A thread on microstructure on high rebate/high taker fee crypto-derivatives exchanges. E.g. BitMEX, Bybit. This may be common sense to some, but I explained this to a tradfi trader and they found it helpful. (1/8)
Stylized fact #1: Despite a relatively thin tick in BTC, these markets trade one tick wide 99% of the time.
Stylized fact #2: When the price moves, it often moves by 20-40 ticks at a time instantaneously. (2/8)
Thinking like a market maker: a 2.5 bps rebate is equivalent to 15 $0.50 cent ticks on a 30k BTC. Providing liquidity at the top of the book, I am still mostly in the green if fair value is less than 15 ticks under my bid or above my ask. (3/8)
Even though the volatility of BTC suggests that it should trade way wider than a $0.50 spread, there is competition for price priority to earn the massive 15 tick rebate. This drives market makers to quote at prices they otherwise know are slightly too cheap/too expensive. (4/8)
Now let's address stylized fact #2. Suppose an external signal in the form of price on every other exchange rising. With a 7.5 bps taker fee, as a liquidity remover I need to be right by a whopping 45 ticks on a 30k BTC for my mark to market PNL to go green. (5/8)
So initially, market makers begin to form a significant imbalance on the best bid. Everyone sees the signal, but no one is willing to act first because removing liquidity is simply too expensive relative to the potential profit. (6/8)
Finally, once our hypothetical cross-exchange arb becomes strong enough to cover the taker fee, there is a market order race to remove liquidity. This is why it is rare to see the price move by just a tick or two on these exchanges. (7/8)
By the time the market order race is on, there is enough pent up order flow here to blast the price 30+ ticks easily. If you're building a HFT strategy, your orderbook features will vastly vary depending on the fee structure of the exchange. (8/8)

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More from @lightspringfox

16 Jul
Figured I would compliment the shitposting with some thoughts. This meme was based completely on a true story. People talk a lot about mental game for discretionary traders, but mental biases for quants are under discussed in my opinion. (1/n)
1. Confirmation bias w.r.t simulations: How often do you look for bugs in your backtesting framework when performance looks good vs bad? When performance looks bad the inclination is to peruse the code. When things look good, ignorance is bliss. (2/n)
Net effect: Your bugs will always be selected to bias results upwards, unless you make a habit of unit testing each component. This mechanism can be quite insidious over time in a complex backtester. (3/n)
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