Machine Learning RSI [BackQuant]Machine Learning RSI
The Machine Learning RSI is a cutting-edge trading indicator that combines the power of Relative Strength Index (RSI) with Machine Learning (ML) clustering techniques to dynamically determine overbought and oversold thresholds. This advanced indicator adapts to market conditions in real-time, offering traders a robust tool for identifying optimal entry and exit points with increased precision.
Core Concept: Relative Strength Index (RSI)
The RSI is a well-known momentum oscillator that measures the speed and change of price movements, oscillating between 0 and 100. Typically, RSI values above 70 are considered overbought, and values below 30 are considered oversold. However, static thresholds may not be effective in all market conditions.
This script enhances the RSI by integrating a dynamic thresholding system powered by Machine Learning clustering, allowing it to adapt thresholds based on historical RSI behavior and market context.
Machine Learning Clustering for Dynamic Thresholds
The Machine Learning (ML) component uses clustering to calculate dynamic thresholds for overbought and oversold levels. Instead of relying on fixed RSI levels, this indicator clusters historical RSI values into three groups using a percentile-based initialization and iterative optimization:
Cluster 1: Represents lower RSI values (typically associated with oversold conditions).
Cluster 2: Represents mid-range RSI values.
Cluster 3: Represents higher RSI values (typically associated with overbought conditions).
Dynamic thresholds are determined as follows:
Long Threshold: The upper centroid value of Cluster 3.
Short Threshold: The lower centroid value of Cluster 1.
This approach ensures that the indicator adapts to the current market regime, providing more accurate signals in volatile or trending conditions.
Smoothing Options for RSI
To further enhance the effectiveness of the RSI, this script allows traders to apply various smoothing methods to the RSI calculation, including:
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Hull Moving Average (HMA)
Linear Regression (LINREG)
Double Exponential Moving Average (DEMA)
Triple Exponential Moving Average (TEMA)
Adaptive Linear Moving Average (ALMA)
T3 Moving Average
Traders can select their preferred smoothing method and adjust the smoothing period to suit their trading style and market conditions. The option to smooth the RSI reduces noise and makes the indicator more reliable for detecting trends and reversals.
Long and Short Signals
The indicator generates long and short signals based on the relationship between the RSI value and the dynamic thresholds:
Long Signals: Triggered when the RSI crosses above the long threshold, signaling bullish momentum.
Short Signals: Triggered when the RSI falls below the short threshold, signaling bearish momentum.
These signals are dynamically adjusted to reflect real-time market conditions, making them more robust than static RSI signals.
Visualization and Clustering Insights
The Machine Learning RSI provides an intuitive and visually rich interface, including:
RSI Line: Plotted in real-time, color-coded based on its position relative to the dynamic thresholds (green for long, red for short, gray for neutral).
Dynamic Threshold Lines: The script plots the long and short thresholds calculated by the ML clustering process, providing a clear visual reference for overbought and oversold levels.
Cluster Plots: Each RSI cluster is displayed with distinct colors (green, orange, and red) to give traders insights into how RSI values are grouped and how the dynamic thresholds are derived.
Customization Options
The Machine Learning RSI is highly customizable, allowing traders to tailor the indicator to their preferences:
RSI Settings : Adjust the RSI length, source price, and smoothing method to match your trading strategy.
Threshold Settings : Define the range and step size for clustering thresholds, allowing you to fine-tune the clustering process.
Optimization Settings : Control the performance memory, maximum clustering steps, and maximum data points for ML calculations to ensure optimal performance.
UI Settings : Customize the appearance of the RSI plot, dynamic thresholds, and cluster plots. Traders can also enable or disable candle coloring based on trend direction.
Alerts and Automation
To assist traders in staying on top of market movements, the script includes alert conditions for key events:
Long Signal: When the RSI crosses above the long threshold.
Short Signal: When the RSI crosses below the short threshold.
These alerts can be configured to notify traders in real-time, enabling timely decisions without constant chart monitoring.
Trading Applications
The Machine Learning RSI is versatile and can be applied to various trading strategies, including:
Trend Following: By dynamically adjusting thresholds, this indicator is effective in identifying and following trends in real-time.
Reversal Trading: The ML clustering process helps identify extreme RSI levels, offering reliable signals for reversals.
Range-Bound Trading: The dynamic thresholds adapt to market conditions, making the indicator suitable for trading in sideways markets where static thresholds often fail.
Final Thoughts
The Machine Learning RSI represents a significant advancement in RSI-based trading indicators. By integrating Machine Learning clustering techniques, this script overcomes the limitations of static thresholds, providing dynamic, adaptive signals that respond to market conditions in real-time. With its robust visualization, customizable settings, and alert capabilities, this indicator is a powerful tool for traders seeking to enhance their momentum analysis and improve decision-making.
As always, thorough backtesting and integration into a broader trading strategy are recommended to maximize the effectiveness!
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PDF Smoothed Moving Average [BackQuant]PDF Smoothed Moving Average
Introducing BackQuant’s PDF Smoothed Moving Average (PDF-MA) — an innovative trading indicator that applies Probability Density Function (PDF) weighting to moving averages, creating a unique, trend-following tool that offers adaptive smoothing to price movements. This advanced indicator gives traders an edge by blending PDF-weighted values with conventional moving averages, helping to capture trend shifts with enhanced clarity.
Core Concept: Probability Density Function (PDF) Smoothing
The Probability Density Function (PDF) provides a mathematical approach to applying adaptive weighting to data points based on a specified variance and mean. In the PDF-MA indicator, the PDF function is used to weight price data, adding a layer of probabilistic smoothing that enhances the detection of trend strength while reducing noise.
The PDF weights are controlled by two key parameters:
Variance: Determines the spread of the weights, where higher values spread out the weighting effect, providing broader smoothing.
Mean : Centers the weights around a particular price value, influencing the trend’s directionality and sensitivity.
These PDF weights are applied to each price point over the chosen period, creating an adaptive and smooth moving average that more closely reflects the underlying price trend.
Blending PDF with Standard Moving Averages
To further improve the PDF-MA, this indicator combines the PDF-weighted average with a traditional moving average, selected by the user as either an Exponential Moving Average (EMA) or Simple Moving Average (SMA). This blended approach leverages the strengths of each method: the responsiveness of PDF smoothing and the robustness of conventional moving averages.
Smoothing Method: Traders can choose between EMA and SMA for the additional moving average layer. The EMA is more responsive to recent prices, while the SMA provides a consistent average across the selected period.
Smoothing Period: Controls the length of the lookback period, affecting how sensitive the average is to price changes.
The result is a PDF-MA that provides a reliable trend line, reflecting both the PDF weighting and traditional moving average values, ideal for use in trend-following and momentum-based strategies.
Trend Detection and Candle Coloring
The PDF-MA includes a built-in trend detection feature that dynamically colors candles based on the direction of the smoothed moving average:
Uptrend: When the PDF-MA value is increasing, the trend is considered bullish, and candles are colored green, indicating potential buying conditions.
Downtrend: When the PDF-MA value is decreasing, the trend is considered bearish, and candles are colored red, signaling potential selling or shorting conditions.
These color-coded candles provide a quick visual reference for the trend direction, helping traders make real-time decisions based on the current market trend.
Customization and Visualization Options
This indicator offers a range of customization options, allowing traders to tailor it to their specific preferences and trading environment:
Price Source : Choose the price data for calculation, with options like close, open, high, low, or HLC3.
Variance and Mean : Fine-tune the PDF weighting parameters to control the indicator’s sensitivity and responsiveness to price data.
Smoothing Method : Select either EMA or SMA to customize the conventional moving average layer used in conjunction with the PDF.
Smoothing Period : Set the lookback period for the moving average, with a longer period providing more stability and a shorter period offering greater sensitivity.
Candle Coloring : Enable or disable candle coloring based on trend direction, providing additional clarity in identifying bullish and bearish phases.
Trading Applications
The PDF Smoothed Moving Average can be applied across various trading strategies and timeframes:
Trend Following : By smoothing price data with PDF weighting, this indicator helps traders identify long-term trends while filtering out short-term noise.
Reversal Trading : The PDF-MA’s trend coloring feature can help pinpoint potential reversal points by showing shifts in the trend direction, allowing traders to enter or exit positions at optimal moments.
Swing Trading : The PDF-MA provides a clear trend line that swing traders can use to capture intermediate price moves, following the trend direction until it shifts.
Final Thoughts
The PDF Smoothed Moving Average is a highly adaptable indicator that combines probabilistic smoothing with traditional moving averages, providing a nuanced view of market trends. By integrating PDF-based weighting with the flexibility of EMA or SMA smoothing, this indicator offers traders an advanced tool for trend analysis that adapts to changing market conditions with reduced lag and increased accuracy.
Whether you’re trading trends, reversals, or swings, the PDF-MA offers valuable insights into the direction and strength of price movements, making it a versatile addition to any trading strategy.
Savitzky-Golay Z-Score [BackQuant]Savitzky-Golay Z-Score
The Savitzky-Golay Z-Score is a powerful trading indicator that combines the precision of the Savitzky-Golay filter with the statistical strength of the Z-Score. This advanced indicator is designed to detect trend shifts, identify overbought or oversold conditions, and highlight potential divergences in the market, providing traders with a unique edge in detecting momentum changes and trend reversals.
Core Concept: Savitzky-Golay Filter
The Savitzky-Golay filter is a widely-used smoothing technique that preserves important signal features such as peak detection while filtering out noise. In this indicator, the filter is applied to price data (default set to HLC3) to smooth out volatility and produce a cleaner trend line. By specifying the window size and polynomial degree, traders can fine-tune the degree of smoothing to match their preferred trading style or market conditions.
Z-Score: Measuring Deviation
The Z-Score is a statistical measure that indicates how far the current price is from its mean in terms of standard deviations. In trading, the Z-Score can be used to identify extreme price moves that are likely to revert or continue trending. A positive Z-Score means the price is above the mean, while a negative Z-Score indicates the price is below the mean.
This script calculates the Z-Score based on the Savitzky-Golay filtered price, enabling traders to detect moments when the price is diverging from its typical range and may present an opportunity for a trade.
Long and Short Conditions
The Savitzky-Golay Z-Score generates clear long and short signals based on the Z-Score value:
Long Signals : When the Z-Score is positive, indicating the price is above its smoothed mean, a long signal is generated. The color of the bars turns green, signaling upward momentum.
Short Signals : When the Z-Score is negative, indicating the price is below its smoothed mean, a short signal is generated. The bars turn red, signaling downward momentum.
These signals allow traders to follow the prevailing trend with confidence, using statistical backing to avoid false signals from short-term volatility.
Standard Deviation Levels and Extreme Levels
This indicator includes several features to help visualize overbought and oversold conditions:
Standard Deviation Levels: The script plots horizontal lines at +1, +2, -1, and -2 standard deviations. These levels provide a reference for how far the current price is from the mean, allowing traders to quickly identify when the price is moving into extreme territory.
Extreme Levels: Additional extreme levels at +3 and +4 (and their negative counterparts) are plotted to highlight areas where the price is highly likely to revert. These extreme levels provide important insight into market conditions that are far outside the norm, signaling caution or potential reversal zones.
The indicator also adapts the color shading of these extreme zones based on the Z-Score’s strength. For example, the area between +3 and +4 is shaded with a stronger color when the Z-Score approaches these values, giving a visual representation of market pressure.
Divergences: Detecting Hidden and Regular Signals
A key feature of the Savitzky-Golay Z-Score is its ability to detect bullish and bearish divergences, both regular and hidden:
Regular Bullish Divergence: This occurs when the price makes a lower low while the Z-Score forms a higher low. It signals that bearish momentum is weakening, and a bullish reversal could be near.
Hidden Bullish Divergence: This divergence occurs when the price makes a higher low while the Z-Score forms a lower low. It signals that bullish momentum may continue after a temporary pullback.
Regular Bearish Divergence: This occurs when the price makes a higher high while the Z-Score forms a lower high, signaling that bullish momentum is weakening and a bearish reversal may be near.
Hidden Bearish Divergence: This divergence occurs when the price makes a lower high while the Z-Score forms a higher high, indicating that bearish momentum may continue after a temporary rally.
These divergences are plotted directly on the chart, making it easier for traders to spot when the price and momentum are out of sync and when a potential reversal may occur.
Customization and Visualization
The Savitzky-Golay Z-Score offers a range of customization options to fit different trading styles:
Window Size and Polynomial Degree: Adjust the window size and polynomial degree of the Savitzky-Golay filter to control how much smoothing is applied to the price data.
Z-Score Lookback Period: Set the lookback period for calculating the Z-Score, allowing traders to fine-tune the sensitivity to short-term or long-term price movements.
Display Options: Choose whether to display standard deviation levels, extreme levels, and divergence labels on the chart.
Bar Color: Color the price bars based on trend direction, with green for bullish trends and red for bearish trends, allowing traders to easily visualize the current momentum.
Divergences: Enable or disable divergence detection, and adjust the lookback periods for pivots used to detect regular and hidden divergences.
Alerts and Automation
To ensure you never miss an important signal, the indicator includes built-in alert conditions for the following events:
Positive Z-Score (Long Signal): Triggers an alert when the Z-Score crosses above zero, indicating a potential buying opportunity.
Negative Z-Score (Short Signal): Triggers an alert when the Z-Score crosses below zero, signaling a potential short opportunity.
Shifting Momentum: Alerts when the Z-Score is shifting up or down, providing early warning of changing market conditions.
These alerts can be configured to notify you via email, SMS, or app notification, allowing you to stay on top of the market without having to constantly monitor the chart.
Trading Applications
The Savitzky-Golay Z-Score is a versatile tool that can be applied across multiple trading strategies:
Trend Following: By smoothing the price and calculating the Z-Score, this indicator helps traders follow the prevailing trend while avoiding false signals from short-term volatility.
Mean Reversion: The Z-Score highlights moments when the price is far from its mean, helping traders identify overbought or oversold conditions and capitalize on potential reversals.
Divergence Trading: Regular and hidden divergences between the Z-Score and price provide early warning of trend reversals, allowing traders to enter trades at opportune moments.
Final Thoughts
The Savitzky-Golay Z-Score is an advanced statistical tool designed to provide a clearer view of market trends and momentum. By applying the Savitzky-Golay filter and Z-Score analysis, this indicator reduces noise and highlights key areas where the market may reverse or accelerate, giving traders a significant edge in understanding price behavior.
Whether you’re a trend follower or a reversal trader, this indicator offers the flexibility and insights you need to navigate complex markets with confidence.
Savitzky Golay Median Filtered RSI [BackQuant]Savitzky Golay Median Filtered RSI
Introducing BackQuant's Savitzky Golay Median Filtered RSI, a cutting-edge indicator that enhances the classic Relative Strength Index (RSI) by applying both a Savitzky-Golay filter and a median filter to provide smoother and more reliable signals. This advanced approach helps reduce noise and captures true momentum trends with greater precision. Let’s break down how the indicator works, the features it offers, and how it can improve your trading strategy.
Core Concept: Relative Strength Index (RSI)
The Relative Strength Index (RSI) is a widely used momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100, with levels above 70 typically indicating overbought conditions and levels below 30 indicating oversold conditions. However, the standard RSI can sometimes generate noisy signals, especially in volatile markets, making it challenging to identify reliable entry and exit points.
To improve upon the traditional RSI, this indicator introduces two powerful filters: the Savitzky-Golay filter and a median filter.
Savitzky-Golay Filter: Smoothing with Precision
The Savitzky-Golay filter is a digital filtering technique used to smooth data while preserving important features, such as peaks and trends. Unlike simple moving averages that can distort important price data, the Savitzky-Golay filter uses polynomial regression to fit the data, providing a more accurate and less lagging result.
In this script, the Savitzky-Golay filter is applied to the RSI values to smooth out short-term fluctuations and provide a more reliable signal. By using a window size of 5 and a polynomial degree of 2, the filter effectively reduces noise without compromising the integrity of the underlying price movements.
Median Filter: Reducing Outliers
After applying the Savitzky-Golay filter, the median filter is applied to the smoothed RSI values. The median filter is particularly effective at removing short-lived outliers, further enhancing the accuracy of the RSI by reducing the impact of sudden and temporary price spikes or drops. This combination of filters creates an ultra-smooth RSI that is better suited for detecting true market trends.
Long and Short Signals
The Savitzky Golay Median Filtered RSI generates long and short signals based on user-defined threshold levels:
Long Signals: A long signal is triggered when the filtered RSI exceeds the Long Threshold (default set at 176). This indicates that momentum is shifting upward, and it may present a good buying opportunity.
Short Signals: A short signal is generated when the filtered RSI falls below the Short Threshold (default set at 162). This suggests that momentum is weakening, potentially signaling a selling opportunity or exit from a long position.
These threshold levels can be adjusted to suit different market conditions and timeframes, allowing traders to fine-tune the sensitivity of the indicator.
Customization and Visualization Options
The Savitzky Golay Median Filtered RSI comes with several customization options, enabling traders to tailor the indicator to their specific needs:
Calculation Source: Select the price source for the RSI calculation (default is OHLC4, but it can be changed to close, open, high, or low prices).
RSI Period: Adjust the lookback period for the RSI calculation (default is 14).
Median Filter Length: Control the length of the median filter applied to the smoothed RSI, affecting how much noise is removed from the signal.
Threshold Levels: Customize the long and short thresholds to define the sensitivity for generating buy and sell signals.
UI Settings: Choose whether to display the RSI and thresholds on the chart, color the bars according to trend direction, and adjust the line width and colors used for long and short signals.
Visual Feedback: Color-Coded Signals and Thresholds
To make the signals easier to interpret, the indicator offers visual feedback by coloring the price bars and the RSI plot according to the current market trend:
Green Bars indicate long signals when momentum is bullish.
Red Bars indicate short signals when momentum is bearish.
Gray Bars indicate neutral or undecided conditions when no clear signal is present.
In addition, the Long and Short Thresholds can be plotted directly on the chart to provide a clear reference for when signals are triggered, allowing traders to visually gauge the strength of the RSI relative to its thresholds.
Alerts for Automation
For traders who prefer automated notifications, the Savitzky Golay Median Filtered RSI includes built-in alert conditions for long and short signals. You can configure these alerts to notify you when a buy or sell condition is met, ensuring you never miss a trading opportunity.
Trading Applications
This indicator is versatile and can be used in a variety of trading strategies:
Trend Following: The combination of Savitzky-Golay and median filtering makes this RSI particularly useful for identifying strong trends without being misled by short-term noise. Traders can use the long and short signals to enter trades in the direction of the prevailing trend.
Reversal Trading: By adjusting the threshold levels, traders can use this indicator to spot potential reversals. When the RSI moves from overbought to oversold levels (or vice versa), it may signal a shift in market direction.
Swing Trading: The smoothed RSI provides a clear signal for short to medium-term price movements, making it an excellent tool for swing traders looking to capitalize on momentum shifts.
Risk Management: The filtered RSI can be used as part of a broader risk management strategy, helping traders avoid false signals and stay in trades only when the momentum is strong.
Final Thoughts
The Savitzky Golay Median Filtered RSI takes the classic RSI to the next level by applying advanced smoothing techniques that reduce noise and improve signal reliability. Whether you’re a trend follower, swing trader, or reversal trader, this indicator provides a more refined approach to momentum analysis, helping you make better-informed trading decisions.
As with all indicators, it is important to backtest thoroughly and incorporate sound risk management strategies when using the Savitzky Golay Median Filtered RSI in your trading system.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Kalman For Loop [BackQuant]Kalman For Loop
Introducing BackQuant's Kalman For Loop (Kalman FL) — a highly adaptive trading indicator that uses a Kalman filter to smooth price data and generate actionable long and short signals. This advanced indicator is designed to help traders identify trends, filter out market noise, and optimize their entry and exit points with precision. Let’s explore how this indicator works, its key features, and how it can enhance your trading strategies.
Core Concept: Kalman Filter
The Kalman Filter is a mathematical algorithm used to estimate the state of a system by filtering noisy data. It is widely used in areas such as control systems, signal processing, and time-series analysis. In the context of trading, a Kalman filter can be applied to price data to smooth out short-term fluctuations, providing a clearer view of the underlying trend.
Unlike moving averages, which use fixed weights to smooth data, the Kalman Filter adjusts its estimate dynamically based on the relationship between the process noise and the measurement noise. This makes the filter more adaptive to changing market conditions, providing more accurate trend detection without the lag associated with traditional smoothing techniques.
Please see the original Kalman Price Filter
In this script, the Kalman For Loop applies the Kalman filter to the price source (default set to the closing price) to generate a smoothed price series, which is then used to calculate signals.
Adaptive Smoothing with Process and Measurement Noise
Two key parameters govern the behavior of the Kalman filter:
Process Noise: This controls the extent to which the model allows for uncertainty in price changes. A lower process noise value will make the filter smoother but slower to react to price changes, while a higher value makes it more sensitive to recent price fluctuations.
Measurement Noise: This represents the uncertainty or "noise" in the observed price data. A higher measurement noise value gives the filter more leeway to ignore short-term fluctuations, focusing on the broader trend. Lowering the measurement noise makes the filter more responsive to minor changes in price.
These settings allow traders to fine-tune the Kalman filter’s sensitivity, adjusting it to match their preferred trading style or market conditions.
For-Loop Scoring Mechanism
The Kalman FL further enhances the effectiveness of the Kalman filter by using a for-loop scoring system. This mechanism evaluates the smoothed price over a range of periods (defined by the Calculation Start and Calculation End inputs), assigning a score based on whether the current filtered price is higher or lower than previous values.
Long Signals: A long signal is generated when the for-loop score surpasses the Long Threshold (default set at 20), indicating a strong upward trend. This helps traders identify potential buying opportunities.
Short Signals: A short signal is triggered when the score crosses below the Short Threshold (default set at -10), signaling a potential downtrend or selling opportunity.
These signals are plotted on the chart, giving traders a clear visual indication of when to enter long or short positions.
Customization and Visualization Options
The Kalman For Loop comes with a range of customization options to give traders full control over how the indicator operates and is displayed on the chart:
Kalman Price Source: Choose the price data used for the Kalman filter (default is the closing price), allowing you to apply the filter to other price points like open, high, or low.
Filter Order: Set the order of the Kalman filter (default is 5), controlling how far back the filter looks in its calculations.
Process and Measurement Noise: Fine-tune the sensitivity of the Kalman filter by adjusting these noise parameters.
Signal Line Width and Colors: Customize the appearance of the signal line and the colors used to indicate long and short conditions.
Threshold Lines: Toggle the display of the long and short threshold lines on the chart for better visual clarity.
The indicator also includes the option to color the candlesticks based on the current trend direction, allowing traders to quickly identify changes in market sentiment. In addition, a background color feature further highlights the overall trend by shading the background in green for long signals and red for short signals.
Trading Applications
The Kalman For Loop is a versatile tool that can be adapted to a variety of trading strategies and markets. Some of the primary use cases include:
Trend Following: The adaptive nature of the Kalman filter helps traders identify the start of new trends with greater precision. The for-loop scoring system quantifies the strength of the trend, making it easier to stay in trades for longer when the trend remains strong.
Mean Reversion: For traders looking to capitalize on short-term reversals, the Kalman filter's ability to smooth price data makes it easier to spot when price has deviated too far from its expected path, potentially signaling a reversal.
Noise Reduction: The Kalman filter excels at filtering out short-term price noise, allowing traders to focus on the broader market movements without being distracted by minor fluctuations.
Risk Management: By providing clear long and short signals based on filtered price data, the Kalman FL helps traders manage risk by entering positions only when the trend is well-defined, reducing the chances of false signals.
Alerts and Automation
To further assist traders, the Kalman For Loop includes built-in alert conditions that notify you when a long or short signal is generated. These alerts can be configured to trigger notifications, helping you stay on top of market movements without constantly monitoring the chart.
Final Thoughts
The Kalman For Loop is a powerful and adaptive trading indicator that combines the precision of the Kalman filter with a for-loop scoring mechanism to generate reliable long and short signals. Whether you’re a trend follower or a reversal trader, this indicator offers the flexibility and accuracy needed to navigate complex markets with confidence.
As always, it’s important to backtest the indicator and adjust the settings to fit your trading style and market conditions. No indicator is perfect, and the Kalman FL should be used alongside other tools and sound risk management practices for the best results.
Fourier For Loop [BackQuant]Fourier For Loop
PLEASE Read the following, as understanding an indicator's functionality is essential before integrating it into a trading strategy. Knowing the core logic behind each tool allows for a sound and strategic approach to trading.
Introducing BackQuant's Fourier For Loop (FFL) — a cutting-edge trading indicator that combines Fourier transforms with a for-loop scoring mechanism. This innovative approach leverages mathematical precision to extract trends and reversals in the market, helping traders make informed decisions. Let's break down the components, rationale, and potential use-cases of this indicator.
Understanding Fourier Transform in Trading
The Fourier Transform decomposes price movements into their frequency components, allowing for a detailed analysis of cyclical behavior in the market. By transforming the price data from the time domain into the frequency domain, this indicator identifies underlying patterns that traditional methods may overlook.
In this script, Fourier transforms are applied to the specified calculation source (defaulted to HLC3). The transformation yields magnitude values that can be used to score market movements over a defined range. This scoring process helps uncover long and short signals based on relative strength and trend direction.
Why Use Fourier Transforms?
Fourier Transforms excel in identifying recurring cycles and smoothing noisy data, making them ideal for fast-paced markets where price movements may be erratic. They also provide a unique perspective on market volatility, offering traders additional insights beyond standard indicators.
Calculation Logic: For-Loop Scoring Mechanism
The For Loop Scoring mechanism compares the magnitude of each transformed point in the series, summing the results to generate a score. This score forms the backbone of the signal generation system.
Long Signals: Generated when the score surpasses the defined long threshold (default set at 40). This indicates a strong bullish trend, signaling potential upward momentum.
Short Signals: Triggered when the score crosses under the short threshold (default set at -10). This suggests a bearish trend or potential downside risk.'
Thresholds & Customization
The indicator offers customizable settings to fit various trading styles:
Calculation Periods: Control how many periods the Fourier transform covers.
Long/Short Thresholds: Adjust the sensitivity of the signals to match different timeframes or risk preferences.
Visualization Options: Traders can visualize the thresholds, change the color of bars based on trend direction, and even color the background for enhanced clarity.
Trading Applications
This Fourier For Loop indicator is designed to be versatile across various market conditions and timeframes. Some of its key use-cases include:
Cycle Detection: Fourier transforms help identify recurring patterns or cycles, giving traders a head-start on market direction.
Trend Following: The for-loop scoring system helps confirm the strength of trends, allowing traders to enter positions with greater confidence.
Risk Management: With clearly defined long and short signals, traders can manage their positions effectively, minimizing exposure to false signals.
Final Note
Incorporating this indicator into your trading strategy adds a layer of mathematical precision to traditional technical analysis. Be sure to adjust the calculation start/end points and thresholds to match your specific trading style, and remember that no indicator guarantees success. Always backtest thoroughly and integrate the Fourier For Loop into a balanced trading system.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future .
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Custom Time Range HighlighterCustom Time Range Highlighter
This versatile indicator allows traders to highlight specific time ranges on their charts, accommodating users worldwide by supporting customizable UTC offsets. Traders can define two distinct time ranges, setting start and end hours in their local time zone.
A toggle option enables the display of highlights for today only , ensuring focus on current trading conditions.
Ideal for day traders and those following specific market sessions, this tool enhances visibility of active trading periods and aids in effective trade management.
Deep Crab Harmonic Pattern [TradingFinder] Reversal Zones🔵 Introduction
The Deep Crab pattern is a 5-point extension harmonic structure (X-A-B-C-D) used in technical analysis to identify potential reversal points in financial markets. Like the original Crab pattern, it heavily relies on a 1.618 XA projection to form the Potential Reversal Zone (PRZ).
However, the key difference lies in the B point, which must be an 0.886 retracement of the XA leg. The D point in this pattern typically extends beyond the X point, signaling a strong potential reversal in price movement.
Bullish Deep Crab :
The Bullish Deep Crab is a pattern used in technical analysis to spot potential trend reversals. It signals a shift from a downtrend to an uptrend. Traders enter a buy position at the D point and set a stop-loss below point X, anticipating a price increase.
Bearish Deep Crab :
The Bearish Deep Crab is a reversal pattern that indicates the potential end of an uptrend. Traders enter a sell position at point D and set a stop-loss above point X, expecting the price to fall afterward.
🟣 Crab Vs Deep Crab
The Crab and Deep Crab patterns are both used to identify reversal points in technical analysis, but they differ in terms of correction depth :
Crab : The B point retraces between 38.2% to 61.8% of the XA leg, and point D extends beyond X, indicating a price reversal after a smaller correction.
Deep Crab : The B point retraces more deeply, around 88.6% of the XA leg, and point D has a stronger extension, signaling a reversal after a deeper correction.
The Deep Crab is more suited for identifying stronger price movements.
🔵 How to Use
To effectively use the Deep Crab pattern, it’s essential to correctly identify its five key points (X, A, B, C, and D) based on Fibonacci retracements and extensions. Traders look for a deep retracement at point B, followed by an extended move to point D, which typically signals a strong price reversal.
Once these points are established, traders can strategically enter positions at point D with appropriate stop-loss and take-profit levels, capitalizing on the anticipated market reversal. Proper use of Fibonacci tools is crucial for accurate pattern identification.
🟣 Bullish Deep Crab
To use the Bullish Deep Crab pattern, a trader identifies point D as the key price reversal point in a downtrend. Using Fibonacci tools, points X, A, B, and C are identified, with point B showing an 88.6% retracement of XA, and CD extending 1.618% of XA.
The trader enters a buy position at point D and sets a stop-loss below X, expecting a reversal from a downtrend to an uptrend.
🟣 Bearish Deep Crab
In the Bearish Deep Crab pattern, point D acts as the reversal point in an uptrend. After identifying points X, A, B, and C, D extends 1.618% of XA. Point B retraces 88.6% of XA. Traders enter a sell position at point D and place a stop-loss above X, anticipating a drop in price.
🔵 Setting
🟣 Logical Setting
ZigZag Pivot Period : You can adjust the period so that the harmonic patterns are adjusted according to the pivot period you want. This factor is the most important parameter in pattern recognition.
Show Valid Forma t: If this parameter is on "On" mode, only patterns will be displayed that they have exact format and no noise can be seen in them. If "Off" is, the patterns displayed that maybe are noisy and do not exactly correspond to the original pattern.
Show Formation Last Pivot Confirm : if Turned on, you can see this ability of patterns when their last pivot is formed. If this feature is off, it will see the patterns as soon as they are formed. The advantage of this option being clear is less formation of fielded patterns, and it is accompanied by the latest pattern seeing and a sharp reduction in reward to risk.
Period of Formation Last Pivot : Using this parameter you can determine that the last pivot is based on Pivot period.
🟣 Genaral Setting
Show : Enter "On" to display the template and "Off" to not display the template.
Color : Enter the desired color to draw the pattern in this parameter.
LineWidth : You can enter the number 1 or numbers higher than one to adjust the thickness of the drawing lines. This number must be an integer and increases with increasing thickness.
LabelSize : You can adjust the size of the labels by using the "size.auto", "size.tiny", "size.smal", "size.normal", "size.large" or "size.huge" entries.
🟣 Alert Setting
Alert : On / Off
Message Frequency : This string parameter defines the announcement frequency. Choices include: "All" (activates the alert every time the function is called), "Once Per Bar" (activates the alert only on the first call within the bar), and "Once Per Bar Close" (the alert is activated only by a call at the last script execution of the real-time bar upon closing). The default setting is "Once per Bar".
Show Alert Time by Time Zone : The date, hour, and minute you receive in alert messages can be based on any time zone you choose. For example, if you want New York time, you should enter "UTC-4". This input is set to the time zone "UTC" by default.
🔵 Conclusion
The Deep Crab pattern is a valuable reversal tool in technical analysis, known for its deep retracement and extended price movements.
Unlike other harmonic patterns, it emphasizes identifying critical points where price action is likely to reverse sharply. This pattern works well in both bullish and bearish market scenarios, offering clear signals for entry and exit points.
However, successful application requires a deep understanding of market behavior and precise use of technical tools like Fibonacci retracement. Overall, mastering this pattern can enhance trading strategies and risk management.
Kalman PSaR [BackQuant]Kalman PSaR
Overview and Innovation
The Kalman PSaR combines the well-known Parabolic SAR (PSaR) with the advanced smoothing capabilities of the Kalman Filter . This innovative tool aims to enhance the traditional PSaR by integrating Kalman filtering, which reduces noise and improves trend detection. The Kalman PSaR adapts dynamically to price movements, making it a highly effective indicator for spotting trend shifts while minimizing the impact of false signals caused by market volatility.
Please Find the Basic Kalman Here:
Kalman Filter Dynamics
The Kalman Filter is a powerful algorithm for estimating the true value of a system amidst noisy data. In the Kalman PSaR, this filter is applied to the high, low, and closing prices, resulting in a smoother and more accurate representation of price action. The filter’s parameters—process noise and measurement noise—are customizable, allowing traders to fine-tune the sensitivity of the indicator to market conditions. By reducing the impact of noise, the Kalman-filtered PSaR offers clearer signals for identifying trend reversals and continuations.
Enhanced PSaR Calculation
The traditional Parabolic SAR is a popular trend-following indicator that highlights potential entry and exit points based on price acceleration. In the Kalman PSaR, this calculation is enhanced by the Kalman-filtered prices, providing a smoother and more reliable signal. The indicator continuously updates based on the acceleration factor and max step values, while the Kalman filter ensures that sudden price spikes or market noise do not trigger false signals.
Min Step and Max Step: These settings control the sensitivity of the PSaR. The Min Step sets the initial acceleration factor, while the Max Step limits how fast the PSaR adapts to price changes, helping traders fine-tune the indicator’s responsiveness.
Optional Smoothing Techniques To further enhance the signal clarity, the Kalman PSaR includes an optional smoothing feature. Traders can choose from various smoothing methods, such as SMA, Hull, EMA, WMA, TEMA, and more, to reduce short-term fluctuations and emphasize the underlying trend. The smoothing period is customizable, allowing traders to adjust the indicator’s behavior according to their preferred trading style and timeframe.
Color-Coded Candle Painting The Kalman PSaR features color-coded candles that change according to the trend direction. When the price is above the PSaR, candles are painted green to indicate a long trend, and when the price is below the PSaR, candles are painted red to signal a short trend. This visual representation makes it easy to interpret market sentiment at a glance, improving decision-making speed during fast-moving markets.
Key Features and Customization
Kalman Filter Customization: The process noise and measurement noise parameters allow traders to adjust how aggressively the filter adapts to price changes, making it suitable for both volatile and stable markets.
Smoothing Options: A variety of moving average types, such as SMA, Hull, EMA, and more, can be applied to smooth the PSaR values, ensuring that the signal remains clear even in choppy markets.
Dynamic Trend Detection: The Kalman PSaR dynamically updates based on price movements, helping traders spot trend reversals early while filtering out false signals caused by short-term volatility.
Bar Coloring and PSaR Plotting: Traders can choose to color candles based on trend direction or plot the PSaR directly on the chart for additional visual clarity.
Practical Applications
Trend-Following Strategies: The Kalman PSaR excels in trend-following strategies by providing timely signals of trend changes. The dynamic nature of the indicator allows traders to capture significant price movements while avoiding market noise.
Reversal Identification: The indicator’s ability to filter out noise and provide smoother signals makes it ideal for identifying reversals in volatile markets.
Risk Management: By plotting clear stop levels based on the PSaR, traders can use this indicator to effectively manage risk, placing stop-loss orders at key points based on the trend direction.
Conclusion
The Kalman PSaR is a fusion of the classic Parabolic SAR and the Kalman filter, offering enhanced trend detection with reduced noise. Its customizable filtering and smoothing options, combined with dynamic trend-following capabilities, make it a versatile tool for traders seeking to improve their timing and signal accuracy. The adaptive nature of the Kalman filter, combined with the robust PSaR logic, helps traders stay on the right side of the market and manage risk more effectively.
Bat Harmonic Pattern [TradingFinder] Bat Chart Indicator🔵 Introduction
The Bat Harmonic Pattern, created by Scott Carney in the 1990s, is a sophisticated tool in technical analysis, used to identify potential reversal points in price movements by leveraging Fibonacci ratios.
This pattern is classified into two primary types: the Bullish Bat Pattern, which signals the end of a downtrend and the beginning of an uptrend, and the Bearish Bat Pattern, which indicates the conclusion of an uptrend and the onset of a downtrend.
🟣 Bullish Bat Pattern
The Bullish Bat Pattern is designed to identify when a downtrend is likely to end and a new uptrend is about to begin. The key feature of this pattern is Point D, which typically aligns near the 88.6% Fibonacci retracement of the XA leg.
This point is considered a strong buy zone. When the price reaches Point D after a significant downtrend, it often indicates a potential reversal, presenting a buying opportunity for traders anticipating the start of an upward movement.
🟣 Bearish Bat Pattern
In contrast, the Bearish Bat Pattern forms when an uptrend is nearing its conclusion. Point D, which also typically aligns near the 88.6% Fibonacci retracement of the XA leg, serves as a critical point for traders.
This point is regarded as a strong sell zone, signaling that the uptrend may be ending, and a downtrend could be imminent. Traders often open short positions when they identify this pattern, aiming to capitalize on the anticipated downward movement.
🔵 How to Use
The Bat Pattern consists of five key points: X, A, B, C, and D, and four waves: XA, AB, BC, and CD. Fibonacci ratios play a crucial role in this pattern, helping traders pinpoint precise entry and exit points. In both the Bullish and Bearish Bat Patterns, the 88.6% retracement of the XA leg is a critical level for identifying potential reversal points.
🟣 Bullish Bat Pattern
Traders typically enter buy positions after Point D forms, expecting the downtrend to end and a new uptrend to start. This point, located near the 88.6% retracement of the XA leg, serves as a reliable buy signal.
🟣 Bearish Bat Pattern
Traders usually open short positions after identifying Point D, expecting the uptrend to end and a downtrend to begin. This point, also near the 88.6% retracement of the XA leg, acts as a valid sell signal.
🟣 Trading Tips for the Bat Pattern
Accurate Fibonacci Point Identification : Accurately identify Points X, A, B, C, and D, and calculate the Fibonacci ratios between these points. Point D should ideally be near the 88.6% retracement of the XA leg.
Signal Confirmation with Other Tools : To enhance the pattern's accuracy, avoid trading solely based on the Bat Pattern.
Risk Management : Always use stop-loss orders. In a Bullish Bat Pattern, place the stop-loss below Point X, and in a Bearish Bat Pattern, above Point X. This helps limit potential losses if the pattern fails.
Wait for Price Movement Confirmation : After identifying Point D, wait for the price to move in the anticipated direction to confirm the pattern's validity before entering a trade.
Set Realistic Profit Targets : Use Fibonacci retracement levels to set realistic profit targets, such as 38.2%, 50%, and 61.8% retracement levels of the CD leg. This strategy helps maximize profits and prevents premature exits.
🔵 Setting
🟣 Logical Setting
ZigZag Pivot Period : You can adjust the period so that the harmonic patterns are adjusted according to the pivot period you want. This factor is the most important parameter in pattern recognition.
Show Valid Forma t: If this parameter is on "On" mode, only patterns will be displayed that they have exact format and no noise can be seen in them. If "Off" is, the patterns displayed that maybe are noisy and do not exactly correspond to the original pattern.
Show Formation Last Pivot Confirm : if Turned on, you can see this ability of patterns when their last pivot is formed. If this feature is off, it will see the patterns as soon as they are formed. The advantage of this option being clear is less formation of fielded patterns, and it is accompanied by the latest pattern seeing and a sharp reduction in reward to risk.
Period of Formation Last Pivot : Using this parameter you can determine that the last pivot is based on Pivot period.
🟣 Genaral Setting
Show : Enter "On" to display the template and "Off" to not display the template.
Color : Enter the desired color to draw the pattern in this parameter.
LineWidth : You can enter the number 1 or numbers higher than one to adjust the thickness of the drawing lines. This number must be an integer and increases with increasing thickness.
LabelSize : You can adjust the size of the labels by using the "size.auto", "size.tiny", "size.smal", "size.normal", "size.large" or "size.huge" entries.
🟣 Alert Setting
Alert : On / Off
Message Frequency : This string parameter defines the announcement frequency. Choices include: "All" (activates the alert every time the function is called), "Once Per Bar" (activates the alert only on the first call within the bar), and "Once Per Bar Close" (the alert is activated only by a call at the last script execution of the real-time bar upon closing). The default setting is "Once per Bar".
Show Alert Time by Time Zone : The date, hour, and minute you receive in alert messages can be based on any time zone you choose. For example, if you want New York time, you should enter "UTC-4". This input is set to the time zone "UTC" by default.
🔵 Conclusion
The Bat Harmonic Pattern is a powerful tool in technical analysis, offering traders the ability to identify critical reversal points using Fibonacci ratios. By recognizing the Bullish and Bearish Bat Patterns, traders can anticipate potential trend reversals and make informed trading decisions.
However, it is essential to combine the Bat Pattern with other technical analysis tools and confirm signals for better trading outcomes. With proper use, this pattern can help traders minimize risk and optimize their entry and exit points in the market.
Volatility with Power VariationVolatility Analysis using Power Variation
The "Volatility with Power Variation" indicator is designed to measure market volatility. It focuses on providing traders with a clear understanding of how much the market is moving and how this movement changes over time.. This indicator helps in identifying potential periods of market expansion or contraction, based on volatility.
What the indicator does:
This indicator analyzes volatility which refers to the degree of variation in the returns of a financial instrument over time. It's an important measure to understand how much the price and returns of a asset fluctuates. High volatility means large price swings, meanwhile low volatility indicates smaller and consolidating movements. Realized (Historical) Volatility refers to volatility based on past price data.
Power Variation
Power Variation is an extension of the traditional methods used to calculate realized volatility. Instead of simply summing up squared returns (as done in calculating variance), Power Variation raises the magnitude of returns to a power p . This allows the indicator to capture different types of market behavior depending on the chosen value of p .
When P = 2, the Power variation behaves like a traditional variance measure. Lower values of p (e.g., p=1) make the indicator more sensitive to smaller price changes, meanwhile higher values make it more responsive to large jumps, but smaller price moves wont affect the measure that much or won't most likely.
Bipower Variation
Bipower variation is another method used to analyze the changes in price. It specifically isolates the continuous part of price movements from the jumps, which can help by understanding whether volatility is coming from regular market activity or from sharp, sudden moves.
How to Use the Indicator.
Understand Realized and Historical Volatility. Volatility after periods of low volatility you can eventually expect a expansion or an increase in volatility. Conversely, after periods of high volatility, the market often contracts and volatility decreases. If the variation plot is really low and you start seeing it increasing, shown by the standard deviation channels and moving average and you see it trending and increasing then that means you can expect for volatility to increase which means more price moves and expansions. Also if the scaling seems messed up, then use the logarithmic chart scale.
DEMA Adaptive DMI [BackQuant]DEMA Adaptive DMI
PLEASE Read the following, knowing what an indicator does at its core before adding it into a system is pivotal. The core concepts can allow you to include it in a logical and sound manner.
Conceptual Foundation and Innovation
The DEMA Adaptive DMI blends the Double Exponential Moving Average (DEMA) with the Directional Movement Index (DMI) to offer a unique approach to trend-following. By applying DEMA to the high and low prices, this indicator refines the traditional DMI calculation, enhancing its responsiveness to price changes. This results in a more adaptive and timely measure of market trends and momentum, providing traders with a more refined tool for capturing directional movements in the market.
Technical Composition and Calculation
At its core, the DEMA Adaptive DMI calculates the DEMA for both the high and low prices over a user-defined period. This dual application of DEMA serves to smooth out price fluctuations while retaining sensitivity to market movements. The DMI is then derived from the changes in these DEMA values, producing a set of plus and minus directional indicators that reflect the prevailing trend. Additionally, an Average Directional Index (ADX) is computed to measure the strength of the trend, with the entire process being dynamically adjusted based on the DEMA calculations.
DEMA Application:
The DEMA is applied to both high and low prices to reduce lag and provide a smoother representation of price action.
Directional Movement Calculation: The DMI is calculated using the smoothed price changes, resulting in plus and minus indicators that accurately reflect market trends.
ADX Calculation:
The ADX is computed to quantify the strength of the trend, offering traders insight into whether the market is trending strongly or is in a phase of consolidation.
Features and User Inputs The DEMA Adaptive DMI offers a range of customizable options to suit different trading styles and market conditions:
DEMA Calculation Period: Users can set the period for the DEMA calculation, allowing for adjustments based on the desired sensitivity.
DMI Length: The length of the DMI calculation can be adjusted, providing flexibility in how trends are measured.
ADX Smoothing Period: The smoothing period for the ADX can be customized to fine-tune the trend strength measurement.
Divergence Detection: Optional divergence detection features allow traders to spot potential reversals based on the DMI and price action.
Visualization options include static high and low levels to mark extreme DMI thresholds, the ability to color bars according to trend direction, and background hues to highlight overbought and oversold conditions.
Practical Applications
The DEMA Adaptive DMI is particularly effective in markets where trend strength and direction are crucial for successful trading. Traders can leverage this indicator to:
Identify Trend Reversals:
Detect potential trend reversals by monitoring the DMI and ADX in conjunction with divergence signals.
Trend Confirmation:
Use the DEMA-based DMI to confirm the strength and direction of a trend, aiding in the timing of entries and exits.
Strategic Positioning:
The indicator's responsiveness allows traders to position themselves effectively in fast-moving markets, reducing the risk of late entries or exits.
Advantages and Strategic Value
By integrating the DEMA with the DMI, this indicator provides a more adaptive and timely measure of market trends. The reduced lag from the DEMA ensures that traders receive signals that are closely aligned with current market conditions, while the dynamic DMI calculation offers a more accurate representation of trend direction and strength. This makes the DEMA Adaptive DMI a valuable tool for traders looking to enhance their trend-following strategies with a focus on precision and adaptability.
Summary and Usage Tips
The DEMA Adaptive DMI is a sophisticated trend-following indicator that combines the benefits of DEMA and DMI into a single, powerful tool. Traders are encouraged to incorporate this indicator into their trading systems for a more nuanced and responsive approach to trend detection and confirmation. Whether used for identifying trend reversals, confirming trend strength, or strategically positioning in the market, the DEMA Adaptive DMI offers a versatile and reliable solution for trend-following strategies.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Shark Harmonic Pattern [TradingFinder] Shark Detector Indicator🔵 Introduction
The Shark harmonic pattern, first introduced by Scott Carney in 2011, is a recognized tool in technical analysis. Since its inception, it has been widely adopted by traders as an essential market analysis tool.
Due to its complexity, the Shark pattern can be challenging for novice traders. Therefore, we have developed the Harmonic Pattern Indicator to help analysts and traders easily identify these patterns.
🟣 Understanding the Types of Shark Pattern
In technical analysis, the Shark harmonic pattern forms at the end of trends and is categorized into two types: Bullish and Bearish Shark Patterns.
Bullish Shark Pattern : This pattern appears at the end of a downtrend, indicating a potential reversal to an uptrend. Traders can use this pattern to identify buy entry points. The image below illustrates the core components of the Bullish Shark Pattern.
Bearish Shark Pattern : Conversely, the Bearish Shark Pattern forms at the end of an uptrend, signaling a possible reversal to a downtrend. This pattern prompts traders to shift their positions from buying to selling. The image below showcases the characteristics of the Bearish Shark Pattern.
🔵 How to Use
🟣 Trading with the Bullish Shark Pattern
The Bullish Shark Pattern acts as a reversal pattern, helping traders identify the end of a downtrend and the beginning of an uptrend. It consists of five key points that indicate alternating bullish and bearish movements.
Upon the complete formation of this pattern, traders can look for opportunities to enter buy trades. To manage risk effectively, it is advisable to set a stop-loss below the lowest price point within the pattern.
🟣 Trading with the Bearish Shark Pattern
Similarly, the Bearish Shark Pattern functions as a reversal pattern but in the opposite direction. It helps traders identify the end of an uptrend and the onset of a downtrend.
After the pattern fully forms, traders can seek sell entry opportunities. As with the bullish pattern, placing a stop-loss above the highest price point within the pattern is recommended for risk management.
🔵 Setting
🟣 Logical Setting
ZigZag Pivot Period : You can adjust the period so that the harmonic patterns are adjusted according to the pivot period you want. This factor is the most important parameter in pattern recognition.
Show Valid Format : If this parameter is on "On" mode, only patterns will be displayed that they have exact format and no noise can be seen in them. If "Off" is, the patterns displayed that maybe are noisy and do not exactly correspond to the original pattern.
Show Formation Last Pivot Confirm : if Turned on, you can see this ability of patterns when their last pivot is formed. If this feature is off, it will see the patterns as soon as they are formed. The advantage of this option being clear is less formation of fielded patterns, and it is accompanied by the latest pattern seeing and a sharp reduction in reward to risk.
Period of Formation Last Pivot : Using this parameter you can determine that the last pivot is based on Pivot period.
🟣 Genaral Setting
Show : Enter "On" to display the template and "Off" to not display the template.
Color : Enter the desired color to draw the pattern in this parameter.
LineWidth : You can enter the number 1 or numbers higher than one to adjust the thickness of the drawing lines. This number must be an integer and increases with increasing thickness.
LabelSize : You can adjust the size of the labels by using the "size.auto", "size.tiny", "size.smal", "size.normal", "size.large" or "size.huge" entries.
🟣 Alert Setting
Alert : On / Off
Message Frequency : This string parameter defines the announcement frequency. Choices include: "All" (activates the alert every time the function is called), "Once Per Bar" (activates the alert only on the first call within the bar), and "Once Per Bar Close" (the alert is activated only by a call at the last script execution of the real-time bar upon closing). The default setting is "Once per Bar".
Show Alert Time by Time Zone : The date, hour, and minute you receive in alert messages can be based on any time zone you choose. For example, if you want New York time, you should enter "UTC-4". This input is set to the time zone "UTC" by default.
🔵 Conclusion
The Shark harmonic pattern is a potent analytical tool in technical analysis that aids traders in identifying critical reversal points in financial markets. Whether in a bullish or bearish context, this pattern provides clear trend change signals, allowing traders to enter trades with greater precision and optimize their strategies.
However, as with all analytical methods, it is essential to supplement the Shark pattern with additional analyses and strict risk management to avoid potential losses. Incorporating this pattern into a comprehensive trading strategy can lead to better trade outcomes and more opportunities for success
Moving Average Z-Score Suite [BackQuant]Moving Average Z-Score Suite
1. What is this indicator
The Moving Average Z-Score Suite is a versatile indicator designed to help traders identify and capitalize on market trends by utilizing a variety of moving averages. This indicator transforms selected moving averages into a Z-Score oscillator, providing clear signals for potential buy and sell opportunities. The indicator includes options to choose from eleven different moving average types, each offering unique benefits and characteristics. It also provides additional features such as standard deviation levels, extreme levels, and divergence detection, enhancing its utility in various market conditions.
2. What is a Z-Score
A Z-Score is a statistical measurement that describes a value's relationship to the mean of a group of values. It is measured in terms of standard deviations from the mean. For instance, a Z-Score of 1.0 means the value is one standard deviation above the mean, while a Z-Score of -1.0 indicates it is one standard deviation below the mean. In the context of financial markets, Z-Scores can be used to identify overbought or oversold conditions by determining how far a particular value (such as a moving average) deviates from its historical mean.
3. What moving averages can be used
The Moving Average Z-Score Suite allows users to select from the following eleven moving averages:
Simple Moving Average (SMA)
Hull Moving Average (HMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Double Exponential Moving Average (DEMA)
Running Moving Average (RMA)
Linear Regression Curve (LINREG) (This script can be found standalone )
Triple Exponential Moving Average (TEMA)
Arnaud Legoux Moving Average (ALMA)
Kalman Hull Moving Average (KHMA)
T3 Moving Average
Each of these moving averages has distinct properties and reacts differently to price changes, allowing traders to select the one that best fits their trading style and market conditions.
4. Why Turning a Moving Average into a Z-Score is Innovative and Its Benefits
Transforming a moving average into a Z-Score is an innovative approach because it normalizes the moving average values, making them more comparable across different periods and instruments. This normalization process helps in identifying extreme price movements and mean-reversion opportunities more effectively. By converting the moving average into a Z-Score, traders can better gauge the relative strength or weakness of a trend and detect potential reversals. This method enhances the traditional moving average analysis by adding a statistical perspective, providing clearer and more objective trading signals.
5. How It Can Be Used in the Context of a Trading System
In a trading system, it can be used to generate buy and sell signals based on the Z-Score values. When the Z-Score crosses above zero, it indicates a potential buying opportunity, suggesting that the price is above its mean and possibly trending upward. Conversely, a Z-Score crossing below zero signals a potential selling opportunity, indicating that the price is below its mean and might be trending downward. Additionally, the indicator's ability to show standard deviation levels and extreme levels helps traders set profit targets and stop-loss levels, improving risk management and trade planning.
6. How It Can Be Used for Trend Following
For trend-following strategies, it can be particularly useful. The Z-Score oscillator helps traders identify the strength and direction of a trend. By monitoring the Z-Score and its rate of change, traders can confirm the persistence of a trend and make informed decisions to enter or exit trades. The indicator's divergence detection feature further enhances trend-following by identifying potential reversals before they occur, allowing traders to capitalize on trend shifts. By providing a clear and quantifiable measure of trend strength, this indicator supports disciplined and systematic trend-following strategies.
No backtests for this indicator due to the many options and ways it can be used,
Enjoy
Exponential Smoothing FilterThe digital exponential filter, in finance known as Exponential Moving Average (EMA) , can be used as a technical indicator for chart analysis to visualize uptrends and downtrends in the market. Unlike the classic simple moving average, the EMA requires only two values for its calculation: the last calculated exponential average price and the current price. This is a simple and fast calculation - even for wide smoothing windows. For further details and the math please refer to the "exponential smoothing" article on Wikipedia.
Here are some additional key points about the exponential moving average:
The EMA can react more quickly to price changes because it can give more weight to current prices - depending on your parameter settings.
Short-term, disruptive price fluctuations are smoothed out well, making prevailing trends more visible.
Despite good smoothing properties, it delays the input values slightly, so it can follow sudden trend changes well.
The EMA is well suited to dynamic markets and trading strategies.
The filter is a good basis for further processing such as gradient analysis.
How to use
When you add the script to your charts, you'll immediately see a thin orange line across your time series, smoothing out price fluctuations.
There are only two parameters to set
smoothing factor between 0.0000 = no smoothing and 0.9999 = strong smoothing
input source : open, high, low, close hl2, etc.
Chart output
In the example chart above, you can see that the orange line follows the highs and lows better than the blue line , which is a simple moving average (SMA).
Additionally, the orange line has a shorter lag, or reacts faster when the trend of the original price data suddenly changes. These characteristics are critical for buying and selling decisions: quickly reacting and tracking highs and lows while providing a smooth line that filters out distracting noise.
Dark & Light Theme [TradingFinder] Switching Colors Library🔵 Introduction
One of the challenges of script users is matching the colors used in indicators or strategies. By default, colors are chosen to display based on either the dark theme or the light theme.
In scripts with a large number of colors used, changing all colors to better display in dark mode or light mode can be a difficult and tedious process.
This library provides developers with the ability to adjust the colors used in their scripts based on the theme of the display.
🔵 Logic
To categorize the color spectrum, the range from 0 to 255 of all three main colors red, green and blue was divided into smaller ranges.
Blue color, which is more effective in darkening or lightening colors, is divided into 8 categories, red color into 5 categories, and green color into 3 categories, because it has little effect on darkening or brightening colors.
The combination of these categories creates 120 different modes for the color range, which leads to a more accurate identification of the color and its brightness, and helps to decide how to change it.
Except for these 120 modes, there are 2 other modes that are related to colors almost white or black, which makes a total of 122 modes.
🔵 How to Use
First, you can add the library to your code as shown in the example below.
import TFlab/Dark_Light_Theme_TradingFinder_Switching_Colors_Library/1 as SC
🟣 Parameters
SwitchingColorMode(Color, Mode) =>
Parameters:
Color (color)
Mode (string)
Color : In this parameter, enter the color you want to adjust based on light mode and dark mode.
Mode : Three modes "Off", "Light" and "Dark" are included in this parameter. "Light" mode is for color adjustment for use in "Light Mode".
"Dark" mode is for color adjustment for use in "Dark Mode" and "Off" mode turns off the color adjustment function and the input color to the function is the same as the output color.
🔵 Function Outputs
OriginalColor = input.color(color.red)
= SC.SwitchingColorMode(OriginalColor, Mode)
Dead Simple Heikin Ashi Candles (HA Candles)Are you looking for a dead simple calculation of the Heikin Ashi candles as they are calculated in tradingview? Here it is!
I was looking through the library and I saw that many have come up with a lot of awesome scripts using heikin ashi candles. But, I can't find anywhere that had the straightforward simple version of how Tradingview calculates them. This was a problem for me because I realized after punching the formula in that TradingView doesn't calculate HA candles in the original way.
You might say they don't calculate them the "right" but, spoiler alert, there is no right in trading. You can only be rational or irrational as you make money or lose money.
This is useful to me for building out some portions of an algo that are not going to be compatible with the built-in function. It happens. So, if you were looking for it too, hopefully it saves you some time.
For reference the original calc of HA candles is:
o = (prev_HA_open + prev_HA_close) / 2
h = math.max(high, ha_open, ha_close)
l = math.min(low, ha_open, ha_close)
c = ohlc4
Trade Well.
Fourier Adjusted Average True Range [BackQuant]Fourier Adjusted Average True Range
1. Conceptual Foundation and Innovation
The FA-ATR leverages the principles of Fourier analysis to dissect market prices into their constituent cyclical components. By applying Fourier Transform to the price data, the FA-ATR captures the dominant cycles and trends which are often obscured in noisy market data. This integration allows the FA-ATR to adapt its readings based on underlying market dynamics, offering a refined view of volatility that is sensitive to both market direction and momentum.
2. Technical Composition and Calculation
The core of the FA-ATR involves calculating the traditional ATR, which measures market volatility by decomposing the entire range of price movements. The FA-ATR extends this by incorporating a Fourier Transform of price data to assess cyclical patterns over a user-defined period 'N'. This process synthesizes both the magnitude of price changes and their rhythmic occurrences, resulting in a more comprehensive volatility indicator.
Fourier Transform Application: The Fourier series is calculated using price data to identify the fundamental frequency of market movements. This frequency helps in adjusting the ATR to reflect more accurately the current market conditions.
Dynamic Adjustment: The ATR is then adjusted by the magnitude of the dominant cycle from the Fourier analysis, enhancing or reducing the ATR value based on the intensity and phase of market cycles.
3. Features and User Inputs
Customizability: Traders can modify the Fourier period, ATR period, and the multiplication factor to suit different trading styles and market environments.
Visualization : The FA-ATR can be plotted directly on the chart, providing a visual representation of volatility. Additionally, the option to paint candles according to the trend direction enhances the usability and interpretative ease of the indicator.
Confluence with Moving Averages: Optionally, a moving average of the FA-ATR can be displayed, serving as a confluence factor for confirming trends or potential reversals.
4. Practical Applications
The FA-ATR is particularly useful in markets characterized by periodic fluctuations or those that exhibit strong cyclical trends. Traders can utilize this indicator to:
Adjust Stop-Loss Orders: More accurately set stop-loss orders based on a volatility measure that accounts for cyclical market changes.
Trend Confirmation: Use the FA-ATR to confirm trend strength and sustainability, helping to avoid false signals often encountered in volatile markets.
Strategic Entry and Exit: The indicator's responsiveness to changing market dynamics makes it an excellent tool for planning entries and exits in a trend-following or a breakout trading strategy.
5. Advantages and Strategic Value
By integrating Fourier analysis, the FA-ATR provides a volatility measure that is both adaptive and anticipatory, giving traders a forward-looking tool that adjusts to changes before they become apparent through traditional indicators. This anticipatory feature makes it an invaluable asset for traders looking to gain an edge in fast-paced and rapidly changing market conditions.
6. Summary and Usage Tips
The Fourier Adjusted Average True Range is a cutting-edge development in technical analysis, offering traders an enhanced tool for assessing market volatility with increased accuracy and responsiveness. Its ability to adapt to the market's cyclical nature makes it particularly useful for those trading in highly volatile or cyclically influenced markets.
Traders are encouraged to integrate the FA-ATR into their trading systems as a supplementary tool to improve risk management and decision-making accuracy, thereby potentially increasing the effectiveness of their trading strategies.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Normalised T3 Oscillator [BackQuant]Normalised T3 Oscillator
The Normalised T3 Oscillator is an technical indicator designed to provide traders with a refined measure of market momentum by normalizing the T3 Moving Average. This tool was developed to enhance trading decisions by smoothing price data and reducing market noise, allowing for clearer trend recognition and potential signal generation. Below is a detailed breakdown of the Normalised T3 Oscillator, its methodology, and its application in trading scenarios.
1. Conceptual Foundation and Definition of T3
The T3 Moving Average, originally proposed by Tim Tillson, is renowned for its smoothness and responsiveness, achieved through a combination of multiple Exponential Moving Averages and a volume factor. The Normalised T3 Oscillator extends this concept by normalizing these values to oscillate around a central zero line, which aids in highlighting overbought and oversold conditions.
2. Normalization Process
Normalization in this context refers to the adjustment of the T3 values to ensure that the oscillator provides a standard range of output. This is accomplished by calculating the lowest and highest values of the T3 over a user-defined period and scaling the output between -0.5 to +0.5. This process not only aids in standardizing the indicator across different securities and time frames but also enhances comparative analysis.
3. Integration of the Oscillator and Moving Average
A unique feature of the Normalised T3 Oscillator is the inclusion of a secondary smoothing mechanism via a moving average of the oscillator itself, selectable from various types such as SMA, EMA, and more. This moving average acts as a signal line, providing potential buy or sell triggers when the oscillator crosses this line, thus offering dual layers of analysis—momentum and trend confirmation.
4. Visualization and User Interaction
The indicator is designed with user interaction in mind, featuring customizable parameters such as the length of the T3, normalization period, and type of moving average used for signals. Additionally, the oscillator is plotted with a color-coded scheme that visually represents different strength levels of the market conditions, enhancing readability and quick decision-making.
5. Practical Applications and Strategy Integration
Traders can leverage the Normalised T3 Oscillator in various trading strategies, including trend following, counter-trend plays, and as a component of a broader trading system. It is particularly useful in identifying turning points in the market or confirming ongoing trends. The clear visualization and customizable nature of the oscillator facilitate its adaptation to different trading styles and market environments.
6. Advanced Features and Customization
Further enhancing its utility, the indicator includes options such as painting candles according to the trend, showing static levels for quick reference, and alerts for crossover and crossunder events, which can be integrated into automated trading systems. These features allow for a high degree of personalization, enabling traders to mold the tool according to their specific trading preferences and risk management requirements.
7. Theoretical Justification and Empirical Usage
The use of the T3 smoothing mechanism combined with normalization is theoretically sound, aiming to reduce lag and false signals often associated with traditional moving averages. The practical effectiveness of the Normalised T3 Oscillator should be validated through rigorous backtesting and adjustment of parameters to match historical market conditions and volatility.
8. Conclusion and Utility in Market Analysis
Overall, the Normalised T3 Oscillator by BackQuant stands as a sophisticated tool for market analysis, providing traders with a dynamic and adaptable approach to gauging market momentum. Its development is rooted in the understanding of technical nuances and the demand for a more stable, responsive, and customizable trading indicator.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Pine Script Chart ViewerDisplay your custom charts exported from anywhere in TradingView.
Put your candles on candles :
var Candle candles = array.from(...)
For instance:
var Candle candles = array.from(Candle.new(2.0, 4.0, 1.0, 3.0), Candle.new(3.0, 5.0, 2.0, 4.0))
Candle details:
Candle.new(open_1, high_1, low_1, close_1)
Gaussian Price Filter [BackQuant]Gaussian Price Filter
Overview and History of the Gaussian Transformation
The Gaussian transformation, often associated with the Gaussian (normal) distribution, is a mathematical function characteristically prominent in statistics and probability theory. The bell-shaped curve of the Gaussian function, expressing the normal distribution, is ubiquitously employed in various scientific and engineering disciplines, including financial market analysis. This transformation's core utility in trading and economic forecasting is derived from its efficacy in smoothing data series and highlighting underlying trends, which are pivotal for making strategic trading decisions.
The Gaussian filter, specifically, is a type of data-smoothing algorithm that mitigates the random "noise" of market price data, thus enhancing the visibility of crucial trend changes and patterns. Historically, this concept was adapted from fields such as signal processing and image editing, where precise extraction of useful information from noisy environments is critical.
1. What is a Gaussian Transformation?
A Gaussian transformation involves the application of a Gaussian function to a set of data points. The function is applied as a filter in the context of trading algorithms to smooth time series data, which helps in identifying the intrinsic trends obscured by market volatility. The transformation is characterized by its parameter, sigma (σ), representing the standard deviation, which determines the width of the Gaussian bell curve. The breadth of this curve impacts the degree of smoothing: a wider curve (higher sigma value) results in more smoothing, beneficial for longer-term trend analysis.
2. Filtering Price with Gaussian Transformation and its Benefits
In the provided Script, the Gaussian transformation is utilized to filter price data. The filtering process involves convolving the price data with Gaussian weights, which are calculated based on the chosen length (the number of data points considered) and sigma. This convolution process smooths out short-term fluctuations and highlights longer-term movements, facilitating a clearer analysis of market trends.
Benefits:
Reduces noise: It filters out minor price movements and random fluctuations, which are often misleading.
Enhances trend recognition: By smoothing the data, it becomes easier to identify significant trends and reversals.
Improves decision-making: Traders can make more informed decisions by focusing on substantive, smoothed data rather than reacting to random noise.
3. Potential Limitations and Issues
While Gaussian filters are highly effective in smoothing data, they are not without limitations:
Lag introduction: Like all moving averages, the Gaussian filter introduces a lag between the actual price movements and the output signal, which can delay decision-making.
Feature blurring: Over-smoothing might obscure significant price movements, especially if a large sigma is used.
Parameter sensitivity: The choice of length and sigma significantly affects the output, requiring optimization and backtesting to determine the best settings for specific market conditions.
4. Extending Gaussian Filters to Other Indicators
The methodology used to filter price data with a Gaussian filter can similarly be applied to other technical indicators, such as RSI (Relative Strength Index) or MACD (Moving Average Convergence Divergence). By smoothing these indicators, traders can reduce false signals and enhance the reliability of the indicators' outputs, leading to potentially more accurate signals and better timing for entering or exiting trades.
5. Application in Trading
In trading, the Gaussian Price Filter can be strategically used to:
Spot trend reversals: Smoothed price data can more clearly indicate when a trend is starting to change, which is crucial for catching reversals early.
Define entry and exit points: The filtered data points can help in setting more precise entry and exit thresholds, minimizing the risk and maximizing the potential return.
Filter other data streams: Apply the Gaussian filter on volume or open interest data to identify significant changes in market dynamics.
6. Functionality of the Script
The script is designed to:
Calculate Gaussian weights (f_gaussianWeights function): Generates the weights used for the Gaussian kernel based on the provided length and sigma.
Apply the Gaussian filter (f_applyGaussianFilter function): Uses the weights to compute the smoothed price data.
Conditional Trend Detection and Coloring: Determines the trend direction based on the filtered price and colors the price bars on the chart to visually represent the trend.
7. Specific Actions of This Code
The Pine Script provided by BackQuant executes several specific actions:
Input Handling: It allows users to specify the source data (src), kernel length, and sigma directly in the chart settings.
Weight Calculation and Normalization: Computes the Gaussian weights and normalizes them to ensure their sum equals one, which maintains the original data scale.
Filter Application: Applies the normalized Gaussian kernel to the price data to produce a smoothed output.
Trend Identification and Visualization: Identifies whether the market is trending upwards or downwards based on the smoothed data and colors the bars green (up) or red (down) to indicate the trend direction.
Volatility Adjusted Weighted DEMA [BackQuant]Volatility Adjusted Weighted DEMA
The Volatility Adjusted Weighted Double Exponential Moving Average (VAWDEMA) by BackQuant is a sophisticated technical analysis tool designed for traders seeking to integrate volatility into their moving average calculations. This innovative indicator adjusts the weighting of the Double Exponential Moving Average (DEMA) according to recent volatility levels, offering a more dynamic and responsive measure of market trends.
Primarily, the single Moving average is very noisy, but can be used in the context of strategy development, where as the crossover, is best used in the context of defining a trading zone/ macro uptrend on higher timeframes.
Why Volatility Adjustment is Beneficial
Volatility is a fundamental aspect of financial markets, reflecting the intensity of price changes. A volatility adjustment in moving averages is beneficial because it allows the indicator to adapt more quickly during periods of high volatility, providing signals that are more aligned with the current market conditions. This makes the VAWDEMA a versatile tool for identifying trend strength and potential reversal points in more volatile markets.
Understanding DEMA and Its Advantages
DEMA is an indicator that aims to reduce the lag associated with traditional moving averages by applying a double smoothing process. The primary benefit of DEMA is its sensitivity and quicker response to price changes, making it an excellent tool for trend following and momentum trading. Incorporating DEMA into your analysis can help capture trends earlier than with simple moving averages.
The Power of Combining Volatility Adjustment with DEMA
By adjusting the weight of the DEMA based on volatility, the VAWDEMA becomes a powerful hybrid indicator. This combination leverages the quick responsiveness of DEMA while dynamically adjusting its sensitivity based on current market volatility. This results in a moving average that is both swift and adaptive, capable of providing more relevant signals for entering and exiting trades.
Core Logic Behind VAWDEMA
The core logic of the VAWDEMA involves calculating the DEMA for a specified period and then adjusting its weighting based on a volatility measure, such as the average true range (ATR) or standard deviation of price changes. This results in a weighted DEMA that reflects both the direction and the volatility of the market, offering insights into potential trend continuations or reversals.
Utilizing the Crossover in a Trading System
The VAWDEMA crossover occurs when two VAWDEMAs of different lengths cross, signaling potential bullish or bearish market conditions. In a trading system, a crossover can be used as a trigger for entry or exit points:
Bullish Signal: When a shorter-period VAWDEMA crosses above a longer-period VAWDEMA, it may indicate an uptrend, suggesting a potential entry point for a long position.
Bearish Signal: Conversely, when a shorter-period VAWDEMA crosses below a longer-period VAWDEMA, it might signal a downtrend, indicating a possible exit point or a short entry.
Incorporating VAWDEMA crossovers into a trading strategy can enhance decision-making by providing timely and adaptive signals that account for both trend direction and market volatility. Traders should combine these signals with other forms of analysis and risk management techniques to develop a well-rounded trading strategy.
Alert Conditions For Trading
alertcondition(vwdema>vwdema , title="VWDEMA Long", message="VWDEMA Long - {{ticker}} - {{interval}}")
alertcondition(vwdema<vwdema , title="VWDEMA Short", message="VWDEMA Short - {{ticker}} - {{interval}}")
alertcondition(ta.crossover(crossover, 0), title="VWDEMA Crossover Long", message="VWDEMA Crossover Long - {{ticker}} - {{interval}}")
alertcondition(ta.crossunder(crossover, 0), title="VWDEMA Crossover Short", message="VWDEMA Crossover Short - {{ticker}} - {{interval}}")
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Kalman Hull Supertrend [BackQuant]Kalman Hull Supertrend
At its core, this indicator uses a Kalman filter of price, put inside of a hull moving average function (replacing the weighted moving averages) and then using that as a price source for the supertrend instead of the normal hl2 (high+low/2).
Therefore, making it more adaptive to price and also sensitive to recent price action.
PLEASE Read the following, knowing what an indicator does at its core before adding it into a system is pivotal. The core concepts can allow you to include it in a logical and sound manner.
1. What is a Kalman Filter
The Kalman Filter is an algorithm renowned for its efficiency in estimating the states of a linear dynamic system amidst noisy data. It excels in real-time data processing, making it indispensable in fields requiring precise and adaptive filtering, such as aerospace, robotics, and financial market analysis. By leveraging its predictive capabilities, traders can significantly enhance their market analysis, particularly in estimating price movements more accurately.
If you would like this on its own, with a more in-depth description please see our Kalman Price Filter.
2. Hull Moving Average (HMA) and Its Core Calculation
The Hull Moving Average (HMA) improves on traditional moving averages by combining the Weighted Moving Average's (WMA) smoothness and reduced lag. Its core calculation involves taking the WMA of the data set and doubling it, then subtracting the WMA of the full period, followed by applying another WMA on the result over the square root of the period's length. This methodology yields a smoother and more responsive moving average, particularly useful for identifying market trends more rapidly.
3. Combining Kalman Filter with HMA
The innovative combination of the Kalman Filter with the Hull Moving Average (KHMA) offers a unique approach to smoothing price data. By applying the Kalman Filter to the price source before its incorporation into the HMA formula, we enhance the adaptiveness and responsiveness of the moving average. This adaptive smoothing method reduces noise more effectively and adjusts more swiftly to price changes, providing traders with clearer signals for market entries or exits.
The calculation is like so:
KHMA(_src, _length) =>
f_kalman(2 * f_kalman(_src, _length / 2) - f_kalman(_src, _length), math.round(math.sqrt(_length)))
4. Integration with Supertrend
Incorporating this adaptive price smoothing technique into the Supertrend indicator further enhances its efficiency. The Supertrend, known for its proficiency in identifying the prevailing market trend and providing clear buy or sell signals, becomes even more powerful with an adaptive price source. This integration allows the Supertrend to adjust more dynamically to market changes, offering traders more accurate and timely trading signals.
5. Application in a Trading System
In a trading system, the Kalman Hull Supertrend indicator can serve as a critical component for identifying market trends and generating signals for potential entry and exit points. Its adaptiveness and sensitivity to price changes make it particularly useful for traders looking to minimize lag in signal generation and improve the accuracy of their market trend analysis. Whether used as a standalone tool or in conjunction with other indicators, its dynamic nature can significantly enhance trading strategies.
6. Core Calculations and Benefits
The core of this indicator lies in its sophisticated filtering and averaging techniques, starting with the Kalman Filter's predictive adjustments, followed by the adaptive smoothing of the Hull Moving Average, and culminating in the trend-detecting capabilities of the Supertrend. This multi-layered approach not only reduces market noise but also adapts to market volatility more effectively. Benefits include improved signal accuracy, reduced lag, and the ability to discern trend changes more promptly, offering traders a competitive edge.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
