StockBot AI Vision Pro (v3.0)

This is a simple, interactive command-line chatbot built with Python and the yfinance module. It allows users to fetch real-time stock data by entering the ticker symbol (e.g., AAPL, TSLA, MSFT). The bot retrieves and displays the current price, day’s high, and day’s low for the selected stock.

Ultimate Features

• Interactive 3D Interface: Features an immersive WebGL particle background powered by Three.js that responds to user mouse movement.
• Holographic Charting Engine: Integrates Chart.js mapped with dynamic bezier curves, gradient fills, and customized tooltips.
• Tailwind CSS Overhaul: Fully responsive, "Apple-tier" frosted glass dashboard (backdrop-blur-xl) with staggered animations.
• Advanced Technical Indicators Engine:
  • Simple Moving Averages (SMA 20 & 50 Crossovers)
  • Relative Strength Index (RSI 14)
  • MACD (Moving Average Convergence Divergence Histogram)
  • Bollinger Bands (Volatility Boundaries)
• Neural Verdict System: A proprietary AI scoring engine that automatically grades data points to generate actionable STRONG BUY / HOLD / STRONG SELL signals alongside risk assessments.
• Global Market Support: Handles both NSE/BSE (e.g., "RELIANCE.NS", "TATASTEEL.NS") and international markets (e.g., "TSLA", "AAPL").

Architecture Stack

The project has been scaled to a full MVT architectural pattern for maximum maintainability:

1. Frontend (Jinja2 + Tailwind + Three.js):
   • layout.html, index.html, dashboard.html. Handles WebGL rendering, CSS grids, and chart population.
2. Backend (Flask Server):
   • app.py: The robust routing and controller layer running asynchronously via Python.
3. Data Core (data_engine.py):
   • Extensive integration with the yfinance API for instant market history fetching and caching.
4. Math Engine (analysis_engine.py):
   • Calculates all statistical formulas via NumPy & Pandas.

Getting Started

Prerequisites

• Python 3.8+
• Internet connection (required for CDN styling and real-time market polling)

Installation

1. Clone the repository:

   git clone https://github.com/pratik1258m/Stockbot.git
   cd Stockbot

2. Install required backend dependencies:

   pip install -r requirements.txt

3. Execute the Application Server:

   python3 app.py

   Access the breathtaking dashboard at http://127.0.0.1:5001.

Evaluation Methodology

The internal "Verdict AI" formulates signals using a weighted scoring matrix:

• Trend Factor: Evaluates the relation of the Close price natively against the SMA-50 baseline.
• Momentum Factor: Assigns gravity based on RSI extremities (Oversold < 30 = Bullish; Overbought > 70 = Bearish).
• Convergence Factor: Interprets positive/negative histogram divergence emitted by the MACD.
• Volatility Factor: Monitors Bollinger Band expansion tests and standard deviation breakouts.

These raw scores are mapped to a human-readable professional diagnostic scale.

Academic Context

Designed explicitly to fulfill requirements for a Computer Science Final Year thesis/project. Code structure is strictly self-documented, highly modular, and production-ready.