Tutorial : R script using quantmod to analyze BTC-USD

Here is a complete, production-ready R script using quantmod to analyze BTC-USD.

 

This script covers the entire pipeline: ingestion, cleaning, technical indicator calculation, statistical analysis, and financial charting.

R

# ==============================================================================
# BTC-USD Quantitative Analysis Script using quantmod
# ==============================================================================

# 1. Load Required Libraries
# If not installed, run: install.packages(c("quantmod", "TTR", "PerformanceAnalytics"))
library(quantmod)
library(TTR)
library(PerformanceAnalytics)

# 2. Ingest Data (Yahoo Finance)
# auto.assign = FALSE prevents messing up your global environment
cat("Fetching BTC-USD historical data...\n")
btc_raw <- getSymbols("BTC-USD", src = "yahoo", 
                      from = "2024-01-01", 
                      to = Sys.Date(), 
                      auto.assign = FALSE)

# Clean missing rows (crypto trades 24/7, but Yahoo occasionally has NA gaps)
btc <- na.omit(btc_raw)

# 3. Quick Data Inspection
print(head(btc))
cat("Data Frequency:", periodicity(btc)$scale, "\n")

# ==============================================================================
# 4. Feature Engineering & Column Extractions
# ==============================================================================
# Using quantmod wrappers to extract specific vectors safely
btc_close  <- Cl(btc)    # Closing price
btc_volume <- Vo(btc)    # Volume
btc_ohlc   <- OHLC(btc)  # 4-column matrix (Open, High, Low, Close)

# Calculate Daily and Monthly Returns
btc_daily_ret   <- dailyReturn(btc_close)
btc_monthly_ret <- monthlyReturn(btc_close)

# Create Lagged features (Useful for predicting tomorrow based on yesterday)
btc$Lag_Close_1 <- Lag(btc_close, k = 1)
btc$Lag_Vol_1   <- Lag(btc_volume, k = 1)

# ==============================================================================
# 5. Statistical Risk & Performance Metrics
# ==============================================================================
cat("\n--- Performance Metrics ---\n")
# Calculate annualized return and Sharpe ratio using PerformanceAnalytics
sharpe  <- SharpeRatio.annualized(btc_daily_ret, Rf = 0.04/365) # assuming 4% risk-free rate
max_dd  <- maxDrawdown(btc_daily_ret)

print(paste("Annualized Sharpe Ratio:", round(sharpe, 2)))
print(paste("Maximum Historical Drawdown:", round(max_dd * 100, 2), "%"))

# ==============================================================================
# 6. Advanced Charting Layouts (Quantmod Native)
# ==============================================================================
# Step A: Open a clean white-themed candlestick chart frame
chartSeries(btc, 
            type = "candlesticks", 
            theme = chartTheme("white"), 
            name = "BTC-USD Analysis Dashboard",
            subset = "last 6 months") # Zoom into the last 6 months

# Step B: Layer Technical Indicators using the TTR wrapper system
addSMA(n = 50, col = "blue")       # 50-day Simple Moving Average
addEMA(n = 200, col = "red")      # 200-day Exponential Moving Average
addBBands(n = 20, sd = 2)         # Bollinger Bands (Volatility)
addRSI(n = 14)                    # Relative Strength Index (Momentum)
addMACD()                         # Moving Average Convergence Divergence

What This Script Does:

1. Dynamic Dates: Automatically sets the end date to today (Sys.Date()) and pulls fresh daily crypto data.

2. Combats Look-Ahead Bias: Implements the Lag() function, ensuring that any statistical models you build only use past data to predict future metrics.

3. Professional Visuals: Rather than plotting standard lines, chartSeries() builds a multi-pane financial dashboard detailing price actions, volumes, and overlays (like Bollinger Bands) automatically synced across the X-axis timeline.