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 ā including
pre- and post-earnings price movements ā 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
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