I will build quantitative trading systems in python

Yes. I can build a Python system from an initial hypothesis or review, test, validate, refactor, and improve an existing one. The scope may include signals, logic, code, data, risk controls, execution, and assumptions.

Share your goals and what you have: market, timeframe, data source, strategy rules, risk constraints, and desired deliverables. Code, backtests, datasets, API documentation, and technical specifications also help. We can define the remaining requirements together.

Quant Research Foundation: hypothesis review, research, initial backtest, and report. Validated Quant System: realistic backtesting, validation, risk modeling, and paper trading preparation. Production Quant System: advanced validation, risk controls, execution, automation, and deployment support.

Validation may include realistic backtests, out-of-sample and walk-forward tests, Monte Carlo, sensitivity and regime analysis, stress tests, transaction costs, slippage, and liquidity. I check for look-ahead bias, data leakage, and excessive complexity, and assess parameter stability.

Yes, when the data, problem formulation, and validation protocol justify it. Depending on the scope, I may use scikit-learn, XGBoost, or PyTorch for feature selection, classification, forecasting, regime detection, and other quantitative applications.

I build systems for crypto, stocks/ETFs, CFDs, futures, options, commodities, FX, and fixed income. Trading venues and exchange groups: NYSE, Nasdaq, LSE, Euronext, Xetra, SIX, JPX, HKEX, ASX, B3, TSX, NSE, SSE, SZSE, KRX, CME Group, Cboe, ICE, Eurex, SGX, and TAIFEX.

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 APIs, WebSocket, FIX, or SDKs, depending on the platform.

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, Coin Metrics, and your own data. Integration depends on access and licensing for each source.

Depending on the scope, I use Python, SQL, C++, pandas, NumPy, SciPy, Polars, statsmodels, scikit-learn, XGBoost, PyTorch. Research: QuantConnect, Jupyter, vectorbt, backtrader. Production: FastAPI, PostgreSQL, Docker, Linux, pytest, Git, CI/CD, Prometheus, Grafana, AWS, Azure, Google Cloud.

I prioritize Fiverr chat and written documentation for clear communication and traceable decisions. Short Fiverr calls can help clarify requirements or demonstrate results. NDA available; your strategy, code, data, documentation, and results remain confidential.