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Robot James (🐊,🐊) Profile picture
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31 Dec 21, 10 tweets, 4 min read
Classic books for buy-side quant stuff.

General:

Inside the Black Box - amazon.com/Inside-Black-B…

High-level, hand-wavey (a good thing), intro to quant trading.
Efficiently Inefficient - amazon.com/Efficiently-In…

High-level, non-technical, intro to hedge-fund strategies.

The Laws of Trading - amazon.com/Laws-Trading-T…

General overview to the whole trading problem - mostly from prop trading / market maker perspective.
Return Prediction:

Expected Returns - amazon.com/Expected-Retur…

Nearly everything widely known that used to make money is in here.
Modelling / Portfolio Construction:

Active Portfolio Management - amazon.com/Active-Portfol…

The classic book on combining weak alphas and combining them into something that attempts to maximize signal, minimize variance, and actually make money after cost.
Quantitative Equity Portfolio Management (the Qian, Hua, Sorensen one) - amazon.com/Quantitative-E…

and

Quantitative Portfolio Management -
amazon.com/Quantitative-P…

Application of the stuff in G&K.

Good stuff in here on maximizing utility after trading frictions.
The Elements of Statistical Learning - amazon.com/Elements-Stati…

I never made it past chapter 4 of this book - but you should probably try.
You wanna skim this stuff at first.

Just try to get the big picture.

Plenty of time to go deep into details when you've got the basic high-level concepts clear in your mind.
Moar books I haven't read (and lots of overlap with these) here:
Books primarily address the question:

"Given some edges (return predictions), how should I trade, given my objectives, constraints and costs?"

That's very important.

But even more fundamental is actually having some edge:
Ideas for edge can come from your own observations, trade data, and those of other traders.

In fact, the best new book I read this year was @TCK_JRubano's, which describes a deeper, more idiosyncratic style of trading than broad quant trading.

amazon.com/gp/product/099…

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More from @therobotjames

Robot James (🐊,🐊) Profile picture
3 Dec 21
Execution performance stuff.

You want to get into $100k of SHIT-PERP within the next 5 minutes.

How do you know if you did a good job?

Here is one way of thinking about it... 👇👇👇
At the time you declare your intent to trade, you want to take a snapshot price to benchmark your execution performance against.

We'll call that the "Arrival Price".

Let's say it's the weighted midpoint (microprice) of SHIT-PERP at the point you decide we want to trade.
Now we'd start trading.

Maybe we just YOLO a market buy for the full amount in one go?

Maybe we split it into bits, and run a simple TWAP algo?

Maybe we do a more sophisticated thing: a mixture of opportunistic quoting and taking liquidity when it's available?
Read 14 tweets
Robot James (🐊,🐊) Profile picture
2 Dec 21
I see discussion about entries and exits in systematic trading. I don't really think like that.

I trade when:
1) I can get a good price
2) I have to.

1) is dependent on others. You can't force people to trade with you at bad prices.

It needs patience.
Let's say you wait and you take an opportunity to buy cheap.

Now you are long.

The market moves and now it's trading at a price you think is fair.

What do you do?
You would like to reduce your exposure because you have no edge anymore.

Carrying positions with no edge costs you in pnl variance, with no expected returns.

But reducing the position involves trading. And trading at a fair price is an immediate loss. It costs you money.
Read 6 tweets
Robot James (🐊,🐊) Profile picture
1 Dec 21
USDTBULL on FTX is up over 50% in a year.

It holds USDT-PERP rebalanced to 3x leverage.

A long USDT-PERP position only paid funding at an average of about 5% p.a. over that period.

So where did the rest of those returns come from?

What am I missing?

Seems... wrong. 🤔
Most likely what @factorialzeros and @ArchipCapital have pointed out.

The token would be buying more and more USDT-PERP as it rebalanced.

And the cashflows would be relatively consistent and USDT-PERP v low vol, it would compound very effectively.
As my man @IDrawCharts said in the comments, you could DIY this much better yourself.

- Long USDT-PERP
- Re-invest funding received back in by buying more USDT-PERP.

Obviously, there's a reason people are pushing those perps down (it's tether innit) so do your own research.
Read 4 tweets
Robot James (🐊,🐊) Profile picture
26 Nov 21
"how do you know when something is cheap?" 👇
@KrisAbdelmessih better at explaining this than me 🙏
And @bennpeifert too.

There's a podcast he did with @choffstein where he talks about "disturbances in the force" and how traders get paid to provide liquidity where its needed and offset it elsewhere.

A really beautiful description of the art.
Read 4 tweets
Robot James (🐊,🐊) Profile picture
26 Nov 21
You see a lot of trade theses that look like sell-side research.

"We think this will happen, which will cause this to happen, which will cause this..."

"This company/project is better than this one, so I think it will outperform.."

You don't need to play such unreliable games.
Most reliable approaches to trading don't involve grand predictions of the future.

Just...

What's being over-offered now?
What's being over-bid now?

I spend most of my time trading the crypto markets at the moment.

I'm spectacularly uninformed about the projects.
I have no idea what the future looks like.

But sometimes I can buy an asset at $100 and sell a future on it for $110.

Sometimes people place limit orders and leave them alone. And I can take them if the market moves and they become attractive.
Read 6 tweets
Robot James (🐊,🐊) Profile picture
16 Nov 21
"Ask questions now? Or ask questions later?"

Imagine you are trading a futures product.

The prices of the futures as a function of their time to expiry looks like below.

You step away from the desk to make some tea.

And, upon your return, you see a different picture...

1/n
What happened to the Feb expiry?

It kinda sticks up.

The curve looks kinky now.

Why?

2/n
Could be many things:

1. Random large demand for that expiry has created temporary price impact, likely to revert
2. New info impacting that month is being priced efficiently
3. Somebody knows something and you're likely to see more demand come in behind in that expiry

3/n
Read 15 tweets

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