Source code for backtrader.indicators.lrsi

#!/usr/bin/env python
"""Laguerre RSI Module - Laguerre filter-based RSI.

This module provides the LaguerreRSI indicator defined by John F. Ehlers
for faster reaction to price changes using Laguerre filters.

Classes:
    LaguerreRSI: Laguerre RSI indicator (alias: LRSI).
    LaguerreFilter: Laguerre filter (alias: LAGF).

Example:
    class MyStrategy(bt.Strategy):
        def __init__(self):
            self.lrsi = bt.indicators.LRSI(self.data, gamma=0.5)
            self.lfilter = bt.indicators.LAGF(self.data, gamma=0.5)

        def next(self):
            if self.lrsi.lrsi[0] > 0.8:
                self.sell()
            elif self.lrsi.lrsi[0] < 0.2:
                self.buy()
"""

from . import PeriodN

__all__ = ["LaguerreRSI", "LRSI", "LaguerreFilter", "LAGF"]




class LaguerreRSI(PeriodN):
    """
    Defined by John F. Ehlers in `Cybernetic Analysis for Stock and Futures`,
    2004, published by Wiley. `ISBN: 978-0-471-46307-8`

    The Laguerre RSI tries to implement a better RSI by providing a sort of
    *Time Warp without Time Travel* using a Laguerre filter. This provides for
    faster reactions to price changes

    ``gamma`` is meant to have values between ``0.2`` and ``0.8``, with the
    best balance found theoretically at the default of ``0.5``
    """

    alias = ("LRSI",)
    lines = ("lrsi",)
    params = (
        ("gamma", 0.5),
        ("period", 6),
    )

    plotinfo = {"plotymargin": 0.15, "plotyticks": [0.0, 0.2, 0.5, 0.8, 1.0]}

    l0, l1, l2, l3 = 0.0, 0.0, 0.0, 0.0



    def next(self):
        """Calculate Laguerre RSI for the current bar.

        Calculates L0-L3 Laguerre filter values and computes the
        RSI-style ratio of upward changes to total changes.
        """
        l0_1 = self.l0  # cache previous intermediate values
        l1_1 = self.l1
        l2_1 = self.l2

        g = self.p.gamma  # avoid more lookups
        self.l0 = l0 = (1.0 - g) * self.data + g * l0_1
        self.l1 = l1 = -g * l0 + l0_1 + g * l1_1
        self.l2 = l2 = -g * l1 + l1_1 + g * l2_1
        self.l3 = l3 = -g * l2 + l2_1 + g * self.l3

        cu = 0.0
        cd = 0.0
        if l0 >= l1:
            cu = l0 - l1
        else:
            cd = l1 - l0

        if l1 >= l2:
            cu += l1 - l2
        else:
            cd += l2 - l1

        if l2 >= l3:
            cu += l2 - l3
        else:
            cd += l3 - l2

        den = cu + cd
        self.lines.lrsi[0] = 1.0 if not den else cu / den






class LaguerreFilter(PeriodN):
    """
    Defined by John F. Ehlers in `Cybernetic Analysis for Stock and Futures`,
    2004, published by Wiley. `ISBN: 978-0-471-46307-8`

    ``gamma`` is meant to have values between ``0.2`` and ``0.8``, with the
    best balance found theoretically at the default of ``0.5``
    """

    alias = ("LAGF",)
    lines = ("lfilter",)
    params = (("gamma", 0.5),)
    plotinfo = {"subplot": False}

    l0, l1, l2, l3 = 0.0, 0.0, 0.0, 0.0



    def next(self):
        """Calculate Laguerre filter for the current bar.

        Computes L0-L3 Laguerre filter values and outputs the
        weighted average (l0 + 2*l1 + 2*l2 + l3) / 6.
        """
        l0_1 = self.l0  # cache previous intermediate values
        l1_1 = self.l1
        l2_1 = self.l2

        g = self.p.gamma  # avoid more lookups
        self.l0 = l0 = (1.0 - g) * self.data + g * l0_1
        self.l1 = l1 = -g * l0 + l0_1 + g * l1_1
        self.l2 = l2 = -g * l1 + l1_1 + g * l2_1
        self.l3 = l3 = -g * l2 + l2_1 + g * self.l3
        self.lines.lfilter[0] = (l0 + (2 * l1) + (2 * l2) + l3) / 6




LRSI = LaguerreRSI
LAGF = LaguerreFilter