人工智能大模型量化炒股项目详细设计

人工智能大模型量化炒股项目详细设计

项目概述

本项目旨在构建一个基于人工智能大模型的量化炒股系统，通过整合市场数据收集、预处理、特征工程、模型训练与预测、策略生成与回测等核心功能，为投资者提供数据驱动的交易决策支持。

系统架构设计

系统采用模块化设计，主要包括以下组件：

1. 数据采集层：负责收集各类金融数据
2. 数据处理层：清洗、转换和存储数据
3. 特征工程层：构建和选择预测特征
4. 模型层：训练和部署 AI 模型进行预测
5. 策略层：生成交易策略
6. 回测层：评估策略表现
7. 风控层：实施风险控制措施
8. 交互层：提供用户界面和 API 接口

数据架构

系统将处理多种类型的数据：

1. 市场数据：股票价格、成交量、开盘价、收盘价等
2. 基本面数据：财务报表、公司公告、行业数据等
3. 情绪数据：新闻、社交媒体、分析师评级等
4. 宏观经济数据：利率、通胀、GDP 等

数据将存储在时序数据库和关系型数据库中，并通过数据湖进行统一管理。

技术选型

1. 数据处理与存储：

   • Python 生态系统 (pandas, numpy, scipy)
   • 时序数据库 (InfluxDB, TimescaleDB)
   • 关系型数据库 (PostgreSQL, MySQL)
   • 分布式计算框架 (Spark)

2. 模型与算法：

   • 深度学习框架 (TensorFlow, PyTorch)
   • 传统机器学习 (Scikit-learn)
   • 大语言模型 (LLM) 接口
   • 强化学习 (Stable Baselines)

3. 系统开发：

   • 微服务架构
   • RESTful API (FastAPI, Flask)
   • 容器化 (Docker, Kubernetes)

4. 可视化与交互：

   • 仪表盘 (Plotly Dash, Streamlit)
   • 前端框架 (React, Vue.js)

核心模块实现

以下是系统核心模块的详细设计和实现思路：

python

运行

# 项目结构示例
project/
├── data/                   # 数据存储目录
├── src/                    # 源代码目录
│   ├── data_collector/     # 数据采集模块
│   ├── data_processor/     # 数据处理模块
│   ├── feature_engineering/# 特征工程模块
│   ├── model_training/     # 模型训练模块
│   ├── strategy/           # 策略生成模块
│   ├── backtest/           # 回测模块
│   ├── risk_management/    # 风险管理模块
│   └── api/                # API接口
├── config/                 # 配置文件
├── tests/                  # 测试代码
├── notebooks/              # 实验和分析笔记
└── docs/                   # 文档

数据采集模块设计

python

运行

# src/data_collector/stock_data_collector.py
import requests
import pandas as pd
from datetime import datetime, timedelta
import logging
import time
from typing import Dict, List, Optional

class StockDataCollector:
    def __init__(self, api_key: str, api_base_url: str):
        self.api_key = api_key
        self.api_base_url = api_base_url
        self.headers = {"Authorization": f"Bearer {api_key}"}
        self.logger = logging.getLogger(__name__)
    
    def get_historical_data(self, 
                           symbol: str, 
                           start_date: str, 
                           end_date: str, 
                           interval: str = "1d") -> pd.DataFrame:
        """获取股票历史数据"""
        try:
            params = {
                "symbol": symbol,
                "start": start_date,
                "end": end_date,
                "interval": interval
            }
            
            url = f"{self.api_base_url}/historical"
            response = requests.get(url, params=params, headers=self.headers)
            
            if response.status_code == 200:
                data = response.json()
                df = pd.DataFrame(data["data"])
                df['date'] = pd.to_datetime(df['date'])
                df.set_index('date', inplace=True)
                return df
            else:
                self.logger.error(f"获取历史数据失败: {response.status_code}, {response.text}")
                return pd.DataFrame()
                
        except Exception as e:
            self.logger.error(f"获取历史数据时发生异常: {str(e)}")
            return pd.DataFrame()
    
    def get_fundamental_data(self, symbol: str) -> Dict:
        """获取股票基本面数据"""
        try:
            url = f"{self.api_base_url}/fundamentals/{symbol}"
            response = requests.get(url, headers=self.headers)
            
            if response.status_code == 200:
                return response.json()
            else:
                self.logger.error(f"获取基本面数据失败: {response.status_code}, {response.text}")
                return {}
                
        except Exception as e:
            self.logger.error(f"获取基本面数据时发生异常: {str(e)}")
            return {}
    
    def get_market_sentiment(self, symbol: str, date_range: int = 7) -> Dict:
        """获取市场情绪数据"""
        try:
            end_date = datetime.now().strftime("%Y-%m-%d")
            start_date = (datetime.now() - timedelta(days=date_range)).strftime("%Y-%m-%d")
            
            url = f"{self.api_base_url}/sentiment"
            params = {
                "symbol": symbol,
                "start": start_date,
                "end": end_date
            }
            
            response = requests.get(url, params=params, headers=self.headers)
            
            if response.status_code == 200:
                return response.json()
            else:
                self.logger.error(f"获取市场情绪数据失败: {response.status_code}, {response.text}")
                return {}
                
        except Exception as e:
            self.logger.error(f"获取市场情绪数据时发生异常: {str(e)}")
            return {}
    
    def batch_fetch_symbols(self, symbols: List[str], start_date: str, end_date: str) -> Dict[str, pd.DataFrame]:
        """批量获取多个股票的数据"""
        all_data = {}
        for symbol in symbols:
            self.logger.info(f"正在获取 {symbol} 的数据")
            data = self.get_historical_data(symbol, start_date, end_date)
            if not data.empty:
                all_data[symbol] = data
            # 添加延迟避免API限流
            time.sleep(1)
        return all_data

数据处理模块设计

python

运行

# src/data_processor/data_processor.py
import pandas as pd
import numpy as np
from typing import Dict, List, Optional
import logging

class DataProcessor:
    def __init__(self):
        self.logger = logging.getLogger(__name__)
    
    def clean_data(self, df: pd.DataFrame) -> pd.DataFrame:
        """清理数据，处理缺失值和异常值"""
        try:
            # 处理缺失值
            df = df.dropna()
            
            # 处理异常值（例如，价格为负的情况）
            for col in ['open', 'high', 'low', 'close', 'volume']:
                if col in df.columns:
                    df = df[df[col] >= 0]
            
            return df
        except Exception as e:
            self.logger.error(f"数据清理时发生异常: {str(e)}")
            return df
    
    def normalize_data(self, df: pd.DataFrame, columns: List[str]) -> pd.DataFrame:
        """对指定列进行归一化处理"""
        try:
            normalized_df = df.copy()
            for col in columns:
                if col in normalized_df.columns:
                    min_val = normalized_df[col].min()
                    max_val = normalized_df[col].max()
                    if max_val - min_val > 0:
                        normalized_df[col] = (normalized_df[col] - min_val) / (max_val - min_val)
                    else:
                        normalized_df[col] = 0.0
            return normalized_df
        except Exception as e:
            self.logger.error(f"数据归一化时发生异常: {str(e)}")
            return df
    
    def add_technical_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
        """添加技术指标"""
        try:
            # 确保数据包含所需的列
            if 'close' not in df.columns or 'high' not in df.columns or 'low' not in df.columns:
                self.logger.warning("数据缺少计算技术指标所需的列")
                return df
            
            # 计算移动平均线
            df['MA5'] = df['close'].rolling(window=5).mean()
            df['MA10'] = df['close'].rolling(window=10).mean()
            df['MA20'] = df['close'].rolling(window=20).mean()
            
            # 计算相对强弱指数(RSI)
            delta = df['close'].diff()
            gain = delta.where(delta > 0, 0)
            loss = -delta.where(delta < 0, 0)
            avg_gain = gain.rolling(window=14).mean()
            avg_loss = loss.rolling(window=14).mean()
            rs = avg_gain / avg_loss
            df['RSI'] = 100 - (100 / (1 + rs))
            
            # 计算布林带
            df['STD20'] = df['close'].rolling(window=20).std()
            df['UpperBand'] = df['MA20'] + (df['STD20'] * 2)
            df['LowerBand'] = df['MA20'] - (df['STD20'] * 2)
            
            # 计算MACD
            df['EMA12'] = df['close'].ewm(span=12, adjust=False).mean()
            df['EMA26'] = df['close'].ewm(span=26, adjust=False).mean()
            df['MACD'] = df['EMA12'] - df['EMA26']
            df['Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
            df['Histogram'] = df['MACD'] - df['Signal']
            
            # 移除包含NaN的行（由滚动计算产生）
            df = df.dropna()
            
            return df
        except Exception as e:
            self.logger.error(f"添加技术指标时发生异常: {str(e)}")
            return df
    
    def create_labels(self, df: pd.DataFrame, forecast_days: int = 5) -> pd.DataFrame:
        """创建预测标签（未来价格变动）"""
        try:
            # 计算未来价格变动百分比
            df['future_price'] = df['close'].shift(-forecast_days)
            df['price_change'] = (df['future_price'] - df['close']) / df['close']
            
            # 创建分类标签：上涨(1)、下跌(0)
            df['label'] = np.where(df['price_change'] > 0, 1, 0)
            
            # 移除包含NaN的行
            df = df.dropna()
            
            return df
        except Exception as e:
            self.logger.error(f"创建标签时发生异常: {str(e)}")
            return df
    
    def process_batch_data(self, data_dict: Dict[str, pd.DataFrame]) -> Dict[str, pd.DataFrame]:
        """处理批量股票数据"""
        processed_data = {}
        for symbol, df in data_dict.items():
            self.logger.info(f"处理 {symbol} 的数据")
            cleaned_df = self.clean_data(df)
            indicator_df = self.add_technical_indicators(cleaned_df)
            labeled_df = self.create_labels(indicator_df)
            processed_data[symbol] = labeled_df
        return processed_data

特征工程模块设计

python

运行

# src/feature_engineering/feature_selector.py
import pandas as pd
import numpy as np
from sklearn.feature_selection import SelectKBest, f_classif, mutual_info_classif
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from typing import List, Tuple, Dict
import logging

class FeatureSelector:
    def __init__(self):
        self.logger = logging.getLogger(__name__)
        self.selected_features = []
    
    def select_features_univariate(self, 
                                  X: pd.DataFrame, 
                                  y: pd.Series, 
                                  k: int = 10) -> List[str]:
        """使用单变量统计方法选择特征"""
        try:
            # 应用SelectKBest进行特征选择
            selector = SelectKBest(score_func=f_classif, k=k)
            selector.fit(X, y)
            
            # 获取特征得分和排名
            scores = selector.scores_
            feature_scores = list(zip(X.columns, scores))
            feature_scores.sort(key=lambda x: x[1], reverse=True)
            
            # 获取选定的特征
            self.selected_features = [feature for feature, _ in feature_scores[:k]]
            self.logger.info(f"单变量特征选择完成，选择了 {k} 个特征")
            
            return self.selected_features
        except Exception as e:
            self.logger.error(f"单变量特征选择时发生异常: {str(e)}")
            return list(X.columns)
    
    def select_features_tree_based(self, 
                                  X: pd.DataFrame, 
                                  y: pd.Series, 
                                  k: int = 10) -> List[str]:
        """使用基于树的方法选择特征"""
        try:
            # 使用随机森林进行特征重要性评估
            rf = RandomForestClassifier(n_estimators=100, random_state=42)
            rf.fit(X, y)
            
            # 获取特征重要性
            importances = rf.feature_importances_
            feature_importances = list(zip(X.columns, importances))
            feature_importances.sort(key=lambda x: x[1], reverse=True)
            
            # 获取选定的特征
            self.selected_features = [feature for feature, _ in feature_importances[:k]]
            self.logger.info(f"基于树的特征选择完成，选择了 {k} 个特征")
            
            return self.selected_features
        except Exception as e:
            self.logger.error(f"基于树的特征选择时发生异常: {str(e)}")
            return list(X.columns)
    
    def prepare_training_data(self, 
                             df: pd.DataFrame, 
                             feature_cols: List[str], 
                             label_col: str = 'label',
                             test_size: float = 0.2,
                             sequence_length: int = 20) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
        """准备训练数据，包括特征缩放和数据集划分"""
        try:
            # 确保所有特征列都存在
            valid_features = [col for col in feature_cols if col in df.columns]
            
            if not valid_features:
                self.logger.error("没有有效的特征列")
                return np.array([]), np.array([]), np.array([]), np.array([])
            
            # 特征缩放
            scaler = StandardScaler()
            X = scaler.fit_transform(df[valid_features])
            y = df[label_col].values
            
            # 创建序列数据（用于时间序列模型）
            X_seq, y_seq = [], []
            for i in range(len(X) - sequence_length):
                X_seq.append(X[i:i+sequence_length])
                y_seq.append(y[i+sequence_length])
            
            X_seq = np.array(X_seq)
            y_seq = np.array(y_seq)
            
            # 划分训练集和测试集
            split_idx = int(len(X_seq) * (1 - test_size))
            X_train, X_test = X_seq[:split_idx], X_seq[split_idx:]
            y_train, y_test = y_seq[:split_idx], y_seq[split_idx:]
            
            self.logger.info(f"训练数据准备完成: 训练集大小={len(X_train)}, 测试集大小={len(X_test)}")
            
            return X_train, X_test, y_train, y_test
        except Exception as e:
            self.logger.error(f"准备训练数据时发生异常: {str(e)}")
            return np.array([]), np.array([]), np.array([]), np.array([])
    
    def get_feature_importance(self, 
                              X: pd.DataFrame, 
                              y: pd.Series) -> Dict[str, float]:
        """获取特征重要性分数"""
        try:
            # 使用随机森林评估特征重要性
            rf = RandomForestClassifier(n_estimators=100, random_state=42)
            rf.fit(X, y)
            
            # 创建特征重要性字典
            importances = rf.feature_importances_
            feature_importance = {feature: score for feature, score in zip(X.columns, importances)}
            
            return feature_importance
        except Exception as e:
            self.logger.error(f"获取特征重要性时发生异常: {str(e)}")
            return {}

模型训练模块设计

python

运行

# src/model_training/model_trainer.py
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout
from typing import Dict, List, Tuple
import logging

class ModelTrainer:
    def __init__(self, model_type: str = "random_forest"):
        self.model_type = model_type
        self.model = None
        self.logger = logging.getLogger(__name__)
    
    def train_random_forest(self, 
                           X_train: np.ndarray, 
                           y_train: np.ndarray,
                           n_estimators: int = 100,
                           random_state: int = 42) -> None:
        """训练随机森林模型"""
        try:
            self.model = RandomForestClassifier(n_estimators=n_estimators, random_state=random_state)
            self.model.fit(X_train, y_train)
            self.logger.info("随机森林模型训练完成")
        except Exception as e:
            self.logger.error(f"训练随机森林模型时发生异常: {str(e)}")
    
    def train_gradient_boosting(self, 
                               X_train: np.ndarray, 
                               y_train: np.ndarray,
                               n_estimators: int = 100,
                               learning_rate: float = 0.1,
                               random_state: int = 42) -> None:
        """训练梯度提升模型"""
        try:
            self.model = GradientBoostingClassifier(n_estimators=n_estimators, 
                                                  learning_rate=learning_rate, 
                                                  random_state=random_state)
            self.model.fit(X_train, y_train)
            self.logger.info("梯度提升模型训练完成")
        except Exception as e:
            self.logger.error(f"训练梯度提升模型时发生异常: {str(e)}")
    
    def train_lstm(self, 
                  X_train: np.ndarray, 
                  y_train: np.ndarray,
                  sequence_length: int,
                  n_features: int,
                  epochs: int = 50,
                  batch_size: int = 32) -> None:
        """训练LSTM模型"""
        try:
            # 构建LSTM模型
            self.model = Sequential([
                LSTM(50, return_sequences=True, input_shape=(sequence_length, n_features)),
                Dropout(0.2),
                LSTM(50, return_sequences=False),
                Dropout(0.2),
                Dense(25),
                Dense(1, activation='sigmoid')
            ])
            
            # 编译模型
            self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
            
            # 训练模型
            self.model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=1)
            self.logger.info("LSTM模型训练完成")
        except Exception as e:
            self.logger.error(f"训练LSTM模型时发生异常: {str(e)}")
    
    def evaluate_model(self, X_test: np.ndarray, y_test: np.ndarray) -> Dict:
        """评估模型性能"""
        try:
            if self.model is None:
                self.logger.error("模型未训练")
                return {}
            
            # 预测
            y_pred = self.model.predict(X_test)
            
            # 对于LSTM模型，预测结果是概率值，需要转换为类别
            if self.model_type == "lstm":
                y_pred = (y_pred > 0.5).astype(int).reshape(-1)
            
            # 计算评估指标
            accuracy = accuracy_score(y_test, y_pred)
            report = classification_report(y_test, y_pred, output_dict=True)
            cm = confusion_matrix(y_test, y_pred)
            
            self.logger.info(f"模型评估完成 - 准确率: {accuracy:.4f}")
            
            return {
                "accuracy": accuracy,
                "report": report,
                "confusion_matrix": cm.tolist()
            }
        except Exception as e:
            self.logger.error(f"评估模型时发生异常: {str(e)}")
            return {}
    
    def predict(self, X: np.ndarray) -> np.ndarray:
        """使用训练好的模型进行预测"""
        try:
            if self.model is None:
                self.logger.error("模型未训练")
                return np.array([])
            
            # 预测
            predictions = self.model.predict(X)
            
            # 对于LSTM模型，预测结果是概率值，需要转换为类别
            if self.model_type == "lstm":
                predictions = (predictions > 0.5).astype(int).reshape(-1)
            
            return predictions
        except Exception as e:
            self.logger.error(f"预测时发生异常: {str(e)}")
            return np.array([])
    
    def save_model(self, path: str) -> None:
        """保存模型"""
        try:
            if self.model is None:
                self.logger.error("模型未训练")
                return
            
            if self.model_type in ["random_forest", "gradient_boosting"]:
                import joblib
                joblib.dump(self.model, path)
            elif self.model_type == "lstm":
                self.model.save(path)
            
            self.logger.info(f"模型已保存至 {path}")
        except Exception as e:
            self.logger.error(f"保存模型时发生异常: {str(e)}")
    
    def load_model(self, path: str) -> None:
        """加载模型"""
        try:
            if self.model_type in ["random_forest", "gradient_boosting"]:
                import joblib
                self.model = joblib.load(path)
            elif self.model_type == "lstm":
                self.model = tf.keras.models.load_model(path)
            
            self.logger.info(f"模型已从 {path} 加载")
        except Exception as e:
            self.logger.error(f"加载模型时发生异常: {str(e)}")

策略生成模块设计

python

运行

# src/strategy/strategy_generator.py
import pandas as pd
import numpy as np
from typing import Dict, List, Tuple
import logging

class StrategyGenerator:
    def __init__(self):
        self.logger = logging.getLogger(__name__)
    
    def generate_basic_signals(self, 
                              df: pd.DataFrame, 
                              prediction_col: str = 'prediction') -> pd.DataFrame:
        """生成基本交易信号"""
        try:
            # 创建信号列，1表示买入，-1表示卖出，0表示持有
            df['signal'] = 0
            
            # 当预测上涨时买入
            df.loc[df[prediction_col] == 1, 'signal'] = 1
            
            # 当预测下跌时卖出
            df.loc[df[prediction_col] == 0, 'signal'] = -1
            
            # 生成交易指令（信号变化时）
            df['position'] = df['signal'].diff()
            
            self.logger.info("基本交易信号生成完成")
            return df
        except Exception as e:
            self.logger.error(f"生成基本交易信号时发生异常: {str(e)}")
            return df
    
    def generate_advanced_signals(self, 
                                 df: pd.DataFrame, 
                                 prediction_col: str = 'prediction',
                                 confidence_col: str = 'confidence',
                                 confidence_threshold: float = 0.6) -> pd.DataFrame:
        """生成高级交易信号，考虑预测置信度"""
        try:
            # 创建信号列，1表示买入，-1表示卖出，0表示持有
            df['signal'] = 0
            
            # 当预测上涨且置信度高于阈值时买入
            df.loc[(df[prediction_col] == 1) & (df[confidence_col] > confidence_threshold), 'signal'] = 1
            
            # 当预测下跌且置信度高于阈值时卖出
            df.loc[(df[prediction_col] == 0) & (df[confidence_col] > confidence_threshold), 'signal'] = -1
            
            # 生成交易指令（信号变化时）
            df['position'] = df['signal'].diff()
            
            self.logger.info("高级交易信号生成完成")
            return df
        except Exception as e:
            self.logger.error(f"生成高级交易信号时发生异常: {str(e)}")
            return df
    
    def calculate_position_size(self, 
                               portfolio_value: float, 
                               stock_price: float, 
                               signal: int,
                               risk_percentage: float = 0.02,
                               max_position_percentage: float = 0.1) -> float:
        """计算头寸规模"""
        try:
            if signal == 0:  # 不交易
                return 0
            
            # 基于风险的头寸规模计算
            risk_amount = portfolio_value * risk_percentage
            position_value = min(risk_amount, portfolio_value * max_position_percentage)
            
            # 计算股数
            shares = position_value / stock_price
            
            # 向下取整为整数股
            shares = int(shares)
            
            # 根据信号方向确定买入或卖出
            return shares * signal if signal > 0 else shares
        except Exception as e:
            self.logger.error(f"计算头寸规模时发生异常: {str(e)}")
            return 0
    
    def backtest_strategy(self, 
                         df: pd.DataFrame, 
                         initial_capital: float = 100000.0) -> Dict:
        """回测交易策略"""
        try:
            # 复制数据
            portfolio = df.copy()
            
            # 初始化投资组合价值和持仓
            portfolio['positions'] = 0
            portfolio['cash'] = initial_capital
            portfolio['portfolio_value'] = initial_capital
            
            # 执行回测
            for i in range(1, len(portfolio)):
                # 获取上一交易日的持仓和现金
                prev_positions = portfolio.iloc[i-1]['positions']
                prev_cash = portfolio.iloc[i-1]['cash']
                
                # 获取当前交易日的信号和价格
                signal = portfolio.iloc[i]['signal']
                price = portfolio.iloc[i]['close']
                
                # 计算交易数量
                if signal != 0:  # 如果有交易信号
                    # 这里简化处理，假设每次交易使用10%的资金
                    trade_value = prev_cash * 0.1
                    shares_to_trade = int(trade_value / price) * signal
                    
                    # 更新持仓和现金
                    portfolio.loc[portfolio.index[i], 'positions'] = prev_positions + shares_to_trade
                    portfolio.loc[portfolio.index[i], 'cash'] = prev_cash - (shares_to_trade * price)
                else:  # 没有交易信号，保持原有持仓和现金
                    portfolio.loc[portfolio.index[i], 'positions'] = prev_positions
                    portfolio.loc[portfolio.index[i], 'cash'] = prev_cash
                
                # 计算投资组合价值
                portfolio.loc[portfolio.index[i], 'portfolio_value'] = (
                    portfolio.iloc[i]['positions'] * price + portfolio.iloc[i]['cash']
                )
            
            # 计算每日收益率
            portfolio['daily_return'] = portfolio['portfolio_value'].pct_change()
            
            # 计算累积收益率
            portfolio['cumulative_return'] = (1 + portfolio['daily_return']).cumprod() - 1
            
            # 计算评估指标
            total_return = portfolio['cumulative_return'].iloc[-1]
            annual_return = ((1 + total_return) ** (252 / len(portfolio))) - 1
            sharpe_ratio = portfolio['daily_return'].mean() / portfolio['daily_return'].std() * np.sqrt(252)
            
            # 计算最大回撤
            portfolio['peak'] = portfolio['portfolio_value'].cummax()
            portfolio['drawdown'] = (portfolio['portfolio_value'] - portfolio['peak']) / portfolio['peak']
            max_drawdown = portfolio['drawdown'].min()
            
            self.logger.info(f"策略回测完成 - 总收益率: {total_return:.4f}, 年化收益率: {annual_return:.4f}")
            
            return {
                "portfolio": portfolio,
                "total_return": total_return,
                "annual_return": annual_return,
                "sharpe_ratio": sharpe_ratio,
                "max_drawdown": max_drawdown
            }
        except Exception as e:
            self.logger.error(f"策略回测时发生异常: {str(e)}")
            return {}

回测模块设计

python

运行

# src/backtest/backtester.py
import pandas as pd
import numpy as np
from typing import Dict, List, Tuple
import logging

class Backtester:
    def __init__(self):
        self.logger = logging.getLogger(__name__)
    
    def run_backtest(self, 
                    df: pd.DataFrame, 
                    initial_capital: float = 100000.0,
                    commission: float = 0.001,  # 0.1% 佣金率
                    slippage: float = 0.0005) -> Dict:  # 0.05% 滑点
        """运行完整回测"""
        try:
            # 确保数据包含所需的列
            required_cols = ['close', 'signal', 'position']
            for col in required_cols:
                if col not in df.columns:
                    self.logger.error(f"回测数据缺少必要的列: {col}")
                    return {}
            
            # 复制数据并初始化投资组合列
            portfolio = df.copy()
            portfolio['positions'] = 0
            portfolio['cash'] = initial_capital
            portfolio['portfolio_value'] = initial_capital
            portfolio['daily_return'] = 0.0
            portfolio['cumulative_return'] = 0.0
            
            # 执行回测
            for i in range(1, len(portfolio)):
                # 获取上一交易日的持仓和现金
                prev_positions = portfolio.iloc[i-1]['positions']
                prev_cash = portfolio.iloc[i-1]['cash']
                prev_portfolio_value = portfolio.iloc[i-1]['portfolio_value']
                
                # 获取当前交易日的信号和价格
                signal = portfolio.iloc[i]['signal']
                position_change = portfolio.iloc[i]['position']
                price = portfolio.iloc[i]['close']
                
                # 计算考虑滑点的实际成交价格
                if position_change > 0:  # 买入
                    execution_price = price * (1 + slippage)
                elif position_change < 0:  # 卖出
                    execution_price = price * (1 - slippage)
                else:  # 不交易
                    execution_price = price
                
                # 计算交易价值和佣金
                trade_value = abs(position_change * execution_price)
                trade_commission = trade_value * commission
                
                # 更新持仓和现金
                if position_change != 0:  # 如果有交易
                    portfolio.loc[portfolio.index[i], 'positions'] = prev_positions + position_change
                    portfolio.loc[portfolio.index[i], 'cash'] = (
                        prev_cash - (position_change * execution_price) - trade_commission
                    )
                else:  # 没有交易
                    portfolio.loc[portfolio.index[i], 'positions'] = prev_positions
                    portfolio.loc[portfolio.index[i], 'cash'] = prev_cash
                
                # 计算投资组合价值
                portfolio_value = (
                    portfolio.iloc[i]['positions'] * price + portfolio.iloc[i]['cash']
                )
                portfolio.loc[portfolio.index[i], 'portfolio_value'] = portfolio_value
                
                # 计算每日收益率
                daily_return = (portfolio_value / prev_portfolio_value) - 1
                portfolio.loc[portfolio.index[i], 'daily_return'] = daily_return
            
            # 计算累积收益率
            portfolio['cumulative_return'] = (1 + portfolio['daily_return']).cumprod() - 1
            
            # 计算评估指标
            total_return = portfolio['cumulative_return'].iloc[-1]
            annual_return = ((1 + total_return) ** (252 / len(portfolio))) - 1
            sharpe_ratio = portfolio['daily_return'].mean() / portfolio['daily_return'].std() * np.sqrt(252)
            
            # 计算最大回撤
            portfolio['peak'] = portfolio['portfolio_value'].cummax()
            portfolio['drawdown'] = (portfolio['portfolio_value'] - portfolio['peak']) / portfolio['peak']
            max_drawdown = portfolio['drawdown'].min()
            max_drawdown_duration = self._calculate_max_drawdown_duration(portfolio['drawdown'])
            
            # 计算胜率
            portfolio['trade_result'] = portfolio['daily_return'].shift(-1) * portfolio['positions']
            portfolio['trade_result'] = portfolio['trade_result'].dropna()
            winning_trades = portfolio[portfolio['trade_result'] > 0]
            win_rate = len(winning_trades) / len(portfolio['trade_result']) if len(portfolio['trade_result']) > 0 else 0
            
            self.logger.info(f"回测完成 - 总收益率: {total_return:.4f}, 年化收益率: {annual_return:.4f}")
            
            return {
                "portfolio": portfolio,
                "performance_metrics": {
                    "total_return": total_return,
                    "annual_return": annual_return,
                    "sharpe_ratio": sharpe_ratio,
                    "max_drawdown": max_drawdown,
                    "max_drawdown_duration": max_drawdown_duration,
                    "win_rate": win_rate,
                    "num_trades": len(portfolio[portfolio['position'] != 0])
                }
            }
        except Exception as e:
            self.logger.error(f"回测时发生异常: {str(e)}")
            return {}
    
    def _calculate_max_drawdown_duration(self, drawdown_series: pd.Series) -> int:
        """计算最大回撤持续时间"""
        try:
            in_drawdown = False
            max_duration = 0
            current_duration = 0
            
            for value in drawdown_series:
                if value < 0:  # 处于回撤中
                    if not in_drawdown:
                        in_drawdown = True
                        current_duration = 1
                    else:
                        current_duration += 1
                    
                    if current_duration > max_duration:
                        max_duration = current_duration
                else:  # 回撤结束
                    in_drawdown = False
                    current_duration = 0
            
            return max_duration
        except Exception as e:
            self.logger.error(f"计算最大回撤持续时间时发生异常: {str(e)}")
            return 0
    
    def analyze_performance(self, results: Dict) -> pd.DataFrame:
        """分析回测结果，生成性能报告"""
        try:
            if not results or 'portfolio' not in results or 'performance_metrics' not in results:
                self.logger.error("无效的回测结果")
                return pd.DataFrame()
            
            portfolio = results['portfolio']
            metrics = results['performance_metrics']
            
            # 创建性能报告
            report = pd.DataFrame({
                '指标': ['总收益率', '年化收益率', '夏普比率', '最大回撤', '最大回撤持续时间', '胜率', '交易次数'],
                '数值': [
                    f"{metrics['total_return']:.2%}",
                    f"{metrics['annual_return']:.2%}",
                    f"{metrics['sharpe_ratio']:.2f}",
                    f"{metrics['max_drawdown']:.2%}",
                    f"{metrics['max_drawdown_duration']} 天",
                    f"{metrics['win_rate']:.2%}",
                    metrics['num_trades']
                ]
            })
            
            return report
        except Exception as e:
            self.logger.error(f"分析回测性能时发生异常: {str(e)}")
            return pd.DataFrame()
    
    def plot_performance(self, results: Dict) -> None:
        """可视化回测结果"""
        try:
            if not results or 'portfolio' not in results:
                self.logger.error("无效的回测结果")
                return
            
            portfolio = results['portfolio']
            
            # 导入绘图库
            import matplotlib.pyplot as plt
            import seaborn as sns
            sns.set_style('whitegrid')
            
            # 创建图表
            fig, axes = plt.subplots(3, 1, figsize=(12, 18))
            
            # 绘制投资组合价值曲线
            axes[0].plot(portfolio.index, portfolio['portfolio_value'])
            axes[0].set_title('投资组合价值')
            axes[0].set_ylabel('价值')
            
            # 绘制累积收益率曲线
            axes[1].plot(portfolio.index, portfolio['cumulative_return'])
            axes[1].set_title('累积收益率')
            axes[1].set_ylabel('收益率')
            
            # 绘制回撤曲线
            axes[2].fill_between(portfolio.index, portfolio['drawdown'], 0, color='red', alpha=0.3)
            axes[2].set_title('回撤')
            axes[2].set_ylabel('回撤幅度')
            
            plt.tight_layout()
            plt.savefig('backtest_results.png')
            self.logger.info("回测结果图表已保存")
            
        except Exception as e:
            self.logger.error(f"可视化回测结果时发生异常: {str(e)}")

风险管理模块设计

python

运行

# src/risk_management/risk_manager.py
import pandas as pd
import numpy as np
from typing import Dict, List, Tuple
import logging

class RiskManager:
    def __init__(self, 
                max_position_percentage: float = 0.1,
                max_drawdown_threshold: float = 0.15,
                daily_loss_threshold: float = 0.05,
                volatility_target: float = 0.2):
        """
        初始化风险管理模块
        
        参数:
            max_position_percentage: 单个股票最大持仓比例
            max_drawdown_threshold: 最大允许回撤阈值
            daily_loss_threshold: 每日最大允许损失阈值
            volatility_target: 波动率目标
        """
        self.max_position_percentage = max_position_percentage
        self.max_drawdown_threshold = max_drawdown_threshold
        self.daily_loss_threshold = daily_loss_threshold
        self.volatility_target = volatility_target
        self.logger = logging.getLogger(__name__)
    
    def position_size_limit(self, 
                           portfolio_value: float, 
                           stock_price: float, 
                           base_position: int) -> int:
        """限制单个股票的头寸规模"""
        try:
            # 计算最大允许头寸价值
            max_position_value = portfolio_value * self.max_position_percentage
            
            # 计算对应的最大股数
            max_shares = int(max_position_value / stock_price)
            
            # 确保不超过最大允许头寸
            return min(abs(base_position), max_shares) * np.sign(base_position)
        except Exception as e:
            self.logger.error(f"限制头寸规模时发生异常: {str(e)}")
            return 0
    
    def check_drawdown(self, portfolio: pd.DataFrame) -> bool:
        """检查当前回撤是否超过阈值"""
        try:
            if len(portfolio) < 2:
                return False
            
            # 计算当前回撤
            portfolio['peak'] = portfolio['portfolio_value'].cummax()
            portfolio['drawdown'] = (portfolio['portfolio_value'] - portfolio['peak']) / portfolio['peak']
            current_drawdown = portfolio['drawdown'].iloc[-1]
            
            # 检查是否超过阈值
            if current_drawdown < -self.max_drawdown_threshold:
                self.logger.warning(f"当前回撤 {current_drawdown:.2%} 超过阈值 {self.max_drawdown_threshold:.2%}")
                return True
            
            return False
        except Exception as e:
            self.logger.error(f"检查回撤时发生异常: {str(e)}")
            return False
    
    def check_daily_loss(self, portfolio: pd.DataFrame) -> bool:
        """检查当日损失是否超过阈值"""
        try:
            if len(portfolio) < 2:
                return False
            
            # 计算当日收益率
            daily_return = portfolio['portfolio_value'].pct_change().iloc[-1]
            
            # 检查是否超过阈值
            if daily_return < -self.daily_loss_threshold:
                self.logger.warning(f"当日损失 {daily_return:.2%} 超过阈值 {self.daily_loss_threshold:.2%}")
                return True
            
            return False
        except Exception as e:
            self.logger.error(f"检查当日损失时发生异常: {str(e)}")
            return False
    
    def volatility_adjustment(self, 
                             portfolio: pd.DataFrame, 
                             position: int) -> int:
        """基于市场波动率调整头寸规模"""
        try:
            if len(portfolio) < 20:  # 需要足够的数据计算波动率
                return position
            
            # 计算最近20天的波动率
            returns = portfolio['portfolio_value'].pct_change().dropna()
            volatility = returns.std() * np.sqrt(252)  # 年化波动率
            
            # 如果波动率高于目标，减少头寸；如果低于目标，增加头寸
            adjustment_factor = self.volatility_target / max(volatility, 0.01)  # 避免除零错误
            
            # 限制调整因子的范围
            adjustment_factor = min(max(adjustment_factor, 0.5), 1.5)
            
            # 调整头寸规模
            adjusted_position = int(position * adjustment_factor)
            
            self.logger.info(f"波动率调整: 原头寸={position}, 调整因子={adjustment_factor:.2f}, 新头寸={adjusted_position}")
            
            return adjusted_position
        except Exception as e:
            self.logger.error(f"波动率调整时发生异常: {str(e)}")
            return position
    
    def apply_risk_management(self, 
                             portfolio: pd.DataFrame, 
                             position: int, 
                             stock_price: float) -> int:
        """应用全面的风险管理措施"""
        try:
            # 获取当前投资组合价值
            portfolio_value = portfolio['portfolio_value'].iloc[-1]
            
            # 1. 应用头寸规模限制
            position = self.position_size_limit(portfolio_value, stock_price, position)
            
            # 2. 检查回撤
            if self.check_drawdown(portfolio):
                # 如果回撤超过阈值，减少头寸
                position = int(position * 0.5)  # 减少50%的头寸
            
            # 3. 检查当日损失
            if self.check_daily_loss(portfolio):
                # 如果当日损失超过阈值，减少头寸
                position = int(position * 0.7)  # 减少30%的头寸
            
            # 4. 应用波动率调整
            position = self.volatility_adjustment(portfolio, position)
            
            return position
        except Exception as e:
            self.logger.error(f"应用风险管理时发生异常: {str(e)}")
            return 0

API 接口设计

python

运行

# src/api/api_server.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import pandas as pd
import numpy as np
from typing import List, Dict, Optional
import joblib
import logging
from datetime import datetime

# 导入项目模块
from data_collector.stock_data_collector import StockDataCollector
from data_processor.data_processor import DataProcessor
from feature_engineering.feature_selector import FeatureSelector
from model_training.model_trainer import ModelTrainer
from strategy.strategy_generator import StrategyGenerator
from risk_management.risk_manager import RiskManager

app = FastAPI(title="AI量化炒股系统API", description="基于人工智能的量化交易系统API")

# 模型和数据初始化
data_collector = StockDataCollector(api_key="your_api_key", api_base_url="https://api.example.com")
data_processor = DataProcessor()
feature_selector = FeatureSelector()
model_trainer = ModelTrainer(model_type="random_forest")
strategy_generator = StrategyGenerator()
risk_manager = RiskManager()

# 加载训练好的模型
try:
    model_trainer.load_model("models/stock_predictor_model.pkl")
    logging.info("模型已成功加载")
except Exception as e:
    logging.error(f"加载模型失败: {str(e)}")
    model_trainer = None

# 请求模型
class PredictionRequest(BaseModel):
    symbols: List[str]
    start_date: str
    end_date: str
    features: Optional[List[str]] = None

class StrategyRequest(BaseModel):
    symbol: str
    start_date: str
    end_date: str
    initial_capital: float = 100000.0
    risk_percentage: float = 0.02

# API端点
@app.get("/")
def read_root():
    return {"message": "欢迎使用AI量化炒股系统API"}

@app.post("/predict")
def predict_stocks(request: PredictionRequest):
    """预测股票价格走势"""
    try:
        if model_trainer is None:
            raise HTTPException(status_code=500, detail="模型未加载")
        
        # 获取历史数据
        stock_data = data_collector.batch_fetch_symbols(
            request.symbols, 
            request.start_date, 
            request.end_date
        )
        
        if not stock_data:
            raise HTTPException(status_code=404, detail="未能获取股票数据")
        
        # 处理数据
        processed_data = data_processor.process_batch_data(stock_data)
        
        predictions = {}
        for symbol, df in processed_data.items():
            # 特征选择
            if request.features:
                valid_features = [f for f in request.features if f in df.columns]
            else:
                # 使用默认特征
                valid_features = ['MA5', 'MA10', 'MA20', 'RSI', 'UpperBand', 'LowerBand', 'MACD', 'Histogram']
            
            if not valid_features:
                raise HTTPException(status_code=400, detail=f"没有有效的特征: {symbol}")
            
            # 准备预测数据
            X = df[valid_features].values
            
            # 预测
            preds = model_trainer.predict(X)
            
            # 构建结果
            prediction_dates = df.index.strftime('%Y-%m-%d').tolist()
            prediction_values = preds.tolist()
            
            predictions[symbol] = {
                "dates": prediction_dates,
                "predictions": prediction_values,
                "last_updated": datetime.now().strftime('%Y-%m-%d %H:%M:%S')
            }
        
        return predictions
    
    except Exception as e:
        logging.error(f"预测时发生错误: {str(e)}")
        raise HTTPException(status_code=500, detail=f"预测时发生错误: {str(e)}")

@app.post("/strategy")
def generate_trading_strategy(request: StrategyRequest):
    """生成交易策略"""
    try:
        # 获取单只股票数据
        stock_data = data_collector.get_historical_data(
            request.symbol, 
            request.start_date, 
            request.end_date
        )
        
        if stock_data.empty:
            raise HTTPException(status_code=404, detail=f"未能获取股票数据: {request.symbol}")
        
        # 处理数据
        processed_data = data_processor.clean_data(stock_data)
        processed_data = data_processor.add_technical_indicators(processed_data)
        
        # 假设我们已经有预测结果
        # 在实际应用中，这里应该调用预测模型
        processed_data['prediction'] = np.random.randint(0, 2, size=len(processed_data))
        
        # 生成交易信号
        signal_data = strategy_generator.generate_basic_signals(processed_data)
        
        # 回测策略
        backtest_results = strategy_generator.backtest_strategy(
            signal_data, 
            initial_capital=request.initial_capital
        )
        
        if not backtest_results:
            raise HTTPException(status_code=500, detail="策略回测失败")
        
        # 分析性能
        performance_report = strategy_generator.analyze_performance(backtest_results)
        
        # 构建响应
        response = {
            "symbol": request.symbol,
            "start_date": request.start_date,
            "end_date": request.end_date,
            "performance_metrics": backtest_results["performance_metrics"],
            "performance_report": performance_report.to_dict(orient='records'),
            "last_updated": datetime.now().strftime('%Y-%m-%d %H:%M:%S')
        }
        
        return response
    
    except Exception as e:
        logging.error(f"生成策略时发生错误: {str(e)}")
        raise HTTPException(status_code=500, detail=f"生成策略时发生错误: {str(e)}")

@app.get("/risk/position-size")
def calculate_position_size(
    portfolio_value: float,
    stock_price: float,
    signal: int,
    risk_percentage: float = 0.02,
    max_position_percentage: float = 0.1
):
    """计算风险调整后的头寸规模"""
    try:
        # 创建模拟投资组合数据
        portfolio_data = pd.DataFrame({
            'date': [datetime.now().strftime('%Y-%m-%d')],
            'portfolio_value': [portfolio_value]
        })
        
        # 计算基础头寸规模
        base_position = strategy_generator.calculate_position_size(
            portfolio_value, 
            stock_price, 
            signal,
            risk_percentage,
            max_position_percentage
        )
        
        # 应用风险管理
        adjusted_position = risk_manager.apply_risk_management(
            portfolio_data, 
            base_position, 
            stock_price
        )
        
        return {
            "base_position": base_position,
            "adjusted_position": adjusted_position,
            "risk_percentage": risk_percentage,
            "max_position_percentage": max_position_percentage,
            "last_updated": datetime.now().strftime('%Y-%m-%d %H:%M:%S')
        }
    
    except Exception as e:
        logging.error(f"计算头寸规模时发生错误: {str(e)}")
        raise HTTPException(status_code=500, detail=f"计算头寸规模时发生错误: {str(e)}")

项目部署与运行

1. 环境准备

bash

# 创建虚拟环境
python3 -m venv ai_quant_env
source ai_quant_env/bin/activate

# 安装依赖
pip install -r requirements.txt

# 配置环境变量
export API_KEY=your_api_key
export DB_CONNECTION_STRING=your_database_connection_string

1. 数据收集与预处理

bash

python src/data_collector/main.py --start-date 2020-01-01 --end-date 2023-12-31 --symbols AAPL,MSFT,GOOG

1. 模型训练

bash

python src/model_training/main.py --model-type random_forest --data-path data/processed_data.csv

1. 策略回测

bash

python src/strategy/main.py --symbol AAPL --start-date 2022-01-01 --end-date 2023-12-31 --initial-capital 100000

1. API 服务启动

bash

uvicorn src.api.api_server:app --host 0.0.0.0 --port 8000 --reload

项目优化与扩展建议

1. 数据层面：

   • 增加更多数据源，如新闻情绪、社交媒体数据
   • 实现实时数据流处理
   • 引入数据质量监控机制

2. 模型层面：

   • 尝试不同的模型架构，如 Transformer、GNN 等
   • 实现模型集成策略
   • 建立模型监控和自动更新机制

3. 系统层面：

   • 实现分布式计算以处理大规模数据
   • 构建微服务架构提高系统可扩展性
   • 开发可视化界面展示策略表现和风险指标

4. 风险管理层面：

   • 实现更复杂的风险度量方法，如 VaR、CVaR
   • 开发动态仓位管理策略
   • 建立黑天鹅事件应对机制

这个设计涵盖了量化交易系统的主要组件，你可以根据实际需求进行调整和扩展。在实际应用中，还需要考虑交易执行、合规性和监管要求等方面的问题。

分享

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