# -*- 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