# 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
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# Unless required by applicable law or agreed to in writing, software
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import numpy as np
import pandas as pd
from stochastic.noise import GaussianNoise
from tensortrade.stochastic.processes.brownian_motion import brownian_motion_log_returns
from tensortrade.stochastic.utils.helpers import get_delta, scale_times_to_generate
from tensortrade.stochastic.utils.parameters import ModelParameters, default
[docs]def ornstein_uhlenbeck_levels(params):
"""
Constructs the rate levels of a mean-reverting ornstein uhlenbeck process.
Arguments:
params : ModelParameters
The parameters for the stochastic model.
Returns:
The interest rate levels for the Ornstein Uhlenbeck process
"""
ou_levels = [params.all_r0]
brownian_motion_returns = brownian_motion_log_returns(params)
for i in range(1, params.all_time):
drift = params.ou_a * (params.ou_mu - ou_levels[i - 1]) * params.all_delta
randomness = brownian_motion_returns[i - 1]
ou_levels.append(ou_levels[i - 1] + drift + randomness)
return np.array(ou_levels)
[docs]def ornstein(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 = ornstein_uhlenbeck_levels(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