Benchmarking
Compare against market benchmarks
Technical comparison of historical portfolio data with market indices, asset classes, and model series.
How Benchmarking Works
Benchmarking compares historical portfolio data with market indices and transparently defined model series.
The software calculates relative performance, volatility, and deviations. It describes past data and assesses neither the suitability of a strategy nor any resulting transaction.
Market Index Comparison
How does the portfolio perform relative to the market?
With a holding period of six months for Bitcoin and Ethereum, the question of relative performance to the overall market arises.
Benchmarking tools compare historical portfolio data with relevant crypto indices such as the Crypto Market Cap Index or DeFi Pulse Index. They calculate how an index model would have developed over the same period. No recommendation to adjust an investment strategy is derived from the result.
Long-term performance trends are more significant than short-term volatility.
Functionality:
- Comparison of portfolio with crypto market indices
- Performance analysis relative to market benchmarks over defined periods
- Calculation of risk-adjusted returns
- Visual representation of relative performance
Asset Class Benchmarking
Technical Data Comparison
Different assets exhibit diverging performance characteristics. Stablecoins correlate differently than volatile altcoins. Lending protocols have different risk profiles than trading strategies.
Benchmarking compares historical metrics for recorded assets with defined asset classes. For example, it can show how a stablecoin series developed relative to average DeFi lending rates. The display contains no assessment of a personal allocation and no rebalancing recommendation.
Functionality:
- Benchmark against specific asset classes (Stablecoins, DeFi, Altcoins)
- Performance comparison within each asset category
- Display of differences within asset classes
- Attribution analysis of return drivers
Strategy Comparison
Active vs. Passive Performance
Comparing active trading strategies (market timing) with passive approaches is essential for performance evaluation.
Active trading results are contrasted with passive strategies like Dollar-Cost Averaging (DCA) or a buy-and-hold approach. This can reveal whether a passive approach with lower risk and lower transaction costs would have achieved superior performance. The goal is the objective evaluation of strategy efficiency.
Functionality:
- Comparison of portfolio with passive investment strategies (DCA, Buy-and-Hold)
- Benchmark against weighted portfolio models (e.g., 80% BTC, 20% ETH)
- Visualization of performance delta (Alpha)
- Metrics for efficiency evaluation of active vs. passive approaches
Risk-Adjusted Returns
Risk-Weighted Performance Analysis
A high nominal return must be evaluated in the context of volatility.
Benchmarking considers not only absolute returns but calculates risk-adjusted metrics like Sharpe Ratio and Sortino Ratio. Comparing risk-adjusted performance with market benchmarks indicates whether the achieved return adequately compensates for the risk taken.
Functionality:
- Calculation of risk-adjusted return metrics (Sharpe Ratio, Sortino Ratio)
- Comparison of risk-adjusted performance with benchmarks
- Representation of volatility-adjusted returns
Period Analysis
Performance Over Different Timeframes
Consistency over time periods is a key indicator of strategy quality.
The tools enable performance comparison over various time periods (daily, weekly, monthly, quarterly, yearly). This helps differentiate between short-term outliers and sustainable under- or outperformance against the benchmark.
Functionality:
- Performance comparison across multiple timeframes
- Analysis of performance under different market conditions
- Identification of trends relative to benchmarks
- Evaluation of investment strategy consistency
FAQ
We compare your portfolio against major crypto market indices, asset classes (stablecoins, DeFi, altcoins), and proven investment strategies like Dollar-Cost Averaging and buy-and-hold approaches. You can also create custom benchmarks based on your specific investment goals.
Benchmark data is updated in real-time based on current market conditions. Historical comparisons use accurate historical data to ensure fair and meaningful comparisons across different time periods.
Yes. Users can configure benchmarks from their own model weights or market indices. Treno does not assess which reference values are suitable for a user's personal circumstances.
Risk-adjusted returns account for the volatility and risk you're taking to achieve your returns. We calculate metrics like Sharpe Ratio and Sortino Ratio, comparing your risk-adjusted performance against market benchmarks. This helps you understand if you're being properly compensated for the risk you're taking.