Study the failure.
Not just the signal.
Most automated systems do not fail because of one bad indicator. Research bias, execution, sizing, costs and operations interact. This section isolates those mechanisms.
RESULTDATASIGNALRISKCOSTOPS
Diagnose before you optimize.
Seven failure modes across research, execution, risk and operations.
↗02MACHINE LEARNINGWhy AI trading bots failTargets, leakage, non-stationarity and unnecessary complexity.
↗03DIAGNOSTICSTrading bot losing money?A troubleshooting order that protects the evidence before changing rules.
↗04EVIDENCE GAPProfitable backtest, losing forwardSelection bias, execution assumptions and changing market structure.
↗05COSTSHow costs destroy small edgesFees, spread, slippage and turnover compound against weak expectancy.
↗Freeze the version
Changing rules after every loss destroys the forward record you need to diagnose the system.
Use net outcomes
Gross signals are not returns. Fees, spread, slippage and sizing belong in the result.
Keep failed systems
Deleting losing versions creates survivorship bias and makes research look cleaner than reality.
Do not redesign before locating the failure.
First confirm that the intended version and data are correct. Then measure net costs and payoff distribution, inspect regime and exposure concentration, and only then decide whether the strategy logic needs a new version.
A losing experiment is still an experiment.
ReplyOpsAI keeps unsuccessful versions visible and separates BACKTEST, PAPER and LIVE evidence. The goal is a useful record of what happened, not a collection of attractive equity curves.
See the experiment log