Adaptive Bollinger Bands Trend Following Strategy with Multi Level Risk Management

Pine ScriptTrend following Breakout Mean reversion EMA SMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 91 lines
Parameters: 0
Backtest bar: 2h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a trend-following system that combines Bollinger Bands and EMA indicators with a multi-layer risk control mechanism to optimize trading performance. The core strategy utilizes Bollinger Bands breakout-reversion patterns to capture market trends while incorporating an EMA trend filter to enhance trading accuracy. The system includes a comprehensive risk management framework with trailing stops, fixed stop losses, profit targets, and time-based position closure.

Strategy Principles

The trading logic is based on the following core elements:

Uses Bollinger Bands with 1.5 standard deviation and 14-period length as the primary trading signal indicator
Triggers short signals when the previous candle closes above the upper band and current candle reverses
Triggers long signals when the previous candle closes below the lower band and current candle turns bullish
Optionally applies an 80-period EMA as a trend filter, only entering positions when trend direction aligns
Activates trailing stops when price crosses the Bollinger Bands middle line
Allows setting fixed stop loss and take profit amounts
Supports automatic position closure based on bar count

Strategy Advantages

Combines trend-following and reversal trading characteristics for stable performance across different market conditions
Multi-layered risk control system provides comprehensive money management
Flexible parameter settings allow strategy adaptation to different market conditions
EMA filter effectively reduces false breakout risks
Trailing stop mechanism effectively locks in profits
Time-dimensional closure mechanism prevents long-term position trapping

Strategy Risks

May generate frequent false breakout signals in ranging markets
Fixed monetary stop losses may not suit all market conditions
EMA filtering might cause missing important trading opportunities
Trailing stops may close positions prematurely in highly volatile markets
Parameter optimization may lead to overfitting historical data

Strategy Optimization Directions

Introduce adaptive Bollinger Bands periods based on market volatility
Develop dynamic stop-loss system based on money management
Add volume analysis to confirm breakout validity
Implement intelligent parameter optimization system
Add market environment recognition module for different parameter settings in different market conditions

Summary

This is a well-designed trend-following system that provides reliable trading signals through the combination of Bollinger Bands and EMA, while ensuring trading safety through multi-layered risk control. The strategy offers strong configurability to adapt to different trading styles and market environments. While there are some inherent risks, the suggested optimization directions can further enhance the strategy's stability and profitability.

Source Pine Script

/*backtest
start: 2025-01-10 00:00:00
end: 2025-02-08 08:00:00
period: 2h
basePeriod: 2h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("AI Bollinger Bands Strategy with SL, TP, and Bars Till Close", overlay=true)

// Input parameters
bb_length           = input.int(14, title="Bollinger Bands Length", minval=1)
bb_stddev           = input.float(1.5, title="Bollinger Bands Standard Deviation", minval=0.1)
use_ema             = input.bool(true, title="Use EMA Filter")
ema_length          = input.int(80, title="EMA Length", minval=1)
use_trailing_stop   = input.bool(true, title="Use Trailing Stop")
use_sl              = input.bool(true, title="Use Stop Loss")
use_tp              = input.bool(false, title="Use Take Profit")
sl_dollars          = input.float(300.0, title="Stop Loss (\$)", minval=0.0)
tp_dollars          = input.float(1000.0, title="Take Profit (\$)", minval=0.0)
use_bars_till_close = input.bool(true, title="Use Bars Till Close")
bars_till_close     = input.int(10, title="Bars Till Close", minval=1)
// New input to toggle indicator plotting
plot_indicators     = input.bool(true, title="Plot Bollinger Bands and EMA on Chart")

// Calculate Bollinger Bands and EMA
basis      = ta.sma(close, bb_length)
upper_band = basis + bb_stddev * ta.stdev(close, bb_length)
lower_band = basis - bb_stddev * ta.stdev(close, bb_length)
ema        = ta.ema(close, ema_length)

// Plot Bollinger Bands and EMA conditionally
plot(plot_indicators  ? basis : na, color=color.blue, linewidth=2, title="Basis (SMA)")
plot(plot_indicators ? upper_band : na, color=color.red, linewidth=2, title="Upper Band")
plot(plot_indicators  ? lower_band : na, color=color.green, linewidth=2, title="Lower Band")
plot(plot_indicators   ? ema   : na, color=color.orange, linewidth=2, title="EMA")

// EMA conditions
ema_long_condition  = ema > ema[1]
ema_short_condition = ema < ema[1]

// Entry conditions
sell_condition = close[1] > upper_band[1] and close[1] > open[1] and close < open
if sell_condition and (not use_ema or ema_short_condition)
    strategy.entry("Sell", strategy.short)

buy_condition = close[1] < lower_band[1] and close > open
if buy_condition and (not use_ema or ema_long_condition)
    strategy.entry("Buy", strategy.long)

// Trailing stop logic
if use_trailing_stop
    if strategy.position_size > 0 and close >= basis
        strategy.exit("Trailing Stop Long", from_entry="Buy", stop=low)
    if strategy.position_size < 0 and close <= basis
        strategy.exit("Trailing Stop Short", from_entry="Sell", stop=high)

// Stop Loss and Take Profit logic
if use_sl or use_tp
    if strategy.position_size > 0
        long_entry = strategy.position_avg_price
        long_sl    = long_entry - sl_dollars
        long_tp    = long_entry + tp_dollars
        if use_sl and close <= long_sl
            strategy.close("Buy", comment="Long SL Hit")
        if use_tp and close >= long_tp
            strategy.close("Buy", comment="Long TP Hit")
    if strategy.position_size < 0
        short_entry = strategy.position_avg_price
        short_sl    = short_entry + sl_dollars
        short_tp    = short_entry - tp_dollars
        if use_sl and close >= short_sl
            strategy.close("Sell", comment="Short SL Hit")
        if use_tp and close <= short_tp
            strategy.close("Sell", comment="Short TP Hit")

// Bars Till Close logic
var int bars_since_entry = na
if strategy.position_size != 0
    bars_since_entry := na(bars_since_entry) ? 0 : bars_since_entry + 1
else
    bars_since_entry := na

if use_bars_till_close and not na(bars_since_entry) and bars_since_entry >= bars_till_close
    strategy.close("Buy", comment="Bars Till Close Hit")
    strategy.close("Sell", comment="Bars Till Close Hit")

// Plot buy/sell signals
plotshape(sell_condition and (not use_ema or ema_short_condition), title="Sell Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="Sell")
plotshape(buy_condition and (not use_ema or ema_long_condition), title="Buy Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="Buy")

Published by ChaoZhang on the FMZ strategy square · last modified 2025-02-10. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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