MinimalMoney • 4/27/2026, 11:56:48 AM
ETHFDUSD | 6MinimalMoney.json | 2026-01-01 - 2026-04-20 | -26.16% | 1797 trades | 69% WR
Strategy: MinimalMoney | Period: 2026-01-01 to 2026-04-20 | Starting Capital: 2,500.00 USDT | Final Value: 1,845.99 USDT | Return: -26.16% | Trades: 1,797 | Win Rate: 68.6% | Best Trade: 0.6117 USDT | Worst Trade: -0.0132 USDT | Total Profit: 59.17 USDT | Max Drawdown: -42.48% | Profit Factor: 15.09 | Sharpe Ratio: -1.08 | Total Fees: 68.78 USDT
Backtest ETHFDUSD (Mode: 6MinimalMoney.json) Period: 2026-01-01 00:00:01 to 2026-04-20 23:59:59 Starting balance: 2,500.00 USDT Final value: 1,845.99 USDT P&L: -654.01 USDT (-26.16%) Result: LOSS Completed trades: 1797 Open orders at end: 99 Win rate: 68.6% Avg. profit/trade: 0.032927 USDT Best trade: 0.611675 USDT Worst trade: -0.013174 USDT Total profit (trades only): 59.169381 USDT Strategy parameters: Buy trigger: -0.1% from last buy Buy splits: 2 Sell targets: [0.2, 0.5] Investment per buy: 50.0 USDT Trailing stop-loss: [0.1, 0.2] Fees: maker 7.5 bps / taker 7.5 bps Elapsed: 14.1s
This run produced a -26.16% return on ETHFDUSD — a clear loss in the tested window. Useful primarily as a negative datapoint about parameter combinations that did not fit ETHFDUSD market conditions over these dates.
About ETHFDUSD: Ethereum sits one tier below Bitcoin in market cap and slightly above it in realised volatility. ETH pairs typically reward strategies that can hold through brief drawdowns to capture larger trend moves.
The 68.6% closed-trade win rate on 1,797 closed ETHFDUSD trades sits in the comfortable range — frequent wins keep equity curves smooth and reduce psychological drawdown when running MinimalMoney live. This figure covers closed trades only and **excludes 99 orders** that were still open at the end of the window.
At roughly 16.3 ETHFDUSD trades per day this is a high-frequency configuration — fee drag and slippage assumptions become critical when extrapolating to live trading on Binance Spot.
The trade payoff distribution is positively skewed — outsized winners drove the bulk of the result, which is characteristic of trend-capturing modes. Best single trade: 0.6117 USDT. Worst: -0.0132 USDT. Average per trade: 0.0329 USDT.
Risk profile (closed trades only): Per-trade exposure was minimal — the worst closed trade only cost 0.00% of starting capital. That low-risk-per-trade footprint is the signature of a tightly-sized configuration; expect smoother equity curves but also slower compounding in strong trend regimes. Best single trade contributed +0.02% to the account, giving a best-vs-worst ratio of roughly 46.43:1 between the extreme closed trades. Note: this is a closed-trade statistic — open positions and unrealized PnL are not included.
Configuration analysis: The MinimalMoney configuration entered on a 0.1% pullback signal across 2 potential buy splits at 50 USDT each. Total deployable notional is therefore 100 USDT — a position-sizing footprint that is defensive at 4% of starting capital — most of the account stays in stablecoins as buffer. No hard stop-loss is configured — the strategy relies on take-profit zones and trailing logic instead, which trades smoother behaviour for higher tail-risk in sustained downtrends. Profit is taken in 2 laddered sell zones, which scales out gradually rather than betting on a single exit price — a structure that smooths returns at the cost of capping the very best winners. Maker/taker fees totalling 15 bps were deducted from every fill, so the headline -26.16% is already net of trading costs — no additional fee adjustment is required when comparing to other runs.
Over the configured 110-day window the strategy reported 59.17 USDT of realised trade profit on a 2500 USDT starting balance, ending at a portfolio value of 1845.99 USDT. Mechanically annualising the -26.16% window return projects to roughly -63.4% per year — since the window is shorter than one year (110 days), the annualisation extrapolates from a partial-year sample and is sensitive to the specific market regime in those months. Treat this number as a unit-conversion of the window result, not as an expected forward return.
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.
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.
This interpretation is generated deterministically from this run's own metrics. Past performance is not indicative of future results — a profitable backtest is necessary but not sufficient evidence that a strategy will work in live trading on ETHFDUSD.
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