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
Adaptive
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
Volume-Weighted RSI with Adaptive SmoothingThis indicator is designed to provide traders with insights into the relative strength of a security by incorporating volume-weighted elements, effectively combining the concepts of Relative Strength Index (RSI) and volume-weighted averages to generate meaningful trading signals.
The indicator calculates the traditional RSI, which measures the speed and change of price movements, as well as the volume-weighted RSI, which considers the influence of trading volume on price action. It then applies adaptive smoothing to the volume-weighted RSI, allowing for customization of the smoothing process. The resulting smoothed volume-weighted RSI is plotted alongside the original RSI, providing traders with a comprehensive view of the price strength dynamics.
The line coloration in this indicator is designed to provide visual cues about the relationship between the RSI and the volume-weighted RSI. When the RSI line is above or equal to the volume-weighted RSI line, it suggests a potentially bullish condition with positive market momentum. In such cases, the line is colored lime. Conversely, when the RSI line (fuchsia) is below the volume-weighted RSI line, it indicates a potentially bearish condition with negative market momentum. The line color is set to fuchsia. By observing the line color, traders can quickly assess the relative strength between the RSI and the volume-weighted RSI, aiding their decision-making process.
The bar color and background color further enhance the visual interpretation of the indicator. The bar color reflects the RSI's relationship with the volume-weighted RSI and the predefined thresholds. If the RSI line is above both the volume-weighted RSI line and the overbought threshold (70), the bar color is set to lime, indicating a potentially overbought condition. Conversely, if the RSI line is below both the volume-weighted RSI line and the oversold threshold (30), the bar color is set to fuchsia, suggesting a potentially oversold condition. When the RSI line is between these two thresholds, the bar color is set to yellow, indicating a neutral or intermediate state. The background color, displayed with a semi-transparent shade, provides additional context by reflecting the prevailing market conditions. It turns lime if the volume-weighted RSI is above the overbought threshold, fuchsia if below the oversold threshold, and yellow if it falls between these two thresholds. This coloration scheme aids traders in quickly assessing market conditions and potential trading opportunities.
Calculations:
-- RSI Calculation : The traditional RSI is calculated based on the price movements of the asset. The up and down movements are determined, and exponential moving averages are used to smooth the values. The RSI value ranges from 0 to 100, with levels above 70 indicating overbought conditions and levels below 30 indicating oversold conditions.
-- Volume-Weighted RSI Calculation : The volume-weighted RSI incorporates the trading volume of the asset into the calculations. The closing price is multiplied by the corresponding volume, and the average is taken over a specific length. The up and down movements are smoothed using exponential moving averages to generate the volume-weighted RSI value.
-- Adaptive Smoothing : The indicator offers an adaptive smoothing option, allowing traders to customize the smoothing process of the volume-weighted RSI. By adjusting the smoothing length, traders can fine-tune the responsiveness of the indicator to changes in market conditions. Smoothing helps reduce noise and enhances the clarity of the signals.
Interpretation:
The indicator provides two main components for interpretation:
-- RSI : The traditional RSI reflects the price momentum and potential overbought or oversold conditions. Traders can look for RSI values above 70 as potential overbought signals, suggesting a possible price reversal or correction. Conversely, RSI values below 30 indicate potential oversold signals, indicating a potential price rebound or rally.
-- Volume-Weighted RSI : The volume-weighted RSI incorporates trading volume, which provides insights into the strength of price movements. When the volume-weighted RSI is above the traditional RSI, it suggests that the buying pressure supported by higher volume is stronger, potentially indicating a more reliable trend. Conversely, when the volume-weighted RSI is below the traditional RSI, it suggests that the selling pressure supported by higher volume is stronger, potentially indicating a more significant price reversal.
Potential Strategies:
-- Overbought and Oversold Signals : Traders can utilize the RSI component of the indicator to identify overbought and oversold conditions. A potential strategy is to consider taking short positions when the RSI is above 70 and long positions when the RSI is below 30. These levels can act as dynamic support and resistance areas, indicating possible price reversals.
-- Confirmation with Volume : Traders can use the volume-weighted RSI as a confirmation tool to validate price movements. When the volume-weighted RSI is above the traditional RSI, it may provide additional confirmation for long positions, suggesting stronger buying pressure. Conversely, when the volume-weighted RSI is below the traditional RSI, it may provide confirmation for short positions, indicating stronger selling pressure.
-- Trend Reversal Strategy : Watch for the volume-weighted RSI to reach extreme levels above 70 (overbought) or below 30 (oversold). Look for a reversal signal where the RSI line (green or fuchsia) crosses below or above the volume-weighted RSI line. Enter a trade when the reversal signal occurs, and the RSI line changes color. Exit the trade when the RSI line crosses back in the opposite direction or reaches the opposite extreme level.
-- Divergence Strategy : Compare the direction of the RSI line (green or fuchsia) with the volume-weighted RSI line. A bullish divergence occurs when the RSI line makes higher lows while the volume-weighted RSI line makes lower lows. A bearish divergence occurs when the RSI line makes lower highs while the volume-weighted RSI line makes higher highs. Once a divergence is identified, wait for the RSI line to cross above or below the volume-weighted RSI line as confirmation of a potential trend reversal. Consider using additional indicators or price action analysis to time the entry more accurately. Use stop-loss orders and profit targets to manage risk and secure profits.
-- Trend Continuation Strategy : Assess the overall trend direction by observing the RSI line's position relative to the volume-weighted RSI line. When the RSI line consistently stays above the volume-weighted RSI line, it indicates a bullish trend, while the opposite suggests a bearish trend. Look for temporary pullbacks within the ongoing trend where the RSI line (green or fuchsia) touches or crosses the volume-weighted RSI line. Enter trades in the direction of the dominant trend when the RSI line crosses back in the trend direction. Exit the trade when the RSI line starts to deviate significantly from the volume-weighted RSI line or when the trend shows signs of weakening through other technical or fundamental factors.
Limitations:
-- False Signals : Like any indicator, the "Volume-Weighted RSI with Adaptive Smoothing" may produce false signals, especially during periods of low liquidity or choppy market conditions. Traders should exercise caution and consider using additional confirmation indicators or tools to validate the signals generated by this indicator.
-- Lagging Nature : The indicator relies on historical price data and volume to calculate the RSI and volume-weighted RSI. As a result, the signals provided may have a certain degree of lag compared to real-time price action. Traders should be aware of this inherent lag and consider combining the indicator with other timely indicators to enhance the accuracy of their trading decisions.
-- Parameter Sensitivity : The indicator's effectiveness can be influenced by the choice of parameters, such as the length of the RSI, smoothing length, and adaptive smoothing option. Different market conditions may require adjustments to these parameters to optimize performance. Traders are encouraged to conduct thorough testing and analysis to determine the most suitable parameter values for their specific trading strategies and preferences.
-- Market Conditions : The indicator's performance may vary depending on the prevailing market conditions. It is essential to understand that no indicator can guarantee accurate predictions or consistently profitable trades. Traders should consider the broader market context, fundamental factors, and other technical indicators to complement the insights provided by the "Volume-Weighted RSI with Adaptive Smoothing" indicator.
-- Subjectivity : Interpretation of the indicator's signals involves subjective judgment. Traders may have varying interpretations of overbought and oversold levels, as well as the significance of the volume-weighted RSI in relation to the traditional RSI. It is crucial to combine the indicator with personal analysis and trading experience to make informed trading decisions.
Remember, no single indicator can provide foolproof trading signals. The "Volume-Weighted RSI with Adaptive Smoothing" indicator serves as a valuable tool for analyzing price strength and volume dynamics. It can assist traders in identifying potential entry and exit points, validating trends, and managing risk. However, it should be used as part of a comprehensive trading strategy that considers multiple factors and indicators to increase the likelihood of successful trades.
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.
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
Adaptive Fisherized Z-scoreHello Fellas,
It's time for a new adaptive fisherized indicator of me, where I apply adaptive length and more on a classic indicator.
Today, I chose the Z-score, also called standard score, as indicator of interest.
Special Features
Advanced Smoothing: JMA, T3, Hann Window and Super Smoother
Adaptive Length Algorithms: In-Phase Quadrature, Homodyne Discriminator, Median and Hilbert Transform
Inverse Fisher Transform (IFT)
Signals: Enter Long, Enter Short, Exit Long and Exit Short
Bar Coloring: Presents the trade state as bar colors
Band Levels: Changes the band levels
Decision Making
When you create such a mod you need to think about which concepts are the best to conclude. I decided to take Inverse Fisher Transform instead of normalization to make a version which fits to a fixed scale to avoid the usual distortion created by normalization.
Moreover, I chose JMA, T3, Hann Window and Super Smoother, because JMA and T3 are the bleeding-edge MA's at the moment with the best balance of lag and responsiveness. Additionally, I chose Hann Window and Super Smoother because of their extraordinary smoothing capabilities and because Ehlers favours them.
Furthermore, I decided to choose the half length of the dominant cycle instead of the full dominant cycle to make the indicator more responsive which is very important for a signal emitter like Z-score. Signal emitters always need to be faster or have the same speed as the filters they are combined with.
Usage
The Z-score is a low timeframe scalper which works best during choppy/ranging phases. The direction you should trade is determined by the last trend change. E.g. when the last trend change was from bearish market to bullish market and you are now in a choppy/ranging phase confirmed by e.g. Chop Zone or KAMA slope you want to do long trades.
Interpretation
The Z-score indicator is a momentum indicator which shows the number of standard deviations by which the value of a raw score (price/source) is above or below the mean value of what is being observed or measured. Easily explained, it is almost the same as Bollinger Bands with another visual representation form.
Signals
B -> Buy -> Z-score crosses above lower band
S -> Short -> Z-score crosses below upper band
BE -> Buy Exit -> Z-score crosses above 0
SE -> Sell Exit -> Z-score crosses below 0
If you were reading till here, thank you already. Now, follows a bunch of knowledge for people who don't know the concepts I talk about.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Inverse Fisher Transform
The Inverse Fisher Transform is a transform used in DSP to alter the Probability Distribution Function (PDF) of a signal or in our case of indicators.
The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals.
Hann Window
The Hann function (aka Hann Window) is named after the Austrian meteorologist Julius von Hann. It is a window function used to perform Hann smoothing.
Super Smoother
The Super Smoother uses a special mathematical process for the smoothing of data points.
The Super Smoother is a technical analysis indicator designed to be smoother and with less lag than a traditional moving average.
Adaptive Length
Length based on the dominant cycle length measured by a "dominant cycle measurement" algorithm.
Happy Trading!
Best regards,
simwai
---
Credits to
@cheatcountry
@everget
@loxx
@DasanC
@blackcat1402
GKD-C PA Adaptive Fisher Transform [Loxx]The Giga Kaleidoscope GKD-C PA Adaptive Fisher Transform is a confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ GKD-C PA Adaptive Fisher Transform
Phase Accumulation Adaptive Fisher Transform is an adaptive Fisher Transform using a modified version of Ehlers Phase Accumulation Cycle Period. This version of Phase Accumulation Cylce Period accepts as inputs: 1) total number of cycles you wish to inject into the calculation, this works as a multiplier so the higher this number, the longer the period output; 2) filter is to change the alpha value of the final smother before returning the period output.
What is the Phase Accumulation Cycle?
The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio.
What is Fisher Transform?
The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution.
The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, and the Average Directional Index (ADX).
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker CC Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Advance Trend Pressure as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
? Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
RSI Volatility Bands [QuantraSystems]RSI Volatility Bands
Introduction
The RSI Volatility Bands indicator introduces a unique approach to market analysis by combining the traditional Relative Strength Index (RSI) with dynamic, volatility adjusted deviation bands. It is designed to provide a highly customizable method of trend analysis, enabling investors to analyze potential entry and exit points in a new and profound way.
The deviation bands are calculated and drawn in a manner which allows investors to view them as areas of dynamic support and resistance.
Legend
Upper and Lower Bands - A dynamic plot of the volatility-adjusted range around the current price.
Signals - Generated when the RSI volatility bands indicate a trend shift.
Case Study
The chart highlights the occurrence of false signals, emphasizing the need for caution when the bands are contracted and market volatility is low.
Juxtaposing this, during volatile market phases as shown, the indicator can effectively adapt to strong trends. This keeps an investor in a position even through a minor drawdown in order to exploit the entire price movement.
Recommended Settings
The RSI Volatility Bands are highly customisable and can be adapted to many assets with diverse behaviors.
The calibrations used in the above screenshots are as follows:
Source = close
RSI Length = 8
RSI Smoothing MA = DEMA
Bandwidth Type = DEMA
Bandwidth Length = 24
Bandwidth Smooth = 25
Methodology
The indicator first calculates the RSI of the price data, and applies a custom moving average.
The deviation bands are then calculated based upon the absolute difference between the RSI and its moving average - providing a unique volatility insight.
The deviation bands are then adjusted with another smoothing function, providing clear visuals of the RSI’s trend within a volatility-adjusted context.
rsiVal = ta.rsi(close, rsiLength)
rsiEma = ma(rsiMA, rsiVal, bandLength)
bandwidth = ma(bandMA, math.abs(rsiVal - rsiEma), bandLength)
upperBand = ma(bandMA, rsiEma + bandwidth, smooth)
lowerBand = ma(bandMA, rsiEma - bandwidth, smooth)
long = upperBand > 50 and not (lowerBand < lowerBand and lowerBand < 50)
short= not (upperBand > 50 and not (lowerBand < lowerBand and lowerBand < 50))
By dynamically adjusting to market conditions, the RSI trend bands offer a unique perspective on market trends, and reversal zones.
Backtesting ModuleDo you often find yourself creating new 'strategy()' scripts for each trading system? Are you unable to focus on generating new systems due to fatigue and time loss incurred in the process? Here's a potential solution: the 'Backtesting Module' :)
INTRODUCTION
Every trading system is based on four basic conditions: long entry, long exit, short entry and short exit (which are typically defined as boolean series in Pine Script).
If you can define the conditions generated by your trading system as a series of integers, it becomes possible to use these variables in different scripts in efficient ways. (Pine Script is a convenient language that allows you to use the integer output of one indicator as a source in another.)
The 'Backtesting Module' is a dynamic strategy script designed to adapt to your signals. It boasts two notable features:
⮞ It produces a backtest report using the entry and exit variables you define.
⮞ It not only serves for system testing but also to combine independent signals into a single system. (This functionality enables to create complex strategies and report on their success!)
The module tests Golden and Death cross signals by default, when you enter your own conditions the default signals will be neutralized. The methodology is described below.
PREPARATION
There are three simple steps to connect your own indicator to the Module.
STEP 1
Firstly, you must define entry and exit variables in your own script. Let's elucidate it with a straightforward example. Consider a system generating long and short signals based on the intersections of two moving averages. Consequently, our conditions would be as follows:
// Signals
long = ta.crossover(ta.sma(close, 14), ta.sma(close, 28))
short = ta.crossunder(ta.sma(close, 14), ta.sma(close, 28))
Now, the question is: How can we convert boolean variables into integer variables? The answer is conditional ternary block, defined as follows:
// Entry & Exit
long_entry = long ? 1 : 0
long_exit = short ? 1 : 0
short_entry = short ? 1 : 0
short_exit = long ? 1 : 0
The mechanics of the Entry & Exit variables are simple. The variable takes on a value of 1 when your trading system generates the signal and if your system does not produce any signal, variable returns 0. In this example, you see how exit signals can be generated in a trading system that only contains entry signals. If you have a system with original exit signals, you can also use them directly. (Please mind the NOTES section below).
STEP 2
To utilize the Entry & Exit variables as source in another script, they must be plotted on the chart. Therefore, the final detail to include in the script containing your trading system would be as follows:
// Plot The Output
plot(long_entry, "Long Entry", display=display.data_window, editable=false)
plot(long_exit, "Long Exit", display=display.data_window, editable=false)
plot(short_entry, "Short Entry", display=display.data_window, editable=false)
plot(short_exit, "Short Exit", display=display.data_window, editable=false)
STEP 3
Now, we are ready to test the system! Load the Backtesting Module indicator onto the chart along with your trading system/indicator. Then set the outputs of your system (Long Entry, Long Exit, Short Entry, Short Exit) as source in the module. That's it.
FEATURES & ORIGINALITY
⮞ Primarily, this script has been created to provide you with an easy and practical method when testing your trading system.
⮞ I thought it might be nice to visualize a few useful results. The Backtesting Module provides insights into the outcomes of both long and short trades by computing the number of trades and the success percentage.
⮞ Through the 'Trade' parameter, users can specify the market direction in which the indicator is permitted to initiate positions.
⮞ Users have the flexibility to define the date range for the test.
⮞ There are optional features allowing users to plot entry prices on the chart and customize bar colors.
⮞ The report and the test date range are presented in a table on the chart screen. The entry price can be monitored in the data window.
⮞ Note that results are based on realized returns, and the open trade is not included in the displayed results. (The only exception is the 'Unrealized PNL' result in the table.)
STRATEGY SETTINGS
The default parameters are as follows:
⮞ Initial Balance : 10000 (in units of currency)
⮞ Quantity : 10% of equity
⮞ Commission : 0.04%
⮞ Slippage : 0
⮞ Dataset : All bars in the chart
For a realistic backtest result, you should size trades to only risk sustainable amounts of equity. Do not risk more than 5-10% on a trade. And ALWAYS configure your commission and slippage parameters according to pessimistic scenarios!
NOTES
⮞ This script is intended solely for development purposes. And it'll will be available for all the indicators I publish.
⮞ In this version of the module, all order types are designed as market orders. The exit size is the sum of the entry size.
⮞ As your trading conditions grow more intricate, you might need to define the outputs of your system in alternative ways. The method outlined in this description is tailored for straightforward signal structures.
⮞ Additionally, depending on the structure of your trading system, the backtest module may require further development. This encompasses stop-loss, take-profit, specific exit orders, quantity, margin and risk management calculations. I am considering releasing improvements that consider these options in future versions.
⮞ An example of how complex trading signals can be generated is the OTT Collection. If you're interested in seeing how the signals are constructed, you can use the link below.
THANKS
Special thanks to PineCoders for their valuable moderation efforts.
I hope this will be a useful example for the TradingView community...
DISCLAIMER
This is just an indicator, nothing more. It is provided for informational and educational purposes exclusively. The utilization of this script does not constitute professional or financial advice. The user solely bears the responsibility for risks associated with script usage. Do not forget to manage your risk. And trade as safely as possible. Best of luck!
Advanced Dynamic Threshold RSI [Elysian_Mind]Advanced Dynamic Threshold RSI Indicator
Overview
The Advanced Dynamic Threshold RSI Indicator is a powerful tool designed for traders seeking a unique approach to RSI-based signals. This indicator combines traditional RSI analysis with dynamic threshold calculation and optional Bollinger Bands to generate weighted buy and sell signals.
Features
Dynamic Thresholds: The indicator calculates dynamic thresholds based on market volatility, providing more adaptive signal generation.
Performance Analysis: Users can evaluate recent price performance to further refine signals. The script calculates the percentage change over a specified lookback period.
Bollinger Bands Integration: Optional integration of Bollinger Bands for additional confirmation and visualization of potential overbought or oversold conditions.
Customizable Settings: Traders can easily customize key parameters, including RSI length, SMA length, lookback bars, threshold multiplier, and Bollinger Bands parameters.
Weighted Signals: The script introduces a unique weighting mechanism for signals, reducing false positives and improving overall reliability.
Underlying Calculations and Methods
1. Dynamic Threshold Calculation:
The heart of the Advanced Dynamic Threshold RSI Indicator lies in its ability to dynamically calculate thresholds based on multiple timeframes. Let's delve into the technical details:
RSI Calculation:
For each specified timeframe (1-hour, 4-hour, 1-day, 1-week), the Relative Strength Index (RSI) is calculated using the standard 14-period formula.
SMA of RSI:
The Simple Moving Average (SMA) is applied to each RSI, resulting in the smoothing of RSI values. This smoothed RSI becomes the basis for dynamic threshold calculations.
Dynamic Adjustment:
The dynamically adjusted threshold for each timeframe is computed by adding a constant value (5 in this case) to the respective SMA of RSI. This dynamic adjustment ensures that the threshold reflects changing market conditions.
2. Weighted Signal System:
To enhance the precision of buy and sell signals, the script introduces a weighted signal system. Here's how it works technically:
Signal Weighting:
The script assigns weights to buy and sell signals based on the crossover and crossunder events between RSI and the dynamically adjusted thresholds. If a crossover event occurs, the weight is set to 2; otherwise, it remains at 1.
Signal Combination:
The weighted buy and sell signals from different timeframes are combined using logical operations. A buy signal is generated if the product of weights from all timeframes is equal to 2, indicating alignment across timeframe.
3. Experimental Enhancements:
The Advanced Dynamic Threshold RSI Indicator incorporates experimental features for educational exploration. While not intended as proven strategies, these features aim to offer users a glimpse into unconventional analysis. Some of these features include Performance Calculation, Volatility Calculation, Dynamic Threshold Calculation Using Volatility, Bollinger Bands Module, Weighted Signal System Incorporating New Features.
3.1 Performance Calculation:
The script calculates the percentage change in the price over a specified lookback period (variable lookbackBars). This provides a measure of recent performance.
pctChange(src, length) =>
change = src - src
pctChange = (change / src ) * 100
recentPerformance1H = pctChange(close, lookbackBars)
recentPerformance4H = pctChange(request.security(syminfo.tickerid, "240", close), lookbackBars)
recentPerformance1D = pctChange(request.security(syminfo.tickerid, "1D", close), lookbackBars)
3.2 Volatility Calculation:
The script computes the standard deviation of the closing price to measure volatility.
volatility1H = ta.stdev(close, 20)
volatility4H = ta.stdev(request.security(syminfo.tickerid, "240", close), 20)
volatility1D = ta.stdev(request.security(syminfo.tickerid, "1D", close), 20)
3.3 Dynamic Threshold Calculation Using Volatility:
The dynamic thresholds for RSI are calculated by adding a multiplier of volatility to 50.
dynamicThreshold1H = 50 + thresholdMultiplier * volatility1H
dynamicThreshold4H = 50 + thresholdMultiplier * volatility4H
dynamicThreshold1D = 50 + thresholdMultiplier * volatility1D
3.4 Bollinger Bands Module:
An additional module for Bollinger Bands is introduced, providing an option to enable or disable it.
// Additional Module: Bollinger Bands
bbLength = input(20, title="Bollinger Bands Length")
bbMultiplier = input(2.0, title="Bollinger Bands Multiplier")
upperBand = ta.sma(close, bbLength) + bbMultiplier * ta.stdev(close, bbLength)
lowerBand = ta.sma(close, bbLength) - bbMultiplier * ta.stdev(close, bbLength)
3.5 Weighted Signal System Incorporating New Features:
Buy and sell signals are generated based on the dynamic threshold, recent performance, and Bollinger Bands.
weightedBuySignal = rsi1H > dynamicThreshold1H and rsi4H > dynamicThreshold4H and rsi1D > dynamicThreshold1D and crossOver1H
weightedSellSignal = rsi1H < dynamicThreshold1H and rsi4H < dynamicThreshold4H and rsi1D < dynamicThreshold1D and crossUnder1H
These features collectively aim to provide users with a more comprehensive view of market dynamics by incorporating recent performance and volatility considerations into the RSI analysis. Users can experiment with these features to explore their impact on signal accuracy and overall indicator performance.
Indicator Placement for Enhanced Visibility
Overview
The design choice to position the "Advanced Dynamic Threshold RSI" indicator both on the main chart and beneath it has been carefully considered to address specific challenges related to visibility and scaling, providing users with an improved analytical experience.
Challenges Faced
1. Differing Scaling of RSI Results:
RSI values for different timeframes (1-hour, 4-hour, and 1-day) often exhibit different scales, especially in markets like gold.
Attempting to display these RSIs on the same chart can lead to visibility issues, as the scaling differences may cause certain RSI lines to appear compressed or nearly invisible.
2. Candlestick Visibility vs. RSI Scaling:
Balancing the visibility of candlestick patterns with that of RSI values posed a unique challenge.
A single pane for both candlesticks and RSIs may compromise the clarity of either, particularly when dealing with assets that exhibit distinct volatility patterns.
Design Solution
Placing the buy/sell signals above/below the candles helps to maintain a clear association between the signals and price movements.
By allocating RSIs beneath the main chart, users can better distinguish and analyze the RSI values without interference from candlestick scaling.
Doubling the scaling of the 1-hour RSI (displayed in blue) addresses visibility concerns and ensures that it remains discernible even when compared to the other two RSIs: 4-hour RSI (orange) and 1-day RSI (green).
Bollinger Bands Module is optional, but is turned on as default. When the module is turned on, the users can see the upper Bollinger Band (green) and lower Bollinger Band (red) on the main chart to gain more insight into price actions of the candles.
User Flexibility
This dual-placement approach offers users the flexibility to choose their preferred visualization:
The main chart provides a comprehensive view of buy/sell signals in relation to candlestick patterns.
The area beneath the chart accommodates a detailed examination of RSI values, each in its own timeframe, without compromising visibility.
The chosen design optimizes visibility and usability, addressing the unique challenges posed by differing RSI scales and ensuring users can make informed decisions based on both price action and RSI dynamics.
Usage
Installation
To ensure you receive updates and enhancements seamlessly, follow these steps:
Open the TradingView platform.
Navigate to the "Indicators" tab in the top menu.
Click on "Community Scripts" and search for "Advanced Dynamic Threshold RSI Indicator."
Select the indicator from the search results and click on it to add to your chart.
This ensures that any future updates to the indicator can be easily applied, keeping you up-to-date with the latest features and improvements.
Review Code
Open TradingView and navigate to the Pine Editor.
Copy the provided script.
Paste the script into the Pine Editor.
Click "Add to Chart."
Configuration
The indicator offers several customizable settings:
RSI Length: Defines the length of the RSI calculation.
SMA Length: Sets the length of the SMA applied to the RSI.
Lookback Bars: Determines the number of bars used for recent performance analysis.
Threshold Multiplier: Adjusts the multiplier for dynamic threshold calculation.
Enable Bollinger Bands: Allows users to enable or disable Bollinger Bands integration.
Interpreting Signals
Buy Signal: Generated when RSI values are above dynamic thresholds and a crossover occurs.
Sell Signal: Generated when RSI values are below dynamic thresholds and a crossunder occurs.
Additional Information
The indicator plots scaled RSI lines for 1-hour, 4-hour, and 1-day timeframes.
Users can experiment with additional modules, such as machine-learning simulation, dynamic real-life improvements, or experimental signal filtering, depending on personal preferences.
Conclusion
The Advanced Dynamic Threshold RSI Indicator provides traders with a sophisticated tool for RSI-based analysis, offering a unique combination of dynamic thresholds, performance analysis, and optional Bollinger Bands integration. Traders can customize settings and experiment with additional modules to tailor the indicator to their trading strategy.
Disclaimer: Use of the Advanced Dynamic Threshold RSI Indicator
The Advanced Dynamic Threshold RSI Indicator is provided for educational and experimental purposes only. The indicator is not intended to be used as financial or investment advice. Trading and investing in financial markets involve risk, and past performance is not indicative of future results.
The creator of this indicator is not a financial advisor, and the use of this indicator does not guarantee profitability or specific trading outcomes. Users are encouraged to conduct their own research and analysis and, if necessary, consult with a qualified financial professional before making any investment decisions.
It is important to recognize that all trading involves risk, and users should only trade with capital that they can afford to lose. The Advanced Dynamic Threshold RSI Indicator is an experimental tool that may not be suitable for all individuals, and its effectiveness may vary under different market conditions.
By using this indicator, you acknowledge that you are doing so at your own risk and discretion. The creator of this indicator shall not be held responsible for any financial losses or damages incurred as a result of using the indicator.
Kind regards,
Ely
Optimal Length BackTester [YinYangAlgorithms]This Indicator allows for a ‘Optimal Length’ to be inputted within the Settings as a Source. Unlike most Indicators and/or Strategies that rely on either Static Lengths or Internal calculations for the length, this Indicator relies on the Length being derived from an external Indicator in the form of a Source Input.
This may not sound like much, but this application may allows limitless implementations of such an idea. By allowing the input of a Length within a Source Setting you may have an ‘Optimal Length’ that adjusts automatically without the need for manual intervention. This may allow for Traditional and Non-Traditional Indicators and/or Strategies to allow modifications within their settings as well to accommodate the idea of this ‘Optimal Length’ model to create an Indicator and/or Strategy that adjusts its length based on the top performing Length within the current Market Conditions.
This specific Indicator aims to allow backtesting with an ‘Optimal Length’ inputted as a ‘Source’ within the Settings.
This ‘Optimal Length’ may be used to display and potentially optimize multiple different Traditional Indicators within this BackTester. The following Traditional Indicators are included and available to be backtested with an ‘Optimal Length’ inputted as a Source in the Settings:
Moving Average; expressed as either a: Simple Moving Average, Exponential Moving Average or Volume Weighted Moving Average
Bollinger Bands; expressed based on the Moving Average Type
Donchian Channels; expressed based on the Moving Average Type
Envelopes; expressed based on the Moving Average Type
Envelopes Adjusted; expressed based on the Moving Average Type
All of these Traditional Indicators likewise may be displayed with multiple ‘Optimal Lengths’. They have the ability for multiple different ‘Optimal Lengths’ to be inputted and displayed, such as:
Fast Optimal Length
Slow Optimal Length
Neutral Optimal Length
By allowing for the input of multiple different ‘Optimal Lengths’ we may express the ‘Optimal Movement’ of such an expressed Indicator based on different Time Frames and potentially also movement based on Fast, Slow and Neutral (Inclusive) Lengths.
This in general is a simple Indicator that simply allows for the input of multiple different varieties of ‘Optimal Lengths’ to be displayed in different ways using Tradition Indicators. However, the idea and model of accepting a Length as a Source is unique and may be adopted in many different forms and endless ideas.
Tutorial:
You may add an ‘Optimal Length’ within the Settings as a ‘Source’ as followed in the example above. This Indicator allows for the input of a:
Neutral ‘Optimal Length’
Fast ‘Optimal Length’
Slow ‘Optimal Length’
It is important to account for all three as they generally encompass different min/max length values and therefore result in varying ‘Optimal Length’s’.
For instance, say you’re calculating the ‘Optimal Length’ and you use:
Min: 1
Max: 400
This would therefore be scanning for 400 (inclusive) lengths.
As a general way of calculating you may assume the following for which lengths are being used within an ‘Optimal Length’ calculation:
Fast: 1 - 199
Slow: 200 - 400
Neutral: 1 - 400
This allows for the calculation of a Fast and Slow length within the predetermined lengths allotted. However, it likewise allows for a Neutral length which is inclusive to all lengths alloted and may be deemed the ‘Most Accurate’ for these reasons. However, just because the Neutral is inclusive to all lengths, doesn’t mean the Fast and Slow lengths are irrelevant. The Fast and Slow length inputs may be useful for seeing how specifically zoned lengths may fair, and likewise when they cross over and/or under the Neutral ‘Optimal Length’.
This Indicator features the ability to display multiple different types of Traditional Indicators within the ‘Display Type’.
We will go over all of the different ‘Display Types’ with examples on how using a Fast, Slow and Neutral length would impact it:
Simple Moving Average:
In this example above have the Fast, Slow and Neutral Optimal Length formatted as a Slow Moving Average. The first example is on the 15 minute Time Frame and the second is on the 1 Day Time Frame, demonstrating how the length changes based on the Time Frame and the effects it may have.
Here we can see that by inputting ‘Optimal Lengths’ as a Simple Moving Average we may see moving averages that change over time with their ‘Optimal Lengths’. These lengths may help identify Support and/or Resistance locations. By using an 'Optimal Length' rather than a static length, we may create a Moving Average which may be more accurate as it attempts to be adaptive to current Market Conditions.
Bollinger Bands:
Bollinger Bands are a way to see a Simple Moving Average (SMA) that then uses Standard Deviation to identify how much deviation has occurred. This Deviation is then Added and Subtracted from the SMA to create the Bollinger Bands which help Identify possible movement zones that are ‘within range’. This may mean that the price may face Support / Resistance when it reaches the Outer / Inner bounds of the Bollinger Bands. Likewise, it may mean the Price is ‘Overbought’ when outside and above or ‘Underbought’ when outside and below the Bollinger Bands.
By applying All 3 different types of Optimal Lengths towards a Traditional Bollinger Band calculation we may hope to see different ranges of Bollinger Bands and how different lookback lengths may imply possible movement ranges on both a Short Term, Long Term and Neutral perspective. By seeing these possible ranges you may have the ability to identify more levels of Support and Resistance over different lengths and Trading Styles.
Donchian Channels:
Above you’ll see two examples of Machine Learning: Optimal Length applied to Donchian Channels. These are displayed with both the 15 Minute Time Frame and the 1 Day Time Frame.
Donchian Channels are a way of seeing potential Support and Resistance within a given lookback length. They are a way of withholding the High’s and Low’s of a specific lookback length and looking for deviation within this length. By applying a Fast, Slow and Neutral Machine Learning: Optimal Length to these Donchian Channels way may hope to achieve a viable range of High’s and Low’s that one may use to Identify Support and Resistance locations for different ranges of Optimal Lengths and likewise potentially different Trading Strategies.
Envelopes / Envelopes Adjusted:
Envelopes are an interesting one in the sense that they both may be perceived as useful; however we deem that with the use of an ‘Optimal Length’ that the ‘Envelopes Adjusted’ may work best. We will start with examples of the Traditional Envelope then showcase the Adjusted version.
Envelopes:
As you may see, a Traditional form of Envelopes even produced with a Machine Learning: Optimal Length may not produce optimal results. Unfortunately this may occur with some Traditional Indicators and they may need some adjustments as you’ll notice with the ‘Envelopes Adjusted’ version. However, even without the adjustments, these Envelopes may be useful for seeing ‘Overbought’ and ‘Oversold’ locations within a Machine Learning: Optimal Length standpoint.
Envelopes Adjusted:
By adding an adjustment to these Envelopes, we may hope to better reflect our Optimal Length within it. This is caused by adding a ratio reflection towards the current length of the Optimal Length and the max Length used. This allows for the Fast and Neutral (and potentially Slow if Neutral is greater) to achieve a potentially more accurate result.
Envelopes, much like Bollinger Bands are a way of seeing potential movement zones along with potential Support and Resistance. However, unlike Bollinger Bands which are based on Standard Deviation, Envelopes are based on percentages +/- from the Simple Moving Average.
We will conclude our Tutorial here. Hopefully this has given you some insight into how useful adding a ‘Optimal Length’ within an external (secondary) Indicator as a Source within the Settings may be. Likewise, how useful it may be for automation sake in the sense that when the ‘Optimal Length’ changes, it doesn’t rely on an alert where you need to manually update it yourself; instead it will update Automatically and you may reap the benefits of such with little manual input needed (aside from the initial setup).
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Fibonacci HH LL TRAMA BandLuxAlgo's Trend Moving Adaptive Moving Average was used as a reference to create bands by reading the highest and lowest prices of past bars based on Fibonacci numbers and then multiplying them by the Fibonacci ratio.
LuxAlgo/ LuxAlgo/
In particular, the so-called TRAMA is characterized by its adaptation to the average of the highest and lowest prices over a specific period of time and is used to identify support/resistance.
In order to apply this feature to the maximum extent possible, I used the high or low prices as the source of input, rather than the closing price.
For example,
src = high
not original like
src = close
In addition, I created 6 levels by multiplying the Fibonacci ratio
//Midline
mah = ama1
mal = ama2
m = (mah + mal)/2
//Half Mean Range
dist = (mah - mal)/2
//Levels
h6 = m + dist * 11.089
h5 = m + dist * 6.857
h4 = m + dist * 4.235
h3 = m + dist * 2.618
h2 = m + dist * 1.618
h1 = m + dist * 0.618
l1 = m - dist * 0.618
l2 = m - dist * 1.618
l3 = m - dist * 2.618
l4 = m - dist * 4.235
l5 = m - dist * 6.857
l6 = m - dist * 11.089
If you want to use it for scalping, such as 15 minutes, you can include Fibonacci numbers such as 21,34,55 for a quick reaction type to detect the trend. Also, by including Fibonacci numbers such as 89,144,233, you can see where you stand in the larger trend. Some examples are included below.
For Investors
BTCUSDT 1day Chart Fibonacci number "55"
For Daytraders
BTCUSDT 4hour Chart Fibonacci number "34"
For Scalpers
BTCUSDT 15min Chart Fibonacci number "55"
BTCUSDT 15min Chart Fibonacci number "89"
BTCUSDT 15min Chart Fibonacci number "233"
Fibonacci numbers are 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, etc.,
Fibonacci ratios are 0.618, 1.618, 2.618, 4.236, 6.854, 11.089, etc.,
PA-Adaptive Hull Parabolic [Loxx]The PA-Adaptive Hull Parabolic is not your typical trading indicator. It synthesizes the computational brilliance of two famed technicians: John Ehlers and John Hull. Let's demystify its sophistication.
█ Ehlers' Phase Accumulation
John Ehlers is well-known in the trading community for his digital signal processing approach to market data. One of his standout techniques is phase accumulation. This method identifies the dominant cycle in the market by accumulating the phases of individual cycles. By doing so, it "adapts" to real-time market conditions.
Here's the brilliance of phase accumulation in this code
The indicator doesn't merely use a static look-back period. Instead, it dynamically determines the dominant market cycle through phase accumulation.
The calcComp function, rooted in Ehlers' methodology, provides a complex computation using a digital signal processing approach to filter out market noise and pinpoint the current cycle's frequency.
By measuring and adapting to the instantaneous period of the market, it ensures that the indicator remains relevant, especially in non-stationary market conditions.
Hull's Moving Average
John Hull introduced the Hull Moving Average (HMA) aiming to reduce lag and improve smoothing. The HMA's essence lies in its weighted average computation, prioritizing more recent prices.
This code takes an adaptive twist on the HMA
Instead of a fixed period, the HMA uses the dominant cycle length derived from Ehlers' phase accumulation. This makes the HMA not just fast and smooth, but also adaptive to the dominant market rhythm.
The intricate iLwmp function in the script provides this adaptive HMA computation. It's a weighted moving average, but its length isn't static; it's based on the previously determined dominant market cycle.
█ Trading Insights
The indicator paints the bars to represent the immediate trend: green for bullish and red for bearish.
Entry points, both long ("L") and short ("S"), are presented visually. These are derived from crossovers of the adaptive HMA, a clear indication of a potential shift in the trend.
Additionally, alert conditions are set, ready to notify a trader when these crossovers occur, ensuring real-time actionable insights.
█ Conclusion
The PA-Adaptive Hull Parabolic is a masterclass in advanced technical indicator design. By marrying John Ehlers' adaptive phase accumulation with John Hull's HMA, it creates a dynamic, responsive, and precise tool for traders. It's not just about capturing the trend; it's about understanding the very rhythm of the market.
Adaptive MACD [LuxAlgo]The Adaptive MACD indicator is an adaptive version of the popular Moving Average Convergence Divergence (MACD) oscillator, returning longer-term variations during trending markets and cyclic variations during ranging markets while filtering out noisy variations.
🔶 USAGE
The proposed oscillator contains all the elements within a regular MACD, such as a signal line and histogram. A MACD value above 0 would indicate up-trending variations, while a value under 0 would be indicating down-trending variations.
Just like most oscillators, our proposed Adaptive MACD is able to return divergences with the price.
As we can see in the image above ranging markets will make the Adaptive MACD more conservative toward more cyclical conservations, filtering out both noise and longer-term variations. However, when longer-term variations (such as in a trending market) are prominent the oscillator will conserve longer-term variations.
The R2 Period setting determines when trending/ranging markets are detected, with higher values returning indications for longer intervals.
The fast and slow settings will act similarly to the regular MACD, however, closer values will return more cyclical outputs.
The image above compares our proposed MACD (top) with a regular MACD (bottom), both using fast = 19 and slow = 20 .
🔶 DETAILS
It is common to be solely interested in the trend component when the market is trending, however, during a ranging market it is more common to observe a more prominent cyclical/noise component. We want to be able to preserve one of the components at the appropriate market conditions, however, the regular MACD lack the ability to preserve cyclical component with high accuracy.
The MACD is an IIR bandpass filter. In order to obtain a lower passband bandwidth and a more symmetrical magnitude response (which would allow to conserve more precise cyclical variations) we can directly change the system calculation:
y = (price - price ) × g + ((1 - a1) + (1 - a2)) × y - (1 - a1) × (1 - a2) × y
where:
a1 = 2/(fast + 1)
a2 = 2/(slow + 1)
g = a1 - a2
Using division instead of multiplication on the second feedback weight allows further weighting the 2 samples lagged output, returning a more desirable magnitude response with a higher degree of filtering on both ends of the spectrum as shown in the image below:
We are interested in conserving cycles during ranging markets, and longer-term variations during trending markets, we can do this by interpolating between our two filter coefficients:
α × + (1 - α) ×
where 1 > α > 0 . α is measuring if the market is trending or ranging, with values closer to 1 indicating a trending market. We see that for higher values of α the original coefficient of the MACD is used. The image below shows various magnitude responses given multiple values of α :
We use a rolling R-Squared as α , this measurement has the benefit of indicating if the market is trending or ranging, as well as being constrained within range (0, 1), and having a U-shaped distribution.
If you are interested to learn more about the MACD see:
🔶 SETTINGS
R2 Period: Calculation window of the R-Squared.
Fast: Fast period for the calculation of the Adaptive MACD, lower values will return more noisy results.
Slow: Slow period for the calculation of the Adaptive MACD, higher values will return result with longer-term conserved variations.
Signal: Period of the EMA applied to the Adaptive MACD.
adaptive_mfi
█ Description
Money flow an indexed value-based price and volume for the specified input length (lookback period). In summary, a momentum indicator that attempt to measure the flow of money (identify buying/selling pressure) through the asset within a specified period of time. MFI will oscillate between 0 to 100, oftentimes comprehend the analysis with oversold (20) or overbought (80) level, and a divergence that spotted to signaling a further change in trend/direction. As similar to many other indicators that use length (commonly a fixed value) as an input parameter, can be optimized by applied an adaptive filter (Ehlers), to solve the measuring cycle period. In this indicator, the adaptive measure of dominant cycle as an input parameter for the lookback period/n, will be applied to the money flow index.
█ Money Flow Index
mfi = 100 - (100/(1 + money_flow_ratio))
where:
n = int(dominant_cycle)
money_flow_ratio = n positive raw_money_flow / n negative raw_money_flow
raw_money_flow = typical_price * volume
typical_price = hlc3
█ Feature
The indicator will have a specified default parameter of: hp_period = 48; source = ohlc4
Horizontal line indicates positive/negative money flow
MFI Color Scheme: Solid; Normalized
Donchian Volatility Indicator - Adaptive Channel WidthThis indicator is designed to help traders assess and analyze market volatility. By calculating the width of the Donchian channels, it provides valuable insights into the range of price movements over a specified period. This indicator helps traders identify periods of high and low volatility, enabling them to make more informed trading decisions.
The indicator is based on the concept of Donchian channels, which consist of the highest high and lowest low over a specified lookback period. The channel width is calculated as the difference between the upper and lower channels. A wider channel indicates higher volatility, suggesting potentially larger price movements and increased trading opportunities. On the other hand, a narrower channel suggests lower volatility, indicating a relatively calmer market environment with potentially fewer trading opportunities.
The adaptive aspect of the indicator refers to its ability to adjust the width of the channels dynamically based on market conditions. The indicator calculates the width of the channels using the Average True Range (ATR) indicator, which measures the average range of price movements over a specified period. By multiplying the ATR value with the user-defined ATR multiplier, the indicator adapts the width of the channels to reflect the current level of volatility. During periods of higher volatility, the channels expand to accommodate larger price movements, providing a broader range for assessing volatility. Conversely, during periods of lower volatility, the channels contract, reflecting the narrower price ranges and signaling a decrease in volatility. This adaptive nature allows traders to have a flexible and responsive measure of volatility, ensuring that the indicator reflects the current market conditions accurately.
To provide further insights, the indicator includes a signal line. The signal line is derived from the channel width and is calculated as a simple moving average over a specified signal period. This signal line acts as a reference level, allowing traders to compare the current channel width with the average width over a given time frame. By assessing whether the current channel width is above or below the signal line, traders can gain additional context on the volatility level in the market.
The colors used in the Donchian Volatility Indicator - Adaptive Channel Width play a vital role in visualizing the volatility levels:
-- Lime Color : When the channel width is above the signal line, it is colored lime. This color signifies that volatility has entered the market, indicating potentially higher price movements and increased trading opportunities. Traders can pay closer attention to the lime-colored channel width as it may suggest favorable conditions for trend-following or breakout trading strategies.
-- Fuchsia Color : When the channel width is below the signal line, it is colored fuchsia. This color represents relatively low volatility, suggesting a calmer market environment with potentially fewer trading opportunities. Traders may consider adjusting their strategies during periods of low volatility, such as employing range-bound or mean-reversion strategies.
-- Aqua Color : The signal line is represented by the aqua color. This color allows traders to easily identify the signal line amidst the channel width. The aqua color provides a visual reference for the average channel width and helps traders assess whether the current width is above or below this average.
The Donchian Volatility Indicator - Adaptive Channel Width has several practical applications for traders:
-- Volatility Assessment : Traders can use this indicator to assess the level of volatility in the market. By observing the width of the Donchian channels and comparing it to the signal line, they can determine whether the current volatility is relatively high or low. This information helps traders set appropriate expectations and adjust their trading strategies accordingly.
-- Breakout Trading : Wide channel widths may indicate an increased likelihood of price breakouts. Traders can use the Donchian Volatility Indicator - Adaptive Channel Width to identify potential breakout opportunities. When the channel width exceeds the signal line, it suggests a higher probability of significant price movements, potentially signaling a breakout. Traders may consider entering trades in the direction of the breakout.
-- Risk Management : The indicator can assist in setting appropriate stop-loss levels based on the current volatility. During periods of high volatility (lime-colored channel width), wider stop-loss orders may be warranted to account for larger price swings. Conversely, during periods of low volatility (fuchsia-colored channel width), narrower stop-loss orders may be appropriate to limit risk in a more range-bound market.
While the Donchian Volatility Indicator - Adaptive Channel Width is a valuable tool, it is important to consider its limitations:
-- Lagging Indicator : The indicator relies on historical price data, making it a lagging indicator. It provides insights based on past price movements and may not capture sudden changes or shifts in volatility. Traders should be aware that the indicator may not generate real-time signals and should be used in conjunction with other indicators and analysis tools.
-- False Signals : Like any technical indicator, the Donchian Volatility Indicator - Adaptive Channel Width is not immune to generating false signals. Traders should exercise caution and use additional analysis to confirm the signals generated by the indicator. Considering the broader market context and employing risk management techniques can help mitigate the impact of false signals.
-- Market Conditions : Market conditions can vary, and volatility levels can differ across different assets and timeframes. Traders should adapt their strategies and consider other market factors when interpreting the signals provided by the indicator. It is crucial to avoid relying solely on the indicator and to incorporate a comprehensive analysis of the market environment.
In conclusion, this indicator is a powerful tool for assessing market volatility. By examining the width of the Donchian channels and comparing it to the signal line, traders can gain insights into the level of volatility and adjust their trading strategies accordingly. The color-coded representation of the channel width and signal line allows for easy visualization and interpretation of the volatility dynamics. Traders should utilize this indicator as part of a broader trading approach, incorporating other technical analysis tools and considering market conditions for a comprehensive assessment of market volatility.
Adaptive Mean Reversion IndicatorThe Adaptive Mean Reversion Indicator is a tool for identifying mean reversion trading opportunities in the market. The indicator employs a dynamic approach by adapting its parameters based on the detected market regime, ensuring optimal performance in different market conditions.
To determine the market regime, the indicator utilizes a volatility threshold. By comparing the average true range (ATR) over a 14-period to the specified threshold, it determines whether the market is trending or ranging. This information is crucial as it sets the foundation for parameter optimization.
The parameter optimization process is an essential step in the indicator's calculation. It dynamically adjusts the lookback period and threshold level based on the identified market regime. In trending markets, a longer lookback period and higher threshold level are chosen to capture extended trends. In ranging markets, a shorter lookback period and lower threshold level are used to identify mean reversion opportunities within a narrower price range.
The mean reversion calculation lies at the core of this indicator. It starts with computing the mean value using the simple moving average (SMA) over the selected lookback period. This represents the average price level. The deviation is then determined by calculating the standard deviation of the closing prices over the same lookback period. The upper and lower bands are derived by adding and subtracting the threshold level multiplied by the deviation from the mean, respectively. These bands serve as dynamic levels that define potential overbought and oversold areas.
In real-time, the indicator's adaptability shines through. If the market is trending, the adaptive mean is set to the calculated mean value. The adaptive upper and lower bands are adjusted by scaling the threshold level with a factor of 0.75. This adjustment allows the indicator to be less sensitive to minor price fluctuations during trending periods, providing more robust mean reversion signals. In ranging market conditions, the regular mean, upper band, and lower band are used as they are more suited to capture mean reversion within a confined price range.
The signal generation component of the indicator identifies potential trading opportunities based on the relationship between the current close price and the adaptive upper and lower bands. If the close price is above the adaptive upper band, it suggests a potential short entry opportunity (-1). Conversely, if the close price is below the adaptive lower band, it indicates a potential long entry opportunity (1). When the close price is within the range defined by the adaptive upper and lower bands, no clear trading signal is generated (0).
To further strengthen the quality of signals, the indicator introduces a confluence condition based on the RSI. When the RSI exceeds the threshold levels of 70 or falls below the threshold level of 30, it indicates a strong momentum condition. By incorporating this confluence condition, the indicator ensures that mean reversion signals align with the prevailing market momentum. It reduces the likelihood of false signals and provides traders with added confidence when entering trades.
The indicator offers alert conditions to notify traders of potential trading opportunities. Alert conditions are set to trigger when a potential long entry signal (1) or a potential short entry signal (-1) aligns with the confluence condition. These alerts allow traders to stay informed about favorable mean reversion setups, even when they are not actively monitoring the charts. By leveraging alerts, traders can efficiently manage their time and take advantage of market opportunities.
To enhance visual interpretation, the indicator incorporates background coloration that provides valuable insights into the prevailing market conditions. When the indicator generates a potential short entry signal (-1) that aligns with the confluence condition, the background color is set to lime. This color suggests a bullish trend that is potentially reaching an exhaustion point and about to revert downwards. Similarly, when the indicator generates a potential long entry signal (1) that aligns with the confluence condition, the background color is set to fuchsia. This color represents a bearish trend that is potentially reaching an exhaustion point and about to revert upwards. By employing background coloration, the indicator enables traders to quickly identify market conditions that may offer mean reversion opportunities with a directional bias.
The indicator further enhances visual clarity by incorporating bar coloring that aligns with the prevailing market conditions and signals. When the indicator generates a potential short entry signal (-1) that aligns with the confluence condition, the bar color is set to lime. This color signifies a bullish trend that is potentially reaching an exhaustion point, indicating a high probability of a downward reversion. Conversely, when the indicator generates a potential long entry signal (1) that aligns with the confluence condition, the bar color is set to fuchsia. This color represents a bearish trend that is potentially reaching an exhaustion point, indicating a high probability of an upward reversion. By using distinct bar colors, the indicator provides traders with a clear visual distinction between bullish and bearish trends, facilitating easier identification of mean reversion opportunities within the context of the broader trend.
While the "Adaptive Mean Reversion Indicator" offers a robust framework for identifying mean reversion opportunities, it's important to remember that no indicator is foolproof. Traders should exercise caution and employ risk management strategies. Additionally, it is recommended to use this indicator in conjunction with other technical analysis tools and fundamental factors to make well-informed trading decisions. Regular backtesting and refinement of the indicator's parameters are crucial to ensure its effectiveness in different market conditions.
