Rough Path Momentum Strategy Backtest BTC-USD 40.52% Return With 75% Win Rate

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Most momentum strategies look at one return window. Rough path methods look at the shape of the return path. In this BTC-USD test, that approach produced a 40.52% return, 75% win rate, and no open trade at the end of the run.

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

RoughPathMomentumStrategy is a path-signature momentum with trailing stops strategy.

The strategy is interesting because it uses path structure instead of a single indicator threshold. It calculates signature-style components from recent return increments, checks whether the structure is stable, and trades only when the momentum signature is strong enough.

The strategy logic is:

1. Store recent return increments as a path.
2. Calculate level 1, level 2, and level 3 signature components.
3. Combine those levels into a momentum signature.
4. Check signature stability by comparing sub-paths.
5. Enter when the signature is strong and stable, then manage risk with a trailing stop.

How The Code Works

Returns become the path increments:

self.returns = bt.indicators.PctChange(self.close, period=1)
self.path_increments = []

The signature calculation includes multiple levels:

level1 = np.sum(increments)

level2 = 0
for i in range(n-1):
    for j in range(i+1, n):
        level2 += increments[i] * increments[j]

The final momentum signature combines direct and higher-order path structure:

momentum_sig = (
    level1 * 0.5 +
    level2 * 0.3 / path_length +
    level3 * 0.2 / (path_length**2)
)

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, 652 flat strategy-equity days were removed, leaving 78 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.