#systematic-trading
9 items tagged systematic-trading. ← all topics
Persist It When It Happens: The Data You Can't Fetch Twice
Some of the most important facts in a trading system — what actually filled, at what price, in what order — are only available for a short window after they occur. Why you write them down at event time, and how to test that you did.
Read →A Sign Convention Is an Undocumented API
Is a negative price a credit or a debit? Multi-leg fills, signed quantities and mixed labels let a system report the wrong direction while every number looks plausible. Why you reconstruct from the legs, and how to test for it.
Read →A Highlight Column Is Not a Trading Signal
Adding a 'worth a look' label to an existing scanner sounds like a cosmetic change. It's actually one of the easiest ways to quietly grow a second, undocumented signal inside a presentation layer — and the two invariants that keep it from doing that.
Read →Paper Trading Is a Software Test, Not a Track Record
A good stretch in a paper account gets read as proof a strategy works. What paper trading actually validates is narrower, more useful, and has almost nothing to do with whether the idea behind it is any good.
Read →Research and Execution Are Not the Same Program
A systematic trading workflow is really two programs sharing one codebase. What breaks when the boundary between them blurs — and the rule that keeps it enforced.
Read →Your Position Size Is a Number in a YAML File
A one-line currency check meant a live strategy sized every position from a hardcoded capital figure instead of the real account balance — and it failed silently for months. What the bug was, why it was invisible, and the validator that now sits between signal and submission.
Read →15-Minute Divergence — The 1-Year Refresh on 5,073 Trades
Two months after the first intraday backtest, the same strategy re-run on a 4.5× larger sample: 5,073 trades across 144 symbols. The noise-stop fix worked, the profit factor held at 1.50× — and the data says this is not a day-trading strategy at all.
Read →15-Minute Divergence on NASDAQ 100 — What a Full Year of Intraday Data Shows
A 1-year backtest of a multi-indicator divergence strategy on 15-minute IBKR bars across NASDAQ 100 stocks — 1,116 trades, a Sharpe of 2.99, and the one mechanical flaw that nearly buried the edge.
Read →Backtesting Divergence on NASDAQ 100 — What 5 Years of Data Shows
A systematic backtest of a multi-indicator divergence strategy across NASDAQ 100 stocks over five years — win rates, profit factors, drawdown, and the structural lessons that change how you think about divergence trading.
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