# Sliding Slope

Calculates the linear slope on a given row, looking backwards for a user-defined window of periods.

Pass in a partition_col, an order_col, and a lookback window size.

NOTE: Your data should be a properly formatted timeseries dataset before applying this transformation. In other words, each period should only appear once, and periods considered zero should be imputed with 0 already. NOTE: Slope calculations are notoriously sensitive to large outliers, especially with smaller windows.

Example use case: On daily stock data, calculate SLOPE by TICKER, with a 14-period lookback window.

Name | Type | Description | Is Optional |
---|---|---|---|

partition_col | column | Grouping column to calculate the slope within. | |

order_col | column | Column to order rows by when calculating the agg window. Slope automatically sorts ascending. | |

value_col | column | Column to calulate slope for. | |

window | int | Number of periods to use as a lookback period, to calculate slope. | |

ds = rasgo.get.dataset(fqtn="RASGOCOMMUNITY.PUBLIC.ZEPL_DAILY_STOCK_FEATURES")

ds2 = ds.sliding_slope(partition_col = 'TICKER',

order_col = 'DATE',

value_col = 'CLOSE',

window = 14)

Last modified 7mo ago