Converting currencies

Another quite common preprocessing step you might encounter while working on financial tasks is converting currencies. Imagine you have a portfolio of multiple assets, priced in different currencies and you would like to arrive at a total portfolio worth. The simplest example might be American and European stocks.

In this recipe, we show how to easily convert stock prices from USD to EUR. However, the very same steps can be used to convert any pair of currencies.

How to do it…

Execute the following steps to convert stock prices from USD to EUR.

1. Import the libraries:

import pandas as pd
import yfinance as yf
from forex_python.converter import CurrencyRates

1. Download Apple's OHLC prices from January 2020:

df = yf.download("AAPL",
                 start="2020-01-01",
                 end="2020-01-31",
                 progress=False)
df = df.drop(columns=["Adj Close", "Volume"])

1. Instantiate the CurrencyRates...

Different ways of aggregating trade data

Before diving into building a machine learning model or designing a trading strategy, we not only need reliable data, but we also need to aggregate it into a format that is convenient for further analysis and appropriate for the models we choose. The term bars refers to a data representation that contains basic information about the price movements of any financial asset. We have already seen one form of bars in Chapter 1, Acquiring Financial Data, in which we have explored how to download financial data from a variety of sources. There, we downloaded OHLCV data sampled by some time period, be it a month, day, or intraday frequencies. This is the most common way of aggregating financial time series data and is known as the time bars.

There are some drawbacks of sampling financial time series by time:

• Time bars disguise the actual rate of activity in the market - they tend to oversample low activity periods (e.g. noon) and undersample high activity...

Summary

In this chapter, we have learned how to preprocess financial time series data. We started by showing how to calculate returns and potentially adjust them for inflation. Then, we covered a few of the popular methods for imputing missing values. Lastly, we explained what are the different approaches to aggregating trade data and why choosing the correct one matters.

We should always pay significant attention to this step, as we not only want to enhance our model's performance but also to ensure the validity of any analysis. In the next chapter, we will continue working with the preprocessed data and learn how to create time series visualization.

Creating interactive visualizations

In the first recipe, we gave a short preview of creating interactive visualizations in Python. In this recipe, we will show how to create interactive line plots using three different libraries: cufflinks, plotly, and bokeh. Naturally, these are not the only available libraries for interactive visualizations. Another popular one you might want to investigate further is altair.

The plotly library is built on top of d3.js (a JavaScript library used for creating interactive visualizations in web browsers) and is known for creating high-quality plots with a significant degree of interactivity (inspecting values of observations, viewing tooltips of a given point, zooming in, and so on). Plotly is also the company responsible for developing this library and it provides hosting for our visualizations. We can create an infinite number of offline visualizations and a few free ones to share online (with a limited number of views per day).

cufflinks...

Creating a candlestick chart

A candlestick chart is a type of financial graph, used to describe a given security’s price movements. A single candlestick (typically corresponding to one day, but a different frequency is possible) combines the open, high, low, and close (OHLC) prices.

The elements of a bullish candlestick (where the close price in a given time period is higher than the open price) are presented in Figure 3.15:

Figure 3.15: Diagram of a bullish candlestick

For a bearish candlestick, we should swap the positions of the open and close prices. Typically, we would also change the candle’s color to red.

In comparison to the plots introduced in the previous recipes, candlestick charts convey much more information than a simple line plot of the adjusted close price. That is why they are often used in real trading platforms, and traders use them for identifying patterns and making trading decisions.

In this recipe, we also add moving average...

Summary

In this chapter, we have covered various ways of visualizing financial (and not only) time series. Plotting the data is very helpful in getting familiar with the analyzed time series. We can identify some patterns (for example, trends or changepoints) that we might subsequently want to confirm with statistical tests. Visualizing data can also help to spot some outliers (extreme values) in our series. This brings us to the topic of the next chapter, that is, automatic pattern identification and outlier detection.