# Install necessary libraries (if not already installed)
install.packages("quantmod")
install.packages("TTR")
install.packages("PerformanceAnalytics")
install.packages("xts")
install.packages("dplyr")
install.packages("ggplot2")
# Load libraries
library(quantmod)
library(TTR)
library(PerformanceAnalytics)
library(xts)
library(dplyr)
library(ggplot2)
# Step 1: Download Historical Data for RVNL
getSymbols("RVNL.NS", src = "yahoo", from = "2020-01-01", to = Sys.Date())
# Convert xts object to a data frame for processing
RVNL_df <- data.frame(Date = index(RVNL.NS), coredata(RVNL.NS))
colnames(RVNL_df) <- c("Date", "Open", "High", "Low", "Close", "Volume", "Adjusted")
# Step 2: Calculate Moving Averages and Add Indicators
RVNL_df <- RVNL_df %>%
mutate(
Change_Percent = (Close - lag(Close)) / lag(Close) * 100, # Daily percentage change
Prev_Volume = lag(Volume), # Previous day's volume
MA7 = SMA(Close, 7), # 7-day moving average
MA65 = SMA(Close, 65), # 65-day moving average
MA_Ratio = MA7 / MA65 # Ratio of MA7 to MA65
)
# Step 3: Define Buy Condition
# Buy when stock is up 4% in a day, volume > previous day, volume >= 100,000, and MA ratio > 1.05
buy_condition <- RVNL_df %>%
filter(
Change_Percent >= 4, # Stock up 4% or more
Volume > Prev_Volume, # Volume greater than previous day
Volume >= 100000, # Volume greater than or equal to 100,000
MA_Ratio > 1.05 # 7-day MA / 65-day MA > 1.05
)
# Step 4: Create Buy Signals and Calculate Stop Losses
buy_signals <- buy_condition %>%
mutate(
Buy_Signal = 1,
Stop_Loss_Day1 = Low, # Initial stop loss is the low of the buy day
Stop_Loss_NextDay = NA, # Placeholder for stop loss of subsequent days
Sell_Date = as.Date(NA), # Placeholder for sell date
Return = NA, # Placeholder for return on each trade
Days_Held = NA # Placeholder for holding days on each trade
)
# Step 5: Apply Dynamic Stop Loss and Sell After 4 Days
for (i in 1:nrow(buy_signals)) {
buy_date <- buy_signals$Date[i]
next_4_days <- RVNL_df %>% filter(Date > buy_date) %>% head(4)
stop_loss_next <- buy_signals$Stop_Loss_Day1[i] # Start with Day 1 stop loss
for (j in 1:nrow(next_4_days)) {
day_data <- next_4_days[j, ]
price_range <- day_data$High - day_data$Low
stop_loss_next <- max(stop_loss_next, day_data$Low - (price_range / 2))
if (day_data$Low <= stop_loss_next) {
# Stop loss hit, sell on this day
buy_signals$Sell_Date[i] <- day_data$Date
buy_signals$Return[i] <- (day_data$Close - buy_signals$Close[i]) / buy_signals$Close[i]
buy_signals$Days_Held[i] <- as.numeric(difftime(day_data$Date, buy_signals$Date[i], units = "days"))
break
} else if (j == 4) {
# Sell at the end of 4th day if stop loss not hit
buy_signals$Sell_Date[i] <- day_data$Date
buy_signals$Return[i] <- (day_data$Close - buy_signals$Close[i]) / buy_signals$Close[i]
buy_signals$Days_Held[i] <- as.numeric(difftime(day_data$Date, buy_signals$Date[i], units = "days"))
}
}
}
# Step 6: Initialize Starting Capital and Apply Compounded Returns
starting_capital <- 100000 # Starting capital of 100,000 INR
capital <- starting_capital
buy_signals$Capital <- NA # To track capital at each step
equity_curve <- numeric(length = nrow(buy_signals) + 1) # To track the equity curve
equity_curve[1] <- starting_capital
# Step 7: Calculate Compounded Returns and Update Capital for Each Trade
for (i in 1:nrow(buy_signals)) {
trade_return <- buy_signals$Return[i]
capital <- capital * (1 + trade_return) # Update capital with compounded return
buy_signals$Capital[i] <- capital # Store updated capital
equity_curve[i + 1] <- capital # Update equity curve
}
# Step 8: Prepare Data for Plotting the Equity Curve
equity_df <- data.frame(Date = c(buy_signals$Date, max(buy_signals$Sell_Date, na.rm = TRUE)),
Capital = equity_curve)
# Step 9: Prepare Data for Plotting Buy and Sell Signals on Price Chart
RVNL_df$Buy_Signal <- ifelse(RVNL_df$Date %in% buy_signals$Date, "Buy", NA)
RVNL_df$Sell_Signal <- ifelse(RVNL_df$Date %in% buy_signals$Sell_Date, "Sell", NA)
# Step 10: Plot Price Chart with Buy (Green) and Sell (Red) Candles Using ggplot2
price_chart <- ggplot(RVNL_df, aes(x = Date)) +
geom_line(aes(y = Close), color = "blue") + # Plot Close prices
geom_point(data = RVNL_df %>% filter(!is.na(Buy_Signal)), aes(y = Low), color = "darkgreen", size = 3, shape = 24, fill = "darkgreen") + # Buy Signal
geom_point(data = RVNL_df %>% filter(!is.na(Sell_Signal)), aes(y = High), color = "red", size = 3, shape = 25, fill = "red") + # Sell Signal
labs(title = "RVNL Trading Strategy: Buy (Green) and Sell (Red) Signals", y = "Price", x = "Date") +
theme_minimal()
# Plot the Price Chart
print(price_chart)
# Step 11: Plot Equity Curve Using ggplot2
equity_chart <- ggplot(equity_df, aes(x = Date, y = Capital)) +
geom_line(color = "blue", size = 1) +
labs(title = "Equity Curve for RVNL Trading Strategy", y = "Capital (INR)", x = "Date") +
theme_minimal()
# Plot the Equity Curve
print(equity_chart)
# Step 12: Display Results Table with Total Return, Total Days Held, and Final Capital
trade_results <- buy_signals %>%
select(Date, Sell_Date, Return, Days_Held, Capital) %>%
filter(!is.na(Sell_Date)) # Filter out rows without Sell Date
# Calculate total return and total days held
final_capital <- capital
total_return <- (final_capital - starting_capital) / starting_capital * 100 # Total return as percentage
total_days_held <- sum(trade_results$Days_Held, na.rm = TRUE)
# Display results
print("Trade Results (Buy Date, Sell Date, Return, Days Held, Capital):")
print(trade_results)
print(paste("Final Capital:", round(final_capital, 2), "INR"))
print(paste("Total Return Generated:", round(total_return, 2), "%"))
print(paste("Total Days Held:", total_days_held))
# If you want to save the results as a CSV file:
write.csv(trade_results, "RVNL_Trading_Strategy_Results.csv", row.names = FALSE)
50% time sold on fourth day, 50% time SL hit on 3/2/1 day.
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