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• Preface
• Contents and Structure
• Who This Book Is For
• Conventions Used in This Book
• Using Code Examples
• O’Reilly Online Learning
• How to Contact Us
• Acknowledgments
• 1. Python and Algorithmic Trading
• Python for Finance
• Python Versus 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
• References and 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 and 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
• References and 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
• References and 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
• References and 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
• References and 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
• References and Further Resources
• Python Scripts
• Backtesting Base Class
• Long-Only Backtesting Class
• Long-Short Backtesting Class
• 7. Working with Real-Time Data and Sockets
• Running a Simple Tick Data Server
• Connecting a Simple Tick Data Client
• Signal Generation in Real Time
• Visualizing Streaming Data with Plotly
• The Basics
• Three Real-Time Streams
• Three Sub-Plots for Three Streams
• Streaming Data as Bars
• Conclusions
• References and Further Resources
• Python Scripts
• Sample Tick Data Server
• Tick Data Client
• Momentum Online Algorithm
• Sample Data Server for Bar Plot
• 8. CFD Trading with Oanda
• Setting Up an Account
• The Oanda API
• Retrieving Historical Data
• Looking Up Instruments Available for Trading
• Backtesting a Momentum Strategy on Minute Bars
• Factoring In Leverage and Margin
• Working with Streaming Data
• Placing Market Orders
• Implementing Trading Strategies in Real Time
• Retrieving Account Information
• Conclusions
• References and Further Resources
• Python Script
• 9. FX Trading with FXCM
• Getting Started
• Retrieving Data
• Retrieving Tick Data
• Retrieving Candles Data
• Working with the API
• Retrieving Historical Data
• Retrieving Streaming Data
• Placing Orders
• Account Information
• Conclusions
• References and Further Resources
• 10. Automating Trading Operations
• Capital Management
• Kelly Criterion in Binomial Setting
• Kelly Criterion for Stocks and Indices
• ML-Based Trading Strategy
• Vectorized Backtesting
• Optimal Leverage
• Risk Analysis
• Persisting the Model Object
• Online Algorithm
• Infrastructure and Deployment
• Logging and Monitoring
• Visual Step-by-Step Overview
• Configuring Oanda Account
• Setting Up the Hardware
• Setting Up the Python Environment
• Uploading the Code
• Running the Code
• Real-Time Monitoring
• Conclusions
• References and Further Resources
• Python Script
• Automated Trading Strategy
• Strategy Monitoring
• Appendix. Python, NumPy, matplotlib, pandas
• Python Basics
• Data Types
• Data Structures
• Control Structures
• Python Idioms
• NumPy
• Regular ndarray Object
• Vectorized Operations
• Boolean Operations
• ndarray Methods and NumPy Functions
• ndarray Creation
• Random Numbers
• matplotlib
• pandas
• DataFrame Class
• Numerical Operations
• Data Selection
• Boolean Operations
• Plotting with pandas
• Input-Output Operations
• Case Study
• Conclusions
• Further Resources
• Index