# 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
from typing import Callable
import numpy as np
import pandas as pd
from stochastic.processes.noise import GaussianNoise
from tensortrade.stochastic.utils.parameters import ModelParameters, default
[docs]def scale_times_to_generate(times_to_generate: int, time_frame: str) -> int:
"""Adjusts the number of times to generate the prices based on a time frame.
Parameters
----------
times_to_generate : int
The number of time to generate prices.
time_frame : str
The time frame for generating.
(e.g. 1h, 1min, 1w, 1d)
Returns
-------
int
The adjusted number of times to generate.
Raises
------
ValueError
Raised if the `time_frame` provided does not match the correct format.
"""
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: str) -> float:
"""Gets the time delta for a given time frame.
Parameters
----------
time_frame : str
The time frame for generating.
(e.g. 1h, 1min, 1w, 1d)
Returns
-------
float
The time delta for the given 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_prices(param: 'ModelParameters', log_returns: 'np.array') -> 'np.array':
"""Converts a sequence of log returns into normal returns (exponentiation)
and then computes a price sequence given a starting price, param.all_s0.
Parameters
----------
param: `ModelParameters`
The model parameters.
log_returns : `np.array`
The log returns.
Returns
-------
`np.array`
The price sequence.
"""
returns = np.exp(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 += [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) -> 'pd.DataFrame':
"""Generates a data frame of OHLCV data based on the price model specified.
Parameters
----------
price_fn : `Callable[[ModelParameters], np.array]`
The price function generate the prices based on the chosen model.
base_price : int, default 1
The base price to use for price generation.
base_volume : int, default 1
The base volume to use for volume generation.
start_date : str, default '2010-01-01'
The start date of the generated data
start_date_format : str, default '%Y-%m-%d'
The format for the start date of the generated data.
times_to_generate : int, default 1000
The number of bars to make.
time_frame : str, default '1h'
The time frame.
params : `ModelParameters`, optional
The model parameters.
Returns
-------
`pd.DataFrame`
The data frame containing the OHLCV bars.
"""
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