ltrd Profile picture
Jun 11, 2023 11 tweets 4 min read Read on X
[1/n] Friday’s night crash was very interesting because there was no big news at this moment and some of the instruments fell about 50% within two hours. I would like to break down the microstructure behaviors and do an overall analysis from that day. 🧵

#trading #cryptocrash Image
Firstly, let’s take a look at the macroscopic view of the market. On the histogram, we can see the changes from the highest price to the lowest price within the first couple of hours of June 10th (UTC). Big moves around the whole crypto space. Image
Some of the instruments fell about 50% and GTC fell over 73% due to the fact that during such crashes, liquidity is poor. This example can show you how liquidity is important when there is huge volatility in the market. Unfortunately, this move happened during the weekend.
Before we move to the market microstructure, let’s look at the performance of the coins during and after the crash. You can see here by the slope of the regression line that the more the coin lost in the crash, the more it rose after the crash. Image
In this kind of analysis, it is important to see what are the outliers here. Is this some basket that performed better than the overall market or it is a coin that performed very well in the last days? It is a moment when you have to dig deep down into particular examples.
Let’s move to the worst coin during the crash - GTC On the plot you have liquidity for the first 25 bids and asks with CVD (cumulative volume delta) and the price of GTC. You can see that before the big crash, liquidity vanished on both the bid and ask sides. Image
Here is the important lesson - in those moments your algorithms can have the best predictions but simultaneously the worst performance due to the lack of liquidity. If your algorithm has to do a rebalancing here, it is worse than a nightmare to do it in those market conditions.
Let’s look at the data. You can see here the impact of the market order for $5k. Before the crash, the cost is marginal in comparison to the cost during the crash and even an hour after the crash. If you do not consider it during your analysis, then you do a big mistake. Image
Here, you can see the impact for BNXUSDT. Doing a $5k market order can rise your costs by almost 5% in the worst cases. Image
The last thing that I would like to show you is the net volume (buy - sell) on a couple of exchanges for BTC. Binance perpetuals still dominates the market, but OKX and Bybit are much more important than were before. Image
I hope that you enjoyed the analysis. If you have any questions or you would like for me to do some next steps related to this crash - please comment before this thread.

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More from @ltrd_

Jun 19
Recently, I got DMs regarding how I use AI on a daily basis, so I’m going to explain it from my PoV. Currently, my job is more business-related rather than quant. I am responsible for strategic partnerships, the research planning, new investments (both financial and technological) and of course leadership as well as recruitment process. That’s the reason why you can find my PoV interesting. 🧵
Use Deep Research Mode - I try to use it constantly before important meetings or in order to get the first exposure to some topic. You have important meeting with a person from business that you are not familiar with? You talk about new financial product that you have not seen before? You talk with startup founder about an idea that you have never heard about? That is the ideal case for Deep Research.
Use AI for the worst job - AI is here not to do all creative job in order for you to do laundry whole day - it is the opposite. Look for the things that it can automate, use it for short scripts or analyses that would last couple of hours but not can take you 30 minutes from start to conclusions. That is what AI is for you. Do not focus too much if “AI reasons or not” - it does not apply to 99% of your cases. Think about “ai-value / risk” matrix - if something has small cost of making mistake then automate it, in case of big cost - just let it be your assistant.
Read 10 tweets
Jun 16
I got a lot of messages regarding materials for perpetual swaps (and CFD). Let's focus more on perps and what to do and read to better understand this instrument.

I provide you with articles, exercises and plan to work on perpetual swaps. 🧵
Firstly, assuming (almost) zero knowledge, I would read exchange docs with explanation. Something simple - read the article, read the docs and try to fully understand the basics. There is no worse situation than starting without deep, fundamental knowledge.

okx.com/help/iv-introd…

guides.delta.exchange/delta-exchange…
After that deep understanding of the basics, you should read one (JUST ONE) paper to understand something that is a bit more complex and will reveal more for you. It can be one of them:

arxiv.org/pdf/2212.06888
papers.ssrn.com/sol3/papers.cf…
papers.ssrn.com/sol3/papers.cf…

Take a look at each of them, choose something that resonates with you and read it.
Read 10 tweets
Jun 10
Why you should not catch a knife on small-cap instruments? 🧵

Today, I’m going to give you one of the most important lessons you can get if you want to be profitable long-term (with statistics and examples).

We all have the desire to think that a 30% drop is just a discount and a time to buy your favorite, solid-looking coin. That is far from the truth.

If you want to read only one thing today, this is the one you should choose.

#CryptoTradingImage
Story:
Imagine your favorite coin — the one you follow on X and engage with on Discord — drops 30% without any specific event. (Let’s assume BTC didn’t drop more than 3% on the same day.)

First thought?
“I’ll buy it — it’s a super cool project, and this is just a discount.”

Unfortunately, on average, that’s the wrong choice.
And I’ll show you why — with data, and a possible explanation.
I took all the data from GATEIO from the last 2 years. I filtered all days where a particular coin had a 30% decline, with less than a 3% decline on BTC. I promise you — no parameter calibration was done. I just picked those two numbers arbitrarily. They simply felt reasonable.

After that, I created plots of price action over the 90 days following the drop. 1 on the Y-axis means no change from the close price on the day of the drop. Everything below 1 indicates a further decline.
Read 9 tweets
May 4
Conclusions after TOKEN2049 🧵

TOKEN2049 in Dubai is behind us, and it's time for reflections. I've prepared a short list of observations from my week in Dubai—I hope it gives you a fresh perspective. Remember, if you want a comprehensive view of TOKEN2049, don't rely solely on my list; read others' reflections too. We all attend TOKEN2049 with different goals, levels of preparation, and meet different people. Also, when I make statements, assume the Pareto principle—meaning it's true 80% of the time, not 100%. There will always be outliers or exceptions.

#Token2049Dubai #Crypto
EXPO is getting worse → The expo is now 60% exchanges (mostly low-quality, but not exclusively) and market makers (also mostly low-quality). Nothing new, important, or interesting. Few good projects or narratives to keep you engaged at the expo, and many important people didn't even bother getting tickets.
Huge number of shady MMs → There's a crazy number of shady market makers (mostly Russian Gotbit spin-offs or something similar) aggressively trying to get contacts. The last time I felt this way was in Florence, with vendors aggressively selling bracelets. I strongly advise founders and people who care about their projects to be cautious—these companies sell visions of huge pumps followed by quick cashouts. If you're planning to scam and pump your project by 1000% to cash out 90%, go with these shady MMs; they're perfect for it.
Read 13 tweets
Apr 27
I have prepared several thought experiments for you to think about in the evening with your favorite herbal tea (or any other drink).

Focus on deep thinking about the problems and possible scenarios.

In thought experiments, one of the most important things is to think about really strange situations (with crazy numbers) and the cause-and-effect chains.
(Often, with outliers and crazy examples, it's actually easier to find hidden dependencies or counterexamples.)

Here are some thought experiments (or puzzles) 🧵🧵🧵:

#Crypto #CryptoTrading #Margin #ADL
Insurance Fund goes to 0, but the exchange applies a “0 ADL Rule” (No ADL):
What can the exchange do to mitigate the big risks of market impacts or even its own default because of excessive liquidation and bankruptcy losses?
Exchange decides to offer 1000x leverage on a quite illiquid token:
What happens?
Does ADL trigger almost all the time?
Who becomes the biggest beneficiary of this situation?
Read 8 tweets
Mar 15
I would like to recommend this book to all of you - Unreasonable Hospitality is simply great. I love it so much that I gave it to a couple of the most important people in the company to share the values from the book. Quick review in the thread 🧵

Read this book!

#books Image
This book is about a lot of topics - philosophy of hospitality, leadership, management, progress, mentoring and great experience at work.
Here is the list of my best quotes from the book:

"People will forget what you do; they’ll forget what you said. But they’ll never forget how you made them feel."
Read 13 tweets

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