diff --git a/freqtrade/rpc/rpc.py b/freqtrade/rpc/rpc.py
index 37c097347..0b47ecf7c 100644
--- a/freqtrade/rpc/rpc.py
+++ b/freqtrade/rpc/rpc.py
@@ -11,7 +11,7 @@ from typing import TYPE_CHECKING, Any
 import psutil
 from dateutil.relativedelta import relativedelta
 from dateutil.tz import tzlocal
-from numpy import inf, int64, isnan, mean, nan
+from numpy import inf, isnan, mean, nan
 from pandas import DataFrame, NaT, read_sql
 from sqlalchemy import func, select
 
@@ -1536,7 +1536,9 @@ class RPC:
                 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"].dt.as_unit("ms").astype(int64)
+            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:
diff --git a/tests/conftest.py b/tests/conftest.py
index a92144659..46601ddfb 100644
--- a/tests/conftest.py
+++ b/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"].dt.as_unit("ms").astype(np.int64)
+    df["date"] = df.loc[:, "date"].dt.as_unit("ms").astype("int64")
     return list(list(x) for x in zip(*(df[x].values.tolist() for x in df.columns), strict=False))
 
 
diff --git a/tests/exchange/test_binance_public_data.py b/tests/exchange/test_binance_public_data.py
index ab299321b..98d3864d3 100644
--- a/tests/exchange/test_binance_public_data.py
+++ b/tests/exchange/test_binance_public_data.py
@@ -69,7 +69,7 @@ def make_response_from_url(start_date, end_date):
             "taker_buy_quote_volume,ignore"
         )
         df = pd.DataFrame(columns=cols.split(","), dtype=float)
-        df["open_time"] = date_col.astype("int64") // 10**6
+        df["open_time"] = date_col.as_unit("ms").astype("int64")
         df["open"] = df["high"] = df["low"] = df["close"] = df["volume"] = 1.0
         return df