Call me cynical but this is a bad idea, and not for Quantopian either. It is well known that only a small fraction of traders ever end up making money consistently.<p>Effectively Quantopian get a vast and relatively cheap set of researchers/traders to develop strategies and they can simply cherry pick and front run the most successful. This means that the actual developers of the successful strategy will quickly be muscled out.<p>Of course Quantopian would deny they would ever do this but they have full access to all your code and results and can do what they like with it. You really think a "Wall Street" company is going to be happy with your $nn a month subscription fee when they can see you are running a strategy that when scaled up a bit could make so much more?
Quantopian suffers from a few problems (not unlike Access or the other 4GLs):<p>1) platforms like thinkorswim get you 90% there (and have an associated brokerage so you can also run it). You can write powerful trading scripts using a wide variety of signals, but for the last 10% you will need something more powerful like C or matlab or excel.<p>2) terms of service are always shaky -- if you have alpha, you want to guard that like a first born child.<p>3) other people (<a href="http://tradingphysics.com/" rel="nofollow">http://tradingphysics.com/</a>) offer market data at very low prices, which is far better than trusting a third party with your code<p>4) oftentimes strategies don't directly translate to production profits, and to a great extent they depend on simulator assumptions (let's say that there are 10K shares offered at the best selling price what happens when you try to buy all of them? What happens if you are stopped due to RegNMS or some other oddity? What happens if the quote is fake or will be canceled by the time your order reaches the exchange -- a common tactic of Chicago firms like GETCO?) for which you have no control. But now, given that they don't have a BD license, you need to go through someone else (adding another layer and process that potentially could cause problems later on -- I've seen a similar situation happen where a person leaves one company to join another, only to find out his trading strategies don't work in the new place )<p>Tl;dr: there are better platforms for getting your feet wet, and if they aren't good enough you are better off going with a real solution.
It's an interesting idea, but most successful retail algo traders probably won't trust their bread-and-butter algos to a cloud-type solution. I've been algorithmically trading for a few years now, and I've invested money into Ninjatrader, where I program my algos and run things from my own computer.<p>If anyone had their own algos, they would probably be too paranoid that Quantopian would run backtesting on every single algo, and cherry pick the best ones for themselves. Whether or not it's true doesn't matter, it's most likely the common thought process that any successful algo trader would have.<p>That would leave only the inexperienced and beginning traders that would be more apt to fail, since algo trading is very, very, very hard.
This is not a good idea. People can barely invest in regulated securities doing a dipshit buy and hold strategy.<p>For professionals - brokerage houses already do most of the hard work and provide decent APIs - so they don't need it either.<p>Algo trading is cool - but it's most definitely NOT for the masses.
I'm happy for Quantoplan making steps to improve the user experience around algorithmic trading, however armchair traders and many hedge funds have been using platforms like Tradestation (and dozens of others) to back-test and develop algorithmic strategy for well over a decade.<p>I spent a year working as a researcher for a now-failed hedge fund (failed due to regulatory issues, not performance, we were doing 20% year over year on commodity futures). As an engineer/math guy, it was an incredibly interesting experience because it opened my mind to all sorts of theoretical possibilities and explorations of pattern matching, noise filtering, exotic concepts like wavelets and more.<p>However, I quickly learned a few things from experienced traders and from seeing my work move from testing to prod. What I learned makes me extremely hesitant to employ automated trading systems on my own money:<p>1. Historical back-testing is a great way to curve fit. You can hyper optimize your algorithm looking for arbitrage opportunities, trends, whatever. You'll get performance reports that make it seem like you're ready to print money. Then you get out and trade and discover that your system can't keep up with market movements because the indicators that you relied upon may have exhibited correlation but not causation.<p>2. The boon of algorithmic trading is that it attempts to remove emotion from the trading process, not that it is a better predictor. Listening to a machine should help alleviate the symptoms of "fear and greed" that lead to abrupt, incorrect decision making. Think about that for a minute, some hedge funds advocate algorithms not because of predictive power but as guarantees of rational decision making.<p>3. Conversely, while developing and testing a system, a smart person will almost inevitably try to bring in exotic concepts into price prediction, order sizing and trend following functions. Given enough time, complexity will increase until it becomes challenging to understand the rationale behind a system's output. Trading this way is scary because real money is being moved without an understanding of fundamental and macro-factors.<p>4. You will <i>very</i> likely lose money. Even at the size of our fund (1B under management) we were sometimes at the mercy of market makers who gave us crap prices on trades or seemingly manipulated prices to hit our stop orders and cause us to exit positions too early.<p>I love seeing the ideas behind algorithmic trading popularized, however I want to make sure that anyone embarking on it understands the market as a system and not just as a time series to be modelled. It's composed of real human beings, with emotions running wild. If you decide to play, then play, but do so wisely and carefully and remember to keep it simple.
There's a lot of criticism here, but as someone who has never done algorithmic trading before - it's very exciting. I don't believe Quantopian's intention was to target veterans, since I'm sure you vets have your own system for testing and trading already worked out.<p>As a newbies with no experience in the field at all, it is unlikely that I would've even tried without a platform such as this. If Quantopian's intention is to bring more people into the field and get them exposure, even now, it's quite an impressive stepping stone.
beat me to it, working on something similar... with a twist. btw they're on github : <a href="https://github.com/quantopian" rel="nofollow">https://github.com/quantopian</a>
Wouldn't you need a broker account with minimal spread and very low broker fees for this to work?<p>The banks who are already doing high frequency trading don't have to worry about this, they can profit off a trade after its gone up a fraction of a point or so and close the trade, your average trader has to make up for the transaction fees or spread which are sometimes 10 to 100 times what a bigger firm has to cover.
Great job. Surprised at the amount of negativity for an app that is meant to help people.<p>This is one of those disruptor-style apps that pierces the veil of an industry and brings professional level tools to the masses.<p>I sat up from my chair after seeing this and said "wow". Have not done that for an app in a long time.
This will be cool if/when it has:<p>- options<p>- futures<p>- fundamental company data<p>Until then, it's matlab.<p>edit: Ok, this is a bit harsh. It's already pretty cool.