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Update CI & add black formatter

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This commit is contained in:
Jactus
2020-10-09 14:33:29 +08:00
committed by you-n-g
parent 143f3f985b
commit 7c6e5e822c
26 changed files with 194 additions and 255 deletions
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4
qlib/contrib/estimator/estimator.py
View File

@@ -186,7 +186,9 @@ class Estimator(object):
# analysis["pred_short"] = risk_analysis(long_short_reports["short"])
# analysis["pred_long_short"] = risk_analysis(long_short_reports["long_short"])
analysis["excess_return_without_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"])
analysis["excess_return_with_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"] - report_normal["cost"])
analysis["excess_return_with_cost"] = risk_analysis(
report_normal["return"] - report_normal["bench"] - report_normal["cost"]
)
analysis_df = pd.concat(analysis) # type: pd.DataFrame
TimeInspector.log_cost_time(
"Finished generating analysis," " average turnover is: {0:.4f}.".format(report_normal["turnover"].mean())

12
qlib/contrib/estimator/handler.py
View File

@@ -558,16 +558,16 @@ class QLibDataHandlerV1(ConfigQLibDataHandler):
class Alpha158(QLibDataHandlerV1):
config_template = {
'kbar': {},
'price': {
'windows': [0],
'feature': ['OPEN', 'HIGH', 'LOW', 'CLOSE'],
"kbar": {},
"price": {
"windows": [0],
"feature": ["OPEN", "HIGH", "LOW", "CLOSE"],
},
'rolling': {}
"rolling": {},
}
def _init_kwargs(self, **kwargs):
kwargs['labels'] = ["Ref($close, -2)/Ref($close, -1) - 1"]
kwargs["labels"] = ["Ref($close, -2)/Ref($close, -1) - 1"]
super(Alpha158, self)._init_kwargs(**kwargs)

11
qlib/contrib/evaluate.py
View File

@@ -34,8 +34,13 @@ def risk_analysis(r, N=252):
annualized_return = mean * N
information_ratio = mean / std * np.sqrt(N)
max_drawdown = (r.cumsum() - r.cumsum().cummax()).min()
data = {"mean": mean, "std": std, "annualized_return": annualized_return,
"information_ratio": information_ratio, "max_drawdown": max_drawdown}
data = {
"mean": mean,
"std": std,
"annualized_return": annualized_return,
"information_ratio": information_ratio,
"max_drawdown": max_drawdown,
}
res = pd.Series(data, index=data.keys()).to_frame("risk")
return res
@@ -230,7 +235,7 @@ def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, **k
limit move 0.1 (10%) for example, long and short with same limit
extract_codes: bool
will we pass the codes extracted from the pred to the exchange.
.. note:: This will be faster with offline qlib.
"""
# check strategy:

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

@@ -167,7 +167,7 @@ class DNNModelPytorch(Model):
# train
self.logger.info("training...")
self._fitted = True
#return
# return
# prepare training data
x_train_values = torch.from_numpy(x_train.values).float()
y_train_values = torch.from_numpy(y_train.values).float()
@@ -210,7 +210,7 @@ class DNNModelPytorch(Model):
# validation
train_loss += loss.val
#print(loss.val)
# print(loss.val)
if step and step % self.eval_steps == 0:
stop_steps += 1
train_loss /= self.eval_steps
@@ -263,7 +263,7 @@ class DNNModelPytorch(Model):
raise ValueError("model is not fitted yet!")
x_test = torch.from_numpy(x_test.values).float().cuda()
self.dnn_model.eval()
with torch.no_grad():
preds = self.dnn_model(x_test).detach().cpu().numpy()
return preds

55
qlib/contrib/report/analysis_model/analysis_model_performance.py
View File

@@ -14,9 +14,7 @@ from scipy import stats
from ..graph import ScatterGraph, SubplotsGraph, BarGraph, HeatmapGraph
def _group_return(
pred_label: pd.DataFrame = None, reverse: bool = False, N: int = 5, **kwargs
) -> tuple:
def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int = 5, **kwargs) -> tuple:
"""
:param pred_label:
@@ -48,9 +46,7 @@ def _group_return(
t_df["long-short"] = t_df["Group1"] - t_df["Group%d" % N]
# Long-Average
t_df["long-average"] = (
t_df["Group1"] - pred_label.groupby(level="datetime")["label"].mean()
)
t_df["long-average"] = t_df["Group1"] - pred_label.groupby(level="datetime")["label"].mean()
t_df = t_df.dropna(how="all") # for days which does not contain label
# FIXME: support HIGH-FREQ
@@ -58,9 +54,7 @@ def _group_return(
# Cumulative Return By Group
group_scatter_figure = ScatterGraph(
t_df.cumsum(),
layout=dict(
title="Cumulative Return", xaxis=dict(type="category", tickangle=45)
),
layout=dict(title="Cumulative Return", xaxis=dict(type="category", tickangle=45)),
).figure
t_df = t_df.loc[:, ["long-short", "long-average"]]
@@ -103,13 +97,9 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t
lambda x: x["label"].rank(pct=True).corr(x["score"].rank(pct=True))
)
else:
ic = pred_label.groupby(level="datetime").apply(
lambda x: x["label"].corr(x["score"])
)
ic = pred_label.groupby(level="datetime").apply(lambda x: x["label"].corr(x["score"]))
_index = (
ic.index.get_level_values(0).astype("str").str.replace("-", "").str.slice(0, 6)
)
_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)],
@@ -186,17 +176,13 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t
def _pred_autocorr(pred_label: pd.DataFrame, lag=1, **kwargs) -> tuple:
pred = pred_label.copy()
pred["score_last"] = pred.groupby(level="instrument")["score"].shift(lag)
ac = pred.groupby(level="datetime").apply(
lambda x: x["score"].rank(pct=True).corr(x["score_last"].rank(pct=True))
)
ac = pred.groupby(level="datetime").apply(lambda x: x["score"].rank(pct=True).corr(x["score_last"].rank(pct=True)))
# FIXME: support HIGH-FREQ
_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)
),
layout=dict(title="Auto Correlation", xaxis=dict(type="category", tickangle=45)),
).figure
return (ac_figure,)
@@ -206,9 +192,7 @@ def _pred_turnover(pred_label: pd.DataFrame, N=5, lag=1, **kwargs) -> tuple:
pred["score_last"] = pred.groupby(level="instrument")["score"].shift(lag)
top = pred.groupby(level="datetime").apply(
lambda x: 1
- x.nlargest(len(x) // N, columns="score")
.index.isin(x.nlargest(len(x) // N, columns="score_last").index)
.sum()
- x.nlargest(len(x) // N, columns="score").index.isin(x.nlargest(len(x) // N, columns="score_last").index).sum()
/ (len(x) // N)
)
bottom = pred.groupby(level="datetime").apply(
@@ -218,14 +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)
),
layout=dict(title="Top-Bottom Turnover", xaxis=dict(type="category", tickangle=45)),
).figure
return (turnover_figure,)
@@ -270,12 +257,12 @@ def model_performance_graph(
.. code-block:: python
instrument datetime score label
SH600004 2017-12-11 -0.013502 -0.013502
2017-12-12 -0.072367 -0.072367
2017-12-13 -0.068605 -0.068605
2017-12-14 0.012440 0.012440
2017-12-15 -0.102778 -0.102778
instrument datetime score label
SH600004 2017-12-11 -0.013502 -0.013502
2017-12-12 -0.072367 -0.072367
2017-12-13 -0.068605 -0.068605
2017-12-14 0.012440 0.012440
2017-12-15 -0.102778 -0.102778
:param lag: `pred.groupby(level='instrument')['score'].shift(lag)`. It will be only used in the auto-correlation computing.

46
qlib/contrib/report/analysis_position/cumulative_return.py
View File

@@ -36,9 +36,7 @@ def _get_cum_return_data_with_position(
end_date=end_date,
).copy()
_cumulative_return_df["label"] = (
_cumulative_return_df["label"] - _cumulative_return_df["bench"]
)
_cumulative_return_df["label"] = _cumulative_return_df["label"] - _cumulative_return_df["bench"]
_cumulative_return_df = _cumulative_return_df.dropna()
df_gp = _cumulative_return_df.groupby(level="datetime")
result_list = []
@@ -105,26 +103,20 @@ def _get_figure_with_position(
:return:
"""
cum_return_df = _get_cum_return_data_with_position(
position, report_normal, label_data, start_date, end_date
)
cum_return_df = _get_cum_return_data_with_position(position, report_normal, label_data, start_date, end_date)
cum_return_df = cum_return_df.set_index("date")
# FIXME: support HIGH-FREQ
cum_return_df.index = cum_return_df.index.strftime('%Y-%m-%d')
cum_return_df.index = cum_return_df.index.strftime("%Y-%m-%d")
# 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"}
),
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
),
"{}_weight".format(_t_name.replace("minus", "plus") if "minus" in _t_name else _t_name),
dict(row=2, col=1),
),
(
@@ -240,13 +232,13 @@ def cumulative_return_graph(
.. code-block:: python
return cost bench turnover
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
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 label_data: `D.features` result; index is `pd.MultiIndex`, index name is [`instrument`, `datetime`]; columns names is [`label`].
@@ -256,12 +248,12 @@ def cumulative_return_graph(
.. code-block:: python
label
instrument datetime
SH600004 2017-12-11 -0.013502
2017-12-12 -0.072367
2017-12-13 -0.068605
2017-12-14 0.012440
2017-12-15 -0.102778
instrument datetime
SH600004 2017-12-11 -0.013502
2017-12-12 -0.072367
2017-12-13 -0.068605
2017-12-14 0.012440
2017-12-15 -0.102778
:param show_notebook: True or False. If True, show graph in notebook, else return figures
@@ -272,9 +264,7 @@ def cumulative_return_graph(
position = copy.deepcopy(position)
report_normal = report_normal.copy()
label_data.columns = ["label"]
_figures = _get_figure_with_position(
position, report_normal, label_data, start_date, end_date
)
_figures = _get_figure_with_position(position, report_normal, label_data, start_date, end_date)
if show_notebook:
BaseGraph.show_graph_in_notebook(_figures)
else:

41
qlib/contrib/report/analysis_position/parse_position.py
View File

@@ -20,13 +20,13 @@ def parse_position(position: dict = None) -> pd.DataFrame:
print(position_df.head())
# status: 0-hold, -1-sell, 1-buy
amount cash count price status weight
instrument datetime
SZ000547 2017-01-04 44.154290 211405.285654 1 205.189575 1 0.031255
SZ300202 2017-01-04 60.638845 211405.285654 1 154.356506 1 0.032290
SH600158 2017-01-04 46.531681 211405.285654 1 153.895142 1 0.024704
SH600545 2017-01-04 197.173093 211405.285654 1 48.607037 1 0.033063
SZ000930 2017-01-04 103.938300 211405.285654 1 80.759453 1 0.028958
amount cash count price status weight
instrument datetime
SZ000547 2017-01-04 44.154290 211405.285654 1 205.189575 1 0.031255
SZ300202 2017-01-04 60.638845 211405.285654 1 154.356506 1 0.032290
SH600158 2017-01-04 46.531681 211405.285654 1 153.895142 1 0.024704
SH600545 2017-01-04 197.173093 211405.285654 1 48.607037 1 0.033063
SZ000930 2017-01-04 103.938300 211405.285654 1 80.759453 1 0.028958
"""
@@ -63,15 +63,12 @@ def parse_position(position: dict = None) -> pd.DataFrame:
# Trading day sell
if not result_df.empty:
_trading_day_sell_df = result_df.loc[
(result_df["date"] == previous_data["date"])
& (result_df.index.isin(_cur_day_sell))
(result_df["date"] == previous_data["date"]) & (result_df.index.isin(_cur_day_sell))
].copy()
if not _trading_day_sell_df.empty:
_trading_day_sell_df["status"] = -1
_trading_day_sell_df["date"] = _trading_date
_trading_day_df = _trading_day_df.append(
_trading_day_sell_df, sort=False
)
_trading_day_df = _trading_day_df.append(_trading_day_sell_df, sort=False)
result_df = result_df.append(_trading_day_df, sort=True)
@@ -85,9 +82,7 @@ def parse_position(position: dict = None) -> pd.DataFrame:
return result_df.set_index(["instrument", "datetime"])
def _add_label_to_position(
position_df: pd.DataFrame, label_data: pd.DataFrame
) -> pd.DataFrame:
def _add_label_to_position(position_df: pd.DataFrame, label_data: pd.DataFrame) -> pd.DataFrame:
"""Concat position with custom label
:param position_df: position DataFrame
@@ -98,16 +93,12 @@ def _add_label_to_position(
_start_time = position_df.index.get_level_values(level="datetime").min()
_end_time = position_df.index.get_level_values(level="datetime").max()
label_data = label_data.loc(axis=0)[:, pd.to_datetime(_start_time) :]
_result_df = pd.concat([position_df, label_data], axis=1, sort=True).reindex(
label_data.index
)
_result_df = pd.concat([position_df, label_data], axis=1, sort=True).reindex(label_data.index)
_result_df = _result_df.loc[_result_df.index.get_level_values(1) <= _end_time]
return _result_df
def _add_bench_to_position(
position_df: pd.DataFrame = None, bench: pd.Series = None
) -> pd.DataFrame:
def _add_bench_to_position(position_df: pd.DataFrame = None, bench: pd.Series = None) -> pd.DataFrame:
"""Concat position with bench
:param position_df: position DataFrame
@@ -135,9 +126,7 @@ def _calculate_label_rank(df: pd.DataFrame) -> pd.DataFrame:
# Sell: -1, Hold: 0, Buy: 1
for i in [-1, 0, 1]:
g_df.loc[g_df["status"] == i, "rank_label_mean"] = g_df[
g_df["status"] == i
]["rank_ratio"].mean()
g_df.loc[g_df["status"] == i, "rank_label_mean"] = g_df[g_df["status"] == i]["rank_ratio"].mean()
g_df["excess_return"] = g_df[_label_name] - g_df[_label_name].mean()
return g_df
@@ -181,7 +170,5 @@ def get_position_data(
_date_list = _position_df.index.get_level_values(level="datetime")
start_date = _date_list.min() if start_date is None else start_date
end_date = _date_list.max() if end_date is None else end_date
_position_df = _position_df.loc[
(start_date <= _date_list) & (_date_list <= end_date)
]
_position_df = _position_df.loc[(start_date <= _date_list) & (_date_list <= end_date)]
return _position_df

14
qlib/contrib/report/analysis_position/rank_label.py
View File

@@ -46,7 +46,7 @@ def _get_figure_with_position(
_res_df = pd.DataFrame.from_dict(res_dict, orient="index")
# FIXME: support HIGH-FREQ
_res_df.index = _res_df.index.strftime('%Y-%m-%d')
_res_df.index = _res_df.index.strftime("%Y-%m-%d")
for _col in _res_df.columns:
yield ScatterGraph(
_res_df.loc[:, [_col]],
@@ -105,12 +105,12 @@ def rank_label_graph(
.. code-block:: python
label
instrument datetime
SH600004 2017-12-11 -0.013502
2017-12-12 -0.072367
2017-12-13 -0.068605
2017-12-14 0.012440
2017-12-15 -0.102778
instrument datetime
SH600004 2017-12-11 -0.013502
2017-12-12 -0.072367
2017-12-13 -0.068605
2017-12-14 0.012440
2017-12-15 -0.102778
:param start_date: start date

44
qlib/contrib/report/analysis_position/report.py
View File

@@ -48,20 +48,12 @@ def _calculate_report_data(df: pd.DataFrame) -> pd.DataFrame:
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["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()
)
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"]
@@ -113,13 +105,7 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]:
)
for i in range(2, 8):
# yaxis
_subplot_layout.update(
{
"yaxis{}".format(i): dict(
zeroline=True, showline=True, showticklabels=True
)
}
)
_subplot_layout.update({"yaxis{}".format(i): dict(zeroline=True, showline=True, showticklabels=True)})
_layout_style = dict(
height=1200,
title=" ",
@@ -134,7 +120,9 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]:
"y1": 1,
"fillcolor": "#d3d3d3",
"opacity": 0.3,
"line": {"width": 0,},
"line": {
"width": 0,
},
},
{
"type": "rect",
@@ -146,7 +134,9 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]:
"y1": 0.55,
"fillcolor": "#d3d3d3",
"opacity": 0.3,
"line": {"width": 0,},
"line": {
"width": 0,
},
},
],
)
@@ -200,13 +190,13 @@ def report_graph(report_df: pd.DataFrame, show_notebook: bool = True) -> [list,
.. code-block:: python
return cost bench turnover
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
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**

52
qlib/contrib/report/analysis_position/risk_analysis.py
View File

@@ -32,13 +32,9 @@ def _get_risk_analysis_data_with_report(
# analysis["pred_long_short"] = risk_analysis(report_long_short_df["long_short"])
if not report_normal_df.empty:
analysis["excess_return_without_cost"] = risk_analysis(
report_normal_df["return"] - report_normal_df["bench"]
)
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"]
report_normal_df["return"] - report_normal_df["bench"] - report_normal_df["cost"]
)
analysis_df = pd.concat(analysis) # type: pd.DataFrame
analysis_df["date"] = date
@@ -67,9 +63,7 @@ def _get_monthly_risk_analysis_with_report(report_normal_df: pd.DataFrame) -> pd
"""
# Group by month
report_normal_gp = report_normal_df.groupby(
[report_normal_df.index.year, report_normal_df.index.month]
)
report_normal_gp = report_normal_df.groupby([report_normal_df.index.year, report_normal_df.index.month])
# report_long_short_gp = report_long_short_df.groupby(
# [report_long_short_df.index.year, report_long_short_df.index.month]
# )
@@ -96,9 +90,7 @@ def _get_monthly_risk_analysis_with_report(report_normal_df: pd.DataFrame) -> pd
return _monthly_df
def _get_monthly_analysis_with_feature(
monthly_df: pd.DataFrame, feature: str = "annualized_return"
) -> pd.DataFrame:
def _get_monthly_analysis_with_feature(monthly_df: pd.DataFrame, feature: str = "annualized_return") -> pd.DataFrame:
"""
:param monthly_df:
@@ -108,9 +100,7 @@ def _get_monthly_analysis_with_feature(
_monthly_df_gp = monthly_df.reset_index().groupby(["level_1"])
_name_df = _monthly_df_gp.get_group(feature).set_index(["level_0", "level_1"])
_temp_df = _name_df.pivot_table(
index="date", values=["risk"], columns=_name_df.index
)
_temp_df = _name_df.pivot_table(index="date", values=["risk"], columns=_name_df.index)
_temp_df.columns = map(lambda x: "_".join(x[-1]), _temp_df.columns)
_temp_df.index = _temp_df.index.strftime("%Y-%m")
@@ -126,9 +116,7 @@ def _get_risk_analysis_figure(analysis_df: pd.DataFrame) -> Iterable[py.Figure]:
if analysis_df is None:
return []
_figure = SubplotsGraph(
_get_all_risk_analysis(analysis_df), kind_map=dict(kind="BarGraph", kwargs={})
).figure
_figure = SubplotsGraph(_get_all_risk_analysis(analysis_df), kind_map=dict(kind="BarGraph", kwargs={})).figure
return (_figure,)
@@ -141,7 +129,7 @@ def _get_monthly_risk_analysis_figure(report_normal_df: pd.DataFrame) -> Iterabl
"""
# if report_normal_df is None and report_long_short_df is None:
# return []
# return []
if report_normal_df is None:
return []
@@ -231,13 +219,13 @@ def risk_analysis_graph(
.. code-block:: python
return cost bench turnover
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
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 report_long_short_df: **df.index.name** must be **date**, df.columns contain **long**, **short**, **long_short**
@@ -245,13 +233,13 @@ def risk_analysis_graph(
.. code-block:: python
long short long_short
long short long_short
date
2017-01-04 -0.001360 0.001394 0.000034
2017-01-05 0.002456 0.000058 0.002514
2017-01-06 0.000120 0.002739 0.002859
2017-01-09 0.001436 0.001838 0.003273
2017-01-10 0.000824 -0.001944 -0.001120
2017-01-04 -0.001360 0.001394 0.000034
2017-01-05 0.002456 0.000058 0.002514
2017-01-06 0.000120 0.002739 0.002859
2017-01-09 0.001436 0.001838 0.003273
2017-01-10 0.000824 -0.001944 -0.001120
:param show_notebook: Whether to display graphics in a notebook, default **True**
@@ -263,7 +251,7 @@ def risk_analysis_graph(
_get_monthly_risk_analysis_figure(
report_normal_df,
# report_long_short_df,
)
)
)
if show_notebook:
ScatterGraph.show_graph_in_notebook(_figure_list)

24
qlib/contrib/report/analysis_position/score_ic.py
View File

@@ -14,18 +14,12 @@ def _get_score_ic(pred_label: pd.DataFrame):
"""
concat_data = pred_label.copy()
concat_data.dropna(axis=0, how="any", inplace=True)
_ic = concat_data.groupby(level="datetime").apply(
lambda x: x["label"].corr(x["score"])
)
_rank_ic = concat_data.groupby(level="datetime").apply(
lambda x: x["label"].corr(x["score"], method="spearman")
)
_ic = concat_data.groupby(level="datetime").apply(lambda x: x["label"].corr(x["score"]))
_rank_ic = concat_data.groupby(level="datetime").apply(lambda x: x["label"].corr(x["score"], method="spearman"))
return pd.DataFrame({"ic": _ic, "rank_ic": _rank_ic})
def score_ic_graph(
pred_label: pd.DataFrame, show_notebook: bool = True
) -> [list, tuple]:
def score_ic_graph(pred_label: pd.DataFrame, show_notebook: bool = True) -> [list, tuple]:
"""score IC
Example:
@@ -47,12 +41,12 @@ def score_ic_graph(
.. code-block:: python
instrument datetime score label
SH600004 2017-12-11 -0.013502 -0.013502
2017-12-12 -0.072367 -0.072367
2017-12-13 -0.068605 -0.068605
2017-12-14 0.012440 0.012440
2017-12-15 -0.102778 -0.102778
instrument datetime score label
SH600004 2017-12-11 -0.013502 -0.013502
2017-12-12 -0.072367 -0.072367
2017-12-13 -0.068605 -0.068605
2017-12-14 0.012440 0.012440
2017-12-15 -0.102778 -0.102778
:param show_notebook: whether to display graphics in notebook, the default is **True**