First, I will introduce their approach (from the article http://ift.tt/2nPv9am):
Yves-Laurent explains that this strategy falls short in a financial context because it means that learning how to trade requires one to model returns for each decision in each state of the market. Financial markets are incredibly complex systems, so the math goes from science to art to pseudoscience quite rapidly. Instead, Pit.ai evaluates trading strategies themselves, taking into account metrics like Sharpe ratios and maximum drawdown — financial tools for evaluating risk. Utilizing this strategy, Pit hopes to best industry stalwarts by not only delivering above average returns, but breaking the traditional two and twenty fee structure of the hedge fund industry. Without the need for large analyst teams to search for macro-economic trends and data to exploit, Pit can stay lean and drop management fees altogether, instead opting only to collect carry from its limited partners.
First, I don't understand how they will be able to live without management fees: yes, they have automatic tools to somehow evaluate strategies, but then they still will need experts to make sure algorithms are working properly and the output is not a random garbage. In fact, they need both people and computing power!
Second, I do not understand their approach to evaluating trading strategies: you have already all the math to estimate the volatility, so what are they bringing to the table in this particular area? Are they claiming they can estimate volatility better that, for example, Heston model or something like that?
I'm not an expert in machine learning or volatility estimations, so please bear with my silly questions (and poor grammar).
Submitted March 23, 2017 at 06:29AM by dewise http://ift.tt/2nayOzJ
