LowMoney • 5/7/2026, 9:15:19 AM
BTCFDUSD | 5LowMoney.json | 2024-01-01 - 2025-01-01 | +36.01% | 46195 trades | 100% WR
Strategy: LowMoney | Period: 2024-01-01 to 2025-01-01 | Starting Capital: 5,000.00 USDT | Final Value: 6,800.61 USDT | Return: +36.01% | Trades: 46,195 | Win Rate: 100.0% | Best Trade: 0.0315 USDT | Worst Trade: 0.0173 USDT | Total Profit: +1,041.14 USDT | Max Drawdown: -12.76% | Sharpe Ratio: 1.33 | Total Fees: 748.66 USDT
A 36.01% return on BTCFDUSD puts this run firmly in the strong performer tier. Most algorithmic strategies on BTCFDUSD struggle to clear double digits net of fees, so this result indicates the LowMoney configuration captured meaningful price movement during the test window.
About BTCFDUSD: Bitcoin is the highest-cap and least volatile of the major crypto pairs. Backtests on BTC tend to produce smoother equity curves but also lower percentage returns than altcoins -- the trade-off is reduced tail risk.
An 100.0% win rate across 46,195 trades on BTCFDUSD is unusually high. Strategies that win this often typically use small take-profits relative to stop-losses, which works until a single large adverse BTCFDUSD move erases many small wins.
At roughly 126.2 BTCFDUSD 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: 0.0315 USDT. Worst: 0.0173 USDT. Average per trade: 0.0225 USDT.
Risk profile: Per-trade exposure was minimal -- the worst 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 +0.00% to the account, giving a single-trade reward-to-risk ratio of roughly 1.82:1 between the extreme outliers.
About the LowMoney strategy: LowMoney is calibrated for small starting balances -- smaller position sizes, tighter risk controls, fewer parallel orders.
Configuration analysis: The LowMoney configuration entered on a 0.1% pullback signal across 2 potential buy splits at 20 USDT each. Total deployable notional is therefore 40 USDT -- a position-sizing footprint that is defensive at 1% 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 36.01% is already net of trading costs -- no additional fee adjustment is required when comparing to other runs.
Over the 366-day test window the strategy generated 1041.14 USDT of profit on a 5000 USDT starting balance, growing the account to 6800.61 USDT. Annualised, the 36.01% return over 366 days projects to roughly +35.9% per year -- a pace that would compound at a rate that comfortably outpaces traditional asset classes. Crypto market regimes shift quickly, so this projection should be treated as a directional indicator rather than a forecast.
This backtest was executed on historical Binance Spot 1-minute candles for BTCFDUSD, 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 every minute sequentially, evaluates the LowMoney 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.
Test window covers approximately 12.0 months of BTCFDUSD 1-minute price action -- a sample size that is large enough to span multiple short-term regimes.
Translating this result to live trading: BTCFDUSD 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 BTCFDUSD.
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