feat(markets): add yfinance integration for stock and crypto data

- Add yfinance wrapper with support for stocks and cryptocurrencies
- Update aggregated models to recognize yfinance products
- Include yfinance in market APIs tool and demo script
- Add comprehensive tests for yfinance functionality
- Update dependencies to include yfinance and required packages

src/app/utils/aggregated_models.py

@@ -77,6 +77,8 @@ class AggregatedProductInfo(ProductInfo):
             return "binance"
         elif "crypto" in product.id.lower() or "cc" in product.id.lower():
             return "cryptocompare"
+        elif "yfinance" in product.id.lower() or "yf" in product.id.lower():
+            return "yfinance"
         else:
             return "unknown"
     

src/app/utils/market_aggregator.py

@@ -1,71 +0,0 @@
-import statistics
-from typing import Dict, Any
-
-class MarketAggregator:
-    """
-    Aggrega dati di mercato da più provider e genera segnali e analisi avanzate.
-    """
-    @staticmethod
-    def aggregate(symbol: str, sources: Dict[str, Dict[str, Any]]) -> Dict[str, Any]:
-        prices = []
-        volumes = []
-        price_map = {}
-        for provider, data in sources.items():
-            price = data.get('price')
-            if price is not None:
-                prices.append(price)
-                price_map[provider] = price
-            volume = data.get('volume')
-            if volume is not None:
-                volumes.append(MarketAggregator._parse_volume(volume))
-        
-        # Aggregated price (mean)
-        agg_price = statistics.mean(prices) if prices else None
-        # Spread analysis
-        spread = (max(prices) - min(prices)) / agg_price if prices and agg_price else 0
-        # Confidence
-        stddev = statistics.stdev(prices) if len(prices) > 1 else 0
-        confidence = max(0.5, 1 - (stddev / agg_price)) if agg_price else 0
-        if spread < 0.005:
-            confidence += 0.1
-        if len(prices) >= 3:
-            confidence += 0.05
-        confidence = min(confidence, 1.0)
-        # Volume trend
-        total_volume = sum(volumes) if volumes else None
-        # Price divergence
-        max_deviation = (max(prices) - min(prices)) / agg_price if prices and agg_price else 0
-        # Signals
-        signals = {
-            "spread_analysis": f"Low spread ({spread:.2%}) indicates healthy liquidity" if spread < 0.01 else f"Spread {spread:.2%} - check liquidity",
-            "volume_trend": f"Combined volume: {total_volume:.2f}" if total_volume else "Volume data not available",
-            "price_divergence": f"Max deviation: {max_deviation:.2%} - {'Normal range' if max_deviation < 0.01 else 'High divergence'}"
-        }
-        return {
-            "aggregated_data": {
-                f"{symbol}_USD": {
-                    "price": round(agg_price, 2) if agg_price else None,
-                    "confidence": round(confidence, 2),
-                    "sources_count": len(prices)
-                }
-            },
-            "individual_sources": price_map,
-            "market_signals": signals
-        }
-    @staticmethod
-    def _parse_volume(volume: Any) -> float:
-        # Supporta stringhe tipo "1.2M" o numeri
-        if isinstance(volume, (int, float)):
-            return float(volume)
-        if isinstance(volume, str):
-            v = volume.upper().replace(' ', '')
-            if v.endswith('M'):
-                return float(v[:-1]) * 1_000_000
-            if v.endswith('K'):
-                return float(v[:-1]) * 1_000
-            try:
-                return float(v)
-            except Exception as e:
-                print(f"Errore nel parsing del volume: {e}")
-                return 0.0
-        return 0.0

src/app/utils/market_data_aggregator.py

@@ -12,8 +12,8 @@ class MarketDataAggregator:
     
     def __init__(self, currency: str = "USD"):
         # Import lazy per evitare circular import
-        from app.markets import MarketAPIs
-        self._market_apis = MarketAPIs(currency)
+        from app.markets import MarketAPIsTool
+        self._market_apis = MarketAPIsTool(currency)
         self._aggregation_enabled = True
     
     def get_product(self, asset_id: str) -> ProductInfo: