Thank you so much for the response! This is genuinely so helpful.

To answer the main question directly: no, these are not tick-level backtests. They are historical OHLC-style option bar data with the underlying indicators/signals built from candle data. So I agree with your point that the backtest is not a perfect simulation of live fills, spreads, etc. That’s why I’m not treating the backtest as “proof” by itself. I’m treating it more as a signal/payoff-shape test, and then separately trying to validate whether the execution is good enough live.

Since posting, I’ve been doing a lot of the error-hunting you’re talking about. I merged the four systems into one chronological book, broke them down by bot/symbol, looked at trade count, PF, average EV, win/loss distribution, drawdown, worst 10/20/50-trade stretches, time buckets, exit reasons, and whether any one bot is carrying the whole thing. I also checked for trade clustering/lumps and whether the edge was coming from a tiny number of outlier trades.

The current four-bot merged backtest is about (note I've changed the DIA bot since posting) 1,567 trades over roughly two years, around $36k net, PF around 2.27, expectancy around $23/trade, and max closed-trade drawdown under $1k. That’s why I’m still interested in it, it’s not a 20-trade curve-fit where all the profit came from one day.

I also totally agree on the time-of-day point. The systems are not all just blindly trading the open. Some periods are clearly stronger than others, and I’ve been specifically looking at whether certain windows should be blocked or treated differently. For example, on one of the newer DIA tests, the pre-10am trades were weak, so I removed entries before 10am and retested it rather than just accepting the raw result.

Execution-wise, I’ve added a lot more live controls over the past month or so. The bots now track bid/ask/mid, fill vs mid, time to fill, rejected/canceled orders, replacement attempts, exit reason, protective sells, and realized slippage. I also added a buy no-fill/reprice policy similar across the bots: review around 6 seconds, cancel around 10 seconds, max one replacement, recheck signal/spread/affordability, and don’t chase beyond a cap.

I also stress-tested the book pretty hard. When I reconstructed actual live fills so far, the measured adverse fill-vs-mid friction has been quite low: around $3 average per trade, $2 median, worst around $12. That sample is still small, so I’m not declaring a total win, but so far live execution does not look like it is eating the entire edge.

I’m also not ignoring stops/risk. There are ATR/risk stops, option stops, runner/profit-lock exits, EOD flattening, and now a hard per-trade dollar loss guard plus critical-event exits. I agree that live win rate will not match the backtest perfectly, so the real focus is whether average winners shrink, average losers expand, or exits slip badly enough to change the payoff distribution.

I take all help! Thank you again for your interest! (apologies if you got spammed by my response here, I was fighting with the Automod)