nearly everything that is a good repeatable trading idea looks like:

"under <some circumstances> this thing is likely to be too cheap/rich because <some people> are being forced or greedy or stupid... so the thing is more likely to go up/down in the future" your job as trader, operating in an efficient, competitive market, is to tell yourself that your idea about that is probably bullshit.

and quickly prove to yourself that it is indeed bullshit.

destroy those hopes and dreams quickly... and move onto something more productive.

all active etfs are trash.

under the premise that all active etfs are trash, i looked at what it would look like if you could shorta bunch of them against an equivalent SPY long.

the legs are sized to equal volatility based on 120 day rolling realized vol. highlighly scientifically, i looked at etfdb and picked 15 active / tactical ETFs based on their name and category.

trading is hard.

if you disagree, that's cos you haven't done it for long enough.

you can get lucky for a while - but your luck will inevitably turn

you can find yourself doing the right thing at the right time for a while - but markets adapt quicker than you can, typically. extracting returns from the market, persistently, over years and decades is tough.

it requires pragmatism and flexibility.

it requires you to be decisive about trade-offs, in a world of incomplete information and massive uncertainty.

i saw a bunch of people saying that high-ish interest rates were very bad for risk assets.

you shouldn't believe it when ppl say stuff like that

ppl say all kinds of dumb stuff

and you can investigate it yourself in five minutes to see if it's bollox or not. this page lists historical total returns on stocks, bonds, and bills, and historical yields since 1928.



we can pull that into excel with Get Data > From Web, then pasting that url.

here is the data pages.stern.nyu.edu/~adamodar/New_…

if you try random trading rules on raw data, you'll find a lot of stuff that would seem to have made money in the past if you'd been trading it.

but you're unlikely to have achieved anything useful, even if your simulation of all the frictions involved was perfect.

https://twitter.com/AgustinLebron3/status/1770094306583536035

the main reason for this is luck.

your raw data contains a lot of non-randomness.

sims on options contacts, especially, are full of unintended bets.

contracts are incomparable with themselves as price moves relative to their strike, and as time approaches contract expiry.

at some point, volatility is going to spike a lot.

and lots of you are going to get rekt cos you didn't have a good plan for what to do, or you didn't stick to it.

i can't have that on my conscience - i got enough already - so pls read this and make a plan. you need to be prepared to TRADE to keep your risk in line.

the market is constantly giving you risk you don't want.

there's no excuse for just accepting that.

if the market gave you risk you don't want, you gotta trade to push your risk back to what you wanted.

if you have been paying attention recently, you may have heard whispers of the dangers that the rapid growth of the Forex Repo Market may pose to the financial system.

but what is the Forex Repo Market? despite its relative obscurity, the Forex Repo Market is a pivotal component of modern finance, facilitating short-term currency liquidity like no other mechanism.

stop trying to beat djokovic at tennis.

the first fundamental problem traders run up against is that there's no beginners' market.

you gotta compete for good prices with the best in the market.

this is a problem.

there are a lot of people better at markets than you. if you approach trading in a gung-ho manner, it's basically like playing in a tennis competition with djokovic.

and that's not going to go well.

cos he's very good at playing tennis and you're bad at it.

(sorry to break it to)

on finding an angle and whacking it

if you wanted to bet a sports game effectively you might:
1. estimate the outcome probabilities yourself and compare them to the odds
2. look for situations where those setting odds tend to get it wrong, on average we might call the first one "handicapping" and the second one "playing angles".

if we're handicapping, we need to be "more right than the market" on average, across all the possible things that can happen.

if we're playing angles, we can afford to care about one situation only.

i tell you a trading edge.

it'll probably look something like "<x> tends to get mispriced when <y> happens because <z>"

ideally we have some causality we understand or strongly suspect

and some empirical evidence of this being the case, in the past, or in similar situations. that's the initial job: finding some exploitable pricing distortion.

not much point doing anything else, until you have something like that.

but you still have to design and run a set of processes to exploit it.

and there are a ton of different ways you'll fk that up.

people were saying i wasn't posting enough about trading, so FINE, here you are you dirty ingrates...

here are the best places to get trading ideas. 1. steal them from other people.

if you know other people are making money trading an idea, it is often a good idea to pursue it too.

this isn't school. the market gods give no prizes for originality or showing your work,

an edge is an edge however you got to it.

managing positions without voodoo

most important points:
1. you dictate your risk exposures, not the market
2. it doesn't matter how you got different exposures to the ones you want, just fix it. basics:

1. you decide what exposures you want
2. over time, the market gives you different ones
3. you don't have to accept that! put the ones you wanted back on
4. when you don't want the exposures anymore, take them off.

a couple of simple trade-off considerations re: kwant trading signals that may or may not be obvious.

here's the price of a thing....

the main job is to predict how it's likely to move. to do this you used information about it.

at any point, new information is appearing (trades, quotes, events, chatter)

and old information, that used to be very important, is becoming less so.

intuition of log returns

when you do anything with data, you should think about the intuition of each thing you do, and what it represents "in the real world".

let's take the example of log returns, which some people tell me they find confusing. consider an asset whose price goes from $100 to $200

assume there are no other cashflows like dividends associated with this thing.

what are the returns for being long?

the intuitively obvious answer is 100%

it went up $100, the same as its price at the start of the period.

if you try to teach yourself quant trading from the internet or from scientific papers, you run the risk of spending a lot of time majoring on minor sh1t.

you see it on here all the time.

the simplest, closest-to-hand tool for the job is what you want.

at least to start. need to adjust for volatility?

good, that's predictably time varying - you should do that.

but don't reach for some fancy forecast you need to fit.

use recent realized, estimated in an easy way, over an intuitively sensible period, based on what effects you're looking for.

The process of trading involves a lot of measuring.

In a trading system, you've made a bunch of assumptions and decisions, added a lot of moving parts.

And you need to isolate and track all those individual bits as best you can. You've made forecasts about future returns.

Do those still hold up? Is the nature of them changing? Is information decaying at the same rate? Are you seeing different behaviour in different places? How confident are you in that?

here's the out-of-sample backtest performance of a sh1tcoin quant mean-reversion strategy.

from a large universe of sh1tcoins, we select 10 pairs to trade based on stationarity tests and unsupervised learning techniques performed on 2021 data.

and simulate trading 'em in 2022 it "trades" on hourly bars.

the trading strategy is just a simple zscore thing.

at any point we hold a position equal to the inverse of the zscore of the spread at the start of the hour.

150% returns in a year on 16 vol. sharpe 9.

we made money on each pair, but more noisily.

if you naively simulate trading illiquid stuff, your assumptions around fills will totally dominate your results.

get a universe of illiquid stuff, normalize it, simulate buying the stuff that looks cheap, selling the stuff that looks expensive. if you simulate that assuming no trading frictions (trade at midprice whenever you want) then it will look like you can make lots of money.

why?

because nobody can do that.

everyone wants to get paid to donk things back into line, but to do that you need to trade with others

for small independent traders who are "full-time", applying "pro" trading approaches at tiny scale or in highly constrained places can work.

>play where your tiny size is actually size
>or do the arb but just trade the good leg
>or be in and out like a ninja on the 1 lot but you have to be careful that you take the "idea", not the "implementation".

talk to some guy that does high frequency trading at scale and nearly everything he will tell you is important for him, isn't important for you.

you gotta steal the "essence" of things

thoughts on statistical inference in adaptive markets via a toy example

say someone starts coming in every day at 2pm and doing a significantly large TWAP buy.

maybe they're hedging or rebalancing or doing some other mechanical price-insensitive thing.

what happens? well, we expect price to go up in that hour

and, cos it was just a demand for liquidity with no information behind it, we expect price to go back down in the next hour when his buying stops.

now... that's not what we necessarily see in the market.

Who remembers the short uvxy / short 2x xiv trade?

Glorious stuff until the grownups appeared. Used to be be able to gear it up like crazy.

Main thing was that, in a volatile day, those funds nearly always massively undershot their index.

So, being short, you were long the tracking error on both sides.

And borrow used to be cheap.

Plus the usual LETF decay stuff.