What?

Django Cryptofeed Werks aggregates candlesticks using tick data from financial exchanges.

Raw ticks are aggregated if equal symbol, timestamp, nanoseconds and tick rule. As described in the accompanying project cryptofeed-werks, aggregating trades in this way can increase information, as they are either orders of size or stop loss cascades.

As well, the number of rows can be reduced by 30-50%

By filtering aggregated rows, for example only writing a row when an aggregated trade is greater than min_volume >= 1000, the number of rows can be reduced more.

Why?

Candlesticks aggregated by django-cryptofeed-werks are informationally dense. Such data can be useful for analyzing financial markets. As an example, refer to "Low-Frequency Traders in a High-Frequency World: A Survival Guide" and "The Volume Clock: Insights into the High Frequency Paradigm". Lopez de Prado recommends volume bars, however they are are computationally expensive to generate.

By filtering and aggregating trades, they can be computed faster, with little loss in precision.

How?

Whenever possible, data is downloaded from the exchange's AWS S3 repositories. Otherwise, it is downloaded using their REST APIs.

A database, preferably PostgreSQL, is required. Data is saved to the database after aggregation and filtering.

Candles are aggregated at 1 minute intervals, and validated with the exchange's historical candle API.

Notes.

Supported exchanges

◻️ Alpaca Markets

✅ Binance REST API (requires API key)

✅ Bitfinex REST API

◻️ Bitflyer REST API

✅ BitMEX REST API, and S3 repository

✅ Bybit REST API, and S3 repository

✅ Coinbase Pro REST API

◻️ Deribit REST API

✅ FTX REST API

Note: Exchanges without paginated REST APIs will never be supported.

For deployment, there are Dockerfiles. As well there are invoke tasks for rapid deployment to Google Cloud Run.

Installation

For convenience, django-cryptofeed-werks can be installed from PyPI:

pip install django-cryptofeed-werks

Environment

To use the scripts or deploy to GCP, rename .env.sample to .env, and add the required settings.

Future Plans

Allow use as an aggr historical data source as decribed by aggr.