# 加载必要的R包
library(quantmod)      # 获取金融数据
library(PortfolioAnalytics)  # 投资组合分析
library(PerformanceAnalytics)  # 绩效分析
library(ggplot2)       # 数据可视化
library(dplyr)         # 数据处理
library(scales)        # 数据转换

# 定义股票代码
tickers <- c("AAPL", "MSFT", "JPM", "PG", "XOM")

# 设置时间范围
start_date <- "2018-01-01"
end_date <- "2023-01-01"

# 创建一个空列表存储股票数据
stock_data <- list()

# 获取每只股票的价格数据
for(ticker in tickers) {
  stock_data[[ticker]] <- getSymbols(ticker, 
                                     from = start_date, 
                                     to = end_date, 
                                     auto.assign = FALSE)
}

# 查看数据结构
str(stock_data[[1]])

# 加载必要的包
library(quantmod)
library(quantstrat)
library(eTTR)
library(PerformanceAnalytics)
library(ggplot2)
library(dplyr)
library(tibble)
library(scales)
library(gridExtra)
# 加载环境重置函数
source("/Users/matrixspk/My-Sites/r-finance/assets/code/reset_strategy_env.R")
# 加载计算胜率的函数
source("/Users/matrixspk/My-Sites/r-finance/assets/code/calculate_portfolio_win_rates.R")
source("/Users/matrixspk/My-Sites/r-finance/assets/code/generateSimpleSignalChain.R")

# 设置获取数据的起始和结束日期
initDate <- as.Date("2017-12-31")
startdate.st <- as.Date("2018-01-01")
enddate.st <- as.Date("2023-06-01")
# 获取苹果公司股票数据
getSymbols("AAPL", src = "yahoo", from = startdate.st, to = enddate.st)

colnames(AAPL) <- c("Open", "High", "Low", "Close", "Volume", "Adjusted")
# 查看数据结构
head(AAPL)

summary(AAPL)

# 加载必要的包
library(quantmod)
library(quantstrat)
library(eTTR)
library(PerformanceAnalytics)
library(ggplot2)
library(dplyr)
library(tibble)
library(scales)
library(gridExtra)
library(showtext)
font_add("SimHei", regular = "SimHei.ttf")
showtext_auto()

# 设置获取数据的起始和结束日期
startdate.st <- as.Date("2018-01-01")
enddate.st <- as.Date("2023-06-01")

# 获取上证指数数据
getSymbols("TSLA", 
           src = "yahoo", 
           from = startdate.st, 
           to = enddate.st)

colnames(TSLA) <- c("Open", "High", "Low", 
                    "Close", "Volume", "Adjusted")

# 查看数据结构
head(TSLA)

summary(TSLA)

# 安装必要包（首次运行需取消注释）
# install.packages(c("quantmod", 
#                    "tidyverse", 
#                    "ggplot2", 
#                    "zoo", 
#                    "corrplot"))

library(quantmod)   # 获取金融数据
library(tidyverse)  # 数据处理
library(ggplot2)    # 可视化
library(zoo)        # 时间序列处理

# 股票代码列表（支持多市场，如A股需加 .SS/.SZ）
# 苹果、谷歌、微软、英伟达
stocks <- c("AAPL", "GOOGL", "MSFT", "NVDA")  
# 时间范围
start_date <- "2023-01-01"
end_date <- Sys.Date()  # 获取当前日期

# 获取数据
getSymbols(stocks, 
           src = "yahoo", 
           from = start_date, 
           to = end_date)

# 处理数据
stock_data <- lapply(stocks, function(x) {
  data <- as_tibble(get(x)) %>%
    mutate(Date = index(get(x))) %>%
    rename_with(~ gsub(paste0("^", x, "\\."), "", .x)) %>%
    select(Date, Close) %>%
    mutate(symbol = x) %>%  # 添加股票代码列
    rename(price = Close)   # 重命名收盘价列
}) %>%
  bind_rows()

# 查看结果
head(stock_data)

library(dplyr)
# 检查缺失值
missing_values <- stock_data %>%
  group_by(symbol) %>%
  summarise(missing = sum(is.na(price)))

# 填充缺失值（使用前向填充）
stock_data <- stock_data %>%
  group_by(symbol) %>%
  mutate(price = na.locf(price))

library(dplyr)
# 生成完整日期序列
full_dates <- tibble(Date = seq(as.Date(start_date), 
                                as.Date(end_date), 
                                by = "day"))

# 左连接填充所有日期
stock_data <- full_dates %>%
  left_join(stock_data, by = "Date") %>%
  group_by(symbol) %>%
  fill(price, .direction = "downup") %>%
  na.omit()

library(dplyr)
ggplot(stock_data, aes(x = Date, y = price, color = symbol)) +
  geom_line(linewidth = 0.8) +
  labs(title = "多只股票价格走势对比",
       x = "日期",
       y = "收盘价",
       color = "股票代码") +
  theme_minimal() +
  theme(legend.position = "top") +  
  scale_color_manual(values = c("AAPL" = "red", 
                                "GOOGL" = "blue", 
                                "MSFT" = "green", 
                                "NVDA" = "purple")
                     )

library(dplyr)
# 计算对数收益率
return_data <- stock_data %>%
  group_by(symbol) %>%
  mutate(log_return = log(price) - log(lag(price))) %>%
  na.omit()

# 绘制收益率曲线
ggplot(return_data, 
       aes(x = Date, y = log_return, color = symbol)) +
  geom_line(alpha = 0.7) +
  labs(title = "对数收益率对比",
       x = "日期",
       y = "对数收益率",
       color = "股票代码") +
  theme_minimal() + 
  theme(legend.position = "top") # 图例放底部

# 加载必要的R包
library(quantmod)
library(PerformanceAnalytics)
library(foreach)
library(doParallel)
library(ggplot2)
library(dplyr)
library(tidyr)
library(eTTR)
library(zoo)

# 设置起止日期
start_date <- "2018-01-01"
end_date <- "2023-01-01"
out_of_sample_date <- "2023-01-02"
end_oos_date <- "2023-12-31"

# 股票代码列表
stock_symbols <- c("AAPL", "MSFT", "GOOG", "AMZN", "TSLA")

# 获取股票数据
stock_data <- list()
for (symbol in stock_symbols) {
   data_raw <- getSymbols(symbol, from = start_date, to = end_date, auto.assign = FALSE)
   colnames(data_raw) <- c("Open", "High", "Low", "Close", "Volume", "Adjusted")
   stock_data[[symbol]] <- data_raw
}

# 获取样本外数据
oos_data <- list()
for (symbol in stock_symbols) {
   data_raw <- getSymbols(symbol, from = out_of_sample_date, to = end_oos_date, auto.assign = FALSE)
   colnames(data_raw) <- c("Open", "High", "Low", "Close", "Volume", "Adjusted")
   oos_data[[symbol]] <- data_raw
}

# 加载必要的R包
library(quantmod)
library(PerformanceAnalytics)
library(foreach)
library(doParallel)
library(ggplot2)
library(dplyr)
library(tidyr)
library(caret)
library(magrittr)

# 设置起止日期
start_date <- "2018-01-01"
end_date <- "2023-01-01"
out_of_sample_date <- "2023-01-02"
end_oos_date <- "2023-12-31"

# 大盘股列表
large_cap_symbols <- c("AAPL", "MSFT", "AMZN", "TSLA")

# 小盘股列表
small_cap_symbols <- c("ARQT", "AVXL", "BPMC", "CELZ")

# 所有股票代码
all_symbols <- c(large_cap_symbols, small_cap_symbols)

# 获取股票数据
stock_data <- list()
for (symbol in all_symbols) {
  tryCatch(
    {
      stock_data_raw <- getSymbols(symbol, 
                                   from = start_date, 
                                   to = end_date, 
                                   auto.assign = FALSE)
      colnames(stock_data_raw) <- c("Open", 
                                    "High", 
                                    "Low", 
                                    "Close", 
                                    "Volume", 
                                    "Adjusted")
      stock_data[[symbol]] <- stock_data_raw
      cat("Successfully downloaded", symbol, "\n")
    },
    error = function(e) {
      cat("Error downloading", symbol, ":", conditionMessage(e), "\n")
    }
  )
}

# 过滤掉下载失败的股票
valid_symbols <- names(stock_data)
large_cap_symbols <- large_cap_symbols[large_cap_symbols %in% valid_symbols]
small_cap_symbols <- small_cap_symbols[small_cap_symbols %in% valid_symbols]

# 获取样本外数据
oos_data <- list()
for (symbol in valid_symbols) {
  tryCatch(
    {
      oss_data_raw <- getSymbols(symbol, 
                                 from = out_of_sample_date, 
                                 to = end_oos_date, 
                                 auto.assign = FALSE)
      colnames(oss_data_raw) <- c("Open", 
                                  "High", 
                                  "Low", 
                                  "Close", 
                                  "Volume", 
                                  "Adjusted")
      oos_data[[symbol]] <- oss_data_raw
      cat("Successfully downloaded OOS data for", symbol, "\n")
    },
    error = function(e) {
      cat("Error downloading OOS data for", symbol, ":", conditionMessage(e), "\n")
    }
  )
}