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Hands-On Financial Trading with Python

You're reading from Hands-On Financial Trading with Python

Product type Book

Published in Apr 2021

Publisher Packt

ISBN-13 9781838982881

Pages 360 pages

Edition 1st Edition

Languages

Concepts

Authors (2):

Jiri Pik

Sourav Ghosh

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1: Introduction to Algorithmic Trading

2. Chapter 1: Introduction to Algorithmic Trading

3. Section 2: In-Depth Look at Python Libraries for the Analysis of Financial Datasets

4. Chapter 2: Exploratory Data Analysis in Python

5. Chapter 3: High-Speed Scientific Computing Using NumPy

6. Chapter 4: Data Manipulation and Analysis with pandas

7. Chapter 5: Data Visualization Using Matplotlib

8. Chapter 6: Statistical Estimation, Inference, and Prediction

9. Section 3: Algorithmic Trading in Python

10. Chapter 7: Financial Market Data Access in Python

11. Chapter 8: Introduction to Zipline and PyFolio

12. Chapter 9: Fundamental Algorithmic Trading Strategies

13. Other Books You May Enjoy

Appendix A: How to Setup a Python Environment

Introduction to scikit-learn regression and classification

scikit-learn is a Python supervised and unsupervised machine learning library built on top of the numpy and scipy libraries.

Let's demonstrate how to forecast price changes on a dataset with RidgeCV regression and classification using scikit-learn.

Generating the dataset

Let's start by generating the dataset for the following examples—a Pandas DataFrame containing daily data for 20 years with BookPressure, TradePressure, RelativeValue, and Microstructure fields to represent some synthetic trading signals built on this dataset (also known as features or predictors). The PriceChange field represents the daily change in prices that we are trying to predict (also known as response or target variable). For simplicity, we make the PriceChange field a linear function of our predictors with random weights and some random noise. The Price field represents the actual price of the instrument generated using the...

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Authors (2)

Jiri Pik

Jiri Pik is an artificial intelligence architect & strategist who works with major investment banks, hedge funds, and other players. He has architected and delivered breakthrough trading, portfolio, and risk management systems, as well as decision support systems, across numerous industries. Jiri's consulting firm, Jiri Pik—RocketEdge, provides its clients with certified expertise, judgment, and execution at the speed of light.

See other products by Jiri Pik

Sourav Ghosh

Sourav Ghosh has worked in several proprietary, high-frequency algorithmic trading firms over the last decade. He has built and deployed extremely low latency, high-throughput automated trading systems for trading exchanges around the world, across multiple asset classes. He specializes in statistical arbitrage market-making and pairs trading strategies with the most liquid global futures contracts. He is currently the vice president at an investment bank based in São Paulo, Brazil. He holds a master's in computer science from the University of Southern California. His areas of interest include computer architecture, FinTech, probability theory and stochastic processes, statistical learning and inference methods, and natural language processing.

See other products by Sourav Ghosh

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