feat: implement aggregate_history_prices function to calculate hourly price averages

src/app/utils/market_aggregation.py

@@ -3,21 +3,33 @@ 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"""
-    raise NotImplementedError("Funzione non ancora implementata per problemi di timestamp he deve essere uniformato prima di usare questa funzione.")
-    # TODO implementare l'aggregazione dopo aver modificato la classe Price in modo che abbia un timestamp integer
-    # aggregated_prices = []
-    # for timestamp in range(len(next(iter(prices.values())))):
-    #     timestamp_prices = [
-    #         price_list[timestamp].price
-    #         for price_list in prices.values()
-    #         if len(price_list) > timestamp and price_list[timestamp].price is not None
-    #     ]
-    #     if timestamp_prices:
-    #         aggregated_prices.append(statistics.mean(timestamp_prices))
-    #     else:
-    #         aggregated_prices.append(None)
-    # return aggregated_prices
+    """
+    Aggrega i prezzi storici per symbol calcolando la media
+
+    """
+    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]] = {}
+    for _, price_list in prices.items():
+        for price in price_list:
+            time = price.timestamp_ms - (price.timestamp_ms % 3600000)  # arrotonda all'ora (non dovrebbe essere necessario)
+            timestamped_prices.setdefault(time, []).append(price)
+
+    # Ora aggregiamo i prezzi per ogni ora
+    aggregated_prices = []
+    for time, price_list in timestamped_prices.items():
+        price = Price()
+        price.timestamp_ms = 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)
+
+    assert(len(aggregated_prices) <= max_list_length)
+    return aggregated_prices
 
 def aggregate_product_info(products: dict[str, list[ProductInfo]]) -> list[ProductInfo]:
     """

tests/utils/test_market_aggregator.py

@@ -94,7 +94,7 @@ class TestMarketDataAggregator:
         assert len(aggregated) == 2
         assert aggregated[0].timestamp_ms == 1685577600000
         assert aggregated[0].high == pytest.approx(50050.0, rel=1e-3)
-        assert aggregated[0].low == pytest.approx(49500.0, rel=1e-3)
+        assert aggregated[0].low == pytest.approx(49550.0, rel=1e-3)
         assert aggregated[1].timestamp_ms == 1685581200000
         assert aggregated[1].high == pytest.approx(50250.0, rel=1e-3)
-        assert aggregated[1].low == pytest.approx(49800.0, rel=1e-3)
+        assert aggregated[1].low == pytest.approx(49850.0, rel=1e-3)