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 👇👇👇

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

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.

Drawing lines on a chart and hoping you can figure out when to buy and sell is *not* a stonkingly obvious way to get paid.

You're putting a huge amount of confidence in your discretion and skill there.

It's a lousy business case. I wouldn't lend you money to do that.

Throwing features into a machine learning algorithm and hoping it can figure out when to buy and sell is a similarly lousy business case.

There's no reason to think that would work. Why would you expect to get paid for that?

I wouldn't lend you money to do that either.

So what IS a stonkingly obvious edge?

We'll go through some examples.

First, we'll start with things you almost certainly can't do, because they illustrate some important points I want to make.

Then we'll look at similar stuff that you actually *can* do.

Some Exchange Traded Funds (ETFs) appear to be rather illiquid. They don't trade that much and their order books are thin.

But if you put in a limit order at a good price for a large number of shares, you often get filled quickly.


Because some traders (called APs) can assemble new ETF shares for you out of bits of the assets the fund holds, and then sell those new shares to you.

This is a useful thing for you.

Due to this, you can buy the ETF for a good price without having to buy all the bits.

The trader makes money if she can buy the bits for cheaper than she can sell you ETF shares she made from those bits.

This is a familiar business model.

The trader provides something useful, takes on some inventory risk, and gets paid if they are running things well.

It works the other way around too.

The same trader can buy the ETF shares off you, disassemble them, and sell the bits on the market.

She makes money if she can sell the bits for more than she paid you for the ETF shares.

You can't do this, so why am I telling you this?

Because it is a great example of a stonkingly obvious edge you can build a business case around.

You get paid for:
- providing a useful service (liquidity at good prices for ETF shares)
- taking on risk
- doing the work well.

What other things look like this?

Market making looks like this too.

Market makers provide a useful service: giving traders the ability to instantly buy or sell an asset at good prices.

In doing that, they take on some risk: they accumulate inventory and tend to be on the wrong side of big moves.

If he does the work well, the MM manages these risks and makes some of the bid/ask spread on each trade, on average.

As before, the MM gets paid for:
- providing a useful service (immediate trading)
- taking on risk (inventory risk)
- doing the work well.

Here's another example...

If you jumped in a time machine and went back to the 1980s, you might notice that commodity futures tended to trade significantly cheaper than the spot value of the commodity itself.

This was because the commodity futures market was dominated by producers looking to hedge against lower prices.

Commodity producers were selling futures contracts to "lock in" guaranteed future prices.

But a futures contract needs a buyer and a seller. Who was buying?


Traders realized that they could take the other side of these flows at a discounted price. (They were less desperate to trade.)

The excess "supply" of futures contracts from commodity producers' hedging created "demand" from traders exploiting the imbalance.

It might not seem that way at first, but traders were providing *a useful service* to the producers.

They provided them with someone to trade with - and the demand from these traders competing for the opportunity resulted in better prices for the producer's hedges.

So there was a stonkingly obvious opportunity here for traders to take the other side of hedging flows.

This was exploited by:
- commodity carry trading
- trend-following
- securitization of comm futures into wealth mgmt products (ultimately killing it in mid 00s)

The "business case" for the trade was the same as before.

Traders fading these flows were:
- providing a useful service (liquidity to hedgers)
- taking on a bit of risk (M2M risk)
- doing the work well (systematic rules, risk mgmt)

Can we have a more recent example please, James?

Sure. Here's a similar and bang-up-to-date example from the cryptocurrency markets.

The FOMO demand for leveraged long exposure to crypto is enormous.

Massive demand from relatively price-insensitive bullish traders has resulted in BTC futures trading far above the spot price. (As high as 40% annualized, on some venues, as of last week)

This is the opposite situation to commodity futures in the 1980s.

There's a stonkingly obvious edge here.

You take the other side.

You sell BTC futures to the rampant YOLO trader demand, and you hedge your directional exposure by buying an equivalent amount of spot BTC.

The "business case" for this looks reasonable:
- providing a useful service (YOLO traders need someone to trade with)
- taking on risk (basis risk, blow up risk)
- doing it well (hedging, smart margin mgmt cos you can't collateralize futures short with long leg)

How about another one you can do?

A classic "stonkingly obvious" high probability edge that nearly everyone should harness is Risk Premia Harvesting.

Assets that are sensitive to certain risks are unattractive compared to those that are not.

This means simply being the person prepared to "hold" these assets is a useful service.

It's a win-win situation where:
- you, as person prepared to take the risk, gets paid in excess returns (higher yield)
- risk-averse person is happy to hold less risky stuff instead.

This thread presented a very simple risk premia harvesting strategy:

This stonkingly obvious edge has provided a tailwind to macro traders ("spoos and blues") and wealth mgmt for decades.

And it's not too good for you either.

As with the other examples, the business case stacks up:

Returns come from:
- providing a useful service (demand for risky assets)
- taking on risk (inflation, real rates, credit, growth etc)
- doing it well (manage risk, sit on hands, don't chase returns)

Wrapping up

If you're serious about making money trading you need some stuff you can really rely on.

You need at least one stonkingly obvious business cases that stack up. Otherwise, you're just LARPing around.

To make soup, you first make stock. Garnish is last.

So put away your vanity projects and get serious about this *first*.

You get paid for:
- providing a useful service (often providing liquidity to more desperate or constrained traders)
- taking on risk (otherwise everyone would do it)
- good implementation

Don't be a hero...

You want good solid business cases.

Solid, obvious things that it is reasonable to think someone would pay you for.

30/30 Fin.

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More from @therobotjames

22 Apr
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. 👇👇👇

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

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.

Read 17 tweets
19 Apr
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.

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.

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.)

Read 22 tweets
14 Apr
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"

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.

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.

Read 5 tweets
11 Apr
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
Read 15 tweets
30 Mar
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.

Read 16 tweets
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

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