| 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) |
|
|
| |
| 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 |
|
|
