jeff.hl Profile picture
Mar 16 9 tweets 3 min read
1/ Liquidity is definitely a factor but the fundamental reason for volatility autocorrelation is information propagation.

One mental model for markets is a viscous fluid. Shocks to the system play out as damped oscillations in the price discovery process.
2/ In this analogy, the autocorrelation of volatility is the amplitude decaying over time at rate proportional to viscosity. It's intuitive if you think about dropping heavy objects into liquid.

The viscosity parameter is the efficiency of the market. What are its components?
3/ Markets are where self-interested actors come together in a dance of price discovery. A simple example, let's say CPI prints lower than expected.

First NLP strategies parse headlines and slam BTC books. Hard coded triggers, for example 6% inflation -> BTC +2%.
4/ At the optimal frontier of sophisticated trading, the fastest are the dumbest. When prices are wrong you just need to get fills.

There is always alpha pushing the price towards fair. Human traders think and adjust. Inverse correlation between time spent thinking and smartness
5/ This human disagreement dance is the primary factor for volatility autocorrelation.

E.g. twitter traders might buy because #moneyprinter baby, past the macro-BTC correlation the tradfi folks are using. The price will settle at a dollar weighted average of their opinions
6/ There are structural causes for short term volatility autocorrelation too. A well known exacerbating factor is triggering cascades of stop loss orders. Where u at tabasco?

But in general, MMs and quant traders actually serve to *increase* the viscosity of the "fluid."
7/ This is counterintuitive, and yes liquidity does dry up during high volatility like @thiccythot_ mentioned.

However in aggregate (dollar-weighted) non-quant actors trade on price, not size. Illiquidity amplifies the jitter but isn't the root cause.
8/ The easiest way to think about the quant effect is that both makers and takers only have positive pnl if on average buys are lower than sells.

This means the MMs in business predictively smooth out the ripples, smoothing out dislocations and decreasing volatility on average.
9/ You can look empirically at electronic market history to see the positive effects of quant trading.

Tighter spreads for retail is often mentioned, but more efficient tracking of fair price is important too.

Btw I summarized the market actors here in more detail:

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More from @chameleon_jeff

Mar 5
1/ Agreed. Papers guide you the wrong way. Academics do not know how to make money quant trading (or why would they be writing?)

How do you get started with real data though? Here's an example: Let's reverse engineer the networking stack at #OKX to minimize order latencies. 🧵
2/ Personally I love studying latency: it's concrete, less noisy than pnl, and forces you to look at the data. The seeds for full-blown strategies often come from tinkering with latency measurements.

Ok let's jump into #OKX. Same process here applies to all exchanges.
3/ First look at the docs and see what stands out. Each OKX API has two endpoints: AWS and AliCloud. This is weird. Do some googling: They launched AWS to make it easier for HFT firms to migrate their tech stack. Makes sense: we were tempted to just use AWS for simplicity.
Read 13 tweets
Mar 4
1/ Controversial take: hard work is more important than smart work.

It's a myth that we only have a few hours of good creative work per day. Train yourself to grind long hours first. You will surprise yourself. The work naturally become higher quality, less distracted.
2/ Train yourself to go from 10 to 40 to 100 hours of focused work a week. I did this for months building an automated trading system from scratch. 5am-10pm 7days/week

99% of people need #WorkLifeBalance or whatever, and that's great. But I'm talking to the people on a mission.
3/ Do you really want something? Then really work. Half-assing doesn't get you anywhere. Don't look for "system hacks," or whatever, just do it. You first need the baseline mentality of going all in, or nothing else matters
Read 6 tweets
Feb 28
1/ A picture is worth a thousand...ideas for a new HFT strategy in crypto?

Ever wonder how to go from raw market data to an automated strategy in crypto HFT? Here's an actual case study our team did. Image
2/ This graph is the BBO (i.e. price of highest bid and lowest offer) of BTCUSDT on #Binance, zoomed in. The y-axis is in USDT, with an arbitrary offset. The x-axis is time, around a 20ms range.

Standard stuff. But HFT is all about thinking deeply about simple things.
3/ Mysteries:

y-axis: that spread is tiny. One cent on BTCUSDT is... less than 0.01bps? What is this liquidity??

x-axis: When the offer ticks up the bid follows in... 3ms? If you've ever measured the round-trip order latency on AWS, you'll know it's never this fast.
Read 11 tweets

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