About

I’m looking for a disciplined and skilled developer to build a basic quantitative model for day trading, combining market analysis and statistical logic. The goal is not high-frequency or AI prediction — I want a solid, rule-based framework that can later be expanded with more data and conditions. This is the foundation stage: clean structure, stable execution, and testable logic matter more than fancy indicators. ⸻ Responsibilities: • Design and code a simple day trading model (Python preferred) using basic quantitative principles such as: • Moving averages, RSI, volume signals, or volatility filters • Entry and exit triggers based on price action or trend • Position sizing and stop-loss logic • Incorporate risk management rules (for example: fixed % risk per trade, max daily loss) • Enable backtesting and paper trading functionality (preferably via APIs such as Alpaca, Interactive Brokers, or Binance if applicable) • Document the logic clearly so it can be understood and extended later • Optional: add performance metrics like win rate, Sharpe ratio, and daily P/L curve ⸻ Requirements: • Proven experience in Python for trading or backtesting (e.g., pandas, NumPy, backtrader, zipline, or QuantConnect) • Understanding of basic market structure and technical indicators • Ability to write clean, well-commented code with modular logic • Good communication — I prefer someone who can explain decisions plainly, not just send code ⸻ Deliverables: 1. Working Python script or Jupyter Notebook with the day trading model 2. Example backtest results on recent market data 3. Short document explaining the logic, parameters, and file structure ⸻ Preferred but Not Required: • Experience with live trading APIs (Alpaca, Interactive Brokers, Binance) • Familiarity with equities, futures, or forex markets • Knowledge of position sizing and money management techniques