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Quantitative Finance📊 Data Science
Algorithmic Trading
A quantitative trading project exploring algorithmic strategies, historical market analysis, and automated decision-making.
Benchmark Results & Metrics
Sharpe Ratio
2.1
Backtest Period
5 Years
Supported Strategies
3
Max Drawdown
8.5%
01 // Overview
This project develops and backtests trading models. By analyzing historical daily and intraday stock datasets, it gauges the profitability of trend-following and mean-reversion rules.
02 // The Problem
Retail traders lack quantitative backtesting tools, exposing them to human bias and excessive market risk without statistical validation.
03 // System Architecture
Historical Backtesting Engine
Data is loaded, technical indicators (MACD, RSI) are computed, and a state engine executes buy/sell signals. Metrics like drawdown and Sharpe ratio are calculated.
ARCHITECTURAL DATA FLOW:Market Data -> Indicator Calculator -> Signal Engine -> Portfolio Execution Simulator -> Performance Report
Architecture Components:
Indicator Calculator
Signal State Engine
Portfolio Backtester
Risk Metric Reporter
04 // Implementation
Implemented in Python using pandas, numpy, and backtrader. Interactive charts are generated using bokeh and matplotlib to view portfolio equity curves.
05 // Execution Workflow
11. Retrieve historical OHLCV data.
22. Calculate technical indicators (moving averages, RSI).
33. Evaluate trading criteria to trigger buy/sell signals.
44. Run portfolio simulation accounting for commission.
55. Analyze risk metrics (Sharpe ratio, drawdown, ROI).
06 // Technology Stack
Financial ML & Analytics
BacktraderPandasNumPy
Data Sources
Yahoo Finance APIQuantopian datasets
07 // Technical Challenges
⚠️Avoiding look-ahead bias and overfitting when parameter-tuning strategies.
⚠️Accounting for transaction commissions and slippage in backtest returns.
Lessons Learned
- •A strategy that works on paper often fails in production if execution slippage is not modeled.
- •Diversifying signals across uncorrelated assets reduces overall maximum drawdown.
Future Improvements
- •Implementing sentiment signals from financial news using fine-tuned FinBERT.
- •Deploying the logic to interact with live paper-trading APIs (e.g. Alpaca).