# Parabolic SAR

get_parabolic_sar(quotes, acceleration_step=0.02, max_acceleration_factor=0.2)

### More overloaded interfaces

**get_parabolic_sar**(quotes, acceleration_step, max_acceleration_factor, initial_factor)

## Parameters

name | type | notes |
---|---|---|

`quotes` | Iterable[Quote] | Iterable of the Quote class or its sub-class. • See here for usage with pandas.DataFrame |

`acceleration_step` | float, default 0.02 | Incremental step size for the Acceleration Factor. Must be greater than 0. |

`max_acceleration_factor` | float, default 0.2 | Maximum factor limit. Must be greater than `acceleration_step` . |

`initial_factor` | float | Initial Acceleration Factor. Must be greater than 0 and not larger than `max_acceleration_factor` . Default is `acceleration_step` . |

### Historical quotes requirements

You must have at least two historical quotes to cover the warmup periods; however, we recommend at least 100 data points. Initial Parabolic SAR values prior to the first reversal are not accurate and are excluded from the results. Therefore, provide sufficient quotes to capture prior trend reversals, before your intended usage period.

`quotes`

is an `Iterable[Quote]`

collection of historical price quotes. It should have a consistent frequency (day, hour, minute, etc). See the Guide for more information.

## Return

```
ParabolicSARResults[ParabolicSARResult]
```

- This method returns a time series of all available indicator values for the
`quotes`

provided. `ParabolicSARResults`

is just a list of`ParabolicSARResult`

.- It always returns the same number of elements as there are in the historical quotes.
- It does not return a single incremental indicator value.
- The first trend will have
`None`

values since it is not accurate and based on an initial guess.

### ParabolicSARResult

name | type | notes |
---|---|---|

`date` | datetime | Date |

`sar` | float, Optional | Stop and Reverse value |

`is_reversal` | bool, Optional | Indicates a trend reversal |

### Utilities

See Utilities and Helpers for more information.

## Example

```
from stock_indicators import indicators
# This method is NOT a part of the library.
quotes = get_history_from_feed("SPY")
# calculate ParabolicSar(0.02,0.2)
results = indicators.get_parabolic_sar(quotes, 0.02, 0.2)
```

## About Parabolic SAR

Created by J. Welles Wilder, Parabolic SAR (stop and reverse) is a price-time based indicator used to determine trend direction and reversals. [Discuss] 💬