Automated Backtester Research Plan (Part 10)

I’ve been getting more organized this year by converting incomplete drafts into finished blog posts. From the mini-series on the automated backtester, here is one further post (Jan 2019) on the off chance that someone out there could possibly benefit.

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Relatively simple adjustment criteria to study: rolling out a short option or closing a losing vertical. Maybe also rolling a wing closer when the market is up X% or when the market reaches the opposite long strike (because it has become cheaper to roll).

The next step will be to tabulate several statistics for the serial, non-overlapping approach. These include number (and temporal distribution) of trades, winning percentage, compound annualized growth rate (CAGR), maximum drawdown percentage, 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 50% initial account value by increments of 5% or 10%.

Allocation may be done based on maximum risk of the position (wing width minus credit received for iron spreads) or stop-loss at max potential profit. When considering this, it would be interesting to see what the distribution of losers looks like as a percentage of max potential profit (credit received, for an iron butterfly).

RAR may have two meanings. As used above, RAR is CAGR (or total return) divided by maximum drawdown percentage. When studied with filters, RAR may mean CAGR (or total return) divided by percentage of time in the market.

Some elementary filters can also be studied. Average true range (ATR) (eight or 14 periods) under a certain value (analyzed as a range) could be an entry criterion. Determining the exact range could be done by studying ATR distribution by date across the whole data set. Maybe we use absolute number or maybe we use a percentile over the last X days, which may have to be optimized as well. We could also study VIX (or RVX) to see if it makes sense to stay out with VIX above Y or above the Z’th percentile over the last W days. A more complicated combination would be to center the trade above (below) the money when momentum is positive (negative) or when we get a Donchian channel breakout to the upside (downside). This may be more a study of mean reversion vs. trend following and probably something to study closely with stability in mind.