Failure in Live Trading

One response to “Failure in Live Trading”

1. 

   Leslie Chan

   06/04/2025

   The discrepancy between strong backtest performance and poor live trading results is a common challenge in algorithmic trading. Here are the key reasons why this happens and how to address them:

   1. Overfitting/Curve-Fitting
   Problem: Your strategy is overly optimized to historical data patterns that don’t repeat in live markets.

   Solution: Use walk-forward optimization, out-of-sample testing, and limit parameter complexity.

   2. Look-Ahead Bias
   Problem: The backtest accidentally uses future data (e.g., adjusted historical prices, future indicators).

   Solution: Implement point-in-time data checks and avoid survivorship bias in your dataset.

   3. Slippage & Execution Assumptions
   Problem: Backtests assume perfect fills at mid-prices, ignoring real-world liquidity issues.

   Solution: Model conservative slippage (0.1-1% for equities, more for crypto) and test with limit orders.

   4. Market Regime Changes
   Problem: The strategy worked in past conditions (e.g., low volatility) but not current ones.

   Solution: Stress-test across multiple regimes (2008, 2020 COVID) and incorporate regime detection.

   5. Transaction Cost Underestimation
   Problem: Not accounting for fees, spreads, or market impact.

   Solution: Apply realistic costs (e.g., 5-10 bps for equities, 20+ bps for crypto).

   6. Data Quality Issues
   Problem: Backtest uses cleaned/stitched data that doesn’t match messy real-time feeds.

   Solution: Test with raw tick data including failed trades and outliers.

   7. Latency & Infrastructure
   Problem: Live execution is slower than assumed (exchange latency, API delays).

   Solution: Measure actual round-trip times and test in simulation mode first.

   8. Parameter Instability
   Problem: Optimal backtest parameters are fragile in live markets.

   Solution: Use robust parameter ranges and Monte Carlo analysis.

   Actionable Steps:
   Run a 3-month paper trading test with real-time data

   Analyze failed trades to identify which assumption broke

   Implement sensitivity analysis – if small input changes cause large P&L swings, the strategy is likely overfit

   Example:
   A mean-reversion strategy backtested on 2020-2022 crypto data may fail in 2023 because:

   Slippage was underestimated (liquidity decreased)

   Volatility regimes changed

   Exchange fees increased

   Backtests are necessary but insufficient – live market dynamics always differ. The best strategies are simple, account for costs conservatively, and work across multiple regimes.