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Autonomous AI-driven trading agent for options and equities using market regime detection, Google Gemini strategy generation, and deterministic risk gating with Alpaca integration
Server has 7 tools with basic descriptions and input schemas, but significant gaps in parameter documentation, output schema clarity, and error handling guidance. Tool names are action-oriented (detect_, get_, evaluate_, generate_, execute_, monitor_, run_) which is good. However, most parameter descriptions are minimal or missing entirely. Output schemas are not documented, tools return JSON strings without specifying field structure. Error handling is generic (catch-all exceptions returning JSON errors) with no recovery guidance. No tool annotations (readOnlyHint/destructiveHint) despite clear risk distinctions. Schemas present but incomplete: many parameters lack descriptions, and complex JSON string parameters (trade_intent_json) lack inline documentation of expected structure.
Fetch market data and classify the current market regime (BULLISH_TRENDING, BEARISH_TRENDING, HIGH_VOLATILITY, LOW_VOLATILITY, SIDEWAYS_CONSOLIDATION) along with technical indicators (SMA 20/50, RSI 14, ATR 14, Realized Volatility).
Run 100% deterministic mathematical Risk Gate verification on an AI TradeIntent proposal. Enforces max 5% position allocation, buying power limits, stop-loss sanity, and defined-risk options.
Execute a cleared TradeIntent order on Alpaca (Paper/Live API) with OCC Option symbol formatting.
Analyze market regime for a ticker and prompt Google Gemini to formulate a validated TradeIntent proposal adhering to strict regime strategy mapping.
Retrieve Alpaca live account equity, buying power, cash, and active position metrics.
JSON string parameters lack inline schema documentation. trade_intent_json is passed as a string but expected structure (fields, required keys, constraints) is never documented in the parameter description. LLMs cannot infer valid JSON structure from a bare 'JSON string representation' description.
Output schemas are not documented. Tools return json.dumps() of various structures (analysis.summary_dict, intent.model_dump(), receipt, results) but the response field names, types, and structure are never specified. LLMs cannot plan downstream calls or extract data reliably.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 64 | 2026-07-28+ | v2 |
Scan all open positions in Alpaca, calculate unrealized P&L %, and execute auto-liquidation for positions hitting +50% Profit Target or -40% Stop Loss.
Execute a complete autonomous scan cycle across the specified watchlist symbols.
No tool annotations (readOnlyHint/destructiveHint/idempotentHint) despite clear risk profile. execute_alpaca_order and monitor_and_liquidate_positions are destructive (WRITE risk) but lack destructiveHint annotation. detect_market_regime and get_account_risk_summary are read-only but lack readOnlyHint. Agents cannot distinguish safe from risky tools without explicit hints.
Error handling provides no recovery guidance. All exceptions return generic JSON errors like {'error': str(e)} with no actionable next steps. Pattern: 'Failed to detect regime for SPY: ...' tells the agent nothing about whether to retry, call a different tool, or ask the user.
Parameter descriptions are minimal or absent. watchlist_csv, max_positions, dry_run in run_autonomous_pipeline have descriptions, but many parameters across tools lack detail on format, constraints, or valid ranges. E.g., symbol parameter has no note on case sensitivity, valid ticker format, or whether it accepts aliases.