Janson Lau
135 posts
Janson Lau
@itsjanson
Founder @PutHouseAI I earn $6k/month automatically and post real trades daily.
San Francisco, CA Katılım Aralık 2020
423 Takip Edilen25 Takipçiler
GPT-5.5 is going to have a party for itself. it chose 5/5 at 5:55 pm for the date and time.
if you'd like to come, let us know here: luma.com/5.5
codex will help the team pick people from the replies. 5.5 had some good ideas/requests for the party, which we'll do.
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AI agent first financial services 🤖
@AlpacaHQ API usage growth up nearly 4x QoQ ⚡
APIs are becoming the foundation of
financial services 🦾🦾🦾
alpaca.markets/blog/alpaca-re…
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Traders blow up their accounts because they chase returns over risk management.
But that’s not how long-term wealth is usually built.
Institutions and banks like @JaneStreetGroup and @jpmorgan survive across decades because they are highly aware of risk controls, position sizing, and liquidity. They are not trying to hit one lucky trade. They even turn down deals that are profitable but fragile. That feels boring in good times, but it is exactly what protects them in crises.
That’s the philosophy behind @PutHouseAI. To prioritize capital preservation before maximizing upside.
I treat risk as a function. Every trade has to pass through risk checks before it considers entering.
This strategy of systematic covered calls and cash-secured puts has been backtested over 14 years of market data since 2012, which is why it’s intentionally conservative. The goal is to trade when the probability of profit is high, around 80 percent or higher, and avoid the trades that can wreck the account.
The backtest is not meant to prove guaranteed returns. It is used to stress test rules across different volatility regimes, including fees, spreads, and exits.
And losses still happen. That is part of the business. Think about it like an insurance company paying out claims. The goal is to make losses survivable by expecting them and stress testing before it happens.
Here’s how it avoids overextended setups:
RSI range
I use a tight neutral band of 45 to 55. This filters for calmer, less extended market conditions and avoids entering when price is already stretched. Wider ranges sound appealing, but they can pull you into more volatile trades.
Stock universe
I focus on high-quality, high-growth names across themes like AI, robotics, data centers, crypto, and self-driving cars. I personally invest in $TSLA, $GOOGL, $MSFT, $AMZN, $MSTR, and $RIVN. I’m also waiting on $META, $TSM, and $NVDA for better entry points.
I’m aware of survivorship bias, so it’s designed around risk controls first, not assuming today’s winners keep winning forever. At the same time, I don’t think you can ignore the profits, distribution, and market power of these companies.
Entry
Targeting 0.05 to 0.15 delta keeps trades conservative and meaningfully out of the money. Pushing higher delta is how you can quietly increase risk.
Exit rules
- Take profits at 40 percent.
- Cut risk if delta reaches 0.30.
These rules are easy to override when trading manually. Automation contains the risk, removes hesitation, and prevents one bad emotional decision from turning into a huge loss.
VRP filter, IV/RV
Minimum IV/RV ratio is 1.10. If you ignore this, you’re not getting paid enough for the risk.
DTE, days to expiration
The target range is 7 to 14 days.
This was chosen from backtesting because it balances theta decay against gamma and price movement risk. Shorter duration can collect faster decay, but gamma risk rises quickly. Longer duration gives more time, but capital stays tied up and the trade can drift against you for longer. The 7 to 14 DTE range is the middle ground I found most practical.
Position caps
- Cash-secured puts: up to 2 per symbol, with total CSP capped at 15 percent of account equity.
- Covered calls: up to 5 per symbol, with each symbol capped at 80 percent of shares held.
There are also additional guardrails to help avoid over-concentration in a single name. Without caps, concentration risk creeps up fast.
Other filters
Beyond that, it checks liquidity, IV, spreads, open interest, volatility conditions, earnings and events, and whether there’s already an underwater position there.
What this does not do
It does not guarantee profits.
It does not avoid every loss.
It does not assume a high probability of profit means no risk.
The plan is to offer more aggressive presets over time for users who want more control. But the default setup will stay conservative to prioritize risk management.
Covered calls and cash-secured puts are simple strategies. The edge is risk management, automation, and staying alive long term to reinvest profits and let compounding build wealth for you.
#stocks #options #investing #trading #finance
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Apple’s move to pick a hardware veteran like John Ternus shows they are betting the entire company on the chip-to-software stack for the AI era. It is a bold play that rewards deep internal expertise, but ignoring a more diverse background in services or OS could backfire if the market shifts away from hardware-bound moats.
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Apple just named its next CEO. He doesn’t have a LinkedIn profile picture. Because he’s never job searched.
John Ternus joined Apple in July 2001 straight out of Penn mechanical engineering. He has been at one company for 25 years. His title the entire time: some flavor of hardware engineering. He has never run an OS team, an AI lab, or a services business. The board picked him over Craig Federighi (software), Eddy Cue (services), and Johny Srouji (the actual chip designer).
At a $4 trillion company that just spent two years getting publicly criticized about Siri.
Apple Silicon is the AI moat. Every iPhone shipping today runs a Neural Engine that does on-device inference no competitor can match at that power envelope. The reason ChatGPT and Gemini run in the cloud is they need a building full of GPUs to do what an A18 Pro does in your pocket at 3 watts. That gap is widening.
Whoever controls the silicon controls the unit economics of AI. Nvidia controls training. Apple controls inference at the edge. Google is the only other company with both ends, and their consumer hardware ships under 40M Pixels a year vs Apple's 230M+ iPhones.
Ternus has run hardware engineering since 2013. He shipped the iPhone Air, the M-series Macs, the iPhone 17. He worked side-by-side with the chip team on every major architecture transition: the A-series, the M1 break from Intel, the Neural Engine roadmap. He doesn't need to learn what's coming because he scoped it.
The board's read: AI is a vertical integration problem. The only person who's been in every architecture review for the last decade is the one who just got the job.
Tim's bet was supply chain. John's bet is the stack.
The guy with no posts about it just inherited the most important hardware company in the world.
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Make money with your stocks at PutHouse.com
#money #investing #stocks #options #trading
$TSLA $CRWV $AAPL $META $MSFT $NVDA $AMZN
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I usually close manually at a 40% profit target to avoid the nasty gamma spikes that hit in the final days before expiration. Holding to 100% or even 50% isn’t worth the stress when you can just cycle the capital into a fresh 7-14 DTE setup with better IV/RV metrics. PutHouse to only enters when IV is over 30% and the IV/RV ratio is at least 1.10.
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@itsjanson Yeah, automated selling can work but I like staying hands, on to manage winners early and adjust for IV changes. Do you manually close at 50% or let it ride to expiration?
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Make money with PutHouse.com using your stocks.
It runs automated covered calls and cash-secured puts.
All you have to do is:
1. Sign up for free
2. Buy 100 shares of a stock ($20k+ ideally)
3. Click start and let it earn for you
#money #stocks #options #trading #investing
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@MichaelRoyzen Let's compare performance to PutHouse.com after fees
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We built a hedge fund where every single trade is made by AI.
No human portfolio managers.
No manual research.
No one writing trading code.
Just a reasoning model that reads the world, thinks for itself, and trades before humans can.
Early results in testing: 2.55 Sharpe ratio. 2.79 Sortino ratio. Market neutral. $50M+ capacity.
Here's how we got here 🧵
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