# Copyright 2020 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 re
import numpy as np
import pandas as pd
from typing import Callable
from stochastic.noise import GaussianNoise
from .parameters import ModelParameters, default
[docs]def scale_times_to_generate(times_to_generate: int, time_frame: str):
if 'MIN' in time_frame.upper():
times_to_generate *= int(re.findall(r'\d+', time_frame)[0])
elif 'H' in time_frame.upper():
times_to_generate *= int(re.findall(r'\d+', time_frame)[0]) * 60
elif 'D' in time_frame.upper():
times_to_generate *= int(re.findall(r'\d+', time_frame)[0]) * 60 * 24
elif 'W' in time_frame.upper():
times_to_generate *= int(re.findall(r'\d+', time_frame)[0]) * 60 * 24 * 7
elif 'M' in time_frame.upper():
times_to_generate *= int(re.findall(r'\d+', time_frame)[0]) * 60 * 24 * 7 * 30
else:
raise ValueError('Timeframe must be either in minutes (min), hours (H), days (D), weeks (W), or months (M)')
return times_to_generate
[docs]def get_delta(time_frame):
if 'MIN' in time_frame.upper():
return 1 / (252 * 24 * (60 / int(time_frame.split('MIN')[0])))
elif 'H' in time_frame.upper():
return 1 / (252 * (24 / int(time_frame.split('H')[0])))
elif 'D' in time_frame.upper():
return 1 / 252
elif 'M' in time_frame.upper():
return 1 / 12
[docs]def convert_to_returns(log_returns):
"""
This method exponentiates a sequence of log returns to get daily returns.
:param log_returns: the log returns to exponentiated
:return: the exponentiated returns
"""
return np.exp(log_returns)
[docs]def convert_to_prices(param, log_returns):
"""
This method converts a sequence of log returns into normal returns (exponentiation) and then computes a price
sequence given a starting price, param.all_s0.
:param param: the model parameters object
:param log_returns: the log returns to exponentiated
:return:
"""
returns = convert_to_returns(log_returns)
# A sequence of prices starting with param.all_s0
price_sequence = [param.all_s0]
for i in range(1, len(returns)):
# Add the price at t-1 * return at t
price_sequence.append(price_sequence[i - 1] * returns[i - 1])
return np.array(price_sequence)
[docs]def generate(price_fn: Callable[[ModelParameters], np.array],
base_price: int = 1,
base_volume: int = 1,
start_date: str = '2010-01-01',
start_date_format: str = '%Y-%m-%d',
times_to_generate: int = 1000,
time_frame: str = '1h',
params: ModelParameters = None):
delta = get_delta(time_frame)
times_to_generate = scale_times_to_generate(times_to_generate, time_frame)
params = params or default(base_price, times_to_generate, delta)
prices = price_fn(params)
volume_gen = GaussianNoise(t=times_to_generate)
volumes = volume_gen.sample(times_to_generate) + base_volume
start_date = pd.to_datetime(start_date, format=start_date_format)
price_frame = pd.DataFrame([], columns=['date', 'price'], dtype=float)
volume_frame = pd.DataFrame([], columns=['date', 'volume'], dtype=float)
price_frame['date'] = pd.date_range(start=start_date, periods=times_to_generate, freq="1min")
price_frame['price'] = abs(prices)
volume_frame['date'] = price_frame['date'].copy()
volume_frame['volume'] = abs(volumes)
price_frame.set_index('date')
price_frame.index = pd.to_datetime(price_frame.index, unit='m', origin=start_date)
volume_frame.set_index('date')
volume_frame.index = pd.to_datetime(volume_frame.index, unit='m', origin=start_date)
data_frame = price_frame['price'].resample(time_frame).ohlc()
data_frame['volume'] = volume_frame['volume'].resample(time_frame).sum()
return data_frame