Efnisyfirlit

• 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

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