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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
简单可工作的多因子模型系统
基于现有数据创建一个完整可用的多因子选股系统
"""
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import warnings
import json
from typing import List, Dict, Any, Tuple
warnings.filterwarnings('ignore')
# 机器学习库
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.preprocessing import RobustScaler
import xgboost as xgb
# Flask相关
from flask import Flask, render_template, request, jsonify
# 数据库相关
from app import create_app
from app.extensions import db
from app.models import (
StockDailyHistory, StockDailyBasic, StockFactor, StockMoneyflow,
StockCyqPerf, FactorDefinition, FactorValues, MLModelDefinition,
MLPredictions, StockBasic
)
class SimpleWorkingSystem:
"""简单可工作的多因子模型系统"""
def __init__(self):
self.app = create_app()
self.models = {}
self.scalers = {}
self.factor_data = None
self.setup_routes()
def setup_routes(self):
"""设置Web路由"""
@self.app.route('/')
def index():
"""主页"""
return """
<!DOCTYPE html>
<html>
<head>
<title>多因子模型系统</title>
<meta charset="utf-8">
<style>
body { font-family: Arial, sans-serif; margin: 40px; background: #f5f5f5; }
.container { max-width: 1200px; margin: 0 auto; background: white; padding: 30px; border-radius: 10px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }
.header { text-align: center; margin-bottom: 30px; }
.card { background: #f8f9fa; padding: 20px; margin: 15px 0; border-radius: 8px; border-left: 4px solid #007bff; }
.btn { background: #007bff; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer; margin: 5px; }
.btn:hover { background: #0056b3; }
.stats { display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 15px; margin: 20px 0; }
.stat-item { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 20px; border-radius: 8px; text-align: center; }
</style>
</head>
<body>
<div class="container">
<div class="header">
<h1>🚀 多因子模型系统</h1>
<p>基于机器学习的智能选股系统</p>
</div>
<div class="stats" id="stats">
<div class="stat-item">
<h3>系统状态</h3>
<p>正在加载...</p>
</div>
</div>
<div class="card">
<h3>🔧 系统功能</h3>
<button class="btn" onclick="initSystem()">初始化系统</button>
<button class="btn" onclick="trainModel()">训练模型</button>
<button class="btn" onclick="predictStocks()">股票预测</button>
<button class="btn" onclick="showReport()">系统报告</button>
</div>
<div class="card">
<h3>📊 预测结果</h3>
<div id="predictions">点击"股票预测"查看结果</div>
</div>
<div class="card">
<h3>📋 系统日志</h3>
<div id="logs" style="background: #f1f1f1; padding: 15px; border-radius: 5px; font-family: monospace; max-height: 300px; overflow-y: auto;">
系统就绪...
</div>
</div>
</div>
<script>
function log(message) {
const logs = document.getElementById('logs');
logs.innerHTML += '<br>' + new Date().toLocaleTimeString() + ' - ' + message;
logs.scrollTop = logs.scrollHeight;
}
function updateStats() {
fetch('/api/status')
.then(response => response.json())
.then(data => {
if (data.success) {
const stats = data.data;
document.getElementById('stats').innerHTML = `
<div class="stat-item">
<h3>股票数量</h3>
<p>${stats.stock_count.toLocaleString()}</p>
</div>
<div class="stat-item">
<h3>因子记录</h3>
<p>${stats.factor_records.toLocaleString()}</p>
</div>
<div class="stat-item">
<h3>模型数量</h3>
<p>${stats.model_count}</p>
</div>
<div class="stat-item">
<h3>预测记录</h3>
<p>${stats.prediction_count.toLocaleString()}</p>
</div>
`;
}
});
}
function initSystem() {
log('开始初始化系统...');
fetch('/api/init', {method: 'POST'})
.then(response => response.json())
.then(data => {
if (data.success) {
log('✅ 系统初始化成功');
updateStats();
} else {
log('❌ 系统初始化失败: ' + data.error);
}
});
}
function trainModel() {
log('开始训练模型...');
fetch('/api/train', {method: 'POST'})
.then(response => response.json())
.then(data => {
if (data.success) {
log('✅ 模型训练成功 - R²: ' + data.data.test_r2.toFixed(4));
} else {
log('❌ 模型训练失败: ' + data.error);
}
});
}
function predictStocks() {
log('开始股票预测...');
fetch('/api/predict', {method: 'POST'})
.then(response => response.json())
.then(data => {
if (data.success) {
const predictions = data.data;
let html = '<h4>前10名预测结果:</h4><table style="width:100%; border-collapse: collapse;">';
html += '<tr style="background:#f8f9fa;"><th style="padding:8px; border:1px solid #ddd;">股票代码</th><th style="padding:8px; border:1px solid #ddd;">预测收益率</th><th style="padding:8px; border:1px solid #ddd;">排名</th></tr>';
predictions.slice(0, 10).forEach((pred, idx) => {
html += `<tr><td style="padding:8px; border:1px solid #ddd;">${pred.ts_code}</td><td style="padding:8px; border:1px solid #ddd;">${(pred.predicted_return * 100).toFixed(2)}%</td><td style="padding:8px; border:1px solid #ddd;">${idx + 1}</td></tr>`;
});
html += '</table>';
document.getElementById('predictions').innerHTML = html;
log('✅ 预测完成，共 ' + predictions.length + ' 只股票');
} else {
log('❌ 预测失败: ' + data.error);
}
});
}
function showReport() {
log('生成系统报告...');
fetch('/api/report')
.then(response => response.json())
.then(data => {
if (data.success) {
log('✅ 报告生成成功');
alert('系统报告:\\n' + JSON.stringify(data.data, null, 2));
} else {
log('❌ 报告生成失败: ' + data.error);
}
});
}
// 页面加载时更新统计
updateStats();
setInterval(updateStats, 30000); // 每30秒更新一次
</script>
</body>
</html>
"""
@self.app.route('/api/status')
def api_status():
"""系统状态API"""
with self.app.app_context():
try:
stats = {
'stock_count': StockBasic.query.count(),
'factor_records': FactorValues.query.count(),
'model_count': MLModelDefinition.query.count(),
'prediction_count': MLPredictions.query.count(),
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
return jsonify({'success': True, 'data': stats})
except Exception as e:
return jsonify({'success': False, 'error': str(e)})
@self.app.route('/api/init', methods=['POST'])
def api_init():
"""系统初始化API"""
with self.app.app_context():
try:
result = self.initialize_system()
return jsonify({'success': True, 'data': result})
except Exception as e:
return jsonify({'success': False, 'error': str(e)})
@self.app.route('/api/train', methods=['POST'])
def api_train():
"""模型训练API"""
with self.app.app_context():
try:
result = self.train_model()
return jsonify({'success': True, 'data': result})
except Exception as e:
return jsonify({'success': False, 'error': str(e)})
@self.app.route('/api/predict', methods=['POST'])
def api_predict():
"""股票预测API"""
with self.app.app_context():
try:
result = self.predict_stocks()
return jsonify({'success': True, 'data': result})
except Exception as e:
return jsonify({'success': False, 'error': str(e)})
@self.app.route('/api/report')
def api_report():
"""系统报告API"""
with self.app.app_context():
try:
result = self.generate_report()
return jsonify({'success': True, 'data': result})
except Exception as e:
return jsonify({'success': False, 'error': str(e)})
def initialize_system(self):
"""初始化系统"""
try:
# 创建数据库表
db.create_all()
# 检查并创建因子定义
self._ensure_factor_definitions()
# 加载因子数据
self._load_factor_data()
# 创建模型定义
self._ensure_model_definitions()
return {
'message': '系统初始化完成',
'factor_count': len(self.factor_data) if self.factor_data is not None else 0
}
except Exception as e:
raise Exception(f"系统初始化失败: {e}")
def _ensure_factor_definitions(self):
"""确保因子定义存在"""
builtin_factors = [
{
'factor_id': 'money_flow_strength',
'factor_name': '资金流向强度',
'factor_type': 'money_flow',
'description': '净流入金额相对市值的比例'
},
{
'factor_id': 'chip_concentration',
'factor_name': '筹码集中度',
'factor_type': 'chip',
'description': '筹码分布集中度指标'
},
{
'factor_id': 'momentum_5d',
'factor_name': '5日动量',
'factor_type': 'technical',
'description': '5日价格动量'
},
{
'factor_id': 'volatility_20d',
'factor_name': '20日波动率',
'factor_type': 'technical',
'description': '20日收益率标准差'
}
]
for factor_def in builtin_factors:
existing = FactorDefinition.query.filter_by(factor_id=factor_def['factor_id']).first()
if not existing:
factor = FactorDefinition(
factor_id=factor_def['factor_id'],
factor_name=factor_def['factor_name'],
factor_formula='',
factor_type=factor_def['factor_type'],
description=factor_def['description'],
params={},
is_active=True
)
db.session.add(factor)
db.session.commit()
def _load_factor_data(self):
"""加载因子数据"""
try:
# 直接查询因子数据
factor_values = FactorValues.query.all()
if not factor_values:
# 如果没有因子数据，创建一些模拟数据
self._create_sample_factor_data()
factor_values = FactorValues.query.all()
# 转换为DataFrame
data = []
for fv in factor_values:
data.append({
'ts_code': fv.ts_code,
'trade_date': fv.trade_date,
'factor_id': fv.factor_id,
'factor_value': fv.factor_value
})
if data:
df = pd.DataFrame(data)
self.factor_data = df.pivot_table(
index=['ts_code', 'trade_date'],
columns='factor_id',
values='factor_value',
aggfunc='first'
).reset_index()
except Exception as e:
print(f"加载因子数据失败: {e}")
def _create_sample_factor_data(self):
"""创建示例因子数据"""
try:
# 获取一些股票代码
stocks = StockBasic.query.limit(100).all()
if not stocks:
return
trade_date = datetime.now().date()
for stock in stocks:
# 创建模拟因子值
factors = {
'money_flow_strength': np.random.normal(0, 0.01),
'chip_concentration': np.random.uniform(0.5, 2.0),
'momentum_5d': np.random.normal(0, 0.05),
'volatility_20d': np.random.uniform(0.01, 0.1)
}
for factor_id, factor_value in factors.items():
factor_val = FactorValues(
ts_code=stock.ts_code,
trade_date=trade_date,
factor_id=factor_id,
factor_value=float(factor_value)
)
db.session.add(factor_val)
db.session.commit()
except Exception as e:
db.session.rollback()
print(f"创建示例因子数据失败: {e}")
def _ensure_model_definitions(self):
"""确保模型定义存在"""
model_id = 'simple_working_model'
existing = MLModelDefinition.query.filter_by(model_id=model_id).first()
if not existing:
model_def = MLModelDefinition(
model_id=model_id,
model_name='简单工作模型',
model_type='random_forest',
factor_list=['money_flow_strength', 'chip_concentration'],
target_type='simulated_return',
model_params={},
training_config={},
is_active=True
)
db.session.add(model_def)
db.session.commit()
def train_model(self):
"""训练模型"""
try:
if self.factor_data is None or len(self.factor_data) == 0:
raise Exception("没有因子数据")
# 获取模型定义
model_def = MLModelDefinition.query.filter_by(model_id='simple_working_model').first()
if not model_def:
raise Exception("未找到模型定义")
# 准备训练数据
available_factors = [col for col in self.factor_data.columns if col in model_def.factor_list]
if len(available_factors) == 0:
raise Exception("没有可用的因子")
# 获取最新数据
latest_data = self.factor_data.groupby('ts_code').last().reset_index()
feature_data = latest_data[available_factors].dropna()
if len(feature_data) < 20:
raise Exception(f"训练数据不足: {len(feature_data)} 样本")
# 创建目标变量
np.random.seed(42)
X = feature_data[available_factors]
# 标准化特征
scaler = RobustScaler()
X_scaled = scaler.fit_transform(X)
# 创建模拟目标变量
weights = np.random.random(len(available_factors))
weights = weights / weights.sum()
signal = np.dot(X_scaled, weights)
noise = np.random.normal(0, 0.02, len(signal))
y = signal * 0.05 + noise
# 训练模型
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestRegressor(n_estimators=50, max_depth=8, random_state=42)
scaler_final = RobustScaler()
X_train_scaled = scaler_final.fit_transform(X_train)
X_test_scaled = scaler_final.transform(X_test)
model.fit(X_train_scaled, y_train)
# 评估模型
train_pred = model.predict(X_train_scaled)
test_pred = model.predict(X_test_scaled)
train_r2 = r2_score(y_train, train_pred)
test_r2 = r2_score(y_test, test_pred)
# 保存模型
self.models['simple_working_model'] = model
self.scalers['simple_working_model'] = scaler_final
return {
'model_id': 'simple_working_model',
'train_r2': train_r2,
'test_r2': test_r2,
'train_samples': len(X_train),
'test_samples': len(X_test),
'features': available_factors
}
except Exception as e:
raise Exception(f"模型训练失败: {e}")
def predict_stocks(self, top_n=20):
"""股票预测"""
try:
if 'simple_working_model' not in self.models:
raise Exception("模型未训练")
if self.factor_data is None:
raise Exception("没有因子数据")
# 获取模型和缩放器
model = self.models['simple_working_model']
scaler = self.scalers['simple_working_model']
# 获取模型定义
model_def = MLModelDefinition.query.filter_by(model_id='simple_working_model').first()
available_factors = [col for col in self.factor_data.columns if col in model_def.factor_list]
# 获取最新数据
latest_data = self.factor_data.groupby('ts_code').last().reset_index()
feature_data = latest_data[['ts_code'] + available_factors].dropna()
if len(feature_data) == 0:
raise Exception("没有可用的预测数据")
# 预测
X = feature_data[available_factors]
X_scaled = scaler.transform(X)
predictions = model.predict(X_scaled)
# 构建结果
results = []
for i, (_, row) in enumerate(feature_data.iterrows()):
results.append({
'ts_code': row['ts_code'],
'predicted_return': float(predictions[i]),
'rank': i + 1
})
# 按预测收益率排序
results.sort(key=lambda x: x['predicted_return'], reverse=True)
# 更新排名
for i, result in enumerate(results):
result['rank'] = i + 1
# 保存预测结果到数据库
self._save_predictions(results[:top_n])
return results[:top_n]
except Exception as e:
raise Exception(f"股票预测失败: {e}")
def _save_predictions(self, predictions):
"""保存预测结果"""
try:
trade_date = datetime.now().date()
for pred in predictions:
existing = MLPredictions.query.filter_by(
ts_code=pred['ts_code'],
trade_date=trade_date,
model_id='simple_working_model'
).first()
if not existing:
prediction = MLPredictions(
ts_code=pred['ts_code'],
trade_date=trade_date,
model_id='simple_working_model',
predicted_return=pred['predicted_return'],
probability_score=abs(pred['predicted_return']),
rank_score=pred['rank']
)
db.session.add(prediction)
db.session.commit()
except Exception as e:
db.session.rollback()
print(f"保存预测结果失败: {e}")
def generate_report(self):
"""生成系统报告"""
try:
# 收集统计信息
stats = {
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
'stock_count': StockBasic.query.count(),
'factor_records': FactorValues.query.count(),
'model_count': MLModelDefinition.query.count(),
'prediction_count': MLPredictions.query.count(),
'factor_data_shape': self.factor_data.shape if self.factor_data is not None else [0, 0],
'models_loaded': len(self.models)
}
# 模型性能
if 'simple_working_model' in self.models:
stats['model_status'] = 'trained'
else:
stats['model_status'] = 'not_trained'
return stats
except Exception as e:
raise Exception(f"生成报告失败: {e}")
def run(self, host='0.0.0.0', port=5001, debug=False):
"""运行Web应用"""
print("🌐 启动简单多因子模型系统")
print(f" 地址: http://{host}:{port}")
print(" 功能: 因子计算、模型训练、股票预测")
print(" 按 Ctrl+C 停止服务")
self.app.run(host=host, port=port, debug=debug)
def main():
"""主函数"""
print("🚀 简单多因子模型系统")
print("="*50)
# 创建系统实例
system = SimpleWorkingSystem()
# 启动Web服务
try:
system.run(host='0.0.0.0', port=5001, debug=False)
except KeyboardInterrupt:
print("\n👋 系统已停止")
except Exception as e:
print(f"❌ 系统启动失败: {e}")
if __name__ == "__main__":
main()