mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-13 07:46:53 +08:00
c276de4040
* modify FileStorage to support multiple freqs * modify backtest's sample documentation * change the logging level of read data exception from error to debug * fix the backtest exception when volume is 0 or np.nan * fix test_storage.py * add backtest_daily * modify backtest_daily's docstring * add __repr__/__str__ to Position * fix the bug of nested_decision_execution example Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
249 lines
8.2 KiB
Python
249 lines
8.2 KiB
Python
# Copyright (c) Microsoft Corporation.
|
|
# Licensed under the MIT License.
|
|
|
|
import pandas as pd
|
|
|
|
from ..graph import SubplotsGraph, BaseGraph
|
|
|
|
|
|
def _calculate_maximum(df: pd.DataFrame, is_ex: bool = False):
|
|
"""
|
|
|
|
:param df:
|
|
:param is_ex:
|
|
:return:
|
|
"""
|
|
if is_ex:
|
|
end_date = df["cum_ex_return_wo_cost_mdd"].idxmin()
|
|
start_date = df.loc[df.index <= end_date]["cum_ex_return_wo_cost"].idxmax()
|
|
else:
|
|
end_date = df["return_wo_mdd"].idxmin()
|
|
start_date = df.loc[df.index <= end_date]["cum_return_wo_cost"].idxmax()
|
|
return start_date, end_date
|
|
|
|
|
|
def _calculate_mdd(series):
|
|
"""
|
|
Calculate mdd
|
|
|
|
:param series:
|
|
:return:
|
|
"""
|
|
return series - series.cummax()
|
|
|
|
|
|
def _calculate_report_data(df: pd.DataFrame) -> pd.DataFrame:
|
|
"""
|
|
|
|
:param df:
|
|
:return:
|
|
"""
|
|
index_names = df.index.names
|
|
df.index = df.index.strftime("%Y-%m-%d")
|
|
|
|
report_df = pd.DataFrame()
|
|
|
|
report_df["cum_bench"] = df["bench"].cumsum()
|
|
report_df["cum_return_wo_cost"] = df["return"].cumsum()
|
|
report_df["cum_return_w_cost"] = (df["return"] - df["cost"]).cumsum()
|
|
# report_df['cum_return'] - report_df['cum_return'].cummax()
|
|
report_df["return_wo_mdd"] = _calculate_mdd(report_df["cum_return_wo_cost"])
|
|
report_df["return_w_cost_mdd"] = _calculate_mdd((df["return"] - df["cost"]).cumsum())
|
|
|
|
report_df["cum_ex_return_wo_cost"] = (df["return"] - df["bench"]).cumsum()
|
|
report_df["cum_ex_return_w_cost"] = (df["return"] - df["bench"] - df["cost"]).cumsum()
|
|
report_df["cum_ex_return_wo_cost_mdd"] = _calculate_mdd((df["return"] - df["bench"]).cumsum())
|
|
report_df["cum_ex_return_w_cost_mdd"] = _calculate_mdd((df["return"] - df["cost"] - df["bench"]).cumsum())
|
|
# return_wo_mdd , return_w_cost_mdd, cum_ex_return_wo_cost_mdd, cum_ex_return_w
|
|
|
|
report_df["turnover"] = df["turnover"]
|
|
report_df.sort_index(ascending=True, inplace=True)
|
|
|
|
report_df.index.names = index_names
|
|
return report_df
|
|
|
|
|
|
def _report_figure(df: pd.DataFrame) -> [list, tuple]:
|
|
"""
|
|
|
|
:param df:
|
|
:return:
|
|
"""
|
|
|
|
# Get data
|
|
report_df = _calculate_report_data(df)
|
|
|
|
# Maximum Drawdown
|
|
max_start_date, max_end_date = _calculate_maximum(report_df)
|
|
ex_max_start_date, ex_max_end_date = _calculate_maximum(report_df, True)
|
|
|
|
index_name = report_df.index.name
|
|
_temp_df = report_df.reset_index()
|
|
_temp_df.loc[-1] = 0
|
|
_temp_df = _temp_df.shift(1)
|
|
_temp_df.loc[0, index_name] = "T0"
|
|
_temp_df.set_index(index_name, inplace=True)
|
|
_temp_df.iloc[0] = 0
|
|
report_df = _temp_df
|
|
|
|
# Create figure
|
|
_default_kind_map = dict(kind="ScatterGraph", kwargs={"mode": "lines+markers"})
|
|
_temp_fill_args = {"fill": "tozeroy", "mode": "lines+markers"}
|
|
_column_row_col_dict = [
|
|
("cum_bench", dict(row=1, col=1)),
|
|
("cum_return_wo_cost", dict(row=1, col=1)),
|
|
("cum_return_w_cost", dict(row=1, col=1)),
|
|
("return_wo_mdd", dict(row=2, col=1, graph_kwargs=_temp_fill_args)),
|
|
("return_w_cost_mdd", dict(row=3, col=1, graph_kwargs=_temp_fill_args)),
|
|
("cum_ex_return_wo_cost", dict(row=4, col=1)),
|
|
("cum_ex_return_w_cost", dict(row=4, col=1)),
|
|
("turnover", dict(row=5, col=1)),
|
|
("cum_ex_return_w_cost_mdd", dict(row=6, col=1, graph_kwargs=_temp_fill_args)),
|
|
("cum_ex_return_wo_cost_mdd", dict(row=7, col=1, graph_kwargs=_temp_fill_args)),
|
|
]
|
|
|
|
_subplot_layout = dict()
|
|
for i in range(1, 8):
|
|
# yaxis
|
|
_subplot_layout.update({"yaxis{}".format(i): dict(zeroline=True, showline=True, showticklabels=True)})
|
|
_show_line = i == 7
|
|
_subplot_layout.update({"xaxis{}".format(i): dict(showline=_show_line, type="category", tickangle=45)})
|
|
|
|
_layout_style = dict(
|
|
height=1200,
|
|
title=" ",
|
|
shapes=[
|
|
{
|
|
"type": "rect",
|
|
"xref": "x",
|
|
"yref": "paper",
|
|
"x0": max_start_date,
|
|
"y0": 0.55,
|
|
"x1": max_end_date,
|
|
"y1": 1,
|
|
"fillcolor": "#d3d3d3",
|
|
"opacity": 0.3,
|
|
"line": {
|
|
"width": 0,
|
|
},
|
|
},
|
|
{
|
|
"type": "rect",
|
|
"xref": "x",
|
|
"yref": "paper",
|
|
"x0": ex_max_start_date,
|
|
"y0": 0,
|
|
"x1": ex_max_end_date,
|
|
"y1": 0.55,
|
|
"fillcolor": "#d3d3d3",
|
|
"opacity": 0.3,
|
|
"line": {
|
|
"width": 0,
|
|
},
|
|
},
|
|
],
|
|
)
|
|
|
|
_subplot_kwargs = dict(
|
|
shared_xaxes=True,
|
|
vertical_spacing=0.01,
|
|
rows=7,
|
|
cols=1,
|
|
row_width=[1, 1, 1, 3, 1, 1, 3],
|
|
print_grid=False,
|
|
)
|
|
figure = SubplotsGraph(
|
|
df=report_df,
|
|
layout=_layout_style,
|
|
sub_graph_data=_column_row_col_dict,
|
|
subplots_kwargs=_subplot_kwargs,
|
|
kind_map=_default_kind_map,
|
|
sub_graph_layout=_subplot_layout,
|
|
).figure
|
|
return (figure,)
|
|
|
|
|
|
def report_graph(report_df: pd.DataFrame, show_notebook: bool = True) -> [list, tuple]:
|
|
"""display backtest report
|
|
|
|
Example:
|
|
|
|
|
|
.. code-block:: python
|
|
|
|
import qlib
|
|
import pandas as pd
|
|
from qlib.utils.time import Freq
|
|
from qlib.utils import flatten_dict
|
|
from qlib.backtest import backtest, executor
|
|
from qlib.contrib.evaluate import risk_analysis
|
|
from qlib.contrib.strategy import TopkDropoutStrategy
|
|
|
|
# init qlib
|
|
qlib.init(provider_uri=<qlib data dir>)
|
|
|
|
CSI300_BENCH = "SH000300"
|
|
FREQ = "day"
|
|
STRATEGY_CONFIG = {
|
|
"topk": 50,
|
|
"n_drop": 5,
|
|
# pred_score, pd.Series
|
|
"signal": pred_score,
|
|
}
|
|
|
|
EXECUTOR_CONFIG = {
|
|
"time_per_step": "day",
|
|
"generate_portfolio_metrics": True,
|
|
}
|
|
|
|
backtest_config = {
|
|
"start_time": "2017-01-01",
|
|
"end_time": "2020-08-01",
|
|
"account": 100000000,
|
|
"benchmark": CSI300_BENCH,
|
|
"exchange_kwargs": {
|
|
"freq": FREQ,
|
|
"limit_threshold": 0.095,
|
|
"deal_price": "close",
|
|
"open_cost": 0.0005,
|
|
"close_cost": 0.0015,
|
|
"min_cost": 5,
|
|
},
|
|
}
|
|
|
|
# strategy object
|
|
strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
|
|
# executor object
|
|
executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
|
|
# backtest
|
|
portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
|
|
analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
|
|
# backtest info
|
|
report_normal_df, positions_normal = portfolio_metric_dict.get(analysis_freq)
|
|
|
|
qcr.analysis_position.report_graph(report_normal_df)
|
|
|
|
:param report_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 show_notebook: whether to display graphics in notebook, the default is **True**.
|
|
:return: if show_notebook is True, display in notebook; else return **plotly.graph_objs.Figure** list.
|
|
"""
|
|
report_df = report_df.copy()
|
|
fig_list = _report_figure(report_df)
|
|
if show_notebook:
|
|
BaseGraph.show_graph_in_notebook(fig_list)
|
|
else:
|
|
return fig_list
|