# 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 pandas as pd
from stochastic.noise import GaussianNoise
from stochastic.continuous import FractionalBrownianMotion
from tensortrade.stochastic.utils import scale_times_to_generate
[docs]def fbm(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,
hurst: float = 0.61,
time_frame: str = '1h'):
times_to_generate = scale_times_to_generate(times_to_generate, time_frame)
price_fbm = FractionalBrownianMotion(t=times_to_generate, hurst=hurst)
price_volatility = price_fbm.sample(times_to_generate, zero=False)
prices = price_volatility + base_price
volume_gen = GaussianNoise(times_to_generate)
volume_volatility = volume_gen.sample(times_to_generate)
volumes = volume_volatility * price_volatility + 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