Terjumpa strategi Pine yang menarik

Beberapa hari yang lalu, saya menyemak imbas strategi dalam Forum Pencipta dan melihat siaran dipanggilPanel Pro+ Quantum SmartPromptSelepas menyemak kod tersebut, saya dapati konsep asasnya agak menarik: ia menggunakan 10 penunjuk teknikal, memberikan pemberat berbeza kepada setiap penunjuk berdasarkan keadaan pasaran, dan akhirnya mengira skor untuk menentukan keputusan beli dan jual. Sebagai contoh, dalam pasaran kenaikan harga, penunjuk arah aliran berwajaran 2.0, dan RSI berwajaran 1.5; dalam pasaran beruang, beratnya berbeza. Rasanya seperti meniru cara orang berfikir: memfokuskan pada perkara yang berbeza dalam situasi yang berbeza.

Jika anda memikirkannya dengan teliti, struktur ini kelihatan seperti rangkaian saraf:

• 10 penunjuk teknikal apabila input
• Klasifikasi keadaan pasaran seperti lapisan tersembunyi
• Matriks berat ialah berat sambungan
• Akhirnya menghasilkan skor

Tetapi masalahnya ialah semua pemberat dikodkan keras, sebagai contoh:

if marketType == "Bull"
    array.set(weights, 0, 2.0) // 趋势权重固定是2.0
    array.set(weights, 1, 1.5) // RSI权重固定是1.5

Nombor ini ditetapkan sepenuhnya oleh pengarang berdasarkan pengalaman pasaran dan belum dikaji atau dioptimumkan dalam apa jua cara.

Idea: Jadikan pemberat boleh dipelajari

Memandangkan strukturnya sudah hampir serupa dengan rangkaian saraf, mengapa tidak menjadikannya benar-benar dapat belajar?

Idea saya mudah sahaja:

1. Kekalkan kaedah pengiraan berat asal dan dapatkan “skor berat”
2. Menggunakan skor wajaran ini sebagai input, latih rangkaian saraf kecil
3. Biarkan rangkaian belajar untuk meramalkan pulangan masa hadapan daripada markah wajaran
4. Tentukan sama ada untuk membuka kedudukan berdasarkan kadar pulangan yang diramalkan

Ini bukan sahaja mengekalkan strategi asal tetapi juga meningkatkan keupayaan pembelajaran.

Bermula di Platform Pencipta

Platform Pencipta dipilih terutamanya kerana ia menyokong Python dan mengandungi data yang kaya.

Langkah 1: Tulis semula penunjuk teknikal

Saya menulis semula semua penunjuk dalam skrip Pine dalam Python, menggunakan perpustakaan Talib untuk memastikan pengiraan yang tepat. Ini termasuk penunjuk biasa seperti EMA, MACD, RSI dan ATR, serta analisis volum dan pengecaman corak candlestick yang mudah.

Langkah 2: Pengesanan status pasaran

Mengikut logik strategi asal, jenis pasaran ditentukan berdasarkan gabungan pelbagai penunjuk: Lembu jantan, Beruang, Helang, Serigala, dll. Bahagian ini pada dasarnya adalah gabungan logik if-else.

Langkah 3: Pengiraan markah berat

Ini adalah bahagian teras. Saya menetapkan dua set berat:

• Berat asas:[2.0, 1.5, 2.0, 1.3, 1.2, …]
• Berat pasaran: diselaraskan mengikut keadaan pasaran yang berbeza

Berat akhir = berat asas × berat pasaran

Wajaran ini kemudiannya digunakan untuk menimbang markah asal 10 penunjuk dan menjumlahkannya untuk mendapatkan “skor wajaran”.

Langkah 4: Peramal Rangkaian Neural

Saya menulis rangkaian yang sangat mudah:

• Input: 1 ciri (skor wajaran)
• Lapisan tersembunyi: 16 neuron, pengaktifan ReLU
• Output: kadar pulangan yang diramalkan, terhad kepada ±5% menggunakan tanh

Objektif latihan: Gunakan skor berat pada masa t-1 untuk meramalkan perubahan harga pada masa t.

Langkah 5: Logik Transaksi

Daripada membeli atau menjual terus berdasarkan penilaian, kami melihat pada kadar pulangan yang diramalkan:

• Kadar pulangan yang diramalkan > 1.5%: Buka kedudukan beli atau tutup kedudukan jual
• Kadar pulangan yang diramalkan < -1.5%: Buka kedudukan jual atau tutup kedudukan beli
• Situasi lain: Kekalkan status quo

Pada masa yang sama, kekalkan henti untung dan henti rugi untuk memastikan risiko boleh dikawal.

Beberapa pemerhatian pada operasi sebenar

Pengumpulan Data

Strategi boleh mengumpul data latihan secara normal. Setiap kali candlestick baharu muncul, skor berat candlestick sebelumnya digunakan sebagai ciri, dan kenaikan atau penurunan candlestick semasa berbanding dengan yang sebelumnya digunakan sebagai label.

Data mungkin seperti ini:

权重评分=15.6, 收益率=+0.8%
权重评分=-8.2, 收益率=-1.2%
权重评分=22.1, 收益率=+0.3%

Latihan model

Rangkaian saraf boleh dilatih secara normal, dan kehilangan MSE akan berkurangan secara beransur-ansur. Tetapkan ia untuk melatih semula setiap 4 jam untuk memastikan model itu boleh menyesuaikan diri dengan perubahan pasaran.

Kesan ramalan

Ramalan model mempunyai korelasi tertentu dengan pulangan sebenar, tetapi ia tidak begitu kuat.

1. Satu ciri adalah terlalu mudah dan mungkin tidak mengandungi maklumat yang mencukupi
2. Turun naik harga jangka pendek adalah sangat rawak
3. Pasaran kontrak agak bising

Prestasi dagangan

Risiko satu transaksi dikawal dengan baik disebabkan oleh perlindungan henti rugi dan ambil untung. Walau bagaimanapun, keuntungan keseluruhan adalah purata, terutamanya disebabkan oleh kekurangan ketepatan ramalan yang tinggi.

Beberapa masalah yang dihadapi

Ciri-ciri terlalu mudahMenggunakan hanya pemberat untuk menjaringkan satu ciri adalah agak mudah. Pasaran adalah sangat kompleks sehingga satu nombor sukar untuk ditangkap sepenuhnya.

Kualiti sampel tidak stabil: Harga kontrak banyak turun naik dalam jangka pendek, dan dalam banyak kes kenaikan dan penurunan sebenarnya adalah rawak, yang menjadikan kualiti sampel latihan tidak stabil.

Risiko untuk terlalu sesuai: Walaupun rangkaian itu mudah, ia mungkin masih terlalu muat apabila saiz sampel adalah terhad.

Keperluan masa nyata: Pembelajaran dalam talian memerlukan keseimbangan antara masa latihan dan prestasi masa nyata.

Masa yang terhad dan pengoptimuman yang tidak mencukupi

Masih terdapat banyak bidang yang strategi ini boleh dipertingkatkan, tetapi masa dan tenaga adalah terhad, jadi kami tidak dapat mengoptimumkannya secara mendalam:

Ciri-ciri: Anda boleh menambah lebih banyak penunjuk teknikal, atau menggunakan ciri statistik siri harga.

Model: Anda boleh mencuba model jujukan seperti LSTM, atau menyepadukan berbilang model.

Data: Meningkatkan kualiti sampel dan meningkatkan pembersihan data.

Pengendalian angin: Meningkatkan henti rugi dinamik dan mengoptimumkan pengurusan kedudukan.

Keuntungan dan pemikiran

Penerokaan ini memberi saya pengajaran: kunci kepada idea yang baik ialah pelaksanaan yang tepat pada masanya! Apabila saya melihat reka bentuk matriks berat dalam skrip Pine, saya segera memikirkan kemungkinan untuk memperbaikinya dengan rangkaian saraf. Jika saya hanya memikirkannya atau menangguhkannya, idea itu mungkin telah dilupakan. Nasib baik, Platform Inventor menyediakan persekitaran Python dan antara muka data, membolehkan saya mengubah idea saya dengan cepat menjadi kod boleh jalan. Dari penjanaan idea kepada pelaksanaan asas, ia hanya mengambil masa sehari. Walaupun prestasi strategi akhir adalah biasa-biasa saja, pelaksanaan sebenar sekurang-kurangnya mengesahkan kebolehlaksanaan idea tersebut. Lebih penting lagi, proses pelaksanaan menjana idea dan penambahbaikan baharu. Tanpa tindakan segera, penemuan dan pandangan seterusnya ini adalah mustahil. Bercakap tentang idea di atas kertas tidak boleh dibandingkan dengan pengalaman dunia sebenar menulis kod, menjalankan data dan memerhatikan hasil. Ini adalah sifat perdagangan kuantitatif. Terdapat banyak idea, tetapi yang benar-benar berharga adalah idea yang dilaksanakan dan disahkan dengan cepat.

”`py “‘backtest start: 2025-07-31 00:00:00 end: 2025-08-07 00:00:00 period: 1h basePeriod: 5m exchanges: [{“eid”:“Futures_Binance”,“currency”:“ETH_USDT”,“balance”:5000000,“fee”:[0.01,0.01]}] “’

import numpy as np from collections import deque import talib as TA

========== 异常类 ==========

class Error_noSupport(BaseException): def init(self): Log(“只支持期货交易！#FF0000”)

class Error_AtBeginHasPosition(BaseException): def init(self): Log(“启动时有期货持仓! #FF0000”)

========== 收益率预测神经网络 ==========

class ReturnPredictor: def init(self, input_size=10, hidden_size=20, output_size=1): “”“收益率预测网络: X[t] -> yt+1”“” self.W1 = np.random.randn(input_size, hidden_size) * 0.1 self.b1 = np.zeros((1, hidden_size)) self.W2 = np.random.randn(hidden_size, output_size) * 0.1 self.b2 = np.zeros((1, output_size)) self.learning_rate = 0.001

def sigmoid(self, x):
    return 1 / (1 + np.exp(-np.clip(x, -250, 250)))

def tanh(self, x):
    return np.tanh(x)

def forward(self, X):
    self.z1 = np.dot(X, self.W1) + self.b1
    self.a1 = self.sigmoid(self.z1)
    self.z2 = np.dot(self.a1, self.W2) + self.b2
    # 输出预测收益率，使用tanh限制在合理范围
    self.a2 = self.tanh(self.z2) * 0.1  # 限制在±10%范围内
    return self.a2

def backward(self, X, y, output):
    m = X.shape[0]

    # MSE损失的梯度
    dZ2 = (output - y) / m
    # tanh的导数
    tanh_derivative = 1 - (output / 0.1) ** 2
    dZ2 = dZ2 * 0.1 * tanh_derivative

    dW2 = np.dot(self.a1.T, dZ2)
    db2 = np.sum(dZ2, axis=0, keepdims=True)

    dA1 = np.dot(dZ2, self.W2.T)
    dZ1 = dA1 * self.a1 * (1 - self.a1)  # sigmoid导数
    dW1 = np.dot(X.T, dZ1)
    db1 = np.sum(dZ1, axis=0, keepdims=True)

    # 更新权重
    self.W2 -= self.learning_rate * dW2
    self.b2 -= self.learning_rate * db2
    self.W1 -= self.learning_rate * dW1
    self.b1 -= self.learning_rate * db1

def train(self, X, y, epochs=100):
    for i in range(epochs):
        output = self.forward(X)
        self.backward(X, y, output)
        if i % 20 == 0:
            loss = np.mean((output - y) ** 2)
            Log(f"收益率预测训练轮次 {i}, MSE损失: {loss:.6f}")

def predict(self, X):
    return self.forward(X)

========== 技术指标计算类 ==========

class TechnicalIndicators: @staticmethod def calculate_indicators(records, use_completed_only=True): “”“计算技术指标和特征”“” if len(records) < 55: return None, None

    # 只使用已完成的K线数据
    if use_completed_only and len(records) > 1:
        working_records = records[:-1]
    else:
        working_records = records

    if len(working_records) < 55:
        return None, None

    closes = np.array([r['Close'] for r in working_records])
    highs = np.array([r['High'] for r in working_records])
    lows = np.array([r['Low'] for r in working_records])
    volumes = np.array([r['Volume'] for r in working_records])
    opens = np.array([r['Open'] for r in working_records])

    try:
        # 基础指标
        ema_55 = TA.EMA(closes, timeperiod=55)
        sma_vol20 = TA.SMA(volumes, timeperiod=20)
        macd, signal_line, _ = TA.MACD(closes)
        rsi_val = TA.RSI(closes, timeperiod=14)
        atr14 = TA.ATR(highs, lows, closes, timeperiod=14)
        range20 = TA.STDDEV(closes, timeperiod=20)

        # 计算派生指标
        sma_atr20 = TA.SMA(atr14, timeperiod=20)
        sma_range20 = TA.SMA(range20, timeperiod=20)
        rvol = volumes / sma_vol20 if sma_vol20[-1] > 0 else np.ones_like(volumes)
        delta = closes - opens

        # 计算量能阈值
        vol_abs_thresh = sma_vol20 * 1.2
        sniper_thresh = np.percentile(volumes[-40:], 80) if len(volumes) >= 40 else sma_vol20[-1]

        # 趋势
        trend = np.where(closes > ema_55, 1, np.where(closes < ema_55, -1, 0))

        # 简化K线形态
        body_size = np.abs(closes - opens)
        total_range = highs - lows

        # 锤子线
        is_hammer = ((total_range > 3 * body_size) & 
                    ((closes - lows) / (total_range + 0.001) > 0.6) & 
                    ((opens - lows) / (total_range + 0.001) > 0.6))

        # 吞噬形态
        is_engulfing = np.zeros_like(closes, dtype=bool)
        if len(closes) >= 2:
            is_engulfing[1:] = ((closes[1:] > opens[:-1]) & 
                               (opens[1:] < closes[:-1]) & 
                               (closes[1:] > opens[1:]) & 
                               (opens[1:] < closes[1:]))

        pattern = np.where(is_hammer, 1, np.where(is_engulfing, 2, 0))

        # 🔥 计算标准化特征向量（用于神经网络输入）
        features = []

        # 1. 趋势特征
        if len(ema_55) > 0 and not np.isnan(ema_55[-1]):
            trend_feature = (closes[-1] - ema_55[-1]) / ema_55[-1]
            features.append(np.tanh(trend_feature * 100))
        else:
            features.append(0)

        # 2. RSI特征
        if len(rsi_val) > 0 and not np.isnan(rsi_val[-1]):
            rsi_feature = (rsi_val[-1] - 50) / 50
            features.append(rsi_feature)
        else:
            features.append(0)

        # 3. MACD特征
        if len(macd) > 0 and not np.isnan(macd[-1]) and not np.isnan(signal_line[-1]):
            macd_feature = (macd[-1] - signal_line[-1]) / closes[-1] if closes[-1] > 0 else 0
            features.append(np.tanh(macd_feature * 1000))
        else:
            features.append(0)

        # 4. 成交量特征
        if len(vol_abs_thresh) > 0 and vol_abs_thresh[-1] > 0:
            vol_feature = volumes[-1] / vol_abs_thresh[-1] - 1
            features.append(np.tanh(vol_feature))
        else:
            features.append(0)

        # 5. 相对成交量特征
        if len(rvol) > 0 and not np.isnan(rvol[-1]):
            rvol_feature = rvol[-1] - 1
            features.append(np.tanh(rvol_feature))
        else:
            features.append(0)

        # 6. Delta特征
        if len(delta) > 0 and not np.isnan(delta[-1]) and closes[-1] > 0:
            delta_feature = delta[-1] / closes[-1]
            features.append(np.tanh(delta_feature * 100))
        else:
            features.append(0)

        # 7. ATR特征
        if len(atr14) > 0 and len(sma_atr20) > 0 and sma_atr20[-1] > 0:
            atr_feature = atr14[-1] / sma_atr20[-1] - 1
            features.append(np.tanh(atr_feature))
        else:
            features.append(0)

        # 8. Blocks特征
        if len(volumes) >= 10:
            highest_vol = np.max(volumes[-10:])
            blocks_feature = volumes[-1] / highest_vol - 0.8 if highest_vol > 0 else 0
            features.append(np.tanh(blocks_feature * 5))
        else:
            features.append(0)

        # 9. Tick特征
        if len(sma_vol20) > 0 and sma_vol20[-1] > 0:
            tick_feature = volumes[-1] / sma_vol20[-1] - 1
            features.append(np.tanh(tick_feature))
        else:
            features.append(0)

        # 10. 形态特征
        pattern_feature = pattern[-1] / 2.0 if len(pattern) > 0 else 0
        features.append(pattern_feature)

        # 确保特征数量正确
        while len(features) < 10:
            features.append(0)

        features = np.array(features[:10]).reshape(1, -1)

        indicators = {
            'ema_55': ema_55,
            'sma_vol20': sma_vol20,
            'macd': macd,
            'signal_line': signal_line,
            'rsi_val': rsi_val,
            'atr14': atr14,
            'range20': range20,
            'sma_atr20': sma_atr20,
            'sma_range20': sma_range20,
            'rvol': rvol,
            'delta': delta,
            'vol_abs_thresh': vol_abs_thresh,
            'sniper_thresh': sniper_thresh,
            'trend': trend,
            'pattern': pattern,
            'volumes': volumes,
            'closes': closes,
            'highs': highs,
            'lows': lows
        }

        return indicators, features

    except Exception as e:
        Log(f"计算技术指标异常: {str(e)}")
        return None, None

========== 市场状态检测类 ==========

class MarketStateDetector: @staticmethod def detect_market_type(indicators): “”“检测市场状态”“” if indicators is None: return “Unknown”

    try:
        # 获取最新值
        close = indicators['closes'][-1]
        ema_55 = indicators['ema_55'][-1]
        macd = indicators['macd'][-1]
        signal_line = indicators['signal_line'][-1]
        rsi_val = indicators['rsi_val'][-1]
        atr14 = indicators['atr14'][-1]
        volume = indicators['volumes'][-1]
        sma_vol20 = indicators['sma_vol20'][-1]
        sma_atr20 = indicators['sma_atr20'][-1]
        range20 = indicators['range20'][-1]
        sma_range20 = indicators['sma_range20'][-1]
        rvol = indicators['rvol'][-1]
        delta = indicators['delta'][-1]

        # 检查有效性
        if (np.isnan(ema_55) or np.isnan(macd) or np.isnan(signal_line) or 
            np.isnan(rsi_val) or np.isnan(atr14) or np.isnan(sma_atr20)):
            return "Unknown"

        # 市场类型判断
        is_bull = (close > ema_55 and macd > signal_line and rsi_val > 50 and rvol > 1)
        is_bear = (close < ema_55 and macd < signal_line and rsi_val < 50 and volume > sma_vol20)
        is_sideways = (abs(close - ema_55) < atr14 * 0.5 and atr14 < sma_atr20)
        is_volatile = (atr14 > sma_atr20 * 1.2)

        # 需要历史数据的判断
        if len(indicators['closes']) >= 2:
            price_change = indicators['closes'][-1] - indicators['closes'][-2]
            is_momentum = (price_change > atr14 * 1.5 and volume > sma_vol20 * 1.5)
            is_wolf = (price_change < -atr14 and close < ema_55)
        else:
            is_momentum = False
            is_wolf = False

        is_mean_rev = (rsi_val > 70 or rsi_val < 30)
        is_box = (is_sideways and range20 < sma_range20 * 0.8)
        is_macro = (abs(delta) > atr14 * 2) if not np.isnan(delta) else False
        is_eagle = (is_bull and atr14 < sma_atr20 * 0.8)

        # 优先级判断
        if is_eagle:
            return "Eagle"
        elif is_bull:
            return "Bull"
        elif is_wolf:
            return "Wolf"
        elif is_bear:
            return "Bear"
        elif is_box:
            return "Box"
        elif is_sideways:
            return "Sideways"
        elif is_volatile:
            return "Volatile"
        elif is_momentum:
            return "Momentum"
        elif is_mean_rev:
            return "MeanRev"
        elif is_macro:
            return "Macro"
        else:
            return "Unknown"

    except Exception as e:
        Log(f"市场状态检测异常: {str(e)}")
        return "Unknown"

========== 动态权重生成器 ==========

class DynamicWeightGenerator: @staticmethod def generate_weights_from_predicted_return(predicted_return, market_type): “”“根据预测收益率和市场状态生成动态权重”“”

    # 基础权重矩阵（不同市场类型）
    base_weights_matrix = {
        "Bull": [2.0, 1.5, 2.0, 1.3, 1.2, 1.0, 1.2, 1.0, 1.0, 1.0],
        "Bear": [2.0, 1.5, 2.0, 1.5, 1.3, 1.1, 1.2, 1.1, 1.0, 1.0],
        "Eagle": [2.2, 1.4, 2.1, 1.2, 1.3, 1.1, 1.1, 1.0, 1.0, 1.1],
        "Wolf": [1.8, 1.6, 1.8, 1.6, 1.2, 1.0, 1.3, 1.2, 1.0, 0.9],
        "Momentum": [1.5, 1.2, 1.8, 2.0, 2.0, 1.5, 1.5, 1.3, 1.2, 1.0],
        "Sideways": [1.0, 1.4, 1.0, 0.8, 0.7, 1.0, 0.9, 0.8, 1.0, 1.3],
        "Volatile": [1.2, 1.5, 1.3, 1.6, 1.8, 1.2, 1.4, 1.3, 1.4, 1.0],
    }

    base_weights = base_weights_matrix.get(market_type, [1.0] * 10)

    # 🔥 根据预测收益率动态调整权重
    adjustment_factors = [1.0] * 10

    # 预测收益率的强度
    return_strength = abs(predicted_return)
    return_direction = 1 if predicted_return > 0 else -1

    if return_strength > 0.02:  # 强预测信号 > 2%
        if return_direction > 0:  # 预测上涨
            adjustment_factors[0] *= 1.3  # 增强趋势权重
            adjustment_factors[2] *= 1.2  # 增强MACD权重
            adjustment_factors[4] *= 1.15 # 增强相对成交量权重
            adjustment_factors[1] *= 0.9  # 降低RSI权重
        else:  # 预测下跌
            adjustment_factors[1] *= 1.3  # 增强RSI权重
            adjustment_factors[3] *= 1.2  # 增强成交量权重
            adjustment_factors[0] *= 0.9  # 降低趋势权重

    elif return_strength > 0.01:  # 中等预测信号 1%-2%
        if return_direction > 0:
            adjustment_factors[0] *= 1.15
            adjustment_factors[2] *= 1.1
        else:
            adjustment_factors[1] *= 1.15
            adjustment_factors[3] *= 1.1

    # 波动性调整
    if return_strength > 0.03:  # 高波动预期 > 3%
        adjustment_factors[4] *= 1.2  # 增强相对成交量权重
        adjustment_factors[6] *= 1.15 # 增强sniper权重
        adjustment_factors[7] *= 1.1  # 增强blocks权重

    # 生成最终动态权重
    dynamic_weights = [base_weights[i] * adjustment_factors[i] for i in range(10)]

    # 权重标准化（可选）
    # total_weight = sum(dynamic_weights)
    # dynamic_weights = [w / total_weight * 10 for w in dynamic_weights]

    return dynamic_weights

========== 智能得分计算系统 ==========

class SmartScoringSystem: def init(self): self.return_predictor = ReturnPredictor() self.weight_generator = DynamicWeightGenerator() self.is_model_trained = False

def calculate_score(self, indicators, market_type, features=None):
    """计算交易得分（使用预测收益率的动态权重）"""
    if indicators is None:
        return 50.0

    try:
        # 🔥 核心逻辑：使用当前指标预测下期收益率
        if self.is_model_trained and features is not None:
            predicted_return = self.return_predictor.predict(features)[0, 0]

        else:
            predicted_return = 0.0
            Log(f"📊 使用基础权重计算")

        # 根据预测收益率生成动态权重
        dynamic_weights = self.weight_generator.generate_weights_from_predicted_return(
            predicted_return, market_type)

        # 获取最新指标值
        trend = indicators['trend'][-1]
        rsi_val = indicators['rsi_val'][-1]
        macd = indicators['macd'][-1]
        signal_line = indicators['signal_line'][-1]
        volume = indicators['volumes'][-1]
        vol_abs_thresh = indicators['vol_abs_thresh'][-1]
        sma_vol20 = indicators['sma_vol20'][-1]
        rvol = indicators['rvol'][-1]
        delta = indicators['delta'][-1]
        sniper_thresh = indicators['sniper_thresh']
        pattern = indicators['pattern'][-1]

        # 计算各项得分
        base_score = 0.0

        # 1. 趋势得分
        trend_score = 20 if trend == 1 else (-20 if trend == -1 else 0)
        base_score += trend_score * dynamic_weights[0]

        # 2. RSI得分
        rsi_score = -10 if rsi_val > 70 else (10 if rsi_val < 30 else 0)
        base_score += rsi_score * dynamic_weights[1]

        # 3. MACD得分
        macd_score = 10 if macd > signal_line else -10
        base_score += macd_score * dynamic_weights[2]

        # 4. 成交量得分
        vol_score = 8 if volume > vol_abs_thresh else (-8 if volume < sma_vol20 else 0)
        base_score += vol_score * dynamic_weights[3]

        # 5. 相对成交量得分
        rvol_score = 7 if rvol > 1.5 else (-7 if rvol < 0.8 else 0)
        base_score += rvol_score * dynamic_weights[4]

        # 6. Delta得分
        delta_score = 6 if delta > 0 else -6
        base_score += delta_score * dynamic_weights[5]

        # 7. Sniper得分
        sniper_score = 8 if volume > sniper_thresh else (-8 if volume < sma_vol20 else 0)
        base_score += sniper_score * dynamic_weights[6]

        # 8. Blocks得分
        if len(indicators['volumes']) >= 10:
            highest_vol = np.max(indicators['volumes'][-10:])
            blocks_score = 5 if volume > highest_vol * 0.8 else (-5 if volume < sma_vol20 else 0)
        else:
            blocks_score = 0
        base_score += blocks_score * dynamic_weights[7]

        # 9. Tick得分
        tick_score = 5 if volume > sma_vol20 else -5
        base_score += tick_score * dynamic_weights[8]

        # 10. 形态得分
        pattern_score = 7 if pattern == 1 else (5 if pattern == 2 else 0)
        base_score += pattern_score * dynamic_weights[9]

        # 转换为百分比得分
        score_pct = max(0, min(100, 50 + base_score))

        return score_pct

    except Exception as e:
        Log(f"得分计算异常: {str(e)}")
        return 50.0

def train_return_predictor(self, X, y):
    """训练收益率预测器"""
    if len(X) < 20:
        Log("训练数据不足，跳过收益率预测器训练")
        return False

    X_array = np.array(X)
    y_array = np.array(y).reshape(-1, 1)

    Log(f"🧠 开始训练收益率预测器，样本数: {len(X_array)}")
    Log(f"📊 收益率范围: [{np.min(y_array)*100:.3f}%, {np.max(y_array)*100:.3f}%]")

    self.return_predictor.train(X_array, y_array, epochs=100)
    self.is_model_trained = True

    # 验证模型预测效果
    predictions = self.return_predictor.predict(X_array)
    mse = np.mean((predictions - y_array) ** 2)
    correlation = np.corrcoef(predictions.flatten(), y_array.flatten())[0, 1]

    Log(f"✅ 收益率预测器训练完成")
    Log(f"📈 MSE: {mse:.6f}, 相关系数: {correlation:.4f}")

    return True

========== 动态参数管理器 ==========

class DynamicParameterManager: def init(self): self.market_params = { “Bull”: {“stop_loss”: 0.02, “take_profit”: 0.05}, “Bear”: {“stop_loss”: 0.02, “take_profit”: 0.05}, “Eagle”: {“stop_loss”: 0.015, “take_profit”: 0.06}, “Wolf”: {“stop_loss”: 0.025, “take_profit”: 0.04}, “Momentum”: {“stop_loss”: 0.025, “take_profit”: 0.06}, “Sideways”: {“stop_loss”: 0.01, “take_profit”: 0.02}, “Volatile”: {“stop_loss”: 0.03, “take_profit”: 0.07}, “Unknown”: {“stop_loss”: 0.02, “take_profit”: 0.03} }

def get_params(self, market_type):
    return self.market_params.get(market_type, self.market_params["Unknown"])

========== 主策略类 ==========

class PredictiveNeuralTradingStrategy: def init(self): self.data_buffer = deque(maxlen=200) self.feature_buffer = deque(maxlen=100) self.label_buffer = deque(maxlen=100) # 存储收益率标签 self.scoring_system = SmartScoringSystem() self.param_manager = DynamicParameterManager()

    # 训练控制
    self.last_retrain_time = 0
    self.retrain_interval = 3600 * 6  # 6小时重新训练
    self.min_train_samples = 30

    # 交易状态
    self.POSITION_NONE = 0
    self.POSITION_LONG = 1
    self.POSITION_SHORT = 2
    self.position_state = self.POSITION_NONE

    # 交易记录
    self.open_price = 0
    self.counter = {'win': 0, 'loss': 0}

    # K线数据管理
    self.last_processed_time = 0

def get_current_position(self):
    """获取当前期货持仓状态"""
    try:
        positions = exchange.GetPosition()
        if not positions:
            return self.POSITION_NONE, 0

        long_amount = 0
        short_amount = 0

        for pos in positions:
            amount = pos.get('Amount', 0)
            pos_type = pos.get('Type', -1)

            if amount > 0:
                if pos_type == 0:  # 多仓
                    long_amount += amount
                elif pos_type == 1:  # 空仓
                    short_amount += amount

        net_position = long_amount - short_amount

        if net_position > 0:
            return self.POSITION_LONG, net_position
        elif net_position < 0:
            return self.POSITION_SHORT, abs(net_position)
        else:
            return self.POSITION_NONE, 0

    except Exception as e:
        Log(f"获取持仓异常: {str(e)}")
        return self.POSITION_NONE, 0

def collect_data(self, records):
    """收集数据并生成训练样本"""
    if not records or len(records) < 55:
        return False

    # 检查是否有新的已完成K线
    if len(records) > 1:
        latest_completed = records[-2]
        current_time = latest_completed['Time']

        # 如果这根K线已经处理过，跳过
        if current_time <= self.last_processed_time:
            return False

        self.last_processed_time = current_time

    # 添加已完成的K线到缓冲区
    completed_records = records[:-1] if len(records) > 1 else []
    if completed_records:
        self.data_buffer.extend(completed_records[-5:])

    # 🔥 生成训练样本：X[t] -> y[t+1]
    if len(self.data_buffer) >= 2:
        # 使用倒数第二条记录作为特征，最后一条记录计算收益率标签
        buffer_list = list(self.data_buffer)

        # 计算t-1时刻的指标作为特征
        feature_records = buffer_list[:-1] if len(buffer_list) > 1 else buffer_list
        indicators, features = TechnicalIndicators.calculate_indicators(
            feature_records, use_completed_only=False)

        if indicators is not None and features is not None:
            # 计算t时刻相对于t-1时刻的收益率作为标签
            if len(buffer_list) >= 2:
                current_close = buffer_list[-1]['Close']
                previous_close = buffer_list[-2]['Close']

                if previous_close > 0:
                    return_rate = (current_close - previous_close) / previous_close

                    # 添加到训练集
                    self.feature_buffer.append(features[0])
                    self.label_buffer.append(return_rate)

                    Log(f"📈 新样本: 收益率={return_rate*100:.3f}%, 特征维度={features.shape}")

    return True

def should_retrain(self):
    """判断是否需要重新训练"""
    import time
    current_time = time.time()
    return (current_time - self.last_retrain_time > self.retrain_interval and 
            len(self.feature_buffer) >= self.min_train_samples)

def train_model(self):
    """训练收益率预测器"""
    if len(self.feature_buffer) < self.min_train_samples:
        Log("训练数据不足，跳过训练")
        return False

    X = list(self.feature_buffer)
    y = list(self.label_buffer)

    success = self.scoring_system.train_return_predictor(X, y)

    if success:
        import time
        self.last_retrain_time = time.time()

    return success

def get_trading_signals(self, records):
    """获取交易信号"""
    # 计算当前时刻的技术指标
    indicators, features = TechnicalIndicators.calculate_indicators(
        list(self.data_buffer), use_completed_only=False)
    if indicators is None:
        return 50.0, "Unknown"

    # 检测市场类型
    market_type = MarketStateDetector.detect_market_type(indicators)

    # 🔥 使用预测收益率的动态权重计算得分
    score = self.scoring_system.calculate_score(indicators, market_type, features)

    return score, market_type

def check_entry_conditions(self, score, market_type):
    """检查开仓条件"""
    # 多头条件
    long_condition = ((market_type in ["Bull", "Eagle", "Momentum"]) and score > 65)

    # 空头条件  
    short_condition = ((market_type in ["Bear", "Wolf"]) and score < 35)

    return long_condition, short_condition

def open_long(self):
    """开多仓"""
    try:
        ticker = exchange.GetTicker()
        if not ticker:
            return False

        buy_price = ticker['Last'] + 20
        order_id = exchange.CreateOrder("", "buy", buy_price, AmountOP)

        if order_id:
            Sleep(2000)
            order_info = exchange.GetOrder(order_id)
            if order_info and order_info.get('Status') == 1:
                self.open_price = order_info.get('AvgPrice', buy_price)
                self.position_state = self.POSITION_LONG
                Log(f"🚀 开多仓成功: 价格={self.open_price}, 数量={AmountOP}")
                return True
            else:
                exchange.CancelOrder(order_id)
                Log("开多仓订单未完全成交，已取消")

        return False

    except Exception as e:
        Log(f"开多仓异常: {str(e)}")
        return False

def open_short(self):
    """开空仓"""
    try:
        ticker = exchange.GetTicker()
        if not ticker:
            return False

        sell_price = ticker['Last'] - 20
        order_id = exchange.CreateOrder("", "sell", sell_price, AmountOP)

        if order_id:
            Sleep(2000)
            order_info = exchange.GetOrder(order_id)
            if order_info and order_info.get('Status') == 1:
                self.open_price = order_info.get('AvgPrice', sell_price)
                self.position_state = self.POSITION_SHORT
                Log(f"🎯 开空仓成功: 价格={self.open_price}, 数量={AmountOP}")
                return True
            else:
                exchange.CancelOrder(order_id)
                Log("开空仓订单未完全成交，已取消")

        return False

    except Exception as e:
        Log(f"开空仓异常: {str(e)}")
        return False

def close_position(self):
    """平仓"""
    try:
        positions = exchange.GetPosition()
        if not positions:
            Log("没有持仓需要平仓")
            self.position_state = self.POSITION_NONE
            self.open_price = 0
            return True

        ticker = exchange.GetTicker()
        if not ticker:
            return False

        close_success = True

        for pos in positions:
            if pos['Amount'] == 0:
                continue

            amount = pos['Amount']
            pos_type = pos['Type']

            if pos_type == 0:  # 平多仓
                close_price = ticker['Last'] - 20
                order_id = exchange.CreateOrder("", "closebuy", close_price, amount)
                Log(f"📤 平多仓: 价格={close_price}, 数量={amount}")

            elif pos_type == 1:  # 平空仓
                close_price = ticker['Last'] + 20
                order_id = exchange.CreateOrder("", "closesell", close_price, amount)
                Log(f"📤 平空仓: 价格={close_price}, 数量={amount}")

            if order_id:
                Sleep(2000)
                order_info = exchange.GetOrder(order_id)
                if order_info and order_info.get('Status') == 1:
                    close_price = order_info.get('AvgPrice', close_price)
                    Log(f"✅ 平仓成功: 成交价格={close_price}")
                    self.update_profit_stats(close_price)
                else:
                    exchange.CancelOrder(order_id)
                    close_success = False
                    Log(f"平仓订单未完全成交，已取消")
            else:
                close_success = False
                Log("平仓订单创建失败")

        if close_success:
            self.position_state = self.POSITION_NONE
            self.open_price = 0

        return close_success

    except Exception as e:
        Log(f"平仓异常: {str(e)}")
        return False

def update_profit_stats(self, close_price):
    """更新盈亏统计"""
    if self.open_price == 0:
        return

    if self.position_state == self.POSITION_LONG:
        if close_price > self.open_price:
            self.counter['win'] += 1
            Log("💰 多仓盈利")
        else:
            self.counter['loss'] += 1
            Log("💸 多仓亏损")
    elif self.position_state == self.POSITION_SHORT:
        if close_price < self.open_price:
            self.counter['win'] += 1
            Log("💰 空仓盈利")
        else:
            self.counter['loss'] += 1
            Log("💸 空仓亏损")

def check_stop_loss_take_profit(self, current_price, params):
    """检查止损止盈并执行平仓"""
    if self.open_price == 0 or self.position_state == self.POSITION_NONE:
        return False

    stop_loss_pct = params["stop_loss"]
    take_profit_pct = params["take_profit"]

    if self.position_state == self.POSITION_LONG:
        profit_pct = (current_price - self.open_price) / self.open_price

        if profit_pct <= -stop_loss_pct:
            Log(f"🔴 多仓止损触发: 开仓价={self.open_price:.2f}, 当前价={current_price:.2f}, 亏损={profit_pct:.4f}")
            return self.execute_close_position("止损")
        elif profit_pct >= take_profit_pct:
            Log(f"🟢 多仓止盈触发: 开仓价={self.open_price:.2f}, 当前价={current_price:.2f}, 盈利={profit_pct:.4f}")
            return self.execute_close_position("止盈")

    elif self.position_state == self.POSITION_SHORT:
        profit_pct = (self.open_price - current_price) / self.open_price

        if profit_pct <= -stop_loss_pct:
            Log(f"🔴 空仓止损触发: 开仓价={self.open_price:.2f}, 当前价={current_price:.2f}, 亏损={profit_pct:.4f}")
            return self.execute_close_position("止损")
        elif profit_pct >= take_profit_pct:
            Log(f"🟢 空仓止盈触发: 开仓价={self.open_price:.2f}, 当前价={current_price:.2f}, 盈利={profit_pct:.4f}")
            return self.execute_close_position("止盈")

    return False

def execute_close_position(self, reason):
    """执行平仓操作（专门用于止盈止损）"""
    try:
        positions = exchange.GetPosition()
        if not positions:
            Log(f"{reason}平仓: 没有持仓")
            self.position_state = self.POSITION_NONE
            self.open_price = 0
            return True

        ticker = exchange.GetTicker()
        if not ticker:
            Log(f"{reason}平仓失败: 无法获取ticker")
            return False

        Log(f"🚨 执行{reason}平仓操作...")
        close_success = True

        for pos in positions:
            if pos['Amount'] == 0:
                continue

            amount = pos['Amount']
            pos_type = pos['Type']
            order_id = None

            if pos_type == 0:  # 平多仓
                close_price = ticker['Last'] - 50
                order_id = exchange.CreateOrder("", "closebuy", close_price, amount)
                Log(f"📤 {reason}平多仓订单: 价格={close_price}, 数量={amount}")

            elif pos_type == 1:  # 平空仓
                close_price = ticker['Last'] + 50
                order_id = exchange.CreateOrder("", "closesell", close_price, amount)
                Log(f"📤 {reason}平空仓订单: 价格={close_price}, 数量={amount}")

            if order_id:
                Log(f"📋 {reason}平仓订单ID: {order_id}")
                Sleep(1500)

                for retry in range(2):
                    order_info = exchange.GetOrder(order_id)
                    if order_info:
                        status = order_info.get('Status', -1)
                        if status == 1:
                            close_price = order_info.get('AvgPrice', close_price)
                            Log(f"✅ {reason}平仓成功: 成交价格={close_price}")
                            self.update_profit_stats(close_price)
                            break
                        elif status == 0:
                            if retry == 0:
                                Log(f"⏳ {reason}平仓订单执行中，等待...")
                                Sleep(1500)
                            else:
                                Log(f"⚠️ {reason}平仓订单未完全成交，强制取消")
                                exchange.CancelOrder(order_id)
                                close_success = False
                        else:
                            Log(f"❌ {reason}平仓订单状态异常: {status}")
                            exchange.CancelOrder(order_id)
                            close_success = False
                            break
                    else:
                        Log(f"⚠️ 无法获取{reason}平仓订单信息，重试 {retry+1}/2")
                        if retry == 1:
                            close_success = False
            else:
                Log(f"❌ {reason}平仓订单创建失败")
                close_success = False

        if close_success:
            Sleep(1000)
            new_positions = exchange.GetPosition()
            total_amount = sum(pos['Amount'] for pos in new_positions) if new_positions else 0

            if total_amount == 0:
                Log(f"✅ {reason}平仓完成，持仓已清零")
                self.position_state = self.POSITION_NONE
                self.open_price = 0
                return True
            else:
                Log(f"⚠️ {reason}平仓不完全，剩余持仓: {total_amount}")
                return False
        else:
            Log(f"❌ {reason}平仓失败")
            return False

    except Exception as e:
        Log(f"❌ {reason}平仓异常: {str(e)}")
        return False

def execute_trade_logic(self, score, market_type, current_price):
    """执行交易逻辑"""
    params = self.param_manager.get_params(market_type)

    # 获取当前实际持仓状态
    actual_position, position_amount = self.get_current_position()

    # 同步内部状态
    self.position_state = actual_position

    # 先检查止损止盈（最高优先级）
    if self.position_state != self.POSITION_NONE:
        if self.check_stop_loss_take_profit(current_price, params):
            Log("🚨 触发止盈止损，已执行平仓，跳过其他交易信号")
            return

    # 获取开仓条件
    long_condition, short_condition = self.check_entry_conditions(score, market_type)

    # 执行交易逻辑
    if long_condition and self.position_state <= self.POSITION_NONE:
        Log(f"📈 开多仓信号: 市场={market_type}, 预测得分={score:.1f} > 65")
        self.open_long()

    if short_condition and self.position_state >= self.POSITION_NONE:
        Log(f"📉 开空仓信号: 市场={market_type}, 预测得分={score:.1f} < 35")
        self.open_short()

    if not long_condition and self.position_state > self.POSITION_NONE:
        Log(f"📤 平多仓信号: 市场={market_type}, 预测得分={score:.1f}")
        self.close_position()

    if not short_condition and self.position_state < self.POSITION_NONE:
        Log(f"📤 平空仓信号: 市场={market_type}, 预测得分={score:.1f}")
        self.close_position()

def CancelPendingOrders(): “”“取消所有挂单”“” while True: orders = exchange.GetOrders() if not orders: break for order in orders: exchange.CancelOrder(order[‘Id’]) Sleep(500)

def main(): global AmountOP, LoopInterval

# 检查初始持仓
initial_positions = exchange.GetPosition()
if initial_positions and any(pos['Amount'] > 0 for pos in initial_positions):
    raise Error_AtBeginHasPosition()

# 取消所有挂单
CancelPendingOrders()

# 初始化策略
strategy = PredictiveNeuralTradingStrategy()

Log("🔮 预测型神经网络期货交易策略启动")
LogProfitReset()

# 数据预热期
Log("进入数据预热期...")
warmup_count = 0
warmup_target = 60

while warmup_count < warmup_target:
    records = exchange.GetRecords()
    if records and len(records) >= 55:
        if strategy.collect_data(records):
            warmup_count += 1
            if warmup_count % 10 == 0:
                Log(f"预热进度: {warmup_count}/{warmup_target}")
    Sleep(5000)

Log("数据预热完成，开始首次收益率预测器训练...")
strategy.train_model()

# 主交易循环
loop_count = 0
while True:
    loop_count += 1

    # 获取K线数据
    records = exchange.GetRecords()
    if not records or len(records) < 55:
        Sleep(LoopInterval * 1000)
        continue

    # 数据处理
    data_updated = strategy.collect_data(records)

    # 检查是否需要重新训练
    if strategy.should_retrain():
        Log("🔄 重新训练收益率预测器...")
        strategy.train_model()

    # 获取交易信号
    score, market_type = strategy.get_trading_signals(records)

    # 获取当前实时价格
    ticker = exchange.GetTicker()
    if ticker:
        current_price = ticker['Last']
    else:
        current_price = records[-1]['Close']

    # 获取当前参数
    params = strategy.param_manager.get_params(market_type)

    # 优先检查止损止盈（使用实时价格）
    if strategy.position_state != strategy.POSITION_NONE:
        if strategy.check_stop_loss_take_profit(current_price, params):
            Log("⚡ 触发止盈止损，已执行平仓")
            Sleep(LoopInterval * 1000)
            continue

    # 执行交易逻辑（只在有新数据时执行）
    if data_updated:
        strategy.execute_trade_logic(score, market_type, current_price)

    # 状态显示
    pos_state_name = {
        strategy.POSITION_NONE: "无仓",
        strategy.POSITION_LONG: "多仓", 
        strategy.POSITION_SHORT: "空仓"
    }.get(strategy.position_state, "未知")

    data_status = "📊新数据" if data_updated else "⏸️等待"
    model_status = "🔮预测" if strategy.scoring_system.is_model_trained else "📊基础"

    # 获取开仓条件用于显示
    long_cond, short_cond = strategy.check_entry_conditions(score, market_type)
    signal_status = ""
    if long_cond:
        signal_status = "📈多头"
    elif short_cond:
        signal_status = "📉空头"
    else:
        signal_status = "🔄观望"

    # 显示训练样本数量
    sample_count = len(strategy.feature_buffer)

    LogStatus(f"循环: {loop_count}, 价格: {current_price:.2f}, "
              f"预测得分: {score:.1f}, 市场: {market_type}, "
              f"持仓: {pos_state_name}, 信号: {signal_status}, "
              f"状态: {data_status}, 模式: {model_status}, "
              f"样本: {sample_count}, "
              f"胜: {strategy.counter['win']}, 负: {strategy.counter['loss']}")

    Sleep(LoopInterval * 1000)

========== 参数设置 ==========

AmountOP = 1 # 期货合约数量 LoopInterval = 3 # 循环间隔（�