trademetrics • Performance Analytics for R • trademetrics

Performance and Risk Analytics for Trading Strategies

Part of the Traderverse ecosystem for quantitative trading in R.

📊 Overview

trademetrics provides comprehensive performance and risk analytics for quantitative trading strategies. Calculate return metrics, risk-adjusted returns, drawdown analysis, rolling statistics, and more.

Key Features

Return Metrics: Total return, CAGR, annualized returns
Risk-Adjusted Metrics: Sharpe, Sortino, Calmar, Information ratios
Drawdown Analysis: Maximum drawdown, average drawdown, recovery time
Rolling Statistics: Rolling Sharpe, volatility, correlation, beta
Performance Summaries: Comprehensive reports with all key metrics

🚀 Installation

# From GitHub (development version)
# install.packages("devtools")
devtools::install_github("Traderverse/trademetrics")

💡 Quick Start

library(trademetrics)

# Sample strategy returns
returns <- rnorm(252, mean = 0.001, sd = 0.02)

# Calculate individual metrics
calc_sharpe(returns, rf_rate = 0.02/252, periods = 252)
calc_max_drawdown(returns = returns)
calc_cagr(returns = returns, periods = 252)

# Or get a comprehensive summary
summary <- performance_summary(returns, rf_rate = 0.02/252, periods = 252)
print(summary)

Output:

==============================================
         Performance Summary
==============================================

Return Metrics:
  Total Return:          25.30%
  CAGR:                  23.40%
  Annualized Return:     25.20%

Risk Metrics:
  Annualized Vol:        31.75%
  Sharpe Ratio:           0.79
  Sortino Ratio:          1.12
  Calmar Ratio:           1.85

Drawdown Metrics:
  Max Drawdown:         -12.65%
  Average Drawdown:      -3.45%
  Recovery Time:             45 periods

Trade Statistics:
  Total Periods:            252
  Winning Periods:          138
  Losing Periods:           114
  Win Rate:               54.76%
  Best Period:             8.23%
  Worst Period:           -7.45%

==============================================

📚 Main Functions

Return Metrics

calc_total_return() - Total return
calc_cagr() - Compound Annual Growth Rate
calc_annualized_return() - Annualized return
calc_annualized_volatility() - Annualized volatility

Risk-Adjusted Metrics

calc_sharpe() - Sharpe ratio
calc_sortino() - Sortino ratio
calc_calmar() - Calmar ratio
calc_information_ratio() - Information ratio vs benchmark

Drawdown Analysis

calc_drawdown() - Drawdown series
calc_max_drawdown() - Maximum drawdown
calc_average_drawdown() - Average drawdown
calc_drawdown_duration() - Drawdown periods and durations
calc_recovery_time() - Time to recover from max drawdown

Rolling Statistics

calc_rolling_sharpe() - Rolling Sharpe ratio
calc_rolling_volatility() - Rolling volatility
calc_rolling_correlation() - Rolling correlation
calc_rolling_beta() - Rolling beta

Utilities

performance_summary() - Comprehensive performance report

🎓 Examples

Compare to Benchmark

# Strategy and benchmark returns
strategy_returns <- rnorm(252, 0.001, 0.02)
benchmark_returns <- rnorm(252, 0.0008, 0.015)

# Calculate Information Ratio
calc_information_ratio(strategy_returns, benchmark_returns, periods = 252)

# Include in summary
summary <- performance_summary(
  returns = strategy_returns,
  benchmark_returns = benchmark_returns,
  rf_rate = 0.02/252,
  periods = 252
)
print(summary)

Rolling Metrics

# Calculate rolling Sharpe ratio
rolling_sharpe <- calc_rolling_sharpe(
  returns = strategy_returns,
  window = 60,  # 3-month window
  rf_rate = 0.02/252,
  periods = 252
)

# Plot rolling Sharpe
plot(rolling_sharpe, type = "l", main = "Rolling 60-Day Sharpe Ratio")
abline(h = 0, col = "gray", lty = 2)

Drawdown Analysis

# Calculate drawdown series
drawdowns <- calc_drawdown(returns = strategy_returns)

# Plot drawdown
plot(drawdowns, type = "l", main = "Drawdown Over Time",
     ylab = "Drawdown %", col = "red")

# Get detailed drawdown periods
dd_periods <- calc_drawdown_duration(returns = strategy_returns)
print(dd_periods)

🔗 Integration with Traderverse

trademetrics works seamlessly with other Traderverse packages:

library(tradeio)        # Data acquisition
library(tradefeatures)  # Technical indicators
library(tradeengine)    # Backtesting
library(trademetrics)   # Performance analytics
library(tradeviz)       # Visualization

# Fetch data
prices <- fetch_prices("AAPL", from = Sys.Date() - 365)

# Add indicators
prices <- prices |>
  add_sma(20) |>
  add_rsi(14)

# Run backtest
results <- backtest(prices, strategy = my_strategy)

# Analyze performance
summary <- performance_summary(results$returns)
print(summary)

# Visualize
plot_equity_curve(results$equity)
plot_drawdown(results$equity)

📖 Documentation

🤝 Contributing

We welcome contributions! See our Contributing Guide for details.

📜 License

MIT License. See LICENSE file for details.

🌟 Part of Traderverse

trademetrics is part of the Traderverse ecosystem:

tradeio - Data acquisition
tradefeatures - Technical indicators
tradeengine - Backtesting engine
trademetrics - Performance analytics ⭐ You are here
tradeviz - Visualization
tradedash - Interactive dashboard

Built with ❤️ for the trading community
_{Making quantitative trading accessible to everyone}