BConnector

The BConnector module acts as the API interface between custom CSuite functionality and the Binance Exchange. Connectivity with Binance is achieved via the binance-python library developed by Sam McHardy; thank you Sam!

The API Client

The object which enables API calls is the client which is derived from a connection via the binance-python lib. The client can be used to connect with the exchange and send, recive data ad-hoc or in real-time and is necessary for most connected functionality such as data retrival and order submission. A client can be easily created:

client = CSuite.connect_client('filename.json')

To initiate the client one must provide a JSON file with the two API keys for Binance, indexed as ‘API KEY’ and ‘SECRET KEY’. Once the client has been called successfully by providing up-to-date keys, the returned client object must be kept for later use.

Setup File Example

{
"API_KEY" : "YOUR_API_KEY_HERE",
"SECRET_KEY" : "SECRET_KEY_HERE"
}

Get Spot Timeseries

timeSeries = CSuite.get_SpotKlines(client, symbol, interval)

Downloading timeseries (OCHL) data for a specific crypto made extremely easy through the Kline function, which returns a Pandas DataFrame with all the data: OCHL & Volume. The client is a required parameter, so is the symbol (Binance pair notation) and the interval which can be from ‘1m’ to ‘4d’. Please note that this function will return the latest 1000 data-points.

Requires: obj: client, str: symbol, str: interval

Returns: Pandas DataFrame

Get Multiple Spot Timeseries

frame = CSuite.get_batch_historic(client, symbols, interval, mode')

Since users often find themselves needing to download multiple timeseries at the same time, we provide the custom get_batch_historic() function. This function allows the download of parrallel historical timeseries data for multiple crypto. Unlike the get_SpotKline() function, this one requires an array of strings in the symbols parameter. It also supports multiple modes through the mode parameter, the following is supported:

Nominal (‘N’): Returns the non-normalised closing price of the pair.
Return (‘R’): Returns the daily percentage returns of the price timeseries.
Volatility (‘V’): Return the 5-Day rolling standard deviation of returns.

Requires: obj: client, array of str: symbols, str: interval, str: mode

Returns: Pandas DataFrame

Get Quote & Limit Order Book

Viewing the Orderbook

book = CSUite.view_book(client, symbol, limit=100)

Retrieving the live limit order book (LOB) is the first step in accessing the real time market on the exchange. Using this limit order book functionality it is possible to have near-real time access to the access via equivalent DMA. The Binance API has a refresh rate of 200ms for the book.

We can simply call the view_book() function to get a snapshot of the book. It returns a ladder-like DataFrame with bid/ask volumes in num of tokens and Prices. The ticks are NOT uniform.

Requires: obj: client, str: symbol, int: limit

Returns: Pandas DataFrame

Get Quote

quote  = CSuite.get_quote(client, symbol)

In certain use cases it may be useful to access only the best bid ask & volume at those ticks in such cases we offer a parsed version of the book function via get_quote() which directly interacts with the Binance API for high speeds.

Requires: obj: client, str: symbol

Returns: Pandas DataFrame

Getting Symbol Trade Parameters

info = get_symbol_info(client, symbol)

We have also created a simplified way to parse the Exchange information necessary to execute automated trading details such as the trading status, tickSize, and minimum notional are included.

Requires: obj: client, str: symbol

Returns: Pandas DataFrame

Get Futures Data

future = CSuite.get_FutureKlines(client, symbol, interval)

Alongside Spot functionality, the library supports retrieval and operation in the Binance Futures Market. It is possible to access futures Timeseries data through the provided functions. The get_FutureKlines() function works exactly like the get_SpotKline() described above.

Requires: obj: client, str: symbol, str: interval

Returns: Pandas DataFrame

Get Futures-Spot Spread

spread = get_FuturesSpread(client, symbol, interval)

We have bundled the functionality of comparing the Spot and Futures by downloading and parsing both get_FutureKlines() & get_SpotKline(). This is possible via the following:

Requires: obj: client, str: symbol, str: interval

Returns: Pandas DataFrame

Note

The term period in the following functions refers to the special interval of derivative statistics which includes the values [5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d]

Get Futures Open Interest

open_interest = get_FuturesOI(client, symbol, period)

The Open Interest statistic refering to the volume of currently open contracts is available through Binance API and can be retived simply using the relevant function.

Requires: obj: client, str: symbol, str: period

Returns: Pandas DataFrame

Get Futures Long-Short

long_short = get_FuturesLS(client, symbol, period)

The Long-Short statistic of the Binance futures portfolios is also easily accessible as a timeseries. This function returns a DataFrame which contains the Accounts, Position and standard Long/Short Ratio as per Binance Docs.

Requires: obj: client, str: symbol, str: period

Returns: Pandas DataFrame

Get Futures Funding Rate

funding_rate = get_FuturesFundingRate(client, symbol, period)

The Futures Funding Rate of the Binance Exchange (i.e. the cost of going long futures), is also packaged.

Requires: obj: client, str: symbol, str: period

Returns: Pandas DataFrame

Options Data

Get Option Skew

skew = get_options_skew(client, maturity, strikes)`

Get Multiple Issue Skew

data = get_omm_skew(client, [''], [''])

Options Vol Smirk

iv = IV_skew(data, price)