Python for Algorithmic Trading

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