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I will audit and validate your trading strategy with python backtesting
Quantitative Developer: Trading Systems from Research to Production
I build quantitative trading systems for professional traders, prop firms, hedge funds, asset managers, and fintechs, from alpha discovery through validation to production deployment. I turn market hy...
About this Gig
I assess the reliability of your strategys results through Python backtesting, risk analysis, and robustness testing.
I can audit existing code and backtests or rebuild the logic in Python, reviewing data, rules, position sizing, and execution assumptions, including for statistical and machine learning models.
Depending on scope, I apply out-of-sample testing, walk-forward analysis, Monte Carlo simulations, sensitivity analysis, regime analysis, stress tests, resampling, and benchmarks, accounting for costs, slippage, and liquidity.
I investigate overfitting, look-ahead bias, data leakage, survivorship bias, and parameter instability.
You receive reproducible code, return and risk metrics, trade analysis, visualizations, and a report with findings, limitations, and technical recommendations.
Findings may indicate that the strategy needs revision or should not move forward. No guarantees of profit or future performance.
NDA available. Send your rules, data, and existing results so we can agree on scope and evaluation criteria before you order.
FAQ
What do I receive, and how do the packages differ?
All include reproducible Python code, metrics, visualizations, and a report. Backtest covers data, rules, and costs; Robustness adds out-of-sample testing, sensitivity, and stability; Advanced extends the analysis with walk-forward, Monte Carlo, regimes, and stress tests.
Can you validate an existing strategy, codebase, or backtest?
Yes. I can audit code, reproduce a backtest, or rebuild the logic in Python from documented rules. I compare results, check discrepancies, and assess data, assumptions, and methodology, including when the original implementation is on another platform.
What do I need to provide before the project starts?
Share the market, instruments, timeframe, entry and exit rules, position sizing, data, broker, and risk constraints. Code, backtests, and documentation help reproduce the expected behavior. We address any gaps and agree on evaluation criteria before starting.
How do you control biases and reduce the risk of overfitting?
I define the evaluation protocol, preserve chronological order, and verify what data was available at each point in time. I investigate look-ahead bias, data leakage, survivorship bias, and multiple testing effects. Data used for tuning is not treated as an independent test set.
How do you simulate costs and execution conditions?
Depending on scope and data, I model commissions, spreads, slippage, and liquidity constraints. I can include latency, partial fills, margin, and financing. Assumptions are documented and tested across cost and execution scenarios.
Which markets and exchanges do you work with?
I work with crypto, stocks/ETFs, CFDs, futures, options, commodities, FX, and fixed income. Exchanges and groups: NYSE, Nasdaq, LSE, Euronext, Xetra, SIX, JPX, HKEX, ASX, B3, TSX, NSE, SSE, SZSE, KRX, CME Group, Cboe, ICE, Eurex, SGX, and TAIFEX, subject to data availability and access.
Which brokers, platforms, and execution infrastructure can you integrate?
I can integrate IBKR, Charles Schwab, TradeStation, tastytrade, Alpaca, OANDA, Webull, Tradier, Public, Binance, Coinbase, Bitfinex, Bybit, Kraken, Deribit, Rithmic, CQG, Bloomberg EMSX, Trading Technologies and SS&C Eze via REST, WebSocket, FIX or SDKs, subject to access and available capabilities.
Which data sources can you integrate?
I can integrate data from Bloomberg, LSEG, FactSet, S&P Global, ICE Data Services, Databento, Nasdaq Data Link, dxFeed, Cboe DataShop, CME DataMine, OptionMetrics, Macrobond, RavenPack, Kaiko, and Coin Metrics, plus your datasets. Integration depends on access and licensing for each source.
What technology stack do you use from research to production?
Depending on scope, I use Python, SQL, C++, pandas, NumPy, SciPy, Polars, statsmodels, scikit-learn, XGBoost, and PyTorch. Research: QuantConnect, Jupyter, vectorbt, and backtrader. Production: FastAPI, PostgreSQL, Docker, Linux, pytest, Git, CI/CD, Prometheus, Grafana, AWS, Azure, and Google Cloud.
How do you handle communication and confidentiality?
I prioritize Fiverr chat and written documentation to keep communication clear and decisions traceable. Short calls through Fiverr can help clarify requirements or demonstrate results. NDA available; your strategy, code, data, documentation, and results remain confidential.
