Refactor: Update ProductInfo and Price classes to include aggregation methods; remove standalone aggregation functions; fix docs

src/app/api/base/markets.py

@@ -5,8 +5,8 @@ from pydantic import BaseModel
 
 class ProductInfo(BaseModel):
     """
-    Informazioni sul prodotto, come ottenute dalle API di mercato.
+    Product information as obtained from market APIs.
-    Implementa i metodi di conversione dai dati grezzi delle API.
+    Implements conversion methods from raw API data.
     """
     id: str = ""
     symbol: str = ""
@@ -14,10 +14,46 @@ class ProductInfo(BaseModel):
     volume_24h: float = 0.0
     currency: str = ""
 
+    @staticmethod
+    def aggregate(products: dict[str, list['ProductInfo']]) -> list['ProductInfo']:
+        """
+        Aggregates a list of ProductInfo by symbol.
+        Args:
+            products (dict[str, list[ProductInfo]]): Map provider -> list of ProductInfo
+        Returns:
+            list[ProductInfo]: List of ProductInfo aggregated by symbol
+        """
+
+        # Costruzione mappa symbol -> lista di ProductInfo
+        symbols_infos: dict[str, list[ProductInfo]] = {}
+        for _, product_list in products.items():
+            for product in product_list:
+                symbols_infos.setdefault(product.symbol, []).append(product)
+
+        # Aggregazione per ogni symbol
+        aggregated_products: list[ProductInfo] = []
+        for symbol, product_list in symbols_infos.items():
+            product = ProductInfo()
+
+            product.id = f"{symbol}_AGGREGATED"
+            product.symbol = symbol
+            product.currency = next(p.currency for p in product_list if p.currency)
+
+            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
+
+            aggregated_products.append(product)
+        return aggregated_products
+
+
+
 class Price(BaseModel):
     """
-    Rappresenta i dati di prezzo per un asset, come ottenuti dalle API di mercato.
+    Represents price data for an asset as obtained from market APIs.
-    Implementa i metodi di conversione dai dati grezzi delle API.
+    Implements conversion methods from raw API data.
     """
     high: float = 0.0
     low: float = 0.0
@@ -25,16 +61,17 @@ class Price(BaseModel):
     close: float = 0.0
     volume: float = 0.0
     timestamp: str = ""
-    """Timestamp con formato YYYY-MM-DD HH:MM"""
+    """Timestamp in format YYYY-MM-DD HH:MM"""
 
     def set_timestamp(self, timestamp_ms: int | None = None, timestamp_s: int | None = None) -> None:
         """
-        Imposta il timestamp a partire da millisecondi o secondi.
+        Sets the timestamp from milliseconds or seconds.
-        IL timestamp viene salvato come stringa formattata 'YYYY-MM-DD HH:MM'.
+        The timestamp is saved as a formatted string 'YYYY-MM-DD HH:MM'.
         Args:
-            timestamp_ms: Timestamp in millisecondi.
+            timestamp_ms: Timestamp in milliseconds.
-            timestamp_s: Timestamp in secondi.
+            timestamp_s: Timestamp in seconds.
         Raises:
+            ValueError: If neither timestamp_ms nor timestamp_s is provided.
         """
         if timestamp_ms is not None:
             timestamp = timestamp_ms // 1000
@@ -46,10 +83,41 @@ class Price(BaseModel):
 
         self.timestamp = datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M')
 
+    @staticmethod
+    def aggregate(prices: dict[str, list['Price']]) -> list['Price']:
+        """
+        Aggregates historical prices for the same symbol by calculating the mean.
+        Args:
+            prices (dict[str, list[Price]]): Map provider -> list of Price.
+                The map must contain only Price objects for the same symbol.
+        Returns:
+            list[Price]: List of Price objects aggregated by timestamp.
+        """
+
+        # Costruiamo una mappa timestamp -> lista di Price
+        timestamped_prices: dict[str, list[Price]] = {}
+        for _, price_list in prices.items():
+            for price in price_list:
+                timestamped_prices.setdefault(price.timestamp, []).append(price)
+
+        # Ora aggregiamo i prezzi per ogni timestamp
+        aggregated_prices: list[Price] = []
+        for time, price_list in timestamped_prices.items():
+            price = Price()
+            price.timestamp = time
+            price.high = statistics.mean([p.high for p in price_list])
+            price.low = statistics.mean([p.low for p in price_list])
+            price.open = statistics.mean([p.open for p in price_list])
+            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)
+        return aggregated_prices
+
 class MarketWrapper:
     """
     Base class for market API wrappers.
     All market API wrappers should inherit from this class and implement the methods.
+    Provides interface for retrieving product and price information from market APIs.
     """
 
     def get_product(self, asset_id: str) -> ProductInfo:
@@ -82,66 +150,3 @@ class MarketWrapper:
             list[Price]: A list of Price objects.
         """
         raise NotImplementedError("This method should be overridden by subclasses")
-
-
-def aggregate_history_prices(prices: dict[str, list[Price]]) -> list[Price]:
-    """
-    Aggrega i prezzi storici per symbol calcolando la media.
-    Args:
-        prices (dict[str, list[Price]]): Mappa provider -> lista di Price
-    Returns:
-        list[Price]: Lista di Price aggregati per timestamp
-    """
-
-    # Costruiamo una mappa timestamp -> lista di Price
-    timestamped_prices: dict[str, list[Price]] = {}
-    for _, price_list in prices.items():
-        for price in price_list:
-            timestamped_prices.setdefault(price.timestamp, []).append(price)
-
-    # Ora aggregiamo i prezzi per ogni timestamp
-    aggregated_prices: list[Price] = []
-    for time, price_list in timestamped_prices.items():
-        price = Price()
-        price.timestamp = time
-        price.high = statistics.mean([p.high for p in price_list])
-        price.low = statistics.mean([p.low for p in price_list])
-        price.open = statistics.mean([p.open for p in price_list])
-        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)
-    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
-    symbols_infos: dict[str, list[ProductInfo]] = {}
-    for _, product_list in products.items():
-        for product in product_list:
-            symbols_infos.setdefault(product.symbol, []).append(product)
-
-    # Aggregazione per ogni symbol
-    aggregated_products: list[ProductInfo] = []
-    for symbol, product_list in symbols_infos.items():
-        product = ProductInfo()
-
-        product.id = f"{symbol}_AGGREGATED"
-        product.symbol = symbol
-        product.currency = next(p.currency for p in product_list if p.currency)
-
-        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
-
-        aggregated_products.append(product)
-    return aggregated_products
-

src/app/api/base/news.py

@@ -2,6 +2,9 @@ from pydantic import BaseModel
 
 
 class Article(BaseModel):
+    """
+    Represents a news article with source, time, title, and description.
+    """
     source: str = ""
     time: str = ""
     title: str = ""
@@ -11,11 +14,12 @@ class NewsWrapper:
     """
     Base class for news API wrappers.
     All news API wrappers should inherit from this class and implement the methods.
+    Provides interface for retrieving news articles from news APIs.
     """
 
     def get_top_headlines(self, limit: int = 100) -> list[Article]:
         """
-        Get top headlines, optionally limited by limit.
+        Retrieve top headlines, optionally limited by the specified number.
         Args:
             limit (int): The maximum number of articles to return.
         Returns:
@@ -25,7 +29,7 @@ class NewsWrapper:
 
     def get_latest_news(self, query: str, limit: int = 100) -> list[Article]:
         """
-        Get latest news based on a query.
+        Retrieve the latest news based on a search query.
         Args:
             query (str): The search query.
             limit (int): The maximum number of articles to return.

src/app/api/base/social.py

@@ -2,12 +2,18 @@ from pydantic import BaseModel
 
 
 class SocialPost(BaseModel):
+    """
+    Represents a social media post with time, title, description, and comments.
+    """
     time: str = ""
     title: str = ""
     description: str = ""
     comments: list["SocialComment"] = []
 
 class SocialComment(BaseModel):
+    """
+    Represents a comment on a social media post.
+    """
     time: str = ""
     description: str = ""
 
@@ -16,11 +22,12 @@ class SocialWrapper:
     """
     Base class for social media API wrappers.
     All social media API wrappers should inherit from this class and implement the methods.
+    Provides interface for retrieving social media posts and comments from APIs.
     """
 
     def get_top_crypto_posts(self, limit: int = 5) -> list[SocialPost]:
         """
-        Get top cryptocurrency-related posts, optionally limited by total.
+        Retrieve top cryptocurrency-related posts, optionally limited by the specified number.
         Args:
             limit (int): The maximum number of posts to return.
         Returns:

src/app/api/markets/__init__.py

@@ -1,6 +1,6 @@
 from agno.tools import Toolkit
 from app.api.wrapper_handler import WrapperHandler
-from app.api.base.markets import MarketWrapper, Price, ProductInfo, aggregate_history_prices, aggregate_product_info
+from app.api.base.markets import MarketWrapper, Price, ProductInfo
 from app.api.markets.binance import BinanceWrapper
 from app.api.markets.coinbase import CoinBaseWrapper
 from app.api.markets.cryptocompare import CryptoCompareWrapper
@@ -68,7 +68,7 @@ class MarketAPIsTool(MarketWrapper, Toolkit):
             Exception: If all wrappers fail to provide results.
         """
         all_products = self.handler.try_call_all(lambda w: w.get_products(asset_ids))
-        return aggregate_product_info(all_products)
+        return ProductInfo.aggregate(all_products)
 
     def get_historical_prices_aggregated(self, asset_id: str = "BTC", limit: int = 100) -> list[Price]:
         """
@@ -83,4 +83,4 @@ class MarketAPIsTool(MarketWrapper, Toolkit):
             Exception: If all wrappers fail to provide results.
         """
         all_prices = self.handler.try_call_all(lambda w: w.get_historical_prices(asset_id, limit))
-        return aggregate_history_prices(all_prices)
+        return Price.aggregate(all_prices)

tests/api/test_binance.py

@@ -1,6 +1,19 @@
 import pytest
+import asyncio
 from app.api.markets.binance import BinanceWrapper
 
+# fix warning about no event loop
+@pytest.fixture(scope="session", autouse=True)
+def event_loop():
+    """
+    Ensure there is an event loop for the duration of the tests.
+    """
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    yield loop
+    loop.close()
+
+
 @pytest.mark.market
 @pytest.mark.api
 class TestBinance:

tests/utils/test_market_aggregator.py

@@ -1,6 +1,6 @@
 import pytest
 from datetime import datetime
-from app.api.base.markets import ProductInfo, Price, aggregate_history_prices, aggregate_product_info
+from app.api.base.markets import ProductInfo, Price
 
 
 @pytest.mark.aggregator
@@ -33,7 +33,7 @@ class TestMarketDataAggregator:
             "Provider3": [self.__product("BTC", 49900.0, 900.0, "USD")],
         }
 
-        aggregated = aggregate_product_info(products)
+        aggregated = ProductInfo.aggregate(products)
         assert len(aggregated) == 1
 
         info = aggregated[0]
@@ -57,7 +57,7 @@ class TestMarketDataAggregator:
             ],
         }
 
-        aggregated = aggregate_product_info(products)
+        aggregated = ProductInfo.aggregate(products)
         assert len(aggregated) == 2
 
         btc_info = next((p for p in aggregated if p.symbol == "BTC"), None)
@@ -80,7 +80,7 @@ class TestMarketDataAggregator:
             "Provider1": [],
             "Provider2": [],
         }
-        aggregated = aggregate_product_info(products)
+        aggregated = ProductInfo.aggregate(products)
         assert len(aggregated) == 0
 
     def test_aggregate_product_info_with_partial_data(self):
@@ -88,7 +88,7 @@ class TestMarketDataAggregator:
             "Provider1": [self.__product("BTC", 50000.0, 1000.0, "USD")],
             "Provider2": [],
         }
-        aggregated = aggregate_product_info(products)
+        aggregated = ProductInfo.aggregate(products)
         assert len(aggregated) == 1
         info = aggregated[0]
         assert info.symbol == "BTC"
@@ -119,7 +119,7 @@ class TestMarketDataAggregator:
         price.set_timestamp(timestamp_s=timestamp_2h_ago)
         timestamp_2h_ago = price.timestamp
 
-        aggregated = aggregate_history_prices(prices)
+        aggregated = Price.aggregate(prices)
         assert len(aggregated) == 2
         assert aggregated[0].timestamp == timestamp_1h_ago
         assert aggregated[0].high == pytest.approx(50050.0, rel=1e-3) # type: ignore