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QuantConnect Deploys Cloud Optimization for Algo Trading Parameter Testing

QuantConnect’s technology gives quants the ability to better tune their algorithms

QuantConnect, an open-source algorithmic trading platform, announced a cloud-based parameter optimization service to help quants detect overfit parameters. Available for all asset classes, this service enables QuantConnect’s users to select robust parameters, making strategies less sensitive to out-of-sample market changes and potentially avoiding costly mistakes.

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Quantitative trading strategies are controlled by a set of parameters, which are often decided by quants making an educated guess. As the selection of these parameters dramatically influences the results of backtests, many quants are prone to overfitting: choosing the parameters that fit the detail and noise of backtesting data, to the extent that it negatively impacts the live performance of the algorithm.

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The technology’s functionality — previously only available at a handful of elite megafunds — allows users to test thousands of combinations of these parameters in a fully spread, fee and slippage adjusted intraday simulation environment. No other public platform in the world provides the ability to optimize high fidelity backtests at scale.

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These results can then be compared en masse via a graphical user interface, providing insights into how different parameters impact a strategy’s alpha. This allows quants to determine a range of acceptable parameters for their strategy.

“Traditionally, quants had to iterate strategies by manually editing parameters and re-running backtests to learn how sensitive their strategy was to their parameters,” QuantConnect Founder and CEO Jared Broad said. “The time and resource intensive nature of this process can lead to the deployment of overfit trading strategies, a potentially costly mistake. Now, the big-data crunching throughput necessary to alleviate those concerns is accessible to the broader quant community at a low cost.”

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