Getting Capital Allocations from Quantopian
Anyone with some coding skill and a mind for finance can be successful. All you need are good ideas, and we'll provide good tools.
How this works

1. You use our platform to create investment algorithms.

2. We evaluate your algorithms, and selected authors receive an offer to license their algorithm.

3. When a selected algorithm generates positive returns, the author gets a cut.

Our job is to raise the capital, handle all day-to-day trading operations, provide useful data sets, and build the best platform in the world for creating investment strategies.

Your compensation

If your algorithm receives an allocation, we will pay you a share of the returns that your algorithm earns on our capital. Our target is to pay you 10% of the net profit on your algorithm's allocation.

Our compensation structure has benefits that you won't find in most other quant compensation.
You will always own your intellectual property. At other firms, your algorithms become the property of the firm.
You don't have to manage the algorithm operations, chained to your desk every day. We will operate the algorithm for you.
You don't have to pay for the platform, pricing data, or corporate fundamental data.
You aren't responsible for raising trading capital. Quantopian will provide it for you.
You will be paid a share of your algorithm's positive returns, regardless of other algorithm writers' performance.
Our tools and data are world-class. Our open-source platform is built with the help and guidance of experts world-wide.
Our process

We are building a portfolio of uncorrelated investments. Financial theory, backed by evidence in practice, shows the benefits of aggregating a large number of uncorrelated returns streams. The key is that the algorithms should be uncorrelated.

With rare exceptions described in our Terms of Use, we don't look at your algorithm code. But we do look at some of the information that comes out of your backtest. We look at things like your performance, your risk metrics, how many securities you trade, how often you trade, and what the size of your positions are.

What we look for
Low Exposure to the Market
We want algorithms that aren't correlated to the rest of the stock market. If your algorithm is correlated with the rest of the stock market, the algorithm isn't providing any quality different from buying a market index. As a practical matter this means that we're looking for algorithms that have a strong hedging component, or are completely hedged at all times. We measure market exposure by calculating your beta to the S&P 500. We are looking for algorithms with a market beta between -0.3 and +0.3.
Consistent Profitability
We are looking for uncorrelated algorithms that show stable profits. If your algorithm is losing money, or makes money in a volatile way, it isn't very helpful. We are looking for algorithms that consistently have a Sharpe ratio over 1.0.
Actively Trading Algorithms
Your algorithm has to actively trade. We measure that by looking at how often your portfolio turns over per year. This is important because we need your algorithm to be predictable. Our algorithm selection model only works if we have sufficient data to analyze about your algorithm. Algorithms that rebalance or trade very infrequently are difficult to model with limited historical data. We are looking for algorithms that turn over their portfolio between 12 and 500 times per year (e.g between once per month and twice a day).
Low Correlation to Peers
Your algorithm must generate a returns stream that has low correlation with other algorithms we select. We measure correlation by looking at your average pairwise correlation with the rest of the algorithms in the pool. We prefer that average to be between -30% and +30% average pairwise correlation with the other algorithms in our portfolio.
Strategic Intent
We are looking for algorithms that are driven by underlying economic reasoning. Examples of economic reasoning could be a supply and demand imbalance, a mixture of technical and fundamental drivers of stock price or a structural mispricing between an index ETF and its constituent stocks.
What we don't want
In the pursuit of a good algorithm, sometimes a writer will overfit the algorithm to the test data set, only to see poor performance on live data. Good practice is to minimize the number of parameters in your model, constrain your parameter search space, and develop your algorithm using a limited data set (in-sample data), followed by cross-validation testing on the rest of the data, the out-of-sample.
Data snooping
Some algorithms, either unintentionally or by design, rely on future information to be profitable. Our tools and data sets are built to prevent this bias from entering your algorithm, but algorithm writers still need to take care. One common form of data snooping is survivorship bias, where the strategy works only on a set of companies that are large and successful today, like Apple, Google and Amazon. Another common error is look-ahead bias, where the strategy incorporates external data into a backtest earlier than it would actually have been available in live trading.
Spurious correlations
If there is no economic underpinning linking the algorithm's signal and its profits, this should be a warning sign. When you don't know why your algorithm works, it is difficult to have confidence about its future behavior.
Infringing or misappropriated content
You should not post or use any copyrightable or trade secret materials or any other protected intellectual property of others (other than Shared Content), including any proprietary materials of your current or past employer, without getting any necessary consents or approvals in advance. For more information, please see our Terms of Use.
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