Quantitative Algorithm Developer (Stock Price Prediction Model with Backtesting)

We are looking for an experienced Quantitative Algorithm Developer to design and build a robust quantitative model that predicts stock price movements based on both actual market data and estimated/projected data. The model should analyze historical and real-time stock data to forecast short-term and medium-term price movements, incorporating behavioral patterns from past earnings announcements to inform future predictions. Key Requirements

• Price Movement Prediction: Build a model that predicts stock price direction and magnitude using actual market data and estimated/projected inputs (e.g., analyst estimates, consensus EPS/revenue forecasts).
• Earnings Event Analysis: Analyze and integrate historical earnings announcement behavior — including pre- and post-earnings price movements — as a predictive factor.
• Past Behavior Integration: Factor in historical price patterns, volatility cycles, and seasonal tendencies into the model.
• Backtesting Engine: Develop a fully functional backtesting framework to validate model performance, including Sharpe ratio, max drawdown, win rate, and alpha generation.
• Data Pipeline: Set up or integrate with data sources (e.g., Yahoo Finance, Bloomberg, Quandl, Alpha Vantage) for clean data ingestion.
• Documentation: Provide clear documentation of model logic, assumptions, parameters, and backtesting results. Preferred Skills & Experience
• Strong background in quantitative finance and algorithmic trading
• Proficiency in Python (Pandas, NumPy, scikit-learn, statsmodels, Backtrader or Zipline)
• Experience with ML or statistical models for financial time series (LSTM, XGBoost, ARIMA, factor models)
• Familiarity with earnings event studies and event-driven strategies
• Knowledge of options pricing or implied volatility is a plus
• Experience with platforms like QuantConnect, Zipline, or Backtrader Apply tot his job