A few years ago developed an algorithm to label momentum and trend patterns in intra-day or daily price data.
In spite of its simplicity, has performed quite well as compared to a number of more complicated statistical approaches.
As is not especially proprietary, hence thought to share this more broadly.
Please note that the labeler makes use of a forward window to achieve 0 lag labeling. Hence if you want to use a technique like this in live trading, would only be useful as a way to identify prior price moves, but cannot indicate the direction of the current time period. The tradeoff is between 0 lag + lookahead or lag + no lookahead.
The labeler behavior is defined by two parameters (which seem intuitive from a trading perspective):
- minimum trend / momentum amplitude of interest
- this should be some multiple of volatility / noise
- maximum amount of noise allowed in move:
- defined by maximum period where no new high (low) is achieved), as well as
- no drawdown in move exceeding the minimum move amplitude
There are other ways to define noise or extension, but these choises resulted in a super-simple model, that works well. In addition an incremental OLS is performed to determine which points best fit the move, discarding outliers around the edges.
Below are some examples of the same (intra-day) data series, parameterized for more noise, less noise, higher or lower minimum amplitudes.
labeler = AmplitudeBasedLabeler (minamp = 20, Tinactive = 10)
labels = labeler.label (df)
labeler.plot()
labeler = AmplitudeBasedLabeler (minamp = 20, Tinactive = 30)
labels = labeler.label (df)
labeler.plot()