Update helpers.py

helpers.py

@@ -1,5 +1,7 @@
-import os, re, json
+import os, re, json, hashlib
 from datetime import datetime
+import pandas as pd
+import numpy as np
 
 def safe_float_conversion(value, default=0.0):
     try:
@@ -127,4 +129,65 @@ def local_re_analyze_trade(trade_data, processed_data):
     return {
         "action": action, "reasoning": reasoning, "new_stop_loss": None, "new_take_profit": None,
         "new_expected_minutes": None, "model_source": "local", "strategy": strategy
-    }
+    }
+
+def validate_candidate_data_enhanced(candidate):
+    try:
+        required_fields = ['symbol', 'current_price', 'final_score', 'enhanced_final_score']
+        for field in required_fields:
+            if field not in candidate: candidate[field] = 0.0 if field.endswith('_score') or field == 'current_price' else 'UNKNOWN'
+        candidate['current_price'] = safe_float_conversion(candidate.get('current_price'), 0.0)
+        candidate['final_score'] = safe_float_conversion(candidate.get('final_score'), 0.5)
+        candidate['enhanced_final_score'] = safe_float_conversion(candidate.get('enhanced_final_score'), candidate['final_score'])
+        if 'reasons_for_candidacy' not in candidate: candidate['reasons_for_candidacy'] = ['unknown_reason']
+        if 'sentiment_data' not in candidate: candidate['sentiment_data'] = {'btc_sentiment': 'NEUTRAL','fear_and_greed_index': 50,'general_whale_activity': {'sentiment': 'NEUTRAL', 'critical_alert': False}}
+        if 'advanced_indicators' not in candidate: candidate['advanced_indicators'] = {}
+        if 'strategy_scores' not in candidate: candidate['strategy_scores'] = {}
+        if 'target_strategy' not in candidate: candidate['target_strategy'] = 'GENERIC'
+        return True
+    except Exception as error:
+        print(f"Failed to validate candidate data for {candidate.get('symbol')}: {error}")
+        return False
+
+def normalize_weights(weights_dict):
+    total = sum(weights_dict.values())
+    if total > 0:
+        for strategy in weights_dict:
+            weights_dict[strategy] /= total
+    return weights_dict
+
+def calculate_market_volatility(market_context):
+    try:
+        btc_price = market_context.get('bitcoin_price_usd', 0)
+        fear_greed = market_context.get('fear_and_greed_index', 50)
+        whale_sentiment = market_context.get('general_whale_activity', {}).get('sentiment', 'NEUTRAL')
+        
+        volatility_score = 0
+        
+        if btc_price > 0:
+            if abs(fear_greed - 50) > 20:
+                volatility_score += 1
+        
+        if whale_sentiment in ['BULLISH', 'BEARISH']:
+            volatility_score += 1
+        elif whale_sentiment == 'SLIGHTLY_BULLISH':
+            volatility_score += 0.5
+        
+        if volatility_score >= 1.5:
+            return "high"
+        elif volatility_score >= 0.5:
+            return "medium"
+        else:
+            return "low"
+            
+    except Exception as e:
+        print(f"Volatility calculation error: {e}")
+        return "medium"
+
+def generate_trade_id():
+    return str(int(time.time()))
+
+def should_update_weights(performance_history_count):
+    if performance_history_count <= 10:
+        return True
+    return performance_history_count % 3 == 0