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Format code with the latest version of black.

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This commit is contained in:
Charles Young
2021-02-22 11:25:42 +08:00
parent 2f9d45e03a
commit 37871389b9
41 changed files with 526 additions and 116 deletions
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24
qlib/config.py
View File

@@ -115,7 +115,12 @@ _default_config = {
"format": "[%(process)s:%(threadName)s](%(asctime)s) %(levelname)s - %(name)s - [%(filename)s:%(lineno)d] - %(message)s"
}
},
"filters": {"field_not_found": {"()": "qlib.log.LogFilter", "param": [".*?WARN: data not found for.*?"],}},
"filters": {
"field_not_found": {
"()": "qlib.log.LogFilter",
"param": [".*?WARN: data not found for.*?"],
}
},
"handlers": {
"console": {
"class": "logging.StreamHandler",
@@ -130,7 +135,10 @@ _default_config = {
"exp_manager": {
"class": "MLflowExpManager",
"module_path": "qlib.workflow.expm",
"kwargs": {"uri": "file:" + str(Path(os.getcwd()).resolve() / "mlruns"), "default_exp_name": "Experiment",},
"kwargs": {
"uri": "file:" + str(Path(os.getcwd()).resolve() / "mlruns"),
"default_exp_name": "Experiment",
},
},
}
@@ -192,8 +200,16 @@ HIGH_FREQ_CONFIG = {
}
_default_region_config = {
REG_CN: {"trade_unit": 100, "limit_threshold": 0.099, "deal_price": "vwap",},
REG_US: {"trade_unit": 1, "limit_threshold": None, "deal_price": "close",},
REG_CN: {
"trade_unit": 100,
"limit_threshold": 0.099,
"deal_price": "vwap",
},
REG_US: {
"trade_unit": 1,
"limit_threshold": None,
"deal_price": "close",
},
}

18
qlib/contrib/backtest/__init__.py
View File

@@ -18,7 +18,13 @@ logger = get_module_logger("backtest caller")
def get_strategy(
strategy=None, topk=50, margin=0.5, n_drop=5, risk_degree=0.95, str_type="dropout", adjust_dates=None,
strategy=None,
topk=50,
margin=0.5,
n_drop=5,
risk_degree=0.95,
str_type="dropout",
adjust_dates=None,
):
"""get_strategy
@@ -69,7 +75,11 @@ def get_strategy(
str_cls = getattr(strategy_pool, str_cls_dict.get(str_type))
strategy = str_cls(
topk=topk, buffer_margin=margin, n_drop=n_drop, risk_degree=risk_degree, adjust_dates=adjust_dates,
topk=topk,
buffer_margin=margin,
n_drop=n_drop,
risk_degree=risk_degree,
adjust_dates=adjust_dates,
)
elif isinstance(strategy, (dict, str)):
# 2) create strategy with init_instance_by_config
@@ -162,7 +172,9 @@ def get_exchange(
def get_executor(
executor=None, trade_exchange=None, verbose=True,
executor=None,
trade_exchange=None,
verbose=True,
):
"""get_executor

23
qlib/contrib/backtest/profit_attribution.py
View File

@@ -12,7 +12,10 @@ from pathlib import Path
def get_benchmark_weight(
bench, start_date=None, end_date=None, path=None,
bench,
start_date=None,
end_date=None,
path=None,
):
"""get_benchmark_weight
@@ -213,7 +216,12 @@ def get_stock_group(stock_group_field_df, bench_stock_weight_df, group_method, g
def brinson_pa(
positions, bench="SH000905", group_field="industry", group_method="category", group_n=None, deal_price="vwap",
positions,
bench="SH000905",
group_field="industry",
group_method="category",
group_n=None,
deal_price="vwap",
):
"""brinson profit attribution
@@ -247,10 +255,17 @@ def brinson_pa(
# suspend stock is NAN. So we have to get more date to forward fill the NAN
shift_start_date = start_date - datetime.timedelta(days=250)
instruments = D.list_instruments(
D.instruments(market="all"), start_time=shift_start_date, end_time=end_date, as_list=True,
D.instruments(market="all"),
start_time=shift_start_date,
end_time=end_date,
as_list=True,
)
stock_df = D.features(
instruments, [group_field, deal_price], start_time=shift_start_date, end_time=end_date, freq="day",
instruments,
[group_field, deal_price],
start_time=shift_start_date,
end_time=end_date,
freq="day",
)
stock_df.columns = [group_field, "deal_price"]

10
qlib/contrib/data/handler.py
View File

@@ -21,7 +21,10 @@ def check_transform_proc(proc_l, fit_start_time, fit_end_time):
fit_start_time is not None and fit_end_time is not None
), "Make sure `fit_start_time` and `fit_end_time` are not None."
pkwargs.update(
{"fit_start_time": fit_start_time, "fit_end_time": fit_end_time,}
{
"fit_start_time": fit_start_time,
"fit_end_time": fit_end_time,
}
)
new_l.append({"class": klass.__name__, "kwargs": pkwargs})
else:
@@ -167,7 +170,10 @@ class Alpha158(DataHandlerLP):
def get_feature_config(self):
conf = {
"kbar": {},
"price": {"windows": [0], "feature": ["OPEN", "HIGH", "LOW", "VWAP"],},
"price": {
"windows": [0],
"feature": ["OPEN", "HIGH", "LOW", "VWAP"],
},
"rolling": {},
}
return self.parse_config_to_fields(conf)

6
qlib/contrib/eva/alpha.py
View File

@@ -35,7 +35,11 @@ def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False
def calc_long_short_return(
pred: pd.Series, label: pd.Series, date_col: str = "datetime", quantile: float = 0.2, dropna: bool = False,
pred: pd.Series,
label: pd.Series,
date_col: str = "datetime",
quantile: float = 0.2,
dropna: bool = False,
) -> Tuple[pd.Series, pd.Series]:
"""
calculate long-short return

7
qlib/contrib/evaluate.py
View File

@@ -244,7 +244,12 @@ def long_short_backtest(
short_returns[date] = np.mean(short_profit) + np.mean(all_profit)
ls_returns[date] = np.mean(short_profit) + np.mean(long_profit)
return dict(zip(["long", "short", "long_short"], map(pd.Series, [long_returns, short_returns, ls_returns]),))
return dict(
zip(
["long", "short", "long_short"],
map(pd.Series, [long_returns, short_returns, ls_returns]),
)
)
def t_run():

16
qlib/contrib/evaluate_portfolio.py
View File

@@ -64,7 +64,12 @@ def get_position_value(evaluate_date, position):
instruments = list(set(instruments) - set(["cash"])) # filter 'cash'
fields = ["$close"]
close_data_df = D.features(
instruments, fields, start_time=evaluate_date, end_time=evaluate_date, freq="day", disk_cache=0,
instruments,
fields,
start_time=evaluate_date,
end_time=evaluate_date,
freq="day",
disk_cache=0,
)
value = _get_position_value_from_df(evaluate_date, position, close_data_df)
return value
@@ -82,7 +87,14 @@ def get_position_list_value(positions):
start_date, end_date = day_list[0], day_list[-1]
# load data
fields = ["$close"]
close_data_df = D.features(instruments, fields, start_time=start_date, end_time=end_date, freq="day", disk_cache=0,)
close_data_df = D.features(
instruments,
fields,
start_time=start_date,
end_time=end_date,
freq="day",
disk_cache=0,
)
# generate value
# return dict for time:position_value
value_dict = OrderedDict()

4
qlib/contrib/model/catboost_model.py
View File

@@ -32,7 +32,9 @@ class CatBoostModel(Model):
**kwargs
):
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L,
["train", "valid"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]

21
qlib/contrib/model/pytorch_alstm.py
View File

@@ -118,7 +118,10 @@ class ALSTM(Model):
torch.manual_seed(self.seed)
self.ALSTM_model = ALSTMModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout,
d_feat=self.d_feat,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
)
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr)
@@ -208,11 +211,17 @@ class ALSTM(Model):
return np.mean(losses), np.mean(scores)
def fit(
self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None,
self,
dataset: DatasetH,
evals_result=dict(),
verbose=True,
save_path=None,
):
df_train, df_valid, df_test = dataset.prepare(
["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L,
["train", "valid", "test"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
x_train, y_train = df_train["feature"], df_train["label"]
@@ -319,12 +328,14 @@ class ALSTMModel(nn.Module):
self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1)
self.att_net = nn.Sequential()
self.att_net.add_module(
"att_fc_in", nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)),
"att_fc_in",
nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)),
)
self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout))
self.att_net.add_module("att_act", nn.Tanh())
self.att_net.add_module(
"att_fc_out", nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False),
"att_fc_out",
nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False),
)
self.att_net.add_module("att_softmax", nn.Softmax(dim=1))

17
qlib/contrib/model/pytorch_alstm_ts.py
View File

@@ -123,7 +123,10 @@ class ALSTM(Model):
torch.manual_seed(self.seed)
self.ALSTM_model = ALSTMModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout,
d_feat=self.d_feat,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
).to(self.device)
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr)
@@ -195,7 +198,11 @@ class ALSTM(Model):
return np.mean(losses), np.mean(scores)
def fit(
self, dataset, evals_result=dict(), verbose=True, save_path=None,
self,
dataset,
evals_result=dict(),
verbose=True,
save_path=None,
):
dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
@@ -302,12 +309,14 @@ class ALSTMModel(nn.Module):
self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1)
self.att_net = nn.Sequential()
self.att_net.add_module(
"att_fc_in", nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)),
"att_fc_in",
nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)),
)
self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout))
self.att_net.add_module("att_act", nn.Tanh())
self.att_net.add_module(
"att_fc_out", nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False),
"att_fc_out",
nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False),
)
self.att_net.add_module("att_softmax", nn.Softmax(dim=1))

22
qlib/contrib/model/pytorch_gats.py
View File

@@ -229,11 +229,17 @@ class GATs(Model):
return np.mean(losses), np.mean(scores)
def fit(
self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None,
self,
dataset: DatasetH,
evals_result=dict(),
verbose=True,
save_path=None,
):
df_train, df_valid, df_test = dataset.prepare(
["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L,
["train", "valid", "test"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
x_train, y_train = df_train["feature"], df_train["label"]
@@ -334,11 +340,19 @@ class GATModel(nn.Module):
if base_model == "GRU":
self.rnn = nn.GRU(
input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout,
input_size=d_feat,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout,
)
elif base_model == "LSTM":
self.rnn = nn.LSTM(
input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout,
input_size=d_feat,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout,
)
else:
raise ValueError("unknown base model name `%s`" % base_model)

18
qlib/contrib/model/pytorch_gats_ts.py
View File

@@ -242,7 +242,11 @@ class GATs(Model):
return np.mean(losses), np.mean(scores)
def fit(
self, dataset, evals_result=dict(), verbose=True, save_path=None,
self,
dataset,
evals_result=dict(),
verbose=True,
save_path=None,
):
dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
@@ -357,11 +361,19 @@ class GATModel(nn.Module):
if base_model == "GRU":
self.rnn = nn.GRU(
input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout,
input_size=d_feat,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout,
)
elif base_model == "LSTM":
self.rnn = nn.LSTM(
input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout,
input_size=d_feat,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout,
)
else:
raise ValueError("unknown base model name `%s`" % base_model)

21
qlib/contrib/model/pytorch_gru.py
View File

@@ -118,7 +118,10 @@ class GRU(Model):
torch.manual_seed(self.seed)
self.gru_model = GRUModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout,
d_feat=self.d_feat,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
)
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.gru_model.parameters(), lr=self.lr)
@@ -208,11 +211,17 @@ class GRU(Model):
return np.mean(losses), np.mean(scores)
def fit(
self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None,
self,
dataset: DatasetH,
evals_result=dict(),
verbose=True,
save_path=None,
):
df_train, df_valid, df_test = dataset.prepare(
["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L,
["train", "valid", "test"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
x_train, y_train = df_train["feature"], df_train["label"]
@@ -296,7 +305,11 @@ class GRUModel(nn.Module):
super().__init__()
self.rnn = nn.GRU(
input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout,
input_size=d_feat,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout,
)
self.fc_out = nn.Linear(hidden_size, 1)

17
qlib/contrib/model/pytorch_gru_ts.py
View File

@@ -123,7 +123,10 @@ class GRU(Model):
torch.manual_seed(self.seed)
self.GRU_model = GRUModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout,
d_feat=self.d_feat,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
).to(self.device)
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.GRU_model.parameters(), lr=self.lr)
@@ -195,7 +198,11 @@ class GRU(Model):
return np.mean(losses), np.mean(scores)
def fit(
self, dataset, evals_result=dict(), verbose=True, save_path=None,
self,
dataset,
evals_result=dict(),
verbose=True,
save_path=None,
):
dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
@@ -279,7 +286,11 @@ class GRUModel(nn.Module):
super().__init__()
self.rnn = nn.GRU(
input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout,
input_size=d_feat,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout,
)
self.fc_out = nn.Linear(hidden_size, 1)

21
qlib/contrib/model/pytorch_lstm.py
View File

@@ -118,7 +118,10 @@ class LSTM(Model):
torch.manual_seed(self.seed)
self.lstm_model = LSTMModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout,
d_feat=self.d_feat,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
)
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.lstm_model.parameters(), lr=self.lr)
@@ -208,11 +211,17 @@ class LSTM(Model):
return np.mean(losses), np.mean(scores)
def fit(
self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None,
self,
dataset: DatasetH,
evals_result=dict(),
verbose=True,
save_path=None,
):
df_train, df_valid, df_test = dataset.prepare(
["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L,
["train", "valid", "test"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
x_train, y_train = df_train["feature"], df_train["label"]
@@ -296,7 +305,11 @@ class LSTMModel(nn.Module):
super().__init__()
self.rnn = nn.LSTM(
input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout,
input_size=d_feat,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout,
)
self.fc_out = nn.Linear(hidden_size, 1)

17
qlib/contrib/model/pytorch_lstm_ts.py
View File

@@ -123,7 +123,10 @@ class LSTM(Model):
torch.manual_seed(self.seed)
self.LSTM_model = LSTMModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout,
d_feat=self.d_feat,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
).to(self.device)
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.LSTM_model.parameters(), lr=self.lr)
@@ -195,7 +198,11 @@ class LSTM(Model):
return np.mean(losses), np.mean(scores)
def fit(
self, dataset, evals_result=dict(), verbose=True, save_path=None,
self,
dataset,
evals_result=dict(),
verbose=True,
save_path=None,
):
dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
@@ -279,7 +286,11 @@ class LSTMModel(nn.Module):
super().__init__()
self.rnn = nn.LSTM(
input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout,
input_size=d_feat,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout,
)
self.fc_out = nn.Linear(hidden_size, 1)

6
qlib/contrib/model/pytorch_nn.py
View File

@@ -154,7 +154,11 @@ class DNNModelPytorch(Model):
self.dnn_model.to(self.device)
def fit(
self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None,
self,
dataset: DatasetH,
evals_result=dict(),
verbose=True,
save_path=None,
):
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L

19
qlib/contrib/model/pytorch_sfm.py
View File

@@ -30,7 +30,14 @@ from ...data.dataset.handler import DataHandlerLP
class SFM_Model(nn.Module):
def __init__(
self, d_feat=6, output_dim=1, freq_dim=10, hidden_size=64, dropout_W=0.0, dropout_U=0.0, device="cpu",
self,
d_feat=6,
output_dim=1,
freq_dim=10,
hidden_size=64,
dropout_W=0.0,
dropout_U=0.0,
device="cpu",
):
super().__init__()
@@ -355,11 +362,17 @@ class SFM(Model):
self.train_optimizer.step()
def fit(
self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None,
self,
dataset: DatasetH,
evals_result=dict(),
verbose=True,
save_path=None,
):
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L,
["train", "valid"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]

14
qlib/contrib/model/pytorch_tabnet.py
View File

@@ -120,7 +120,9 @@ class TabnetModel(Model):
os.makedirs("pretrain")
[df_train, df_valid] = dataset.prepare(
["pretrain", "pretrain_validation"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L,
["pretrain", "pretrain_validation"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
df_train.fillna(df_train.mean(), inplace=True)
@@ -154,7 +156,11 @@ class TabnetModel(Model):
break
def fit(
self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None,
self,
dataset: DatasetH,
evals_result=dict(),
verbose=True,
save_path=None,
):
if self.pretrain:
# there is a pretrained model, load the model
@@ -166,7 +172,9 @@ class TabnetModel(Model):
# adding one more linear layer to fit the final output dimension
self.tabnet_model = FinetuneModel(self.out_dim, self.final_out_dim, self.tabnet_model).to(self.device)
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L,
["train", "valid"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
df_train.fillna(df_train.mean(), inplace=True)
x_train, y_train = df_train["feature"], df_train["label"]

4
qlib/contrib/model/xgboost.py
View File

@@ -29,7 +29,9 @@ class XGBModel(Model):
):
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L,
["train", "valid"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]

24
qlib/contrib/online/executor.py
View File

@@ -150,13 +150,21 @@ class SimulatorExecutor(BaseExecutor):
if order.direction == Order.SELL: # sell
print(
"[I {:%Y-%m-%d}]: sell {}, price {:.2f}, amount {}, value {:.2f}.".format(
trade_date, order.stock_id, trade_price, order.deal_amount, trade_val,
trade_date,
order.stock_id,
trade_price,
order.deal_amount,
trade_val,
)
)
else:
print(
"[I {:%Y-%m-%d}]: buy {}, price {:.2f}, amount {}, value {:.2f}.".format(
trade_date, order.stock_id, trade_price, order.deal_amount, trade_val,
trade_date,
order.stock_id,
trade_price,
order.deal_amount,
trade_val,
)
)
@@ -263,13 +271,21 @@ def load_order_list(user_path, trade_date):
for stock_id in order_dict["sell"]:
amount, factor = order_dict["sell"][stock_id]
order = Order(
stock_id=stock_id, amount=amount, trade_date=pd.Timestamp(trade_date), direction=Order.SELL, factor=factor,
stock_id=stock_id,
amount=amount,
trade_date=pd.Timestamp(trade_date),
direction=Order.SELL,
factor=factor,
)
order_list.append(order)
for stock_id in order_dict["buy"]:
amount, factor = order_dict["buy"][stock_id]
order = Order(
stock_id=stock_id, amount=amount, trade_date=pd.Timestamp(trade_date), direction=Order.BUY, factor=factor,
stock_id=stock_id,
amount=amount,
trade_date=pd.Timestamp(trade_date),
direction=Order.BUY,
factor=factor,
)
order_list.append(order)
return order_list

6
qlib/contrib/online/manager.py
View File

@@ -84,10 +84,12 @@ class UserManager:
raise ValueError("Cannot find user {}".format(user_id))
self.users[user_id].account.save_account(self.data_path / user_id)
save_instance(
self.users[user_id].strategy, self.data_path / user_id / "strategy_{}.pickle".format(user_id),
self.users[user_id].strategy,
self.data_path / user_id / "strategy_{}.pickle".format(user_id),
)
save_instance(
self.users[user_id].model, self.data_path / user_id / "model_{}.pickle".format(user_id),
self.users[user_id].model,
self.data_path / user_id / "model_{}.pickle".format(user_id),
)
def add_user(self, user_id, config_file, add_date):

8
qlib/contrib/online/operator.py
View File

@@ -125,7 +125,9 @@ class Operator:
trade_date=trade_date,
)
save_order_list(
order_list=order_list, user_path=(pathlib.Path(path) / user_id), trade_date=trade_date,
order_list=order_list,
user_path=(pathlib.Path(path) / user_id),
trade_date=trade_date,
)
self.logger.info("Generate order list at {} for {}".format(trade_date, user_id))
um.save_user_data(user_id)
@@ -158,7 +160,9 @@ class Operator:
order_list = load_order_list(user_path=(pathlib.Path(path) / user_id), trade_date=trade_date)
trade_info = executor.execute(order_list=order_list, trade_account=user.account, trade_date=trade_date)
executor.save_executed_file_from_trade_info(
trade_info=trade_info, user_path=(pathlib.Path(path) / user_id), trade_date=trade_date,
trade_info=trade_info,
user_path=(pathlib.Path(path) / user_id),
trade_date=trade_date,
)
self.logger.info("execute order list at {} for {}".format(trade_date.date(), user_id))

6
qlib/contrib/online/utils.py
View File

@@ -79,7 +79,11 @@ def prepare(um, today, user_id, exchange_config=None):
log.warning("user_id:{}, last trading date {} after today {}".format(user_id, latest_trading_date, today))
return [pd.Timestamp(latest_trading_date)], None
dates = D.calendar(start_time=pd.Timestamp(latest_trading_date), end_time=pd.Timestamp(today), future=True,)
dates = D.calendar(
start_time=pd.Timestamp(latest_trading_date),
end_time=pd.Timestamp(today),
future=True,
)
dates = list(dates)
dates.append(get_next_trading_date(dates[-1], future=True))
if exchange_config:

66
qlib/contrib/report/analysis_model/analysis_model_performance.py
View File

@@ -53,7 +53,8 @@ def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int
t_df.index = t_df.index.strftime("%Y-%m-%d")
# Cumulative Return By Group
group_scatter_figure = ScatterGraph(
t_df.cumsum(), layout=dict(title="Cumulative Return", xaxis=dict(type="category", tickangle=45)),
t_df.cumsum(),
layout=dict(title="Cumulative Return", xaxis=dict(type="category", tickangle=45)),
).figure
t_df = t_df.loc[:, ["long-short", "long-average"]]
@@ -61,7 +62,12 @@ def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int
group_hist_figure = SubplotsGraph(
t_df,
kind_map=dict(kind="DistplotGraph", kwargs=dict(bin_size=_bin_size)),
subplots_kwargs=dict(rows=1, cols=2, print_grid=False, subplot_titles=["long-short", "long-average"],),
subplots_kwargs=dict(
rows=1,
cols=2,
print_grid=False,
subplot_titles=["long-short", "long-average"],
),
).figure
return group_scatter_figure, group_hist_figure
@@ -96,12 +102,15 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t
_index = ic.index.get_level_values(0).astype("str").str.replace("-", "").str.slice(0, 6)
_monthly_ic = ic.groupby(_index).mean()
_monthly_ic.index = pd.MultiIndex.from_arrays(
[_monthly_ic.index.str.slice(0, 4), _monthly_ic.index.str.slice(4, 6)], names=["year", "month"],
[_monthly_ic.index.str.slice(0, 4), _monthly_ic.index.str.slice(4, 6)],
names=["year", "month"],
)
# fill month
_month_list = pd.date_range(
start=pd.Timestamp(f"{_index.min()[:4]}0101"), end=pd.Timestamp(f"{_index.max()[:4]}1231"), freq="1M",
start=pd.Timestamp(f"{_index.min()[:4]}0101"),
end=pd.Timestamp(f"{_index.max()[:4]}1231"),
freq="1M",
)
_years = []
_month = []
@@ -133,15 +142,32 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t
_bin_size = ((_ic_df.max() - _ic_df.min()) / 20).min()
_sub_graph_data = [
("ic", dict(row=1, col=1, name="", kind="DistplotGraph", graph_kwargs=dict(bin_size=_bin_size),),),
(
"ic",
dict(
row=1,
col=1,
name="",
kind="DistplotGraph",
graph_kwargs=dict(bin_size=_bin_size),
),
),
(_qqplot_fig, dict(row=1, col=2)),
]
ic_hist_figure = SubplotsGraph(
_ic_df.dropna(),
kind_map=dict(kind="HistogramGraph", kwargs=dict()),
subplots_kwargs=dict(rows=1, cols=2, print_grid=False, subplot_titles=["IC", "IC %s Dist. Q-Q" % dist_name],),
subplots_kwargs=dict(
rows=1,
cols=2,
print_grid=False,
subplot_titles=["IC", "IC %s Dist. Q-Q" % dist_name],
),
sub_graph_data=_sub_graph_data,
layout=dict(yaxis2=dict(title="Observed Quantile"), xaxis2=dict(title=f"{dist_name} Distribution Quantile"),),
layout=dict(
yaxis2=dict(title="Observed Quantile"),
xaxis2=dict(title=f"{dist_name} Distribution Quantile"),
),
).figure
return ic_bar_figure, ic_heatmap_figure, ic_hist_figure
@@ -155,7 +181,8 @@ def _pred_autocorr(pred_label: pd.DataFrame, lag=1, **kwargs) -> tuple:
_df = ac.to_frame("value")
_df.index = _df.index.strftime("%Y-%m-%d")
ac_figure = ScatterGraph(
_df, layout=dict(title="Auto Correlation", xaxis=dict(type="category", tickangle=45)),
_df,
layout=dict(title="Auto Correlation", xaxis=dict(type="category", tickangle=45)),
).figure
return (ac_figure,)
@@ -175,11 +202,17 @@ def _pred_turnover(pred_label: pd.DataFrame, N=5, lag=1, **kwargs) -> tuple:
.sum()
/ (len(x) // N)
)
r_df = pd.DataFrame({"Top": top, "Bottom": bottom,})
r_df = pd.DataFrame(
{
"Top": top,
"Bottom": bottom,
}
)
# FIXME: support HIGH-FREQ
r_df.index = r_df.index.strftime("%Y-%m-%d")
turnover_figure = ScatterGraph(
r_df, layout=dict(title="Top-Bottom Turnover", xaxis=dict(type="category", tickangle=45)),
r_df,
layout=dict(title="Top-Bottom Turnover", xaxis=dict(type="category", tickangle=45)),
).figure
return (turnover_figure,)
@@ -197,7 +230,11 @@ def ic_figure(ic_df: pd.DataFrame, show_nature_day=True, **kwargs) -> go.Figure:
# FIXME: support HIGH-FREQ
ic_df.index = ic_df.index.strftime("%Y-%m-%d")
ic_bar_figure = BarGraph(
ic_df, layout=dict(title="Information Coefficient (IC)", xaxis=dict(type="category", tickangle=45),),
ic_df,
layout=dict(
title="Information Coefficient (IC)",
xaxis=dict(type="category", tickangle=45),
),
).figure
return ic_bar_figure
@@ -240,7 +277,12 @@ def model_performance_graph(
figure_list = []
for graph_name in graph_names:
fun_res = eval(f"_{graph_name}")(
pred_label=pred_label, lag=lag, N=N, reverse=reverse, rank=rank, show_nature_day=show_nature_day,
pred_label=pred_label,
lag=lag,
N=N,
reverse=reverse,
rank=rank,
show_nature_day=show_nature_day,
)
figure_list += fun_res

36
qlib/contrib/report/analysis_position/cumulative_return.py
View File

@@ -13,7 +13,11 @@ from ..analysis_position.parse_position import get_position_data
def _get_cum_return_data_with_position(
position: dict, report_normal: pd.DataFrame, label_data: pd.DataFrame, start_date=None, end_date=None,
position: dict,
report_normal: pd.DataFrame,
label_data: pd.DataFrame,
start_date=None,
end_date=None,
):
"""
@@ -25,7 +29,11 @@ def _get_cum_return_data_with_position(
:return:
"""
_cumulative_return_df = get_position_data(
position=position, report_normal=report_normal, label_data=label_data, start_date=start_date, end_date=end_date,
position=position,
report_normal=report_normal,
label_data=label_data,
start_date=start_date,
end_date=end_date,
).copy()
_cumulative_return_df["label"] = _cumulative_return_df["label"] - _cumulative_return_df["bench"]
@@ -79,7 +87,11 @@ def _get_cum_return_data_with_position(
def _get_figure_with_position(
position: dict, report_normal: pd.DataFrame, label_data: pd.DataFrame, start_date=None, end_date=None,
position: dict,
report_normal: pd.DataFrame,
label_data: pd.DataFrame,
start_date=None,
end_date=None,
) -> Iterable[go.Figure]:
"""Get average analysis figures
@@ -99,12 +111,18 @@ def _get_figure_with_position(
# Create figures
for _t_name in ["buy", "sell", "buy_minus_sell", "hold"]:
sub_graph_data = [
("cum_{}".format(_t_name), dict(row=1, col=1, graph_kwargs={"mode": "lines+markers", "xaxis": "x3"}),),
(
"cum_{}".format(_t_name),
dict(row=1, col=1, graph_kwargs={"mode": "lines+markers", "xaxis": "x3"}),
),
(
"{}_weight".format(_t_name.replace("minus", "plus") if "minus" in _t_name else _t_name),
dict(row=2, col=1),
),
("{}_value".format(_t_name), dict(row=1, col=2, kind="HistogramGraph", graph_kwargs={}),),
(
"{}_value".format(_t_name),
dict(row=1, col=2, kind="HistogramGraph", graph_kwargs={}),
),
]
_default_xaxis = dict(showline=False, zeroline=True, tickangle=45)
@@ -143,7 +161,13 @@ def _get_figure_with_position(
[{"rowspan": 1}, None],
]
subplots_kwargs = dict(
vertical_spacing=0.01, rows=2, cols=2, row_width=[1, 2], column_width=[3, 1], print_grid=False, specs=specs,
vertical_spacing=0.01,
rows=2,
cols=2,
row_width=[1, 2],
column_width=[3, 1],
print_grid=False,
specs=specs,
)
yield SubplotsGraph(
cum_return_df,

5
qlib/contrib/report/analysis_position/parse_position.py
View File

@@ -72,7 +72,10 @@ def parse_position(position: dict = None) -> pd.DataFrame:
result_df = result_df.append(_trading_day_df, sort=True)
previous_data = dict(date=_trading_date, code_list=_trading_day_df[_trading_day_df["status"] != -1].index,)
previous_data = dict(
date=_trading_date,
code_list=_trading_day_df[_trading_day_df["status"] != -1].index,
)
result_df.reset_index(inplace=True)
result_df.rename(columns={"date": "datetime", "index": "instrument"}, inplace=True)

16
qlib/contrib/report/analysis_position/rank_label.py
View File

@@ -23,7 +23,11 @@ def _get_figure_with_position(
:return:
"""
_position_df = get_position_data(
position, label_data, calculate_label_rank=True, start_date=start_date, end_date=end_date,
position,
label_data,
calculate_label_rank=True,
start_date=start_date,
end_date=end_date,
)
res_dict = dict()
@@ -47,14 +51,20 @@ def _get_figure_with_position(
yield ScatterGraph(
_res_df.loc[:, [_col]],
layout=dict(
title=_col, xaxis=dict(type="category", tickangle=45), yaxis=dict(title="lable-rank-ratio: %"),
title=_col,
xaxis=dict(type="category", tickangle=45),
yaxis=dict(title="lable-rank-ratio: %"),
),
graph_kwargs=dict(mode="lines+markers"),
).figure
def rank_label_graph(
position: dict, label_data: pd.DataFrame, start_date=None, end_date=None, show_notebook=True,
position: dict,
label_data: pd.DataFrame,
start_date=None,
end_date=None,
show_notebook=True,
) -> Iterable[go.Figure]:
"""Ranking percentage of stocks buy, sell, and holding on the trading day.
Average rank-ratio(similar to **sell_df['label'].rank(ascending=False) / len(sell_df)**) of daily trading

15
qlib/contrib/report/analysis_position/report.py
View File

@@ -123,7 +123,9 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]:
"y1": 1,
"fillcolor": "#d3d3d3",
"opacity": 0.3,
"line": {"width": 0,},
"line": {
"width": 0,
},
},
{
"type": "rect",
@@ -135,13 +137,20 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]:
"y1": 0.55,
"fillcolor": "#d3d3d3",
"opacity": 0.3,
"line": {"width": 0,},
"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,
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,

6
qlib/contrib/report/graph.py
View File

@@ -311,7 +311,11 @@ class SubplotsGraph:
_temp_row_data = (
column_name,
dict(
row=row, col=col, name=res_name, kind=self._kind_map["kind"], graph_kwargs=self._kind_map["kwargs"],
row=row,
col=col,
name=res_name,
kind=self._kind_map["kind"],
graph_kwargs=self._kind_map["kwargs"],
),
)
self._sub_graph_data.append(_temp_row_data)

5
qlib/contrib/strategy/cost_control.py
View File

@@ -57,7 +57,10 @@ class SoftTopkStrategy(WeightStrategyBase):
final_stock_weight[stock_id] -= sw
if self.buy_method == "first_fill":
for stock_id in buy_signal_stocks:
add_weight = min(max(1 / self.topk - final_stock_weight.get(stock_id, 0), 0.0), sold_stock_weight,)
add_weight = min(
max(1 / self.topk - final_stock_weight.get(stock_id, 0), 0.0),
sold_stock_weight,
)
final_stock_weight[stock_id] = final_stock_weight.get(stock_id, 0.0) + add_weight
sold_stock_weight -= add_weight
elif self.buy_method == "average_fill":

12
qlib/contrib/strategy/order_generator.py
View File

@@ -102,10 +102,14 @@ class OrderGenWInteract(OrderGenerator):
# strategy 1 : generate amount_position by weight_position
# Use API in Exchange()
target_amount_dict = trade_exchange.generate_amount_position_from_weight_position(
weight_position=target_weight_position, cash=current_tradable_value, trade_date=trade_date,
weight_position=target_weight_position,
cash=current_tradable_value,
trade_date=trade_date,
)
order_list = trade_exchange.generate_order_for_target_amount_position(
target_position=target_amount_dict, current_position=current_amount_dict, trade_date=trade_date,
target_position=target_amount_dict,
current_position=current_amount_dict,
trade_date=trade_date,
)
return order_list
@@ -160,6 +164,8 @@ class OrderGenWOInteract(OrderGenerator):
else:
continue
order_list = trade_exchange.generate_order_for_target_amount_position(
target_position=amount_dict, current_position=current.get_stock_amount_dict(), trade_date=trade_date,
target_position=amount_dict,
current_position=current.get_stock_amount_dict(),
trade_date=trade_date,
)
return order_list

6
qlib/contrib/tuner/launcher.py
View File

@@ -13,7 +13,11 @@ from .config import TunerConfigManager
args_parser = argparse.ArgumentParser(prog="tuner")
args_parser.add_argument(
"-c", "--config_path", required=True, type=str, help="config path indicates where to load yaml config.",
"-c",
"--config_path",
required=True,
type=str,
help="config path indicates where to load yaml config.",
)
args = args_parser.parse_args()

5
qlib/contrib/tuner/space.py
View File

@@ -10,5 +10,8 @@ TopkAmountStrategySpace = {
}
QLibDataLabelSpace = {
"labels": hp.choice("labels", [["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["Ref($close, -5)/$close - 1"]],)
"labels": hp.choice(
"labels",
[["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["Ref($close, -5)/$close - 1"]],
)
}

26
qlib/contrib/tuner/tuner.py
View File

@@ -28,7 +28,10 @@ class Tuner:
self.optim_config = optim_config
self.max_evals = self.tuner_config.get("max_evals", 10)
self.ex_dir = os.path.join(self.tuner_config["experiment"]["dir"], self.tuner_config["experiment"]["name"],)
self.ex_dir = os.path.join(
self.tuner_config["experiment"]["dir"],
self.tuner_config["experiment"]["name"],
)
self.best_params = None
self.best_res = None
@@ -39,7 +42,10 @@ class Tuner:
TimeInspector.set_time_mark()
fmin(
fn=self.objective, space=self.space, algo=tpe.suggest, max_evals=self.max_evals,
fn=self.objective,
space=self.space,
algo=tpe.suggest,
max_evals=self.max_evals,
)
self.logger.info("Local best params: {} ".format(self.best_params))
TimeInspector.log_cost_time(
@@ -153,7 +159,8 @@ class QLibTuner(Tuner):
estimator_config["data"]["args"].update(params["data_label_space"])
estimator_path = os.path.join(
self.tuner_config["experiment"].get("dir", "../"), QLibTuner.ESTIMATOR_CONFIG_NAME,
self.tuner_config["experiment"].get("dir", "../"),
QLibTuner.ESTIMATOR_CONFIG_NAME,
)
with open(estimator_path, "w") as fp:
@@ -166,20 +173,27 @@ class QLibTuner(Tuner):
model_space_name = self.tuner_config["model"].get("space", None)
if model_space_name is None:
raise ValueError("Please give the search space of model.")
model_space = getattr(importlib.import_module(".space", package="qlib.contrib.tuner"), model_space_name,)
model_space = getattr(
importlib.import_module(".space", package="qlib.contrib.tuner"),
model_space_name,
)
# 2. Setup strategy space
strategy_space_name = self.tuner_config["strategy"].get("space", None)
if strategy_space_name is None:
raise ValueError("Please give the search space of strategy.")
strategy_space = getattr(importlib.import_module(".space", package="qlib.contrib.tuner"), strategy_space_name,)
strategy_space = getattr(
importlib.import_module(".space", package="qlib.contrib.tuner"),
strategy_space_name,
)
# 3. Setup data label space if given
if self.tuner_config.get("data_label", None) is not None:
data_label_space_name = self.tuner_config["data_label"].get("space", None)
if data_label_space_name is not None:
data_label_space = getattr(
importlib.import_module(".space", package="qlib.contrib.tuner"), data_label_space_name,
importlib.import_module(".space", package="qlib.contrib.tuner"),
data_label_space_name,
)
else:
data_label_space_name = None

3
qlib/data/client.py
View File

@@ -26,7 +26,8 @@ class Client:
self.logger = get_module_logger(self.__class__.__name__)
# bind connect/disconnect callbacks
self.sio.on(
"connect", lambda: self.logger.debug("Connect to server {}".format(self.sio.connection_url)),
"connect",
lambda: self.logger.debug("Connect to server {}".format(self.sio.connection_url)),
)
self.sio.on("disconnect", lambda: self.logger.debug("Disconnect from server!"))

69
qlib/data/data.py
View File

@@ -328,7 +328,14 @@ class DatasetProvider(abc.ABC):
raise NotImplementedError("Subclass of DatasetProvider must implement `Dataset` method")
def _uri(
self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=1, **kwargs,
self,
instruments,
fields,
start_time=None,
end_time=None,
freq="day",
disk_cache=1,
**kwargs,
):
"""Get task uri, used when generating rabbitmq task in qlib_server
@@ -407,13 +414,29 @@ class DatasetProvider(abc.ABC):
for inst, spans in instruments_d.items():
data[inst] = p.apply_async(
DatasetProvider.expression_calculator,
args=(inst, start_time, end_time, freq, normalize_column_names, spans, C,),
args=(
inst,
start_time,
end_time,
freq,
normalize_column_names,
spans,
C,
),
)
else:
for inst in instruments_d:
data[inst] = p.apply_async(
DatasetProvider.expression_calculator,
args=(inst, start_time, end_time, freq, normalize_column_names, None, C,),
args=(
inst,
start_time,
end_time,
freq,
normalize_column_names,
None,
C,
),
)
p.close()
@@ -575,7 +598,12 @@ class LocalInstrumentProvider(InstrumentProvider):
start_time = pd.Timestamp(start_time or cal[0])
end_time = pd.Timestamp(end_time or cal[-1])
_instruments_filtered = {
inst: list(filter(lambda x: x[0] <= x[1], [(max(start_time, x[0]), min(end_time, x[1])) for x in spans],))
inst: list(
filter(
lambda x: x[0] <= x[1],
[(max(start_time, x[0]), min(end_time, x[1])) for x in spans],
)
)
for inst, spans in _instruments.items()
}
_instruments_filtered = {key: value for key, value in _instruments_filtered.items() if value}
@@ -695,7 +723,14 @@ class LocalDatasetProvider(DatasetProvider):
for inst in instruments_d:
p.apply_async(
LocalDatasetProvider.cache_walker, args=(inst, start_time, end_time, freq, column_names,),
LocalDatasetProvider.cache_walker,
args=(
inst,
start_time,
end_time,
freq,
column_names,
),
)
p.close()
@@ -728,7 +763,12 @@ class ClientCalendarProvider(CalendarProvider):
def calendar(self, start_time=None, end_time=None, freq="day", future=False):
self.conn.send_request(
request_type="calendar",
request_content={"start_time": str(start_time), "end_time": str(end_time), "freq": freq, "future": future,},
request_content={
"start_time": str(start_time),
"end_time": str(end_time),
"freq": freq,
"future": future,
},
msg_queue=self.queue,
msg_proc_func=lambda response_content: [pd.Timestamp(c) for c in response_content],
)
@@ -792,7 +832,14 @@ class ClientDatasetProvider(DatasetProvider):
self.queue = queue.Queue()
def dataset(
self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=0, return_uri=False,
self,
instruments,
fields,
start_time=None,
end_time=None,
freq="day",
disk_cache=0,
return_uri=False,
):
if Inst.get_inst_type(instruments) == Inst.DICT:
get_module_logger("data").warning(
@@ -895,7 +942,13 @@ class BaseProvider:
return Inst.list_instruments(instruments, start_time, end_time, freq, as_list)
def features(
self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=None,
self,
instruments,
fields,
start_time=None,
end_time=None,
freq="day",
disk_cache=None,
):
"""
Parameters:

5
qlib/data/dataset/utils.py
View File

@@ -32,7 +32,10 @@ def get_level_index(df: pd.DataFrame, level=Union[str, int]) -> int:
def fetch_df_by_index(
df: pd.DataFrame, selector: Union[pd.Timestamp, slice, str, list], level: Union[str, int], fetch_orig=True,
df: pd.DataFrame,
selector: Union[pd.Timestamp, slice, str, list],
level: Union[str, int],
fetch_orig=True,
) -> pd.DataFrame:
"""
fetch data from `data` with `selector` and `level`

7
qlib/data/filter.py
View File

@@ -341,7 +341,12 @@ class ExpressionDFilter(SeriesDFilter):
# do not use dataset cache
try:
_features = DatasetD.dataset(
instruments, [self.rule_expression], fstart, fend, freq=self.filter_freq, disk_cache=0,
instruments,
[self.rule_expression],
fstart,
fend,
freq=self.filter_freq,
disk_cache=0,
)
except TypeError:
# use LocalDatasetProvider

6
qlib/tests/__init__.py
View File

@@ -18,6 +18,10 @@ class TestAutoData(unittest.TestCase):
print(f"Qlib data is not found in {provider_uri}")
GetData().qlib_data(
name="qlib_data_simple", region="cn", interval="1d", target_dir=provider_uri, delete_old=False,
name="qlib_data_simple",
region="cn",
interval="1d",
target_dir=provider_uri,
delete_old=False,
)
init(provider_uri=provider_uri, region=REG_CN, **cls._setup_kwargs)

5
qlib/workflow/record_temp.py
View File

@@ -193,7 +193,10 @@ class SigAnaRecord(SignalRecord):
}
)
objects.update(
{"long_short_r.pkl": long_short_r, "long_avg_r.pkl": long_avg_r,}
{
"long_short_r.pkl": long_short_r,
"long_avg_r.pkl": long_avg_r,
}
)
self.recorder.log_metrics(**metrics)
self.recorder.save_objects(**objects, artifact_path=self.get_path())