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