Automated Backtester Research Plan (Part 1)

Today I begin outlining a research plan for the automated backtester.

I want to start with naked puts because they employ the least leverage.

We can study trades entered every day between 30-64 DTE. We can choose the first strike under -0.10 to -0.50 delta by increments of -0.10. We can hold to expiration, manage winners at 25% (ATM options only?) or 50%, or exit at 7-21 DTE by increments of seven. We can also exit at 20-80% of the original DTE by increments of 15%. We can manage losers at 2x, 3x, 4x, and 5x initial credit. I’d like to track and plot maximum adverse (favorable) excursion (no management) for the winners (losers) along with final PnL and total number of trades. I want to monitor winning percentage, average win, average loss, largest loss, profit factor, average trade (average PnL), PnL per day, standard deviation of winning trades, standard deviation of losing trades, average days in trade (DIT), average DIT for winning trades, and average DIT for losing trades.

Return on investment (ROI) does not seem relevant for naked puts because of the large notional risk. At the moment, I cannot think of a need to track buying power reduction, but this is something I will keep in mind.

Speaking of notional risk, unless normalized the average win/loss can vary significantly based on underlying price (and option prices). We can apply a fixed position size (e.g. $5M) and calculate number of contracts for each trade. If I am selling a 1500 put, for example, then $5M divided by $150,000 (notional risk) is 33 contracts ($4,950,000) and change (truncate). If I sell a 1000 put then 50 contracts would amount to $5M notional risk. Regardless of underlying price, this will give a variable number of contracts to keep notional risk relatively constant thereby keeping profits and losses commensurate.

If we don’t normalize for notional risk then we would get numbers that don’t make as much sense. With the underlying at 1000 vs. 2000, for example, the contribution to the total PnL would be roughly twice as large at the higher prices. The overall contribution should not significantly vary based on an arbitrary factor.

I want to briefly discuss the relative constancy around target position size. I mentioned that $4,950,000 is 1% less than $5,000,000. As discussed here, I would expect this error to be inversely proportional to number of contracts because the percentage difference between consecutively decreasing integers increases (e.g. 19 is 5% lower than 20 whereas 9 is 10% lower than 10). If we deem this error to be too large—especially for lower-priced underlyings like RUT—then the target position size can be increased (e.g. from $5M to $10M).

I would like to see a distribution of losers in time and in magnitude. Date can be on the x-axis with underlying closing price (line graph) on the right y-axis and trade PnL (histogram) on the left y-axis. It certainly makes sense to do these graphs for expiration. We can also do the graphs for managing winners at 50%. I think it also makes sense to do these graphs for managing early (e.g. 7-21 DTE or X% of the original DTE) as well as managing losers.

I will continue next time.