Short trend phase (ta.short_run)

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Identify bearish phases from directional changes of fast and slow series.

Indicator catalog · How to use indicators

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()
data["fast"] = data.Close.rolling(3).mean()
data["slow"] = data.Close.rolling(10).mean()

class Example(Strategy):
    def init(self):
        self.value = self.ta.short_run(inputs={'fast': 'fast', 'slow': 'slow'})
        self.latest_value = None

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

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

Example inputs: fast is the three-bar mean of close prices; slow is the ten-bar mean of close prices.

Call and parameters

self.ta.short_run(*, length=2, 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	2	Minimum: 1

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: fast, slow.

Channel	Meaning
short_run	Short trend phase value

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.

Indicator catalog · Custom calculations