Python for Finance and Algorithmic Trading with QuantConnect
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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 | 20.5 hours on-demand video course
Welcome to the ultimate online course to go from zero to hero in Python for Finance, including Algorithmic Trading with LEAN Engine! This course will guide you through everything you need to know to use Python for Finance and conducting Algorithmic Trading on the QuantConnect platform with the powerful LEAN engine! This course is specifically design to connect core financial concepts to clear Python code. You will learn about in-demand real world skills that are highly sought after in the fintech ecosystem.
What you’ll 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
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