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Gym CryptoTrading Environment

license dep2 dep3 dep4

Gym Environment API based Bitcoin trading simulator with continuous observation space and discrete action space. It uses real world transactions from CoinBaseUSD exchange to sample per minute closing, lowest and highest prices along with volume of the currency traded in the particular minute interval.

Contents of this document

Installation

git clone https://github.com/samre12/gym-cryptotrading.git
cd gym-cryptotrading
pip install -e .

Usage

Importing the module into the current session using import gym_cryptotrading will register the environment with gym after which it can be used as any other gym environment.

import gym
import gym_cryptotrading
env = gym.make('CryptoTrading-v0')

  • Use env.reset() to start a new random episode.

    • returns history of observations prior to the starting point of the episode. Look Parameters for more information.
    state = env.reset() # use state to make initial prediction

    Note: Make sure to reset the environment before first use else gym.error.ResetNeeded() will be raised.

  • Use env.step(action) to take one step in the environment.

    • returns (observation, reward, is_terminal) in respective order
    observation, reward, is_terminal = env.step(action)

    Note: Calling env.step(action) after the terminal state is reached will raise gym.error.ResetNeeded().

  • With the current implementation, the environment does not support env.render().

Setting the logging level of gym using gym.logger.set_level(level) to a value less than or equal 10 will allow to track all the logs (debug and info levels) generated by the environment.
These include human readable timestamps of Bitcoin prices used to simulate an episode.

Environment

Observation Space

  • Observation at a time step is (closing, lowest, highest, volume) of Bitcoin in the corresponding minute interval.

  • Since the price of Bitcoin varies from a few dollars to 15K dollars, the observation for time step i + 1 is normalized by the prices at time instant i.

Each entry in the observation is the ratio of increase (value greater than 1.0) or decrease (value lessar than 1.0) from the price at previos time instant.

Action Space

At each time step, the agent can either go LONG or SHORT in a unit (for more information , refer to Parameters) of Bitcoin of can stay NEUTRAL.
Action space thus becomes discrete with three possible actions:

  • NEUTRAL corresponds to 0

  • LONG corresponds to 1

  • SHORT corresponds to 2

Note: Use env.action_space.get_action(action) to lookup action names corresponding to their respective values.

Parameters

The environment is characterized with these parameters:

  • history_length lag in the observations that is used for the state representation of the trading agent.

    • every call to env.reset() returns a numpy array of shape (history_length,) + shape(observation) that corresponds to observations of length history_length prior to the starting point of the episode.

    • trading agent can use the returned array to predict the first action

    • defaults to 100.

    • supplied value must be greater than or equal to 0

  • horizon alternatively episode length is the number trades that the agent does in a single episode

    • defaults to 5.

    • supplied value must be greater than 0

  • unit is the fraction of Bitcoin that can be traded in each time step

    • defaults to 5e-4.

    • supplied value must be greater than 0

Usage

env = gym.make('CryptoTrading-v0')
env.env.set_params(history_length, horizon, unit)

Note: parameters can only be set before first reset of the environment, that is, before the first call to env.reset(), else gym_cryptotrading.errors.EnvironmentAlreadyLoaded will be raised

Simulator

  • Dataset for per minute prices of Bitcoin is not continuos and compute due to the downtime of the exchanges.

  • Current implementation does not make any assumptions about the missing values.

  • It rather finds continuos blocks with lengths greater than history_length + horizon + 1 and use them to simulate episodes. This avoids any discrepancies in results due to random subsitution of missing values

Important Information

Upon first use, the environment downloads latest transactions dataset from the exchange which are then cached in tempory directory of the operating system for future use.

  • A user can also update the latest transactions dataset by calling gym_cryptotrading.Generator.update_gen() prior to making the environment.

    • Updating the latest transactions won't reflect in environments made earlier.

  • If you are running the environment behind a proxy, export suitalble http proxy settings to allow the environment to download transactions from the exchange

Examples

Coming soon.