"""2026年2月BTC数据 - DLinear, N-BEATS, TTM-R2 准确率测试 (ONNX版)

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
DLinear预测逻辑：整个df一次性预处理，按索引遍历窗口
输出：每小时详细数据、每天准确率、总准确率、三模型集成、美股开盘标记
"""

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

import numpy as np
import pandas as pd
import pickle
import pandas_ta as ta
import onnxruntime as ort
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2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_feb_2026 = {
        pd.Timestamp('2026-02-16'),  # 总统日 Presidents' Day
    }

    if et_date.weekday() >= 5:
        return False
    if et_date in us_holidays_feb_2026:
        return False
    return True

# ==================== 预处理器定义 ====================

class ClassificationPreprocessor:
    """DLinear分类预处理器 - pickle反序列化占位类"""
    def __init__(self, lookback=168, forecast_horizon=1):
        self.lookback = lookback
        self.forecast_horizon = forecast_horizon
        self.scaler = StandardScaler()
        self.is_fitted = False

class NBeatsPreprocessor:
    """N-BEATS预处理器"""
    def __init__(self, lookback=168, forecast_horizon=1):
        self.lookback = lookback
        self.forecast_horizon = forecast_horizon
        self.price_scaler = StandardScaler()
        self.is_fitted = False

    def preprocess(self, df, fit=True):
        df = df.copy()
        prices = df['close'].values.reshape(-1, 1)

        if fit:
            scaled_prices = self.price_scaler.fit_transform(prices)
            self.is_fitted = True
        else:
            scaled_prices = self.price_scaler.transform(prices)

        X = []
        valid_indices = []

        for i in range(len(scaled_prices) - self.lookback):
            X.append(scaled_prices[i:i + self.lookback, 0])
            valid_indices.append(i + self.lookback - 1)

        return np.array(X), df, valid_indices


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("=" * 110)
    print("2026年3月 BTC 1h 涨跌预测准确率测试 (DLinear, N-BEATS, TTM-R2) - ONNX Version")
    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("DLinear预测逻辑：整个df一次性预处理，按索引遍历窗口")
    print("=" * 110)

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

    # Load DLinear ONNX
    try:
        dlinear_session = ort.InferenceSession('./models/checkpoints/dlinear_classification_2026/model.onnx')
        print("  ✓ DLinear (ONNX) 已加载")
        
        with open('./models/checkpoints/dlinear_classification_2026/preprocessor.pkl', 'rb') as f:
            dlinear_preprocessor = pickle.load(f)
    except Exception as e:
        print(f"  ✗ DLinear 加载失败: {e}")
        return

    # Load N-BEATS ONNX
    try:
        nbeats_session = ort.InferenceSession('./models/checkpoints/nbeats_btc_1h/model.onnx')
        print("  ✓ N-BEATS (ONNX) 已加载")
        
        with open('./models/checkpoints/nbeats_btc_1h/preprocessor.pkl', 'rb') as f:
            nbeats_preprocessor = pickle.load(f)
    except Exception as e:
        print(f"  ✗ N-BEATS 加载失败: {e}")
        return

    # Load TTM-R2 ONNX
    ttm_session = None
    try:
        temp_session = ort.InferenceSession('./models/checkpoints/ttm_r2_onnx/ttm_r2_crypto_channel_mixing.onnx')
        
        # Test inference with dummy input
        print("  Testing TTM-R2 ONNX inference...")
        dummy_input = np.random.randn(1, 512, 1).astype(np.float32)
        ort_inputs = {}
        input_names = [i.name for i in temp_session.get_inputs()]
        print(f"Model inputs: {input_names}")  
        # 根据 session 中的名称映射输入
        for input_meta in temp_session.get_inputs():
            name = input_meta.name
            if 'past_values' in name or name == 'input':
                ort_inputs[name] = dummy_input
            elif 'mask' in name:
                # 创建全 1 mask
                mask = np.ones_like(dummy_input)
                # 检查期望的类型
                if input_meta.type == 'tensor(int64)':
                    mask = mask.astype(np.int64)
                else:
                    mask = mask.astype(np.float32)
                ort_inputs[name] = mask
        print(f"Session run input keys: {list(ort_inputs.keys())}")
        for k, v in ort_inputs.items():
            print(f"Input {k} shape: {v.shape}, dtype: {v.dtype}")
                
        temp_session.run(None, ort_inputs)
        
        ttm_session = temp_session
        print("  ✓ TTM-R2 (ONNX) 已加载且可用 (Optimizations Disabled)")
    except Exception as e:
        print(f"  ⚠ TTM-R2 (ONNX) 加载或推理失败: {e}")
        print("    将在本次测试中跳过 TTM-R2 模型")
        ttm_session = None

    # ========== 步骤2: 获取测试数据 ==========
    print("\n[2/4] 获取2026年3月数据...")
    fetcher = BinanceDataFetcher()
    df = fetcher.fetch_ohlcv('BTC', '1h', start_date='2026-01-01', end_date='2026-04-01', 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...")

    # DLinear 整体预处理（无create_labels，整个df一次性处理）
    dl_lookback = dlinear_preprocessor.lookback
    df_dl = df.copy()
    df_dl['rsi'] = ta.rsi(df_dl['close'], length=14)
    macd = ta.macd(df_dl['close'])
    if macd is not None and not macd.empty:
        df_dl['macd'] = macd.iloc[:, 0]
        df_dl['macd_hist'] = macd.iloc[:, 1]
        df_dl['macd_signal'] = macd.iloc[:, 2]
    df_dl = df_dl.dropna().reset_index(drop=True)

    feature_cols = ['open', 'high', 'low', 'close', 'volume',
                   'rsi', 'macd', 'macd_hist', 'macd_signal']
    scaled_dl = dlinear_preprocessor.scaler.transform(df_dl[feature_cols])

    X_dl = []
    idx_dl = []
    for i in range(len(scaled_dl) - dl_lookback):
        X_dl.append(scaled_dl[i:i + dl_lookback])
        idx_dl.append(i + dl_lookback - 1)
    X_dl = np.array(X_dl, dtype=np.float32)
    # 构建 timestamp -> 窗口索引映射
    dl_ts_map = {}
    for i, idx in enumerate(idx_dl):
        ts = df_dl.iloc[idx]['timestamp']
        dl_ts_map[ts] = i

    # N-BEATS 整体预处理（N-BEATS只用close，不受create_labels影响）
    X_nb, df_nb, idx_nb = nbeats_preprocessor.preprocess(df, fit=False)
    X_nb = X_nb.astype(np.float32) # Ensure float32

    # TTM-R2 整体预处理
    ttm_context_len = 512
    scaler_ttm = StandardScaler()
    scaled_close_ttm = scaler_ttm.fit_transform(df[['close']])
    X_ttm = []
    idx_ttm = []
    for i in range(len(scaled_close_ttm) - ttm_context_len):
        X_ttm.append(scaled_close_ttm[i:i+ttm_context_len])
        idx_ttm.append(i + ttm_context_len - 1)
    X_ttm = np.array(X_ttm, dtype=np.float32)
    idx_ttm_map = {v: k for k, v in enumerate(idx_ttm)}

    # 找出3月（北京时间）的所有预测目标K线索引
    # df 中每一行都是一根已收盘的K线，timestamp 是 UTC
    jan_targets = []
    for target_idx in range(1, len(df)):
        # 同样在筛选样本时也将时间向后移动一个小时
        target_time_utc = df.iloc[target_idx]['timestamp'] + timedelta(hours=1)
        target_time_bj = utc_to_beijing(target_time_utc)
        if target_time_bj.year == 2026 and target_time_bj.month == 3:
            jan_targets.append(target_idx)

    print(f"3月（北京时间）目标K线数: {len(jan_targets)}")

    results = []

    for target_idx in jan_targets:
        # 原始timestamp表示k线的开始时间，因此预测的结果实际对应下一小时的k线。
        # 所以展示时需要将时间向后移动1小时。
        target_time_utc = df.iloc[target_idx]['timestamp'] + timedelta(hours=1)
        target_time_bj = utc_to_beijing(target_time_utc)

        # 现在我们将预测目标设为下一根k线（即target_idx+1），
        # 输入使用它的前一根（等于原先的target_idx）。
        predicted_idx = target_idx + 1
        if predicted_idx >= len(df):
            # 已经到达数据末尾，无法构建完整样本，跳过
            continue
        input_idx = target_idx
        # 开盘/收盘对应预测k线
        open_price = df.iloc[predicted_idx]['open']
        close_price = df.iloc[predicted_idx]['close']
        # 实际涨跌以预测k线的开盘为基准
        actual_label = 1 if close_price > open_price else 0

        # --- DLinear 预测 (ONNX) ---
        dl_pred = None
        dl_prob = None
        input_ts = df.iloc[input_idx]['timestamp']
        dl_i = dl_ts_map.get(input_ts)
        if dl_i is not None and dl_i < len(X_dl):
            x_dl_input = X_dl[dl_i:dl_i+1] # (1, 168, 9)
            
            # ONNX Inference
            # Input name: 'input'
            logits = dlinear_session.run(['output'], {'input': x_dl_input})[0]
            
            prob = 1 / (1 + np.exp(-logits))
            dl_pred = int(prob.item() > 0.5)
            dl_prob = float(prob.item())

        # --- N-BEATS 预测 (ONNX) ---
        nb_pred = None
        if input_idx in idx_nb:
            nb_i = idx_nb.index(input_idx)
            x_nb_input = X_nb[nb_i:nb_i+1] # (1, 168)
            
            # ONNX Inference
            # Input name: 'input'
            nb_out = nbeats_session.run(['forecast'], {'input': x_nb_input})[0]
            
            if nb_out.ndim == 1:
                pred_price_scaled = nb_out[0]
            else:
                pred_price_scaled = nb_out[0, 0]
            current_price_scaled = X_nb[nb_i, -1]
            nb_pred = 1 if pred_price_scaled > current_price_scaled else 0

        # --- TTM-R2 预测 (ONNX) ---
        ttm_pred = None
        ttm_i = idx_ttm_map.get(input_idx)
        if ttm_i is not None and ttm_session is not None:
            x_ttm_input = X_ttm[ttm_i:ttm_i+1] # (1, 512, 1)
            
            # Debug shape
            # print(f"TTM input shape: {x_ttm_input.shape}, dtype: {x_ttm_input.dtype}")

            # ONNX Inference
            # Input name: 'past_values'
            # Try transposing to (1, 1, 512) just in case
            # x_ttm_input = x_ttm_input.transpose(0, 2, 1)
            
            ttm_out = ttm_session.run(None, {'past_values': x_ttm_input})[0]
            
            ttm_pred_scaled = ttm_out[0, 0, 0]
            ttm_current_scaled = X_ttm[ttm_i, -1, 0]
            ttm_pred = 1 if ttm_pred_scaled > ttm_current_scaled else 0

        # 集成预测（多数投票）
        votes = [v for v in [dl_pred, nb_pred, ttm_pred] if v is not None]
        ens_pred = (1 if sum(votes) >= 2 else 0) if len(votes) == 3 else None

        results.append({
            'time_utc': target_time_utc,
            'time_bj': target_time_bj,
            'open': open_price,
            'close': close_price,
            'actual': actual_label,
            'dlinear': dl_pred,
            'nbeats': nb_pred,
            'ttm': ttm_pred,
            'ensemble': ens_pred,
        })

    # ========== 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3月，北京时间)")
    print(f"总样本数: {total_samples}")

    # ========== 每小时详细数据 ==========
    print("\n" + "=" * 110)
    print("每小时详细数据")
    print("=" * 110)

    # 改为显示开盘/收盘
    hourly_header = (
        f"{'北京时间':<18} {'开盘':>10} {'收盘':>10} {'实际':>4} "
        f"{'DL':>3} {'':>1} {'NB':>3} {'':>1} {'TTM':>3} {'':>1} {'集成':>4} {'':<1}"
    )

    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_acc = (day_group['dlinear'] == day_group['actual']).mean()
        nb_day_valid = day_group.dropna(subset=['nbeats'])
        nb_day_acc = (nb_day_valid['nbeats'] == nb_day_valid['actual']).mean() if len(nb_day_valid) > 0 else None
        ttm_day_valid = day_group.dropna(subset=['ttm'])
        ttm_day_acc = (ttm_day_valid['ttm'] == ttm_day_valid['actual']).mean() if len(ttm_day_valid) > 0 else None
        ens_day_valid = day_group.dropna(subset=['ensemble'])
        ens_day_acc = (ens_day_valid['ensemble'] == ens_day_valid['actual']).mean() if len(ens_day_valid) > 0 else None

        nb_acc_str = f"{nb_day_acc:.1%}" if nb_day_acc is not None else "N/A"
        ttm_acc_str = f"{ttm_day_acc:.1%}" if ttm_day_acc is not None else "N/A"
        ens_acc_str = f"{ens_day_acc:.1%}" if ens_day_acc is not None else "N/A"

        print(f"\n--- {date_bj} ({us_str}) --- DL:{dl_day_acc:.1%} | NB:{nb_acc_str} | TTM:{ttm_acc_str} | 集成:{ens_acc_str}")
        print(hourly_header)
        print("-" * 110)

        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'])
            nb_str = fmt_pred(row['nbeats'])
            nb_r = fmt_result(row['nbeats'], row['actual'])
            ttm_str = fmt_pred(row['ttm'])
            ttm_r = fmt_result(row['ttm'], row['actual'])
            ens_str = fmt_pred(row['ensemble'])
            ens_r = fmt_result(row['ensemble'], row['actual'])

            print(
                f"{time_str:<18} {row['open']:>10.2f} {row['close']:>10.2f} {actual_str:>4} "
                f"{dl_str:>3} {dl_r:>1} {nb_str:>3} {nb_r:>1} {ttm_str:>3} {ttm_r:>1} {ens_str:>4} {ens_r:<1}"
            )

    # ========== 每日准确率汇总表 ==========
    print("\n\n" + "=" * 110)
    print("每日准确率汇总 (北京时间)")
    print("=" * 110)

    header = f"{'日期':<14} {'样本':>4} {'美股':>6}  {'DLinear':>8} {'N-BEATS':>8} {'TTM-R2':>8} {'集成(投票)':>10}"
    print(header)
    print("-" * 110)

    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_acc = (group['dlinear'] == group['actual']).mean()

        nb_valid = group.dropna(subset=['nbeats'])
        nb_acc = (nb_valid['nbeats'] == nb_valid['actual']).mean() if len(nb_valid) > 0 else None

        ttm_valid = group.dropna(subset=['ttm'])
        ttm_acc = (ttm_valid['ttm'] == ttm_valid['actual']).mean() if len(ttm_valid) > 0 else None

        ens_valid = group.dropna(subset=['ensemble'])
        ens_acc = (ens_valid['ensemble'] == ens_valid['actual']).mean() if len(ens_valid) > 0 else None

        dl_str = f"{dl_acc:.1%}"
        nb_str = f"{nb_acc:.1%}" if nb_acc is not None else "N/A"
        ttm_str = f"{ttm_acc:.1%}" if ttm_acc is not None else "N/A"
        ens_str = f"{ens_acc:.1%}" if ens_acc is not None else "N/A"

        print(f"{str(date_bj):<14} {total:>4} {us_str:>6}  {dl_str:>8} {nb_str:>8} {ttm_str:>8} {ens_str:>10}")

    print("-" * 110)

    # --- 总准确率 ---
    dl_total = (df_res['dlinear'] == df_res['actual']).mean()

    nb_all = df_res.dropna(subset=['nbeats'])
    nb_total = (nb_all['nbeats'] == nb_all['actual']).mean() if len(nb_all) > 0 else None

    ttm_all = df_res.dropna(subset=['ttm'])
    ttm_total = (ttm_all['ttm'] == ttm_all['actual']).mean() if len(ttm_all) > 0 else None

    ens_all = df_res.dropna(subset=['ensemble'])
    ens_total = (ens_all['ensemble'] == ens_all['actual']).mean() if len(ens_all) > 0 else None

    nb_t_str = f"{nb_total:.2%}" if nb_total is not None else "N/A"
    ttm_t_str = f"{ttm_total:.2%}" if ttm_total is not None else "N/A"
    ens_t_str = f"{ens_total:.2%}" if ens_total is not None else "N/A"

    print(f"{'总计':<14} {total_samples:>4} {'':>6}  {dl_total:.2%}  {nb_t_str:>8} {ttm_t_str:>8} {ens_t_str:>10}")
    print("=" * 110)

    # --- 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("-" * 80)
    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_a = (subset['dlinear'] == subset['actual']).mean()

        nb_s = subset.dropna(subset=['nbeats'])
        nb_a = (nb_s['nbeats'] == nb_s['actual']).mean() if len(nb_s) > 0 else None

        ttm_s = subset.dropna(subset=['ttm'])
        ttm_a = (ttm_s['ttm'] == ttm_s['actual']).mean() if len(ttm_s) > 0 else None

        ens_s = subset.dropna(subset=['ensemble'])
        ens_a = (ens_s['ensemble'] == ens_s['actual']).mean() if len(ens_s) > 0 else None

        nb_str = f"{nb_a:.2%}" if nb_a is not None else "N/A"
        ttm_str = f"{ttm_a:.2%}" if ttm_a is not None else "N/A"
        ens_str = f"{ens_a:.2%}" if ens_a is not None else "N/A"

        print(f"  {label} ({n}样本): DLinear={dl_a:.2%}  N-BEATS={nb_str}  TTM-R2={ttm_str}  集成={ens_str}")

    print("-" * 80)

    # --- 预测分布统计 ---
    print("\n[预测分布统计]")
    print("-" * 80)
    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%})")

    for name, col in [("DLinear", "dlinear"), ("N-BEATS", "nbeats"), ("TTM-R2", "ttm"), ("集成", "ensemble")]:
        valid = df_res[col].dropna()
        if len(valid) == 0:
            continue
        up = (valid == 1).sum()
        down = (valid == 0).sum()
        print(f"  {name}: 预测上涨={up} ({up/len(valid):.1%})  预测下跌={down} ({down/len(valid):.1%})")

    print("=" * 110)


if __name__ == "__main__":
    main()