BUG: Cast datetime_arr.astype(np.int64) to avoid error on Widows

Fixes error:

> TypeError: Converting from datetime64[ns] to int32 is not supported. Do obj.astype('int64').astype(dtype) instead

Contrary to numpy docs:

* https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int_

even 64-bit Windos apparently has 32-bit `long`s.

* https://stackoverflow.com/questions/36278590/numpy-array-dtype-is-coming-as-int32-by-default-in-a-windows-10-64-bit-machine
* https://learn.microsoft.com/en-us/cpp/cpp/data-type-ranges?view=msvc-170

Fixes #1214

Author: kernc

backtesting/_plotting.py

@@ -165,10 +165,10 @@ def _weighted_returns(s, trades=trades):
         return ((df['Size'].abs() * df['ReturnPct']) / df['Size'].abs().sum()).sum()
 
     def _group_trades(column):
-        def f(s, new_index=pd.Index(df.index.astype(int)), bars=trades[column]):
+        def f(s, new_index=pd.Index(df.index.astype(np.int64)), bars=trades[column]):
             if s.size:
                 # Via int64 because on pandas recently broken datetime
-                mean_time = int(bars.loc[s.index].astype(int).mean())
+                mean_time = int(bars.loc[s.index].astype(np.int64).mean())
                 new_bar_idx = new_index.get_indexer([mean_time], method='nearest')[0]
                 return new_bar_idx
         return f

backtesting/_stats.py

@@ -15,7 +15,7 @@ def compute_drawdown_duration_peaks(dd: pd.Series):
     iloc = np.unique(np.r_[(dd == 0).values.nonzero()[0], len(dd) - 1])
     iloc = pd.Series(iloc, index=dd.index[iloc])
     df = iloc.to_frame('iloc').assign(prev=iloc.shift())
-    df = df[df['iloc'] > df['prev'] + 1].astype(int)
+    df = df[df['iloc'] > df['prev'] + 1].astype(np.int64)
 
     # If no drawdown since no trade, avoid below for pandas sake and return nan series
     if not len(df):

backtesting/backtesting.py

@@ -1536,7 +1536,7 @@ def _optimize_sambo() -> Union[pd.Series,
                 if values.dtype.kind in 'mM':  # timedelta, datetime64
                     # these dtypes are unsupported in SAMBO, so convert to raw int
                     # TODO: save dtype and convert back later
-                    values = values.astype(int)
+                    values = values.astype(np.int64)
 
                 if values.dtype.kind in 'iumM':
                     dimensions.append((values.min(), values.max() + 1))