Backtest results are not live results. Every automated strategy will show some difference between historical backtesting performance and real-time execution. Understanding why these differences exist — and how to account for them — is essential before committing capital to any automated futures strategy.
Why do backtest results differ from live trading?
Several factors create a gap between what a strategy produces in backtesting and what it delivers in live markets:
- Slippage — Backtests typically assume fills at exact price levels. In live trading, your order may fill one or more ticks away from the intended price, especially during fast-moving markets or around news events. On NQ, one tick of slippage equals $5 per contract per side.
- Fill assumptions — Historical backtests assume your limit order gets filled whenever price touches your level. In reality, your order sits in a queue. Price may touch your limit and reverse without filling you, particularly at support and resistance levels where many orders compete.
- Market impact — A backtest does not account for your order's effect on the market. While this is negligible for single-contract retail traders, it becomes relevant when scaling to multiple contracts or trading thinner markets like SI.
- Latency — There is always a delay between your strategy generating a signal and the broker executing the order. This delay can range from milliseconds to seconds depending on your connection, hardware, and broker infrastructure.
- Data quality — Backtests depend on the accuracy of historical data. Gaps, bad ticks, or differences in data providers can produce results that do not reflect actual market conditions during the tested period.
How does HuntersAlgo account for these differences?
HuntersAlgo strategies are backtested with conservative assumptions designed to narrow the gap between backtest and live performance:
- Fixed stop losses and profit targets — Every trade has predefined exit levels. This eliminates ambiguity in exit logic and makes backtest results more reproducible in live conditions.
- Commission and slippage included — Backtest reports include realistic commission costs and slippage estimates per trade. Results shown on the results page reflect these deductions.
- No curve fitting — Strategies use a limited number of parameters to avoid over-optimization on historical data. Fewer parameters mean less risk that the strategy is tuned to past patterns that will not repeat.
- Session filters — Trading is restricted to specific market sessions with sufficient liquidity, reducing the likelihood of poor fills during thin overnight periods.
How should you interpret strategy performance data?
When reviewing any strategy's backtest results, keep these principles in mind:
- Expect degradation — A reasonable rule of thumb is that live performance may be 10-30% worse than backtest performance on key metrics. If a strategy remains net positive after applying that haircut, it has a more credible edge.
- Focus on risk metrics — Max drawdown and average losing trade size matter more than total profit. You need to survive the worst periods to capture the good ones.
- Require sufficient sample size — A strategy needs hundreds of trades to produce statistically meaningful results. Be skeptical of any performance data based on fewer than 200 trades.
- Compare across market conditions — A strategy that only works in trending markets may struggle during consolidation. Look for consistent performance across different volatility regimes.
Where can you learn more?
Review the detailed methodology page to understand exactly how HuntersAlgo backtests are configured, including data sources, parameter settings, and assumptions. The FAQ addresses common questions about performance expectations, and the results page provides full transparency on every strategy's historical performance.
Past performance, whether backtested or live, does not guarantee future results. Always trade with capital you can afford to lose.