Stock Data Interpretation Using Python

Interpreting stock data is an essential part of investment analysis and decision-making. In recent years, the use of data analysis tools and programming languages has become popular in the finance industry. In this article, we will explore how to interpret Indian stock data using Python.

Python is a powerful programming language that has many libraries and tools for data analysis, visualization, and machine learning. We will use the following libraries to interpret Indian stock data:

Pandas: a library for data manipulation and analysis.

Numpy: a library for numerical computing.

Matplotlib: a library for data visualization.

Seaborn: a library for advanced data visualization.

Getting Stock Data:

Before we start analyzing stock data, we need to obtain it. We can use various methods to get stock data, such as using APIs provided by stock market websites or downloading CSV files. Here, we will use the Yahoo Finance API to obtain stock data.

The following code imports the required libraries and gets the stock data of Reliance Industries Limited (RELIANCE.NS) from January 1, 2016, to December 31, 2021:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

import yfinance as yf

ticker = "RELIANCE.NS"

start_date = "2016-01-01"

end_date = "2021-12-31"

data = yf.download(ticker, start_date, end_date)

Data Exploration:

Once we have obtained the stock data, we need to explore it to gain insights into the company's performance. We can start by examining the data's structure and summary statistics:

print(data.head())

print(data.tail())

print(data.describe())

The head() function returns the first five rows of the data, and the tail() function returns the last five rows. The describe() function returns the summary statistics of the data, such as the mean, standard deviation, minimum, maximum, and quartiles.

Data Visualization:

Visualization is a powerful tool for understanding complex data. We can use various visualization techniques to gain insights into the stock data. Here, we will use line plots to visualize the stock price over time and candlestick charts to visualize the stock price trends.

Line Plot:

The following code creates a line plot of Reliance Industries Limited's stock price over time:

plt.plot(data["Close"])

plt.title("Reliance Industries Limited Stock Price")

plt.xlabel("Date")

plt.ylabel("Price (INR)")

plt.show()

This code creates a line plot of the closing stock price, which is the price at which the stock was traded at the end of each day. The x-axis represents time, and the y-axis represents the stock price in Indian Rupees.

Candlestick Chart:

The following code creates a candlestick chart of Reliance Industries Limited's stock price:

import mplfinance as mpf

mpf.plot(data, type="candle", volume=True, show_nontrading=True)

This code uses the mplfinance library to create a candlestick chart. A candlestick chart is a chart that displays the opening, closing, high, and low prices of a stock over a certain period. The body of each candle represents the opening and closing prices, and the shadows represent the high and low prices. The volume bars at the bottom represent the trading volume.

Technical Analysis:

Technical analysis is a method of evaluating securities based on statistics generated by market activity, such as past prices and volume. We can use various technical indicators to gain insights into the stock's trend and momentum. Here, we will use the moving average and relative strength index (RSI) indicators.

Moving Average

A moving average is a widely used technical indicator that smooths out the price data by creating a constantly updated average price. It is used to identify trends and support and resistance levels. We can calculate the moving average using the rolling() function of Pandas.

The following code calculates the 20-day moving average of Reliance Industries Limited's stock price and plots it along with the actual stock price:

data["MA20"] = data["Close"].rolling(window=20).mean()

plt.plot(data["Close"], label="Actual Price")

plt.plot(data["MA20"], label="MA20")

plt.legend()

plt.title("Reliance Industries Limited Stock Price with Moving Average")

plt.xlabel("Date")

plt.ylabel("Price (INR)")

plt.show()

This code creates a plot of the actual stock price and the 20-day moving average. The moving average smooths out the price data and highlights the trend. When the stock price crosses the moving average, it is considered a buy or sell signal, depending on whether the stock is trading above or below the moving average.

RSI:

The relative strength index (RSI) is a momentum oscillator that measures the speed and change of price movements. It is used to identify overbought and oversold conditions. We can calculate the RSI using the ta() function of Pandas.

pip install pandas_ta

import pandas_ta as pta

The following code calculates the 14-day RSI of Reliance Industries Limited's stock price and plots it:

data["RSI"] = pta.rsi(data['Close'], length = 14)

plt.plot(data["RSI"])

plt.title("Reliance Industries Limited RSI")

plt.xlabel("Date")

plt.ylabel("RSI")

plt.show()

This code creates a plot of the 14-day RSI. The RSI ranges from 0 to 100 and is considered overbought when it is above 70 and oversold when it is below 30. When the RSI crosses these levels, it is considered a buy or sell signal, depending on the direction of the cross.

Conclusion:

In this article, we explored how to interpret Indian stock data using Python. We used the Pandas, Numpy, Matplotlib, and Seaborn libraries to obtain, explore, and visualize the stock data. We also used technical analysis techniques such as moving average and RSI to gain insights into the stock's trend and momentum. These techniques can be used to make informed investment decisions based on the stock's performance.