Source code for backtrader.indicators.contrib.center_of_gravity_indicator

#!/usr/bin/env python
"""Functional-test indicators migrated to contrib.

Generated from a single functional strategy module to preserve file-local
helper functions and constants without cross-test name collisions.
"""

from .. import (
    EMA,
    SMA,
    Indicator,
    WeightedMovingAverage,
)

__all__ = [
    "CenterOfGravityIndicator",
]




class CenterOfGravityIndicator(Indicator):
    """Center of Gravity indicator composed of price selection and smoothed center line."""

    lines = (
        "center",
        "signal",
        "state",
    )
    params = (
        ("period", 10),
        ("smooth_period", 3),
        ("ma_method", "sma"),
        ("applied_price", "close"),
        ("point", 0.01),
    )

    def __init__(self):
        """Initialize base MAs and derived center/signal/state lines."""
        ma_cls = SMA if str(self.p.ma_method).lower() == "sma" else EMA
        price = self._price_line()
        sma = SMA(price, period=self.p.period)
        lwma = WeightedMovingAverage(price, period=self.p.period)
        self.lines.center = (sma * lwma) / self.p.point
        self.lines.signal = ma_cls(self.lines.center, period=self.p.smooth_period)
        self.addminperiod(self.p.period + self.p.smooth_period + 5)

    def _price_line(self):
        mode = str(self.p.applied_price).lower()
        if mode == "open":
            return self.data.open
        if mode == "high":
            return self.data.high
        if mode == "low":
            return self.data.low
        if mode == "median":
            return (self.data.high + self.data.low) / 2.0
        if mode == "typical":
            return (self.data.high + self.data.low + self.data.close) / 3.0
        if mode == "weighted":
            return (self.data.high + self.data.low + self.data.close + self.data.close) / 4.0
        if mode == "simpl":
            return (self.data.open + self.data.close) / 2.0
        if mode == "quarter":
            return (self.data.high + self.data.low + self.data.open + self.data.close) / 4.0
        return self.data.close



    def next(self):
        """Compute the state classification for the current bar."""
        self.lines.state[0] = (
            2.0 if float(self.lines.center[0]) < float(self.lines.signal[0]) else 1.0
        )