File size: 3,265 Bytes
f2361f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | import numpy as np
import pandas as pd
from qlib.constant import EPS
from qlib.data.dataset.processor import Processor
from qlib.data.dataset.utils import fetch_df_by_index
class HighFreqNorm(Processor):
def __init__(self, fit_start_time, fit_end_time):
self.fit_start_time = fit_start_time
self.fit_end_time = fit_end_time
def fit(self, df_features):
fetch_df = fetch_df_by_index(df_features, slice(self.fit_start_time, self.fit_end_time), level="datetime")
del df_features
df_values = fetch_df.values
names = {
"price": slice(0, 10),
"volume": slice(10, 12),
}
self.feature_med = {}
self.feature_std = {}
self.feature_vmax = {}
self.feature_vmin = {}
for name, name_val in names.items():
part_values = df_values[:, name_val].astype(np.float32)
if name == "volume":
part_values = np.log1p(part_values)
self.feature_med[name] = np.nanmedian(part_values)
part_values = part_values - self.feature_med[name]
self.feature_std[name] = np.nanmedian(np.absolute(part_values)) * 1.4826 + EPS
part_values = part_values / self.feature_std[name]
self.feature_vmax[name] = np.nanmax(part_values)
self.feature_vmin[name] = np.nanmin(part_values)
def __call__(self, df_features):
df_features["date"] = pd.to_datetime(
df_features.index.get_level_values(level="datetime").to_series().dt.date.values
)
df_features.set_index("date", append=True, drop=True, inplace=True)
df_values = df_features.values
names = {
"price": slice(0, 10),
"volume": slice(10, 12),
}
for name, name_val in names.items():
if name == "volume":
df_values[:, name_val] = np.log1p(df_values[:, name_val])
df_values[:, name_val] -= self.feature_med[name]
df_values[:, name_val] /= self.feature_std[name]
slice0 = df_values[:, name_val] > 3.0
slice1 = df_values[:, name_val] > 3.5
slice2 = df_values[:, name_val] < -3.0
slice3 = df_values[:, name_val] < -3.5
df_values[:, name_val][slice0] = (
3.0 + (df_values[:, name_val][slice0] - 3.0) / (self.feature_vmax[name] - 3) * 0.5
)
df_values[:, name_val][slice1] = 3.5
df_values[:, name_val][slice2] = (
-3.0 - (df_values[:, name_val][slice2] + 3.0) / (self.feature_vmin[name] + 3) * 0.5
)
df_values[:, name_val][slice3] = -3.5
idx = df_features.index.droplevel("datetime").drop_duplicates()
idx.set_names(["instrument", "datetime"], inplace=True)
# Reshape is specifically for adapting to RL high-freq executor
feat = df_values[:, [0, 1, 2, 3, 4, 10]].reshape(-1, 6 * 240)
feat_1 = df_values[:, [5, 6, 7, 8, 9, 11]].reshape(-1, 6 * 240)
df_new_features = pd.DataFrame(
data=np.concatenate((feat, feat_1), axis=1),
index=idx,
columns=["FEATURE_%d" % i for i in range(12 * 240)],
).sort_index()
return df_new_features
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