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Profitable People

I have been in the investment industry for a number of years and have a degree in financial mathematics. It seems unlikely to me that people can actually profit on a site like this. It is a great platform I just am skeptical that people actually have the ability to develop profitable algorithms on their own when competing against full time bank and hedge fund trading desks that have far greater assets and likely more training. Do some people on here consistently profit?

7 responses

Notwithstanding the direction Fawcett et al. have for Quantopian, to which I cannot presume any privilege, I'll boldly share my perspective: Putting aside the lofty goal of consistent profit, opening up such tools to non-industry professionals taps into a potentially massive pool of diverse capabilities. Quantopian may only need one good algo story to tell and they can strike it big with the masses. As for myself, yes of course I believe I've got a better algo than the pros! Just needs a little tweaking here and there and it may take a while..;) In the meantime, I'm enjoying every moment of it.

Hi Ben,

I think your question is critical, and we are scrutinizing the small amount of data we have. We need much more time with live trading to make any firm conclusions, but we have been looking at the percentage of algorithms that are making money. Looking at strategies that have been running for 20 days or more, about 75% are profitable. I know this isn't a complete answer to your question, but I wanted to share the information we have. I would also love suggestions for how we should evaluate our population of algorithms and quants.

Sekuri - gathering talent from around the world is exactly our mission. Then we want to provide a great platform and tons of data. I'm thrilled with our start, and excited for what lies ahead.



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Both institutional and retail traders have their own advantages. Retail quant traders has the following advantages:
1) Smaller fund. This means a retail investor suffer much less in market impact, and thus allowing her to utilize strategies that are not suitable for larger funds.
2) Flexibility in picking the low hanging fruits. There are profitable strategies out there that are suitable only for smaller funds because they do not scale well.

Hello Fawce,

In response to your request for "suggestions for how we should evaluate our population of algorithms and quants" it is gonna depend on the objectives of each algorithm. Basically, each algorithm needs a kind of prospectus, against which its execution and financial performance can be measured. With your demo live algo, you've basically done this by writing an algo to mirror an ETF ( So, it is a straightforward exercise to analyze your demo live algo.

To illustrate my point, you present the fact that "strategies that have been running for 20 days or more, about 75% are profitable" but it does not reveal whether the algos are successful against their objectives. As an (unlikely) example, what if a significant fraction of the algos were designed to hedge against a big market correction, and should not be profiting in the current bull market? Then the fact that 75% are profiting (contrary to their objectives) would indicate failure rather than success.

It might turn out that your customer base is taking a fraction of their investable net worth and intentionally speculating in the market. If only 50% of them profit consistently (relative to an appropriate benchmark), it might still be a win compared to less quantitative approaches to speculating (e.g. manually "day-trading"). Do you have a sense what your live traders are trying to accomplish with their algos, in terms of risk-reward? There's also the case of investors willing to take a hit on return in exchange for reduced volatility. So, it might be a good thing if somebody puts their diversified retirement portfolio into Q/IB and underperforms SPY, for example.

So, you could consider a kind of online guided prospectus template/questionaire that would automatically generate an appropriate set of metrics for a given algo, and then apply them. Then, you could roll them up periodically to see how everyone is doing against their individual objectives.


I believe Chee Soon is right. The main reason is that you have much more liquidity with smaller accounts and can try many more strategies than there is available in open big funds.
Besides, you do not need complex techniques to make money in the markets.
Btw, I also think it would be great if we could see some statistics about our user-base results.


Your point may be valid, but in the spirit of quantitativeness, is there any evidence (e.g. academic/industry reports) that retail investors/traders have an edge? And if so, in what areas? It would seem that if retail traders (on average) were making money hand-over-fist, folks like Interactive Brokers would have published supporting analyses a long time ago.


It has been pointed out that so called experts have a hard time beating the return you get just by throwing your money into an index fund so I say "Pffffft" when it comes to the magic of "experts". Before Quantopian I engaged in a tremendous amount of backtesting and I'm happy to say that after throwing real money in I have really cleaned up the last few years (25 stocks bought in 2011, 2012 and 2013, avg return over all of them now is 50.853%) so I can't learn Python fast enough to use this system. I'm more than happy to share how I came up with this. It isn't like suddenly the entire market is going to shift to use whatever works for me, but I am firmly convinced that you can find literally countless formulas that work. Find them, stick to your knitting and use the few that really work for you. (In my case it was an adaptation of one of James O'Shaughnessy's formulas in "What works on Wall Street").