港美股投资：AI时代的全球资产配置策略-[AI生成]

# 港美股价值投资筛选模型
class GlobalValueScreener:
    def __init__(self):
        self.hk_criteria = {
            'pe_ratio': (0, 15),      # 港股估值普遍较低
            'dividend_yield': (3, 10), # 港股高股息特征明显
            'pb_ratio': (0, 1.5),      # 破净股机会较多
            'market_cap': (1000, 50000) # 市值范围（百万港币）
        }

        self.us_criteria = {
            'pe_ratio': (10, 30),     # 美股估值相对较高
            'roic': (15, 100),        # 资本回报率要求高
            'growth_rate': (10, 50),  # 成长性要求
            'market_cap': (10000, 1000000) # 市值范围（百万美元）
        }

    def screen_hk_stocks(self, stocks_data):
        """筛选港股价值股"""
        filtered = []
        for stock in stocks_data:
            if (self.hk_criteria['pe_ratio'][0] <= stock['pe'] <= self.hk_criteria['pe_ratio'][1] and
                self.hk_criteria['dividend_yield'][0] <= stock['dividend_yield'] <= self.hk_criteria['dividend_yield'][1] and
                stock['pb'] <= self.hk_criteria['pb_ratio'][1]):
                filtered.append(stock)
        return filtered

    def screen_us_growth_stocks(self, stocks_data):
        """筛选美股成长股"""
        filtered = []
        for stock in stocks_data:
            if (self.us_criteria['pe_ratio'][0] <= stock['pe'] <= self.us_criteria['pe_ratio'][1] and
                stock['roic'] >= self.us_criteria['roic'][0] and
                stock['revenue_growth'] >= self.us_criteria['growth_rate'][0]):
                filtered.append(stock)
        return filtered

import pandas as pd
import numpy as np
from datetime import datetime, timedelta

class CurrencyRiskManager:
    def __init__(self, usd_cny_rate=7.2, hkd_usd_rate=0.128):
        self.usd_cny = usd_cny_rate
        self.hkd_usd = hkd_usd_rate

    def calculate_hedging_cost(self, investment_amount, holding_period):
        """
        计算汇率对冲成本
        investment_amount: 投资金额（人民币）
        holding_period: 持有期（月）
        """
        # 远期汇率成本估算
        forward_points = 0.0005 * holding_period  # 每月5个基点
        hedging_cost = investment_amount * forward_points

        # 期权对冲成本估算
        option_premium = investment_amount * 0.02  # 2%期权费

        return {
            'forward_cost': hedging_cost,
            'option_cost': option_premium,
            'recommended_hedge': 'forward' if holding_period < 6 else 'option'
        }

    def optimize_currency_allocation(self, total_capital):
        """
        优化货币配置比例
        """
        # 基于风险平价原则
        usd_allocation = 0.6  # 美元资产60%
        hkd_allocation = 0.3  # 港币资产30%
        cny_allocation = 0.1  # 人民币资产10%

        return {
            'USD': total_capital * usd_allocation / self.usd_cny,
            'HKD': total_capital * hkd_allocation / (self.usd_cny * self.hkd_usd),
            'CNY': total_capital * cny_allocation
        }

from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
import talib

class AIGlobalStockPicker:
    def __init__(self):
        self.models = {
            'hk': RandomForestClassifier(n_estimators=100),
            'us': XGBClassifier(n_estimators=100)
        }

    def extract_features(self, stock_data, market_type):
        """提取多维度特征"""
        features = []

        # 基本面特征
        features.extend([
            stock_data['pe_ratio'],
            stock_data['pb_ratio'],
            stock_data['roe'],
            stock_data['debt_to_equity']
        ])

        # 技术面特征
        if 'price_history' in stock_data:
            prices = stock_data['price_history']
            features.extend([
                talib.RSI(prices, timeperiod=14)[-1],  # RSI指标
                talib.MACD(prices)[0][-1],             # MACD
                talib.BBANDS(prices)[0][-1],           # 布林带上轨
            ])

        # 市场情绪特征（港股特有）
        if market_type == 'hk':
            features.append(stock_data['southbound_flow'])  # 南向资金流向
            features.append(stock_data['short_interest'])   # 沽空比例

        return features

    def predict_performance(self, stock_features, market_type):
        """预测股票未来表现"""
        model = self.models[market_type]
        # 这里需要训练好的模型
        # prediction = model.predict([stock_features])
        # return prediction
        return 0.75  # 示例返回值

import jieba
from transformers import pipeline
from collections import Counter

class MarketSentimentAnalyzer:
    def __init__(self):
        self.sentiment_analyzer = pipeline("sentiment-analysis")
        self.hk_keywords = ['港股', '恒生指数', 'H股', '红筹股', '南向资金']
        self.us_keywords = ['美股', '纳斯达克', '标普500', '美联储', '财报季']

    def analyze_news_sentiment(self, news_text, market_type):
        """分析新闻情绪"""
        # 中文新闻分词
        if market_type == 'hk':
            words = jieba.lcut(news_text)
            keyword_count = sum(1 for word in words if word in self.hk_keywords)
        else:
            # 英文新闻简单分词
            words = news_text.lower().split()
            keyword_count = sum(1 for word in words if word in self.us_keywords)

        # 情感分析
        sentiment_result = self.sentiment_analyzer(news_text[:512])  # 限制长度

        return {
            'sentiment_score': sentiment_result[0]['score'],
            'sentiment_label': sentiment_result[0]['label'],
            'keyword_relevance': keyword_count / len(words) if words else 0
        }

    def detect_market_events(self, social_media_posts):
        """从社交媒体检测市场事件"""
        event_keywords = {
            'earnings': ['财报', '业绩', '盈利', '收入', 'earning'],
            'merger': ['并购', '收购', '合并', 'merger', 'acquisition'],
            'regulation': ['监管', '政策', '法规', 'regulation', 'policy'],
            'macro': ['加息', '通胀', 'GDP', '利率', 'inflation', 'GDP']
        }

        events_detected = []
        for post in social_media_posts:
            for event_type, keywords in event_keywords.items():
                if any(keyword in post.lower() for keyword in keywords):
                    events_detected.append({
                        'type': event_type,
                        'post': post[:100],  # 截取前100字符
                        'timestamp': datetime.now()
                    })

        return events_detected