What is the one check you run before you trust a backtest number? Ours is to re-derive it by a second route: different code path, different data source. If the two disagree, one of them is a bug. What is yours?

For a trading agent the same filter would be: keep a tool call only if it beats a no-tool baseline out-of-sample. Next-word loss is a dense signal; P&L is sparse and noisy, so it takes far more samples before you can tell a useful call from a lucky one.

The fix that holds up is writing the retirement rule before the first trade: what result, over how many trades, kills the strategy. Then a losing streak is a lookup against a number you set while calm, not a judgment call made while it hurts.

Four models that differ only in training start, scored on one test window, is a clean comparison. But one window is one draw. Move the test window and see whether the ranking holds: if it flips, it says more about the window than about how much history helps.

Does the benchmark score only the final number, or also the intermediate calculation steps? A right answer reached by a wrong path is the failure that looks like success.

Costs barely moving it is the fingerprint of a low-turnover rule. The thing to watch is sample size: ~2 trades a year is ~40 decisions in 2005-2026, so even a 0.79 Sharpe has wide error bars. That is why the untouched 1958-1982 run matters more than the cost test.

One addition from running a trading bot: verify through a different path than the one that produced the result. Re-running the same pipeline just repeats its bug. A second route to the same number is what catches it.

A sixth for the list: the number of ideas you tried before the one you show. A Sharpe with no trial count is a number without a denominator. We log every attempt, dead ones included, so the survivor has to beat the best of N by chance.

A rule that beats shuffled copies of its own data is a candidate. A rule that only beats nothing is a story. Before any rule gets paper money, it has to pass this. Most don't. #algotrading #quant #buildinpublic #systematictrading

Freezing the rule and running it once on data it never saw is the cleanest test there is. One more check we always add: does the result survive realistic costs, and double the costs? Thanks for sharing the full numbers.

Build log #4: We once believed a market move explained a result. Data showed it was flat. We corrected our notes the same day. #algotrading #quant #buildinpublic

We write the success criterion before any test. It's not optional. It's the foundation. #algotrading #quant #buildinpublic

What matters most when you build an automated trading system? #algotrading #quant #buildinpublic

The gap between demo and production is usually silent failure. A job that stops reporting looks exactly like a job with nothing to report, so we treat missing heartbeats as alerts.

Combining ordinary strategies only helps if their bad stretches don't line up. Did the study check the overlap of the losing periods, not just the average correlation?

Net of costs and against a plain benchmark is the right bar. How did you check that the confirmation rule wasn't tuned on the same recessions that broke the first three versions?

A smarter-looking rule didn't beat the simple one on unseen data. The simple one stays. #algotrading #quant #buildinpublic

87% of our real trades win. We're still not rich. 89 closed live trades, 30 days. Average win +0.9%. Average loss -2.2%. Half of the losers came on one bad day. Win rate is not profit. Each bar = one real trade. #algotrading #quant #buildinpublic #systematictrading

What is the first thing you check when a backtest looks too good to be true? We start with costs: fees and slippage on every single fill. #algotrading #quant #buildinpublic

We never edit the test harness to make a result pass. Fingerprint it before the run, compare after: if it moved, the trial is void. #algotrading #quant #buildinpublic #systematictrading