Fibonacci ATR Fusion - Strategy [presentTrading]Open-script again! This time is also an ATR-related strategy. Enjoy! :)
If you have any questions, let me know, and I'll help make this as effective as possible.
█ Introduction and How It Is Different
The Fibonacci ATR Fusion Strategy is an advanced trading approach that uniquely integrates Fibonacci-based weighted averages with the Average True Range (ATR) to identify and capitalize on significant market trends.
Unlike traditional strategies that rely on single indicators or static parameters, this method combines multiple timeframes and dynamic volatility measurements to enhance precision and adaptability. Additionally, it features a 4-step Take Profit (TP) mechanism, allowing for systematic profit-taking at various levels, which optimizes both risk management and return potential in long and short market positions.
BTCUSD 6hr Performance
█ Strategy, How It Works: Detailed Explanation
The Fibonacci ATR Fusion Strategy utilizes a combination of technical indicators and weighted averages to determine optimal entry and exit points. Below is a breakdown of its key components and operational logic.
🔶 1. Enhanced True Range Calculation
The strategy begins by calculating the True Range (TR) to measure market volatility accurately.
TR = max(High - Low, abs(High - Previous Close), abs(Low - Previous Close))
High and Low: Highest and lowest prices of the current trading period.
Previous Close: Closing price of the preceding trading period.
max: Selects the largest value among the three calculations to account for gaps and limit movements.
🔶 2. Buying Pressure (BP) Calculation
Buying Pressure (BP) quantifies the extent to which buyers are driving the price upwards within a period.
BP = Close - True Low
Close: Current period's closing price.
True Low: The lower boundary determined in the True Range calculation.
🔶 3. Ratio Calculation for Different Periods
To assess the strength of buying pressure relative to volatility, the strategy calculates a ratio over various Fibonacci-based timeframes.
Ratio = 100 * (Sum of BP over n periods) / (Sum of TR over n periods)
n: Length of the period (e.g., 8, 13, 21, 34, 55).
Sum of BP: Cumulative Buying Pressure over n periods.
Sum of TR: Cumulative True Range over n periods.
This ratio normalizes buying pressure, making it comparable across different timeframes.
🔶 4. Weighted Average Calculation
The strategy employs a weighted average of ratios from multiple Fibonacci-based periods to smooth out signals and enhance trend detection.
Weighted Avg = (w1 * Ratio_p1 + w2 * Ratio_p2 + w3 * Ratio_p3 + w4 * Ratio_p4 + Ratio_p5) / (w1 + w2 + w3 + w4 + 1)
w1, w2, w3, w4: Weights assigned to each ratio period.
Ratio_p1 to Ratio_p5: Ratios calculated for periods p1 to p5 (e.g., 8, 13, 21, 34, 55).
This weighted approach emphasizes shorter periods more heavily, capturing recent market dynamics while still considering longer-term trends.
🔶 5. Simple Moving Average (SMA) of Weighted Average
To further smooth the weighted average and reduce noise, a Simple Moving Average (SMA) is applied.
Weighted Avg SMA = SMA(Weighted Avg, m)
- m: SMA period (e.g., 3).
This smoothed line serves as the primary signal generator for trade entries and exits.
🔶 6. Trading Condition Thresholds
The strategy defines specific threshold values to determine optimal entry and exit points based on crossovers and crossunders of the SMA.
Long Condition = Crossover(Weighted Avg SMA, Long Entry Threshold)
Short Condition = Crossunder(Weighted Avg SMA, Short Entry Threshold)
Long Exit = Crossunder(Weighted Avg SMA, Long Exit Threshold)
Short Exit = Crossover(Weighted Avg SMA, Short Exit Threshold)
Long Entry Threshold (T_LE): Level at which a long position is triggered.
Short Entry Threshold (T_SE): Level at which a short position is triggered.
Long Exit Threshold (T_LX): Level at which a long position is exited.
Short Exit Threshold (T_SX): Level at which a short position is exited.
These conditions ensure that trades are only executed when clear trends are identified, enhancing the strategy's reliability.
Previous local performance
🔶 7. ATR-Based Take Profit Mechanism
When enabled, the strategy employs a 4-step Take Profit system to systematically secure profits as the trade moves in the desired direction.
TP Price_1 Long = Entry Price + (TP1ATR * ATR Value)
TP Price_2 Long = Entry Price + (TP2ATR * ATR Value)
TP Price_3 Long = Entry Price + (TP3ATR * ATR Value)
TP Price_1 Short = Entry Price - (TP1ATR * ATR Value)
TP Price_2 Short = Entry Price - (TP2ATR * ATR Value)
TP Price_3 Short = Entry Price - (TP3ATR * ATR Value)
- ATR Value: Calculated using ATR over a specified period (e.g., 14).
- TPxATR: User-defined multipliers for each take profit level.
- TPx_percent: Percentage of the position to exit at each TP level.
This multi-tiered exit strategy allows for partial position closures, optimizing profit capture while maintaining exposure to potential further gains.
█ Trade Direction
The Fibonacci ATR Fusion Strategy is designed to operate in both long and short market conditions, providing flexibility to traders in varying market environments.
Long Trades: Initiated when the SMA of the weighted average crosses above the Long Entry Threshold (T_LE), indicating strong upward momentum.
Short Trades: Initiated when the SMA of the weighted average crosses below the Short Entry Threshold (T_SE), signaling robust downward momentum.
Additionally, the strategy can be configured to trade exclusively in one direction—Long, Short, or Both—based on the trader’s preference and market analysis.
█ Usage
Implementing the Fibonacci ATR Fusion Strategy involves several steps to ensure it aligns with your trading objectives and market conditions.
1. Configure Strategy Parameters:
- Trading Direction: Choose between Long, Short, or Both based on your market outlook.
- Trading Condition Thresholds: Set the Long Entry, Short Entry, Long Exit, and Short Exit thresholds to define when to enter and exit trades.
2. Set Take Profit Levels (if enabled):
- ATR Multipliers: Define how many ATRs away from the entry price each take profit level is set.
- Take Profit Percentages: Allocate what percentage of the position to close at each TP level.
3. Apply to Desired Chart:
- Add the strategy to the chart of the asset you wish to trade.
- Observe the plotted Fibonacci ATR and SMA Fibonacci ATR indicators for visual confirmation.
4. Monitor and Adjust:
- Regularly review the strategy’s performance through backtesting.
- Adjust the input parameters based on historical performance and changing market dynamics.
5. Risk Management:
- Ensure that the sum of take profit percentages does not exceed 100% to avoid over-closing positions.
- Utilize the ATR-based TP levels to adapt to varying market volatilities, maintaining a balanced risk-reward ratio.
█ Default Settings
Understanding the default settings is crucial for optimizing the Fibonacci ATR Fusion Strategy's performance. Here's a precise and simple overview of the key parameters and their effects:
🔶 Key Parameters and Their Effects
1. Trading Direction (`tradingDirection`)
- Default: Both
- Effect: Determines whether the strategy takes both long and short positions or restricts to one direction. Selecting Both allows maximum flexibility, while Long or Short can be used for directional bias.
2. Trading Condition Thresholds
Long Entry (long_entry_threshold = 58.0): Higher values reduce false positives but may miss trades.
Short Entry (short_entry_threshold = 42.0): Lower values capture early short trends but may increase false signals.
Long Exit (long_exit_threshold = 42.0): Exits long positions early, securing profits but potentially cutting trends short.
Short Exit (short_exit_threshold = 58.0): Delays short exits to capture favorable movements, avoiding premature exits.
3. Take Profit Configuration (`useTakeProfit` = false)
- Effect: When enabled, the strategy employs a 4-step TP mechanism to secure profits at multiple levels. By default, it is disabled to allow users to opt-in based on their trading style.
4. ATR-Based Take Profit Multipliers
TP1 (tp1ATR = 3.0): Sets the first TP at 3 ATRs for initial profit capture.
TP2 (tp2ATR = 8.0): Targets larger trends, though less likely to be reached.
TP3 (tp3ATR = 14.0): Optimizes for extreme price moves, seldom triggered.
5. Take Profit Percentages
TP Level 1 (tp1_percent = 12%): Secures 12% at the first TP.
TP Level 2 (tp2_percent = 12%): Exits another 12% at the second TP.
TP Level 3 (tp3_percent = 12%): Closes an additional 12% at the third TP.
6. Weighted Average Parameters
Ratio Periods: Fibonacci-based intervals (8, 13, 21, 34, 55) balance responsiveness.
Weights: Emphasizes recent data for timely responses to market trends.
SMA Period (weighted_avg_sma_period = 3): Smoothens data with minimal lag, balancing noise reduction and responsiveness.
7. ATR Period (`atrPeriod` = 14)
Effect: Sets the ATR calculation length, impacting TP sensitivity to volatility.
🔶 Impact on Performance
- Sensitivity and Responsiveness:
- Shorter Ratio Periods and Higher Weights: Make the weighted average more responsive to recent price changes, allowing quicker trade entries and exits but increasing the likelihood of false signals.
- Longer Ratio Periods and Lower Weights: Provide smoother signals with fewer false positives but may delay trade entries, potentially missing out on significant price moves.
- Profit Taking:
- ATR Multipliers: Higher multipliers set take profit levels further away, targeting larger price movements but reducing the probability of reaching these levels.
- Fixed Percentages: Allocating equal percentages at each TP level ensures consistent profit realization and risk management, preventing overexposure.
- Trade Direction Control:
- Selecting Specific Directions: Restricting trades to Long or Short can align the strategy with market trends or personal biases, potentially enhancing performance in trending markets.
- Risk Management:
- Take Profit Percentages: Dividing the position into smaller percentages at multiple TP levels helps lock in profits progressively, reducing risk and allowing the remaining position to ride further trends.
- Market Adaptability:
- Weighted Averages and ATR: By combining multiple timeframes and adjusting to volatility, the strategy adapts to different market conditions, maintaining effectiveness across various asset classes and timeframes.
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If you want to know more about ATR, can also check "SuperATR 7-Step Profit".
Enjoy trading.
Takeprofit
SuperATR 7-Step Profit - Strategy [presentTrading] Long time no see!
█ Introduction and How It Is Different
The SuperATR 7-Step Profit Strategy is a multi-layered trading approach that integrates adaptive Average True Range (ATR) calculations with momentum-based trend detection. What sets this strategy apart is its sophisticated 7-step take-profit mechanism, which combines four ATR-based exit levels and three fixed percentage levels. This hybrid approach allows traders to dynamically adjust to market volatility while systematically capturing profits in both long and short market positions.
Traditional trading strategies often rely on static indicators or single-layered exit strategies, which may not adapt well to changing market conditions. The SuperATR 7-Step Profit Strategy addresses this limitation by:
- Using Adaptive ATR: Enhances the standard ATR by making it responsive to current market momentum.
- Incorporating Momentum-Based Trend Detection: Identifies stronger trends with higher probability of continuation.
- Employing a Multi-Step Take-Profit System: Allows for gradual profit-taking at predetermined levels, optimizing returns while minimizing risk.
BTCUSD 6hr Performance
█ Strategy, How It Works: Detailed Explanation
The strategy revolves around detecting strong market trends and capitalizing on them using an adaptive ATR and momentum indicators. Below is a detailed breakdown of each component of the strategy.
🔶 1. True Range Calculation with Enhanced Volatility Detection
The True Range (TR) measures market volatility by considering the most significant price movements. The enhanced TR is calculated as:
TR = Max
Where:
High and Low are the current bar's high and low prices.
Previous Close is the closing price of the previous bar.
Abs denotes the absolute value.
Max selects the maximum value among the three calculations.
🔶 2. Momentum Factor Calculation
To make the ATR adaptive, the strategy incorporates a Momentum Factor (MF), which adjusts the ATR based on recent price movements.
Momentum = Close - Close
Stdev_Close = Standard Deviation of Close over n periods
Normalized_Momentum = Momentum / Stdev_Close (if Stdev_Close ≠ 0)
Momentum_Factor = Abs(Normalized_Momentum)
Where:
Close is the current closing price.
n is the momentum_period, a user-defined input (default is 7).
Standard Deviation measures the dispersion of closing prices over n periods.
Abs ensures the momentum factor is always positive.
🔶 3. Adaptive ATR Calculation
The Adaptive ATR (AATR) adjusts the traditional ATR based on the Momentum Factor, making it more responsive during volatile periods and smoother during consolidation.
Short_ATR = SMA(True Range, short_period)
Long_ATR = SMA(True Range, long_period)
Adaptive_ATR = /
Where:
SMA is the Simple Moving Average.
short_period and long_period are user-defined inputs (defaults are 3 and 7, respectively).
🔶 4. Trend Strength Calculation
The strategy quantifies the strength of the trend to filter out weak signals.
Price_Change = Close - Close
ATR_Multiple = Price_Change / Adaptive_ATR (if Adaptive_ATR ≠ 0)
Trend_Strength = SMA(ATR_Multiple, n)
🔶 5. Trend Signal Determination
If (Short_MA > Long_MA) AND (Trend_Strength > Trend_Strength_Threshold):
Trend_Signal = 1 (Strong Uptrend)
Elif (Short_MA < Long_MA) AND (Trend_Strength < -Trend_Strength_Threshold):
Trend_Signal = -1 (Strong Downtrend)
Else:
Trend_Signal = 0 (No Clear Trend)
🔶 6. Trend Confirmation with Price Action
Adaptive_ATR_SMA = SMA(Adaptive_ATR, atr_sma_period)
If (Trend_Signal == 1) AND (Close > Short_MA) AND (Adaptive_ATR > Adaptive_ATR_SMA):
Trend_Confirmed = True
Elif (Trend_Signal == -1) AND (Close < Short_MA) AND (Adaptive_ATR > Adaptive_ATR_SMA):
Trend_Confirmed = True
Else:
Trend_Confirmed = False
Local Performance
🔶 7. Multi-Step Take-Profit Mechanism
The strategy employs a 7-step take-profit system
█ Trade Direction
The SuperATR 7-Step Profit Strategy is designed to work in both long and short market conditions. By identifying strong uptrends and downtrends, it allows traders to capitalize on price movements in either direction.
Long Trades: Initiated when the market shows strong upward momentum and the trend is confirmed.
Short Trades: Initiated when the market exhibits strong downward momentum and the trend is confirmed.
█ Usage
To implement the SuperATR 7-Step Profit Strategy:
1. Configure the Strategy Parameters:
- Adjust the short_period, long_period, and momentum_period to match the desired sensitivity.
- Set the trend_strength_threshold to control how strong a trend must be before acting.
2. Set Up the Multi-Step Take-Profit Levels:
- Define ATR multipliers and fixed percentage levels according to risk tolerance and profit goals.
- Specify the percentage of the position to close at each level.
3. Apply the Strategy to a Chart:
- Use the strategy on instruments and timeframes where it has been tested and optimized.
- Monitor the positions and adjust parameters as needed based on performance.
4. Backtest and Optimize:
- Utilize TradingView's backtesting features to evaluate historical performance.
- Adjust the default settings to optimize for different market conditions.
█ Default Settings
Understanding default settings is crucial for optimal performance.
Short Period (3): Affects the responsiveness of the short-term MA.
Effect: Lower values increase sensitivity but may produce more false signals.
Long Period (7): Determines the trend baseline.
Effect: Higher values reduce noise but may delay signals.
Momentum Period (7): Influences adaptive ATR and trend strength.
Effect: Shorter periods react quicker to price changes.
Trend Strength Threshold (0.5): Filters out weaker trends.
Effect: Higher thresholds yield fewer but stronger signals.
ATR Multipliers: Set distances for ATR-based exits.
Effect: Larger multipliers aim for bigger moves but may reduce hit rate.
Fixed TP Levels (%): Control profit-taking on smaller moves.
Effect: Adjusting these levels affects how quickly profits are realized.
Exit Percentages: Determine how much of the position is closed at each TP level.
Effect: Higher percentages reduce exposure faster, affecting risk and reward.
Adjusting these variables allows you to tailor the strategy to different market conditions and personal risk preferences.
By integrating adaptive indicators and a multi-tiered exit strategy, the SuperATR 7-Step Profit Strategy offers a versatile tool for traders seeking to navigate varying market conditions effectively. Understanding and adjusting the key parameters enables traders to harness the full potential of this strategy.
VIDYA ProTrend Multi-Tier ProfitHello! This time is about a trend-following system.
VIDYA is quite an interesting indicator that adjusts dynamically to market volatility, making it more responsive to price changes compared to traditional moving averages. Balancing adaptability and precision, especially with the more aggressive short trade settings, challenged me to fine-tune the strategy for a variety of market conditions.
█ Introduction and How it is Different
The "VIDYA ProTrend Multi-Tier Profit" strategy is a trend-following system that combines the VIDYA (Variable Index Dynamic Average) indicator with Bollinger Bands and a multi-step take-profit mechanism.
Unlike traditional trend strategies, this system allows for more adaptive profit-taking, adjusting for long and short positions through distinct ATR-based and percentage-based targets. The innovation lies in its dynamic multi-tier approach to profit-taking, especially for short trades, where more aggressive percentages are applied using a multiplier. This flexibility helps adapt to various market conditions by optimizing trade management and profit allocation based on market volatility and trend strength.
BTCUSD 6hr performance
█ Strategy, How it Works: Detailed Explanation
The core of the "VIDYA ProTrend Multi-Tier Profit" strategy lies in the dual VIDYA indicators (fast and slow) that analyze price trends while accounting for market volatility. These indicators work alongside Bollinger Bands to filter trade entries and exits.
🔶 VIDYA Calculation
The VIDYA indicator is calculated using the following formula:
Smoothing factor (𝛼):
alpha = 2 / (Length + 1)
VIDYA formula:
VIDYA(t) = alpha * k * Price(t) + (1 - alpha * k) * VIDYA(t-1)
Where:
k = |Chande Momentum Oscillator (MO)| / 100
🔶 Bollinger Bands as a Volatility Filter
Bollinger Bands are calculated using a rolling mean and standard deviation of price over a specified period:
Upper Band:
BB_upper = MA + (K * stddev)
Lower Band:
BB_lower = MA - (K * stddev)
Where:
MA is the moving average,
K is the multiplier (typically 2), and
stddev is the standard deviation of price over the Bollinger Bands length.
These bands serve as volatility filters to identify potential overbought or oversold conditions, aiding in the entry and exit logic.
🔶 Slope Calculation for VIDYA
The slopes of both fast and slow VIDYAs are computed to assess the momentum and direction of the trend. The slope for a given VIDYA over its length is:
Slope = (VIDYA(t) - VIDYA(t-n)) / n
Where:
n is the length of the lookback period. Positive slope indicates bullish momentum, while negative slope signals bearish momentum.
LOCAL picture
🔶 Entry and Exit Conditions
- Long Entry: Occurs when the price moves above the slow VIDYA and the fast VIDYA is trending upward. Bollinger Bands confirm the signal when the price crosses the upper band, indicating bullish strength.
- Short Entry: Happens when the price drops below the slow VIDYA and the fast VIDYA trends downward. The signal is confirmed when the price crosses the lower Bollinger Band, showing bearish momentum.
- Exit: Based on VIDYA slopes flattening or reversing, or when the price hits specific ATR or percentage-based profit targets.
🔶 Multi-Step Take Profit Mechanism
The strategy incorporates three levels of take profit for both long and short trades:
- ATR-based Take Profit: Each step applies a multiple of the ATR (Average True Range) to the entry price to define the exit point.
The first level of take profit (long):
TP_ATR1_long = Entry Price + (2.618 * ATR)
etc.
█ Trade Direction
The strategy offers flexibility in defining the trading direction:
- Long: Only long trades are considered based on the criteria for upward trends.
- Short: Only short trades are initiated in bearish trends.
- Both: The strategy can take both long and short trades depending on the market conditions.
█ Usage
To use the strategy effectively:
- Adjust the VIDYA lengths (fast and slow) based on your preference for trend sensitivity.
- Use Bollinger Bands as a filter for identifying potential breakout or reversal scenarios.
- Enable the multi-step take profit feature to manage positions dynamically, allowing for partial exits as the price reaches specified ATR or percentage levels.
- Leverage the short trade multiplier for more aggressive take profit levels in bearish markets.
This strategy can be applied to different asset classes, including equities, forex, and cryptocurrencies. Adjust the input parameters to suit the volatility and characteristics of the asset being traded.
█ Default Settings
The default settings for this strategy have been designed for moderate to trending markets:
- Fast VIDYA Length (10): A shorter length for quick responsiveness to price changes. Increasing this length will reduce noise but may delay signals.
- Slow VIDYA Length (30): The slow VIDYA is set longer to capture broader market trends. Shortening this value will make the system more reactive to smaller price swings.
- Minimum Slope Threshold (0.05): This threshold helps filter out weak trends. Lowering the threshold will result in more trades, while raising it will restrict trades to stronger trends.
Multi-Step Take Profit Settings
- ATR Multipliers (2.618, 5.0, 10.0): These values define how far the price should move before taking profit. Larger multipliers widen the profit-taking levels, aiming for larger trend moves. In higher volatility markets, these values might be adjusted downwards.
- Percentage Levels (3%, 8%, 17%): These percentage levels define how much the price must move before taking profit. Increasing the percentages will capture larger moves, while smaller percentages offer quicker exits.
- Short TP Multiplier (1.5): This multiplier applies more aggressive take profit levels for short trades. Adjust this value based on the aggressiveness of your short trade management.
Each of these settings directly impacts the performance and risk profile of the strategy. Shorter VIDYA lengths and lower slope thresholds will generate more trades but may result in more whipsaws. Higher ATR multipliers or percentage levels can delay profit-taking, aiming for larger trends but risking partial gains if the trend reverses too early.
Multi-Step Vegas SuperTrend - strategy [presentTrading]Long time no see! I am back : ) Please allow me to gain some warm-up.
█ Introduction and How it is Different
The "Vegas SuperTrend Strategy" is an enhanced trading strategy that leverages both the Vegas Channel and SuperTrend indicators to generate buy and sell signals.
What sets this strategy apart from others is its dynamic adjustment to market volatility and its multi-step take profit mechanism. Unlike traditional single-step profit-taking approaches, this strategy allows traders to systematically scale out of positions at predefined profit levels, thereby optimizing their risk-reward ratio and maximizing potential gains.
BTCUSD 6hr performance
█ Strategy, How it Works: Detailed Explanation
The Vegas SuperTrend Strategy combines the strengths of the Vegas Channel and SuperTrend indicators to identify market trends and generate trade signals. The following subsections delve into the details of how each component works and how they are integrated.
🔶 Vegas Channel Calculation
The Vegas Channel is based on a simple moving average (SMA) and the standard deviation (STD) of the closing prices over a specified period. The channel is defined by upper and lower bounds that are dynamically adjusted based on market volatility.
Simple Moving Average (SMA):
SMA_vegas = (1/N) * Σ(Close_i) for i = 0 to N-1
where N is the length of the Vegas Window.
Standard Deviation (STD):
STD_vegas = sqrt((1/N) * Σ(Close_i - SMA_vegas)^2) for i = 0 to N-1
Vegas Channel Upper and Lower Bounds:
VegasChannelUpper = SMA_vegas + STD_vegas
VegasChannelLower = SMA_vegas - STD_vegas
The details are here:
🔶 Trend Detection and Trade Signals
The strategy determines the current market trend based on the closing price relative to the SuperTrend bounds:
Market Trend:
MarketTrend = 1 if Close > SuperTrendPrevLower
-1 if Close < SuperTrendPrevUpper
Previous Trend otherwise
Trade signals are generated when there is a shift in the market trend:
Bullish Signal: When the market trend shifts from -1 to 1.
Bearish Signal: When the market trend shifts from 1 to -1.
🔶 Multi-Step Take Profit Mechanism
The strategy incorporates a multi-step take profit mechanism that allows for partial exits at predefined profit levels. This helps in locking in profits gradually and reducing exposure to market reversals.
Take Profit Levels:
The take profit levels are calculated as percentages of the entry price:
TakeProfitLevel_i = EntryPrice * (1 + TakeProfitPercent_i/100) for long positions
TakeProfitLevel_i = EntryPrice * (1 - TakeProfitPercent_i/100) for short positions
Multi-steps take profit local picture:
█ Trade Direction
The trade direction can be customized based on the user's preference:
Long: The strategy only takes long positions.
Short: The strategy only takes short positions.
Both: The strategy can take both long and short positions based on the market trend.
█ Usage
To use the Vegas SuperTrend Strategy, follow these steps:
Configure Input Settings:
- Set the ATR period, Vegas Window length, SuperTrend Multiplier, and Volatility Adjustment Factor.
- Choose the desired trade direction (Long, Short, Both).
- Enable or disable the take profit mechanism and set the take profit percentages and amounts for each step.
█ Default Settings
The default settings of the strategy are designed to provide a balanced approach to trading. Below is an explanation of each setting and its effect on the strategy's performance:
ATR Period (10): This setting determines the length of the ATR used in the SuperTrend calculation. A longer period smoothens the ATR, making the SuperTrend less sensitive to short-term volatility. A shorter period makes the SuperTrend more responsive to recent price movements.
Vegas Window Length (100): This setting defines the period for the Vegas Channel's moving average. A longer window provides a broader view of the market trend, while a shorter window makes the channel more responsive to recent price changes.
SuperTrend Multiplier (5): This base multiplier adjusts the sensitivity of the SuperTrend to the ATR. A higher multiplier makes the SuperTrend less sensitive, reducing the frequency of trade signals. A lower multiplier increases sensitivity, generating more signals.
Volatility Adjustment Factor (5): This factor dynamically adjusts the SuperTrend multiplier based on the width of the Vegas Channel. A higher factor increases the sensitivity of the SuperTrend to changes in market volatility, while a lower factor reduces it.
Take Profit Percentages (3.0%, 6.0%, 12.0%, 21.0%): These settings define the profit levels at which portions of the trade are exited. They help in locking in profits progressively as the trade moves in favor.
Take Profit Amounts (25%, 20%, 10%, 15%): These settings determine the percentage of the position to exit at each take profit level. They are distributed to ensure that significant portions of the trade are closed as the price reaches the set levels, reducing exposure to reversals.
Adjusting these settings can significantly impact the strategy's performance. For instance, increasing the ATR period or the SuperTrend multiplier can reduce the number of trades, potentially improving the win rate but also missing out on some profitable opportunities. Conversely, lowering these values can increase trade frequency, capturing more short-term movements but also increasing the risk of false signals.
Strategic Multi-Step Supertrend - Strategy [presentTrading]The code is mainly developed for me to stimulate the multi-step taking profit function for strategies. The result shows the drawdown can be reduced but at the same time reduced the profit as well. It can be a heuristic for futures leverage traders.
█ Introduction and How it is Different
The "Strategic Multi-Step Supertrend" is a trading strategy designed to leverage the power of multiple steps to optimize trade entries and exits across the Supertrend indicator. Unlike traditional strategies that rely on single entry and exit points, this strategy employs a multi-step approach to take profit, allowing traders to lock in gains incrementally. Additionally, the strategy is adaptable to both long and short trades, providing a comprehensive solution for dynamic market conditions.
This template strategy lies in its dual Supertrend calculation, which enhances the accuracy of trend detection and provides more reliable signals for trade entries and exits. This approach minimizes false signals and increases the overall profitability of trades by ensuring that positions are entered and exited at optimal points.
BTC 6h L/S Performance
█ Strategy, How It Works: Detailed Explanation
The "Strategic Multi-Step Supertrend Trader" strategy utilizes two Supertrend indicators calculated with different parameters to determine the direction and strength of the market trend. This dual approach increases the robustness of the signals, reducing the likelihood of entering trades based on false signals. Here is a detailed breakdown of how the strategy operates:
🔶 Supertrend Indicator Calculation
The Supertrend indicator is a trend-following overlay on the price chart, typically used to identify the direction of the trend. It is calculated using the Average True Range (ATR) to ensure that the indicator adapts to market volatility. The formula for the Supertrend indicator is:
Upper Band = (High + Low) / 2 + (Factor * ATR)
Lower Band = (High + Low) / 2 - (Factor * ATR)
Where:
- High and Low are the highest and lowest prices of the period.
- Factor is a user-defined multiplier.
- ATR is the Average True Range over a specified period.
The Supertrend changes its direction based on the closing price in relation to these bands.
🔶 Entry-Exit Conditions
The strategy enters long positions when both Supertrend indicators signal an uptrend, and short positions when both indicate a downtrend. Specifically:
- Long Condition: Supertrend1 < 0 and Supertrend2 < 0
- Short Condition: Supertrend1 > 0 and Supertrend2 > 0
- Long Exit Condition: Supertrend1 > 0 and Supertrend2 > 0
- Short Exit Condition: Supertrend1 < 0 and Supertrend2 < 0
🔶 Multi-Step Take Profit Mechanism
The strategy features a multi-step take profit mechanism, which allows traders to lock in profits incrementally. This is achieved through four user-configurable take profit levels. For each level, the strategy specifies a percentage increase (for long trades) or decrease (for short trades) in the entry price at which a portion of the position is exited:
- Step 1: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent1 / 100)
- Step 2: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent2 / 100)
- Step 3: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent3 / 100)
- Step 4: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent4 / 100)
This staggered exit strategy helps in locking profits at multiple levels, thereby reducing risk and increasing the likelihood of capturing the maximum possible profit from a trend.
BTC Local
█ Trade Direction
The strategy is highly flexible, allowing users to specify the trade direction. There are three options available:
- Long Only: The strategy will only enter long trades.
- Short Only: The strategy will only enter short trades.
- Both: The strategy will enter both long and short trades based on the Supertrend signals.
This flexibility allows traders to adapt the strategy to various market conditions and their own trading preferences.
█ Usage
1. Add the strategy to your trading platform and apply it to the desired chart.
2. Configure the take profit settings under the "Take Profit Settings" group.
3. Set the trade direction under the "Trade Direction" group.
4. Adjust the Supertrend settings in the "Supertrend Settings" group to fine-tune the indicator calculations.
5. Monitor the chart for entry and exit signals as indicated by the strategy.
█ Default Settings
- Use Take Profit: True
- Take Profit Percentages: Step 1 - 6%, Step 2 - 12%, Step 3 - 18%, Step 4 - 50%
- Take Profit Amounts: Step 1 - 12%, Step 2 - 8%, Step 3 - 4%, Step 4 - 0%
- Number of Take Profit Steps: 3
- Trade Direction: Both
- Supertrend Settings: ATR Length 1 - 10, Factor 1 - 3.0, ATR Length 2 - 11, Factor 2 - 4.0
These settings provide a balanced starting point, which can be customized further based on individual trading preferences and market conditions.
VAWSI and Trend Persistance Reversal Strategy SL/TPThis is a completely revamped version of my "RSI and ATR Trend Reversal Strategy."
What's New?
The RSI has been replaced with an original indicator of mine, the "VAWSI," as I've elected to call it.
The standard RSI measures a change in an RMA to determine the strength of a movement.
The VAWSI performs very similarly, except it uses another original indicator of mine, the VAWMA.
VAWMA stands for "Volume (and) ATR Weight Moving Average." It takes an average of the volume and ATR and uses the ratio of each bar to weigh a moving average of the source.
It has the same formula as an RSI, but uses the VAWMA instead of an RMA.
Next we have the Trend Persistence indicator, which is an index on how long a trend has been persisting for. It is another original indicator. It takes the max deviation the source has from lowest/highest of a specified length. It then takes a cumulative measure of that amount, measures the change, then creates a strength index with that amount.
The VAWSI is a measure of an emerging trend, and the Trend Persistence indicator is a measure of how long a trend has persisted.
Finally, the 3rd main indicator, is a slight variation of an ATR. Rather than taking the max of source - low or high- source and source - source , it instead takes the max of high-low and the absolute value of source - the previous source. It then takes the absolute value of the change of this, and normalizes it with the source.
Inputs
Minimum SL/TP ensures that the Stop Loss and Take Profit still exist in untrendy markets. This is the minimum Amount that will always be applied.
VAWSI Weight is a divided by 100 multiplier for the VAWSI. So value of 200 means it is multiplied by 2. Think of it like a percentage.
Trend Persistence weight and ATR Weight are applied the same. Higher the number, the more impactful on the final calculation it is.
Combination Mult is an outright multiplier to the final calculation. So a 2.0 = * 2.0
Trend Persistence Smoothing Length is the length of the weighted moving average applied to the Trend Persistence Strength index.
Length Cycle Decimal is a replacement of length for the script.
Here we used BlackCat1402's Dynamic Length Calculation, which can be found on his page. With his permission we have implemented it into this script. Big shout out to them for not only creating, but allowing us to use it here.
The Length Cycle Decimal is used to calculate the dynamic length. Because TradingView only allows series int for their built-in library, a lot of the baseline indicators we use have to be manually recreated as functions in the following section.
The Strategy
As usual, we use Heiken Ashi values for calculations.
We begin by establishing the minimum SL/TP for use later.
Next we determine the amount of bars back since the last crossup or crossdown of our threshold line.
We then perform some normalization of our multipliers. We want a larger trend or larger VAWSI amount to narrow the threshold, so we have 1 divide them. This way, a higher reading outputs a smaller number and vice versa. We do this for both Trend Persistence, and the VAWSI.
The VAWSI we also normalize, where rather than it being a 0-100 reading of trend direction and strength, we absolute it so that as long as a trend is strong, regardless of direction, it will have a higher reading. With these normalized values, we add them together and simply subtract the ATR measurement rather than having 1 divide it.
Here you can see how the different measurements add up. A lower final number suggests imminent reversal, and a higher final number suggests an untrendy or choppy market.
ATR is in orange, the Trend Persistence is blue, the VAWSI is purple, and the final amount is green.
We take this final number and depending on the current trend direction, we multiply it by either the Highest or Lowest source since the last crossup or crossdown. We then take the highest or lowest of this calculation, and have it be our Stop Loss or Take Profit. This number cannot be higher/lower than the previous source to ensure a rapid spike doesn't immediately close your position on a still continuing trend. As well, the threshold cannot be higher/ lower than the the specified Stop Loss and Take Profit
Only after the source has fully crossed these lines do we consider it a crossup or crossdown. We confirm this with a barstate.isconfirmed to prevent repainting. Next, each time there is a crossup or crossdown we enter a long or a short respectively and plot accordingly.
I have the strategy configured to "process on order close" to ensure an accurate backtesting result. You could also set this to false and add a 1 bar delay to the "if crossup" and "if crossdown" lines under strategy so that it is calculated based on the open of the next bar.
Final Notes
The amounts have been preconfigured for performance on RIOT 5 Minute timeframe. Other timeframes are viable as well. With a few changes to the parameters, this strategy has backtested well on NVDA, AAPL, TSLA, and AMD. I recommend before altering settings to try other timeframes first.
This script does not seem to perform nearly as well in typically untrendy and choppy markets such as crypto and forex. With some setting changes, I have seen okay results with crypto, but overfitting could be the cause there.
Thank you very much, and please enjoy.
Trailing Take Profit - Close Based📝 Description
This script demonstrates a new approach to the trailing take profit.
Trailing Take Profit is a price-following technique. When used, instead of setting a limit order for the take profit target exiting from your position at the specified price, a stop order is conditionally set when the take profit target is reached. Then, the stop price (a.k.a trailing price), is placed below the take profit target at a distance defined by the user percentagewise. On regular time intervals, the stop price gets updated by following the "Trail Barrier" price (high by default) upwards. When the current price hits the stop price you exit the trade. Check the chart for more details.
This script demonstrates how to implement the close-based Trailing Take Profit logic for long positions, but it can also be applied for short positions if the logic is "reversed".
📢 NOTE
To generate some entries and showcase the "Trailing Take Profit" technique, this script uses the crossing of two moving averages. Please keep in mind that you should not relate the Backtesting results you see in the "Strategy Tester" tab with the success of the technique itself.
This is not a complete strategy per se, and the backtest results are affected by many parameters that are outside of the scope of this publication. If you choose to use this new approach of the "Trailing Take Profit" in your logic you have to make sure that you are backtesting the whole strategy.
⚔️ Comparison
In contrast to my older "Trailing Take Profit" publication where the trailing take profit implementation was tick-based, this new approach is close-based, meaning that the update of the stop price occurs at the bar close instead of every tick.
While comparing the real-time results of the two implementations is like comparing apples to oranges, because they have different dynamic behavior, the new approach offers better consistency between the backtesting results and the real-time results.
By updating the stop price on every bar close, you do not rely on the backtester assumptions anymore (check the Reasoning section below for more info).
The new approach resembles the conditional "Trailing Exit" technique, where the condition is true when the current price crosses over the take profit target. Then, the stop order is placed at the trailing price and it gets updated on every bar close to "follow" the barrier price (high). On the other hand, the older tick-based approach had more "tight" dynamics since the trailing price gets updated on every tick leaving less room for price fluctuations by making it more probable to reach the trailing price.
🤔 Reasoning
This new close-based approach addresses several practical issues the older tick-based approach had. Those issues arise mainly from the technicalities of the TV Backtester. More specifically, due to the assumptions the Broker Emulator makes for the price action of the history bars, the backtesting results in the TV Backtester are exaggerated, and depending on the timeframe, the backtesting results look way better than they are in reality.
The effect above, and the inability to reason about the performance of a strategy separated people into two groups. Those who never use this feature, because they couldn't know for sure the actual effect it might have in their strategy, (even if it turned out to be more profitable) and those who abused this type of "repainting" behavior to show off, and hijack some boosts from the community by boasting about the "fake" results of their strategies.
Even if there are ways to evaluate the effectiveness of the tick-based approach that is applied in an existing strategy (this is out of the topic of this publication), it requires extra effort to do the analysis. Using this closed-based approach we can have more predictable results, without surprises.
⚠️ Caveats
Since this approach updates the trailing price on bar close, you must wait for at least one bar to close after the price crosses over the take profit target.
RSI and ATR Trend Reversal SL/TPQuick History:
I was frustrated with a standard fixed percent TP/SL as they often were not receptive to quick market rallies/reversals. I developed this TP/SL and eventually made it into a full fledge strategy and found it did well enough to publish. This strategy can be used as a standalone or tacked onto another strategy as a TP/SL. It does function as both with a single line. This strategy has been tested with TSLA , AAPL, NVDA, on the 15 minutes timeframe.
HOW IT WORKS:
Inputs:
Length: Simple enough, it determines the length of the RSI and ATR used.
Multiplier: This multiplies the RSI and ATR calculation, more on this later.
Delay to prevent Idealization: TradingView will use the open of the bar the strategy triggers on when calculating the backtest. This can produce unrealistic results depending on the source. If your source is open, set to 0, if anything else, set to 1.
Minimum Difference: This is essentially a traditional SL/TP, it is borderline unnecessary, but if the other parameters are wacky this can be used to ensure the SL/TP. It multiplies the source by the percent, so if it is set to 10, the SL/TP is initialized at src +- 10%.
Source input: Self Explanatory, be sure to update the Delay if you use open.
CALCULATION:
Parameters Initialization:
The strategy uses Heikinashi values for calculations, this is not toggleable in parameters, but can be easily changed by changing hclose to equal src.
FUNCTION INITIALIZATION:
highest_custom and lowest_custom do the same thing as ta.highest and ta.lowest, however the built in ta library does not allow for var int input, so I had to create my own functions to be used here. I actually developed these years ago and have used them in almost every strategy since. Feel especially free to use these in your own scripts.
The rsilev is where the magic happens.
SL/TP min/max are initially calculated to be used later.
Then we begin by establishing variables.
BullGuy is used to determine the length since the last crossup or crossdown, until one happens, it returns na, breaking the function. BearGuy is used in all the calculations, and is the same as BullGuy, unless BullGuy is na, where BearGuy counts up from 1 on each bar from 0.
We create our rsi and have to modify the second one to suit the function. In the case of the upper band, we mirror the lower one. So if the RSI is 80, we want it to be 20 on the upper band.
the upper band and lower band are calculated the exact same way, but mirrored. For the purpose of writing, I'm going to talk about the lower band. Assume everything is mirrored for the upper one. It finds the highest source since the last crossup or crossdown. It then multiplies from 1 / the RSI, this means that a rapid RSI increase will increase the band dramatically, so it is able to capture quick rally/reversals. We add this to the atr to source ratio, as the general volatility is a massive factor to be included. We then multiply this number by our chosen amount, and subtract it from the highest source, creating the band.
We do this same process but mirrored with both bands and compared it to the source. If the source is above the lower band, it suggests an uptrend, so the lower band is outputted, and vice versa for the upper one.
PLOTTING:
We also determine the line color in the same manner as we do the trend direction.
STRATEGY:
We then use the source again, and if it crosses up or down relative to the selected band, we enter a long or short respectively.
This may not be the most superb independent strategy, but it can be very useful as a TP/SL for your chosen entry conditions, especially in volatile markets or tickers.
Thank you for taking the time to read, and please enjoy.
Machine Learning: SuperTrend Strategy TP/SL [YinYangAlgorithms]The SuperTrend is a very useful Indicator to display when trends have shifted based on the Average True Range (ATR). Its underlying ideology is to calculate the ATR using a fixed length and then multiply it by a factor to calculate the SuperTrend +/-. When the close crosses the SuperTrend it changes direction.
This Strategy features the Traditional SuperTrend Calculations with Machine Learning (ML) and Take Profit / Stop Loss applied to it. Using ML on the SuperTrend allows for the ability to sort data from previous SuperTrend calculations. We can filter the data so only previous SuperTrends that follow the same direction and are within the distance bounds of our k-Nearest Neighbour (KNN) will be added and then averaged. This average can either be achieved using a Mean or with an Exponential calculation which puts added weight on the initial source. Take Profits and Stop Losses are then added to the ML SuperTrend so it may capitalize on Momentum changes meanwhile remaining in the Trend during consolidation.
By applying Machine Learning logic and adding a Take Profit and Stop Loss to the Traditional SuperTrend, we may enhance its underlying calculations with potential to withhold the trend better. The main purpose of this Strategy is to minimize losses and false trend changes while maximizing gains. This may be achieved by quick reversals of trends where strategic small losses are taken before a large trend occurs with hopes of potentially occurring large gain. Due to this logic, the Win/Loss ratio of this Strategy may be quite poor as it may take many small marginal losses where there is consolidation. However, it may also take large gains and capitalize on strong momentum movements.
Tutorial:
In this example above, we can get an idea of what the default settings may achieve when there is momentum. It focuses on attempting to hit the Trailing Take Profit which moves in accord with the SuperTrend just with a multiplier added. When momentum occurs it helps push the SuperTrend within it, which on its own may act as a smaller Trailing Take Profit of its own accord.
We’ve highlighted some key points from the last example to better emphasize how it works. As you can see, the White Circle is where profit was taken from the ML SuperTrend simply from it attempting to switch to a Bullish (Buy) Trend. However, that was rejected almost immediately and we went back to our Bearish (Sell) Trend that ended up resulting in our Take Profit being hit (Yellow Circle). This Strategy aims to not only capitalize on the small profits from SuperTrend to SuperTrend but to also capitalize when the Momentum is so strong that the price moves X% away from the SuperTrend and is able to hit the Take Profit location. This Take Profit addition to this Strategy is crucial as momentum may change state shortly after such drastic price movements; and if we were to simply wait for it to come back to the SuperTrend, we may lose out on lots of potential profit.
If you refer to the Yellow Circle in this example, you’ll notice what was talked about in the Summary/Overview above. During periods of consolidation when there is little momentum and price movement and we don’t have any Stop Loss activated, you may see ‘Signal Flashing’. Signal Flashing is when there are Buy and Sell signals that keep switching back and forth. During this time you may be taking small losses. This is a normal part of this Strategy. When a signal has finally been confirmed by Momentum, is when this Strategy shines and may produce the profit you desire.
You may be wondering, what causes these jagged like patterns in the SuperTrend? It's due to the ML logic, and it may be a little confusing, but essentially what is happening is the Fast Moving SuperTrend and the Slow Moving SuperTrend are creating KNN Min and Max distances that are extreme due to (usually) parabolic movement. This causes fewer values to be added to and averaged within the ML and causes less smooth and more exponential drastic movements. This is completely normal, and one of the perks of using k-Nearest Neighbor for ML calculations. If you don’t know, the Min and Max Distance allowed is derived from the most recent(0 index of data array) to KNN Length. So only SuperTrend values that exhibit distances within these Min/Max will be allowed into the average.
Since the KNN ML logic can cause these exponential movements in the SuperTrend, they likewise affect its Take Profit. The Take Profit may benefit from this movement like displayed in the example above which helped it claim profit before then exhibiting upwards movement.
By default our Stop Loss Multiplier is kept quite low at 0.0000025. Keeping it low may help to reduce some Signal Flashing while not taking extra losses more so than not using it at all. However, if we increase it even more to say 0.005 like is shown in the example above. It can really help the trend keep momentum. Please note, although previous results don’t imply future results, at 0.0000025 Stop Loss we are currently exhibiting 69.27% profit while at 0.005 Stop Loss we are exhibiting 33.54% profit. This just goes to show that although there may be less Signal Flashing, it may not result in more profit.
We will conclude our Tutorial here. Hopefully this has given you some insight as to how Machine Learning, combined with Trailing Take Profit and Stop Loss may have positive effects on the SuperTrend when turned into a Strategy.
Settings:
SuperTrend:
ATR Length: ATR Length used to create the Original Supertrend.
Factor: Multiplier used to create the Original Supertrend.
Stop Loss Multiplier: 0 = Don't use Stop Loss. Stop loss can be useful for helping to prevent false signals but also may result in more loss when hit and less profit when switching trends.
Take Profit Multiplier: Take Profits can be useful within the Supertrend Strategy to stop the price reverting all the way to the Stop Loss once it's been profitable.
Machine Learning:
Only Factor Same Trend Direction: Very useful for ensuring that data used in KNN is not manipulated by different SuperTrend Directional data. Please note, it doesn't affect KNN Exponential.
Rationalized Source Type: Should we Rationalize only a specific source, All or None?
Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None?
Machine Learning Smoothing Type: How should we smooth our Fast and Slow ML Datas to be used in our KNN Distance calculation? SMA, EMA or VWMA?
KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using.
Machine Learning Length: How far back is our Machine Learning going to keep data for.
k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for?
Fast ML Data Length: What is our Fast ML Length?? This is used with our Slow Length to create our KNN Distance.
Slow ML Data Length: What is our Slow ML Length?? This is used with our Fast Length to create our KNN Distance.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
2Mars strategy [OKX]The strategy is based on the intersection of two moving averages, which requires adjusting the parameters (ratio and multiplier) for the moving average.
Basis MA length: multiplier * ratio
Signal MA length: multiplier
The SuperTrend indicator is used for additional confirmation of entry into a position.
Bollinger Bands and position reversal are used for take-profit.
About stop loss:
If activated, the stop loss price will be updated on every entry.
Basic setup:
Additional:
Alerts for OKX:
FFT Strategy Bi-Directional Stop/Profit/Trailing + VMA + AroonThis strategy uses the Fast Fourier Transform inspired from the source code of @tbiktag for the Fast Fourier Transform & @lazybear for the VMA filter.
If you are not familiar with the Fast Fourier transform it is a variation of the Discrete Fourier Transform. Veritasium on youtube has a great video on it with a follow up recommendation from 3brown1blue. In short it will extract all the frequencies from a set of data. @tbiktag laid the groundwork for creating the indicator which will allow you to isolate only those signals which are the most relevant and remove the noise. I recommend having @tbiktag's FFT Transform indicator side by side with this to understand what my variation is doing by setting similar settings .
Using this idea, you can then optimize a strategy to the frequencies that are best. The main entry signal is when the FFT Signal crosses above or below the 0 line .
Included with this strategy is the ability to optionally bi-directionally set:
Stop Loss
Trailing Stop Loss
Take Profit
Trailing Take Profit
Entries are optionally further filtered by use of the VMA using the algorithm from LazyBear which allows you to adjust a variable moving average with 3 market trend detections. Green represents upwards momentum; Blue sideways trading and Red downwards momentum. The idea being to filter out buy or sell entries unless the market is moving in that direction, and this makes a big difference as you can see for yourself when you turn it off or on. Turning it off will change the color of the FFT signal to orange instead of the green, blue, red colors .
I have added 2 custom stop loss types as well for experimentation:
1. VMA Filter stop loss to exit the trade if the VMA detects a market trend direction change matching the rules you have set. I have set this to off by default, but it is there so you can see what affect it may have on other tickers. It can increase the profit factor but usually at a cost of net profit.
2. The Aroon Filter stop loss with different lengths for the short or long direction. For the Aroon strategy (which is a trend change detector) it is considered bullish if the upper line (green in my code) is above 70 and the lower line (red in my code) is below 30 and the opposite for the bearish case. With this in mind, I have set it to filter by default only the extreme ends (99 and 1) to increase profit factor and net profit but I encourage you to try different settings and see how it affects things. Turning this off yields much higher net profit but at the cost of the profit factor and drawdown . To disable this just uncheck the 'Use Aroon Filter Long' (or short) and it will also hide the aroon graphics and crosses on the plot.
I will be adding more features in an attempt to lower the drawdown on this strategy but I hope you enjoy what I have so far!
Take Profit On Trend v2 (by BHD_Trade_Bot)The purpose of strategy is to detect long-term uptrend and short-term downtrend so that you can easy to take profit.
The strategy also using BHD unit to detect how big you win and lose, so that you can use this strategy for all coins without worry about it have different percentage of price change.
ENTRY
The buy order is placed on assets that have long-term uptrend and short-term downtrend:
- Long-term uptrend condition: ema200 is going up
- Short-term downtrend condition: 2 last candles are down price (use candlestick for less delay)
CLOSE
The sell order is placed when take profit or stop loss:
- Take profit: price increase 2 BHD unit
- Stop loss: price decrease 3 BHD unit
The strategy use $1000 for initial capital and trading fee is 0.1% for each order.
Pro tip: The 1-hour time frame for ETH/USDT has the best results on average.
Squeeze Momentum Strategy [LazyBear] Buy Sell TP SL Alerts-Modified version of Squeeze Momentum Indicator by @LazyBear.
-Converted to version 5,
-Taken inspiration from @KivancOzbilgic for its buy sell calculations,
-Used @Bunghole strategy template with Take Profit, Stop Loss and Enable/Disable Toggles
-Added Custom Date Backtesting Module
------------------------------------------------------------------------------------------------------------------------
All credit goes to above
Problem with original version:
The original Squeeze Momentum Strategy did not have buy sell signals and there was alot of confusion as to when to enter and exit.
There was no proper strategy that would allow backtesting on which further analysis could be carried out.
There are 3 aspects this strategy:
1 ) Strategy Logic (easily toggleable from the dropdown menu from strategy settings)
- LazyBear (I have made this simple by using Kivanc technique of Momentums Moving Average Crossover, BUY when MA cross above signal line, SELL when crossdown signal line)
- Zero Crossover Line (BUY signal when crossover zero line, and SELL crossdown zero line)
2) Long Short TP and SL
- In strategies there is usually only 1 SL and 1 TP, and it is assumed that if a 2% SL giving a good profit %, then it would be best for both long and short. However this is not the case for many. Many markets/pairs, go down with much more speed then they go up with. Hence once we have a profitable backtesting setting, then we should start optimizing Long and Short SL's seperately. Once that is done, we should start optimizing for Long and Short TP's separately, starting with Longs first in both cases.
3) Enable and Disable Toggles of Long and Short Trades
- Many markets dont allow short trades, or are not suitable for short trades. In this case it would be much more feasible to disable "Short" Trading and see results of Long Only as a built in graphic view of backtestor provides a more easy to understand data feed as compared to the performance summary in which you have to review long and short profitability separately.
4) Custom Data Backtesting
- One of most crucial aspects while optimizing for backtesting is to check a strategies performance on uptrends, downtrend and sideways markets seperately as to understand the weak points of strategy.
- Once you enable custom date backtesting, you will see lines on the chart which can be dragged left right based on where you want to start and end the backtesting from and to.
Note:
- Not a financial advise
- Open to feedback, questions, improvements, errors etc.
- More info on how the squeeze momentum works visit LazyBear indicator link:
Happy Trading!
Cheers
M Tahreem Alam @mtahreemalam
Strategy Backtesting Template [MYN]A few people have been asking me to share my backtesting template. Currently I use this as my starting point for validating existing strategies and developing new ones.
Features:
Trading Date Range
Trade Direction
4 progressive take profits with target percents and percentage of position to take profit on (Thanks adolgo)
Variable percentage Stop Loss
Automatic ProfitView Alert Syntax builder for Longs and Shorts
ADX checkbox to automatically add conditional logic to your strategy
Oversold RSI with tight SL Strategy (by Coinrule)This is one of the best strategies that can be used to get familiar with technical indicators and start to include them in your trading bot rules.
ENTRY
1. This trading system uses the RSI ( Relative Strength Index ) to anticipate good points to enter positions. RSI is a technical indicator frequently used in trading. It works by measuring the speed and change of price movements to determine whether a coin is oversold (indicating a good entry point) or overbought (indicating a point of exit/entry for a short position). The RSI oscillates between 0 and 100 and is traditionally considered overbought when over 70 and oversold when below 30.
2. To pick the right moment to buy, the strategy enters a trade when the RSI falls below 30 indicating the coin is oversold and primed for a trend reversal.
EXIT
The strategy then exits the position when the price appreciates 7% from the point of entry. The position also maintains a tight stop-loss and closes the position if the price depreciates 1% from the entry price. The idea behind this is to cut your losing trades fast and let your winners ride.
The best time frame for this strategy based on our backtesting data is the daily. Shorter time frames can also work well on certain coins, however in our experience, the daily works best. Feel free to experiment with this script and test it on a variety of your coins! With our backtesting data a trading fee of 0.1% is taken into account. The fee is aligned to the base fee applied on Binance, which is the largest cryptocurrency exchange by volume. In the example shown, this strategy made a handsome net profit of 39.31% on Chainlink with 61.54% of trades being profitable.
Supertrend with TP by Furkan SancuSimple Supertrend strategy with Take Profit levels.
Note: Make sure TP amounts add up to 100.
Opens a position with Supertrends strategy then adds Take Profit points with certain amounts of current position.
RSI+PA+PrTPHi everybody,
This strategy is a RSI, Price Averaging, Pyramiding Strategy based on the earlier RSI+PA+DCA strategy. See below.
For this slightly different strategy I left the DCA option out and instead focused on the Take Profit calculation. In the previous strategy the Take Profit was directly connected to the Average Price level with a specified take profit %. When the price reached the Take Profit all positions where exited. The strategy opened multiple position based on the PA price levels. The separate positions can close when they reach separately specified Take Profit Limit. Each time the prices crosses the PA layer again the position can be re-opened. This causes the average price to drop each time a separate position is opened and closed.
I thought it was an interesting way to minimize losses and in general it works fine. Only when the market goes bearish it can cause significant losses
For the lack of a better word, I dubbed it Progressive Take Profit. The PrTP works different and is less risky. It doesn't directly follow the average price development and is calculated for a part based on the estimated profits of the separate closed positions. Every time a separate position is closed, the profit of that position is deducted of the Take Profit Limit. This causes the Take Profit Limit to drop les drastically then the average price and the whole position will only be closed when the separately opened and closed positions made up for the biggest losses.
There are still some aspects in the puzzle that are not fully worked out yet and I am still working on it, but I wanted to share this idea already and maybe you have some thoughts about it.
The next step is to re-implement a better worked out DCA function.
To be continued.
Take profit Multi timeframeRepublish:
Take profit Multi timeframe:
In this scipts, I build risk-reward system managemant. You can take profit in two way: percent or at resistant in higher timeframe or both.
Strategy in this scripts, I use Wave trend indicator as example strategy.
Strategy Execution Template - Master PatternThis script is a template to execute your strategy with Stop Loss and Take Profit and showing Trades.
Then, you can write your strategy without taking care of Stop Loss and Take Profit method.
3x Fixed Percent Stop Loss, Trailing Percent Stop Loss, Trailing ATR Stop Loss, Tailing HHLL Stop Loss
3x Fixed Percent, Take Profit
Kahlman HullMA / WT Cross StrategyA strategy created using Hull Moving Average and WT Cross .
Hull Moving Average turns green and WT Cross crossover this is a long. Otherwise short.
Stop Loss and Take Profit settings are available. You can set it to the level you want or turn it off.
According to my measurements, it shows the best performance in the 4-hour period. But you can find the best settings that are correct from the Strategy settings.
multiple orders - strategy - educationalHi,
Here is a 'template', using array's, for multiple orders and different SL/TP levels per trade (This is an example with max 5 open trades)
The 'switch' makes sure that the first available position will be used,
for example, when 'L1' is closed in the past, and a buy condition is triggered, position 'L1' will be filled,
should it be that 'L1', 'L2', 'L3' are already filled, then position 'L4' will be filled, ...
An extra table is added with data of the trades
Be aware, the 'Buy and Hold' resembles the profit when 100% of the available equity has been bought at the time of the very first trade and sold now. On the other hand, the positions work with a % of equity, 20% per trade (5 x 20 = 100%)
You can see that every trade exits on its own terms, without interference of other trades
Important, this technique only works if in the strategy() function:
- close_entries_rule -> set at 'ANY'
- pyramiding is set at max amount of trades or higher (in this case 5 or higher)
Cheers!
RSI_OTT - TP/SLWhile creating this strategy, I was inspired by "ott(by Anıl Özekşi)" and "RSI + Bollinger Double Strategy(by ChartArt)".
Basically, the strategy uses ott bands instead of bollinger bands in the "RSI+Bollinger Double Strategy".
User can select take profit, stop loss, position direction(long, short or both) and the other ott parameters via interface.
Buy Monday, Exit Tuesday with Stop Loss and Take ProfitStrategy to go long at end of Monday before market closes and exit before Tuesday close, or at stop loss or take profit percentages
This runs strategy on entering by weekday name and also by session time.
Implemented are also Take Profit and Stop Loss to exit position using custom inputs.
Added stoploss and take profit plot lines
