A Python-based quantitative finance toolkit for portfolio optimization and analysis.

Features

Find and optimise Buy/Sell signals based on momentum (moving average): buy_sell_signals
Find Call/Put prices of options using Alpaca-API and Black-Scholes-Merton model: options_pricing
Efficient frontier calculation and visualization, Risk-return optimization, portfolio rebalancing tools : portfolio_rebalance
Find safer stocks for bear market: find_safer_stocks_for_bear_market]
Find best stocks for value investing: find_value_stocks
Use Bollinger Bands/Keltner Channels for optimum trading strategy: bollinger_bands
Generate Monte Carlo simulation and calculate Value at Risk (VaR): monte_carlo_simulation

Check the documentation to begin optimizing your investment portfolio using advanced quantitative methods.

Name		Name	Last commit message	Last commit date
Latest commit History 35 Commits
figures		figures
README.md		README.md
backtesting.py		backtesting.py
bollinger_bands.ipynb		bollinger_bands.ipynb
buy_sell_signals.ipynb		buy_sell_signals.ipynb
find_safer_stocks_for_bear_market.ipynb		find_safer_stocks_for_bear_market.ipynb
find_value_stocks.ipynb		find_value_stocks.ipynb
lstm.ipynb		lstm.ipynb
monte_carlo_simulation.ipynb		monte_carlo_simulation.ipynb
options_pricing.ipynb		options_pricing.ipynb
portfolio_rebalance.ipynb		portfolio_rebalance.ipynb