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I will backtest and validate your trading strategy in python
Quantitative Developer: Research, Backtest and Deploy Trading Systems
I work with professional traders, prop firms, hedge funds, managers and fintechs to research, validate, and develop quantitative trading systems—from alpha discovery to production deployment. I turn m...
Level 2
Has met high performance criteria and has a proven track record for meeting client expectations.
About this Gig
A strategy only deserves confidence when its results remain defensible after every assumption has been tested.
I subject trading strategies to rigorous quantitative validation, turning apparent performance into clear evidence for real-world decisions. I can audit or rebuild the strategy in Python, review data, rules, risk, and architecture, and assess statistical and machine learning models.
Depending on scope, I analyze costs, slippage, liquidity, and execution; and apply out-of-sample testing, walk-forward analysis, Monte Carlo simulations, sensitivity analysis, parameter stability, market regimes, stress tests, resampling, and benchmarks.
The process identifies overfitting, look-ahead bias, data leakage, survivorship bias, parameter fragility, and unrealistic assumptions.
You receive reproducible code, performance and risk metrics, trade diagnostics, visualizations, and a technical report with conclusions, limitations, and objective recommendations.
I do not promise profits. I deliver clarity about what your strategy truly supports.
Contact me before ordering so we can align on the market, data, rules, and scope.
FAQ
What do I receive, and how do the packages differ?
All packages include reproducible Python code, metrics, visualizations, and a technical report. Quantitative Backtest covers data, rules, and costs; Robust Validation adds OOS testing, sensitivity, and stability; Institutional Validation includes WFA, Monte Carlo, regimes, and stress tests.
Can you validate an existing strategy, codebase, or backtest?
Yes. I can assess existing strategies from code, backtests, specifications, or documentation. The audit reviews logical consistency, data integrity, biases, parameter stability, execution risks, and methodological gaps, regardless of the technology used.
What do I need to provide before the project?
Provide the market, assets, timeframe, strategy rules, data source, broker or exchange, risk constraints, and validation objective. Also share any existing code, documents, and results. If anything is missing, I can help turn the logic into testable technical requirements.
How do you control bias and assess robustness?
I separate research, validation, and final testing; preserve temporal causality; verify historical data availability; control optimization; and test costs, parameters, and adverse scenarios. The goal is to detect overfitting, look-ahead bias, data leakage, and unrealistic assumptions.
Which robustness tests can be included?
Tests are selected based on the strategy’s nature, maturity, and risks. The scope may include OOS, sensitivity and parameter stability, cost scenarios, WFA, Monte Carlo, resampling, market regimes, stress tests, benchmarks, and realistic execution modeling.
Which markets and exchanges do you work with?
I work with equities, ETFs, indices, futures, options, commodities, FX, CFDs, fixed income, crypto, and prediction markets. Venues include NYSE, Nasdaq, LSE, Euronext, Xetra, SIX, JPX, HKEX, ASX, B3, CME, CBOT, NYMEX, COMEX, Cboe, ICE, Eurex, SGX, Binance, Coinbase, Kraken, OKX, Bybit, and Deribit.
Which brokers, infrastructure providers, and data sources can you integrate?
I integrate IBKR, Trading Technologies, CQG, Rithmic, FlexTrade, and Bloomberg EMSX via REST, WebSocket, or FIX. Data sources include Bloomberg, LSEG, FactSet, S&P Global, ICE Data Services, MSCI, Databento, and RavenPack. Licenses and third-party costs are excluded.
What technical stack do you use from research to production?
I use Python, SQL, and C++; pandas, NumPy, statsmodels, scikit-learn, XGBoost, and PyTorch; plus QuantConnect, vectorbt, and backtrader. For production: FastAPI, Git, Linux, Docker, pytest, CI/CD, databases, APIs, VPS, and cloud infrastructure, depending on scope.
Does validation guarantee profits or future performance?
No. No backtest eliminates market uncertainty or guarantees that historical relationships will persist in production. Validation measures robustness, weaknesses, and implementation risks, helping determine whether the strategy is ready for paper trading or live execution.
How do you communicate and protect confidentiality?
I prioritize written documentation and communication through Fiverr chat. Short calls may be used for introductions, alignment, or demonstrations. I can also work under an NDA to protect strategies, data, research, infrastructure, and source code.
