rmr.py

import numpy as np
import pandas as pd

from .. import tools
from ..algo import Algo
from .olmar import OLMAR


def norm(x):
    if isinstance(x, pd.Series):
        axis = 0
    else:
        axis = 1
    return np.sqrt((x ** 2).sum(axis=axis))


class RMR(OLMAR):
    """Robust Median Reversion. Strategy exploiting mean-reversion by robust
    L1-median estimator. Practically the same as OLMAR.

    Reference:
        Dingjiang Huang, Junlong Zhou, Bin Li, Steven C.H. Hoi, Shuigeng Zhou
        Robust Median Reversion Strategy for On-Line Portfolio Selection, 2013.
        http://ijcai.org/papers13/Papers/IJCAI13-296.pdf
    """

    PRICE_TYPE = "raw"
    REPLACE_MISSING = True

    def __init__(self, window=5, eps=10.0, tau=0.001):
        """
        :param window: Lookback window.
        :param eps: Constraint on return for new weights on last price (average of prices).
            x * w >= eps for new weights w.
        :param tau: Precision for finding median. Recommended value is around 0.001. Strongly
                    affects algo speed.
        """
        super().__init__(window, eps)
        self.tau = tau

    def predict(self, x, history):
        """find L1 median to historical prices"""
        y = history.mean()
        y_last = None
        while y_last is None or norm(y - y_last) / norm(y_last) > self.tau:
            y_last = y
            d = norm(history - y)
            y = history.div(d, axis=0).sum() / (1.0 / d).sum()
        return y / x


if __name__ == "__main__":
    tools.quickrun(RMR())