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Quick Start

Get Quant101 running locally in 5 minutes.

Prerequisites

  • Python 3.12+
  • Poetry for dependency management
  • Polygon.io flat file data (or any OHLCV source in Parquet)

Installation

git clone git@github.com:JerryHong08/jerryquant101.git
cd jerryquant101
poetry install

Configuration

Copy the example config and edit your data paths:

cp basic_config.yaml.example basic_config.yaml

Key fields in basic_config.yaml:

data:
  data_dir: /path/to/your/polygon_data    # Root of your data directory

update:
  mode: standalone                         # standalone | server | client

Run the Test Suite

python -m pytest --tb=short -q
# Expected: 201 passed

Run a Pipeline Backtest

The fastest way to see the system work end-to-end:

cd src && python -m backtest.backtester --mode pipeline

This runs the default alpha pipeline (BBIBOLL factor, equal-weight sizing, daily rebalancing) on the US Large Cap 50 universe and prints a Sharpe ratio.

Run a Strategy Backtest

For the legacy trade-level BBIBOLL backtest:

cd src && python -m backtest.backtester --mode strategy

Explore Notebooks

The research notebooks are the best way to understand each module:

Notebook What It Shows
pipeline_demo.ipynb Full pipeline: OHLCV → factor → weights → Sharpe
factor_diagnostics.ipynb Per-factor IC/IR, L/S returns, direction check
risk_analysis.ipynb VaR, CVaR, drawdown, 4 sizing methods compared
cost_analysis.ipynb Transaction costs, breakeven analysis, rebalancing frequency
validation.ipynb Walk-forward, bootstrap CI, PSR/DSR, multiple testing
alpha_research.ipynb End-to-end BBIBOLL factor analysis
alpha_iteration.ipynb Multi-factor: STR, Vol Ratio, IC correlation

Next Steps