- OpenBacktesting
- Indicator catalog
- Aberration volatility channel (ta.aberration)
Aberration volatility channel (ta.aberration)¶
Construct upper and lower channels around smoothed prices and output true range.
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()
class Example(Strategy):
def init(self):
self.value = self.ta.aberration()
self.latest_value = None
def next(self):
channel = self.value.middle
if channel.valid[-1]:
self.latest_value = float(channel[-1])
result = Backtest(data, Example).run()
print(result["_strategy"].latest_value)
Call and parameters¶
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 |
5 |
Minimum: 1 |
atr_length |
ATR lookback in bars. | int |
15 |
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: high, low, close.
| Channel | Meaning |
|---|---|
middle |
Middle line |
upper |
Upper band |
lower |
Lower band |
atr |
Atr output |
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.