Adaptive Gaussian MA For Loop [BackQuant]Adaptive Gaussian MA For Loop
PLEASE Read the following carefully before applying this indicator to your trading system. Knowing the core logic behind the tools you're using allows you to integrate them into your strategy with confidence and precision.
Introducing BackQuant's Adaptive Gaussian Moving Average For Loop (AGMA FL) — a sophisticated trading indicator that merges the Gaussian Moving Average (GMA) with adaptive volatility to provide dynamic trend analysis. This unique indicator further enhances its effectiveness by utilizing a for-loop scoring mechanism to detect potential shifts in market direction. Let's dive into the components, the rationale behind them, and how this indicator can be practically applied to your trading strategies.
Understanding the Gaussian Moving Average (GMA)
The Gaussian Moving Average (GMA) is a smoothed moving average that applies Gaussian weighting to price data. Gaussian weighting gives more significance to data points near the center of the lookback window, making the GMA particularly effective at reducing noise while maintaining sensitivity to changes in price direction. In contrast to simpler moving averages like the SMA or EMA, GMA provides a more refined smoothing function, which can help traders follow the true trend in volatile markets.
In this script, the GMA is calculated over a defined Calculation Period (default 14), applying a Gaussian filter to smooth out market fluctuations and provide a clearer view of underlying trends.
Adaptive Volatility: A Dynamic Edge
The Adaptive feature in this indicator gives it the ability to adjust its sensitivity based on current market volatility. If the Adaptive option is enabled, the GMA uses a standard deviation-based volatility measure (with a default period of 20) to dynamically adjust the width of the Gaussian filter, allowing the GMA to react faster in volatile markets and more slowly in calm conditions. This dynamic nature ensures that the GMA stays relevant across different market environments.
When the Adaptive setting is disabled, the script defaults to a constant standard deviation value (default 1.0), providing a more stable but less responsive smoothing function.
Why Use Adaptive Gaussian Moving Average?
The Gaussian Moving Average already provides smoother results than standard moving averages, but by adding an adaptive component, the indicator becomes even more responsive to real-time price changes. In fast-moving or highly volatile markets, this adaptation allows traders to react quicker to emerging trends. Conversely, in quieter markets, it reduces over-sensitivity to minor fluctuations, thus lowering the risk of false signals.
For-Loop Scoring Mechanism
The heart of this indicator lies in its for-loop scoring system, which evaluates the smoothed price data (the GMA) over a specified range, comparing it to previous values. This scoring system assigns a numerical value based on whether the current GMA is higher or lower than previous values, creating a trend score.
Long Signals: These are generated when the for-loop score surpasses the Long Threshold (default set at 40), signaling that the GMA is gaining upward momentum, potentially identifying a favorable buying opportunity.
Short Signals: These are triggered when the score crosses below the Short Threshold (default set at -10), indicating that the market may be losing strength and that a selling or shorting opportunity could be emerging.
Thresholds & Customization Options
This indicator offers a high degree of flexibility, allowing you to fine-tune the settings according to your trading style and risk preferences:
Calculation Period: Adjust the lookback period for the Gaussian filter, affecting how smooth or responsive the indicator is to price changes.
Adaptive Mode: Toggle the adaptive feature on or off, allowing the GMA to dynamically adjust based on market volatility or remain consistent with a fixed standard deviation.
Volatility Settings: Control the standard deviation period for adaptive mode, fine-tuning how quickly the GMA responds to shifts in volatility.
For-Loop Settings: Modify the start and end points for the for-loop score calculation, adjusting the depth of analysis for trend signals.
Thresholds for Signals: Set custom long and short thresholds to determine when buy or sell signals should be generated.
Visualization Options: Choose to color bars based on trend direction, plot signal lines, or adjust the background color to reflect current market sentiment visually.
Trading Applications
The Adaptive Gaussian MA For Loop can be applied to a variety of trading styles and markets. Here are some key ways you can use this indicator in practice:
Trend Following: The combination of Gaussian smoothing and adaptive volatility helps traders stay on top of market trends, identifying significant momentum shifts while filtering out noise. The for-loop scoring system enhances this by providing a numerical representation of trend strength, making it easier to spot when a new trend is emerging or when an existing one is gaining strength.
Mean Reversion: For traders looking to capitalize on short-term market corrections, the adaptive nature of this indicator makes it easier to identify when price action is deviating too far from its smoothed trend, allowing for strategic entries and exits based on overbought or oversold conditions.
Swing Trading: With its ability to capture medium-term price movements while avoiding the noise of short-term fluctuations, this indicator is well-suited for swing traders who aim to profit from market reversals or short-to-mid-term trends.
Volatility Management: The adaptive feature allows the indicator to adjust dynamically in volatile markets, ensuring that it remains responsive in times of increased uncertainty while avoiding unnecessary noise in calmer periods. This makes it an effective tool for traders who want to manage risk by staying in tune with changing market conditions.
Final Thoughts
The Adaptive Gaussian MA For Loop is a powerful and flexible indicator that merges the elegance of Gaussian smoothing with the adaptability of volatility-based adjustments. By incorporating a for-loop scoring mechanism, this indicator provides traders with a comprehensive view of market trends and potential trade opportunities.
It’s important to test the settings on historical data and adapt them to your specific trading style, timeframe, and market conditions. As with any technical tool, the AGMA For Loop should be used in conjunction with other indicators and solid risk management practices for the best results.
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
Adaptive
Two Pole Butterworth For Loop [BackQuant]Two Pole Butterworth For Loop
PLEASE read the following carefully, as understanding the underlying concepts and logic behind the indicator is key to incorporating it into your trading system in a sound and methodical manner.
Introducing BackQuant's Two Pole Butterworth For Loop (2P BW FL) — an advanced indicator that fuses the power of the Two Pole Butterworth filter with a dynamic for-loop scoring mechanism. This unique approach is designed to extract actionable trading signals by smoothing out price data and then analyzing it using a comparative scoring method. Let's delve into how this indicator works, why it was created, and how it can be used in various trading scenarios.
Understanding the Two Pole Butterworth Filter
The Butterworth filter is a signal processing tool known for its smooth response and minimal distortion. It's often used in electronic and communication systems to filter out unwanted noise. In trading, the Butterworth filter can be applied to price data to smooth out the volatility, providing traders with a clearer view of underlying trends without the whipsaws often associated with market noise.
The Two Pole Butterworth variant further enhances this effect by applying the filter with two poles, effectively creating a sharper transition between the passband and stopband. In simple terms, this allows the filter to follow the price action more closely, reacting to changes while maintaining smoothness.
In this script, the Two Pole Butterworth filter is applied to the Calculation Source (default is set to the closing price), creating a smoothed price series that serves as the foundation for further analysis.
Why Use a Two Pole Butterworth Filter?
The Two Pole Butterworth filter is chosen for its ability to reduce lag while maintaining a smooth output. This makes it an ideal choice for traders who want to capture trends without being misled by short-term volatility or market noise. By filtering the price data, the Two Pole Butterworth enables traders to focus on the broader market movements and avoid false signals.
The For-Loop Scoring Mechanism
In addition to the Butterworth filter, this script uses a for-loop scoring system to evaluate the smoothed price data. The for-loop compares the current value of the filtered price (referred to as "subject") to previous values over a defined range (set by the start and end input). The score is calculated based on whether the subject is higher or lower than the previous points, and the cumulative score is used to determine the strength of the trend.
Long and Short Signal Logic
Long Signals: A long signal is triggered when the score surpasses the Long Threshold (default set at 40). This suggests that the price has built sufficient upward momentum, indicating a potential buying opportunity.
Short Signals: A short signal is triggered when the score crosses under the Short Threshold (default set at -10). This indicates weakening price action or a potential downtrend, signaling a possible selling or shorting opportunity.
By utilizing this scoring system, the indicator identifies moments when the price momentum is shifting, helping traders enter positions at opportune times.
Customization and Visualization Options
One of the strengths of this indicator is its flexibility. Traders can customize various settings to fit their personal trading style or adapt it to different markets and timeframes:
Calculation Periods: Adjust the lookback period for the Butterworth filter, allowing for shorter or longer smoothing depending on the desired sensitivity.
Threshold Levels: Set the long and short thresholds to define when signals should be triggered, giving you control over the balance between sensitivity and specificity.
Signal Line Width and Colors: Customize the visual presentation of the indicator on the chart, including the width of the signal line and the colors used for long and short conditions.
Candlestick and Background Colors: If desired, the indicator can color the candlesticks or the background according to the detected trend, offering additional clarity at a glance.
Trading Applications
This Two Pole Butterworth For Loop indicator is versatile and can be adapted to various market conditions and trading strategies. Here are a few use cases where this indicator shines:
Trend Following: The Butterworth filter smooths the price data, making it easier to follow trends and identify when they are gaining or losing strength. The for-loop scoring system enhances this by providing a clear indication of how strong the current trend is compared to recent history.
Mean Reversion: For traders looking to identify potential reversals, the indicator’s ability to compare the filtered price to previous values over a range of periods allows it to spot moments when the trend may be losing steam, potentially signaling a reversal.
Swing Trading: The combination of smoothing and scoring allows swing traders to capture short to medium-term price movements by filtering out the noise and focusing on significant shifts in momentum.
Risk Management: By providing clear long and short signals, this indicator helps traders manage their risk by offering well-defined entry and exit points. The smooth nature of the Butterworth filter also reduces the risk of getting caught in false signals due to market noise.
Final Thoughts
The Two Pole Butterworth For Loop indicator offers traders a powerful combination of smoothing and scoring to detect meaningful trends and shifts in price momentum. Whether you are a trend follower, swing trader, or someone looking to refine your entry and exit points, this indicator provides the tools to make more informed trading decisions.
As always, it's essential to backtest the indicator on historical data and tailor the settings to your specific trading style and market. While the Butterworth filter helps reduce noise and smooth trends, no indicator can predict the future with absolute certainty, so it should be used in conjunction with other tools and sound risk management practices.
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
Octopus Nest Strategy Hello Fellas,
Hereby, I come up with a popular strategy from YouTube called Octopus Nest Strategy. It is a no repaint, lower timeframe scalping strategy utilizing PSAR, EMA and TTM Squeeze.
The strategy considers these market factors:
PSAR -> Trend
EMA -> Trend
TTM Squeeze -> Momentum and Volatility by incorporating Bollinger Bands and Keltner Channels
Note: As you can see there is a potential improvement by incorporating volume.
What's Different Compared To The Original Strategy?
I added an option which allows users to use the Adaptive PSAR of @loxx, which will hopefully improve results sometimes.
Signals
Enter Long -> source above EMA 100, source crosses above PSAR and TTM Squeeze crosses above 0
Enter Short -> source below EMA 100, source crosses below PSAR and TTM Squeeze crosses below 0
Exit Long and Exit Short are triggered from the risk management. Thus, it will just exit on SL or TP.
Risk Management
"High Low Stop Loss" and "Automatic High Low Take Profit" are used here.
High Low Stop Loss: Utilizes the last high for short and the last low for long to calculate the stop loss level. The last high or low gets multiplied by the user-defined multiplicator and if no recent high or low was found it uses the backup multiplier.
Automatic High Low Take Profit: Utilizes the current stop loss level of "High Low Stop Loss" and gets calculated by the user-defined risk ratio.
Now, follows the bunch of knowledge for the more inexperienced readers.
PSAR: Parabolic Stop And Reverse; Developed by J. Welles Wilders and a classic trend reversal indicator.
The indicator works most effectively in trending markets where large price moves allow traders to capture significant gains. When a security’s price is range-bound, the indicator will constantly be reversing, resulting in multiple low-profit or losing trades.
TTM Squeeze: TTM Squeeze is a volatility and momentum indicator introduced by John Carter of Trade the Markets (now Simpler Trading), which capitalizes on the tendency for price to break out strongly after consolidating in a tight trading range.
The volatility component of the TTM Squeeze indicator measures price compression using Bollinger Bands and Keltner Channels. If the Bollinger Bands are completely enclosed within the Keltner Channels, that indicates a period of very low volatility. This state is known as the squeeze. When the Bollinger Bands expand and move back outside of the Keltner Channel, the squeeze is said to have “fired”: volatility increases and prices are likely to break out of that tight trading range in one direction or the other. The on/off state of the squeeze is shown with small dots on the zero line of the indicator: red dots indicate the squeeze is on, and green dots indicate the squeeze is off.
EMA: Exponential Moving Average; Like a simple moving average, but with exponential weighting of the input data.
Don't forget to check out the settings and keep it up.
Best regards,
simwai
---
Credits to:
@loxx
@Bjorgum
@Greeny
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.
Fisher Divergence Overlay [BackQuant]Fisher Divergence Overlay
You can find the other Fisher Script Here !
Overlay Adaptation The Fisher Divergence Overlay is a newly enhanced version of the original Fisher Transformation indicator, designed specifically to be plotted directly on price charts. This adaptation allows traders to visualize Fisher Transform signals, divergences, and trend shifts directly over the price action, offering a more intuitive way to monitor market trends and potential reversals without the need for separate indicator windows. The overlay structure is particularly useful for spotting divergences and shifts in momentum as they relate to key price levels.
Why Turn the Fisher into an Overlay?
By transforming the Fisher Divergence indicator into an overlay, traders gain a more direct view of the relationship between price movements and the Fisher Transformation's signals. Divergences and midline crossovers, key components of the Fisher strategy, can now be clearly seen relative to the current price action. The decision to integrate this functionality as an overlay allows for a cleaner and more insightful trading experience, helping traders make quicker, more informed decisions based on market dynamics.
Midline Cross Signals : The overlay makes it easy to see when the Fisher Transform crosses above or below the midline, a critical signal for potential trend reversals.
Divergence Signals : Both regular and hidden divergences are plotted directly over price bars, offering immediate visual confirmation of potential trend shifts.
Key Features of the Overlay Version
Kaufman Adaptive Moving Average (KAMA): The Fisher Transformation in this overlay version can be adapted using Kaufman’s Adaptive Moving Average (KAMA). This enhances the Fisher's responsiveness to current market volatility, smoothing out price data while maintaining the accuracy of trend signals.
Divergence Detection: The overlay includes both regular and hidden bullish and bearish divergence detection, with these divergences plotted directly on the price chart. This visual feedback makes it easier for traders to spot when the momentum of the Fisher Transform deviates from the actual price movement, often signaling potential reversals.
Dynamic Bar Coloring: The bars are color-coded based on either the Fisher trend or divergences, allowing traders to visually interpret market sentiment without additional analysis. Green bars signal an upward trend or bullish divergence, while red bars indicate a downward trend or bearish divergence.
Take Profit Hues: In conjunction with a normalized RSI, the overlay includes background hues for overbought and oversold conditions, providing additional context for exit points or potential reversals.
How to Use the Fisher Overlay Traders can use this overlay to streamline their workflow by having both the Fisher signals and price action in the same visual space. The key signals include:
Midline Cross Signals: A crossover of the Fisher Transform above the midline often indicates a shift toward bullish momentum, while a cross below suggests bearish momentum.
Divergences: Regular and hidden divergences, displayed directly on the chart, help traders identify moments when the momentum of the Fisher Transform is in contrast with price movements, signaling potential reversals.
RSI Confluence: Overbought and oversold signals, provided by the integrated RSI, give further insight into potential exhaustion points in the market, marked by background color changes on the chart.
Strategic Value of the Fisher Divergence Overlay
This overlay offers a streamlined, efficient way to interpret Fisher Transform signals, divergences, and confluence signals like RSI in real-time. The visual integration of these signals with price action enhances decision-making by providing immediate context, making it easier to spot high-probability trade setups.
Trend Confirmation: The overlay version helps confirm trends by visually aligning Fisher Transform signals with price levels. Traders can use this feature to strengthen their conviction before entering or exiting a trade.
Adaptability: With the option to use KAMA for adaptive price smoothing, this overlay remains responsive across different market environments, making it suitable for both trending and volatile markets.
Summary and Interpretation Tips
It enhances the traditional Fisher Transform with visual elements like divergence detection, RSI confluence, and midline cross signals. By overlaying these elements directly on the price chart, traders can quickly interpret key signals and make better trading decisions.
Use this indicator to identify trend shifts and potential reversals by focusing on midline crossovers and divergences. The visual cues—bar colors, divergence labels, and background hues—make it easy to spot actionable moments without cluttering the chart. For best results, combine this overlay with other trend-following tools to confirm your trades and maximize the utility of Fisher Transform signals.
Multi-Kernel CCI [BackQuant]Multi-Kernel CCI
Conceptual Foundation and Innovation
It offers a fresh take on the Commodity Channel Index (CCI) by integrating three distinct kernel functions—Exponential Decay, Gaussian Decay, and Cosine Decay—to create a more robust and adaptive momentum indicator. The use of these kernel functions allows the CCI calculation to be more responsive to price changes while smoothing out noise, providing traders with clearer trend signals and reducing false alerts in varying market conditions.
Technical Composition and Calculation
The core of this indicator is a multi-kernel approach to calculating the CCI, where three different decay kernels are applied to the price source. Each kernel provides a unique weighting mechanism for price data over a user-defined lookback period. The result is an average of these three kernel calculations, which serves as the foundation for the CCI calculation. This innovative approach makes the Multi-Kernel CCI more adaptive to different market conditions compared to traditional CCI calculations.
Exponential Decay Kernel: Applies an exponential weighting to recent price data, giving more importance to recent values while smoothing out older data.
Gaussian Decay Kernel: Weights data using a Gaussian function, ensuring smooth transitions between price points and reducing outliers' impact.
Cosine Decay Kernel: Utilizes a cosine function to apply a unique oscillating weight to the data, capturing cyclical market movements more effectively.
Adaptive Thresholding: Like the Adaptive Momentum Oscillator, this indicator adjusts its long and short thresholds dynamically using percentile-based calculations over historical CCI values.
Features and User Inputs The Multi-Kernel CCI offers a wide range of customization options for traders:
Kernel Calculation Length & Alpha: Traders can fine-tune the sensitivity of the CCI by adjusting the length of the kernel calculation and the alpha parameter for the Exponential Decay Kernel.
Adaptive Thresholds: The indicator provides percentile-based thresholds for both long and short signals, allowing traders to dynamically adjust their signals based on historical data.
Extreme Value Detection: This feature highlights extreme overbought and oversold conditions with customizable thresholds and background hues, visually aiding in identifying high-probability reversal zones.
Divergence Detection: The script includes a divergence detection feature, identifying regular and hidden bullish or bearish divergences to help traders spot potential trend reversals.
Practical Applications The Multi-Kernel CCI excels in markets where adaptive trend detection and momentum confirmation are critical. Traders can leverage this tool in several ways:
Adaptive Trend Following: The dynamically adjusting thresholds allow traders to capture trends more effectively while avoiding false signals during consolidations or choppy markets.
Reversal Detection: The multi-kernel approach ensures that reversals are detected with greater precision, particularly in volatile markets where traditional indicators might fail.
Divergence Identification: With built-in divergence detection, the indicator provides traders with an early warning of potential trend reversals, helping to time their entries and exits more effectively.
Advantages and Strategic Value The Multi-Kernel CCI offers several strategic advantages over traditional CCI indicators:
Multi-Kernel Smoothing:
By using multiple decay kernels, this CCI calculation is better suited to detect subtle changes in market momentum, reducing the impact of noise and providing clearer trend signals.
Dynamic Thresholds:
The adaptive percentile-based thresholds ensure that the indicator remains relevant across different market conditions, enhancing signal accuracy.
Visual and Analytical Aids:
With features like extreme value detection and divergence spotting, this indicator equips traders with powerful tools to confirm trend strength and identify potential reversals.
Summary and Usage Tips
The Multi-Kernel CCI is a highly versatile tool for traders seeking a more adaptive and robust momentum indicator. Its multi-kernel foundation provides smoother, more reliable signals, while the adaptive thresholds and divergence detection features help traders refine their entries and exits. The dynamic nature of this indicator makes it ideal for both trend-following and reversal strategies in volatile markets.
Traders should experiment with the kernel calculation length and alpha parameter to align the indicator's sensitivity with their specific trading style and market conditions. Additionally, the adaptive thresholds can be fine-tuned to ensure the CCI captures the most significant trend changes without being overly reactive to short-term fluctuations.
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 Filter RoC with Adaptive Thresholds [BackQuant]Kalman Filter RoC with Adaptive Thresholds
Another Kalman Script !!
Please Find the Basic Kalman Here:
Overview and Purpose
The Kalman Filter RoC with Adaptive Thresholds is an advanced tool designed for traders seeking to refine their trend detection and momentum analysis. By combining the robustness of the Kalman filter with the Rate of Change (RoC) indicator, this tool offers a highly responsive and adaptive method to identify shifts in market trends. The inclusion of adaptive thresholding further enhances the indicator’s precision by dynamically adjusting to market volatility, providing traders with reliable entry and exit signals.
Kalman Filter Dynamics
The Kalman Filter is renowned for its ability to estimate the true state of a system amidst noisy data. In this indicator, the Kalman filter is applied to the price data to smooth out fluctuations and generate a more accurate representation of the underlying trend. This is particularly useful in volatile markets where noise can obscure the true direction of price movements. The Kalman filter adapts in real-time based on user-defined parameters, such as process noise and measurement noise, making it highly customizable for different market conditions.
Rate of Change (RoC) and Smoothing The Rate of Change (RoC) is a classic momentum indicator that measures the percentage change in price over a specific period. By integrating it with the Kalman-filtered price, the RoC becomes more responsive to genuine price trends while filtering out short-term noise. An optional smoothing feature using the ALMA (Arnaud Legoux Moving Average) further refines the signal, allowing traders to adjust the calculation length and smoothing factor (sigma) for even greater precision.
Adaptive Thresholds A key innovation in this indicator is the adaptive thresholding mechanism. Traditional RoC indicators rely on static thresholds to identify overbought or oversold conditions, but the Kalman Filter RoC adapts these thresholds dynamically. The adaptive thresholds are calculated based on the historical volatility of the filtered RoC values, allowing the indicator to adjust in response to changing market conditions. This feature reduces the risk of false signals in choppy or highly volatile markets.
Divergence Detection The Kalman Filter RoC also includes divergence detection, helping traders identify when the momentum of the RoC diverges from the price action. Divergences can often signal potential reversals or trend continuations, making them a valuable tool in any trader’s toolkit. Regular and hidden divergences are plotted directly on the chart, providing visual cues for traders to act upon.
Customization and Flexibility This indicator offers a wide range of customization options, making it suitable for various trading strategies and market conditions:
Process Noise & Measurement Noise: These parameters control how sensitive the Kalman filter is to price changes and help traders fine-tune the balance between noise reduction and signal responsiveness.
ALMA Smoothing: Traders can apply ALMA smoothing to the RoC signal to reduce short-term volatility and improve signal clarity.
Adaptive Threshold Calculation Period: The length of the lookback period for the adaptive thresholds can be adjusted, allowing traders to tailor the indicator to fit their specific trading style.
Practical Applications
Trend Detection: The Kalman-filtered RoC helps identify shifts in momentum, making it easier for traders to spot emerging trends early. The dynamic thresholding ensures that these signals are reliable, even in volatile markets.
Divergence Trading: Divergences between the RoC and price action are clear indicators of potential trend reversals. The visual plotting of divergences simplifies the process of identifying these opportunities.
Momentum Analysis: The combination of Kalman filtering and RoC provides a smoother, more accurate view of market momentum, helping traders stay on the right side of the market.
Conclusion
The Kalman Filter RoC is a powerful and adaptable tool that merges advanced filtering techniques with momentum analysis. Its real-time responsiveness and dynamic thresholding make it a highly effective indicator for identifying trends, managing risk, and capitalizing on divergence signals. Traders looking to enhance their trend-following or momentum strategies will find this indicator to be a valuable addition to their toolkit.
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
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
Adaptive RSI BandsThe RSI Band Optimizer is an innovative technical analysis tool designed to identify and display the most effective Relative Strength Index (RSI) band values for any given trading instrument. This powerful indicator dynamically calculates optimal overbought and oversold levels, moving beyond the traditional static 70/30 or 80/20 bands.
Core Functionality:
Dynamic RSI Band Calculation:
The indicator analyzes historical price data to determine the most effective RSI levels for identifying overbought and oversold conditions specific to the current trading instrument and timeframe.
Adaptive Optimization:
Rather than relying on external factors, the tool uses a proprietary algorithm that focuses solely on the relationship between historical RSI values and subsequent price movements. This pure RSI-based approach ensures that the bands are optimized for the indicator's own dynamics.
Continuous Recalibration:
The optimal RSI bands are continuously recalculated as new price data becomes available, ensuring that the indicator adapts to changing market conditions and remains relevant over time.
Key Inputs:
RSI Length:
Allows users to set the period for the RSI calculation. While the default is typically 14, users can adjust this to suit their trading style and the characteristics of the instrument they're trading.
Optimization Lookback:
Defines the historical period the indicator uses to calculate optimal bands. This balance between recent market behavior and longer-term patterns.
Band Sensitivity:
Enables fine-tuning of how aggressively the indicator adjusts the RSI bands. Higher sensitivity results in more frequent band adjustments, while lower sensitivity provides more stable levels.
What Makes It Unique:
Self-Contained Optimization:
Unlike indicators that rely on external data sources or comparisons, this tool focuses purely on optimizing RSI bands based on the indicator's own historical performance.
Instrument-Specific Bands:
By calculating optimal bands for each specific instrument, the indicator acknowledges that different assets may have different typical RSI ranges and behaviors.
Timeframe Adaptability:
The optimization process adapts to the selected timeframe, recognizing that optimal RSI bands may differ between short-term and long-term charts.
Dynamic Band Adjustment:
The continuous recalibration of bands allows the indicator to adapt to changing market volatility and trends, providing more relevant signals over time.
Enhanced RSI Interpretation:
By providing optimized, asset-specific overbought and oversold levels, the indicator offers a more nuanced and potentially more accurate interpretation of RSI values.
The RSI Band Optimizer represents a significant advancement in the application of the Relative Strength Index. By dynamically calculating optimal band values, it addresses one of the main criticisms of traditional RSI usage – the reliance on static, one-size-fits-all overbought and oversold levels. This tool empowers traders to make more informed decisions based on RSI readings that are truly tailored to the specific characteristics of the asset they're trading.
TrendMaster ProTrendMaster Pro: A Comprehensive Trend Analysis Tool for Long-Term Investors
TrendMaster Pro is an advanced technical indicator designed to provide long-term investors with a robust and comprehensive analysis of market trends. This sophisticated tool operates exclusively on daily timeframes, making it ideal for those focused on long-term investment strategies. By combining multiple analytical approaches, TrendMaster Pro offers investors a powerful means to assess trend quality and make informed decisions.
Automatic Trend Detection
At the heart of TrendMaster Pro lies its ability to automatically identify the most statistically significant trend. The indicator analyzes various timeframes ranging from 1000 to 5000 days, selecting the one that exhibits the highest correlation. This feature ensures that investors are always working with the most relevant trend data, eliminating the subjectivity often associated with manual trend identification.
The trend detection algorithm employs a regression analysis approach, evaluating approximately 80,000 different trend alternatives each day. Each potential trend is assigned a score based on criteria such as trend density, deviation from regression, and the number of price points near the trend's floor and ceiling. The trend with the highest score is then selected and displayed on the chart.
Comprehensive Scoring System
TrendMaster Pro employs a multi-faceted scoring system that evaluates four key aspects of a trend, providing a holistic view of its quality and potential. Each aspect is scored on a scale of 0 to 10, with the overall trend quality score being a weighted average of these individual scores.
1. Length Score
The Length Score measures the duration of the detected trend. Longer trends receive higher scores, reflecting increased reliability and significance. This score is calculated by normalizing the auto-selected period (which ranges from 1000 to 5000 days) to a scale of 5 to 10.
For example, if the auto-selected period is 3000 days, it would receive a score of around 7.5. This emphasizes the importance of long-term trends in investment decision-making, as they tend to be more stable and indicative of underlying market forces.
2. Strength Score
The Strength Score utilizes Pearson's Correlation Coefficient to assess trend strength. This statistical measure gauges the linear relationship between price and trend projection. A value closer to 1 indicates a strong positive correlation, reinforcing confidence in the trend direction based on historical price movements.
The indicator translates the Pearson's Correlation Coefficient into a score from 0 to 10. For instance, a correlation coefficient of 0.95 might translate to a Strength Score of 8, indicating a strong and reliable trend.
3. Performance Score
The Performance Score compares the asset's Compound Annual Growth Rate (CAGR) to a chosen benchmark, typically a major index like the S&P 500. This score provides insight into how well the asset is performing relative to the broader market.
The CAGR is calculated using the formula: CAGR = (Ending Value / Beginning Value)^(1/n) - 1, where n is the number of years. The Performance Score is then determined by comparing this CAGR to the benchmark's CAGR over the same period. A higher score indicates outperformance relative to the benchmark.
4. Level Score
The Level Score evaluates the current price position within the trend channel. Lower prices within the channel receive higher scores, suggesting potential value or buying opportunities. This score helps identify possible entry points based on historical trend behavior.
For example, if the current price is near the lower boundary of the trend channel, it might receive a Level Score of 9, indicating a potentially attractive entry point.
Visual Representation
TrendMaster Pro provides a clear visual representation of the detected trend by displaying a regression channel on the chart. This channel consists of three lines: a middle line representing the main trend, and upper and lower lines representing standard deviations from the main trend.
The channel offers a quick visual reference for support and resistance levels, helping investors identify potential entry and exit points. The color and style of these lines can be customized to suit individual preferences.
Detailed Information Table
A comprehensive table presents all scores and relevant data, allowing for quick and easy interpretation of the trend analysis. This table includes:
The auto-selected trend length
The Pearson's Correlation Coefficient
The asset's CAGR and the benchmark's CAGR
Individual scores for Length, Strength, Performance, and Level
The overall Trend Quality Score
This table provides investors with a clear, at-a-glance summary of the trend's key characteristics and quality.
Practical Application
To use TrendMaster Pro effectively, investors should consider the following:
Focus on the overall Trend Quality Score as a primary indicator of trend strength and reliability.
Use the Length Score to gauge the trend's longevity and potential stability.
Pay attention to the Strength Score to assess how well the price action aligns with the identified trend.
Utilize the Performance Score to compare the asset's performance against the broader market.
Consider the Level Score when timing entries, looking for opportunities when prices are relatively low within the trend channel.
Use the visual trend channel as a guide for potential support and resistance levels.
Limitations and Considerations
While TrendMaster Pro offers powerful insights, it's important to remember that no indicator can predict future market movements with certainty. The tool should be used in conjunction with fundamental analysis and other market information.
Additionally, as the indicator is designed for daily charts and long-term analysis, it may not be suitable for short-term trading strategies. Users should also be aware that past performance does not guarantee future results, even with strong trend indications.
Conclusion
TrendMaster Pro represents a significant advancement in trend analysis for long-term investors. By combining automatic trend detection, comprehensive scoring, and benchmark comparison, it offers a powerful tool for those seeking to make informed, data-driven investment decisions. Its ability to objectively assess trend quality across multiple dimensions provides investors with a valuable edge in navigating complex market conditions.
For investors looking to deepen their understanding of market trends and enhance their long-term investment strategies, TrendMaster Pro offers a sophisticated yet accessible solution. As with any investment tool, users are encouraged to thoroughly familiarize themselves with its features and interpret its outputs in the context of their overall investment approach.
Efficiency Weighted OrderFlow [AlgoAlpha]Introducing the Efficiency Weighted Orderflow Indicator by AlgoAlpha! 📈✨
Elevate your trading game with our cutting-edge Efficiency Weighted Orderflow Indicator, designed to provide clear insights into market trends and potential reversals. This tool is perfect for traders seeking to understand the underlying market dynamics through efficiency-weighted volume calculations.
🌟 Key Features 🌟
✨ Smooth OrderFlow Calculation : Option to smooth order flow data for more consistent signals.
🔧 Customizable Parameters : Adjust the Order Flow Period and HMA Smoothing Length to fit your trading strategy.
🔍 Visual Clarity : Easily distinguish between bullish and bearish trends with customizable colors.
📊 Standard Deviation Normalization : Keeps order flow values normalized for better comparison across different market conditions.
🔔 Trend Reversal Alerts : Stay ahead with built-in alert conditions for significant order flow changes.
🚀 Quick Guide to Using the Efficiency Weighted Orderflow Indicator
🛠 Add the Indicator: Search for "Efficiency Weighted Orderflow " in TradingView's Indicators & Strategies. Customize settings like smoothing and order flow period to fit your trading style.
📊 Market Analysis: Watch for trend reversal alerts to capture trading opportunities by studying the behaviour of the indicator.
🔔 Alerts: Enable notifications for significant order flow changes to stay updated on market trends.
🔍 How It Works
The Efficiency Weighted Orderflow Indicator starts by calculating the efficiency of price movements using the absolute difference between the close and open prices, divided by volume. The order flow is then computed by summing these efficiency-weighted volumes over a specified period, with an option to apply Hull Moving Average (HMA) smoothing for enhanced signal stability. To ensure robust comparison, the order flow is normalized using standard deviation. The indicator plots these values as columns, with distinct colors representing bullish and bearish trends. Customizable parameters for period length and smoothing allow traders to tailor the indicator to their strategies. Additionally, visual cues and alert conditions for trend reversals and significant order flow changes keep traders informed and ready to act. This indicator improves on the Orderflow aspect of our Standardized Orderflow indicator. The Efficiency Weighted Orderflow is less susceptible to noise and is also quicker at detecting trend changes.
Log Regression Channel [UAlgo]The "Log Regression Channel " channel is useful for analyzing price trends and volatility in a financial instrument over a specified period. By using logarithmic scaling, this indicator can more effectively handle the wide range of price movements seen in many financial markets, making it particularly valuable for assets with exponential growth characteristics.
The indicator plots the central regression line along with upper and lower deviation bands, providing a visual representation of potential support and resistance levels.
🔶 Key Features
Logarithmic Regression Line: The central line represents the logarithmic regression, which fits the price data over the specified length using a logarithmic scale. This helps in identifying the overall trend direction.
Deviation Bands: The upper and lower bands are plotted at a specified multiple of the standard deviation from the regression line, highlighting areas of potential overbought and oversold conditions.
Customizable Parameters: Users can adjust the length of the regression, the deviation multiplier, the color of the labels, and the size of the text labels to suit their preferences.
R-Squared Display: The R-squared value, which measures the goodness of fit of the regression model, is displayed on the chart. This helps traders assess the reliability of the regression line.
🔶 Calculations
The indicator performs several key calculations to plot the logarithmic regression channel:
Logarithmic Transformation: The prices and time indices are transformed using the natural logarithm to handle exponential growth in price data.
Regression Coefficients: The slope and intercept of the regression line are calculated using the least squares method on the transformed data.
Predicted Values: The regression equation is used to calculate predicted values for each data point.
Standard Deviation: The standard deviation of the residuals (differences between actual and predicted values) is computed to determine the width of the deviation bands.
Deviation Bands: Upper and lower bands are plotted at a specified multiple of the standard deviation above and below the regression line.
R-Squared Value: The R-squared value is calculated to measure how well the regression line fits the data. This value is displayed on the chart to inform the user of the model's reliability.
🔶 Disclaimer
The "Log Regression Channel " indicator is provided for educational and informational purposes only.
It is not intended as investment advice or a recommendation to buy or sell any financial instrument. Trading financial instruments involves substantial risk and may not be suitable for all investors.
Past performance is not indicative of future results. Users should conduct their own research.
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
Kalman Hull RSI [BackQuant]Kalman Hull RSI
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 the RSI, very similar to the Kalman Hull Supertrend just processing price for a different indicator.
This also allows it to make it more adaptive to price and also sensitive to recent price action. This indicator is also mainly built for trend-following systems
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.
OR our Kalman Hull Supertrend
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)))
Use Case
The Kalman Hull RSI is particularly suited for traders who require a highly adaptive indicator that can respond to rapid market changes without the excessive noise associated with typical RSI calculations. It can be effectively used in markets with high volatility where traditional indicators might lag or produce misleading signals.
Application in a Trading System
The Kalman Hull RSI is versatile in application, suitable for:
Trend Identification: Quickly identify potential reversals or confirmations of existing trends.
Overbought/Oversold Conditions: Utilize the dynamic RSI thresholds to pinpoint potential entry and exit points, adapting to current market conditions.
Risk Management: Enhance trading strategies by integrating a more reliable measure of momentum, which can lead to improved stop-loss placements and exit strategies.
Core Calculations and Benefits
Dynamic State Estimation: By applying the Kalman Filter, the indicator continually adjusts its calculations based on incoming price data, providing a real-time, smoothed response to price movements.
Reduced Lag: The integration with HMA significantly reduces lag, offering quicker responses to price changes than traditional moving averages or RSI alone.
Increased Accuracy: The dual filtering effect minimizes the impact of price spikes and noise, leading to more accurate signaling for trades.
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
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
DEMA RSI Overlay [BackQuant]DEMA RSI Overlay
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.
Anyways,
BackQuant's new trading indicator that blends the Double Exponential Moving Average (DEMA) with the Relative Strength Index (RSI) to create a unique overlay on the trading chart. This combination is not arbitrary; both the DEMA and RSI are revered for their distinct advantages in trading strategy development. Let's delve into the core components of this script, the rationale behind choosing DEMA and RSI, the logic of long and short signals, and its practical trading applications.
Understanding DEMA
DEMA is an enhanced version of the conventional exponential moving average that aims to reduce the lag inherent in traditional averages. It does this by applying more weight to recent prices. The reduction in lag makes DEMA an excellent tool for tracking price trends more closely. In the context of this script, DEMA serves as the foundation for the RSI calculation, offering a smoother and more responsive signal line that can provide clearer trend indications.
Why DEMA?
DEMA is chosen for its responsiveness to price changes. This characteristic is particularly beneficial in fast-moving markets where entering and exiting positions quickly is crucial. By using DEMA as the price source, the script ensures that the signals generated are timely and reflective of the current market conditions, reducing the risk of entering or exiting a trade based on outdated information.
Integrating RSI
The RSI, a momentum oscillator, measures the speed and change of price movements. It oscillates between zero and 100 and is typically used to identify overbought or oversold conditions. In this script, the RSI is calculated based on DEMA, which means it inherits the responsiveness of DEMA, allowing traders to spot potential reversals or continuation signals sooner.
Why RSI?
Incorporating RSI offers a measure of price momentum and market conditions relative to past performance. By setting thresholds for long (buy) and short (sell) signals, the script uses RSI to identify potential turning points in the market, providing traders with strategic entry and exit points.
Calculating Long and Short Signals
Long Signals : These are generated when the RSI of the DEMA crosses above the longThreshold (set at 70 by default) and the closing price is not above the upper volatility band. This suggests that the asset is gaining upward momentum while not being excessively overbought, presenting a potentially favorable buying opportunity.
Short Signals : Generated when the RSI of the DEMA falls below the shortThreshold (set at 55 by default). This indicates that the asset may be losing momentum or entering a downtrend, signaling a possible selling or shorting opportunity.
Logical Soundness
The logic of combining DEMA with RSI for generating trade signals is sound for several reasons:
Timeliness : The use of DEMA ensures that the price source for RSI calculation is up-to-date, making the momentum signals more relevant.
Balance : By setting distinct thresholds for long and short signals, the script balances sensitivity and specificity, aiming to minimize false signals while capturing genuine market movements.
Adaptability : The inclusion of user inputs for periods and thresholds allows traders to customize the indicator to fit various trading styles and timeframes.
Trading Use-Cases
This DEMA RSI Overlay indicator is versatile and can be applied across different markets and timeframes. Its primary use-cases include:
Trend Following: Traders can use it to identify the start of a new trend or the continuation of an existing trend.
Swing Trading: The indicator's sensitivity to price changes makes it ideal for swing traders looking to capitalize on short to medium-term price movements.
Risk Management: By providing clear long and short signals, it helps traders manage their positions more effectively, potentially reducing the risk of significant losses.
Final Note
We have also decided to add in the option of standard deviation bands, calculated on the DEMA, this can be used as a point of confluence rendering trading ranges. Expanding when volatility is high and compressing when it is low.
For example:
This provides the user with a 1, 2, 3 standard deviation band of the DEMA.
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
Relative Strength Overlay [BackQuant]Relative Strength Overlay
Relative Strength Overlay is a new innovative proprietary adaptive calculation to get an assets relative strength. To ensure this is well put together and easy for traders to use we have made it into an overlay. Allowing traders and investors to spot clear trends in both the up and down directions. Providing clear signals, and an option for a gradient to allow users to screen assets with strong relative strength and potentially define a trading period.
Please take the time to read the following.
Importance and Concepts
1. Adaptive Relative Strength Calculation:
At the heart of this indicator lies an adaptive relative strength calculation, a pivotal concept that goes beyond the traditional RSI (Relative Strength Index) by dynamically adjusting its sensitivity based on recent price action. This adaptability ensures that the indicator is more responsive to current market conditions, enhancing its effectiveness in signaling potential reversals or continuations.
2. Volatility and Price Action Adaptivity:
Incorporating an adaptive approach to both volatility and price action, the indicator refines its signals to reflect the current market environment more accurately. This adaptability is achieved through a custom calculation that considers the volatility (using ATR - Average True Range) and price action (through DEMA - Double Exponential Moving Average), ensuring that the indicator remains sensitive to sudden changes in market dynamics.
3. DEMA Utilization:
The use of DEMA provides a price-adaptive mechanism that smoothens the indicator's output, making it more reliable during volatile periods. DEMA helps in reducing the lag associated with traditional moving averages, offering a quicker response to price changes and enhancing the adaptive nature of the relative strength calculation.
Main Features and Trading Applications
Comprehensive UI Settings:
The indicator comes with extensive user interface settings, allowing traders to customize various parameters according to their trading preferences. These settings include adjustment options for calculation periods, standard deviation factors, and the ability to toggle features like volatility bands and signal lines on or off.
Volatility-Adjusted Bands:
Utilizing a custom ATR calculation, the indicator plots volatility bands that adjust according to current market volatility. These bands serve as dynamic support and resistance levels, providing traders with potential entry and exit points based on the confluence of relative strength signals and band breaches.
Calibrated Trading Conditions:
The indicator features pre-modeled long and short conditions that have been backtested to ensure robustness. These conditions help in identifying high-probability trading setups, making the indicator a valuable tool for both discretionary and systematic traders, mainly looking to either define a trading period, or capture clear trends in confluence with other metrics.
Trading Range Identification:
By filtering assets based on their relative strength, traders can use the indicator to identify securities with strong momentum. This feature is particularly useful for portfolio selection and asset screening, allowing traders to focus on the most promising opportunities.
Gradient Background Hue:
The indicator offers a unique visual aid in the form of a gradient background hue, which assists in quickly screening assets based on their relative strength. This color-coding feature aids in identifying potential reversals as it highlights changes in the strength's direction.
Adaptive Volatility Bands with Standard Deviations:
The inclusion of three sets of volatility bands, each corresponding to different standard deviations, provides a probabilistic view of price movements. These bands adapt to current market volatility, offering traders insights into the likelihood of price staying within certain ranges. This goes up to +-3 Standard Deviations.
Alert Conditions and Signal Visualization:
With built-in alert conditions for long and short signals, along with the ability to paint candles according to the prevailing trend, traders can stay informed about significant market movements. This feature enhances the decision-making process by visually representing the strength and direction of the trend.
alertcondition(ta.crossover(BackQuant, 0), title="Positive RS", message="Positive RS {{exchange}}:{{ticker}}")
alertcondition(ta.crossunder(BackQuant, 0), title="Negative RS", message="Negative RS {{exchange}}:{{ticker}}")
Concluding Remarks.
In conclusion our Relative Strength Overlay indicator is a comprehensive tool that leverages adaptive calculations and volatility adjustments to provide traders with nuanced insights into market conditions. By combining traditional concepts with innovative features, this indicator offers a versatile solution for traders seeking to enhance their market analysis and identify high-probability trading opportunities.
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 Price Filter [BackQuant]Kalman Price Filter
The Kalman Filter, named after Rudolf E. Kálmán, is a algorithm used for estimating the state of a linear dynamic system from a series of noisy measurements. Originally developed for aerospace applications in the early 1960s, such as guiding Apollo spacecraft to the moon, it has since been applied across numerous fields including robotics, economics, and, notably, financial markets. Its ability to efficiently process noisy data in real-time and adapt to new measurements has made it a valuable tool in these areas.
Use Cases in Financial Markets
1. Trend Identification:
The Kalman Filter can smooth out market price data, helping to identify the underlying trend amidst the noise. This is particularly useful in algorithmic trading, where identifying the direction and strength of a trend can inform trade entry and exit decisions.
2. Market Prediction:
While no filter can predict the future with certainty, the Kalman Filter can be used to forecast short-term market movements based on current and historical data. It does this by estimating the current state of the market (e.g., the "true" price) and projecting it forward under certain model assumptions.
3. Risk Management:
The Kalman Filter's ability to estimate the volatility (or noise) of the market can be used for risk management. By dynamically adjusting to changes in market conditions, it can help traders adjust their position sizes and stop-loss orders to better manage risk.
4. Pair Trading and Arbitrage:
In pair trading, where the goal is to capitalize on the price difference between two correlated securities, the Kalman Filter can be used to estimate the spread between the pair and identify when the spread deviates significantly from its historical average, indicating a trading opportunity.
5. Optimal Asset Allocation:
The filter can also be applied in portfolio management to dynamically adjust the weights of different assets in a portfolio based on their estimated risks and returns, optimizing the portfolio's performance over time.
Advantages in Financial Applications
Adaptability: The Kalman Filter continuously updates its estimates with each new data point, making it well-suited to markets that are constantly changing.
Efficiency: It processes data and updates estimates in real-time, which is crucial for high-frequency trading strategies.
Handling Noise: Its ability to distinguish between the signal (e.g., the true price trend) and noise (e.g., random fluctuations) is particularly valuable in financial markets, where price data can be highly volatile.
Challenges and Considerations
Model Assumptions: The effectiveness of the Kalman Filter in financial applications depends on the accuracy of the model used to describe market dynamics. Financial markets are complex and influenced by numerous factors, making model selection critical.
Parameter Sensitivity: The filter's performance can be sensitive to the choice of parameters, such as the process and measurement noise values. These need to be carefully selected and potentially adjusted over time.
Despite these challenges, the Kalman Filter remains a potent tool in the quantitative trader's arsenal, offering a sophisticated method to extract useful information from noisy financial data. Its use in trading strategies should, however, be complemented with sound risk management practices and an awareness of the limitations inherent in any model-based approach to trading.
Adaptive Fisher [BackQuant]Adaptive Fisher
What is it at its core:
Custom Kaufman Adaptive Moving Average Smoothed Price Data, Fisher Transformation.
Why did we choose to make an Adaptive Fisher ?
The Adaptive Fisher Transformation Indicator is an advanced technical tool designed to signal potential turning points in market prices by transforming asset price data into a nearly Gaussian normal distribution. This transformation, initially conceptualized by John F. Ehlers, aims to make extreme price behavior, which could indicate potential market reversals, more identifiable. Unlike the standard distribution of asset prices, the Gaussian normal distribution provides a clearer framework for identifying price extremes and trends.
With that being considered there are key things to take into consideration:
As the transformation seeks to normalize price data, it's crucial to remember that asset prices inherently do not follow a normal distribution. Thus, traders should use this tool in conjunction with other analyses to confirm potential trading signals. The effectiveness can vary across different assets and market conditions, underscoring the importance of customization and adaptation to specific trading strategies. As the same for all tools, all must be backtested. Past performance is not a guarantee for future results.
Now for the Key Features
Normalization of Prices: The Adaptive Fisher Transformation normalizes price data, enhancing the visibility of turning points. This normalization is critical for identifying moments when the price movement is statistically significant, thereby aiding in decision-making.
Adaptivity through Kaufman's Adaptive Moving Average (KAMA): Unlike traditional indicators, this version employs KAMA to dynamically adjust to market volatility. By doing so, it smoothens the price data more effectively, providing signals that are more responsive to current market conditions.
Divergence Detection: It includes the capability to detect divergences between the indicator and price movement, a powerful signal of potential trend reversals. Traders can specify the length over which divergences are calculated, allowing for customization based on their trading strategy.
Visual Enhancements: The indicator features color gradients to delineate strength levels and extreme values, improving readability and the quick assessment of market conditions.
Customizable Smoothing Mechanism: To accommodate different assets and timeframes, the indicator includes an option to select from various moving averages for smoothing, with an Exponential Moving Average (EMA) recommended for its effectiveness.
Application and Interpretation:
Traders can utilise this tool to identify potential reversal points by looking for extreme values in the transformed price data. Changes in the direction of the indicator can also signal shifts in market trends.
The inclusion of a normalized Relative Strength Index (RSI) provides additional confluence, aiding traders in recognizing overbought and oversold conditions through color-coded background hues in the chart.
Alert conditions are programmed for various scenarios, including trend shifts, Fisher Transform crossings over the midline, and both regular and hidden divergences, enabling traders to react promptly to potential market movements.
Empirical Soundness
Mathematical Foundation in Gaussian Distribution: At its core, the Fisher Transformation's application to financial markets is based on transforming prices to conform more closely to a Gaussian normal distribution, which is a fundamental concept in statistics. This transformation aims to make the identification of price extremes more reliable. Empirical studies have shown that while raw financial data may not follow a normal distribution, the application of transformations can facilitate the identification of critical turning points in market data (Ehlers, John F., "Cybernetic Analysis for Stocks and Futures", Wiley & Sons, 2004).
Adaptivity through KAMA: The use of Kaufman's Adaptive Moving Average introduces a dynamic element to the indicator, allowing it to adjust to market volatility automatically. This adaptivity is particularly relevant in today's financial markets, where volatility patterns can shift rapidly due to economic news, geopolitical events, and changes in market sentiment. The empirical strength of KAMA lies in its foundational logic, designed to account for market noise and smoothing price data more effectively than traditional moving averages (Kaufman, Perry J., "Trading Systems and Methods", Wiley & Sons, 2013).
Innovative Divergence Detection Mechanism: Divergence detection adds an empirical layer to the Adaptive Fisher Transformation by highlighting discrepancies between price action and the indicator's performance. This feature is grounded in the principle that divergences can often precede reversals, providing early warning signs of potential shifts in market direction. The ability to customize the calculation length for divergences enables the indicator to be fine-tuned to the characteristics of specific assets or market conditions, enhancing its practical application.
User Inputs Explained:
Calculation Source (price): This input determines the base price used for calculations, typically the closing price (close). Traders can adjust this to open, high, low, or another average, tailoring the indicator to focus on specific aspects of price action.
Fisher Lookback (ftPeriod): Defines the period over which the Fisher Transform is calculated. A shorter period makes the indicator more sensitive to price movements, while a longer period smoothens the output, reducing sensitivity.
Make Fisher Adaptive (adapt): A boolean input that enables the adaptation feature of the Fisher Transform using KAMA. When set to true, it dynamically adjusts the Fisher Transform according to market volatility, enhancing its responsiveness to recent price changes.
Adaptive Period (length), Fast Length (fast), Slow Length (slow): These inputs configure the KAMA calculation, affecting its sensitivity to price movements. The length determines the lookback period for volatility calculation, while fast and slow set the speed of adjustment to market conditions.
Smooth Fisher (smooth): Allows for additional smoothing of the Fisher Transform output to reduce noise. This is particularly useful in highly volatile markets or when the indicator is too reactive to price changes.
Smoothing Type (modeSwitch) and Smooth Period (smoothlen): Determine the method and period for smoothing. Options include various moving averages (EMA, SMA, etc.), providing flexibility in how the smoothing is applied.
Show Fisher, Show Fisher Moving Average, Moving Average Period (malen): These inputs control the visibility of the Fisher Transform and its moving average on the chart, as well as the period of the moving average. This helps in identifying trends and the direction of the market.
Show Detected Trend Shifts (trendshift): Enables the highlighting of moments when the indicator suggests a potential shift in market trend, providing early signals for traders.
Show Fisher Strength levels (showextreme): Displays predefined levels indicating extreme values of the Fisher Transform, which could suggest overbought or oversold conditions.
Show Confluence RSI (showrsi), RSI Period (rsiPeriod): These inputs add a normalized Relative Strength Index to the chart for additional analysis, offering a secondary measure of market conditions.
Show Overbought and Oversold Signals: When enabled, the background color changes to highlight overbought or oversold conditions based on the RSI, aiding in visual identification of potential trading opportunities.
Use Case of Midline Crossover Fisher:
Midline Crossover Fisher: The Fisher Transform's midline crossover is a critical signal for traders. A crossover above the midline indicates a bullish market sentiment, suggesting that it might be a good time to consider entering a long position. Conversely, a crossover below the midline suggests bearish sentiment, potentially signaling an opportunity to go short. This is based on the principle that the Fisher Transform makes turning points more evident, and crossing the midline reflects a change in momentum.
Overbought and Oversold Hues:
RSI Overbought and Oversold Background Color: The background color feature for RSI OB (overbought) and OS (oversold) conditions enhances visual cues for market extremes. When the RSI exceeds upper thresholds (Above 70), indicating overbought conditions, the background will turn to warn traders of potential price reversals. Similarly, when the RSI falls below lower thresholds (Below 30), suggesting oversold conditions, green can highlight potential opportunities for buying.
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.
This is using the Midline Crossover:
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Momentum Velocity [BackQuant]Momentum Velocity
Main Features:
- Momentum Based Oscillator
- Divergences
- Overbought and Oversold Conditions based off a VZO
- Alert Conditions
- Ability to make Adaptive
- Big User input menu for customisation
The Momentum Velocity indicator is based on the principle of momentum , which is a measure of the rate of change or the speed at which prices move over a specified time period. The underlying assumption of momentum trading is that assets that have performed well in the recent past will continue to perform well in the near future, and conversely, assets that have performed poorly will continue to perform poorly. This concept is widely accepted and empirically supported in financial literature, making the Momentum Velocity indicator empirically sound for several reasons:
Empirical Evidence on Momentum
Academic Research: A foundational piece of research that supports the momentum strategy is Jegadeesh and Titman's study, "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," published in the Journal of Finance in 1993. The authors find that strategies which buy stocks that have performed well in the past and sell stocks that have performed poorly generate significantly higher than expected returns over 3- to 12-month holding periods. This study is one of many that empirically validate the momentum effect in stock returns.
Behavioural Finance Theories:
Behavioural finance provides explanations for the momentum effect that go beyond the efficient market hypothesis. Theories such as investor herding, overreaction and under reaction to news, and the disposition effect can cause price trends to continue. The momentum strategy exploits these behavioural biases by assuming that prices will continue to move in their current direction for some time.
Global Evidence:
The momentum effect is not limited to specific markets or asset classes. Studies have documented momentum profits across various countries, markets, and asset types (stocks, bonds, commodities, and currencies). For instance, Asness, Moskowitz, and Pedersen in their paper, "Value and Momentum Everywhere," published in the Journal of Finance in 2013, show that momentum strategies can yield positive returns in different international markets.
Risk Factors:
Some researchers argue that the returns to momentum strategies are compensation for bearing certain risks. However, the empirical evidence suggests that momentum returns are difficult to explain by traditional risk factors alone, adding to the strategy’s attractiveness. The factor model of Carhart (1997), which adds a momentum factor to the Fama and French three-factor model, highlights the importance of momentum as a distinct source of returns.
Empirical Evidence Application
The Momentum Velocity indicator applies these empirical insights by quantitatively measuring the speed and direction of price movements over a given period, adjusting for recent market conditions through adaptive filtering, and normalizing the results to identify potential trading signals. By doing so, it provides traders with a tool that not only captures the essence of the momentum anomaly but also enhances it with modern technical analysis techniques for real-time market application.
Trading Application
Due to the robustness of momentum, traders are able to use this as a confluence metric into their system on any timeframe. Providing robust signals, that by extention are adaptive to the market. This is also further enabled by using adaptive filtering.
Conclusion
In summary, the empirical soundness of the Momentum Velocity indicator is grounded in the well-documented momentum effect observed in financial markets. By leveraging historical price data to predict future price movements, it aligns with both academic research and observed market behavior, making it a potentially valuable tool for traders seeking to exploit momentum-based trading opportunities.
User Inputs:
Calculation Source: Choose the price component (e.g., close) to base calculations on.
Lookback Period: Define the period over which momentum and normalization are calculated.
Use Adaptive Filtering?: Toggle the use of DEMA for more responsive momentum calculation.
Adaptive Lookback Period: Set the period for the adaptive filter when enabled.
Show Momentum Moving Average?: Option to display a moving average of the plotosc for trend smoothing.
MA Period: Specify the period for the momentum moving average.
Show Static High and Low Levels: Display predefined levels indicating extreme momentum thresholds.
Color Bars According to Trend?: Color price bars based on the momentum direction for quick visual reference.
Show Overbought and Oversold Signals: Highlight extreme volume conditions as potential buy/sell signals.
Signal Calculation Period: Set the period for calculating volume-based signals.
Show Detected Divergences?: Enable or disable the visualization of bullish and bearish divergences.
How it can be used in the context of a Trading System
Momentum and momentum divergences are pivotal concepts in trading systems, offering traders insights into the strength and potential reversal points of market trends. Momentum, a measure of the rate of price changes, helps traders identify the velocity of market movements, allowing them to ride the wave of prevailing trends for profits. When momentum divergences occur—where price movement and momentum indicators move in opposite directions—they signal a weakening of the current trend and potential for reversal. Traders can use these signals to adjust their positions, entering or exiting trades based on the anticipation of trend changes. Incorporating momentum and its divergences into a trading system provides a dynamic strategy that leverages the market's natural cycles of trend strength and exhaustion, aiming to capitalize on both continuation and reversal opportunities for enhanced trading outcomes.
We have also added a volume based component for traders to use as a point of confluence. It is shown on the chart giving background hues for overbought and oversold signals.
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
Median Supertrend [BackQuant]Median Supertrend Concept by BackQuant ©
This was created since the normal supertrend is noisy, in the attempts to remove that and still get a good signal we decided to use a special median calculation as the source to a modified supertrend. This allows us to reduce noise, and make the supertrend adaptive to volatility. The full description and reasoning, including definitions and backtests are as follows:
1. Definition of Median
The median is a statistical measure that identifies the middle value in a given set of numbers when those numbers are arranged in either ascending or descending order. If the dataset has an even number of observations, the median is calculated as the average of the two middle numbers. This measure is particularly useful in understanding the central tendency of data, especially in cases where the dataset may contain outliers that could skew the mean. For example, in a dataset representing the earnings of families, the median provides a more accurate reflection of the typical income than the mean if the dataset includes extreme values.
2. Understanding Supertrend and Its Use Case
Supertrend is a popular trend-following indicator used in technical analysis. It is computed using the Average True Range (ATR) to capture volatility, combined with a moving average. The indicator provides clear signals to traders about bullish or bearish trends, indicating potential entry and exit points. Traders often use Supertrend in various market conditions to enhance their trading strategies, leveraging its simplicity and effectiveness in identifying ongoing trends and reversals.
3. Rationale Behind Combining Median with Supertrend
The integration of the median into the Supertrend indicator seeks to mitigate the impact of outliers and sudden market spikes that can affect trend analysis. By using the median value of price data for trend determination, the Median Supertrend aims to offer a more stable and reliable indicator that reflects the underlying market conditions more accurately than traditional methods. This modification is intended to improve the timing of trend detection and the precision of entry and exit signals.
4. Key Differences and Benefits
Enhanced Stability: The use of median values reduces sensitivity to extreme price movements, offering a smoother trend line that can lead to more reliable trading signals.
Adaptive Sensitivity: Users can adjust the indicator's sensitivity to align with different trading styles and market conditions through customizable parameters like the ATR multiplier and lookback period.
Explicit Trading Signals: The indicator simplifies the trading process by providing clear, actionable long and short signals based on trend reversals, aiding in decision-making.
Customizability: Options to use Heikin Ashi candles, paint candles based on the trend, and toggle signal visibility allow traders to personalize the indicator to their preference.
5. User Inputs
The Median Supertrend indicator includes several user inputs to tailor its operation:
Use HA Candles as Source?: Option to base calculations on Heikin Ashi candles for smoother price data.
Paint Candles According to Trend?: Visual aid that colors candles based on the current trend direction, enhancing chart readability.
ATR Period and Multiplier: Parameters to adjust the sensitivity of the trend detection, allowing users to fine-tune the indicator.
Adaptive Lookback Period: Defines the period for the median calculation, offering flexibility in trend assessment.
Show Long and Short Signals: Enables traders to visualize entry signals directly on the chart.
6. Application in Trading
Traders can incorporate the Median Supertrend into their strategies as a standalone indicator for trend following or as a filter in a multi-indicator system. It is particularly useful in markets known for having outliers or sudden price jumps, as the median-based calculation provides a grounded trend analysis. This indicator can be applied across various timeframes and asset classes, making it a versatile tool for day traders, swing traders, and long-term investors alike.
7. Summary and Empirical Soundness
The integration of median values into the Supertrend indicator represents an innovative approach to trend analysis, addressing some of the volatility and outlier-related challenges inherent in traditional methods. This combination is empirically sound as it leans on the statistical robustness of the median to offer a more stable and reliable trend determination mechanism.
8. Relavant Backtests on Major Assets (1D Timeframe)
We include these backtests as a general proxy for how they work.
Please do your own calibrating to suit it to your own needs and backtest.
Past results don't = future results but they can help you understand how it functions.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
AI Adaptive Money Flow Index (Clustering) [AlgoAlpha]🌟🚀 Dive into the future of trading with our latest innovation: the AI Adaptive Money Flow Index by AlgoAlpha Indicator! 🚀🌟
Developed with the cutting-edge power of Machine Learning, this indicator is designed to revolutionize the way you view market dynamics. 🤖💹 With its unique blend of traditional Money Flow Index (MFI) analysis and advanced k-means clustering, it adapts to market conditions like never before.
Key Features:
📊 Adaptive MFI Analysis: Utilizes the classic MFI formula with a twist, adjusting its parameters based on AI-driven clustering.
🧠 AI-Driven Clustering: Applies k-means clustering to identify and adapt to market states, optimizing the MFI for current conditions.
🎨 Customizable Appearance: Offers adjustable settings for overbought, neutral, and oversold levels, as well as colors for uptrends and downtrends.
🔔 Alerts for Key Market Movements: Set alerts for trend reversals, overbought, and oversold conditions, ensuring you never miss a trading opportunity.
Quick Guide to Using the AI Adaptive MFI (Clustering):
🛠 Customize the Indicator: Customize settings like MFI source, length, and k-means clustering parameters to suit your analysis.
📈 Market Analysis: Monitor the dynamically adjusted overbought, neutral, and oversold levels for insights into market conditions. Watch for classification symbols ("+", "0", "-") for immediate understanding of the current market state. Look out for reversal signals (▲, ▼) to get potential entry points.
🔔 Set Alerts: Utilize the built-in alert conditions for trend changes, overbought, and oversold signals to stay ahead, even when you're not actively monitoring the charts.
How It Works:
The AI Adaptive Money Flow Index employs the k-means clustering machine learning algorithm to refine the traditional Money Flow Index, dynamically adjusting overbought, neutral, and oversold levels based on market conditions. This method analyzes historical MFI values, grouping them into initial clusters using the traditional MFI's overbought, oversold and neutral levels, and then finding the mean of each cluster, which represent the new market states thresholds. This adaptive approach ensures the indicator's sensitivity in real-time, offering a nuanced understanding of market trend and volume analysis.
By recalibrating MFI thresholds for each new data bar, the AI Adaptive MFI intelligently conforms to changing market dynamics. This process, assessing past periods to adjust the indicator's parameters, provides traders with insights finely tuned to recent market behavior. Such innovation enhances decision-making, leveraging the latest data to inform trading strategies. 🌐💥
Regression Sloped RSI [QuantraSystems]Regression Sloped RSI
Introduction
The Regression Sloped RSI (𝓡𝓢-𝓡𝓢𝓘) enhances the classical RSI by incorporating a form of linear regression analysis, which adjusts the traditional RSI in relation to the calculated slope over a specified lookback period.
Its innovative approach reduces the occurrence of false signals compared to the classical RSI. Furthermore, it is particularly effective in markets characterized by strong trends. This is because it responds faster while retaining a high level of whipsaw resistance. The Heikin-Ashi style processing is critical to this.
It also provides robust reversal signals from dynamic overbought and oversold zones to further enhance mean-reversion trading.
Legend
The coloring of the 𝓡𝓢-𝓡𝓢𝓘 changes based on trend direction: A bright green when upwards, lilac when downwards. The strength of the trend is expressed in its distance to Null. Its acceleration is found in the Heikin-Ashi (HA) candles.
The 𝓡𝓢-𝓡𝓢𝓘 in combination with the HA bars can be used to achieve earlier entries, when the former passes across the latter in an obvious divergence.
Case Study
In this example the 𝓡𝓢-𝓡𝓢𝓘 is used to make a few intra-day trades on the Ethereum 15 minute chart. Each trade was open for approximately 5 hours. On the first trade we enter a long in an early entry. The indicator gives us three confirmations which we should all check for. First we have a positive candle developing, secondly the 𝓡𝓢-𝓡𝓢𝓘 (line) rises above the Heikin-Ashi candles, thirdly the classical RSI (the saturated surface in the background) rises as well.
The trader should then calculate their position sizing responsibly and enter into a short daytrade. Please always have invalidation rules, for example a) if the initial HA candle closes negative b) you can place your stop loss at 1SD into the opposite direction.
Always use adequate risk management, never risk more than 1% of your portfolio, unless you are a seasoned trader with your own calculated position sizes.
Always forward test your rules, assets, timeframe and settings sufficiently.
It is always recommended to use multiple Quantra indicators to add confirmations to your signals - this is by design.
Recommended Settings
Please reset to defaults before enabling recommended settings.
Intra-Day Trading (15min chart)
RSI Length: 22
LR Length: 25
Smoothing: EMA
Toggle SD Bands: On
Mode for Coloring: Candles
Trend Following (4H chart)
RSI Length: 40
LR Length: 35
Smoothing: LSMA
Toggle SD Bands: Off
Mode for Coloring: Extremes or Trend Following
Notes
Quantra Standard Value Contents:
The Heikin-Ashi (HA) candle visualization smoothes out the signal line to provide more informative insights into momentum and trends. This allows earlier entries and exits by observing the indicator values transformed by the HA.
Various visualization options are available to adjust the indicator to the user’s preference: Aside from HA, a classic line, or a hybrid of both.
A special feature of Quantra’s indicators is that they are probabilistically built - therefore they work well as confluence and can easily be stacked to increase signal accuracy.
To add to Quantra's indicators’ utility we have added the option to change the price bars colors based on different signals:
Choose Mode for Coloring
Trend Following (Indicator above mid line counts as uptrend, below is downtrend)
Extremes (Everything beyond the SD bands is highlighted to signal mean reversion)
Candles (Color of HA candles as barcolor)
Reversions (Only for HA) (Reversion Signals via the triangles if HA candles change trend while beyond the SD bands, high probability entries/exits)
The 𝓡𝓢-𝓡𝓢𝓘 is finely tuned to detect divergences.
Primarily utilized for trend following, the 𝓡𝓢-𝓡𝓢𝓘 also demonstrates effectiveness in identifying reversions, intensity of movements and the navigation of range-bound markets.
Allows for easy identification of slowdowns in momentum and thus negative rate of change.
Methodology
The 𝓡𝓢-𝓡𝓢𝓘 takes the classical RSI using a specified lookback length and computes the slope of a linear regression line applied to the RSI values. This slope is used to adjust the RSI.
This sloped RSI can be further smoothed using various Moving Averages with customizable lengths.
For a more nuanced view of market trends, the 𝓡𝓢-𝓡𝓢𝓘 applies a specialized Heikin Ashi method. This transformation modifies the Sloped RSI values in order to weigh and reflect the average price, offering a smoother representation compared to traditional candlestick patterns.
The 𝓡𝓢-𝓡𝓢𝓘 calculates upper and lower bounds based on a specified standard deviation multiplier and adjustable lookback period, providing a dynamic framework to identify extrema and thus overbought and oversold conditions.
Particularly in the Heikin Ashi mode, the 𝓡𝓢-𝓡𝓢𝓘 can display reversion signals. These are plotted as shapes on the chart, indicating high probability reversal points in the market trend.
