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streamlit-fyp's Introduction

License: MIT Streamlit App

  • Final Year Project - BSc Computer Science @ City University of Hong Kong, 2022
  • Liaison with Industry:
    • Finance / Fintech Industry
  • End Users:
    • Users who are interested in Trading. Traders with high risk tolerance

Data Collection, Data Pre-Processing, Exploratory Data Analysis, Sentiment Analysis, Topic Modeling Procedures

Getting Started

There are two ways to run this web application

  1. You can navigate to https://share.streamlit.io/icasso/streamlit-fyp/main.py , perferred on Chrome browser
  2. You can clone this Project and run with Streamlit Python

2.1 Install required libaries

pip install -r requirements.txt

2.2 Run it

streamlit run main.py

Skill Sets but not limited to:

  • Language: Python 3.10, Python 3.9
  • Front-End: Streamlit (Python)
  • Back-End: Streamlit (Python)
  • Database: PostgreSQL

Python Libraries included but not limited to

  • streamlit
  • numpy
  • pandas
  • plotly
  • matplotlib
  • psycopg2

Libraries included but not limited to (Juypter Notebook Related)

  • pmaw
  • sqlalchemy
  • sklearn
  • nltk
  • textblob
  • wordcloud

Cloud Technologies

  • Microsoft Azure : Azure Database for PostgreSQL flexible server
  • Streamlit Cloud

MISC / API

Stock Data / Time Series / End of Date / Real Time Data

  • Twelve Data API
  • Yahoo Finance

Citation & Worth Mentions

Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.

Research Source

https://ojs.aaai.org/index.php/ICWSM/article/view/14550

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