Filtered Squeeze Strategy Backtest BTC-USD 41.43% Return With 10 Closed Trades

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A volatility squeeze can mark a market preparing for expansion. The problem is that many squeezes fail. This strategy filters squeeze breakouts with volume, MACD, and trend direction, producing a 41.43% BTC-USD return with 10 closed trades.

The test used BTC-USD daily candles from July 27, 2024 to July 27, 2026. The benchmark was BTC buy-and-hold over the same period.

Why This Strategy Matters

FilteredSqueezeStrategy is a Bollinger/Keltner squeeze breakout with volume and trend filters strategy.

The strategy is useful because it does not buy every contraction. It waits for compression first, then asks whether the breakout has momentum, volume, and trend support.

The strategy logic is:

1. Detect squeeze when Bollinger Bands sit inside the Keltner Channel.
2. Require volume to spike above its moving average.
3. Require price to break outside the Bollinger Band.
4. Confirm direction with MACD.
5. Use an ATR trailing stop after entry.

How The Code Works

The squeeze combines Bollinger Bands and a custom Keltner Channel:

self.bband = bt.indicators.BollingerBands(self.data, period=self.p.bb_period, devfactor=self.p.bb_devfactor)
self.keltner = CustomKeltnerChannel(self.data, period=self.p.kc_period, devfactor=self.p.kc_devfactor)

A squeeze exists when Bollinger Bands contract inside Keltner bounds:

is_squeeze = (
    self.bband.top < self.keltner.top and
    self.bband.bot > self.keltner.bot
)

The entry requires squeeze, breakout, MACD, volume, and trend alignment:

if not self.position and is_squeeze and has_volume_spike:
    price_breaks_up = self.data.close[0] > self.bband.top[0]
    macd_is_bullish = self.macd.macd[0] > self.macd.signal[0]
    if price_breaks_up and macd_is_bullish and is_long_term_uptrend:
        self.order = self.buy()

This is the benefit of packaging the idea as a Backtrader strategy: the indicator setup, signal rules, position management, and output metrics all run through the same repeatable research workflow.

Backtest Setup

Performance Results

The run ended with no open trade, so the trade count and final equity are fully closed-position results.

Equity Curve vs Benchmark

The equity curve shows whether the strategy built its return steadily or relied on one isolated jump. Here the comparison is especially useful because BTC buy-and-hold finished negative while the strategy finished strongly positive.

Drawdown vs Benchmark

The drawdown chart shows how much pain the strategy had to absorb during the test. Return alone is not enough; a publishable strategy review needs the drawdown path next to the benchmark.

Rolling Return vs Benchmark

The rolling return chart shows when the strategy gained or lost its edge. This helps separate one lucky ending from a result that developed across the test window.

Daily Return Distribution

The daily return distribution excludes zero-return strategy days. In this run, 455 flat strategy-equity days were removed, leaving 275 non-zero strategy return days plotted.

What Traders Can Learn From This Test

The main lesson is not that one backtest is automatically tradable. The lesson is that the research workflow matters. A useful strategy package should let you move from idea, to code, to batch test, to metrics, to charts, to publishable research without rebuilding the same infrastructure over and over.

That is exactly what the Mega Backtrader Strategy Pack is designed to do. It gives you hundreds of ready-to-run strategy modules plus the tooling to test them across assets and time windows.

Get the full package here: Mega Backtrader Strategy Pack.

Disclaimer

This article is for research and educational use only. Backtest results are not financial advice, investment advice, or a guarantee of future performance. Always validate strategy logic, data quality, execution assumptions, costs, slippage, and risk before using any trading system.