Python for Finance

Apply Python to financial analysis and systematic trading.

3 daysIntermediate6 modules

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Overview

About this course

Apply Python to financial analysis and systematic trading. You will structure market data with NumPy and pandas, chart trends with Matplotlib, model portfolios with Modern Portfolio Theory and the capital asset pricing model (CAPM), measure risk-adjusted returns with Sharpe and Sortino ratios, then build, backtest and evaluate algorithmic strategies for shares, futures and options on QuantConnect.

Curriculum

6 modules

Overview of Python’s role in finance and algorithmic trading.
Introduction to QuantConnect and the Lean engine.

NumPy - Formatting and structuring financial data.
pandas - Data manipulation and time-series analysis.
Matplotlib - Data visualization for financial trends.

Modern Portfolio Theory (MPT) - Diversification and efficient frontier modeling.
Capital Asset Pricing Model (CAPM) - Valuing securities based on risk and return.
Sharpe Ratio & Sortino Ratio - Measuring risk-adjusted returns.
Effective Market Hypothesis (EMH) - Understanding market efficiency in trading.

How to use QuantConnect for strategy development and execution.
Buying and selling shares using automated trading strategies.
Implementing custom algorithms for systematic trading.
Applying Bollinger Bands and other classic technical analysis techniques.

Using algorithmic trading to trade derivative futures contracts.
Understanding risk management techniques for leveraged trading.

How to read and interpret backtest results.
Understanding Probabilistic Sharpe Ratios for performance analysis.
Conducting full universe stock selection screening in QuantConnect.

Who it's for

Finance professionals, analysts and Python programmers interested in quantitative finance.

Before you start

Introduction to Python

3 days

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