5 BIG Mistakes Traders Make When Backtesting Options Strategies

Five Biggest Mistakes in Option Strategy Backtesting

Key Concepts:

• Slippage: The difference between the expected price of a trade and the price at which the trade is actually executed.
• Profit Factor: The ratio of gross profit to gross loss. A profit factor greater than 1 indicates profitability.
• Win Rate: The percentage of trades that result in a profit.
• Max Drawdown: The largest peak-to-trough decline during a specific period.
• Robustness: The ability of a strategy to perform consistently well under varying market conditions.
• Position Sizing: Determining the appropriate amount of capital to allocate to each trade.
• Curve Fitting: Optimizing a strategy to perform well on historical data, but failing to generalize to future data.
• Zero DTE (Day to Expiration): Options expiring on the same day they are traded.
• Iron Condor: A neutral options strategy involving the sale of an out-of-the-money call spread and an out-of-the-money put spread.
• VIX: The CBOE Volatility Index, a measure of market expectations of near-term volatility.
• FOMC: Federal Open Market Committee, the monetary policymaking body of the Federal Reserve System.
• CPI, PPI, PCE: Consumer Price Index, Producer Price Index, and Personal Consumption Expenditures Price Index – economic indicators measuring inflation.
• Triple Witching: The simultaneous expiration of stock options, stock index options, and stock index futures contracts.

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1. Neglecting Slippage

Slippage, the difference between the expected trade price and the actual execution price, is a critical factor often overlooked in backtesting. While not always guaranteed, assuming some slippage, particularly on entry, provides a more realistic assessment of a strategy’s robustness.

Example: An S&P zero DTE iron condor strategy initially showed a profit factor of 1.25. However, incorporating 5 cents of slippage on entry reduced the profit factor to 1.03 and the P&L from approximately $5,000 to $1,200. Increasing slippage to 10 cents resulted in a negative P&L, demonstrating the strategy’s vulnerability to even minor execution discrepancies.

Recommendation: Always consider including slippage, at least on entry, to gauge a strategy’s resilience. Slippage on exit should be considered if profits are being actively taken.

2. Unrealistic Position Sizing

Backtests often employ position sizes that are impractical for real-world trading. Allocating excessively large capital to each trade, or assuming the ability to fill extremely large orders, can distort results.

Example 1: A backtest allocated $100,000 capital with a consistent position size of 300 contracts (totaling $100,000 risk per trade). This implies filling 1,200 contracts daily for an iron condor, which is unlikely due to liquidity constraints in GLD options.

Example 2: Another test used 25% of $100,000 capital ($25,000) per position. While seemingly more reasonable, the speaker argues even this is optimistic, especially for less liquid options.

Solution: Adjust position sizes to reflect realistic fill capabilities and capital allocation strategies. Reducing the position size to 1-2 contracts and incorporating slippage yielded more plausible results.

3. Unrealistic Rules & Strategy Timing

Strategies based on overly specific or improbable timing rules can produce misleading backtest results. Rules that rely on perfect execution or anticipate market behavior with unrealistic accuracy should be scrutinized.

Example: A strategy entering trades at precisely 3:55 PM, with a position size of $25,000, was identified as unrealistic. The speaker questioned who would consistently take the opposing side of such a large trade so close to market close, especially given the inherent risks of QQQ options.

Key Consideration: Question the plausibility of the rules and the availability of liquidity to support the strategy’s execution.

4. Focusing Solely on Win Rates & P&Ls

Evaluating a strategy based solely on win rate and P&L provides an incomplete picture. A comprehensive assessment requires considering all relevant metrics and, crucially, how the strategy interacts with other potential strategies within a portfolio.

Example: A long put strategy, individually, showed limited profitability. However, when combined with an S&P iron condor strategy, with rules dictating which strategy to deploy based on daily market direction, the combined portfolio performance improved, demonstrating the potential for synergistic effects.

Recommendation: Analyze the strategy’s overall profile, including risk metrics, and consider its integration into a broader portfolio context.

5. Attempting to Eliminate All Loss Scenarios

The pursuit of a strategy that eliminates all potential loss scenarios is often counterproductive. Overly restrictive rules can limit trading opportunities and ultimately hinder profitability.

Example: Backtesting an iron condor strategy with filters to avoid high volatility days (VIX changes of 0-5%) and major market events (FOMC, CPI, etc.) resulted in suboptimal performance. The strategy that traded through all market conditions, including these events, proved most profitable.

Rationale: Market conditions that lead to losses sometimes offer increased compensation for taking on risk. A robust strategy should be able to withstand a variety of environments, rather than attempting to avoid them entirely.

Notable Quotes:

• “Slippage…shows the robustness of a strategy. Like how resilient is a strategy to a little bit of slippage.” – Kirk (Optional Alpha)
• “I’m not saying that this is like something you can’t trade, but you can see at 5 cents of slippage made a considerable difference in the P&L.” – Kirk (Optional Alpha)
• “My goal is not necessarily to get you to find the perfect strategy or a profitable versus a loser. It's just to get you to find more realistic testing that you can do moving forward.” – Kirk (Optional Alpha)

Conclusion:

Effective option strategy backtesting requires a realistic and comprehensive approach. Ignoring slippage, employing unrealistic position sizes, imposing overly restrictive rules, focusing solely on superficial metrics, and attempting to eliminate all potential losses can all lead to misleading results. By addressing these five common mistakes, traders can develop more robust and reliable strategies that are better prepared for the challenges of live trading. The emphasis should be on creating a realistic simulation of market conditions and evaluating the strategy’s performance within a broader portfolio context.