Source code for tensortrade.exchanges.simulated.simulated_exchange

# Copyright 2019 The TensorTrade Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License

import numpy as np
import pandas as pd

from abc import abstractmethod
from gym.spaces import Space, Box
from typing import List, Dict, Generator

from tensortrade.trades import Trade, TradeType
from tensortrade.exchanges import InstrumentExchange
from tensortrade.slippage import RandomUniformSlippageModel


[docs]class SimulatedExchange(InstrumentExchange): """An instrument exchange, in which the price history is based off the supplied data frame and trade execution is largely decided by the designated slippage model. If the `data_frame` parameter is not supplied upon initialization, it must be set before the exchange can be used within a trading environment. """ def __init__(self, data_frame: pd.DataFrame = None, **kwargs): super().__init__(base_instrument=kwargs.get('base_instrument', 'USD'), dtype=kwargs.get('dtype', np.float16), feature_pipeline=kwargs.get('feature_pipeline', None)) self._should_pretransform_obs = kwargs.get('should_pretransform_obs', False) if data_frame is not None: self.data_frame = data_frame.astype(self._dtype) self._commission_percent = kwargs.get('commission_percent', 0.3) self._base_precision = kwargs.get('base_precision', 2) self._instrument_precision = kwargs.get('instrument_precision', 8) self._initial_balance = kwargs.get('initial_balance', 1E4) self._min_order_amount = kwargs.get('min_order_amount', 1E-3) self._window_size = kwargs.get('window_size', 1) self._min_trade_price = kwargs.get('min_trade_price', 1E-6) self._max_trade_price = kwargs.get('max_trade_price', 1E6) self._min_trade_amount = kwargs.get('min_trade_amount', 1E-3) self._max_trade_amount = kwargs.get('max_trade_amount', 1E6) max_allowed_slippage_percent = kwargs.get('max_allowed_slippage_percent', 1.0) SlippageModelClass = kwargs.get('slippage_model', RandomUniformSlippageModel) self._slippage_model = SlippageModelClass(max_allowed_slippage_percent) @property def data_frame(self) -> pd.DataFrame: """The underlying data model backing the price and volume simulation.""" return self._data_frame @data_frame.setter def data_frame(self, data_frame: pd.DataFrame): self._data_frame = data_frame if self._should_pretransform_obs and self._feature_pipeline is not None: self._data_frame = self._feature_pipeline.transform( self._data_frame, self.generated_space) @property def initial_balance(self) -> float: return self._initial_balance @property def balance(self) -> float: return self._balance @property def portfolio(self) -> Dict[str, float]: return self._portfolio @property def trades(self) -> pd.DataFrame: return self._trades @property def performance(self) -> pd.DataFrame: return self._performance @property def generated_space(self) -> Space: low = np.array([self._min_trade_price, ] * 4 + [self._min_trade_amount, ]) high = np.array([self._max_trade_price, ] * 4 + [self._max_trade_amount, ]) return Box(low=low, high=high, dtype='float') @property def generated_columns(self) -> List[str]: return list(['open', 'high', 'low', 'close', 'volume']) @property def has_next_observation(self) -> bool: return self._current_step < len(self._data_frame) - 1 def _create_observation_generator(self) -> Generator[pd.DataFrame, None, None]: for step in range(self._current_step, len(self._data_frame)): self._current_step = step obs = self._data_frame.iloc[step - self._window_size + 1:step + 1] if not self._should_pretransform_obs and self._feature_pipeline is not None: obs = self._feature_pipeline.transform(obs, self.generated_space) yield obs raise StopIteration
[docs] def current_price(self, symbol: str) -> float: if len(self._data_frame) is 0: self.next_observation() return float(self._data_frame['close'].values[self._current_step])
def _is_valid_trade(self, trade: Trade) -> bool: if trade.trade_type is TradeType.MARKET_BUY or trade.trade_type is TradeType.LIMIT_BUY: return trade.amount >= self._min_order_amount and self._balance >= trade.amount * trade.price elif trade.trade_type is TradeType.MARKET_SELL or trade.trade_type is TradeType.LIMIT_SELL: return trade.amount >= self._min_order_amount and self._portfolio.get(trade.symbol, 0) >= trade.amount return True def _update_account(self, trade: Trade): if trade.amount > 0: self._trades = self._trades.append({ 'step': self._current_step, 'symbol': trade.symbol, 'type': trade.trade_type, 'amount': trade.amount, 'price': trade.price }, ignore_index=True) if trade.is_buy: self._balance -= trade.amount * trade.price self._portfolio[trade.symbol] = self._portfolio.get(trade.symbol, 0) + trade.amount elif trade.is_sell: self._balance += trade.amount * trade.price self._portfolio[trade.symbol] = self._portfolio.get(trade.symbol, 0) - trade.amount self._portfolio[self._base_instrument] = self._balance self._performance = self._performance.append({ 'balance': self.balance, 'net_worth': self.net_worth, }, ignore_index=True)
[docs] def execute_trade(self, trade: Trade) -> Trade: current_price = self.current_price(symbol=trade.symbol) commission = self._commission_percent / 100 filled_trade = trade.copy() if filled_trade.is_hold or not self._is_valid_trade(filled_trade): filled_trade.amount = 0 elif filled_trade.is_buy: price_adjustment = price_adjustment = (1 + commission) filled_trade.price = max(round(current_price * price_adjustment, self._base_precision), self.base_precision) filled_trade.amount = round( (filled_trade.price * filled_trade.amount) / filled_trade.price, self._instrument_precision) elif filled_trade.is_sell: price_adjustment = (1 - commission) filled_trade.price = round(current_price * price_adjustment, self._base_precision) filled_trade.amount = round(filled_trade.amount, self._instrument_precision) filled_trade = self._slippage_model.fill_order(filled_trade, current_price) self._update_account(filled_trade) return filled_trade
[docs] def reset(self): super().reset() self._balance = self._initial_balance self._portfolio = {self._base_instrument: self._balance} self._trades = pd.DataFrame([], columns=['step', 'symbol', 'type', 'amount', 'price']) self._performance = pd.DataFrame([], columns=['balance', 'net_worth']) self._current_step = 0