统计函数综合策略示例

def main():
    while True:
        records = exchange.GetRecords()
        if len(records) < 100:
            Sleep(1000)
            continue
        
        closes = [r['Close'] for r in records]
        current_price = closes[-1]
        
        # 线性回归及统计指标
        lr = TA.LINEARREG(closes, 20)
        slope = TA.LINEARREG_SLOPE(closes, 20)
        stddev = TA.STDDEV(closes, 20)
        
        # 价格偏离（标准差单位）
        deviation = (current_price - lr[-1]) / stddev[-1]
        
        Log(f"偏离度: {deviation:.2f}σ")
        Log(f"斜率: {slope[-1]:.4f}")
        
        # 交易逻辑
        if deviation < -2 and slope[-1] > 0:
            Log("✅ 上升趋势 + 超卖，买入")
            # exchange.Buy(...)
        elif deviation > 2 and slope[-1] < 0:
            Log("❌ 下降趋势 + 超买，卖出")
            # exchange.Sell(...)
        elif abs(deviation) < 0.5:
            Log("💰 价格回归，平仓")
        
        Sleep(60000)

def main():
    while True:
        # 获取两个相关资产
        records_btc = exchange.GetRecords()
        exchange.IO("currency", "ETH_USDT")
        records_eth = exchange.GetRecords()
        exchange.IO("currency", "BTC_USDT")
        
        if len(records_btc) < 100:
            Sleep(1000)
            continue
        
        btc_close = [r['Close'] for r in records_btc]
        eth_close = [r['Close'] for r in records_eth]
        
        # 相关性分析
        correl = TA.CORREL(btc_close, eth_close, 30)
        
        # 只在高相关时交易
        if correl[-1] > 0.8:
            # 价格比率
            ratio = [btc_close[i] / eth_close[i] for i in range(len(btc_close))]
            ratio_ma = sum(ratio[-30:]) / 30
            ratio_std = TA.STDDEV(ratio, 30)[-1]
            
            # Z-score
            z_score = (ratio[-1] - ratio_ma) / ratio_std
            
            Log(f"相关性: {correl[-1]:.3f}")
            Log(f"比率Z-score: {z_score:.2f}")
            
            # 套利信号
            if z_score > 2:
                Log("✅ BTC相对超买，做空BTC做多ETH")
            elif z_score < -2:
                Log("✅ BTC相对超卖，做多BTC做空ETH")
            elif abs(z_score) < 0.5:
                Log("💰 比率回归，平仓")
        else:
            Log(f"⚠️ 相关性较低 ({correl[-1]:.3f})，不适合配对交易")
        
        Sleep(60000)

def main():
    while True:
        records = exchange.GetRecords()
        if len(records) < 100:
            Sleep(1000)
            continue
        
        closes = [r['Close'] for r in records]
        current_price = closes[-1]
        
        # 多周期标准差
        std_5 = TA.STDDEV(closes, 5)
        std_20 = TA.STDDEV(closes, 20)
        std_60 = TA.STDDEV(closes, 60)
        
        # 波动率比率
        volatility_ratio = std_5[-1] / std_60[-1]
        
        # 标准差百分比
        std_percent = (std_20[-1] / current_price) * 100
        
        Log(f"短期/长期波动比: {volatility_ratio:.2f}")
        Log(f"标准差占价格: {std_percent:.2f}%")
        
        # 根据波动率调整策略
        if volatility_ratio > 1.5:
            Log("⚠️ 短期波动率显著升高")
            stop_multiplier = 3  # 扩大止损
            position_size = 0.5  # 减小仓位
        elif volatility_ratio < 0.7:
            Log("💤 短期波动率较低")
            stop_multiplier = 2  # 标准止损
            position_size = 1.0  # 正常仓位
        else:
            Log("📊 正常波动环境")
            stop_multiplier = 2.5
            position_size = 0.8
        
        # 止损距离
        stop_distance = stop_multiplier * std_20[-1]
        
        Log(f"建议止损距离: {stop_distance:.2f}")
        Log(f"建议仓位比例: {position_size:.1%}")
        
        Sleep(60000)

def main():
    while True:
        records = exchange.GetRecords()
        if len(records) < 100:
            Sleep(1000)
            continue
        
        closes = [r['Close'] for r in records]
        current_price = closes[-1]
        
        # TSF预测
        forecast = TA.TSF(closes, 20)
        predicted = forecast[-1]
        
        # 线性回归斜率
        slope = TA.LINEARREG_SLOPE(closes, 20)
        
        # 标准差
        stddev = TA.STDDEV(closes, 20)
        
        # 预测偏离
        forecast_deviation = (current_price - predicted) / stddev[-1]
        
        Log(f"当前: {current_price:.2f}")
        Log(f"预测: {predicted:.2f}")
        Log(f"偏离: {forecast_deviation:.2f}σ")
        Log(f"斜率: {slope[-1]:.4f}")
        
        # 综合判断
        if slope[-1] > 0.1 and forecast_deviation < -1:
            Log("✅ 上升趋势 + 价格低于预测，买入")
        elif slope[-1] < -0.1 and forecast_deviation > 1:
            Log("❌ 下降趋势 + 价格高于预测，卖出")
        
        Sleep(60000)