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NewQuant/futures_trading_strategies/hc/TrendlineBreakoutStrategy/TrendlineBreakoutStrategy_ML.ipynb
liaozhaorun 4712565749 1、vp策略
2025-10-05 00:09:59 +08:00

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{
"cells": [
{
"cell_type": "code",
"id": "522f09ca7b3fe929",
"metadata": {
"ExecuteTime": {
"end_time": "2025-09-23T07:14:21.841076Z",
"start_time": "2025-09-23T07:14:21.809777Z"
}
},
"source": [
"from datetime import datetime\n",
"\n",
"from src.data_processing import load_raw_data\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import sys\n",
"\n",
"if '/mnt/d/PyProject/NewQuant/' not in sys.path:\n",
" sys.path.append('/mnt/d/PyProject/NewQuant/')"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"execution_count": 30
},
{
"cell_type": "code",
"id": "4f7e4b438cea750e",
"metadata": {
"ExecuteTime": {
"end_time": "2025-09-23T07:14:21.879680Z",
"start_time": "2025-09-23T07:14:21.856562Z"
}
},
"source": [
"from turtle import down\n",
"from src.analysis.result_analyzer import ResultAnalyzer\n",
"# 导入所有必要的模块\n",
"from src.data_manager import DataManager\n",
"from src.backtest_engine import BacktestEngine\n",
"from src.indicators.indicator_list import INDICATOR_LIST\n",
"from src.indicators.indicators import *\n",
"\n",
"# 导入您自己的 SMC 策略\n",
"from src.strategies.TrendlineBreakoutStrategy.TrendlineBreakoutStrategy import TrendlineBreakoutStrategy\n",
"\n",
"# --- 配置参数 ---\n",
"# 获取当前脚本所在目录，假设数据文件在项目根目录下的 data 文件夹内\n",
"data_file_path = '/mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_SA/KQ_m@CZCE_SA_min15.csv'\n",
"# data_file_path = \"/mnt/d/PyProject/NewQuant/data/data/KQ_m@DCE_m/KQ_m@DCE_m_min60.csv\"\n",
"# data_file_path = \"/mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_SA/KQ_m@CZCE_SA_min15.csv\"\n",
"# data_file_path = \"/mnt/d/PyProject/NewQuant/data/data/KQ_m@DCE_m/KQ_m@DCE_m_min60.csv\""
],
"outputs": [],
"execution_count": 31
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-09-23T07:14:21.911306Z",
"start_time": "2025-09-23T07:14:21.891023Z"
}
},
"cell_type": "code",
"source": [
"\n",
"initial_capital = 100000.0\n",
"slippage_rate = 0.000 # 假设每笔交易0.1%的滑点\n",
"commission_rate = 0.0000 # 假设每笔交易0.02%的佣金\n",
"\n",
"global_config = {\n",
" 'symbol': 'KQ_m@CZCE_SA', # 确保与数据文件中的 symbol 匹配\n",
"}\n",
"\n",
"# 回测时间范围\n",
"start_time = datetime(2021, 1, 1)\n",
"end_time = datetime(2024, 3, 12)\n",
"\n",
"# start_time = datetime(2024, 1, 1)\n",
"# end_time = datetime(2025, 8, 1)\n",
"\n",
"\n",
"indicators = INDICATOR_LIST\n",
"indicators = []\n",
"\n",
"# 确保 DataManager 能够重置以进行多次回测\n",
"# data_manager.reset() # 首次运行不需要重置"
],
"id": "9ee53c41eaaefabb",
"outputs": [],
"execution_count": 32
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-09-23T07:15:11.965857Z",
"start_time": "2025-09-23T07:14:21.922851Z"
}
},
"cell_type": "code",
"source": [
"from src.indicators.indicators import ROC_MA\n",
"\n",
"# --- 1. 初始化数据管理器 ---\n",
"print(\"初始化数据管理器...\")\n",
"data_manager = DataManager(file_path=data_file_path, symbol=global_config['symbol'], start_time=start_time,\n",
" end_time=end_time)\n",
"\n",
"strategy_parameters = {\n",
" 'main_symbol': 'SA', # <-- 替换为你的交易品种代码，例如 'GC=F' (黄金期货), 'ZC=F' (玉米期货)\n",
" 'trade_volume': 1,\n",
" 'order_direction': ['BUY', 'SELL'],\n",
" # 'indicators': [PriceRangeToVolatilityRatio(7, 14, 2.8, 4.3), ROC_MA(10, 10, -2.7, -0.4)],\n",
" 'enable_log': False\n",
"}\n",
"\n",
"# --- 2. 初始化回测引擎并运行 ---\n",
"print(\"\\n初始化回测引擎...\")\n",
"engine = BacktestEngine(\n",
" data_manager=data_manager,\n",
" strategy_class=TrendlineBreakoutStrategy, # <--- 更改为您的 SMC 策略类\n",
" # current_segment_symbol 参数已从 SMCPureH1LongStrategy 中移除，不需要设置\n",
" strategy_params=strategy_parameters,\n",
" initial_capital=initial_capital,\n",
" slippage_rate=slippage_rate,\n",
" commission_rate=commission_rate,\n",
" roll_over_mode=True,\n",
" start_time=start_time,\n",
" end_time=end_time,\n",
" indicators=indicators # 如果您的 SMC 策略不使用这些指标，也可以考虑移除\n",
")\n",
"\n",
"print(\"\\n开始运行回测...\")\n",
"engine.run_backtest()\n",
"print(\"\\n回测运行完毕。\")\n",
"\n",
"# --- 3. 获取回测结果 ---\n",
"results = engine.get_backtest_results()\n",
"portfolio_snapshots = results[\"portfolio_snapshots\"]\n",
"trade_history = results[\"trade_history\"]\n",
"initial_capital_result = results[\"initial_capital\"]\n",
"bars = results[\"all_bars\"]\n",
"\n",
"# --- 4. 结果分析与可视化 ---\n",
"if portfolio_snapshots:\n",
" analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, bars, initial_capital_result, INDICATOR_LIST)\n",
"\n",
" analyzer.generate_report()\n",
"\n",
" analyzer.analyze_indicators()\n",
"else:\n",
" print(\"\\n没有生成投资组合快照，无法进行结果分析。\")"
],
"id": "f903fd2761d446cd",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"初始化数据管理器...\n",
"数据加载成功: /mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_SA/KQ_m@CZCE_SA_min15.csv\n",
"数据范围从 2020-12-31 14:45:00 到 2025-08-26 13:45:00\n",
"总计 25662 条记录。\n",
"\n",
"初始化回测引擎...\n",
"模拟器初始化：初始资金=100000.00, 滑点率=0.0, 佣金率=0.0\n",
"\n",
"--- 回测引擎初始化完成 ---\n",
" 策略: TrendlineBreakoutStrategy\n",
" 初始资金: 100000.00\n",
" 换月模式: 启用\n",
"\n",
"开始运行回测...\n",
"\n",
"--- 回测开始 ---\n",
"TrendlineBreakoutStrategy 策略初始化回调被调用。\n",
"开始将 DataFrame 转换为 Bar 对象流...\n",
"Bar 对象流生成完毕。\n",
"\n",
"--- 回测结束，检查并平仓所有剩余持仓 ---\n",
"--- 回测结束 ---\n",
"总计处理了 17565 根K线。\n",
"总计发生了 1974 笔交易。\n",
"最终总净值: 101664.00\n",
"总收益率: 1.66%\n",
"\n",
"回测运行完毕。\n"
]
}
],
"execution_count": 33
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-09-23T07:15:18.149306Z",
"start_time": "2025-09-23T07:15:11.986337Z"
}
},
"cell_type": "code",
"source": [
"from matplotlib import pyplot as plt\n",
"from sklearn.metrics import confusion_matrix\n",
"from src.core_data import Trade\n",
"import pandas as pd\n",
"import numpy as np\n",
"import joblib\n",
"import talib\n",
"from dataclasses import dataclass, field\n",
"from typing import Dict, List, Tuple, Optional\n",
"\n",
"# --- 机器学习库 ---\n",
"import xgboost as xgb\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import accuracy_score, classification_report\n",
"import seaborn as sns\n",
"\n",
"\n",
"# 这是您提供的趋势线计算函数，我们将其独立出来用于特征计算\n",
"def _calculate_latest_trendline_values(prices: np.ndarray) -> Tuple[Optional[float], Optional[float]]:\n",
" n = len(prices)\n",
" if n < 2 or np.isnan(prices).any(): return None, None\n",
" x = np.arange(n)\n",
" high_point_idx = np.argmax(prices)\n",
" high_point_price = prices[high_point_idx]\n",
" best_upper_slope = None\n",
" min_upper_distance_sum = float('inf')\n",
" for i in range(n):\n",
" if i == high_point_idx or (i - high_point_idx) == 0: continue\n",
" candidate_slope = (prices[i] - high_point_price) / (i - high_point_idx)\n",
" intercept = high_point_price - candidate_slope * high_point_idx\n",
" candidate_line = candidate_slope * x + intercept\n",
" if np.all(candidate_line >= prices - 1e-9):\n",
" distance_sum = np.sum(candidate_line - prices)\n",
" if distance_sum < min_upper_distance_sum:\n",
" min_upper_distance_sum = distance_sum\n",
" best_upper_slope = candidate_slope\n",
" if best_upper_slope is None: return None, None\n",
" upper_intercept = high_point_price - best_upper_slope * high_point_idx\n",
" latest_upper_value = best_upper_slope * (n - 1) + upper_intercept\n",
" low_point_idx = np.argmin(prices)\n",
" low_point_price = prices[low_point_idx]\n",
" best_lower_slope = None\n",
" min_lower_distance_sum = float('inf')\n",
" for i in range(n):\n",
" if i == low_point_idx or (i - low_point_idx) == 0: continue\n",
" candidate_slope = (prices[i] - low_point_price) / (i - low_point_idx)\n",
" intercept = low_point_price - candidate_slope * low_point_idx\n",
" candidate_line = candidate_slope * x + intercept\n",
" if np.all(candidate_line <= prices + 1e-9):\n",
" distance_sum = np.sum(prices - candidate_line)\n",
" if distance_sum < min_lower_distance_sum:\n",
" min_lower_distance_sum = distance_sum\n",
" best_lower_slope = candidate_slope\n",
" if best_lower_slope is None: return None, None\n",
" lower_intercept = low_point_price - best_lower_slope * low_point_idx\n",
" latest_lower_value = best_lower_slope * (n - 1) + lower_intercept\n",
" return latest_upper_value, latest_lower_value\n",
"\n",
"\n",
"def calculate_features(df: pd.DataFrame, trendline_n: int = 50) -> pd.DataFrame:\n",
" \"\"\"\n",
" 【单一事实来源】\n",
" 接收一个OHLCV DataFrame，计算并返回所有模型需要的特征。\n",
" 训练和预测都必须调用此函数。\n",
" \"\"\"\n",
" # 确保数据按时间排序\n",
" df = df.sort_values('datetime').reset_index(drop=True)\n",
"\n",
" # --- 基础指标 ---\n",
" df['roc'] = talib.ROC(df['close'], timeperiod=10)\n",
" df['roc_ma'] = talib.MA(df['roc'], timeperiod=5)\n",
" df['rsi'] = talib.RSI(df['close'], timeperiod=14)\n",
" df['adx'] = talib.ADX(df['high'], df['low'], df['close'], timeperiod=14)\n",
"\n",
" # --- 【修正】统一的趋势线和价差计算 ---\n",
" close_prices = df['close'].values\n",
" trend_results = [\n",
" _calculate_latest_trendline_values(close_prices[i - trendline_n:i]) if i >= trendline_n else (np.nan, np.nan)\n",
" for i in range(len(close_prices))\n",
" ]\n",
" trend_df = pd.DataFrame(trend_results, index=df.index, columns=['upper_trendline', 'lower_trendline'])\n",
"\n",
" df['trendline_slope'] = (trend_df['upper_trendline'] - trend_df['lower_trendline']) / trendline_n\n",
"\n",
" # 【关键修正】根据错误信息，训练时使用的是 'gap_from_prev_close'\n",
" # 它的计算方式很可能就是当前close与上一根close的价差，我们在这里重现它\n",
" df['gap_from_prev_close'] = df['close'].diff()\n",
"\n",
" # 同时，我们保留 price_gap_to_lower_trendline，因为这可能是个有用的特征\n",
" # 如果你确定不再需要它，可以注释掉下面这行\n",
" df['price_gap_to_lower_trendline'] = df['close'] - trend_df['lower_trendline']\n",
"\n",
" return df\n",
"\n",
"\n",
"# ==============================================================================\n",
"# 3. 核心功能：特征工程与数据预处理\n",
"# ==============================================================================\n",
"def create_features_from_ohlc(df: pd.DataFrame, trendline_n: int = 50) -> pd.DataFrame:\n",
" \"\"\"\n",
" 现在这个函数只是一个包装器，直接调用共享的特征计算函数。\n",
" \"\"\"\n",
" print(\"\\n开始计算特征...\")\n",
" df_featured = calculate_features(df, trendline_n=trendline_n)\n",
" df_featured = df_featured.dropna().reset_index(drop=True)\n",
" print(\"特征计算和清洗完成。\")\n",
" return df_featured\n",
"\n",
"\n",
"import pandas as pd\n",
"from typing import List\n",
"\n",
"\n",
"# 假设 Trade class 和其他依赖已定义\n",
"\n",
"def build_training_set(trade_history: List[Trade], df_with_features: pd.DataFrame, feature_columns: List[str]) -> (pd.DataFrame, pd.Series):\n",
" \"\"\"\n",
" 将交易历史与带有特征的行情数据链接，构建训练集。\n",
" 【已修正以兼容新版pandas】\n",
" \"\"\"\n",
" # 确保两个DataFrame的datetime列都是datetime对象\n",
" df_with_features['datetime'] = pd.to_datetime(df_with_features['datetime'])\n",
"\n",
" # 为了快速查找，将datetime设置为索引并排序\n",
" df_indexed = df_with_features.set_index('datetime').sort_index()\n",
"\n",
" features_list = []\n",
" labels_list = []\n",
"\n",
" print(f\"\\n开始构建训练集，共 {len(trade_history) // 2} 对交易...\")\n",
"\n",
" # 提取所有开仓交易的时间和盈亏信息\n",
" open_times = []\n",
" trade_outcomes = []\n",
" for i in range(0, len(trade_history), 2):\n",
" open_trade = trade_history[i]\n",
" close_trade = trade_history[i + 1]\n",
" if open_trade.is_open_trade and close_trade.is_close_trade:\n",
" open_times.append(open_trade.fill_time)\n",
" trade_outcomes.append(1 if close_trade.realized_pnl > 0 else 0)\n",
"\n",
" # 将交易时间转换为pandas的DatetimeIndex\n",
" trade_times_idx = pd.to_datetime(open_times)\n",
"\n",
" # --- 【核心修正】使用 get_indexer 进行定位 ---\n",
" # get_indexer 会返回每个交易时间在df_indexed中的位置索引。\n",
" # method='ffill' (forward fill) 确保如果某个交易时间不存在，\n",
" # 它会找到该时间点之前最近的一个有效索引。\n",
" locations = df_indexed.index.get_indexer(trade_times_idx, method='ffill')\n",
"\n",
" # 过滤掉无法找到位置的交易 (例如，交易时间早于所有行情数据)\n",
" valid_locations = locations[locations != -1]\n",
" valid_outcomes = [outcome for i, outcome in enumerate(trade_outcomes) if locations[i] != -1]\n",
"\n",
" if len(valid_locations) == 0:\n",
" raise ValueError(\"未能将任何交易时间匹配到行情数据！请检查时间戳是否对齐。\")\n",
"\n",
" # 根据找到的位置索引，一次性提取所有对应的特征行\n",
" matched_features_df = df_indexed.iloc[valid_locations]\n",
"\n",
" X = matched_features_df.reset_index(drop=True)\n",
" y = pd.Series(valid_outcomes, name=\"is_profitable\")\n",
"\n",
" # 确保所有需要的列都存在\n",
" missing_cols = [col for col in feature_columns if col not in X.columns]\n",
" if missing_cols:\n",
" raise KeyError(f\"以下特征列在数据中缺失: {missing_cols}\")\n",
"\n",
" X = X[feature_columns]\n",
"\n",
" print(f\"训练集构建完成。生成了 {len(X)} 个样本。\")\n",
" print(f\"盈利样本比例: {y.mean() * 100:.2f}%\")\n",
"\n",
" return X, y\n",
"\n",
"\n",
"# ==============================================================================\n",
"# 4. 模型训练与评估 (与上一版基本相同)\n",
"# ==============================================================================\n",
"def train_and_evaluate_filter_model(X: pd.DataFrame, y: pd.Series, model_path: str, scaler_path: str):\n",
" print(\"\\n--- 开始模型训练流程 ---\")\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
" scaler = StandardScaler()\n",
" X_train_scaled = scaler.fit_transform(X_train)\n",
" X_test_scaled = scaler.transform(X_test)\n",
" # joblib.dump(scaler, scaler_path)\n",
" print(f\"特征缩放器 (Scaler) 已保存至: {scaler_path}\")\n",
" model = xgb.XGBClassifier(eval_metric='logloss', random_state=42)\n",
" model.fit(X_train_scaled, y_train)\n",
" # joblib.dump(model, model_path)\n",
" print(f\"训练好的模型已保存至: {model_path}\")\n",
" print(\"\\n--- 模型评估 ---\")\n",
" y_pred = model.predict(X_test_scaled)\n",
" print(f\"测试集准确率 (Accuracy): {accuracy_score(y_test, y_pred):.4f}\")\n",
" print(\"\\n分类报告 (Classification Report):\")\n",
" print(classification_report(y_test, y_pred, target_names=[\"亏损(0)\", \"盈利(1)\"]))\n",
"\n",
" return model, scaler\n",
"\n",
"\n",
"# ==============================================================================\n",
"# 5. 主执行流程\n",
"# ==============================================================================\n",
"MODEL_SAVE_PATH = \"trade_filter_model_v2.joblib\"\n",
"SCALER_SAVE_PATH = \"trade_filter_scaler_v2.joblib\"\n",
"TRENDLINE_PERIOD = 50\n",
"\n",
"\n",
"feature_columns = [\n",
" 'roc',\n",
" 'roc_ma',\n",
" 'rsi',\n",
" 'adx',\n",
" 'trendline_slope',\n",
" 'gap_from_prev_close'\n",
"]\n",
"\n",
"# 加载数据并获取日期范围以生成匹配的交易\n",
"ohlc_df = pd.read_csv(data_file_path, parse_dates=['datetime'])\n",
"\n",
"# --- 步骤 2: 特征工程 ---\n",
"df_with_features = create_features_from_ohlc(ohlc_df, trendline_n=TRENDLINE_PERIOD)\n",
"\n",
"# --- 步骤 3: 构建训练集 ---\n",
"X_features, y_labels = build_training_set(trade_history, df_with_features, feature_columns)\n",
"# --- 在这里加入验证代码 ---\n",
"print(\"\\n--- 验证标签集合 ---\")\n",
"unique_labels_in_data = sorted(y_labels.unique())\n",
"print(f\"数据集中实际存在的标签类别: {unique_labels_in_data}\")\n",
"if len(unique_labels_in_data) < 4:\n",
" print(\"警告：训练数据中未包含全部四种交易结果，模型将被训练为少于4类的分类器。\")\n",
"# -------------------------\n",
"# --- 步骤 4: 训练并保存模型 ---\n",
"model, scaler = train_and_evaluate_filter_model(X_features, y_labels, MODEL_SAVE_PATH, SCALER_SAVE_PATH)\n",
"\n",
"print(f\"\\n训练流程完成！模型和Scaler已保存。\")"
],
"id": "7aa58a71f10abb2d",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"开始计算特征...\n",
"特征计算和清洗完成。\n",
"\n",
"开始构建训练集，共 987 对交易...\n",
"训练集构建完成。生成了 987 个样本。\n",
"盈利样本比例: 36.07%\n",
"\n",
"--- 验证标签集合 ---\n",
"数据集中实际存在的标签类别: [np.int64(0), np.int64(1)]\n",
"警告：训练数据中未包含全部四种交易结果，模型将被训练为少于4类的分类器。\n",
"\n",
"--- 开始模型训练流程 ---\n",
"特征缩放器 (Scaler) 已保存至: trade_filter_scaler_v2.joblib\n",
"训练好的模型已保存至: trade_filter_model_v2.joblib\n",
"\n",
"--- 模型评估 ---\n",
"测试集准确率 (Accuracy): 0.6498\n",
"\n",
"分类报告 (Classification Report):\n",
" precision recall f1-score support\n",
"\n",
" 亏损(0) 0.72 0.74 0.73 190\n",
" 盈利(1) 0.51 0.49 0.50 107\n",
"\n",
" accuracy 0.65 297\n",
" macro avg 0.62 0.61 0.62 297\n",
"weighted avg 0.65 0.65 0.65 297\n",
"\n",
"\n",
"训练流程完成！模型和Scaler已保存。\n"
]
}
],
"execution_count": 34
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-09-23T07:15:18.184361Z",
"start_time": "2025-09-23T07:15:18.162545Z"
}
},
"cell_type": "code",
"source": [
"# =-============================================================================\n",
"# 【最终修正版 V2】机器学习过滤器指标\n",
"# ==============================================================================\n",
"class MLFilterIndicator(Indicator):\n",
" \"\"\"\n",
" 一个将【已加载的】机器学习模型封装为标准指标的过滤器。\n",
" 它接收模型、scaler对象以及一个明确的特征列表。\n",
" \"\"\"\n",
"\n",
" def __init__(self,\n",
" model: xgb.XGBClassifier,\n",
" scaler: StandardScaler,\n",
" feature_names: List[str], # <-- 【新增】显式传入特征名称列表\n",
" trendline_n: int = 50,\n",
" down_bound: float = 0.5,\n",
" up_bound: float = None,\n",
" shift_window: int = 0):\n",
"\n",
" super().__init__(down_bound, up_bound, shift_window)\n",
" self.trendline_n = trendline_n\n",
"\n",
" # --- 【修改】更稳健的初始化 ---\n",
" # 不再使用try...except来隐藏问题，而是进行明确的检查\n",
" if model is None or scaler is None:\n",
" raise ValueError(\"传入的model或scaler对象不能为空。\")\n",
" if not feature_names or not isinstance(feature_names, list):\n",
" raise ValueError(\"必须传入一个包含特征名称的列表 (feature_names)。\")\n",
"\n",
" self.model = model\n",
" self.scaler = scaler\n",
" self.feature_names = feature_names # <-- 直接使用传入的列表\n",
"\n",
" print(\"ML过滤器已成功初始化。\")\n",
" print(f\"将使用的特征列表: {self.feature_names}\")\n",
"\n",
" def get_name(self) -> str:\n",
" return f\"MLFilter_TrendN{self.trendline_n}\"\n",
"\n",
" def get_values(self, close: np.array, open: np.array, high: np.array, low: np.array,\n",
" volume: np.array) -> np.ndarray:\n",
" # 【关键】现在 get_values 的实现变得非常简洁\n",
" df = pd.DataFrame(\n",
" {'datetime': pd.to_datetime(np.arange(len(close))), 'open': open, 'high': high, 'low': low, 'close': close,\n",
" 'volume': volume})\n",
"\n",
" # 直接调用统一的特征计算函数\n",
" df_with_features = calculate_features(df, trendline_n=self.trendline_n)\n",
"\n",
" # --- 后续的预测流程与之前相同 ---\n",
" required_cols = self.feature_names\n",
" df_filtered = df_with_features[required_cols].dropna()\n",
"\n",
" if df_filtered.empty:\n",
" return np.full_like(close, np.nan, dtype=float)\n",
"\n",
" X = df_filtered[self.feature_names]\n",
" X_scaled = self.scaler.transform(X)\n",
"\n",
" profit_proba = self.model.predict_proba(X_scaled)[:, 1]\n",
"\n",
" prediction_series = pd.Series(profit_proba, index=X.index)\n",
" aligned_predictions = prediction_series.reindex(df.index)\n",
"\n",
" return aligned_predictions.values\n",
"\n"
],
"id": "bfd8ff1e602c6545",
"outputs": [],
"execution_count": 35
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-09-23T08:06:51.838537Z",
"start_time": "2025-09-23T08:01:45.486079Z"
}
},
"cell_type": "code",
"source": [
"from src.indicators.indicators import ROC_MA\n",
"from src.strategies.TrendlineBreakoutStrategy.TrendlineBreakoutStrategy import TrendlineBreakoutStrategy\n",
"\n",
"start_time = datetime(2024, 1, 1)\n",
"end_time = datetime(2025, 8, 1)\n",
"\n",
"\n",
"# --- 1. 初始化数据管理器 ---\n",
"print(\"初始化数据管理器...\")\n",
"data_manager = DataManager(file_path=data_file_path, symbol=global_config['symbol'], start_time=start_time,\n",
" end_time=end_time)\n",
"\n",
"strategy_parameters = {\n",
" 'main_symbol': 'SA', # <-- 替换为你的交易品种代码，例如 'GC=F' (黄金期货), 'ZC=F' (玉米期货)\n",
" 'trade_volume': 1,\n",
" 'order_direction': ['BUY', 'SELL'],\n",
" 'indicators': [MLFilterIndicator(model, scaler, feature_names), MLFilterIndicator(model, scaler, feature_columns)],\n",
" 'enable_log': False\n",
"}\n",
"\n",
"# --- 2. 初始化回测引擎并运行 ---\n",
"print(\"\\n初始化回测引擎...\")\n",
"engine = BacktestEngine(\n",
" data_manager=data_manager,\n",
" strategy_class=TrendlineBreakoutStrategy, # <--- 更改为您的 SMC 策略类\n",
" # current_segment_symbol 参数已从 SMCPureH1LongStrategy 中移除，不需要设置\n",
" strategy_params=strategy_parameters,\n",
" initial_capital=initial_capital,\n",
" slippage_rate=slippage_rate,\n",
" commission_rate=commission_rate,\n",
" roll_over_mode=True,\n",
" start_time=start_time,\n",
" end_time=end_time,\n",
" indicators=indicators # 如果您的 SMC 策略不使用这些指标，也可以考虑移除\n",
")\n",
"\n",
"print(\"\\n开始运行回测...\")\n",
"engine.run_backtest()\n",
"print(\"\\n回测运行完毕。\")\n",
"\n",
"# --- 3. 获取回测结果 ---\n",
"results = engine.get_backtest_results()\n",
"portfolio_snapshots = results[\"portfolio_snapshots\"]\n",
"trade_history = results[\"trade_history\"]\n",
"initial_capital_result = results[\"initial_capital\"]\n",
"bars = results[\"all_bars\"]\n",
"\n",
"# --- 4. 结果分析与可视化 ---\n",
"if portfolio_snapshots:\n",
" analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, bars, initial_capital_result, INDICATOR_LIST)\n",
"\n",
" analyzer.generate_report()\n",
" analyzer.plot_performance()\n",
" metrics = analyzer.calculate_all_metrics()\n",
" print(metrics)\n",
"\n",
" analyzer.analyze_indicators()\n",
"else:\n",
" print(\"\\n没有生成投资组合快照，无法进行结果分析。\")"
],
"id": "c7452f42eb4e9345",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"初始化数据管理器...\n",
"数据加载成功: /mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_SA/KQ_m@CZCE_SA_min15.csv\n",
"数据范围从 2020-12-31 14:45:00 到 2025-08-26 13:45:00\n",
"总计 25662 条记录。\n",
"ML过滤器已成功初始化。\n",
"将使用的特征列表: ['roc', 'roc_ma', 'rsi', 'adx', 'trendline_slope', 'gap_from_prev_close']\n",
"ML过滤器已成功初始化。\n",
"将使用的特征列表: ['roc', 'roc_ma', 'rsi', 'adx', 'trendline_slope', 'gap_from_prev_close']\n",
"\n",
"初始化回测引擎...\n",
"模拟器初始化：初始资金=100000.00, 滑点率=0.0, 佣金率=0.0\n",
"\n",
"--- 回测引擎初始化完成 ---\n",
" 策略: TrendlineBreakoutStrategy\n",
" 初始资金: 100000.00\n",
" 换月模式: 启用\n",
"\n",
"开始运行回测...\n",
"\n",
"--- 回测开始 ---\n",
"TrendlineBreakoutStrategy 策略初始化回调被调用。\n",
"开始将 DataFrame 转换为 Bar 对象流...\n",
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]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mKeyboardInterrupt\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[38]\u001B[39m\u001B[32m, line 38\u001B[39m\n\u001B[32m 23\u001B[39m engine = BacktestEngine(\n\u001B[32m 24\u001B[39m data_manager=data_manager,\n\u001B[32m 25\u001B[39m strategy_class=TrendlineBreakoutStrategy, \u001B[38;5;66;03m# <--- 更改为您的 SMC 策略类\u001B[39;00m\n\u001B[32m (...)\u001B[39m\u001B[32m 34\u001B[39m indicators=indicators \u001B[38;5;66;03m# 如果您的 SMC 策略不使用这些指标，也可以考虑移除\u001B[39;00m\n\u001B[32m 35\u001B[39m )\n\u001B[32m 37\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[33m开始运行回测...\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m---> \u001B[39m\u001B[32m38\u001B[39m \u001B[43mengine\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrun_backtest\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 39\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[33m回测运行完毕。\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m 41\u001B[39m \u001B[38;5;66;03m# --- 3. 获取回测结果 ---\u001B[39;00m\n",
"\u001B[36mFile \u001B[39m\u001B[32m/mnt/d/PyProject/NewQuant/src/backtest_engine.py:160\u001B[39m, in \u001B[36mBacktestEngine.run_backtest\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 157\u001B[39m \u001B[38;5;66;03m# 3. 更新策略关注的当前合约 symbol\u001B[39;00m\n\u001B[32m 158\u001B[39m \u001B[38;5;28mself\u001B[39m.strategy.symbol = current_bar.symbol\n\u001B[32m--> \u001B[39m\u001B[32m160\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mstrategy\u001B[49m\u001B[43m.\u001B[49m\u001B[43mon_open_bar\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcurrent_bar\u001B[49m\u001B[43m.\u001B[49m\u001B[43mopen\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcurrent_bar\u001B[49m\u001B[43m.\u001B[49m\u001B[43msymbol\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 162\u001B[39m current_indicator_dict = {}\n\u001B[32m 163\u001B[39m close_array = np.array(\u001B[38;5;28mself\u001B[39m.close_list)\n",
"\u001B[36mFile \u001B[39m\u001B[32m<string>:39\u001B[39m, in \u001B[36mon_open_bar\u001B[39m\u001B[34m(self, open_price, symbol)\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32m/mnt/d/PyProject/NewQuant/src/indicators/base_indicators.py:32\u001B[39m, in \u001B[36mIndicator.is_condition_met\u001B[39m\u001B[34m(self, close, open, high, low, volume)\u001B[39m\n\u001B[32m 26\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mis_condition_met\u001B[39m(\u001B[38;5;28mself\u001B[39m,\n\u001B[32m 27\u001B[39m close: np.array,\n\u001B[32m 28\u001B[39m \u001B[38;5;28mopen\u001B[39m: np.array,\n\u001B[32m 29\u001B[39m high: np.array,\n\u001B[32m 30\u001B[39m low: np.array,\n\u001B[32m 31\u001B[39m volume: np.array):\n\u001B[32m---> \u001B[39m\u001B[32m32\u001B[39m value = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mget_latest_value\u001B[49m\u001B[43m(\u001B[49m\u001B[43mclose\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mhigh\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlow\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvolume\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 33\u001B[39m condition_met = \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[32m 34\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m value \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
"\u001B[36mFile \u001B[39m\u001B[32m/mnt/d/PyProject/NewQuant/src/indicators/base_indicators.py:22\u001B[39m, in \u001B[36mIndicator.get_latest_value\u001B[39m\u001B[34m(self, close, open, high, low, volume)\u001B[39m\n\u001B[32m 21\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mget_latest_value\u001B[39m(\u001B[38;5;28mself\u001B[39m, close: np.array, \u001B[38;5;28mopen\u001B[39m: np.array, high: np.array, low: np.array, volume: np.array):\n\u001B[32m---> \u001B[39m\u001B[32m22\u001B[39m values = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mget_values\u001B[49m\u001B[43m(\u001B[49m\u001B[43mclose\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mhigh\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlow\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvolume\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 23\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m values[-(\u001B[38;5;28mself\u001B[39m.shift_window + \u001B[32m1\u001B[39m)].item() \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(values) > \u001B[38;5;28mself\u001B[39m.shift_window + \u001B[32m1\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[35]\u001B[39m\u001B[32m, line 47\u001B[39m, in \u001B[36mMLFilterIndicator.get_values\u001B[39m\u001B[34m(self, close, open, high, low, volume)\u001B[39m\n\u001B[32m 42\u001B[39m df = pd.DataFrame(\n\u001B[32m 43\u001B[39m {\u001B[33m'\u001B[39m\u001B[33mdatetime\u001B[39m\u001B[33m'\u001B[39m: pd.to_datetime(np.arange(\u001B[38;5;28mlen\u001B[39m(close))), \u001B[33m'\u001B[39m\u001B[33mopen\u001B[39m\u001B[33m'\u001B[39m: \u001B[38;5;28mopen\u001B[39m, \u001B[33m'\u001B[39m\u001B[33mhigh\u001B[39m\u001B[33m'\u001B[39m: high, \u001B[33m'\u001B[39m\u001B[33mlow\u001B[39m\u001B[33m'\u001B[39m: low, \u001B[33m'\u001B[39m\u001B[33mclose\u001B[39m\u001B[33m'\u001B[39m: close,\n\u001B[32m 44\u001B[39m \u001B[33m'\u001B[39m\u001B[33mvolume\u001B[39m\u001B[33m'\u001B[39m: volume})\n\u001B[32m 46\u001B[39m \u001B[38;5;66;03m# 直接调用统一的特征计算函数\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m47\u001B[39m df_with_features = \u001B[43mcalculate_features\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrendline_n\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mtrendline_n\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 49\u001B[39m \u001B[38;5;66;03m# --- 后续的预测流程与之前相同 ---\u001B[39;00m\n\u001B[32m 50\u001B[39m required_cols = \u001B[38;5;28mself\u001B[39m.feature_names\n",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[34]\u001B[39m\u001B[32m, line 79\u001B[39m, in \u001B[36mcalculate_features\u001B[39m\u001B[34m(df, trendline_n)\u001B[39m\n\u001B[32m 76\u001B[39m \u001B[38;5;66;03m# --- 【修正】统一的趋势线和价差计算 ---\u001B[39;00m\n\u001B[32m 77\u001B[39m close_prices = df[\u001B[33m'\u001B[39m\u001B[33mclose\u001B[39m\u001B[33m'\u001B[39m].values\n\u001B[32m 78\u001B[39m trend_results = [\n\u001B[32m---> \u001B[39m\u001B[32m79\u001B[39m \u001B[43m_calculate_latest_trendline_values\u001B[49m\u001B[43m(\u001B[49m\u001B[43mclose_prices\u001B[49m\u001B[43m[\u001B[49m\u001B[43mi\u001B[49m\u001B[43m \u001B[49m\u001B[43m-\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrendline_n\u001B[49m\u001B[43m:\u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mif\u001B[39;00m i >= trendline_n \u001B[38;5;28;01melse\u001B[39;00m (np.nan, np.nan)\n\u001B[32m 80\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(close_prices))\n\u001B[32m 81\u001B[39m ]\n\u001B[32m 82\u001B[39m trend_df = pd.DataFrame(trend_results, index=df.index, columns=[\u001B[33m'\u001B[39m\u001B[33mupper_trendline\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33mlower_trendline\u001B[39m\u001B[33m'\u001B[39m])\n\u001B[32m 84\u001B[39m df[\u001B[33m'\u001B[39m\u001B[33mtrendline_slope\u001B[39m\u001B[33m'\u001B[39m] = (trend_df[\u001B[33m'\u001B[39m\u001B[33mupper_trendline\u001B[39m\u001B[33m'\u001B[39m] - trend_df[\u001B[33m'\u001B[39m\u001B[33mlower_trendline\u001B[39m\u001B[33m'\u001B[39m]) / trendline_n\n",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[34]\u001B[39m\u001B[32m, line 50\u001B[39m, in \u001B[36m_calculate_latest_trendline_values\u001B[39m\u001B[34m(prices)\u001B[39m\n\u001B[32m 48\u001B[39m intercept = low_point_price - candidate_slope * low_point_idx\n\u001B[32m 49\u001B[39m candidate_line = candidate_slope * x + intercept\n\u001B[32m---> \u001B[39m\u001B[32m50\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43mall\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcandidate_line\u001B[49m\u001B[43m \u001B[49m\u001B[43m<\u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mprices\u001B[49m\u001B[43m \u001B[49m\u001B[43m+\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m1e-9\u001B[39;49m\u001B[43m)\u001B[49m:\n\u001B[32m 51\u001B[39m distance_sum = np.sum(prices - candidate_line)\n\u001B[32m 52\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m distance_sum < min_lower_distance_sum:\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/miniconda3/envs/quant/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:2584\u001B[39m, in \u001B[36m_all_dispatcher\u001B[39m\u001B[34m(a, axis, out, keepdims, where)\u001B[39m\n\u001B[32m 2479\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 2480\u001B[39m \u001B[33;03m Test whether any array element along a given axis evaluates to True.\u001B[39;00m\n\u001B[32m 2481\u001B[39m \n\u001B[32m (...)\u001B[39m\u001B[32m 2578\u001B[39m \n\u001B[32m 2579\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m 2580\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m _wrapreduction_any_all(a, np.logical_or, \u001B[33m'\u001B[39m\u001B[33many\u001B[39m\u001B[33m'\u001B[39m, axis, out,\n\u001B[32m 2581\u001B[39m keepdims=keepdims, where=where)\n\u001B[32m-> \u001B[39m\u001B[32m2584\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_all_dispatcher\u001B[39m(a, axis=\u001B[38;5;28;01mNone\u001B[39;00m, out=\u001B[38;5;28;01mNone\u001B[39;00m, keepdims=\u001B[38;5;28;01mNone\u001B[39;00m, *,\n\u001B[32m 2585\u001B[39m where=\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[32m 2586\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m (a, where, out)\n\u001B[32m 2589\u001B[39m \u001B[38;5;129m@array_function_dispatch\u001B[39m(_all_dispatcher)\n\u001B[32m 2590\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mall\u001B[39m(a, axis=\u001B[38;5;28;01mNone\u001B[39;00m, out=\u001B[38;5;28;01mNone\u001B[39;00m, keepdims=np._NoValue, *, where=np._NoValue):\n",
"\u001B[31mKeyboardInterrupt\u001B[39m: "
]
}
],
"execution_count": 38
}
],
"metadata": {
"kernelspec": {
"display_name": "quant",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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