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AI-Optimized Adaptive Stop-Loss Trading System with Multiple Technical Indicator Integration

Author: ChaoZhang, Date: 2024-11-27 15:10:57
Tags: RSIBBATRSTMA

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Overview

This strategy is an adaptive trading system that combines AI optimization with multiple technical indicators. It primarily uses Bollinger Bands, Relative Strength Index (RSI), and Supertrend indicators to generate trading signals, with AI optimization for parameter adjustment. The system includes an ATR-based adaptive stop-loss mechanism, allowing the strategy to automatically adjust risk management parameters based on market volatility.

Strategy Principles

The strategy employs a multi-layer filtering mechanism to determine trading signals. First, Bollinger Bands are used to identify market volatility ranges, generating long signals when price breaks below the lower band and RSI is in oversold territory. Conversely, short signals are considered when price breaks above the upper band and RSI is in overbought territory. The Supertrend indicator serves as a trend confirmation tool, executing trades only when the price-to-Supertrend relationship aligns with the trading direction. The AI module optimizes various parameters to enhance strategy adaptability. Both stop-loss and profit targets are dynamically calculated based on ATR, ensuring risk management measures adapt to changes in market volatility.

Strategy Advantages

  1. Multiple technical indicators reduce the impact of false signals
  2. AI optimization module enhances strategy adaptability and stability
  3. ATR-based dynamic stop-loss mechanism effectively controls risk
  4. Strategy parameters can be flexibly adjusted based on actual needs
  5. Comprehensive risk management system including stop-loss and take-profit settings
  6. Good visualization effects for monitoring and analysis

Strategy Risks

  1. Over-optimization of parameters may lead to overfitting
  2. Multiple indicators may generate conflicting signals during extreme volatility
  3. AI module requires sufficient historical data for training
  4. High-frequency trading may incur significant transaction costs
  5. Stop-losses may experience slippage during rapid market changes
  6. High system complexity requires regular maintenance and adjustment

Optimization Directions

  1. Introduce more market sentiment indicators to improve signal accuracy
  2. Optimize AI module training methods and parameter selection
  3. Add volume analysis to support decision-making
  4. Implement additional risk control measures
  5. Develop adaptive parameter adjustment mechanisms
  6. Optimize computational efficiency to reduce resource consumption

Summary

This is a comprehensive trading strategy that combines traditional technical analysis with modern artificial intelligence technology. Through the coordinated use of multiple technical indicators, the strategy can effectively identify market opportunities, while the AI optimization module provides strong adaptability. The dynamic stop-loss mechanism provides excellent risk control capabilities. Although there are still aspects that need optimization, the overall design approach is rational, offering good practical value and development potential.

/*backtest
start: 2024-10-01 00:00:00
end: 2024-10-31 23:59:59
period: 1h
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("AI-Optimized Crypto Trading with Trailing Stop", overlay=true, precision=4)

// Input settings for AI optimization
risk_per_trade = input.float(1.0, title="Risk per Trade (%)", minval=0.1, maxval=100) / 100
atr_period = input.int(14, title="ATR Period")  // ATR период должен быть целым числом
atr_multiplier = input.float(2.0, title="ATR Multiplier for Stop Loss")
take_profit_multiplier = input.float(2.0, title="Take Profit Multiplier")
ai_optimization = input.bool(true, title="Enable AI Optimization")

// Indicators: Bollinger Bands, RSI, Supertrend
rsi_period = input.int(14, title="RSI Period")
upper_rsi = input.float(70, title="RSI Overbought Level")
lower_rsi = input.float(30, title="RSI Oversold Level")
bb_length = input.int(20, title="Bollinger Bands Length")
bb_mult = input.float(2.0, title="Bollinger Bands Multiplier")
supertrend_factor = input.int(3, title="Supertrend Factor")  // Изменено на целое число

// Bollinger Bands
basis = ta.sma(close, bb_length)
dev = bb_mult * ta.stdev(close, bb_length)
upper_band = basis + dev
lower_band = basis - dev

// RSI
rsi = ta.rsi(close, rsi_period)

// Supertrend calculation
atr = ta.atr(atr_period)
[supertrend, _] = ta.supertrend(atr_multiplier, supertrend_factor)

// AI-based entry/exit signals (dynamic optimization)
long_signal = (rsi < lower_rsi and close < lower_band) or (supertrend[1] < close and ai_optimization)
short_signal = (rsi > upper_rsi and close > upper_band) or (supertrend[1] > close and ai_optimization)

// Trade execution with trailing stop-loss
if (long_signal)
    strategy.entry("Long", strategy.long, stop=close - atr * atr_multiplier, limit=close + atr * take_profit_multiplier)

if (short_signal)
    strategy.entry("Short", strategy.short, stop=close + atr * atr_multiplier, limit=close - atr * take_profit_multiplier)

// Plotting the MAs and Ichimoku Cloud for visualization
plot(upper_band, color=color.red, title="Upper Bollinger Band")
plot(lower_band, color=color.green, title="Lower Bollinger Band")
plot(supertrend, color=color.blue, title="Supertrend")
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