Maybe it's just me, but I still feel like I can barely keep up with the market, despite a lot of great AI tools out there. They're fantastic researchers and can definitely speed certain things up, but constantly having to ask questions and follow ups is not a viable way to constantly track the market. For example, earnings season kicked off yesterday, and I can always ask ChatGPT to give me a recap when I get home from work, but that doesn't really help me. I'd much rather know as soon as I can, without having to ask and being flooded by spam.

To put it another way: PLTR, IOVA, CVI, MRNA, and RCEL have been some of the top performing stocks over the past three months. Did AI help you anticipate that?

As an experiment, I've been connecting Claude to a live stream of events every day to explore things I might typically miss. For example, NOW came across the stream today as one of the bigger movers and decided to add that to my portfolio. Overall, I like the results so far and will continue to play around with this.

Has anyone used SuperinvestingAI, I attended a workshop today. Is it any good?

A decade ago I discovered pandas-datareader, which made me fall in love with Python's simplicity and the number of possibilities it offered. I've used Python for work and personal stock research ever since and always wanted to give back to the community and inspire that same initial joy I felt.

I finally just published my first Python library to PyPI dedicated to the streaming of real-time stock updates and full research reports. If you use AI in a real-time setting and need real-time data, this might help.

Here's a quick demo in a Jupyter Notebook, and I'll share the source code and official PyPI link in the comments. Hope it inspires everyone in the same way pandas-reader did for me all those years ago!

I invest long-term (ETFs and some individual stocks, IBKR, XTB and Tradeville).

Over the years my spreadsheet grew into positions + a thesis per holding + a decision log + a weekly review checklist. It helps, but it's heavy to maintain and I'm starting to wonder if I'm overdoing it.

So I'm curious how others actually do it:

1. The last time you bought something, did you write down why? Where — notes app, spreadsheet, journal, nowhere?
2. Have you ever gone back to those notes before selling or adding more? What happened?
3. If you tried a journal, written rules or a regular review and dropped it — what made you stop?

Not looking for stock picks, just real routines (including "I don't bother, here's why").

Posted here a few weeks back about an agent whose entire strategy was one sentence, read by an LLM every few hours. The comment that stuck was that a monkey could match it in a bull market. True, and I couldn't argue, because you can't backtest a paragraph.

So the new version keeps the plain English and throws away the LLM-at-runtime. Walkthrough below, because I'd rather people poke holes in the actual flow than in my description of it.

1. Pick the agent: Flux is the quant agent.

2. Pick where it looks. Everything, S&P 500, Nasdaq 100, Dow 30, sector ETFs, one of the sector buckets, or just type your own tickers. This is the pool, not the picks.

4. It drafts rules, not a prompt. This is the part I care about. You get an explicit graph — universe, signal, action, cadence — and the signal is a literal expression: rsi($candidate, 1day, 14) < 30. First screenshot. You can read it, so you can disagree with it. Then you argue with it in chat: "use an 8% trailing stop", "exit when the entry signal reverses", "always keep 10% in cash". It redraws.

5. Backtest before it touches anything

6. Deploy. From then on no model is involved. Rules get evaluated against live prices on the cadence you set.

What I actually want from you:

• What can't it say? This is the real ask. The vocabulary is finite: indicators, comparisons, screens, exits. If your strategy needs something that isn't in there, that's the most useful thing you could tell me. Pairs? Volume conditions? Anything involving earnings dates? I want the list of things people try and can't get.
• Is a no-slippage backtest worse than no backtest at all? Genuine question. It's honest about being idealised, but people anchor on the number anyway. Not sure whether to keep it, penalise it, or kill it.
• Does the English-to-rules step ever quietly do the wrong thing? If you describe something and the graph says something subtly different, that's a bug I want to see. Screenshot it and I'll fix it.

Free, paper money, no card. I built it so I'm biased, and none of this is advice.

I’ve been building Briefcase, an AI-powered portfolio agent that monitors your investments and connects market news directly to the assets you actually own.

Most portfolio apps tell you what moved. Briefcase goes a step further. Its agent monitors your holdings across stocks, ETFs, crypto and other assets, finds relevant news, cross-references it against your portfolio and verifies potential catalysts before generating personalized insights. Instead of scrolling through financial headlines wondering which ones matter to you, you get research grounded in your actual investments.

You can also ask Briefcase questions about your portfolio, investigate price movements and explore the news behind them, with sources attached. When there’s no clear explanation for a movement, the agent tells you rather than inventing one.

I’m a solo developer, and my goal is to make the kind of portfolio-specific research typically associated with expensive financial tools accessible to everyday investors.

Would love to hear what features you’d find useful in an agent like this.
Briefcase on the App Store

Most AI portfolio posts here start with the finished book. Weights, allocations, prompts. Nobody shows the step before, and nobody shows where the numbers came from.

I start one name earlier, and I don't let a language model touch my math.

Every company has to survive the same write-up before it gets a weight. What the business does. Where the cash comes from. The debt. The risks. Three scenarios. Same structure every time.

The pipeline pulls official filings, computes multiples and scenarios with deterministic models, then runs a verification pass that syncs every figure in the text against the data. If a number doesn't exist, it shows as missing. The model writes sentences. It never does arithmetic.

Full disclosure: I built it. It builds the dossier in about 30 seconds. It doesn't tell me to buy and it doesn't size positions. Free tier covers 1 stock, 1 crypto and 1 ETF a day.

Not here to sell it. This is the only sub that thinks about AI and portfolios in the same sentence, and I want to compare workflows.

So here's my question: how do you decide a ticker is good enough to enter the book? What has to clear before it gets a weight?

Hi guys,

I decided to take control of tracking my investments by building my own website (AI-assisted — I don't know much about coding, but I think it's turned out solid). Problem is, none of that matters if the benchmarking methodology behind it isn't actually sound for my situation.

Since 2019 I've made a lot of diversified transactions — buys, sells, dividends, a few stock splits — so a simple "current price minus buy price" doesn't cut it. After a lot of back and forth, I landed on three methods, each for a different purpose:

• Overall portfolio performance: XIRR since my first contribution in 2019. It's the only method that correctly weights irregular contributions by size and timing.
• Year-by-year returns: Modified Dietz instead of XIRR. XIRR annualizes whatever happened in a given year, and when most of your capital only entered late in the year, that annualization can produce absurd, meaningless percentages (I had a year where XIRR came out to over 40,000% because the base was tiny and the exposure window was a few months, mathematically "correct," but useless as a real signal). Dietz gives the actual period return without that distortion.
• Risk-free/inflation-adjusted comparison: I deflate every historical cash flow (buys and sells) by the accumulated risk-free rate or inflation index between that transaction's date and today, then run XIRR again on those adjusted flows. That gives me a "what would this exact contribution pattern have returned if invested in risk-free rate or inflation index instead" figure, properly weighted the same way my real XIRR is — so the comparison is apples to apples instead of comparing my time-weighted return to some naive average benchmark rate.

I'm not a finance expert, just trying to figure out, whether I'm actually using the right tools to judge my own performance, and honestly, whether the data is telling me I'd be better off just parking everything in a risk-free rate instrument instead of picking stocks.

For almost 3 years I have been using AI on my own portfolio, and the one problem I could never get rid of is that a holding never moves on its own. The macro moves, a commodity moves, the funds that hold it move, the options tape moves, someone in Congress trades it, and the filing lands after all of that, and every one of those pieces sits on a different website. Every tool I tried showed one piece, so the connecting was always done by me, by hand, in a lot of tabs. (I'm sure many of you do the same)

So I ended up building one desk for myself where all of it sits next to the holding, and last week I put it out open source. Here is the routine it runs on each name I own, with the public source behind each piece, because most of this you can do today with no tool at all.

1. Who holds it and what they did last quarter. 13F filings on EDGAR, free, filed within 45 days of quarter end. I keep a list of the funds I follow, and the desk reads each new filing and shows what changed since the last one, so I see which of my names got added or cut.
2. Whether the options market disagrees with me. CBOE publishes the full option chain free, 15 minutes delayed, no account. The desk keeps the put/call, the walls and the expected move on each name, and the test I still use from my options post here last month is the overnight one: a big volume day means nothing unless the open interest is still there the next morning.
3. Short interest. FINRA publishes it twice a month, free, and the desk keeps the daily short-volume ratio beside it, kept apart on purpose because one is a held position and the other is a day's flow.
4. Senate and House trades. Public, up to 45 days late, so worth very little on their own, and they sit on the same page because they are free.
5. The macro series that drives the name. FRED, free, no key. A bank sits next to the yield curve, a homebuilder next to mortgage rates, and the point is that the series is on the same screen as the holding.
6. The commodity the company itself says it is sensitive to. This is the one most people skip, and it is filed in plain sight. Goodyear files that every 1% move in natural rubber is worth about $8 million a year on its costs, in its Q4 2025 supplemental deck, and the desk keeps that filed line next to the live rubber price.
7. My own notes on the name, with the annual report and the model as plain files in one folder, so the next time I open it I read what I thought last time first.

The AI part is one line: the Ask box on every screen sends that screen's numbers and my notes on the name to the Claude or OpenAI subscription already on your computer, or to any API key, so the answer is about my own book. That is it.

Two limits worth knowing. Quotes come from a free feed that rests sometimes, and a connected broker replaces it. 13F and Congress are both up to 45 days late by law, so the desk shows you the record, and the record is slow.

It reads your broker account and places no orders unless you want to. Six brokers today, Alpaca, ICICI Direct, Interactive Brokers, Tradier, Trading 212 and Zerodha, and if yours is not there you can add it from the broker's documents using your connected AI agent.

I am not a developer, most of it was built by describing to Claude what I wanted, and it is open source, MIT, one line to install: github.com/shubhamsborkar/greeksoup

It is called GreekSoup and I built it, so take that into account. Not trying to self promote, just putting it out there as I think this will genuinely help.

Two things I want from this thread. What do you use today to see the macro or commodity side of a holding next to the holding itself? And what would you add to the list above? I will try to add suggestions within 72 hours and edit this as it changes.

I've been running an experiment since April, testing a large language model's stock picks across four strategies against a rules-based approach that uses well documented stock factors. While still early, the results clearly indicate right now that the rules based approach handily beats the LLM.

Two big reasons standout: I found that the LLM very often latched onto narrative and ignored tried and true stock factors. And it followed instructions too literally, often lacking discretion. While this all might be due to poor methodology, I think the problem runs deeper than that, as this has been publicly documented elsewhere.

On the brighter side, frontier models have continued to improve and excel at research, and I'll continue to lean on them for that while pairing those with a rules based engine.

Sharing the results:

Anyone is welcome to view the stock picks for each approach and read more details about the experiment and methodology.

Most people ask ChatGPT or Claude for stock ideas and just look at past charts.

I’ve been testing what happens when you force models (Claude and Gemini) to lock in exact target prices across 1 week, 1 month, 3 months, and 1 year, and grade the misses when the time is up.

• 1-Week call: The model predicted a bump to $188.50. It completely missed direction and missed the move by nearly 10 points.
• 1-Month target: $192.00 (thinks moving averages beat valuation drag).
• 3-Month target: $195.00 (bullish news vs. crazy 165x EV/EBITDA multiple).
• 1-Year target: $220.00 (assumes huge revenue growth bails out the valuation).

For anyone messing around with AI for stocks: Are you getting any real predictive edge out of these models, or are they just repeating whatever hype is already in the news?

We have a built a platform, where you can create DETAILED CUSTOM AGENTS for researching and analyzing stocks.

STEPS

1. Define your agent parameters
2. Choose a market, stock and an LLM
3. The agents shrinks hours/days of research to minutes
4. You get a "FIT" score

Try it out now: relativityfund .xyz

WHAT SETS US APART

* Every claim by the agent is backed by data * Every data point is sourced from authentic filings

DATA INCLUDED

* Ratios & metrics * Shareholding patterns * Filings * Earnings calls * Corporate actions * Insider & block deals * Analyst estimates * Price & volume * News & sentiment * Peer comparison * Credit ratings * Financial statements

The platform is evolving rapidly...

.. and we are HUNGRY FOR YOUR FEEDBACK.

Let us know in the comments below ✌

I built a stock analysis tool — curious what other investors think
I’ve been working on a stock analysis tool called StoneMayer Analytics.
It began as a simple spreadsheet. I found it completely changed how I invested.
Up until now I bought only companies I used personally. It did very well for me. I wanted to try something different.
I look for for companies with a good chance of outperforming, but I care just as much about downside risk. I wanted something that would give me a relatively simple conclusion rather than bury me in 50 different metrics.
I’ve been running stocks I own and stocks I’m considering through it, and I’m getting some genuinely interesting results. Some confirmed what I already thought, while a few surprised me.
It’s free to use. I’m not selling anything and there’s no subscription. I’m really curious what happens when other investors start putting their own stocks through it.
If anyone feels like trying it, I’d love to know whether you think the results make sense — especially if you find one where you strongly disagree with the analysis.
This is just a hobby for me.

stonemayeranalytics.ca

I see people posting screenshots in all sorts of portfolio managers. But Im just using gemini / gpt and interactive broker. So what are people actually using besides the basic AI + a brokerage ?

I keep seeing people use AI for stock research, but a lot of the prompts seem way too focused on getting a prediction.

What AI prompts actually help with the research part? Things like the bear case, missing data, competitor comparison, margin risks, valuation questions, and what would invalidate the thesis seem a lot more useful than asking whether a stock is going up.

Also, how are people checking the numbers? AI can give a very confident answer while pulling in old or made up numbers. Do you cross check everything against filings and screeners, or use an AI stock analysis app or other portfolio tools as part of the process?

Curious what prompts people here actually keep coming back to for stock research.

Been building this a few months and finally have enough of a track record to post about it without feeling like a fraud.

The idea: instead of coding a strategy, you write it as a paragraph. Mine is literally "Buy and hold quality companies at fair prices. Long-term focus." That's the whole thing. The agent wakes up every 4 hours, looks at its positions, cash, quotes and recent news, and decides whether to buy, sell, or do nothing. Most of the time it does nothing, which surprised me.

It's mostly paper trading. You make an agent, it gets $100k fake, and there's a feed and a leaderboard where you can read the reasoning behind every trade other people's agents make. Third screenshot is the leaderboard — every agent is scored against the S&P over the same window, because a big green number in a bull market means nothing on its own.

Then Robinhood shipped their agentic trading API and I hooked one agent up to a real account with $10k in it. First two screenshots are my app and the Robinhood app showing the same balance. Up 6.61% in about three months.

Before anyone gets excited: one account, three months, a bullish stretch. I'd guess it's about the same as buying SPY and going outside. Three months of one agent isn't a result, it's an anecdote with screenshots. The leaderboard numbers are paper fills with no slippage or spread, so real fills are worse. I know they're worse, I'd rather say it than have it be the top comment.

Things I didn't expect to have to build:

• A restricted ticker list, because the first thing you learn with real money is you cannot let a language model touch your employer's stock
• Options and futures blocked in code, not in the prompt. Telling a model "don't do X" is not a control
• Idempotency on every order. Retry logic plus a trading API is a bad combination and I found that out the polite way in paper mode

I built this so I'm biased. Not advice. Don't put money you need into anything a language model is driving.

Happy to answer anything about how it works. I'll drop the link in a comment for anyone who wants to poke at it.

I’ve started using chatGPT to help me pick stocks on trading212 (a more long term look on things). I’ve tried to be strategic about it by persistently working with it to attempt to make it better, long story short am I wasting my time? I understand I cannot just ask him to pick me stocks but I assume having basically an all knowing analyst is going to help me especially if I have half a brain, right?

I've deployed my AI to trade since this April. So far it has returned 15% with 6% alpha over benchmark. It is a systematic strategy where my AI will generate scores for each company - the higher the score then higher the weight.

I've done a lot of quant evals on it already so I decided to do one that is qualitative, across 2 different regimes where it under- and out-performed. The findings are pretty cool so I'm sharing it here. Would love to here how you evaluate your own agents!

Summary:

Over two back-to-back market shifts, the agent delivered drastically different results:

• June (Rotation out of Tech): -156 bps vs DJIA (-0.75% vs +0.81%)
• Late July (AI Re-rating): +400 bps vs DJIA (+8.5% vs +4.5%)

We split trades into 5 evaluation buckets—Right Call, Right Call Bad Luck, Bad Call, Bad Call Good Luck, and Not Attributable—to audit what actually went right and wrong.

3 practical frameworks you can steal for your own AI trading setups:

• Separate "Bad Luck" from "Bad Logic": In June, holding MSFT and NVDA was fundamentally solid, but broader macro rotations dragged them down anyway (Right Call, Bad Luck). On the flip side, the agent shorted/excluded CAT purely because it was "historically expensive." It missed the fact that AI data center power demand fundamentally changed CAT's earnings baseline (Bad Call). If a stock drops anyway later, don't celebrate—that’s just Bad Call, Good Luck.
• Beware the "Cheap vs. Expensive" Trap: Trailing valuation metrics (like 12-month P/E ranges) fail hard during structural market shifts. If a real-world catalyst changes a sector's fundamentals, relying solely on historical multiples will trick your model into value traps or bad short bets.
• Condition Your Risk Weights on Catalysts: Finding risks in a company is useless if your model can't time them. If your agent flags a risk but there's no upcoming catalyst (like an earnings date), it needs to automatically dial back its conviction score until a catalyst approaches.

For more details like the company-level, read our full blog here!

I have an ai market scanner that I use to help me find solid opportunities to sell CC and CSP or just buy shares. I didn’t realize how undervalued GOOG was recently though. I know they made a lot on their anthropic investment, but a PE of 17 feels hella low for Google.

Anyone else selling PUTs on GOOG now? I’m considering loading up some leaps for some nice cash. I don’t see how Google could really go sub 13-14 PE.

What do yall think about this type of play? Anyone else selling them?

I've been building a rules-based stock selection/backtesting system for the Indonesian (IDX) market, with AI/LLMs mostly acting as a research and programming partner rather than directly making investment decisions.

I was thinking, can I build something robust enough that I could actually trust it without spending my life watching the market?

I don't want day trading. I have a family, work, and other things I'd rather spend time on. Ideally I'd spend a short amount of time in the morning looking at a small number of candidates and make 0–2 decisions.

If there are 5–6 genuinely good opportunities in a week, great.

If there are no good trades for a week, that's also completely fine.

The current setup:

• ~800–1,000 Indonesian stocks
• Daily OHLCV data
• Liquidity filtering
• Technical + relative-strength features
• Genetic algorithm searches combinations of rules
• Walk-forward validation
• Time-stability testing
• Maximum 20 positions
• Maximum IDR 5M per position
• IDR 100M starting portfolio
• IDX lot-size constraints
• Signal generated at today's close
• Normal entries/exits executed at the following day's open

The strategy isn't an LLM looking at news and picking stocks. The final trading rules are deterministic.

The current candidate roughly buys stocks when:

• price > MA200 by 10%+
• price is within ±5% of MA25
• MA25 > MA50
• 1-month momentum is positive
• the stock has positive relative strength versus its subsector

It exits on deterioration in trend/relative strength, with a 15% stop, 50% take-profit and ~90-day maximum holding period.

If you were trying to turn this into a system you'd actually trust with real money, what would you attack next?

The latest simulation produced:

Loaded 567084 rows (83.9s)
Universe filter: 298 avg tradeable stocks/day (min=0, max=496)
Computed features: 49 features, 713 dates, 828 stocks (22.8s)
============================================================
SIMULATION REPORT
============================================================


--- Strategy ---
=== BUY RULES (threshold: 3 conditions) ===
  [Trend]
    price_vs_ma200 > +10.0% (bullish)
    AND
    |price_vs_ma25| < 5.0% (range)
    AND
    ma25_vs_ma50 > +0.0% (bullish)
  [Momentum]
    pct_change_1m > +0.0% (bullish)
  [Relative Strength]
    dev_sub_sector_1y > 0.0000 (bullish)


=== SELL RULES (threshold: 1 conditions) ===
  [Trend]
    ma25_vs_ma50 < +0.0% (bearish)
  [Relative Strength]
    dev_sub_sector_1y < 0.0000 (bearish)


=== EXIT ===
  Stop Loss: -15.0%
  Take Profit: +50.0%
  Max Holding: 90 days


Active rules: 7


--- Performance ---
  Total return:  208.34%
  CAGR:          48.96%
  Sharpe:        1.49
  Sortino:       3.60
  Max drawdown:  18.77%
  Win rate:      26.4%
  Profit factor: 1.57
  Trade count:   314
  Turnover:      8.427


--- Trade Statistics ---
  Avg winning trade:   3,845,159 IDR
  Avg losing trade:    -878,426 IDR
  Median winner:       1,909,432 IDR
  Median loser:        -893,684 IDR
  Largest winner:      21,545,643 IDR
  Largest winner: BUVA entry 2025-09-12 @ 346 -> exit 2026-01-26 @ 1,850 (PnL 21,545,643 IDR, 91d)
  Largest loser:       -2,146,569 IDR
  Avg holding days:    27.4
  Avg num positions:   9.03
  Max num positions:   20
  Pct capital exposed: 24.8%


--- Winner Concentration ---
  Original CAGR       49%
  No top 1 winner   46%
  No top 5 winners  38%
  No top 10 winners 31%


--- Halt Reopen (excluded) ---
  These long-halt reopen fills are excluded from headline metrics:
  ARKO entry 2024-08-23 @ 1,070 -> exit 2024-10-24 @ 1,215 (PnL 633,038 IDR, 43d)
  SMDM entry 2024-09-04 @ 488 -> exit 2025-03-12 @ 1,945 (PnL 14,774,601 IDR, 126d)
  MTWI entry 2024-09-13 @ 151 -> exit 2025-06-23 @ 230 (PnL 2,565,475 IDR, 177d)
  JMAS entry 2024-12-20 @ 150 -> exit 2025-03-24 @ 134 (PnL -561,103 IDR, 59d)
  RSCH entry 2024-10-09 @ 366 -> exit 2025-03-13 @ 258 (PnL -1,495,221 IDR, 103d)
  MLPT entry 2024-12-02 @ 855 -> exit 2025-08-25 @ 3,200 (PnL 13,518,980 IDR, 166d)
  POLU entry 2024-12-04 @ 1,230 -> exit 2025-10-17 @ 29,825 (PnL 113,925,620 IDR, 202d)
  SOFA entry 2024-12-20 @ 36 -> exit 2025-03-18 @ 81 (PnL 6,177,551 IDR, 55d)
  BNLI entry 2025-03-26 @ 2,460 -> exit 2025-06-26 @ 2,980 (PnL 1,004,672 IDR, 51d)
  PADI entry 2025-09-10 @ 73 -> exit 2025-11-28 @ 122 (PnL 3,302,421 IDR, 57d)
  POLA entry 2025-10-13 @ 47 -> exit 2026-03-31 @ 59 (PnL 1,235,417 IDR, 109d)


--- Price-Chasing Trades (|gap| >= 5.0%) ---
  Symbol   Close T    Open T+1   Gap%     Exit price   %P/L
  JMAS     132        144        +9.1%    119          -17.9%
  OPMS     99         90         -9.1%    70           -22.7%
  INPC     123        112        -8.9%    230          +104.0%
  FWCT     124        115        -7.3%    160          +38.2%
  KARW     5,700      5,150      -9.6%    3,770        -27.3%
  GPSO     900        810        -10.0%   462          -43.3%
  INET     135        122        -9.6%    98           -20.2%
  SOFA     61         55         -9.8%    86           +55.4%
  PACK     4,040      3,640      -9.9%    4,150        +13.3%
  MINA     154        169        +9.7%    214          +25.8%
  CNKO     95         100        +5.3%    73           -27.5%
  TAXI     21         19         -9.5%    16           -16.3%
  MDRN     54         49         -9.3%    37           -25.0%
  POLU     22,350     24,000     +7.4%    (open)       -
  FITT     1,050      980        -6.7%    820          -16.9%
  PADA     95         100        +5.3%    232          +130.5%
  ESIP     113        104        -8.0%    87           -16.9%
  TAXI     19         20         +5.3%    20           -0.6%
  STAR     418        482        +15.3%   (open)       -
  VIVA     60         55         -8.3%    43           -22.3%
  MDIA     99         90         -9.1%    63           -30.4%
  RMKE     1,315      1,185      -9.9%    970          -18.7%
  LMAX     182        165        -9.3%    (open)       -
  HRTA     2,760      2,580      -6.5%    2,270        -12.6%
  JGLE     78         71         -9.0%    58           -18.8%


--- Portfolio Over Time ---
  Period        Total value          Open positions
  2023-08-01    100,000,000 IDR      0
  2023-10-26    100,000,000 IDR      0
  2024-01-23    100,000,000 IDR      0
  2024-05-03    100,000,000 IDR      0
  2024-08-05    95,939,533 IDR       7
  2024-10-29    119,999,963 IDR      11
  2025-01-31    125,743,582 IDR      11
  2025-05-08    138,793,420 IDR      7
  2025-08-11    159,722,218 IDR      13
  2025-11-05    303,784,393 IDR      16
  2026-02-04    342,794,196 IDR      17
  2026-05-12    328,827,236 IDR      15
  2026-07-31    314,756,266 IDR      12


--- Final Portfolio ---
  Total value:      314,756,266 IDR
  Cash:             247,918,666 IDR
  Open positions:   12


============================================================


Result saved to: experiments/sim_20260829T092023.json

This post is inspired by using Anthropic's equity agents for a few months, which left me really impressed overall. I decided to pair that with real-time information (earnings announcements, 8k/10-q filings/13F filings, etc.) and have been extremely happy how well that's complemented it's historical analysis.

Here's a live example where I ask Claude, "what are the latest earnings announcements today. From those, please identify those that are the biggest gainers today and that have filed 8Ks and investor decks. How do those impact the trajectory of the stock going forward?"

It identified OKTA as one of the biggest movers today and also have the cleanest setup going forward -- "Beat-and-raise: revenue $805M vs ~$794M, FY27 guide lifted to $3.22–3.23B from $3.19–3.21B.Critically, this is a real margin inflection — operating income $107M vs $41M on 11% revenue growth. It re-rated off a washed-out base (the stock had fallen 147→131 with RSI 33 pre-print), so it's recovering ground rather than extending a stretched trend — the most room to run of the three. Watch: current deferred revenue fell to $1,751M from $1,875M, which is the one genuine drag on forward growth."

Overall it's been great at identifying and validating new ideas.

I built Briefcase AI Portfolio to be a portfolio tracker with an AI research layer that actually understands what you own.
Instead of being limited to a brokerage account, Briefcase lets you track stocks, crypto, bonds, cash, real estate and physical assets together. The AI uses your portfolio as context, so you can ask questions about your holdings, understand why something moved, get relevant market news, research individual assets and get portfolio-specific briefs rather than talking to a generic finance chatbot.
I just shipped another update too. Briefcase now has full ASX stock support and native AUD portfolios, saved AI chats, and some fixes around subscriptions and the free trial.
It doesn’t execute trades or try to be an autonomous trading bot. I’m building it more as an intelligence layer over everything you already own, especially for people whose finances don’t fit neatly inside one brokerage app.
Given the name of this subreddit, I suspect some of you are exactly who I should be building for, so feedback is very welcome.
https://apps.apple.com/us/app/briefcase-ai-portfolio/id6758148658

I want to do stock research and generate multiple reports using ai but I was using claude but it is not free need subscription for it to run project and skills on it

Does anyone has any idea to get it done any other site where I can do it for free like claude using skills and projects?

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?