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

Enriching data points with annotations

The matplotlib.Axes.text(...) method adds a text box to our plots:

ax.text(1, 10000, 'Generated using numpy and matplotlib')
fig

The output is as follows:

Figure 5.8 – Plot displaying Matplotlib text annotations

Figure 5.8 – Plot displaying Matplotlib text annotations

The matplotlib.Axes.annotate(...) method provides more control over the annotations.

The code block that follows uses the following parameters to control the annotation:

  • The xy= parameter specifies the location of the data point.
  • The xytext= parameter specifies the location of the text box.
  • The arrowprops= parameter accepts a dictionary specifying parameters to control the arrow from the text box to the data point.
  • The facecolor= parameter specifies the color and the shrink= parameter specifies the size of the arrow.
  • The horizontalalignment= and verticalalignment= parameters specify the orientation of the text box relative to the data point.

The code...