""" Analysis Memory System 2.0 Simplified memory for fast analysis service. Features: 1. Store analysis decisions with market context 2. Retrieve similar historical patterns 3. Track decision outcomes for learning """ import json import time import hashlib from typing import Dict, Any, List, Optional from datetime import datetime, timedelta from app.utils.logger import get_logger from app.utils.db import get_db_connection logger = get_logger(__name__) def _safe_json_parse(val, default=None): """Safely parse JSON - handle cases where it's already a Python object or string""" if val is None: return default if isinstance(val, (dict, list)): return val # Already a Python object (PostgreSQL JSONB automatically converted) if isinstance(val, str): try: return json.loads(val) except (json.JSONDecodeError, TypeError): return default return default class AnalysisMemory: """ Simple but effective memory system for AI analysis. Uses PostgreSQL for persistence. """ def __init__(self): self._ensure_table() def _ensure_table(self): """Create memory table if not exists, and add missing columns if needed.""" try: with get_db_connection() as db: cur = db.cursor() # Create table if it does not exist cur.execute(""" CREATE TABLE IF NOT EXISTS qd_analysis_memory ( id SERIAL PRIMARY KEY, user_id INT, market VARCHAR(50) NOT NULL, symbol VARCHAR(50) NOT NULL, decision VARCHAR(10) NOT NULL, confidence INT DEFAULT 50, price_at_analysis DECIMAL(24, 8), summary TEXT, reasons JSONB, scores JSONB, indicators_snapshot JSONB, raw_result JSONB, consensus_score DECIMAL(24, 8), consensus_abs DECIMAL(24, 8), agreement_ratio DECIMAL(10, 6), quality_multiplier DECIMAL(10, 6), task_status VARCHAR(20) DEFAULT 'completed', task_error TEXT, updated_at TIMESTAMP DEFAULT NOW(), created_at TIMESTAMP DEFAULT NOW(), validated_at TIMESTAMP, actual_outcome VARCHAR(20), actual_return_pct DECIMAL(10, 4), was_correct BOOLEAN, user_feedback VARCHAR(20), feedback_at TIMESTAMP ); """) # Check and add missing columns (for existing tables) cur.execute(""" DO $$ BEGIN -- Add the user_id column if it does not exist IF NOT EXISTS ( SELECT 1 FROM information_schema.columns WHERE table_name = 'qd_analysis_memory' AND column_name = 'user_id' ) THEN ALTER TABLE qd_analysis_memory ADD COLUMN user_id INT; END IF; -- Add the raw_result column if it does not exist IF NOT EXISTS ( SELECT 1 FROM information_schema.columns WHERE table_name = 'qd_analysis_memory' AND column_name = 'raw_result' ) THEN ALTER TABLE qd_analysis_memory ADD COLUMN raw_result JSONB; END IF; IF NOT EXISTS ( SELECT 1 FROM information_schema.columns WHERE table_name = 'qd_analysis_memory' AND column_name = 'consensus_score' ) THEN ALTER TABLE qd_analysis_memory ADD COLUMN consensus_score DECIMAL(24, 8); END IF; IF NOT EXISTS ( SELECT 1 FROM information_schema.columns WHERE table_name = 'qd_analysis_memory' AND column_name = 'consensus_abs' ) THEN ALTER TABLE qd_analysis_memory ADD COLUMN consensus_abs DECIMAL(24, 8); END IF; IF NOT EXISTS ( SELECT 1 FROM information_schema.columns WHERE table_name = 'qd_analysis_memory' AND column_name = 'agreement_ratio' ) THEN ALTER TABLE qd_analysis_memory ADD COLUMN agreement_ratio DECIMAL(10, 6); END IF; IF NOT EXISTS ( SELECT 1 FROM information_schema.columns WHERE table_name = 'qd_analysis_memory' AND column_name = 'quality_multiplier' ) THEN ALTER TABLE qd_analysis_memory ADD COLUMN quality_multiplier DECIMAL(10, 6); END IF; IF NOT EXISTS ( SELECT 1 FROM information_schema.columns WHERE table_name = 'qd_analysis_memory' AND column_name = 'task_status' ) THEN ALTER TABLE qd_analysis_memory ADD COLUMN task_status VARCHAR(20) DEFAULT 'completed'; END IF; IF NOT EXISTS ( SELECT 1 FROM information_schema.columns WHERE table_name = 'qd_analysis_memory' AND column_name = 'task_error' ) THEN ALTER TABLE qd_analysis_memory ADD COLUMN task_error TEXT; END IF; IF NOT EXISTS ( SELECT 1 FROM information_schema.columns WHERE table_name = 'qd_analysis_memory' AND column_name = 'updated_at' ) THEN ALTER TABLE qd_analysis_memory ADD COLUMN updated_at TIMESTAMP DEFAULT NOW(); END IF; END $$; """) #Create index cur.execute(""" CREATE INDEX IF NOT EXISTS idx_analysis_memory_symbol ON qd_analysis_memory(market, symbol); CREATE INDEX IF NOT EXISTS idx_analysis_memory_created ON qd_analysis_memory(created_at DESC); CREATE INDEX IF NOT EXISTS idx_analysis_memory_validated ON qd_analysis_memory(validated_at) WHERE validated_at IS NOT NULL; CREATE INDEX IF NOT EXISTS idx_analysis_memory_user ON qd_analysis_memory(user_id); """) db.commit() cur.close() logger.debug("Analysis memory table ensured successfully") except Exception as e: logger.warning(f"Memory table creation/update skipped: {e}") def store(self, analysis_result: Dict[str, Any], user_id: int = None) -> Optional[int]: """ Store an analysis result for future reference. Args: analysis_result: Result from FastAnalysisService.analyze() user_id: User ID who created this analysis Returns: Memory ID or None if failed """ try: with get_db_connection() as db: cur = db.cursor() # Prepare data market = analysis_result.get("market") symbol = analysis_result.get("symbol") decision = analysis_result.get("decision") confidence = analysis_result.get("confidence") price = analysis_result.get("market_data", {}).get("current_price") summary = analysis_result.get("summary") reasons = json.dumps(analysis_result.get("reasons", [])) scores = json.dumps(analysis_result.get("scores", {})) indicators = json.dumps(analysis_result.get("indicators", {})) raw = json.dumps(analysis_result) consensus = analysis_result.get("consensus") or {} consensus_score = consensus.get("consensus_score") consensus_abs = consensus.get("consensus_abs") agreement_ratio = consensus.get("agreement_ratio") quality_multiplier = consensus.get("quality_multiplier") cur.execute(""" INSERT INTO qd_analysis_memory ( user_id, market, symbol, decision, confidence, price_at_analysis, summary, reasons, scores, indicators_snapshot, raw_result, consensus_score, consensus_abs, agreement_ratio, quality_multiplier, task_status, task_error, updated_at ) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, NOW()) RETURNING id """, ( user_id, market, symbol, decision, confidence, price, summary, reasons, scores, indicators, raw, consensus_score, consensus_abs, agreement_ratio, quality_multiplier, "completed", "", )) # Use the lastrowid attribute to get the ID (execute has already processed RETURNING internally) memory_id = cur.lastrowid db.commit() cur.close() logger.info(f"Stored analysis memory #{memory_id} for {symbol} by user {user_id}") return memory_id except Exception as e: logger.error(f"Failed to store analysis memory: {e}", exc_info=True) return None def get_recent(self, market: str, symbol: str, days: int = 7, limit: int = 5) -> List[Dict]: """ Get recent analysis history for a symbol. Args: market: Market type symbol: Symbol days: Look back period limit: Max results Returns: List of historical analyses """ try: with get_db_connection() as db: cur = db.cursor() cur.execute(f""" SELECT id, decision, confidence, price_at_analysis, summary, reasons, scores, created_at, validated_at, was_correct, actual_return_pct, task_status, task_error, updated_at FROM qd_analysis_memory WHERE market = %s AND symbol = %s AND created_at > NOW() - INTERVAL '{int(days)} days' ORDER BY created_at DESC LIMIT %s """, (market, symbol, limit)) rows = cur.fetchall() or [] cur.close() results = [] for row in rows: results.append({ "id": row['id'], "decision": row['decision'], "confidence": row['confidence'], "price": float(row['price_at_analysis']) if row['price_at_analysis'] else None, "summary": row['summary'], "reasons": _safe_json_parse(row['reasons'], []), "scores": _safe_json_parse(row['scores'], {}), "status": row.get('task_status') or 'completed', "error_message": row.get('task_error') or '', "created_at": row['created_at'].isoformat() if row['created_at'] else None, "updated_at": row['updated_at'].isoformat() if row.get('updated_at') else None, "was_correct": row['was_correct'], "actual_return_pct": float(row['actual_return_pct']) if row['actual_return_pct'] else None, }) return results except Exception as e: logger.error(f"Failed to get recent memories: {e}") return [] def get_all_history(self, user_id: int = None, page: int = 1, page_size: int = 20) -> Dict: """ Get all analysis history with pagination. Args: user_id: User ID filter (required to show only user's own history) page: Page number (1-indexed) page_size: Items per page Returns: Dict with items list and total count """ try: offset = (page - 1) * page_size with get_db_connection() as db: cur = db.cursor() # Build WHERE clause based on user_id where_clause = "WHERE user_id = %s" if user_id else "" params_count = (user_id,) if user_id else () # Get total count cur.execute(f"SELECT COUNT(*) as cnt FROM qd_analysis_memory {where_clause}", params_count) total_row = cur.fetchone() total = total_row['cnt'] if total_row else 0 # Get paginated results params = (user_id, page_size, offset) if user_id else (page_size, offset) cur.execute(f""" SELECT id, market, symbol, decision, confidence, price_at_analysis, summary, reasons, scores, indicators_snapshot, raw_result, created_at, validated_at, was_correct, actual_return_pct, task_status, task_error, updated_at FROM qd_analysis_memory {where_clause} ORDER BY created_at DESC LIMIT %s OFFSET %s """, params) rows = cur.fetchall() or [] cur.close() items = [] for row in rows: items.append({ "id": row['id'], "market": row['market'], "symbol": row['symbol'], "decision": row['decision'], "confidence": row['confidence'], "price": float(row['price_at_analysis']) if row['price_at_analysis'] else None, "summary": row['summary'], "reasons": _safe_json_parse(row['reasons'], []), "scores": _safe_json_parse(row['scores'], {}), "indicators": _safe_json_parse(row['indicators_snapshot'], {}), "full_result": _safe_json_parse(row['raw_result'], None), "status": row.get('task_status') or 'completed', "error_message": row.get('task_error') or '', "created_at": row['created_at'].isoformat() if row['created_at'] else None, "updated_at": row['updated_at'].isoformat() if row.get('updated_at') else None, "was_correct": row['was_correct'], "actual_return_pct": float(row['actual_return_pct']) if row['actual_return_pct'] else None, }) return { "items": items, "total": total, "page": page, "page_size": page_size } except Exception as e: logger.error(f"Failed to get all history: {e}") return {"items": [], "total": 0, "page": page, "page_size": page_size} def delete_history(self, memory_id: int, user_id: int = None) -> bool: """ Delete a history record by ID. Args: memory_id: The ID of the analysis memory to delete user_id: User ID to ensure user can only delete their own records Returns: True if deleted successfully, False otherwise """ try: with get_db_connection() as db: cur = db.cursor() if user_id: # Only delete if it belongs to the user cur.execute("DELETE FROM qd_analysis_memory WHERE id = %s AND user_id = %s", (memory_id, user_id)) else: cur.execute("DELETE FROM qd_analysis_memory WHERE id = %s", (memory_id,)) db.commit() affected = cur.rowcount cur.close() return affected > 0 except Exception as e: logger.error(f"Failed to delete memory {memory_id}: {e}") return False def create_pending_task(self, market: str, symbol: str, language: str, model: str, timeframe: str, user_id: int = None) -> Optional[int]: """Create a processing record in history before long-running analysis starts.""" try: with get_db_connection() as db: cur = db.cursor() summary = f"Analysis submitted ({timeframe})..." reasons = json.dumps([]) scores = json.dumps({}) indicators = json.dumps({}) raw = json.dumps({ "market": market, "symbol": symbol, "language": language, "model": model, "timeframe": timeframe, "task_status": "processing", }) cur.execute(""" INSERT INTO qd_analysis_memory ( user_id, market, symbol, decision, confidence, summary, reasons, scores, indicators_snapshot, raw_result, task_status, task_error, updated_at, created_at ) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, NOW(), NOW()) RETURNING id """, ( user_id, market, symbol, "HOLD", 0, summary, reasons, scores, indicators, raw, "processing", "", )) # PostgresCursor.execute() will consume the RETURNING result in advance from fetchone() during INSERT. # So don’t use cur.fetchone() here, just get lastrowid. memory_id = cur.lastrowid db.commit() cur.close() return memory_id except Exception as e: logger.error(f"Failed to create pending task: {e}") return None def finalize_pending_task(self, memory_id: int, result: Dict[str, Any]) -> bool: """Overwrite pending record with final analysis result.""" try: consensus = result.get("consensus") or {} with get_db_connection() as db: cur = db.cursor() cur.execute(""" UPDATE qd_analysis_memory SET decision = %s, confidence = %s, price_at_analysis = %s, summary = %s, reasons = %s, scores = %s, indicators_snapshot = %s, raw_result = %s, consensus_score = %s, consensus_abs = %s, agreement_ratio = %s, quality_multiplier = %s, task_status = %s, task_error = %s, updated_at = NOW() WHERE id = %s """, ( result.get("decision"), result.get("confidence"), result.get("market_data", {}).get("current_price"), result.get("summary"), json.dumps(result.get("reasons", [])), json.dumps(result.get("scores", {})), json.dumps(result.get("indicators", {})), json.dumps(result), consensus.get("consensus_score"), consensus.get("consensus_abs"), consensus.get("agreement_ratio"), consensus.get("quality_multiplier"), "completed" if not result.get("error") else "failed", str(result.get("error") or ""), int(memory_id), )) ok = cur.rowcount > 0 db.commit() cur.close() return ok except Exception as e: logger.error(f"Failed to finalize pending task {memory_id}: {e}") return False def fail_pending_task(self, memory_id: int, error_message: str) -> bool: """Mark pending task as failed.""" try: with get_db_connection() as db: cur = db.cursor() cur.execute(""" UPDATE qd_analysis_memory SET task_status = 'failed', task_error = %s, summary = %s, updated_at = NOW() WHERE id = %s """, ( str(error_message or "analysis failed"), f"Analysis failed: {str(error_message or '')}", int(memory_id), )) ok = cur.rowcount > 0 db.commit() cur.close() return ok except Exception as e: logger.error(f"Failed to mark task failed {memory_id}: {e}") return False def get_similar_patterns(self, market: str, symbol: str, current_indicators: Dict, limit: int = 3) -> List[Dict]: """ Find historical analyses with similar technical patterns. Multi-indicator weighted similarity: - RSI: ±15 range, weighted 0.3 - MACD signal: exact match, weighted 0.3 - MA trend: exact match, weighted 0.25 - Volatility level: similar band, weighted 0.15 - Time decay: prefer recent validated outcomes """ try: rsi = float(current_indicators.get("rsi", {}).get("value") or 50) macd_signal = str(current_indicators.get("macd", {}).get("signal") or "neutral").lower() ma_trend = str(current_indicators.get("moving_averages", {}).get("trend") or "sideways").lower() vol_level = str(current_indicators.get("volatility", {}).get("level") or "normal").lower() with get_db_connection() as db: cur = db.cursor() cur.execute(""" SELECT id, decision, confidence, price_at_analysis, summary, reasons, indicators_snapshot, created_at, was_correct, actual_return_pct FROM qd_analysis_memory WHERE market = %s AND symbol = %s AND validated_at IS NOT NULL AND was_correct IS NOT NULL ORDER BY validated_at DESC NULLS LAST, created_at DESC LIMIT %s """, (market, symbol, limit * 5)) rows = cur.fetchall() or [] cur.close() scored = [] for row in rows: ind = _safe_json_parse(row['indicators_snapshot'], {}) hist_rsi = float(ind.get("rsi", {}).get("value") or 50) hist_macd = str(ind.get("macd", {}).get("signal") or "neutral").lower() hist_ma = str(ind.get("moving_averages", {}).get("trend") or "sideways").lower() hist_vol = str(ind.get("volatility", {}).get("level") or "normal").lower() rsi_diff = abs(hist_rsi - rsi) rsi_score = max(0, 1 - rsi_diff / 30) * 0.3 macd_score = 0.3 if hist_macd == macd_signal else 0 ma_score = 0.25 if hist_ma == ma_trend else 0 vol_score = 0.15 if hist_vol == vol_level else (0.08 if _vol_bands_similar(vol_level, hist_vol) else 0) sim = rsi_score + macd_score + ma_score + vol_score if sim < 0.25: continue bonus = 0.1 if row['was_correct'] else 0 scored.append((sim + bonus, { "id": row['id'], "decision": row['decision'], "confidence": row['confidence'], "price": float(row['price_at_analysis']) if row['price_at_analysis'] else None, "summary": row['summary'], "was_correct": row['was_correct'], "actual_return_pct": float(row['actual_return_pct']) if row['actual_return_pct'] else None, "similarity_score": round(sim + bonus, 3), })) scored.sort(key=lambda x: -x[0]) return [p[1] for p in scored[:limit]] except Exception as e: logger.error(f"Failed to get similar patterns: {e}") return [] def record_feedback(self, memory_id: int, feedback: str) -> bool: """ Record user feedback on an analysis. Args: memory_id: Analysis memory ID feedback: 'helpful' | 'not_helpful' | 'accurate' | 'inaccurate' """ try: with get_db_connection() as db: cur = db.cursor() cur.execute(""" UPDATE qd_analysis_memory SET user_feedback = %s, feedback_at = NOW() WHERE id = %s """, (feedback, memory_id)) db.commit() cur.close() return True except Exception as e: logger.error(f"Failed to record feedback: {e}") return False def validate_past_decisions(self, days_ago: int = 7) -> Dict[str, Any]: """ Validate historical decisions by comparing with actual price movements. Run this periodically (e.g., daily) to build learning data. Args: days_ago: Validate decisions from N days ago Returns: Validation statistics """ from app.services.market_data_collector import MarketDataCollector collector = MarketDataCollector() stats = { "validated": 0, "correct": 0, "incorrect": 0, "errors": 0, } try: with get_db_connection() as db: cur = db.cursor() # Get unvalidated decisions from N days ago cur.execute(f""" SELECT id, market, symbol, decision, price_at_analysis FROM qd_analysis_memory WHERE validated_at IS NULL AND created_at < NOW() - INTERVAL '{int(days_ago)} days' AND created_at > NOW() - INTERVAL '{int(days_ago + 1)} days' LIMIT 50 """) rows = cur.fetchall() or [] for row in rows: try: price_data = collector._get_price(row['market'], row['symbol']) current_price = float(price_data.get('price', 0)) if price_data else None if not current_price or current_price <= 0: continue analysis_price = float(row['price_at_analysis']) if analysis_price <= 0: continue # Calculate return return_pct = ((current_price - analysis_price) / analysis_price) * 100 # Determine if decision was correct decision = row['decision'] was_correct = False if decision == 'BUY' and return_pct > 2: # 2% threshold was_correct = True elif decision == 'SELL' and return_pct < -2: was_correct = True elif decision == 'HOLD' and abs(return_pct) <= 5: was_correct = True # Update record cur.execute(""" UPDATE qd_analysis_memory SET validated_at = NOW(), actual_return_pct = %s, was_correct = %s WHERE id = %s """, (return_pct, was_correct, row['id'])) stats["validated"] += 1 if was_correct: stats["correct"] += 1 else: stats["incorrect"] += 1 except Exception as e: logger.warning(f"Failed to validate memory {row['id']}: {e}") stats["errors"] += 1 db.commit() cur.close() except Exception as e: logger.error(f"Validation batch failed: {e}") accuracy = (stats["correct"] / stats["validated"] * 100) if stats["validated"] > 0 else 0 stats["accuracy_pct"] = round(accuracy, 2) logger.info(f"Validation completed: {stats}") return stats def validate_unvalidated_older_than(self, min_age_days: int = 7, limit: int = 200) -> Dict[str, Any]: """ Best-effort backfill: Validate unvalidated decisions older than `min_age_days`. This is used by offline AI calibration so the system can tune itself automatically. """ from app.services.market_data_collector import MarketDataCollector collector = MarketDataCollector() stats = { "validated": 0, "correct": 0, "incorrect": 0, "errors": 0, } try: with get_db_connection() as db: cur = db.cursor() cur.execute( f""" SELECT id, market, symbol, decision, price_at_analysis FROM qd_analysis_memory WHERE validated_at IS NULL AND created_at < NOW() - INTERVAL '{int(min_age_days)} days' LIMIT {int(limit)} """ ) rows = cur.fetchall() or [] for row in rows: try: price_data = collector._get_price(row["market"], row["symbol"]) current_price = float(price_data.get("price", 0)) if price_data else None if not current_price or current_price <= 0: continue analysis_price = float(row.get("price_at_analysis") or 0.0) if analysis_price <= 0: continue return_pct = ((float(current_price) - analysis_price) / analysis_price) * 100.0 decision = str(row.get("decision") or "HOLD") was_correct = False if decision == "BUY" and return_pct > 2: was_correct = True elif decision == "SELL" and return_pct < -2: was_correct = True elif decision == "HOLD" and abs(return_pct) <= 5: was_correct = True cur.execute( """ UPDATE qd_analysis_memory SET validated_at = NOW(), actual_return_pct = %s, was_correct = %s WHERE id = %s """, (return_pct, was_correct, int(row["id"])), ) stats["validated"] += 1 if was_correct: stats["correct"] += 1 else: stats["incorrect"] += 1 except Exception as e: logger.warning(f"Failed to validate memory {row.get('id')}: {e}", exc_info=True) stats["errors"] += 1 db.commit() cur.close() except Exception as e: logger.error(f"validate_unvalidated_older_than failed: {e}", exc_info=True) return stats def get_confidence_accuracy_by_bucket( self, market: str = None, symbol: str = None, days: int = 90 ) -> Dict[str, float]: """ Compute actual accuracy by confidence bucket for calibration. Buckets: (50,60), (60,70), (70,80), (80,90), (90,100). Returns e.g. {"60_70": 0.58, "70_80": 0.62} - bucket_key -> accuracy. """ try: with get_db_connection() as db: cur = db.cursor() where = ["validated_at IS NOT NULL", "was_correct IS NOT NULL", "confidence IS NOT NULL"] params = [] if market: where.append("market = %s") params.append(market) if symbol: where.append("symbol = %s") params.append(symbol) where.append(f"created_at > NOW() - INTERVAL '{int(days)} days'") params = tuple(params) if params else () cur.execute(f""" SELECT confidence, was_correct FROM qd_analysis_memory WHERE {' AND '.join(where)} """, params) rows = cur.fetchall() or [] cur.close() buckets = [(50, 60), (60, 70), (70, 80), (80, 90), (90, 101)] out = {} for lo, hi in buckets: subset = [r for r in rows if lo <= (r.get("confidence") or 0) < hi] if len(subset) < 5: continue correct = sum(1 for r in subset if r.get("was_correct")) out[f"{lo}_{hi}"] = correct / len(subset) return out except Exception as e: logger.warning(f"get_confidence_accuracy_by_bucket failed: {e}") return {} def get_adjusted_confidence( self, raw_confidence: int, market: str = None, symbol: str = None ) -> int: """ Adjust confidence based on historical accuracy in that bucket. If model is overconfident (low actual accuracy), dampen. Underconfident -> boost slightly. """ buckets = [(50, 60, "50_60"), (60, 70, "60_70"), (70, 80, "70_80"), (80, 90, "80_90"), (90, 101, "90_100")] bucket_key = None for lo, hi, key in buckets: if lo <= raw_confidence < hi: bucket_key = key break if not bucket_key: return max(1, min(99, int(raw_confidence))) acc_map = self.get_confidence_accuracy_by_bucket(market=market, symbol=symbol) acc = acc_map.get(bucket_key) if acc is None or acc <= 0: return max(1, min(99, int(raw_confidence))) expected = 0.5 + (raw_confidence - 50) / 100 if expected <= 0: return raw_confidence factor = acc / expected adjusted = int(raw_confidence * factor) return max(1, min(99, adjusted)) def get_performance_stats(self, market: str = None, symbol: str = None, days: int = 30) -> Dict[str, Any]: """ Get AI performance statistics. Returns: Performance metrics for display """ try: with get_db_connection() as db: cur = db.cursor() where_clauses = ["validated_at IS NOT NULL"] params = [] if market: where_clauses.append("market = %s") params.append(market) if symbol: where_clauses.append("symbol = %s") params.append(symbol) # Use f-string for interval since psycopg2 doesn't support placeholder in INTERVAL where_clauses.append(f"created_at > NOW() - INTERVAL '{int(days)} days'") where_sql = " AND ".join(where_clauses) cur.execute(f""" SELECT COUNT(*) as total, SUM(CASE WHEN was_correct = true THEN 1 ELSE 0 END) as correct, AVG(actual_return_pct) as avg_return, SUM(CASE WHEN decision = 'BUY' THEN 1 ELSE 0 END) as buy_count, SUM(CASE WHEN decision = 'SELL' THEN 1 ELSE 0 END) as sell_count, SUM(CASE WHEN decision = 'HOLD' THEN 1 ELSE 0 END) as hold_count, SUM(CASE WHEN user_feedback = 'helpful' THEN 1 ELSE 0 END) as helpful_count, SUM(CASE WHEN user_feedback IS NOT NULL THEN 1 ELSE 0 END) as feedback_count FROM qd_analysis_memory WHERE {where_sql} """, tuple(params) if params else None) row = cur.fetchone() cur.close() if not row or not row['total']: return { "total_analyses": 0, "accuracy_pct": 0, "avg_return_pct": 0, "user_satisfaction_pct": 0, } total = row['total'] correct = row['correct'] or 0 return { "total_analyses": total, "accuracy_pct": round((correct / total * 100) if total > 0 else 0, 2), "avg_return_pct": round(float(row['avg_return'] or 0), 2), "decision_distribution": { "buy": row['buy_count'] or 0, "sell": row['sell_count'] or 0, "hold": row['hold_count'] or 0, }, "user_satisfaction_pct": round( (row['helpful_count'] / row['feedback_count'] * 100) if row['feedback_count'] and row['feedback_count'] > 0 else 0, 2 ), "period_days": days, } except Exception as e: logger.error(f"Failed to get performance stats: {e}") return { "total_analyses": 0, "accuracy_pct": 0, "error": str(e), } def _vol_bands_similar(a: str, b: str) -> bool: """Check if two volatility levels are in similar band.""" low = {"low", "normal", "normal_low"} high = {"high", "elevated", "volatile", "very_high"} a, b = a.lower(), b.lower() if a in low and b in low: return True if a in high and b in high: return True return False # Singleton _memory_instance = None def get_analysis_memory() -> AnalysisMemory: """Get singleton AnalysisMemory instance.""" global _memory_instance if _memory_instance is None: _memory_instance = AnalysisMemory() return _memory_instance