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时间:2025/7/11 8:28:06来源：https://blog.csdn.net/weixin_39521144/article/details/145592443 浏览次数:0次

- 0. 承前
- 1. 解题思路
- - 1.1 应用场景维度
  - 1.2 技术实现维度
  - 1.3 实践应用维度
- 2. 市场预测模型
- - 2.1 趋势预测
  - 2.2 模型训练与评估
- 3. 风险评估模型
- - 3.1 信用风险评估
- 4. 投资组合优化
- - 4.1 资产配置模型
- 5. 回答话术

0. 承前

本文通过通俗易懂的方式介绍监督学习在量化金融中的应用，包括市场预测、风险评估、投资组合优化等方面。

如果想更加全面清晰地了解金融资产组合模型进化论的体系架构，可参考：
0. 金融资产组合模型进化全图鉴

1. 解题思路

理解监督学习在量化金融中的应用，需要从以下几个维度进行分析：

1.1 应用场景维度

市场趋势预测
风险评估模型
投资组合优化

1.2 技术实现维度

特征工程
模型选择
性能评估

1.3 实践应用维度

数据处理
模型训练
策略实现

2. 市场预测模型

2.1 趋势预测

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScalerclass MarketPredictor:def __init__(self):self.model = RandomForestClassifier(n_estimators=100)self.scaler = StandardScaler()def create_features(self, df):"""创建技术指标特征"""df = df.copy()# 价格特征df['returns'] = df['close'].pct_change()df['ma5'] = df['close'].rolling(5).mean()df['ma20'] = df['close'].rolling(20).mean()# 波动率特征df['volatility'] = df['returns'].rolling(20).std()# 动量特征df['momentum'] = df['returns'].rolling(10).sum()# RSI指标delta = df['close'].diff()gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()rs = gain / lossdf['rsi'] = 100 - (100 / (1 + rs))return df.dropna()def prepare_data(self, df, target_days=5):"""准备训练数据"""# 创建目标变量（未来n天的涨跌）df['target'] = np.where(df['close'].shift(-target_days) > df['close'], 1, 0)# 选择特征features = ['returns', 'ma5', 'ma20', 'volatility', 'momentum', 'rsi']X = df[features]y = df['target']# 标准化特征X = self.scaler.fit_transform(X)return X[:-target_days], y[:-target_days]

2.2 模型训练与评估

class ModelEvaluator:def __init__(self):passdef evaluate_strategy(self, predictions, actual_returns):"""评估策略性能"""# 计算策略收益strategy_returns = predictions * actual_returns# 计算累积收益cumulative_returns = (1 + strategy_returns).cumprod()# 计算夏普比率sharpe_ratio = np.sqrt(252) * (strategy_returns.mean() / strategy_returns.std())# 计算最大回撤rolling_max = cumulative_returns.expanding().max()drawdowns = cumulative_returns / rolling_max - 1max_drawdown = drawdowns.min()return {'sharpe_ratio': sharpe_ratio,'max_drawdown': max_drawdown,'total_return': cumulative_returns[-1] - 1}

3. 风险评估模型

3.1 信用风险评估

class CreditRiskModel:def __init__(self):from sklearn.linear_model import LogisticRegressionself.model = LogisticRegression()def prepare_features(self, financial_data):"""准备金融特征"""features = pd.DataFrame()# 偿债能力指标features['current_ratio'] = (financial_data['current_assets'] / financial_data['current_liabilities'])features['debt_ratio'] = (financial_data['total_debt'] / financial_data['total_assets'])# 盈利能力指标features['roe'] = (financial_data['net_income'] / financial_data['total_equity'])features['operating_margin'] = (financial_data['operating_income'] / financial_data['revenue'])# 效率指标features['asset_turnover'] = (financial_data['revenue'] / financial_data['total_assets'])return featuresdef predict_default_prob(self, features):"""预测违约概率"""proba = self.model.predict_proba(features)return proba[:, 1]  # 返回违约概率

4. 投资组合优化

4.1 资产配置模型

class PortfolioOptimizer:def __init__(self):from sklearn.covariance import LedoitWolfself.covariance_estimator = LedoitWolf()def optimize_portfolio(self, returns, risk_tolerance=0.2):"""优化投资组合权重"""# 估计协方差矩阵cov_matrix = self.covariance_estimator.fit(returns).covariance_# 计算预期收益exp_returns = returns.mean()# 优化目标函数def objective(weights):portfolio_return = np.sum(exp_returns * weights)portfolio_risk = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))return -portfolio_return + risk_tolerance * portfolio_risk# 约束条件constraints = [{'type': 'eq', 'fun': lambda x: np.sum(x) - 1},  # 权重和为1{'type': 'ineq', 'fun': lambda x: x}  # 权重非负]# 优化from scipy.optimize import minimizen_assets = returns.shape[1]result = minimize(objective, x0=np.ones(n_assets)/n_assets,constraints=constraints)return result.x