股债期组合策略全解析：跨市场资产配置与量化实现

# 资产配置引擎
class AssetAllocator:
    def __init__(self):
        self.stock_ratio = 0.6  # 股票基础权重
        self.bond_ratio = 0.3   # 债券基础权重
        self.futures_ratio = 0.1 # 期货基础权重
        
    def adjust_weights(self, volatility):
        """基于波动率动态调整权重"""
        if volatility > 0.25:
            self.stock_ratio *= 0.8
            self.bond_ratio *= 1.2
        elif volatility < 0.15:
            self.stock_ratio *= 1.2
            self.futures_ratio *= 1.5
        return self.stock_ratio, self.bond_ratio, self.futures_ratio

def stock_selection():
    # 高股息筛选（股息率>3%，ROE>15%）
    high_div = filter_stocks(div_yield>0.03, roe>0.15)  
    
    # 科技成长筛选（研发费用增速>30%，机构持股增加）
    tech_growth = filter_stocks(rd_growth>0.3, inst_hold_chg>0)
    
    # 组合优化（风险平价模型）
    portfolio = risk_parity(high_div, tech_growth, max_drawdown=0.15)
    return portfolio

def bond_strategy():
    # 30年期国债久期对冲
    treasury = fetch_bond_data('019627.IB')  # 30年国债代码
    duration_hedge = calc_duration(treasury)
    
    # 可转债套利（转股溢价率<10%）
    cbond = filter_bonds(convert_premium<0.1, rating>'AA+')
    
    return {
        'treasury_ratio': 0.7, 
        'cbond_ratio': 0.3,
        'duration': duration_hedge
    }

class FuturesMomentum:
    def __init__(self):
        self.lookback = 20  # 动量周期
        self.threshold = 0.05  # 突破阈值
        
    def generate_signal(self, df):
        # 多品种动量计算
        df['momentum'] = df.close.pct_change(self.lookback)
        df['signal'] = np.where(df.momentum>self.threshold, 1, 
                               np.where(df.momentum<-self.threshold, -1, 0))
        return df[['code','signal']]

def rebalance_portfolio(holdings, target_ratios):
    """季度再平衡逻辑"""
    total_value = sum(holdings.values())
    adjustments = {}
    
    for asset in target_ratios:
        target_value = total_value * target_ratios[asset]
        current_value = holdings.get(asset, 0)
        adjustments[asset] = target_value - current_value
        
    return adjustments  # 生成调仓指令

def risk_control(portfolio):
    # 波动率监测（20日滚动波动）
    volatility = portfolio.returns.rolling(20).std()
    
    # 熔断机制（单日回撤>5%暂停交易）
    if portfolio.daily_drawdown > 0.05:
        pause_trading()
        
    # 保证金监控（期货杠杆<5倍）
    futures_margin = calc_margin_ratio()
    if futures_margin > 5:
        reduce_leverage()

# 引入LSTM预测资产相关性
model = Sequential()
model.add(LSTM(50, input_shape=(60,3)))  # 输入60天3资产数据
model.add(Dense(3, activation='softmax'))  # 输出配置权重
model.compile(loss='categorical_crossentropy', optimizer='adam')

def cross_border_arbitrage():
    # 中美利差套利（需考虑汇率对冲）
    cn_10y = 2.5  # 中国10年国债收益率
    us_10y = 4.2  # 美国10年国债收益率
    fx_forward = 6.35  # 1年期远期汇率
    
    carry = (us_10y - cn_10y) - (fx_forward - spot_rate)
    return carry > 0.03  # 利差超过3%触发