pyharmonics 1.3.7

No project description provided

pyharmonics

pyharmonics detects harmonic patterns in OHLC candle data for any stock or crypto asset. See http://www.harmonictrader.com for more information on harmonic patterns and follow author Scott Carney.

Installation

From git

$ git clone [e-mail]:niall-oc/pyharmonics.git
$ cd pyharmonics
$ pip install .
$ cd src
$ python
>>> from pyharmonics.marketdata import BinanceCandleData
...

From pypi

$ pip install pyharmonics
$ python
>>> from pyharmonics.marketdata import BinanceCandleData

Complete Guide

https://pyharmonics.readthedocs.io/en/latest/

Quick Guide

Use the market data features or generate your own market data matching the dataframe schema below. close_time, dts can be omitted

>>> from pyharmonics.marketdata import BinanceCandleData
>>> b = BinanceCandleData()
>>> b.get_candles('BTCUSDT', b.MIN_15, 1000)
>>> b.df
>>> b.df
                               open      high       low     close     volume  close_time                       dts
index                                                                                                             
2023-07-09 07:44:59+01:00  30249.04  30267.04  30233.79  30262.33   79.71611  1688885099 2023-07-09 07:44:59+01:00
2023-07-09 07:59:59+01:00  30262.32  30267.87  30235.00  30254.79  136.31718  1688885999 2023-07-09 07:59:59+01:00
2023-07-09 08:14:59+01:00  30254.80  30283.50  30233.33  30283.50  185.04086  1688886899 2023-07-09 08:14:59+01:00
2023-07-09 08:29:59+01:00  30283.50  30283.50  30263.37  30263.37   74.17937  1688887799 2023-07-09 08:29:59+01:00
2023-07-09 08:44:59+01:00  30263.37  30270.09  30243.10  30257.30  121.15791  1688888699 2023-07-09 08:44:59+01:00
...                             ...       ...       ...       ...        ...         ...                       ...
2023-07-19 16:29:59+01:00  29841.37  29902.00  29841.36  29878.00  267.42077  1689780599 2023-07-19 16:29:59+01:00
2023-07-19 16:44:59+01:00  29878.00  29933.00  29866.15  29890.01  245.03318  1689781499 2023-07-19 16:44:59+01:00
2023-07-19 16:59:59+01:00  29890.01  29995.16  29890.00  29956.46  611.16786  1689782399 2023-07-19 16:59:59+01:00
2023-07-19 17:14:59+01:00  29956.46  29979.00  29901.70  29930.57  365.35485  1689783299 2023-07-19 17:14:59+01:00
2023-07-19 17:29:59+01:00  29930.57  29930.57  29870.00  29901.40  244.14513  1689784199 2023-07-19 17:29:59+01:00

[1000 rows x 7 columns]

Create a technicals object for further analysis.

>>> from pyharmonics.technicals import Technicals
>>> t = Technicals(b.df, b.symbol, b.interval)

Search for a harmonic pattern.

>>> from pyharmonics.search import HarmonicSearch
>>> m = HarmonicSearch(t)
>>> m.search()

Plot the findings.

>>> from pyharmonics.plotter import Plotter
>>> p = Plotter(t, 'BTCUSDT', b.MIN_15)
>>> p.add_harmonic_plots(m.get_patterns(family=m.XABCD))
>>> p.show()

You will see something like this.

See all harmonic patterns.

>>> p = Plotter(t, 'BTCUSDT', b.HOUR_1)
>>> p.add_harmonic_plots(m.get_patterns())
>>> p.show()

You will see something like this.

See all forming patterns.

>>> h = HarmonicSearch(t)
>>> h.forming()
>>> p = Plotter(t, 'BTCUSDT', b.HOUR_1)
>>> p.add_harmonic_plots(h.get_patterns(formed=False))
>>> p.show()

etc.