Allow me to ramble for a bit about how I think about edge in trading. 👇👇👇

First, what we're trying to do is trade deviations from fair value.

We want to repeatedly:
- buy what's cheap
- sell what's expensive
- offset risk as cheaply and efficiently as possible.

We'll concentrate on the first two here.

Let's take a really simple example to start with. Imagine you have the same asset trading on two different exchanges.

Let's pretend it's some altcoin trading on two crypto exchanges (cos I want to look cool.)

Remember we want to be trading deviations from fair value?

Well, I don't have a clue what the fair value of some altcoin should be.

But maybe I don't need to.

I can certainly identify when it might be *relatively* cheap or expensive on each exchange.

In this case, it makes sense to think about the *spread* - the difference between the two prices.

I can't get a decent fair value for the asset itself, but I can get a fair value for the spread.

Ignoring real-world frictions, risks etc the FV of the spread should be $0.

(Spread = price at Binance - price at OKEx)
I drew it badly but you probably get the idea.

Now we've got a clear idea of how we might trade this - at least in our frictionless world.

We want to be short the spread when it's above fair value and long when it's above.

We can estimate the expected value of the position as the distance between the spread and the $0 fair value line.

This is useful because this isn't going to be the only thing we're trading.

If the EV of getting short this spread is only 5% annualised and we have a 50% annualised opportunity elsewhere - we're not going to be trading this.

Or, at least, we won't be trading it in size.

Our general trading problem (at least in terms of edge) becomes to:
- identify deviations from "fair value"
- pick the best ones
- hold them until there are other opportunities with better expected returns available.

"Hang on a minute," you say. "You picked a totally trivial example of the same thing trading in two different places and different prices. It's easy to know how to trade that, but most things don't look like that."

True true.

But the concepts are the same, it's just harder to model the fair value line.

You need some skill in determining fair value, either through:
- quant skills - such as modelling lead/lag effects to other assets, corrs to a basket, liquidations etc
- discretional skill

So now we take a leap and assume you can calculate a fair value line.

Plot FV and the tradeable price of the asset..

Now the FV line is no longer anchored to $0 - but we can still work out the expected value as the difference between FV and the price you can trade.

And if it helps you picture things, you can just plot your expected value of holding a trade till it had no edge anymore...

Which is the equivalent of what the chart would look like if you flattened the FV line to be a horizontal line at $0, like before.

This is, essentially, the whole "edge" side of active trading.

You're normalising all your opportunities as best you can vs "fair value" (which is easier to do with spreads / relative opportunities - but noisily possible everywhere with skill.)

Then you're trying to hold the opportunities with the highest expected value.

And when the expected value of your positions isn't as attractive as others, you rotate into more attractive opportunities.

This is the conceptual side of "edge".

Conceptually, it is simple.

Practically, of course, it is not so simple.

There is a lot of measuring and modelling and adaptation to be done.

And when it comes to trading you have to think about risks and other constraints. (Which is usually the bigger problem.)

But this is the basics of edge.


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More from @therobotjames

21 Mar
Most beginner traders don't realize just how variable the p&l of even a very high-performing trading strategy is.

I simulated 10 5 year GBM processes with annual return 20% / annual vol 10%.

(Simulating a strategy within known Sharpe 2 characteristics.)
I plotted the path with the highest ending equity (green), median (black) and lowest (red).

All paths are from exactly the same process, with the same known return distribution.

You might think of the green line as trading a strategy with a known large edge and being lucky.
You might think of the red line as trading a strategy with a known large edge and being unlucky.

Even when you were really lucky, you were underwater for 130 days.

When you were unlucky, you were underwater for 508 days (about 2 years)
Read 8 tweets
21 Mar
@InBraised If you can trade for free, then your optimal trading strategy (given reasonable return estimates) would be incredibly hyperactive.

You would continuously change portfolio weights according to your latest return estimates.
@InBraised In the real world, this would kill you, because trading frictions would eat away at your PnL.

So, one way to avoid hyperactive rebalancing is to only calculate your return estimates periodically (say once every day, or every week, or something).
@InBraised But this isn't optimal because, if your alpha is good, you want to be calculating it as often as possible. You just only want to be trading when the increase in expected returns from the new position is much better than the old position.
Read 4 tweets
20 Mar
Risk and Reward: A Quant Tragedy

Through careful research, you have assembled a collection of alphas that are correlated with future asset returns.

There's some conventional stuff (momentum, ST reversal, valuation, quality, short interest etc)...

There's some totally unique stuff (you think)...

And there's some stuff you're still clinging onto because you don't want to admit you wasted all that time and money on alternative data.

You take these alphas and you combine them into an expected return for each asset.

You run some simple sense checks. Each period you sort the assets by expected return, long the top and short the bottom.

It looks good. You are encouraged.

But you wouldn't trade it like that.

Some assets are more volatile than others, many are driven by similar risks

Read 7 tweets
19 Mar
Examples of "elevator pitches" for retail-friendly trades, that I would find reasonable 👇👇👇

"Wealth management equity/bond rebalance flows are massive and, due to their size, may not be fully dispersed when performance differences (and therefore rebalance trades) are very large.

We might get paid for buying what they're selling around month-end"
"Institutional yield enhancement programs are massive and tend to be info-insensitive sellers of volatility on an up-tick in vol.

This may keep IVs depressed in the short-term, leading to trend effects in IV on significant bad news, which we could profitably trend-follow"
Read 4 tweets
18 Mar
How do I know if I have an edge?

A thread... 👇👇👇👇

I've been helping a family friend with his trading. I've given him a simple systematic strategy to trade by hand.

We can plot the distribution of historic trade returns from past trading or a backtest as a histogram.

The trade P&L is on the x-axis and the frequency (# of trades with that P&L) on the y-axis.

This is useful because it gives us a hint as to what the "edge" of our strategy might be - if we could ever truly *know* such a thing.

In this case, our strategy had positive mean and negative skew.

We saw winning trades about 58% of the time but losers were bigger, on average, than winners.

(As many things that make money tend do, regrettably)

Read 15 tweets
9 Mar
On "stationarity"...

When we talk about something being "stationary" we mean that the observations look like they could be drawn from the same "bag of observations" (distribution), regardless of what time we choose to look at.

You can observe this by eyeballing charts.

VIX (blue) stays within a range the whole time. If it gets to extreme values, it's likely to revert back to moderate values.

By contrast, the SPX price (orange) just seems to drift away. It doesn't appear anchored to any range.

We can also see this by sampling from the distributions at different times.

Let's divide our sample roughly in half (2004-2012 vs 2013-2021).

From the histogram, it's clear that SPX prices are not drawn from the same distribution in the first and second periods

Read 6 tweets

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