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qlib/contrib/report/analysis_position/risk_analysis.py Normal file
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from typing import Iterable
import pandas as pd
import plotly.graph_objs as py
from ...evaluate import risk_analysis
from ..graph import SubplotsGraph, ScatterGraph
def _get_risk_analysis_data_with_report(
report_normal_df: pd.DataFrame,
# report_long_short_df: pd.DataFrame,
date: pd.Timestamp,
) -> pd.DataFrame:
"""Get risk analysis data with report
:param report_normal_df: report data
:param report_long_short_df: report data
:param date: date string
:return:
"""
analysis = dict()
# if not report_long_short_df.empty:
# analysis["pred_long"] = risk_analysis(report_long_short_df["long"])
# analysis["pred_short"] = risk_analysis(report_long_short_df["short"])
# analysis["pred_long_short"] = risk_analysis(report_long_short_df["long_short"])
if not report_normal_df.empty:
analysis["sub_bench"] = risk_analysis(
report_normal_df["return"] - report_normal_df["bench"]
)
analysis["sub_cost"] = risk_analysis(
report_normal_df["return"]
- report_normal_df["bench"]
- report_normal_df["cost"]
)
analysis_df = pd.concat(analysis) # type: pd.DataFrame
analysis_df["date"] = date
return analysis_df
def _get_all_risk_analysis(risk_df: pd.DataFrame) -> pd.DataFrame:
"""risk_df to standard
:param risk_df: risk data
:return:
"""
if risk_df is None:
return pd.DataFrame()
risk_df = risk_df.unstack()
risk_df.columns = risk_df.columns.droplevel(0)
return risk_df.drop("mean", axis=1)
def _get_monthly_risk_analysis_with_report(report_normal_df: pd.DataFrame) -> pd.DataFrame:
"""Get monthly analysis data
:param report_normal_df:
# :param report_long_short_df:
:return:
"""
# Group by month
report_normal_gp = report_normal_df.groupby(
[report_normal_df.index.year, report_normal_df.index.month]
)
# report_long_short_gp = report_long_short_df.groupby(
# [report_long_short_df.index.year, report_long_short_df.index.month]
# )
gp_month = sorted(set(report_normal_gp.size().index))
_monthly_df = pd.DataFrame()
for gp_m in gp_month:
_m_report_normal = report_normal_gp.get_group(gp_m)
# _m_report_long_short = report_long_short_gp.get_group(gp_m)
if len(_m_report_normal) < 3:
# The month's data is less than 3, not displayed
# FIXME: If the trading day of a month is less than 3 days, a breakpoint will appear in the graph
continue
month_days = pd.Timestamp(year=gp_m[0], month=gp_m[1], day=1).days_in_month
_temp_df = _get_risk_analysis_data_with_report(
_m_report_normal,
# _m_report_long_short,
pd.Timestamp(year=gp_m[0], month=gp_m[1], day=month_days),
)
_monthly_df = _monthly_df.append(_temp_df, sort=False)
return _monthly_df
def _get_monthly_analysis_with_feature(
monthly_df: pd.DataFrame, feature: str = "annual"
) -> pd.DataFrame:
"""
:param monthly_df:
:param feature:
:return:
"""
_monthly_df_gp = monthly_df.reset_index().groupby(["level_1"])
_name_df = _monthly_df_gp.get_group(feature).set_index(["level_0", "level_1"])
_temp_df = _name_df.pivot_table(
index="date", values=["risk"], columns=_name_df.index
)
_temp_df.columns = map(lambda x: "_".join(x[-1]), _temp_df.columns)
_temp_df.index = _temp_df.index.strftime("%Y-%m")
return _temp_df
def _get_risk_analysis_figure(analysis_df: pd.DataFrame) -> Iterable[py.Figure]:
"""Get analysis graph figure
:param analysis_df:
:return:
"""
if analysis_df is None:
return []
_figure = SubplotsGraph(
_get_all_risk_analysis(analysis_df), kind_map=dict(kind="BarGraph", kwargs={})
).figure
return (_figure,)
def _get_monthly_risk_analysis_figure(report_normal_df: pd.DataFrame) -> Iterable[py.Figure]:
"""Get analysis monthly graph figure
:param report_normal_df:
:param report_long_short_df:
:return:
"""
# if report_normal_df is None and report_long_short_df is None:
# return []
if report_normal_df is None:
return []
# if report_normal_df is None:
# report_normal_df = pd.DataFrame(index=report_long_short_df.index)
# if report_long_short_df is None:
# report_long_short_df = pd.DataFrame(index=report_normal_df.index)
_monthly_df = _get_monthly_risk_analysis_with_report(
report_normal_df=report_normal_df,
# report_long_short_df=report_long_short_df,
)
for _feature in ["annual", "mdd", "sharpe", "std"]:
_temp_df = _get_monthly_analysis_with_feature(_monthly_df, _feature)
yield ScatterGraph(
_temp_df,
layout=dict(title=_feature, xaxis=dict(type="category", tickangle=45)),
graph_kwargs={"mode": "lines+markers"},
).figure
def risk_analysis_graph(
analysis_df: pd.DataFrame = None,
report_normal_df: pd.DataFrame = None,
report_long_short_df: pd.DataFrame = None,
show_notebook: bool = True,
) -> Iterable[py.Figure]:
"""Generate analysis graph and monthly analysis
Example:
.. code-block:: python
from qlib.contrib.evaluate import risk_analysis, backtest, long_short_backtest
from qlib.contrib.strategy import TopkDropoutStrategy
from qlib.contrib.report import analysis_position
# backtest parameters
bparas = {}
bparas['limit_threshold'] = 0.095
bparas['account'] = 1000000000
sparas = {}
sparas['topk'] = 50
sparas['n_drop'] = 230
strategy = TopkDropoutStrategy(**sparas)
report_normal_df, positions = backtest(pred_df, strategy, **bparas)
# long_short_map = long_short_backtest(pred_df)
# report_long_short_df = pd.DataFrame(long_short_map)
analysis = dict()
# analysis['pred_long'] = risk_analysis(report_long_short_df['long'])
# analysis['pred_short'] = risk_analysis(report_long_short_df['short'])
# analysis['pred_long_short'] = risk_analysis(report_long_short_df['long_short'])
analysis['sub_bench'] = risk_analysis(report_normal_df['return'] - report_normal_df['bench'])
analysis['sub_cost'] = risk_analysis(report_normal_df['return'] - report_normal_df['bench'] - report_normal_df['cost'])
analysis_df = pd.concat(analysis)
analysis_position.risk_analysis_graph(analysis_df, report_normal_df)
:param analysis_df: analysis data, index is **pd.MultiIndex**; columns names is **[risk]**.
.. code-block:: python
risk
sub_bench mean 0.000662
std 0.004487
annual 0.166720
sharpe 2.340526
mdd -0.080516
sub_cost mean 0.000577
std 0.004482
annual 0.145392
sharpe 2.043494
mdd -0.083584
:param report_normal_df: **df.index.name** must be **date**, df.columns must contain **return**, **turnover**, **cost**, **bench**
.. code-block:: python
return cost bench turnover
date
2017-01-04 0.003421 0.000864 0.011693 0.576325
2017-01-05 0.000508 0.000447 0.000721 0.227882
2017-01-06 -0.003321 0.000212 -0.004322 0.102765
2017-01-09 0.006753 0.000212 0.006874 0.105864
2017-01-10 -0.000416 0.000440 -0.003350 0.208396
:param report_long_short_df: **df.index.name** must be **date**, df.columns contain **long**, **short**, **long_short**
.. code-block:: python
long short long_short
date
2017-01-04 -0.001360 0.001394 0.000034
2017-01-05 0.002456 0.000058 0.002514
2017-01-06 0.000120 0.002739 0.002859
2017-01-09 0.001436 0.001838 0.003273
2017-01-10 0.000824 -0.001944 -0.001120
:param show_notebook: Whether to display graphics in a notebook, default **True**
If True, show graph in notebook
If False, return graph figure
:return:
"""
_figure_list = list(_get_risk_analysis_figure(analysis_df)) + list(
_get_monthly_risk_analysis_figure(
report_normal_df,
# report_long_short_df,
)
)
if show_notebook:
ScatterGraph.show_graph_in_notebook(_figure_list)
else:
return _figure_list