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我希望能够为每个时期制作一个权重向量，在这里的示例中，权重向量在开始时是固定的并且没有变化，我希望能够改变权重。我也有不断变化的股票数量，因此在我的情况下，例如“GOOG”可能会在几年后消失并被一种或多种不同的股票所取代，fe“TSLA”（我也有数千只股票。）这可以在 tidyquant 中完成还是有其他选择？

library(tidyquant)
library(tidyverse)

# Asset Period Returns
stock_returns_monthly <- c("AAPL", "GOOG", "NFLX") %>%
  tq_get(get  = "stock.prices",
         from = "2010-01-01",
         to   = "2015-12-31") %>%
  group_by(symbol) %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "monthly", 
               col_rename = "Ra")
stock_returns_monthly 

# Baseline Period Returns
baseline_returns_monthly <- "XLK" %>%
  tq_get(get  = "stock.prices",
         from = "2010-01-01",
         to   = "2015-12-31") %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "monthly", 
               col_rename = "Rb")
baseline_returns_monthly
# scaling a single portfolio to many, 3 in this case
stock_returns_monthly_multi <- stock_returns_monthly %>%
  tq_repeat_df(n = 3)
stock_returns_monthly_multi


# Create Vector of Weights
# not all symbols need to be specified. Any symbol not specified by default gets a weight of zero.
weights <- c(
  0.50, 0.25, 0.25,
  0.25, 0.50, 0.25,
  0.25, 0.25, 0.50
)
stocks <- c("AAPL", "GOOG", "NFLX")
weights_table <-  tibble(stocks) %>%
  tq_repeat_df(n = 3) %>%
  bind_cols(tibble(weights)) %>%
  group_by(portfolio)
weights_table



# Aggregate a Portfolio using Vector of Weights
portfolio_returns_monthly_multi  <-
  stock_returns_monthly_multi %>%
  tq_portfolio(assets_col  = symbol, 
               returns_col = Ra, 
               weights     = weights_table, 
               col_rename  = "Ra")
portfolio_returns_monthly_multi 

# Merging Ra and Rb
RaRb_single_portfolio <- left_join(portfolio_returns_monthly_multi , 
                                   baseline_returns_monthly,
                                   by = "date")
RaRb_single_portfolio

#Step 4: Computing the CAPM Table
RaRb_single_portfolio %>%
  tq_performance(Ra = Ra, Rb = Rb, performance_fun = table.CAPM) %>%
  t()
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