投资组合再平衡策略：动态优化与税务效率

import pandas as pd
import numpy as np

def calendar_rebalance(weights, target_weights, rebalance_freq='M'):
    """
    日历再平衡策略
    
    Parameters:
    -----------
    weights : DataFrame - 实际权重
    target_weights : Series - 目标权重
    rebalance_freq : str - 再平衡频率 ('D', 'W', 'M', 'Q', 'Y')
    """
    rebalanced_weights = weights.copy()
    
    # 按频率筛选再平衡日期
    rebalance_dates = weights.resample(rebalance_freq).last().index
    
    for date in rebalance_dates:
        if date in weights.index:
            rebalanced_weights.loc[date] = target_weights
    
    # 前向填充
    rebalanced_weights = rebalanced_weights.fillna(method='ffill')
    
    return rebalanced_weights

def threshold_rebalance(weights, target_weights, threshold=0.05):
    """
    阈值再平衡策略
    
    Parameters:
    -----------
    threshold : float - 触发再平衡的偏离阈值
    """
    current_weights = weights.iloc[0]
    rebalance_dates = [weights.index[0]]
    
    for i in range(1, len(weights)):
        # 计算当前权重与目标的偏离
        deviation = abs(weights.iloc[i] - target_weights)
        
        # 如果任一资产偏离超过阈值，触发再平衡
        if (deviation > threshold).any():
            current_weights = target_weights
            rebalance_dates.append(weights.index[i])
        else:
            current_weights = weights.iloc[i]
        
        weights.iloc[i] = current_weights
    
    return weights, rebalance_dates

def volatility_adjusted_rebalance(returns, target_weights, vol_window=20):
    """
    基于波动率的动态再平衡
    """
    # 计算滚动波动率
    rolling_vol = returns.rolling(window=vol_window).std() * np.sqrt(252)
    
    # 根据波动率调整再平衡阈值
    # 高波动 -> 窄阈值（频繁再平衡）
    # 低波动 -> 宽阈值（减少交易）
    dynamic_threshold = 0.05 + 0.05 * (rolling_vol.mean(axis=1) - rolling_vol.mean().mean()) / rolling_vol.mean().mean()
    
    # 应用阈值再平衡
    weights = calculate_weights(returns)
    rebalanced = threshold_rebalance(weights, target_weights, dynamic_threshold)
    
    return rebalanced

def tax_aware_rebalance(positions, target_weights, returns, cost_basis, tax_rate_short=0.35, tax_rate_long=0.15):
    """
    税务感知再平衡
    
    Parameters:
    -----------
    positions : Series - 当前持仓市值
    cost_basis : Series - 持仓成本基础
    tax_rate_short : float - 短期资本利得税率
    tax_rate_long : float - 长期资本利得税率
    """
    # 计算持有期
    holding_period = (pd.Timestamp.now() - cost_basis['purchase_date']).days
    
    # 计算未实现损益
    unrealized_gain = positions - cost_basis['cost']
    tax_impact = pd.Series(0, index=positions.index)
    
    # 短期 vs 长期税务影响
    tax_impact[holding_period <= 365] = unrealized_gain * tax_rate_short
    tax_impact[holding_period > 365] = unrealized_gain * tax_rate_long
    
    # 优先卖出税务成本最低的持仓
    sell_order = tax_impact.sort_values().index
    
    # 执行再平衡（考虑税务影响）
    trades = calculate_trades(positions, target_weights)
    optimized_trades = optimize_tax(trades, sell_order, tax_impact)
    
    return optimized_trades

def vwap_execution(order, price_data, volume_data, lookback=30):
    """
    VWAP (Volume Weighted Average Price) 执行算法
    """
    # 计算历史VWAP
    historical_vwap = (price_data * volume_data).sum() / volume_data.sum()
    
    # 预测当日交易量分布
    volume_profile = predict_intraday_volume(volume_data, lookback)
    
    # 根据交易量分布分配订单
    executed_price = 0
    for period, vol_pct in volume_profile.items():
        order_size = order * vol_pct
        period_price = get_period_price(price_data, period)
        executed_price += order_size * period_price
    
    vwap_price = executed_price / order
    
    return vwap_price