Machine Learning Driven Technical Analysis Library in Python.
Technical Analysis has long been a stable for generating insights for trading. While the predictive power of each individual indicator is up for a debate, it is no denying that technical indicators are useful tools to create summary statistics for time-series data. This usefulness is not limited to finance, but rather any domain with necessity to deal with time series.
However, most indicators are interpreted in a too simplistic way. For example, candlestick patterns classify each candle into one of a couple classes. However, we would get more useful information if we can also measure how close a candle matches each class.
MLTA enables this possibility. Instead of returning classes, MLTA generates the likelihood of a candle belonging to a given class. The result is more granula information which leads to an improvement in accuracy.
MLTA is not yet on pypi. You can install the package through the following command:
pip install git+https://github.com/palmbook/MLTA.git
from MLTA import Candlestick
candle_model = Candlestick()
# df must have columns open, high, low, and close (in lower letters)
# class_prob contains columns of probabilities for each possible class
class_prob = candle_model.CDL3INSIDE(df)
Available methods are
- CDL3INSIDE
- CDL3LINESTRIKE
- CDL3OUTSIDE
- CDL3WHITESOLDIERS
- CDLCLOSINGMARUBOZU
- CDLCOUNTERATTACK
- CDLDOJI
- CDLDRAGONFLYDOJI
- CDLGAPSIDESIDEWHITE
- CDLGRAVESTONEDOJI
- CDLHAMMER
- CDLHARAMI
- CDLHOMINGPIGEON
- CDLINVERTEDHAMMER
- CDLLADDERBOTTOM
- CDLLONGLEGGEDDOJI
- CDLLONGLINE
- CDLMARUBOZU
- CDLMATCHINGLOW
- CDLMORNINGDOJISTAR
- CDLMORNINGSTAR
- CDLRICKSHAWMAN
- CDLRISEFALL3METHODS
- CDLSEPARATINGLINES
- CDLSHORTLINE
- CDLSTICKSANDWICH
- CDLTAKURI
- CDLTASUKIGAP
- CDLUNIQUE3RIVER
- bullishPin
- bearishPin