Customization

Custom reward function

Use the History object to create your custom reward function. Below is an example with a really basic reward function \(r_{t} = ln(\frac{p_{t}}{p_{t-1}})\text{ with }p_{t}\text{ = portofolio valuation at timestep }t\) (this is the default reward function).

import gymnasium as gym
import numpy as np
def reward_function(history):
        return np.log(history["portfolio_valuation", -1] / history["portfolio_valuation", -2])

env = gym.make("TradingEnv",
        ...
        reward_function = reward_function
        ...
    )

Custom logs

Use the History object to add custom logs. If the verbose parameter of your trading environment is set to 1 or 2, the environment displays a quick summary of your episode. By default Market Return and Portfolio Return are the displayed metrics.

Market Return :  25.30%   |   Portfolio Return : 45.24%

You can add custom metrics using the method .add_metric(name, function) after initializing your environment :

env = gym.make("TradingEnv",
       ...
   )
env.add_metric('Position Changes', lambda history : np.sum(np.diff(history['position']) != 0) )
env.add_metric('Episode Lenght', lambda history : len(history['position']) )
# Then, run your episode(s)

Market Return :  25.30%   |   Portfolio Return : 45.24%   |   Position Changes : 28417   |   Episode Lenght : 33087

The .add_metric method takes 2 parameters :

• name : The displayed name of the metrics

• function : The function that takes the History object as parameters and returns a value (you might want to prefer string over other types to avoid error).

Note

If you want to use your metrics to feed a custom logger, to visualize data or to track performance, you can access to results with env.get_metrics() at the end of an episode. In this case, it returns :

{ "Market Return" :  "25.30%", "Portfolio Return" : "45.24%", "Position Changes" : 28417, "Episode Lenght" : 33087 }

Note

If you want to use your metrics to feed a custom logger, to visualize data or to track performance, you can access to results with env.get_metrics() at the end of an episode. In this case, it returns :

{ "Market Return" :  "25.30%", "Portfolio Return" : "45.24%", "Position Changes" : 28417, "Episode Lenght" : 33087 }