Using the OHLCTechnicals object on OHLC Data¶
>>> from pyharmonics.marketdata import BinanceCandleData
>>> from pyharmonics.technicals import OHLCTechnicals
>>> b = BinanceCandleData()
>>> t = OHLCTechnicals(b.df, b.symbol, b.interval, peak_spacing=20)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xual/Software/pyharmonics/src/pyharmonics/technicals.py", line 129, in __init__
raise ValueError('Candle DataFrame is None! call cd.get_candles(ASSET, INTERVAL) first.')
ValueError: Candle DataFrame is None! call cd.get_candles(ASSET, INTERVAL) first.
Note
Always remember to get candle data :-)
>>> b.get_candles('ETHUSDT', b.HOUR_4, 400)
>>> b.df
open high low close volume close_time dts
index
2023-05-13 08:59:59+01:00 1805.62 1806.84 1796.80 1802.98 30365.3283 1683964799 2023-05-13 08:59:59+01:00
2023-05-13 12:59:59+01:00 1802.98 1811.46 1801.24 1803.78 29164.0211 1683979199 2023-05-13 12:59:59+01:00
2023-05-13 16:59:59+01:00 1803.79 1809.91 1785.23 1795.90 46713.5684 1683993599 2023-05-13 16:59:59+01:00
2023-05-13 20:59:59+01:00 1795.91 1806.45 1786.12 1792.83 40715.0401 1684007999 2023-05-13 20:59:59+01:00
2023-05-14 00:59:59+01:00 1792.84 1804.89 1792.38 1795.11 24692.1556 1684022399 2023-05-14 00:59:59+01:00
... ... ... ... ... ... ... ...
2023-07-18 04:59:59+01:00 1911.21 1917.19 1906.25 1908.88 19832.6141 1689652799 2023-07-18 04:59:59+01:00
2023-07-18 08:59:59+01:00 1908.88 1911.72 1893.36 1898.99 37921.2814 1689667199 2023-07-18 08:59:59+01:00
2023-07-18 12:59:59+01:00 1898.99 1909.64 1890.80 1894.15 39215.5098 1689681599 2023-07-18 12:59:59+01:00
2023-07-18 16:59:59+01:00 1894.15 1903.66 1885.08 1897.20 49833.1236 1689695999 2023-07-18 16:59:59+01:00
2023-07-18 20:59:59+01:00 1897.21 1903.58 1875.73 1891.50 46447.8182 1689710399 2023-07-18 20:59:59+01:00
[400 rows x 7 columns]
>>> t = Technicals(b.df, b.symbol, b.interval)
>>> t.df
open high low close volume close_time ... price_peaks price_dips macd_peaks macd_dips rsi_peaks rsi_dips
index ...
2023-05-13 08:59:59+01:00 1805.62 1806.84 1796.80 1802.98 30365.3283 1683964799 ... 0 0 0 0 0 0
2023-05-13 12:59:59+01:00 1802.98 1811.46 1801.24 1803.78 29164.0211 1683979199 ... 0 0 0 0 0 0
2023-05-13 16:59:59+01:00 1803.79 1809.91 1785.23 1795.90 46713.5684 1683993599 ... 0 0 0 0 0 0
2023-05-13 20:59:59+01:00 1795.91 1806.45 1786.12 1792.83 40715.0401 1684007999 ... 0 0 0 0 0 0
2023-05-14 00:59:59+01:00 1792.84 1804.89 1792.38 1795.11 24692.1556 1684022399 ... 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2023-07-18 04:59:59+01:00 1911.21 1917.19 1906.25 1908.88 19832.6141 1689652799 ... 0 0 0 0 0 0
2023-07-18 08:59:59+01:00 1908.88 1911.72 1893.36 1898.99 37921.2814 1689667199 ... 0 0 0 0 0 0
2023-07-18 12:59:59+01:00 1898.99 1909.64 1890.80 1894.15 39215.5098 1689681599 ... 0 0 0 0 0 0
2023-07-18 16:59:59+01:00 1894.15 1903.66 1885.08 1897.20 49833.1236 1689695999 ... 0 0 0 0 0 0
2023-07-18 20:59:59+01:00 1897.21 1903.58 1875.73 1891.50 46447.8182 1689710399 ... 0 0 0 0 0 0
[400 rows x 27 columns]
Peak sensitivity can be increased like so.
>>> t = Technicals(b.df, peak_spacing=12)
Note
More peaks will mean more points to search through when looking for harmonic patterns. In some cases this can lead to too many patterns being identified.
Using the standard Technicals object on single trend data¶
>>> from pyharmonics.technicals import Technicals
>>> from pyharmonics.utils import UER
>>> t = Technicals(UER, 'Unemployment', 'monthly', peak_spacing=6)
>>> t.df
year close price_peaks price_dips macd rsi stoch_rsi ... ema 21 ema 34 ema 55 macd_peaks macd_dips rsi_peaks rsi_dips
0 2013-01-01 8.0 1 0 NaN NaN NaN ... NaN NaN NaN 0 0 0 0
1 2013-02-01 7.7 0 0 NaN NaN NaN ... NaN NaN NaN 0 0 0 0
2 2013-03-01 7.5 0 0 NaN NaN NaN ... NaN NaN NaN 0 0 0 0
3 2013-04-01 7.6 0 0 NaN NaN NaN ... NaN NaN NaN 0 0 0 0
4 2013-05-01 7.5 0 0 NaN NaN NaN ... NaN NaN NaN 0 0 0 0
.. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
122 2023-03-01 3.5 0 0 0.036866 38.289828 0.591382 ... 4.069905 4.441627 4.740887 0 0 0 0
123 2023-04-01 3.4 0 1 0.042426 37.160633 0.686581 ... 4.009005 4.382106 4.692998 0 0 0 0
124 2023-05-01 3.7 1 0 0.068006 42.627002 0.736196 ... 3.980914 4.343128 4.657534 0 0 1 0
125 2023-06-01 3.6 0 0 0.079110 41.336196 0.769141 ... 3.946285 4.300664 4.619765 0 0 0 0
126 2023-07-01 3.5 0 0 0.080380 40.030762 0.794174 ... 3.905714 4.254912 4.579774 1 0 0 0
[127 rows x 22 columns]
Technicals.df schema¶
>>> t.df.columns
Index(['open', 'high', 'low', 'close', 'volume', 'close_time', 'dts', 'macd',
'rsi', 'stoch_rsi', 'bb%', 'sma 50', 'sma 100', 'sma 150', 'sma 200',
'ema 5', 'ema 8', 'ema_13', 'ema 21', 'ema 34', 'ema 55', 'price_peaks',
'price_dips', 'macd_peaks', 'macd_dips', 'rsi_peaks', 'rsi_dips'],
dtype='object')
`'macd', 'rsi', 'stoch_rsi', 'bb%'`are the MACD ( Moving Avg. Convergence Divergence ), RSI ( Relative strength index ), Stochastic RSI and Bollinger Band deviation reading.`'sma 50', 'sma 100', 'sma 150', 'sma 200'`are Simple Moving Avergaes SMA. 50, 100, 150, 200 candle average. All useful for plotting support/resistance levels.`'ema 5', 'ema 8', 'ema_13', 'ema 21', 'ema 34', 'ema 55'`are Exponential moving averages all fibonacci numbers. Very accurate in plotting support/resistance as swings move.`'price_peaks', 'price_dips', 'macd_peaks', 'macd_dips', 'rsi_peaks', 'rsi_dips'`the indexes where the price is at a peak or dip. Similar for the MACD and RSI. This informatoin is key for detecting divergence patterns which confirm harmonic patterns.