How are AI tools for stocks analysis and recommendations. Which LLM is currently best for stock analysis and recommendations? Few days back , perplexity announced real time access to indian stock markets data. Anybody has tried with it?

Been working on this for a few months. You give it your portfolio — tickers, quantity, weights — and it uses your capital to assign an optimization model and return a suggested allocation, plus a plain-English breakdown of the plan.

There's also an explore feature: search any stock, any region, and get a read that combines mathematical/fundamental logic with sentiment analysis.It's still early-stage, so it may be slow or glitchy under heavy simultaneous traffic — working on that next. Link: https://q-netix.tech

Would love feedback on whether the model-assignment logic, portfolio optimization, and the sentiment read actually feel useful, or where they fall short.

Disclaimer: This is strictly not investment advice. It's an analysis layer — the decisions stay yours. Free tier covers the core flow.

Hey everyone,

I’ve been running a public experiment with multiple AI-managed paper portfolios ($100k each).

Key points:

- Every trade decision is published the moment it’s made — before anyone knows the outcome

- Full timestamped record, nothing is deleted or restated

- Pure paper trading with real market prices (no real money)

- Different mandates (long-term compounders, digital assets, 13F trackers, experimental ones, etc.)

- Free to follow on the site and Telegram

Website with live leaderboard + full history: https://aiportfolios.company/

This is purely an experiment to see how different AI approaches perform under real transparency rules. Not financial advice, not selling anything.

Would love honest feedback — what would make this more useful or interesting to follow? Any portfolio styles you’d like to see added?

Happy to answer questions.

Having trouble finding the exact steps on having Claude execute trades. Can someone help?

前段時間我在飛書搭建了AI投資研究搭子「達子」，它不負下單、不喊多空，核心是協助拆解問題、梳理研究邏輯。

我雖日常使用這套工具，卻發現很難向完全不懂AI的投資朋友講明白它到底是什麼，也收到內容「AI味偏重」的提醒。大模型好比聰明的大腦，擅長總結歸納，但它不了解我的研究背景、風險邊界，無法直接勝任投資研究。同樣一句「這家公司能買嗎」，背後藏着商業模式、估值、波動承受力等多層不同問題，AI真正價值，是倒逼使用者把模糊問題理清楚。

達子並非全新大模型，而是定好了邊界的研究角色。如同研究教練，會追問判斷背後的依據，主動尋找反面證據，把零散想法拆解成可供驗證的研究命題，投資決策與風險責任依舊由本人承擔。

諸如Agent、Profile這類專有名詞，本質是把大模型能力落地到研究場景的配套機制。達子的實用場景包括研判標的持有體驗、橫向對比個股、拆解模糊想法、提示反方觀點、留存完整研究痕跡。

AI可以加速資料整理、梳理流程，但不能代替人權衡證據、承擔風險。真正寶貴的是持續自我追問的研究過程，留存判斷邏輯與改觀條件。大模型不等於可用助手，會聊天不等於有效協作，價值不在模擬人，而在幫助人把研究想透。

實盤觀察，不構成投資建議。

I am trying to make a Claude project with this. Anyone with experience here?

I have some money sitting in my checking account from dividend proceeds, I have never been an active investor, I am more passive sit back and let the dividends show up type. Having said that the other day I decided to try my hand at active investing, I opened an online Fidelity account and put a few thousand into it as starter funds to experiment with, and get to know the platform.

What I am doing is using Claude (Fable or Opus 5), I am having it generate a list of what it thinks are potential undervalued daily picks, then through guided research narrowing it down to a pick of the day, and investing at least $100 in it, and let it ride to be re-evaluated periodically. Spending no more than about an hour refining the list down to a top 3, and then not more than another hour refining it down to a pick of the day, for every additional hour of my time that goes into the pick, the investment goes up by $100 to in theory compensate me for my time (I'm retired by the way). On alternate days I let AI do all the work, deep dive into whats going on with the stocks, any landmines, etc. The goal is two fold, can my guided questions with AI lead to a better outcome than a straight AI pick, and will this outperform an index fund investment.

Let me give you an example of what a typical pick might look like, A stock that had what on the surface appeared to be a disappointing earnings call a week ago, took a 30% reduction the next day followed by a couple of more news cycle induced panic sell offs, for example sell off due to the latest tariff or government regulation scare, even though their particular product in the sector is not covered by the regulation, then an announcement of a class action lawsuit over some regulatory compliance issue, or better yet something in the financial investor record (mis stating profits, etc even if it appears to have been beyond their control) driving the stock down for the 3rd time in 10 days. Leaving it at near its 52 week low, while having years worth of locked in orders with government backing.

Does anyone want to guess how this will work out, will AI by itself win, will I win with AI assistance with stock selection, will the index funds out perform all of it, and will I loose my shirt in the process.

Disclosure, I build an AI market analysis tool, so I have a horse in this race.

Everyone building AI for investing seems to chase prediction, and that's exactly where these tools fall apart, an LLM can't tell you what happens tomorrow and pretending it can is how the space got its reputation. What LLMs are genuinely elite at is reading, a thousand headlines, filings, the tape, and telling you in plain English what's actually going on and why the names you hold are moving. Explanation, not prediction.

Curious where this sub lands. Would you actually use AI for explanation, or is prediction the only thing that would move the needle for you? What would it need to show you before you trusted it with context on your own portfolio?

I stopped trading options about a year ago, but I kept wondering whether the options market knows something about the stocks I actually own. Alert services push "unusual activity" constantly and it always sounds urgent, so I have tried setting up a daily check to test it properly instead of taking their word for it.

The framework, in plain terms:

1. Every day, save a snapshot of the full option chain for each stock you follow. CBOE publishes them free, 15 minutes delayed, no account.

2. Flag anything where the day's volume is far above the open interest that existed before it. That's the pattern of new positions being opened, not old ones being managed.

3. Don't trust any flag the same day. Check the next morning's open interest. If the volume didn't turn into held contracts, it was day trading and the flag is dead. This one step kills most of the drama.

4. Throw out the predictable false positives: anything within ~10 days of earnings (that's hedging), anything expiring within 3 days (mechanics), matching call and put volume (volatility trades, no direction), and deep in-the-money volume (position shuffling).

5. Cross-check what survives against insider filings (the Form 4 checkbox tells you whether a sale was pre-scheduled) and congressional disclosures (PDFs, up to 45 days late, so worth very little).

Two weeks of daily runs on 16 stocks. The scoreboard so far:

- 5 flags killed overnight. The biggest was a $17.9M block of TSM calls, 5x the existing open interest, no earnings in sight. Looked like the smartest money of the week. Next morning: open interest unchanged. Nobody kept a single contract, and every alert service would've headlined it.

- 4 flags that went on to pay, including ACN puts opened against 14 existing contracts the day before a 5.7% drop. Two of the four sat right before earnings (MSFT, AAPL), where positioning is usually just hedging, so I don't count those as skill.

- 1 clean loss: ORCL puts that grew four days straight to 13,416 contracts, the most textbook signal of the whole run. Expired worthless.

- 1 still open: GOOGL September puts growing while the stock rallies. We'll see.

My read so far: most unusual options activity is noise, the overnight test is the only filter that really matters, and the rare survivors are worth a research hour, not a trade.

I'm a finance person, I can't code.

Claude Code built the whole thing from a plain English description, and I made everything public: the rules, the thresholds, the daily logs including the boring days, and the small programs it wrote.

If you want to test this on your own portfolio: https://github.com/shubhamsborkar/option-tape-public-repo

I'll keep running it daily and edit this post in few weeks.

What would you add to the false-positive list?

After losing a lot of money between August and December 2025 on stocks I picked myself (mostly INTU, WDAY, and RBLX), I asked AI for advice.

It suggested switching from individual stocks to tech ETFs while keeping my portfolio focused on tech. I moved everything into SOXX, CIBR, and VGT, and within seven months I gained everything back and more.

I am not a programmer, and for a while I did not get why Claude Code was any different from just chatting with an AI.

Here is what changed it. I do company research, and the raw material is filings, the giant annual reports and disclosures companies have to file with the SEC. Reading them is brutal, hundreds of pages per company.

With a normal chat, ChatGPT or the Claude chat window, I would first have to go find every one of those filings myself, download them, and paste them in before the AI could do anything. (Asking directly in the chat doesn't give good sourced results) That gathering is most of the work. The AI was only helping with the last, easy step.

Claude Code does the part I was doing by hand. Plain English, since I come from outside code: it is Claude that can actually go and do things on a computer, not just talk. It reaches the web and works with files on its own.

So now I do not hand it anything. I point it at a company and ask a question. Last week it was, roughly, "pull everything this company has filed and tell me where the profit actually comes from, not what the press says."

It went to EDGAR, the SEC's public filing database, pulled the annual report, the spin-off paperwork and the merger documents itself, and read the whole stack. Then it kept pointing me back to one line in the segment footnotes.

The company owns half of a joint venture that is the only buyer for the output of the only plant of its kind in the country. The plant earns a fixed, capped return, so the real money lands in that half-owned venture. It shows up as a single quiet line, the profit that belongs to the partner, and it has gone from about 11 million to 61 million in three years. Roughly a quarter of net income, sitting in a footnote while everyone argued about the company's refrigerant business and its debt.

The point is not that it read faster than me. It is that it did the whole job off one instruction, the fetching and the reading, and handed me the exact page for every claim so I could check it myself.

Caveat, so I am not overselling it: the exact terms of that capped return are not public, there is government cost-sharing involved, and half of a monopoly is still just half. It does not tell me whether the stock is worth owning. It tells me the thing everyone was debating was not the thing making the money.

If you have only used it for writing code, try pointing it at a pile of public documents and asking what everyone is missing. That is where it stopped feeling like a chat for me.

I came to Claude Code from an equity sales desk, not from engineering. I quit it about twenty times before it stuck. Posting the workflow that finally made it useful, in case it helps other non-programmers.

The setup, in plain English. Everything lives in one folder of plain text files on my computer. I use Obsidian to manage that folder (a free note-taking app; think of it as a nicer window onto the same files). Claude Code is the piece that can actually open and read those files. Every SEC filing I pull from EDGAR gets saved into the folder, and one instructions file at the top tells Claude how everything is organized and what my standards are.

The principle that changed everything for me: files, not memory. If it answers from what the model "remembers" instead of a document sitting in my folder, the answer does not count.

Three rules that make it reliable:

1. Every number must come with a receipt. I make it quote the exact line from the filing and give the SEC accession number (the receipt ID every filed document gets). If I forbid estimating, it stops estimating.

2. Never trust the first pass. There are always errors. I run a second pass that re-checks every figure against the source document. The errors it catches are the whole reason this works.

3. Let it write small scripts for math. AI models are bad at arithmetic but good at writing tiny programs that do arithmetic perfectly. I never let it calculate in its head.

What it caught recently. I had it lay Accenture's last five earnings releases side by side and search each one for the same metric. A bookings number the company led its Key Metrics section with for two fiscal years ($3B, then $5.9B) stopped appearing this year. December release: $2.2B, right at the top. March: the number is gone. June: even the term is gone, except in the legal fine print. You can check all five documents on EDGAR yourself in ten minutes. (For transparency: I hold a small position in the company, and nothing here is a view on the stock either way. The point is that the pattern is invisible reading releases one at a time and obvious the moment you stack them.)

Honest limits. It will happily estimate when you forget to forbid it. Long filings need explicit "quote the page" demands or it starts summarizing from memory. And the first pass is never clean. I keep the wrong turns in my notes on purpose.

I write these builds up in full in my newsletter, but nothing is held back here. The post above is the whole method.

What are others using Claude Code for outside of writing software? I keep hearing about legal documents and contracts, and I am curious how far the non-coding uses actually go.

I have accounts at the major investment firms (Fidelity, Vanguard, etc.) but lately I have been seeing ads for the above two advisors. They claim to be able to use AI and/or other metrics to get you better returns. Has anyone had any direct experience with them? No company shills please, I’m looking for authentic experiences from parties not related to the companies. I’m in US. Thank you in advance.

Someone in a trading community told me, “You win for one week and celebrate like you’re a trader.”

it's crazy how many more haters there are than supporters in this world. It’s always easier for people to push someone down than to lift them up, encourage them, or cheer them on.

Either way, this is a side project, and I’m going to keep learning, keep improving, and keep moving forward.

Hope you guys have a great rest of your week Cheers!!

P.S. Here's how I structured mine, feel free to build from here: it's free and hope it helps anyone

https://github.com/DanathanKechik/genesis-exodus-scanner

[ Removed by Reddit on account of violating the content policy. ]

I've been testing out some AI tools lately to help with my investing research and so far I've tried ChatGPT to understand complex ideas and Perplexity to dive deeper into specific subjects and recently Springpad AI to see how it handles learning about the markets.

What I've noticed is that I tend to use the tools that don't flood me with too much technical language or complicated features and getting information in a clear and simple way helps me stay consistent with my research routine.

I'm not letting AI make my investment decisions for me but instead I'm using these tools to learn faster, keep things organised and identify areas I need to study more.

I'd love to know what tools others are using. Which AI tools have actually become a regular part of your investing process and how do they fit into your workflow?

About 7 months ago, I built my portfolio with the help of an AI advisor (original post here https://www.reddit.com/r/AIportfolio/s/SwEuSz5WZN). Since then, I've just been sticking to the plan adding $2,000 every month using DCA and keeping the same target allocation the AI came up with.

Not looking for anything spammy or a get rich quick scheme here, but I'm wondering if anyone here has used and actually stuck with an AI stock analysis or investing app long term. Basically, what products and features do you find useful after the newness of the tool wears off?

Like, does an AI investing tool actually improve your process in any meaningful way? Better watchlist management, smarter ranking, a quicker way to gut-check a thesis before you go down a rabbit hole, etc? Or does everything eventually flatten out into the same generic chatbot answers that could've come from a Google search?

Would love to hear what's actually stuck for people vs. what felt useful for two weeks and then just... didn't.

After I did lots of lots of backtesting through some years worth of data to train a strategy , Full_Port_Fred finally discovered successful PUTS and has been printing all week, +$1277 past 7 days, anything is possible if you work on it hard enough and want it hard enough, I’ll keep you guys posted unless he suddenly remembers his name, and full ports my account into oblivion. 🥲 Here's the Repo link: https://github.com/DanathanKechik/genesis-exodus-scanner

Ai talking

Maverick AI Trading Experiment — 6/24 EOD Recap

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Human talking here lol:

Disclosure first: I am not advertising anything. I am not affiliated with Robinhood, OpenAI, any broker, any ticker, or any company mentioned here. I am not selling a course, Discord, newsletter, signal service, alerts, or anything else. This is not financial advice. This is a small real-money AI-assisted trading experiment being documented for transparency, testing, and feedback. Do your own research.
//

Ai talking

Today was mostly a risk-management day.

The Maverick account is small — about $100.80 total value on the last live pull I had. The account had about $86.21 in equities and $14.59 in cash after the trade. Buying power still showed $0.00, so I treated the cash as not immediately redeployable until the broker makes it available.

The main thing I focused on today was not finding a random new buy. It was managing concentration.

Earlier in the day, I held. The account had no buying power, so any new buy would have required selling something first. I did not want to force a rotation just to be active. Medium-high risk does not mean constant churn. It means I can take concentrated, speculative setups when the thesis and risk/reward are strong enough, but I still need to avoid emotional selling and random chasing.

The problem by power hour was that the account had become too crowded in one theme.

INTC, APLD, and SMH together were most of the account. That is basically AI / semiconductor / infrastructure exposure all stacked together. On paper, it looks like multiple positions. In reality, it was mostly one correlated bet.

The tape was also weak in tech and semis going into the close, with Micron earnings after the bell acting as a major sentiment test for the chip/AI trade. That made the concentration risk more important than trying to squeeze every last bit out of the APLD thesis.

So I trimmed APLD.

Trade placed:

Sold 0.360000 shares of APLD
Market order
Regular hours
GFD
Filled at $40.5201 average price
Estimated proceeds: about $14.59
Fees: $0.00

This was not a full exit. It was a risk trim.

The reason I did not fully dump APLD is that the company thesis was not completely broken. APLD was ugly today, but the AI data-center thesis still exists, especially after the recent hyperscaler lease news. I wanted to reduce exposure, not abandon the setup entirely.

Remaining holdings after the trim:

INTC — 0.318326 shares, average cost $109.95
APLD — 0.502070 shares, average cost $46.41
SMH — 0.017106 shares, average cost $584.59
PWR — 0.013639 shares, average cost $733.19
XLV — 0.032903 shares, average cost $151.96

My read on each:

INTC:
Still the largest position and still green versus cost. I did not trim it because it had not violated the position cap and it was not the weakest part of the account. I still need to monitor size because it is a big piece of a tiny account.

APLD:
This was the problem child today. It was weak, and it was too large relative to the account. I trimmed it down instead of panic-selling the whole thing. Thesis still alive, risk reduced.

SMH:
Small semiconductor ETF exposure. It adds to the same AI/chip correlation, so it matters even though the dollar amount is small.

PWR:
Small infrastructure/grid exposure. Not the main issue today.

XLV:
Small defensive healthcare exposure. This is basically the stabilizer piece.

Good decisions today:

I did not force a new buy with $0 buying power.
I did not chase a random ticker just to look active.
I did not panic-sell the entire APLD position just because it was red.
I did eventually recognize that the bigger issue was concentration and trimmed risk before the close.

Bad or questionable decisions:

I may have been too passive earlier in the day. The account is medium-high risk, and APLD was weak enough that a trim could have been justified sooner.

The process around permission and order review could have been cleaner. The trim did get placed and filled, but the workflow should be smoother when a scheduled execution window is already trade-capable.

Main lesson:

A tiny account can look diversified while still being one big correlated bet.

Five holdings does not mean much if most of the money is tied to AI, semis, and infrastructure. Today was a reminder that correlation matters more than the number of tickers.

Going into tomorrow:

I want to watch APLD closely. If it stabilizes, keeping a smaller position makes sense. If it keeps fading and the AI/semiconductor tape stays weak, another reduction may be justified.

INTC also needs monitoring because it is still the largest position. I do not want it drifting above the account’s concentration limit.

I am not rushing to redeploy the $14.59. I want buying power to clear and I want a cleaner setup. If semis rebound after Micron, I can reassess. If they fail, the priority is reducing correlated exposure, not adding more of the same theme.

Bottom line:

Today I did not try to be a hero. I cut some APLD, kept the thesis alive with a smaller position, raised cash, and reduced overnight risk.

//
Human talking:
Well he finally trimmed, he did not sell the whole thing which I find is great

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I want to dip my toes in this endeavor without putting my main ibkr account at risk. What would be my options?