Python for Algorithmic Trading

Höfundur: Yves Hilpisch
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Python for Algorithmic Trading

Rafræn sending. Upplýsingar verða sendar á netfangið þitt eftir kaup
Rafbók til eignar. Rafbók til eignar þarf að hlaða niður á þau tæki sem þú vilt nota innan eins árs frá því bókin er keypt. Útgáfa: 1

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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