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c687548 | 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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | # -*- coding: utf-8 -*-
# @Time : 2025/7/4 19:42
# @Author : Lukax
# @Email : Lukarxiang@gmail.com
# @File : inplemental.py
# -*- presentd: PyCharm -*-
import torch
import numpy as np
import pandas as pd
from Settings import Config
from torch.utils.data import DataLoader, TensorDataset
def getDataLoader(X, Y, hparams, device, shuffle = True):
X = torch.tensor(X, dtype = torch.float32, device = device)
if Y is None:
dataset = TensorDataset(X)
else:
Y = torch.tensor(Y.values if hasattr(Y, 'values') else Y,
dtype = torch.float32, device = device).unsqueeze(1) # y need 2 dimensions
dataset = TensorDataset(X, Y)
dataloader = DataLoader(dataset, batch_size = hparams['batch_size'], shuffle = shuffle,
generator = torch.Generator().manual_seed(hparams['seed']))
return dataloader
class Config:
TRAIN_PATH = "/AI4M/users/mjzhang/workspace/DRW/new_data/train.parquet"
TEST_PATH = "/AI4M/users/mjzhang/workspace/DRW/new_data/test.parquet"
SUBMISSION_PATH = "/AI4M/users/mjzhang/workspace/DRW/new_data/sample_submission.csv"
# Original features plus additional market features
FEATURES = [
"X175", "X198", "X179", "X173", "X169", "X181", "X94",
"X197", "X137", "X133", "X163", "X196", "sell_qty",
"bid_qty", "ask_qty", "buy_qty", "volume"]
EX_FEATURES = [
'X598', 'X385', 'X603', 'X674', 'X415', 'X345', 'X174',
'X302', 'X178', 'X168', 'X612', 'X421', 'X333', 'X586', 'X292'
]
TARGET = "label"
N_FOLDS = 3
RANDOM_STATE = 42
def add_featrues1(df):
# Original features
df['bid_ask_interaction'] = df['bid_qty'] * df['ask_qty']
df['bid_buy_interaction'] = df['bid_qty'] * df['buy_qty']
df['bid_sell_interaction'] = df['bid_qty'] * df['sell_qty']
df['ask_buy_interaction'] = df['ask_qty'] * df['buy_qty']
df['ask_sell_interaction'] = df['ask_qty'] * df['sell_qty']
df['volume_weighted_sell'] = df['sell_qty'] * df['volume']
df['buy_sell_ratio'] = df['buy_qty'] / (df['sell_qty'] + 1e-10)
df['selling_pressure'] = df['sell_qty'] / (df['volume'] + 1e-10)
df['log_volume'] = np.log1p(df['volume'])
df['effective_spread_proxy'] = np.abs(df['buy_qty'] - df['sell_qty']) / (df['volume'] + 1e-10)
df['bid_ask_imbalance'] = (df['bid_qty'] - df['ask_qty']) / (df['bid_qty'] + df['ask_qty'] + 1e-10)
df['order_flow_imbalance'] = (df['buy_qty'] - df['sell_qty']) / (df['buy_qty'] + df['sell_qty'] + 1e-10)
df['liquidity_ratio'] = (df['bid_qty'] + df['ask_qty']) / (df['volume'] + 1e-10)
# === NEW MICROSTRUCTURE FEATURES ===
# Price Pressure Indicators
df['net_order_flow'] = df['buy_qty'] - df['sell_qty']
df['normalized_net_flow'] = df['net_order_flow'] / (df['volume'] + 1e-10)
df['buying_pressure'] = df['buy_qty'] / (df['volume'] + 1e-10)
df['volume_weighted_buy'] = df['buy_qty'] * df['volume']
# Liquidity Depth Measures
df['total_depth'] = df['bid_qty'] + df['ask_qty']
df['depth_imbalance'] = (df['bid_qty'] - df['ask_qty']) / (df['total_depth'] + 1e-10)
df['relative_spread'] = np.abs(df['bid_qty'] - df['ask_qty']) / (df['total_depth'] + 1e-10)
df['log_depth'] = np.log1p(df['total_depth'])
# Order Flow Toxicity Proxies
df['kyle_lambda'] = np.abs(df['net_order_flow']) / (df['volume'] + 1e-10)
df['flow_toxicity'] = np.abs(df['order_flow_imbalance']) * df['volume']
df['aggressive_flow_ratio'] = (df['buy_qty'] + df['sell_qty']) / (df['total_depth'] + 1e-10)
# Market Activity Indicators
df['volume_depth_ratio'] = df['volume'] / (df['total_depth'] + 1e-10)
df['activity_intensity'] = (df['buy_qty'] + df['sell_qty']) / (df['volume'] + 1e-10)
df['log_buy_qty'] = np.log1p(df['buy_qty'])
df['log_sell_qty'] = np.log1p(df['sell_qty'])
df['log_bid_qty'] = np.log1p(df['bid_qty'])
df['log_ask_qty'] = np.log1p(df['ask_qty'])
# Microstructure Volatility Proxies
df['realized_spread_proxy'] = 2 * np.abs(df['net_order_flow']) / (df['volume'] + 1e-10)
df['price_impact_proxy'] = df['net_order_flow'] / (df['total_depth'] + 1e-10)
df['quote_volatility_proxy'] = np.abs(df['depth_imbalance'])
# Complex Interaction Terms
df['flow_depth_interaction'] = df['net_order_flow'] * df['total_depth']
df['imbalance_volume_interaction'] = df['order_flow_imbalance'] * df['volume']
df['depth_volume_interaction'] = df['total_depth'] * df['volume']
df['buy_sell_spread'] = np.abs(df['buy_qty'] - df['sell_qty'])
df['bid_ask_spread'] = np.abs(df['bid_qty'] - df['ask_qty'])
# Information Asymmetry Measures
df['trade_informativeness'] = df['net_order_flow'] / (df['bid_qty'] + df['ask_qty'] + 1e-10)
df['execution_shortfall_proxy'] = df['buy_sell_spread'] / (df['volume'] + 1e-10)
df['adverse_selection_proxy'] = df['net_order_flow'] / (df['total_depth'] + 1e-10) * df['volume']
# Market Efficiency Indicators
df['fill_probability'] = df['volume'] / (df['buy_qty'] + df['sell_qty'] + 1e-10)
df['execution_rate'] = (df['buy_qty'] + df['sell_qty']) / (df['total_depth'] + 1e-10)
df['market_efficiency'] = df['volume'] / (df['bid_ask_spread'] + 1e-10)
# Non-linear Transformations
df['sqrt_volume'] = np.sqrt(df['volume'])
df['sqrt_depth'] = np.sqrt(df['total_depth'])
df['volume_squared'] = df['volume'] ** 2
df['imbalance_squared'] = df['order_flow_imbalance'] ** 2
# Relative Measures
df['bid_ratio'] = df['bid_qty'] / (df['total_depth'] + 1e-10)
df['ask_ratio'] = df['ask_qty'] / (df['total_depth'] + 1e-10)
df['buy_ratio'] = df['buy_qty'] / (df['buy_qty'] + df['sell_qty'] + 1e-10)
df['sell_ratio'] = df['sell_qty'] / (df['buy_qty'] + df['sell_qty'] + 1e-10)
# Market Stress Indicators
df['liquidity_consumption'] = (df['buy_qty'] + df['sell_qty']) / (df['total_depth'] + 1e-10)
df['market_stress'] = df['volume'] / (df['total_depth'] + 1e-10) * np.abs(df['order_flow_imbalance'])
df['depth_depletion'] = df['volume'] / (df['bid_qty'] + df['ask_qty'] + 1e-10)
# Directional Indicators
df['net_buying_ratio'] = df['net_order_flow'] / (df['volume'] + 1e-10)
df['directional_volume'] = df['net_order_flow'] * np.log1p(df['volume'])
df['signed_volume'] = np.sign(df['net_order_flow']) * df['volume']
# Replace infinities and NaNs
df = df.replace([np.inf, -np.inf], 0).fillna(0)
return df
def load_data():
features = list(set(Config.FEATURES + Config.MLP_FEATURES))
train = pd.read_parquet(Config.TRAIN_PATH, columns = features + [Config.TARGET])
train = train.dropna(subset=[Config.TARGET]).reset_index(drop=True)
assert not train[Config.TARGET].isna().any(), "label still has NaN"
test = pd.read_parquet(Config.TEST_PATH, columns = features)
submission = pd.read_csv(Config.SUBMISSION_PATH)
print(f'Origin: train {train.shape}, test {test.shape}, submission {submission.shape}')
train, test = add_featrues1(train), add_featrues1(test)
Config.FEATURES = test.columns.tolist()
return train.reset_index(drop = True), test.reset_index(drop = True), submission
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