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by Yves Hilpisch
Python for Algorithmic Trading
1. Python and Algorithmic Trading
Python for Finance
Python vs. Pseudo-Code
NumPy and Vectorization
pandas and the DataFrame Class
Algorithmic Trading
Python for Algorithmic Trading
Focus and Prerequisites
Trading Strategies
Simple Moving Averages
Momentum
Mean-Reversion
Machine and Deep Learning
Conclusions
Further Resources
2. Python Infrastructure
Conda as a Package Manager
Installing Miniconda
Basic Operations with Conda
Conda as a Virtual Environment Manager
Using Docker Containers
Docker Images and Containers
Building a Ubuntu & Python Docker Image
Using Cloud Instances
RSA Public and Private Keys
Jupyter Notebook Configuration File
Installation Script for Python and Jupyter Lab
Script to Orchestrate the Droplet Set-up
Conclusions
Further Resources
3. Working with Financial Data
Reading Financial Data From Different Sources
The Data Set
Reading from a CSV File with Python
Reading from a CSV File with pandas
Exporting to Excel and JSON
Reading from Excel and JSON
Working with Open Data Sources
Eikon Data API
Retrieving Historical Structured Data
Retrieving Historical Unstructured Data
Storing Financial Data Efficiently
Storing DataFrame Objects
Using TsTables
Storing Data with SQLite3
Conclusions
Further Resources
Python Scripts
4. Mastering Vectorized Backtesting
Making Use of Vectorization
Vectorization with NumPy
Vectorization with pandas
Strategies based on Simple Moving Averages
Getting into the Basics
Generalizing the Approach
Strategies based on Momentum
Getting into the Basics
Generalizing the Approach
Strategies based on Mean-Reversion
Getting into the Basics
Generalizing the Approach
Data Snooping and Overfitting
Conclusions
Further Resources
Python Scripts
SMA Backtesting Class
Momentum Backtesting Class
Mean Reversion Backtesting Class
5. Predicting Market Movements with Machine Learning
Using Linear Regression for Market Movement Prediction
A Quick Review of Linear Regression
The Basic Idea for Price Prediction
Predicting Index Levels
Predicting Future Returns
Predicting Future Market Direction
Vectorized Backtesting of Regression-based Strategy
Generalizing the Approach
Using Machine Learning for Market Movement Prediction
Linear Regression with scikit-learn
A Simple Classification Problem
Using Logistic Regression to Predict Market Direction
Generalizing the Approach
Using Deep Learning for Market Movement Prediction
The Simple Classification Problem Revisited
Using Deep Neural Networks to Predict Market Direction
Adding Different Types of Features
Conclusions
Further Resources
Python Scripts
Linear Regression Backtesting Class
Classification Algorithm Backtesting Class
6. Building Classes for Event-based Backtesting
Backtesting Base Class
Long Only Backtesting Class
Long Short Backtesting Class
Conclusions
Further Resources
Python Scripts
Backtesting Base Class
Long Only Backtesting Class
Long Short Backtesting Class
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