https://github.com/vvipjain/stock-trend-analysis
Stock Trend Analysis
https://github.com/vvipjain/stock-trend-analysis
matplotlib numpy numpy-arrays numpy-library pandas pandas-dataframe pandas-library pandas-python pyhton3 python seaborn
Last synced: 3 months ago
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Stock Trend Analysis
- Host: GitHub
- URL: https://github.com/vvipjain/stock-trend-analysis
- Owner: VVipJain
- Created: 2024-08-06T09:50:03.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-08-06T10:20:34.000Z (11 months ago)
- Last Synced: 2025-02-10T05:14:15.754Z (4 months ago)
- Topics: matplotlib, numpy, numpy-arrays, numpy-library, pandas, pandas-dataframe, pandas-library, pandas-python, pyhton3, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 321 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Stock-Trend-Analysis
This repository contains a comprehensive analysis of stock market trends using Python, Pandas, Numpy , Matplotlib. The project aims to provide insights into stock price movements, volume trends, and other key financial metrics through interactive visualizations.
INTRODUCTION ->
In this project, we analyze stock market data to uncover trends and patterns. By leveraging the powerful data manipulation capabilities of Pandas and the interactive visualization features of Plotly, we aim to provide valuable insights into stock price movements, trading volumes, and other financial metrics. This project is ideal for data analysts, financial analysts, and anyone interested in stock market analytics.
DATASET ->
The dataset used in this analysis contains historical stock price data, including open, high, low, close prices, and trading volumes. The data is typically stored in a CSV file named NFLX.csv.
DATA FIELDS ->
* Date: Date of the stock trading session
* Open: Opening price of the stock
* High: Highest price of the stock during the session
* Low: Lowest price of the stock during the session
* Close: Closing price of the stock
* Adj Close: Adjusted closing price of the stock
* Volume: Trading volume of the stock
ANALYSIS ->
The analysis is divided into several sections:
* Loading and Cleaning Data: Importing the dataset and performing initial cleaning operations such as handling missing values and converting data types.
* Data Exploration: Exploring the dataset to understand its structure, including summary statistics and unique values.
* Trend Analysis: Analyzing stock price trends over time.
* Volume Analysis: Analyzing trading volume trends over time.
* Moving Averages: Calculating and visualizing moving averages to identify long-term trends.
* Volatility Analysis: Analyzing stock price volatility.
VISUALISATIONS ->
We use matplotlib and seaborn libraries to create interactive visualizations. Some of the key visualizations are as follows:
