Python for Algorithmic Trading: From Idea to Cloud Deployment

Cover
Copyright
Table of Contents
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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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 A. 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
About the Author
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