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

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Apply Now