Update App/routers/stocks/models.py

App/routers/stocks/models.py

@@ -27,6 +27,29 @@ class Stock(Model):
         table = "stocks"
 
 
+# No changes needed for the Stock model itself
+class Stock(Model):
+    id = fields.IntField(primary_key=True)
+    symbol = fields.CharField(max_length=10, unique=True)
+    name = fields.CharField(max_length=200)
+    created_at = fields.DatetimeField(auto_now_add=True)
+    updated_at = fields.DatetimeField(auto_now=True)
+    
+    # Relationships
+    price_data: fields.ReverseRelation["StockPriceData"]
+    dividends: fields.ReverseRelation["Dividend"]
+    portfolio_holdings: fields.ReverseRelation["PortfolioHolding"] # Assuming this model exists
+
+    class Meta:
+        table = "stocks"
+
+# Create a lean Pydantic model for Stock that excludes deep relationships
+StockPydantic_Slim = pydantic_model_creator(
+    Stock, 
+    name="StockPydantic_Slim", 
+    exclude=("dividends", "portfolio_holdings", "price_data")
+)
+
 class StockPriceData(Model):
     id = fields.IntField(primary_key=True)
     stock = fields.ForeignKeyField("models.Stock", related_name="price_data")
@@ -35,28 +58,38 @@ class StockPriceData(Model):
     closing_price = fields.DecimalField(max_digits=15, decimal_places=2)
     high = fields.DecimalField(max_digits=15, decimal_places=2)
     low = fields.DecimalField(max_digits=15, decimal_places=2)
-    volume = fields.BigIntField()  # Use BigIntField for large numbers
-    turnover = fields.BigIntField()  # Use BigIntField instead of IntField
-    shares_in_issue = fields.BigIntField()  # Use BigIntField for large numbers
-    market_cap = fields.BigIntField()  # Use BigIntField for large numbers
+    volume = fields.BigIntField()
+    turnover = fields.BigIntField()
+    shares_in_issue = fields.BigIntField()
+    market_cap = fields.BigIntField()
     created_at = fields.DatetimeField(auto_now_add=True)
 
     @staticmethod
-    async def get_list(data):
-        if type(data) == QuerySet:
-            parser = pydantic_queryset_creator(StockPriceData)
-            return await parser.from_queryset(data)
+    async def get_list(data: QuerySet):
+        # Create a Pydantic model for StockPriceData that uses our slim Stock model
+        PriceDataPydantic = pydantic_model_creator(
+            StockPriceData,
+            name="PriceDataWithSlimStock",
+            # This is the key part: we explicitly define the nested model to use
+            computed=("stock", StockPydantic_Slim) 
+        )
+        
+        # Create a Pydantic model for a list of the above
+        PriceDataListPydantic = pydantic_queryset_creator(
+            StockPriceData,
+            pydantic_model=PriceDataPydantic
+        )
+        
+        return await PriceDataListPydantic.from_queryset(data)
 
     async def to_dict(self):
-        if type(self) == Model:
-            parser = pydantic_model_creator(StockPriceData)
-            return await parser.from_tortoise_orm(self)
+        # This can remain as is, or you can create a specific Pydantic model for it too
+        parser = pydantic_model_creator(StockPriceData)
+        return await parser.from_tortoise_orm(self)
 
     class Meta:
         table = "stock_price_data"
-        unique_together = (
-            ("stock", "date"),
-        )  # Prevent duplicate entries for same stock/date
+        unique_together = (("stock", "date"),)
 
 
 class Dividend(Model):