of 2. We have chosen the longer period for the momentum length to highlight the performance of this indicator compared to its ancestors. A major point to consider with this indicator is that you can only achieve so much smoothing for a chosen delay. This is because more data is required to produce a smoother signal at a specified length. Once you have selected your desired momentum length you can then select your desired momentum smoothing . This is made possible by the use of the windowed sinc low pass algorithm because it includes a frequency cutoff argument. This means that you can have as little or as much smoothing as you please without impacting the period of the indicator. In the provided examples above this paragraph is a visual representation of what is going on under the hood of this indicator. The blue line is the filtered signal being compared to the current closing price. As you can see, the filtered signal is very smooth and accurately represents the underlying price action without noise.
We hope that users can find the same utility as we did in this indicator and that it levels up your analysis utilizing the momentum oscillator or rate of change.
Enjoy
Dynamic Trailing (Zeiierman)█ Overview
The Dynamic Trailing (Zeiierman) indicator enhances the traditional SuperTrend approach by providing a more nuanced, adaptable tool for trend analysis and market volatility assessment. It combines techniques to identify dynamic support and resistance levels, trend directions, and market volatility. By integrating the Average True Range (ATR) with a unique multiplier system and smoothing mechanisms, this indicator offers a nuanced approach to trend-following strategies, making it a valuable asset for traders looking to leverage SuperTrend methodologies with additional insights into market dynamics.
█ How It Works
At its core, this indicator builds on the traditional SuperTrend formula by utilizing a modified ATR calculation to define the deviation for dynamic support and resistance levels. These levels are dynamically adjusted based on market volatility. The innovation lies in the addition of the Hull Moving Average (HMA) and the Triple Exponential Moving Average (TEMA) for an enhanced smoothing effect, making the indicator's trend signals more reliable and less prone to market noise. The trend direction is determined by comparing the closing price with the dynamic levels, facilitating clear bullish or bearish signals.
The indicator incorporates a 'Supertrend' function, which uses the dynamic levels and the price’s position relative to them to determine the trend direction. This determination is visualized through color-coded lines and a cloud zone, which expands or contracts based on the ATR and a user-defined width setting, illustrating the market's volatility and trend strength.
ATR Calculation: Utilizes the Average True Range (ATR) to measure market volatility. The ATR is a cornerstone of this indicator, helping to dynamically adjust the support and resistance levels according to the market’s changing conditions.
Supertrend Calculation: Implements a supertrend formula that combines the ATR with user-defined multipliers to plot potential trend directions. This feature helps in identifying whether the market is in an uptrend or downtrend, offering visual cues for potential reversals.
TEMA Calculation: Employs the Triple Exponential Moving Average (TEMA) through a Hull Moving Average (HMA) calculation to smooth out price data. This smoothing process helps in reducing market noise and makes the trend direction clearer.
Dynamic Support and Resistance: Calculates dynamic support and resistance levels by applying a deviation (derived from the ATR and user-defined multiplier) to the smoothed price data. These levels adapt to market conditions, providing areas where price might experience support or resistance.
Trend and Cloud Calculation: Determines the overall trend direction and plots a 'Cloud' zone around it, which adjusts in width based on the ATR and a user-defined cloud width setting. This cloud acts as a visual buffer, indicating the strength and stability of the current trend.
█ How to Use
Trend Identification: The primary function of this indicator is to help traders quickly identify the prevailing market trend. A change in the color of the dynamic trailing line or its position relative to the price can signal potential trend reversals.
Dynamic Support and Resistance: Unlike static levels, the dynamic levels adjust with market conditions, providing current areas where the price might experience support or resistance.
Dynamic Support
Dynamic Resistance
█ Settings
Mult (Multiplier): Adjusts the multiplier for the ATR calculation, affecting the deviation distance for support and resistance levels. Higher values decrease sensitivity and vice versa.
Len (Length): Sets the period for the HMA in the TEMA calculation, influencing the indicator's responsiveness to price changes.
Smoothness: Determines the smoothness of the dynamic support and resistance lines by setting the SMA length. Higher values result in smoother lines.
Cloud Width : Modifies the width of the cloud, providing a visual representation of market volatility.
Color Settings (upcol and dncol): Allows users to customize the colors of the indicator's lines and cloud, aiding in visual trend identification.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
MBAND 200 4H BTC/USDT - By MGS-TradingMBAND 200 4H BTC/USDT with RSI and Volume by MGS-Trading: A Neural Network-Inspired Indicator
Introduction:
The MBAND 200 4H BTC/USDT with RSI and Volume represents a groundbreaking achievement in the integration of artificial intelligence (AI) into cryptocurrency market analysis. Developed by MGS-Trading, this indicator is the culmination of extensive research and development efforts aimed at leveraging AI's power to enhance trading strategies. By synthesizing neural network concepts with traditional technical analysis, the MBAND indicator offers a dynamic, multi-dimensional view of the market, providing traders with unparalleled insights and actionable signals.
Innovative Approach:
Our journey to create the MBAND indicator began with a simple question: How can we mimic the decision-making prowess of a neural network in a trading indicator? The answer lay in the weighted aggregation of Exponential Moving Averages (EMAs) from multiple timeframes, each serving as a unique input akin to a neuron in a neural network. These weights are not arbitrary; they were painstakingly optimized through backtesting across various market conditions to ensure they reflect the significance of each timeframe’s contribution to overall market dynamics.
Core Features:
Neural Network-Inspired Weights: The heart of the MBAND indicator lies in its AI-inspired weighting system, which treats each timeframe’s EMA as an input node in a neural network. This allows the indicator to process complex market data in a nuanced and sophisticated manner, leading to more refined and informed trading signals.
Multi-Timeframe EMA Analysis: By analyzing EMAs from 15 minutes to 3 days, the MBAND indicator captures a comprehensive snapshot of market trends, enabling traders to make informed decisions based on a broad spectrum of data.
RSI and Volume Integration: The inclusion of the Relative Strength Index (RSI) and volume data adds layers of confirmation to the signals generated by the EMA bands. This multi-indicator approach helps in identifying high-probability setups, reinforcing the neural network’s concept of leveraging multiple data points for decision-making.
Usage Guidelines:
Signal Interpretation: The MBAND bands provide a visual representation of the market’s momentum and direction. A price moving above the upper band signals strength and potential continuation of an uptrend, while a move below the lower band suggests weakness and a possible downtrend.
Overbought/Oversold Conditions: The RSI component identifies when the asset is potentially overbought (>70) or oversold (<30). Traders should watch for these conditions near the MBAND levels for potential reversal opportunities.
Volume Confirmation: An increase in volume accompanying a price move towards or beyond an MBAND level serves as confirmation of the strength behind the move. This can indicate whether a breakout is likely to sustain or if a reversal has substantial backing.
Strategic Entry and Exit Points: Combine the MBAND readings with RSI and volume indicators to pinpoint strategic entry and exit points. For example, consider entering a long position when the price is near the lower MBAND, RSI indicates oversold conditions, and there is a notable volume increase.
About MGS-Trading:
At MGS-Trading, we are passionate about harnessing the transformative power of AI to revolutionize cryptocurrency trading. Our indicators and tools are designed to provide traders with advanced analytics and insights, drawing on the latest AI techniques and methodologies. The MBAND 200 4H BTC/USDT with RSI and Volume indicator is a prime example of our commitment to innovation, offering traders a sophisticated, AI-enhanced tool for navigating the complexities of the cryptocurrency markets.
Disclaimer:
The MBAND indicator is provided for informational purposes only and does not constitute investment advice. Trading cryptocurrencies involves significant risk and can result in the loss of your investment. We recommend conducting your own research and consulting with a qualified financial advisor before making any trading decisions.
SMA Angular Trends [Yosiet]This indicator uses two specific SMA configurations conditioned by an angular slope that is always repeated in trend markets, which are usually beneficial in swing or long-term strategies.
SETTINGS
- Fast Angle Threshold: Is the value in degrees for the condition of the fast sma
- Slow Angle Threshold: Is the value in degrees for the condition of the slow sma
- Linear Mode: When is active, it shows the sma curves only when the condition is satisfied. When is inactive, it shows color of the trends
HOW TO USE
This indicator it helps to see clearly the trends and the oppotunities to entry/exit in breakouts and retests
WHY THOSE SMAs
The SMAs are sma(7, low) and sma(30, high), those setups came from analyze several others indicators with machine learning searching for convergence points in 2018.
THOUGHTS
This indicator only pretends to help traders to take decissions with extra data confirmation
IMPROVEMENTS
You can comment your ideas and sugestions to improve this indicator
Kalman Filtered RSI Oscillator [BackQuant]Kalman Filtered RSI Oscillator
The Kalman Filtered RSI Oscillator is BackQuants new free indicator designed for traders seeking an advanced, empirical approach to trend detection and momentum analysis. By integrating the robustness of a Kalman filter with the adaptability of the Relative Strength Index (RSI), this tool offers a sophisticated method to capture market dynamics. This indicator is crafted to provide a clearer, more responsive insight into price trends and momentum shifts, enabling traders to make informed decisions in fast-moving markets.
Core Principles
Kalman Filter Dynamics:
At its core, the Kalman Filtered RSI Oscillator leverages the Kalman filter, renowned for its efficiency in predicting the state of linear dynamic systems amidst uncertainties. By applying it to the RSI calculation, the tool adeptly filters out market noise, offering a smoothed price source that forms the basis for more accurate momentum analysis. The inclusion of customizable parameters like process noise, measurement noise, and filter order allows traders to fine-tune the filter’s sensitivity to market changes, making it a versatile tool for various trading environments.
RSI Adaptation:
The RSI is a widely used momentum oscillator that measures the speed and change of price movements. By integrating the RSI with the Kalman filter, the oscillator not only identifies the prevailing trend but also provides a smoothed representation of momentum. This synergy enhances the indicator's ability to signal potential reversals and trend continuations with a higher degree of reliability.
Advanced Smoothing Techniques:
The indicator further offers an optional smoothing feature for the RSI, employing a selection of moving averages (HMA, THMA, EHMA, SMA, EMA, WMA, TEMA, VWMA) for traders seeking to reduce volatility and refine signal clarity. This advanced smoothing mechanism is pivotal for traders looking to mitigate the effects of short-term price fluctuations on the RSI's accuracy.
Empirical Significance:
Empirically, the Kalman Filtered RSI Oscillator stands out for its dynamic adjustment to market conditions. Unlike static indicators, the Kalman filter continuously updates its estimates based on incoming price data, making it inherently more responsive to new market information. This dynamic adaptation, combined with the RSI's momentum analysis, offers a powerful approach to understanding market trends and momentum with a depth not available in traditional indicators.
Trend Identification and Momentum Analysis:
Traders can use the Kalman Filtered RSI Oscillator to identify strong trends and momentum shifts. The color-coded RSI columns provide immediate visual cues on the market's direction and strength, aiding in quick decision-making.
Optimal for Various Market Conditions:
The flexibility in tuning the Kalman filter parameters makes this indicator suitable for a wide range of assets and market conditions, from volatile to stable markets. Traders can adjust the settings based on empirical testing to find the optimal configuration for their trading strategy.
Complementary to Other Analytical Tools:
While powerful on its own, the Kalman Filtered RSI Oscillator is best used in conjunction with other analytical tools and indicators. Combining it with volume analysis, price action patterns, or other trend-following indicators can provide a comprehensive view of the market, allowing for more nuanced and informed trading decisions.
The Kalman Filtered RSI Oscillator is a groundbreaking tool that marries empirical precision with advanced trend analysis techniques. Its innovative use of the Kalman filter to enhance the RSI's performance offers traders an unparalleled ability to navigate the complexities of modern financial markets. Whether you're a novice looking to refine your trading approach or a seasoned professional seeking advanced analytical tools, the Kalman Filtered RSI Oscillator represents a significant step forward in technical analysis capabilities.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Kyrie Crossover ( @zaytradellc )Unlocking Market Dynamics: Kyrie Crossover Script by @zaytradellc
personalized trading success with the "Kyrie Crossover" script, meticulously crafted by @zaytrade. This innovative Pine Script, tailored to the birthdays of Kyrie and the script creator, combines the power of technical analysis with a touch of personalization to revolutionize your trading experience.
**Exponential Moving Average (EMA) Crossover Strategy:**
At the heart of the "Kyrie Crossover" script lies a sophisticated EMA crossover strategy. By utilizing a 10-period EMA and a 323-period EMA (symbolizing long term price action ), the strategy effectively captures market trends with precision and insight.
- **Short-Term EMA (10-period):** This EMA reacts swiftly to recent price changes, offering heightened sensitivity to short-term fluctuations. It excels in identifying immediate shifts in market sentiment, making it invaluable for pinpointing short-lived trends and potential reversal points.
- **Long-Term EMA (323-period):** In contrast, the long-term EMA provides a broader perspective by smoothing out short-term noise and focusing on longer-term trend direction. Its extended length filters out market noise effectively, providing a clear representation of the underlying trend's momentum and sustainability.
**Directional Movement Index (DMI) Metrics:**
The "Kyrie Crossover" script goes beyond traditional indicators by incorporating DMI metrics across multiple timeframes. By assessing trend strength and direction, traders gain valuable insights into market dynamics, allowing for informed decision-making.
**Simple Instructions to Profit:**
1. **Identify EMA Crossovers:** Look for instances where the short-term EMA (10-period) crosses above the long-term EMA (323-period) for a bullish signal, indicating a potential buying opportunity. Conversely, a crossover where the short-term EMA crosses below the long-term EMA signals a bearish trend and a potential selling opportunity.
2. **Confirm with DMI Metrics:** Validate EMA crossovers by checking DMI metrics across different timeframes (5 minutes, 15 minutes, 30 minutes, and 1 hour). Pay attention to color-coded indicators, with green indicating a bullish trend, red indicating a bearish trend, and white indicating no clear trend.
3. **Manage Risk:** Implement proper risk management techniques, such as setting stop-loss orders and position sizing based on your risk tolerance and trading objectives.
4. **Stay Informed:** Regularly monitor market conditions and adjust your trading strategy accordingly based on new signals and emerging trends.
Cauchy Distribution Trend AnalysisThis custom Pine Script indicator is designed to analyze assets, including cryptocurrencies, through a lens inspired by the Cauchy distribution's characteristics. It focuses on identifying potential long and short opportunities by evaluating the asset's price position relative to a dynamically calculated median price and a scale parameter. Here's a breakdown of its components and how to use it:
Components
Median Length: The period over which the median price is calculated. The median price acts as a proxy for the Cauchy distribution's location parameter, representing a central value around which the market price fluctuates.
MA Length: The length for calculating the moving average, which is used to determine the scale parameter. The scale parameter estimates the average volatility around the median price, adjusted for the selected averaging method.
Moving Average Type: Offers a choice between HMA (Hull Moving Average), SMA (Simple Moving Average), and EMA (Exponential Moving Average) to calculate the scale parameter. This flexibility allows users to tailor the sensitivity of the scale parameter to the asset's price volatility.
Median Price Calculation: Uses the close price (by default) to calculate the median price over the specified period.
Scale Parameter Calculation: A function that calculates the scale parameter based on the chosen average source. This parameter is used to identify the threshold for long and short conditions.
Strategy Logic
Long Condition: Triggered when the asset's close price is greater than the sum of the median price and the scale parameter. This indicates that the asset's price has moved significantly above the median price, suggesting bullish momentum.
Short Condition: Triggered when the asset's close price is less than the difference between the median price and the scale parameter. This indicates that the asset's price has moved significantly below the median price, suggesting bearish momentum.
EHRHART Algo Premium (V.2)EHRHART Algo Premium is a indicator designed to help traders analyze market flow. It work with multiple EMA for identifying the sentiment of market. It's very simple calculation but it's a good help for people who use price action. I think the visual of the chart is very important and and I wanted to create an indicator very visual. I'm price action lover like lots of people and I personally think it's very important to identify the flow of market because buying when the flow of market is up give you better chance to win your trade. It's not BUY and SELL signal, this indicator don't tell u when u need buy or when u need sell, it's principally here for helping the visual of trading chart (have a good clear chart). I decided to post this indicator because people were asking me how it worked and were curious about these colors, so here we go !
This indicator show:
The main flow ( green candle=buy pressure /red candle=seller pressure ), it's based on two EMA cross over, this two EMA are editable so u can take the combination you want depending on your trading strategy. When the first EMA is above the second EMA candle becoming green and when the second EMA is above the first EMA candle becoming red.
The trend of two EMA crossover (blue=bullish and violet=bearish), it's based on two EMA (two different than main flow) cross over, this two EMA are editable so u can take the combination you want depending on your trading strategy. When the first EMA is above the second EMA the trend becoming blue and when the second EMA is above the first EMA the trend becoming violet.
Potential trend reversals (violet candle), it's calculate with the two EMA of the main flow, when these two EMA becoming closer, the candle becoming violet. It meaning that the trend may reversals. I added sensitivity parameter, so u can adjust it depending on your trading strategy, the more sensitive it is, the more candle will be colored violet.
A system of RSI print on the chart, when the RSI becoming overbought (more than 75) a red triangle will pop up on the chart, and when the RSI becoming oversold (less than 25) a green triangle will pop up on the chart. U can show or hidden these setting.
Bullish candles are represented by hollow candles.
Bearish candles are represented by full candles.
You can use this indicator with multiple strategy, I personally use it with price action (support/resistance) and I made it for that (but it's your choice).
This is an example of how I'll use it:
Here we can see that the price is coming testing our weakly support, however the main flow is bullish (red candle), so I'm waiting my first signal (violet candle). When the first candle passed violet I decided to enter the trade because violet candle after red candle means that the two EMA start closed to themselves meaning that's the flow may turn green. My second signal will be candle passed green, because it meaning the two EMA start deviate from themselves, buyer are taking advantage. In this situation a green triangle on the support will be my third signal.
CAPACE MARKETThis custom indicator combines the Moving Average Convergence Divergence (MACD) and the Relative Strength Index (RSI) into a single trading tool. It calculates the MACD and RSI values, then averages these two indicators to create a composite line. This average line is intended to capture the momentum and relative strength of the market simultaneously, potentially offering a more nuanced view of market conditions.
Key features of the indicator include:
Visualization of MACD and RSI Lines: It plots the MACD and RSI values as separate lines on the chart, allowing traders to see the behavior of each indicator clearly.
Average Line: A line representing the average of the MACD and RSI indicators is plotted, providing a synthesized view of both momentum and strength.
Entry Points Indication: The indicator uses red dots to mark the points where the average line crosses over or under the MACD or RSI lines. These intersections are meant to signal potential entry points for traders.
Market Condition Highlighting: The background color changes based on whether the average line is above or below zero. A green background suggests a positive market condition (bullish), while a red background indicates a negative market condition (bearish).
This tool aims to offer traders an integrated perspective by combining the insights of both MACD and RSI, potentially aiding in the identification of entry and exit points as well as the overall market sentiment.
Bitcoin Momentum StrategyThis is a very simple long-only strategy I've used since December 2022 to manage my Bitcoin position.
I'm sharing it as an open-source script for other traders to learn from the code and adapt it to their liking if they find the system concept interesting.
General Overview
Always do your own research and backtesting - this script is not intended to be traded blindly (no script should be) and I've done limited testing on other markets beyond Ethereum and BTC, it's just a template to tweak and play with and make into one's own.
The results shown in the strategy tester are from Bitcoin's inception so as to get a large sample size of trades, and potential returns have diminished significantly as BTC has grown to become a mega cap asset, but the script includes a date filter for backtesting and it has still performed solidly in recent years (speaking from personal experience using it myself - DYOR with the date filter).
The main advantage of this system in my opinion is in limiting the max drawdown significantly versus buy & hodl. Theoretically much better returns can be made by just holding, but that's also a good way to lose 70%+ of your capital in the inevitable bear markets (also speaking from experience).
In saying all of that, the future is fundamentally unknowable and past results in no way guarantee future performance.
System Concept:
Capture as much Bitcoin upside volatility as possible while side-stepping downside volatility as quickly as possible.
The system uses a simple but clever momentum-style trailing stop technique I learned from one of my trading mentors who uses this approach on momentum/trend-following stock market systems.
Basically, the system "ratchets" up the stop-loss to be much tighter during high bearish volatility to protect open profits from downside moves, but loosens the stop loss during sustained bullish momentum to let the position ride.
It is invested most of the time, unless BTC is trading below its 20-week EMA in which case it stays in cash/USDT to avoid holding through bear markets. It only trades one position (no pyramiding) and does not trade short, but can easily be tweaked to do whatever you like if you know what you're doing in Pine.
Default parameters:
HTF: Weekly Chart
EMA: 20-Period
ATR: 5-period
Bar Lookback: 7
Entry Rule #1:
Bitcoin's current price must be trading above its higher-timeframe EMA (Weekly 20 EMA).
Entry Rule #2:
Bitcoin must not be in 'caution' condition (no large bearish volatility swings recently).
Enter at next bar's open if conditions are met and we are not already involved in a trade.
"Caution" Condition:
Defined as true if BTC's recent 7-bar swing high minus current bar's low is > 1.5x ATR, or Daily close < Daily 20-EMA.
Trailing Stop:
Stop is trailed 1 ATR from recent swing high, or 20% of ATR if in caution condition (ie. 0.2 ATR).
Exit on next bar open upon a close below stop loss.
I typically use a limit order to open & exit trades as close to the open price as possible to reduce slippage, but the strategy script uses market orders.
I've never had any issues getting filled on limit orders close to the market price with BTC on the Daily timeframe, but if the exchange has relatively low slippage I've found market orders work fine too without much impact on the results particularly since BTC has consistently remained above $20k and highly liquid.
Cost of Trading:
The script uses no leverage and a default total round-trip commission of 0.3% which is what I pay on my exchange based on their tier structure, but this can vary widely from exchange to exchange and higher commission fees will have a significantly negative impact on realized gains so make sure to always input the correct theoretical commission cost when backtesting any script.
Static slippage is difficult to estimate in the strategy tester given the wide range of prices & liquidity BTC has experienced over the years and it largely depends on position size, I set it to 150 points per buy or sell as BTC is currently very liquid on the exchange I trade and I use limit orders where possible to enter/exit positions as close as possible to the market's open price as it significantly limits my slippage.
But again, this can vary a lot from exchange to exchange (for better or worse) and if BTC volatility is high at the time of execution this can have a negative impact on slippage and therefore real performance, so make sure to adjust it according to your exchange's tendencies.
Tax considerations should also be made based on short-term trade frequency if crypto profits are treated as a CGT event in your region.
Summary:
A simple, but effective and fairly robust system that achieves the goals I set for it.
From my preliminary testing it appears it may also work on altcoins but it might need a bit of tweaking/loosening with the trailing stop distance as the default parameters are designed to work with Bitcoin which obviously behaves very differently to smaller cap assets.
Good luck out there!
Adaptive Moving Average (AMA) Signals (Zeiierman)█ Overview
The Adaptive Moving Average (AMA) Signals indicator, enhances the classic concept of moving averages by making them adaptive to the market's volatility. This adaptability makes the AMA particularly useful in identifying market trends with varying degrees of volatility.
The core of the AMA's adaptability lies in its Efficiency Ratio (ER), which measures the directionality of the market over a given period. The ER is calculated by dividing the absolute change in price over a period by the sum of the absolute differences in daily prices over the same period.
⚪ Why It's Useful
The AMA Signals indicator is particularly useful because of its adaptability to changing market conditions. Unlike static moving averages, it dynamically adjusts, providing more relevant signals that can help traders capture trends earlier or identify reversals with greater accuracy. Its configurability makes it suitable for various trading strategies and timeframes, from day trading to swing trading.
█ How It Works
The AMA Signals indicator operates on the principle of adapting to market efficiency through the calculation of the Efficiency Ratio (ER), which measures the directionality of the market over a specified period. By comparing the net price change to total price movements, the AMA adjusts its sensitivity, becoming faster during trending markets and slower during sideways markets. This adaptability is enhanced by a gamma parameter that filters signals for either trend continuation or reversal, making it versatile across different market conditions.
change = math.abs(close - close )
volatility = math.sum(math.abs(close - close ), n)
ER = change / volatility
Efficiency Ratio (ER) Calculation: The AMA begins with the computation of the Efficiency Ratio (ER), which measures the market's directionality over a specified period. The ER is a ratio of the net price change to the total price movements, serving as a measure of the efficiency of price movements.
Adaptive Smoothing: Based on the ER, the indicator calculates the smoothing constants for the fastest and slowest Exponential Moving Averages (EMAs). These constants are then used to compute a Scaled Smoothing Coefficient (SC) that adapts the moving average to the market's efficiency, making it faster during trending periods and slower in sideways markets.
Signal Generation: The AMA applies a filter, adjusted by a "gamma" parameter, to identify trading signals. This gamma influences the sensitivity towards trend or reversal signals, with options to adjust for focusing on either trend-following or counter-trend signals.
█ How to Use
Trend Identification: Use the AMA to identify the direction of the trend. An upward moving AMA indicates a bullish trend, while a downward moving AMA suggests a bearish trend.
Trend Trading: Look for buy signals when the AMA is trending upwards and sell signals during a downward trend. Adjust the fast and slow EMA lengths to match the desired sensitivity and timeframe.
Reversal Trading: Set the gamma to a positive value to focus on reversal signals, identifying potential market turnarounds.
█ Settings
Period for ER calculation: Defines the lookback period for calculating the Efficiency Ratio, affecting how quickly the AMA responds to changes in market efficiency.
Fast EMA Length and Slow EMA Length: Determine the responsiveness of the AMA to recent price changes, allowing traders to fine-tune the indicator to their trading style.
Signal Gamma: Adjusts the sensitivity of the filter applied to the AMA, with the ability to focus on trend signals or reversal signals based on its value.
AMA Candles: An innovative feature that plots candles based on the AMA calculation, providing visual cues about the market trend and potential reversals.
█ Alerts
The AMA Signals indicator includes configurable alerts for buy and sell signals, as well as positive and negative trend changes.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
DEMA Adjusted Average True Range [BackQuant]The use of the Double Exponential Moving Average (DEMA) within your Adjusted Average True Range (ATR) calculation serves as a cornerstone for enhancing the indicator's responsiveness to market changes. To delve deeper into why DEMA is employed specifically in the context of your ATR calculation, let's explore the inherent qualities of DEMA and its impact on the ATR's performance.
DEMA and Its Advantages
As previously mentioned, DEMA was designed to offer a more responsive alternative to the traditional Exponential Moving Average (EMA). By giving more weight to recent price data, DEMA reduces the lag typically associated with moving averages. This reduction in lag is especially beneficial for short-term traders looking to capitalize on trend reversals and other market movements as swiftly as possible.
The calculation of DEMA involves the following steps:
Calculate EMA1: This is the Exponential Moving Average of the price.
Calculate EMA2: This is the Exponential Moving Average of EMA1, thus it is a smoothing of a smoothing, leading to a greater lag.
Formulate DEMA: The formula
EMA1 = EMA of price
EMA2 = EMA of EMA1
DEMA = (2 x EMA1) - EMA2
effectively doubles the weighting of the most recent data points by subtracting the lagged, double-smoothed EMA2 from twice the single-smoothed EMA1.
This process enhances the moving average's sensitivity to recent price movements, allowing the DEMA to adhere more closely to the price bars than either EMA1 or EMA2 alone.
Integration with ATR
In the context of your ATR calculation, the integration of DEMA plays a crucial role in defining the indicator's core functionality. Here's a detailed explanation of how DEMA affects the ATR calculation:
Initial Determination of DEMA : By applying the DEMA formula to the chosen source data (which can be adjusted to use Heikin Ashi candle close prices for an even smoother analysis), you set a foundation for a more reactive trend-following mechanism within the ATR framework.
Application to ATR Bands : The calculated DEMA serves as the central line from which the ATR bands are derived. The ATR value, multiplied by a user-defined factor, is added to and subtracted from the DEMA to form the upper and lower bands, respectively. This dynamic adjustment not only reflects the volatility based on the ATR but does so in a way that is closely aligned with the most recent price action, thanks to the utilization of DEMA.
Enhanced Signal Quality : The responsiveness of DEMA ensures that the ATR bands adjust more promptly to changes in market conditions. This quality is vital for traders who rely on the ATR bands to identify potential entry and exit points, trend reversals, or to assess market volatility.
By employing DEMA as the core component in calculating the Adjusted Average True Range, your indicator leverages DEMA's reduced lag and increased weight on recent data to provide a more timely and accurate measure of market volatility. This innovative approach enhances the utility of the ATR by making it not only a tool for assessing volatility but also a more reactive indicator for trend analysis and trading signal generation.
The main concept of combining these is to reduce lag, get a more robust signal and still capture clear trends over medium time horizons.
For me, this is best used in confluence with other indicators, it can be made faster in order to get fasters response time, or slower. This is all depending on the needs of you as a trader.
User Inputs:
The script offers several user-configurable inputs, such as the period lengths for DEMA and ATR calculations, the multiplication factor for the ATR, and options to use Heikin Ashi candles or standard price data. Additionally, it allows for the toggling of visual features, like the plotting of the DEMA ATR and its moving average, and the application of color-coded trends on price bars.
Additional Features:
Moving Average Confluence: Traders can opt to display a moving average of the DEMA ATR, choosing from various types (e.g., SMA, EMA, HMA). This feature provides a layer of confluence, aiding in the identification of trend direction and strength.
Trend Identification :
The script employs logical conditions to ascertain the trend direction based on the movement of the DEMA ATR. It assigns colors to represent bullish or bearish trends, which are reflected in the plotted lines and the coloring of price bars.
Alerts :
Customizable alert conditions for trend reversals enhance the utility of the indicator for active trading, notifying users of significant changes in trend direction.
1D Backtests
We include these backtests as a general proxy for how they work.
Please do your own calibrating to suit it to your own needs and backtest.
Past results don't = future results but they can help you understand how it functions.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Volume Based S/R with EMA Crossover SignalsThis Pine Script indicator, titled "Volume Based S/R with EMA Crossover Signals," is designed for use on the TradingView platform and overlays on price charts to help traders identify potential buy and sell opportunities based on volume changes and EMA (Exponential Moving Average) crossovers. Let's break down its components for a detailed understanding:
Inputs
length: The number of bars used to calculate the standard deviation of the volume change. This parameter helps in identifying significant changes in volume over a specified period.
threshold: A multiplier applied to the standard deviation of volume change to determine significant spikes in volume, which are then used to identify support and resistance levels.
smoothLength: The length of the EMA used to smooth the price data, providing a clearer view of the overall price trend and helping to confirm trade signals.
fastEMALength and slowEMALength: The lengths of the fast and slow EMAs, respectively. These are used to generate crossover signals, where the crossing of the fast EMA over the slow EMA may indicate a potential entry or exit point.
Calculations
Volume Change and Standard Deviation: The script calculates the percentage change in volume from one bar to the next and then computes the standard deviation of these changes over the specified length. This process helps identify unusual volume activity, which can precede significant price movements.
Signal Generation Based on Volume: When the absolute value of the volume change divided by its standard deviation exceeds the threshold, it signals significant volume activity, potentially indicating strong support or resistance levels at previous highs or lows.
Smoothed Price: An EMA applied to the closing prices over smoothLength bars helps to confirm the trend direction and filter out noise.
EMA Crossover Signals: The script calculates two EMAs based on the fastEMALength and slowEMALength inputs. A crossover of these two averages generates potential buy or sell signals.
Logic for Buy/Sell Signals
Buy Signal: Generated when the price is above the identified support level (determined by significant volume activity), the fast EMA crosses above the slow EMA, and the price is also above the smoothed price. This confluence of conditions suggests upward momentum and potential buying opportunity.
Sell Signal: The opposite conditions generate a sell signal — when the price is below the identified resistance level, the fast EMA crosses below the slow EMA, and the price is below the smoothed price, indicating downward momentum and a potential selling opportunity.
Plotting
Support and Resistance Levels: Plotted as circles on the chart, with resistance levels in red and support levels in green, based on significant volume activity.
Smoothed Price and EMAs: The smoothed price line and both EMAs are plotted on the chart to help visually assess the trend and the crossover signals.
Buy and Sell Signals: Represented by shapes plotted on the chart, indicating the recommended trading action (buy or sell) based on the combined indicator logic.
Filling Between Support and Resistance: For visual clarity, the area between the identified support and resistance levels is filled, highlighting the range within which the price is expected to fluctuate.
This indicator offers a multi-faceted approach to trading, combining volume analysis with trend following via EMA crossovers. By identifying significant volume-based support and resistance levels and confirming trend direction with EMA crossovers and smoothed price trends, traders can make more informed decisions regarding entry and exit points. However, it's important to use this indicator as part of a comprehensive trading strategy, considering other factors such as market conditions, news, and technical analysis from other indicators.
Hull Trend and CompareThis Pine Script is a TradingView indicator called "Hull Trend and Compare." Its main purpose is to provide a visual representation of price trends and a comparative analysis between the selected symbol and another symbol chosen for comparison.
The key components and functionalities:
Price Trend Visualization:
1.Mode Selection:
Offers three modes: "Normal," "Linear," and "Heikin-Ashi."
Allows users to choose between a standard chart, linear regression, or Heikin-Ashi candlesticks.
2.Hull Moving Average (HullMA):
Calculates the HullMA for the selected mode and length.
Plots the HullMA on the chart.
Colors the background based on the relationship between HullMA and the closing price.
Generates buy and sell signals when the price crosses over or under the HullMA.
Symbol Comparison:
1.Comparison with Another Symbol:
Allows users to compare the selected symbol with another symbol (specified in the sym input).
Provides options to choose the method of calculation for the compared symbol (open, high, low, close).
Users can choose whether to use a different method of calculation (usem), adjust the length (len), and enable or disable comparison (usecmp).
Table Display:
1.Table for Technical Indicators:
Optionally displays a table showing technical indicators for both symbols.
Includes Stochastic Momentum, RSI (Relative Strength Index), and MACD (Moving Average Convergence Divergence).
Colors the table cells based on the direction of the indicators.
Users can customize the table's position, text size, and visibility (shwtbl).
Technical Indicators:
1.Stochastic Momentum (StochMoM):
Calculates %K and %D using the Stochastic formula.
Displays StochMoM values and colors cells based on bullish or bearish conditions.
2.Relative Strength Index (RSI):
Computes the RSI values and colors cells based on the direction of the trend.
3.MACD (Moving Average Convergence Divergence):
Calculates MACD and Signal line values.
Displays MACD values and colors cells based on bullish or bearish conditions.
Summary:
This script provides traders with a versatile tool for analyzing price trends, comparing symbols, and viewing key technical indicators. The combination of visual elements on the chart and a detailed table enhances the ability to make informed trading decisions.
This script is provided for educational purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
F2X IndexThis script is designed to analyze financial market data, particularly focusing on trends and volatility. It allows users to input parameters such as index length and signal length. The script calculates moving averages and differences between the source data and the moving averages. It also optionally adjusts for volatility using the Average True Range (ATR) and can color the signal based on trend direction. The output includes plots for the index and signal, with customizable colors based on trend and volatility. The script provides a visual representation of market dynamics to aid in decision-making for traders and investors.
Multi Time Frame Exponential Moving Average and dasboardThis Pine script, titled "Multi Time Frame Exponential Moving Average (MTF EMA)," provides an innovative approach for traders who wish to track trends across multiple timeframes without having to switch between different charts. It combines two main features: an indicator displaying exponential moving averages (EMA) on five different time periods, as well as a compact dashboard that synthesizes this information on a single chart window.
The originality of this script lies in its ability to provide a comprehensive analysis of EMA trends across different time intervals, allowing traders to quickly and clearly understand the market dynamics without having to navigate between multiple charts. Rather than switching from one chart to another to observe trends on different time scales, traders can now consult a single dashboard to obtain all the necessary information.
The script uses exponential moving averages (EMA) to identify trends over five time periods: 5 minutes, 15 minutes, 1 hour, 4 hours, and 1 day. The values of the EMAs are calculated based on the closing prices of candles. Bullish or bearish trends are indicated by upward or downward arrows respectively, making it easy to interpret the information on the dashboard.
To use this script, traders can simply add it to their chart on the TradingView platform. They can customize the parameters of the exponential moving averages according to their preferences and choose between a dark or light theme for the dashboard. Then, they can observe trends on different time scales directly on the dashboard, enabling them to make informed trading decisions.
In summary, this script offers a practical and innovative solution for tracking trends across multiple timeframes, combining the efficiency of exponential moving averages with the convenience of a dashboard centralized on a single chart. This allows traders to save time and stay informed about market movements effectively and efficiently.
Volatility Adjusted EMA - by CrunchsterApplies recent volatility adjustment to the exponential moving average, where the smoothing factor is 2/(N + 1) - N being the lookback period or span
Volatility of recent 30 days returns is calculated using standard deviation with a thirty day lookback.
Increased smoothing compared to a standard EMA, which also adjusts to market conditions, as first described by Chande in 1991.
Composite Trend Oscillator [ChartPrime]CODE DUELLO:
Have you ever stopped to wonder what the underlying filters contained within complex algorithms are actually providing for you? Wouldn't it be nice to actually visually inspect for that? Those would require some kind of wild west styled quick draw duel or some comparison method as a proper 'code duello'. Then it can be determined which filter can 'draw' the quickest from it's computational holster with the least amount of lag and smoothness.
In Pine we can do so, discovering how beneficial that would be. This can be accomplished by quickly switching from one filter to another by input() back and forth, requiring visual memory. A better way could be done by placing two indicators added to the chart and then eventually placed into one indicator pane on top of each other.
By adding a filter() helper function that calls other moving average functions chosen for comparison, it can put to the test which moving average is the best drawing filter suited to our expected needs. PhiSmoother was formerly debuted and now it is utilized in a more complex environment in a multitude of ways along side other commonly utilized filters. Now, you the reader, get to judge for yourself...
FILTER VERSATILITY:
Having the capability to adjust between various smoothing methods such as PhiSmoother, TEMA, DEMA, WMA, EMA, and SMA on historical market data within the code provides an advantage. Each of these filter methods offers distinct advantages and hinderances. PhiSmoother stands out often by having superb noise rejection, while also being able to manipulate the fine-tuning of the phase or lag of the indicator, enhancing responsiveness to price movements.
The following are more well-known classic filters. TEMA (Triple Exponential Moving Average) and DEMA (Double Exponential Moving Average) offer reduced transient response times to price changes fluctuations. WMA (Weighted Moving Average) assigns more weight to recent data points, making it particularly useful for reduced lag. EMA (Exponential Moving Average) strikes a balance between responsiveness and computational efficiency, making it a popular choice. SMA (Simple Moving Average) provides a straightforward calculation based on the arithmetic mean of the data. VWMA and RMA have both been excluded for varying reasons, both being unworthy of having explanation here.
By allowing for adjustment refinements between these filter methods, traders may garner the flexibility to adapt their analysis to different market dynamics, optimizing their algorithms for improved decision-making and performance on demand.
INDICATOR INTRODUCTION:
ChartPrime's Composite Trend Oscillator operates as an oscillator based on the concept of a moving average ribbon. It utilizes up to 32 filters with progressively longer periods to assess trend direction and strength. Embedded within this indicator is an alternative view that utilizes the separation of the ribbon filaments to assess volatility. Both versions are excellent candidates for trend and momentum, both offering visualization of polarity, directional coloring, and filter crossings. Anyone who has former experience using RSI or stochastics may have ease of understanding applying this to their chart.
COMPOSITE CLUSTER MODES EXPLAINED:
In Trend Strength mode, the oscillator behavior signifies market direction and movement strength. When the oscillator is rising and above zero, the market is within a bullish phase, and visa versa. If the signal filter crosses the composite trend, this indicates a potential dynamic shift signaling a possible reversal. When the oscillator is teetering on its extremities, the market is more inclined to reverse later.
With Volatility mode, the oscillator undergoes a transformation, displaying an unbounded oscillator driven by market volatility. While it still employs the same scoring mechanism, it is now scaled according to the strength of the market move. This can aid with identification of ranging scenarios. However, one side effect is that the oscillator no longer has minimum or maximum boundaries. This can still be advantageous when considering divergences.
NOTEWORTHY SETTINGS FEATURES:
The following input settings described offer comprehensive control over the indicator's behavior and visualization.
Common Controls:
Price Source Selection - The indicator offers flexibility in choosing the price source for analysis. Traders can select from multiple options.
Composite Cluster Mode - Choose between "Trend Strength" and "Volatility" modes, providing insights into trend directionality or volatility weighting.
Cluster Filter and Length - Selects a filter for the cluster composition. This includes a length parameter adjustment.
Cluster Options:
Cluster Dispersion - Users can adjust the separation between moving averages in the cluster, influencing the sensitivity of the analysis.
Cluster Trimming - By modifying upper and lower trim parameters, traders can adjust the sensitivity of the moving averages within the cluster, enhancing its adaptability.
PostSmooth Filter and Length - Choose a filter to refine the composite cluster's post-smoothing with a length parameter adjustment.
Signal Filter and Length - Users can select a filter for the lagging signal plot, also having a length parameter adjustment.
Transition Easing - Sensitivity adjustment to influence the transition between bullish and bearish colors.
Enjoy
Exponentially Weighted Moving Average Oscillator [BackQuant]Exponentially Weighted Moving Average (EWMA)
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, the main applications being technical analysis and volatility modeling.
The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted.
Applications of the EWMA
The EWMA is widely used in technical analysis. It may not be used directly, but it is used in conjunction with other indicators to generate trading signals. A well-known example is the Negative Volume Index (NVI), which is used in conjunction with its EWMA.
Why is it different from the In-Built TradingView EWMA
Adaptive Algorithms: If your strategy requires the alpha parameter to change adaptively based on certain conditions (for example, based on market volatility), a for loop can be used to adjust the weights dynamically within the loop as opposed to the fixed decay rate in the standard EWMA.
Customization: A for loop allows for more complex and nuanced calculations that may not be directly supported by built-in functions. For example, you might want to adjust the weights in a non-standard way that the typical EWMA calculation doesn't allow for.
Use of the Oscillator
This mainly comes from 3 main premises, this is something I like to do personally since it is easier to work with them in the context of my system. E.g. Using them to spot clear trends without noise on longer timeframes.
Clarity: Plotting the EWMA as an oscillator provides a clear visual representation of the momentum or trend strength. It allows traders to see overbought or oversold conditions relative to a normalized range.
Comparison: An oscillator can make it easier to compare different securities or timeframes on a similar scale, especially when normalized. This is because the oscillator values are typically bounded within a range (like -1 to 1 or 0 to 100), whereas the actual price series can vary significantly.
Focus on Change: When plotted as an oscillator, the focus is on the rate of change or the relative movement of the EWMA, not on the absolute price levels. This can help traders spot divergences or convergences that may not be as apparent when the EWMA is plotted directly on the price chart. This is also one reason there is a conditional plotting on the chart.
Trend Strength: When normalized, the distance of the oscillator from its midpoint can be interpreted as the strength of the trend, providing a quantitative measure that can be used to make systematic trading decisions.
Here are the backtests on the 1D Timeframe for
BITSTAMP:BTCUSD
BITSTAMP:ETHUSD
COINBASE:SOLUSD
When using this script the user is able to define a source and period, which by extension calculates the alpha. An option to colour the bars accord to trend.
This makes it super easy to use in a system.
I recommend using this as above the midline (0) for a positive trend and below the midline for negative trend.
Hence why I put a label on the last bar to ensure it is easier for traders to read.
Lastly, The decreasing colour relative to RoC, this also helps traders to understand the strength of the indicator and gain insight into when to potentially reduce position size.
This indicator is best used in the medium timeframe.
Trend Deviation strategy - BTC [IkkeOmar]Intro:
This is an example if anyone needs a push to get started with making strategies in pine script. This is an example on BTC, obviously it isn't a good strategy, and I wouldn't share my own good strategies because of alpha decay.
This strategy integrates several technical indicators to determine market trends and potential trade setups. These indicators include:
Directional Movement Index (DMI)
Bollinger Bands (BB)
Schaff Trend Cycle (STC)
Moving Average Convergence Divergence (MACD)
Momentum Indicator
Aroon Indicator
Supertrend Indicator
Relative Strength Index (RSI)
Exponential Moving Average (EMA)
Volume Weighted Average Price (VWAP)
It's crucial for you guys to understand the strengths and weaknesses of each indicator and identify synergies between them to improve the strategy's effectiveness.
Indicator Settings:
DMI (Directional Movement Index):
Length: This parameter determines the number of bars used in calculating the DMI. A higher length may provide smoother results but might lag behind the actual price action.
Bollinger Bands:
Length: This parameter specifies the number of bars used to calculate the moving average for the Bollinger Bands. A longer length results in a smoother average but might lag behind the price action.
Multiplier: The multiplier determines the width of the Bollinger Bands. It scales the standard deviation of the price data. A higher multiplier leads to wider bands, indicating increased volatility, while a lower multiplier results in narrower bands, suggesting decreased volatility.
Schaff Trend Cycle (STC):
Length: This parameter defines the length of the STC calculation. A longer length may result in smoother but slower-moving signals.
Fast Length: Specifies the length of the fast moving average component in the STC calculation.
Slow Length: Specifies the length of the slow moving average component in the STC calculation.
MACD (Moving Average Convergence Divergence):
Fast Length: Determines the number of bars used to calculate the fast EMA (Exponential Moving Average) in the MACD.
Slow Length: Specifies the number of bars used to calculate the slow EMA in the MACD.
Signal Length: Defines the number of bars used to calculate the signal line, which is typically an EMA of the MACD line.
Momentum Indicator:
Length: This parameter sets the number of bars over which momentum is calculated. A longer length may provide smoother momentum readings but might lag behind significant price changes.
Aroon Indicator:
Length: Specifies the number of bars over which the Aroon indicator calculates its values. A longer length may result in smoother Aroon readings but might lag behind significant market movements.
Supertrend Indicator:
Trendline Length: Determines the length of the period used in the Supertrend calculation. A longer length results in a smoother trendline but might lag behind recent price changes.
Trendline Factor: Specifies the multiplier used in calculating the trendline. It affects the sensitivity of the indicator to price changes.
RSI (Relative Strength Index):
Length: This parameter sets the number of bars over which RSI calculates its values. A longer length may result in smoother RSI readings but might lag behind significant price changes.
EMA (Exponential Moving Average):
Fast EMA: Specifies the number of bars used to calculate the fast EMA. A shorter period results in a more responsive EMA to recent price changes.
Slow EMA: Determines the number of bars used to calculate the slow EMA. A longer period results in a smoother EMA but might lag behind recent price changes.
VWAP (Volume Weighted Average Price):
Default settings are typically used for VWAP calculations, which consider the volume traded at each price level over a specific period. This indicator provides insights into the average price weighted by trading volume.
backtest range and rules:
You can specify the start date for backtesting purposes.
You can can select the desired trade direction: Long, Short, or Both.
Entry and Exit Conditions:
LONG:
DMI Cross Up: The Directional Movement Index (DMI) indicates a bullish trend when the positive directional movement (+DI) crosses above the negative directional movement (-DI).
Bollinger Bands (BB): The price is below the upper Bollinger Band, indicating a potential reversal from the upper band.
Momentum Indicator: Momentum is positive, suggesting increasing buying pressure.
MACD (Moving Average Convergence Divergence): The MACD line is above the signal line, indicating bullish momentum.
Supertrend Indicator: The Supertrend indicator signals an uptrend.
Schaff Trend Cycle (STC): The STC indicates a bullish trend.
Aroon Indicator: The Aroon indicator signals a bullish trend or crossover.
When all these conditions are met simultaneously, the strategy considers it a favorable opportunity to enter a long trade.
SHORT:
DMI Cross Down: The Directional Movement Index (DMI) indicates a bearish trend when the negative directional movement (-DI) crosses above the positive directional movement (+DI).
Bollinger Bands (BB): The price is above the lower Bollinger Band, suggesting a potential reversal from the lower band.
Momentum Indicator: Momentum is negative, indicating increasing selling pressure.
MACD (Moving Average Convergence Divergence): The MACD line is below the signal line, signaling bearish momentum.
Supertrend Indicator: The Supertrend indicator signals a downtrend.
Schaff Trend Cycle (STC): The STC indicates a bearish trend.
Aroon Indicator: The Aroon indicator signals a bearish trend or crossover.
When all these conditions align, the strategy considers it an opportune moment to enter a short trade.
Disclaimer:
THIS ISN'T AN OPTIMAL STRATEGY AT ALL! It was just an old project from when I started learning pine script!
The backtest doesn't promise the same results in the future, always do both in-sample and out-of-sample testing when backtesting a strategy. And make sure you forward test it as well before implementing it!
Furthermore this strategy uses both trend and mean-reversion systems, that is usually a no-go if you want to build robust trend systems .
Don't hesitate to comment if you have any questions or if you have some good notes for a beginner.
Dynamic Gradient Filter
Sigmoid Functions:
History and Mathematical Basis:
Sigmoid functions have a rich history in mathematics and are widely used in various fields, including statistics, machine learning, and signal processing.
The term "sigmoid" originates from the Greek words "sigma" (meaning "S-shaped") and "eidos" (meaning "form" or "type").
The sigmoid curve is characterized by its smooth S-shaped appearance, which allows it to map any real-valued input to a bounded output range, typically between 0 and 1.
The most common form of the sigmoid function is the logistic function:
Logistic Function (σ):
Defined as σ(x) = 1 / (1 + e^(-x)), where:
'x' is the input value,
'e' is Euler's number (approximately 2.71828).
This function was first introduced by Belgian mathematician Pierre François Verhulst in the 1830s to model population growth with limiting factors.
It gained popularity in the early 20th century when statisticians like Ronald Fisher began using it in regression analysis.
Specific Sigmoid Functions Used in the Indicator:
sig(val):
The 'sig' function in this indicator is a modified version of the logistic function, clamping a value between 0 and 1 on the sigmoid curve.
siga(val):
The 'siga' function adjusts values between -1 and 1 on the sigmoid curve, offering a centered variation of the sigmoid effect.
sigmoid(val):
The 'sigmoid' function provides a standard implementation of the logistic function, calculating the sigmoid value of the input data.
Adaptive Smoothing Factor:
The ' adaptiveSmoothingFactor(gradient, k)' function computes a dynamic smoothing factor for the filter based on the gradient of the price data and the user-defined sensitivity parameter 'k' .
Gradient:
The gradient represents the rate of change in price, calculated as the absolute difference between the current and previous close prices.
Sensitivity (k):
The 'k' parameter adjusts how quickly the filter reacts to changes in the gradient. Higher values of 'k' lead to a more responsive filter, while lower values result in smoother outputs.
Usage in the Indicator:
The "close" value refers to the closing price of each period in the chart's time frame
The indicator calculates the gradient by measuring the absolute difference between the current "close" price and the previous "close" price.
This gradient represents the strength or magnitude of the price movement within the chosen time frame.
The "close" value plays a pivotal role in determining the dynamic behavior of the "Dynamic Gradient Filter," as it directly influences the smoothing factor.
What Makes This Special:
The "Dynamic Gradient Filter" indicator stands out due to its adaptive nature and responsiveness to changing market conditions.
Dynamic Smoothing Factor:
The indicator's dynamic smoothing factor adjusts in real-time based on the rate of change in price (gradient) and the user-defined sensitivity '(k)' parameter.
This adaptability allows the filter to respond promptly to both minor fluctuations and significant price movements.
Smoothed Price Action:
The final output of the filter is a smoothed representation of the price action, aiding traders in identifying trends and potential reversals.
Customizable Sensitivity:
Traders can adjust the 'Sensitivity' parameter '(k)' to suit their preferred trading style, making the indicator versatile for various strategies.
Visual Clarity:
The plotted "Dynamic Gradient Filter" line on the chart provides a clear visual guide, enhancing the understanding of market dynamics.
Usage:
Traders and analysts can utilize the "Dynamic Gradient Filter" to:
Identify trends and reversals in price movements.
Filter out noise and highlight significant price changes.
Fine-tune trading strategies by adjusting the sensitivity parameter.
Enhance visual analysis with a dynamically adjusting filter line on the chart.
Literature:
en.wikipedia.org
medium.com
en.wikipedia.org
