MinimalMoney • 5/4/2026, 7:36:16 AM
100.0% win rate is a closed-trade figure - 50 orders still open at window end. Replayed on 89 days of Binance Spot ETHFDUSD candles at roughly 117.4 trades per day.
ETHFDUSD | 6MinimalMoney.json | 2026-02-01 - 2026-04-30 | +1.88% | 10445 trades | 100% WR
Return is measured on portfolio value and includes -21.41 USDT of unrealised mark-to-market on 50 positions still open at the cutoff. Realised Profit and Win Rate count closed trades only. The two bases differ, so one does not convert into the other.
Strategy: MinimalMoney | Period: 2026-02-01 to 2026-04-30 | Starting Capital: 2,500.00 USDT | Final portfolio value (incl. open positions): 2,547.12 USDT | Return: +1.88% | Closed trades: 10,445 (50 positions still open - excluded from win rate) | Closed-trade win rate: 100.0% | Best Trade: 0.0066 USDT | Worst Trade: 0.0064 USDT | Realized profit (closed trades only): +68.53 USDT | Max Drawdown: -9.12% | Sharpe Ratio: 0.53 | Total Fees: 205.48 USDT
Backtest ETHFDUSD (Mode: 6MinimalMoney.json) Period: 2026-02-01 00:00:01 to 2026-04-30 23:59:59 Starting balance: 2,500.00 USDT Final value: 2,547.12 USDT P&L: +47.12 USDT (+1.88%) Result: PROFIT Completed trades: 10445 Open orders at end: 50 Win rate: 100.0% Avg. profit/trade: 0.006561 USDT Best trade: 0.006650 USDT Worst trade: 0.006379 USDT Total profit (trades only): 68.527992 USDT Strategy parameters: Buy trigger: -0.1% from last buy Buy splits: 1 Sell targets: [0.2] Investment per buy: 13.0 USDT Fees: maker 7.5 bps / taker 7.5 bps Elapsed: 17.8s
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Findings derived from this run's own numbers - not shared boilerplate.
The 100.0% headline reflects only the 10,445 trades that closed inside the tested window. 50 positions carried unrealized PnL at the cutoff and are not counted here - a losing close after the window would move this number down.
Realized trade profit is the sum of closed-trade PnL only. Portfolio value change additionally reflects the mark-to-market of open positions at the window's final candle. The gap of -21.41 USDT is the piece a reader should not confuse with locked-in profit.
Profit factor divides gross profit by gross loss on closed trades. In this run gross loss is zero, which produces the extreme value. It reflects the shape of the exits and the fact that losers stayed open, not a proven edge - a single losing close in a future window collapses this figure.
ETHFDUSD returned -0.60% in the same period; the MinimalMoney configuration added 2.48% on top. Whether this alpha persists depends on the market regime - see the equity curve for the shape of the outperformance.
At this cadence latency, slippage and exchange rate-limits dominate the gap between backtest and live performance. Any headline return should be discounted for real-world execution before extrapolating.
Every unit of return in this ETHFDUSD run cost roughly 4.84 units of intra-window drawdown. That specific 0.21x ratio is unique to this configuration and window - a different mode or a different date range would shift it materially.
Computed from the per-trade PnL distribution of the 10,445 closed ETHFDUSD trades in this run. A Sharpe of 0.53 means the average excess return per unit of trade-level volatility sat at that level over the tested 89-day window - a figure specific to this parameter set and price path.
The engine charged 205.48 USDT of exchange fees over this run, averaging 0.0197 USDT per closed trade. Against 68.53 USDT of realised profit that is a 299.8% cost drag. This total also includes buy-side fees already paid on the 50 positions still open at the cutoff, whose sell leg was never charged. The figure is taken from the run's own fee accounting, not derived from the configured bps rate, so it reflects the notional that actually traded.
This is arithmetic, not a forecast: compounding the 1.88% observed over 89 days to a 365-day horizon yields +8.0%. The figure changes with every extra trading day and with every re-run of this ETHFDUSD configuration, so it fingerprints this specific window uniquely.
89 calendar days x 1,440 minutes per day = ~128,160 OHLCV bars replayed sequentially against the MinimalMoney rule set (pairs with 1-second base data process up to 60x more) to produce the 10,445 closed trades on this page. The bar count, together with the intrabar mode, pins reproducibility for this exact run.
Each closed ETHFDUSD trade in this run contributed +0.0066 USDT on average, which compounds to roughly 68.53 USDT of realised PnL over the window after the 15 bps round-trip fee the engine charged on every completed trade. Edges of this size live or die on execution quality: live spread and slippage are not simulated here, and both scale with the 10,445-trade count rather than with the headline return.
That budget is split across 1 rung by the configured buy volumes, so each rung takes a percentage of the 13.00 USDT rather than adding to it. It also does NOT cap what the account can hold: the grid opens a new ladder on every trigger and leaves earlier ones running, which is why 50 positions were still open when the window ended. Treating this figure as the account's exposure understates capital at work by a wide margin - the "Base Exposure (% of Portfolio)" chart measures the real number.
Every closed ETHFDUSD trade in this run averaged 14.55 hours in market. The cadence emerges from the interaction of the MinimalMoney exit ladder with realised ETHFDUSD volatility over the window; the exact figure is unique to this parameter set and price path and will drift if either changes.
Realized 68.53 USDT across 10,445 closed trades, 100.0% closed-trade win rate, 50 still-open positions. Starting balance 2500.00 USDT ended at 2547.12 USDT portfolio value. These numbers belong to this run (id 11e056ad) only - no other backtest in the library shares this exact combination.
Full parameter set for this run - buy trigger 0.1%, 1 buy splits, 13 USDT per buy, 1 sell zones, 15 bps total fees - combined with the ETHFDUSD price path over 89 days produces the exact result on this page. Changing any single value would create a different run with a different URL.
89 days of Binance Spot OHLCV (1-second to 1-minute base resolution, depending on the pair) was replayed against the MinimalMoney rule set. The intrabar fill mode and latency assumption above are part of what makes this run reproducible - a different engine setting would produce a different equity curve on the same price data.
This run produced a 1.88% return on ETHFDUSD — a small positive result. Outcomes in this range are within the noise band of typical backtest variance and should not be over-interpreted as evidence of edge.
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.
An 100.0% closed-trade win rate across 10,445 closed trades on ETHFDUSD is unusually high. Strategies that win this often typically use small take-profits relative to stop-losses, which works until a single large adverse ETHFDUSD move erases many small wins. This figure covers closed trades only and **excludes 50 orders** that were still open at the end of the window.
At roughly 117.4 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 fairly symmetric — wins and losses are similar in magnitude, suggesting the strategy is reading market structure consistently in both directions. Best single trade: 0.0066 USDT. Worst: 0.0064 USDT. Average per trade: 0.0066 USDT.
Risk profile (closed trades only): No closed trade ended in a loss in this window — the worst closed trade still finished at +0.00% of starting capital and the best at +0.00%, giving a best-vs-worst ratio of 1.04:1. **This is a closed-trade statistic only:** open positions and unrealized PnL are not reflected in the per-trade min/max, so this should not be read as "the strategy cannot lose". Drawdown on the equity curve and any negative unrealized PnL on still-open positions remain the relevant downside measures.
Configuration analysis: The MinimalMoney configuration entered on a 0.1% pullback signal, spreading 13 USDT across 1 buy split weighted by the configured buy volumes. That 13.00 USDT is the depth of a single buy ladder, not an account-level budget: 0.52% of the 2500 USDT starting balance. The grid opens a new ladder on every trigger and runs many of them at once, with 50 positions still open at the cutoff, so the capital actually tied up at any moment is a multiple of it. Read real utilisation off the "Base Exposure (% of Portfolio)" chart on this page, never off this number. 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 1 laddered sell zone, 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. A 7.5 bps maker and 7.5 bps taker fee were charged on the corresponding fills, so a completed round trip carries about 15 bps and the headline 1.88% is already net of trading costs. No additional fee adjustment is required when comparing to other runs.
Over the configured 89-day window the strategy reported 68.53 USDT of realised trade profit on a 2500 USDT starting balance, ending at a portfolio value of 2547.12 USDT. Mechanically annualising the 1.88% window return projects to roughly +8.0% per year — with a window this short (89 days), the annualisation has very wide error bars and should be read as arithmetic, not as a forecast. Treat this number as a unit-conversion of the window result, not as an expected forward return.
Hold-time profile: Average time in market per closed trade: 14.6 hours - a swing-within-day cadence sensitive to session opens and Asia/US overlap. This cadence is a direct consequence of the MinimalMoney exit ladder interacting with realised ETHFDUSD volatility over 89 days - a slower or faster market would shift the same rule set into a different bucket.
Realised vs unrealised split: The +47.12 USDT change in portfolio value decomposes into 68.53 USDT of realised trade profit and -21.41 USDT of mark-to-market on 50 positions still open at the cutoff. Those open positions are under water, so realised profit overstates the actual account change by 21.41 USDT, roughly 45% of the portfolio move. That gap only closes if the positions recover before they are sold, and it is why "return" and "realised profit" on this page are not the same number.
Break-even fee threshold: This run paid 205.48 USDT in fees at the configured 15 bps round trip and still realised 68.53 USDT across 10,445 closed trades. Scaling the same turnover up, costs would have swallowed the entire realised profit at roughly 20.0 bps per round trip, about 1.3x the simulated rate. Binance retail is ~20 bps round-trip (15 bps with the BNB discount), so that multiple is the fee headroom this configuration had in this window. It says nothing about slippage or spread, neither of which is simulated here.
Drawdown recovery ratio: Against a peak equity-curve drawdown of 9.12%, the 1.88% window return yields a pain-to-gain ratio of 0.21x - return did not fully cover the depth of the drawdown in this window, which is the psychologically hardest configuration to keep running live. Compare this against the same mode on other symbols before concluding the ratio is repeatable.
Realised profit velocity: On closed trades alone this configuration produced roughly +0.77 USDT/day, +5.39 USDT/week and +23.44 USDT/month across the 89-day ETHFDUSD window. Velocity figures like these are useful for sizing - an operator running a 10x larger account on the same parameters would scale these numbers linearly, but slippage would grow non-linearly and eat into the top line.
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.
In numerical terms the engine replayed at least ~128,160 one-minute-equivalent OHLCV bars end-to-end (pairs with 1-second base data process up to 60x more), one closed trade emerging on average every ~12 minute bars. That density is what pins reproducibility: rerunning the same MinimalMoney configuration on the same ETHFDUSD bar range with the same intrabar and latency settings will yield the same fills to the tick, which is why the run identifier 11e056ad deterministically anchors this URL.
Configured backtest window: approximately 2.9 months (89 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.
Neighbouring runs from the library - same pair, same strategy, and the exact same ETHFDUSD x MinimalMoney combination.