Volatility Adjusted Momentum Strategy Backtest: ETH-USD 84.42% Return With 61.54% Win Rate

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Simple rules can outperform more elaborate systems. VolatilityAdjustedMomentum combines a seven-day and 30-day moving-average crossover with a one-ATR trailing threshold and 95% position sizing.

On ETH-USD daily candles from August 4, 2024 through August 4, 2026, the strategy returned 84.42% while buy-and-hold lost 30.15%. It completed 13 trades, won eight, and finished flat.

The result was sustained across the test: strategy equity was ahead on 85.3% of days after day 30. It also beat ETH in 69.1% of 126-day windows and 90.4% of 252-day windows.

A Compact Momentum Model

The trend signal is a conventional moving-average crossover:

self.short_ma = bt.indicators.SimpleMovingAverage(
    self.data.close,
    period=self.params.short_ma_period,
)
self.long_ma = bt.indicators.SimpleMovingAverage(
    self.data.close,
    period=self.params.long_ma_period,
)
self.atr = bt.indicators.AverageTrueRange(
    self.data,
    period=self.params.atr_period,
)

A bullish cross opens a long; a bearish cross opens a short. The position uses 95% of current account value:

position_size = int(
    (account_value * self.params.size_pct)
    / self.data.close[0]
)

if self.short_ma[0] > self.long_ma[0] \
        and self.short_ma[-1] <= self.long_ma[-1]:
    self.order = self.buy(size=position_size)

The strategy does not exit merely because the averages cross back. It tracks the best close after entry and maintains a stop one current ATR away:

self.trailing_stop_price = max(
    self.trailing_stop_price,
    self.data.close[0] - stop_distance,
)

if self.data.close[0] <= self.trailing_stop_price:
    self.order = self.close()

Because ATR is recalculated every bar, the effective trailing distance adapts to changing market volatility.

Backtest Setup

Equity Curve vs Buy-and-Hold

The equity curve remained above the benchmark for most of the period and finished with an excess return above 114 percentage points.

Drawdown vs Buy-and-Hold

Maximum drawdown was 14.33%, producing a 1.36 Sharpe ratio despite aggressive 95% allocation.

Rolling Return vs Buy-and-Hold

The strategy won 56.4% of 63-day windows, 69.1% of 126-day windows, and 90.4% of 252-day windows. Median 126-day excess return was 22.32%.

Daily Return Distribution

The return distribution shows the daily risk produced by a near-fully invested long-and-short system.

Research Takeaway

This is the cleanest result in the group: familiar indicators, 13 closed trades, a 61.54% win rate, strong Sharpe, controlled drawdown, and long-horizon rolling dominance. Its simplicity makes it especially suitable for parameter sensitivity and out-of-sample testing.

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Disclaimer

This article is for research and educational use only. Backtests are not financial advice and do not guarantee future performance. Validate leverage, sizing, costs, execution, and out-of-sample behavior before trading.