feat: add detailed market instructions and improve error handling in price aggregation methods

src/app/utils/market_aggregation.py

@@ -4,10 +4,12 @@ from app.markets.base import ProductInfo, Price
 
 def aggregate_history_prices(prices: dict[str, list[Price]]) -> list[Price]:
     """
-    Aggrega i prezzi storici per symbol calcolando la media
-
+    Aggrega i prezzi storici per symbol calcolando la media oraria.
+    Args:
+        prices (dict[str, list[Price]]): Mappa provider -> lista di Price
+    Returns:
+        list[Price]: Lista di Price aggregati per ora
     """
-    max_list_length = max(len(p) for p in prices.values())
 
     # Costruiamo una mappa timestamp_h -> lista di Price
     timestamped_prices: dict[int, list[Price]] = {}
@@ -27,13 +29,15 @@ def aggregate_history_prices(prices: dict[str, list[Price]]) -> list[Price]:
         price.close = statistics.mean([p.close for p in price_list])
         price.volume = statistics.mean([p.volume for p in price_list])
         aggregated_prices.append(price)
-
-    assert(len(aggregated_prices) <= max_list_length)
     return aggregated_prices
 
 def aggregate_product_info(products: dict[str, list[ProductInfo]]) -> list[ProductInfo]:
     """
     Aggrega una lista di ProductInfo per symbol.
+    Args:
+        products (dict[str, list[ProductInfo]]): Mappa provider -> lista di ProductInfo
+    Returns:
+        list[ProductInfo]: Lista di ProductInfo aggregati per symbol
     """
 
     # Costruzione mappa symbol -> lista di ProductInfo
@@ -48,15 +52,16 @@ def aggregate_product_info(products: dict[str, list[ProductInfo]]) -> list[Produ
     for symbol, product_list in symbols_infos.items():
         product = ProductInfo()
 
-        product.id = f"{symbol}_AGG"
+        product.id = f"{symbol}_AGGREGATED"
         product.symbol = symbol
         product.quote_currency = next(p.quote_currency for p in product_list if p.quote_currency)
 
-        prices = [p.price for p in product_list]
-        product.price = statistics.mean(prices)
+        volume_sum = sum(p.volume_24h for p in product_list)
+        product.volume_24h = volume_sum / len(product_list) if product_list else 0.0
+
+        prices = sum(p.price * p.volume_24h for p in product_list)
+        product.price = (prices / volume_sum) if volume_sum > 0 else 0.0
 
-        volumes = [p.volume_24h for p in product_list]
-        product.volume_24h = sum([p * v for p, v in zip(prices, volumes)]) / sum(volumes)
         aggregated_products.append(product)
 
         confidence = _calculate_confidence(product_list, sources) # TODO necessary?