Smarter Agents
Not "ask GPT to trade." Multiple agents already deployed and trading. Three distinct approaches running in production.
Multi-Agent Debate + Skills
5 subagents analyze every trade in parallel: technical, sentiment, market structure analysts + bull/bear researchers. 6 skills on demand: trading session, deep analysis, portfolio review, strategy update, morning brief, alert review.
Deterministic Quant Trader
Python agent with 3-stage mathematical brain: market health filter, asset rotation selector, position sizing engine. Pure math for stops and sizing - no hallucination in the core layer. 4 concurrent loops.
Backtesting Arena
Multiple strategies evaluated via backtesting framework. Best-performing agent selected for live trading. Market simulator with MCP stubs for offline evaluation.
Python Code Sandbox
Isolated Docker container per agent. Downloads market data, writes and runs custom analysis scripts. Persistent state across sessions. The agent codes its own trading tools.
Self-Improving
Persistent memory across sessions. Detailed session reports with trade reasoning. Self-reflection after losses - analyzes what went wrong, creates permanent rules. Strategy evolves from exploration to fine-tuned edge.
Harness Runtime
Event-driven agent harness (think OpenClaw, but for trading and 100% isolated). Orchestrates Claude Code CLI via stream-json protocol. Session lifecycle, alert injection, Telegram streaming, error recovery. Works with API keys or Claude subscription for flat-cost operation.
Operator in the loop.
Agent in control.
Agent streams output in real-time to Telegram. Operator can message mid-session - ask questions, request analysis, guide decisions. The agent acknowledges and incorporates.
- Live output streaming as the agent thinks and acts
- Two-way communication - ask agent to check an idea or trade
- Risk engine alerts: halts, circuit breakers, blocked actions
- Agent alerts: price triggers, news events, session summaries
