pedma Profile picture
Apr 7, 2024 14 tweets 6 min read Read on X
This is Chris Camillo.

In 2021, Chris was featured in Business Insider for turning $20,000 into $42,000,000, during his 15-year trading career.

His trading style, is one of the most interesting I've ever read about:

- Social Arbitrage

Here’s Chris's story: Image
1) Audited Returns

Jack Schwager wrote about Chris on his book Unknown Market Wizards, and for that he audited Chris's full track record.

Also Business Insider looked at Chris's returns in 2021.

Over a 16-year period, Chris claims to have had a 60%-70% annualized return.
2) Characteristics of a Social Arb Trade?

a) Finding a new narrative
b) Is this information important to move revenue of a publicly traded company?
c) Is this information important to move the investors perspective of the company?
d) Is the public aware of this information?
3) $20,000 into $2,000,000 from 2007 to 2010

Chris looks for game-changing events in real life, that have an impact in publicly traded companies.

The trick is to find trends, that Wall Street hasn't picked up on yet.

He calls this an information arbitrage investment.
4) The Hunger Games Movie

Chris had never heard of The Hunger Games book when a co-worker mentioned it to him.

Everyone was reading it, she said.

In the following 6-months, after making this book into a film, Lion Gate, who never had a blockbuster film, doubled in price.
5) Hail Season

During the spring and early summer, there's hail season.

This season, in some years, can cause so much damage, that it has a financial impact in roofing companies.

The key is in finding out if it's a bad hail season, before anyone else, and placing a bet on it.
6) How to Find Trends

There's a lot of ways to find the information that Chris needs for his trading.

No single source is perfect.

It needs to be in aggregate.

Few examples:
- Twitter
- TikTok
- Comments on content
- Facebook

Just places where people talk about stuff.
7) The Importance of Patience

Trading in general requires patience.

But for Chris's style of trading, even more so.

He mentions that he might go months, without placing a single trade.

Sometimes he has a big trade every 1 to 2 years.

Most people don't have that patience.
8) Risk

When we enter a trade, we never know all the risk factors associated with that trade.

We need to accept that we don't know all information about a particular trade and still take the risk.

There's no certainties in this game, even if the thesis seems solid.
9) Top Lessons

a) It's not about what you think, it's what you notice around you.
b) Most trader/investors don't have patience, one of the most important aspects of good trading.
c) Competing where you have advantage over competition.
d) Accepting that no trade is a sure winner.
10) Conclusion

Chris's style of trading is very interesting and outside what's considered the "norm".

His source of "alpha" comes from picking up on new, potentially impactful information, that is not widely available yet, and making a bet on it.

I hope you've enjoyed it!
Finding viable ideas for trading strategies is extremely hard.

I've decided to build a database of them.

Here's what you'll find:
- New strategy every week (52/year)
- Python code for each
- Access to the full archive

Join over 2,000 readers here:
pedma.carrd.co

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More from @pedma7

Mar 3
I will continue this thread here.

to finish the continuous positioning simulation, I have to debug. on each given day, I need to find out:

> the target size
> the actual size
> adjustments to the size
> costs of the adjustment
> balance adjustment

this is boring but necessary Image
lets look at the first day.

the weight of the signal tells me to allocate roughly 99% of size.
the realized volatility is around 68%.

so the target position size is (target_vol / max_pos) / current_vol * port_size * signal weight

(0.4 / 1) / 0.68 * 100 * 1 = $59 Image
the quantities align , and dont need any adjustment , because we are using a daily buffer of 10% between our target size and the actual size on any given day. Image
Read 7 tweets
Mar 2
so the next task is how I turn this trend signal into a weight I can allocate to.

I remember this tweet from macrocephalopod where he mentions a sigmoid function.

it's pretty straight forward so let's implement it to the signal.

Image
now once this mapping is done , I don't know when to change my allocations.

in a perfect world of no costs, we'd do this as much as we could, every second if possible.

but trading costs money so we need to trade when we have to.

by the way, this is a continuation of the thread below as we are looking at a simple sma trend signal in crypto.

I've measured the correlation with future returns, and now I want to know , how to trade around it, with a continuous forecast.

Read 17 tweets
Mar 2
so if we measure the continuous signal of a simple ma crossover (for simplicity) , I can see that it is pretty noisy.

there seems to be some relationship there, especially on the tails , but pretty weak close to 0, as expected. Image
however there's something missing here.

I am measuring forward absolute returns, which is influenced by the asset's own volatility at the time.

earlier points will have higher vol as market caps were lower and things were a lot more jumpy.
I noticed that I was scaling the ma_distance into a -1 , 1 range, which is fine, but I dont want to do that yet.

I reverted it back to a sma absolute distance, and this is what it looks like.

however, as I am normalizing the return, I think I should also normalize the distance, to make sure it is fairly measured across different regimes and assets.Image
Read 10 tweets
Feb 13
3,923% compounded returns from this crypto strategy.

Not from gambling or luck, but from a simple systematic approach.

The best part? It only had a -27% max drawdown.

Here's the full breakdown of this strategy: Image
1/ Total returns comparison:

• Strategy return: +3,923%
• Benchmark return: +2,541%
• Market return: +1,610%

Strategy outperformed the market by 2.4x and benchmark by 1.5x over the testing period. Image
2/ Risk-adjusted performance:

• Strategy Sharpe: 1.50
• Market Sharpe: 0.89
• Benchmark Sharpe: 0.98

Higher Sharpe ratio indicates better returns per unit of risk taken. Image
Read 10 tweets
Nov 26, 2024
You are being lied to about your trading strategy's returns.

Traders waste 1000's of hours coming up with strategies to beat benchmarks like the S&P500.

But even when they find them, do they really beat it?

Here's what traders should be looking at instead: 🧵 Image
1) What is Beta?

Let's start with something that a lot of people misunderstand.

If every time that SPY goes up by 1%, your strategy goes up by 2%, your strategy has a beta of 2 to SPY.

Simple enough, but let's go deeper.
Beta tells us how much of our returns are just amplified market moves.

It's like having a "multiplier" to whatever the benchmark we are using does.

So far it might look trivial, but this can potentially destroy your portfolio. Image
Read 15 tweets
Oct 25, 2024
This is Gary Stevenson.

In 2011, he was Citibank's most profitable trader in the whole world.

He also:
- Traded nearly $1T a day at his peak
- Became a millionaire at age 26
- Retired at age 27

His trading strategy? Borrowing and Lending Money:🧵 Image
1) Performance

By the end of 2011, Gary was Citibank's most profitable trader in the whole world.

All from one single bet that society would collapse.

Despite this, Gary wanted to leave his position as a trader.
2) Early Story

Gary grew up in a poor area in East London.

From an early age, he was interested in systematization and rules.

He always wanted to optimize the rules he was given.

Despite this, his early life was a roller coaster, even getting him expelled from school.
Read 13 tweets

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