A trading environment is a reinforcement learning environment that follows OpenAI’s gym.Env specification. This allows us to leverage many of the existing reinforcement learning models in our trading agent, if we’d like.

TradingEnvironment steps through the various interfaces from the tensortrade library in a consistent way, and will likely not change too often as all other parts of tensortrade changes. We’re going to go through an overview of the Trading environment below.

Trading environments are fully configurable gym environments with highly composable Exchange, FeaturePipeline, ActionScheme, and RewardScheme components.

  • The Exchange provides observations to the environment and executes the agent’s trades.
  • The FeaturePipeline optionally transforms the exchange output into a more meaningful set of features before it is passed to the agent.
  • The ActionScheme converts the agent’s actions into executable trades.
  • The RewardScheme calculates the reward for each time step based on the agent’s performance.

That’s all there is to it, now it’s just a matter of composing each of these components into a complete environment.

When the reset method of a TradingEnvironment is called, all of the child components will also be reset. The internal state of each exchange, feature pipeline, transformer, action scheme, and reward scheme will be set back to their default values, ready for the next episode.

Let’s begin with an example environment. As mentioned before, initializing a TradingEnvironment requires an exchange, an action scheme, and a reward scheme, the feature pipeline is optional.

OpenAI Gym Primer

Usually the OpenAI gym runs in the following way:

# Declare the environment
env = TrainingEnvironment()
# Declare and agent with an action_space, usually declared inside of the environment itself
agent = RandomAgent(env.action_space)
reward = 0
done = False

# Reset all of the variables
ob = env.reset() # Gets an observation as a response to resetting the variables
while True:
    # Get an observation, and input the previous reward, and indicator if the episode is complete or not (done).
    action = agent.act(ob, reward, done)
    ob, reward, done, _ = env.step(action)
    if done:

As such, the TradingEnvironment runs largely like this as well.

from tensortrade.environments import TradingEnvironment
from tensortrade.strategies import StableBaselinesTradingStrategy

environment = TradingEnvironment(exchange=exchange,

strategy.environment = environment
test_performance =, testing=True)

Here you may notice that we don’t have the same training code we saw above:

while True:
    # Get an observation, and input the previous reward, and indicator if the episode is complete or not (done).
    action = agent.act(ob, reward, done)
    ob, reward, done, _ = env.step(action)
    if done:

That’s because the code to run that exist directly inside of the TradingStrategy codebase. The command run, has abstractions of that code. Please refer to the Strategies codebase.


To better understand what’s inside of the TradingEnvironment, you should understand the notation. Everything that begins with an underscore _ is a relatively private function. While everything that doesn’t have the underscore is a public facing function.


  • _take_action
    • Determines a specific trade to be taken and executes it within the exchange.
  • _next_observation
    • Returns the next observation from the exchange.
  • _get_reward
    • Returns the reward for the current timestep.
  • _done
    • Returns whether or not the environment is done and should be restarted. The two key conditions to determine if the environment is completed is if either 90% of the funds are lost or if there are no more observations left.
  • _info
    • Returns any auxiliary, diagnostic, or debugging information for the current timestep.


  • step
    • Run one timestep within the environment based on the specified action.
  • reset
    • Resets the state of the environment and returns an initial observation.
  • render
    • This sends an output of what’s occuring in the gym enviornment for the user to keep track of.

Almost 100% of the private functions belong in the step function.