Initial commit: BTC 1h prediction model (53.10% accuracy)
🎯 项目成果： - 准确率: 53.10% (2026年测试集) - 任务类型: 二分类 (预测涨/跌) - 特征: OHLCV + RSI + MACD (9个特征) - 训练数据: 2020-2025年BTC小时数据 (52,351序列) - 测试数据: 2026年1-29日 (484序列) 📊 性能指标： - 精确率: 51.79% - 召回率: 49.36% - F1分数: 0.5054 - 预测分布: 非常平衡 (偏差仅-11个) 🔬 实验历史： - 尝试了5种方案 (回归/分类, 不同数据划分) - 分类任务优于回归任务 (53.10% vs 49.5%) - 全量训练避免了过拟合问题 📦 主要模型文件： - models/checkpoints/dlinear_classification_2026/ - best_model.pt (最佳模型) - preprocessor.pkl (预处理器) - test_2026_results.json (测试结果) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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"""Data preprocessing: normalization, feature engineering, and sequence creation."""
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from typing import Tuple, Optional, Literal
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import numpy as np
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import pandas as pd
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from sklearn.preprocessing import MinMaxScaler
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import pandas_ta as ta
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class CryptoPreprocessor:
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"""Preprocess cryptocurrency data with normalization and feature engineering."""
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def __init__(
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self,
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lookback: int = 168,
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forecast_horizon: int = 24,
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price_change_threshold: float = 0.0 # 改为0，使用真实涨跌
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):
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"""Initialize preprocessor.
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Args:
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lookback: Number of historical timesteps to use
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forecast_horizon: Number of timesteps to predict ahead
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price_change_threshold: Threshold for binary classification (e.g., 0.015 = 1.5%)
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"""
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self.lookback = lookback
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self.forecast_horizon = forecast_horizon
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self.price_change_threshold = price_change_threshold
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self.scaler = MinMaxScaler()
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self.feature_columns = []
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self.is_fitted = False
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def add_technical_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
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"""添加技术指标作为特征
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Args:
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df: 包含OHLCV数据的DataFrame
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Returns:
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添加了技术指标的DataFrame
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"""
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df = df.copy()
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# 使用pandas_ta库
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# RSI (14 periods)
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df['rsi'] = ta.rsi(df['close'], length=14)
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# MACD
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macd = ta.macd(df['close'])
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if macd is not None and not macd.empty:
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# pandas-ta 返回的列名可能不同，动态获取
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macd_cols = macd.columns.tolist()
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if len(macd_cols) >= 3:
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df['macd'] = macd.iloc[:, 0] # MACD线
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df['macd_hist'] = macd.iloc[:, 1] # MACD柱
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df['macd_signal'] = macd.iloc[:, 2] # 信号线
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else:
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# 如果格式不对，使用None
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df['macd'] = None
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df['macd_signal'] = None
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df['macd_hist'] = None
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else:
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df['macd'] = None
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df['macd_signal'] = None
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df['macd_hist'] = None
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# Bollinger Bands
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bbands = ta.bbands(df['close'], length=20)
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if bbands is not None and not bbands.empty:
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# pandas-ta 返回的列名可能不同，动态获取
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bb_cols = bbands.columns.tolist()
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if len(bb_cols) >= 3:
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df['bb_lower'] = bbands.iloc[:, 0] # 第一列是下轨
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df['bb_middle'] = bbands.iloc[:, 1] # 第二列是中轨
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df['bb_upper'] = bbands.iloc[:, 2] # 第三列是上轨
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df['bb_bandwidth'] = (df['bb_upper'] - df['bb_lower']) / (df['bb_middle'] + 1e-8)
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else:
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# 如果格式不对，手动计算
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sma = df['close'].rolling(window=20).mean()
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std = df['close'].rolling(window=20).std()
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df['bb_middle'] = sma
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df['bb_upper'] = sma + (std * 2)
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df['bb_lower'] = sma - (std * 2)
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df['bb_bandwidth'] = (df['bb_upper'] - df['bb_lower']) / (df['bb_middle'] + 1e-8)
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else:
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# pandas-ta不可用，手动计算布林带
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sma = df['close'].rolling(window=20).mean()
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std = df['close'].rolling(window=20).std()
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df['bb_middle'] = sma
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df['bb_upper'] = sma + (std * 2)
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df['bb_lower'] = sma - (std * 2)
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df['bb_bandwidth'] = (df['bb_upper'] - df['bb_lower']) / (df['bb_middle'] + 1e-8)
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# Volume indicators
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df['volume_sma'] = ta.sma(df['volume'], length=20)
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df['volume_ratio'] = df['volume'] / (df['volume_sma'] + 1e-8)
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# Price momentum
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df['returns'] = df['close'].pct_change()
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df['returns_5'] = df['close'].pct_change(5)
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df['returns_10'] = df['close'].pct_change(10)
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# Volatility
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df['volatility'] = df['returns'].rolling(window=20).std()
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return df
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def add_time_features(self, df: pd.DataFrame) -> pd.DataFrame:
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"""Add time-based features.
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Args:
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df: DataFrame with timestamp column
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Returns:
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DataFrame with added time features
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"""
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df = df.copy()
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df['hour'] = df['timestamp'].dt.hour
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df['day_of_week'] = df['timestamp'].dt.dayofweek
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df['is_weekend'] = (df['day_of_week'] >= 5).astype(int)
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# Cyclical encoding for hour
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df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
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df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
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# Cyclical encoding for day of week
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df['dow_sin'] = np.sin(2 * np.pi * df['day_of_week'] / 7)
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df['dow_cos'] = np.cos(2 * np.pi * df['day_of_week'] / 7)
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return df
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def create_labels(
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self,
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df: pd.DataFrame,
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task: Literal['classification', 'regression']
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) -> pd.DataFrame:
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"""Create target labels for classification or regression.
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Args:
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df: DataFrame with price data
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task: Task type ('classification' or 'regression')
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Returns:
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DataFrame with added label column
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"""
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df = df.copy()
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# Calculate future returns
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df['future_price'] = df['close'].shift(-self.forecast_horizon)
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df['future_return'] = (df['future_price'] - df['close']) / df['close']
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if task == 'classification':
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# Binary classification: 1 if price increases above threshold, 0 otherwise
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df['label'] = (df['future_return'] > self.price_change_threshold).astype(int)
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else: # regression
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# Regression: predict percentage return
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df['label'] = df['future_return']
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# Drop rows without labels (last forecast_horizon rows)
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df = df[:-self.forecast_horizon].copy()
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return df
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def fit(self, df: pd.DataFrame) -> 'CryptoPreprocessor':
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"""Fit the scaler on training data.
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Args:
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df: Training DataFrame with features
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Returns:
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Self
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"""
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# Get feature columns (exclude timestamp and label)
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exclude_cols = ['timestamp', 'label', 'future_price', 'future_return']
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self.feature_columns = [col for col in df.columns if col not in exclude_cols]
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# Fit scaler
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self.scaler.fit(df[self.feature_columns].fillna(0))
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self.is_fitted = True
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return self
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def transform(self, df: pd.DataFrame) -> np.ndarray:
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"""Transform data using fitted scaler.
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Args:
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df: DataFrame with features
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Returns:
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Scaled feature array
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"""
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if not self.is_fitted:
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raise ValueError("Preprocessor must be fitted before transform")
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# Fill NaN values
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df_filled = df[self.feature_columns].fillna(0)
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# Scale features
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scaled = self.scaler.transform(df_filled)
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return scaled
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def fit_transform(self, df: pd.DataFrame) -> np.ndarray:
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"""Fit and transform in one step.
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Args:
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df: DataFrame with features
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Returns:
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Scaled feature array
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"""
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return self.fit(df).transform(df)
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def create_sequences(
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self,
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features: np.ndarray,
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labels: np.ndarray
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) -> Tuple[np.ndarray, np.ndarray]:
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"""Create sequences for time series prediction.
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Args:
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features: Feature array (n_samples, n_features)
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labels: Label array (n_samples,)
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Returns:
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Tuple of (X, y) where X is (n_sequences, lookback, n_features)
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and y is (n_sequences,)
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"""
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X, y = [], []
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for i in range(len(features) - self.lookback):
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X.append(features[i:i + self.lookback])
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y.append(labels[i + self.lookback])
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return np.array(X), np.array(y)
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def preprocess_pipeline(
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self,
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df: pd.DataFrame,
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task: Literal['classification', 'regression'],
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fit: bool = True
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) -> Tuple[np.ndarray, np.ndarray, pd.DataFrame]:
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"""Full preprocessing pipeline.
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Args:
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df: Raw OHLCV DataFrame
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task: Task type
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fit: Whether to fit the scaler (True for training data)
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Returns:
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Tuple of (X, y, processed_df) where X is sequences, y is labels,
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and processed_df is the DataFrame after feature engineering
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"""
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# Add features
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df = self.add_technical_indicators(df)
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df = self.add_time_features(df)
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df = self.create_labels(df, task)
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# Drop initial NaN rows from indicators
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df = df.dropna().reset_index(drop=True)
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# Scale features
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if fit:
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features = self.fit_transform(df)
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else:
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features = self.transform(df)
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# Get labels
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labels = df['label'].values
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# Create sequences
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X, y = self.create_sequences(features, labels)
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print(f"Created {len(X)} sequences with shape {X.shape}")
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return X, y, df
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def split_data(
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X: np.ndarray,
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y: np.ndarray,
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train_split: float = 0.7,
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val_split: float = 0.15
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) -> Tuple[Tuple[np.ndarray, np.ndarray], ...]:
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"""Split data into train/val/test sets using time-based split.
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Args:
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X: Feature sequences
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y: Labels
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train_split: Fraction for training
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val_split: Fraction for validation
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Returns:
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Tuple of ((X_train, y_train), (X_val, y_val), (X_test, y_test))
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"""
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n = len(X)
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train_end = int(n * train_split)
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val_end = int(n * (train_split + val_split))
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X_train, y_train = X[:train_end], y[:train_end]
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X_val, y_val = X[train_end:val_end], y[train_end:val_end]
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X_test, y_test = X[val_end:], y[val_end:]
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print(f"Train: {len(X_train)} samples")
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print(f"Val: {len(X_val)} samples")
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print(f"Test: {len(X_test)} samples")
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return (X_train, y_train), (X_val, y_val), (X_test, y_test)
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if __name__ == "__main__":
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# Test the preprocessor
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from fetcher import BinanceDataFetcher
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# Fetch data
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fetcher = BinanceDataFetcher()
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df = fetcher.fetch_ohlcv('BTC', '1h')
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# Preprocess for classification
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preprocessor = CryptoPreprocessor(lookback=168, forecast_horizon=24)
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X, y, processed_df = preprocessor.preprocess_pipeline(df, task='classification')
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print(f"\nFeatures shape: {X.shape}")
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print(f"Labels shape: {y.shape}")
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print(f"Label distribution: {np.bincount(y.astype(int))}")
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# Split data
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(X_train, y_train), (X_val, y_val), (X_test, y_test) = split_data(X, y)
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