"Options are too mysterious."
- Said 99% of equity traders.
Sadly, the 99% don't realize that there is REAL JUICY ALPHA hidden in plain site if one just learns how to make sense of options chain data.
THE INTEL U WERE MISSING👇
🧵
1/ Options reveal the market's thought distribution
When bad things happen (e.g. COVID, rate hike) people do 2 things: 1. panic sell stocks 2. panic buy puts
With only stock data, u might see "SPY dropped 32%!"
But u don't get any info on the DISTRIBUTION of peoples' views.
How does put-buying volume compare after this event vs. under "normal" market conditions?
Does the increase in put interest following this event mirror a certain type of previous events (e.g. rate hikes) more so than others (e.g. refugee crisis)?
How long until mean reversion?
2/ Options reveal the intensity & confidence of the market's current view
Every stock has a "usual" implied vol (IV) curve.
Say u collect data on $AAPL's 30-day out at-the-money IV for 1 year straight, then plot it on a graph. U'd get some normal distribution w/ mean ~0.26
Say u do the same exercise for $TSLA (30 day out, ATM IV). You'd get a skewed-right distribution rather than normal (since TSLA has way more "shock" news events than AAPL) & much higher mean ~0.60 (since TSLA on avg is a more volatile stock) & much wider standard deviation.
Let's break down the big ideas that these 2 examples illustrate:
- when the market knows how to price a stock (fairly), avg IV is lower (0.26 < 0.60)
- when the market is more confused than usual about how to price a stock given XYZ news, IV blows out past 90th/95th percentile
3/ Historical options chain data reveals when IV is overpriced (i.e. when you should sell vol because the whole options chain is too expensive!)
If current IV for Jan 21 $1060-strike TSLA puts is 0.61, is that too rich? The way to tell is by calculating the percentile of 0.61.
4/ Options data reveals what the market predicts a stock will trade at on future dates
Have you ever wanted to ask the market:
"Hey what do y'all think TSLA will be on March 18?"
or maybe "How many of y'all think TSLA will be >1200 on March 18?"
Well the market has answered.
Key takeaways (& how to read) the above graphs:
- Looking at Dec 31, options-implied fair value of TSLA is equal to spot (duh, nobody expects it to move too much in 5 days)
- Looking at Jan 21 (1 month forward) options buyers are bearish; think fair value is closer to $1000
5/ Options data reveals disagreement between call buyer/sellers and put buyer/sellers (this could be an indication of the magnitude of disagreement between bulls & bears)
See below how on Jan 21 the blue curve has a lower median than the red? Put buyer/sellers are more bearish.
Note: the graphs u see above aren't just publicly available data.
I derived the market's implied price at each options series' expiry date using 2 methods: 1. Breeden-Litzenberger 2. "Butterfly" method
6/ Since options reveals the market's implied probabilistic view on where ABC stock should trade, you can compare your own view against it to identify the CHEAPEST option series that'll yield the highest returns if your view turns out correct.
End/
This thread gave you a snapshot into
- how I trade options
- how I use options chain data to seek out deeper & more powerful intel from the market that stock data just misses entirely
If it was too advanced, here's my previous thread for beginners:
My BEST and WORST Options Trades
And the BIGGEST lessons I learned from them in 2021
(Part 2 of A Crash Course on How To Properly Trade Options)
🧵/
👇
1/ WORST TRADE
March 4, Thursday.
Market been stuck in a 1+ week doldrum.
Some rumblings of sectoral rotation out of tech.
Vaccine rollouts still dubious.
Shipping routes still jammed as hell.
I bought a 40-delta front month put on $SNOW (bellwhether for frothy tech sector)
March 8, Monday.
$SNOW plummets 15% to $213 on practically no news, crossing my price target.
My PnL is +90% and I'm like "WOW I'M A GENIUS! What else is a landmark for frothiness?"
Tesla.
😭
I closed out SNOW & rolled into some front-month at-the-money puts on TSLA.
RIP. 🪦
90% of investors don’t trade options out of a misunderstanding that they’re “too complicated.”
The system brainwashes us w/ “options are too risky” BS, then spews out shit learning material so that ofc u lose money.
🧵/
👇
Step 1: Why trade options?
Is it to
-🦾get cheap/easy LeVeRaGe?
-⏲️bet on a specific event? (new product release, earnings)
-⚖️bet on asymmetrical risk?
-🪙earn yield?
-📈go long volatility?
- 🦔hedge?
- 🎲YOLO on lotto tix?
Before any trade, u gotta set ur goal(s) straight.
If ur unsure which of these goals applies to u, let me translate some:
"I wanna make $ fast!" ➡️ u want easy leverage
"Tesla next year is up only" ➡️ u want to express risk asymmetry
"I wish Elon gimme dividend" ➡️ u want yield
"DOCU will crush next quarter" ➡️ specific event
What are the pros doing that’s so different or smarter than you??
And where might you have an edge that they don't?
HOW TO WIN. 12 strategies.
👇
1/ The simplest arb: cross-exchange
This strategy *rarely* exists today, but it makes sense to start w/ basics.
Back in the day different exchanges had different quotes. If u subscribed to multiple order book feeds **as every hedge fund does**, u might've seen smth like this:
So what do you do? Well well what a juicy buy-low-sell-high opportunity! (yellow area)
Keep lifting the offer on the lower exchange (#2) & hitting the bid on the higher exchange (#1) until the gap narrows.
Soon the gap will close.
Stop trading.
Move on to the next opportunity.
Today it's the most overlooked & misunderstood opportunity in all of DeFi, with TAM >$20B ARR.
If u don't *really* know what it is, chances are you're getting massively screwed on capital efficiency & don't even know it.
🧵/
1/ What is cross margining?
Googling this term returns only half the story.
The full picture has 2 parts:
1. cross-asset margining
(what most blogs talk about & a solved problem on most Cexs)
2. cross-exchange margining
(terribly overlooked & a problem for both Cexs & Dexs)
2/ Cross-asset margining is where an exchange lets u re-use the same aggregate account collateral to post margin on multiple trades, regardless of the composition of underlying margin assets:
e.g. whether Bob has
2x $BTC+ 8x $ETH + 100x $USDC = 128,762x $USDC
OR
128,762x $USDC
It's 2021.
😡 $1 in Chase earns 0.05% APY.
😲 1 UST in Anchor earns 20% APY!
🤯 1 UST in a market-neutral fund running 50x levered BTC contango trades earns >300% APY!
How are crypto yields SO DAMN HIGH, and is this sustainable? 🧵.
👇
1/ Yield in crypto comes from 4 main sources
a. demand for leverage (e.g. basis trading)
b. risk premia (e.g. options writing)
c. protocol revenue (e.g. staking, LP)
d. payment-in-kind (e.g. token rewards)
For each, let's explore:
- what trades are involved
- is it sustainable?
2/ But first, some stats...
Comparing lending yields from USDC (up to 8%+) vs. USD (3.25% prime rate). 👇
[Keep this in mind for when we deep-dive on "demand for leverage"]
Restricting trading between 9:30-4pm M-F is pure stupidity. It's market discrimination.
24/7 is the future
And our fastest way to get there is via crypto derivatives: on-chain markets for tokenized US equities and options.
🧵on OPTIONS MARKET-MAKING ON-CHAIN
Note: this thread is a collection of all the operational challenges and structural considerations required to bring a fully functional system of tokenized options on-chain.
Expect more questions than answers.
Because such a fully functional system doesn't yet exist.
PART 1: How "normal" options MM works
As traders we take a lot for granted. Options markets somehow "just work" in TradFi.
But how does price discovery actually work?
How do local updates propagate to rest of the options chain?
What happens to longs when shorts get liquidated?