fix: return exception error

models/price.py

@@ -1,5 +1,13 @@
-from pydantic import BaseModel
+from pydantic import BaseModel, validator
 
 
 class PricePayload(BaseModel):
     symbol: str
+    count_back: int = 150
+    source: str = "database"
+
+    @validator("source")
+    def valid_source(cls, v):
+        if v not in ["database", "vnstock"]:
+            raise ValueError('source must be database or vnstock')
+        return v

routes/data.py

@@ -61,7 +61,9 @@ async def update_entire_macd():
     status_code=status.HTTP_200_OK
 )
 async def get_price_data(payload: PricePayload) -> Sequence[dict]:
-    price_df = Indicator.get_price(payload.symbol)
+    price_df = Indicator.get_price(payload.symbol,
+                                   payload.count_back,
+                                   payload.source)
     if price_df is None:
         return [{"message": "Error"}]
     return json.loads(price_df.to_json(orient="records"))

services/indicator.py

@@ -155,27 +155,53 @@ class Indicator:
             updating_price = False
 
     @staticmethod
-    def get_price(symbol) -> pd.DataFrame:
+    def get_price(symbol: str,
+                  count_back: int = 150,
+                  source: str = "database") -> pd.DataFrame:
         try:
             symbol = symbol.upper()
-            uri = os.environ.get("MONGODB_URI")
-            client = MongoClient(uri)
-            database = client.get_database("data")
-            collection = database.get_collection("price")
-            result = list(collection.find())
-            tmp_df = pd.DataFrame(result[0]["value"])
-            lst_symbols = tmp_df["ticker"].values
-            if symbol not in lst_symbols:
-                return None
-            symbol_index = np.argwhere(lst_symbols == symbol)[0][0]
-            lst_values = []
-            for record in result:
-                value = record["value"][symbol_index]
-                value["time"] = record["time"]
-                lst_values.append(value)
-            return pd.DataFrame(lst_values)
-        except Exception:
-            return None
+            if source == "database":
+                uri = os.environ.get("MONGODB_URI")
+                client = MongoClient(uri)
+                database = client.get_database("data")
+                collection = database.get_collection("price")
+                result = list(collection.find())
+                tmp_df = pd.DataFrame(result[0]["value"])
+                lst_symbols = tmp_df["ticker"].values
+                if symbol not in lst_symbols:
+                    return pd.DataFrame(
+                        [{"message": "The symbol is not existed"}]
+                        )
+                symbol_index = np.argwhere(lst_symbols == symbol)[0][0]
+                lst_values = []
+                for record in result:
+                    value = record["value"][symbol_index]
+                    value["time"] = record["time"]
+                    lst_values.append(value)
+                return pd.DataFrame(lst_values)
+            elif source == "vnstock":
+                datetime_now = datetime.utcnow()
+                end_date = datetime_now.date()
+                delta = timedelta(days=count_back)
+                start_date = end_date - delta
+                start_date = start_date.strftime(DATE_FORMAT)
+                end_date = end_date.strftime(DATE_FORMAT)
+                df = longterm_ohlc_data(symbol,
+                                        start_date,
+                                        end_date,
+                                        resolution="D",
+                                        type="stock"
+                                        ).reset_index(drop=True)
+                df[["open", "high", "low", "close"]] = \
+                    df[["open", "high", "low", "close"]] * 1000
+                # convert open, high, low, close to int
+                df[["open", "high", "low", "close"]] = \
+                    df[["open", "high", "low", "close"]].astype(int)
+                return df[["time", "ticker", "open", "high", "low", "close"]]
+        except Exception as e:
+            return pd.DataFrame(
+                [{"message": f"Caught error {e}"}]
+                )
 
     @staticmethod
     def update_daily_rsi() -> None:
@@ -288,10 +314,7 @@ class Indicator:
             client = MongoClient(uri)
             database = client.get_database("data")
             collection = database.get_collection("rsi")
-            try:
-                records = list(collection.find())
-            except Exception as e:
-                print(f"Error: {e}")
+            records = list(collection.find())
             record_df = pd.DataFrame(records).drop(columns=["_id"])
             record_df = \
                 record_df[["time", symbol]].rename(columns={symbol: "rsi"})
@@ -303,8 +326,10 @@ class Indicator:
                 record_df["stoch_rsi_smooth_k"], smooth_d
             )
             return record_df
-        except Exception:
-            return None
+        except Exception as e:
+            return pd.DataFrame(
+                [{"message": f"Caught error {e}"}]
+                )
 
     @staticmethod
     def stoch_rsi(rsi: pd.Series, periods: int = 14) -> pd.Series:
@@ -384,16 +409,15 @@ class Indicator:
             client = MongoClient(uri)
             database = client.get_database("data")
             collection = database.get_collection("macd")
-            try:
-                records = list(collection.find())
-            except Exception as e:
-                print(f"Error: {e}")
+            records = list(collection.find())
             record_df = pd.DataFrame(records).drop(columns=["_id"])
             record_df = \
                 record_df[["time", symbol]].rename(columns={symbol: "macd"})
             return record_df
-        except Exception:
-            return None
+        except Exception as e:
+            return pd.DataFrame(
+                [{"message": f"Caught error {e}"}]
+                )
 
     @staticmethod
     def get_ichimoku_cloud(