# Benchmark¶

## Performance Metrics¶

FinRL-Meta provides the following unified metrics to measure the trading performance:

• Cumulative return: $$R = \frac{V - V_0}{V_0}$$, where V is final portfolio value, and $$V_0$$ is original capital.

• Annualized return: $$r = (1+R)^\frac{365}{t}-1$$, where t is the number of trading days.

• Annualized volatility: $${\sigma}_a = \sqrt{\frac{\sum_{i=1}^{n}{(r_i-\bar{r})^2}}{n-1}}$$, where $$r_i$$ is the annualized return in year i, $$\bar{r}$$ is the average annualized return, and n is the number of years.

• Sharpe ratio: $$S = \frac{r - r_f}{{\sigma}_a}$$, where $$r_f$$ 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¶

For educational purposes, we provide Jupyter notebooks as tutorials to help newcomers get familiar with the whole pipeline. Notebooks can be found here

• 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.