Dynamic Moving Average and Bollinger Bands Cross Strategy with Fixed Stop Loss Optimization Model

Pine ScriptTrend following Breakout Volatility SMA Bollinger Bands ATR Volume

Author: ChaoZhang
Complexity: Starter · 57 lines
Parameters: 0
Backtest bar: 3h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a trend-following trading system that combines Moving Average (MA) and Bollinger Bands indicators. It identifies market trends by analyzing price relationships with the 200-period moving average and Bollinger Bands position, while incorporating a fixed percentage stop-loss mechanism for risk control. The strategy employs a 2.86% position management, compatible with 35x leverage, demonstrating prudent fund management principles.

Strategy Principles

The core logic of the strategy is based on the following key elements:

Uses 200-period moving average as the primary trend indicator
Combines 20-period Bollinger Bands' upper and lower channels for volatility range assessment
Opens long positions when:
- Price is above the 200 MA
- Bollinger Bands middle band is above 200 MA
- Price crosses above the lower Bollinger Band
Opens short positions when:
- Price is below the 200 MA
- Bollinger Bands middle band is below 200 MA
- Price crosses below the upper Bollinger Band
Implements 3% fixed stop-loss percentage for risk control
Closes long positions at upper Bollinger Band, shorts at lower band

Strategy Advantages

Strong Trend Following Capability

Effectively identifies long-term trends using 200 MA
Bollinger Bands assist in detecting medium-short term trend changes

Comprehensive Risk Control

Fixed stop-loss mechanism effectively controls per-trade risk
Dynamic take-profit design enhances profit opportunities

Flexible Parameter Optimization

MA period and Bollinger Bands parameters adjustable to market characteristics
Stop-loss percentage adjustable to risk tolerance

High Systematization

Clear trading signals without subjective judgment
Suitable for automated trading execution

Strategy Risks

Sideways Market Risk

False breakout signals may occur frequently in ranging markets
Recommended to trade only in clear trending markets

Slippage Risk

Significant slippage possible during volatile periods
Recommend setting reasonable slippage protection

Systematic Risk

Market events may cause stop-loss failure
Recommend combining with other risk control measures

Parameter Optimization Risk

Over-optimization may lead to overfitting
Recommend backtesting across different timeframes

Strategy Optimization Directions

Dynamic Stop-Loss Optimization

Introduce ATR indicator for dynamic stop-loss adjustment
Adjust stop-loss percentage based on market volatility

Entry Signal Optimization

Add volume confirmation indicators
Implement trend strength filters

Position Management Optimization

Implement dynamic position sizing
Adjust leverage based on market volatility

Trading Timing Optimization

Add market sentiment indicators
Implement time filters

Summary

This strategy builds a complete trading system by combining classic technical indicators, demonstrating good trend capture ability and risk control effects. The core advantages lie in its high systematization and parameter adjustability, while achieving effective risk control through fixed stop-loss mechanisms. Although performance may be suboptimal in ranging markets, implementing the suggested optimizations can further enhance strategy stability and profitability. Traders are advised to consider market conditions when implementing live trading and adjust parameters according to their risk tolerance.

Source Pine Script

/*backtest
start: 2024-11-26 00:00:00
end: 2024-12-25 08:00:00
period: 3h
basePeriod: 3h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/


//@version=5
strategy("MA 200 and Bollinger Bands Strategy", overlay=true) // 2.86% for 35x leverage

// inputs
ma_length = input(200, title="MA Length")
bb_length = input(20, title="Bollinger Bands Length")
bb_mult = input(2.0, title="Bollinger Bands Multiplier")

// calculations
ma_200 = ta.sma(close, ma_length)
bb_basis = ta.sma(close, bb_length)
bb_upper = bb_basis + (ta.stdev(close, bb_length) * bb_mult)
bb_lower = bb_basis - (ta.stdev(close, bb_length) * bb_mult)

// plot indicators
plot(ma_200, color=color.blue, title="200 MA")
plot(bb_upper, color=color.red, title="Bollinger Upper Band")
plot(bb_basis, color=color.gray, title="Bollinger Basis")
plot(bb_lower, color=color.green, title="Bollinger Lower Band")

// strategy logic
long_condition = close > ma_200 and bb_basis > ma_200 and ta.crossover(close, bb_lower)
short_condition = close < ma_200 and bb_basis < ma_200 and ta.crossunder(close, bb_upper)

// fixed stop loss percentage
fixed_stop_loss_percent = 3.0 / 100.0

if (long_condition)
    strategy.entry("Long", strategy.long)
    strategy.exit("Stop Long", "Long", stop=strategy.position_avg_price * (1 - fixed_stop_loss_percent))

if (short_condition)
    strategy.entry("Short", strategy.short)
    strategy.exit("Stop Short", "Short", stop=strategy.position_avg_price * (1 + fixed_stop_loss_percent))

// take profit conditions
close_long_condition = close >= bb_upper
close_short_condition = close <= bb_lower

if (close_long_condition)
    strategy.close("Long")

if (close_short_condition)
    strategy.close("Short")

Published by ChaoZhang on the FMZ strategy square · last modified 2024-12-27. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

Build this with AgenKit

A spec is not a trading system. Paste this into your coding agent and AgenKit runs a specialist team — architecture, test-first build, risk gates, review — with paper trading on by default.

in Claude Code, Cursor, Codex & 4 more

/agenkit build a production-grade trading bot with a backtest engine, risk controls and paper trading by default from strategies/Dynamic-Moving-Average-and-Bollinger-Bands-Cross-Strategy-with-Fixed-Stop-Loss-Optimization-Model.md

npx agenkit install engineering-kit in your project
Paste the command into your agent and approve each phase
Get tests, backtest, risk controls and a paper-trading switch

Get AgenKit How /agenkit works

Understand it first

Before you run this, read the lesson behind the idea and how to backtest it honestly. Specs like this one are the survivors of an unknown number of trials; deflate their results accordingly.