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Spent the last quarter building a simple logging system to measure the gap between theoretical and realized P&L on my options strategies. The results changed how I size trades and time execution.
Background. I run systematic short vol on SPX weeklies, mostly iron condors and strangles. Everything is rules-based, entries trigger off a vol surface model I built in Python, exits are mechanical at fixed percentage of max profit or DTE cutoff. Mid six figure account, 15-40 contracts a week. The execution itself is still semi-manual through IBKR's API but the signal generation is fully automated.
The problem I was trying to solve: my realized returns were consistently 15-20% below what my backtest projected, and I couldn't find the leak in my model. Spent weeks tweaking my vol surface assumptions, adjusting delta targets on the short legs, changing DTE windows. None of it closed the gap.
**The logging system**
Pretty basic. Every time my signal fires and I submit an order, the script logs three things: the theoretical mid of the spread at signal time (calculated from my own vol surface, not the broker's mark), the NBBO mid at submission, and the actual fill price. On the exit side it logs the same three numbers plus the timestamp.
I also poll the options chain every 60 seconds during market hours and log the bid-ask width on each leg of my open positions. This gives me an intraday spread width profile for each position over its entire life.
After 90 days I had about 180 round trips and roughly 45,000 spread width observations.
**What the data showed**
Single legs: fill vs theoretical mid gap averaged 2-4%. Not great but not the problem.
Verticals: 8-12% gap. The compound error from two legs with independent bid-ask spreads starts to bite.
Iron condors: 15-22% gap. Four legs, four independent fictions stacked together. On a 4 leg IC where my model priced theoretical mid at $2.80, fills were consistently $2.55-$2.65. That 15-25 cent drag per spread, multiplied across hundreds of contracts per month, was the entire gap between backtested and realized returns.
The spread width data was even more interesting. Bid-ask width on SPX weekly options follows a very consistent intraday curve. Widest in the first 30 minutes, compresses through the morning, tightest window is roughly 10:30-12:30 ET, widens modestly into the afternoon, then compresses again before the 3:30 close. The difference between filling at 9:35 and filling at 11:00 was 10-15 cents per spread on average. Completely deterministic, completely avoidable.
**What I changed in the system**
First, I added an execution window filter. Signal can fire whenever, but the order doesn't submit until the spread width on all legs drops below a threshold calculated from the trailing 5-day average spread width for that specific strike and DTE. If it doesn't compress by 1pm, the order submits anyway with a more aggressive limit. This alone recovered about 40% of the slippage.
Second, I rewrote my backtester to apply a realistic fill model instead of assuming mid fills. I sample from a distribution fitted to my actual fill data, parameterized by number of legs, DTE, and time of day. Any strategy that doesn't clear my minimum return threshold after this simulated slippage gets rejected. This killed about 20% of the trades my old backtest was greenlighting, and my live win rate went up because the surviving signals had real edge, not theoretical edge that existed only at mid.
Third, I started tracking what I call "realizable theta." The Greeks my broker displays are based on theoretical mid. When I compare displayed theta with actual daily P&L change measured at the prices I could actually close at, there's a consistent 18-22% haircut. A position showing $14/day theta is really collecting $11/day in realizable terms. I now use the haircut-adjusted number for all position sizing.
**Quantified impact**
Over the 90 day tracking period, cumulative gap between theoretical and realized P&L was just over $14K. My total commissions over the same period were about $6K. Slippage was 2.3x my commission costs and nobody talks about it because it's invisible unless you build the tracking infrastructure.
After implementing the changes, the last 60 days have shown roughly 11% improvement in net P&L versus the prior 60 days, on fewer total contracts. Fewer trades, less gross premium, but keeping more of it.
**What I haven't solved**
Legging. I've experimented with selling the short strike first and adding the long wing after a favorable move. When it works the improvement is 8-12 cents per spread. But automating the decision of when to leg versus when to submit as a combo is hard. The two times it went wrong cost me more than a month of spread savings. I have some ideas around using real-time gamma exposure to size the legging risk but haven't backtested it properly yet.
The logging code is pretty straightforward, just polling IBKR's API for chain data and writing to a SQLite database. Happy to discuss the schema and the fill distribution model if anyone is doing something similar. Particularly interested in whether people trading RUT or individual names see even worse slippage given the wider markets on those chains.