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Fix backtest (#719)

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* 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>
This commit is contained in:
Pengrong Zhu
2021-12-07 19:04:23 +08:00
committed by GitHub
parent 84103c7d43
commit c276de4040
19 changed files with 663 additions and 232 deletions
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70
qlib/contrib/report/analysis_position/risk_analysis.py
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@@ -170,32 +170,64 @@ def risk_analysis_graph(
.. code-block:: python
from qlib.contrib.evaluate import risk_analysis, backtest, long_short_backtest
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
from qlib.contrib.report import analysis_position
# backtest parameters
bparas = {}
bparas['limit_threshold'] = 0.095
bparas['account'] = 1000000000
# init qlib
qlib.init(provider_uri=<qlib data dir>)
sparas = {}
sparas['topk'] = 50
sparas['n_drop'] = 230
strategy = TopkDropoutStrategy(**sparas)
CSI300_BENCH = "SH000300"
FREQ = "day"
STRATEGY_CONFIG = {
"topk": 50,
"n_drop": 5,
# pred_score, pd.Series
"signal": pred_score,
}
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)
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)
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['excess_return_without_cost'] = risk_analysis(report_normal_df['return'] - report_normal_df['bench'])
analysis['excess_return_with_cost'] = risk_analysis(report_normal_df['return'] - report_normal_df['bench'] - report_normal_df['cost'])
analysis_df = pd.concat(analysis)
analysis["excess_return_without_cost"] = risk_analysis(
report_normal_df["return"] - report_normal_df["bench"], freq=analysis_freq
)
analysis["excess_return_with_cost"] = risk_analysis(
report_normal_df["return"] - report_normal_df["bench"] - report_normal_df["cost"], freq=analysis_freq
)
analysis_df = pd.concat(analysis) # type: pd.DataFrame
analysis_position.risk_analysis_graph(analysis_df, report_normal_df)