Ashish Lal@ashish1

TradingHive is an autonomous AI-powered trading agent designed to transform market data into disciplined, risk-aware trading decisions using Alpaca’s trading infrastructure. Instead of relying on a single static strategy, TradingHive combines market-state analysis, technical indicators, reinforcement learning, dynamic position sizing, options intelligence, and a centralized risk governor into one coordinated system. The system continuously processes market information, identifies market regimes, evaluates directional opportunities, selects suitable option contracts, calculates position size according to available capital and risk tolerance, and validates every order before execution. Its reinforcement-learning layer allows the system to learn from previous trading outcomes through state-action-reward experiences, while the Hive architecture enables multiple strategy-driven bots to be evaluated and evolved across generations. TradingHive is built in Rust with a modular architecture covering market data, strategy intelligence, reinforcement learning, risk management, execution, persistence, reconciliation, and observability. Alpaca provides the broker and market-data connectivity, allowing the system to operate against real market conditions while maintaining strict execution and risk controls. The goal is not simply to build another trading bot, but an adaptive trading intelligence system capable of learning, evaluating, and coordinating trading agents under explicit risk constraints.

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🌎 Online Hackathon | 💻 Dates: 28 August–4 September 2026 | 🏆 $6,000 Prize Pool Build AI trading agents on Alpaca — autonomous agents and trading apps using Alpaca's Trading API, MCP server and CLI. 🌍 Fully online hackathon. Join and build from anywhere in the world.