The path to profitable trading is straight and narrow.
Having a theory on inflation, or memetics, or an in-depth knowledge of the VIX expiration, is a waste of time if I'm quoting corn options.
So good training concentrates the trader on things that matter.
2/n
You need to concentrate on micro things (information, motivations, constraints, game theory) when you're playing micro games.
And you need to concentrate on macro things (stats, broad behavioural tendencies) when you're playing macro games.
3/n
Twitter lets us observe people's learning journeys in real time. So we can see some patterns of where people get stuck.
On this site, you see many examples of smart but inexperienced traders trying to play micro games and macro level and vice versa...
4/n
Often this looks like an attempt to identify cause->effect relationships in markets/timescales where noise is dominant and the inefficiencies that persist are weak tendencies at best...
A good training program keeps you on the straight and narrow.
5/n
To trade a very sloppy market, you'll want to concentrate on imbalances you can observe directly.
You'll concentrate on understanding your competitors, and carving out a win/win niche for yourself thru understanding their constraints + motivations.
6/n
Good training for this market has you concentrating on the easy arbs, on watching your competition like a hawk, on understanding "the game", knowing how to compete whilst avoiding an "arms race" etc.
Good training has you majoring in the stuff that matters for the trade.
7/n
What about medium frequency trades in more liquid stuff?
That's a different game...
The understanding of motivations, constraints, game theory, is still important - but you must use that info differently.
The market is very good at dispersing wrinkles and imbalances.
8/n
So a cause and effect type understanding: "he's got to do this, then she'll do this, and this will have this effect" is not so explicitly helpful at this level.
You're operating in an environment that is a lot noisier than that...
9/n
So you need to turn your understanding of market actors and "the game" into blunt observable statistical factors.
You need to concentrate on finding blunt, tendencies, which make sense and are (ideally) observable in the past data.
10/n
Game theory and micro stuff are important here - but ultimately you need to boil this understanding into factors that are almost embarrassingly over-simplified.
You're just trying to quantify noisy tendencies as robustly as possible.
And then "ensemble go brrr"....
11/n
At this level, statistical modelling is important. In the first example, less so...
It is this "understand what makes the difference and major in it" / "ignore the stuff that doesn't matter" that I think is hardest for the beginner without a structured program.
12/12 fin
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1. What exposures do I want? 2. What exposures do I have? 3. How do I get closer to 1 from 2, given that:
a) it costs to switch positions
b) my estimates are noisy
c) co-movements of assets are somewhat predictable
1/n
Concepts like "open trades" and "unrealized p&l" tend to be unhelpful in this paradigm.
If you don't like the exposures you have, then move them closer to the ones you want.
It makes no difference if you're underwater or in profit in your "position accounting"
2/n
There is no difference between a position that you have kept on the book for a while and one you just opened. It's exactly the same exposure either way.
Let's run through these 3 questions using a simple toy trading approach...
3/n
I think of the returns from "fundamental investing" coming from two sources...
1. Risk Premium - The tendency of risky assets to be relatively cheap vs their expected cashflows. This leads them to "carry" more than they would if their real cashflows were riskless
2/10
2. Mispricing - For behavioural/structural reasons, some assets are under/over-priced vs a reasonable estimate of their ex-ante "fair value".
On average, we expect them to converge towards fair value over some long time horizon.
First, put aside any expectation that you can isolate and quantify effects with great precision.
The market is a highly efficient beast - why means that any non-random effects we observe tend to be extremely noisy.
But just cos something is hard, doesn't mean we shouldn't try.
In fact, it's essential that we try to understand and isolate effects as best we can.
The best tools for the job (at least to start) are:
- economic intuition
- very simple data analysis (the kind of thing you could do in an excel pivot table)
Shall we do some analysis on a *really dumb* factor which might predict relative returns in stocks?
"Are cheap stocks expensive?"
A research thread 👇👇👇
Options on stocks with a low share price tend to be overpriced.
Equity options (at 100 shares a pop) are quite big for a small retail trader. So we might say there is excess retail demand for options on cheap stocks - which would result in them being overpriced.
But are low priced stocks also expensive?
The AMZN share price is $3k+. There are Robinhooders who can't afford a single stock.
Do we see the same effect in Stocks as we do in the options?