Multi Level Quantitative Trading Strategy Based on Bollinger Bands Trend Divergence

Pine ScriptTrend following Breakout Volatility EMA SMA Bollinger Bands

Author: ChaoZhang
Complexity: Starter · 71 lines
Parameters: 0
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a multi-level quantitative trading system based on Bollinger Bands trend divergence and dynamic bandwidth changes. The strategy constructs a complete trading decision framework by monitoring Bollinger Bands width dynamics, price breakouts, and EMA200 coordination. It employs an adaptive volatility tracking mechanism to effectively capture market trend turning points.

Strategy Principles

The strategy is based on the following key elements:

Bollinger Bands calculation using 20-period moving average and 2 standard deviations
Trend strength determination through bandwidth changes across three consecutive time points
Breakout validation using candle body to bandwidth ratio
EMA200 as a medium-long term trend filter
Long entry when price breaks above upper band with expanding bandwidth conditions
Exit when price breaks below lower band with contracting bandwidth conditions

Strategy Advantages

Forward-looking signal system that identifies potential trend turning points
Multiple technical indicator cross-validation reduces false signals
Bandwidth change rate indicator adapts well to market volatility
Clear entry and exit logic, easy to implement programmatically
Comprehensive risk control mechanisms effectively control drawdowns

Strategy Risks

May generate frequent trades in ranging markets
Potential lag during sudden trend changes
Parameter optimization faces overfitting risk
Slippage risk during high market volatility periods
Requires constant monitoring of bandwidth indicator effectiveness

Strategy Optimization Directions

Introduce adaptive parameter optimization mechanisms
Add volume and other auxiliary indicators for validation
Optimize stop-loss and take-profit conditions
Improve quantitative standards for trend strength assessment
Incorporate additional market environment filters

Summary

The strategy builds a robust trading system through Bollinger Bands trend divergence and dynamic bandwidth changes. While performing excellently in trending markets, improvements are needed for ranging markets and parameter optimization. Overall, the strategy demonstrates good practical value and room for expansion.

Source Pine Script

/*backtest
start: 2019-12-23 08:00:00
end: 2024-12-25 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=6
strategy("BBDIV_Strategy", overlay=true)

// Inputs for Bollinger Bands
length = input.int(20, title="BB Length")
mult = input.float(2.0, title="BB Multiplier")

// Calculate Bollinger Bands
basis = ta.sma(close, length)
deviation = mult * ta.stdev(close, length)
upperBB = basis + deviation
lowerBB = basis - deviation

// Calculate Bollinger Band width
bb_width = upperBB - lowerBB
prev_width = ta.valuewhen(not na(bb_width[1]), bb_width[1], 0)
prev_prev_width = ta.valuewhen(not na(bb_width[2]), bb_width[2], 0)

// Determine BB state
bb_state = bb_width > prev_width and prev_width > prev_prev_width ? 1 : bb_width < prev_width and prev_width < prev_prev_width ? -1 : 0

// Assign colors based on BB state
bb_color = bb_state == 1 ? color.green : bb_state == -1 ? color.red : color.gray

// Highlight candles closed outside BB
candle_size = high - low
highlight_color = (candle_size > bb_width / 2 and close > upperBB) ? color.new(color.green, 50) : (candle_size > bb_width / 2 and close < lowerBB) ? color.new(color.red, 50) : na

bgcolor(highlight_color, title="Highlight Candles")

// Plot Bollinger Bands
plot(upperBB, title="Upper BB", color=bb_color, linewidth=2, style=plot.style_line)
plot(lowerBB, title="Lower BB", color=bb_color, linewidth=2, style=plot.style_line)
plot(basis, title="Middle BB", color=color.blue, linewidth=1, style=plot.style_line)

// Calculate EMA 200
ema200 = ta.ema(close, 200)

// Plot EMA 200
plot(ema200, title="EMA 200", color=color.orange, linewidth=2, style=plot.style_line)

// Strategy logic
enter_long = highlight_color == color.new(color.green, 50)
exit_long = highlight_color == color.new(color.red, 50)

if (enter_long)
    strategy.entry("Buy", strategy.long)

if (exit_long)
    strategy.close("Buy")

// Display profit at close
if (exit_long)
    var float entry_price = na
    var float close_price = na
    var float profit = na

    if (strategy.opentrades > 0)
        entry_price := strategy.opentrades.entry_price(strategy.opentrades - 1)
        close_price := close
        profit := (close_price - entry_price) * 100 / entry_price * 2 * 10 // Assuming 1 pip = 0.01 for XAUUSD
        label.new(bar_index, high + (candle_size * 2), str.tostring(profit, format.mintick) + " USD", style=label.style_label_up, color=color.green)

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.

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