The New Version of Quantiacs is now Live!

Starting today you have access to a new version of the Quantiacs platform. This article summarizes the new competitions, lists the main new features and provides a simple example for getting started with the new version. The submission window for Futures systems and for Bitcoin Futures systems is open. Winners will get 4M USD in guaranteed allocations.

Quantiacs started Futures contests in 2014 and has allocated over 30M USD to top performing systems. The live evaluation phase of the current Q14 contest has started on January 1st, 2021 and will end on April 30th, 2021.

We are proud to announce that contests will run on a completely new version of the platform.

New Data and Competitions

We are starting two new competitions on different liquid asset classes:

  • the “Classic” Quantiacs Futures contest on 75 liquid Futures;

You can submit your code to the Futures and Bitcoin Futures contest. The submission window will be closed end of May 2021.

Note that we introduced some simple rules to limit backtesting issues and select strategies which work with liquid assets as well as a new prize scheme: 2M USD are assigned to each contest and the top 7 systems for each contest will get allocations.

New Features

We redesigned Quantiacs to offer you the best tools for your quant experience:

  • An online space to develop code and test your ideas for forecasting global financial markets based on Jupyter Notebook and JupyterLab;

The following section will show you how to write a simple trend-following strategy on the new Quantiacs version. A distinctive feature of the new version is the simplification of the strategy code.

Legacy code can be used with the original version of Quantiacs. We described an example in detail in a previous post.

A similar strategy running on selected Futures contracts can be coded in the new platform as follows:

import xarray as xrimport qnt.ta as qnta
import qnt.backtester as qnbt
import as qndata
import qnt.output as qnout
def load_data(period):
return qndata.futures_load_data(
assets = ['F_AD','F_BO','F_BP','F_C'],
tail = period)
def strategy(data):
close = data.sel(field='close')
sma_long = qnta.sma(close, 200).isel(time=-1)
sma_short = qnta.sma(close, 10).isel(time=-1)
return xr.where(sma_short > sma_long, 1, -1)
weights = qnbt.backtest(
competition_type = "futures",
load_data = load_data,
lookback_period = 365,
test_period = 16 * 365,
strategy = strategy)

This is a multi-pass implementation. The procedure forbids looking-forward issues which arise in backtesting with a single-pass implementation.

The following single-pass implementation is considerably faster but all data are accessed at once, and there is the risk of implicit looking forward. It is good practice to always cross-check the results of the fast single-pass implementation with the slower but safe multi-pass one before submission.

import xarray as xrimport qnt.ta as qnta
import as qndata
import qnt.output as qnout
data = qndata.futures_load_data(
assets = ['F_AD','F_BO','F_BP','F_C'],
tail = 16 * 365)
close = data.sel(field='close')
sma_long = qnta.sma(close, 200)
sma_short = qnta.sma(close, 20)
weights = xr.where(sma_short > sma_long, 1, -1)
weights = qnout.clean(weights, data)
qnout.check(weights, data)qnout.write(weights)

After registering to the platform you will see in your user platform several pre-coded examples for getting started. These represent the ideal starting point as they contain a quick summary of the most useful functions you will need. More material can be found in the documentation.

Try the new platform and send us your feedback at!



Quantiacs is building a crowdsourced quant fund and provides quants worldwide with free data, software and computational resources.

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Quantiacs is building a crowdsourced quant fund and provides quants worldwide with free data, software and computational resources.