# Relative Strength Index (RSI)

get_rsi(quotes, lookback_periods=14)

## 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 14 | Number of periods (`N` ) in the lookback period. Must be greater than 0. |

### Historical quotes requirements

You must have at least `N+100`

periods of `quotes`

to cover the convergence periods. Since this uses a smoothing technique, we recommend you use at least `10×N`

data points prior to the intended usage date for better precision.

`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.

## Returns

```
RSIResults[RSIResult]
```

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

provided. `RSIResults`

is just a list of`RSIResult`

.- 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-1`

periods will have`None`

values since there’s not enough data to calculate.

⚞

Convergence warning: The first`10×N`

periods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.

### RSIResult

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

`date` | datetime | Date |

`rsi` | float, Optional | RSI over prior `N` lookback periods |

### 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 RSI(14)
results = indicators.get_rsi(quotes, 14)
```

## About Relative Strength Index (RSI)

Created by J. Welles Wilder, the Relative Strength Index measures strength of the winning/losing streak over `N`

lookback periods on a scale of 0 to 100, to depict overbought and oversold conditions. [Discuss] 💬