fix: use more stable "date to ms" method

Author: xmatthias

freqtrade/data/btanalysis/bt_fileutils.py

@@ -308,7 +308,7 @@ def get_backtest_market_change(filename: Path, include_ts: bool = True) -> pd.Da
     else:
         df = pd.read_feather(filename)
     if include_ts:
-        df.loc[:, "__date_ts"] = df.loc[:, "date"].astype(np.int64) // 1000 // 1000
+        df.loc[:, "__date_ts"] = df.loc[:, "date"].dt.as_unit("ms").astype(np.int64)
     return df
 
 
@@ -326,7 +326,7 @@ def get_backtest_wallet_change(filename: Path, strategy_name: str) -> pd.DataFra
         data = load_file_from_zip(filename, f"{filename.stem}_{strategy_name}_wallet.feather")
         df = pd.read_feather(BytesIO(data))
 
-        df.loc[:, "__date_ts"] = df.loc[:, "date"].astype(np.int64) // 1000 // 1000
+        df.loc[:, "__date_ts"] = df.loc[:, "date"].dt.as_unit("ms").astype(np.int64)
         return df
     except ValueError:
         pass

freqtrade/data/history/datahandlers/jsondatahandler.py

@@ -35,8 +35,8 @@ def ohlcv_store(
         filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
         self.create_dir_if_needed(filename)
         _data = data.copy()
-        # Convert date to int
-        _data["date"] = _data["date"].astype(np.int64) // 1000 // 1000
+        # Convert date to int (milliseconds)
+        _data["date"] = _data["date"].dt.as_unit("ms").astype(np.int64)
 
         # Reset index, select only appropriate columns and save as json
         _data.reset_index(drop=True).loc[:, self._columns].to_json(

freqtrade/rpc/rpc.py

@@ -794,7 +794,7 @@ def _rpc_get_historic_balance(self) -> tuple[DataFrame, int]:
         results = read_sql("wallet_history", con=Trade.session.bind, parse_dates=["timestamp"])
 
         results = results.rename({"timestamp": "date"}, axis=1)
-        results.loc[:, "__date_ts"] = results.loc[:, "date"].astype("int64") // 1000 // 1000
+        results.loc[:, "__date_ts"] = results.loc[:, "date"].dt.as_unit("ms").astype("int64")
         # Exclude non-bot managed for now
         results_filtered = results.loc[results["bot_managed"]]
 
@@ -1536,7 +1536,7 @@ def _convert_dataframe_to_dict(
                 df_cols = [col for col in dataframe_columns if col in cols_set]
                 dataframe = dataframe.loc[:, df_cols]
 
-            dataframe.loc[:, "__date_ts"] = dataframe.loc[:, "date"].astype(int64) // 1000 // 1000
+            dataframe.loc[:, "__date_ts"] = dataframe.loc[:, "date"].dt.as_unit("ms").astype(int64)
             # Move signal close to separate column when signal for easy plotting
             for sig_type in signals.keys():
                 if sig_type in dataframe.columns:

tests/conftest.py

@@ -207,7 +207,7 @@ def generate_test_data(
 def generate_test_data_raw(timeframe: str, size: int, start: str = "2020-07-05", random_seed=42):
     """Generates data in the ohlcv format used by ccxt"""
     df = generate_test_data(timeframe, size, start, random_seed)
-    df["date"] = df.loc[:, "date"].astype(np.int64) // 1000 // 1000
+    df["date"] = df.loc[:, "date"].dt.as_unit("ms").astype(np.int64)
     return list(list(x) for x in zip(*(df[x].values.tolist() for x in df.columns), strict=False))