Jane Street is accusing Millennium of poaching 2 employees and replicating a proprietary trading strategy. I’m curious what an example would be of a strategy that is simultaneously unique but replicable by two employees being poached.
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Based on the WSO Dataset, while I can't comment on specific allegations or situations involving companies like Jane Street and Millennium, I can provide insight into what constitutes a unique yet replicable quantitative trading strategy. These strategies often involve complex mathematical models and algorithms designed to identify and exploit market inefficiencies. Here's an illustrative example that aligns with the general knowledge shared within the WSO community:
Example: Statistical Arbitrage Strategy
Statistical arbitrage is a broad class of strategies that use statistical models to identify mispricings between a pair of securities or within a basket of securities. A unique yet replicable example could involve:
Pair Trading: Identifying two historically correlated stocks that have recently diverged in price. The strategy would be to short the outperformer and go long on the underperformer, betting on the convergence of their prices.
Signal Generation: Using machine learning algorithms to analyze historical price data and identify patterns that predict this divergence and convergence. This could involve complex feature engineering to extract predictive signals from raw data.
Risk Management: Implementing dynamic risk management techniques to adjust the exposure based on real-time market volatility and the pair's historical behavior. This might include value-at-risk (VaR) calculations or conditional value-at-risk (CVaR) to manage potential losses.
Execution Algorithm: Developing a sophisticated execution algorithm that minimizes market impact and transaction costs. This could involve techniques like iceberg orders, which hide the actual order size, or using predictive models to choose optimal trading times.
This strategy is unique in its specific combination of securities, the exact mathematical models used, the data features considered, and the execution tactics. However, if two employees deeply involved in developing or operating this strategy were to move to another firm, they could potentially replicate the strategy by applying the same principles to a different set of securities or markets, assuming they do not violate any non-compete agreements or intellectual property laws.
It's important to note that while the core idea of statistical arbitrage is not new, the uniqueness comes from the specific implementation details, which can be highly proprietary and valuable to trading firms.
Sources: Trading Own Quant Strategy at Prop Shop, Q&A: Head of Macro Strategy at Hedge Fund Taking Questions, Stock Pitch Sample Template - Proven Examples to Help Ace Your Interview, Q&A: Current junior quantitative researcher, Q&A: Head of Macro Strategy at Hedge Fund Taking Questions
del
I can give you one concrete example.
FTX offered leveraged crypto products. It would rebalance client positions hourly (not sure if I remember details exactly) but would only rebalance their own hedging every 24 hours (at the same time). If you knew this you could front run their rebalance pretty aggressively as you knew a substantial amount of forced liquidity would enter the market at a specific time. Completely portable strategy. If you know it you can create it in 5 minutes.
A more theoretical example might be URL leaking - imagine the s&p500 committee makes a decision on index inclusion. Then a press release is created and released. Imagine the main decisions are included in the decision URL. If you can repeatably identify the url locations before the press release goes out then you can front run the inclusion. Using s&p500 as an illustrative example, but I know very similar strategies have actually been used. Again can be created in 5 minutes with no infra if you know the alpha.
These are very cool - thank you!
With the theoretical s&p example, where does the quantitative part come in?
What do you mean? It’s not something you could do non-systematically.
That I understand, I'm just curious what quantitatively/systematically goes into trying to do that.
Roughly I would describe a possible theoretical url scraping process as follows:
- let’s say all sp500 announcement urls have the format spglobal.com/releases/{inclusion_1}_{exclusion_1}
- our goal is to identify the urls programmatically
- we get a list of potential inclusions and inclusions from a research provider (suppose 10 possible inclusions and 10 possible exclusions)
- we build the 100 possible urls that could come from inclusion and exclusion options.
- we make http requests to all 100 endpoints just before we expect the announcement
- if we get a 404 code then that announcement doesn’t exist
- if we get a 403/401 code then the announcement exists but isn’t public yet and so we know what inclusion and exclusion is coming.
You can see an actual announcement url here - you can see the types of information you might try to extract from such a url and how you could programmatically buils and request upcoming announcements: https://press.spglobal.com/2024-03-01-Super-Micro-Computer-and-Deckers-…
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