I will develop quantitative trading systems in python

Yes. I can turn a market hypothesis into a Python strategy or review, test, refactor, and improve existing systems. The scope may include signals, factors, data pipelines, statistical modeling, backtesting, validation, risk controls, and execution logic.

No. We can start with a hypothesis, partial rules, or a research question. Send your objectives, markets, timeframe, data, and risk constraints. Code and documentation also help. We define the evaluation criteria and deliverables before work begins.

The protocol may combine out-of-sample tests, walk-forward analysis, Monte Carlo, sensitivity analysis, regime tests, and stress tests. Backtests account for costs, slippage, and liquidity. I check for look-ahead bias, data leakage, and selection bias from multiple tests.

Foundation: hypothesis, signals, initial backtest, and report. Validation: modular pipeline, robustness tests, risk modeling, and preparation for paper trading. Production: advanced validation, risk controls, execution, automation, and deployment support.

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.

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, depending on data availability and trading rules.

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, depending on access and supported features.

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 own datasets. Integration depends on each source’s access, licensing, and coverage.

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

I prioritize Fiverr chat and written documentation to keep communication clear and maintain a clear record of decisions. Short calls on Fiverr can help clarify requirements or demonstrate results. NDA available; your strategy, code, data, documentation, and results remain confidential.