Steal ideas, not implementation.

I see you, with your "small but beautiful" pot of capital, trying to make it bigger.

A🧵on easy games, stealing ideas, and not competing in games you don't need to compete in.

1/n

First, the Market Gods give no prizes for difficulty.

So, to start with, you'll want to play the easiest, most reliable, hardest-to-screw-up, least-dependent-on-skill games you possibly can.

See linked thread:

https://twitter.com/therobotjames/status/1384361563558010882

2/n

Second, the Market Gods give no prizes for originality.

So you want to know what traders who are taking the game seriously are doing. (Especially with their own money.)

Proprietary trading firms
Hedge fund prop capital
Serious solo traders
Hedge funds

3/n

We recently looked at VIX Futures and why they tend to trade at a premium to the VIX index most of the time.

How might you apply this understanding?

Let's discuss how you might think about a systematic VIX carry trade based on these concepts.

https://twitter.com/therobotjames/status/1437922467801034754?s=20

1/n

In the original thread we noted:
- you can't trade VIX
- so there's no market mechanism to stop it from being predictable
- but VIX futures do trade and their price incorporates where the market thinks VIX is likely to go

2/n

If the market thinks VIX is going to go up, the futures will likely already be trading at a premium.

Sellers won't sell low if it's likely to go up.
Buyers will be happy to buy higher if it's likely to go up.

3/n

If you weren't there, you have no idea how disgustingly decadent pre-GFC sell side finance was.

Whatever you imagine x10.

Silicon Valley is amateur hour choirboy stuff in comparison.

Need a burner account to share stories 😂

Why do VIX Futures trade at different prices to VIX?

Derivatives can be complicated, but the answer to this question is not.

If you understand how the market prices risk then you'll know a lot without needing to know a lot.

Let's walk through it. 🧵👇

1/n

Pull up a chart of the VIX index.

tradingview.com/chart/D5QuNI5X…

If you're an experienced trader, you'll recognize immediately that this is not a thing you can trade.

Why?

Cos it wouldn't look like that if people could trade it.

2/n

Cos, just by eyeballing the time series chart, you can tell VIX is very predictable:

- It stays about the same in the short term
- But if it's low it's more likely to go up
- And if it's high it's more likely to go down
- It has a floor under which it's unlikely to go lower

3/n

My focus recently has been on the crypto markets.

I don't have all the answers.

But I thought it would be useful to ramble a bit about the experience of entering a new market.

My perspective here is professional trading, but the concepts are valid for individuals too

1/n

First, you've got to work out whether it's worth expending time, effort, and money in a new market.

There's an opportunity cost associated with looking at and implementing new things.

So you put together some "high-level business case" to see if it stacks up

2/n

This can be tricky because you don't know what you don't know.

So you seek out people who are doing it and ask them to share some of their experiences.

If you are serious, people will generally be very happy to talk to you. This game isn't as secretive as you might think.

3/n

Tail hedging for degenerates.

For most of my time, I just thought of tail hedging as the "cost of entry".

A "ticket to the dance" if you like.

You can't predict what happens in the tails - so pay up to cover them & go play hard in the peak of the bell curve, where your tools and models are most valid.

If you're a good trader, you'll tend to find that your highest expected return opportunities appear after massive moves.

Disconnections happen when others risk models are flashing red and they are FORCED to trade (rather than want to).

You want dry powder for these times.

In the "win-lose" games of active trading, your "edge" comes from:
- Buying from someone too cheap
- Selling to someone too expensive

At least on average.

To do this, you need to know who you are playing against.

🧵on "edge", where to find it, and how you can compete 👇

1/n

If you are a market maker, it is relatively clear to understand who you are trading against.

If you're a positional trader, it is perhaps less clear.

On a trivial level, you're probably trading with a market maker.

2/n

But understand that "the market line" is set by the supply/demand pressures of other aggressive traders.
- End users (wealth mgmt, retail)
- Aggressive prop traders doing short term risky arbs
- Informed positional traders with pricing models + (maybe) info advantages

3/n

A simple thread about position sizing and volatility targeting 👇

You have $1,000
You buy $1,000 of SPY
You leave it alone
The volatility of SPY over the period was 18%

What is the volatility of your portfolio?

Not a trick question. It's 18%

1/n

Imagine instead you buy $500 of SPY in your $1000 account.

At the start, you have half your money in cash and half in SPY.

What is the volatility of your portfolio now?

It's 9%: half what it was before.

2/n

Now, let's say you could buy $2000 of SPY in your $1000 account (and don't pay anything to borrow)

What is the volatility of your portfolio now?

It's 36%: twice the figure when you were fully invested.

This is a useful result. You can prove it to yourself easily in Excel

3/n

Tips for doing financial analysis with OHLC bar data.

Many of you doing quanty analysis with OHLC bar data.

Here's some boring but crucial stuff you need to understand if you're doing that. 👇👇👇

1/n

An OHLC bar represents a summary of trades that happened in a certain period.

Open -the price of the first trade in the period
High - the highest price traded in the period
Low - the lowest price traded in the period
Close - the price of the final trade in the period

2/n

For daily stock data, the Close price will be the price arrived at in the closing auction.

This is set by balancing the supply and demand of MOO (market on close) and LOO (limit on close) orders to maximize the amount of stock traded.

3/n

In Australia, if you're serious about getting the job done effectively and efficiently, you might say:

"I'm not here to f*** spiders"

Many traders act like they are, indeed, here to f*** spiders.

A thread about getting serious about making money trading 👇👇👇

1/n

If you're making soup, you first need a good stock.

Stock isn't exciting. Everyone has stock.

Garnish is exciting, but you can't make soup from just garnish.

You need some stock in your trading portfolio

You need at least one reliable, stonkingly obvious way to get paid

2/n

Here's a non-soup analogy...

If you start a business venture, it's clear that you need an obvious, reliable way to make money.

You wouldn't just try to blag it.

"I am smart and hard-working" is not a business case.

You need a stonkingly obvious way to get paid.

3/n

In my 20 years of trading I have noticed this cycle play out again and again with traders that "make it":

1. Overconfidently reach for returns
2. Get humbled by the market
3. Simplify + concentrate on clear, high probability edges.

1/n

Nearly everyone starts with a lack of respect for how hard it is to consistently make money trading.

That leads them to pass over high-probability sources of returns in favor of more marginal ideas.

Or they overcomplicate the trading of a good edge.

2/n

Here's an example...

It is 2015. You look at a simple strategy.

You hold an equal dollar exposure to:
- Cap Weighted US Stocks (VTI)
- 20+ Yr US Treasury Bonds (TLT)
- Gold (GLD)

And rebalance each month.

(I've extended back a bit with mutual fund prices.)

3/n

A common mistake is to make implementation decisions or parameter choices based on "what improves the summary performance of a backtest".

Quant research is not "changing random stuff and picking the best performing backtest"

1/n

https://twitter.com/macrocephalopod/status/1382299510945284100

A backtest is a very complicated thing.

In the best case, the cause -> effect relationship between what you are changing and the performance of the backtest (say) is highly non-linear.

More likely there is no clear relationship.

2/n

Quant trading is not "changing stuff until you get a backtest you're happy with".

You need to split what you're doing into small component chunks and model those chunks as best you can.

3/n

It's easy to lose money trading if you:
1. Trade too much (paying fees + impact on each txn)
2. Size positions too big (high vol hurts compounding ability + gets u rekt)
3. Shorting positive drift/risk premia

It's hard to lose money consistently if you avoid these things.

However clueless you are, you get to trade at market prices.

Imagine we can know that an asset has a fair value of $100.

You might think it's worth $150.

But if it's quoted $99 / $101, you can buy now at $101.

You were totally wrong but you still bought close to fair value.

The same mechanisms that make it hard to get an edge also make it hard for you to trade at really bad prices.

In a simple model, you might say that prices are set by:
- (risky) arbitrage and relative value in the short term
- pricing/valuation models in the long term

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.

1/n

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.)

2/n

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.

3/n

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)

@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.

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)...

1/7

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.

2/7

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

3/7

Examples of "elevator pitches" for retail-friendly trades, that I would find reasonable 👇👇👇

1/4

https://twitter.com/therobotjames/status/1372731067635953664

"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"

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.

1/n

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.

2/n

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)

3/n

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.

1/5

https://twitter.com/TimmiHendricks/status/1369313259640283142

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.

2/5

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

3/5

Let me try to be helpful.

If you have some kind of factor that you think predicts future stock returns (or similar) and you are making charts like below, then here are some tips...

We'll go through an example of trying to "time" SPX with the level of VIX.

A thread 👇👇👇👇

https://twitter.com/macrocephalopod/status/1368955289672839172

You get daily SPX index prices and daily VIX close data

You align them by date and plot them on dual axes, in true RealVision style.

"SPX tends to go down when VIX is high. I can therefore time an SPX allocation based on VIX. Let me share this on twitter" you say.

No. Very no.

There are two main problems with what you did:

1. The SPX price drifts. We can't directly compare the price of SPX in 2004 with its price in 2021

2. As traders, we are more interested in whether high VIX is *followed by* decreasing SPX prices, not *coincident with* them.