TrendShift is a builder-like library that appends some trend aggregations about onto a given pandas DataFrame.
In the following image the blue line, with the value on the right side, draws the EUR/USD price along a few hours and the red line, with the value on the left, draws the difference between every price movement, the trends.
pip install trendshift
The following example implementation builds all the available TrendShift features for a dataframe in the column "target_column".
my_new_df: DataFrame = = TrendShift(input_dataframe, "target_column")\
.with_sum()\
.with_numbered_steps()\
.with_simple_moving_avg()\
.with_steps_by_trend()\
.with_difference_by_trend()\
.with_trend_countdown()\
.build()
The snippet above over an example "input_dataframe.target_column" will output the following data set. "target_column" values are the original ones.
| target_column | trends_sum | step_number | simple_moving_avg | trend_difference | trend_steps | trend_countdown | remaining | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.0 | 20.0 | |||||
| 1 | 16 | 15.0 | 1.0 | 15.0 | 20.0 | 2.0 | 2.0 | 5.0 |
| 2 | 21 | 20.0 | 2.0 | 10.0 | 1.0 | 0.0 | ||
| 3 | 21 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ||
| 4 | 21 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ||
| 5 | 21 | 0.0 | 0.0 | 0.0 | 0.0 | -11.0 | ||
| 6 | 20 | -1.0 | 1.0 | -1.0 | -11.0 | 3.0 | 3.0 | -10.0 |
| 7 | 17 | -4.0 | 2.0 | -2.0 | 2.0 | -7.0 | ||
| 8 | 10 | -11.0 | 3.0 | -3.6666666666666665 | 1.0 |
More information within the main interface src/trendshift/trendshift.py
A Trend is a pattern found in incremental or at least continuous series datasets, like for example time series, that describes whether the data is moving upward or downward.
A Shift is a group of states within the input series dataset in one only direction, upward or downward. When the trend of the next value is different from the current one, the current Shift ends and the next Shift starts in the following value.
A Step is a state in the dataset series during an upward or downward Shift. A step of the input dataframe is a row within a Shift.
TrendShift applies some cumulative calculations over the target column. It appends a column for every calculation as a result to the original DataFrame.
Cumulative sum of every Step in a Shift.
Cumulative Step count of every Shift.
Simple Moving Average is the average of the difference between all Step in a Shift.
Total difference between the first Step and the last one in a Shift. This value is placed in the first Step only.
Total number of Step in a Shift. This value is placed in the first Step only.
Reversed upside down cumulative Step count in a Shift.
Difference between any trend step and the step at the end of the trend.
Tests are an important part in this project. My developing methodology is BDD and I developed this whole project from tests/features.
There you can find the most specific documentation about any service or entity of this project.
If you are not only a user of this library but a developer who wants to adapt or maintain this code, you should follow the same way, BDD.
The package https://behave.readthedocs.io/ is a development dependency you must install.
pip install behave
Execute the tests by the following command:
behave tests/features
Jaume Mila Bea jaume@westial.com