https://github.com/parth1899/financial-sentiment-analysis-and-stock-forecasting-platform
Built for INDOvateAI Sprint 2025—where we secured second prize 🏆—this project empowers investors to make data-driven decisions in real-time.
https://github.com/parth1899/financial-sentiment-analysis-and-stock-forecasting-platform
finbert flask lstm-neural-networks react tensorflow
Last synced: over 1 year ago
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Built for INDOvateAI Sprint 2025—where we secured second prize 🏆—this project empowers investors to make data-driven decisions in real-time.
- Host: GitHub
- URL: https://github.com/parth1899/financial-sentiment-analysis-and-stock-forecasting-platform
- Owner: parth1899
- Created: 2025-03-01T06:08:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-08T11:34:25.000Z (over 1 year ago)
- Last Synced: 2025-03-08T12:26:01.503Z (over 1 year ago)
- Topics: finbert, flask, lstm-neural-networks, react, tensorflow
- Language: Python
- Homepage:
- Size: 135 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# 📈 ArthaYukti – A Deep Learning-Driven Financial Sentiment Analysis & Stock Forecasting Platform
Built for INDOvateAI Sprint 2025 | Secured Second Prize 🏆
This project integrates FinBERT-based sentiment analysis with an LSTM-based stock price prediction model to provide a comprehensive market analysis. It dynamically assigns weightage to sentiment and price forecasts to improve investment decision-making.
---
## 🚨 Problem Statement
📉 **Investors face an overwhelming volume of real-time data, leading to delayed decisions and missed opportunities.**
❌ **Extracting accurate sentiment from unstructured sources is complex and error-prone, posing high-stakes risks.**
---
## 🛠️ Solution Approach
✅ **Custom Nifty50 database (2014–2025, 129,377 rows)** → Cleaned & preprocessed for time-series forecasting.
✅ **FinBERT-based Sentiment Extraction** → Trained on **1.4M financial headlines** to extract **bullish, bearish, or neutral** sentiment.
✅ **LSTM-based Time-Series Prediction** → Forecasts stock price trends based on historical market data.
✅ **User-Friendly Dashboard** → Displays **prediction charts, source citations, analytics, and investment recommendations**.
---
## 🏗️ System Architecture
1️⃣ **Data Acquisition & Reliability**
- Customizable ETL from `yfinance` for accurate real-time data.
- Fully documented & version-controlled codebase on GitHub.
2️⃣ **NLP & Sentiment Analysis**
- **Structured LLMs** for multi-language financial news processing.
- Sentiment classification (**bearish, bullish, neutral**) via **FinBERT**.
3️⃣ **Forecasting & Dynamic Analysis**
- LSTM-based stock price forecasting.
- Weighted analysis combining sentiment & confidence scores using a custom formula.
- Provides **actionable insights** for investors.
4️⃣ **Real-Time Processing & Scalability**
- Low latency real-time input processing.
- Extensive **Flask endpoints** for API-driven predictions.
- Cache-based state management for multi-user support.
5️⃣ **Visualization & User Empowerment**
- Multiple interactive **graph view options**.
- **Source verification** for user confidence & validation.
---
## 🎥 Demo
https://github.com/user-attachments/assets/4c9511a8-e48e-4f45-85fd-0fcb628f0f8a
---
## 🚀 Features
✅ **LSTM & FinBERT Integration** – Combines deep learning & NLP for robust stock forecasting.
✅ **Sentiment Analysis from Financial News** – Extracts real-time news sentiment.
✅ **Custom Dynamic Weight Assignment** – Adjusts importance of sentiment vs. prediction confidence.
✅ **Real-Time Market Predictions** – Generates **buy/hold/sell** signals.
✅ **Market Analysis Dashboard** – Displays **real-time sentiment, trend predictions, and historical analysis**.
✅ **Multi-Market Adaptability** – Can be extended to **crypto, forex, and commodities**.
✅ **Research & Analytics Tool** – Useful for **financial researchers & institutions**.
---
## ⚙️ Installation & Setup
### 1️⃣ Clone the Repository
```bash
git clone https://github.com/parth1899/IndovateAI.git
```
### 2️⃣ Backend Setup
```bash
cd Backend
# Create and activate a virtual environment (Recommended)
python -m venv venv
source venv/bin/activate # On Windows, use: venv\Scripts\activate
# Install required dependencies
pip install -r requirements.txt
# Running the server
python ./app.py
```
### 3️⃣ Frontend Setup
```bash
cd ../Frontend
# Install dependencies
npm install
# Start the development server
npm run dev
```