RakshaQuant
Unverified ML strategy on Multi by HimanshuMohanty-Git24. BotFinder score 18 out of 100.
🛡️ RakshaQuant - AI-powered paper trading for NSE using LangGraph multi-agent orchestration. Features 4 LLM agents, learning feedback loop, LangSmith observability & risk guardrail
Source: github
BotFinder analysis pending.
RakshaQuant
🛡️ RakshaQuant Agentic Paper Trading System for NSE Where Large Language Models Meet Financial Markets --- 🎯 About This Project RakshaQuant (रक्षा = Protection in Sanskrit) is an autonomous agentic trading system designed for the Indian NSE market. It leverages LangGraph to orchestrate a team of specialized AI agents that analyze market data, formulate strategies, validate signals, and manage risk in real-time. Unlike traditional algorithmic trading that relies solely on hardcoded logic, RakshaQuant introduces cognitive flexibility—using LLMs to reason about market regimes (bull/bear/ranging) and adapt its strategies accordingly. Key Capabilities - 🤖 Cognitive Agents: Multi-agent system that "thinks" before it trades - 🌐 Live Market Analysis: Real-time multi-stock monitoring via WebSocket - 🛡️ Dynamic Risk Management: Deterministic rules engine + a kill switch that gates execution - 📊 Professional Dashboard: Real-time CLI interface for monitoring agent thought processes - 📝 Self-Improving Memory: A closed learn-from-losses loop — classifies closed trades into lessons and tracks wheth
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