Backtest quality
Modelling assumptions, test period, symbol behaviour, trade count, curve shape, drawdown depth, and whether the report gives enough evidence to trust the result.
A focused review of your MT5, MQL5, or Python strategy results so you can understand where the risk sits before increasing capital or live exposure.
The goal is to turn platform output into a clear risk picture: what worked, where it failed, how fragile the curve may be, and which assumptions need to be checked before deployment.
This is useful when you have an MT5 backtest, an MQL5 Expert Advisor, Python research output, or a trading report that looks promising but does not yet explain the risk clearly enough.
Modelling assumptions, test period, symbol behaviour, trade count, curve shape, drawdown depth, and whether the report gives enough evidence to trust the result.
Largest losses, loss clustering, trade distribution, equity path weakness, value at risk, and conditions where the strategy appears most vulnerable.
A concise report with findings, observations, risks to investigate, and suggested next steps for stress testing or implementation review.
Review of MT5, MQL5, Python, or account-history outputs provided by the client.
Summary of profit, drawdown, trade distribution, value at risk, and curve confidence observations.
Risk notes covering concentration, path dependency, largest losses, and weak market conditions.
Recommendations for further stress testing, sizing review, or implementation changes.