Finlab Github Guide

# Example feature: Technical Analysis Dashboard class FinLab: def __init__(self, data_source): self.data = data_source def technical_indicators(self, ticker): """Calculate RSI, MACD, Bollinger Bands""" pass

Because financial time series are autocorrelated (today’s price depends on yesterday’s), standard statistical tests fail. FinLab’s GitHub includes a bootstrap_significance.py script that runs Monte Carlo simulations to validate if your strategy is genuinely profitable or just lucky.

: Includes tools to analyze the probability of strategy overfitting using combinatorial symmetric cross-validation. finlab github

We buy when momentum is positive AND volatility is below the 12-month median.

conda create -n finlab_env python=3.9 conda activate finlab_env We buy when momentum is positive AND volatility

Have you built a strategy using FinLab? Fork the repository and submit a pull request with your notebook. The quantitative community is waiting to see your alpha.

: Uses Pandas for fast, vectorized backtesting of trading strategies. The quantitative community is waiting to see your alpha

Yes, it has the standard RSI and MACD. But the GitHub repository shines with its proprietary indicators: