src/app/markets/__init__.py

@@ -82,7 +82,25 @@ class MarketAPIsTool(BaseWrapper, Toolkit):
         all_prices = self.wrappers.try_call_all(lambda w: w.get_historical_prices(asset_id, limit))
         return aggregate_history_prices(all_prices)
 
-# TODO definire istruzioni per gli agenti di mercato
 MARKET_INSTRUCTIONS = """
+**TASK:** You are a specialized **Crypto Price Data Retrieval Agent**. Your primary goal is to fetch the most recent and/or historical price data for requested cryptocurrency assets (e.g., 'BTC', 'ETH', 'SOL'). You must provide the data in a clear and structured format.
 
+**AVAILABLE TOOLS:**
+1.  `get_products(asset_ids: list[str])`: Get **current** product/price info for a list of assets. **(PREFERITA: usa questa per i prezzi live)**
+2.  `get_historical_prices(asset_id: str, limit: int)`: Get historical price data for one asset. Default limit is 100. **(PREFERITA: usa questa per i dati storici)**
+3.  `get_products_aggregated(asset_ids: list[str])`: Get **aggregated current** product/price info for a list of assets. **(USA SOLO SE richiesto 'aggregato' o se `get_products` fallisce)**
+4.  `get_historical_prices_aggregated(asset_id: str, limit: int)`: Get **aggregated historical** price data for one asset. **(USA SOLO SE richiesto 'aggregato' o se `get_historical_prices` fallisce)**
+
+**USAGE GUIDELINE:**
+* **Asset ID:** Always convert common names (e.g., 'Bitcoin', 'Ethereum') into their official ticker/ID (e.g., 'BTC', 'ETH').
+* **Cost Management (Cruciale per LLM locale):**
+    * **Priorità Bassa per Aggregazione:** **Non** usare i metodi `*aggregated` a meno che l'utente non lo richieda esplicitamente o se i metodi non-aggregati falliscono.
+    * **Limitazione Storica:** Il limite predefinito per i dati storici deve essere **20** punti dati, a meno che l'utente non specifichi un limite diverso.
+* **Fallimento Tool:** Se lo strumento non restituisce dati per un asset specifico, rispondi per quell'asset con: "Dati di prezzo non trovati per [Asset ID]."
+
+**REPORTING REQUIREMENT:**
+1.  **Format:** Output the results in a clear, easy-to-read list or table.
+2.  **Live Price Request:** If an asset's *current price* is requested, report the **Asset ID**, **Latest Price**, and **Time/Date of the price**.
+3.  **Historical Price Request:** If *historical data* is requested, report the **Asset ID**, the **Limit** of points returned, and the **First** and **Last** entries from the list of historical prices (Date, Price). Non stampare l'intera lista di dati storici.
+4.  **Output:** For all requests, fornire un **unico e conciso riepilogo** dei dati reperiti.
 """

src/app/markets/base.py

@@ -14,7 +14,7 @@ class BaseWrapper:
         Returns:
             ProductInfo: An object containing product information.
         """
-        raise NotImplementedError
+        raise NotImplementedError("This method should be overridden by subclasses")
 
     def get_products(self, asset_ids: list[str]) -> list['ProductInfo']:
         """
@@ -24,7 +24,7 @@ class BaseWrapper:
         Returns:
             list[ProductInfo]: A list of objects containing product information.
         """
-        raise NotImplementedError
+        raise NotImplementedError("This method should be overridden by subclasses")
 
     def get_historical_prices(self, asset_id: str = "BTC", limit: int = 100) -> list['Price']:
         """
@@ -35,7 +35,7 @@ class BaseWrapper:
         Returns:
             list[Price]: A list of Price objects.
         """
-        raise NotImplementedError
+        raise NotImplementedError("This method should be overridden by subclasses")
 
 class ProductInfo(BaseModel):
     """

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?

tests/utils/test_market_aggregator.py

@@ -40,10 +40,10 @@ class TestMarketDataAggregator:
         info = aggregated[0]
         assert info is not None
         assert info.symbol == "BTC"
-        assert info.price == pytest.approx(50000.0, rel=1e-3)
 
-        avg_weighted_volume = (50000.0 * 1000.0 + 50100.0 * 1100.0 + 49900.0 * 900.0) / (1000.0 + 1100.0 + 900.0)
-        assert info.volume_24h == pytest.approx(avg_weighted_volume, rel=1e-3)
+        avg_weighted_price = (50000.0 * 1000.0 + 50100.0 * 1100.0 + 49900.0 * 900.0) / (1000.0 + 1100.0 + 900.0)
+        assert info.price == pytest.approx(avg_weighted_price, rel=1e-3)
+        assert info.volume_24h == pytest.approx(1000.0, rel=1e-3)
         assert info.quote_currency == "USD"
 
     def test_aggregate_product_info_multiple_symbols(self):
@@ -65,17 +65,38 @@ class TestMarketDataAggregator:
         eth_info = next((p for p in aggregated if p.symbol == "ETH"), None)
 
         assert btc_info is not None
-        assert btc_info.price == pytest.approx(50050.0, rel=1e-3)
-        avg_weighted_volume_btc = (50000.0 * 1000.0 + 50100.0 * 1100.0) / (1000.0 + 1100.0)
-        assert btc_info.volume_24h == pytest.approx(avg_weighted_volume_btc, rel=1e-3)
+        avg_weighted_price_btc = (50000.0 * 1000.0 + 50100.0 * 1100.0) / (1000.0 + 1100.0)
+        assert btc_info.price == pytest.approx(avg_weighted_price_btc, rel=1e-3)
+        assert btc_info.volume_24h == pytest.approx(1050.0, rel=1e-3)
         assert btc_info.quote_currency == "USD"
 
         assert eth_info is not None
-        assert eth_info.price == pytest.approx(4025.0, rel=1e-3)
-        avg_weighted_volume_eth = (4000.0 * 2000.0 + 4050.0 * 2100.0) / (2000.0 + 2100.0)
-        assert eth_info.volume_24h == pytest.approx(avg_weighted_volume_eth, rel=1e-3)
+        avg_weighted_price_eth = (4000.0 * 2000.0 + 4050.0 * 2100.0) / (2000.0 + 2100.0)
+        assert eth_info.price == pytest.approx(avg_weighted_price_eth, rel=1e-3)
+        assert eth_info.volume_24h == pytest.approx(2050.0, rel=1e-3)
         assert eth_info.quote_currency == "USD"
 
+    def test_aggregate_product_info_with_no_data(self):
+        products = {
+            "Provider1": [],
+            "Provider2": [],
+        }
+        aggregated = aggregate_product_info(products)
+        assert len(aggregated) == 0
+
+    def test_aggregate_product_info_with_partial_data(self):
+        products = {
+            "Provider1": [self.__product("BTC", 50000.0, 1000.0, "USD")],
+            "Provider2": [],
+        }
+        aggregated = aggregate_product_info(products)
+        assert len(aggregated) == 1
+        info = aggregated[0]
+        assert info.symbol == "BTC"
+        assert info.price == pytest.approx(50000.0, rel=1e-3)
+        assert info.volume_24h == pytest.approx(1000.0, rel=1e-3)
+        assert info.quote_currency == "USD"
+
     def test_aggregate_history_prices(self):
         """Test aggregazione di prezzi storici usando aggregate_history_prices"""