结合 AI 与交易：如何在 2026 年使用机器学习实现加密货币交易自动化

基于规则（Rule-Based）：
IF RSI < 30 → Buy
IF RSI > 70 → Sell

优点：简单、可解释、可预测
缺点：无法适应市场变化；只能捕捉简单模式

数据驱动（Data-Driven）：
INPUT: price, volume, technical indicators, news sentiment, on-chain data
MODEL: learns hidden patterns from dozens to thousands of variables
OUTPUT: Buy/Sell/hold probability and expected return

优点：能够捕捉复杂模式；可以在一定程度上适应市场变化
缺点：黑箱、存在过拟合风险、需要大量数据

pip install pandas numpy scikit-learn xgboost ta ccxt matplotlib

import pandas as pd
import numpy as np
from ta import add_all_ta_features

def create_features(df: pd.DataFrame) -> pd.DataFrame:
    """OHLCV generate ML features from data"""

# basic technical indicators (using the ta library)
    df = add_all_ta_features(
        df, open="open", high="high", low="low",
        close="close", volume="volume", fillna=True
    )

# price change-rate features
    for period in [1, 3, 7, 14, 30]:
        df[f'return_{period}d'] = df['close'].pct_change(period)

# volatility features
    df['volatility_7d'] = df['close'].pct_change().rolling(7).std()
    df['volatility_30d'] = df['close'].pct_change().rolling(30).std()

# volume anomaly detection
    df['volume_ratio'] = df['volume'] / df['volume'].rolling(20).mean()

# target variable: 1 if the price rises 1% or more over the next 4 hours, otherwise 0
    df['target'] = (df['close'].shift(-4) > df['close'] * 1.01).astype(int)

    return df.dropna()

from xgboost import XGBClassifier
from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import classification_report

def train_model(df: pd.DataFrame):

# separate features and target
    exclude_cols = ['open', 'high', 'low', 'close', 'volume', 'target']
    feature_cols = [c for c in df.columns if c not in exclude_cols]

    X = df[feature_cols]
    y = df['target']

# time-series cross-validation (prevents future data leakage)
    tscv = TimeSeriesSplit(n_splits=5)

    model = XGBClassifier(
        n_estimators=200,
        max_depth=5,
        learning_rate=0.05,
        subsample=0.8,
        colsample_bytree=0.8,
        random_state=42
    )

# use the last 20% as the test set
    split = int(len(X) * 0.8)
    X_train, X_test = X[:split], X[split:]
    y_train, y_test = y[:split], y[split:]

    model.fit(X_train, y_train)

# performance evaluation
    y_pred = model.predict(X_test)
    print(classification_report(y_test, y_pred))

    return model

# usage example

# model = train_model(df_with_features)

def ml_trading_signal(model, current_features: pd.DataFrame) -> str:
    """ML generate trading signals with the model"""

    proba = model.predict_proba(current_features)[0]
    buy_prob = proba[1]

# upward probability

# probability-based signals (stay on the sidelines when uncertain)
    if buy_prob > 0.65:
        return 'BUY'
    elif buy_prob < 0.35:
        return 'SELL'
    else:
        return 'HOLD'

import ccxt
import pandas as pd
from datetime import datetime, timedelta

def fetch_historical_data(symbol: str, timeframe: str, days: int) -> pd.DataFrame:
    """collect historical data from Bithumb"""

    exchange = ccxt.bithumb()
    since = exchange.parse8601(
        (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%dT%H:%M:%S')
    )

    all_candles = []
    while since < exchange.milliseconds():
        candles = exchange.fetch_ohlcv(symbol, timeframe, since=since, limit=200)
        if not candles:
            break
        all_candles.extend(candles)
        since = candles[-1][0] + 1

    df = pd.DataFrame(all_candles, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
    df.set_index('timestamp', inplace=True)

    return df