Sensei AI — Institutional-Grade Stock Analysis Platform

Sensei AI — Institutional-Grade Stock Analysis Platform I built Sensei AI, a real-time trading intelligence platform covering all 50 Nifty stocks, designed to bring institutional-level AI analysis to retail investors. The system unifies five distinct AI model families into a single scored decision engine (−5.0 to +5.0): - Classical ML — Random Forest with SHAP-based feature attribution for explainable UP/DOWN probability - Deep Learning — Custom LSTM and causal Temporal CNN (PyTorch) for 5-day return forecasting - Reinforcement Learning — PPO agent (Stable-Baselines3) trained via a custom Gymnasium trading environment for BUY/SELL/HOLD actions - Regime Detection — Gaussian Hidden Markov Model to classify BULL/BEAR market states - Financial NLP — FinBERT sentiment analysis on live Google News RSS feeds These signals feed a weighted voting decision engine that outputs a confidence-scored BUY/SELL/HOLD call, backed by a 5-method Support & Resistance engine, automated intraday/swing trade setup generation (Entry/SL/Target/R:R), and backtesting (Sharpe Ratio, Max Drawdown, Total Return). The platform ships as a full-stack Streamlit web app with a FastAPI backend, containerized with Docker, and pulls live market data via yfinance and nsepython. Tech stack: Python 3.13, PyTorch, Stable-Baselines3, Scikit-learn, SHAP, HuggingFace Transformers, hmmlearn, Streamlit, Docker