Tool to calculate technical analysis indicators in a continuous time series data
Project description
python_storage_timeline_ta_indicators
This library reads, cleans and sorts data acquired from web database. Values in a storage expected to be in a json format with a following structure:
{"time":1684968241256,"value":"{\"r\":[49208.040463174424,730195614842.3989]}"}
Class data aggregation takes link as an argument to create instance of a database. The data from the database could be updated manualy via update
function.
To calculate ta indicators function print_indicators
should be used that takes next arguments:
time
in "2D", "4S" or "W" format that denotes length of even interwals time series will be divided.
length
takes int input and stands for number of intervals that will be used to calculate the indicator.
indicators
takes one (or multiple) of 'RSI', 'STOCH', 'STOCHRSI', 'ADX', 'MACD', 'WILLIAMS', 'CCI', 'ATR', 'HIGHLOW', 'ULTOSC', 'ULTOSC', 'ROC', 'MA'. If is None
calculates all of listed.
IMPORTANT
Some indicators require additional arguments. They should be added to print_indicators
function with names used in ta-lib
.
CODE EXAMPLE
from dataaggregation.aggregation import data_aggregation
my_data = data_aggregation('thelinkgoeshere')
my_data.get_data()
my_data.print_indicators('D', length = 14, smooth_step=6, indicators = ['RSI'])
Anomaly detection
Should only be used with a great understanding of features of data it was trained on. Works best on a volatile time series with high liquidity.
CODE EXAMPLE
from dataaggregation.aggregation import lstm_anomaly_detection
anms = lstm_anomaly_detection(url = 'thelinkgoeshere')
anms.detect_anomalies(threshold=1.25)
Output example####
NOW AVALIABLE ON PIP!
pip install python_storage_timeline_ta_indicators
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