A protocol for quant funds and researchers to work together without revealing their IP.
<aside> 👉 Functions to integrate Judge Research into your fund’s research environment in minutes, not hours; as well as functions to improve your research & data environment.
</aside>
Judge Research is meant to plug into your fund’s research environment with maximum flexibility. Most of the functions here are built to make that plugging-in process a matter of minutes. Some by extension increase the ease-of-use of research environments in general.
We have simplified the process to its basic steps: You simply (1) set the historical data parameters, (2) choose your data provider, (3) set your API key and (4) fetch your data, which shows up as nicely organized timstamped OHLCV data.
The HDParams() class defines the parameters of the historical data you are about to pull.
It is good practice to align your historical data with that of the GA series to which you are submitting.
startDateString = "2019-07-01 00:00:00"
periodsPerDay = 6 # Make sure this aligns with asset.interval
startDate = datetime.strptime(startDateString, "%Y-%m-%d %H:%M:%S")
timeBack = datetime.now() - startDate
nObs = timeBack.total_seconds() / ((60*60*24) / periodsPerDay)
nObs = math.floor(nObs)
print(nObs)
asset1 = HDParams()
asset1.exchange = "BINANCE"
asset1.ticker = "BTC-USD-SPOT"
asset1.set_start_date("2019-06-01 00:00:00")
asset1.interval = "4h"
asset1.num_periods = nObs
HD = Coinalytix()
The Coinalytix() class is a conduit to historical data for crypto spot, futures and options markets.
You use .with_api_key() to supply it with an authentication key. It takes any string as an argument.
HD.with_api_key("hHV1QUTclB653YLvFJBJh5Pz0BayF251at64c9x9")
x1 = HD.fetch_hd(asset)
Finally, the .fetch_hd() method accepts an HDParams object and returns a Pandas data frame with timestamped OHLCV data.
Once you have done your feature engineering and you are ready to submit features to Judge Research, the SDK makes it easy to do so.