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Quantiacs is a platform for quantitative trading and algorithmic strategy development. It offers a robust platform for quantitative traders and researchers, specializing in algorithm development and backtesting. The platform allows users to learn, develop, and test their trading algorithms, which can then be submitted for evaluation and potential allocation of funds for trading. Quantiacs hosts various contests for different asset classes and investment styles, such as futures, cryptocurrencies, and stock indices like the S&P 500 and Nasdaq-100. Winners of these contests can receive significant allocations for their trading strategies, and they earn a percentage of the profits generated by their systems without any downside risk. The platform provides access to market data for stocks, cryptocurrencies, and futures, along with free research and open-source libraries for local algorithm testing. Users can code trading strategies in Python and customize environments with libraries like TensorFlow. They can run their strategies on the Quantiacs cloud or develop locally and submit their algorithms. Quantiacs leverages various datasets, including survivorship-bias-free stock data, futures data, cryptocurrency data, and macroeconomic data from sources like the U.S. Bureau of Labor Statistics and the International Monetary Fund. The platform has been used by individuals with diverse backgrounds, including students, engineers, and finance professionals, to develop successful trading algorithms and earn allocations and quant fees. Quantiacs has a track record of hosting quantitative trading contests and allocating significant funds to winning algorithms. It emphasizes the importance of positive performance during the live contest period and has specific rules for submissions, including limits on the number of running submissions, positive performance requirements, and restrictions on the use of assets that were members of the S&P 500 index at a given point in time. The platform's use of cookies is in line with standard online practices, and it provides clear explanations of its privacy policy, terms and conditions, and cookies policy. Overall, Quantiacs appears to be a legitimate and established platform for quantitative trading and algorithmic strategy development, with a focus on fostering innovation in the field of quantitative finance."

• Robust platform for quantitative trading and algorithmic strategy development. Hosts contests for different asset classes and investment styles, such as futures, cryptocurrencies, and stock indices like the S&P 500 and Nasdaq-100. Winners of contests can receive significant allocations for their trading strategies. Users earn a percentage of the profits generated by their systems without any downside risk. Provides access to market data for stocks, cryptocurrencies, and futures. Offers free research and open-source libraries for local algorithm testing. Users can code trading strategies in Python and customize environments with libraries like TensorFlow. Allows running strategies on the Quantiacs cloud or developing locally and submitting algorithms. Leverages various datasets, including survivorship-bias-free stock data, futures data, cryptocurrency data, and macroeconomic data from sources like the U.S. Bureau of Labor Statistics and the International Monetary Fund. Used by individuals with diverse backgrounds, including students, engineers, and finance professionals, to develop successful trading algorithms and earn allocations and quant fees. Track record of hosting quantitative trading contests and allocating significant funds to winning algorithms. Emphasizes the importance of positive performance during the live contest period. Specific rules for submissions, including limits on the number of running submissions, positive performance requirements, and restrictions on the use of assets that were members of the S&P 500 index at a given point in time. Use of cookies in line with standard online practices. Provides clear explanations of its privacy policy, terms and conditions, and cookies policy.