Platonic Solids Visualization-Scret Geometry-AYNETExplanation:
Input Options:
solid: Choose the type of Platonic Solid (Tetrahedron, Cube, Octahedron, etc.).
size: Adjust the size of the geometry.
color_lines: Choose the color for the edges.
line_width: Set the width of the edges.
Geometry Calculations:
Each solid is drawn based on predefined coordinates and connected using the line.new function.
Geometric Types Supported:
Tetrahedron: A triangular pyramid.
Cube: A square-based 2D projection.
Octahedron: Two pyramids joined at the base.
Unsupported Solids:
Dodecahedron and Icosahedron are geometrically more complex and not rendered in this basic implementation.
Visualization:
The chosen Platonic Solid will be drawn relative to the center position (center_y) on the chart.
Adjust the size and center_y inputs to position the shape correctly.
Let me know if you need improvements or have a specific geometry to implement!
Cycles
Sri Yantra-Scret Geometry - AYNETExplanation of the Script
Inputs:
periods: Number of bars used for calculating the moving average and standard deviation.
yloc: Chooses the display location (above or below the bars).
Moving Average and Standard Deviation:
ma: Moving average of the close price for the specified period.
std: Standard deviation, used to set the range for the Sri Yantra triangle points.
Triangle Points:
p1, p2, and p3 are the points for constructing the triangle, with p1 and p2 set at two standard deviations above and below the moving average, and p3 at the moving average itself.
Sri Yantra Triangle Drawing:
Three lines form a triangle, with the moving average line serving as the midpoint anchor.
The triangle pattern shifts across bars as new moving average values are calculated.
Moving Average Plot:
The moving average is plotted in red for visual reference against the triangle pattern.
This basic script emulates the Sri Yantra pattern using price data, creating a spiritual and aesthetic overlay on price charts, ideal for users looking to incorporate sacred geometry into their technical analysis.
Holt-Winters Forecast BandsDescription:
The Holt-Winters Adaptive Bands indicator combines seasonal trend forecasting with adaptive volatility bands. It uses the Holt-Winters triple exponential smoothing model to project future price trends, while Nadaraya-Watson smoothed bands highlight dynamic support and resistance zones.
This indicator is ideal for traders seeking to predict future price movements and visualize potential market turning points. By focusing on broader seasonal and trend data, it provides insight into both short- and long-term market directions. It’s particularly effective for swing trading and medium-to-long-term trend analysis on timeframes like daily and 4-hour charts, although it can be adjusted for other timeframes.
Key Features:
Holt-Winters Forecast Line: The core of this indicator is the Holt-Winters model, which uses three components — level, trend, and seasonality — to project future prices. This model is widely used for time-series forecasting, and in this script, it provides a dynamic forecast line that predicts where price might move based on historical patterns.
Adaptive Volatility Bands: The shaded areas around the forecast line are based on Nadaraya-Watson smoothing of historical price data. These bands provide a visual representation of potential support and resistance levels, adapting to recent volatility in the market. The bands' fill colors (red for upper and green for lower) allow traders to identify potential reversal zones without cluttering the chart.
Dynamic Confidence Levels: The indicator adapts its forecast based on market volatility, using inputs such as average true range (ATR) and price deviations. This means that in high-volatility conditions, the bands may widen to account for increased price movements, helping traders gauge the current market environment.
How to Use:
Forecasting: Use the forecast line to gain insight into potential future price direction. This line provides a directional bias, helping traders anticipate whether the price may continue along a trend or reverse.
Support and Resistance Zones: The shaded bands act as dynamic support and resistance zones. When price enters the upper (red) band, it may be in an overbought area, while the lower (green) band may indicate oversold conditions. These bands adjust with volatility, so they reflect the current market conditions rather than fixed levels.
Timeframe Recommendations:
This indicator performs best on daily and 4-hour charts due to its reliance on trend and seasonality. It can be used on lower timeframes, but accuracy may vary due to increased price noise.
For traders looking to capture swing trades, the daily and 4-hour timeframes provide a balance of trend stability and signal reliability.
Adjustable Settings:
Alpha, Beta, and Gamma: These settings control the level, trend, and seasonality components of the forecast. Alpha is generally the most sensitive setting for adjusting responsiveness to recent price movements, while Beta and Gamma help fine-tune the trend and seasonal adjustments.
Band Smoothing and Deviation: These settings control the lookback period and width of the volatility bands, allowing users to customize how closely the bands follow price action.
Parameters:
Prediction Length: Sets the length of the forecast, determining how far into the future the prediction line extends.
Season Length: Defines the seasonality cycle. A setting of 14 is typical for bi-weekly cycles, but this can be adjusted based on observed market cycles.
Alpha, Beta, Gamma: These parameters adjust the Holt-Winters model's sensitivity to recent prices, trends, and seasonal patterns.
Band Smoothing: Determines the smoothing applied to the bands, making them either more reactive or smoother.
Ideal Use Cases:
Swing Trading and Trend Following: The Holt-Winters model is particularly suited for capturing larger market trends. Use the forecast line to determine trend direction and the bands to gauge support/resistance levels for potential entries or exits.
Identifying Reversal Zones: The adaptive bands act as dynamic overbought and oversold zones, giving traders potential reversal areas when price reaches these levels.
Important Notes:
No Buy/Sell Signals: This indicator does not produce direct buy or sell signals. It’s intended for visual trend analysis and support/resistance identification, leaving trade decisions to the user.
Not for High-Frequency Trading: Due to the nature of the Holt-Winters model, this indicator is optimized for higher timeframes like the daily and 4-hour charts. It may not be suitable for high-frequency or scalping strategies on very short timeframes.
Adjust for Volatility: If using the indicator on lower timeframes or more volatile assets, consider adjusting the band smoothing and prediction length settings for better responsiveness.
Performance-INDIA & GLOBAL MARKETS-MADGrowth vs. Stability: India is expected to maintain relatively strong economic growth compared to many other global markets, which are facing slower growth or even recession risks. The Indian economy is benefiting from a large domestic market, young population, and rising digital and infrastructure investments.
Volatility: Indian markets are often more volatile due to domestic factors, such as political changes, policy announcements, and inflationary pressures. Global markets, on the other hand, tend to experience volatility based on external economic factors and geopolitical risks.
Inflation and Interest Rates: Both India and global markets are dealing with inflation, but India’s central bank (RBI) is seen as being proactive in controlling inflation through interest rate hikes. Globally, major central banks like the Fed and ECB are tightening their monetary policies, which is contributing to global economic slowdown concerns.
Central Bank Liquidity YOY % Change - Second DerivativeThis indicator measures the acceleration or deceleration in the yearly growth rate of central bank liquidity.
By calculating the year-over-year percentage change of the YoY growth rate, it highlights shifts in the pace of liquidity changes, providing insights into market momentum or potential reversals influenced by central bank actions.
This can help reveal impulses in liquidity by identifying changes in the growth rate's acceleration or deceleration. When central bank liquidity experiences a rapid increase or decrease, the second derivative captures these shifts as sharp upward or downward movements.
These impulses often signal pivotal liquidity shifts, which may correspond to major policy changes, market interventions, or financial stability measures, offering an early signal of potential market impacts.
CAGR ProjectionThe CAGR Projection Indicator is a tool designed to visualize the potential growth of an asset over time based on a specified annual growth rate. This indicator overlays a projection line on the price chart, allowing traders and investors to compare actual price movements with a hypothetical growth trajectory.
One of the key features of this indicator is the ability for users to input their expected annual growth rate as a percentage. This flexibility allows for various scenarios to be modeled, from conservative estimates to more optimistic projections. Additionally, the indicator allows users to set a specific start date for the projection, enabling analysis from any chosen point in time.
The projection calculation is dynamic, adjusting for different timeframes and updating with each new bar on the chart. The indicator initializes either at the specified start date or when the first valid price is encountered. Using the initial price as a base, the indicator calculates the projected price for each subsequent bar using the compound growth formula. The calculation accounts for the specific timeframe of the chart, ensuring accurate projections regardless of whether the chart displays daily, weekly, or other intervals.
The projected growth is plotted as a blue line on the chart, providing a clear visual comparison between the actual price movement and the hypothetical growth trajectory. This visual representation makes it easy for users to quickly assess how an asset is performing relative to the expected growth rate.
This tool has several practical applications. Investors can use it to set realistic growth targets for their investments. By comparing actual price movements to the projection line, users can quickly assess if an asset is outperforming or underperforming relative to the expected growth rate. Furthermore, multiple instances of the indicator can be used with different growth rates to visualize various potential outcomes, facilitating scenario analysis.
The indicator also offers customization options, such as displaying a label showing the annual growth rate used for the projection, and the ability to adjust the color of the projection line to suit individual preferences or chart setups.
In summary, this CAGR Projection indicator serves as a valuable tool for both long-term investors and traders, offering a simple yet effective way to visualize potential growth scenarios and assess investment performance over time. It combines ease of use with powerful analytical capabilities, making it a useful addition to any trader's or investor's toolkit.
Crypto Wallets Profitability & Performance [LuxAlgo]The Crypto Wallets Profitability & Performance indicator provides a comprehensive view of the financial status of cryptocurrency wallets by leveraging on-chain data from IntoTheBlock. It measures the percentage of wallets profiting, losing, or breaking even based on current market prices.
Additionally, it offers performance metrics across different timeframes, enabling traders to better assess market conditions.
This information can be crucial for understanding market sentiment and making informed trading decisions.
🔶 USAGE
🔹 Wallets Profitability
This indicator is designed to help traders and analysts evaluate the profitability of cryptocurrency wallets in real-time. It aggregates data gathered from the blockchain on the number of wallets that are in profit, loss, or breaking even and presents it visually on the chart.
Breaking even line demonstrates how realized gains and losses have changed, while the profit and the loss monitor unrealized gains and losses.
The signal line helps traders by providing a smoothed average and highlighting areas relative to profiting and losing levels. This makes it easier to identify and confirm trading momentum, assess strength, and filter out market noise.
🔹 Profitability Meter
The Profitability Meter is an alternative display that visually represents the percentage of wallets that are profiting, losing, or breaking even.
🔹 Performance
The script provides a view of the financial health of cryptocurrency wallets, showing the percentage of wallets in profit, loss, or breaking even. By combining these metrics with performance data across various timeframes, traders can gain valuable insights into overall wallet performance, assess trend strength, and identify potential market reversals.
🔹 Dashboard
The dashboard presents a consolidated view of key statistics. It allows traders to quickly assess the overall financial health of wallets, monitor trend strength, and gauge market conditions.
🔶 DETAILS
🔹 The Chart Occupation Option
The chart occupation option adjusts the occupation percentage of the chart to balance the visibility of the indicator.
🔹 The Height in Performance Options
Crypto markets often experience significant volatility, leading to rapid and substantial gains or losses. Hence, plotting performance graphs on top of the chart alongside other indicators can result in a cluttered display. The height option allows you to adjust the plotting for balanced visibility, ensuring a clearer and more organized chart.
🔶 SETTINGS
The script offers a range of customizable settings to tailor the analysis to your trading needs.
Chart Occupation %: Adjust the occupation percentage of the chart to balance the visibility of the indicator.
🔹 Profiting Wallets
Profiting Percentage: Toggle to display the percentage of wallets in profit.
Smoothing: Adjust the smoothing period for the profiting percentage line.
Signal Line: Choose a signal line type (SMA, EMA, RMA, or None) to overlay on the profiting percentage.
🔹 Losing Wallets
Losing Percentage: Toggle to display the percentage of wallets in loss.
Smoothing: Adjust the smoothing period for the losing percentage line.
Signal Line: Choose a signal line type (SMA, EMA, RMA, or None) to overlay on the losing percentage.
🔹 Breaking Even Wallets
Breaking-Even Percentage: Toggle to display the percentage of wallets breaking even.
Smoothing: Adjust the smoothing period for the breaking-even percentage line.
🔹 Profitability Meter
Profitability Meter: Enable or disable the meter display, set its width, and adjust the offset.
🔹 Performance
Performance Metrics: Choose the timeframe for performance metrics (Day to Date, Week to Date, etc.).
Height: Adjust the height of the chart visuals to balance the visibility of the indicator.
🔹 Dashboard
Block Profitability Stats: Toggle the display of profitability stats.
Performance Stats: Toggle the display of performance stats.
Dashboard Size and Position: Customize the size and position of the performance dashboard on the chart.
🔶 RELATED SCRIPTS
Market-Sentiment-Technicals
Multi-Chart-Widget
Exposure Oscillator (Cumulative 0 to ±100%)
Exposure Oscillator (Cumulative 0 to ±100%)
This Pine Script indicator plots an "Exposure Oscillator" on the chart, which tracks the cumulative market exposure from a range of technical buy and sell signals. The exposure is measured on a scale from -100% (maximum short exposure) to +100% (maximum long exposure), helping traders assess the strength of their position in the market. It provides an intuitive visual cue to aid decision-making for trend-following strategies.
Buy Signals (Increase Exposure Score by +10%)
Buy Signal 1 (Cross Above 21 EMA):
This signal is triggered when the price crosses above the 21-period Exponential Moving Average (EMA), where the current bar closes above the EMA21, and the previous bar closed below the EMA21. This indicates a potential upward price movement as the market shifts into a bullish trend.
buySignal1 = ta.crossover(close, ema21)
Buy Signal 2 (Trending Above 21 EMA):
This signal is triggered when the price closes above the 21-period EMA for each of the last 5 bars, indicating a sustained bullish trend. It confirms that the price is consistently above the EMA21 for a significant period.
buySignal2 = ta.barssince(close <= ema21) > 5
Buy Signal 3 (Living Above 21 EMA):
This signal is triggered when the price has closed above the 21-period EMA for each of the last 15 bars, demonstrating a strong, prolonged uptrend.
buySignal3 = ta.barssince(close <= ema21) > 15
Buy Signal 4 (Cross Above 50 SMA):
This signal is triggered when the price crosses above the 50-period Simple Moving Average (SMA), where the current bar closes above the 50 SMA, and the previous bar closed below it. It indicates a shift toward bullish momentum.
buySignal4 = ta.crossover(close, sma50)
Buy Signal 5 (Cross Above 200 SMA):
This signal is triggered when the price crosses above the 200-period Simple Moving Average (SMA), where the current bar closes above the 200 SMA, and the previous bar closed below it. This suggests a long-term bullish trend.
buySignal5 = ta.crossover(close, sma200)
Buy Signal 6 (Low Above 50 SMA):
This signal is true when the lowest price of the current bar is above the 50-period SMA, indicating strong bullish pressure as the price maintains itself above the moving average.
buySignal6 = low > sma50
Buy Signal 7 (Accumulation Day):
An accumulation day occurs when the closing price is in the upper half of the daily range (greater than 50%) and the volume is larger than the previous bar's volume, suggesting buying pressure and accumulation.
buySignal7 = (close - low) / (high - low) > 0.5 and volume > volume
Buy Signal 8 (Higher High):
This signal occurs when the current bar’s high exceeds the highest high of the previous 14 bars, indicating a breakout or strong upward momentum.
buySignal8 = high > ta.highest(high, 14)
Buy Signal 9 (Key Reversal Bar):
This signal is generated when the stock opens below the low of the previous bar but rallies to close above the previous bar’s high, signaling a potential reversal from bearish to bullish.
buySignal9 = open < low and close > high
Buy Signal 10 (Distribution Day Fall Off):
This signal is triggered when a distribution day (a day with high volume and a close near the low of the range) "falls off" the rolling 25-bar period, indicating the end of a bearish trend or selling pressure.
buySignal10 = ta.barssince(close < sma50 and close < sma50) > 25
Sell Signals (Decrease Exposure Score by -10%)
Sell Signal 1 (Cross Below 21 EMA):
This signal is triggered when the price crosses below the 21-period Exponential Moving Average (EMA), where the current bar closes below the EMA21, and the previous bar closed above it. It suggests that the market may be shifting from a bullish trend to a bearish trend.
sellSignal1 = ta.crossunder(close, ema21)
Sell Signal 2 (Trending Below 21 EMA):
This signal is triggered when the price closes below the 21-period EMA for each of the last 5 bars, indicating a sustained bearish trend.
sellSignal2 = ta.barssince(close >= ema21) > 5
Sell Signal 3 (Living Below 21 EMA):
This signal is triggered when the price has closed below the 21-period EMA for each of the last 15 bars, suggesting a strong downtrend.
sellSignal3 = ta.barssince(close >= ema21) > 15
Sell Signal 4 (Cross Below 50 SMA):
This signal is triggered when the price crosses below the 50-period Simple Moving Average (SMA), where the current bar closes below the 50 SMA, and the previous bar closed above it. It indicates the start of a bearish trend.
sellSignal4 = ta.crossunder(close, sma50)
Sell Signal 5 (Cross Below 200 SMA):
This signal is triggered when the price crosses below the 200-period Simple Moving Average (SMA), where the current bar closes below the 200 SMA, and the previous bar closed above it. It indicates a long-term bearish trend.
sellSignal5 = ta.crossunder(close, sma200)
Sell Signal 6 (High Below 50 SMA):
This signal is true when the highest price of the current bar is below the 50-period SMA, indicating weak bullishness or a potential bearish reversal.
sellSignal6 = high < sma50
Sell Signal 7 (Distribution Day):
A distribution day is identified when the closing range of a bar is less than 50% and the volume is larger than the previous bar's volume, suggesting that selling pressure is increasing.
sellSignal7 = (close - low) / (high - low) < 0.5 and volume > volume
Sell Signal 8 (Lower Low):
This signal occurs when the current bar's low is less than the lowest low of the previous 14 bars, indicating a breakdown or strong downward momentum.
sellSignal8 = low < ta.lowest(low, 14)
Sell Signal 9 (Downside Reversal Bar):
A downside reversal bar occurs when the stock opens above the previous bar's high but falls to close below the previous bar’s low, signaling a reversal from bullish to bearish.
sellSignal9 = open > high and close < low
Sell Signal 10 (Distribution Cluster):
This signal is triggered when a distribution day occurs three times in the rolling 7-bar period, indicating significant selling pressure.
sellSignal10 = ta.valuewhen((close < low) and volume > volume , 1, 7) >= 3
Theme Mode:
Users can select the theme mode (Auto, Dark, or Light) to match the chart's background or to manually choose a light or dark theme for the oscillator's appearance.
Exposure Score Calculation: The script calculates a cumulative exposure score based on a series of buy and sell signals.
Buy signals increase the exposure score, while sell signals decrease it. Each signal impacts the score by ±10%.
Signal Conditions: The buy and sell signals are derived from multiple conditions, including crossovers with moving averages (EMA21, SMA50, SMA200), trend behavior, and price/volume analysis.
Oscillator Visualization: The exposure score is visualized as a line on the chart, changing color based on whether the exposure is positive (long position) or negative (short position). It is limited to the range of -100% to +100%.
Position Type: The indicator also indicates the position type based on the exposure score, labeling it as "Long," "Short," or "Neutral."
Horizontal Lines: Reference lines at 0%, 100%, and -100% visually mark neutral, increasing long, and increasing short exposure levels.
Exposure Table: A table displays the current exposure level (in percentage) and position type ("Long," "Short," or "Neutral"), updated dynamically based on the oscillator’s value.
Inputs:
Theme Mode: Choose "Auto" to use the default chart theme, or manually select "Dark" or "Light."
Usage:
This oscillator is designed to help traders track market sentiment, gauge exposure levels, and manage risk. It can be used for long-term trend-following strategies or short-term trades based on moving average crossovers and volume analysis.
The oscillator operates in conjunction with the chart’s price action and provides a visual representation of the market’s current trend strength and exposure.
Important Considerations:
Risk Management: While the exposure score provides valuable insight, it should be combined with other risk management tools and analysis for optimal trading decisions.
Signal Sensitivity: The accuracy and effectiveness of the signals depend on market conditions and may require adjustments based on the user’s trading strategy or timeframe.
Disclaimer:
This script is for educational purposes only. Trading involves significant risk, and users should carefully evaluate all market conditions and apply appropriate risk management strategies before using this tool in live trading environments.
Self-Adaptive RSI with Fractal Dimension and Entropy ScalingSelf-Adaptive RSI with Fractal Dimension and Entropy Scaling
This advanced oscillator is a refined version of the RSI that integrates multi-timeframe analysis, fractal scaling, and entropy to create an adaptive, highly responsive indicator. The script leverages a range of techniques to dynamically adjust to market conditions and enhance sensitivity to trend and volatility. Here’s a breakdown of the core features:
Base and Fixed Adaptive Lengths:
A base length (input by the user) seeds the initial length for calculations. The script then calculates a fixed adaptive length as a multiplier of this base, providing consistency across different calculations.
Multi-Timeframe RSI Calculation:
The script calculates RSI across multiple timeframes (5 minutes to daily) and aggregates these values using a weighted average based on the Golden Ratio. This multi-timeframe RSI accounts for both short-term and long-term trends, making it more robust and responsive to shifts in market direction.
Enhanced RSI Using Adaptive Volume Weighting:
Price differences are smoothed and adjusted incorporating volume-based weights, allowing the RSI to adapt to changes in trading volume. This volume impact factor enhances trend detection accuracy.
Adaptive Zero-Lag RSI with Golden Ratio Smoothing:
To eliminate lag, the multi-timeframe RSI is smoothed using a zero-lag EMA based on a Golden Ratio length, adding precision to the RSI’s responsiveness while minimizing delay.
Fractal Dimension Scaling:
The oscillator is scaled to expand its range using fractal dimensions, capturing market complexity and adjusting for periods of high or low volatility. This scaling enhances sensitivity to price fluctuations.
Entropy-Based Trend Sensitivity and Volatility Compression:
The final RSI incorporates entropy scaling, achieved through a trend factor derived from a linear regression. This factor adjusts the RSI output based on market volatility and directional strength, compressing the indicator during stable periods and expanding it in high-volatility conditions.
Overbought and Oversold Thresholds Using Statistical Percentiles:
Rather than fixed thresholds, the overbought and oversold levels are set dynamically using percentile ranks (99th and 1st percentiles) over a long period, making them adaptive and reflective of historical price extremes.
This self-adaptive RSI, combining multi-timeframe weighting, fractal scaling, and entropy, provides a nuanced view of market trends and momentum. It dynamically adjusts to market volatility and structure, offering a sophisticated tool for traders seeking adaptive trend analysis and reliable entry/exit signals.
Dynamic Movement-Based OscillatorDynamic Movement-Based Oscillator
This oscillator is designed to adapt its calculations based on market volatility, creating a dynamic and movement-sensitive indicator without using fixed or arbitrary lengths. It works by adjusting its sensitivity and smoothing based on the volatility of recent price action. The script utilizes the following core components:
Volatility-Driven Adaptive Length:
The adaptive length is calculated from the Average True Range (ATR) over a long period. This length dynamically adjusts between a minimum length and the maximum length allowed, ensuring that the oscillator's responsiveness aligns with current market conditions.
Directional Movement with Adaptive Smoothing:
Using an exponential moving average (EMA) of up and down price movements, this component calculates adaptive averages for upward and downward movement. The length of the EMA is set by the adaptive length, creating a response that mirrors recent volatility.
Ratio-Based Oscillator Calculation:
The oscillator value is calculated based on the ratio of average upward to downward movement. This ratio is transformed into a range centered around zero, with values oscillating between positive and negative regions based on the strength of directional movement.
Dynamic Normalization:
To stabilize the oscillator and provide a bounded range, the script normalizes it against the highest and lowest values over a large window (4999 bars or the adaptive length, whichever is greater). This scaling ensures that the oscillator is calibrated to recent highs and lows, eliminating the need for arbitrary limits.
Adaptive Smoothing:
The final oscillator output is smoothed with a secondary adaptive EMA, where the smoothing factor is dynamically set to half of the current volatility length. This creates a responsive but stable line that adapts as market volatility changes.
Multi-Level Visual Reference Lines:
Several horizontal reference lines are plotted to guide interpretation:
High (50): Indicates potential overbought levels.
Tending/Rejection (25) and Rejection/Trending (-25): Mark areas where reversals or continuations might be expected.
Mid (0): The central line around which the oscillator oscillates.
Low (-50): Represents potential oversold levels.
This oscillator aims to capture directional momentum dynamically, allowing for adaptable, real-time analysis of price action with smooth, volatility-adjusted responses. It’s useful for detecting shifts in market momentum, particularly in trending or highly volatile environments.
Because the lengths are so long this can be used on really small time frames
Trending days will often live in the top or bottom quartile
Divergences work extremely well
BTCUSD Price Overextension from Configurable SMAsBTCUSD Price Overextension Indicator with Configurable SMAs
This indicator helps identify potential correction points for BTCUSD by detecting overextended conditions based on customizable short-term and long-term SMAs, average price deviation, and divergence.
Key Features:
Customizable SMAs: Set your own lengths for short-term (default 20) and long-term (default 50) SMAs, allowing you to tailor the indicator to different market conditions.
Overextension Detection: Detects when the average price over a set period (default 10 bars) is overextended above the short-term SMA by a configurable adjustment factor.
Divergence Threshold: Highlights when the short-term and long-term SMAs diverge beyond a specified threshold, signaling potential trend continuation.
Conditional Highlight: Displays a red background only when all conditions are met, and the current candle closes at or above the previous candle. A label "Overextended" appears only on the first bar of each overextended sequence for clear identification.
How to Use:
Identify Correction Signals: Look for red background highlights, which indicate a potential overextension based on the configured SMA and divergence thresholds.
Adjust Parameters: Use the adjustment factor, divergence threshold, and SMA lengths to fine-tune the indicator for different market environments or trading strategies.
This tool is ideal for BTCUSD traders looking to spot potential pullback areas or continuation zones by analyzing trend strength and overextension relative to key moving averages.
Sentient FLDOverview of the FLD
The Future Line of Demarcation (FLD) was first proposed by JM Hurst in the 1970s as a cycle analysis tool. It is a smoothed median price plotted on a time-based chart, and displaced into the future (to the right on the chart). The amount of displacement is determined by performing a cycle analysis, the line then plotted to extend beyond the right hand edge of the chart by half a cycle wavelength.
Interactions between price and the FLD
As price action unfolds, price interacts with the FLD line, either by crossing over the line, or by finding support or resistance at the line.
Targets
When price crosses an FLD a target for the price move is generated. The target consists of a price level and also expected time.
When price reaches that target it is an indication that the cycle influencing price to move up or down has completed that action and is about to turn around.
If price fails to reach a target by the expected time, it indicates bullish or bearish pressure from longer cycles, and a change in mood of the market.
Sequence of interactions
Price interacts with the FLD in a regular sequence of 8 interactions which are labelled using the letters A - H, in alphabetical order. This sequence of interactions occurs between price and a cycle called the Signal cycle. The full sequence plays out over a single wave of a longer cycle, called the Sequence cycle. The interactions are:
A category interaction is where price crosses above the FLD as it rises out of a trough of the Sequence cycle.
B & C category interactions often occur together as a pair, where price comes back to the FLD line and finds support at the level of the FLD as the first trough of the Signal cycle forms.
D category interaction is where price crosses below the FLD as it falls towards the second trough of the Signal cycle.
E category interaction is where price crosses above the FLD again as it rises out of the second trough of the Signal cycle.
F category interaction is where price crosses below the FLD as it falls towards the next trough of the Sequence cycle.
G & H category interactions often occur together as a pair, where price comes back to the FLD line and finds resistance at the level of the FLD before a final move down into the next Sequence cycle trough.
Trading Opportunities
This sequence of interactions provides the trader with trading opportunities:
A and E category interactions involve price crossing over the FLD line, for a long trading opportunity.
D and F category interactions involve price crossing below the FLD line, for a short trading opportunity.
B and C category interactions occur where price finds support at the FLD, another long trading opportunity.
G and H category interactions occur where price finds resistance at the FLD, another short trading opportunity.
3 FLD Lines Plotted
The Sentient FLD indicator plots three FLD lines, for three primary cycles on your time-based charts:
The Signal cycle (pink color, can be changed in the settings), which is used to generate trading signals on the basis of the sequence of interactions between price and the FLD
The Mid cycle (orange color, can be changed in the settings), which is used for confirmation of the signals from the signal cycle FLD.
The Sequence cycle (green color, can be changed in the settings) which is the cycle over which the entire A - H sequence of interactions plays out.
Cycle Analysis
In addition to plotting the three FLD lines, the Sentient FLD indicator performs a cycle phasing analysis and identifies the positions of the troughs of five cycles on your chart (The Signal, Mid & Sequence cycles and two longer cycles for determining the underlying trend).
The results of this analysis are plotted by using diamond symbols to mark the timing of past troughs of the cycles, and circles to mark the timing of the next expected troughs, with lines extending to each side to represent the range of time in which the trough is expected to form. These are called circles-and-whiskers. The diamonds are stacked vertically because the troughs are synchronized in time. The circles-and-whiskers therefore are also stacked, creating a nest-of-lows which is a high probability period for a trough to form.
Identifying the Interactions
The Sentient FLD also identifies the interactions between price and each one of the three FLDs plotted on your chart, and those interactions are labelled so that you can keep track of the unfolding A - H sequence.
Next Expected Interaction
Because the Sentient FLD is able to identify the sequence of interactions, it is also able to identify the next expected interaction between price and the FLD. This enables you to anticipate levels of support or resistance, or acceleration levels where price is expected to cross through the FLD.
Cycle Table
A cycle table is displayed on the chart (position can be changed in settings). The cycle table comprises 6 columns:
The Cycle Name (CYCLE): the name of the cycle which is its nominal wavelength in words.
The Nominal Wavelength (NM): The nominal wavelength of the cycle measured in bars.
The Current Wavelength (CR): The current recent wavelength of the cycle measured in bars.
The Variation (VAR): The variation between the nominal wavelength and current wavelength as a percentage (%).
The relevant Sequence Cycle (SEQ): The cycle over which the sequence of interactions with this FLD plays out.
The Mode (MODE): Whether the cycle is currently Bearish, Neutral or Bullish.
Benefits of using the Sentient FLD
The cycle analysis shown with diamonds and circles marking the troughs, and next expected troughs of the cycles enable you to anticipate the timing of market turns (troughs and peaks in the price), because of the fact that cycles, by definition, repeat with some regularity.
The results of the cycle analysis are also displayed on your chart in a table, and enable you to understand at a glance what the current mode of each cycle is, whether bullish, bearish or neutral.
The identification of the sequence of interactions between price and the FLD enables you to anticipate the next interaction, and thereby expect either a price cross of the FLD or dynamic levels of support and resistance at the levels of the FLD lines, only visible to the FLD trader.
When the next expected interaction between price and the FLD is an acceleration point (price is expected to cross over the FLD), that level can be used as a signal for entry into a trade.
Similarly when the next expected interaction between price and the FLD is either support or resistance, that level can be used as a signal for entry into a trade when price reacts as expected, finding support or resistance.
The targets that are generated as a result of price crossing the FLD represent cycle exhaustion levels and times, and can be used as take profit exits, or as levels after which stops should be tightened.
The indicator optionally also calculates targets for longer timeframes, and displays them on your chart providing useful context for the influence of longer cycles without needing to change timeframe.
Example
In this image you can see an example of the different aspects of the indicator working on a 5 minute chart (details below):
This is what the indicator shows:
The 3 FLD lines are for the 100 minute (pink), 3 hour (orange) and 6 hour (green) cycles (refer to the cycle table for the cycle names).
Previous targets can be seen, shown as pointed labels, with the same colors.
The cycle table at the bottom left of the chart is colour coded, and indicates that the cycles are all currently running a bit long, by about 14%.
Note also the grey-colored 6 hour target generated by the 15 x minute timeframe at 12:20. When targets are close together their accuracy is enhanced.
At the foot of the chart we can see a collection of circles-and-whiskers in a nest-of-lows, indicating that a 12 hour cycle trough has been due to form in the past hour.
The past interactions between price and the signal cycle are labelled and we can see the sequence of E (with some +E post-interaction taps), F and then G-H.
The next interaction between price and the signal is the A category interaction - a long trading opportunity as price bounces out of the 12 hour cycle trough.
Notice the green upward pointing triangles on the FLD lines, indicating that they are expected to provide acceleration points, where price will cross over the FLD and move towards a target above the FLD.
The cycle table shows that the cycles of 6 hours and longer are all expected to be bullish (with the 12 hour cycle neutral to bullish).
On the basis that we are expecting a 12 hour trough to form, and the 6 hour cycle targets have been reached, and the next interaction with the signal cycle is an A category acceleration point, we can plan to enter into a long trade.
Two hours later
This screenshot shows the situation almost 2 hours later:
Notes:
The expected 12 hour cycle trough has been confirmed in the cycle analysis, and now displayed as a stack of diamonds at 12:25
Price did cross over the signal cycle FLD (the 100 minute cycle, pink FLD line) as expected. That price cross is labelled as an A category interaction at 13:00.
A 100 minute target was generated. That target was almost, but not quite reached in terms of price, indicating that the move out of the 12 hour cycle trough is not quite as bullish as would be expected (remember the 12 hour cycle is expected to be neutral-bullish). The time element of the target proved accurate however with a peak forming at the expected time. Stops could have been tightened at that time.
Notice that price then came back to the signal FLD (100 minute) line at the time that the next 100 minute cycle trough was expected (see the pink circle-and-whiskers between 13:40 and 14:25, with the circle at 14:05.
Price found support (as was expected) when it touched the signal FLD at 13:55 and 14:00, and that interaction has been labelled as a B-C category interaction pair.
We also have a 3 hour target above us at about 6,005. That could be a good target for the move.
Another 2 hours later
This screenshot shows the situation another 2 hours later:
Notes:
We can see that the 100 minute cycle trough has been confirmed at 13:45
The nest-of-lows marking the time the 3 hour cycle trough was expected is between 15:00 and 15:45, with a probable trough in price at 15:00
The sequence of interactions is labelled: A at 13:00; B-C at 14:00; another B-C (double B-C interactions are common) at 14:30; E at 15:10; +E (a post E tap) at 16:20
Price has just reached a cluster of targets at 6005 - 6006. The 3 hour target we noted before, as well as a 6 hour target and a 12 hour target from the 15 x minute timeframe.
Notice how after those targets were achieved, price has exhausted its upward move, and has turned down.
The next expected interaction with the signal cycle FLD is an F category interaction. The downward pointing red triangles on the line indicate that the interaction is expected to be a price cross down, as price moves down into the next 6 hour cycle trough.
Other Details
The Sentient FLD indicator works on all time-based charts from 10 seconds up to monthly.
The indicator works on all actively traded instruments, including forex, stocks, indices, commodities, metals and crypto.
Bitcoin: The Puell MultipleBitcoin: The Puell Multiple Indicator Overview
The Puell Multiple is an indicator originally used to analyze Bitcoin's valuation based on mining revenue. However, this approximate version uses Bitcoin's current price to give us a similar perspective. It’s helpful for understanding whether Bitcoin’s price is currently high or low compared to its historical trend.
Recommended Timeframe:
For optimal insights, it’s recommended to use this indicator on the weekly timeframe. This timeframe smooths out daily fluctuations, making it easier to capture long-term valuation trends and better understand market cycles.
What Does the Indicator Show?
This indicator compares the current price of Bitcoin to its average price over the past 365 days. Here’s what it helps you see:
When Bitcoin Might Be Undervalued:
If the indicator value is below a certain low threshold (e.g., 0.51 by default), it suggests that Bitcoin might be undervalued compared to its long-term trend. Historically, periods where the indicator is low have sometimes coincided with good buying opportunities, as Bitcoin is seen as “cheap” in relation to its recent average.
When Bitcoin Might Be Overvalued:
If the indicator value is above a certain high threshold (e.g., 3.4 by default), it suggests that Bitcoin might be overvalued. In the past, these high points have sometimes signaled times to consider selling, as Bitcoin is viewed as “expensive” relative to its recent trend.
How to Read the Indicator
Indicator Line: The main line in the indicator panel shows the value of the Puell Multiple over time, fluctuating based on the comparison between current and past prices.
Threshold Lines: Two horizontal lines represent the high and low thresholds:
Bottom Threshold (Red Line): Indicates a high value, suggesting that Bitcoin might be overvalued.
Top Threshold (Green Line): Indicates a low value, suggesting that Bitcoin might be undervalued.
Color Coding:
The background may appear green when the indicator is below the low threshold (suggesting undervaluation) or red when it’s above the high threshold (suggesting overvaluation).
How You Can Use This Indicator
Long-Term Investment Insights: This indicator can help you identify favorable buying or selling conditions based on historical price trends. When the value is low, Bitcoin might be in a more attractive price range; when it’s high, the price might be inflated compared to its yearly trend.
Market Timing: This tool is best used alongside other indicators, as it’s primarily helpful for understanding broader trends rather than predicting short-term movements.
The Puell Multiple (Approximate) indicator thus offers a historical lens on Bitcoin’s valuation, helping you make decisions informed by past price trends. For best results, keep in mind the weekly timeframe recommendation to capture meaningful market cycles.
William Fractals + SignalsWilliams Fractals + Trading Signals
This indicator identifies Williams Fractals and generates trading signals based on price sweeps of these fractal levels.
Williams Fractals are specific candlestick patterns that identify potential market turning points. Each fractal requires a minimum of 5 bars (2 before, 1 center, 2 after), though this indicator allows you to customize the number of bars checked.
Up Fractal (High Point) forms when you have a center bar whose HIGH is higher than the highs of 'n' bars before and after it. For example, with n=2, you'd see a pattern where the center bar's high is higher than 2 bars before and 2 bars after it. The indicator also recognizes patterns where up to 4 bars after the center can have equal highs before requiring a lower high.
Down Fractal (Low Point) forms when you have a center bar whose LOW is lower than the lows of 'n' bars before and after it. For example, with n=2, you'd see a pattern where the center bar's low is lower than 2 bars before and 2 bars after it. The indicator also recognizes patterns where up to 4 bars after the center can have equal lows before requiring a higher low.
Trading Signals:
The indicator generates signals when price "sweeps" these fractal levels:
Buy Signal (Green Triangle) triggers when price sweeps a down fractal. This requires price to go BELOW the down fractal's low level and then CLOSE ABOVE it . This pattern often indicates a failed breakdown and potential reversal upward.
Sell Signal (Red Triangle) triggers when price sweeps an up fractal. This requires price to go ABOVE the up fractal's high level and then CLOSE BELOW it. This pattern often indicates a failed breakout and potential reversal downward.
Customizable Settings:
1. Periods (default: 10) - How many bars to check before and after the center bar (minimum value: 2)
2. Maximum Stored Fractals (default: 1) - How many fractal levels to keep in memory. Older levels are removed when this limit is reached to prevent excessive signals and maintain indicator performance.
Important Notes:
• The indicator checks the actual HIGH and LOW prices of each bar, not just closing prices
• Fractal levels are automatically removed after generating a signal to prevent repeated triggers
• Signals are only generated on bar close to avoid false triggers
• Alerts include the ticker symbol and the exact price level where the sweep occurred
Common Use Cases:
• Identifying potential reversal points
• Finding stop-hunt levels where price might reverse
• Setting stop-loss levels above up fractals or below down fractals
• Trading failed breakouts/breakdowns at fractal levels
Economic Seasons [Daveatt]Ever wondered what season your economy is in?
Just like Mother Nature has her four seasons, the economy cycles through its own seasons! This indicator helps you visualize where we are in the economic cycle by tracking two key metrics:
📊 What We're Tracking:
1. Interest Rates (USIRYY) - The yearly change in interest rates
2. Inflation Rate (USINTR) - The rate at which prices are rising
The magic happens when we normalize these values (fancy math that makes the numbers play nice together) and compare them to their recent averages. We use a lookback period to calculate the standard deviation and determine if we're seeing higher or lower than normal readings.
🔄 The Four Economic Seasons & Investment Strategy:
1. 🌸 Goldilocks (↑Growth, ↓Inflation)
"Not too hot, not too cold" - The economy is growing steadily without overheating.
BEST TIME TO: Buy growth stocks, technology, consumer discretionary
WHY: Companies can grow earnings in this ideal environment of low rates and stable prices
2. 🌞 Reflation (↑Growth, ↑Inflation)
"Party time... but watch your wallet!" - The economy is heating up.
BEST TIME TO: Buy commodities, banking stocks, real estate
WHY: These sectors thrive when inflation rises alongside growth
3. 🌡️ Inflation (↓Growth, ↑Inflation)
"Ouch, my purchasing power!" - Growth slows while prices keep rising.
BEST TIME TO: Rotate into value stocks, consumer staples, healthcare
WHY: These defensive sectors maintain pricing power during inflationary periods
4. ❄️ Deflation (↓Growth, ↓Inflation)
"Winter is here" - Both growth and inflation are falling.
BEST TIME TO: Focus on quality bonds, cash positions, and dividend aristocrats
WHY: Capital preservation becomes key; high-quality fixed income provides safety
🎯 Strategic Trading Points:
- BUY AGGRESSIVELY: During late Deflation/early Goldilocks (the spring thaw)
- HOLD & ACCUMULATE: Throughout Goldilocks and early Reflation
- START TAKING PROFITS: During late Reflation/early Inflation
- DEFENSIVE POSITIONING: Throughout Inflation and Deflation
⚠️ Warning Signs to Watch:
- Goldilocks → Reflation: Time to reduce growth stock exposure
- Reflation → Inflation: Begin rotating into defensive sectors
- Inflation → Deflation: Quality becomes crucial
- Deflation → Goldilocks: Start building new positions
The blue dot shows you where we are right now in this cycle.
The red arrows in the middle remind us that this is a continuous cycle - one season flows into the next, just like in nature!
💡 Pro Tip: The transitions between seasons often provide the best opportunities - but also the highest risks. Use additional indicators and fundamental analysis to confirm these shifts.
Remember: Just like you wouldn't wear a winter coat in summer, you shouldn't use a Goldilocks strategy during Inflation! Time your trades with the seasons. 🎯
Happy Trading! 📈
Previous Day High and Low Count with Probabilities
Indicator Explanation
This indicator displays the number of days on which the previous day's high or low prices were not reached and calculates probabilities for future price movements based on this information. It stores the high and low values of the last 45 days and checks daily whether these levels were touched. Based on the number of days without touching either the high or the low, the indicator calculates the probability of future price movements in either direction (Up or Down).
The indicator offers customization options for label placement and color on the chart. The counts for the high and low touches, along with the calculated probabilities (in percentages), are displayed as labels on the chart. These labels can be shifted along the X-axis by up to 50 bars and can be customized in color and size. Additionally, the text for the labels can be freely chosen, giving the user improved flexibility and overview.
In summary, this indicator helps to:
- Track how often previous day's high and low levels were not reached.
- Estimate probabilities for future price movements based on this information.
- Customize the chart display for easier interpretation.
Strategy Concept
Probability and Touch Conditions:
A long position is entered only if:
The probability of reaching the high is at least 60%.
The price has not touched the previous day’s high in the last three days.
Similarly, for short positions:
The probability of reaching the low is at least 60%.
The price has not touched the previous day’s low in the last three days.
Incremental Position Size Increase:
On the 3rd consecutive day without a high/low touch and with the probability condition met, an initial position of 0.01 lots is opened.
On the 4th day, an additional position of 0.01 lots is added.
On the 5th day, an extra position of 0.02 lots is opened.
After a two-day pause, the situation is re-evaluated, and if conditions are still met, a 0.04-lot position is considered.
Trend Reversal Detection:
The strategy also includes a simple trend reversal check. If the market shows clear reversal signals, no new positions will be opened.
Adjustments and Risk Management
This strategy can be adjusted by modifying the probability values, the number of days without a high/low touch, and the lot sizes. Additionally, stop-loss and take-profit levels can be added to further control the risk and secure profits.
Strategy Concept
Probability and Touch Conditions:
A long position is entered only if:
The probability of reaching the high is at least 60%.
The price has not touched the previous day’s high in the last three days.
Similarly, for short positions:
The probability of reaching the low is at least 60%.
The price has not touched the previous day’s low in the last three days.
Incremental Position Size Increase:
On the 3rd consecutive day without a high/low touch and with the probability condition met, an initial position of 0.01 lots is opened.
On the 4th day, an additional position of 0.01 lots is added.
On the 5th day, an extra position of 0.02 lots is opened.
After a two-day pause, the situation is re-evaluated, and if conditions are still met, a 0.04-lot position is considered.
Trend Reversal Detection:
The strategy also includes a simple trend reversal check. If the market shows clear reversal signals, no new positions will be opened.
Risk Disclaimer
The author of this strategy does not assume any liability for potential losses or gains that may arise from the use of this strategy. Trading involves significant risk, and it is important to only trade with capital that you can afford to lose. The strategy presented is for educational purposes only and should not be considered as financial advice. Always conduct your own research and consider seeking advice from a professional financial advisor before making any trading decisions.
Spreads between contractsA simple indicator that automatically calculates and charts the difference between the nearby futures contract (1!) and the next contract (2!), enabling contango and backwardation analysis. If needed, any two contracts can also be manually entered.
Seasonality v1.33.Seasonality v1.33 - Seasonal Indicator for Trading Trends
Seasonality v1.33 is a tailored indicator designed to analyze seasonal trends in historical price movements, assisting traders in making informed decisions. In its beta version, Seasonality v1.33 allows users to select up to two specific months and compare price changes for these months across several years, helping to identify potential seasonal patterns.
Indicator Features
Identifying Seasonal Trends: By choosing up to two months and a range of years, Seasonality v1.33 offers a visual representation of average price changes and highlights potential positive or negative trends. This supports traders in spotting recurring seasonal price movements that may be influenced by yearly cycles or market conditions.
Historical Comparison Across Multiple Years: The indicator displays the percentage price changes for the selected months over up to 10 years, allowing traders to observe consistency in price fluctuations across different years.
Visual Presentation: A color-coded table shows the dominant trend, either positive or negative, and highlights monthly trends for easy reference. The table size and position can be customized, allowing integration into each user’s preferred chart layout.
How to Use
Month and Year Selection: In the current beta version, traders can select two specific months and a range of years to check for potential seasonal effects.
Trend Summary: The table provides both individual yearly data and an overall trend signal for the selected months, giving a quick overview of prevailing tendencies.
Customizable Display: The table’s position and text size are adjustable to fit seamlessly into the user’s charting interface.
Limitations and Considerations
Data Dependency: The accuracy of analysis relies on the availability of historical price data, which may vary depending on the market or asset.
No Guarantee of Future Trends: While past trends provide insights, they do not guarantee future results. This indicator serves as a supportive tool but should be complemented by thorough analysis and sound risk management.
Feedback and Suggestions
The Seasonality v1.33 indicator is available in beta for free use and testing until the end of the month. Your feedback is highly valued! Comments and suggestions will help us improve future versions and tailor them to the needs of traders.
Bullrun Profit Maximizer [QuantraSystems]Bullrun Profit Maximizer
Quantra Systems guarantees that the information created and published within this document and on the Tradingview platform is fully compliant with applicable regulations, does not constitute investment advice, and is not exclusively intended for qualified investors.
Important Note!
The system equity curve presented here has been generated as part of the process of testing and verifying the methodology behind this script.
Crucially, it was developed after the system was conceptualized, designed, and created, which helps to mitigate the risk of overfitting to historical data. In other words, the system was built for robustness, not for simply optimizing past performance.
This ensures that the system is less likely to degrade in performance over time, compared to hyper optimized systems that are tailored to past data. No tweaks or optimizations were made to this system post backtest.
Even More Important Note!!
The nature of markets is that they change quickly and unpredictably. Past performance does not guarantee future results - this is a fundamental rule in trading and investing.
While this system is designed with broad, flexible conditions to adapt quickly to a range of market environments, it is essential to understand that no assumptions should be made about future returns based on historical data. Markets are inherently uncertain, and this system - like all trading systems - cannot predict future outcomes.
Introduction
The "Adaptive Pairwise Momentum System" is not a prototype to the Bullrun Profit Maximizer (BPM) . The Bullrun Profit Maximizer is a fully re-engineered, higher frequency momentum system.
The Bullrun Profit Maximizer (BPM) uses a completely different filter logic and refines momentum calculations, specifically to support higher frequency trading on Crypto's Blue Chip assets. It correctly calculates fees and slippage by compounding them against System Profit before plotting the equity curve.
Unlike prior systems, this script utilizes a completely new filter logic and refined momentum calculation, specifically built to support higher frequency trading on blue-chip assets, while minimizing the impact of fees and slippage.
While the APMS focuses on Macro Trend Alignment, the BPM instead applies an equity curve based filter, allowing for targeted precision on the current asset’s trend without relying on broader market conditions. This approach delivers more responsive and asset specific signals, enhancing agility in today’s fast paced crypto markets.
The BPM dynamically optimizes capital allocation across up to four high performing assets, ensuring that the portfolio adapts swiftly to changing market conditions. The system logic consists of sophisticated quantitative methods, rapid momentum analysis and alpha cyclicality/seasonality optimizations. The overarching goal is to ensure that the portfolio is always invested in the highest performing asset based on dynamic market conditions, while at the same time managing risk through rapid asset filters and internal mechanisms like alpha cyclicality, volatility and beta analysis.
In addition to these core functionalities, the BPM comes with the typical Quantra Systems UI design, structured to reduce data clutter and provide users with only the most essential, impactful information. The BPM UI format delivers clear and easy to read signals. It enables rapid decision making in a high frequency environment without compromising on depth or accuracy.
Bespoke Logic Filtering with Equity Curve Precision
The BPM script utilizes a completely new methodology and focuses on intraday rotations of blue-chip crypto assets, while previously built systems were designed with a longer term focus in mind.
In response to the need for more precise signal generation, the BPM replaces the previous macro trend filter with a new, highly specific equity curve activation filter. This unique logic filter is driven solely by the performance trends of the asset currently held by the system. By analyzing the equity curve directly, this system can make more targeted, timely allocations based on asset specific momentum, allowing for quick adjustments that are more relevant to the held asset rather than general market conditions.
The benefits of this new, unique approach are twofold: first, it avoids premature allocation shifts based on broader macro movements, and second, it enables the system to adapt dynamically to the performance of each asset individually. This asset specific filtering allows traders to capitalize on localized strength within individual blue-chip cryptoassets without being affected by lags in the overall market trend.
High Frequency Momentum Calculation for Enhanced Flexibility
The BPM incorporates a newly designed momentum calculation that increases its suitability across lower timeframes. This new momentum indicator captures and processes more data points within a shorter window than ever before, rather than extending bar intervals and potentially losing high frequency detail. This creates a smooth, data rich featureset that is especially suited for blue-chip assets, where liquidity reduces slippage and fees, making higher frequency trading viable.
By retaining more data, this system captures subtle shifts in momentum more effectively than traditional approaches, offering higher resolution insights. These modifications result in a system capable of generating highly responsive signals on faster timeframes, empowering traders to act quickly in volatile markets.
User Interface and Enhanced Readability
The BPM also features a reimagined, streamlined user interface, making it easier than ever to monitor essential signals at a glance. The new layout minimizes extraneous data points in the tables, leaving only the most actionable information for traders. This cleaner presentation is purpose built to help traders identify the strongest asset in real time, with clear, color coded signals to facilitate swift decision making in fast moving markets.
Equity Stats Table : Designed for clarity, the stats table focuses on the current allocation’s performance metrics, emphasizing the most critical metrics without unnecessary clutter.
Color Coded Highlights : The interface includes the option to highlight both the current top performing asset, and historical allocations - with indicators of momentum shifts and performance metrics readily accessible.
Clear Signals : Visual cues are presented in an enhanced way to improve readability, including simplified line coloring, and improve visualization of the outperforming assets in the allocation table.
Dynamic Asset Reallocation
The BPM dynamically allocates capital to the strongest performing asset in a selected pool. This system incorporates a re-engineered, pairwise momentum measurement designed to operate at higher frequencies. The system evaluates each asset against others in real time, ensuring only the highest momentum asset receives allocation. This approach keeps the portfolio positioned for maximum efficiency, with an updated weighting logic that favors assets showing both strength and sustainability.
Position Changes and Slippage Calculation
Position changes are optimized for faster reallocation, with realistic slippage and fee calculations factored into each trade. The system’s structure minimizes the impact of these costs on blue-chip assets, allowing for more active management on short timeframes without incurring significant drag on performance.
A Special Note on Fees + Slippage
In the image above, the system has been applied to four different timeframes - 12h, 8h, 4h and 1h - using identical settings and a selected slippage and fees amount of 0.2%. In this stress test, we isolate the choppy downwards period from the previous Bitcoin all time high - set in March 2024, to the current date where Bitcoin is currently sitting at around the same level.
This illustrates an important concept: starting at the 12h, the system performed better as the timeframes decreased. In fact, only on the 4hr chart did the system equity curve make a new all time high alongside Bitcoin. It is worth noting that market phases that are “non-trending” are generally the least profitable periods to use a momentum/trend system - as most systems will get caught by false momentum and will “buy the top,” and then proceed to “sell the bottom.”
Lower timeframes typically offer more data points for the algorithm to compute over, and enable quicker entries and exits within a robust system, often reducing downside risk and compounding gains more effectively - in all market environments.
However, slippage, fees, and execution constraints are still limiting factors. Although blue-chip cryptocurrencies are more liquid and can be traded with lower fees compared to low cap assets, frequent trading on lower timeframes incurs cumulative slippage costs. With the BPM system set to a realistic slippage rate of 0.2% per trade, this example emphasizes how even lower fees impact performance as trade frequency increases.
Finding the optimal balance between timeframe and slippage impact requires careful consideration of factors such as portfolio size, liquidity of selected tokens, execution speed, and the fee rate of the exchange you execute trades on.
Number of Position Changes
Understanding the number of position changes in a strategy is critical to assessing its feasibility in real world trading. Frequent position changes can lead to increased costs due to slippage and fees. Monitoring the number of position changes provides insight into the system’s behavior - helping to evaluate how active the strategy is and whether it aligns with the trader's desired time input for position management.
Equity Curve and Performance Calculations
To provide a benchmark, the script also generates a Buy-and-Hold (or "HODL") equity curve that represents a 100% allocation to Bitcoin, the highest market cap cryptoasset. This allows users to easily compare the performance of the dynamic rotation system with that of a more traditional investment strategy.
The script tracks key performance metrics for both the dynamic portfolio and the HODL strategy, including:
Sharpe Ratio
The Sharpe Ratio is a key metric that evaluates a portfolio’s risk adjusted return by comparing its ‘excess’ return to its volatility. Traditionally, the Sharpe Ratio measures returns relative to a risk-free rate. However, in our system’s calculation, we omit the risk-free rate and instead measure returns above a benchmark of 0%. This adjustment provides a more universal comparison, especially in the context of highly volatile assets like cryptocurrencies, where a traditional risk-free benchmark, such as the usual 3-month T-bills, is often irrelevant or too distant from the realities of the crypto market.
By using 0% as the baseline, we focus purely on the strategy's ability to generate raw returns in the face of market risk, which makes it easier to compare performance across different strategies or asset classes. In an environment like cryptocurrency, where volatility can be extreme, the importance of relative return against a highly volatile backdrop outweighs comparisons to a risk-free rate that bears little resemblance to the risk profile of digital assets.
Sortino Ratio
The Sortino Ratio improves upon the Sharpe Ratio by specifically targeting downside risk and leaves the upside potential untouched. In contrast to the Sharpe Ratio (which penalizes both upside and downside volatility), the Sortino Ratio focuses only on negative return deviations. This makes it a more suitable metric for evaluating strategies like the Bullrun Profit Maximizer - that aim to minimize drawdowns without restricting upside capture. By measuring returns relative to a 0% baseline, the Sortino ratio provides a clearer assessment of how well the system generates gains while avoiding substantial losses in highly volatile markets like crypto.
Omega Ratio
The Omega Ratio is calculated as the ratio of gains to losses across all return thresholds, providing a more complete view of how the system balances upside and downside risk even compared to the Sortino Ratio. While it achieves a similar outcome to the Sortino Ratio by emphasizing the system's ability to capture gains while limiting losses, it is technically a mathematically superior method. However, we include both the Omega and Sortino ratios in our metric table, as the Sortino Ratio remains more widely recognized and commonly understood by traders and investors of all levels.
Usage Summary:
While the backtests in this description are generated as if a trader held a portfolio of just the strongest tokens, this was mainly designed as a method of logical verification and not a recommended investment strategy. In practice, this system can be used in multiple ways.
It can be used as above, or as a factor in forming part of a broader asset selection tool, or even a method of filtering tokens by strength in order to inform a day trader which tokens might be optimal to look at, for long-only trading setups on an intrabar timeframe.
Summary
The Bullrun Profit Maximizer is an advanced tool tailored for traders, offering the precision and agility required in today’s markets. With its asset specific equity curve filter, reworked momentum analysis, and streamlined user interface, this system is engineered to maximize gains and minimize risk during bullmarkets, with a strong focus on risk adjusted performance.
Its refined approach, focused on high resolution data processing and adaptive reallocation, makes it a powerful choice for traders looking to capture high quality trends on clue-chip assets, no matter the market’s pace.
Trend Titan Neutronstar [QuantraSystems]Trend Titan NEUTRONSTAR
Credits
The Trend Titan NEUTRONSTAR is a comprehensive aggregation of nearly 100 unique indicators and custom combinations, primarily developed from unique and public domain code.
We'd like to thank our TradingView community members: @IkKeOmar for allowing us to add his well-built "Normalized KAMA Oscillator" and "Adaptive Trend Lines " indicators to the aggregation, as well as @DojiEmoji for his valuable "Drift Study (Inspired by Monte Carlo Simulations with BM)".
Introduction
The Trend Titan NEUTRONSTAR is a robust trend following algorithm meticulously crafted to meet the demands of crypto investors. Designed with a multi layered aggregation approach, NEUTRONSTAR excels in navigating the unique volatility and rapid shifts of the cryptocurrency market. By stacking and refining a variety of carefully selected indicators, it combines their individual strengths while reducing the impact of noise or false signals. This "aggregation of aggregators" approach enables NEUTRONSTAR to produce a consistently reliable trend signal across assets and timeframes, making it an exceptional tool for investors focused on medium to long term market positioning.
NEUTRONSTAR ’s powerful trend following capabilities provide investors with straightforward, data driven analysis. It signals when tokens exhibit sustained upward momentum and systematically removes allocations from assets showing signs of weakness. This structure aids investors in recognizing peak market phases. In fact, one of NEUTRONSTAR ’s most valuable applications is its potential to help investors time exits near the peak of bull markets. This aims to maximize gains while mitigating exposure to downturns.
Ultimately, NEUTRONSTAR equips investors with a high precision, adaptable framework for strategic decision making. It offers robust support to identify strong trends, manage risk, and navigate the dynamic crypto market landscape.
With over a year of rigorous forward testing and live trading, NEUTRONSTAR demonstrates remarkable robustness and effectiveness, maintaining its performance without succumbing to overfitting. The system has been purposefully designed to avoid unnecessary optimization to past data, ensuring it can adapt as market conditions evolve. By focusing on aggregating valuable trend signals rather than tuning to historical performance, the NEUTRONSTAR serves as a reliable universal trend following system that aligns with the natural market cycles of growth and correction.
Core Methodology
The foundation of the NEUTRONSTAR lies in its multi aggregated structure, where five custom developed trend models are combined to capture the dominant market direction. Each of these aggregates has been carefully crafted with a specific trend signaling period in mind, allowing it to adapt seamlessly across various timeframes and asset classes. Here’s a breakdown of the key components:
FLARE - The original Quantra Signaling Matrix (QSM) model, best suited for timeframes above 12 hours. It forms the foundation of long term trend detection, providing stable signals.
FLAREV2 - A refined and more sophisticated model that performs well across both high and low timeframes, adding a layer of adaptability to the system.
NEBULA - An advanced model combining FLARE and FLAREV2. NEBULA brings the advantages of both components together, enhancing reliability and capturing smoother, more accurate trends.
SPARK - A high speed trend aggregator based on the QSM Universal model. It focuses on fast moving trends, providing early signals of potential shifts.
SUNBURST - A balanced aggregate that combines elements of SPARK and FLARE, confirming SPARK’s signals while minimizing false positives.
Each of these models contributes its own unique perspective on market movement. By layering fast, medium, and slower trend following signals, NEUTRONSTAR can confirm strong trends while filtering out shorter term noise. The result is a comprehensive tool that signals clear market direction with minimized false signals.
A Unique Approach to Trend Aggregation
One of the defining characteristics of NEUTRONSTAR is its deliberate choice to avoid perfectly time coherent indicators within its aggregation. In simpler terms, NEUTRONSTAR purposefully incorporates trend following indicators with slightly different signal periods, rather than synchronizing all components to a single signaling period. This choice brings significant benefits in terms of diversification, adaptability, and robustness of the overall trend signal.
When aggregating multiple trend following components, if all indicators were perfectly time coherent - meaning they responded to market changes in exactly the same way and over the time periods - the resulting signal would effectively be no different from a single trend following indicator. This uniformity would limit the system’s ability to capture a variety of market conditions, leaving it vulnerable to the same noise or false signals that any single indicator might encounter. Instead, NEUTRONSTAR leverages a balanced mix of indicators with varied timing: some fast, some slower, and some in the medium range. This choice allows the system to extract the unique strengths of each component, creating a combined signal that is stronger and more reliable than any single indicator.
By incorporating different signal periods, NEUTRONSTAR achieves what can be thought of as a form of edge accumulation. The fast components within NEUTRONSTAR , for example, are highly sensitive to quick shifts in market direction. These indicators excel at identifying early trend signals, enabling NEUTRONSTAR to react swiftly to emerging momentum. However, these fast indicators alone would be prone to reacting to market noise, potentially generating too many premature signals. This is where the medium term indicators come into play. These components operate with a slower reaction time, filtering out the short term fluctuations and confirming the direction of the trend established by the faster indicators. The combination of these varying signal speeds results in a balanced, adaptive response to market changes.
This approach also allows NEUTRONSTAR to adapt to different market regimes seamlessly. In fast moving, volatile markets, the faster indicators provide an early alert to potential trend shifts, while the slower components offer a stabilizing influence, preventing overreaction to temporary noise. Conversely, in steadier or trending markets, the medium and slower indicators sustain the trend signal, reducing the likelihood of premature exits. This flexible design enhances NEUTRONSTAR ’s ability to operate effectively across multiple asset classes and timeframes, from short term fluctuations to longer term market cycles.
The result is a powerful, multi-layered trend following tool that remains adaptive, capturing the benefits of both fast and medium paced reactions without becoming overly sensitive to short term noise. This unique aggregation methodology also supports NEUTRONSTAR ’s robustness, reducing the risk of overfitting to historical data and ensuring that the system can perform reliably in forward testing and live trading environments. The slightly staggered signal periods provide a greater degree of resilience, making NEUTRONSTAR a dependable choice for traders looking to capitalize on sustained trends while minimizing exposure during periods of market uncertainty.
In summary, the lack of perfect time coherence among NEUTRONSTAR ’s sub components is not a flaw - but a deliberate, robust design choice.
Risk Management through Market Mode Analysis
An essential part of NEUTRONSTAR is its ability to assess the market's underlying behavior and adapt accordingly. It employs a Market Mode Analysis mechanism that identifies when the market is either in a “Trending State” or a “Mean Reverting State.” When enough confidence is established that the market is trending, the system confirms and signals a “Trending State,” which is optimal for maintaining positions in the direction of the trend. Conversely, if there’s insufficient confidence, it labels the market as “Mean Reverting,” alerting traders to potentially avoid trend trades during likely sideways movement.
This distinction is particularly valuable in crypto, where asset prices often oscillate between aggressive trends and consolidation periods. The Market Mode Analysis keeps traders aligned with the broader market conditions, minimizing exposure during periods of potential whipsaws and maximizing gains during sustained trends.
Zero Overfitting: Design and Testing for Real World Resilience
Unlike many trend following indicators that rely heavily on backtesting and optimization, NEUTRONSTAR was built to perform well in forward testing and live trading without post design adjustments. Over a year of live market exposure has all but proven its robustness, with the system’s methodology focused on universal applicability and simplicity rather than curve fitting to past data. This approach ensures the aggregator remains effective across different market cycles and maintains relevance as new data unfolds.
By avoiding overfitting, NEUTRONSTAR is inherently more resistant to the common issue of strategy degradation over time, making it a valuable tool for traders seeking reliable market analysis you can trust for the long term.
Settings and Customization Options
To accommodate a range of trading styles and market conditions, NEUTRONSTAR includes adjustable settings that allow for fine tuning sensitivity and signal generation:
Calculation Method - Users can choose between calculating the NEUTRONSTAR score based on aggregated scores or by using the state of individual aggregates (long, neutral, short). The score method provides faster signals with slightly more noise, while the state based approach offers a smoother signal.
Sensitivity Threshold - This setting adjusts the system’s sensitivity, defining the width of the neutral zone. Higher thresholds reduce sensitivity, allowing for a broader range of volatility before triggering a trend reversal.
Market Regime Sensitivity - A sensitivity adjustment, ranging from 0 to 100, that affects the sensitivity of the sub components in market regime calculation.
These settings offer flexibility for users to tailor NEUTRONSTAR to their specific needs, whether for medium term investment strategies or shorter term trading setups.
Visualization and Legend
For intuitive usability, NEUTRONSTAR uses color coded bar overlays to indicate trend direction:
Green - indicates an uptrend.
Gray - signals a neutral or transition phase.
Purple - denotes a downtrend.
An optional background color can be enabled for market mode visualization, indicating the overall market state as either trending or mean reverting. This feature allows traders to assess trend direction and strength at a glance, simplifying decision making.
Additional Metrics Table
To support strategic decision making, NEUTRONSTAR includes an additional metrics table for in depth analysis:
Performance Ratios - Sharpe, Sortino, and Omega ratios assess the asset’s risk adjusted returns.
Volatility Insights - Provides an average volatility measure, valuable for understanding market stability.
Beta Measurement - Calculates asset beta against BTC, offering insight into asset volatility in the context of the broader market.
These metrics provide deeper insights into individual asset behavior, supporting more informed trend based allocations. The table is fully customizable, allowing traders to adjust the position and size for a seamless integration into their workspace.
Final Summary
The Trend Titan NEUTRONSTAR indicator is a powerful and resilient trend following system for crypto markets, built with a unique aggregation of high performance models to deliver dependable, noise reduced trend signals. Its robust design, free from overfitting, ensures adaptability across various assets and timeframes. With customizable sensitivity settings, intuitive color coded visualization, and an advanced risk metrics table, NEUTRONSTAR provides traders with a comprehensive tool for identifying and riding profitable trends, while safeguarding capital during unfavorable market phases.
Heisenberg Uncertainty Moving Average (HUMA)Overview
This script introduces and approximation of the Heisenberg Uncertainty Moving Average (HUMA), inspired by the principles of quantum physics, particularly the Heisenberg Uncertainty Principle. The indicator dynamically adjusts its moving average length based on price and momentum uncertainty, ensuring adaptability to market conditions. It also features dynamic coloring to indicate the slope of the moving average.
Step-by-Step Explanation
Calculate Uncertainty in Price (Δx):
The price uncertainty is measured over a specified lookback period (length).
This is done by finding the difference between the highest high and lowest low over the period
Momentum uncertainty is defined using the Rate of Change (ROC) of the closing price over the same lookback period (length).
This indicates how much the price has changed over that period, providing a measure of momentum uncertainty.
Introduce Planck’s Constant (h):
Planck’s constant (h) is scaled down for financial use to set a theoretical minimum threshold for the product of uncertainties.
The threshold is defined as h / (4 * π) to simulate a limit that aligns with the Heisenberg Uncertainty Principle in physics.
Calculate the Uncertainty Product (Δx ⋅ Δp):
The product of price uncertainty (Δx) and the absolute value of momentum uncertainty (Δp) is calculated.
To ensure the product respects the minimum threshold set by quantum principles, the value is capped using math.max(uncertainty_product, threshold).
Normalize the Uncertainty Product to Determine the Moving Average Window Size:
The uncertainty product is used to adjust the length of the moving average dynamically.
The formula inversely adjusts the moving average length based on uncertainty: higher uncertainty results in a shorter (more responsive) window and lower uncertainty results in a longer (smoother) window.
Calculate the Heisenberg Uncertainty Moving Average (HUMA):
The slope is determined by finding the difference between the current HUMA value and the value from the previous period, smoothed with a Double Exponential Moving Average (DEMA).
This helps identify the direction of the trend: positive slope indicates an uptrend, and negative slope indicates a downtrend.
Dynamic Coloring Based on the Slope:
Bidirectional MoM w/ Time Weighting | Optional Intrabar DataBidirectional MoM w/ Time Weighting | Optional Intrabar Data
Core Components:
Intrabar Data Extraction:
The script optionally harnesses lower time frame data (e.g., per-second intervals) for high and low prices within each primary bar. You can set it to the current chart time but if you want to use intrabar data it uses the request.security_lower_tf() to properly pull intrabar data.
This fine-grained data enables an in-depth examination of the price action that occurs within a standard timeframe, enhancing the ability to detect subtle market movements.
A key threshold based on Average True Range (ATR) is used to measure significant price changes intrabar, adding a robust filter for volatility sensitivity.
Cumulative Time-to-Threshold Analysis:
The indicator tracks how long it takes for price changes to reach specified thresholds, marking critical time points when upward or downward price movements exceed these levels. This approach provides insights into the speed and intensity of directional shifts within the market.
The calculated time-to-threshold values act as temporal markers that influence subsequent momentum weighting.
Bidirectional Momentum Calculation:
Momentum is assessed in two directions (upward and downward) using a comprehensive array of price changes.
Adaptive Weighting Mechanism:
Each momentum value is weighted by the calculated time-to-threshold, giving preference to momentum that occurs more rapidly and aligning with potential breakout conditions.
The script also factors in correlations between momentum and price change, ensuring that only the most relevant signals contribute to the final analysis.
Iterative Length Analysis:
By iterating over a range of lengths (e.g., 100 to 200 periods), the script aggregates data to assess momentum across different time scales. This provides a more holistic view of market behavior, accommodating both short-term fluctuations and longer-term trends.
Each length is evaluated using moving averages and correlations to determine its contribution to the total weighted momentum.
Final Aggregated Output:
The weighted sums of upward and downward momentum are normalized by the total weight to produce a final composite metric.
The indicator plots these results as separate upward and downward momentum lines, offering traders a visual representation of which direction holds more momentum strength over various intervals.
Practical Application:
This indicator's advanced design is tailored for traders who require a deeper understanding of price movement dynamics and the underlying forces driving market momentum. By incorporating intrabar data, adaptive time-to-threshold calculations, and iterative analysis, this tool seeks to provide a clearer view of potential market direction shifts and their timing.
The indicator can be used to:
Identify potential breakout or reversal points by observing significant shifts in weighted momentum.
Gauge the relative strength of uptrends and downtrends through the plotted momentum lines.
Enhance decision-making with an additional layer of granularity from intrabar data.
In essence, this script is an ambitious attempt to blend multi-scale analysis, momentum dynamics, and time-weighted evaluation, creating a unique approach to understanding market behavior beyond conventional indicators.
RSI and Dev Advanced Volatility IndexEnglish Explanation of the "RSI and Dev Advanced Volatility Index" Pine Script Code
Understanding the Code
Purpose:
This Pine Script code creates a custom indicator that combines the Relative Strength Index (RSI) and Deviation (DEV) to provide insights into market volatility.
Key Components:
* Deviation (DEV): Calculates the difference between the closing price and the 10-period simple moving average. This measures the extent to which the price deviates from its recent average, indicating volatility.
* RSI: The traditional RSI is then applied to the calculated deviations. This helps to smooth the data and identify overbought or oversold conditions in terms of volatility.
Calculation Steps:
* Deviation Calculation: The difference between the closing price and its 10-period simple moving average is calculated.
* RSI Calculation: The RSI is calculated on the deviations, providing a measure of the speed and change of volatility relative to recent volatility changes.
* Plotting:
* The RSI of the deviations is plotted on the chart.
* Horizontal lines are plotted at 50, 0, and 110 to visually represent different volatility zones.
* The area between the lines is filled with color to highlight low and high volatility regions.
Interpretation and Usage
* Volatility Analysis:
* High Volatility: When the RSI is above 50, it indicates high volatility, suggesting the market might be in a consolidation or trend reversal phase.
* Low Volatility: When the RSI is below 50, it indicates low volatility, suggesting a relatively calm market.
* Trading Signals:
* Buy Signal: When the RSI crosses above 50 from below, it might signal increasing volatility, which could be a buying opportunity.
* Sell Signal: When the RSI crosses below 50 from above, it might signal decreasing volatility, which could be a selling opportunity.
* Risk Management:
* By monitoring volatility, traders can better manage their risk. During periods of high volatility, traders might reduce their position size or adopt more conservative strategies.
Advantages
* Comprehensive: Combines RSI and DEV for a more holistic view of volatility.
* Sensitivity: Quickly responds to changes in market volatility.
* Visual Clarity: Color-coded zones provide a clear visual representation of different volatility levels.
Limitations
* Parameter Sensitivity: The indicator's performance is sensitive to parameter changes, such as the lookback period for the moving average.
* Lag: Like most technical indicators, it has some lag and might not capture every market movement.
* Not Predictive: It can only indicate current and past volatility, not future movements.
Summary
This custom indicator offers a valuable tool for analyzing market volatility. By combining RSI and DEV, it provides a more nuanced perspective on price fluctuations. However, it should be used in conjunction with other technical indicators and fundamental analysis for more robust trading decisions.
Key points to remember:
* Higher RSI values indicate higher volatility.
* Lower RSI values indicate lower volatility.
* Crossovers of the RSI line above or below 50 can provide potential trading signals.
* The indicator should be used in conjunction with other analysis tools for a more complete picture of the market.
