# 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.