Trading Benchmarks and Performance Metrics for Strategy Evaluation
Summary
This document outlines a common set of measures for evaluating trading performance: cumulative and annualized returns, annualized volatility, the Sharpe ratio, and maximum drawdown. It gives mathematical definitions for the return, volatility, and Sharpe calculations, with portfolio value, time, periodic returns, and the risk-free rate as inputs. These measures summarize growth and risk from different angles, but the page does not provide a worked example or results from a particular strategy.
It also lists passive buy-and-hold, mean-variance, minimum-variance, and equally weighted portfolios as comparison baselines. The accompanying tutorial topics span stock and cryptocurrency trading, portfolio allocation, liquidation, ensemble approaches, paper trading, and parameter tuning. This is a catalogue of evaluation conventions and learning examples, rather than an empirical benchmark study. The annualization convention and volatility description depend on the stated time units and return data, so comparisons require consistent definitions, benchmarks, and assumptions.
Key ideas
- Cumulative return measures the portfolio’s total change relative to its starting value.
- Annualized return, volatility, and Sharpe ratio summarize performance and risk over time.
- Maximum drawdown captures the largest percentage decline in portfolio value.
- Passive, mean-variance, minimum-variance, and equal-weight portfolios are offered as comparison baselines.
- The document lists tutorial areas but supplies no benchmark results or worked calculations.
Tags
Full text
# Benchmark
:github_url: https://github.com/AI4Finance-Foundation/FinRL
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Benchmark
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Performance Metrics
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FinRL-Meta provides the following unified metrics to measure the trading performance:
- **Cumulative return:** :math:`R = \frac{V - V_0}{V_0}`, where V is final portfolio value, and :math:`V_0` is original capital.
- **Annualized return:** :math:`r = (1+R)^\frac{365}{t}-1`, where t is the number of trading days.
- **Annualized volatility:** :math:`{\sigma}_a = \sqrt{\frac{\sum_{i=1}^{n}{(r_i-\bar{r})^2}}{n-1}}`, where :math:`r_i` is the annualized return in year i, :math:`\bar{r}` is the average annualized return, and n is the number of years.
- **Sharpe ratio:** :math:`S = \frac{r - r_f}{{\sigma}_a}`, where :math:`r_f` is the risk-free rate.
- **Max. drawdown** The maximal percentage loss in portfolio value.
The following baseline trading strategies are provided for comparisons:
• **Passive trading strategy**, a well-known long-term strategy. The investors just buy and hold selected stocks or indexes without further activities.
• ****Mean-variance and min-variance strategy**, both strategies look for a balance between risks and profits. It selects a diversified portfolio to achieve higher profits at lower risk.
• **Equally weighted strategy**, a portfolio allocation strategy that gives equal weights to different assets, avoiding allocating overly high weights on particular stocks.
Tutorials in Jupyter Notebooks
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For educational purposes, we provide Jupyter notebooks as tutorials to help newcomers get familiar with the whole pipeline. Notebooks can be found `here <https://github.com/AI4Finance-Foundation/FinRL-Tutorials>`_
• Stock trading: We apply popular DRL algorithms to trade multiple stocks.
• Portfolio allocation: We use DRL agents to optimize asset allocation in a set of stocks.
• Cryptocurrency trading: We reproduce the experiment on 10 popular cryptocurrencies.
• Multi-agent RL for liquidation strategy analysis: We reproduce the experiment in [7]. The multi-agent optimizes the shortfalls in the liquidation task, which is to sell given shares of one stock sequentially within a given period, considering the costs arising from the market impact and the risk aversion.
• Ensemble strategy for stock trading: We reproduce the experiment in that employed an ensemble strategy of several DRL algorithms on the stock trading task.
• Paper trading demo: We provide a demo for paper trading. Users could combine their own strategies or trained agents in paper trading.
• China A-share demo: We provide a demo based on the China A-share market data.
• Hyperparameter tuning: We provide several demos for hyperparameter tuning using Optuna or Ray Tune, since hyperparameter tuning is critical for better performance.Shown in full with attribution under the source's licence. Licence: MIT
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.