# Hurst Exponent

get_hurst(quotes, lookback_periods=100)

## Parameters

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

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

`lookback_periods` | int, default 100 | Number of periods (`N` ) in the Hurst Analysis. Must be greater than 20. |

### Historical quotes requirements

You must have at least `N+1`

periods of `quotes`

to cover the warmup periods.

`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

```
HurstResults[HurstResult]
```

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

provided. `HurstResults`

is just a list of`HurstResult`

.- 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
`N`

periods will have`None`

values since there’s not enough data to calculate.

### HurstResult

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

`date` | datetime | Date |

`hurst_exponent` | float, Optional | Hurst Exponent (`H` ) |

### 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_historical_quotes("SPY")
# Calculate 20-period Hurst
results = indicators.get_hurst(quotes, 20)
```

## About Hurst Exponent

The Hurst Exponent (`H`

) is part of a Rescaled Range Analysis, a random-walk path analysis that measures trending and mean-reverting tendencies of incremental return values. When `H`

is greater than 0.5 it depicts trending. When `H`

is less than 0.5 it is is more likely to revert to the mean. When `H`

is around 0.5 it represents a random walk. [Discuss] 💬