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)