Kamiaken

[Kris Sidial🇺🇸 @Ksidiii]

Let’s assume you notice that, over the last six months, stock XYZ has consistently outperformed its sector peers between 10:00 a.m. and 12:00 p.m. on Mondays.

During that specific window, when the sector is down, XYZ tends to remain flat. When the sector is up, XYZ is often up twice as much.

You run a series of tests to isolate the observation, control for other variables, and determine whether the effect is statistically meaningful. Eventually, you have enough evidence to believe that, during this specific window, XYZ has a persistent upward bias.

Why does it exist? Maybe it’s the result of a mandate from an end user. Maybe it’s a recurring rebalance effect. Maybe a dealer consistently works an order a certain way. You may not know the exact mechanism. What you do know is that the tendency appears to be real and repeatable.

Well, now what?

Identifying the edge is only the first step. Now you have to figure out the most efficient way to capture it while staying within your risk constraints.

Maybe you decide to buy a 1 week, 25delta call. But when you test that specific expression, you discover that as spot drifts higher during the window, implied volatility consistently comes in. The vol compression overwhelms the benefit from the directional move, and net-net, the trade loses money.

Maybe instead you simply go long the stock. That solves the volatility problem, but now you discover that the amount of capital required to make the trade economically meaningful creates an unacceptable left-tail exposure. One unexpected headline or flash move lower can do disproportionate damage.

So maybe you go long the stock and buy a 0DTE put as protection, effectively cutting off the left tail. But after testing the combined position, you realize that the repeated cost of the hedge and its theta bleed overwhelms the underlying edge. What began as a profitable signal has now become a losing strategy.

Maybe you test a 3 month ATM call instead. The win rate looks great, the risk profile is acceptable, and the trade makes money. But the return on capital still isn’t attractive enough to justify pulling capital away from another strategy with a superior risk-adjusted return.

And this is ultimately the distinction between finding an edge and monetizing an edge.

First, you identify and validate the underlying market behavior. Then you determine which instrument or combination of instruments captures that behavior most efficiently.

Hope this helps!

[Kris Sidial🇺🇸 @Ksidiii]

This is how I think about strategies at a volatility hedge fund.

I like to consider ourselves a blend of quantitative and discretionary trading. The primary reason is that trading from the long-volatility side of the market constantly reminds you of a fundamental problem: the events that matter most are often the events for which you have the least amount of data.

Outlier events, structural breaks, changes in market microstructure, and shifts in participant behavior do not always provide enough historical observations to build a purely quantitative system with complete confidence. At the same time, relying entirely on human intuition creates its own set of problems: bias, inconsistency, emotion, and the tendency to see patterns that may not actually exist.

This hybrid approach makes the two disciplines serve as safeguards for one another.

Here is an example of how that infrastructure works:

1) Idea generation begins at screen observation.

One of the traders notices something interesting in the market: a recurring behavior, an unusual relationship, etc.

Rather than immediately searching through data for something profitable, the observation comes first and is then passed to the quantitative side of the business. I think this distinction is extremely important. It creates a natural safeguard against data mining and overfitting. We are generally starting with a market hypothesis and asking the data whether it is real, rather than starting with the data and searching endlessly until we find something that looks profitable.

2) The quantitative arm attempts to validate or kill the idea.

Once the idea reaches the quant side, statistical validation begins.

Can we isolate this specific source of alpha? Does it persist across different periods and regimes? Is the relationship statistically meaningful? The objective at this stage is not to prove the trader right. It is to independently validate or negate the hypothesis.

3) If the edge survives, we begin designing the strategy.

Assuming the edge holds up, the discussion moves back and forth between the trading and quantitative teams. This is where instrument selection, tenor, strike, sizing, liquidity, and implementation all begin to matter.

An edge can be completely real and still be capitalized on poorly. Identifying the underlying phenomenon is only half the battle. But at this stage, we have our first real version of a “strategy.”

4) Then the real testing begins.

We strip out an OOS and begin testing the strategy historically.

We incorporate practical assumptions around transaction costs, market impact, capacity (NBBO tests). We want to know whether the edge remains economically meaningful after accounting for the realities of actually trading it.

The standard should be whether that evidence remains convincing after you have done everything reasonable to try to break it.

5) Once validated, the strategy is formally documented. Before anything goes into production, a formal write-up is completed and signed off on by every member of the team.

The document defines exactly what we believe the edge is, why we believe it exists, the statistical evidence supporting it, how we intend to monetize it, and the risks surrounding the implementation.

Just as importantly, we explicitly define failure conditions and escape valves. What would cause us to reduce risk? What would cause us to stop trading the strategy entirely? Conversely, what milestones would justify increasing capital?

I think this is an underrated part of systematic trading. You want to define what failure/ success looks like before you are emotionally or financially invested in the outcome.

6) The strategy enters production slowly.

Once approved, the strategy is introduced with a relatively small amount of capital. We monitor realized transaction costs, fills, liquidity, market impact, signal decay, and whether the live return distribution resembles what we expected.

(Continued below)

[TaylorMade Golf @TaylorMadeGolf]

Charlie Woods getting creative around TPC Sawgrass. 🤌 #TeamTaylorMade

[Kris @KrisAbdelmessih]

I saw that @AgustinLebron3 recommended this and while I'm only a few paragraphs in (just got to the word Vaclav) it's already fantastic which explains why I read everything Agustin boosts (well the subset I'm actually capable of understanding)

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[Prompter @PromptLLM]

This is how Fable 5.1 would beat Procrastination

[Danny Noonan @DannyNoonanGolf]

In the early ’90s, nothing tested a golfer’s willpower like watching this dude yell “POW!” at 3 a.m. and trying not to dial the 1-800 number, convinced this was the answer to all your golf problems.

[Kamiaken @Kamiaken50]

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[Kamiaken @Kamiaken50]

A short game lesson with Tiger Woods. 🐅 https://t.co/rKRk3Zy2kG via @YouTube

[Kamiaken @Kamiaken50]

RARE footage of 16-year-old Tiger Woods on the Range at Riviera 🐯 https://t.co/rj5CSgkODB via @YouTube

[Kamiaken @Kamiaken50]

What’s happening in the release? #golf https://t.co/umOmKmz19H via @YouTube

[Kamiaken @Kamiaken50]

The best golfers flip the club, and you should too! #golf https://t.co/Pg14EplGsr via @YouTube

[Kris Sidial🇺🇸 @Ksidiii]

Oftentimes, I’ll get asked by non-investors how my trading performs in an environment where volatility is already elevated.

The question usually comes from a false perception: if volatility is high, it must be expensive, and therefore it must be a bad time to buy volatility. Conversely, when volatility is low, people naturally assume it must be cheap. I think this is one of the biggest misunderstandings around volatility.

The absolute level of volatility does not determine whether volatility is rich or cheap. Volatility is only rich or cheap relative to what ultimately gets delivered.

You can buy volatility at 12 and realize 10. Volatility looked “cheap” because the absolute level was low, but you still overpaid for it. You can also buy volatility at 35 and realize 65. Volatility looked incredibly “expensive” based on its absolute level, but in hindsight, you bought it extremely cheap. That distinction is ultimately what drives P&L.

A great example of this was COVID. As volatility began moving higher, there were plenty of points where implied volatility looked extraordinarily expensive compared to historical norms. If your framework was simply “vol is high, therefore vol is rich,” you would have naturally wanted to sell it in the 30s. Whereas the right play in hindsight was to buy it.

Why?

Because volatility regimes tend to cluster. A market capable of producing a 35 vol environment is fundamentally different from a market calmly trading at 12. The distribution is changing. Correlations can change, liquidity can deteriorate, intraday ranges can expand, and the market’s sensitivity to new information can increase dramatically.

In those environments, a 35 vol can actually be far cheaper than a 12 vol in a quiet regime. This is why I have never liked defining volatility through an absolute lens.

12 vol is not inherently cheap.
35 vol is not inherently expensive.

Here are a two helpful things I like to focus on:

1) How reactive is that vol?

Are you noticing large shifts in local vol from relatively minor spot declines? If so, that means very small changes in spot can give you the ability to generate outsized returns. This is ultimately a function of end-user demand and the market’s need for hedging. If you can fine tune this to specific tenors you can have a cleaner idea of where on the term structure you want to bet.

2) How well does that vol carry up the skew curve?

You can buy $100k in vega today in a 6M option, and tomorrow that vega can be cut in half. Or you can buy $100k in vega today in a 6M option, and three months later it can remain roughly unchanged.
This is entirely dependent on the shape and movement of the vol surface, skew, and term structure. The amount of vega you own today matters far less than how that vega evolves as spot, time, and the surface move.

[Stingr Golf @StingrGolf]

“Wow I hit that thin, and it still carried 310”

[Nostalgic 90s @90stalgic]

One of the best cartoon theme song ever

[Green Lark @the_green_lark]

I'm long ORR ETF and was wondering where the returns have come from so far: Factor decomposition into Japan tilted small, value, and yen hedged. Plus All-Country-World ex-US.

[Kris Sidial🇺🇸 @Ksidiii]

One thing I’ve observed over the years:

A lot of traditional asset managers view convexity and long volatility strictly through a defensive lens. It’s insurance. Something you own to protect the portfolio when things go wrong.

Some of the best derivatives prop trading firms in the world view it very differently.
They treat convexity offensively. Long volatility and convexity as a whole is utilized as a source of alpha and traditionally accepted as a form of absolute returns.

[Finn Hulse @finn_hulse]

if you’re in venture/trading, i highly recommend that you get really good at a game called blotto

it’s all about who can be the most strategically contrarian with finite resource allocation, and many top firms teach it

tonight’s round ends in an hour

https://t.co/7xSvjTD4OU

[Kamiaken @Kamiaken50]

This Is MORE IMPORTANT Than Your Hand Speed | Jon Tattersall Golf https://t.co/vZbAagClHB via @YouTube

[Kamiaken @Kamiaken50]

Jon Tattersall: How to Properly Release the Driver https://t.co/Yalf4pEBPJ via @YouTube

[Snax @aRobotNamedSnax]

Yes