Homework 8

if (!require("quantmod")) install.packages("quantmod", dependencies = TRUE)

## Loading required package: quantmod

## Loading required package: xts

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Loading required package: TTR

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

if (!require("PerformanceAnalytics")) install.packages("PerformanceAnalytics", dependencies = TRUE)

## Loading required package: PerformanceAnalytics

## 
## Attaching package: 'PerformanceAnalytics'

## The following object is masked from 'package:graphics':
## 
##     legend

if (!require("TTR")) install.packages("TTR", dependencies = TRUE)
library(quantmod)
library(PerformanceAnalytics)
library(TTR)

tickers <- c("AAPL", "MSFT", "GOOG", "AMZN", "META", "TSLA", "NVDA", "JPM", "V", "UNH")
start_date <- as.Date("2015-01-01")
end_date <- as.Date("2024-12-31")
lookback_days <- 90
top_n <- 3
rebalance_days <- 30

getSymbols(tickers, from = start_date, to = end_date, auto.assign = TRUE)

##  [1] "AAPL" "MSFT" "GOOG" "AMZN" "META" "TSLA" "NVDA" "JPM"  "V"    "UNH"

prices <- do.call(merge, lapply(tickers, function(x) Ad(get(x))))
colnames(prices) <- tickers

rebalance_dates <- seq(from = start_date + lookback_days, to = end_date, by = rebalance_days)

portfolio_returns <- xts(order.by = index(prices))
portfolio_returns <- portfolio_returns[, 1, drop = FALSE]
colnames(portfolio_returns) <- "Strategy"
portfolio_returns[] <- NA

for (i in 1:(length(rebalance_dates) - 1)) {
  lookback_start <- rebalance_dates[i] - lookback_days
  lookback_end <- rebalance_dates[i]
  forward_end <- rebalance_dates[i + 1]
}

momentum_scores <- sapply(tickers, function(tk) {
    px <- prices[, tk]
    px_lb <- px[paste0(lookback_start, "/", lookback_end)]
    if (length(px_lb) < 2 || any(is.na(px_lb))) return(NA_real_)
    return(as.numeric(last(px_lb)) / as.numeric(first(px_lb)) - 1)
  })

  momentum_scores <- as.numeric(momentum_scores)
  names(momentum_scores) <- tickers

  valid_scores <- sort(momentum_scores[!is.na(momentum_scores)], decreasing = TRUE)
  top_tickers <- names(valid_scores)[1:min(top_n, length(valid_scores))]

  if (length(top_tickers) > 0) {
    forward_prices <- prices[paste0(lookback_end + 1, "/", forward_end), top_tickers]
    forward_returns <- na.omit(Return.calculate(forward_prices))

    if (nrow(forward_returns) > 0) {
      port_ret <- Return.portfolio(forward_returns, weights = rep(1 / length(top_tickers), length(top_tickers)))
      portfolio_returns[index(port_ret), ] <- port_ret
    }
  }

portfolio_returns <- na.omit(portfolio_returns)
charts.PerformanceSummary(portfolio_returns, main = "Momentum Strategy Backtest")

print(table.AnnualizedReturns(portfolio_returns))

##                           Strategy
## Annualized Return           1.0123
## Annualized Std Dev          0.2577
## Annualized Sharpe (Rf=0%)   3.9287

print(paste("Max Drawdown:", round(maxDrawdown(portfolio_returns) * 100, 2), "%"))

## [1] "Max Drawdown: 4.22 %"