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Update quantstats.reports.metrics to include key dates of maximum drawdown. #322

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Update quantstats.reports.metrics to include key dates of maximum drawdown. #322

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f915292
Updated to allow matplotlib to display plots.
git-shogg Dec 8, 2023
3c96edf
Metrics report updated to include key dates of maximum drawdown +blac…
git-shogg Dec 8, 2023
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105 changes: 80 additions & 25 deletions quantstats/reports.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,7 +66,6 @@ def html(
match_dates=True,
**kwargs,
):

if output is None and not _utils._in_notebook():
raise ValueError("`output` must be specified")

Expand Down Expand Up @@ -501,7 +500,6 @@ def full(
match_dates=True,
**kwargs,
):

# prepare timeseries
if match_dates:
returns = returns.dropna()
Expand Down Expand Up @@ -651,7 +649,6 @@ def basic(
match_dates=True,
**kwargs,
):

# prepare timeseries
if match_dates:
returns = returns.dropna()
Expand Down Expand Up @@ -731,7 +728,6 @@ def metrics(
match_dates=True,
**kwargs,
):

if match_dates:
returns = returns.dropna()
returns.index = returns.index.tz_localize(None)
Expand Down Expand Up @@ -816,13 +812,6 @@ def metrics(
if kwargs.get("as_pct", False):
pct = 100

# return df
dd = _calc_dd(
df,
display=(display or "internal" in kwargs),
as_pct=kwargs.get("as_pct", False),
)

metrics = _pd.DataFrame()
metrics["Start Period"] = _pd.Series(s_start)
metrics["End Period"] = _pd.Series(s_end)
Expand Down Expand Up @@ -863,6 +852,9 @@ def metrics(

metrics["~~~~~~~~"] = blank
metrics["Max Drawdown %"] = blank
metrics["Max Drawdown Date"] = blank
metrics["Max Drawdown Period Start"] = blank
metrics["Max Drawdown Period End"] = blank
metrics["Longest DD Days"] = blank

if mode.lower() == "full":
Expand Down Expand Up @@ -930,13 +922,20 @@ 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
Expand Down Expand Up @@ -1004,25 +1003,40 @@ 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
# return drawdown (dd) df
dd = _calc_dd(
df,
display=(display or "internal" in kwargs),
as_pct=kwargs.get("as_pct", False),
)

# drawdown (dd) detail
metrics["~~~~"] = blank
for ix, row in dd.iterrows():
metrics[ix] = row

metrics["Recovery Factor"] = _stats.recovery_factor(df)
metrics["Ulcer Index"] = _stats.ulcer_index(df)
metrics["Serenity Index"] = _stats.serenity_index(df, rf)
Expand All @@ -1031,20 +1045,35 @@ 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:
Expand Down Expand Up @@ -1212,7 +1241,6 @@ 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")
Expand Down Expand Up @@ -1500,6 +1528,15 @@ def _calc_dd(df, display=True, as_pct=False):
.sort_values(by="max drawdown", ascending=True)["max drawdown"]
.values[0]
/ 100,
"Max Drawdown Date": ret_dd[col]
.sort_values(by="max drawdown", ascending=True)["valley"]
.values[0],
"Max Drawdown Period Start": ret_dd[col]
.sort_values(by="max drawdown", ascending=True)["start"]
.values[0],
"Max Drawdown Period End": ret_dd[col]
.sort_values(by="max drawdown", ascending=True)["end"]
.values[0],
"Longest DD Days": str(
_np.round(
ret_dd[col]
Expand All @@ -1519,6 +1556,15 @@ def _calc_dd(df, display=True, as_pct=False):
"max drawdown"
].values[0]
/ 100,
"Max Drawdown Date": ret_dd.sort_values(
by="max drawdown", ascending=True
)["valley"].values[0],
"Max Drawdown Period Start": ret_dd.sort_values(
by="max drawdown", ascending=True
)["start"].values[0],
"Max Drawdown Period End": ret_dd.sort_values(
by="max drawdown", ascending=True
)["end"].values[0],
"Longest DD Days": str(
_np.round(
ret_dd.sort_values(by="days", ascending=False)["days"].values[0]
Expand All @@ -1535,6 +1581,15 @@ def _calc_dd(df, display=True, as_pct=False):
"max drawdown"
].values[0]
/ 100,
"Max Drawdown Date": bench_dd.sort_values(
by="max drawdown", ascending=True
)["valley"].values[0],
"Max Drawdown Period Start": bench_dd.sort_values(
by="max drawdown", ascending=True
)["start"].values[0],
"Max Drawdown Period End": bench_dd.sort_values(
by="max drawdown", ascending=True
)["end"].values[0],
"Longest DD Days": str(
_np.round(
bench_dd.sort_values(by="days", ascending=False)["days"].values[0]
Expand Down
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