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Source code for qiskit_finance.data_providers.random_data_provider

# This code is part of a Qiskit project.
#
# (C) Copyright IBM 2019, 2023.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

""" Pseudo-randomly generated mock stock-market data provider """

from typing import Optional, Union, List
import datetime
import logging
import pandas as pd
import numpy as np

from ._base_data_provider import BaseDataProvider

logger = logging.getLogger(__name__)


class RandomDataProvider(BaseDataProvider):
    """Pseudo-randomly generated mock stock-market data provider."""

    def __init__(
        self,
        tickers: Optional[Union[str, List[str]]] = None,
        start: datetime.datetime = datetime.datetime(2016, 1, 1),
        end: datetime.datetime = datetime.datetime(2016, 1, 30),
        seed: Optional[int] = None,
    ) -> None:
        """
        Args:
            tickers: tickers
            start: first data point
            end: last data point precedes this date
            seed: optional random seed
        """
        super().__init__()
        tickers = tickers if tickers is not None else ["TICKER1", "TICKER2"]
        if isinstance(tickers, list):
            self._tickers = tickers
        else:
            self._tickers = tickers.replace("\n", ";").split(";")
        self._n = len(self._tickers)

        self._start = start
        self._end = end
        self._seed = seed

    def run(self) -> None:
        """
        Generates data pseudo-randomly, thus enabling get_similarity_matrix
        and get_covariance_matrix methods in the base class.
        """

        length = (self._end - self._start).days
        generator = np.random.default_rng(self._seed)
        self._data = []
        for _ in self._tickers:
            d_f = pd.DataFrame(generator.standard_normal(length)).cumsum() + generator.integers(
                1, 101
            )
            trimmed = np.maximum(d_f[0].values, np.zeros(len(d_f[0].values)))
            trimmed_list = trimmed.tolist()
            # find index of first 0 element
            zero_idx = next((idx for idx, val in enumerate(trimmed_list) if val == 0), -1)
            if zero_idx >= 0:
                # set to 0 all values after first 0
                trimmed_list = [
                    val if idx < zero_idx else 0 for idx, val in enumerate(trimmed_list)
                ]
            self._data.append(trimmed_list)