Binance Order Book Data Aggregator

2017 - Present

Binance provides a simple Websockets API which I made use of to create an Orderbook Data Aggregator.

Key features

Provide the OrderBookFrame class which has several parameters for customizing the output which is a Pandas Dataframe.

from exchange_data.data.orderbook_frame import OrderBookFrame

df = OrderBookFrame().frame

print(df)

Output


best_ask  best_bid                                      orderbook_img                    dtype  macd_diff
time
2023-04-20 18:53:30+00:00  5.003906  5.003906  [[[0.0], [0.0], [0.0], [0.0], [0.0], [0.5], [0...  <class 'numpy.float16'>        NaN
2023-04-20 18:54:00+00:00  5.015625  5.011719  [[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0...  <class 'numpy.float16'>        NaN
2023-04-20 18:54:30+00:00  5.011719  5.011719  [[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0...  <class 'numpy.float16'>        NaN
2023-04-20 18:55:00+00:00  5.007812  5.003906  [[[0.0], [0.5], [0.5], [0.5], [0.5], [0.5], [0...  <class 'numpy.float16'>        NaN
2023-04-20 18:55:30+00:00  5.003906  5.003906  [[[0.5], [0.5], [0.5], [0.5], [0.5], [0.5], [0...  <class 'numpy.float16'>        NaN
...                             ...       ...                                                ...                      ...        ...
2023-04-20 20:51:30+00:00  5.039062  5.035156  [[[0.5], [0.5], [0.5], [0.5], [0.5], [0.5], [0...  <class 'numpy.float16'>        NaN
2023-04-20 20:52:00+00:00  5.031250  5.031250  [[[0.5], [0.5], [0.0], [0.0], [0.0], [0.5], [0...  <class 'numpy.float16'>  -0.001544
2023-04-20 20:52:30+00:00  5.031250  5.027344  [[[0.5], [0.5], [0.5], [0.5], [0.5], [0.5], [0...  <class 'numpy.float16'>        NaN
2023-04-20 20:53:00+00:00  5.027344  5.027344  [[[0.5], [0.0], [0.0], [0.5], [0.5], [0.5], [0...  <class 'numpy.float16'>  -0.002028
2023-04-20 20:53:30+00:00  5.027344  5.023438  [[[0.5], [0.0], [0.5], [0.5], [0.5], [0.5], [0...  <class 'numpy.float16'>        NaN

[241 rows x 5 columns]

High Performance In-Memory Binary Tree Order Book implementation OrderBook class.
Select any aggregation time interval necessary. From milliseconds to several seconds or minutes by selecting the available intervals in TimeEmitter class.
High Resolution. Can store thousands of Order Book levels compared to pay-to-use SaaS platforms which only allow 1 minute resolution and limited number of Order Book levels which can be set by using the group_by parameter in OrderBookFrame class.
Observability and alerts built with Grafana and Telegraf.
Extensibility - Currently the aggregator can capture and store orderbook levels from binance.com. However, by copying and modifying SymbolEmitter anyone can easily add other exchanges.

Tech Stack:

Python
Pandas
Numpy
InfluxDB (Time Series Database)
Websockets
Redis
Docker
Grafana
Telegraf