Pandas update: Fixed some bugs that caused future warnings in pandas

.gitignore

@@ -14,3 +14,4 @@ Icon
 /tests
 .vscode
 Icon
+.DS_Store

quantstats/_plotting/core.py

@@ -26,19 +26,13 @@
     pass
 
 import matplotlib.dates as _mdates
-from matplotlib.ticker import (
-    FormatStrFormatter as _FormatStrFormatter,
-    FuncFormatter as _FuncFormatter,
-)
-
-import pandas as _pd
 import numpy as _np
+import pandas as _pd
 import seaborn as _sns
-from .. import (
-    stats as _stats,
-    utils as _utils,
-)
+from matplotlib.ticker import FormatStrFormatter as _FormatStrFormatter
+from matplotlib.ticker import FuncFormatter as _FuncFormatter
 
+from .. import stats as _stats
 
 _sns.set(
     font_scale=1.1,
@@ -112,7 +106,6 @@ def plot_returns_bars(
     savefig=None,
     show=True,
 ):
-
     if match_volatility and benchmark is None:
         raise ValueError("match_volatility requires passing of " "benchmark.")
     if match_volatility and benchmark is not None:
@@ -265,7 +258,6 @@ def plot_timeseries(
     savefig=None,
     show=True,
 ):
-
     colors, ls, alpha = _get_colors(grayscale)
 
     returns.fillna(0, inplace=True)
@@ -419,7 +411,6 @@ def plot_histogram(
     savefig=None,
     show=True,
 ):
-
     # colors = ['#348dc1', '#003366', 'red']
     # if grayscale:
     #     colors = ['silver', 'gray', 'black']
@@ -616,7 +607,6 @@ def plot_rolling_stats(
     savefig=None,
     show=True,
 ):
-
     colors, _, _ = _get_colors(grayscale)
 
     fig, ax = _plt.subplots(figsize=figsize)
@@ -745,7 +735,6 @@ def plot_rolling_beta(
     savefig=None,
     show=True,
 ):
-
     colors, _, _ = _get_colors(grayscale)
 
     fig, ax = _plt.subplots(figsize=figsize)
@@ -889,7 +878,6 @@ def plot_longest_drawdowns(
     savefig=None,
     show=True,
 ):
-
     colors = ["#348dc1", "#003366", "red"]
     if grayscale:
         colors = ["#000000"] * 3
@@ -1001,7 +989,6 @@ def plot_distribution(
     savefig=None,
     show=True,
 ):
-
     colors = _FLATUI_COLORS
     if grayscale:
         colors = ["#f9f9f9", "#dddddd", "#bbbbbb", "#999999", "#808080"]
@@ -1013,16 +1000,16 @@ def plot_distribution(
     apply_fnc = _stats.comp if compounded else _np.sum
 
     port["Weekly"] = port["Daily"].resample("W-MON").apply(apply_fnc)
-    port["Weekly"].ffill(inplace=True)
+    port["Weekly"] = port["Weekly"].ffill()
 
-    port["Monthly"] = port["Daily"].resample("M").apply(apply_fnc)
-    port["Monthly"].ffill(inplace=True)
+    port["Monthly"] = port["Daily"].resample("ME").apply(apply_fnc)
+    port["Monthly"] = port["Monthly"].ffill()
 
-    port["Quarterly"] = port["Daily"].resample("Q").apply(apply_fnc)
-    port["Quarterly"].ffill(inplace=True)
+    port["Quarterly"] = port["Daily"].resample("QE").apply(apply_fnc)
+    port["Quarterly"] = port["Quarterly"].ffill()
 
-    port["Yearly"] = port["Daily"].resample("A").apply(apply_fnc)
-    port["Yearly"].ffill(inplace=True)
+    port["Yearly"] = port["Daily"].resample("YE").apply(apply_fnc)
+    port["Yearly"] = port["Yearly"].ffill()
 
     fig, ax = _plt.subplots(figsize=figsize)
     ax.spines["top"].set_visible(False)
@@ -1118,7 +1105,6 @@ def plot_table(
     savefig=None,
     show=False,
 ):
-
     if columns is not None:
         try:
             tbl.columns = columns

quantstats/reports.py

@@ -18,21 +18,29 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import pandas as _pd
-import numpy as _np
-from math import sqrt as _sqrt, ceil as _ceil
-from datetime import datetime as _dt
-from base64 import b64encode as _b64encode
 import re as _regex
-from tabulate import tabulate as _tabulate
-from . import __version__, stats as _stats, utils as _utils, plots as _plots
-from dateutil.relativedelta import relativedelta
+from base64 import b64encode as _b64encode
+from datetime import datetime as _dt
 from io import StringIO
+from math import ceil as _ceil
+from math import sqrt as _sqrt
+
+import numpy as _np
+import pandas as _pd
+from dateutil.relativedelta import relativedelta
+from tabulate import tabulate as _tabulate
+
+from . import __version__
+from . import plots as _plots
+from . import stats as _stats
+from . import utils as _utils
 
 try:
-    from IPython.display import display as iDisplay, HTML as iHTML
+    from IPython.display import HTML as iHTML
+    from IPython.display import display as iDisplay
 except ImportError:
-    from IPython.core.display import display as iDisplay, HTML as iHTML
+    from IPython.core.display import HTML as iHTML
+    from IPython.core.display import display as iDisplay
 
 
 def _get_trading_periods(periods_per_year=252):
@@ -66,7 +74,6 @@ def html(
     match_dates=True,
     **kwargs,
 ):
-
     if output is None and not _utils._in_notebook():
         raise ValueError("`output` must be specified")
 
@@ -501,7 +508,6 @@ def full(
     match_dates=True,
     **kwargs,
 ):
-
     # prepare timeseries
     if match_dates:
         returns = returns.dropna()
@@ -651,7 +657,6 @@ def basic(
     match_dates=True,
     **kwargs,
 ):
-
     # prepare timeseries
     if match_dates:
         returns = returns.dropna()
@@ -731,7 +736,6 @@ def metrics(
     match_dates=True,
     **kwargs,
 ):
-
     if match_dates:
         returns = returns.dropna()
     returns.index = returns.index.tz_localize(None)
@@ -930,13 +934,20 @@ def metrics(
         metrics["~~~~~~~~~~"] = blank
 
         metrics["Expected Daily %%"] = (
-            _stats.expected_return(df, compounded=compounded, prepare_returns=False) * pct
+            _stats.expected_return(df, compounded=compounded, prepare_returns=False)
+            * pct
         )
         metrics["Expected Monthly %%"] = (
-            _stats.expected_return(df, compounded=compounded, aggregate="M", prepare_returns=False) * pct
+            _stats.expected_return(
+                df, compounded=compounded, aggregate="M", prepare_returns=False
+            )
+            * pct
         )
         metrics["Expected Yearly %%"] = (
-            _stats.expected_return(df, compounded=compounded, aggregate="A", prepare_returns=False) * pct
+            _stats.expected_return(
+                df, compounded=compounded, aggregate="A", prepare_returns=False
+            )
+            * pct
         )
         metrics["Kelly Criterion %"] = (
             _stats.kelly_criterion(df, prepare_returns=False) * pct
@@ -957,7 +968,7 @@ def metrics(
         metrics["Max Consecutive Losses *int"] = _stats.consecutive_losses(df)
 
     metrics["Gain/Pain Ratio"] = _stats.gain_to_pain_ratio(df, rf)
-    metrics["Gain/Pain (1M)"] = _stats.gain_to_pain_ratio(df, rf, "M")
+    metrics["Gain/Pain (1M)"] = _stats.gain_to_pain_ratio(df, rf, "ME")
     # if mode.lower() == 'full':
     #     metrics['GPR (3M)'] = _stats.gain_to_pain_ratio(df, rf, "Q")
     #     metrics['GPR (6M)'] = _stats.gain_to_pain_ratio(df, rf, "2Q")
@@ -1004,19 +1015,26 @@ def metrics(
     # best/worst
     if mode.lower() == "full":
         metrics["~~~"] = blank
-        metrics["Best Day %"] = _stats.best(df, compounded=compounded, prepare_returns=False) * pct
+        metrics["Best Day %"] = (
+            _stats.best(df, compounded=compounded, prepare_returns=False) * pct
+        )
         metrics["Worst Day %"] = _stats.worst(df, prepare_returns=False) * pct
         metrics["Best Month %"] = (
-            _stats.best(df, compounded=compounded, aggregate="M", prepare_returns=False) * pct
+            _stats.best(df, compounded=compounded, aggregate="M", prepare_returns=False)
+            * pct
         )
         metrics["Worst Month %"] = (
             _stats.worst(df, aggregate="M", prepare_returns=False) * pct
         )
         metrics["Best Year %"] = (
-            _stats.best(df, compounded=compounded, aggregate="A", prepare_returns=False) * pct
+            _stats.best(df, compounded=compounded, aggregate="A", prepare_returns=False)
+            * pct
         )
         metrics["Worst Year %"] = (
-            _stats.worst(df, compounded=compounded, aggregate="A", prepare_returns=False) * pct
+            _stats.worst(
+                df, compounded=compounded, aggregate="A", prepare_returns=False
+            )
+            * pct
         )
 
     # dd
@@ -1031,20 +1049,35 @@ def metrics(
     if mode.lower() == "full":
         metrics["~~~~~"] = blank
         metrics["Avg. Up Month %"] = (
-            _stats.avg_win(df, compounded=compounded, aggregate="M", prepare_returns=False) * pct
+            _stats.avg_win(
+                df, compounded=compounded, aggregate="M", prepare_returns=False
+            )
+            * pct
         )
         metrics["Avg. Down Month %"] = (
-            _stats.avg_loss(df, compounded=compounded, aggregate="M", prepare_returns=False) * pct
+            _stats.avg_loss(
+                df, compounded=compounded, aggregate="M", prepare_returns=False
+            )
+            * pct
         )
         metrics["Win Days %%"] = _stats.win_rate(df, prepare_returns=False) * pct
         metrics["Win Month %%"] = (
-            _stats.win_rate(df, compounded=compounded, aggregate="M", prepare_returns=False) * pct
+            _stats.win_rate(
+                df, compounded=compounded, aggregate="M", prepare_returns=False
+            )
+            * pct
         )
         metrics["Win Quarter %%"] = (
-            _stats.win_rate(df, compounded=compounded, aggregate="Q", prepare_returns=False) * pct
+            _stats.win_rate(
+                df, compounded=compounded, aggregate="Q", prepare_returns=False
+            )
+            * pct
         )
         metrics["Win Year %%"] = (
-            _stats.win_rate(df, compounded=compounded, aggregate="A", prepare_returns=False) * pct
+            _stats.win_rate(
+                df, compounded=compounded, aggregate="A", prepare_returns=False
+            )
+            * pct
         )
 
         if "benchmark" in df:
@@ -1212,7 +1245,6 @@ def plots(
     match_dates=True,
     **kwargs,
 ):
-
     benchmark_colname = kwargs.get("benchmark_title", "Benchmark")
     strategy_colname = kwargs.get("strategy_title", "Strategy")
     active = kwargs.get("active", "False")
@@ -1579,9 +1611,7 @@ def _download_html(html, filename="quantstats-tearsheet.html"):
     a.download="{{filename}}";
     a.hidden=true;document.body.appendChild(a);
     a.innerHTML="download report";
-    a.click();</script>""".replace(
-            "\n", ""
-        ),
+    a.click();</script>""".replace("\n", ""),
     )
     jscode = jscode.replace("{{html}}", _regex.sub(" +", " ", html.replace("\n", "")))
     if _utils._in_notebook():
@@ -1594,9 +1624,7 @@ def _open_html(html):
         " ",
         """<script>
     var win=window.open();win.document.body.innerHTML='{{html}}';
-    </script>""".replace(
-            "\n", ""
-        ),
+    </script>""".replace("\n", ""),
     )
     jscode = jscode.replace("{{html}}", _regex.sub(" +", " ", html.replace("\n", "")))
     if _utils._in_notebook():

quantstats/utils.py

@@ -17,13 +17,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import io as _io
 import datetime as _dt
-import pandas as _pd
+import inspect
+import io as _io
+
 import numpy as _np
+import pandas as _pd
 import yfinance as _yf
+
 from . import stats as _stats
-import inspect
 
 
 def _mtd(df):
@@ -259,10 +261,9 @@ def _prepare_benchmark(benchmark=None, period="max", rf=0.0, prepare_returns=Tru
         benchmark = benchmark[benchmark.columns[0]].copy()
 
     if isinstance(period, _pd.DatetimeIndex) and set(period) != set(benchmark.index):
-
         # Adjust Benchmark to Strategy frequency
         benchmark_prices = to_prices(benchmark, base=1)
-        new_index = _pd.date_range(start=period[0], end=period[-1], freq="D")
+        new_index = _pd.date_range(start=period[0], end=period[-1], freq="d")
         benchmark = (
             benchmark_prices.reindex(new_index, method="bfill")
             .reindex(period)