Dual Strategy Selector V2 - CryptogyaniOverview:
This script provides traders with a dual-strategy system that they can toggle between using a simple dropdown menu in the input settings. It is designed to cater to different trading styles and needs, offering both simplicity and advanced filtering techniques. The strategies are built around moving average crossovers, enhanced by configurable risk management tools like take profit levels, trailing stops, and ATR-based stop-loss.
Key Features:
Two Strategies in One Script:
Strategy 1: A classic moving average crossover strategy for identifying entry signals based on trend reversals. Includes user-defined take profit and trailing stop-loss options for profit locking.
Strategy 2: An advanced trend-following system that incorporates:
A higher timeframe trend filter to confirm entry signals.
ATR-based stop-loss for dynamic risk management.
Configurable partial take profit to secure gains while letting the trade run.
Highly Customizable:
All key parameters such as SMA lengths, take profit levels, ATR multiplier, and timeframe for the trend filter are adjustable via the input settings.
Dynamic Toggle:
Traders can switch between Strategy 1 and Strategy 2 with a single dropdown, allowing them to adapt the strategy to market conditions.
How It Works:
Strategy 1:
Entry Logic: A long trade is triggered when the fast SMA crosses above the slow SMA.
Exit Logic: The trade exits at either a user-defined take profit level (percentage or pips) or via an optional trailing stop that dynamically adjusts based on price movement.
Strategy 2:
Entry Logic: Builds on the SMA crossover logic but adds a higher timeframe trend filter to align trades with the broader market direction.
Risk Management:
ATR-Based Stop-Loss: Protects against adverse moves with a volatility-adjusted stop-loss.
Partial Take Profit: Allows traders to secure a percentage of gains while keeping some exposure for extended trends.
How to Use:
Select Your Strategy:
Use the dropdown in the input settings to choose Strategy 1 or Strategy 2.
Configure Parameters:
Adjust SMA lengths, take profit, and risk management settings to align with your trading style.
For Strategy 2, specify the higher timeframe for trend filtering.
Deploy and Monitor:
Apply the script to your preferred asset and timeframe.
Use the backtest results to fine-tune settings for optimal performance.
Why Choose This Script?:
This script stands out due to its dual-strategy flexibility and enhanced features:
For beginners: Strategy 1 provides a simple yet effective trend-following system with minimal setup.
For advanced traders: Strategy 2 includes powerful tools like trend filters and ATR-based stop-loss, making it ideal for challenging market conditions.
By combining simplicity with advanced features, this script offers something for everyone while maintaining full transparency and user customization.
Default Settings:
Strategy 1:
Fast SMA: 21, Slow SMA: 49
Take Profit: 7% or 50 pips
Trailing Stop: Optional (disabled by default)
Strategy 2:
Fast SMA: 20, Slow SMA: 50
ATR Multiplier: 1.5
Partial Take Profit: 50%
Higher Timeframe: 1 Day (1D)
Komut dosyalarını "profit" için ara
Fractional Accumulation Distribution Strategy🔹 INTRODUCTION:
As traders and investors, we often find ourselves searching for ways to maximize our market positioning—trying to capture the best price, manage risk, and adapt to ever-changing volatility. Through years of working with a variety of traders and investors, a common theme emerged: the most successful market participants were those who accumulated positions strategically over time, rather than relying on one-off, rigid entry points. However, even the best of them struggled to consistently time their entries and exits for optimal results.
That's why I created the Fractional Accumulation/Distribution Strategy (FADS)—an adaptable solution designed to dynamically adjust position sizing and entry points based on changing market conditions, enabling both passive and active market participants to optimize their approach.
The FADS trading strategy combines volatility-based trend detection and adaptive position scaling to maximize profitability across varied market conditions. By using the price ranges from higher timeframes, FADS pinpoints extreme demand and supply zones with a high statistical probability of reversal, making it effective in both high and low volatility environments. By applying adjustable threshold settings, users can focus on meaningful price movements to reduce unnecessary trades. Adaptive position scaling further enhances this approach by adjusting position sizes based on entry level distances, allowing for strategic position building that balances risk and reward in uncertain markets. This systematic scaling begins with smaller positions, expanding as the trend solidifies, creating a refined, robust trading experience.
🔹 FEATURES:
Multi-Timeframe Volatility-Based Trend Detection
Accumulation/Distribution Level Filter
Customizable Period for Highest/Lowest Prices Capture
Adjustable Sensitivity & Frequency in Positioning
Broad control settings of Strategy
Adaptive Position Scaling
🔹 SETTINGS:
Volatility : Determines trading range based on market volatility . Highest range value number of periods.
Factor : Adjusts the width of the Accumulation & Distribution bands separately. The Level Filter feature offers customizable triggering bands, allowing users to fine-tune the initiation point for the Accumulation/Distribution sequence. This flexibility enables traders to align entries more precisely with market conditions, setting optimal thresholds for initiating trade chains, whether in accumulating positions during uptrends or distributing in downtrends.
Lowest : Choose the price source (e.g., Close, Low). Number of bars considered when determining the lowest price level. Selecting the checkbox generate a signal when the price crosses below the previous lowest value for calculating the lowest value used for trade signals.
Highest : Choose the price source (e.g., Close, High). Number of bars considered when determining the highest price levels. Selecting the checkbox generate a signal when the price crosses above the previous highest value for calculating the highest value used for trade signals.
Accumulation Spread : Adjusts the buying frequency sensitivity by setting the distance between entries based on personal risk tolerance. Larger values for less frequent buys; smaller values for more frequent buys.
Distribution Spread : Adjusts the selling frequency sensitivity by setting the distance between exits based on reward preference. Larger values for less frequent sells; smaller values for more frequent sells.
Percentage of Capital Allocation : Sets the portion of total capital used for the initial trade in a strategy. It sets the scale for subsequent trades during accumulation phase.
🔹 APPLICATIONS:
❖ Accumulation and Distribution Phases
Early entries are avoided by initiating accumulation only after a trend reversal is confirmed and price breaks below long-term range.
Position sizes are determined by the distance between consecutive trades, smaller distance results in smaller position sizes and vice versa.
Average position cost is reduced by accumulating larger positions at the lower prices, potentially resulting in improved profitability.
Early exits are avoided by initiating distribution only after trend reversal is confirmed and price breaks above long-term range.
The pace of distribution can be tracked by the violet line that represents average positions during distribution phase
❖ Use Cases (Different than default setting input is used for illustration purposes)
If the starting point of accumulation starts too high for the risk preference, Accumulation Level Filter can be lowered by increasing the 🟢 threshold Factor.
If the starting point of distribution is too low for the reward preference, the Distribution Level Filter can be raised by increasing the 🔴 threshold Factor.
In lower timeframes, positions during the accumulation phase could be purchased at higher levels relative to prior entry positions. To optimize for this, consider extending the period used to capture the lowest prices. Similarly, during the distribution phase, increasing the period for identifying higher prices can improve accuracy.
🔹 Strategy Properties:
Adjusting properties within the script settings is recommended to align with specific accounts and trading platforms, ensuring realistic strategy results.
Balance (default): $100,000
Initial Order Size: 1% of the default balance
Commission: 0.1%
Slippage: 5 Ticks
Backtesting: Backtested using TradingView’s built-in strategy testing tool with default commission rates of 0.1% and slippage of 5 ticks. It reflects average market conditions for Apple Inc. (APPL) on 1-hour timeframe
Disclaimers: Commission and slippage varies with market conditions and brokerage policies. The assumed value may not represent all trading environments.
PAST PERFORMANCE DOESN’T GUARANTEE FUTURE RESULTS!
Disclaimer: Please remember that past performance may not be indicative of future results. Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting. This post and the script don’t provide any financial advice.
This invite-only script is being published as part of my commitment to developing tools that align with TradingView’s community standards. Access requests will be reviewed carefully after the script passes TradingView's moderation process.
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.
---
If you want to know more about ATR, can also check "SuperATR 7-Step Profit".
Enjoy trading.
XAUUSD 10-Minute StrategyThis XAUUSD 10-Minute Strategy is designed for trading Gold vs. USD on a 10-minute timeframe. By combining multiple technical indicators (MACD, RSI, Bollinger Bands, and ATR), the strategy effectively captures both trend-following and reversal opportunities, with adaptive risk management for varying market volatility. This approach balances high-probability entries with robust volatility management, making it suitable for traders seeking to optimise entries during significant price movements and reversals.
Key Components and Logic:
MACD (12, 26, 9):
Generates buy signals on MACD Line crossovers above the Signal Line and sell signals on crossovers below the Signal Line, helping to capture momentum shifts.
RSI (14):
Utilizes oversold (below 35) and overbought (above 65) levels as a secondary filter to validate entries and avoid overextended price zones.
Bollinger Bands (20, 2):
Uses upper and lower Bollinger Bands to identify potential overbought and oversold conditions, aiming to enter long trades near the lower band and short trades near the upper band.
ATR-Based Stop Loss and Take Profit:
Stop Loss and Take Profit levels are dynamically set as multiples of ATR (3x for stop loss, 5x for take profit), ensuring flexibility with market volatility to optimise exit points.
Entry & Exit Conditions:
Buy Entry: T riggered when any of the following conditions are met:
MACD Line crosses above the Signal Line
RSI is oversold
Price drops below the lower Bollinger Band
Sell Entry: Triggered when any of the following conditions are met:
MACD Line crosses below the Signal Line
RSI is overbought
Price moves above the upper Bollinger Band
Exit Strategy: Trades are closed based on opposing entry signals, with adaptive spread adjustments for realistic exit points.
Backtesting Configuration & Results:
Backtesting Period: July 21, 2024, to October 30, 2024
Symbol Info: XAUUSD, 10-minute timeframe, OANDA data source
Backtesting Capital: Initial capital of $700, with each trade set to 10 contracts (equivalent to approximately 0.1 lots based on the broker’s contract size for gold).
Users should confirm their broker's contract size for gold, as this may differ. This script uses 10 contracts for backtesting purposes, aligned with 0.1 lots on brokers offering a 100-contract specification.
Key Backtesting Performance Metrics:
Net Profit: $4,733.90 USD (676.27% increase)
Total Closed Trades: 526
Win Rate: 53.99%
Profit Factor: 1.44 (1.96 for Long trades, 1.14 for Short trades)
Max Drawdown: $819.75 USD (56.33% of equity)
Sharpe Ratio: 1.726
Average Trade: $9.00 USD (0.04% of equity per trade)
This backtest reflects realistic conditions, with a spread adjustment of 38 points and no slippage or commission applied. The settings aim to simulate typical retail trading conditions. However, please adjust the initial capital, contract size, and other settings based on your account specifics for best results.
Usage:
This strategy is tuned specifically for XAUUSD on a 10-minute timeframe, ideal for both trend-following and reversal trades. The ATR-based stop loss and take profit levels adapt dynamically to market volatility, optimising entries and exits in varied conditions. To backtest this script accurately, ensure your broker’s contract specifications for gold align with the parameters used in this strategy.
Supertrend StrategyThe Supertrend Strategy was created based on the Supertrend and Relative Strength Index (RSI) indicators, widely respected tools in technical analysis. This strategy combines these two indicators to capture market trends with precision and reliability, looking for optimizing exit levels at oversold or overbought price levels.
The Supertrend indicator identifies trend direction based on price and volatility by using the Average True Range (ATR). The ATR measures market volatility by calculating the average range between an asset’s high and low prices over a set period. It provides insight into price fluctuations, with higher ATR values indicating increased volatility and lower values suggesting stability. The Supertrend Indicator plots a line above or below the price, signaling potential buy or sell opportunities: when the price closes above the Supertrend line, an uptrend is indicated, while a close below the line suggests a downtrend. This line shifts as price movements and volatility levels change, acting as both a trailing stop loss and trend confirmation.
To enhance the Supertrend strategy, the Relative Strength Index (RSI) has been added as an exit criterion. As a momentum oscillator, the RSI indicates overbought (usually above 70) or oversold (usually below 30) conditions. This integration allows trades to close when the asset is overbought or oversold, capturing gains before a possible reversal, even if the percentage take profit level has not been reached. This mechanism aims to prevent losses due to market reversals before the Supertrend signal changes.
### Key Features
1. **Entry criteria**:
- The strategy uses the Supertrend indicator calculated by adding or subtracting a multiple of the ATR from the closing price, depending on the trend direction.
- When the price crosses above the Supertrend line, the strategy signals a long (buy) entry. Conversely, when the price crosses below, it signals a short (sell) entry.
- The strategy performs a reversal if there is an open position and a change in the direction of the supertrend occurs
2. **Exit criteria**:
- Take profit of 30% (default) on the average position price.
- Oversold (≤ 5) or overbought (≥ 95) RSI
- Reversal when there is a change in direction of the Supertrend
3. **No Repainting**:
- This strategy is not subject to repainting, as long as the timeframe configured on your chart is the same as the supertrend timeframe .
4. **Position Sizing by Equity and risk management**:
- This strategy has a default configuration to operate with 35% of the equity. At the time of opening the position, the supertrend line is typically positioned at about 12 to 16% of the entry price. This way, the strategy is putting at risk about 16% of 35% of equity, that is, around 5.6% of equity for each trade. The percentage of equity can be adjusted by the user according to their risk management.
5. **Backtest results**:
- This strategy was subjected to deep backtesting and operations in replay mode, including transaction fees of 0.12%, and slippage of 5 ticks.
- The past results in deep backtest and replay mode were compatible and profitable (Variable results depending on the take profit used, supertrend and RSI parameters). However, it should be noted that few operations were evaluated, since the currency in question has been created for a short time and the frequency of operations is relatively small.
- Past results are no guarantee of future results. The strategy's backtest results may even be due to overfitting with past data.
Default Settings
Chart timeframe: 2h
Supertrend Factor: 3.42
ATR period: 14
Supertrend timeframe: 2 h
RSI timeframe: 15 min
RSI Lenght: 5 min
RSI Upper limit: 95
RSI Lower Limit: 5
Take Profit: 30%
BYBIT:1000000MOGUSDT.P
Statistical ArbitrageThe Statistical Arbitrage Strategy, also known as pairs trading, is a quantitative trading method that capitalizes on price discrepancies between two correlated assets. The strategy assumes that over time, the prices of these two assets will revert to their historical relationship. The core idea is to take advantage of mean reversion, a principle suggesting that asset prices will revert to their long-term average after deviating significantly.
Strategy Mechanics:
1. Selection of Correlated Assets:
• The strategy focuses on two historically correlated assets (e.g., equity index futures like Dow Jones Mini and S&P 500 Mini). These assets tend to move in the same direction due to similar underlying fundamentals, such as overall market conditions. By tracking their relative prices, the strategy seeks to exploit temporary mispricings.
2. Spread Calculation:
• The spread is the difference between the prices of the two assets. This spread represents the relationship between the assets and serves as the basis for determining when to enter or exit trades.
3. Mean and Standard Deviation:
• The historical average (mean) of the spread is calculated using a Simple Moving Average (SMA) over a chosen period. The strategy also computes the standard deviation (volatility) of the spread, which measures how far the spread has deviated from the mean over time. This allows the strategy to define statistically significant price deviations.
4. Entry Signal (Mean Reversion):
• A buy signal is triggered when the spread falls below the mean by a multiple (e.g., two) of the standard deviation. This indicates that one asset is temporarily undervalued relative to the other, and the strategy expects the spread to revert to its mean, generating profits as the prices converge.
5. Exit Signal:
• The strategy exits the trade when the spread reverts to the mean. At this point, the mispricing has been corrected, and the profit from the mean reversion is realized.
Academic Support:
Statistical arbitrage has been widely studied in finance and economics. Gatev, Goetzmann, and Rouwenhorst’s (2006) landmark study on pairs trading demonstrated that this strategy could generate excess returns in equity markets. Their research found that by focusing on historically correlated stocks, traders could identify pricing anomalies and profit from their eventual correction.
Additionally, Avellaneda and Lee (2010) explored statistical arbitrage in different asset classes and found that exploiting deviations in price relationships can offer a robust, market-neutral trading strategy. In these studies, the strategy’s success hinges on the stability of the relationship between the assets and the timely execution of trades when deviations occur.
Risks of Statistical Arbitrage:
1. Correlation Breakdown:
• One of the primary risks is the breakdown of correlation between the two assets. Statistical arbitrage assumes that the historical relationship between the assets will hold in the future. However, market conditions, company fundamentals, or external shocks (e.g., macroeconomic changes) can cause these assets to deviate permanently, leading to potential losses.
• For instance, if two equity indices historically move together but experience divergent economic conditions or policy changes, their prices may no longer revert to the expected mean.
2. Execution Risk:
• This strategy relies on efficient execution and tight spreads. In volatile or illiquid markets, the actual price at which trades are executed may differ significantly from expected prices, leading to slippage and reduced profits.
3. Market Risk:
• Although statistical arbitrage is designed to be market-neutral (i.e., not dependent on the overall market direction), it is not entirely risk-free. Systematic market shocks, such as financial crises or sudden shifts in market sentiment, can affect both assets simultaneously, causing the spread to widen rather than revert to the mean.
4. Model Risk:
• The assumptions underlying the strategy, particularly regarding mean reversion, may not always hold true. The model assumes that asset prices will return to their historical averages within a certain timeframe, but the timing and magnitude of mean reversion can be uncertain. Misestimating this timeframe can lead to extended drawdowns or unrealized losses.
5. Overfitting:
• Over-reliance on historical data to fine-tune the strategy parameters (e.g., the lookback period or standard deviation thresholds) may result in overfitting. This means that the strategy works well on past data but fails to perform in live markets due to changing conditions.
Conclusion:
The Statistical Arbitrage Strategy offers a systematic and quantitative approach to trading that capitalizes on temporary price inefficiencies between correlated assets. It has been proven to generate returns in academic studies and is widely used by hedge funds and institutional traders for its market-neutral characteristics. However, traders must be aware of the inherent risks, including correlation breakdown, execution risks, and the potential for prolonged deviations from the mean. Effective risk management, diversification, and constant monitoring are essential for successfully implementing this strategy in live markets.
Velocity/Volatility/Volume StrategyThe "Vel/Vty/Vol Strategy" is a momentum-based trading approach designed to take advantage of strong price movements that are confirmed by both volatility and volume (if enabled). It provides a high level of customization, allowing traders to adjust various settings based on market conditions and individual preferences. By combining three critical indicators—velocity, volatility (measured through Bollinger Band Width), and an optional volume filter—the strategy generates trade signals for both long and short positions. Here’s a comprehensive explanation of how the strategy works, how the parameters can be customized, and how those adjustments benefit users.
At its core, the strategy focuses on velocity, which measures the speed at which price is changing over time. This is a key indicator of momentum, with a "StrongUp" signal indicating bullish momentum and a "StrongDown" signal suggesting bearish momentum. In addition to velocity, the strategy factors in acceleration, which helps gauge whether momentum is building or weakening. The second essential component is Bollinger Band Width (BBW), which measures volatility in the market. When the BBW expands, it signals increasing volatility, a condition that must be met in combination with a velocity signal to generate a trade. Lastly, the strategy includes an optional Volume Oscillator to filter trades. When this volume filter is enabled, trades will only be executed if there’s an increase in volume, further validating market activity.
The strategy generates long and short trade signals based on specific conditions. A long trade is triggered when there is a strong upward velocity, accompanied by an increase in Bollinger Band Width, indicating both momentum and heightened volatility. If the volume filter is toggled on, a rise in volume must also confirm the signal. Similarly, a short trade is initiated when a strong downward velocity is detected, again paired with an increase in volatility and, optionally, a volume rise. This ensures that trades occur during periods of heightened market activity, reducing the likelihood of false signals.
To help manage risk, the strategy includes several customizable tools. Users can set take profit levels to automatically close positions and lock in gains once a predefined profit percentage is reached. For example, if a 2% take profit is set, a long position will be closed once the price has risen by 2%. Additionally, a trailing take profit option can be enabled, allowing the strategy to dynamically adjust the take-profit target as the market moves in the user’s favor. This ensures that profits are locked in as long as the market continues to trend positively, while providing protection in case of a reversal. The strategy also includes a trailing stop-loss feature, which adjusts the stop price as the market moves in favor of the trade, helping to minimize losses and protect gains.
The strategy offers a variety of parameters that can be customized to suit different trading styles and market conditions. The velocity lookback period controls how far back the strategy looks to calculate velocity. A shorter lookback makes the strategy more sensitive to recent price changes, generating more signals, which can benefit day traders or those seeking to capture short-term price swings. Conversely, a longer lookback smooths out the velocity calculation, reducing false signals and making the strategy more suitable for traders seeking to capture larger trends. Similarly, the Bollinger Band Width (BBW) length can be adjusted to control how far back the strategy looks to calculate volatility. A shorter BBW length makes the strategy more sensitive to volatility spikes, useful in rapidly changing markets. In contrast, a longer BBW length filters out short-term noise and focuses on more sustainable volatility shifts, better suited for slower, more stable markets.
The volume filter is another powerful feature that can be toggled on or off. When turned on, the strategy will only execute trades if there is an increase in volume alongside velocity and volatility signals. This helps filter out false signals in low-volume markets, ensuring that price movements are supported by actual market activity. If the volume filter is turned off, the strategy focuses purely on price and volatility changes, which can be useful in markets where volume data is unreliable or less relevant.
The take profit percentage can be adjusted to define how aggressively or conservatively profits are locked in. A lower take profit percentage allows traders to capture smaller, quicker profits, which can be advantageous in volatile markets. A higher take profit percentage suits traders who prefer to capture larger moves, allowing them to stay in trades longer to benefit from extended trends. Similarly, the trailing take profit percentage determines how tightly the strategy follows market prices as they move in favor of the trade. A tighter trailing percentage ensures that profits are locked in quickly, while a wider trailing percentage gives trades more room to run, ideal for capturing large trends.
The stop loss percentage is another key setting that controls how much risk a trader is willing to take before the position is closed. A tighter stop loss minimizes losses but may result in more frequent stop-outs, particularly in volatile markets. A wider stop loss provides more room for trades to develop, which is useful for traders aiming to capture longer trends despite short-term fluctuations. Additionally, the velocity thresholds can be adjusted to set how sensitive the strategy is to price movements. Lower thresholds increase sensitivity, generating more signals in fast-moving markets, while higher thresholds filter out weaker signals, focusing on larger momentum shifts.
The strategy also allows users to define a time range during which it is active, offering flexibility in backtesting and optimizing for specific market conditions. By limiting the strategy to certain periods, users can tailor it to seasonal trends or historical data that matches their current trading environment.
The flexibility of this strategy makes it suitable for a wide range of traders. Day traders can benefit from adjusting the velocity and BBW lookback periods, tightening take profit and stop loss settings to capture short, fast price movements in highly volatile markets. Trend traders can lengthen the lookback periods and widen the velocity thresholds to capture larger, sustained moves while riding out short-term volatility. Traders with a lower risk tolerance can enable the volume filter and tighten stop losses to reduce false signals and minimize losses. On the other hand, aggressive traders can widen the take profit and trailing stop percentages to allow trades to develop fully, maximizing potential gains in trending markets.
Ultimate Oscillator Trading StrategyThe Ultimate Oscillator Trading Strategy implemented in Pine Script™ is based on the Ultimate Oscillator (UO), a momentum indicator developed by Larry Williams in 1976. The UO is designed to measure price momentum over multiple timeframes, providing a more comprehensive view of market conditions by considering short-term, medium-term, and long-term trends simultaneously. This strategy applies the UO as a mean-reversion tool, seeking to capitalize on temporary deviations from the mean price level in the asset’s movement (Williams, 1976).
Strategy Overview:
Calculation of the Ultimate Oscillator (UO):
The UO combines price action over three different periods (short-term, medium-term, and long-term) to generate a weighted momentum measure. The default settings used in this strategy are:
Short-term: 6 periods (adjustable between 2 and 10).
Medium-term: 14 periods (adjustable between 6 and 14).
Long-term: 20 periods (adjustable between 10 and 20).
The UO is calculated as a weighted average of buying pressure and true range across these periods. The weights are designed to give more emphasis to short-term momentum, reflecting the short-term mean-reversion behavior observed in financial markets (Murphy, 1999).
Entry Conditions:
A long position is opened when the UO value falls below 30, indicating that the asset is potentially oversold. The value of 30 is a common threshold that suggests the price may have deviated significantly from its mean and could be due for a reversal, consistent with mean-reversion theory (Jegadeesh & Titman, 1993).
Exit Conditions:
The long position is closed when the current close price exceeds the previous day’s high. This rule captures the reversal and price recovery, providing a defined point to take profits.
The use of previous highs as exit points aligns with breakout and momentum strategies, as it indicates sufficient strength for a price recovery (Fama, 1970).
Scientific Basis and Rationale:
Momentum and Mean-Reversion:
The strategy leverages two well-established phenomena in financial markets: momentum and mean-reversion. Momentum, identified in earlier studies like those by Jegadeesh and Titman (1993), describes the tendency of assets to continue in their direction of movement over short periods. Mean-reversion, as discussed by Poterba and Summers (1988), indicates that asset prices tend to revert to their mean over time after short-term deviations. This dual approach aims to buy assets when they are temporarily oversold and capitalize on their return to the mean.
Multi-timeframe Analysis:
The UO’s incorporation of multiple timeframes (short, medium, and long) provides a holistic view of momentum, unlike single-period oscillators such as the RSI. By combining data across different timeframes, the UO offers a more robust signal and reduces the risk of false entries often associated with single-period momentum indicators (Murphy, 1999).
Trading and Market Efficiency:
Studies in behavioral finance, such as those by Shiller (2003), show that short-term inefficiencies and behavioral biases can lead to overreactions in the market, resulting in price deviations. This strategy seeks to exploit these temporary inefficiencies, using the UO as a signal to identify potential entry points when the market sentiment may have overly pushed the price away from its average.
Strategy Performance:
Backtests of this strategy show promising results, with profit factors exceeding 2.5 when the default settings are optimized. These results are consistent with other studies on short-term trading strategies that capitalize on mean-reversion patterns (Jegadeesh & Titman, 1993). The use of a dynamic, multi-period indicator like the UO enhances the strategy’s adaptability, making it effective across different market conditions and timeframes.
Conclusion:
The Ultimate Oscillator Trading Strategy effectively combines momentum and mean-reversion principles to trade on temporary market inefficiencies. By utilizing multiple periods in its calculation, the UO provides a more reliable and comprehensive measure of momentum, reducing the likelihood of false signals and increasing the profitability of trades. This aligns with modern financial research, showing that strategies based on mean-reversion and multi-timeframe analysis can be effective in capturing short-term price movements.
References:
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.
Williams, L. (1976). Ultimate Oscillator. Market research and technical trading analysis.
Central Pivot Point Cross & Retrace Strategy // AlgoFyreThe Central Pivot Point Cross & Retrace Strategy uses pivot points for trend identification and trade entry. It combines accumulation/distribution indicators with pivot point levels to generate signals. The strategy incorporates dynamic position sizing based on a fixed risk amount and allows for both long and short positions with customizable stop-loss levels.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Pivot Point-Based Trading
🔸Accumulation/Distribution
🔸Dynamic Position Sizing
🔸Customizable Risk Management
🔶 FUNCTIONALITY
🔸Indicators
🞘 Pivot Points
🞘 Accumulation/Distribution
🔸Conditions
🞘 Long Entry
🞘 Short Entry
🞘 Take Profit
🞘 Stop Loss
🔶 INSTRUCTIONS
🔸Adding the Strategy to the Chart
🔸Configuring the Strategy
🔸Backtesting and Practice
🔸Market Awareness
🔸Visual Customization
🔶 CONCLUSION
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🔶 ORIGINALITY The Central Pivot Point Cross & Retrace Strategy uniquely combines pivot point analysis with accumulation/distribution indicators to identify optimal entry and exit points. It employs dynamic position sizing based on a fixed risk amount, ensuring consistent risk management across trades. This approach allows traders to adapt to varying market conditions by adjusting position sizes according to predefined risk parameters, enhancing both flexibility and control in trading decisions. The strategy's integration of customizable stop-loss levels further refines its risk management capabilities.
🔸Pivot Point-Based Trading This strategy utilizes daily pivot points to identify key support and resistance levels, providing a framework for trend identification and trade entry. The central pivot point serves as the intraday point of balance between buyers and sellers, with the largest amount of trading volume assumed to take place in this area.
🔸Accumulation/Distribution The strategy incorporates the Accumulation/Distribution (A/D) line, an underrated volume-based indicator, to establish the main trend. The A/D line is used in conjunction with a trend based indicator like the 200-period Exponential Moving Average (EMA) to confirm trend direction and strength.
🔸Dynamic Position Sizing Position sizes are calculated dynamically based on a fixed risk amount, allowing traders to maintain consistent risk exposure across trades.
🔸Customizable Risk Management Traders can set flexible risk-reward ratios and adjust stop-loss and take-profit levels, tailoring the strategy to their risk tolerance and market conditions. The strategy recommends taking partial profits at S1 or R1 levels and moving the stop-loss to break-even for remaining positions.
🔶 FUNCTIONALITY The Central Pivot Point Cross & Retrace Strategy leverages pivot points and accumulation/distribution indicators to identify optimal trading opportunities. This strategy is designed to capitalize on price movements around key pivot levels by dynamically adjusting position sizes based on predefined risk parameters. It allows traders to manage risk effectively while taking advantage of both long and short positions.
🔸Indicators 🞘 Pivot Points: Calculates daily pivot points (PP, R1, R2, S1, S2) to identify key support and resistance levels. The central pivot point is crucial for determining market bias and entry points.
🞘 Accumulation/Distribution: Uses the A/D line and with a trend based indicator like the 200 EMA to determine market direction and trend strength. This combination helps eliminate noise and provides more reliable trend signals. We recommend using the Adaptive MAs (Hurst, CVaR, Fractal) // AlgoFyre , but any moving average could be used.
🔸Conditions 🞘 Long Entry: Initiates a long position when the price crosses above the central pivot point (PP), retraces back to it and the A/D line is above its 200 EMA, indicating an uptrend. A limit entry order is set at the PP for entering the long trade.
🞘 Short Entry: Initiates a short position when the price crosses below the central pivot point (PP), retraces back to it and the A/D line is below its 200 EMA, indicating a downtrend. A limit entry order is set at the PP for entering the short trade.
🞘 Take Profit: 50% of the position is closed as profit when R1 for Longs and S1 for Shorts is reached. The position is fully closed when R2 for Longs and S2 for Shorts is reached.
🞘 Stop Loss: Stop loss is set via strategy settings. When the first 50% take profit for both long and shorts is taken, stop loss for both will be moved to break-even/entry.
🔶 INSTRUCTIONS
The Central Pivot Point Cross & Retrace Strategy can be set up by adding it to your TradingView chart and configuring parameters such as the accumulation/distribution source, stop-loss percentage, and risk management settings. This strategy is designed to capitalize on price movements around key pivot levels by dynamically adjusting position sizes based on predefined risk parameters. Enhance the accuracy of signals by combining this strategy with additional indicators like trend-following or momentum-based tools. Adjust settings to better manage risk and optimize entry and exit points.
🔸Adding the Strategy to the Chart Go to your TradingView chart.
Click on the "Pine Editor" button at the bottom of the chart.
Copy and paste the strategy code into the Pine Editor.
Click "Add to Chart" to apply the strategy.
Add the technical indicator "Accumulation/Distribution" to the chart.
Add the trend indicator " Adaptive MAs (Hurst, CVaR, Fractal) // AlgoFyre " or any other MA to the chart and move it to the "Accumulation/Distribution" pane.
Set the source of your trend indicator to "Accumulation/Distribution".
🔸Configuring the Strategy Open the strategy settings by clicking on the gear icon next to its name on the chart.
Accumulation/Distribution Source: Select the source for the accumulation/distribution indicator.
Accumulation/Distribution EMA Source: Select the source for the trend indicator.
Stop Loss Percentage: Set the stop loss distance from the pivot point as a percentage.
Risk Amount: Define the fixed risk amount for position sizing.
Base Order Size: Set the base order size for position calculations.
Number of Positions: Specify the maximum number of positions allowed.
Time Frame: Adjust the time frame based on the currency pair or asset being traded (e.g., 15-minute for EUR/USD, 30-minute for GBP/USD).
🔸Backtesting and Practice Backtest the strategy on historical data to understand how it performs in various market environments.
Practice using the strategy on a demo account before implementing it in live trading.
Test different time frames and asset pairs to find the most suitable combinations.
🔸Market Awareness Keep an eye on market news and events that might cause extreme price movements. The strategy reacts to price data and might not account for news-driven events that can cause large deviations.
Remember that this strategy is not recommended for stocks due to the A/D line's inability to account for gaps in its calculation.
🔸Visual Customization Visualization Settings: Customize the display of entry price, take profit, and stop loss levels.
Color Settings: Switch to the AlgoFyre theme or set custom colors for bullish, bearish, and neutral states.
Table Settings: Enable or disable the information table and adjust its position.
🔶 CONCLUSION
The Central Pivot Point Cross & Retrace Strategy provides a robust framework for capitalizing on price movements around key pivot levels by combining pivot point analysis with accumulation/distribution indicators. This strategy leverages pivot point crossovers to identify entry points and utilizes the A/D line crossover with its 200 EMA for trend confirmation, ensuring trades align with prevailing market conditions. By incorporating dynamic position sizing based on a fixed risk amount, traders can effectively manage risk and adapt to varying market conditions. The strategy's focus on trading around the central pivot point and its customizable stop-loss and take-profit levels further enhance its risk management capabilities, making it a versatile tool for both trending and ranging markets. With its strategic blend of technical indicators and risk management, the Central Pivot Point Cross & Retrace Strategy offers traders a comprehensive approach to optimizing trade execution and maximizing potential returns across various currency pairs and commodities.
Gauss KenJi Robot
Gauss KenJi Trading Robot: Precision and Automation for Traders
The Gauss KenJi robot is a cutting-edge trading solution designed for experienced traders seeking to enhance their decision-making through advanced statistical models and automation. Unlike traditional trading tools that rely on generic indicators prone to false signals, the Gauss KenJi robot offers an innovative approach by utilizing two unique indicators: the Kenji Indicator v.2.0 and the Gauss Indicator .
Kenji Indicator v.2.0
Traditional moving averages and related indicators often fail in flat market conditions, where frequent crossovers lead to confusing signals and false trends. The Kenji Indicator addresses this issue by using a combination of correlation analysis and moving averages to more accurately identify the market’s state. This real-time insight allows for better navigation of local trends, reducing noise and increasing the precision of trade signals.
Gauss Indicator
The Gauss Indicator brings the power of statistical analysis into trading by applying the 3 sigmas rule. It calculates and predicts the likely price ranges for specific time frames (hourly, daily, weekly) with probabilities of 68%, 95%, and 99%. This offers traders an actionable framework for setting stop-loss, take-profit, and identifying key support and resistance levels. By providing a clearer view of potential price movements, the Gauss Indicator improves decision-making, ensuring that traders enter and exit the market at optimal points.
Gauss KenJi Robot: How it Works
The Gauss KenJi robot operates on a statistical algorithm based on the Gaussian function, which uses market volatility as a core indicator of price movements. The robot opens positions in the direction of the trend when the price reaches the predetermined Gauss border. Position sizes are calculated according to the “Initial_lot” parameter, with stop-loss and take-profit levels defined by the “Pips” parameter. Trades are automatically closed either when profit targets or stop-loss limits are reached, or if local trend reversals are detected by the Kenji Indicator.
This highly adaptable algorithm can be applied to any asset class (stocks, forex, crypto, commodities) and any time frame, providing traders with a versatile tool to navigate various markets.
Why Gauss KenJi is Essential for Traders
1. Time Efficiency: The robot operates autonomously, allowing traders to step away from constant chart monitoring while still capitalizing on market movements.
2. Profit Maximization: By leveraging machine learning and advanced statistical models, the robot identifies opportunities faster than human traders, ensuring more profitable trades.
3. Risk Management: The robot strictly adheres to predefined rules, helping traders minimize losses and protect their capital in volatile market conditions.
4. Cross-market Versatility: Whether you’re trading forex, stocks, crypto, or commodities, Gauss KenJi adapts to different markets and time frames, making it a versatile tool for professional traders.
The Gauss KenJi robot is a comprehensive, scientifically driven trading solution designed to eliminate common pitfalls associated with traditional indicators. Its combination of the Kenji Indicator’s trend identification and the Gauss Indicator’s price prediction capabilities makes it an indispensable tool for traders looking to enhance both the precision of their trades and the automation of their strategies. Whether you are aiming for consistent daily profits or optimizing long-term trading strategies, Gauss KenJi offers the efficiency and accuracy required to stay ahead in today’s competitive markets.
InvoTrading - Swing High and Low with BreakoutInvoTrading - Swing High and Low with Breakout Strategy
This strategy is designed to identify trading opportunities based on swing highs and lows, combined with breakout confirmations. It utilizes pivot points to detect potential reversal levels and initiates trades when the price breaks out of these levels under specific conditions.
Key Features:
- Pivot Points: The strategy calculates pivot highs and lows using customizable left and right bars. These pivots represent potential swing points in the market.
- Breakout Detection: It monitors for breakouts above pivot highs (Bullish Break of Structure - BOS) and below pivot lows (Bearish Break of Structure).
- Strong Swings (Optional): You can enable "Strong Swing" detection, which considers only those pivots where the price attempted but failed to break the pivot level, indicating stronger support or resistance.
- Trade Management: The strategy sets entry points, stop losses, and take profits based on a customizable risk-reward ratio.
- Trade Table: An optional table displays recent trades, including their status (Pending, Success, or Failed).
- Visual Aids: Customizable colors and line settings help visualize pivot points, strong swings, and breakout candles on the chart.
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Settings:
1. Pivot Settings:
- Left Bars: Number of bars to the left of the pivot point (default: 5).
- Right Bars: Number of bars to the right of the pivot point (default: 5).
- Pivot Based On: Choose between "High/Low" or "Close" prices for pivot calculations.
2. Color Settings:
- Pivot High Color: Color for Pivot High markers (default: Blue).
- Pivot Low Color: Color for Pivot Low markers (default: Red).
- Strong Swing High Color: Color for Strong Swing High markers (default: Black).
- Strong Swing Low Color: Color for Strong Swing Low markers (default: Black).
- Breakout Candle Color (BOS): Color for the breakout candle (default: Yellow).
3. Line Settings:
- Line Width: Width of the pivot lines (default: 1).
- Line Length (Bars): Length of the pivot lines in bars (default: 20).
- Maximum Number of Lines to Keep: Limits the number of pivot lines displayed to avoid clutter (default: 100).
4. Trade Settings:
- Enable Buy and Sell Signals: Activates trade entries and exits on the chart (default: False).
- Show Trades Table: Displays a table summarizing recent trades (default: False).
- Risk-Reward Ratio: Sets the desired risk-reward ratio for trades (default: 1.5).
- Number of Trades to Display: Maximum number of recent trades shown in the table (default: 5).
- Enable Strong Trade: Only triggers trades when a "Strong Swing" is detected (default: False).
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How It Works:
- Pivot Detection: The script identifies pivot highs and lows based on the specified number of left and right bars.
- Strong Swings: If enabled, the strategy marks a pivot as a strong swing if the price attempts to break it but closes back within the pivot level.
- Breakout Confirmation:
- Long Entry: Occurs when the price closes above a pivot high, signaling a bullish breakout. If "Strong Trade" is enabled, it must be a strong swing high.
- Short Entry: Occurs when the price closes below a pivot low, signaling a bearish breakout. If "Strong Trade" is enabled, it must be a strong swing low.
- Trade Execution: Upon a valid breakout, the strategy places a trade with a stop loss set at the previous candle's low (for longs) or high (for shorts). The take profit is calculated based on the specified risk-reward ratio.
- Trade Monitoring: The strategy updates the status of each trade (Pending, Success, Failed) based on whether the take profit or stop loss is hit.
- Visualization: Breakout candles are highlighted, and pivot lines are drawn with customizable colors and widths. Strong swings are marked distinctly.
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Usage Tips:
- Backtesting: Before using this strategy live, backtest it on different time frames and instruments to assess its performance.
- Customization: Adjust the pivot settings and risk-reward ratio to match your trading style and the volatility of the instrument you're trading.
- Risk Management: Always use proper risk management techniques, even though the strategy calculates stop losses and take profits.
Multi-Step FlexiMA - Strategy [presentTrading]It's time to come back! hope I can not to be busy for a while.
█ Introduction and How It Is Different
The FlexiMA Variance Tracker is a unique trading strategy that calculates a series of deviations between the price (or another indicator source) and a variable-length moving average (MA). Unlike traditional strategies that use fixed-length moving averages, the length of the MA in this system varies within a defined range. The length changes dynamically based on a starting factor and an increment factor, creating a more adaptive approach to market conditions.
This strategy integrates Multi-Step Take Profit (TP) levels, allowing for partial exits at predefined price increments. It enables traders to secure profits at different stages of a trend, making it ideal for volatile markets where taking full profits at once might lead to missed opportunities if the trend continues.
BTCUSD 6hr Performance
█ Strategy, How It Works: Detailed Explanation
🔶 FlexiMA Concept
The FlexiMA (Flexible Moving Average) is at the heart of this strategy. Unlike traditional MA-based strategies where the MA length is fixed (e.g., a 50-period SMA), the FlexiMA varies its length with each iteration. This is done using a **starting factor** and an **increment factor**.
The formula for the moving average length at each iteration \(i\) is:
`MA_length_i = indicator_length * (starting_factor + i * increment_factor)`
Where:
- `indicator_length` is the user-defined base length.
- `starting_factor` is the initial multiplier of the base length.
- `increment_factor` increases the multiplier in each iteration.
Each iteration applies a **simple moving average** (SMA) to the chosen **indicator source** (e.g., HLC3) with a different length based on the above formula. The deviation between the current price and the moving average is then calculated as follows:
`deviation_i = price_current - MA_i`
These deviations are normalized using one of the following methods:
- **Max-Min normalization**:
`normalized_i = (deviation_i - min(deviations)) / range(deviations)`
- **Absolute Sum normalization**:
`normalized_i = deviation_i / sum(|deviation_i|)`
The **median** and **standard deviation (stdev)** of the normalized deviations are then calculated as follows:
`median = median(normalized deviations)`
For the standard deviation:
`stdev = sqrt((1/(N-1)) * sum((normalized_i - mean)^2))`
These values are plotted to provide a clear indication of how the price is deviating from its variable-length moving averages.
For more detail:
🔶 Multi-Step Take Profit
This strategy uses a multi-step take profit system, allowing for exits at different stages of a trade based on the percentage of price movement. Three take-profit levels are defined:
- Take Profit Level 1 (TP1): A small, quick profit level (e.g., 2%).
- Take Profit Level 2 (TP2): A medium-level profit target (e.g., 8%).
- Take Profit Level 3 (TP3): A larger, more ambitious target (e.g., 18%).
At each level, a corresponding percentage of the trade is exited:
- TP Percent 1: E.g., 30% of the position.
- TP Percent 2: E.g., 20% of the position.
- TP Percent 3: E.g., 15% of the position.
This approach ensures that profits are locked in progressively, reducing the risk of market reversals wiping out potential gains.
Local
🔶 Trade Entry and Exit Conditions
The entry and exit signals are determined by the interaction between the **SuperTrend Polyfactor Oscillator** and the **median** value of the normalized deviations:
- Long entry: The SuperTrend turns bearish, and the median value of the deviations is positive.
- Short entry: The SuperTrend turns bullish, and the median value is negative.
Similarly, trades are exited when the SuperTrend flips direction.
* The SuperTrend Toolkit is made by @EliCobra
█ Trade Direction
The strategy allows users to specify the desired trade direction:
- Long: Only long positions will be taken.
- Short: Only short positions will be taken.
- Both: Both long and short positions are allowed based on the conditions.
This flexibility allows the strategy to adapt to different market conditions and trading styles, whether you're looking to buy low and sell high, or sell high and buy low.
█ Usage
This strategy can be applied across various asset classes, including stocks, cryptocurrencies, and forex. The primary use case is to take advantage of market volatility by using a flexible moving average and multiple take-profit levels to capture profits incrementally as the market moves in your favor.
How to Use:
1. Configure the Inputs: Start by adjusting the **Indicator Length**, **Starting Factor**, and **Increment Factor** to suit your chosen asset. The defaults work well for most markets, but fine-tuning them can improve performance.
2. Set the Take Profit Levels: Adjust the three **TP levels** and their corresponding **percentages** based on your risk tolerance and the expected volatility of the market.
3. Monitor the Strategy: The SuperTrend and the FlexiMA variance tracker will provide entry and exit signals, automatically managing the positions and taking profits at the pre-set levels.
█ Default Settings
The default settings for the strategy are configured to provide a balanced approach that works across different market conditions:
Indicator Length (10):
This controls the base length for the moving average. A lower length makes the moving average more responsive to price changes, while a higher length smooths out fluctuations, making the strategy less sensitive to short-term price movements.
Starting Factor (1.0):
This determines the initial multiplier applied to the moving average length. A higher starting factor will increase the average length, making it slower to react to price changes.
Increment Factor (1.0):
This increases the moving average length in each iteration. A larger increment factor creates a wider range of moving average lengths, allowing the strategy to track both short-term and long-term trends simultaneously.
Normalization Method ('None'):
Three methods of normalization can be applied to the deviations:
- None: No normalization applied, using raw deviations.
- Max-Min: Normalizes based on the range between the maximum and minimum deviations.
- Absolute Sum: Normalizes based on the total sum of absolute deviations.
Take Profit Levels:
- TP1 (2%): A quick exit to capture small price movements.
- TP2 (8%): A medium-term profit target for stronger trends.
- TP3 (18%): A long-term target for strong price moves.
Take Profit Percentages:
- TP Percent 1 (30%): Exits 30% of the position at TP1.
- TP Percent 2 (20%): Exits 20% of the position at TP2.
- TP Percent 3 (15%): Exits 15% of the position at TP3.
Effect of Variables on Performance:
- Short Indicator Lengths: More responsive to price changes but prone to false signals.
- Higher Starting Factor: Slows down the response, useful for longer-term trend following.
- Higher Increment Factor: Widens the variability in moving average lengths, making the strategy adapt to both short-term and long-term price trends.
- Aggressive Take Profit Levels: Allows for quick profit-taking in volatile markets but may exit positions prematurely in strong trends.
The default configuration offers a moderate balance between short-term responsiveness and long-term trend capturing, suitable for most traders. However, users can adjust these variables to optimize performance based on market conditions and personal preferences.
ETH Signal 15m
This strategy uses the Supertrend indicator combined with RSI to generate buy and sell signals, with stop loss (SL) and take profit (TP) conditions based on ATR (Average True Range). Below is a detailed explanation of each part:
1. General Information BINANCE:ETHUSDT.P
Strategy Name: "ETH Signal 15m"
Designed for use on the 15-minute time frame for the ETH pair.
Default capital allocation is 15% of total equity for each trade.
2. Backtest Period
start_time and end_time: Define the start and end time of the backtest period.
start_time = 2024-08-01: Start date of the backtest.
end_time = 2054-01-01: End date of the backtest.
The strategy will only run when the current time falls within this specified range.
3. Supertrend Indicator
Supertrend is a trend-following indicator that provides buy or sell signals based on the direction of price changes.
factor = 2.76: The multiplier used in the Supertrend calculation (increasing this value makes the Supertrend less sensitive to price movements).
atrPeriod = 12: Number of periods used to calculate ATR.
Output:
direction: Determines the buy/sell direction based on Supertrend.
If direction decreases, it signals a buy (Long).
If direction increases, it signals a sell (Short).
4. RSI Indicator
RSI (Relative Strength Index) is a momentum indicator, often used to identify overbought or oversold conditions.
rsiLength = 12: Number of periods used to calculate RSI.
rsiOverbought = 70: RSI level considered overbought.
rsiOversold = 30: RSI level considered oversold.
5. Entry Conditions
Long Entry:
Supertrend gives a buy signal (ta.change(direction) < 0).
RSI must be below the overbought level (rsi < rsiOverbought).
Short Entry:
Supertrend gives a sell signal (ta.change(direction) > 0).
RSI must be above the oversold level (rsi > rsiOversold).
The strategy will only execute trades if the current time is within the backtest period (in_date_range).
6. Stop Loss (SL) and Take Profit (TP) Conditions
ATR (Average True Range) is used to calculate the distance for Stop Loss and Take Profit based on price volatility.
atr = ta.atr(atrPeriod): ATR is calculated using 12 periods.
Stop Loss and Take Profit are calculated as follows:
Long Trade:
Stop Loss: Set at close - 4 * atr (current price minus 4 times the ATR).
Take Profit: Set at close + 2 * atr (current price plus 2 times the ATR).
Short Trade:
Stop Loss: Set at close + 4 * atr (current price plus 4 times the ATR).
Take Profit: Set at close - 2.237 * atr (current price minus 2.237 times the ATR).
Summary:
This strategy enters a Long trade when the Supertrend indicates an upward trend and RSI is not in the overbought region. Conversely, a Short trade is entered when Supertrend signals a downtrend, and RSI is not oversold.
The trade is exited when the price reaches the Stop Loss or Take Profit levels, which are determined based on price volatility (ATR).
Disclaimer:
The content provided in this strategy is for informational and educational purposes only. It is not intended as financial, investment, or trading advice. Trading in cryptocurrency, stocks, or any financial markets involves significant risk, and you may lose more than your initial investment. Past performance is not indicative of future results, and no guarantee of profit can be made. You should consult with a professional financial advisor before making any investment decisions. The creator of this strategy is not responsible for any financial losses or damages incurred as a result of following this strategy. All trades are executed at your own risk.
HFT V.2 EnhancedTitle: HFT V.2 Enhanced - ATR Dynamic Stop-Loss & Take-Profit
Description:
The HFT V.2 Enhanced strategy is designed for high-frequency trading with dynamic trade management and robust entry/exit logic. This strategy uses simple moving averages (SMA) for trend identification and the relative strength index (RSI) for momentum confirmation. In this enhanced version, the strategy also incorporates dynamic stop-loss and take-profit levels based on the Average True Range (ATR), offering better adaptability to market volatility.
Features:
Moving Average Crossover: Uses a fast and slow SMA to capture trend reversals and generate trade entries.
RSI Confirmation: Ensures momentum is in the direction of the trade by incorporating the RSI threshold for both long and short entries.
Dynamic Stop-Loss and Take-Profit: Stop-loss and take-profit levels are calculated based on the ATR, allowing the strategy to adjust its exit points according to market volatility. This helps manage risk more effectively and capture larger trends.
Auto-Close Opposing Positions: Automatically closes any open long positions when a short entry is triggered, and vice versa.
Once-Per-Bar Execution: Ensures that a position is entered only once per bar, avoiding multiple trades within the same bar.
Parameters:
Fast MA Length: Defines the length of the fast-moving average.
Slow MA Length: Defines the length of the slow-moving average.
RSI Length: Sets the period for the RSI indicator.
RSI Threshold: Controls the RSI level for confirming momentum (50 by default).
ATR Length: Determines the period for the ATR calculation.
ATR Multiplier for Stop-Loss/Take-Profit: Adjusts the sensitivity of the stop-loss and take-profit levels based on ATR.
How it Works:
Long Entry: The strategy opens a long trade when the fast SMA crosses above the slow SMA, and the RSI is above the user-defined threshold. A dynamic stop-loss is placed below the entry price, and a take-profit target is set based on ATR.
Short Entry: The strategy opens a short trade when the fast SMA crosses below the slow SMA, and the RSI is below the inverse threshold. A stop-loss is placed above the entry price, and a take-profit target is set using ATR.
Risk Management: The strategy adapts to changing market conditions by dynamically adjusting its stop-loss and take-profit levels, ensuring it remains responsive to market volatility.
This script is ideal for traders looking for a high-frequency strategy with advanced trade management, including dynamic exits and volatility-based risk management.
Disclaimer: Always backtest and optimize the parameters to fit your trading style and risk tolerance before using the strategy in live trading.
Larry Conners SMTP StrategyThe Spent Market Trading Pattern is a strategy developed by Larry Connors, typically used for short-term mean reversion trading. This strategy takes advantage of the exhaustion in market momentum by entering trades when the market is perceived as "spent" after extended trends or extreme moves, expecting a short-term reversal. Connors uses indicators like RSI (Relative Strength Index) and price action patterns to identify these opportunities.
Key Elements of the Strategy:
Overbought/Oversold Conditions: The strategy looks for extreme overbought or oversold conditions, often indicated by low RSI values (below 30 for oversold and above 70 for overbought).
Mean Reversion: Connors believed that markets, especially in short-term scenarios, tend to revert to the mean after periods of strong momentum. The "spent" market is assumed to have expended its energy, making a reversal likely.
Entry Signals:
In an uptrend, a stock or market index making a significant number of consecutive up days (e.g., 5-7 consecutive days with higher closes) indicates overbought conditions.
In a downtrend, a similar number of consecutive down days indicates oversold conditions.
Reversal Anticipation: Once an extreme in price movement is identified (such as consecutive gains or losses), the strategy places trades anticipating a reversion to the mean, which is usually the 5-day or 10-day moving average.
Exit Points: Trades are exited when prices move back toward their mean or when the extreme conditions dissipate, usually based on RSI or moving average thresholds.
Why the Strategy Works:
Human Psychology: The strategy capitalizes on the fact that markets, in the short term, often behave irrationally due to the emotions of traders—fear and greed lead to overextended moves.
Mean Reversion Tendency: Financial markets often exhibit mean-reverting behavior, where prices temporarily deviate from their historical norms but eventually return. Short-term exhaustion after a strong rally or sell-off offers opportunities for quick profits.
Overextended Moves: Markets that rise or fall too quickly tend to become overextended, as buyers or sellers get exhausted, making reversals more probable. Connors’ approach identifies these moments when the market is "spent" and ripe for a reversal.
Risks of the Spent Market Trading Pattern Strategy:
Trend Continuation: One of the key risks is that the market may not revert as expected and instead continues in the same direction. In trending markets, mean-reversion strategies can suffer because strong trends can last longer than anticipated.
False Signals: The strategy relies heavily on technical indicators like RSI, which can produce false signals in volatile or choppy markets. There can be times when a market appears "spent" but continues in its current direction.
Market Timing: Mean reversion strategies often require precise market timing. If the entry or exit points are mistimed, it can lead to losses, especially in short-term trades where small price movements can significantly impact profitability.
High Transaction Costs: This strategy requires frequent trades, which can lead to higher transaction costs, especially in markets with wide bid-ask spreads or high commissions.
Conclusion:
Larry Connors’ Spent Market Trading Pattern strategy is built on the principle of mean reversion, leveraging the concept that markets tend to revert to a mean after extreme moves. While effective in certain conditions, such as range-bound markets, it carries risks—especially during strong trends—where price momentum may not reverse as quickly as expected.
For a more in-depth explanation, Larry Connors’ books such as "Short-Term Trading Strategies That Work" provide a comprehensive guide to this and other strategies .
RSI Trend Following StrategyOverview
The RSI Trend Following Strategy utilizes Relative Strength Index (RSI) to enter the trade for the potential trend continuation. It uses Stochastic indicator to check is the price is not in overbought territory and the MACD to measure the current price momentum. Moreover, it uses the 200-period EMA to filter the counter trend trades with the higher probability. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Two layers trade filtering system: Strategy utilizes MACD and Stochastic indicators measure the current momentum and overbought condition and use 200-period EMA to filter trades against major trend.
Trailing take profit level: After reaching the trailing profit activation level script activates the trailing of long trade using EMA. More information in methodology.
Wide opportunities for strategy optimization: Flexible strategy settings allows users to optimize the strategy entries and exits for chosen trading pair and time frame.
Methodology
The strategy opens long trade when the following price met the conditions:
RSI is above 50 level.
MACD line shall be above the signal line
Both lines of Stochastic shall be not higher than 80 (overbought territory)
Candle’s low shall be above the 200 period EMA
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with trailing EMA(by default = 20 period). If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
MACD Fast Length (by default = 12, period of averaging fast MACD line)
MACD Fast Length (by default = 26, period of averaging slow MACD line)
MACD Signal Smoothing (by default = 9, period of smoothing MACD signal line)
Oscillator MA Type (by default = EMA, available options: SMA, EMA)
Signal Line MA Type (by default = EMA, available options: SMA, EMA)
RSI Length (by default = 14, period for RSI calculation)
Trailing EMA Length (by default = 20, period for EMA, which shall be broken close the trade after trailing profit activation)
Justification of Methodology
This trading strategy is designed to leverage a combination of technical indicators—Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Stochastic Oscillator, and the 200-period Exponential Moving Average (EMA)—to determine optimal entry points for long trades. Additionally, the strategy uses the Average True Range (ATR) for dynamic risk management to adapt to varying market conditions. Let's look in details for which purpose each indicator is used for and why it is used in this combination.
Relative Strength Index (RSI) is a momentum indicator used in technical analysis to measure the speed and change of price movements in a financial market. It helps traders identify whether an asset is potentially overbought (overvalued) or oversold (undervalued), which can indicate a potential reversal or continuation of the current trend.
How RSI Works? RSI tracks the strength of recent price changes. It compares the average gains and losses over a specific period (usually 14 periods) to assess the momentum of an asset. Average gain is the average of all positive price changes over the chosen period. It reflects how much the price has typically increased during upward movements. Average loss is the average of all negative price changes over the same period. It reflects how much the price has typically decreased during downward movements.
RSI calculates these average gains and losses and compares them to create a value between 0 and 100. If the RSI value is above 70, the asset is generally considered overbought, meaning it might be due for a price correction or reversal downward. Conversely, if the RSI value is below 30, the asset is considered oversold, suggesting it could be poised for an upward reversal or recovery. RSI is a useful tool for traders to determine market conditions and make informed decisions about entering or exiting trades based on the perceived strength or weakness of an asset's price movements.
This strategy uses RSI as a short-term trend approximation. If RSI crosses over 50 it means that there is a high probability of short-term trend change from downtrend to uptrend. Therefore RSI above 50 is our first trend filter to look for a long position.
The MACD (Moving Average Convergence Divergence) is a popular momentum and trend-following indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in an asset's price.
The MACD consists of three components:
MACD Line: This is the difference between a short-term Exponential Moving Average (EMA) and a long-term EMA, typically calculated as: MACD Line = 12 period EMA − 26 period EMA
Signal Line: This is a 9-period EMA of the MACD Line, which helps to identify buy or sell signals. When the MACD Line crosses above the Signal Line, it can be a bullish signal (suggesting a buy); when it crosses below, it can be a bearish signal (suggesting a sell).
Histogram: The histogram shows the difference between the MACD Line and the Signal Line, visually representing the momentum of the trend. Positive histogram values indicate increasing bullish momentum, while negative values indicate increasing bearish momentum.
This strategy uses MACD as a second short-term trend filter. When MACD line crossed over the signal line there is a high probability that uptrend has been started. Therefore MACD line above signal line is our additional short-term trend filter. In conjunction with RSI it decreases probability of following false trend change signals.
The Stochastic Indicator is a momentum oscillator that compares a security's closing price to its price range over a specific period. It's used to identify overbought and oversold conditions. The indicator ranges from 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions.
It consists of two lines:
%K: The main line, calculated using the formula (CurrentClose−LowestLow)/(HighestHigh−LowestLow)×100 . Highest and lowest price taken for 14 periods.
%D: A smoothed moving average of %K, often used as a signal line.
This strategy uses stochastic to define the overbought conditions. The logic here is the following: we want to avoid long trades in the overbought territory, because when indicator reaches it there is a high probability that the potential move is gonna be restricted.
The 200-period EMA is a widely recognized indicator for identifying the long-term trend direction. The strategy only trades in the direction of this primary trend to increase the probability of successful trades. For instance, when the price is above the 200 EMA, only long trades are considered, aligning with the overarching trend direction.
Therefore, strategy uses combination of RSI and MACD to increase the probability that price now is in short-term uptrend, Stochastic helps to avoid the trades in the overbought (>80) territory. To increase the probability of opening long trades in the direction of a main trend and avoid local bounces we use 200 period EMA.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -3.94%
Maximum Single Profit: +15.78%
Net Profit: +1359.21 USDT (+13.59%)
Total Trades: 111 (36.04% win rate)
Profit Factor: 1.413
Maximum Accumulated Loss: 625.02 USDT (-5.85%)
Average Profit per Trade: 12.25 USDT (+0.40%)
Average Trade Duration: 40 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Multi-Step FlexiSuperTrend - Strategy [presentTrading]At the heart of this endeavor is a passion for continuous improvement in the art of trading
█ Introduction and How it is Different
The "Multi-Step FlexiSuperTrend - Strategy " is an advanced trading strategy that integrates the well-known SuperTrend indicator with a nuanced and dynamic approach to market trend analysis. Unlike conventional SuperTrend strategies that rely on static thresholds and fixed parameters, this strategy introduces multi-step take profit mechanisms that allow traders to capitalize on varying market conditions in a more controlled and systematic manner.
What sets this strategy apart is its ability to dynamically adjust to market volatility through the use of an incremental factor applied to the SuperTrend calculation. This adjustment ensures that the strategy remains responsive to both minor and major market shifts, providing a more accurate signal for entries and exits. Additionally, the integration of multi-step take profit levels offers traders the flexibility to scale out of positions, locking in profits progressively as the market moves in their favor.
BTC 6hr Long/Short Performance
█ Strategy, How it Works: Detailed Explanation
The Multi-Step FlexiSuperTrend strategy operates on the foundation of the SuperTrend indicator, but with several enhancements that make it more adaptable to varying market conditions. The key components of this strategy include the SuperTrend Polyfactor Oscillator, a dynamic normalization process, and multi-step take profit levels.
🔶 SuperTrend Polyfactor Oscillator
The SuperTrend Polyfactor Oscillator is the heart of this strategy. It is calculated by applying a series of SuperTrend calculations with varying factors, starting from a defined "Starting Factor" and incrementing by a specified "Increment Factor." The indicator length and the chosen price source (e.g., HLC3, HL2) are inputs to the oscillator.
The SuperTrend formula typically calculates an upper and lower band based on the average true range (ATR) and a multiplier (the factor). These bands determine the trend direction. In the FlexiSuperTrend strategy, the oscillator is enhanced by iteratively applying the SuperTrend calculation across different factors. The iterative process allows the strategy to capture both minor and significant trend changes.
For each iteration (indexed by `i`), the following calculations are performed:
1. ATR Calculation: The Average True Range (ATR) is calculated over the specified `indicatorLength`:
ATR_i = ATR(indicatorLength)
2. Upper and Lower Bands Calculation: The upper and lower bands are calculated using the ATR and the current factor:
Upper Band_i = hl2 + (ATR_i * Factor_i)
Lower Band_i = hl2 - (ATR_i * Factor_i)
Here, `Factor_i` starts from `startingFactor` and is incremented by `incrementFactor` in each iteration.
3. Trend Determination: The trend is determined by comparing the indicator source with the upper and lower bands:
Trend_i = 1 (uptrend) if IndicatorSource > Upper Band_i
Trend_i = 0 (downtrend) if IndicatorSource < Lower Band_i
Otherwise, the trend remains unchanged from the previous value.
4. Output Calculation: The output of each iteration is determined based on the trend:
Output_i = Lower Band_i if Trend_i = 1
Output_i = Upper Band_i if Trend_i = 0
This process is repeated for each iteration (from 0 to 19), creating a series of outputs that reflect different levels of trend sensitivity.
Local
🔶 Normalization Process
To make the oscillator values comparable across different market conditions, the deviations between the indicator source and the SuperTrend outputs are normalized. The normalization method can be one of the following:
1. Max-Min Normalization: The deviations are normalized based on the range of the deviations:
Normalized Value_i = (Deviation_i - Min Deviation) / (Max Deviation - Min Deviation)
2. Absolute Sum Normalization: The deviations are normalized based on the sum of absolute deviations:
Normalized Value_i = Deviation_i / Sum of Absolute Deviations
This normalization ensures that the oscillator values are within a consistent range, facilitating more reliable trend analysis.
For more details:
🔶 Multi-Step Take Profit Mechanism
One of the unique features of this strategy is the multi-step take profit mechanism. This allows traders to lock in profits at multiple levels as the market moves in their favor. The strategy uses three take profit levels, each defined as a percentage increase (for long trades) or decrease (for short trades) from the entry price.
1. First Take Profit Level: Calculated as a percentage increase/decrease from the entry price:
TP_Level1 = Entry Price * (1 + tp_level1 / 100) for long trades
TP_Level1 = Entry Price * (1 - tp_level1 / 100) for short trades
The strategy exits a portion of the position (defined by `tp_percent1`) when this level is reached.
2. Second Take Profit Level: Similar to the first level, but with a higher percentage:
TP_Level2 = Entry Price * (1 + tp_level2 / 100) for long trades
TP_Level2 = Entry Price * (1 - tp_level2 / 100) for short trades
The strategy exits another portion of the position (`tp_percent2`) at this level.
3. Third Take Profit Level: The final take profit level:
TP_Level3 = Entry Price * (1 + tp_level3 / 100) for long trades
TP_Level3 = Entry Price * (1 - tp_level3 / 100) for short trades
The remaining portion of the position (`tp_percent3`) is exited at this level.
This multi-step approach provides a balance between securing profits and allowing the remaining position to benefit from continued favorable market movement.
█ Trade Direction
The strategy allows traders to specify the trade direction through the `tradeDirection` input. The options are:
1. Both: The strategy will take both long and short positions based on the entry signals.
2. Long: The strategy will only take long positions.
3. Short: The strategy will only take short positions.
This flexibility enables traders to tailor the strategy to their market outlook or current trend analysis.
█ Usage
To use the Multi-Step FlexiSuperTrend strategy, traders need to set the input parameters according to their trading style and market conditions. The strategy is designed for versatility, allowing for various market environments, including trending and ranging markets.
Traders can also adjust the multi-step take profit levels and percentages to match their risk management and profit-taking preferences. For example, in highly volatile markets, traders might set wider take profit levels with smaller percentages at each level to capture larger price movements.
The normalization method and the incremental factor can be fine-tuned to adjust the sensitivity of the SuperTrend Polyfactor Oscillator, making the strategy more responsive to minor market shifts or more focused on significant trends.
█ Default Settings
The default settings of the strategy are carefully chosen to provide a balanced approach between risk management and profit potential. Here is a breakdown of the default settings and their effects on performance:
1. Indicator Length (10): This parameter controls the lookback period for the ATR calculation. A shorter length makes the strategy more sensitive to recent price movements, potentially generating more signals. A longer length smooths out the ATR, reducing sensitivity but filtering out noise.
2. Starting Factor (0.618): This is the initial multiplier used in the SuperTrend calculation. A lower starting factor makes the SuperTrend bands closer to the price, generating more frequent trend changes. A higher starting factor places the bands further away, filtering out minor fluctuations.
3. Increment Factor (0.382): This parameter controls how much the factor increases with each iteration of the SuperTrend calculation. A smaller increment factor results in more gradual changes in sensitivity, while a larger increment factor creates a wider range of sensitivity across the iterations.
4. Normalization Method (None): The default is no normalization, meaning the raw deviations are used. Normalization methods like Max-Min or Absolute Sum can make the deviations more consistent across different market conditions, improving the reliability of the oscillator.
5. Take Profit Levels (2%, 8%, 18%): These levels define the thresholds for exiting portions of the position. Lower levels (e.g., 2%) capture smaller profits quickly, while higher levels (e.g., 18%) allow positions to run longer for more significant gains.
6. Take Profit Percentages (30%, 20%, 15%): These percentages determine how much of the position is exited at each take profit level. A higher percentage at the first level locks in more profit early, reducing exposure to market reversals. Lower percentages at higher levels allow for a portion of the position to benefit from extended trends.
MACD with 1D Stochastic Confirmation Reversal StrategyOverview
The MACD with 1D Stochastic Confirmation Reversal Strategy utilizes MACD indicator in conjunction with 1 day timeframe Stochastic indicators to obtain the high probability short-term trend reversal signals. The main idea is to wait until MACD line crosses up it’s signal line, at the same time Stochastic indicator on 1D time frame shall show the uptrend (will be discussed in methodology) and not to be in the oversold territory. Strategy works on time frames from 30 min to 4 hours and opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Higher time frame confirmation: Strategy utilizes 1D Stochastic to establish the major trend and confirm the local reversals with the higher probability.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
MACD line of MACD indicator shall cross over the signal line of MACD indicator.
1D time frame Stochastic’s K line shall be above the D line.
1D time frame Stochastic’s K line value shall be below 80 (not overbought)
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 3.25, value multiplied by ATR to be subtracted from position entry price to setup stop loss)
ATR Trailing Profit Activation Level (by default = 4.25, value multiplied by ATR to be added to position entry price to setup trailing profit activation level)
Trailing EMA Length (by default = 20, period for EMA, when price reached trailing profit activation level EMA will stop out of position if price closes below it)
User can choose the optimal parameters during backtesting on certain price chart, in our example we use default settings.
Justification of Methodology
This strategy leverages 2 time frames analysis to have the high probability reversal setups on lower time frame in the direction of the 1D time frame trend. That’s why it’s recommended to use this strategy on 30 min – 4 hours time frames.
To have an approximation of 1D time frame trend strategy utilizes classical Stochastic indicator. The Stochastic Indicator is a momentum oscillator that compares a security's closing price to its price range over a specific period. It's used to identify overbought and oversold conditions. The indicator ranges from 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions.
It consists of two lines:
%K: The main line, calculated using the formula (CurrentClose−LowestLow)/(HighestHigh−LowestLow)×100 . Highest and lowest price taken for 14 periods.
%D: A smoothed moving average of %K, often used as a signal line.
Strategy logic assumes that on 1D time frame it’s uptrend in %K line is above the %D line. Moreover, we can consider long trade only in %K line is below 80. It means that in overbought state the long trade will not be opened due to higher probability of pullback or even major trend reversal. If these conditions are met we are going to our working (lower) time frame.
On the chosen time frame, we remind you that for correct work of this strategy you shall use 30min – 4h time frames, MACD line shall cross over it’s signal line. The MACD (Moving Average Convergence Divergence) is a popular momentum and trend-following indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in a stock's price.
The MACD consists of three components:
MACD Line: This is the difference between a short-term Exponential Moving Average (EMA) and a long-term EMA, typically calculated as: MACD Line=12-period EMA−26-period
Signal Line: This is a 9-period EMA of the MACD Line, which helps to identify buy or sell signals. When the MACD Line crosses above the Signal Line, it can be a bullish signal (suggesting a buy); when it crosses below, it can be a bearish signal (suggesting a sell).
Histogram: The histogram shows the difference between the MACD Line and the Signal Line, visually representing the momentum of the trend. Positive histogram values indicate increasing bullish momentum, while negative values indicate increasing bearish momentum.
In our script we are interested in only MACD and signal lines. When MACD line crosses signal line there is a high chance that short-term trend reversed to the upside. We use this strategy on 45 min time frame.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -4.79%
Maximum Single Profit: +20.14%
Net Profit: +2361.33 USDT (+44.72%)
Total Trades: 123 (44.72% win rate)
Profit Factor: 1.623
Maximum Accumulated Loss: 695.80 USDT (-5.48%)
Average Profit per Trade: 19.20 USDT (+0.59%)
Average Trade Duration: 30 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe between 30 min and 4 hours and chart (optimal performance observed on 45 min BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Double CCI Confirmed Hull Moving Average Reversal StrategyOverview
The Double CCI Confirmed Hull Moving Average Strategy utilizes hull moving average (HMA) in conjunction with two commodity channel index (CCI) indicators: the slow and fast to increase the probability of entering when the short and mid-term uptrend confirmed. The main idea is to wait until the price breaks the HMA while both CCI are showing that the uptrend has likely been already started. Moreover, strategy uses exponential moving average (EMA) to trail the price when it reaches the specific level. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Double trade setup confirmation: Strategy utilizes two different period CCI indicators to confirm the breakouts of HMA.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
Short-term period CCI indicator shall be above 0.
Long-term period CCI indicator shall be above 0.
Price shall cross the HMA and candle close above it with the same candle
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
CCI Fast Length (by default = 25, used for calculation short term period CCI
CCI Slow Length (by default = 50, used for calculation long term period CCI)
Hull MA Length (by default = 34, period of HMA, which shall be broken to open trade)
Trailing EMA Length (by default = 20)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is CCI and HMA.
The Commodity Channel Index (CCI) is a momentum-based technical indicator used in trading to measure a security's price relative to its average price over a given period. Developed by Donald Lambert in 1980, the CCI is primarily used to identify cyclical trends in a security, helping traders to spot potential buying or selling opportunities.
The CCI formula is:
CCI = (Typical Price − SMA) / (0.015 × Mean Deviation)
Typical Price (TP): This is calculated as the average of the high, low, and closing prices for the period.
Simple Moving Average (SMA): This is the average of the Typical Prices over a specific number of periods.
Mean Deviation: This is the average of the absolute differences between the Typical Price and the SMA.
The result is a value that typically fluctuates between +100 and -100, though it is not bounded and can go higher or lower depending on the price movement.
The Hull Moving Average (HMA) is a type of moving average that was developed by Alan Hull to improve upon the traditional moving averages by reducing lag while maintaining smoothness. The goal of the HMA is to create an indicator that is both quick to respond to price changes and less prone to whipsaws (false signals).
How the Hull Moving Average is Calculated?
The Hull Moving Average is calculated using the following steps:
Weighted Moving Average (WMA): The HMA starts by calculating the Weighted Moving Average (WMA) of the price data over a period square root of n (sqrt(n))
Speed Adjustment: A WMA is then calculated for half of the period n/2, and this is multiplied by 2 to give more weight to recent prices.
Lag Reduction: The WMA of the full period n is subtracted from the doubled n/2 WMA.
Final Smoothing: To smooth the result and reduce noise, a WMA is calculated for the square root of the period n.
The formula can be represented as:
HMA(n) = WMA(WMA(n/2) × 2 − WMA(n), sqrt(n))
The Weighted Moving Average (WMA) is a type of moving average that gives more weight to recent data points, making it more responsive to recent price changes than a Simple Moving Average (SMA). In a WMA, each data point within the selected period is multiplied by a weight, with the most recent data receiving the highest weight. The sum of these weighted values is then divided by the sum of the weights to produce the WMA.
This strategy leverages HMA of user given period as a critical level which shall be broken to say that probability of trend change to the upside increased. HMA reacts faster than EMA or SMA to the price change, that’s why it increases chances to enter new trade earlier. Long-term period CCI helps to have an approximation of mid-term trend. If it’s above 0 the probability of uptrend increases. Short-period CCI allows to have an approximation of short-term trend reversal from down to uptrend. This approach increases chances to have a long trade setup in the direction of mid-term trend when the short-term trend starts to reverse.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements. It’s also important to make a note, that script uses HMA to enter the trade, but for trailing it leverages EMA. It’s used because EMA has no such fast reaction to price move which increases probability not to be stopped out from any significant uptrend move.
Backtest Results
Operating window: Date range of backtests is 2022.07.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 100%
Maximum Single Position Loss: -4.67%
Maximum Single Profit: +19.66%
Net Profit: +14897.94 USDT (+148.98%)
Total Trades: 104 (36.54% win rate)
Profit Factor: 2.312
Maximum Accumulated Loss: 1302.66 USDT (-9.58%)
Average Profit per Trade: 143.25 USDT (+0.96%)
Average Trade Duration: 34 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Self Optimizing RSI and Self Adaptive TP/SL [Starbots]Self Optimizing RSI and Self Adaptive TP/SL Strategy. (non-repainting)
This script continuously backtests 20 different combinations of RSI Buy conditions across 5 different Take Profit/Stop Loss combinations. In total, it tests 100 variants on every bar close and records the Net Profit gained for each combination. The strategy then selects and uses the best-performing combination of settings currently available for you to trade.
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The Relative Strength Index (RSI), developed by J. Welles Wilder, is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Traditionally the RSI is considered overbought when above 70 and oversold when below 30. Signals can be generated by looking for divergences and failure swings. RSI can also be used to identify the general trend.
To improve our results we are calculating Multiple Length RSI - Average RSI based on the multiple periods. You can use just 1 Length or Multiple.
Set Inputs to Min=14, Max=14 if you want to use just 1 period.
= RSI(14)
3 RSI Lengths example (12,13 and 14):
Min=12, Max=14
(12+13+14) / 3 = avg. RSI
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Backtester - Optimizer Explained:
The backtester runs numerous backtests in the background to optimize trading strategies. Here’s how it works:
Default Inputs (Combinations of TP/SL)
TP 1%, SL4%
TP 2%, SL4%
TP 3%, SL4%
TP 2%, SL5%
TP 4.5%, SL10%
Default Inputs (RSI Crossover Buys) :
18 ,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,45,55, 69
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Backtest RSI Crossover 18:
TP1%, SL4% => Save net profit
TP2%, SL4% => Save net profit
TP3%, SL4% => Save net profit
TP2%, SL5% => Save net profit
TP4.5%, SL10% => Save net profit
,...
,...
Backtest RSI Crossover 69:
TP1%, SL4% => Save net profit
TP2%, SL4% => Save net profit
TP3%, SL4% => Save net profit
TP2%, SL5% => Save net profit
TP4.5%, SL10% => Save net profit
Self Optimizing Buy Condition and Self Optimizing Take Profit - Stop Los
This process involves testing various combinations of RSI crossover values with different Take Profit (TP) and Stop Loss (SL) percentages. The net profit for each combination is saved, allowing the optimizer to select the best-performing settings for trading.
It recalculates on every bar close. If one combination starts performing better than others—achieving a higher net profit gain (essentially like running 100 backtests with different settings in the background)—the strategy switches to that combination of TP/SL and Buy condition. It continues trading with the new settings until another parameter starts performing better and the strategy switches to that setting.
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If you wish to use it as INDICATOR - turn on 'Recalculate - On every tick' in Properties tab to have this script updating constantly and use it as a normal Indicator tool for manual trading.
Other functions:
Set the %fee for optimizing engine. If you set this % higher, you also punish small average trades and make the strategy prefer larger avg. trades, giving you better chances to make your strategy profitable.
Trade with trend and optimize the strategy only when the market is uptrending with EMA/HMA
Use Moving Average of avg.RSI and smooth the values for indicator even more. (Yes strategy is self optimizing RSI or avg.RSI or RSI-MA, you can select all sorts of this indicator for optimizing)
All trading alerts are working and functional, if you want to automate the strategy
This script is simple to use for any trader as it saves a lot of time for searching good parameters on your own. It's self-optimizing and adjusting to the markets on the go.
IsAlgo - Manual Channel► Overview:
Manual Channel is a strategy that allows traders to manually insert channel lines and set the lines’ width. Trades are opened when the price hits one of the lines and bounces back, with the expectation that it will move towards the opposite line. This strategy offers flexibility in configuring channel lines and trading behavior.
► Description:
The Manual Channel strategy is based on the use of manually defined channel lines to guide trading decisions. Traders start by marking four key points on the chart to create the channel. The first two points share the same time but different prices, and the last two points also share the same time but different prices. This method allows traders to place the channel lines precisely based on their analysis and insights. Additionally, the strategy allows for adjusting the width of the channel lines, which acts as a buffer zone around the main lines.
Once the channel is established, the strategy continuously monitors the price movements in relation to these lines. When the price touches one of the channel lines, the strategy opens a trade with the expectation that the price will bounce back and move towards the opposite line. For example, if the price hits the lower channel line, a long trade (buy) might be opened with the anticipation that the price will rise to the upper channel line. Conversely, if the price hits the upper channel line, a short trade (sell) might be opened expecting the price to fall to the lower channel line.
The strategy offers several options for managing trades. Traders can choose to close a trade when the price reaches the opposite channel line, capturing the expected movement within the channel. Additionally, if the price breaks outside the channel, traders have the option to close trades immediately or stop further trade executions to avoid potential losses.
↑ Channel Example:
↓ Channel Example:
► Features and Settings:
⚙︎ Channel: Define the time and prices of the four main points of the channel lines, and set the lines’ width.
⚙︎ Entry Candle: Specify the minimum and maximum body size and the body-to-candle size ratio for entry candles.
⚙︎ Trading Session: Define specific trading hours during which the strategy operates, restricting trades to preferred market periods.
⚙︎ Trading Days: Specify active trading days to avoid certain days of the week.
⚙︎ Backtesting: Perform backtesting for a selected period to evaluate strategy performance. This feature can be deactivated if not needed.
⚙︎ Trades: Configure trade direction (long, short, or both), position sizing (fixed or percentage-based), maximum number of open trades, and daily trade limits.
⚙︎ Trades Exit: Set profit/loss limits, specify trade duration, or exit based on channel breaks.
⚙︎ Stop Loss: Choose from various stop-loss methods, including fixed pips, ATR-based, or highest/lowest price points within a specified number of candles. Trades can also be closed after a certain number of adverse candle movements.
⚙︎ Break Even: Adjust stop loss to break even once predefined profit levels are reached, protecting gains.
⚙︎ Trailing Stop: Implement a trailing stop to adjust the stop loss as the trade becomes profitable, securing gains and potentially capturing further upside.
⚙︎ Take Profit: Set up to three take-profit levels using methods such as fixed pips, ATR, or risk-to-reward ratios. Alternatively, specify a set number of candles moving in the trade’s direction.
⚙︎ Alerts: Comprehensive alert system to notify users of significant actions, including trade openings and closings. Supports dynamic placeholders for take-profit levels and stop-loss prices.
⚙︎ Dashboard: Visual display on the chart providing detailed information about ongoing and past trades, aiding users in monitoring strategy performance and making informed decisions.
► Backtesting Details:
Timeframe: 15-minute EURUSD chart
Initial Balance: $10,000
Order Size: 10 units
Commission: 0.05%
Slippage: 5 ticks
This strategy opens trades around a manually drawn channel, which results in a smaller number of closed trades.
IsAlgo - Manual TrendLine► Overview:
Manual TrendLine is a strategy that allows traders to manually insert a trendline and opens trades when the trendline is retested or when the price hits a new highest high or lowest low. It provides flexibility in trendline configuration and trading behavior, enabling responsive and adaptable trading strategies.
► Description:
The Manual TrendLine strategy revolves around using manually defined trendlines as the primary tool for making trading decisions. Traders start by specifying two key points on the chart to establish the trendline. Each point is defined by a specific time and price, enabling precise placement according to the trader’s analysis and insights. Additionally, the strategy allows for the adjustment of the trendline’s width, which acts as a buffer zone around the trendline, providing flexibility in how closely price movements must align with the trendline to trigger trades.
Once the trendline is established, the strategy continuously monitors price movements relative to this line. One of its core functions is to execute trades when the price retests the trendline. A retest occurs when the price approaches the trendline after initially diverging from it, indicating potential continuation of the prevailing trend. This behavior is often seen as a confirmation of the trend’s strength, and the strategy takes advantage of these moments to enter trades in the direction of the trend.
Beyond retests, the strategy also tracks the formation of new highest highs and lowest lows in relation to the trendline. When the price reaches a new highest high or lowest low, it signifies strong momentum in the trend’s direction. The strategy can be configured to open trades at these critical points.
Another key feature of the strategy is its response to trendline breaks. A break occurs when the price moves through the trendline, potentially signaling a reversal or a significant shift in market sentiment. The strategy can be set to open reverse trades upon such breaks, enabling traders to quickly adapt to changing market conditions. Additionally, traders have the option to stop opening new trades after a trendline break, helping to avoid trades during periods of uncertainty or increased volatility.
↑ Up Trend Example:
↓ Down Trend Example:
► Features and Settings:
⚙︎ TrendLine: Define the time and price of the two main points of the trendline, and set the trendline width.
⚙︎ Entry Candle: Specify the minimum and maximum body size and the body-to-candle size ratio for entry candles.
⚙︎ Trading Session: Define specific trading hours during which the strategy operates, restricting trades to preferred market periods.
⚙︎ Trading Days: Specify active trading days to avoid certain days of the week.
⚙︎ Backtesting: backtesting for a selected period to evaluate strategy performance. This feature can be deactivated if not needed.
⚙︎ Trades: Configure trade direction (long, short, or both), position sizing (fixed or percentage-based), maximum number of open trades, and daily trade limits.
⚙︎ Trades Exit: Set profit/loss limits, specify trade duration, or exit based on band reversal signals.
⚙︎ Stop Loss: Choose from various stop-loss methods, including fixed pips, ATR-based, or highest/lowest price points within a specified number of candles. Trades can also be closed after a certain number of adverse candle movements.
⚙︎ Break Even: Adjust stop loss to break even once predefined profit levels are reached, protecting gains.
⚙︎ Trailing Stop: Implement a trailing stop to adjust the stop loss as the trade becomes profitable, securing gains and potentially capturing further upside.
⚙︎ Take Profit: Set up to three take-profit levels using methods such as fixed pips, ATR, or risk-to-reward ratios. Alternatively, specify a set number of candles moving in the trade’s direction.
⚙︎ Alerts: Comprehensive alert system to notify users of significant actions, including trade openings and closings. Supports dynamic placeholders for take-profit levels and stop-loss prices.
⚙︎ Dashboard: Visual display on the chart providing detailed information about ongoing and past trades, aiding users in monitoring strategy performance and making informed decisions.
► Backtesting Details:
Timeframe: 30-minute EURUSD chart
Initial Balance: $10,000
Order Size: 500 units
Commission: 0.05%
Slippage: 5 ticks
This strategy opens trades around a manually drawn trendline, which results in a smaller number of closed trades.
Smart Money Concept Strategy - Uncle SamThis strategy combines concepts from two popular TradingView scripts:
Smart Money Concepts (SMC) : The strategy identifies key levels in the market (swing highs and lows) and draws trend lines to visualize potential breakouts. It uses volume analysis to gauge the strength of these breakouts.
Smart Money Breakouts : This part of the strategy incorporates the idea of "Smart Money" – institutional traders who often lead market movements. It looks for breakouts of established levels with significant volume, aiming to catch the beginning of new trends.
How the Strategy Works:
Identification of Key Levels: The script identifies swing highs and swing lows based on a user-defined lookback period. These levels are considered significant points where price has reversed in the past.
Drawing Trend Lines: Trend lines are drawn connecting these key levels, creating a visual representation of potential support and resistance zones.
Volume Analysis: The script analyzes the volume during the formation of these levels and during breakouts. Higher volume suggests stronger moves and increases the probability of a successful breakout.
Entry Conditions:
Long Entry: A long entry is triggered when the price breaks above a resistance line with significant volume, and the moving average trend filter (optional) is bullish.
Short Entry: A short entry is triggered when the price breaks below a support line with significant volume, and the moving average trend filter (optional) is bearish.
Exit Conditions:
Stop Loss: Customizable stop loss percentages are implemented to protect against adverse price movements.
Take Profit: Customizable take profit percentages are used to lock in profits.
Credits and Compliance:
This strategy is inspired by the concepts and code from "Smart Money Concepts (SMC) " and "Smart Money Breakouts ." I've adapted and combined elements of both scripts to create this strategy. Full credit is given to the original authors for their valuable contributions to the TradingView community.
To comply with TradingView's House Rules, I've made the following adjustments:
Clearly Stated Inspiration: The description explicitly mentions the original scripts and authors as the inspiration for this strategy.
No Direct Copying: The code has been modified and combined, not directly copied from the original scripts.
Educational Purpose: The primary purpose of this strategy is for learning and backtesting. It's not intended as financial advice.
Important Note:
This strategy is intended for educational and backtesting purposes only. It should not be used for live trading without thorough testing and understanding of the underlying concepts. Past performance is not indicative of future results.
