When we started making allocations in April, we also announced our goal of making allocations up to $50 million to a single algorithm by the end of the year. What would a $50 million dollar allocation mean to an author?
An author's royalty payments essentially depend on three factors: 1) the size of their allocation 2) the performance of their algorithm and 3) the fraction of the net profit that the author receives as a royalty payment.
I'll give you a completely hypothetical, back-of-the-envelope example answer. Please understand that this is for illustration purposes only and that the actual details of any future allocation will vary. The details of the calculation of net profit and the payment schedule, which are included in our author licensing agreement, are not covered in this simple example.
- allocation received: $50 million trading allocation on January 1 (that's gross market value, which includes leverage)
- algorithm net profit: $1.5 million through December 31 of the same year (reflects a 3% annual return on gross market value)
- author's share of net profit: 10%
- author's annualized royalty payment: $150,000 USD
Keep in mind that our allocation process includes a 6 month out-of-sample evaluation period. When we make allocations in January 2018, many of the algorithms will have been written during this month of June. All you have to do is run a backtest or enter a contest. When you do, we store a snapshot of your code and evaluate the performance of those snapshots in 6 months, making you eligible for an allocation. As always, we don’t look at your code during our evaluation process; instead, we look only at the algorithm’s simulation exhaust.
The $50 million dollar question, then, is how can you, a Quantopian community member, dramatically improve your chances of receiving an allocation? This post is here to help, expanding on what you'll read on the allocation page.
1. Seek Alpha While Managing Your Risk Exposures
That 7-word title packs in a lot of meaning and is worth reading again once or twice. Our selection criteria excludes the common risk exposures, including market beta, sector risk, the Fama-French factors, and more. We are looking for algorithms that are profitable while minimizing their exposure to these common risk factors. If your strategy performs well, but has high exposure to common factors, then it’s not really doing anything new and will not be as attractive. When you run a tearsheet on your algorithm, that tearsheet will show you your exposure to many of the common risk factors.
I can offer a shortcut of sorts: Consider writing an algorithm that finds its alpha in one of the data sets that you find on the data page. Those data sets don't generally depend on stock price and volume, and the alpha you find there is more likely to be free of factor risk. Perhaps more importantly, there are fewer people constantly surveying these data sets and trying to come up with trading signals. Data sets like price are so exhausted at this point that it is very difficult to come up with a model that forecasts returns. Newer data sets or data that gets at a novel way of forecasting returns will meaningfully increase your likelihood of finding a tradeable signal. Note that you don't have to buy a data set in order to find alpha, or in order to be eligible for an allocation. Every data set offered on Quantopian comes with a substantial amount of free sample data - if you build a great algo using free sample data our selection process will identify it, and we will validate the out of sample performance on our internal infrastructure.
Another way to avoid common risk factors, particularly relating to equities, is to write an algorithm that trades futures. We haven't made any allocations using futures yet, but we look forward to doing just that in a few months.
Regardless of whether your algorithm finds alpha in price data or our other data sets, it will still have to minimize its exposures to the common risk factors.
Learn more about using alternative data and futures in these posts.
2. Problems that Prevent Allocations
After reviewing literally millions of algorithms (thankfully, with the assistance of good automation!), we have compiled a short list of common mistakes that ordinarily take an algorithm out of consideration for a sizable allocation. When we find common mistakes, we start teaching the community how to avoid them.
For these most common mistakes, you should watch this QuantCon talk, delivered by Jess Stauth and co-written by Delaney. The presentation takes 24 minutes, with 12 minutes of Q&A at the end. This is what you should take away from the presentation:
Overfitting: Overfitting is a real challenge because you can never be sure you’ve avoided it. Your best test of overfitting is to apply your predictions on new (out of sample) data, which often means you must wait for time to pass. Look at slide 8 - that algorithm looks great at first, but in slide 9 you can see the performance collapses once it goes out of sample. Slides 11 and 17, though, you can see keep performing. If you spend an hour listening to The Dangers of Overfitting you'll be in a good position to avoid this mistake.
Long the Market: This one is seductive, but relatively easy to stamp out. The market has historically mostly gone up; to take advantage of this you can just buy an index or basket of long stocks. Vanguard provides this service with an expense ratio of .04% (as of Jun-09-2017). Neither Quantopian, nor Quantopian's clients, wants to pay 10% of the returns for a beta that can be bought much cheaper elsewhere! Look at slide 12 and you'll see an algorithm that is too long, but slide 19 has an algo that is not long the market. Your algorithm should be market neutral, with equal weights long and short on the exposure chart.
Beta to the Market: Market beta is very similar to being long the market in that you end up exposed to market movements, and that is very cheap to find. The difference is that you can unintentionally have market beta emerge even in strategies that are equally long and short. Systemic effects from your forecasting models can cause you to select stocks in a way that subtly increases your market beta. We provide tools to check for this. Slides 12 and 16 show tearsheets that have uncontrolled beta, but the tearsheet on slide 18 has essentially reduced their beta exposure. The beta hedging lecture is good for learning about controlling your beta
Liquidity Risk (aka not using the Q1500US: We see a lot of algorithms that look good at small allocations but fall apart when the allocation gets bigger. It's one thing to put $10k into an illiquid stock, but it's another to put in $100k, let alone $1m. The large orders move the price dramatically, or are totally unfillable, even when you use state-of-the-art execution algorithms like we do. We cover this in detail in our lecture on slippage. Jess also covers this point starting at 18:10 of her talk. One of the best ways to avoid this problem is to use the Q1500US. The Q1500US is a dynamic universe that contains only stocks that can handle large orders with relatively small price impact. Setting this universe before doing any kind of research or analysis can save you a lot of time.
Too Few Positions, or Excessive Exposure to a Single Stock: When a portfolio has a heavy weight in a stock, the portfolio takes a concentrated risk. An unexpected merger or acquisition, corporate fraud, or even a bad quarter can cause a steep drop in the portfolio value. We are looking for portfolios that avoid undue concentration risk and minimize their chances of having a big drawdown. One great way to guard against large drawdowns is to avoid letting your portfolio become too highly exposed to a single stock. A portfolio that holds hundreds to thousands of individual stocks will be well diversified with respect to outlier events that affect a single stock. The concentration risk lecture gives a real world example of the benefits of diversification across many holdings. Simply put, if you really think you can systematically beat the market, then you want to place as many bets as possible in an effort to do so. See the lecture for more information.
Sector Exposure: The US stock market can be broadly divided into a number of sub-groups of similar types of companies, referred to as ‘sectors,’ whose stock price returns tend to experience a tighter relationship to each other than to the rest of the broad market. Quantopian’s sector classification (sourced through Morningstar) classifies stocks into one of 11 sectors (e.g. Technology, Energy, Healthcare, etc). When building a market neutral trading algorithm, it is important to look for unintended exposures across these sectors. Some algorithms may be intended by design to be applied to only one or a few sectors, where other algorithms can be applied in a similar fashion across all sectors. In either case, the key is to study and control the net exposure (long or short) that your strategy can take on in any single sector. Algorithms which maintain a very low net exposure across all sectors, avoiding a large long or short bet within a sector, will be eligible for larger allocations in our selection process. Here is an example algorithm that uses optimize to constrain sector exposure.
Inappropriate Risk-Driven ETFs: Different ETFs are constructed to be different risk packages. They may be leveraged 3 times, creating excessive single name risk (and high fees!). Other ETFs track risk indices like VIX, and have a history of market pricing failures. Quantopian doesn't give allocations to algorithms that are based on ETF risks like these. It is possible to use some ETFs in thoughtful ways, but only with careful risk controls.
In his book Inside the Black Box, Rishi Narang makes this pithy comment on risk: "So the key to understanding risk exposures as they relate to quant trading strategies is that risk exposures are those that are not intentionally sought out by the nature of whatever forecast the quant is making in the alpha model."
Narang’s insight summarizes everything I've discussed so far. When your algorithm is making money, you have to understand why it is making money. Is it riding a single hot sector, or a Fama-French factor, or the market as a whole? In order to construct a good algorithm, you need to know where the alpha comes from.
We’re making allocations already, and we plan on making more of them, and larger, as the year goes along. If you can find alpha while managing your risk, you could get one of them.