# Indicators: calculate, compose, and read values
Use `self.ta` inside `Strategy.init()` for built-in indicators, `self.ind` for a custom function, and `indicators.compute` for a calculation outside a backtest. Browse the [indicator catalog](https://docs.openbacktesting.com/reference/indicators/index.md) for every formula, its parameters, inputs, and outputs.
[compute](https://docs.openbacktesting.com/reference/indicator-api/#openbacktesting-indicators-compute) · [catalog](https://docs.openbacktesting.com/reference/indicator-api/#openbacktesting-indicators-catalog) · [describe](https://docs.openbacktesting.com/reference/indicator-api/#openbacktesting-indicators-describe) · [IndicatorHandle](https://docs.openbacktesting.com/reference/indicator-api/#openbacktesting-indicators-types-indicatorhandle) · [IndicatorResult](https://docs.openbacktesting.com/reference/indicator-api/#openbacktesting-indicators-api-indicatorresult) · [is_cross](https://docs.openbacktesting.com/reference/indicator-api/#openbacktesting-indicators-methods-technicalanalysis-is-cross) · [is_crossover](https://docs.openbacktesting.com/reference/indicator-api/#openbacktesting-indicators-methods-technicalanalysis-is-crossover) · [is_crossunder](https://docs.openbacktesting.com/reference/indicator-api/#openbacktesting-indicators-methods-technicalanalysis-is-crossunder)
## Single and multiple outputs
```python
from openbacktesting import Backtest, Strategy
from openbacktesting.test import GOOG
class IndicatorExample(Strategy):
def init(self):
self.ema = self.ta.ema(self.close, length=20)
self.macd = self.ta.macd(fast=12, slow=26, signal=9)
self.smooth = self.ta.sma(self.macd.histogram, length=3)
def next(self):
if self.ema.valid[-1] and self.smooth.valid[-1]:
if self.close[-1] > self.ema[-1] and self.smooth[-1] > 0:
if not self.position:
self.buy(qty=1)
elif self.position:
self.position.close()
result = Backtest(GOOG, IndicatorExample).run()
print(result["Return [%]"])
```
An EMA returns an `IndicatorHandle`. A multi-output formula such as MACD returns an `IndicatorResult`; select `.macd`, `.signal`, or `.histogram` before passing a channel to another indicator. Bracket access such as `result["histogram"]` also works. Built-in calculations are registered only in `init()`.
## Bind inputs and read outputs
| Argument or operation | Meaning |
| --- | --- |
| `self.ta.ema(length=20)` | Use that indicator's default market column (Close here). |
| `self.ta.ema(self.hl2, length=20)` | Use a same-run single-channel handle. A column name such as `"Close"` is also accepted. |
| `inputs={"high": "High", "low": "Low", "close": "Close"}` | Explicit mapping of formula inputs to data columns or same-run channels; do not combine with positional source arguments. |
| Formula keywords such as `length`, `fast`, `slow` | See each indicator's parameter table for defaults, allowed values, and meaning. |
| `backend=None` | Use the formula's default implementation. Only documented alternatives are accepted. |
| `required_outputs=None` | Use default outputs for readiness diagnostics; an explicit channel list changes readiness, not whether `next()` runs. |
| `handle[-1]`, `handle[-2]` | Current and previous visible values; check history length. |
| `handle.valid[-1]` | Whether this channel has a consumable current value. Check it before trading. |
| `handle.ready[-1]` | Minimum-history/readiness diagnostic. It does not skip callbacks. |
| `handle.s`, `handle.df`, `numpy.asarray(handle)` | Independent snapshots of the visible prefix. |
During `next()`, saved handles and their slices follow the current bar. A saved array/Series or arithmetic result does not. `False` and zero may be valid signals; NaN is not a usable signal. Do not use `bool(NaN)` as a validity check. For a custom calculation, see [Strategy.ind](https://docs.openbacktesting.com/reference/strategy/index.md#openbacktesting-strategy-ind).
## Crosses and confirmed events
`self.ta.is_crossover(a, b)` returns a boolean at the current bar; `is_crossunder` reverses direction and `is_cross` accepts either direction. Both operands need two valid visible observations. These predicates can be called in `next()`.
`self.ta.crossover(a, b)` instead registers a whole signal history in `init()`. Use it when composing signals with `valuewhen` or `barssince`. A pivot becomes available when its right-hand confirmation window finishes; it does not backfill a tradable signal at the earlier extreme. Check the [pivot](https://docs.openbacktesting.com/reference/indicators/pivothigh/index.md) and [session-level](https://docs.openbacktesting.com/reference/indicators/pivot_points/index.md) pages before using those results.
## Calculate outside a strategy
```python
from openbacktesting import indicators
from openbacktesting.test import GOOG
ema = indicators.compute("ta.ema", GOOG, parameters={"length": 20})
print(ema.s.tail())
description = indicators.describe("ta.ema")
catalog = indicators.catalog()
```
`compute` returns an `IndicatorHandle` covering the full input. Multi-output offline results select channels by name, for example `macd["histogram"]`. `parameters` supplies formula options; `inputs` maps input names to frame columns. Ordinary offline calls can omit `cache` and `cursor`. `describe` returns one formula's specification and `catalog` lists the available definitions.
## compute
```python
indicators.compute(
indicator,
frame,
*,
parameters=None,
backend=None,
inputs=None,
cache=None,
cursor=None,
required_outputs=None,
)
```
Compute reviewed indicators in bulk on explicit market data and return read-only result handles.
## catalog
```python
indicators.catalog()
```
Return a tuple of all reviewed definitions, including deferred entries that cannot be called.
## describe
```python
indicators.describe(indicator)
```
Look up an indicator definition by stable ID without computing it.
## IndicatorHandle
Time is the final dimension; all derived views apply cursors and selectors only when read.
### Attributes
| Attribute | Meaning |
| --- | --- |
| `df` | pandas DataFrame copy of all channels' current prefixes. |
| `dtype` | Data type of the underlying read-only numeric buffer. |
| `metadata` | Return a deep copy of provenance metadata; modifications do not affect the registered result. |
| `ndim` | Output dimensions: 1 for a single channel, 2 for multiple channels. |
| `ready` | Read-only mask indicating whether the current prefix meets minimum-history conditions. |
| `s` | pandas Series copy of the first channel's current prefix; saved copies do not grow with the cursor. |
| `shape` | Shape of the currently visible prefix; time is the last axis for multiple outputs. |
| `valid` | Read-only validity masks for each channel's current prefix; False does not mean numeric zero. |
### IndicatorHandle.astype
```python
IndicatorHandle.astype(dtype)
```
Convert the current prefix to a NumPy array snapshot with dtype; do not retain a dynamic cursor.
### IndicatorHandle.copy
```python
IndicatorHandle.copy()
```
Return a writable NumPy copy of the current prefix that no longer follows the cursor.
### IndicatorHandle.first_ready
```python
IndicatorHandle.first_ready()
```
Return the earliest ready position for required outputs, or total length if never ready; does not control callbacks.
### IndicatorHandle.ready_at
```python
IndicatorHandle.ready_at(bar)
```
Query readiness of required outputs and minimum-history conditions; does not control strategy callbacks.
## IndicatorResult
Read-only attribute container for multiple outputs; channel views are created only during initialization.
### Attributes
| Attribute | Meaning |
| --- | --- |
| `df` | Return a pandas DataFrame copy of the currently visible prefix of all channels. |
| `metadata` | Return an independent copy of the underlying handle's provenance metadata. |
| `ready` | Minimum-history mask shared by all outputs. |
| `valid` | Underlying validity masks for each channel; check the selected channel before trading. |
## is_cross
```python
indicators.methods.TechnicalAnalysis.is_cross(left, right)
```
Check whether either-direction crossing occurs on the last currently visible bar.
## is_crossover
```python
indicators.methods.TechnicalAnalysis.is_crossover(left, right)
```
Check whether the current left value is strictly above the right, with the preceding left value at or below the right.
## is_crossunder
```python
indicators.methods.TechnicalAnalysis.is_crossunder(left, right)
```
Check whether the current left value is strictly below the right, with the preceding left value at or above the right.
[Indicator catalog](https://docs.openbacktesting.com/reference/indicators/index.md) · [Strategy](https://docs.openbacktesting.com/reference/strategy/index.md) · [User manual](https://docs.openbacktesting.com/index.md)