Live · NSE · BSE · NASDAQ

AI-powered
market intelligence

StockSense is a self-hosted trading intelligence platform that combines live market data, ML price forecasts, news sentiment analysis, and end-to-end portfolio management — built for retail investors who want a private, extensible alternative to premium terminals.

★ View on GitHub Read the Wiki
32+
ML features per symbol
3
Exchanges (NSE, BSE, NASDAQ)
6
RSS feeds polled
15m
Data refresh interval
30d
Price forecast horizon
Everything you need to track markets

Six core feature areas covering the full investment workflow, from data ingestion to tax reporting.

🧠

ML Price Forecasting

Prophet + Gradient Boosting ensemble trained on 32+ features. Confidence-tiered predictions with inverse-MAPE blending. Auto-retrains every 24 hours per symbol.

📰

News Sentiment Analysis

Polls 6 RSS feeds every 15 minutes. VADER sentiment scoring with finance-specific lexicon. 7-day and 30-day rolling averages feed directly into the prediction model.

💼

Portfolio Tracker

FIFO cost basis across all holdings. Realized and unrealized P&L breakdown. AI-powered advisor scores each position for buy/hold/sell signals.

Live Market Data

Real-time quotes and OHLCV history for NSE, BSE, and NASDAQ via yfinance. 15-minute cache with background polling during market hours (09:15–15:30 IST).

📊

Strategy Backtesting

Define signal conditions using RSI, MACD, or Bollinger Bands. Run backtests against historical data. Review equity curve, Sharpe ratio, and full trade log.

📋

Tax Reports

Capital gains classified as short-term or long-term under Indian tax rules. Automatic ₹1 lakh LTCG exemption. Tax-loss harvesting suggestions with savings calculated.

From raw data to actionable insight

Three stages, fully automated. You make the decision; StockSense handles the analysis.

1

Connect Market Data

Autonomous data-fetcher agent pulls live OHLCV quotes and news from 6 RSS feeds every 15 minutes during market hours. Everything is normalized and stored locally.

Continuous
2

AI Analyzes & Forecasts

A blended Prophet + GBM model assembles 32+ features per symbol — candlestick patterns, technical indicators, sentiment, macro data — and generates 30-day price forecasts.

Every 24 hours
3

You Decide

Review predictions with confidence tiers, screener results, portfolio P&L, strategy backtest outcomes, and AI-generated signals. The advisor explains its reasoning.

On demand
Built with
FastAPI SQLAlchemy 2.0 Python 3.11 yfinance Prophet scikit-learn ta-lib VADER Sentiment React 19 TailwindCSS 4 TanStack Query Recharts GCP Cloud Run Terraform Docker GitHub Actions
Running in under 5 minutes

Requires Python 3.11+, Node 20+, and ta-lib (brew install ta-lib on macOS).

1

Clone the repository

Pull the source code and enter the project directory.

2

Configure environment

Copy the example env file. The only required variable to get started is JWT_SECRET_KEY.

3

Start the application

The start.sh script boots both backend and frontend. Default login is admin / changeme.

# 1. Clone
git clone https://github.com/rupakc/stocksense.git
cd stocksense

# 2. Configure
cp .env.example .env
#    Set JWT_SECRET_KEY (required)

# 3. Run
./start.sh

# Backend API  → http://localhost:8000
# Frontend     → http://localhost:5173
# API docs     → http://localhost:8000/docs
# Login        → admin / changeme

Full installation guide including ta-lib setup and GCP deployment in the project wiki.

Ready to explore the markets?

Clone the repo, point it at your watchlist, and let the models do the heavy lifting.

★ Star on GitHub Full Documentation →
Where to go next

Everything you need to understand, extend, and deploy StockSense.