回测系统 is the process of testing a 交易策略 against historical data to verify its edge 在投入真实资金之前. For SMC traders, it answers the critical question: "Does this actually work, or does it just look good on cherry-picked examples?"
为什么 Most 返回tests Are Worthless
The #1 problem: hindsight 偏向. 何时 you manually scroll through charts and "spot" order blocks that worked, you're unconsciously skipping the ones that failed. This produces a wildly inflated win rate that won't replicate in live trading. A legitimate backtest must be forward-walking: you cover the chart's right side and make decisions bar-by-bar, exactly as you would in real time.
The Forward-Walk Method
第一步: Choose your asset, timeframe, and date range (minimum 100 candles on your 设置 TF). 第二步: Use TradingView's "Replay" mode to hide future 价格行为. 第三步: Apply your SMC rules exactly as you would live — mark structure, 识别 设置s, note entries/stops/目标s. 步骤 4: Advance bar by bar and record results. 步骤 5: After 50-100 trades, calculate: win rate, average R, maximum consecutive losses, and maximum drawdown.
什么 Numbers to Target
Win rate: 50-65% is realistic for SMC strategies. If your backtest shows 80%+, you're doing it wrong (hindsight 偏向). 平均 R: 1.5-2.5R per winning trade. Expectancy: (Win% × Avg Win) − (Loss% × Avg Loss) should be positive. 示例: 60% wins at 2R average, 40% losses at 1R average = (0.6 × 2) − (0.4 × 1) = 0.8R per trade. This means every trade has an expected value of 0.8R — highly profitable over time.
Quantum Algo 回测系统
Quantum Algo includes built-in backtesting that eliminates hindsight 偏向 entirely. Because all 信号s are 不重绘 and confirmed on candle close, the backtest results show exactly what you would have seen in real time. You can test any 信号 configuration against historical data directly in TradingView, instantly seeing win rates, R-multiples, and drawdown statistics without manual forward-walking.
Optimization Without Overfitting
After your initial backtest, you may want to adjust settings. Rule: never 优化 on your test data. Split your data into two halves — 优化 on the first half, then validate on the second half (out-of-sample testing). If results hold on unseen data, your optimization is valid. If they degrade significantly, you've overfit.