"""2026年1月BTC数据 - DLinear 准确率测试 (create_labels版)

与test_dlinear_jan_2026.py对比：
- 本脚本使用create_labels预处理方式（auto_trade旧逻辑）
- 每个目标K线截取到input_idx的df，调用create_labels后取X[-1]预测
- 用于验证create_labels对预测结果的影响

输入数据：预测K线的前一根（即用截止到t的数据预测t+1）
价格对比：预测结果与预测K线(t+1)的收盘价做对比
时间：币安数据UTC，输出转为北京时间(UTC+8)，按北京时间分天
"""

import sys
import os
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

import torch
import numpy as np
import pandas as pd
import pickle
import pandas_ta as ta
from datetime import timedelta
from sklearn.preprocessing import StandardScaler
from data.fetcher import BinanceDataFetcher

# ==================== 时区与美股判断 ====================

def utc_to_beijing(ts):
    """UTC时间转北京时间(UTC+8)"""
    return ts + timedelta(hours=8)

def is_us_stock_open_by_utc(utc_ts):
    """根据UTC时间判断美股当天是否开盘

    美股交易日基于美东时间(ET)，UTC-5(冬令时)
    2026年1月在冬令时范围内（夏令时3月第二个周日开始）
    所以美东 = UTC - 5h
    """
    et_ts = utc_ts - timedelta(hours=5)
    et_date = pd.Timestamp(et_ts.date())

    us_holidays_jan_2026 = {
        pd.Timestamp('2026-01-01'),  # 元旦 New Year's Day
        pd.Timestamp('2026-01-19'),  # 马丁·路德·金日 MLK Day
    }

    if et_date.weekday() >= 5:
        return False
    if et_date in us_holidays_jan_2026:
        return False
    return True

# ==================== pickle反序列化占位类 ====================

class ClassificationPreprocessor:
    """pickle.load时需要能找到这个类名"""
    def __init__(self, lookback=168, forecast_horizon=1):
        self.lookback = lookback
        self.forecast_horizon = forecast_horizon
        self.scaler = StandardScaler()
        self.is_fitted = False

# ==================== create_labels预处理 ====================

def preprocess_with_create_labels(df, scaler, lookback=168, forecast_horizon=1):
    """使用create_labels方式预处理（auto_trade旧逻辑）

    步骤：加特征 -> create_labels(shift + 砍尾) -> dropna -> scaler.transform -> 构建窗口
    """
    df = df.copy()
    # 加特征
    df['rsi'] = ta.rsi(df['close'], length=14)
    macd = ta.macd(df['close'])
    if macd is not None and not macd.empty:
        df['macd'] = macd.iloc[:, 0]
        df['macd_hist'] = macd.iloc[:, 1]
        df['macd_signal'] = macd.iloc[:, 2]

    # create_labels: shift + 砍最后一行
    df['future_price'] = df['close'].shift(-forecast_horizon)
    df['future_return'] = (df['future_price'] - df['close']) / df['close']
    df['label'] = (df['future_return'] > 0).astype(int)
    df = df[:-forecast_horizon]

    df = df.dropna().reset_index(drop=True)

    feature_cols = ['open', 'high', 'low', 'close', 'volume',
                   'rsi', 'macd', 'macd_hist', 'macd_signal']

    scaled_features = scaler.transform(df[feature_cols])

    X = []
    for i in range(len(scaled_features) - lookback):
        X.append(scaled_features[i:i + lookback])

    return np.array(X), df

# ==================== 工具函数 ====================

def fmt_pred(val):
    """格式化预测结果"""
    if val is None or (isinstance(val, float) and np.isnan(val)):
        return "N/A"
    return "涨" if int(val) == 1 else "跌"

def fmt_result(pred, actual):
    """格式化对错"""
    if pred is None or (isinstance(pred, float) and np.isnan(pred)):
        return "  "
    return "✓" if int(pred) == int(actual) else "✗"


def main():
    print("=" * 90)
    print("2026年1月 BTC 1h 涨跌预测准确率测试 (DLinear - create_labels版)")
    print("输入：预测K线的前一根 | 对比：预测结果 vs 预测K线收盘价 | 时间：北京时间(UTC+8)")
    print("预处理：截取到input的df，调用create_labels后取X[-1]")
    print("=" * 90)

    device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')
    print(f"使用设备: {device}")

    # ========== 步骤1: 加载模型 ==========
    print("\n[1/4] 加载模型...")

    from models.dlinear.model import DLinear
    checkpoint = torch.load('./models/checkpoints/dlinear_classification_2026/best_model.pt',
                            map_location=device, weights_only=False)
    model = DLinear(checkpoint['config'])
    model.load_state_dict(checkpoint['model_state_dict'])
    model = model.to(device).eval()
    with open('./models/checkpoints/dlinear_classification_2026/preprocessor.pkl', 'rb') as f:
        preprocessor = pickle.load(f)
    print("  ✓ DLinear已加载")

    # ========== 步骤2: 获取测试数据 ==========
    print("\n[2/4] 获取数据...")
    fetcher = BinanceDataFetcher()
    df = fetcher.fetch_ohlcv('BTC', '1h', start_date='2025-10-01', end_date='2026-02-01', use_cache=False)

    print(f"数据范围(UTC): {df['timestamp'].min()} -> {df['timestamp'].max()}")
    print(f"总行数: {len(df)}")

    # ========== 步骤3: 预处理与预测 ==========
    print("\n[3/4] 运行预测...")

    lookback = preprocessor.lookback
    scaler = preprocessor.scaler

    # 找出1月（北京时间）的所有预测目标K线索引
    jan_targets = []
    for target_idx in range(1, len(df)):
        target_time_utc = df.iloc[target_idx]['timestamp']
        target_time_bj = utc_to_beijing(target_time_utc)
        if target_time_bj.year == 2026 and target_time_bj.month == 1:
            jan_targets.append(target_idx)

    print(f"1月（北京时间）目标K线数: {len(jan_targets)}")

    results = []

    for target_idx in jan_targets:
        target_time_utc = df.iloc[target_idx]['timestamp']
        target_time_bj = utc_to_beijing(target_time_utc)

        # 输入K线索引（前一根）
        input_idx = target_idx - 1
        current_close = df.iloc[input_idx]['close']
        target_close = df.iloc[target_idx]['close']
        actual_label = 1 if target_close > current_close else 0

        # --- DLinear 预测（create_labels方式：截取到input_idx，取X[-1]） ---
        dl_pred = None
        dl_prob = None

        df_up_to_input = df.iloc[:input_idx + 1]
        X, _ = preprocess_with_create_labels(df_up_to_input, scaler, lookback)

        if len(X) > 0:
            x_tensor = torch.FloatTensor(X[-1:]).to(device)
            with torch.no_grad():
                logits = model(x_tensor).cpu().numpy()
                prob = 1 / (1 + np.exp(-logits))
                dl_pred = int(prob.item() > 0.5)
                dl_prob = float(prob.item())

        results.append({
            'time_utc': target_time_utc,
            'time_bj': target_time_bj,
            'current_close': current_close,
            'target_close': target_close,
            'actual': actual_label,
            'dlinear': dl_pred,
            'dl_prob': dl_prob,
        })

    # ========== 步骤4: 输出结果 ==========
    if not results:
        print("没有找到符合条件的测试样本")
        return

    df_res = pd.DataFrame(results)
    df_res['date_bj'] = df_res['time_bj'].dt.date
    df_res['us_open'] = df_res['time_utc'].apply(is_us_stock_open_by_utc)

    total_samples = len(df_res)

    print(f"\n[4/4] 准确率统计 (2026年1月，北京时间)")
    print(f"总样本数: {total_samples}")

    # ========== 每小时详细数据 ==========
    print("\n" + "=" * 90)
    print("每小时详细数据")
    print("=" * 90)

    hourly_header = (
        f"{'北京时间':<18} {'前收盘':>10} {'现收盘':>10} {'实际':>4} "
        f"{'DL':>3} {'':>1} {'概率':>6}"
    )

    for date_bj, day_group in df_res.groupby('date_bj'):
        us_open = day_group['us_open'].iloc[0]
        us_str = "美股开盘" if us_open else "美股休市"

        dl_day_correct = (day_group['dlinear'] == day_group['actual']).sum()
        dl_day_total = day_group['dlinear'].notna().sum()
        dl_day_acc = dl_day_correct / dl_day_total if dl_day_total > 0 else 0

        print(f"\n--- {date_bj} ({us_str}) --- DL准确率:{dl_day_acc:.1%} ({dl_day_correct}/{dl_day_total})")
        print(hourly_header)
        print("-" * 90)

        for _, row in day_group.iterrows():
            time_str = row['time_bj'].strftime('%m-%d %H:%M')
            actual_str = fmt_pred(row['actual'])
            dl_str = fmt_pred(row['dlinear'])
            dl_r = fmt_result(row['dlinear'], row['actual'])
            prob_str = f"{row['dl_prob']:.3f}" if row['dl_prob'] is not None else "  N/A"

            print(
                f"{time_str:<18} {row['current_close']:>10.2f} {row['target_close']:>10.2f} {actual_str:>4} "
                f"{dl_str:>3} {dl_r:>1} {prob_str:>6}"
            )

    # ========== 每日准确率汇总表 ==========
    print("\n\n" + "=" * 90)
    print("每日准确率汇总 (北京时间)")
    print("=" * 90)

    header = f"{'日期':<14} {'样本':>4} {'美股':>6}  {'DLinear':>8}"
    print(header)
    print("-" * 90)

    for date_bj, group in df_res.groupby('date_bj'):
        total = len(group)
        us_open = group['us_open'].iloc[0]
        us_str = "开盘" if us_open else "休市"

        dl_valid = group.dropna(subset=['dlinear'])
        dl_acc = (dl_valid['dlinear'] == dl_valid['actual']).mean() if len(dl_valid) > 0 else 0
        dl_str = f"{dl_acc:.1%}"

        print(f"{str(date_bj):<14} {total:>4} {us_str:>6}  {dl_str:>8}")

    print("-" * 90)

    # --- 总准确率 ---
    dl_valid_all = df_res.dropna(subset=['dlinear'])
    dl_total = (dl_valid_all['dlinear'] == dl_valid_all['actual']).mean() if len(dl_valid_all) > 0 else 0

    print(f"{'总计':<14} {total_samples:>4} {'':>6}  {dl_total:.2%}")
    print("=" * 90)

    # --- 美股开盘日 vs 休市日对比 ---
    print("\n[美股开盘日 vs 休市日 准确率对比]")
    print("-" * 60)
    for label, flag in [("开盘日", True), ("休市日", False)]:
        subset = df_res[df_res['us_open'] == flag]
        if len(subset) == 0:
            continue
        n = len(subset)
        dl_s = subset.dropna(subset=['dlinear'])
        dl_a = (dl_s['dlinear'] == dl_s['actual']).mean() if len(dl_s) > 0 else 0
        print(f"  {label} ({n}样本): DLinear={dl_a:.2%}")

    print("-" * 60)

    # --- 预测分布统计 ---
    print("\n[预测分布统计]")
    print("-" * 60)
    actual_up = (df_res['actual'] == 1).sum()
    actual_down = (df_res['actual'] == 0).sum()
    print(f"  实际: 上涨={actual_up} ({actual_up/total_samples:.1%})  下跌={actual_down} ({actual_down/total_samples:.1%})")

    dl_valid = df_res['dlinear'].dropna()
    if len(dl_valid) > 0:
        up = (dl_valid == 1).sum()
        down = (dl_valid == 0).sum()
        print(f"  DLinear: 预测上涨={up} ({up/len(dl_valid):.1%})  预测下跌={down} ({down/len(dl_valid):.1%})")

    print("=" * 90)


if __name__ == "__main__":
    main()