CAGR
CALIFORNIA GRAPES INTL
stock OTC

I am looking to invest for my retirement swp, so I have around 20-25 years of investment horizon.
Risk Appetite: Moderate according to the survey
Allocations:
Parag Parikh Flexi Cap Fund: 35.0%
Kotak Midcap Fund : 25.0%
Motilal Oswal Midcap Fund: 15.0%
Nippon India Small Cap Fund : 12.5%
Invesco India Small Cap Fund : 12.5%
Why these funds:
Very little overlap in the funds.
Flexi: PPFCS, as I have good exposure to mid and small cap wanted a safe fund for this.
Midcap: Kotak mid cap again for stability and Motilal midcap for bull runs
Small Cap: Started with Nippon but due to AUM size added Invesco as well, didn't want to remove Nippon all together as it is still performing well and is well diversified.
I am aiming for minimum of 13% CAGR at the end of the term.
Building my liquid funds on the side along with this but no investment in gold.

I would not choose one metric. My first layer is net return after fees/slippage, max drawdown, time underwater, expectancy with the number of trades, turnover and exposure. CAGR/Calmar are useful for comparing return to drawdown; Sharpe/Sortino are summaries, not validation. Win rate and profit factor are secondary because they can look good with a bad payoff distribution. The real filter is walk-forward/OOS stability, parameter sensitivity, bootstrap uncertainty and performance in both bull and bear regimes. A 70% win rate can still be a terrible strategy if the losses are larger and the tail risk is hidden.

0.31 is a lot lower than I'd have guessed. that worst week is the whole story really, same average exposure but on the day it matters they're two different strategies, so I'd pick the lookback on drawdown behaviour, not CAGR.

As I am typing this, I am still shocked this is possible and how easy it is to set this up for anyone.
A few weeks back, I kept hearing about Muse, which is basically an AI assistant that can use its own computer and keep doing work for you automatically.
So I gave it a task I have wanted to automate for years:
Monitor my portfolio exactly how I want, and every day search the market for new stock opportunities.
I honestly expected the results to be trash but they were very good.
The first thing I tested was whether it could find stocks across the entire set of US markets that meet my specific criteria.
My criteria right now looks like this:
Three-year revenue CAGR 10% or higher
Latest annual revenue growth 10% or higher
Latest net-income growth 15% or higher
Gross margin 45% or higher
Operating margin 10.7% or higher
Free-cash-flow margin 16% or higher
Approximate ROIC 6.3% or higher
Annual share-count dilution -2% max but ideally positive
I connected Muse to an affordable stock API so it can screen thousands of US-listed stocks every day at 10am. But instead of just returning a giant list, it launches specialized agents to research every company that passes the filters.
My workflow looks like this:
* Growth analyst: forward revenue growth, EPS expectations, company guidance and industry outlook
* Valuation analyst: tests my valuation rules, including growth-adjusted pe and ev/sales relative to growth
* Moat analyst: analyzes what protects the business from competitors
* Financial health analyst: balance sheet, fcf, roic, leverage and margins
* Management analyst: CEO tenure, guidance accuracy, acquisitions, buybacks, dilution, insider ownership and capital allocation
* Durability/catalyst analyst: what keeps growth going and what could make the market recognize the opportunity
* Bear analyst: builds the strongest case against the investment
* Bull analyst: builds the strongest case for it
* CIO agent: reads everything and gives me a shortlist of the companies actually worth researching further
The end result each morning is basically:
"Here is what changed, here are the stocks that passed your screen, here’s the bull/bear research, and here are the 2–3 companies worth looking at today."
Then I can spend my time actually analyzing those few companies in depth.
The second thing I tested was my existing portfolio.
I gave Muse access to my portfolio and asked it to monitor every holding for new opportunities, threats and anything that might change my original investment thesis.
I now get that update every morning at 10am, and it has already made it much easier to keep track of what is happening across my holdings.
The point isn’t my particular investing criteria or my exact list of agents.
The interesting part is that you can replace all of this with your own investing process and have agents run your excat checklist and prcess for you.
And then have the system run it for you every day and get an update on your phone.
The setup was surprisingly simple: connect Muse to financial data, give it your prompts, and tell it what you want checked and how often.
So far I have also been able to run this daily without hitting any usage limits.
The next experiment I want to try is changing the screening rules based on the characteristics that the most successful stocks of the last 20 years had before their biggest runs, then seeing what the system finds.
If anyone tries building something similar, I’d be very interested to hear what agents or screening criteria you use.

Ran it. Top 30, weekly, 0.12%/side, \~2.5 years. Your read holds, mostly.
Same exposure each week spread over the whole top 30 (same timing, no picking) already gets most of it: CAGR 16–18% vs 4% buy-and-hold for 14–56d lookbacks, Sharpe \~0.58 vs 0.38. The actual picks add another 5–8 points CAGR on all four lookbacks with a slightly smaller drawdown. But the per-week edge of the picks vs the full top 30 is tiny and not significant (t between −0.1 and 0.9). It only seems to show up in weeks when many coins are trending.
One thing I didn't expect: a plain BTC switch (whole top 30 only when BTC's own trend is positive) does worse than the breadth version. How many coins are trending matters more than BTC alone.
So: a regime filter, with "how broad is the trend" as the regime, and at best a weak picker on top. Thanks, that's a cleaner way to describe what the forward test actually measures.

You can't go wrong hodl'ing the OG. Like most things in life, Coinbase's card favors the rich, with scaling rewards up to 4% bitcoin back based on assets held with them. STRC is basically a 12% yielding perpetual bond, over-collateralized by Strategy's cash and Bitcoin assets. It will be paying daily dividends this time next month. You are essentially trading off a lot of the volatility of Bitcoin in exchange for a decent amount of the upside, depending on your expectations for the annual CAGR going forward. In my mind, this is the new standard for the bond portion in the traditional 60/40 equity / bond portfolio. I'm about 30% STRC at the moment.

9.5% CAGR btw.

month-to-month shifts are almost always noise for dividend playbooks; the real story is the long‑term CAGR and drawdowns. CGDV's 2.58% loss is the shallowest of the bunch, matching its larger weighting in US large‑caps and lower expense. DGRO still pulls ahead in total return when we look beyond a single month, but CGDG can offer a higher yield if you need cash flow before retirement. Depends on your tax situation, I'd keep CGDV in the taxable side and reserve CGDG or a higher‑yield ETF for a tax‑advantaged bucket.

Yeah, that's the right way to read it. The median just needs the mix of good and bad months to be about right, the order barely matters, which is why that CAGR column basically sat at 8.8% the whole way down. Drawdown is the opposite, it lives in the runs, so block length is really a dial on how much clustering you let back in. 2008 and the early 30s are exactly the stretches a length of 1 shuffles away. I'd weight the 12 and 24 rows the same as you, long enough to carry a bad run, short enough that there are still a few hundred blocks behind the number instead of thirty.

SP500 (VOO/SPY) CAGR in past 15 years is higher than its several decades long CAGR, so just something to keep in mind. A difference of 3-4% might not seem like a lot, but compounding over the course of 15 years it turns out to be substantial.

As I am typing this, I am still shocked this is possible and how easy it is to set this up for anyone.
A few weeks back, I kept hearing about Muse, which is basically an AI assistant that can use its own computer and keep doing work for you automatically.
So I gave it a task I have wanted to automate for years:
Monitor my portfolio exactly how I want, and every day search the market for new stock opportunities.
I honestly expected the results to be trash but they were very good.
The first thing I tested was whether it could find stocks across the entire set of US markets that meet my specific criteria.
My criteria right now looks like this:
Three-year revenue CAGR 10% or higher
Latest annual revenue growth 10% or higher
Latest net-income growth 15% or higher
Gross margin 45% or higher
Operating margin 10.7% or higher
Free-cash-flow margin 16% or higher
Approximate ROIC 6.3% or higher
Annual share-count dilution -2% max but ideally positive
I connected Muse to an affordable stock API so it can screen thousands of US-listed stocks every day at 10am. But instead of just returning a giant list, it launches specialized agents to research every company that passes the filters.
My workflow looks like this:
* Growth analyst: forward revenue growth, EPS expectations, company guidance and industry outlook
* Valuation analyst: tests my valuation rules, including growth-adjusted pe and ev/sales relative to growth
* Moat analyst: analyzes what protects the business from competitors
* Financial health analyst: balance sheet, fcf, roic, leverage and margins
* Management analyst: CEO tenure, guidance accuracy, acquisitions, buybacks, dilution, insider ownership and capital allocation
* Durability/catalyst analyst: what keeps growth going and what could make the market recognize the opportunity
* Bear analyst: builds the strongest case against the investment
* Bull analyst: builds the strongest case for it
* CIO agent: reads everything and gives me a shortlist of the companies actually worth researching further
The end result each morning is basically:
"Here is what changed, here are the stocks that passed your screen, here’s the bull/bear research, and here are the 2–3 companies worth looking at today."
Then I can spend my time actually analyzing those few companies in depth.
The second thing I tested was my existing portfolio.
I gave Muse access to my portfolio and asked it to monitor every holding for new opportunities, threats and anything that might change my original investment thesis.
I now get that update every morning at 10am, and it has already made it much easier to keep track of what is happening across my holdings.
The point isn’t my particular investing criteria or my exact list of agents.
The interesting part is that you can replace all of this with your own investing process and have agents run your excat checklist and prcess for you.
And then have the system run it for you every day and get an update on your phone.
The setup was surprisingly simple: connect Muse to financial data, give it your prompts, and tell it what you want checked and how often.
So far I have also been able to run this daily without hitting any usage limits.
The next experiment I want to try is changing the screening rules based on the characteristics that the most successful stocks of the last 20 years had before their biggest runs, then seeing what the system finds.
If anyone tries building something similar, I’d be very interested to hear what agents or screening criteria you use.

Happy for you.
Yes silver has done well past 6 years. And past 26 years, but not as good as gold.
Long-Term Returns Comparison (2000–2026)
Metric
Jan 2000 
Total Return

gold
~1,376% silver

~1,052%
Annualized Return (CAGR)

Gold ~10.7%
silver~9.7%

I ran a block-length sensitivity test because a Monte Carlo chart can hide its most important assumption: how much historical ordering survives the resampling. In this test, the median return barely moved while the drawdown tail moved by 23.78 percentage points.
The study used Classic 60/40 monthly returns from 1923-01 through 2026-08: 1,244 monthly observations and an 8.72% historical CAGR. Each setting generated 5,000 moving-block bootstrap paths over a 30-year horizon with seed 42.
Only the block length changed: 1, 3, 6, 12, 24 and 36 months. Here's the full table. Drawdowns are positive loss magnitudes, as reported in the study.
|Block length|Median CAGR|Median maximum drawdown|95th-percentile maximum drawdown|
|:-|:-|:-|:-|
|1 month|8.72%|24.57%|38.97%|
|3 months|8.70%|26.38%|42.42%|
|6 months|8.76%|27.40%|44.57%|
|12 months|8.82%|28.93%|51.65%|
|24 months|8.85%|30.78%|57.76%|
|36 months|8.82%|30.78%|62.75%|
[All 3 metrics against block length, on a shared percentage scale. Drawdowns are loss magnitudes.](https://preview.redd.it/65ssrfb1avsh1.png?width=2250&format=png&auto=webp&s=702b6b99f28a5e10784a9f951ff6b50fffc902cc)
Between the endpoint settings, median CAGR went from 8.72% to 8.82%, a reported difference of 0.10 percentage points. The 95th-percentile maximum drawdown went from 38.97% to 62.75%. A return-only summary would leave out the sensitivity I'd most want to inspect before trusting the paths.
A 1-month block samples individual months. A 36-month block preserves 3-year sequences inside each draw. Short blocks break stress sequences apart; a good month inserted among bad months can interrupt a developing drawdown. Longer blocks retain more of the historical bear-market continuity. Every setting still draws from the same return history.
I don't read the longest block as automatically correct. The result is conditional on the selected block rule and this sample. At 62.75%, 5% of simulated paths were worse under that setting; that doesn't establish a 5% real-world probability. Rearranging history can't create a new market structure absent from the sample.
For a Monte Carlo report, I'd want this sensitivity table beside the terminal-return chart. A stable median doesn't establish that the path assumptions are harmless.
Which block length do you use for monthly portfolio returns, and why do you consider it appropriate for the dependence you're trying to preserve?

It matters for the stock market because nominal return might seem good at 15% CAGR (hypothetical), but real return is 0% when you account for inflation

Nifty was 12k in 2019..... dude, that's an 86% return in 7 years with a CAGR of only 9.3% , That's much lower compared to the US market of 14% . Plus if you adjust for usd value loss, The return in dollar terms is only 36% ....
And CAGR returns in dollar terms is only 4.5% .... stop justifying bad returns

The market is asking a lot from Taiwan Semiconductor Manufacturing.
At $456.19, today's price only works in my model if cash flow grows about 28.9% a year for the next 10 years. Recently, reported FCF grew about 24% a year (FY2022-FY2025). The hurdle uses unlevered cash flow; the historical figure above measures reported FCF.
Two operating details behind the valuation:
\- Q2 2026: Taiwan Semiconductor Manufacturing's latest-quarter revenue grew 36.0% year over year. For context, its multi-year revenue CAGR is 18.9%; that comparison spans a different time horizon.
\- Taiwan Semiconductor Manufacturing's reported FCF margin is 25.3%, up from 23.0% in the oldest comparable period.
Even my bull case is only $291, below today's $456.19 price. That is a demanding hurdle. Either the company keeps outperforming for years, or the stock has little room for disappointment.
Model assumptions: 11.7% discount rate and 2.5% long-run growth. These are assumptions, not a forecast of the share price.
What could break the case: Semiconductors are cyclical.
Is the market right to expect more, or is the stock priced for too much?
The video goes through the assumptions behind the range, the stress test, and what would change my conclusion. I broke down the full case in a video: [https://youtu.be/DrCF1rhzNsM](https://youtu.be/DrCF1rhzNsM)
Snapshot: 2026-10-01. I built the model; video production is AI-assisted. Not financial advice.

I spent the last couple weeks digging into Intuit ($INTU), a $76B software company that currently trades at its lowest valuation multiple in over a decade. Most people know them through TurboTax, QuickBooks, Credit Karma, and Mailchimp. The market has hammered the stock down over concerns about near-term guidance and artificial intelligence disruption, but when you look closely at the underlying business and cash flows, I think Mr. Market is misinterpreting a temporary seasonal lull for permanent impairment. Wanted to share my notes and numbers.
## What They Do
Intuit provides the core operating plumbing and compliance infrastructure for over 10 million small businesses and tens of millions of consumers. If you run a local business--whether a dental practice, a contractor, or a bakery--QuickBooks is the central nervous system tracking your invoices, bank feeds, inventory, and employee payroll.
On the consumer side, TurboTax handles federal and state tax filings, increasingly paired with licensed human CPAs through TurboTax Live. Over 80% of their total revenue is recurring software subscriptions or predictable annual compliance volume.
The switching costs here are extraordinarily painful. Once a business owner wires five to ten years of customer records, tax histories, and payroll automation into QuickBooks, migrating to another software platform is like ripping the electrical wiring out of a building while the power is running. Furthermore, over 1 million professional accountants use and actively recommend Intuit products to their clients, serving as a massive, unpaid distribution network that no startup can easily replicate.
## Why The Stock Is Cheap
The market is currently anxious about three main issues. First, artificial intelligence fear. The bear narrative claims that autonomous AI agents, open-source LLMs, and the IRS Direct File program will automate bookkeeping and tax returns for free, disintermediating Intuit's software entirely.
Second, the company is digesting a multi-billion dollar acquisition of Mailchimp, where growth has slowed as email marketing faces commoditization and platform transition friction.
Third, Wall Street reacted poorly to conservative near-term guidance, compounded by unit churn among low-income tax filers earning under $50,000. Inexperienced investors also routinely misunderstand Intuit's seasonal clock: because tax filings occur in the spring, the company always experiences a seasonal operating lull in the autumn, which the market treats as a surprise year after year.
## Why I Think The Market Is Wrong
The AI disintermediation bear case makes for a scary headline, but it misunderstands how financial and legal compliance works in the real world: the regulatory liability barrier.
When an AI text generator hallucinates, you edit a sentence. When an AI hallucinates a tax deduction or miscalculates employee payroll withholdings, you get hit with IRS penalties, state audits, or legal liabilities. Small business owners and taxpayers do not pay Intuit simply to run math; they pay for guaranteed regulatory compliance, software accuracy, and certified audit defense.
This is why customers are actually upgrading rather than leaving. TurboTax Live revenue--which pairs AI automation with human CPAs who sign off on the return--grew 36% this past year. Meanwhile, QuickBooks Online accounting revenue grew 22% year-over-year, and their mid-market Intuit Enterprise Suite contracts expanded over 35%.
Intuit is an asset-light toll bridge that requires virtually zero physical capital to expand. Maintenance CapEx historically runs at just 1.3% of revenue. When you have gross margins of nearly 78% and operating margins expanding to 27.5%, the business is demonstrating clear pricing power, not structural erosion.
## Adjusted Free Cash Flow
| Line Item | Amount |
|---|---|
| Operating Cash Flow | $8931M |
| Less: Stock-Based Comp | -$2056M |
| Less: Maintenance CapEx (5yr avg) | -$278M |
| Less: Working Capital Adj. | -$140M |
| **Adjusted Free Cash Flow (FCF)** | **$6457M** |
| Diluted Shares | 277.0M |
| **FCF Per Share** | **$23.31** |
- Return on Invested Capital (ROIC): 17.4%
- 7-year FCF Per Share CAGR: 21.0%
- Core SMB customer retention: 99%+
- Gross margin: 77.6%
## The Balance Sheet
| Line Item | Amount |
|---|---|
| Cash & Liquid Assets | $7200M |
| Total Debt | $8336M |
| Net Debt | $1136M |
| Market Cap | $76394M |
| **Enterprise Value** | **$77530M** |
| **EV / Adjusted FCF** | **12.0x** |
Intuit carries $1.14B in net debt, which is remarkably conservative for a company producing over $6.4B in annual adjusted free cash flow. Operating income covers annual interest expense 24 times over, meaning the entire debt load could be erased in under 16 months of cash flow if management chose to do so.
At 12.0x EV / Adjusted FCF, Intuit is trading at the 0th percentile of its 10-year historical valuation range (the 10-year median multiple sits around 47x). The market is pricing a premier compounder as if it were a struggling, low-growth legacy hardware business.
## Capital Allocation
Management has taken decisive action to maintain capital discipline. They recently eliminated 17% of corporate overhead to flatten organizational layers, cut operational bloat, and reallocate engineering resources directly toward AI product development.
At the same time, they are repurchasing shares at these depressed levels. Intuit deployed $3.18B into net buybacks over the past year and authorized a fresh $8 billion repurchase facility. They also raised the dividend by 15%, returning the vast majority of their annual cash flow directly to shareholders.
## What Would Make Me Sell
A few operational risks keep me cautious here:
1. Growth stall: Management guided fiscal 2027 revenue growth to 9-10% as they invest in volume pricing for TurboTax and integrate AI tools. If this deceleration turns out to be structural rather than temporary, and top-line growth fails to reaccelerate back toward 12-14% by fiscal 2028, the multiple will stay compressed.
2. Churn acceleration: If low-cost cloud competitors like Xero or Wave start taking meaningful market share from QuickBooks among established small businesses (rather than just entry-level solopreneurs), or if their 99% retention rate begins to drop, my thesis is invalidated.
3. Mailchimp capital destruction: If Mailchimp continues to lose active subscribers and requires further write-downs without generating incremental cash flow, it confirms the acquisition was a permanent capital drag.
## Valuation & Verdict
So what is the business actually worth today? I calculated the Adjusted Free Cash Flow and ran the conservative multiples to find a ~52% Margin of Safety. Rather than dumping more numbers here, I prefer to walk through the valuation math and final intrinsic value target verbally. You can see my complete Intrinsic Value calculation and final verdict in this short video: https://youtu.be/sWfc2CasjLg
*Disclosure: I hold a position in $INTU. Hard data from filings, AI-assisted writing, personal review and position. This is not financial advice.*

schd's dividend growth has decelerated a bit, landing in the 2-3% range, but its yield keeps it attractive for pure income needs. if you're chasing total return I'd still lean toward v ig for the \~12% CAGR, though its yield is lower than schd. as an alternative, consider cg dv for a higher CAGR but higher expense, or pair dg ro with a high-yield ETF to balance growth and cash flow.

Why timing can fluctuate your long term CAGR by upto 100 percent.

Check out the triumverate. NNN, MAIN, and EPD. Three dividend stocks that all yield more than 6%, and have raised dividends for more than 15 years straight, and all three average increasing the dividend by 4.6% CAGR. One is a REIT, one a BDC, and one an MLP. All different sectors, and all pay me very nicely every month or quarter, and pay me more year over year even if I never buy another share. Never have to sell shares.