update

scratch/hougaard_analyzer.py

@@ -0,0 +1,132 @@
+import pandas as pd
+import json
+import os
+import numpy as np
+from datetime import datetime
+import matplotlib.pyplot as plt
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report, confusion_matrix
+
+class HougaardAnalyzer:
+    def __init__(self, file_path):
+        self.file_path = file_path
+        self.df = None
+        self.results = {}
+
+    def load_data(self):
+        """Loads Freqtrade/Bybit JSON format and converts to DataFrame."""
+        print(f"Loading data from {self.file_path}...")
+        with open(self.file_path, 'r') as f:
+            data = json.load(f)
+        
+        # Format: [timestamp, open, high, low, close, volume]
+        cols = ['date', 'open', 'high', 'low', 'close', 'volume']
+        self.df = pd.DataFrame(data, columns=cols)
+        
+        # Convert timestamp (ms) to datetime
+        self.df['date'] = pd.to_datetime(self.df['date'], unit='ms', utc=True)
+        self.df.set_index('date', inplace=True)
+        print(f"Loaded {len(self.df)} candles.")
+
+    def engineer_features(self):
+        """Creates Hougaard-style features."""
+        print("Engineering features...")
+        df = self.df
+        
+        # 1. Time-based features
+        df['hour'] = df.index.hour
+        df['day_of_week'] = df.index.dayofweek
+        
+        # 2. Overnight Range (00:00 - 08:00 UTC)
+        # We group by day and calculate High-Low for the 0-8h window
+        df['date_only'] = df.index.date
+        
+        overnight = df.between_time('00:00', '08:00').groupby('date_only').agg({
+            'high': 'max',
+            'low': 'min',
+            'open': 'first'
+        }).rename(columns={'high': 'on_high', 'low': 'on_low', 'open': 'on_open'})
+        
+        overnight['on_range_pct'] = (overnight['on_high'] - overnight['on_low']) / overnight['on_open']
+        
+        # Map back to main DF
+        df = df.join(overnight, on='date_only')
+        
+        # 3. Distance from Overnight High/Low at 08:00
+        df['dist_from_on_high'] = (df['close'] - df['on_high']) / df['on_high']
+        df['dist_from_on_low'] = (df['close'] - df['on_low']) / df['on_low']
+        
+        # 4. Volatility (ATR-like)
+        df['body_size'] = abs(df['close'] - df['open']) / df['open']
+        df['wick_size'] = (df['high'] - np.maximum(df['open'], df['close'])) / df['open']
+        
+        self.df = df.dropna()
+
+    def label_data(self, target_pct=0.01, stop_pct=0.005, horizon_candles=48):
+        """
+        Labels a 'Long' setup at 08:00 UTC.
+        1: Hits target before stop
+        0: Hits stop before target or expires
+        """
+        print(f"Labeling data (Target: {target_pct*100}%, Stop: {stop_pct*100}%)...")
+        # We only look at the 08:00 candle (London Open)
+        setups = self.df[self.df.index.hour == 8].copy()
+        
+        labels = []
+        for idx, row in setups.iterrows():
+            entry_price = row['close']
+            target_price = entry_price * (1 + target_pct)
+            stop_price = entry_price * (1 - stop_pct)
+            
+            # Look ahead
+            future_data = self.df.loc[idx:].iloc[1:horizon_candles]
+            
+            success = 0
+            for f_idx, f_row in future_data.iterrows():
+                if f_row['high'] >= target_price:
+                    success = 1
+                    break
+                if f_row['low'] <= stop_price:
+                    success = 0
+                    break
+            labels.append(success)
+            
+        setups['label'] = labels
+        return setups
+
+    def run_analysis(self):
+        self.load_data()
+        self.engineer_features()
+        
+        setups = self.label_data()
+        
+        # Features for the model
+        features = ['hour', 'day_of_week', 'on_range_pct', 'dist_from_on_high', 'dist_from_on_low', 'body_size', 'wick_size']
+        X = setups[features]
+        y = setups['label']
+        
+        if len(y.unique()) < 2:
+            print("Error: Not enough variance in labels. Adjust target/stop.")
+            return
+
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+        
+        model = RandomForestClassifier(n_estimators=100, random_state=42)
+        model.fit(X_train, y_train)
+        
+        # Importance
+        importances = pd.Series(model.feature_importances_, index=features).sort_values(ascending=False)
+        print("\n--- Feature Importance (Hougaard Insights) ---")
+        print(importances)
+        
+        # Accuracy
+        y_pred = model.predict(X_test)
+        print("\n--- Model Performance ---")
+        print(classification_report(y_test, y_pred))
+
+if __name__ == "__main__":
+    # Test with BTC 5m data
+    data_path = "/Volumes/Alpha SSD/Coding/freqtrade/user_data/data/bybit/BTC_USDT-5m-futures.json"
+    analyzer = HougaardAnalyzer(data_path)
+    analyzer.run_analysis()

scratch/verify_silence.py

@@ -0,0 +1,18 @@
+import logging
+import sys
+
+# Setup logging to stdout
+logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+
+# Mock QdrantClient to fail
+import qdrant_client
+from unittest.mock import MagicMock
+
+# Force failure
+from GramAddict.core.qdrant_memory import QdrantBase, HeuristicMemoryDB, UIMemoryDB, BannedPathsDB
+
+print("--- Starting Qdrant Silence Test ---")
+h = HeuristicMemoryDB()
+u = UIMemoryDB()
+b = BannedPathsDB()
+print("--- End of Qdrant Silence Test ---")