Here’s an example calculating TSI (True Strength Index). pandas_ta does this by adding an extension to the pandas data frame. When you import pandas_ta, it lets you add new indicators in a nice object-oriented fashion.

Let’s get started with pandas_ta by installing it with pip: The pandas_ta libraryĪn alternative to ta is the pandas_ta library. To learn more about ta check out its documentation here. Similarly, we could use the trend module to calculate MACD.ĭata = macd(data.adjclose, window_slow = 26, window_fast = 12) Rsi_21 = RSIIndicator(close = data.adjclose, window = 21) For example, if you want to calculate the 21-day RSI, rather than the default 14-day calculation, you can use the momentum module. These modules allow you to get more nuanced variations of the indicators. Ta also has several modules that can calculate individual indicators rather than pulling them all in at once. This single call automatically adds in over 80 technical indicators, including RSI, stochastics, moving averages, MACD, ADX, and more.ĭata, open='open', high='high', low='low', close='adjclose', volume='volume')įor example, here’s the RSI values (using the standard 14-day calculation): Below, we just need to specify what fields correspond to the open, high, low, close, and volume. Next, let’s use ta to add in a collection of technical features. Now, data contains the historical prices for AAPL. We’ll be using yahoo_fin to pull in stock price data. To get started, install the ta library using pip: One of the nicest features of the ta package is that it allows you to add dozen of technical indicators all at once. We’re going to compare three libraries – ta, pandas_ta, and bta-lib. Python has several libraries for performing technical analysis of investments. In this post, we will introduce how to do technical analysis with Python.

Pip install pandas