Savitzky Golay Median Filtered RSI [BackQuant]Savitzky Golay Median Filtered RSI
Introducing BackQuant's Savitzky Golay Median Filtered RSI, a cutting-edge indicator that enhances the classic Relative Strength Index (RSI) by applying both a Savitzky-Golay filter and a median filter to provide smoother and more reliable signals. This advanced approach helps reduce noise and captures true momentum trends with greater precision. Let’s break down how the indicator works, the features it offers, and how it can improve your trading strategy.
Core Concept: Relative Strength Index (RSI)
The Relative Strength Index (RSI) is a widely used momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100, with levels above 70 typically indicating overbought conditions and levels below 30 indicating oversold conditions. However, the standard RSI can sometimes generate noisy signals, especially in volatile markets, making it challenging to identify reliable entry and exit points.
To improve upon the traditional RSI, this indicator introduces two powerful filters: the Savitzky-Golay filter and a median filter.
Savitzky-Golay Filter: Smoothing with Precision
The Savitzky-Golay filter is a digital filtering technique used to smooth data while preserving important features, such as peaks and trends. Unlike simple moving averages that can distort important price data, the Savitzky-Golay filter uses polynomial regression to fit the data, providing a more accurate and less lagging result.
In this script, the Savitzky-Golay filter is applied to the RSI values to smooth out short-term fluctuations and provide a more reliable signal. By using a window size of 5 and a polynomial degree of 2, the filter effectively reduces noise without compromising the integrity of the underlying price movements.
Median Filter: Reducing Outliers
After applying the Savitzky-Golay filter, the median filter is applied to the smoothed RSI values. The median filter is particularly effective at removing short-lived outliers, further enhancing the accuracy of the RSI by reducing the impact of sudden and temporary price spikes or drops. This combination of filters creates an ultra-smooth RSI that is better suited for detecting true market trends.
Long and Short Signals
The Savitzky Golay Median Filtered RSI generates long and short signals based on user-defined threshold levels:
Long Signals: A long signal is triggered when the filtered RSI exceeds the Long Threshold (default set at 176). This indicates that momentum is shifting upward, and it may present a good buying opportunity.
Short Signals: A short signal is generated when the filtered RSI falls below the Short Threshold (default set at 162). This suggests that momentum is weakening, potentially signaling a selling opportunity or exit from a long position.
These threshold levels can be adjusted to suit different market conditions and timeframes, allowing traders to fine-tune the sensitivity of the indicator.
Customization and Visualization Options
The Savitzky Golay Median Filtered RSI comes with several customization options, enabling traders to tailor the indicator to their specific needs:
Calculation Source: Select the price source for the RSI calculation (default is OHLC4, but it can be changed to close, open, high, or low prices).
RSI Period: Adjust the lookback period for the RSI calculation (default is 14).
Median Filter Length: Control the length of the median filter applied to the smoothed RSI, affecting how much noise is removed from the signal.
Threshold Levels: Customize the long and short thresholds to define the sensitivity for generating buy and sell signals.
UI Settings: Choose whether to display the RSI and thresholds on the chart, color the bars according to trend direction, and adjust the line width and colors used for long and short signals.
Visual Feedback: Color-Coded Signals and Thresholds
To make the signals easier to interpret, the indicator offers visual feedback by coloring the price bars and the RSI plot according to the current market trend:
Green Bars indicate long signals when momentum is bullish.
Red Bars indicate short signals when momentum is bearish.
Gray Bars indicate neutral or undecided conditions when no clear signal is present.
In addition, the Long and Short Thresholds can be plotted directly on the chart to provide a clear reference for when signals are triggered, allowing traders to visually gauge the strength of the RSI relative to its thresholds.
Alerts for Automation
For traders who prefer automated notifications, the Savitzky Golay Median Filtered RSI includes built-in alert conditions for long and short signals. You can configure these alerts to notify you when a buy or sell condition is met, ensuring you never miss a trading opportunity.
Trading Applications
This indicator is versatile and can be used in a variety of trading strategies:
Trend Following: The combination of Savitzky-Golay and median filtering makes this RSI particularly useful for identifying strong trends without being misled by short-term noise. Traders can use the long and short signals to enter trades in the direction of the prevailing trend.
Reversal Trading: By adjusting the threshold levels, traders can use this indicator to spot potential reversals. When the RSI moves from overbought to oversold levels (or vice versa), it may signal a shift in market direction.
Swing Trading: The smoothed RSI provides a clear signal for short to medium-term price movements, making it an excellent tool for swing traders looking to capitalize on momentum shifts.
Risk Management: The filtered RSI can be used as part of a broader risk management strategy, helping traders avoid false signals and stay in trades only when the momentum is strong.
Final Thoughts
The Savitzky Golay Median Filtered RSI takes the classic RSI to the next level by applying advanced smoothing techniques that reduce noise and improve signal reliability. Whether you’re a trend follower, swing trader, or reversal trader, this indicator provides a more refined approach to momentum analysis, helping you make better-informed trading decisions.
As with all indicators, it is important to backtest thoroughly and incorporate sound risk management strategies when using the Savitzky Golay Median Filtered RSI in your trading system.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Recherche dans les scripts pour "backtest"
Chande Momentum Oscillator StrategyThe Chande Momentum Oscillator (CMO) Trading Strategy is based on the momentum oscillator developed by Tushar Chande in 1994. The CMO measures the momentum of a security by calculating the difference between the sum of recent gains and losses over a defined period. The indicator offers a means to identify overbought and oversold conditions, making it suitable for developing mean-reversion trading strategies (Chande, 1997).
Strategy Overview:
Calculation of the Chande Momentum Oscillator (CMO):
The CMO formula considers both positive and negative price changes over a defined period (commonly set to 9 days) and computes the net momentum as a percentage.
The formula is as follows:
CMO=100×(Sum of Gains−Sum of Losses)(Sum of Gains+Sum of Losses)
CMO=100×(Sum of Gains+Sum of Losses)(Sum of Gains−Sum of Losses)
This approach distinguishes the CMO from other oscillators like the RSI by using both price gains and losses in the numerator, providing a more symmetrical measurement of momentum (Chande, 1997).
Entry Condition:
The strategy opens a long position when the CMO value falls below -50, signaling an oversold condition where the price may revert to the mean. Research in mean-reversion, such as by Poterba and Summers (1988), supports this approach, highlighting that prices often revert after sharp movements due to overreaction in the markets.
Exit Conditions:
The strategy closes the long position when:
The CMO rises above 50, indicating that the price may have become overbought and may not provide further upside potential.
Alternatively, the position is closed 5 days after the buy signal is triggered, regardless of the CMO value, to ensure a timely exit even if the momentum signal does not reach the predefined level.
This exit strategy aligns with the concept of time-based exits, reducing the risk of prolonged exposure to adverse price movements (Fama, 1970).
Scientific Basis and Rationale:
Momentum and Mean-Reversion:
The strategy leverages the well-known phenomenon of mean-reversion in financial markets. According to research by Jegadeesh and Titman (1993), prices tend to revert to their mean over short periods following strong movements, creating opportunities for traders to profit from temporary deviations.
The CMO captures this mean-reversion behavior by monitoring extreme price conditions. When the CMO reaches oversold levels (below -50), it signals potential buying opportunities, whereas crossing overbought levels (above 50) indicates conditions for selling.
Market Efficiency and Overreaction:
The strategy takes advantage of behavioral inefficiencies and overreactions, which are often the drivers behind sharp price movements (Shiller, 2003). By identifying these extreme conditions with the CMO, the strategy aims to capitalize on the market’s tendency to correct itself when price deviations become too large.
Optimization and Parameter Selection:
The 9-day period used for the CMO calculation is a widely accepted timeframe that balances responsiveness and noise reduction, making it suitable for capturing short-term price fluctuations. Studies in technical analysis suggest that oscillators optimized over such periods are effective in detecting reversals (Murphy, 1999).
Performance and Backtesting:
The strategy's effectiveness is confirmed through backtesting, which shows that using the CMO as a mean-reversion tool yields profitable opportunities. The use of time-based exits alongside momentum-based signals enhances the reliability of the strategy by ensuring that trades are closed even when the momentum signal alone does not materialize.
Conclusion:
The Chande Momentum Oscillator Trading Strategy combines the principles of momentum measurement and mean-reversion to identify and capitalize on short-term price fluctuations. By using a widely tested oscillator like the CMO and integrating a systematic exit approach, the strategy effectively addresses both entry and exit conditions, providing a robust method for trading in diverse market environments.
References:
Chande, T. S. (1997). The New Technical Trader: Boost Your Profit by Plugging into the Latest Indicators. John Wiley & Sons.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
Volume-Supported Linear Regression Trend TableThe "Volume-Supported Linear Regression Trend Table" (VSLRT Table) script helps traders identify buy and sell opportunities by analyzing price trends and volume dynamics across multiple timeframes. It uses linear regression to calculate the trend direction and volume strength, visually representing this data with color-coded signals on the chart and in a table. Green signals indicate buying opportunities, while red signals suggest selling, with volume acting as confirmation of trend strength. Traders can use these signals for both short and long positions, with additional risk management and multi-timeframe validation to enhance the strategy.
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To use the "Volume-Supported Linear Regression Trend Table" (VSLRT Table) script in a trading strategy, you would incorporate it into your decision-making process to identify potential buy and sell opportunities based on the trend and volume dynamics. Here’s how you could apply it for trading:
1. Understanding the Key Elements:
Trend Direction (Slope of Price): The script uses linear regression to assess the trend direction of the price. If the price slope is positive, the asset is likely in an uptrend; if it's negative, the asset is in a downtrend.
Volume-Backed Signals: The buy or sell signal is not only based on the price trend but also on volume. Volume is crucial in validating the strength of a trend; large volume often indicates strong interest in a direction.
2. Interpreting the Table and Signals:
The table displayed at the bottom-right of your TradingView chart gives you a clear overview of the trends across different timeframes:
Trend Colors:
Green hues (e.g., ccol11, ccol12, etc.): Indicate a buying trend supported by volume.
Red hues (e.g., ccol21, ccol22, etc.): Indicate a selling trend supported by volume.
Gray: Indicates weak or unclear trends where no decisive direction is present.
Buy/Sell Signals:
The script plots triangles on the chart:
Upward triangle below the bar signals a potential buy.
Downward triangle above the bar signals a potential sell.
3. Building a Trading Strategy:
Here’s how you can incorporate the script’s information into a trading strategy:
Buy Signal (Long Entry):
Look for green triangles (indicating a buy signal) below a bar.
Confirm that the trend color in the table for the relevant timeframe is green, which shows that the buy signal is supported by strong volume.
Ensure that the price is in an uptrend (positive slope) and that volume is increasing on upward moves, as this indicates buying interest.
Execute a long position when these conditions align.
Sell Signal (Short Entry):
Look for red triangles (indicating a sell signal) above a bar.
Confirm that the trend color in the table for the relevant timeframe is red, which shows that the sell signal is supported by strong volume.
Ensure that the price is in a downtrend (negative slope) and that volume is increasing on downward moves, indicating selling pressure.
Execute a short position when these conditions align.
Exiting the Trade:
Exit a long position when a sell signal (red triangle) appears, or when the trend color in the table shifts to red.
Exit a short position when a buy signal (green triangle) appears, or when the trend color in the table shifts to green.
4. Multi-Timeframe Confirmation:
The script provides trends across multiple timeframes (tf1, tf2, tf3), which can help in validating your trade:
Short-Term Trading: Use shorter timeframes (e.g., 3, 5 minutes) for intraday trades. If both short and medium timeframes align in trend direction (e.g., both showing green), it strengthens the signal.
Longer-Term Trading: If you are trading on a higher timeframe (e.g., daily or weekly), confirm that the lower timeframes align with your intended trade direction.
5. Adding Risk Management:
Stop-Loss: Place stop-losses below recent lows (for long trades) or above recent highs (for short trades) to minimize risk.
Take Profit: Consider taking profit at key support/resistance levels or based on a fixed risk-to-reward ratio (e.g., 2:1).
Example Strategy Flow:
For Long (Buy) Trade:
Signal: A green triangle appears below a candle (Buy signal).
Trend Confirmation: Check that the color in the table for your selected timeframe is green, confirming the trend is supported by volume.
Execute Long: Enter a long trade if the price is trending upward (positive price slope).
Exit Long: Exit when a red triangle appears above a candle (Sell signal) or if the trend color shifts to red in the table.
For Short (Sell) Trade:
Signal: A red triangle appears above a candle (Sell signal).
Trend Confirmation: Check that the color in the table for your selected timeframe is red, confirming the trend is supported by volume.
Execute Short: Enter a short trade if the price is trending downward (negative price slope).
Exit Short: Exit when a green triangle appears below a candle (Buy signal) or if the trend color shifts to green in the table.
6. Fine-Tuning:
Backtesting: Before trading live, use TradingView’s backtesting features to test the strategy on historical data and optimize the settings (e.g., length of linear regression, timeframe).
Combine with Other Indicators: Use this strategy alongside other technical indicators (e.g., RSI, MACD) for better confirmation.
In summary, the script helps identify trends with volume support, giving more confidence in buy/sell decisions. Combining these signals with risk management and multi-timeframe analysis can create a solid trading strategy.
Two Pole Butterworth For Loop [BackQuant]Two Pole Butterworth For Loop
PLEASE read the following carefully, as understanding the underlying concepts and logic behind the indicator is key to incorporating it into your trading system in a sound and methodical manner.
Introducing BackQuant's Two Pole Butterworth For Loop (2P BW FL) — an advanced indicator that fuses the power of the Two Pole Butterworth filter with a dynamic for-loop scoring mechanism. This unique approach is designed to extract actionable trading signals by smoothing out price data and then analyzing it using a comparative scoring method. Let's delve into how this indicator works, why it was created, and how it can be used in various trading scenarios.
Understanding the Two Pole Butterworth Filter
The Butterworth filter is a signal processing tool known for its smooth response and minimal distortion. It's often used in electronic and communication systems to filter out unwanted noise. In trading, the Butterworth filter can be applied to price data to smooth out the volatility, providing traders with a clearer view of underlying trends without the whipsaws often associated with market noise.
The Two Pole Butterworth variant further enhances this effect by applying the filter with two poles, effectively creating a sharper transition between the passband and stopband. In simple terms, this allows the filter to follow the price action more closely, reacting to changes while maintaining smoothness.
In this script, the Two Pole Butterworth filter is applied to the Calculation Source (default is set to the closing price), creating a smoothed price series that serves as the foundation for further analysis.
Why Use a Two Pole Butterworth Filter?
The Two Pole Butterworth filter is chosen for its ability to reduce lag while maintaining a smooth output. This makes it an ideal choice for traders who want to capture trends without being misled by short-term volatility or market noise. By filtering the price data, the Two Pole Butterworth enables traders to focus on the broader market movements and avoid false signals.
The For-Loop Scoring Mechanism
In addition to the Butterworth filter, this script uses a for-loop scoring system to evaluate the smoothed price data. The for-loop compares the current value of the filtered price (referred to as "subject") to previous values over a defined range (set by the start and end input). The score is calculated based on whether the subject is higher or lower than the previous points, and the cumulative score is used to determine the strength of the trend.
Long and Short Signal Logic
Long Signals: A long signal is triggered when the score surpasses the Long Threshold (default set at 40). This suggests that the price has built sufficient upward momentum, indicating a potential buying opportunity.
Short Signals: A short signal is triggered when the score crosses under the Short Threshold (default set at -10). This indicates weakening price action or a potential downtrend, signaling a possible selling or shorting opportunity.
By utilizing this scoring system, the indicator identifies moments when the price momentum is shifting, helping traders enter positions at opportune times.
Customization and Visualization Options
One of the strengths of this indicator is its flexibility. Traders can customize various settings to fit their personal trading style or adapt it to different markets and timeframes:
Calculation Periods: Adjust the lookback period for the Butterworth filter, allowing for shorter or longer smoothing depending on the desired sensitivity.
Threshold Levels: Set the long and short thresholds to define when signals should be triggered, giving you control over the balance between sensitivity and specificity.
Signal Line Width and Colors: Customize the visual presentation of the indicator on the chart, including the width of the signal line and the colors used for long and short conditions.
Candlestick and Background Colors: If desired, the indicator can color the candlesticks or the background according to the detected trend, offering additional clarity at a glance.
Trading Applications
This Two Pole Butterworth For Loop indicator is versatile and can be adapted to various market conditions and trading strategies. Here are a few use cases where this indicator shines:
Trend Following: The Butterworth filter smooths the price data, making it easier to follow trends and identify when they are gaining or losing strength. The for-loop scoring system enhances this by providing a clear indication of how strong the current trend is compared to recent history.
Mean Reversion: For traders looking to identify potential reversals, the indicator’s ability to compare the filtered price to previous values over a range of periods allows it to spot moments when the trend may be losing steam, potentially signaling a reversal.
Swing Trading: The combination of smoothing and scoring allows swing traders to capture short to medium-term price movements by filtering out the noise and focusing on significant shifts in momentum.
Risk Management: By providing clear long and short signals, this indicator helps traders manage their risk by offering well-defined entry and exit points. The smooth nature of the Butterworth filter also reduces the risk of getting caught in false signals due to market noise.
Final Thoughts
The Two Pole Butterworth For Loop indicator offers traders a powerful combination of smoothing and scoring to detect meaningful trends and shifts in price momentum. Whether you are a trend follower, swing trader, or someone looking to refine your entry and exit points, this indicator provides the tools to make more informed trading decisions.
As always, it's essential to backtest the indicator on historical data and tailor the settings to your specific trading style and market. While the Butterworth filter helps reduce noise and smooth trends, no indicator can predict the future with absolute certainty, so it should be used in conjunction with other tools and sound risk management practices.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Bitcoin CME-Spot Z-Spread - Strategy [presentTrading]This time is a swing trading strategy! It measures the sentiment of the Bitcoin market through the spread of CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. By applying Bollinger Bands to the spread, the strategy seeks to capture mean-reversion opportunities when prices deviate significantly from their historical norms
█ Introduction and How it is Different
The Bitcoin CME-Spot Bollinger Bands Strategy is designed to capture mean-reversion opportunities by exploiting the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. The strategy uses Bollinger Bands to detect when the spread between these two correlated assets has deviated significantly from its historical norm, signaling potential overbought or oversold conditions.
What sets this strategy apart is its focus on spread trading between futures and spot markets rather than price-based indicators. By applying Bollinger Bands to the spread rather than individual prices, the strategy identifies price inefficiencies across markets, allowing traders to take advantage of the natural reversion to the mean that often occurs in these correlated assets.
BTCUSD 8hr Performance
█ Strategy, How It Works: Detailed Explanation
The strategy relies on Bollinger Bands to assess the volatility and relative deviation of the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. Bollinger Bands consist of a moving average and two standard deviation bands, which help measure how much the spread deviates from its historical mean.
🔶 Spread Calculation:
The spread is calculated by subtracting the Bitfinex spot price from the CME Bitcoin futures price:
Spread = CME Price - Bitfinex Price
This spread represents the difference between the futures and spot markets, which may widen or narrow based on supply and demand dynamics in each market. By analyzing the spread, the strategy can detect when prices are too far apart (potentially overbought or oversold), indicating a trading opportunity.
🔶 Bollinger Bands Calculation:
The Bollinger Bands for the spread are calculated using a simple moving average (SMA) and the standard deviation of the spread over a defined period.
1. Moving Average (SMA):
The simple moving average of the spread (mu_S) over a specified period P is calculated as:
mu_S = (1/P) * sum(S_i from i=1 to P)
Where S_i represents the spread at time i, and P is the lookback period (default is 200 bars). The moving average provides a baseline for the normal spread behavior.
2. Standard Deviation:
The standard deviation (sigma_S) of the spread is calculated to measure the volatility of the spread:
sigma_S = sqrt((1/P) * sum((S_i - mu_S)^2 from i=1 to P))
3. Upper and Lower Bollinger Bands:
The upper and lower Bollinger Bands are derived by adding and subtracting a multiple of the standard deviation from the moving average. The number of standard deviations is determined by a user-defined parameter k (default is 2.618).
- Upper Band:
Upper Band = mu_S + (k * sigma_S)
- Lower Band:
Lower Band = mu_S - (k * sigma_S)
These bands provide a dynamic range within which the spread typically fluctuates. When the spread moves outside of these bands, it is considered overbought or oversold, potentially offering trading opportunities.
Local view
🔶 Entry Conditions:
- Long Entry: A long position is triggered when the spread crosses below the lower Bollinger Band, indicating that the spread has become oversold and is likely to revert upward.
Spread < Lower Band
- Short Entry: A short position is triggered when the spread crosses above the upper Bollinger Band, indicating that the spread has become overbought and is likely to revert downward.
Spread > Upper Band
🔶 Risk Management and Profit-Taking:
The strategy incorporates multi-step take profits to lock in gains as the trade moves in favor. The position is gradually reduced at predefined profit levels, reducing risk while allowing part of the trade to continue running if the price keeps moving favorably.
Additionally, the strategy uses a hold period exit mechanism. If the trade does not hit any of the take-profit levels within a certain number of bars, the position is closed automatically to avoid excessive exposure to market risks.
█ Trade Direction
The trade direction is based on deviations of the spread from its historical norm:
- Long Trade: The strategy enters a long position when the spread crosses below the lower Bollinger Band, signaling an oversold condition where the spread is expected to narrow.
- Short Trade: The strategy enters a short position when the spread crosses above the upper Bollinger Band, signaling an overbought condition where the spread is expected to widen.
These entries rely on the assumption of mean reversion, where extreme deviations from the average spread are likely to revert over time.
█ Usage
The Bitcoin CME-Spot Bollinger Bands Strategy is ideal for traders looking to capitalize on price inefficiencies between Bitcoin futures and spot markets. It’s especially useful in volatile markets where large deviations between futures and spot prices occur.
- Market Conditions: This strategy is most effective in correlated markets, like CME futures and spot Bitcoin. Traders can adjust the Bollinger Bands period and standard deviation multiplier to suit different volatility regimes.
- Backtesting: Before deployment, backtesting the strategy across different market conditions and timeframes is recommended to ensure robustness. Adjust the take-profit steps and hold periods to reflect the trader’s risk tolerance and market behavior.
█ Default Settings
The default settings provide a balanced approach to spread trading using Bollinger Bands but can be adjusted depending on market conditions or personal trading preferences.
🔶 Bollinger Bands Period (200 bars):
This defines the number of bars used to calculate the moving average and standard deviation for the Bollinger Bands. A longer period smooths out short-term fluctuations and focuses on larger, more significant trends. Adjusting the period affects the responsiveness of the strategy:
- Shorter periods (e.g., 100 bars): Makes the strategy more reactive to short-term market fluctuations, potentially generating more signals but increasing the risk of false positives.
- Longer periods (e.g., 300 bars): Focuses on longer-term trends, reducing the frequency of trades and focusing only on significant deviations.
🔶 Standard Deviation Multiplier (2.618):
The multiplier controls how wide the Bollinger Bands are around the moving average. By default, the bands are set at 2.618 standard deviations away from the average, ensuring that only significant deviations trigger trades.
- Higher multipliers (e.g., 3.0): Require a more extreme deviation to trigger trades, reducing trade frequency but potentially increasing the accuracy of signals.
- Lower multipliers (e.g., 2.0): Make the bands narrower, increasing the number of trade signals but potentially decreasing their reliability.
🔶 Take-Profit Levels:
The strategy has four take-profit levels to gradually lock in profits:
- Level 1 (3%): 25% of the position is closed at a 3% profit.
- Level 2 (8%): 20% of the position is closed at an 8% profit.
- Level 3 (14%): 15% of the position is closed at a 14% profit.
- Level 4 (21%): 10% of the position is closed at a 21% profit.
Adjusting these take-profit levels affects how quickly profits are realized:
- Lower take-profit levels: Capture gains more quickly, reducing risk but potentially cutting off larger profits.
- Higher take-profit levels: Let trades run longer, aiming for bigger gains but increasing the risk of price reversals before profits are locked in.
🔶 Hold Days (20 bars):
The strategy automatically closes the position after 20 bars if none of the take-profit levels are hit. This feature prevents trades from being held indefinitely, especially if market conditions are stagnant. Adjusting this:
- Shorter hold periods: Reduce the duration of exposure, minimizing risks from market changes but potentially closing trades too early.
- Longer hold periods: Allow trades to stay open longer, increasing the chance for mean reversion but also increasing exposure to unfavorable market conditions.
By understanding how these default settings affect the strategy’s performance, traders can optimize the Bitcoin CME-Spot Bollinger Bands Strategy to their preferences, adapting it to different market environments and risk tolerances.
GannLSVZO Indicator [Algo Alert]The Volume Zone oscillator breaks up volume activity into positive and negative categories. It is positive when the current closing price is greater than the prior closing price and negative when it's lower than the prior closing price. The resulting curve plots through relative percentage levels that yield a series of buy and sell signals, depending on level and indicator direction.
The Gann Laplace Smoothed Volume Zone Oscillator GannLSVZO is a refined version of the Volume Zone Oscillator, enhanced by the implementation of the upgraded Discrete Fourier Transform, the Laplace Stieltjes Transform. Its primary function is to streamline price data and diminish market noise, thus offering a clearer and more precise reflection of price trends.
By combining the Laplace with Gann Swing Entries and Exits (orange X) and with Ehler's white noise histogram, users gain a comprehensive perspective on volume-related market conditions.
HOW TO USE THE INDICATOR:
The default period is 2 but can be adjusted after backtesting. (I suggest 5 VZO length and NoiceR max length 8 as-well)
The VZO points to a positive trend when it is rising above the 0% level, and a negative trend when it is falling below the 0% level. 0% level can be adjusted in setting by adjusting VzoDifference. Oscillations rising below 0% level or falling above 0% level result in a natural trend.
ORIGINALITY & USFULLNESS:
Personal combination of Gann swings and Laplace Stieltjes Transform of a price which results in less noise Volume Zone Oscillator.
The Laplace Stieltjes Transform is a mathematical technique that transforms discrete data from the time domain into its corresponding representation in the frequency domain. This process involves breaking down a signal into its individual frequency components, thereby exposing the amplitude and phase characteristics inherent in each frequency element.
This indicator utilizes the concept of Ehler's Universal Oscillator and displays a histogram, offering critical insights into the prevailing levels of market noise. The Ehler's Universal Oscillator is grounded in a statistical model that captures the erratic and unpredictable nature of market movements. Through the application of this principle, the histogram aids traders in pinpointing times when market volatility is either rising or subsiding.
The Gann swings and the Gan swing strategy is developed by meomeo105, this Gann high and low algorithm forms the basis of the EMA modification.
DETAILED DESCRIPTION:
My detailed description of the indicator and use cases which I find very valuable.
What is oscillator?
Oscillators are chart indicators that can assist a trader in determining overbought or oversold conditions in ranging (non-trending) markets.
What is volume zone oscillator?
Price Zone Oscillator measures if the most recent closing price is above or below the preceding closing price.
Volume Zone Oscillator is Volume multiplied by the 1 or -1 depending on the difference of the preceding 2 close prices and smoothed with Exponential moving Average.
What does this mean?
If the VZO is above 0 and VZO is rising. We have a bullish trend. Most likely.
If the VZO is below 0 and VZO is falling. We have a bearish trend. Most likely.
Rising means that VZO on close is higher than the previous day.
Falling means that VZO on close is lower than the previous day.
What if VZO is falling above 0 line?
It means we have a high probability of a bearish trend.
Thus the indicator returns 0 and Strategy closes all it's positions when falling above 0 (or rising bellow 0) and we combine higher and lower timeframes to gauge the trend.
What is approximation and smoothing?
They are mathematical concepts for making a discrete set of numbers a
continuous curved line.
Laplace Stieltjes Transform approximation of a close price are taken from aprox library.
Key Features:
You can tailor the Indicator/Strategy to your preferences with adjustable parameters such as VZO length, noise reduction settings, and smoothing length.
Volume Zone Oscillator (VZO) shows market sentiment with the VZO, enhanced with Exponential Moving Average (EMA) smoothing for clearer trend identification.
Noise Reduction leverages Euler's White noise capabilities for effective noise reduction in the VZO, providing a cleaner and more accurate representation of market dynamics.
Choose between the traditional Fast Laplace Stieltjes Transform (FLT) and the innovative Double Discrete Fourier Transform (DTF32) soothed price series to suit your analytical needs.
Use dynamic calculation of Laplace coefficient or the static one. You may modify those inputs and Strategy entries with Gann swings.
I suggest using "Close all" input False when fine-tuning Inputs for 1 TimeFrame. When you export data to Excel/Numbers/GSheets I suggest using "Close all" input as True, except for the lowest TimeFrame. I suggest using 100% equity as your default quantity for fine-tune purposes. I have to mention that 100% equity may lead to unrealistic backtesting results. Be avare. When backtesting for trading purposes use Contracts or USDT.
Fine-tune Inputs: Gann + Laplace Smooth Volume Zone OscillatorUse this Strategy to Fine-tune inputs for the GannLSVZ0 Indicator.
Strategy allows you to fine-tune the indicator for 1 TimeFrame at a time; cross Timeframe Input fine-tuning is done manually after exporting the chart data.
I suggest using "Close all" input False when fine-tuning Inputs for 1 TimeFrame. When you export data to Excel/Numbers/GSheets I suggest using "Close all" input as True, except for the lowest TimeFrame.
MEANINGFUL DESCRIPTION:
The Volume Zone oscillator breaks up volume activity into positive and negative categories. It is positive when the current closing price is greater than the prior closing price and negative when it's lower than the prior closing price. The resulting curve plots through relative percentage levels that yield a series of buy and sell signals, depending on level and indicator direction.
The Gann Laplace Smoothed Volume Zone Oscillator GannLSVZO is a refined version of the Volume Zone Oscillator, enhanced by the implementation of the upgraded Discrete Fourier Transform, the Laplace Stieltjes Transform. Its primary function is to streamline price data and diminish market noise, thus offering a clearer and more precise reflection of price trends.
By combining the Laplace with Gann Swing Entries and with Ehler's white noise histogram, users gain a comprehensive perspective on volume-related market conditions.
HOW TO USE THE INDICATOR:
The default period is 2 but can be adjusted after backtesting. (I suggest 5 VZO length and NoiceR max length 8 as-well)
The VZO points to a positive trend when it is rising above the 0% level, and a negative trend when it is falling below the 0% level. 0% level can be adjusted in setting by adjusting VzoDifference. Oscillations rising below 0% level or falling above 0% level result in a natural trend.
HOW TO USE THE STRATEGY:
Here you fine-tune the inputs until you find a combination that works well on all Timeframes you will use when creating your Automated Trade Algorithmic Strategy. I suggest 4h, 12h, 1D, 2D, 3D, 4D, 5D, 6D, W and M.
When Indicator/Strategy returns 0 or natural trend, Strategy Closes All it's positions.
ORIGINALITY & USFULLNESS:
Personal combination of Gann swings and Laplace Stieltjes Transform of a price which results in less noise Volume Zone Oscillator.
The Laplace Stieltjes Transform is a mathematical technique that transforms discrete data from the time domain into its corresponding representation in the frequency domain. This process involves breaking down a signal into its individual frequency components, thereby exposing the amplitude and phase characteristics inherent in each frequency element.
This indicator utilizes the concept of Ehler's Universal Oscillator and displays a histogram, offering critical insights into the prevailing levels of market noise. The Ehler's Universal Oscillator is grounded in a statistical model that captures the erratic and unpredictable nature of market movements. Through the application of this principle, the histogram aids traders in pinpointing times when market volatility is either rising or subsiding.
The Gann swing strategy is developed by meomeo105, this Gann high and low algorithm forms the basis of the EMA modification.
DETAILED DESCRIPTION:
My detailed description of the indicator and use cases which I find very valuable.
What is oscillator?
Oscillators are chart indicators that can assist a trader in determining overbought or oversold conditions in ranging (non-trending) markets.
What is volume zone oscillator?
Price Zone Oscillator measures if the most recent closing price is above or below the preceding closing price.
Volume Zone Oscillator is Volume multiplied by the 1 or -1 depending on the difference of the preceding 2 close prices and smoothed with Exponential moving Average.
What does this mean?
If the VZO is above 0 and VZO is rising. We have a bullish trend. Most likely.
If the VZO is below 0 and VZO is falling. We have a bearish trend. Most likely.
Rising means that VZO on close is higher than the previous day.
Falling means that VZO on close is lower than the previous day.
What if VZO is falling above 0 line?
It means we have a high probability of a bearish trend.
Thus the indicator returns 0 and Strategy closes all it's positions when falling above 0 (or rising bellow 0) and we combine higher and lower timeframes to gauge the trend.
What is approximation and smoothing?
They are mathematical concepts for making a discrete set of numbers a
continuous curved line.
Laplace Stieltjes Transform approximation of a close price are taken from aprox library.
Key Features:
You can tailor the Indicator/Strategy to your preferences with adjustable parameters such as VZO length, noise reduction settings, and smoothing length.
Volume Zone Oscillator (VZO) shows market sentiment with the VZO, enhanced with Exponential Moving Average (EMA) smoothing for clearer trend identification.
Noise Reduction leverages Euler's White noise capabilities for effective noise reduction in the VZO, providing a cleaner and more accurate representation of market dynamics.
Choose between the traditional Fast Laplace Stieltjes Transform (FLT) and the innovative Double Discrete Fourier Transform (DTF32) soothed price series to suit your analytical needs.
Use dynamic calculation of Laplace coefficient or the static one. You may modify those inputs and Strategy entries with Gann swings.
I suggest using "Close all" input False when fine-tuning Inputs for 1 TimeFrame. When you export data to Excel/Numbers/GSheets I suggest using "Close all" input as True, except for the lowest TimeFrame. I suggest using 100% equity as your default quantity for fine-tune purposes. I have to mention that 100% equity may lead to unrealistic backtesting results. Be avare. When backtesting for trading purposes use Contracts or USDT.
Retest Confirm Point TibbuCreating a "Retest Confirm Point" indicator that generates buy and sell signals involves defining criteria to confirm that a price retest is valid before issuing a trade signal. This generally requires identifying a key level (such as support, resistance, or a trendline), detecting a retest of this level, and then confirming the validity of the retest.
Here’s a Pine Script example to help you create such an indicator. This script identifies and confirms retests of previous highs and lows, and generates buy and sell signals based on those retests: Explanation:
Recent High and Low:
The script identifies the highest and lowest prices over a specified lookback period.
These levels are plotted on the chart as reference points.
Retest Conditions:
Retest High: The closing price is within a buffer range around the recent high.
Retest Low: The closing price is within a buffer range around the recent low.
Confirmation:
Confirm High: The closing price reaches a new high over a set number of bars after the retest condition.
Confirm Low: The closing price reaches a new low over a set number of bars after the retest condition.
Signals:
Buy Signal: Issued when a confirmed retest of the recent high occurs.
Sell Signal: Issued when a confirmed retest of the recent low occurs.
Customization:
Lookback Period: Adjust to determine the historical range for finding recent highs and lows.
Confirmation Bars: Change the number of bars used to confirm the retest.
Retest Buffer: Adjust the percentage buffer to fine-tune the retest conditions.
Testing and Optimization:
Backtest: Always backtest the strategy on historical data to ensure it behaves as expected.
Adjust Parameters: Modify parameters based on the asset, timeframe, and market conditions.
Feel free to modify this script further based on your specific trading strategy and needs. If you need help with any additional features or further customization, let me know!
ChatGPT can make mistakes. Check important info.
Persistent Homology Based Trend Strength OscillatorPersistent Homology Based Trend Strength Oscillator
The Persistent Homology Based Trend Strength Oscillator is a unique and powerful tool designed to measure the persistence of market trends over a specified rolling window. By applying the principles of persistent homology, this indicator provides traders with valuable insights into the strength and stability of uptrends and downtrends, helping to inform better trading decisions.
What Makes This Indicator Original?
This indicator's originality lies in its application of persistent homology , a method from topological data analysis, to financial markets. Persistent homology examines the shape and features of data across multiple scales, identifying patterns that persist as the scale changes. By adapting this concept, the oscillator tracks the persistence of uptrends and downtrends in price data, offering a novel approach to trend analysis.
Concepts Underlying the Calculations:
Persistent Homology: This method identifies features such as clusters, holes, and voids that persist as the scale changes. In the context of this indicator, it tracks the duration and stability of price trends.
Rolling Window Analysis: The oscillator uses a specified window size to calculate the average length of uptrends and downtrends, providing a dynamic view of trend persistence over time.
Threshold-Based Trend Identification: It differentiates between uptrends and downtrends based on specified thresholds for price changes, ensuring precision in trend detection.
How It Works:
The oscillator monitors consecutive changes in closing prices to identify uptrends and downtrends.
An uptrend is detected when the closing price increase exceeds a specified positive threshold.
A downtrend is detected when the closing price decrease exceeds a specified negative threshold.
The lengths of these trends are recorded and averaged over the chosen window size.
The Trend Persistence Index is calculated as the difference between the average uptrend length and the average downtrend length, providing a measure of trend persistence.
How Traders Can Use It:
Identify Trend Strength: The Trend Persistence Index offers a clear measure of the strength and stability of uptrends and downtrends. A higher value indicates stronger and more persistent uptrends, while a lower value suggests stronger and more persistent downtrends.
Spot Trend Reversals: Significant shifts in the Trend Persistence Index can signal potential trend reversals. For instance, a transition from positive to negative values might indicate a shift from an uptrend to a downtrend.
Confirm Trends: Use the Trend Persistence Index alongside other technical indicators to confirm the strength and duration of trends, enhancing the accuracy of your trading signals.
Manage Risk: Understanding trend persistence can help traders manage risk by identifying periods of high trend stability versus periods of potential volatility. This can be crucial for timing entries and exits.
Example Usage:
Default Settings: Start with the default settings to get a feel for the oscillator’s behavior. Observe how the Trend Persistence Index reacts to different market conditions.
Adjust Thresholds: Fine-tune the positive and negative thresholds based on the asset's volatility to improve trend detection accuracy.
Combine with Other Indicators: Use the Persistent Homology Based Trend Strength Oscillator in conjunction with other technical indicators such as moving averages, RSI, or MACD for a comprehensive analysis.
Backtesting: Conduct backtesting to see how the oscillator would have performed in past market conditions, helping you to refine your trading strategy.
Fine-tune Inputs: Fourier Smoothed Volume zone oscillator WFSVZ0Use this Strategy to Fine-tune inputs for the (W&)FSVZ0 Indicator.
Strategy allows you to fine-tune the indicator for 1 TimeFrame at a time; cross Timeframe Input fine-tuning is done manually after exporting the chart data.
I suggest using "Close all" input False when fine-tuning Inputs for 1 TimeFrame. When you export data to Excel/Numbers/GSheets I suggest using "Close all" input as True, except for the lowest TimeFrame.
MEANINGFUL DESCRIPTION:
The Volume Zone oscillator breaks up volume activity into positive and negative categories. It is positive when the current closing price is greater than the prior closing price and negative when it's lower than the prior closing price. The resulting curve plots through relative percentage levels that yield a series of buy and sell signals, depending on level and indicator direction.
The Wavelet & Fourier Smoothed Volume Zone Oscillator (W&)FSVZO is a refined version of the Volume Zone Oscillator, enhanced by the implementation of the Discrete Fourier Transform . Its primary function is to streamline price data and diminish market noise, thus offering a clearer and more precise reflection of price trends.
By combining the Wavalet and Fourier aproximation with Ehler's white noise histogram, users gain a comprehensive perspective on volume-related market conditions.
HOW TO USE THE INDICATOR:
The default period is 2 but can be adjusted after backtesting. (I suggest 5 VZO length and NoiceR max length 8 as-well)
The VZO points to a positive trend when it is rising above the 0% level, and a negative trend when it is falling below the 0% level. 0% level can be adjusted in setting by adjusting VzoDifference. Oscillations rising below 0% level or falling above 0% level result in a natural trend.
HOW TO USE THE STRATEGY:
Here you fine-tune the inputs until you find a combination that works well on all Timeframes you will use when creating your Automated Trade Algorithmic Strategy. I suggest 4h, 12h, 1D, 2D, 3D, 4D, 5D, 6D, W and M.
When I ndicator/Strategy returns 0 or natural trend , Strategy Closes All it's positions.
ORIGINALITY & USFULLNESS:
Personal combination of Fourier and Wavalet aproximation of a price which results in less noise Volume Zone Oscillator.
The Wavelet Transform is a powerful mathematical tool for signal analysis, particularly effective in analyzing signals with varying frequency or non-stationary characteristics. It dissects a signal into wavelets, small waves with varying frequency and limited duration, providing a multi-resolution analysis. This approach captures both frequency and location information, making it especially useful for detecting changes or anomalies in complex signals.
The Discrete Fourier Transform (DFT) is a mathematical technique that transforms discrete data from the time domain into its corresponding representation in the frequency domain. This process involves breaking down a signal into its individual frequency components, thereby exposing the amplitude and phase characteristics inherent in each frequency element.
This indicator utilizes the concept of Ehler's Universal Oscillator and displays a histogram, offering critical insights into the prevailing levels of market noise. The Ehler's Universal Oscillator is grounded in a statistical model that captures the erratic and unpredictable nature of market movements. Through the application of this principle, the histogram aids traders in pinpointing times when market volatility is either rising or subsiding.
DETAILED DESCRIPTION:
My detailed description of the indicator and use cases which I find very valuable.
What is oscillator?
Oscillators are chart indicators that can assist a trader in determining overbought or oversold conditions in ranging (non-trending) markets.
What is volume zone oscillator?
Price Zone Oscillator measures if the most recent closing price is above or below the preceding closing price.
Volume Zone Oscillator is Volume multiplied by the 1 or -1 depending on the difference of the preceding 2 close prices and smoothed with Exponential moving Average.
What does this mean?
If the VZO is above 0 and VZO is rising. We have a bullish trend. Most likely.
If the VZO is below 0 and VZO is falling. We have a bearish trend. Most likely.
Rising means that VZO on close is higher than the previous day.
Falling means that VZO on close is lower than the previous day.
What if VZO is falling above 0 line?
It means we have a high probability of a bearish trend.
Thus the indicator returns 0 and Strategy closes all it's positions when falling above 0 (or rising bellow 0) and we combine higher and lower timeframes to gauge the trend.
In the next Image you can see that trend is negative on 4h, negative on 12h and positive on 1D. That means trend is negative.
I am sorry, the chart is a bit messy. The idea is to use the indicator over more than 1 Timeframe.
What is approximation and smoothing?
They are mathematical concepts for making a discrete set of numbers a
continuous curved line.
Fourier and Wavelet approximation of a close price are taken from aprox library.
Key Features:
You can tailor the Indicator/Strategy to your preferences with adjustable parameters such as VZO length, noise reduction settings, and smoothing length.
Volume Zone Oscillator (VZO) shows market sentiment with the VZO, enhanced with Exponential Moving Average (EMA) smoothing for clearer trend identification.
Noise Reduction leverages Euler's White noise capabilities for effective noise reduction in the VZO, providing a cleaner and more accurate representation of market dynamics.
Choose between the traditional Fast Fourier Transform (FFT) , the innovative Double Discrete Fourier Transform (DTF32) and Wavelet soothed Fourier soothed price series to suit your analytical needs.
Image of Wavelet transform with FAST settings, Double Fourier transform with FAST settings. Improved noice reduction with SLOW settings, and standard FSVZO with SLOW settings:
Fast setting are setting by default:
VZO length = 2
NoiceR max Length = 2
Slow settings are:
VZO length = 5 or 7
NoiceR max Length = 8
As you can see fast setting are more volatile. I suggest averaging fast setting on 4h 12h 1d 2d 3d 4d W and M Timeframe to get a clear view on market trend.
What if I want long only when VZO is rising and above 15 not 0?
You have set Setting VzoDifference to 15. That reduces the number of trend changes.
Example of W&FSVZO with VzoDifference 15 than 0:
VZO crossed 0 line but not 15 line and that's why Indicator returns 0 in one case an 1 in another.
What is Smooth length setting?
A way of calculating Bullish or Bearish (W&)FSVZO .
If smooth length is 2 the trend is rising if:
rising = VZO > ta.ema(VZO, 2)
Meaning that we check if VZO is higher that exponential average of the last 2 elements.
If smooth length is 1 the trend is rising if:
rising = VZO_ > VZO_
Use this Strategy to fine-tune inputs for the (W&)FSVZO Indicator.
(Strategy allows you to fine-tune the indicator for 1 TimeFrame at a time; cross Timeframe Input fine-tuning is done manually after exporting the chart data)
I suggest using " Close all " input False when fine-tuning Inputs for 1 TimeFrame . When you export data to Excel/Numbers/GSheets I suggest using " Close all " input as True , except for the lowest TimeFrame . I suggest using 100% equity as your default quantity for fine-tune purposes. I have to mention that 100% equity may lead to unrealistic backtesting results. Be avare. When backtesting for trading purposes use Contracts or USDT.
RSI Graphique and Dashboard MTFMTF RSI Indicator - User Guide
Introduction:
The MTF RSI (Multi-Timeframe Relative Strength Index) Pine Script is designed to provide traders with a comprehensive view of the RSI (Relative Strength Index) across multiple timeframes. The script includes a primary chart displaying RSI values and a dashboard summarizing RSI trends for different time intervals.
Installation:
Copy the provided Pine Script.
Open the TradingView platform.
Create a new script.
Paste the copied code into the script editor.
Save and apply the script to your chart.
Primary Chart:
The primary chart displays RSI values for the selected timeframe (5, 15, 60, 240, 1440 minutes).
different color lines represent RSI values for different timeframes.
Overbought and Oversold Levels:
Overbought levels (70) are marked in red, while oversold levels (30) are marked in blue for different timeframes.
Dashboard:
The dashboard is a quick reference for RSI trends across multiple timeframes.
Each row represents a timeframe with corresponding RSI trend information.
Arrows (▲ for bullish, ▼ for bearish) indicate the current RSI trend.
Arrow colors represent the trend: blue for bullish, red for bearish.
Settings:
Users can customize the RSI length, background color, and other parameters.
The background color of the dashboard can be adjusted for light or dark themes.
Interpretation:
Bullish Trend: ▲ arrow and blue color.
Bearish Trend: ▼ arrow and red color.
RSI values above 70 may indicate overbought conditions, while values below 30 may indicate oversold conditions.
Practical Tips:
Timeframe Selection: Consider the trend alignment across different timeframes for comprehensive market analysis.
Confirmation: Use additional indicators or technical analysis to confirm RSI signals.
Backtesting: Before applying in live trading, conduct thorough backtesting to evaluate the script's performance.
Adjustment: Modify settings according to your trading preferences and market conditions.
Disclaimer:
This script is a tool for technical analysis and should be used in conjunction with other indicators. It is not financial advice, and users should conduct their own research before making trading decisions. Adjust settings based on personal preferences and risk tolerance. Use the script responsibly and at your own risk.
LineBreakIntroduction:
The LineBreak Indicator is a technical tool designed to assist traders in identifying potential trend reversals or continuations using a unique charting method known as Line Break charts. This indicator overlays Line Break chart patterns on the main price chart and generates Buy and Sell signals based on specific price movements. In this guide, we will explore the LineBreak Indicator's functionality and how to utilize it effectively in your trading strategy.
Indicator Components:
The LineBreak Indicator comprises several components that work together to identify potential trade signals:
Line Break Chart Creation:
The script starts with an indicator declaration, "@version=5," followed by the creation of the LineBreak chart overlay on the main price chart. Line Break charts focus solely on price movements, omitting time entirely.
Line Break Chart Data Retrieval:
The indicator requests Line Break chart data using the "ticker.linebreak" function, which generates Line Break brick patterns based on a specified brick size (in this case, 3). The script then retrieves the Line Break open, high, low, and close prices for analysis.
Buy and Sell Signal Generation:
The script generates Buy and Sell signals using plotshape functions and specific conditions based on Line Break chart patterns. These patterns involve the relationship between consecutive brick prices and their opening prices.
Alert Conditions:
The script establishes alert conditions for both Buy and Sell signals. These alerts notify traders when specific Line Break chart patterns are detected, ensuring timely awareness of potential trading opportunities.
How to Use the LineBreak Indicator:
Line Break Chart Analysis:
Begin by understanding the Line Break chart patterns displayed on the main price chart. Line Break charts focus on price movements rather than time intervals. An upward Line Break brick suggests bullish momentum, while a downward brick indicates bearish momentum.
Buy Signal Interpretation:
Pay attention to Buy signals generated by the indicator. A Buy signal is triggered when specific Line Break brick conditions are met, indicating a potential reversal from a downtrend to an uptrend. This suggests a potential opportunity to enter a long (Buy) trade.
Sell Signal Interpretation:
Likewise, be attentive to Sell signals produced by the indicator. A Sell signal occurs when predefined Line Break brick conditions are fulfilled, suggesting a potential reversal from an uptrend to a downtrend. This could signal a chance to enter a short (Sell) trade.
Alert Notifications:
To ensure you stay informed, set up alert conditions for Buy and Sell signals. Alerts can be customized to your preferences and communication channels, enabling you to promptly respond to potential trade setups.
Risk Management and Considerations:
Confirmation: While the LineBreak Indicator provides valuable insights, use it in conjunction with other technical analysis tools and indicators to confirm signals.
Backtesting: Before deploying the indicator in live trading, perform comprehensive backtesting on historical data to assess its performance and suitability for your trading strategy.
Position Sizing: Determine appropriate position sizes based on your risk tolerance and the signals provided by the LineBreak Indicator. Avoid overleveraging your trades.
Market Awareness: Stay aware of market conditions and news events that could influence price movements. The LineBreak Indicator is a tool to enhance your decision-making process, not a standalone strategy.
Conclusion:
The LineBreak Indicator introduces a different perspective on price movements through its unique charting method. By interpreting Line Break chart patterns and acting on generated Buy and Sell signals, traders can make informed trading decisions. Practice proper risk management and integrate the LineBreak Indicator into a comprehensive trading strategy to achieve consistent and successful trading outcomes.
Please remember that this guide provides a high-level overview of the LineBreak Indicator and its usage. It's essential to thoroughly test and validate any trading strategy before implementing it in a live trading environment.
RenkoIndicatorIntroduction:
The Renko Indicator is a powerful tool designed to help traders identify trends and potential trade opportunities in the financial markets. This indicator overlays a Renko chart on the main price chart and generates Buy and Sell signals based on Renko brick movements. Renko charts are unique in that they focus solely on price movements, ignoring the element of time. In this guide, we will walk you through how to use the Renko Indicator effectively in your trading strategy.
Indicator Components:
The Renko Indicator consists of several components, each serving a specific purpose in aiding your trading decisions.
Market Sentiment Calculation:
At the top of the script, the indicator calculates market sentiment by analyzing recent price action. It determines whether the market sentiment is Bullish, Bearish, or Neutral based on the highest and lowest prices within specific time periods. This information provides you with a broader context for potential trading decisions.
Renko Chart Creation:
The indicator creates a Renko chart overlay on the main price chart using the Average True Range (ATR) method. ATR is used to calculate the brick size for the Renko chart, allowing you to adjust the sensitivity of the chart to price movements.
Renko Open and Close Midpoint:
The script plots the midpoint of Renko open and close prices as a line on the main chart. This visualization helps you understand the direction of Renko bricks and identify trends.
Buy and Sell Signal Generation:
The script generates Buy and Sell signals as label shapes on the chart. A Buy signal is generated when the Renko close price crosses above the Renko open price, indicating potential upward momentum. Conversely, a Sell signal is generated when the Renko close price crosses below the Renko open price, suggesting potential downward momentum.
Alert Conditions:
To ensure you never miss a trading opportunity, the script sets up alert conditions for Buy and Sell signals. These alerts notify you when the specified conditions for potential trades are met. Alerts can be customized to your preference, allowing you to receive notifications via your chosen communication channels.
How to Use the Renko Indicator:
Market Sentiment Analysis:
Start by analyzing the calculated market sentiment. This information helps you understand the broader trend in the market. A Bullish sentiment indicates potential upward movement, a Bearish sentiment suggests potential downward movement, and a Neutral sentiment signals uncertainty.
Renko Chart Interpretation:
Observe the Renko chart overlay and its midpoint line. Upward-trending Renko bricks suggest Bullish momentum, while downward-trending bricks indicate Bearish momentum. Use the Renko chart to identify trends and confirm your trading bias.
Buy and Sell Signals:
Pay close attention to the Buy and Sell signals generated by the indicator. A Buy signal occurs when the Renko close price crosses above the Renko open price. Conversely, a Sell signal occurs when the Renko close price crosses below the Renko open price. These signals highlight potential entry points for trades.
Alert Notifications:
Make use of the alert conditions to receive real-time notifications for Buy and Sell signals. Alerts help you stay informed even when you're not actively watching the charts, allowing you to promptly take action on potential trade opportunities.
Risk Management and Considerations:
Confirmation: While the Renko Indicator provides valuable insights, it's crucial to use it in conjunction with other technical and fundamental analysis tools for confirmation.
Backtesting: Before implementing the indicator in live trading, conduct thorough backtesting on historical data to assess its performance and suitability for your trading strategy.
Position Sizing: Determine appropriate position sizes based on your risk tolerance and the signals provided by the indicator. Avoid overleveraging your trades.
Market Conditions: Be mindful of market conditions and news events that could impact price movements. Use the Renko Indicator as a tool to enhance your decision-making process, not as a standalone strategy.
Conclusion:
The Renko Indicator offers a unique perspective on price movements and can be a valuable addition to your trading toolkit. By analyzing market sentiment, interpreting Renko chart patterns, and acting on Buy and Sell signals, you can make informed trading decisions. Remember to practice proper risk management and integrate the Renko Indicator into a comprehensive trading strategy to achieve consistent and successful trading outcomes.
Pivot Highs&lows: Short/Medium/Long-term + Spikeyness FilterShows Pivot Highs & Lows defined or 'Graded' on a fractal basis: Short-term, medium-term and long-term. Also applies 'Spikeyness' condition by default to filter-out weak/rounded pivots
ES1! 4hr chart (CME) shown above, with lookback = 15; clearly identifying the major highs & lows on the basis of how they are fractally 'nested' within lesser Pivots.
-- in the above chart Short term pivot highs (STH) are simply represented by green 'ʌ', and short-term pivot lows (STL) are simply represented by orange 'v'.
//Basics: (as applying to pivot highs, the following is reversed for pivot lows)
-Short term highs (STH) are simple pivot highs, albeit refined from standard with the 'spikeyness' filter.
-Medium-term highs (MTH) are defined as having a lower STH on either side of them.
-Long-term highs (LTH) are defined as having a lower MTH on either side of them.
//Purpose:
-Education: Quick and easy visualization of the strength or importance of a pivot high or low; a way of grading them based on their larger context.
-Backtesting: use in combination with other trading methods when backtesting to see the relative significance and price sensitivity of LTHs/LTLs compared to lower grade highs and lows.
//Settings:
-Choose Pivot lookback/lookforward bars: One setting, the basis from which all further pivot calculations are done.
-Toggle on/off 'Spikeyness' condition to filter-out weak/rounded/unimpressive pivot highs or lows (default is ON).
-Toggle on/off each of STH, MTH, LTH, STL, MTL, LTL; and choose label text-styles/colors/sizes independently.
-Set text Vertically, horizonally, or simply use 'ʌ' or 'v' symbols if you want to declutter your chart.
//Usage notes:
-Pivots take time to print (lookback bars must have elapsed before confirmation). Fractally nested pivots as here (i.e. a LTH), take even longer to print/confirm, so please be patient.
-Works across timeframes & Assets. Different timeframes may require slightly tweaked lookback/forward settings for optimal use; default is 15 bars.
Example usage with just symbolic labels short-term, med-term, long-term with 1x, 2x and 3x ʌ/v respectively:
Booz StrategyBooz Backtesting : Booz Backtesting is a method for analyzing the performance of your current trading strategy . Booz Backtesting aims to help you generate results and evaluate risk and return without risking real capital.
The Booz Backtesting is the Booz Super Swing Indicator equivalent but gives you the ability to backtest data on different charts.
This is an Indicator created for the purpose of identifying trends in Multiple Markets, it is based on Moving Average Crossover and extra features.
Swing Trading: This function allows you to navigate the entire trend until it is not strong enough, so you can compare it with fixed parameters such as Take Profit and Stop Loss.
Take Profit and Stop Loss function: With this function you will be able to choose the most optimal parameters and see in real time the results in order to choose the best combination of parameters.
Leverage : We have this function for the futures markets where you can check which is the most appropriate leverage for your operation.
Trend Filter: allows you to take multiple entries in the same direction of the market.
If the market crosses below the 200 moving average, it will take only short entries.
If the market crosses above the 200 moving average, it will take only long entries.
Timeframes
Charting from 1 Hour, 4 Hour, Daily, Weekly, Weekly
Markets :Booz Backtesting can be tested in Cryptocurrency, Stocks and Futures markets.
Background Color : at a glance, you can see what cycle the market is in.
Green background : Shows that the market is in a bullish cycle.
Red background: Shows that the market is in a bearish cycle.
Bozz Strategy
Booz Backtesting : Booz Backtesting is a method for analyzing the performance of your current trading strategy . Booz Backtesting aims to help you generate results and evaluate risk and return without risking real capital.
The Booz Backtesting is the Booz Super Swing Indicator equivalent but gives you the ability to backtest data on different charts.
This is an Indicator created for the purpose of identifying trends in Multiple Markets, it is based on Moving Average Crossover and extra features.
Swing Trading: This function allows you to navigate the entire trend until it is not strong enough, so you can compare it with fixed parameters such as Take Profit and Stop Loss.
Take Profit and Stop Loss function: With this function you will be able to choose the most optimal parameters and see in real time the results in order to choose the best combination of parameters.
Leverage : We have this function for the futures markets where you can check which is the most appropriate leverage for your operation.
Trend Filter: allows you to take multiple entries in the same direction of the market.
If the market crosses below the 200 moving average, it will take only short entries.
If the market crosses above the 200 moving average, it will take only long entries.
Timeframes
Charting from 1 Hour, 4 Hour, Daily, Weekly, Weekly
Markets :Booz Backtesting can be tested in Cryptocurrency, Stocks and Futures markets.
Background Color : at a glance, you can see what cycle the market is in.
Green background : Shows that the market is in a bullish cycle.
Red background: Shows that the market is in a bearish cycle.
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SimpleCrossOver_BotThis is a simple example of how you can compile your own strategy
This script contains the code for alerts and for backtesting.
In order to use the backtester, comment out the sections to be used for signals, and comment in the sections to be used on the back tester, and visa versa for using the script for alerts in order to automate your own bot.
Line Break StrategyLine Break Strategy
Entry rule:
Long on a bullish line and short on a bearish line.
Backtest:
Profit factors are shown below for three-line break.
Daily time frame, FXCM broker.
EURUSD: 1.267, USDJPY: 1.039, GBPUSD: -0.816, AUDUSD: -0.959
S&P500: -0.783, Nikkei225: 1.099
CrudeOil: 1.03, Gold: 1.196
BTCUSD: -0.883
Reference:
Steve Nison, Beyond Candlesticks - New Japanese Charting Techniques Revealed
Note:
This strategy doesn't work properly on the linebreak chart.
A good example is shown below. The entry prices are not always correct.
If you have signal, but the next candle moves in the opposite direction, the entry price is drawn at the Open of the new candle instead of the Close of the previous candle.
The results of backtest are unreliable due to this reason.
BOCS Channel Scalper Strategy - Automated Mean Reversion System# BOCS Channel Scalper Strategy - Automated Mean Reversion System
## WHAT THIS STRATEGY DOES:
This is an automated mean reversion trading strategy that identifies consolidation channels through volatility analysis and executes scalp trades when price enters entry zones near channel boundaries. Unlike breakout strategies, this system assumes price will revert to the channel mean, taking profits as price bounces back from extremes. Position sizing is fully customizable with three methods: fixed contracts, percentage of equity, or fixed dollar amount. Stop losses are placed just outside channel boundaries with take profits calculated either as fixed points or as a percentage of channel range.
## KEY DIFFERENCE FROM ORIGINAL BOCS:
**This strategy is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities:
- **Original BOCS**: 1-3 signals per channel (only on breakout)
- **Scalper Version**: 5-15+ signals per channel (every touch of entry zones)
- **Trade Style**: Mean reversion vs trend following
- **Hold Time**: Seconds to minutes vs minutes to hours
- **Best Markets**: Ranging/choppy conditions vs trending breakouts
This makes the scalper ideal for active day traders who want continuous opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased trade frequency also means higher commission costs and requires tighter risk management.
## TECHNICAL METHODOLOGY:
### Price Normalization Process:
The strategy normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis.
### Volatility Detection:
A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The strategy uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars).
### Channel Formation Logic:
When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The strategy tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas.
### Entry Signal Generation:
The strategy uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%):
- **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2)
- **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2)
Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. This captures mean reversion opportunities as price reaches channel extremes.
### Cooldown Filter:
An optional cooldown period (measured in bars) prevents signal spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long signal will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period.
### ATR Volatility Filter:
The strategy includes a multi-timeframe ATR filter to avoid trading during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while trading on 5-minute charts). The filter compares current ATR to a user-defined minimum threshold:
- If ATR ≥ threshold: Trading enabled
- If ATR < threshold: No signals fire
This prevents entries during dead zones where mean reversion is unreliable due to insufficient price movement.
### Take Profit Calculation:
Two TP methods are available:
**Fixed Points Mode**:
- Long TP = Entry + (TP_Ticks × syminfo.mintick)
- Short TP = Entry - (TP_Ticks × syminfo.mintick)
**Channel Percentage Mode**:
- Long TP = Entry + (ChannelRange × TP_Percent)
- Short TP = Entry - (ChannelRange × TP_Percent)
Default 50% targets the channel midline, a natural mean reversion target. Larger percentages aim for opposite channel edge.
### Stop Loss Placement:
Stop losses are placed just outside the channel boundary by a user-defined tick offset:
- Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick)
- Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick)
This logic assumes channel breaks invalidate the mean reversion thesis. If price breaks through, the range is no longer valid and position exits.
### Trade Execution Logic:
When entry conditions are met (price in zone, cooldown satisfied, ATR filter passed, no existing position):
1. Calculate entry price at zone boundary
2. Calculate TP and SL based on selected method
3. Execute strategy.entry() with calculated position size
4. Place strategy.exit() with TP limit and SL stop orders
5. Update info table with active trade details
The strategy enforces one position at a time by checking strategy.position_size == 0 before entry.
### Channel Breakout Management:
Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations.
### Position Sizing System:
Three methods calculate position size:
**Fixed Contracts**:
- Uses exact contract quantity specified in settings
- Best for futures traders (e.g., "trade 2 NQ contracts")
**Percentage of Equity**:
- position_size = (strategy.equity × equity_pct / 100) / close
- Dynamically scales with account growth
**Cash Amount**:
- position_size = cash_amount / close
- Maintains consistent dollar exposure regardless of price
## INPUT PARAMETERS:
### Position Sizing:
- **Position Size Type**: Choose Fixed Contracts, % of Equity, or Cash Amount
- **Number of Contracts**: Fixed quantity per trade (1-1000)
- **% of Equity**: Percentage of account to allocate (1-100%)
- **Cash Amount**: Dollar value per position ($100+)
### Channel Settings:
- **Nested Channels**: Allow multiple overlapping channels vs single channel
- **Normalization Length**: Lookback for high/low calculation (1-500, default 100)
- **Box Detection Length**: Period for volatility detection (1-100, default 14)
### Scalping Settings:
- **Enable Long Scalps**: Toggle long entries on/off
- **Enable Short Scalps**: Toggle short entries on/off
- **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%)
- **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5)
- **Cooldown Period (Bars)**: Minimum spacing between signals (0 = no cooldown)
### ATR Filter:
- **Enable ATR Filter**: Toggle volatility filter on/off
- **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.)
- **ATR Length**: Smoothing period (1-100, default 14)
- **Min ATR Value**: Threshold for trade enablement (0.1+, default 10.0)
### Take Profit Settings:
- **TP Method**: Choose Fixed Points or % of Channel
- **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30)
- **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%)
### Appearance:
- **Show Entry Zones**: Toggle zone labels on channels
- **Show Info Table**: Display real-time strategy status
- **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right)
- **Color Settings**: Customize long/short/TP/SL colors
## VISUAL INDICATORS:
- **Channel boxes** with semi-transparent fill showing consolidation zones
- **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼"
- **Entry signal arrows** below/above bars marking long/short entries
- **Active TP/SL lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL)
- **Info table** showing position status, channel state, last signal, entry/TP/SL prices, and ATR status
## HOW TO USE:
### For 1-3 Minute Scalping (NQ/ES):
- ATR Timeframe: "1" (1-minute)
- ATR Min Value: 10.0 (for NQ), adjust per instrument
- Entry Zone %: 20-25%
- TP Method: Fixed Points, 20-40 ticks
- SL Offset: 5-10 ticks
- Cooldown: 2-3 bars
- Position Size: 1-2 contracts
### For 5-15 Minute Day Trading:
- ATR Timeframe: "5" or match chart
- ATR Min Value: Adjust to instrument (test 8-15 for NQ)
- Entry Zone %: 20-30%
- TP Method: % of Channel, 40-60%
- SL Offset: 5-10 ticks
- Cooldown: 3-5 bars
- Position Size: Fixed contracts or 5-10% equity
### For 30-60 Minute Swing Scalping:
- ATR Timeframe: "15" or "30"
- ATR Min Value: Lower threshold for broader market
- Entry Zone %: 25-35%
- TP Method: % of Channel, 50-70%
- SL Offset: 10-15 ticks
- Cooldown: 5+ bars or disable
- Position Size: % of equity recommended
## BACKTEST CONSIDERATIONS:
- Strategy performs best in ranging, mean-reverting markets
- Strong trending markets produce more stop losses as price breaks channels
- ATR filter significantly reduces trade count but improves quality during low volatility
- Cooldown period trades signal quantity for signal quality
- Commission and slippage materially impact sub-5-minute timeframe performance
- Shorter timeframes require tighter entry zones (15-20%) to catch quick reversions
- % of Channel TP adapts better to varying channel sizes than fixed points
- Fixed contract sizing recommended for consistent risk per trade in futures
**Backtesting Parameters Used**: This strategy was developed and tested using realistic commission and slippage values to provide accurate performance expectations. Recommended settings: Commission of $1.40 per side (typical for NQ futures through discount brokers), slippage of 2 ticks to account for execution delays on fast-moving scalp entries. These values reflect real-world trading costs that active scalpers will encounter. Backtest results without proper cost simulation will significantly overstate profitability.
## COMPATIBLE MARKETS:
Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features require data feed with volume information but are optional for core functionality.
## KNOWN LIMITATIONS:
- Immediate touch entry can fire multiple times in choppy zones without adequate cooldown
- Channel deletion at 10-tick breaks may be too aggressive or lenient depending on instrument tick size
- ATR filter from lower timeframes requires higher-tier TradingView subscription (request.security limitation)
- Mean reversion logic fails in strong breakout scenarios leading to stop loss hits
- Position sizing via % of equity or cash amount calculates based on close price, may differ from actual fill price
- No partial closing capability - full position exits at TP or SL only
- Strategy does not account for gap openings or overnight holds
## RISK DISCLOSURE:
Trading involves substantial risk of loss. Past performance does not guarantee future results. This strategy is for educational purposes and backtesting only. Mean reversion strategies can experience extended drawdowns during trending markets. Stop losses may not fill at intended levels during extreme volatility or gaps. Thoroughly test on historical data and paper trade before risking real capital. Use appropriate position sizing and never risk more than you can afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Automated trading systems can malfunction - monitor all live positions actively.
## ACKNOWLEDGMENT & CREDITS:
This strategy is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community.
Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based signals, multi-timeframe ATR volatility filtering, flexible position sizing (fixed/percentage/cash), cooldown period filtering, dual TP methods (fixed points vs channel percentage), automated strategy execution with exit management, and real-time position monitoring table.
EMA inFusion Pro - Multiple SourcesEMA Fusion Pro: Dynamic Trend & Momentum Strategy with Three Exit Modes
EMA Fusion Pro is a highly customizable, multi-exit trend-following strategy designed for traders who value both precision and flexibility. By leveraging exponential moving averages (EMA), average directional index (ADX), and volume analysis, this strategy aims to capture trending market moves while offering three distinct exit modes for optimal risk management across varying market conditions.
Strategy Overview
This strategy systematically identifies potential entry points using a moving average crossover with highly configurable data sources (including price, volume, rate of change, or their Heikin Ashi versions) and filters signal quality with ADX trend strength and volume spikes. Each trade is managed with one of three advanced exit methodologies—reverse signal, ATR-based stop/take profit, or fixed percentage—giving you the control to adapt your risk profile to different market regimes.
Key Features
Customizable EMA Source: Calculate the core trend-filtering EMA from price (default), volume, rate of change, or their Heikin Ashi counterparts for unique market perspectives.
Trend Filter with ADX: Confirm entries only when the trend is strong, as measured by the user-adjustable ADX threshold.
Volume Spike Confirmation: Optional filter to only take trades with above-average volume activity, reducing false signals.
Three Exit Modes:
Reverse Signal: Exit trades when a new, opposite entry signal occurs.
ATR-Based Stop/Take Profit: Dynamic risk management using multiples of the average true range (ATR) for both take profit and stop loss.
Percent-Based Stop/Take Profit: Fixed-percentage risk management with user-defined thresholds.
Visual Annotations: Signal markers, EMA line color-coded by source, trend background coloring, and optional ATR/percent-based TP/SL levels.
Info Panel: Real-time display of all core indicators, current trading mode, exit parameters, and position status for quick oversight.
How It Works
Entry Logic: A crossover signal (above/below the EMA) triggers a new entry, but only if both ADX trend strength and (optionally) volume spike conditions are met.
Exit Logic: Three selectable modes allow you to exit trades on reverse signals, at a dynamic ATR-based profit or loss, or at a fixed percentage gain/loss.
Flexible Data Analysis: The EMA source can be chosen from six options—standard price, volume, rate of change, or their Heikin Ashi variants—allowing experimentation with different market dimensions.
Risk Management: All exits are precisely controlled, either by the next opposing signal, by volatility-adjusted levels, or by fixed risk/reward ratios.
Backtest & Optimization: The strategy is fully backtestable within TradingView’s Strategy Tester, with adjustable parameters for optimization.
Customization & Usage
Indicator Source: Select your preferred data type for EMA calculation, opening the door to creative strategy variations (e.g., volume momentum, pure price trend, rate of change divergence).
Filter Toggles: Enable/disable ADX and volume filters as desired—useful for different market environments.
Exit Mode Selection: Switch between reverse, ATR, or percent-based exits with a single parameter—ideal for adapting to ranging vs. trending markets.
Visual Clarity: The EMA line color reflects its underlying source, and the info panel summarizes all critical values for easy monitoring.
Who Should Use This Strategy?
Trend Followers seeking to ride strong moves with multiple exit options.
Experienced Traders who want to experiment with different data types (volume, momentum, Heikin Ashi) for trend analysis.
Algorithmic Traders looking for a robust, flexible base to build upon with their own ideas.
Getting Started
Apply the script to your chart and review default settings.
Customize parameters—EMA length, ADX threshold, volume settings, exit type—as desired.
Backtest on multiple instruments and timeframes to evaluate performance.
Optimize filters, exit rules, and risk parameters for your preferred trading style.
Monitor with the real-time info panel and trade alerts.
Disclaimer
This script is for educational and entertainment purposes only. It is not financial advice. Past performance is not indicative of future results. Always conduct thorough testing and consider your risk tolerance before trading real capital.
— Happy Trading —
Feel free to adapt, share, and contribute to this open-source strategy!
Simple DCA Strategy----
### 📌 **Simple DCA Strategy with Backtest Date Filter**
This strategy implements a **Dollar-Cost Averaging (DCA)** approach for long positions, including:
* ✅ **Base Order Entry:** Starts a position with a fixed dollar amount when no position is open.
* 🔁 **Safety Orders:** Buys additional positions when the price drops by a defined percentage, increasing position size with each new entry using a multiplier.
* 🎯 **Take Profit Exit:** Closes all positions when the price reaches a profit target (in % above average entry).
* 🗓️ **Backtest Date Range:** Allows users to specify a custom start and optional end date to run the strategy only within that time window.
* 📊 **Plots:** Visualizes average entry, take profit level, and safety order trigger line.
#### ⚙️ Customizable Inputs:
* Base Order Size (\$)
* Price Deviation for Safety Orders (%)
* Maximum Safety Orders
* Order Size Multiplier
* Take Profit Target (%)
* Start and End Dates for Backtesting
This is a **long-only strategy** and is best used for backtesting performance of DCA-style accumulation under different market conditions.
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OBV ATR Strategy (OBV Breakout Channel) bas20230503ผมแก้ไขจาก OBV+SMA อันเดิม ของเดิม ดูที่เส้น SMA สองเส้นตัดกันมั่นห่วยแตกสำหรับที่ผมลองเทรดจริง และหลักการเบรค ได้แรงบันดาลใจ ATR จาก เทพคอย ที่ใช้กับราคา แต่นี้ใช้กับ OBV แทน
และผมใช้เจมินี้ เพื่อแก้ ให้ เป็น strategy เพื่อเช็คย้อนหลังได้ง่ายกว่าเดิม
หลักการง่ายคือถ้ามันขึ้น มันจะขึ้นเรื่อยๆ
เขียน แบบสุภาพ (น่าจะอ่านได้ง่ายกว่าผมเขียน)
สคริปต์นี้ได้รับการพัฒนาต่อยอดจากแนวคิด OBV+SMA Crossover แบบดั้งเดิม ซึ่งจากการทดสอบส่วนตัวพบว่าประสิทธิภาพยังไม่น่าพอใจ กลยุทธ์ใหม่นี้จึงเปลี่ยนมาใช้หลักการ "Breakout" ซึ่งได้รับแรงบันดาลใจมาจากการใช้ ATR สร้างกรอบของราคา แต่เราได้นำมาประยุกต์ใช้กับ On-Balance Volume (OBV) แทน นอกจากนี้ สคริปต์ได้ถูกแปลงเป็น Strategy เต็มรูปแบบ (โดยความช่วยเหลือจาก Gemini AI) เพื่อให้สามารถทดสอบย้อนหลัง (Backtest) และประเมินประสิทธิภาพได้อย่างแม่นยำ
หลักการของกลยุทธ์: กลยุทธ์นี้ทำงานบนแนวคิดโมเมนตัมที่ว่า "เมื่อแนวโน้มได้เกิดขึ้นแล้ว มีโอกาสที่มันจะดำเนินต่อไป" โดยจะมองหาการทะลุของพลังซื้อ-ขาย (OBV) ที่แข็งแกร่งเป็นพิเศษเป็นสัญญาณเข้าเทร
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สคริปต์นี้เป็นกลยุทธ์ (Strategy) ที่ใช้ On-Balance Volume (OBV) ซึ่งเป็นอินดิเคเตอร์ที่วัดแรงซื้อและแรงขายสะสม แทนที่จะใช้การตัดกันของเส้นค่าเฉลี่ย (SMA Crossover) ที่เป็นแบบพื้นฐาน กลยุทธ์นี้จะมองหาการ "ทะลุ" (Breakout) ของพลัง OBV ออกจากกรอบสูงสุด-ต่ำสุดของตัวเองในรอบที่ผ่านมา
สัญญาณกระทิง (Bull Signal): เกิดขึ้นเมื่อพลังการซื้อ (OBV) แข็งแกร่งจนสามารถทะลุจุดสูงสุดของตัวเองในอดีตได้ บ่งบอกถึงโอกาสที่แนวโน้มจะเปลี่ยนเป็นขาขึ้น
สัญญาณหมี (Bear Signal): เกิดขึ้นเมื่อพลังการขาย (OBV) รุนแรงจนสามารถกดดันให้ OBV ทะลุจุดต่ำสุดของตัวเองในอดีตได้ บ่งบอกถึงโอกาสที่แนวโน้มจะเปลี่ยนเป็นขาลง
ส่วนประกอบบนกราฟ (Indicator Components)
เส้น OBV
เส้นหลัก ที่เปลี่ยนเขียวเป็นแดง เป็นทั้งแนวรับและแนวต้าน และ จุด stop loss
เส้นนี้คือหัวใจของอินดิเคเตอร์ ที่แสดงถึงพลังสะสมของ Volume
เมื่อเส้นเป็นสีเขียว (แนวรับ): จะปรากฏขึ้นเมื่อกลยุทธ์เข้าสู่ "โหมดกระทิง" เส้นนี้คือระดับต่ำสุดของ OBV ในอดีต และทำหน้าที่เป็นแนวรับไดนามิก
เมื่อเส้นกลายเป็นสีแดงสีแดง (แนวต้าน): จะปรากฏขึ้นเมื่อกลยุทธ์เข้าสู่ "โหมดหมี" เส้นนี้คือระดับสูงสุดของ OBV ในอดีต และทำหน้าที่เป็นแนวต้านไดนามิก
สัญลักษณ์สัญญาณ (Signal Markers):
Bull 🔼 (สามเหลี่ยมขึ้นสีเขียว): คือสัญญาณ "เข้าซื้อ" (Long) จะปรากฏขึ้น ณ จุดที่ OBV ทะลุขึ้นไปเหนือกรอบด้านบนเป็นครั้งแรก
Bear 🔽 (สามเหลี่ยมลงสีแดง): คือสัญญาณ "เข้าขาย" (Short) จะปรากฏขึ้น ณ จุดที่ OBV ทะลุลงไปต่ำกว่ากรอบด้านล่างเป็นครั้งแรก
วิธีการใช้งาน (How to Use)
เพิ่มสคริปต์นี้ลงบนกราฟราคาที่คุณสนใจ
ไปที่แท็บ "Strategy Tester" ด้านล่างของ TradingView เพื่อดูผลการทดสอบย้อนหลัง (Backtest) ของกลยุทธ์บนสินทรัพย์และไทม์เฟรมต่างๆ
ใช้สัญลักษณ์ "Bull" และ "Bear" เป็นตัวช่วยในการตัดสินใจเข้าเทรด
ข้อควรจำ: ไม่มีกลยุทธ์ใดที่สมบูรณ์แบบ 100% ควรใช้สคริปต์นี้ร่วมกับการวิเคราะห์ปัจจัยอื่นๆ เช่น โครงสร้างราคา, แนวรับ-แนวต้านของราคา และการบริหารความเสี่ยง (Risk Management) ของตัวคุณเองเสมอ
การตั้งค่า (Inputs)
SMA Length 1 / SMA Length 2: ใช้สำหรับพล็อตเส้นค่าเฉลี่ยของ OBV เพื่อดูเป็นภาพอ้างอิง ไม่มีผลต่อตรรกะการเข้า-ออกของ Strategy อันใหม่ แต่มันเป็นของเก่า ถ้าชอบ ก็ใช้ได้ เมื่อ SMA สองเส้นตัดกัน หรือตัดกับเส้น OBV
High/Low Lookback Length: (ค่าพื้นฐาน30/แก้ตรงนี้ให้เหมาะสมกับ coin หรือหุ้น ตามความผันผวน ) คือระยะเวลาที่ใช้ในการคำนวณกรอบสูงสุด-ต่ำสุดของ OBV
ค่าน้อย: ทำให้กรอบแคบลง สัญญาณจะเกิดไวและบ่อยขึ้น แต่อาจมีสัญญาณหลอก (False Signal) เยอะขึ้น
ค่ามาก: ทำให้กรอบกว้างขึ้น สัญญาณจะเกิดช้าลงและน้อยลง แต่มีแนวโน้มที่จะเป็นสัญญาณที่แข็งแกร่งกว่า
แน่นอนครับ นี่คือคำแปลฉบับภาษาอังกฤษที่สรุปใจความสำคัญ กระชับ และสุภาพ เหมาะสำหรับนำไปใช้ในคำอธิบายสคริปต์ (Description) ของ TradingView ครับ
---Translate to English---
OBV Breakout Channel Strategy
This script is an evolution of a traditional OBV+SMA Crossover concept. Through personal testing, the original crossover method was found to have unsatisfactory performance. This new strategy, therefore, uses a "Breakout" principle. The inspiration comes from using ATR to create price channels, but this concept has been adapted and applied to On-Balance Volume (OBV) instead.
Furthermore, the script has been converted into a full Strategy (with assistance from Gemini AI) to enable precise backtesting and performance evaluation.
The strategy's core principle is momentum-based: "once a trend is established, it is likely to continue." It seeks to enter trades on exceptionally strong breakouts of buying or selling pressure as measured by OBV.
Core Concept
This is a Strategy that uses On-Balance Volume (OBV), an indicator that measures cumulative buying and selling pressure. Instead of relying on a basic Simple Moving Average (SMA) Crossover, this strategy identifies a "Breakout" of the OBV from its own highest-high and lowest-low channel over a recent period.
Bull Signal: Occurs when the buying pressure (OBV) is strong enough to break above its own recent highest high, indicating a potential shift to an upward trend.
Bear Signal: Occurs when the selling pressure (OBV) is intense enough to push the OBV below its own recent lowest low, indicating a potential shift to a downward trend.
On-Screen Components
1. OBV Line
This is the main indicator line, representing the cumulative volume. Its color changes to green when OBV is rising and red when it is falling.
2. Dynamic Support & Resistance Line
This is the thick Green or Red line that appears based on the strategy's current "mode." This line serves as a dynamic support/resistance level and can be used as a reference for stop-loss placement.
Green Line (Support): Appears when the strategy enters "Bull Mode." This line represents the lowest low of the OBV in the recent past and acts as dynamic support.
Red Line (Resistance): Appears when the strategy enters "Bear Mode." This line represents the highest high of the OBV in the recent past and acts as dynamic resistance.
3. Signal Markers
Bull 🔼 (Green Up Triangle): This is the "Long Entry" signal. It appears at the moment the OBV first breaks out above its high-low channel.
Bear 🔽 (Red Down Triangle): This is the "Short Entry" signal. It appears at the moment the OBV first breaks down below its high-low channel.
How to Use
Add this script to the price chart of your choice.
Navigate to the "Strategy Tester" panel at the bottom of TradingView to view the backtesting results for the strategy on different assets and timeframes.
Use the "Bull" and "Bear" signals as aids in your trading decisions.
Disclaimer: No strategy is 100% perfect. This script should always be used in conjunction with other forms of analysis, such as price structure, key price-based support/resistance levels, and your own personal risk management rules.
Inputs
SMA Length 1 / SMA Length 2: These are used to plot moving averages on the OBV for visual reference. They are part of the legacy logic and do not affect the new breakout strategy. However, they are kept for traders who may wish to observe their crossovers for additional confirmation.
High/Low Lookback Length: (Most Important Setting) This determines the period used to calculate the highest-high and lowest-low OBV channel. (Default is 30; adjust this to suit the asset's volatility).
A smaller value: Creates a narrower channel, leading to more frequent and faster signals, but potentially more false signals.
A larger value: Creates a wider channel, leading to fewer and slower signals, which are likely to be more significant.
Best SMA FinderThis script, Best SMA Finder, is a tool designed to identify the most robust simple moving average (SMA) length for a given chart, based on historical backtest performance. It evaluates hundreds of SMA values (from 10 to 1000) and selects the one that provides the best balance between profitability, consistency, and trade frequency.
What it does:
The script performs individual backtests for each SMA length using either "Long Only" or "Buy & Sell" logic, as selected by the user. For each tested SMA, it computes:
- Total number of trades
- Profit Factor (total profits / total losses)
- Win Rate
- A composite Robustness Score, which integrates Profit Factor, number of trades (log-scaled), and win rate.
Only SMA configurations that meet the user-defined minimum trade count are considered valid. Among all valid candidates, the script selects the SMA length with the highest robustness score and plots it on the chart.
How to use it:
- Choose the strategy type: "Long Only" or "Buy & Sell"
- Set the minimum trade count to filter out statistically irrelevant results
- Enable or disable the summary stats table (default: enabled)
The selected optimal SMA is plotted on the chart in blue. The optional table in the top-right corner shows the corresponding SMA length, trade count, Profit Factor, Win Rate, and Robustness Score for transparency.
Key Features:
- Exhaustive SMA optimization across 991 values
- Customizable trade direction and minimum trade filters
- In-chart visualization of results via table and plotted optimal SMA
- Uses a custom robustness formula to rank SMA lengths
Use cases:
Ideal for traders who want to backtest and auto-select a historically effective SMA without manual trial-and-error. Useful for swing and trend-following strategies across different timeframes.
📌 Limitations:
- Not a full trading strategy with position sizing or stop-loss logic
- Only one entry per direction at a time is allowed
- Designed for exploration and optimization, not as a ready-to-trade system
This script is open-source and built entirely from original code and logic. It does not replicate any closed-source script or reuse significant external open-source components.
