Menemukan strategi Pine yang menarik

Beberapa hari yang lalu, saya sedang menelusuri strategi di Forum Penemu dan melihat sebuah postingan yang disebutPanel Pro+ Quantum SmartPromptSetelah meninjau kodenya, saya merasa konsep dasarnya cukup menarik: kode tersebut menggunakan 10 indikator teknikal, memberikan bobot berbeda untuk setiap indikator berdasarkan kondisi pasar, dan akhirnya menghitung skor untuk menentukan keputusan beli dan jual. Misalnya, dalam pasar bullish, indikator tren diberi bobot 2,0, dan RSI diberi bobot 1,5; dalam pasar bearish, bobotnya berbeda. Rasanya seperti meniru cara berpikir orang: berfokus pada hal yang berbeda dalam situasi yang berbeda.

Jika Anda memikirkannya dengan cermat, struktur ini sangat mirip dengan jaringan saraf:

• 10 indikator teknis saat input
• Klasifikasi status pasar seperti lapisan tersembunyi
• Matriks bobot adalah bobot koneksi
• Akhirnya keluarkan skor

Namun masalahnya adalah semua bobot dikodekan secara keras, misalnya:

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

Angka-angka ini sepenuhnya ditetapkan oleh penulis berdasarkan pengalaman pasar dan belum dipelajari atau dioptimalkan dengan cara apa pun.

Ide: Jadikan beban bisa dipelajari

Karena strukturnya sudah sangat mirip dengan jaringan saraf, mengapa tidak membuatnya benar-benar mampu belajar?

Ide saya sederhana:

1. Pertahankan metode perhitungan berat asli dan dapatkan “skor berat”
2. Menggunakan skor tertimbang ini sebagai input, latih jaringan saraf kecil
3. Biarkan jaringan belajar memprediksi pengembalian masa depan dari skor tertimbang
4. Tentukan apakah akan membuka posisi berdasarkan tingkat pengembalian yang diprediksi

Ini tidak hanya mempertahankan strategi asli tetapi juga meningkatkan kemampuan belajar.

Memulai di Platform Inventor

Platform Inventor dipilih terutama karena mendukung Python dan berisi data yang kaya.

Langkah 1: Tulis ulang indikator teknis

Saya menulis ulang semua indikator dalam skrip Pine dengan Python, menggunakan pustaka Talib untuk memastikan perhitungan yang akurat. Ini mencakup indikator umum seperti EMA, MACD, RSI, dan ATR, serta analisis volume dan pengenalan pola candlestick sederhana.

Langkah 2: Deteksi status pasar

Mengikuti logika strategi asli, jenis pasar ditentukan berdasarkan kombinasi berbagai indikator: Bull, Bear, Eagle, Wolf, dll. Bagian ini pada dasarnya merupakan kombinasi logika if-else.

Langkah 3: Perhitungan skor berat badan

Ini bagian intinya. Saya memasang dua set beban:

• Berat dasar:[2.0, 1.5, 2.0, 1.3, 1.2, …]
• Bobot pasar: disesuaikan dengan kondisi pasar yang berbeda

Berat akhir = berat dasar × berat pasar

Bobot ini kemudian digunakan untuk memberi bobot pada skor asli 10 indikator dan menjumlahkannya guna memperoleh “skor tertimbang”.

Langkah 4: Prediktor Jaringan Saraf

Saya menulis jaringan yang sangat sederhana:

• Input: 1 fitur (skor tertimbang)
• Lapisan tersembunyi: 16 neuron, aktivasi ReLU
• Output: tingkat pengembalian yang diprediksi, dibatasi hingga ±5% menggunakan tanh

Tujuan pelatihan: Menggunakan skor bobot pada waktu t-1 untuk memprediksi perubahan harga pada waktu t.

Langkah 5: Logika Transaksi

Alih-alih membeli atau menjual secara langsung berdasarkan peringkat, kami melihat tingkat pengembalian yang diprediksi:

• Tingkat pengembalian yang diperkirakan > 1,5%: Buka posisi panjang atau tutup posisi pendek
• Tingkat pengembalian yang diperkirakan < -1,5%: Buka posisi short atau tutup posisi long
• Situasi lain: Mempertahankan status quo

Pada saat yang sama, pertahankan stop-profit dan stop-loss untuk memastikan risiko dapat dikendalikan.

Beberapa pengamatan pada operasi aktual

Pengumpulan Data

Strategi ini dapat mengumpulkan data pelatihan secara normal. Setiap kali candlestick baru muncul, skor bobot candlestick sebelumnya digunakan sebagai fitur, dan kenaikan atau penurunan candlestick saat ini relatif terhadap candlestick sebelumnya digunakan sebagai label.

Datanya 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%

Pelatihan model

Jaringan saraf dapat dilatih secara normal, dan kerugian MSE akan berkurang secara bertahap. Atur agar pelatihan ulang dilakukan setiap 4 jam untuk memastikan model dapat beradaptasi dengan perubahan pasar.

Efek prediksi

Prediksi model tersebut memang memiliki korelasi tertentu dengan hasil aktual, tetapi tidak terlalu kuat.

1. Satu fitur saja terlalu sederhana dan mungkin tidak mengandung cukup informasi
2. Fluktuasi harga jangka pendek sangat acak
3. Pasar kontrak cukup ramai

Kinerja perdagangan

Risiko satu transaksi terkendali dengan baik berkat perlindungan stop-loss dan take-profit. Namun, profitabilitas keseluruhannya rata-rata, terutama karena kurangnya akurasi perkiraan yang tinggi.

Beberapa masalah yang dihadapi

Fiturnya terlalu sederhanaMenggunakan bobot saja untuk menilai satu fitur agak terlalu sederhana. Pasar begitu kompleks sehingga angka tunggal sulit untuk dipahami sepenuhnya.

Kualitas sampel tidak stabil:Harga kontrak sangat berfluktuasi dalam jangka pendek, dan dalam banyak kasus kenaikan dan penurunannya sebenarnya acak, yang membuat kualitas sampel pelatihan tidak stabil.

Risiko Terlalu Cocok:Meskipun jaringannya sederhana, jaringan tersebut mungkin masih mengalami overfitting apabila ukuran sampelnya terbatas.

Persyaratan waktu nyata:Pembelajaran daring memerlukan keseimbangan antara waktu pelatihan dan kinerja waktu nyata.

Waktu terbatas dan optimasi tidak memadai

Masih banyak area di mana strategi ini dapat ditingkatkan, namun waktu dan energi terbatas, sehingga kami tidak dapat mengoptimalkannya secara mendalam:

Fitur: Anda dapat menambahkan lebih banyak indikator teknis, atau menggunakan karakteristik statistik dari rangkaian harga.

ModelAnda dapat mencoba model sekuens seperti LSTM, atau mengintegrasikan beberapa model.

Data: Meningkatkan kualitas sampel dan meningkatkan pembersihan data.

Pengendalian angin: Meningkatkan stop loss dinamis dan mengoptimalkan manajemen posisi.

Keuntungan dan pikiran

Eksplorasi ini memberi saya pelajaran: kunci ide yang baik adalah implementasi yang tepat waktu! Ketika saya melihat desain matriks bobot dalam skrip Pine, saya langsung memikirkan kemungkinan untuk memperbaikinya dengan jaringan saraf. Jika saya hanya memikirkannya atau menundanya, ide itu kemungkinan besar akan terlupakan. Untungnya, Platform Inventor menyediakan lingkungan Python dan antarmuka data, yang memungkinkan saya dengan cepat mengubah ide saya menjadi kode yang dapat dijalankan. Dari pembuatan ide hingga implementasi dasar, prosesnya hanya membutuhkan waktu satu hari. Meskipun kinerja strategi akhir biasa-biasa saja, implementasi yang sebenarnya setidaknya memverifikasi kelayakan ide tersebut. Lebih penting lagi, proses implementasi menghasilkan ide-ide baru dan penyempurnaan. Tanpa tindakan cepat, penemuan dan wawasan selanjutnya ini mustahil. Membicarakan ide di atas kertas tidak akan pernah sebanding dengan pengalaman dunia nyata dalam menulis kode, menjalankan data, dan mengamati hasilnya. Inilah sifat perdagangan kuantitatif. Ada banyak ide, tetapi yang benar-benar berharga adalah ide-ide yang cepat diimplementasikan dan diverifikasi.

'''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] -> y[t+1] (收益率)"""
        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（秒）

if __name__ == "__main__":
    main()