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A tool for stock price visulization and forecasting

Project description

Python Package using Conda

StockTool

A Tool for visualize and forecast the stock price, and give user investment strategy.

Overview

One investor might want to predict the trend for a specific stock, and to know which stock is better to invest in based on the past stock price. As stock market changed so rapidly, it might be hard to make the right investment strategy by analyzing the data manually. Thus, we built a tool for users to visualize, analyze and forecast stocks

Requirements

StockPrice requires a Python environment higher than Python 3.0.

Installation

  • Method 1: Install from the Pipit

    pip install stocktool

    If this does not work, run the follow command first

    pip install datetime pandas_datareader plotly sktime tbats requests_cache pandas_market_calendars

  • Method 2: Install from the GitHub repo

    pip install git+https://github.com/Ko2259/StockTool.git

  • Method 3: Clone this repository and set up a virtual environment(suggested method due to the dependencies)

    1. Open the terminal
    2. Clone the repository using git clone git@github.com:Ko2259/StockTool.git
    3. Change the directory to StockPrice using cd StockPrice
    4. Set up a new virtual environment using condo env create -f environment.yml
    5. Activate the virtual environment using condo activate stockprice
    6. After finish analyzing, deactivate the virtual environment using condo deactivate

Usage

Repository Structure

.
├── LICENSE
├── README.md
├── stockprice
│   ├── evaluation
│   ├── ml
│   ├── visualization
│   └── tests
├── docs
│   ├── Functional_Specification.md
│   ├── Technology Review.key
│   ├── milestone.mdg
└── example

The stockprice directory includes visualization module for visualize stocks, ml module for forecast future stock price, evaluation module for evaluate invest profit, and unit tests in test module. The example directory provides an example which help new users learn how to use this tool.

Data access

Visualization

Forecasting

Evaluation

Contributions

Acknowledgements

Project details


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