PyPhoenix

The Python versions of the Phoenix Ascending 2.201 and Bad Ass B-Bands indicators can be found in this repository. This code is written for personal use and analysis based on data analysis tools in Python.

Important Note:

• This code is Open Source and like the original code, is licensed under the Mozilla Public License Version 2.0.
• David Ward Jr at wyckoffmode.com wrote the two indicators used in this code in Pine Script, and all credit goes to him.
• Source of Phoenix Ascending 2.201
• Source of Bollinger Bands (Bad Ass B-Bands) - Wyckoff Mode
• I'd like to thank David Ward Jr. for allowing me to use his indicators to build this code.

How to use:

Prerequirements:

Check that the packages listed in requirements.txt are installed on your system or environment.

Usage:

1. First, clone the package in your system:

git clone https://github.com/siniorone/PyPhoenix.git

2. To use PyPhoenix go to the main repo directory, run the following command in your terminal to add src to your PYTHONPATH:

cd PyPhoenix
export PYTHONPATH=${PWD}

3. Then run run.py file:

python src/run.py

Explore the code's functions:

• First of all we need to import the main class:

from src.phoenix import Phoenix

• Then we make an object from Phoenix:

btc = Phoenix("BTC-USD", "2021-02-06", "2022-02-06")

• Use the .graph() method to display an interactive graph:

btc.graph()

• Use save=True to save the graph as a photo at the same time:

btc.graph(save=True)

• If you want only the phoenix indicator to be displayed, you can use the following arguments:

btc.graph(phoenix=True, chart=False)

• Method .graph2() allows you to use another visualization that makes use of Matplotlib and seaborn, and save the result as a high-quality image:

btc.graph2(save=True)

• To get all the data of indicators and prices in the form of a pandas.DataFrame, you can use method .to_dataframe():

df = btc.to_dataframe()
df.shape
# Output: (366, 23)

df.columns

# Output: Index(['open', 'high', 'low', 'close', 'hl2', 'hlc3', 'ohlc4', 'volume',
#      'green', 'red', 'blue', 'energy', 'basis', 'u1std', 'l1std', 'u16std',
#       'l16std', 'u26std', 'l26std', 'u36std', 'l36std', 'u46std', 'l46std'],
#     dtype='object')

df.index

# DatetimeIndex(['2021-02-05', '2021-02-06', '2021-02-07', '2021-02-08',
#               '2021-02-09', '2021-02-10', '2021-02-11', '2021-02-12',
#               '2021-02-13', '2021-02-14',
#               ...
#               '2022-01-27', '2022-01-28', '2022-01-29', '2022-01-30',
#               '2022-01-31', '2022-02-01', '2022-02-02', '2022-02-03',
#               '2022-02-04', '2022-02-05'],
#              dtype='datetime64[ns]', name='Date', length=366, freq=None)

• You can modify the get_stock_data() function in the src/utils/dataminer.py file base on your need and according to your conditions, as long as the output of the function is a pandas.DataFrame with an index of Date and its columns Open, High, Low, Close, Volume, the code will run correctly.

Index	Open	High	Low	Close	Volume
Date
2021-02-05	36931.546	38225.906	36658.761	38144.308	58598066
...	...	...	...	...	...