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This is a thread on the basics of quantitative trading & the strategies it employs. To understand today’s markets, we have to understand today’s traders. Most aren’t even human. They’re finely-tuned machines, built for the sole purpose of making money. How do they do it? 1/7
Quant trading programs can be split into 3 parts: Data parsing: normalizing & feeding market data into your program Trading strategy: Deciding what trades to make Order entry: Converts strategy into orders that can be accepted by the exchange 2/7pic.twitter.com/5j2GHiktre
There are 3 strategies a quant firm can pursue: -Arbitrage: solve a market mis-pricing. -Market Taking: profit from a predicted price change. -Market Making: Provide passive liquidity and profit from the spread. I’ll explain these in more detail below: 3/7
-Arbitrage- Firms that are competitive have one of 3 advantages: -Scale: Can manage a lot of data and have low fees -Speed: First to an opportunity gets the trade -Queue position: If you don’t have to move the market with your trade, you save alpha 4/7
-Market Taking – Succeeding here requires predictive models and low latency to capture opportunities. An example would be to ride the waves of larger traders – buy the remaining liquidity of a security after a large trade and flip it for a profit (or vice versa). 5/7
-Market Making- Market makers connect buyers & sellers who don’t come to the market at the same time. When exchanges tout “deep liquidity” in their markets, they’re relying on market makers to help validate this claim. Below is an example using Virtu: 6/7pic.twitter.com/zcicsCunLq
Headlands Technologies, a quant trading firm, lays out their basic strategy in a blog post back in 2017: https://blog.headlandstech.com/2017/08/03/quantitative-trading-summary/ … 7/7
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