Stochastic Momentum Index (ta.smi_stochastic)

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Smooth the price's distance from the window midpoint and normalize by the range; this differs from SMI Ergodic.

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Run this example

This complete script uses bundled data. It observes the selected output without placing trades.

import numpy as np
from openbacktesting import Backtest, Strategy
from openbacktesting.test import GOOG

data = GOOG.copy()

class Example(Strategy):
    def init(self):
        self.value = self.ta.smi_stochastic()
        self.latest_value = None

    def next(self):
        channel = self.value.smi
        if channel.valid[-1]:
            self.latest_value = float(channel[-1])

result = Backtest(data, Example).run()
print(result["_strategy"].latest_value)

Call and parameters

self.ta.smi_stochastic(
    *,
    length=13,
    smooth=25,
    double=2,
    signal=3,
    inputs=None,
    backend=None,
    required_outputs=None,
)

Register in init(). Read the returned channel in next() after checking valid[-1].

Parameter	Meaning	Type	Default	Allowed values
length	Calculation window in observations.	int	13	Minimum: 1; Maximum: 100000
smooth	Additional smoothing period in bars.	int	25	Minimum: 1; Maximum: 100000
double	Second smoothing period for stochastic momentum.	int	2	Minimum: 1; Maximum: 100000
signal	Signal-line smoothing period in bars.	int	3	Minimum: 1; Maximum: 100000

source/named source arguments accept a column name or same-run single-channel handle. Alternatively, inputs maps each input below to a column or handle. backend selects a supported implementation; required_outputs selects channels for readiness diagnostics. See shared arguments and handles.

Inputs and outputs

Required inputs: high, low, close.

Channel	Meaning
smi	Smi output
signal	Signal line

Reading values safely

Insufficient history or missing inputs can make a channel invalid. Check that channel's valid mask; ready is diagnostic and never suppresses next(). Multi-output results may have different valid channels on the same bar.

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