Setting Up Your Technical Analysis Toolbox

Yeo Yong Kiat

By Yeo Yong Kiat in Finanalytics

5 min read
Real market data. That's what you need before you run off on your horses to learn about technical analysis. Constructing models and verifying what you've learnt against real market data makes for a realistic learning experience. I generally use Python for making API calls - while you are free to use any scripting language, I strongly recommend setting up a framework to make API calls so that you have access to live and updated datasets on a wide range of financial assets. And for the aspiring beginner? I recommend Yahoo Finance.

Installation

For beginners, Yahoo Finance suffices as a data playground, which provides sufficiently long historical prices of both traditional assets (e.g. equities) and cryptocurrencies. For the beginner who doesn't wish to invest too much upfront, it's free, and no registration is required. Most importantly, there are ready Python libraries out there that allow for data calls via Yahoo Finance's APIs.

We'll be working with three python libraries: "pandas", "yfinance" and "plotly". If you haven't installed them yet, go to your terminal and install them via the following pip commands:

pip install plotly yfinance pandas

Once installed, you'll be able to load the three libraries in the header portion of your Python code: 

## Loading Libraries
import pandas as pd 
import yfinance as yf from yahoofinancials 
import YahooFinancials

Accessing Price Data

With the libraries loaded, you now have access to the stock and crypto prices available on Yahoo Finance. For example, suppose you wanted the stock prices of Tesla Inc, which has the ticker TSLA: 

  ## Create a TSLA ticker object
  tsla_ticker=yf.Ticker('TSLA')
  
  ## Download TSLA historical prices into a df
  tsla_df=tsla_ticker.history(period="ytd")
  
  ## Displaying first 5 rows of df, prices to 2 dp
  tsla_df.head().round(2)
  
  Output:
                 Open     High      Low    Close  Adj Close    Volume
  Date                                                               
  2022-01-03  1147.75  1201.07  1136.04  1199.78    1199.78  34643800
  2022-01-04  1189.55  1208.00  1123.05  1149.59    1149.59  33416100
  2022-01-05  1146.65  1170.34  1081.01  1088.12    1088.12  26706600
  2022-01-06  1077.00  1088.00  1020.50  1064.70    1064.70  30112200
  2022-01-07  1080.37  1080.93  1010.00  1026.96    1026.96  28054900

Creating a Ticker Object

Many prefer this way of accessing stock data, because it's more "Pythonic". Essentially, you create a ticker object with methods embedded within that you can call on to access various data beyond stock prices. Let's regenerate the same dataframe above using this method: 

## Create a TSLA ticker object
tsla_ticker=yf.Ticker('TSLA')

## Download TSLA historical prices into a df
tsla_df=tsla_ticker.history(period="ytd")

## Displaying first 5 rows of df, prices to 2 dp
tsla_df.head().round(2)

Output:
               Open     High      Low    Close  Adj Close    Volume
Date                                                               
2022-01-03  1147.75  1201.07  1136.04  1199.78    1199.78  34643800
2022-01-04  1189.55  1208.00  1123.05  1149.59    1149.59  33416100
2022-01-05  1146.65  1170.34  1081.01  1088.12    1088.12  26706600
2022-01-06  1077.00  1088.00  1020.50  1064.70    1064.70  30112200
2022-01-07  1080.37  1080.93  1010.00  1026.96    1026.96  28054900

With this ticker object, you can then obtain various useful company-level data, such as its corporate actions (e.g. dividends, splits), quarterly financials, and even its annual earnings. Let's try to generate one such dataset called "recommendations, which is a historical list of recommendations made by different analysts regarding the stock and whether to buy it or not: 

  ## Retrieve TSLA stock recommendations
  tsla_ticker.recommendations
  
  Output:
                                Firm        To Grade      From Grade Action
  Date                                                                     
  2012-02-16 07:42:00      JP Morgan      Overweight                   main
  2012-02-16 13:53:00     Wunderlich            Hold                   down
  2012-02-17 06:17:00     Oxen Group             Buy                   init
  2012-03-26 07:31:00     Wunderlich             Buy                     up
  2012-05-22 05:57:00    Maxim Group             Buy                   init
  ...                            ...             ...             ...    ...
  2022-06-01 12:08:20  Goldman Sachs             Buy                   main
  2022-06-03 11:22:15    Cowen & Co.  Market Perform                   main
  2022-06-09 08:58:31            UBS             Buy         Neutral     up
  2022-06-10 10:30:27       Barclays     Underweight                   main
  2022-06-13 09:12:55    RBC Capital      Outperform  Sector Perform     up

Issues with the yfinance Library

For such a beginner-friendly tool, the library has its drawbacks. For one thing, some of its methods are extremely fragile. For most of its methods, yfinance works by making API calls to Yahoo Finance to access data, but some methods work by unofficially scraping the Yaho Finance webpages to consolidate data. If Yahoo changes its web structure, these methods will fail.

This library lacks specialised features as well. For example, it doesn't allow access to any of the news reports or analyses that are available on Yahoo Finance. You would have to write your own web scraper to complete yfinance. You also have to generate all of your technical analysis indicators from scratch, which may not be optimum for web developers who want ready functions to code into their web interface or trading algorithm.

In future posts, we will delve into making actual API calls with Python. This will vastly increase the quality and repertoire of data you have access to. The novice may wish to consider adding API calls to his arsenal after mastering some of the basics of technical analysis.

Now, how shall we visualise these data?

Find more Finalytics stories on my blog. Have a suggestion? Contact me at [email protected].