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import json |
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import traceback |
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from typing import Dict, Any, TYPE_CHECKING |
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from .schemas import TraceLog, ReflectorOutput |
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from .memory_store import MemoryStore |
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if TYPE_CHECKING: |
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from LLM import LLMService |
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class Reflector: |
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def __init__(self, llm_service: 'LLMService', memory_store: MemoryStore): |
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self.llm_service = llm_service |
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self.memory_store = memory_store |
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print("✅ Learning Hub: Reflector (Hybrid Aware) loaded") |
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async def analyze_trade_outcome(self, trade_object: Dict[str, Any], close_reason: str): |
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try: |
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decision_data = trade_object.get('decision_data', {}) |
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trace_log = TraceLog( |
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decision_context=decision_data, |
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market_context_at_decision=decision_data.get('market_context_at_decision', {}), |
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indicators_at_decision=decision_data.get('indicators_at_decision', {}), |
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hybrid_weights_used=decision_data.get('hybrid_weights_at_entry', {}), |
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closed_trade_object=trade_object, |
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actual_outcome_reason=close_reason |
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) |
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prompt = self._create_reflector_prompt(trace_log, decision_data) |
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response_text = await self.llm_service._call_llm(prompt) |
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if not response_text: raise ValueError("LLM returned no response.") |
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reflector_json = self.llm_service._parse_llm_response_enhanced( |
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response_text, fallback_strategy="reflection", symbol=trade_object.get('symbol', 'N/A') |
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) |
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if not reflector_json: raise ValueError("Failed to parse LLM response") |
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reflector_output = ReflectorOutput(**reflector_json) |
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domain = self._determine_domain(trade_object.get('strategy', 'general'), reflector_output.error_mode) |
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await self.memory_store.save_new_delta(reflector_output, trade_object, domain) |
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print(f"✅ [Reflector] Analyzed {trade_object.get('symbol')}. New Delta created.") |
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except Exception as e: |
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print(f"❌ [Reflector] Analysis failed: {e}") |
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traceback.print_exc() |
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def _determine_domain(self, strategy: str, error_mode: str) -> str: |
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em = error_mode.lower() |
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if "pattern" in em: return "pattern" |
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if "indicator" in em or "rsi" in em: return "indicator" |
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if "monte_carlo" in em: return "monte_carlo" |
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if "titan" in em or "model" in em: return "strategy" |
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if "news" in em: return "general" |
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return "strategy" |
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def _create_reflector_prompt(self, trace_log: TraceLog, decision_data: Dict) -> str: |
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trade = trace_log.closed_trade_object |
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pnl = trade.get('pnl_percent', 0) |
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is_success = pnl > 0.1 |
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comps = decision_data.get('components', {}) |
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hybrid_context = f""" |
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* Hybrid Score Breakdown (Total: {trade.get('score', 'N/A'):.4f}): |
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- Titan Model Score: {comps.get('titan_score', 'N/A'):.2f} |
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- Chart Patterns Score: {comps.get('patterns_score', 'N/A'):.2f} |
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- Monte Carlo Score: {comps.get('mc_score', 'N/A'):.2f} |
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* Weights Used at Entry: {json.dumps(trace_log.hybrid_weights_used)} |
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""" |
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news_context = f""" |
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* News VADER Score: {decision_data.get('news_score', 0.0):.4f} |
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* News Text: {decision_data.get('news_text', 'N/A')} |
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""" |
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return f""" |
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SYSTEM: You are an expert trading analyst Reflector. Analyze this "Trace" of a hybrid AI trading decision. |
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Did the primary model (Titan) mislead us, or did the secondary models (Patterns/MC) cause a false positive? |
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--- TRACE LOG START --- |
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1. DECISION CONTEXT: |
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* Strategy: {trade.get('strategy', 'N/A')} |
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{hybrid_context} |
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* Entry Reasoning: {decision_data.get('reasoning', 'N/A')[:300]}... |
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2. NEWS CONTEXT: |
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{news_context} |
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3. OUTCOME: |
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* Final PnL: {pnl:+.2f}% |
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* Close Reason: {trace_log.actual_outcome_reason} |
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--- TRACE LOG END --- |
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TASK: Identify the root cause of success/failure. Focus on which hybrid component was most accurate/inaccurate. |
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SUGGEST RULE (Delta): Max 25 words. E.g., "If Titan > 0.95 but Patterns < 0.60, reduce position size." |
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OUTPUT JSON: |
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{{ |
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"success": {str(is_success).lower()}, |
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"score": 0.0, |
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"error_mode": "Short description (e.g., 'titan_false_positive_ignored_patterns')", |
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"suggested_rule": "Concise 25-word rule.", |
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"confidence": 0.0 |
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}} |
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""" |
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return prompt |
