Automated Backtester Research Plan (Part 8)

After studying put credit spreads (PCS) as daily trades, the next step is to study them as non-overlapping trades.

As discussed earlier, I would like to tabulate several statistics for the serial approach. These include number (and temporal distribution) of trades, winning percentage, compound annualized growth rate (CAGR), maximum drawdown, average days in trade, PnL per day, risk-adjusted return (RAR), and profit factor (PF). Equity curves will represent just one potential sequence of trades and some consideration could be given to Monte Carlo simulation. We can plot equity curves for different account allocations such as 10% to 70% initial account value by increments of 5% or 10% for a $50M account. A 30% allocation (for example) would then be $15M per trade. By holding spread width constant, drawdowns throughout the backtesting interval may be considered normalized.

As an example of the serial approach, I would like to backtest “The Bull” with the following guidelines:

• Entry: at 65 days to expiration (DTE), sell closest 30-point PCS to $2.50/contract
• Exit: when next month’s trade is at the entry date (65 DTE) or position down $250/contract
• Max profit: $250/contract (at expiration; $200-$225 would be more reasonable to manage early)
• Max loss: -$250/contract
• Max risk: $2,750/contract (assuming at least $2.50 credit obtained)

I will not detail a research plan for call credit spreads. If we see encouraging results from looking at naked calls then this can be done as described for PCS.

I also am not interested in backtesting rolling adjustments for spreads due to potential execution difficulty.

Thus far, the automated backtester research plan has two major components: study of daily trades to maximize sample size and study of non-overlapping trades. I alluded to a third possibility when discussing filters and the concentration criterion: multiple open trades not to exceed or match one per day.

This is suggestive of traditional backtesting I have seen over the years where trades are opened at a specified DTE. For trades lasting longer than 28 (or 35 every three months) days, overlapping positions will result. As discussed here, I am not a proponent of this approach. Nevertheless, for completeness I think it would be interesting to do this analysis from 30-64 DTE and compare results between groups, which I hypothesize would be similar. To avoid future leaks, position sizing should be done assuming two overlapping trades at all times. ROI should also be calculated based on double the capital.

Another aspect of traditional backtesting I have eschewed in this trading plan is the use of standard deviation (SD) units. I have discussed backtesting many trades from (for example) 0.10-0.40 delta by units of 0.10. More commonly used are 1 SD (0.16 delta corresponding to 68%), 1.5 SD (0.07 delta corresponding to 86.6%), and 2 SD (0.025 delta corresponding to 95%). Although not necessary, we could run additional backtests based on these unit measures for completeness.