Exchange

Exchanges determine the universe of tradable instruments within a trading environment, return observations to the environment on each time step, and execute trades made within the environment. There are two types of exchanges: live and simulated.

Live exchanges are implementations of Exchange backed by live pricing data and a live trade execution engine. For example, CCXTExchange is a live exchange, which is capable of returning pricing data and executing trades on hundreds of live cryptocurrency exchanges, such as Binance and Coinbase.

import ccxt
from tensortrade.exchanges.live import CCXTExchange

coinbase = ccxt.coinbasepro()
exchange = CCXTExchange(exchange=coinbase, base_instrument='USD')

There are also exchanges for stock and ETF trading, such as RobinhoodExchange and InteractiveBrokersExchange, but these are still works in progress.

Simulated exchanges, on the other hand, are implementations of Exchange backed by simulated pricing data and trade execution.

For example, FBMExchange is a simulated exchange, which generates pricing and volume data using fractional brownian motion (FBM). Since its price is simulated, the trades it executes must be simulated as well. The exchange uses a simple slippage model to simulate price and volume slippage on trades, though like almost everything in TensorTrade, this slippage model can easily be swapped out for something more complex.

from tensortrade.exchanges.simulated import FBMExchange

exchange = FBMExchange(base_instrument='BTC', timeframe='1h')

Though the FBMExchange generates fake price and volume data using a stochastic model, it is simply an implementation of SimulatedExchange. Under the hood, SimulatedExchange only requires a data_frame of price history to generate its simulations. This data_frame can either be provided by a coded implementation such as FBMExchange, or at runtime.

import pandas as pd
from tensortrade.exchanges.simulated import SimulatedExchange

df = pd.read_csv('./data/btc_ohclv_1h.csv')
exchange = SimulatedExchange(data_frame=df, base_instrument='USD')