Python implementation of Lorentzian Classification algorithm.
Project description
This module is a python implementation of Lorentzian Classification algorithm developed by @jdehorty in pinescript. The original work can be found here - https://www.tradingview.com/script/WhBzgfDu-Machine-Learning-Lorentzian-Classification/
Prerequisites
- Ensure that TA-Lib is downloaded and built for your platform. Set
TA_INCLUDE_PATH
andTA_LIBRARY_PATH
as mentioned in ta-lib-python. TA-Lib package itself will be installed as a dependency ofadvanced-ta
. - Windows users can use below commands to install TA-Lib,
pip install pipwin pipwin install TA-Lib
Usage
At the most simplest, you can just do this:
from advanced_ta import LorentzianClassification
.
.
# df here is the dataframe containing stock data as [['open', 'high', 'low', 'close', 'volume']]. Notice that the column names are in lower case.
lc = LorentzianClassification(df)
lc.dump('output/result.csv')
lc.plot('output/result.jpg')
.
.
For advanced use, you can do:
from advanced_ta import LorentzianClassification
import TA-Lib as ta
.
.
# df here is the dataframe containing stock data as [['open', 'high', 'low', 'close', 'volume']]. Notice that the column names are in lower case.
lc = LorentzianClassification(
df,
features=[
LorentzianClassification.Feature("RSI", 14, 2), # f1
LorentzianClassification.Feature("WT", 10, 11), # f2
LorentzianClassification.Feature("CCI", 20, 2), # f3
LorentzianClassification.Feature("ADX", 20, 2), # f4
LorentzianClassification.Feature("RSI", 9, 2), # f5
ta.MFI(df['open'], df['high'], df['low'], df['close'], df['volume']) #f6
],
settings=LorentzianClassification.Settings(
source='close',
neighborsCount=8,
maxBarsBack=2000,
useDynamicExits=False
),
filterSettings=LorentzianClassification.FilterSettings(
useVolatilityFilter=True,
useRegimeFilter=True,
useAdxFilter=False,
regimeThreshold=-0.1,
adxThreshold=20,
kernelFilter = LorentzianClassification.KernelFilter(
useKernelSmoothing = False
lookbackWindow = 8
relativeWeight = 8.0
regressionLevel = 25
crossoverLag = 2
)
))
lc.dump('output/result.csv')
lc.plot('output/result.jpg')
.
.
Sample Plot
Generated
Reference From TradingView
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