Book Image

Hands-On Financial Trading with Python

By : Jiri Pik, Sourav Ghosh
Book Image

Hands-On Financial Trading with Python

By: Jiri Pik, Sourav Ghosh

Overview of this book

Creating an effective system to automate your trading can help you achieve two of every trader’s key goals; saving time and making money. But to devise a system that will work for you, you need guidance to show you the ropes around building a system and monitoring its performance. This is where Hands-on Financial Trading with Python can give you the advantage. This practical Python book will introduce you to Python and tell you exactly why it’s the best platform for developing trading strategies. You’ll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. As you progress, you’ll pick up lots of skills like time series forecasting, covering pmdarima and Facebook Prophet. By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization to help you get —and stay—ahead of the markets.
Table of Contents (15 chapters)
1
Section 1: Introduction to Algorithmic Trading
3
Section 2: In-Depth Look at Python Libraries for the Analysis of Financial Datasets
9
Section 3: Algorithmic Trading in Python

Learning mean-reversion strategies

Mean-reversion strategies are based on the assumption that some statistics will revert to their long-term mean values.

Bollinger band strategy

The Bollinger band strategy is based on identifying periods of short-term volatility.

It depends on three lines:

  • The middle band line is the simple moving average, usually 20-50 days.
  • The upper band is the 2 standard deviations above the middle base line.
  • The lower band is the 2 standard deviations below the middle base line.

One way of creating trading signals from Bollinger bands is to define the overbought and oversold market state:

  • The market is overbought when the price of the financial asset rises above the upper band and so is due for a pullback.
  • The market is oversold when the price of the financial asset drops below the lower band and so is due to bounce back.

This is a mean-reversion strategy, meaning that long term, the price should remain within...