I will build institutional quantitative trading systems in python

Yes. I can research a hypothesis, implement rules, or review existing data, models, and code. Share your objective, market, timeframe, and available materials. Together, we define requirements, risk constraints, integrations, and deliverables.

Research assesses the hypothesis and structures data, features, and an initial model. Validated builds the system, compares models, and tests robustness for paper trading. Production adds advanced validation, automated execution, model monitoring, and assisted deployment.

ML/DL can support forecasting, signal selection, and regime detection. NLP turns news, reports, and text into quantitative features. Reinforcement learning can be researched for allocation and execution. I compare models against simple baselines to assess their incremental value.

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. Coverage depends on available data and execution.

IBKR, Charles Schwab, TradeStation, tastytrade, Alpaca, OANDA, Webull, Tradier, Public, Saxo Bank, Binance, Coinbase, Bitfinex, Bybit, OKX, Kraken, Deribit, Rithmic, CQG, Bloomberg EMSX, Trading Technologies, FlexTrade, and SS&C Eze. REST, WebSocket, FIX, or SDKs, depending on access and platform.

Bloomberg, LSEG, FactSet, S&P Global, ICE Data Services, Databento, Nasdaq Data Link, dxFeed, Cboe DataShop, CME DataMine, OptionMetrics, Macrobond, RavenPack, Kaiko, Coin Metrics, MSCI, and your own data. Access and licensing are required; external costs are handled separately.

Depending on scope, I use OOS, walk-forward, Monte Carlo, sensitivity, regime, and stress tests with costs, slippage, and liquidity. I check for overfitting, look-ahead bias, and data leakage. Test data stays out of training and feature selection. I compare models against simple baselines.

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

Depending on scope, I add tests, logs, exposure limits, order reconciliation, and failure recovery. This may include model versioning, monitoring for data shifts, and retraining criteria. I provide operating instructions; ongoing support is agreed on separately.

I prioritize Fiverr chat and written documentation, with traceable updates and decisions. Short Fiverr calls can support alignment and demos. NDA available; your strategy, code, data, models, documentation, and results remain confidential.