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fintech

https://github.com/mihirpatel6996/fintech

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
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    Academic email domains
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (11.3%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: Mihirpatel6996
  • Language: Python
  • Default Branch: main
  • Size: 15.9 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 10 months ago · Last pushed 10 months ago
Metadata Files
Readme Citation

README.md

Stock Market Prediction Web App

A FastAPI-based web application that predicts stock market prices using machine learning algorithms (ARIMA, LSTM, and Linear Regression).

Features

  • Real-time stock data fetching
  • Price predictions using multiple algorithms
  • 7-day price forecasting
  • Simple and intuitive interface
  • Sentiment analysis of financial news

Installation

  1. Clone the repository: bash git clone https://github.com/Mihirpatel6996/FinTech.git cd FinTech

  2. Create a virtual environment: bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

  3. Install dependencies: bash pip install -r requirements.txt

  4. Set up environment variables (if needed): ```bash

    For sentiment analysis

    export NEWSAPIKEY="yourapikey_here"

    On Windows: set NEWSAPIKEY=yourapikey_here

    ```

  5. Run the application: bash uvicorn main:app --reload

  6. Open http://localhost:8000 in your browser

Usage

  1. Enter a valid stock symbol (e.g., AAPL, GOOGL)
  2. Click "Predict" to see the forecast
  3. View current stock data and predictions

Built With

  • FastAPI
  • Python
  • scikit-learn
  • TensorFlow/Keras
  • yfinance
  • Bootstrap

License

MIT License - see LICENSE file for details

Environment Setup

  1. Copy the example environment file: bash cp .env.example .env

  2. Edit the .env file and add your API keys: ```

    Google Gemini API Key

    GEMINIAPIKEY=yourgeminiapi_key

NewsAPI Key

NEWSAPIKEY=yournewsapi_key ```

These API keys are required for: - Google Gemini API: Powering the AI chatbot - NewsAPI: Fetching financial news for sentiment analysis

Owner

  • Login: Mihirpatel6996
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Jadhav"
  given-names: "Kaushik"
orcid: "https://orcid.org/0000-0000-0000-0000"
title: "Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis"
version: 2.0.0
doi: 10.5281/zenodo.10498988
date-released: 2023-03-29
url: "https://github.com/kaushikjadhav01/Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis"

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Dependencies

requirements.txt pypi
  • alpha_vantage >=2.3.1
  • fastapi >=0.103.2
  • google-generativeai >=0.3.0
  • jinja2 >=3.1.2
  • keras *
  • matplotlib *
  • newsapi-python ==0.2.7
  • numpy >=1.26.2
  • pandas *
  • plotly >=5.10.0
  • pydantic >=2.7.4
  • python-dotenv >=1.0.0
  • python-multipart >=0.0.6
  • requests >=2.26.0
  • scikit_learn *
  • scipy >=1.10.0
  • starlette >=0.27.0
  • statsmodels *
  • ta >=0.10.1
  • tensorflow *
  • textblob >=0.15.3
  • uvicorn >=0.23.2
  • vaderSentiment >=3.3.2
  • yfinance *
sentiment_analysis/requirements.txt pypi
  • newsapi-python ==0.2.7
  • requests >=2.26.0
  • textblob >=0.15.3
  • vaderSentiment >=3.3.2