Multi Moving Average and Bollinger Bands Crossover Options Quantitative Trading Strategy

Pine ScriptTrend following Breakout Mean reversion SMA Bollinger Bands ATR Volume

Author: ianzeng123
Complexity: Starter · 55 lines
Parameters: 0
Backtest bar: 5m · intraday
Tested on: BTC/USDT · spot

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Overview

This strategy is an advanced quantitative trading system based on multiple moving averages and Bollinger Bands indicators. The core strategy uses the crossover signals of 5-period and 11-period moving averages as the primary entry criteria, combined with 55-period moving average and Bollinger Bands for signal filtering and risk control. This strategy is particularly suitable for options trading, especially for ATM options on 3-minute and 5-minute timeframes.

Strategy Principles

The core logic of the strategy includes the following key elements:

Utilizing crossovers between 5-period and 11-period moving averages for initial trading signals
Confirming overall trend direction with 55-period moving average
Using Bollinger Bands (22-period) with 1.5 standard deviation for overbought/oversold conditions
Implementing dynamic stop-loss and take-profit targets using 14-period ATR Specifically, long signals are generated when price is below the lower band and the 5-period MA crosses above the 11-period MA, while price remains above the 55-period MA. Conversely, short signals occur when price is above the upper band and the 5-period MA crosses below the 11-period MA, while price is below the 55-period MA.

Strategy Advantages

Multiple timeframe confirmation enhances trading success rate
Adaptive volatility-based stop-loss effectively controls risk
Integration with Bollinger Bands improves entry timing accuracy
Clear trading rules, easy to implement and backtest
Achieves minimum 1:2 reward-to-risk ratio
Particularly suitable for options trading, especially ATM options buying strategies

Strategy Risks

May generate frequent false breakout signals in ranging markets
Moving average system has inherent lag
Bollinger Bands parameters require optimization for different market conditions
ATR-based stops may be too wide during high volatility periods Mitigation measures:

Add volume confirmation
Recommend trading in clear trend environments
Regular review and adjustment of Bollinger Bands parameters
Consider implementing fixed stop-loss limits

Strategy Optimization Directions

Introduce volume indicators for signal confirmation
Develop adaptive Bollinger Bands parameter adjustment mechanism
Add market environment identification module
Optimize stop-loss strategy, consider implementing trailing stops
Include time filters to avoid trading during inactive periods These optimizations will help improve strategy stability and profitability, particularly adaptability across different market conditions.

Summary

This strategy constructs a relatively complete trading system by combining multiple technical indicators. Its core strengths lie in the multi-layered signal confirmation mechanism and dynamic risk management approach. While there is room for optimization, the basic framework is robust and particularly suitable for options traders. Through continuous optimization and improvement, the strategy has the potential to achieve better performance in actual trading.

Source Pine Script

/*backtest
start: 2025-02-12 00:00:00
end: 2025-02-18 08:00:00
period: 5m
basePeriod: 5m
exchanges: [{"eid":"Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("MA5 MA11 Bollinger Bands 22 Strategy", overlay=true)

// Define indicators
ma5 = ta.sma(close, 5)
ma11 = ta.sma(close, 11)
ma55 = ta.sma(close, 55)
basis = ta.sma(close, 22)
dev = 1.5
upperBB = basis + dev * ta.stdev(close, 22)
lowerBB = basis - dev * ta.stdev(close, 22)

// Plot the indicators
plot(ma5, color=color.blue, linewidth=2, title="MA5")
plot(ma11, color=color.red, linewidth=2, title="MA11")
plot(ma55, color=color.green, linewidth=2, title="MA55")
plot(upperBB, color=color.orange, linewidth=1, title="Upper Bollinger Band")
plot(lowerBB, color=color.orange, linewidth=1, title="Lower Bollinger Band")

// Entry conditions
longCondition = ta.crossover(ma5, ma11) and close > ma55 and close < lowerBB
shortCondition = ta.crossunder(ma5, ma11) and close < ma55 and close > upperBB

// Exit conditions
closeLongCondition = ta.crossunder(close, ma5) or close < ma55
closeShortCondition = ta.crossover(close, ma5) or close > ma55

// Execute trades
if (longCondition)
    strategy.entry("Long", strategy.long)
    
if (shortCondition)
    strategy.entry("Short", strategy.short)

if (closeLongCondition)
    strategy.close("Long")
    
if (closeShortCondition)
    strategy.close("Short")
    
// Optional: Add Stop Loss and Take Profit (e.g., ATR-based)
atrValue = ta.atr(14)
stopLoss = atrValue * 1.5
takeProfit = atrValue * 3

strategy.exit("Exit Long", "Long", stop=close - stopLoss, limit=close + takeProfit)
strategy.exit("Exit Short", "Short", stop=close + stopLoss, limit=close - takeProfit)

Published by ianzeng123 on the FMZ strategy square · last modified 2025-02-20. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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