TradFi Fundamentals: Momentum Trading with Macroeconomic DataIntroduction
This indicator combines traditional price momentum with key macroeconomic data. By retrieving GDP, inflation, unemployment, and interest rates using security calls, the script automatically adapts to the latest economic data. The goal is to blend technical analysis with fundamental insights to generate a more robust momentum signal.
Original Research Paper by Mohit Apte, B. Tech Scholar, Department of Computer Science and Engineering, COEP Technological University, Pune, India
Link to paper
Explanation
Price Momentum Calculation:
The indicator computes price momentum as the percentage change in price over a configurable lookback period (default is 50 days). This raw momentum is then normalized using a rolling simple moving average and standard deviation over a defined period (default 200 days) to ensure comparability with the economic indicators.
Fetching and Normalizing Economic Data:
Instead of manually inputting economic values, the script uses TradingView’s security function to retrieve:
GDP from ticker "GDP"
Inflation (CPI) from ticker "USCCPI"
Unemployment rate from ticker "UNRATE"
Interest rates from ticker "USINTR"
Each series is normalized over a configurable normalization period (default 200 days) by subtracting its moving average and dividing by its standard deviation. This standardization converts each economic indicator into a z-score for direct integration into the momentum score.
Combined Momentum Score:
The normalized price momentum and economic indicators are each multiplied by user-defined weights (default: 50% price momentum, 20% GDP, and 10% each for inflation, unemployment, and interest rates). The weighted components are then summed to form a comprehensive momentum score. A horizontal zero line is plotted for reference.
Trading Signals:
Buy signals are generated when the combined momentum score crosses above zero, and sell signals occur when it crosses below zero. Visual markers are added to the chart to assist with trade timing, and alert conditions are provided for automated notifications.
Settings
Price Momentum Lookback: Defines the period (in days) used to compute the raw price momentum.
Normalization Period for Price Momentum: Sets the window over which the price momentum is normalized.
Normalization Period for Economic Data: Sets the window over which each macroeconomic series is normalized.
Weights: Adjust the influence of each component (price momentum, GDP, inflation, unemployment, and interest rate) on the overall momentum score.
Conclusion
This implementation leverages TradingView’s economic data feeds to integrate real-time macroeconomic data into a momentum trading strategy. By normalizing and weighting both technical and economic inputs, the indicator offers traders a more holistic view of market conditions. The enhanced momentum signal provides additional context to traditional momentum analysis, potentially leading to more informed trading decisions and improved risk management.
The next script I release will be an improved version of this that I have added my own flavor to, improving the signals.
Cerca negli script per "如何用wind搜索股票的发行价和份数"
[SHORT ONLY] ATR Sell the Rip Mean Reversion Strategy█ STRATEGY DESCRIPTION
The "ATR Sell the Rip Mean Reversion Strategy" is a contrarian system that targets overextended price moves on stocks and ETFs. It calculates an ATR‐based trigger level to identify shorting opportunities. When the current close exceeds this smoothed ATR trigger, and if the close is below a 200-period EMA (if enabled), the strategy initiates a short entry, aiming to profit from an anticipated corrective pullback.
█ HOW IS THE ATR SIGNAL BAND CALCULATED?
This strategy computes an ATR-based signal trigger as follows:
Calculate the ATR
The strategy computes the Average True Range (ATR) using a configurable period provided by the user:
atrValue = ta.atr(atrPeriod)
Determine the Threshold
Multiply the ATR by a predefined multiplier and add it to the current close:
atrThreshold = close + atrValue * atrMultInput
Smooth the Threshold
Apply a Simple Moving Average over a specified period to smooth out the threshold, reducing noise:
signalTrigger = ta.sma(atrThreshold, smoothPeriodInput)
█ SIGNAL GENERATION
1. SHORT ENTRY
A Short Signal is triggered when:
The current close is above the smoothed ATR signal trigger.
The trade occurs within the specified trading window (between Start Time and End Time).
If the EMA filter is enabled, the close must also be below the 200-period EMA.
2. EXIT CONDITION
An exit Signal is generated when the current close falls below the previous bar’s low (close < low ), indicating a potential bearish reversal and prompting the strategy to close its short position.
█ ADDITIONAL SETTINGS
ATR Period: The period used to calculate the ATR, allowing for adaptability to different volatility conditions (default is 20).
ATR Multiplier: The multiplier applied to the ATR to determine the raw threshold (default is 1.0).
Smoothing Period: The period over which the raw ATR threshold is smoothed using an SMA (default is 10).
Start Time and End Time: Defines the time window during which trades are allowed.
EMA Filter (Optional): When enabled, short entries are only executed if the current close is below the 200-period EMA, confirming a bearish trend.
█ PERFORMANCE OVERVIEW
This strategy is designed for use on the Daily timeframe, targeting stocks and ETFs by capitalizing on overextended price moves.
It utilizes a dynamic, ATR-based trigger to identify when prices have potentially peaked, setting the stage for a mean reversion short entry.
The optional EMA filter helps align trades with broader market trends, potentially reducing false signals.
Backtesting is recommended to fine-tune the ATR multiplier, smoothing period, and EMA settings to match the volatility and behavior of specific markets.
[SHORT ONLY] Consecutive Close>High[1] Mean Reversion Strategy█ STRATEGY DESCRIPTION
The "Consecutive Close > High " Mean Reversion Strategy is a contrarian daily trading system for stocks and ETFs. It identifies potential shorting opportunities by counting consecutive days where the closing price exceeds the previous day's high. When this consecutive day count reaches a predetermined threshold, and if the close is below a 200-period EMA (if enabled), a short entry is triggered, anticipating a corrective pullback.
█ HOW ARE THE CONSECUTIVE BULLISH COUNTS CALCULATED?
The strategy uses a counter variable called `bullCount` to track how many consecutive bars meet a bullish condition. Here’s a breakdown of the process:
Initialize the Counter
var int bullCount = 0
Bullish Bar Detection
Every time the close exceeds the previous bar's high, increment the counter:
if close > high
bullCount += 1
Reset on Bearish Bar
When there is a clear bearish reversal, the counter is reset to zero:
if close < low
bullCount := 0
█ SIGNAL GENERATION
1. SHORT ENTRY
A Short Signal is triggered when:
The count of consecutive bullish closes (where close > high ) reaches or exceeds the defined threshold (default: 3).
The signal occurs within the specified trading window (between Start Time and End Time).
2. EXIT CONDITION
An exit Signal is generated when the current close falls below the previous bar’s low (close < low ), prompting the strategy to exit the position.
█ ADDITIONAL SETTINGS
Threshold: The number of consecutive bullish closes required to trigger a short entry (default is 3).
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
EMA Filter (Optional): When enabled, short entries are only triggered if the current close is below the 200-period EMA.
█ PERFORMANCE OVERVIEW
This strategy is designed for Stocks and ETFs on the Daily timeframe and targets overextended bullish moves.
It aims to capture mean reversion by entering short after a series of consecutive bullish closes.
Further optimization is possible with additional filters (e.g., EMA, volume, or volatility).
Backtesting should be used to fine-tune the threshold and filter settings for specific market conditions.
[SHORT ONLY] Internal Bar Strength (IBS) Mean Reversion Strategy█ STRATEGY DESCRIPTION
The "Internal Bar Strength (IBS) Strategy" is a mean-reversion strategy designed to identify trading opportunities based on the closing price's position within the daily price range. It enters a short position when the IBS indicates overbought conditions and exits when the IBS reaches oversold levels. This strategy is Short-Only and was designed to be used on the Daily timeframe for Stocks and ETFs.
█ WHAT IS INTERNAL BAR STRENGTH (IBS)?
Internal Bar Strength (IBS) measures where the closing price falls within the high-low range of a bar. It is calculated as:
IBS = (Close - Low) / (High - Low)
- Low IBS (≤ 0.2) : Indicates the close is near the bar's low, suggesting oversold conditions.
- High IBS (≥ 0.8) : Indicates the close is near the bar's high, suggesting overbought conditions.
█ SIGNAL GENERATION
1. SHORT ENTRY
A Short Signal is triggered when:
The IBS value rises to or above the Upper Threshold (default: 0.9).
The Closing price is greater than the previous bars High (close>high ).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
An exit Signal is generated when the IBS value drops to or below the Lower Threshold (default: 0.3). This prompts the strategy to exit the position.
█ ADDITIONAL SETTINGS
Upper Threshold: The IBS level at which the strategy enters trades. Default is 0.9.
Lower Threshold: The IBS level at which the strategy exits short positions. Default is 0.3.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for Stocks and ETFs markets and performs best when prices frequently revert to the mean.
The strategy can be optimized further using additional conditions such as using volume or volatility filters.
It is sensitive to extreme IBS values, which help identify potential reversals.
Backtesting results should be analyzed to optimize the Upper/Lower Thresholds for specific instruments and market conditions.
End-of-Session ProbabilityThis indicator estimates the probability that the market will finish the session above a specified target price. It blends a statistical probability model with directional bias and optional morning momentum weighting to help traders gauge end-of-day market expectations.
Key Features:
• Statistical Probability Model:
Uses a normal distribution (with a custom normal CDF approximation) scaled by the square-root-of-time rule. The indicator dynamically adjusts the standard deviation for the remaining session time to compute a z‑score and ultimately the probability that the session close exceeds the target.
• Directional Bias via Daily HullMA (Exponential):
A daily Hull Moving Average (calculated using an exponential method) is used as a big-picture trend indicator. The model allows you to select your bias method—either by comparing the current price to the daily HullMA (Price method) or by using the HullMA’s slope (Slope method). A drift multiplier scales this bias, which then shifts the mean used in the probability calculations.
• Optional Morning Momentum Weight:
For traders who believe that early session moves provide useful clues about the day’s momentum, you can enable an optional weighting. The indicator captures the percentage change from the morning open (within a user-defined time window) and adjusts the expected move accordingly. A multiplier lets you control the strength of this adjustment.
• Visual Outputs:
The indicator plots quantile lines (approximately the 25%, 50%, and 75% levels) for the expected price distribution at session end. An abbreviated on-chart label displays key information:
• Target: The target price (current price plus a user-defined offset)
• Prob Above: The probability (in percentage) that the session close will exceed the target price
• Time: The time remaining in the session (in minutes)
How to Use:
1. Set Your Parameters:
• Expected Session Move: Input your estimated standard deviation for the full-session move in price units.
• Daily Hull MA Settings: Adjust the period for the daily HullMA and choose the bias method (Price or Slope). Modify the drift multiplier to tune the strength of the directional bias.
• Target Offset: Specify an offset from the current price to set your target level.
• Morning Momentum (Optional): Enable the morning momentum weight if you want the indicator to adjust the expected move based on early session price changes. Define the morning session window and set the momentum multiplier.
2. Interpret the Output:
• Quantile Lines: These represent the range of possible end-of-session prices based on your model.
• Abbreviated Label: Provides a quick snapshot of the target price, probability of finishing above that target, and time remaining in the session.
3. Trading Application:
Use the probability output as a guide to assess if the market is likely to continue in the current direction or reverse by session close. The indicator can help you decide on trade entries, exits, or adjustments based on your overall strategy and risk management approach.
This tool is designed to offer a dynamic, statistically driven snapshot of the market’s expected end-of-day behavior, combining both longer-term trend bias and short-term momentum cues.
EMA Study Script for Price Action Traders, v2JR_EMA Research Tool Documentation
Version 2 Enhancements
Version 2 of the JR_EMA Research Tool introduces several powerful features that make it particularly valuable for studying price action around Exponential Moving Averages (EMAs). The key improvements focus on tracking and analyzing price-EMA interactions:
1. Cross Detection and Counting
- Implements flags for crossing bars that instantly identify when price crosses above or below the EMA
- Maintains running counts of closes above and below the EMA
- This feature helps students understand the persistence of trends and the frequency of EMA interactions
2. Bar Number Tracking
- Records the specific bar number when EMA crosses occur
- Stores the previous crossing bar number for reference
- Enables precise measurement of time between crosses, helping identify typical trend durations
3. Variable Reset Management
- Implements sophisticated reset logic for all counting variables
- Ensures accuracy when analyzing multiple trading sessions
- Critical for maintaining clean data when studying patterns across different timeframes
4. Cross Direction Tracking
- Monitors the direction of the last EMA cross
- Helps students identify the current trend context
- Essential for understanding trend continuation vs reversal scenarios
Educational Applications
Price-EMA Relationship Studies
The tool provides multiple ways to study how price interacts with EMAs:
1. Visual Analysis
- Customizable EMA bands show typical price deviation ranges
- Color-coded fills help identify "normal" vs "extreme" price movements
- Three different band calculation methods offer varying perspectives on price volatility
2. Quantitative Analysis
- Real-time tracking of closes above/below EMA
- Running totals help identify persistent trends
- Cross counting helps understand typical trend duration
Research Configurations
EMA Configuration
- Adjustable EMA period for studying different trend timeframes
- Customizable EMA color for visual clarity
- Ideal for comparing different EMA periods' effectiveness
Bands Configuration
Three distinct calculation methods:
1. Full Average Bar Range (ABR)
- Uses the entire range of price movement
- Best for studying overall volatility
2. Body Average Bar Range
- Focuses on the body of the candle
- Excellent for studying conviction in price moves
3. Standard Deviation
- Traditional statistical approach
- Useful for comparing to other technical studies
Signal Configuration
- Optional signal plotting for entry/exit studies
- Helps identify potential trading opportunities
- Useful for backtesting strategy ideas
Using the Tool for Study
Basic Analysis Steps
1. Start with the default 20-period EMA
2. Observe how price interacts with the EMA line
3. Monitor the data window for quantitative insights
4. Use band settings to understand normal price behavior
Advanced Analysis
1. Pattern Recognition
- Use the cross counting system to identify typical pattern lengths
- Study the relationship between cross frequency and trend strength
- Compare different timeframes for fractal analysis
2. Volatility Studies
- Compare different band calculation methods
- Identify market regimes through band width changes
- Study the relationship between volatility and trend persistence
3. Trend Analysis
- Use the closing price count system to measure trend strength
- Study the relationship between trend duration and subsequent reversals
- Compare different EMA periods for optimal trend following
Best Practices for Research
1. Systematic Approach
- Start with longer timeframes and work down
- Document observations about price behavior in different market conditions
- Compare results across multiple symbols and timeframes
2. Data Collection
- Use the data window to record significant events
- Track the number of bars between crosses
- Note market conditions when signals appear
3. Optimization Studies
- Test different EMA periods for your market
- Compare band calculation methods for your trading style
- Document which settings work best in different market conditions
Technical Implementation Notes
This tool is particularly valuable for educational purposes because it combines visual and quantitative analysis in a single interface, allowing students to develop both intuitive and analytical understanding of price-EMA relationships.
Internal Bar Strength (IBS) Strategy█ STRATEGY DESCRIPTION
The "Internal Bar Strength (IBS) Strategy" is a mean-reversion strategy designed to identify trading opportunities based on the closing price's position within the daily price range. It enters a long position when the IBS indicates oversold conditions and exits when the IBS reaches overbought levels. This strategy was designed to be used on the daily timeframe.
█ WHAT IS INTERNAL BAR STRENGTH (IBS)?
Internal Bar Strength (IBS) measures where the closing price falls within the high-low range of a bar. It is calculated as:
IBS = (Close - Low) / (High - Low)
- **Low IBS (≤ 0.2)**: Indicates the close is near the bar's low, suggesting oversold conditions.
- **High IBS (≥ 0.8)**: Indicates the close is near the bar's high, suggesting overbought conditions.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The IBS value drops below the Lower Threshold (default: 0.2).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
A Sell Signal is generated when the IBS value rises to or above the Upper Threshold (default: 0.8). This prompts the strategy to exit the position.
█ ADDITIONAL SETTINGS
Upper Threshold: The IBS level at which the strategy exits trades. Default is 0.8.
Lower Threshold: The IBS level at which the strategy enters long positions. Default is 0.2.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for ranging markets and performs best when prices frequently revert to the mean.
It is sensitive to extreme IBS values, which help identify potential reversals.
Backtesting results should be analyzed to optimize the Upper/Lower Thresholds for specific instruments and market conditions.
Buy on 5 day low Strategy█ STRATEGY DESCRIPTION
The "Buy on 5 Day Low Strategy" is a mean-reversion strategy designed to identify potential buying opportunities when the price drops below the lowest low of the previous five days. It enters a long position when specific conditions are met and exits when the price exceeds the high of the previous day. This strategy is optimized for use on daily or higher timeframes.
█ WHAT IS THE 5-DAY LOW?
The 5-Day Low is the lowest price observed over the last five days. This level is used as a reference to identify potential oversold conditions and reversal points.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The close price is below the lowest low of the previous five days (`close < _lowest `).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
A Sell Signal is generated when the current closing price exceeds the high of the previous day (`close > high `). This indicates that the price has shown strength, potentially confirming the reversal and prompting the strategy to exit the position.
█ ADDITIONAL SETTINGS
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for mean-reverting markets and performs best when the price frequently oscillates around key support levels.
It is sensitive to oversold conditions, as indicated by the 5-Day Low, and overbought conditions, as indicated by the previous day's high.
Backtesting results should be analyzed to optimize the strategy for specific instruments and market conditions.
3-Bar Low Strategy█ STRATEGY DESCRIPTION
The "3-Bar Low Strategy" is a mean-reversion strategy designed to identify potential buying opportunities when the price drops below the lowest low of the previous three bars. It enters a long position when specific conditions are met and exits when the price exceeds the highest high of the previous seven bars. This strategy is suitable for use on various timeframes.
█ WHAT IS THE 3-BAR LOW?
The 3-Bar Low is the lowest price observed over the last three bars. This level is used as a reference to identify potential oversold conditions and reversal points.
█ WHAT IS THE 7-BAR HIGH?
The 7-Bar High is the highest price observed over the last seven bars. This level is used as a reference to identify potential overbought conditions and exit points.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The close price is below the lowest low of the previous three bars (`close < _lowest `).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
If the EMA Filter is enabled, the close price must also be above the 200-period Exponential Moving Average (EMA).
2. EXIT CONDITION
A Sell Signal is generated when the current closing price exceeds the highest high of the previous seven bars (`close > _highest `). This indicates that the price has shown strength, potentially confirming the reversal and prompting the strategy to exit the position.
█ ADDITIONAL SETTINGS
MA Period: The lookback period for the 200-period EMA used in the EMA Filter. Default is 200.
Use EMA Filter: Enables or disables the EMA Filter for long entries. Default is disabled.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for mean-reverting markets and performs best when the price frequently oscillates around key support and resistance levels.
It is sensitive to oversold conditions, as indicated by the 3-Bar Low, and overbought conditions, as indicated by the 7-Bar High.
Backtesting results should be analyzed to optimize the MA Period and EMA Filter settings for specific instruments.
Bollinger Bands Reversal + IBS Strategy█ STRATEGY DESCRIPTION
The "Bollinger Bands Reversal Strategy" is a mean-reversion strategy designed to identify potential buying opportunities when the price deviates below the lower Bollinger Band and the Internal Bar Strength (IBS) indicates oversold conditions. It enters a long position when specific conditions are met and exits when the IBS indicates overbought conditions. This strategy is suitable for use on various timeframes.
█ WHAT ARE BOLLINGER BANDS?
Bollinger Bands consist of three lines:
- **Basis**: A Simple Moving Average (SMA) of the price over a specified period.
- **Upper Band**: The basis plus a multiple of the standard deviation of the price.
- **Lower Band**: The basis minus a multiple of the standard deviation of the price.
Bollinger Bands help identify periods of high volatility and potential reversal points.
█ WHAT IS INTERNAL BAR STRENGTH (IBS)?
Internal Bar Strength (IBS) is a measure of where the closing price is relative to the high and low of the bar. It is calculated as:
IBS = (Close - Low) / (High - Low)
A low IBS value (e.g., below 0.2) indicates that the close is near the low of the bar, suggesting oversold conditions. A high IBS value (e.g., above 0.8) indicates that the close is near the high of the bar, suggesting overbought conditions.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The IBS value is below 0.2, indicating oversold conditions.
The close price is below the lower Bollinger Band.
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
A Sell Signal is generated when the IBS value exceeds 0.8, indicating overbought conditions. This prompts the strategy to exit the position.
█ ADDITIONAL SETTINGS
Length: The lookback period for calculating the Bollinger Bands. Default is 20.
Multiplier: The number of standard deviations used to calculate the upper and lower Bollinger Bands. Default is 2.0.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for mean-reverting markets and performs best when the price frequently deviates from the Bollinger Bands.
It is sensitive to oversold and overbought conditions, as indicated by the IBS, which helps to identify potential reversals.
Backtesting results should be analyzed to optimize the Length and Multiplier parameters for specific instruments.
Average High-Low Range + IBS Reversal Strategy█ STRATEGY DESCRIPTION
The "Average High-Low Range + IBS Reversal Strategy" is a mean-reversion strategy designed to identify potential buying opportunities when the price deviates significantly from its average high-low range and the Internal Bar Strength (IBS) indicates oversold conditions. It enters a long position when specific conditions are met and exits when the price shows strength by exceeding the previous bar's high. This strategy is suitable for use on various timeframes.
█ WHAT IS THE AVERAGE HIGH-LOW RANGE?
The Average High-Low Range is calculated as the Simple Moving Average (SMA) of the difference between the high and low prices over a specified period. It helps identify periods of increased volatility and potential reversal points.
█ WHAT IS INTERNAL BAR STRENGTH (IBS)?
Internal Bar Strength (IBS) is a measure of where the closing price is relative to the high and low of the bar. It is calculated as:
IBS = (Close - Low) / (High - Low)
A low IBS value (e.g., below 0.2) indicates that the close is near the low of the bar, suggesting oversold conditions.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The close price has been below the buy threshold (calculated as `upper - (2.5 * hl_avg)`) for a specified number of consecutive bars (`bars_below_threshold`).
The IBS value is below the specified buy threshold (`ibs_buy_treshold`).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
A Sell Signal is generated when the current closing price exceeds the high of the previous bar (`close > high `). This indicates that the price has shown strength, potentially confirming the reversal and prompting the strategy to exit the position.
█ ADDITIONAL SETTINGS
Length: The lookback period for calculating the average high-low range. Default is 20.
Bars Below Threshold: The number of consecutive bars the price must remain below the buy threshold to trigger a Buy Signal. Default is 2.
IBS Buy Threshold: The IBS value below which a Buy Signal is triggered. Default is 0.2.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for mean-reverting markets and performs best when the price frequently deviates from its average high-low range.
It is sensitive to oversold conditions, as indicated by the IBS, which helps to identify potential reversals.
Backtesting results should be analyzed to optimize the Length, Bars Below Threshold, and IBS Buy Threshold parameters for specific instruments.
Turn of the Month Strategy on Steroids█ STRATEGY DESCRIPTION
The "Turn of the Month Strategy on Steroids" is a seasonal mean-reversion strategy designed to capitalize on price movements around the end of the month. It enters a long position when specific conditions are met and exits when the Relative Strength Index (RSI) indicates overbought conditions. This strategy is optimized for use on daily or higher timeframes.
█ WHAT IS THE TURN OF THE MONTH EFFECT?
The Turn of the Month effect refers to the observed tendency of stock prices to rise around the end of the month. This strategy leverages this phenomenon by entering long positions when the price shows signs of a reversal during this period.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The current day of the month is greater than or equal to the specified `dayOfMonth` threshold (default is 25).
The close price is lower than the previous day's close (`close < close `).
The previous day's close is also lower than the close two days ago (`close < close `).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
There is no existing open position (`strategy.position_size == 0`).
2. EXIT CONDITION
A Sell Signal is generated when the 2-period RSI exceeds 65, indicating overbought conditions. This prompts the strategy to exit the position.
█ ADDITIONAL SETTINGS
Day of Month: The day of the month threshold for triggering a Buy Signal. Default is 25.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed to exploit seasonal price patterns around the end of the month.
It performs best in markets where the Turn of the Month effect is pronounced.
Backtesting results should be analyzed to optimize the `dayOfMonth` threshold and RSI parameters for specific instruments.
Consecutive Bars Above/Below EMA Buy the Dip Strategy█ STRATEGY DESCRIPTION
The "Consecutive Bars Above/Below EMA Buy the Dip Strategy" is a mean-reversion strategy designed to identify potential buying opportunities when the price dips below a moving average for a specified number of consecutive bars. It enters a long position when the dip condition is met and exits when the price shows strength by exceeding the previous bar's high. This strategy is suitable for use on various timeframes.
█ WHAT IS THE MOVING AVERAGE?
The strategy uses either a Simple Moving Average (SMA) or an Exponential Moving Average (EMA) as a reference for identifying dips. The type and length of the moving average can be customized in the settings.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The close price is below the selected moving average for a specified number of consecutive bars (`consecutiveBarsTreshold`).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
A Sell Signal is generated when the current closing price exceeds the high of the previous bar (`close > high `). This indicates that the price has shown strength, potentially confirming the reversal and prompting the strategy to exit the position.
█ ADDITIONAL SETTINGS
Consecutive Bars Threshold: The number of consecutive bars the price must remain below the moving average to trigger a Buy Signal. Default is 3.
MA Type: The type of moving average used (SMA or EMA). Default is SMA.
MA Length: The length of the moving average. Default is 5.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for mean-reverting markets and performs best when the price frequently oscillates around the moving average.
It is sensitive to the number of consecutive bars below the moving average, which helps to identify potential dips.
Backtesting results should be analysed to optimize the Consecutive Bars Threshold, MA Type, and MA Length for specific instruments.
Turn around Tuesday on Steroids Strategy█ STRATEGY DESCRIPTION
The "Turn around Tuesday on Steroids Strategy" is a mean-reversion strategy designed to identify potential price reversals at the start of the trading week. It enters a long position when specific conditions are met and exits when the price shows strength by exceeding the previous bar's high. This strategy is optimized for ETFs, stocks, and other instruments on the daily timeframe.
█ WHAT IS THE STARTING DAY?
The Starting Day determines the first day of the trading week for the strategy. It can be set to either Sunday or Monday, depending on the instrument being traded. For ETFs and stocks, Monday is recommended. For other instruments, Sunday is recommended.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The current day is the first day of the trading week (either Sunday or Monday, depending on the Starting Day setting).
The close price is lower than the previous day's close (`close < close `).
The previous day's close is also lower than the close two days ago (`close < close `).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
If the MA Filter is enabled, the close price must also be above the 200-period Simple Moving Average (SMA).
2. EXIT CONDITION
A Sell Signal is generated when the current closing price exceeds the high of the previous bar (`close > high `). This indicates that the price has shown strength, potentially confirming the reversal and prompting the strategy to exit the position.
█ ADDITIONAL SETTINGS
Starting Day: Determines the first day of the trading week. Options are Sunday or Monday. Default is Sunday.
Use MA Filter: Enables or disables the 200-period SMA filter for long entries. Default is disabled.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for markets with frequent weekly reversals.
It performs best in volatile conditions where price movements are significant at the start of the trading week.
Backtesting results should be analysed to optimize the Starting Day and MA Filter settings for specific instruments.
Consecutive Bearish Candle Strategy█ STRATEGY DESCRIPTION
The "Consecutive Bearish Candle Strategy" is a momentum-based strategy designed to identify potential reversals after a sustained bearish move. It enters a long position when a specific number of consecutive bearish candles occur and exits when the price shows strength by exceeding the previous bar's high. This strategy is optimized for use on various timeframes and instruments.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The close price has been lower than the previous close for at least `Lookback` consecutive bars. This indicates a sustained bearish move, suggesting a potential reversal.
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
A Sell Signal is generated when the current closing price exceeds the high of the previous bar (`close > high `). This indicates that the price has shown strength, potentially confirming the reversal and prompting the strategy to exit the position.
█ ADDITIONAL SETTINGS
Lookback: The number of consecutive bearish bars required to trigger a Buy Signal. Default is 3.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for markets with frequent momentum shifts.
It performs best in volatile conditions where price movements are significant.
Backtesting results should be analysed to optimize the `Lookback` parameter for specific instruments.
4 Bar Momentum Reversal strategy█ STRATEGY DESCRIPTION
The "4 Bar Momentum Reversal Strategy" is a mean-reversion strategy designed to identify price reversals following a sustained downward move. It enters a long position when a reversal condition is met and exits when the price shows strength by exceeding the previous bar's high. This strategy is optimized for indices and stocks on the daily timeframe.
█ WHAT IS THE REFERENCE CLOSE?
The Reference Close is the closing price from X bars ago, where X is determined by the Lookback period. Think of it as a moving benchmark that helps the strategy assess whether prices are trending upwards or downwards relative to past performance. For example, if the Lookback is set to 4, the Reference Close is the closing price 4 bars ago (`close `).
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The close price has been lower than the Reference Close for at least `Buy Threshold` consecutive bars. This indicates a sustained downward move, suggesting a potential reversal.
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
A Sell Signal is generated when the current closing price exceeds the high of the previous bar (`close > high `). This indicates that the price has shown strength, potentially confirming the reversal and prompting the strategy to exit the position.
█ ADDITIONAL SETTINGS
Buy Threshold: The number of consecutive bearish bars needed to trigger a Buy Signal. Default is 4.
Lookback: The number of bars ago used to calculate the Reference Close. Default is 4.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for trending markets with frequent reversals.
It performs best in volatile conditions where price movements are significant.
Backtesting results should be analysed to optimize the Buy Threshold and Lookback parameters for specific instruments.
Adaptive Momentum Reversion StrategyThe Adaptive Momentum Reversion Strategy: An Empirical Approach to Market Behavior
The Adaptive Momentum Reversion Strategy seeks to capitalize on market price dynamics by combining concepts from momentum and mean reversion theories. This hybrid approach leverages a Rate of Change (ROC) indicator along with Bollinger Bands to identify overbought and oversold conditions, triggering trades based on the crossing of specific thresholds. The strategy aims to detect momentum shifts and exploit price reversions to their mean.
Theoretical Framework
Momentum and Mean Reversion: Momentum trading assumes that assets with a recent history of strong performance will continue in that direction, while mean reversion suggests that assets tend to return to their historical average over time (Fama & French, 1988; Poterba & Summers, 1988). This strategy incorporates elements of both, looking for periods when momentum is either overextended (and likely to revert) or when the asset’s price is temporarily underpriced relative to its historical trend.
Rate of Change (ROC): The ROC is a straightforward momentum indicator that measures the percentage change in price over a specified period (Wilder, 1978). The strategy calculates the ROC over a 2-period window, making it responsive to short-term price changes. By using ROC, the strategy aims to detect price acceleration and deceleration.
Bollinger Bands: Bollinger Bands are used to identify volatility and potential price extremes, often signaling overbought or oversold conditions. The bands consist of a moving average and two standard deviation bounds that adjust dynamically with price volatility (Bollinger, 2002).
The strategy employs two sets of Bollinger Bands: one for short-term volatility (lower band) and another for longer-term trends (upper band), with different lengths and standard deviation multipliers.
Strategy Construction
Indicator Inputs:
ROC Period: The rate of change is computed over a 2-period window, which provides sensitivity to short-term price fluctuations.
Bollinger Bands:
Lower Band: Calculated with a 18-period length and a standard deviation of 1.7.
Upper Band: Calculated with a 21-period length and a standard deviation of 2.1.
Calculations:
ROC Calculation: The ROC is computed by comparing the current close price to the close price from rocPeriod days ago, expressing it as a percentage.
Bollinger Bands: The strategy calculates both upper and lower Bollinger Bands around the ROC, using a simple moving average as the central basis. The lower Bollinger Band is used as a reference for identifying potential long entry points when the ROC crosses above it, while the upper Bollinger Band serves as a reference for exits, when the ROC crosses below it.
Trading Conditions:
Long Entry: A long position is initiated when the ROC crosses above the lower Bollinger Band, signaling a potential shift from a period of low momentum to an increase in price movement.
Exit Condition: A position is closed when the ROC crosses under the upper Bollinger Band, or when the ROC drops below the lower band again, indicating a reversal or weakening of momentum.
Visual Indicators:
ROC Plot: The ROC is plotted as a line to visualize the momentum direction.
Bollinger Bands: The upper and lower bands, along with their basis (simple moving averages), are plotted to delineate the expected range for the ROC.
Background Color: To enhance decision-making, the strategy colors the background when extreme conditions are detected—green for oversold (ROC below the lower band) and red for overbought (ROC above the upper band), indicating potential reversal zones.
Strategy Performance Considerations
The use of Bollinger Bands in this strategy provides an adaptive framework that adjusts to changing market volatility. When volatility increases, the bands widen, allowing for larger price movements, while during quieter periods, the bands contract, reducing trade signals. This adaptiveness is critical in maintaining strategy effectiveness across different market conditions.
The strategy’s pyramiding setting is disabled (pyramiding=0), ensuring that only one position is taken at a time, which is a conservative risk management approach. Additionally, the strategy includes transaction costs and slippage parameters to account for real-world trading conditions.
Empirical Evidence and Relevance
The combination of momentum and mean reversion has been widely studied and shown to provide profitable opportunities under certain market conditions. Studies such as Jegadeesh and Titman (1993) confirm that momentum strategies tend to work well in trending markets, while mean reversion strategies have been effective during periods of high volatility or after sharp price movements (De Bondt & Thaler, 1985). By integrating both strategies into one system, the Adaptive Momentum Reversion Strategy may be able to capitalize on both trending and reverting market behavior.
Furthermore, research by Chan (1996) on momentum-based trading systems demonstrates that adaptive strategies, which adjust to changes in market volatility, often outperform static strategies, providing a compelling rationale for the use of Bollinger Bands in this context.
Conclusion
The Adaptive Momentum Reversion Strategy provides a robust framework for trading based on the dual concepts of momentum and mean reversion. By using ROC in combination with Bollinger Bands, the strategy is capable of identifying overbought and oversold conditions while adapting to changing market conditions. The use of adaptive indicators ensures that the strategy remains flexible and can perform across different market environments, potentially offering a competitive edge for traders who seek to balance risk and reward in their trading approaches.
References
Bollinger, J. (2002). Bollinger on Bollinger Bands. McGraw-Hill Professional.
Chan, L. K. C. (1996). Momentum, Mean Reversion, and the Cross-Section of Stock Returns. Journal of Finance, 51(5), 1681-1713.
De Bondt, W. F., & Thaler, R. H. (1985). Does the Stock Market Overreact? Journal of Finance, 40(3), 793-805.
Fama, E. F., & French, K. R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.
VPSA-VTDDear Sir/Madam,
I am pleased to present the next iteration of my indicator concept, which, in my opinion, serves as a highly useful tool for analyzing markets using the Volume Spread Analysis (VSA) method or the Wyckoff methodology.
The VPSA (Volume-Price Spread Analysis), the latest version in the family of scripts I’ve developed, appears to perform its task effectively. The combination of visualizing normalized data alongside their significance, achieved through the application of Z-Score standardization, proved to be a sound solution. Therefore, I decided to take it a step further and expand my project with a complementary approach to the existing one.
Theory
At the outset, I want to acknowledge that I’m aware of the existence of other probabilistic models used in financial markets, which may describe these phenomena more accurately. However, in line with Occam's Razor, I aimed to maintain simplicity in the analysis and interpretation of the concepts below. For this reason, I focused on describing the data using the Gaussian distribution.
The data I read from the chart — primarily the closing price, the high-low price difference (spread), and volume — exhibit cyclical patterns. These cycles are described by Wyckoff's methodology, while VSA complements and presents them from a different perspective. I will refrain from explaining these methods in depth due to their complexity and broad scope. What matters is that within these cycles, various events occur, described by candles or bars in distinct ways, characterized by different spreads and volumes. When observing the chart, I notice periods of lower volatility, often accompanied by lower volumes, as well as periods of high volatility and significant volumes. It’s important to find harmony within this apparent chaos. I think that chart interpretation cannot happen without considering the broader context, but the more variables I include in the analytical process, the more challenges arise. For instance, how can I determine if something is large (wide) or small (narrow)? For elements like volume or spread, my script provides a partial answer to this question. Now, let’s get to the point.
Technical Overview
The first technique I applied is Min-Max Normalization. With its help, the script adjusts volume and spread values to a range between 0 and 1. This allows for a comparable bar chart, where a wide bar represents volume, and a narrow one represents spread. Without normalization, visually comparing values that differ by several orders of magnitude would be inconvenient. If the indicator shows that one bar has a unit spread value while another has half that value, it means the first bar is twice as large. The ratio is preserved.
The second technique I used is Z-Score Standardization. This concept is based on the normal distribution, characterized by variables such as the mean and standard deviation, which measures data dispersion around the mean. The Z-Score indicates how many standard deviations a given value deviates from the population mean. The higher the Z-Score, the more the examined object deviates from the mean. If an object has a Z-Score of 3, it falls within 0.1% of the population, making it a rare occurrence or even an anomaly. In the context of chart analysis, such strong deviations are events like climaxes, which often signal the end of a trend, though not always. In my script, I assigned specific colors to frequently occurring Z-Score values:
Below 1 – Blue
Above 1 – Green
Above 2 – Red
Above 3 – Fuchsia
These colors are applied to both spread and volume, allowing for quick visual interpretation of data.
Volume Trend Detector (VTD)
The above forms the foundation of VPSA. However, I have extended the script with a Volume Trend Detector (VTD). The idea is that when I consider market structure - by market structure, I mean the overall chart, support and resistance levels, candles, and patterns typical of spread and volume analysis as well as Wyckoff patterns - I look for price ranges where there is a lack of supply, demand, or clues left behind by Smart Money or the market's enigmatic identity known as the Composite Man. This is essential because, as these clues and behaviors of market participants — expressed through the chart’s dynamics - reflect the actions, decisions, and emotions of all players. These behaviors can help interpret the bull-bear battle and estimate the probability of their next moves, which is one of the key factors for a trader relying on technical analysis to make a trade decision.
I enhanced the script with a Volume Trend Detector, which operates in two modes:
Step-by-Step Logic
The detector identifies expected volume dynamics. For instance, when looking for signs of a lack of bullish interest, I focus on setups with decreasing volatility and volume, particularly for bullish candles. These setups are referred to as No Demand patterns, according to Tom Williams' methodology.
Simple Moving Average (SMA)
The detector can also operate based on a simple moving average, helping to identify systematic trends in declining volume, indicating potential imbalances in market forces.
I’ve designed the program to allow the selection of candle types and volume characteristics to which the script will pay particular attention and notify me of specific market conditions.
Advantages and Disadvantages
Advantages:
Unified visualization of normalized spread and volume, saving time and improving efficiency.
The use of Z-Score as a consistent and repeatable relative mechanism for marking examined values.
The use of colors in visualization as a reference to Z-Score values.
The possibility to set up a continuous alert system that monitors the market in real time.
The use of EMA (Exponential Moving Average) as a moving average for Z-Score.
The goal of these features is to save my time, which is the only truly invaluable resource.
Disadvantages:
The assumption that the data follows a normal distribution, which may lead to inaccurate interpretations.
A fixed analysis period, which may not be perfectly suited to changing market conditions.
The use of EMA as a moving average for Z-Score, listed both as an advantage and a disadvantage depending on market context.
I have included comments within the code to explain the logic behind each part. For those who seek detailed mathematical formulas, I invite you to explore the code itself.
Defining Program Parameters:
Numerical Conditions:
VPSA Period for Analysis – The number of candles analyzed.
Normalized Spread Alert Threshold – The expected normalized spread value; defines how large or small the spread should be, with a range of 0-1.00.
Normalized Volume Alert Threshold – The expected normalized volume value; defines how large or small the volume should be, with a range of 0-1.00.
Spread Z-SCORE Alert Threshold – The Z-SCORE value for the spread; determines how much the spread deviates from the average, with a range of 0-4 (a higher value can be entered, but from a logical standpoint, exceeding 4 is unnecessary).
Volume Z-SCORE Alert Threshold – The Z-SCORE value for volume; determines how much the volume deviates from the average, with a range of 0-4 (the same logical note as above applies).
Logical Conditions:
Logical conditions describe whether the expected value should be less than or equal to or greater than or equal to the numerical condition.
All four parameters accept two possibilities and are analogous to the numerical conditions.
Volume Trend Detector:
Volume Trend Detector Period for Analysis – The analysis period, indicating the number of candles examined.
Method of Trend Determination – The method used to determine the trend. Possible values: Step by Step or SMA.
Trend Direction – The expected trend direction. Possible values: Upward or Downward.
Candle Type – The type of candle taken into account. Possible values: Bullish, Bearish, or Any.
The last available setting is the option to enable a joint alert for VPSA and VTD.
When enabled, VPSA will trigger on the last closed candle, regardless of the VTD analysis period.
Example Use Cases (Labels Visible in the Script Window Indicate Triggered Alerts):
The provided labels in the chart window mark where specific conditions were met and alerts were triggered.
Summary and Reflections
The program I present is a strong tool in the ongoing "game" with the Composite Man.
However, it requires familiarity and understanding of the underlying methodologies to fully utilize its potential.
Of course, like any technical analysis tool, it is not without flaws. There is no indicator that serves as a perfect Grail, accurately signaling Buy or Sell in every case.
I would like to thank those who have read through my thoughts to the end and are willing to take a closer look at my work by using this script.
If you encounter any errors or have suggestions for improvement, please feel free to contact me.
I wish you good health and accurately interpreted market structures, leading to successful trades!
CatTheTrader
Pseudo Super Smoother [PSS]The Pseudo Super Smoother (PSS) is a a Finite Impulse Response (FIR) filter. It provides a smoothed representation of the underlying data. This indicator can be considered a variation of a moving average, offering a unique approach to filtering price or other data series.
The PSS is inspired by the Super Smoother filter, known for its ability to reduce noise while maintaining a relatively low delay. However, the Super Smoother is an Infinite Impulse Response (IIR) filter. The PSS attempts to approximate some characteristics of the Super Smoother using an FIR design, which offers inherent stability.
The indicator offers two distinct filter types, selectable via the "Filter Style" input: Type 1 and Type 2 . Type 1 provides a smoother output with a more gradual response to changes in the input data. It is characterized by a greater attenuation of high-frequency components. Type 2 exhibits increased reactivity compared to Type 1 , allowing for a faster response to shifts in the underlying data trend, albeit with a potential overshoot. The choice between these two types will depend on the specific application and the preference for responsiveness versus smoothness.
The PSS calculates the FIR filter coefficients based on a decaying exponential function, adjusted according to the selected filter type and the user-defined period. The filter then applies these coefficients to a window of past data, effectively creating a weighted average that emphasizes more recent data points to varying degrees. The PSS uses a specific initialization technique that uses the first non-null data point to pre-fill the input window, which helps it start right away.
The PSS is an approximation of the Super Smoother filter using an FIR design. While it try's to emulate some of the Super Smoother's smoothing characteristics, users should be aware that the frequency response and overall behavior will differ due to it being a rough approximation. The PSS should be considered an experimental indicator and used in conjunction with other analysis techniques. This is, effectively, just another moving average, but its novelty lies in its attempt to bridge the gap between FIR and IIR filter designs for a specific smoothing goal.
N-Degree Moment-Based Adaptive Detection🙏🏻 N-Degree Moment-Based Adaptive Detection (NDMBAD) method is a generalization of MBAD since the horizontal line fit passing through the data's mean can be simply treated as zero-degree polynomial regression. We can extend the MBAD logic to higher-degree polynomial regression.
I don't think I need to talk a lot about the thing there; the logic is really the same as in MBAD, just hit the link above and read if you want. The only difference is now we can gather cumulants not only from the horizontal mean fit (degree = 0) but also from higher-order polynomial regression fit, including linear regression (degree = 1).
Why?
Simply because residuals from the 0-degree model don't contain trend information, and while in some cases that's exactly what you need, in other cases, you want to model your trend explicitly. Imagine your underlying process trends in a steady manner, and you want to control the extreme deviations from the process's core. If you're going to use 0-degree, you'll be treating this beautiful steady trend as a residual itself, which "constantly deviates from the process mean." It doesn't make much sense.
How?
First, if you set the length to 0, you will end up with the function incrementally applied to all your data starting from bar_index 0. This can be called the expanding window mode. That's the functionality I include in all my scripts lately (where it makes sense). As I said in the MBAD description, choosing length is a matter of doing business & applied use of my work, but I think I'm open to talk about it.
I don't see much sense in using degree > 1 though (still in research on it). If you have dem curves, you can use Fourier transform -> spectral filtering / harmonic regression (regression with Fourier terms). The job of a degree > 0 is to model the direction in data, and degree 1 gets it done. In mean reversion strategies, it means that you don't wanna put 0-degree polynomial regression (i.e., the mean) on non-stationary trending data in moving window mode because, this way, your residuals will be contaminated with the trend component.
By the way, you can send thanks to @aaron294c , he said like mane MBAD is dope, and it's gonna really complement his work, so I decided to drop NDMBAD now, gonna be more useful since it covers more types of data.
I wanned to call it N-Order Moment Adaptive Detection because it abbreviates to NOMAD, which sounds cool and suits me well, because when I perform as a fire dancer, nomad style is one of my outfits. Burning Man stuff vibe, you know. But the problem is degree and order really mean two different things in the polynomial context, so gotta stay right & precise—that's the priority.
∞
EMAs CrossThis script generates a line that changes color when evaluating the values and intersections of the 7-, 14-, and 42-day exponential averages, indicating possible entry and exit points.
In general, yellow indicates the beginning of an uptrend, green confirms an uptrend, and brown indicates a downtrend.
Using this script together with the RSI can help you make decisions about the best times to enter and exit positions.
The script was created to generate an indicator in a separate window from the main chart, but adding this indicator to the main window can help you visualize and interpret market movements.
Watchlist & Symbols Distribution [Daveatt]TLDR;
I got bored so I just coded the TradingView watchlist interface in Pinescript :)
TLDR 2:
Sharing it open-source what took me 1 full day to code - haven't coded in Pinescript in a long time, so I'm a bit slow for now :)
█ OVERVIEW
This script offers a comprehensive market analysis tool inspired by TradingView's native watchlist interface features.
It combines an interactive watchlist with powerful distribution visualization capabilities and a performance comparison panel.
The script was developed with a focus on providing multiple visualization methods while working within PineScript's limitations.
█ DEVELOPMENT BACKGROUND
The pie chart implementation was greatly inspired by the ( "Crypto Map Dashboard" script / )
adapting its circular visualization technique to create dynamic distribution charts. However, due to PineScript's 500-line limitation per script, I had to optimize the code to allow users to switch between pie chart analysis and performance comparison modes rather than displaying both simultaneously.
█ SETUP AND DISPLAY
For optimal visualization, users need to adjust the chart's display settings manually.
This involves:
Expanding the indicator window vertically to accommodate both the watchlist and graphical elements
Adjusting the Y-axis scale by dragging it to ensure proper spacing for the comparison panel grid
Modifying the X-axis scale to achieve the desired time window display
Fine-tuning these adjustments whenever switching between pie chart and comparison panel modes
These manual adjustments are necessary due to PineScript's limitations in controlling chart scaling programmatically. While this requires some initial setup, it allows users to customize the display to their preferred viewing proportions.
█ MAIN FEATURES
Distribution Analysis
The script provides three distinct distribution visualization modes through a pie chart.
Users can analyze their symbols by exchanges, asset types (such as Crypto, Forex, Futures), or market sectors.
If you can't see it well at first, adjust your chart scaling until it's displayed nicely.
Asset Exchanges
www.tradingview.com
Asset Types
Asset Sectors
The pie charts feature an optional 3D effect with adjustable depth and angle parameters. To enhance visual customization, four different color schemes are available: Default, Pastel, Dark, and Neon.
Each segment of the pie chart includes interactive tooltips that can be configured to show different levels of detail. Importantly, the pie chart only visualizes the distribution of selected assets (those marked with a checkmark in the watchlist), providing a focused view of the user's current interests.
Interactive Watchlist
The watchlist component displays real-time data for up to 10 user-defined symbols. Each entry shows current price, price changes (both absolute and percentage), volume metrics, and a comparison toggle.
The table is dynamically updated and features color-coded entries that correspond to their respective performance lines in the comparison chart. The watchlist serves as both an information display and a control panel for the comparison feature.
Performance Comparison
One of the script's most innovative features is its performance comparison panel.
Using polylines for smooth visualization, it tracks the 30-day performance of selected symbols relative to a 0% baseline.
The comparison chart includes a sophisticated grid system with 5% intervals and a dynamic legend showing current performance values.
The polyline implementation allows for fluid, continuous lines that accurately represent price movements, providing a more refined visual experience than traditional line plots. Like the pie charts, the comparison panel only displays performance lines for symbols that have been selected in the watchlist, allowing users to focus on their specific assets of interest.
█ TECHNICAL IMPLEMENTATION
The script utilizes several advanced PineScript features:
Dynamic array management for symbol tracking
Polyline-based charting for smooth performance visualization
Real-time data processing with security calls
Interactive tooltips and labels
Optimized drawing routines to maintain performance
Selective visualization based on user choices
█ CUSTOMIZATION
Users can personalize almost every aspect of the script:
Symbol selection and comparison preferences
Visual theme selection with four distinct color schemes
Pie chart dimensions and positioning
Tooltip information density
Component visibility toggles
█ LIMITATIONS
The primary limitation stems from PineScript's 500-line restriction per script.
This constraint necessitated the implementation of a mode-switching system between pie charts and the comparison panel, as displaying both simultaneously would exceed the line limit. Additionally, the script relies on manual chart scale adjustments, as PineScript doesn't provide direct control over chart scaling when overlay=false is enabled.
However, these limitations led to a more focused and efficient design approach that gives users control over their viewing experience.
█ CONCLUSION
All those tools exist in the native TradingView watchlist interface and they're better than what I just did.
However, now it exists in Pinescript... so I believe it's a win lol :)
Value at Risk [OmegaTools]The "Value at Risk" (VaR) indicator is a powerful financial risk management tool that helps traders estimate the potential losses in a portfolio over a specified period of time, given a certain level of confidence. VaR is widely used by financial institutions, traders, and risk managers to assess the probability of portfolio losses in both normal and volatile market conditions. This TradingView script implements a comprehensive VaR calculation using several models, allowing users to visualize different risk scenarios and adjust their trading strategies accordingly.
Concept of Value at Risk
Value at Risk (VaR) is a statistical technique used to measure the likelihood of losses in a portfolio or financial asset due to market risks. In essence, it answers the question: "What is the maximum potential loss that could occur in a given portfolio over a specific time horizon, with a certain confidence level?" For instance, if a portfolio has a one-day 95% VaR of $10,000, it means that there is a 95% chance the portfolio will not lose more than $10,000 in a single day. Conversely, there is a 5% chance of losing more than $10,000. VaR is a key risk management tool for portfolio managers and traders because it quantifies potential losses in monetary terms, allowing for better-informed decision-making.
There are several ways to calculate VaR, and this indicator script incorporates three of the most commonly used models:
Historical VaR: This approach uses historical returns to estimate potential losses. It is based purely on past price data, assuming that the past distribution of returns is indicative of future risks.
Variance-Covariance VaR: This model assumes that asset returns follow a normal distribution and that the risk can be summarized using the mean and standard deviation of past returns. It is a parametric method that is widely used in financial risk management.
Exponentially Weighted Moving Average (EWMA) VaR: In this model, recent data points are given more weight than older data. This dynamic approach allows the VaR estimation to react more quickly to changes in market volatility, which is particularly useful during periods of market stress. This model uses the Exponential Weighted Moving Average Volatility Model.
How the Script Works
The script starts by offering users a set of customizable input settings. The first input allows the user to choose between two main calculation modes: "All" or "OCT" (Only Current Timeframe). In the "All" mode, the script calculates VaR using all available methodologies—Historical, Variance-Covariance, and EWMA—providing a comprehensive risk overview. The "OCT" mode narrows the calculation to the current timeframe, which can be particularly useful for intraday traders who need a more focused view of risk.
The next input is the lookback window, which defines the number of historical periods used to calculate VaR. Commonly used lookback periods include 21 days (approximately one month), 63 days (about three months), and 252 days (roughly one year), with the script supporting up to 504 days for more extended historical analysis. A longer lookback period provides a more comprehensive picture of risk but may be less responsive to recent market conditions.
The confidence level is another important setting in the script. This represents the probability that the loss will not exceed the VaR estimate. Standard confidence levels are 90%, 95%, and 99%. A higher confidence level results in a more conservative risk estimate, meaning that the calculated VaR will reflect a more extreme loss scenario.
In addition to these core settings, the script allows users to customize the visual appearance of the indicator. For example, traders can choose different colors for "Bullish" (Risk On), "Bearish" (Risk Off), and "Neutral" phases, as well as colors for highlighting "Breaks" in the data, where returns exceed the calculated VaR. These visual cues make it easy to identify periods of heightened risk at a glance.
The actual VaR calculation is broken down into several models, starting with the Historical VaR calculation. This is done by computing the logarithmic returns of the asset's closing prices and then using linear interpolation to determine the percentile corresponding to the desired confidence level. This percentile represents the potential loss in the asset over the lookback period.
Next, the script calculates Variance-Covariance VaR using the mean and standard deviation of the historical returns. The standard deviation is multiplied by a z-score corresponding to the chosen confidence level (e.g., 1.645 for 95% confidence), and the resulting value is subtracted from the mean return to arrive at the VaR estimate.
The EWMA VaR model uses the EWMA for the sigma parameter, the standard deviation, obtaining a specific dynamic in the volatility. It is particularly useful in volatile markets where recent price behavior is more indicative of future risk than older data.
For traders interested in intraday risk management, the script provides several methods to adjust VaR calculations for lower timeframes. By using intraday returns and scaling them according to the chosen timeframe, the script provides a dynamic view of risk throughout the trading day. This is especially important for short-term traders who need to manage their exposure during high-volatility periods within the same day. The script also incorporates an EWMA model for intraday data, which gives greater weight to the most recent intraday price movements.
In addition to calculating VaR, the script also attempts to detect periods where the asset's returns exceed the estimated VaR threshold, referred to as "Breaks." When the returns breach the VaR limit, the script highlights these instances on the chart, allowing traders to quickly identify periods of extreme risk. The script also calculates the average of these breaks and displays it for comparison, helping traders understand how frequently these high-risk periods occur.
The script further visualizes the risk scenario using a risk phase classification system. Depending on the level of risk, the script categorizes the market as either "Risk On," "Risk Off," or "Risk Neutral." In "Risk On" mode, the market is considered bullish, and the indicator displays a green background. In "Risk Off" mode, the market is bearish, and the background turns red. If the market is neither strongly bullish nor bearish, the background turns neutral, signaling a balanced risk environment.
Traders can customize whether they want to see this risk phase background, along with toggling the display of the various VaR models, the intraday methods, and the break signals. This flexibility allows traders to tailor the indicator to their specific needs, whether they are day traders looking for quick intraday insights or longer-term investors focused on historical risk analysis.
The "Risk On" and "Risk Off" phases calculated by this Value at Risk (VaR) script introduce a novel approach to market risk assessment, offering traders an advanced toolset to gauge market sentiment and potential risk levels dynamically. These risk phases are built on a combination of traditional VaR methodologies and proprietary logic to create a more responsive and intuitive way to manage exposure in both normal and volatile market conditions. This method of classifying market conditions into "Risk On," "Risk Off," or "Risk Neutral" is not something that has been traditionally associated with VaR, making it a groundbreaking addition to this indicator.
How the "Risk On" and "Risk Off" Phases Are Calculated
In typical VaR implementations, the focus is on calculating the potential losses at a given confidence level without providing an overall market outlook. This script, however, introduces a unique risk classification system that takes the output of various VaR models and translates it into actionable signals for traders, marking whether the market is in a Risk On, Risk Off, or Risk Neutral phase.
The Risk On and Risk Off phases are primarily determined by comparing the current returns of the asset to the average VaR calculated across several different methods, including Historical VaR, Variance-Covariance VaR, and EWMA VaR. Here's how the process works:
1. Threshold Setting and Effect Calculation: The script first computes the average VaR using the selected models. It then checks whether the current returns (expressed as a negative value to signify loss) exceed the average VaR value. If the current returns surpass the calculated VaR threshold, this indicates that the actual market risk is higher than expected, signaling a potential shift in market conditions.
2. Break Analysis: In addition to monitoring whether returns exceed the average VaR, the script counts the number of instances within the lookback period where this breach occurs. This is referred to as the "break effect." For each period in the lookback window, the script checks whether the returns surpass the calculated VaR threshold and increments a counter. The percentage of periods where this breach occurs is then calculated as the "effect" or break percentage.
3. Dual Effect Check (if "Double" Risk Scenario is selected): When the user chooses the "Double" risk scenario mode, the script performs two layers of analysis. First, it calculates the effect of returns exceeding the VaR threshold for the current timeframe. Then, it calculates the effect for the lower intraday timeframe as well. Both effects are compared to the user-defined confidence level (e.g., 95%). If both effects exceed the confidence level, the market is deemed to be in a high-risk situation, thus triggering a Risk Off phase. If both effects fall below the confidence level, the market is classified as Risk On.
4. Risk Phases Determination: The final risk phase is determined by analyzing these effects in relation to the confidence level:
- Risk On: If the calculated effect of breaks is lower than the confidence level (e.g., fewer than 5% of periods show returns exceeding the VaR threshold for a 95% confidence level), the market is considered to be in a relatively safe state, and the script signals a "Risk On" phase. This is indicative of bullish conditions where the potential for extreme loss is minimal.
- Risk Off: If the break effect exceeds the confidence level (e.g., more than 5% of periods show returns breaching the VaR threshold), the market is deemed to be in a high-risk state, and the script signals a "Risk Off" phase. This indicates bearish market conditions where the likelihood of significant losses is higher.
- Risk Neutral: If the break effect hovers near the confidence level or if there is no clear trend indicating a shift toward either extreme, the market is classified as "Risk Neutral." In this phase, neither bulls nor bears are dominant, and traders should remain cautious.
The phase color that the script uses helps visualize these risk phases. The background will turn green in Risk On conditions, red in Risk Off conditions, and gray in Risk Neutral phases, providing immediate visual feedback on market risk. In addition to this, when the "Double" risk scenario is selected, the background will only turn green or red if both the current and intraday timeframes confirm the respective risk phase. This double-checking process ensures that traders are only given a strong signal when both longer-term and short-term risks align, reducing the likelihood of false signals.
A New Way of Using Value at Risk
This innovative Risk On/Risk Off classification, based on the interaction between VaR thresholds and market returns, represents a significant departure from the traditional use of Value at Risk as a pure risk measurement tool. Typically, VaR is employed as a backward-looking measure of risk, providing a static estimate of potential losses over a given timeframe with no immediate actionable feedback on current market conditions. This script, however, dynamically interprets VaR results to create a forward-looking, real-time signal that informs traders whether they are operating in a favorable (Risk On) or unfavorable (Risk Off) environment.
By incorporating the "break effect" analysis and allowing users to view the VaR breaches as a percentage of past occurrences, the script adds a predictive element that can be used to time market entries and exits more effectively. This **dual-layer risk analysis**, particularly when using the "Double" scenario mode, adds further granularity by considering both current timeframe and intraday risks. Traders can therefore make more informed decisions not just based on historical risk data, but on how the market is behaving in real-time relative to those risk benchmarks.
This approach transforms the VaR indicator from a risk monitoring tool into a decision-making system that helps identify favorable trading opportunities while alerting users to potential market downturns. It provides a more holistic view of market conditions by combining both statistical risk measurement and intuitive phase-based market analysis. This level of integration between VaR methodologies and real-time signal generation has not been widely seen in the world of trading indicators, marking this script as a cutting-edge tool for risk management and market sentiment analysis.
I would like to express my sincere gratitude to @skewedzeta for his invaluable contribution to the final script. From generating fresh ideas to applying his expertise in reviewing the formula, his support has been instrumental in refining the outcome.
