Quick Start

Playground

Start up a jupyter lab instance with all the tutorial notebooks.

docker run -p 8888:8888 ionomy-playground:latest

Copy and Paste the url with token from terminal output into your browser.

Open the tutorial notebook of interest or create your own to experiment with!

Install

pip install ionomy-python

Imports and Common Params

The primary classes for use are IonPanda and BitTA.

There are lower level classes if needed: Ionomy, BitTrex, and BitPanda

Ionomy is a raw API wrapper, no extras.

The same with BitTrex, which is a raw api wrapper.

BitPanda is the bittrex equivalent of IonPanda, but without the TA methods.

from Ionomy import IonPanda, BitTA

# Common Params
MARKET = 'btc-hive'
CURRENCY = 'hive'
BASE = 'btc'
TIME = 'day'

Instantiate

You must provide your Ionomy or BitTrex API key/secret, respectively.

IMPORTANT: I opted for the package user to manually load and update ohlcv data

This allows for control of the number of API calls, since there are limits (See Exchanges Websites).

ionpd = IonPanda(IONOMY_KEY, IONOMY_SECRET)
bta = BitTA(BITTREX_KEY, BITTREX_SECRET)

# IMPORTANT - call this method to load/update ohlcv data
bta.update(CURRENCY, BASE, TIME)

Common Methods

The "tutorials" provides examples of each method and their return values.

The "modules" provides the detailed code documentation.

Here, I will show one method from each classe being used by the highest order classes.

# returns a regular dictionary from the raw json
# I saw no benefit from having a single row dataframe returned
market_summary = ionpd.market_summary(MARKET)

# returns a pandas dataframe
order_book_df = ionpd.order_book(MARKET)

# returns a regular dictionary from the raw json
market_summary = bta.market_summary(MARKET)

# returns a pandas dataframe
order_book_df = bta.order_book(MARKET)

# TA methods are returned as DataFrames or Series
rsi_series = rsi_series = bta.rsi(length=1, drift=1, offset=0)

The most common TA methods are implemented and available Due to python limitations, such as floating point arthimetic, etc, results will/may differ from the standard talib package and other TA implementations on the same data. This difference should be within a very small margin of error from a c++ implementation.