# 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,
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import numpy as np
from tensortrade.stochastic.utils import ModelParameters, convert_to_prices
[docs]def brownian_motion_log_returns(params: ModelParameters):
"""
Constructs a Wiener process (Brownian Motion).
References:
- http://en.wikipedia.org/wiki/Wiener_process
Arguments:
params : ModelParameters
The parameters for the stochastic model.
Returns:
brownian motion log returns
"""
sqrt_delta_sigma = np.sqrt(params.all_delta) * params.all_sigma
return np.random.normal(loc=0, scale=sqrt_delta_sigma, size=params.all_time)
[docs]def brownian_motion_levels(params: ModelParameters):
"""
Constructs a price sequence whose returns evolve according to brownian
motion.
Arguments:
params : ModelParameters
The parameters for the stochastic model.
Returns:
A price sequence which follows brownian motion
"""
return convert_to_prices(params, brownian_motion_log_returns(params))