ohlcvish takes OHLCV data, generate multiple technical indicators on it and then gives you all existing Buy-Hold-Sell combinations in the dataset. Mean, median, min and max are the clustered results of the respective signal combination.
To use ohlcvish you need your OHLCV data to be in a pandas.DataFrame like this:
import pandas as pd
eth = pd.read_csv("data/ETH.csv", index_col="datetime", parse_dates=True)
eth.head() close high low open volume
datetime
2015-08-07 3.00 3.0 0.6747 0.6747 123.93
2015-08-08 1.20 3.0 0.1500 3.0000 2119.43
2015-08-09 1.20 1.2 1.2000 1.2000 0.00
2015-08-10 1.20 1.2 1.2000 1.2000 0.00
2015-08-11 0.99 1.2 0.6504 1.2000 9486.09
Use ohlcvish() function to get all signals:
from ohlcvish import ohlcvish
signals = ohlcvish(eth)
signals.head() macd rsi stoch adx aroon bbands sar ma amount forecast_mean forecast_median forecast_min forecast_max
0 -1 -1 0 0 0 0 -1 0 1 59.947906 59.947906 59.947906 59.947906
1 -1 0 0 -1 -1 0 0 0 1 -2.904930 -2.904930 -2.904930 -2.904930
2 -1 0 0 -1 0 0 -1 0 3 -7.415414 -6.642701 -11.645688 -3.957853
3 -1 0 0 -1 0 0 0 0 1 298.919554 298.919554 298.919554 298.919554
4 -1 0 0 0 -1 1 -1 -1 1 -54.082750 -54.082750 -54.082750 -54.082750
Define forecast_period to change forecast for mean, median, min and max.
signals = ohlcvish(eth, forecast_period=10)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent