ETHFDUSD MinimalMoney 2026 Backtest

Methodology & data

This backtest was executed on historical Binance Spot candles for ETHFDUSD at a base resolution between 1 second and 1 minute (1-second for liquid pairs, 1-minute where finer data is unavailable), with intrabar fill simulation in "OLHC" mode and a synthetic order latency of 2s applied to each fill to approximate real-world routing delay. The simulator processes each base candle sequentially, evaluates the MinimalMoney rule set, and books fills against the next available bar, a standard event-driven backtesting approach that avoids look-ahead bias. Equity is marked-to-market on every closed trade and aggregated into the equity curve shown above.

Configured backtest window: approximately 3.6 months (110 days from `config.from` to `config.to`) of ETHFDUSD price action at 1-second to 1-minute resolution — a sample size that is useful for spotting near-term edge but limited for regime-cycle conclusions. Note: the equity series may cover fewer days if the engine omits leading or trailing flat periods (e.g. dates before the asset began trading); see the Overview section for the exact equity-coverage span.

Live trading considerations

Translating this result to live trading: ETHFDUSD liquidity should be checked separately — fill assumptions can drift if the order book is thin during volatile windows. The high trade frequency means cumulative slippage and exchange-side latency will erode a few percent of the headline return over a full year — budget for that gap. Without a hard stop-loss, the live system depends on the take-profit ladder firing during recovery legs; a prolonged downtrend without recovery will hold positions open longer than backtest aggregates suggest. Additionally, exchange downtime, API rate limits, and funding-rate changes (on perp variants) are not modelled here and should be accounted for in production deployment.

Frequently asked questions

Is a -26.07% return on ETHFDUSD a good backtest result?

Not on its own. The -26.07% return looks strong in isolation, but simply holding ETHFDUSD over the same window returned -22.95%, so this configuration underperformed buy-and-hold by -3.12% (negative alpha). A high headline return is not a good result when holding the coin would have done better.

What does the 100.0% win rate mean here?

It means 100.0 out of every 100 closed trades ended profitable. Frequent wins are emotionally easier to operate but say nothing about size — one large loss can offset many small wins.

What is the annualised return for this ETHFDUSD backtest?

If the -26.07% over 110 days continued at the same rate, it would extrapolate to roughly -63.3% per year. This is a hypothetical directional indicator, not a forecast — crypto regimes change, and strategies rarely sustain peak performance year-over-year.

Can I run this exact MinimalMoney configuration live?

The configuration shown in the Strategy Configuration block is the same JSON schema the live unCoded TradingBot consumes, so it can be loaded into a live instance. That is a technical compatibility statement, not a recommendation: a passing backtest is necessary but not sufficient evidence that a configuration will be profitable in live trading. Before any live use, validate on an out-of-sample window, paper-trade it, confirm exchange-side fees match the simulated 7.5/7.5 bps, and start with a position size well below the backtested capital to absorb live slippage and execution differences.

How is this backtest different from others on ETHFDUSD?

Every run on the platform uses the same intrabar-fill engine and historical Binance Spot data, so the comparison is apples-to-apples. What differs between runs is the MinimalMoney parameter set (buy trigger, sell zones, splits, stop-loss) and the time window — both are visible above so you can rerun, tune, or fork this configuration.