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Fiverr Pro
Nederlands
$
USD
I will optimize your trading strategy without overfitting
Quant engineer, I stress test and upgrade trading strategies
I stress-test trading strategies so you know whether they hold up out-of-sample before you risk capital.
Full-stack + ML engineer (Python, TypeScript) and builder of Entropable, a crypto quant resear...
Over deze dienst
Parameter sweeps find the settings that fit the past best. That is not a bug in your process it is the definition of curve-fitting, and it is why optimized strategies stop working.
What you get
Genetic-algorithm search over your parameters, scored on walk-forward OUT-OF-SAMPLE windows never on in-sample profit
Risk constraints inside the fitness function: max drawdown, exposure, minimum trade count
Sensitivity map around the winner a flat plateau is robust, a single bright cell is luck
Before/after table on data the search never saw, with the trade count beside every metric
Updated strategy file in your format, PDF report and the raw run data
Why me
I built Entropable, a quant platform with a DEAP genetic-algorithm optimizer, walk-forward fitness and a CPCV validator. Your strategy runs through the same engine.
How it works
1. Send your rules plus the parameters you want searched, with a plausible range for each
2. I confirm the encoded logic and the risk limits before anything runs
3. Search, validation, report and a short debrief
Not included: writing a strategy for you, live trading, exchange connections.
No financial advice, no signals,
Platform:
TradingView
•
MT5
•
Binance
Mijn portfolio
Veelgestelde vragen
What if the optimizer finds something that only worked by luck?
Then the report says so. On the sample run in my gallery the winner scored a 9.19 Sharpe in-sample on nine trades — and nine trades cannot support that number. The before/after table puts the trade count next to every metric for exactly this reason. A result I cannot defend is reported as one.
