def test_create_injected_simulated_exchange(trade_context):
with trade_context:
exchange = SimulatedExchange()
exchange.reset()
assert exchange.base_instrument == 'EURO'
assert exchange.initial_balance == 1e4
assert exchange._current_step == 0
def test_simlulated_from_config():
class NoSlippage(SlippageModel):
def fill_order(self, trade: Trade, **kwargs) -> Trade:
return trade
config = {
'base_instrument': 'EURO',
'instruments': ['BTC', 'ETH'],
'exchanges': {
'commission_percent': 0.5,
'base_precision': 0.3,
'instrument_precision': 10,
'min_trade_price': 1e-7,
'max_trade_price': 1e7,
'min_trade_amount': 1e-4,
'max_trade_amount': 1e4,
'initial_balance': 1e5,
'window_size': 5,
'should_pretransform_obs': True,
'max_allowed_slippage_percent': 3.0,
'slippage_model': NoSlippage
}
}
with TradingContext(**config):
exchange = SimulatedExchange()
exchange.base_instrument == 'EURO'
exchange._commission_percent == 0.5
def test_simulated_from_config():
class NoSlippage(SlippageModel):
def adjust_trade(self, trade: Trade, **kwargs) -> Trade:
pass
config = {
'base_instrument': 'EURO',
'instruments': ['BTC', 'ETH'],
'exchanges': {
'commission': 0.5,
'base_precision': 0.3,
'instrument_precision': 10,
'min_trade_price': 1e-7,
'max_trade_price': 1e7,
'min_trade_size': 1e-4,
'max_trade_size': 1e4,
'initial_balance': 1e5,
'window_size': 5,
'should_pretransform_obs': True,
'max_allowed_slippage_percent': 3.0,
'slippage_model': NoSlippage
}
}
with TradingContext(**config):
df = pd.DataFrame([[900, 849, 9023, 94039, 943]],
columns=["open", "high", "low", "close", "volume"])
exchange = SimulatedExchange(data_frame=df)
assert exchange._base_instrument == 'EURO'
assert exchange._commission == 0.5
def test_injects_base_instrument():
with TradingContext(**config):
df = pd.DataFrame([[900, 849, 9023, 94039, 943]],
columns=["open", "high", "low", "close", "volume"])
exchange = SimulatedExchange(data_frame=df)
assert exchange._base_instrument == EUR
def get_env(file_path):
df = load_data(file_path)
normalize = MinMaxNormalizer(inplace=True)
difference = FractionalDifference(difference_order=0.6, inplace=True)
feature_pipeline = FeaturePipeline(steps=[normalize, difference])
reward_strategy = SimpleProfitStrategy()
action_strategy = DiscreteActionStrategy(n_actions=20, instrument_symbol='BTC/USDT')
exchange = SimulatedExchange(base_instrument='USDT',
should_pretransform_obs=True,
feature_pipeline=feature_pipeline
)
exchange.data_frame = df[:STEP]
environment = TradingEnvironment(exchange=exchange,
action_strategy=action_strategy,
reward_strategy=reward_strategy,
feature_pipeline=feature_pipeline)
return environment
def main():
df = load_dataframe()
exchange = SimulatedExchange(df)
wallet_usd = Wallet(exchange, 0 * USD)
wallet_btc = Wallet(exchange, .01 * BTC)
portfolio = Portfolio(BTC, wallets=[wallet_usd, wallet_btc])
trading_pair = TradingPair(USD, BTC)
action = DynamicOrders(trading_pair)
env = TradingEnvironment(portfolio, exchange, action, 'simple',
window_size=20)
times = []
for _ in range(300):
action = 0
if random.random() > .9:
action = int(random.uniform(1, 20))
env.step(action)
t1 = time.time()
env.render('human')
times.append(time.time() - t1)
if len(times) > 120:
times.pop(0)
print(f'FPS: {1 / np.mean(times):.1f}', end='\r')
def test_injects_base_instrument():
with TradingContext(**config):
exchange = SimulatedExchange()
assert exchange.base_instrument == 'EURO'
from tensortrade.instruments import *
from tensortrade.exchanges.simulated import SimulatedExchange
from tensortrade.orders import Order, Broker
from tensortrade.orders.recipe import Recipe
from tensortrade.orders.criteria import StopLoss, StopDirection
from tensortrade.wallets import Portfolio, Wallet
from tensortrade.trades import TradeSide, TradeType
PRICE_COLUMN = "close"
data_frame = pd.read_csv("tests/data/input/coinbase-1h-btc-usd.csv")
data_frame.columns = map(str.lower, data_frame.columns)
data_frame = data_frame.rename(columns={'volume btc': 'volume'})
exchange = SimulatedExchange(data_frame=data_frame,
price_column=PRICE_COLUMN,
randomize_time_slices=True)
broker = Broker(exchange)
broker.reset()
def test_init():
wallets = [Wallet(exchange, 10000 * USD), Wallet(exchange, 0 * BTC)]
portfolio = Portfolio(base_instrument=USD, wallets=wallets)
base_wallet = portfolio.get_wallet(exchange.id, USD)
quantity = (1 / 10) * base_wallet.balance
order = Order(side=TradeSide.BUY,
trade_type=TradeType.MARKET,
pair=USD / BTC,
quantity=quantity,
exchange = CCXTExchange(exchange=coinbase, base_instrument='USD')
# RobinhoodExchange and InteractiveBrokersExchange are works in progress and allow you to trade stocks and ETF
# FBMExchnage is a simulated exchange which is simply an implementation of SimulatedExchange
from tensortrade.exchanges.simulated import FBMExchange
exchange = FBMExchange(base_instrument='BTC', timeframe='1h')
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')
# Feature Pipelines
from tensortrade.features import FeaturePipeline
from tensortrade.features.scalers import MinMaxNormalizer
from tensortrade.features.stationarity import FractionalDifference
from tensortrade.features.indicators import SimpleMovingAverage
price_columns = ['open', 'high', 'low', 'close']
normalize_price = MinMaxNormalizer(price_columns)
moving_averages = SimpleMovingAverage(price_columns)
difference_all = FractionalDifference(difference_order=0.6)
feature_pipeline = FeaturePipeline(
from tensortrade.features.indicators import TAlibIndicator
from tensortrade.environments import TradingEnvironment
from tensortrade.features import FeaturePipeline
from tensortrade.actions import DiscreteActions
from tensortrade.rewards import SimpleProfit
from tensortrade.strategies import StableBaselinesTradingStrategy
from stable_baselines import PPO2
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
candles = BitmxMinCandle.objects.all().order_by('id')[:1000]
df = pd.DataFrame(list(candles.values()))
# df = pd.read_csv('./data/Coinbase_BTCUSD_1h.csv')
exchange = SimulatedExchange(data_frame=df,
base_instrument='USD',
window_size=5,
should_pretransform_obs=True)
# -------------------------- Feature Pipelines ------------------------#
price_columns = ["open", "high", "low", "close"]
volume_column = ["volume"]
normalized_price = MinMaxNormalizer(price_columns)
normalized_volume = MinMaxNormalizer(volume_column)
sma = SimpleMovingAverage(columns=price_columns, window_size=50)
indicators = TAlibIndicator(
indicators=["EMA", "RSI", "CCI", "Stochastic", "MACD"],
lows=[30, -100, 20],
highs=[70, 100, 80])
difference_all = FractionalDifference(difference_order=0.6)
feature_pipeline = FeaturePipeline(steps=[
normalized_price, sma, difference_all, normalized_volume, indicators
])