Advanced Quantitative Trading Strategy Combining Dynamic Bollinger Bands with PSAR Indicator

Pine ScriptBreakout Trend following Volatility SMA Bollinger Bands Parabolic SAR

Author: ChaoZhang
Complexity: Starter · 80 lines
Parameters: 0
Backtest bar: 2h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This is a comprehensive trading strategy that combines Bollinger Bands and Parabolic SAR (PSAR) indicators, utilizing a fixed risk-reward ratio for trade management. The strategy operates during intraday trading hours, identifying trading opportunities through price breakouts of Bollinger Bands and candlestick patterns, while using the PSAR indicator for trend confirmation. The strategy implements dynamic stop-loss and take-profit targets, maintaining a 1:3 risk-reward ratio.

Strategy Principles

The strategy employs multiple technical indicators for trade signal confirmation:

Uses 20-period Bollinger Bands as the primary price volatility indicator
Incorporates PSAR indicator (initial value 0.02, maximum 0.2) as a trend confirmation tool
Calculates candlestick body ratio (body length/total length ≥0.33) to ensure signal reliability
Executes trades within specified trading window (GMT-5 7:30-16:00)
Long entry conditions: close above upper band with qualifying candle body ratio
Short entry conditions: close below lower band with qualifying candle body ratio

Strategy Advantages

Integration of multiple technical indicators enhances signal reliability
Fixed risk-reward ratio (1:3) promotes long-term stable returns
Time filtering prevents interference during low liquidity periods
Candlestick body ratio filtering reduces false breakouts
Dynamic stop-loss and take-profit targets adapt to market volatility
Clear strategy logic facilitates understanding and optimization

Strategy Risks

Potential slippage in high volatility markets
Fixed risk-reward ratio might miss some profit opportunities
Time filtering could miss important market moves
Multiple indicators may lead to signal lag
Consecutive losses possible in ranging markets

Strategy Optimization Directions

Implement adaptive Bollinger Band periods for different market conditions
Develop dynamic risk-reward ratio mechanism
Add volume indicators for confirmation
Optimize PSAR parameters for better trend following
Incorporate market volatility filters
Develop smarter time filtering mechanisms

Summary

The strategy creates a complete trading system through the combined use of Bollinger Bands, PSAR indicators, and candlestick analysis. Its core strength lies in the synergy of multiple technical indicators and strict risk management. While inherent risks exist, the suggested optimization directions can further enhance strategy stability and profitability. The strategy is particularly suitable for intraday traders, capable of generating stable returns while maintaining risk control.

Source Pine Script

/*backtest
start: 2024-02-18 00:00:00
end: 2025-02-17 00:00:00
period: 2h
basePeriod: 2h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=6
strategy("Estrategia Bollinger con PSAR y TP Máximo/ Mínimo", overlay=true)

// Parámetros de las Bandas de Bollinger
bb_length = input.int(20, title="Periodo de Bandas de Bollinger", minval=1)
bb_stddev = input.float(2.0, title="Desviación Estándar", step=0.1)

// Parámetros del Parabolic SAR
psar_start = input.float(0.02, title="PSAR Factor Inicial", step=0.01)
psar_increment = input.float(0.02, title="PSAR Incremento", step=0.01)
psar_max = input.float(0.2, title="PSAR Máximo", step=0.01)

// Cálculo de Bandas de Bollinger
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)

// Cálculo del Parabolic SAR
psar = ta.sar(psar_start, psar_increment, psar_max)

// Cálculo del cuerpo de la vela
body_high = math.max(open, close)
body_low = math.min(open, close)
body_length = body_high - body_low
total_length = high - low
body_ratio = body_length / total_length

// Condiciones de Entrada
long_condition = close > upper_band and body_ratio >= 0.33
short_condition = close < lower_band and body_ratio >= 0.33

// Filtro de tiempo: Operar solo de 7:30 AM a 4:00 PM hora colombiana
start_time = timestamp("GMT-5", year, month, dayofmonth, 7, 30)
end_time = timestamp("GMT-5", year, month, dayofmonth, 16, 0)
time_condition = (time >= start_time) and (time <= end_time)

// Variables para mantener el TP máximo y mínimo
var float max_tp = na
var float min_tp = na
var float dynamic_stop = na

// Condiciones de Entrada y Salida
if (long_condition and time_condition)
    entry_price = close  // Precio de entrada
    stop_loss = low  // SL en el mínimo de la vela
    take_profit = entry_price + 3 * (entry_price - stop_loss)  // TP con relación 1:3
    strategy.entry("Compra", strategy.long)
    strategy.exit("Exit Compra", "Compra", stop=stop_loss, limit=take_profit)

    // Dibujar las etiquetas para SL y TP para la operación larga
    label.new(bar_index, stop_loss, text="SL: " + str.tostring(stop_loss), style=label.style_label_up, color=color.red, textcolor=color.white, size=size.small)
    label.new(bar_index, take_profit, text="TP: " + str.tostring(take_profit), style=label.style_label_down, color=color.green, textcolor=color.white, size=size.small)

if (short_condition and time_condition)
    entry_price = close  // Precio de entrada
    stop_loss = high  // SL en el máximo de la vela
    take_profit = entry_price - 3 * (stop_loss - entry_price)  // TP con relación 1:3
    strategy.entry("Venta", strategy.short)
    strategy.exit("Exit Venta", "Venta", stop=stop_loss, limit=take_profit)

    // Dibujar las etiquetas para SL y TP para la operación corta
    label.new(bar_index, stop_loss, text="SL: " + str.tostring(stop_loss), style=label.style_label_down, color=color.red, textcolor=color.white, size=size.small)
    label.new(bar_index, take_profit, text="TP: " + str.tostring(take_profit), style=label.style_label_up, color=color.green, textcolor=color.white, size=size.small)

// Dibujar Bandas de Bollinger
plot(upper_band, color=color.red, title="Banda Superior")
plot(lower_band, color=color.green, title="Banda Inferior")
plot(basis, color=color.blue, title="Media Base")

// Dibujar Parabolic SAR
plot(psar, style=plot.style_circles, color=color.orange, title="Parabolic SAR")

Published by ChaoZhang on the FMZ strategy square · last modified 2025-02-18. 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/Advanced-Quantitative-Trading-Strategy-Combining-Dynamic-Bollinger-Bands-with-PSAR-Indicator.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.