Python for Finance and Algorithmic Trading with QuantConnect

Learn to use Python, Pandas, Matplotlib, and the QuantConnect Lean Engine to perform financial analysis and trading

Created by: Jose Portilla
Head of Data Science at Pierian Training

Created by: Pierian Training
Data Science and Machine Learning Training

What Will I Learn?

• Learn to use powerful Python libraries such as NumPy, Pandas, and Matplotlib
• Understand Modern Portfolio Theory
• Use Monte Carlo simulation techniques to optimize portfolio allocation
• Understand SciPy minimization algorithms to create optimized portfolio holdings
• Use and understand stock fundamentals data, such as CFC, Revenue, and EPS
• Calculate the Sharpe Ratio for any stock
• Understand cumulative returns and daily average returns in stocks
• Learn to use QuantConnect's LEAN engine for automated trading
• Learn about Bollinger Bands and other classic technical analysis
• Use algorithmic trading to trade derivative futures contracts
• Dive into understanding CAPM - Capital Asset Pricing Model
• Use fundamental stock company data to create rules based trading algorithms
• Learn about alternatives to the Sharpe Ratio, such as the Sortino Ratio
• Learn to read and understand a Backtest, including Probabilistic Sharpe Ratios
• Conduct Research on QuantConnect, including full universe stock selection screening

Requirements

• Basic Python Experience

Target audience

• Python developers interested in learning more about finance, markets, and algorithmic trading.