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Multi-Period RSI Divergence with Support/Resistance Quantitative Trading Strategy

Author: ChaoZhang, Date: 2024-12-20 17:01:44
Tags: RSI

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Overview

This strategy is a quantitative trading system that combines RSI technical indicator, price divergence, and support/resistance levels. The strategy identifies trading signals through RSI-price divergence relationships and support/resistance breakouts, while incorporating stop-loss and take-profit mechanisms for risk control.

Strategy Principles

The strategy is based on several core components:

  1. RSI Calculation: Uses a 14-period Relative Strength Index (RSI) to measure price momentum
  2. Support/Resistance Identification: Determines key price levels using 50-period highs and lows
  3. Divergence Detection:
    • Bullish Divergence: When price makes new low but RSI doesn’t, and price is above support
    • Bearish Divergence: When price makes new high but RSI doesn’t, and price is below resistance
  4. Risk Management:
    • 1% stop-loss after entry
    • 2% take-profit target

Strategy Advantages

  1. Multiple Confirmation Mechanism: Combines momentum indicator (RSI), price pattern (divergence), and market structure (support/resistance) for more reliable signals
  2. Comprehensive Risk Control: Preset stop-loss and take-profit mechanisms effectively control risk per trade
  3. High Adaptability: Strategy parameters can be adjusted for different market conditions
  4. Clear Signals: Trading conditions are well-defined for easy implementation and backtesting

Strategy Risks

  1. False Breakout Risk: Frequent false signals may occur in ranging markets
  2. Parameter Sensitivity: Choice of RSI period and support/resistance period significantly impacts strategy performance
  3. Slippage Impact: Actual execution prices may deviate from signal prices in fast markets
  4. Market Environment Dependency: Performs better in trending markets, may generate false signals in ranging markets

Optimization Directions

  1. Timeframe Optimization: Add multiple timeframe confirmation mechanism to improve signal reliability
  2. Stop-Loss Enhancement: Introduce dynamic stop-loss mechanisms, such as trailing stops
  3. Filter Implementation: Add volume and volatility filters to reduce false signals
  4. Parameter Adaptation: Develop adaptive parameter mechanisms for automatic adjustment based on market conditions

Summary

The strategy constructs a relatively complete trading system by combining multiple important concepts in technical analysis. Its strengths lie in multiple confirmation mechanisms and comprehensive risk control, while facing challenges in parameter selection and market environment dependency. Through the suggested optimization directions, the strategy’s stability and adaptability can be further improved. In practical application, it is recommended to determine the most suitable strategy configuration through thorough historical data backtesting and parameter optimization.

/*backtest
start: 2024-12-12 00:00:00
end: 2024-12-19 00:00:00
period: 10m
basePeriod: 10m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=6
strategy("Агрессивная стратегия с дивергенциями по RSI и уровнями поддержки/сопротивления", overlay=true)

// Параметры для RSI
rsiLength = input.int(14, title="Период для RSI", minval=1)   // Период для расчета RSI
rsiOverbought = input.int(70, title="Уровень перекупленности", minval=1, maxval=100)
rsiOversold = input.int(30, title="Уровень перепроданности", minval=1, maxval=100)

// Параметры для стоп-лосса и тейк-профита
stopLossPercent = input.float(1, title="Стоп-лосс (%)", minval=0.1) / 100
takeProfitPercent = input.float(2, title="Тейк-профит (%)", minval=0.1) / 100

// Период для уровней поддержки и сопротивления
supportResistanceLength = input.int(50, title="Период для уровней поддержки и сопротивления", minval=1)

// Рассчитываем RSI
rsi = ta.rsi(close, rsiLength)

// Рассчитываем уровни поддержки и сопротивления
support = ta.lowest(close, supportResistanceLength)  // Находим минимумы за период для поддержки
resistance = ta.highest(close, supportResistanceLength)  // Находим максимумы за период для сопротивления

// Определяем дивергенцию RSI с ценой
priceHigh = ta.highest(close, rsiLength)
priceLow = ta.lowest(close, rsiLength)
rsiHigh = ta.highest(rsi, rsiLength)
rsiLow = ta.lowest(rsi, rsiLength)

// Дивергенция на покупку (бычья): цена делает новый минимум, а RSI этого не делает
bullishDivergence = priceLow < priceLow[1] and rsiLow > rsiLow[1] and close > support

// Дивергенция на продажу (медвежья): цена делает новый максимум, а RSI этого не делает
bearishDivergence = priceHigh > priceHigh[1] and rsiHigh < rsiHigh[1] and close < resistance

// Отображаем уровни поддержки и сопротивления
plot(support, title="Поддержка", color=color.green, linewidth=2, style=plot.style_line)
plot(resistance, title="Сопротивление", color=color.red, linewidth=2, style=plot.style_line)

// Условия для покупки по бычьей дивергенции
if (bullishDivergence)
    strategy.entry("Long", strategy.long)
    stopLoss = close * (1 - stopLossPercent)   // Стоп-лосс
    takeProfit = close * (1 + takeProfitPercent) // Тейк-профит
    strategy.exit("Exit Long", from_entry="Long", stop=stopLoss, limit=takeProfit)

// Условия для продажи по медвежьей дивергенции
if (bearishDivergence)
    strategy.entry("Short", strategy.short)
    stopLoss = close * (1 + stopLossPercent)   // Стоп-лосс для шорта
    takeProfit = close * (1 - takeProfitPercent) // Тейк-профит для шорта
    strategy.exit("Exit Short", from_entry="Short", stop=stopLoss, limit=takeProfit)

// Отображаем RSI на отдельном графике
plot(rsi, title="RSI", color=color.blue, linewidth=2)
hline(rsiOverbought, "Перекупленность", color=color.red)
hline(rsiOversold, "Перепроданность", color=color.green)
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