I will backtest and optimize your trading strategy in python
Software Architect and Quant Developer
Vetted by Fiverr Pro
Robert Brendler was selected by the Fiverr Pro team for their expertise.
Vetted for
Trading Bots Development
About this Gig
Vetted Pro
Is your edge real or just a pretty backtest? Let's find out, honestly.
Most backtests look profitable because of lookahead, leakage, unrealistic fills, or curve-fitting. I test the way a quant does with realistic costs and proper validation, so the numbers actually mean something before you risk capital.
Clean Backtest
one strategy tested properly: realistic spread/commission/slippage, key metrics (net profit, profit factor, win rate, expectancy, max drawdown, Sharpe), and an equity curve.
Optimize + Walk-Forward
parameter optimization with walk-forward / out-of-sample validation across multiple symbols and timeframes, plus overfitting/robustness checks so I'm tuning an edge, not curve-fitting the past.
Quant Research Report
the full treatment: leakage and lookahead audit, Monte Carlo / stress testing, market-regime analysis, and the reusable backtest code delivered.
Bring a Pine, MQL, or Python strategy, or a clear written description. Please contact me before you order, so we can select the right approach for you.
I'll give you a straight verdict, not a hopeful one.
Edge looks real? Let's automate it. Edge looks weak? Let's fix it. See my other gigs for next steps.
Platform:
TradingView
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Custom
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Other
My Portfolio
FAQ
Will a good backtest guarantee profits?
No — and anyone who promises that is selling you something. A backtest shows whether an edge held historically under realistic costs. The value is a result you can actually trust.
Why do my backtests look better than live?
Usually lookahead/leakage, unrealistic fills, or overfitting. I test leakage-free with realistic costs and out-of-sample validation, so the numbers hold up.
What metrics do I get?
Net profit, profit factor, win rate, expectancy, max drawdown, Sharpe/Sortino, and an equity curve — plus robustness and overfitting flags on higher tiers.
Can you optimize my parameters?
Yes (Standard and up), with walk-forward / out-of-sample so it's a real edge, not a curve fit.
What platforms and data?
Python (backtrader, vectorbt, backtesting.py) or TradingView/MT5 native. Bring your data, or I'll source standard historical data.

