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pass the whole workflow

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Young
2020-10-28 14:07:33 +00:00
parent 1a9ee6cef8
commit a50c9008b8
10 changed files with 296 additions and 221 deletions
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17
qlib/contrib/backtest/backtest.py
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@@ -10,6 +10,7 @@ from ...data import D
from .account import Account
from ...config import C
from ...log import get_module_logger
from ...data.dataset.utils import get_level_index
LOG = get_module_logger("backtest")
@@ -18,7 +19,8 @@ def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark)
"""Parameters
----------
pred : pandas.DataFrame
predict should has <instrument, datetime> index and one `score` column
predict should has <datetime, instrument> index and one `score` column
Qlib want to support multi-singal strategy in the future. So pd.Series is not used.
strategy : Strategy()
strategy part for backtest
trade_exchange : Exchange()
@@ -43,6 +45,12 @@ def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark)
`benchmark` is str, will use the daily change as the 'bench'.
benchmark code, default is SH000905 CSI500
"""
# Convert format if the input format is not expected
if get_level_index(pred, level='datetime') == 1:
pred = pred.swaplevel().sort_index()
if isinstance(pred, pd.Series):
pred = pred.to_frame('score')
trade_account = Account(init_cash=account)
_pred_dates = pred.index.get_level_values(level="datetime")
predict_dates = D.calendar(start_time=_pred_dates.min(), end_time=_pred_dates.max())
@@ -71,10 +79,9 @@ def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark)
# 1. Load the score_series at pred_date
try:
score = pred.loc(axis=0)[:, pred_date] # (stock_id, trade_date) multi_index, score in pdate
score_series = score.reset_index(level="datetime", drop=True)[
"score"
] # pd.Series(index:stock_id, data: score)
score = pred.loc(axis=0)[pred_date, :] # (trade_date, stock_id) multi_index, score in pdate
score_series = score.reset_index(level="datetime",
drop=True)["score"] # pd.Series(index:stock_id, data: score)
except KeyError:
LOG.warning("No score found on predict date[{:%Y-%m-%d}]".format(trade_date))
score_series = None