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composer require beziworld/laravel-proxmox
php artisan vendor:publish --tag=proxmox-config   # optional

PROXMOX_URL=https://pve1.example.com:8006
PROXMOX_TOKEN_ID=automation@pve!laravel          # Datacenter > Permissions > API Tokens
PROXMOX_TOKEN_SECRET=b3c6a8e2-....
PROXMOX_NODE=pve1                                # default node for create/list calls
PROXMOX_VERIFY_SSL=/etc/ssl/pve-root-ca.pem      # true, false or a CA bundle path

# or a ticket login instead of the token:
# PROXMOX_USERNAME=root@pam
# PROXMOX_PASSWORD=

php artisan proxmox:check

'connections' => [
    'default' => [...],
    'dc2' => [
        'url' => 'https://dc2-pve1.example.com:8006',
        'token_id' => env('PROXMOX_DC2_TOKEN_ID'),
        'token_secret' => env('PROXMOX_DC2_TOKEN_SECRET'),
        'node' => 'dc2-pve1',
        'verify_ssl' => storage_path('certs/dc2-pve-root-ca.pem'),
    ],
],

use Beziworld\Proxmox\Facades\Proxmox;

$vmid = Proxmox::cluster()->nextId();

Proxmox::vm(9000)->clone($vmid, ['name' => 'shop-42', 'full' => true, 'storage' => 'local-lvm'])->wait();

$vm = Proxmox::vm($vmid);
$vm->updateConfig(['memory' => 4096, 'cores' => 2, 'ipconfig0' => 'ip=10.0.0.42/24,gw=10.0.0.1'])?->wait();
$vm->resize('scsi0', '+20G')?->wait();
$vm->start()->wait();

$vm = Proxmox::vm(100);                      // node looked up in /cluster/resources
$vm = Proxmox::vm(100, 'pve1');              // or given explicitly
$ct = Proxmox::container(200);
$guest = Proxmox::guest(100);                // type and node detected

$vm->status()->isRunning();
$vm->start(); $vm->shutdown(timeout: 120, forceStop: true); $vm->stop();
$vm->reboot(); $vm->reset(); $vm->suspend(); $vm->resume();
$vm->config(); $vm->config(current: true);
$vm->migrate('pve2', online: true)->wait(timeout: 3600);
$vm->convertToTemplate();
$vm->destroy(purge: true);

$vm->snapshot('before-upgrade', 'PHP 8.4 upgrade', withRam: false)->wait();
$vm->snapshots();                              // list<Snapshot>
$vm->rollback('before-upgrade', start: true)->wait();
$vm->deleteSnapshot('before-upgrade');

$vm->backup('pbs', mode: 'snapshot', compress: 'zstd', options: ['notes-template' => ''])->wait();
$vm->backups('pbs');                           // list<StorageVolume>
Proxmox::storage('pve1')->deleteVolume('pbs', $backup->volid);

$task = $vm->start();                          // Task
$upid = (string) $task;                        // store it, e.g. in a queued job

$status = Proxmox::task($upid)->wait(timeout: 600, interval: 2, onPoll: fn($s) => logger()->info($s->status));
$status->succeeded(); $status->exitStatus;     // "OK", "WARNINGS: 1" or the error message

Proxmox::task($upid)->log();                   // list<string>
Proxmox::tasks()->forNode('pve1', ['vmid' => 100, 'errors' => true]);
Proxmox::tasks()->recent();                    // cluster tasks

php artisan proxmox:task "UPID:pve1:000A1B2C:0123ABCD:6730A1B2:qmstart:100:root@pam:" --timeout=300

Proxmox::nodes()->list();                      // list<Node>
Proxmox::nodes()->status('pve1');
Proxmox::cluster()->resources('vm');           // list<ClusterResource>
Proxmox::cluster()->guests();
Proxmox::cluster()->isIdFree(150);
Proxmox::qemu('pve1')->list();                 // list<GuestStatus>
Proxmox::lxc('pve1')->create([
    'hostname' => 'ct1',
    'ostemplate' => 'local:vztmpl/debian-12-standard_12.7-1_amd64.tar.zst',
    'rootfs' => 'local-lvm:8',
    'net0' => 'name=eth0,bridge=vmbr0,ip=dhcp',
])->wait();
Proxmox::storage('pve1')->list('backup');      // list<Storage>
Proxmox::storage('pve1')->content('local', 'iso');

$token = Proxmox::access()->createToken('ci@pve', 'deploy', comment: 'CI', expiresAt: now()->addYear());
$token->value;                                 // shown only once, store it
Proxmox::access()->updateAcl('/vms/100', ['PVEVMUser'], tokens: [$token->fullTokenId]);
Proxmox::access()->can('/vms/100', 'VM.PowerMgmt');
Proxmox::access()->users();

Proxmox::using([
    'url' => $cluster->url,
    'token_id' => $cluster->token_id,
    'token_secret' => decrypt($cluster->token_secret),
])->vm(100)->status();

Proxmox::request('GET', '/cluster/ha/resources');
Proxmox::request('POST', '/nodes/pve1/qemu/100/agent/ping');

composer install
composer ci   # pint --test, phpstan (level 8), pest

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It’s hard to imagine Yellowstone or the Grand Canyon no longer being national parks, but, technically, it could happen. The government can demote national parks and reassign them to other land managers, and they’ve done it in the past. Many national parks and monuments have lost their elevated classification over the decades, for a multitude of reasons. Sometimes, after the fact, Congress decides the area isn’t important enough to merit a special designation. Other times, the sites just never receive visitors.

Even though these areas lost their special status, the public can still visit many of them and participate in a variety of outdoor activities. Here are some of the National Park Service (NPS) sites time forgot, and how to explore them today.

Former National Parks

Mackinac National Park

This island, located on Lake Huron in between Michigan’s Upper and Lower Peninsulas, became the country’s second-ever national park in 1875. It’s home to Fort Mackinac, the state’s oldest surviving military outpost, which served an important role in the War of 1812.

The NPS wasn’t created until 1916, so the Department of War managed the park. By the 1890s, the base had become militarily unnecessary, and the agency balked at the high cost of maintaining the park. Some in the department wanted to sell the land, but locals revolted, so in 1894 they handed it off to the state of Michigan, which created Mackinac Island State Park.

Visiting today: Mackinac Island has become a beloved summer tourist destination. Visitors flock to the island to enjoy its car-free atmosphere and lake views. The state owns around 80% of the island, preserving its historic character.

The 6.3-mile Tranquil Bluff Trail is one of the most scenic on the island, with clear vistas and excellent bird watching opportunities. The Island is known as the “fudge capital of the world,” and streets abound with vintage-style candy shops that can help fuel your hikes. To understand the island’s plant life, stroll the Botanical Trail, with signage that explains the unique flora here.

Platt National Park

The history of this part of Oklahoma dates back to 1902, when the government purchased 33 mineral springs from the Choctaw and Chickasaw Nations to create the Sulphur Springs Reservation. In 1906, Congress designated it a national park, and spent decades building infrastructure and visitor services. The area was popular because the springs here were one of the few ways to find relief during hot, sweltering summers.

By the 1960s, the conservation movement supported leaving wild spaces natural, as opposed to the heavy development that had occurred in Platt. Since the area was small and not home to impressive natural features like mountains or rivers Congress combined it with the nearby Arbuckle Recreation Area in 1976 to create the Chickasaw National Recreation Area.

Visiting today: Chickasaw National Recreation Area is still a popular destination, with over 1.8 million visitors in 2025. It can be crowded in the summer, so try visiting in the shoulder seasons (March to May, and September to October) instead. Entry is free.

Deciduous forests meet prairies to create a unique landscape. Hiking trails abound; the 1-mile Antelope Springs trail is perfect for a casual evening stroll by the water. Check out the 2-mile West Bison Pasture Loop for a chance to see the trail’s namesake animal.

There are six campgrounds throughout the park, which cost $20 to $30/night and can be reserved online. Fishing in the area’s creeks is a popular pastime; be sure to follow all Oklahoma state fishing regulations.

Hill National Park

Conservation icon Theodore Roosevelt created this national park in North Dakota in 1904. The area served as an important big-game preserve to save species like bison. Its importance in conservation eventually led President Herbert Hoover to make it a wildlife refuge and transfer it to the agency that would become the U.S. Fish & Wildlife Service (USFWS). In 2019, it was renamed the White Horse Hill National Game Preserve.

Visiting today: The refuge is open daily to visitors. No overnight stays are allowed. Slowly drive along a designated 4.5-mile car route for the chance to view bison, elk, and deer. There’s also a large prairie dog town near the visitors’ center that is a short walk away. The area is home to over 210 species of birds, so be sure to bring along your binoculars.

General Grant National Park

In many cases, former NPS sites get transferred to other agencies, but in this case, one park got subsumed into another. In 1890, right after the creation of Sequoia National Park, President Benjamin Harrison created nearby General Grant National Park to protect its giant Sequoias from logging. In 1940, the government created the much larger Kings Canyon National Park, which surrounded General Grant. It didn’t make sense to have one park encircled by another, so Kings Canyon took over the smaller park.

Visiting today: The area, now known as General Grant’s Grove, is open to visitors to Kings Canyon. Park entry requires a $35 fee or an America the Beautiful Pass ($80).

The main highlight is the gigantic tree named after the Civil War hero. General Grant’s tree is the world’s second-largest tree by trunk volume. The larger area is home to many other impressive giant Sequoias, as well as meadows and a small waterfall. Combine the North Grove Loop and Dead Giant Trail for a 2.5-mile journey through the area that also provides views of Sequoia Lake.

Former National Monuments

Papago Saguaro National Monument

Before Saguaro National Park existed, there was Papago Saguaro National Monument. Under the 1906 Antiquities Act, a president can declare a national monument with a stroke of a pen, and President Woodrow Wilson did just that in 1914. The land, which the local Maricopa and Pima Tribes once held, was home to distinct red sandstone rock formations and fields of the namesake cacti. The monument, however, quickly fell into disarray, with inadequate funding for upkeep. By 1930, the government had had enough, and passed the land over to the state.

Visiting today: Papago Park has become a popular outdoor recreation destination for the residents of Phoenix and Tempe. It’s a great spot for a post-work hike; popular trails include the 2.3-mile Double Butte Loop and 2.7-mile Elliot Ramada Loop. Other must-visit attractions inside the park include the Phoenix Zoo and the Desert Botanical Garden.

Lewis & Clark Caverns National Monument

Local deer hunters first noticed this series of caves in Jefferson County, Montana, in 1898. Then, in 1911, President Howard Taft declared the impressive caverns a national monument. During the Great Depression, the Civilian Conservation Corps built much of the infrastructure and tunnels that allowed visitors to tour the caves. In 1937, Congress directed the Secretary of the Interior to hand over the land to the state, because the land did not possess enough “national significance.” It then became Montana’s first state park.

Visiting today: Located less than an hour from Bozeman, Lewis & Clark Caverns State Park is a great add-on stop for folks visiting Yellowstone National Park. Be sure to book a guided tour in advance, since it’s the only way to tour the massive limestone caves. Above ground, other popular activities include hiking and biking trails, fishing and canoeing on the Jefferson River, and birdwatching. There’s also campsites and cabins available for reservation online. The best time to visit is May through September.

Father Millet Cross National Monument

The nation’s smallest national monument was also one of its most short-lived. In the late 1600s, a group of French colonists endured a harrowing winter at a fort near Niagara Falls, where many died of starvation. In 1688, Father Pierre Millet, a missionary, had a cross erected there to remember the dead. In 1925, President Coolidge ordered a new cross built on the same site, and declared the 18-square-foot portion of land a national monument.

In 1949, Congress declared the tiny plot “extraneous,” and handed it over to New York as part of Fort Niagara State Park and Old Fort Niagara State Historic Site.

Visiting today: The Father Millet Cross is just one of many pieces of history visitors to the state park can learn about. The French first established Fort Niagara in 1679, and it served as an important military outpost during the French & Indian War and the War of 1812. It served as an active military outpost until 1963, and visitors can tour the grounds and learn from historical interpreters and reenactors. Admission to the historic site costs $17 per adult, while admission to the larger state park costs $8 per vehicle.

Wheeler National Monument

Unique geology is sometimes the reason behind an area’s elevation to national monument status, and Wheeler National Monument is a prime example. Located in the San Juan Mountains in southern Colorado, eruptions from super volcanoes millions of years ago produced to unusual rock and tufa formations, spires, and needles. President Roosevelt signed its special status into law in 1908. The U.S. Forest Service (USFS) managed it until 1933, when the NPS took over. In 1950, Congress demoted it, and passed it back to the USFS. The area was extremely remote and difficult to access, and thus attracted very few visitors.

Visiting today: Wheeler Geological Area is now part of the La Garita Wilderness in the Rio Grande National Forest. Getting there involves a 7-mile hike or a rough 14-mile off-road drive. The USFS advises visitors to devote a full, long day to the trip.

For those considering driving, be prepared. “The road is extremely rough with deep ruts, narrow turns, and some steep hills. High-clearance 4×4 vehicles or ATVs are recommended. This road is not recommended for travel during monsoonal storms,” USFS warns.

Camping is not allowed among the formations, but you can disperse camp nearby for free at Hansen Mill site and at the end of Wheeler Road #600. If you’re looking for a unique, remote weekend backpacking trip, Wheeler deserves to be on your list.

Holy Cross National Monument

Mount of the Holy Cross, a 14er located in the Sawatch Range of the Rocky Mountains in Colorado, got its name for the distinctive cross-shaped snowfield on its face. President Hoover made it a national monument in 1929 in order to protect it. Unsurprisingly, given its remote location and high elevation, it didn’t bring in many visitors. In 1950, Congress handed it over to the Forest Service, and in 1980, it became a designated wilderness area.

Visiting today: The climb up this 14,007-foot peak starts at the Half Moon Pass trailhead (10,320 feet). The 11.25-mile round-trip journey is strenuous, with over 8,000 feet of elevation gain. Weather can quickly turn for the worse, so be prepared with adequate layers and protection. The road to the trailhead is closed annually from May 1 to June 21, and its reopening date depends on when it’s cleared of snow.

Old Kasaan National Monument

National monuments often serve to protect important cultural or historic sites, and that was President Wilson’s intention when he created Old Kasaan National Monument in Alaska in 1916. The area on Prince of Wales Island was formerly a major settlement for the Haida, an Native American tribe in Alaska and Canada whose history there spanned thousands of years. Despite its monument status, Old Kasaan fell into neglect, with no resources spent on restoration.

In 1955, Congress transferred it to the USFS, which manages it as part of Tongass National Forest.

Visiting today: The Organized Village of Kasaan, run by the tribal government, offers guided tours of the Totem Pole Park and Náay I’waans Whale House. The village also owns and operates two cabins for rent located near the forest, with views of the ocean.

Not Quite National Parks: The Public Lands America Almost Crowned

October 30, 2026
|
13 MIN READ

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The year was 1998. Britney Spears topped the charts, Bill Clinton was impeached, and a couple of little websites called Google and Ars Technica came online. It was also the year Nintendo released The Legend of Zelda: Ocarina of Time on the Nintendo 64. Google, which has come to dominate the Internet (Ars is still working on that), is celebrating the imminent release of the Ocarina of Time remaster by temporarily transporting us back to that storied year with a search Easter egg.

To access the Google time machine, you just have to search for either "Zelda" or "Ocarina of Time." Google will load the standard 2026 results page, but you’ll see an animated Master Sword in the lower right corner. It appears on both mobile and desktop—just click the sword and enjoy the show.

After a little time-skipping animation and a Link shout (you know the one), you’ll swear it’s 1998 again. The search page is transformed into a simple list of blue links with retro Google branding. This is particularly apt given the time-shifting mechanics in Ocarina of Time.

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Ars Technica – All content

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Since the 1970s, self-taught engineer Hugh Piggott has been building wind turbines from scratch in his native Scotland. In the 2000s, he started holding workshops showing participants how to build their own. Jonathan Schreiber, an Austrian renewable-energy practitioner, took Piggott’s course in 2012.

Piggott no longer teaches, but now Schreiber has picked up the mantle. He formed PureSelfMade, an independent-energy education project, and since 2013 has hosted workshops showing participants how to build Piggott’s design.

At these workshops, you do not assemble a wind turbine kit. Instead you literally build the entire thing, including the generator, from scratch. You learn to carve the blades, wind copper wire to make the coils, use an angle grinder to cut the steel, et cetera:

PureSelfMade’s upcoming wind turbine workshops are held in Germany and Austria, and conducted in German. They’re also working on setting up English-language workshops in the UK. 

Core77

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Short answer: $10,000 put into SPY on January 3, 2000 grew to $83,195 by October 30, 2026 with every dividend reinvested. Taking the dividends as cash left you with $59,123: $51,962 in shares plus $7,161 in collected dividends. That makes reinvesting worth $24,072, or 41% more money, on a fund that yields only about 1% a year.

This dividend reinvestment calculator doesn’t assume a smooth 7% return. It replays every real SPY, VYM, SCHD and QQQ dividend from Yahoo Finance, day by day, with and without reinvestment. The Python script is below, so you can run it on your own tickers.

How the calculator works (assumptions labeled)

The script, drip.py, does what a broker’s DRIP does, and labels its simplifications:

• Data: daily closes plus per-share dividends from Yahoo Finance via yfinance, through October 30, 2026.
• Assumption 1: each dividend is reinvested at the close on its ex-date. Real brokers buy on the payment date, a few days later. That’s a small difference over 27 years.
• Assumption 2: in the “no DRIP” case the cash just sits there at 0%. This is the honest version of “I’ll spend it or leave it in the account”.
• Assumption 3: no taxes or fees unless I say so. Taxes get their own section below.

As a sanity check, Yahoo’s dividend-adjusted price series turns the same $10,000 into $83,131. My day-by-day replay lands at $83,195, a 0.1% difference. So the replay matches total return.

The results: DRIP vs cash, five real histories

The SPY row is the headline. Without reinvestment you still owned your original 68.8 shares. With it, quarterly dividends quietly bought another 41 shares, so you finish with 110.1 shares. Each of those shares is now worth about $756 and pays its own dividends.

Go back further and the gap widens. From SPY’s first trading day in January 1993, $10,000 grew to $314,411 with DRIP versus $197,803 without. That’s $116,608 (59%) more, and the only difference is what you did with a 1–2% yield.

Two dials decide how much reinvesting adds:

1. Yield. QQQ paid only 0.45% over the last 12 months, so 27 years of reinvesting added just 14%. VYM, at 2.25%, added 43% in only 20 years.
2. Time. SCHD yields the most (2.95%), but its history only starts in October 2011. Fifteen years of reinvesting added 28% ($15,050). It simply hasn’t had SPY’s 33 years yet.

Napkin rule: the DRIP edge grows roughly with yield × years. A 1% yield over 27 years (SPY) and a 2.25% yield over 20 years (VYM) both landed near +41–43%.

The surprise: reinvesting barely mattered for 12 years

Here’s the part no compound-interest calculator shows you. Reinvesting doesn’t pull ahead in a straight line. In the SPY run that started in 2000, the DRIP account was behind the cash account at the end of 2000, 2001, 2002 and 2008.

The reason is simple. Every dividend reinvested in 2000–2002 bought shares that then fell during the dot-com crash. The cash version held those dollars at full value. At the end of 2008 the DRIP account was 3.2% behind. Its lead was still only 3.2% at the end of 2011.

After that, compounding took over. The lead hit 10% in 2013, 26% in 2020, and 40.7% by October 30, 2026. If you check your DRIP after a few years and see nothing, that’s normal. The payoff comes in the second decade.

It also explains why the 60/40 portfolio’s max drawdown and the time to recover from a crash both matter here. A DRIP buys more shares when prices are low, but it only pays off if you keep holding until the recovery.

The income you end up with

People rarely reinvest for the final balance alone. They want a larger dividend check later. So the calculator multiplies the last 12 months of per-share payouts by the shares you hold now:

From one $10,000 start, SPY now pays $835 a year to the DRIP investor versus $521 to the cash investor, 60% more income from the same original money. SCHD pays $2,038 versus $1,267. With reinvestment, each new share pays dividends too, so the income grows on its own.

With monthly contributions the gap reaches six figures

Most people don’t invest once and walk away. So I ran the calculator again with $10,000 up front plus $500 on the first trading day of every month into SPY from January 2000. That’s $170,500 contributed in total (the same approach as the monthly plan in my lump sum vs DCA on the S&P 500 backtest).

With reinvestment the account crosses $1,049,823. Without it, it reaches $846,717. The difference is $203,106, more than the total amount contributed. Monthly money shrinks the gap in percentage terms (24% vs 41%), because fresh cash hasn’t had time to compound. In dollars, though, the gap grows a lot.

Taxes: reinvesting doesn’t dodge them

In a taxable US account, the IRS treats reinvested dividends exactly like cash dividends. Publication 550 says that if you use your dividends to buy more stock, “you must still report the dividends as income”. So DRIP isn’t a tax strategy. It’s a compounding strategy.

Qualified dividends are taxed at long-term capital gain rates (IRS Topic 404). For 2025 those rates are 0%, 15% or 20% depending on taxable income, and “no higher than 15% for most individuals” (IRS Topic 409).

Assumption: a flat 15% on every SPY dividend, with tax paid out of the dividend before reinvesting. The DRIP result falls from $83,195 to $77,535, a $5,660 drag over 27 years. In a tax-advantaged account like an IRA or 401(k), there’s no yearly drag and the untaxed numbers above apply.

The Python dividend reinvestment calculator

Install yfinance and pandas, then save the prices once (so reruns don’t hit Yahoo every time) and run the script:

import yfinance as yf, pandas as pd
d = {t: yf.Ticker(t).history(start="1993-01-01", end="2026-11-01", auto_adjust=False, actions=True)
     for t in ["SPY", "SCHD", "VYM", "QQQ"]}
pd.to_pickle(d, "src/prices.pkl")

auto_adjust=False keeps the raw (split-adjusted) close and the separate Dividends column. If you replayed dividends on top of the dividend-adjusted close, you’d count them twice.

"""Dividend reinvestment calculator: replay real ETF history (Yahoo Finance closes + dividends),
with vs without reinvesting dividends, optional monthly contributions and dividend tax."""
import pandas as pd

def load(path="src/prices.pkl"):
    d = pd.read_pickle(path)
    return {t: h[["Close", "Dividends"]].tz_localize(None).loc[:"2026-10-30"] for t, h in d.items()}

def drip(h, start, initial=10_000, monthly=0, reinvest=True, tax=0.0):
    """Daily replay. Dividends paid on the ex-date at that day's close (a common approximation)."""
    h = h.loc[start:]
    shares, cash, paid, invested, month, rows = 0.0, 0.0, 0.0, 0.0, None, []
    for day, (px, div) in h.iterrows():
        if month is None:
            shares, invested = initial / px, initial
        elif day.month != month and monthly:
            shares += monthly / px; invested += monthly
        month = day.month
        if div > 0:
            net = shares * div * (1 - tax); paid += net
            if reinvest: shares += net / px
            else: cash += net
        rows.append((day, shares * px + cash, shares * px, cash, invested, paid))
    return pd.DataFrame(rows, columns=["date", "total", "stock", "cash", "invested", "divs"]).set_index("date")

def cagr(s):
    yrs = (s.index[-1] - s.index[0]).days / 365.25
    return (s.iloc[-1] / s.iloc[0]) ** (1 / yrs) - 1

if __name__ == "__main__":
    P = load()
    for t, start in (("SPY", "2000-01-03"), ("SPY", "1993-01-29"), ("SCHD", "2011-10-20"), ("VYM", "2006-11-16"), ("QQQ", "2000-01-03")):
        a, b = drip(P[t], start), drip(P[t], start, reinvest=False)
        yrs = (a.index[-1] - a.index[0]).days / 365.25
        print(f"{t} {a.index[0]:%b %Y}-{a.index[-1]:%b %Y} ({yrs:.1f} yrs) $10k: DRIP ${a.total.iloc[-1]:,.0f} "
              f"(CAGR {cagr(a.total):.2%}) | no DRIP ${b.total.iloc[-1]:,.0f} = ${b.stock.iloc[-1]:,.0f} shares + ${b.cash.iloc[-1]:,.0f} cash "
              f"(CAGR {cagr(b.total):.2%}) | DRIP edge ${a.total.iloc[-1]-b.total.iloc[-1]:,.0f} ({a.total.iloc[-1]/b.total.iloc[-1]-1:.0%}) | "
              f"shares {a.stock.iloc[-1]/P[t].Close.iloc[-1]:.1f} vs {b.stock.iloc[-1]/P[t].Close.iloc[-1]:.1f}")
    a = drip(P["SPY"], "2000-01-03", monthly=500); b = drip(P["SPY"], "2000-01-03", monthly=500, reinvest=False)
    print(f"SPY 2000 $10k + $500/mo (invested ${a.invested.iloc[-1]:,.0f}): DRIP ${a.total.iloc[-1]:,.0f} | no DRIP ${b.total.iloc[-1]:,.0f}")
    t15 = drip(P["SPY"], "2000-01-03", tax=0.15)
    print(f"SPY 2000 $10k DRIP with 15% dividend tax: ${t15.total.iloc[-1]:,.0f}")
    # annual dividend income at the end (last 12 months of per-share payouts x shares held)
    for t, start in (("SPY", "2000-01-03"), ("VYM", "2006-11-16"), ("SCHD", "2011-10-20"), ("QQQ", "2000-01-03")):
        ttm = P[t].Dividends.loc["2025-10-31":].sum(); px = P[t].Close.iloc[-1]
        a, b = drip(P[t], start), drip(P[t], start, reinvest=False)
        print(f"{t} trailing-12m payout ${ttm:.3f}/share = {ttm/px:.2%} yield; yearly income DRIP ${ttm*a.stock.iloc[-1]/px:,.0f} vs no DRIP ${ttm*b.stock.iloc[-1]/px:,.0f}")

Output on October 30, 2026 data:

SPY Jan 2000-Oct 2026 (26.8 yrs) $10k: DRIP $83,195 (CAGR 8.22%) | no DRIP $59,123 = $51,962 shares + $7,161 cash (CAGR 6.85%) | DRIP edge $24,072 (41%) | shares 110.1 vs 68.8
SPY Jan 1993-Oct 2026 (33.7 yrs) $10k: DRIP $314,411 (CAGR 10.76%) | no DRIP $197,803 = $171,999 shares + $25,804 cash (CAGR 9.25%) | DRIP edge $116,608 (59%) | shares 416.0 vs 227.6
SCHD Oct 2011-Oct 2026 (15.0 yrs) $10k: DRIP $69,132 (CAGR 13.73%) | no DRIP $54,081 = $42,970 shares + $11,111 cash (CAGR 11.89%) | DRIP edge $15,050 (28%) | shares 1932.1 vs 1201.0
VYM Nov 2006-Oct 2026 (20.0 yrs) $10k: DRIP $59,485 (CAGR 9.35%) | no DRIP $41,668 = $32,449 shares + $9,218 cash (CAGR 7.41%) | DRIP edge $17,818 (43%) | shares 363.3 vs 198.2
QQQ Jan 2000-Oct 2026 (26.8 yrs) $10k: DRIP $86,575 (CAGR 8.38%) | no DRIP $75,786 = $72,875 shares + $2,911 cash (CAGR 7.84%) | DRIP edge $10,789 (14%) | shares 125.4 vs 105.5
SPY 2000 $10k + $500/mo (invested $170,500): DRIP $1,049,823 | no DRIP $846,717
SPY 2000 $10k DRIP with 15% dividend tax: $77,535
SPY trailing-12m payout $7.583/share = 1.00% yield; yearly income DRIP $835 vs no DRIP $521
VYM trailing-12m payout $3.676/share = 2.25% yield; yearly income DRIP $1,335 vs no DRIP $728
SCHD trailing-12m payout $1.055/share = 2.95% yield; yearly income DRIP $2,038 vs no DRIP $1,267
QQQ trailing-12m payout $3.091/share = 0.45% yield; yearly income DRIP $388 vs no DRIP $326

To use it for your own holdings, change the ticker, start, initial, monthly and tax. If you also want to see whether your picks beat the index after dividends, my guide to comparing your portfolio to the S&P 500 uses the same total-return logic.

What to do with this

1. Turn DRIP on in tax-advantaged accounts unless you need the income now. Across these five histories, that one setting was worth 14–59% more money.
2. In taxable accounts, reinvest anyway if you were going to reinvest by hand. The tax is the same either way. DRIP just stops cash from sitting idle.
3. Judge it after 10+ years, not 3. The SPY run was behind or within about 3% of the cash version for its first 12 years.
4. Don’t chase yield for the DRIP edge alone. QQQ’s tiny 0.45% yield still finished with a higher DRIP balance than SPY ($86,575 vs $83,195), because price growth did the work.

Limitations: ex-date reinvestment, 0% on idle cash, no fees, and one 15% tax assumption. SCHD and VYM have shorter histories. This is history, not a forecast, and not investment advice.

From calculator to finance ebook

If you’ve worked through numbers like these, you already have the outline of a short, useful ebook: “DRIP vs cash, with real data”. You could use it as a lead magnet for a finance newsletter or sell it on KDP. ImagineYourBook is an AI book-writing tool that turns a prompt or your notes into a complete draft with a designed cover and Word and EPUB downloads. It also has a Manuscript Rewrite feature for updating an existing book, for example when next year’s dividend data comes in.

Takeaway: reinvesting SPY’s roughly 1% yield turned $10,000 into $83,195 instead of $59,123. The edge was invisible for 12 years and worth 41% after 27. Run the script on your own tickers, keep DRIP on, and give it a decade.

If you want to package what you learned into a book, ImagineYourBook‘s trial is $0 today with a card required. It includes one 10,000-word Small Book, the first $29.99 charge comes after 7 days, and you can cancel any time before day 8. Treat the result as a draft: check every number before you publish.

Sources (checked November 2, 2026): Yahoo Finance: SPY · VYM · SCHD · QQQ · yfinance on GitHub · IRS Publication 550 · IRS Topic 404 · IRS Topic 409 · ImagineYourBook pricing and trial terms. Script: drip.py (shown above). Not investment advice.

Be on the Right Side of Change

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How often to rebalance a portfolio? Once a year is enough for most people. I backtested a 60/40 portfolio on real Vanguard fund data from December 1986 to September 2026. Rebalancing every December took 39 trades. Monthly rebalancing took 477. Both landed on the same Sharpe ratio (0.64 vs 0.63), and the yearly version actually had the smaller worst drawdown (−30.8% vs −32.5%).

Never rebalancing is the one choice that really changes the result. Leave a “60/40” alone for 40 years and it ends up 93.9% stocks. You get a higher return, but you’re also holding a much riskier portfolio than the one you picked.

Below you’ll find the numbers, the charts, and the full Python script, so you can rerun it on your own funds.

The setup: two real funds, five rules

I wanted to test funds you could actually have owned, not idealized indexes:

• Stocks: VFINX (Vanguard 500 Index Fund, tracks the S&P 500)
• Bonds: VBMFX (Vanguard Total Bond Market Index Fund)

Both series are Yahoo Finance adjusted closes, so dividends and interest are reinvested and fund fees are already included. Each test starts with $10,000 at 60% stocks and 40% bonds on December 31, 1986, and runs on month-end data for 477 months through September 30, 2026. The 3-month T-bill rate from FRED is the cash rate for the Sharpe ratio.

The only thing that changes between tests is the rebalancing rule:

1. Never (buy and hold)
2. Monthly, back to 60/40 at every month-end
3. Quarterly, every March, June, September and December
4. Once a year, every December
5. 5-point band: check every month, but only trade when stocks drift more than 5 percentage points from 60% (below 55% or above 65%)

Here is the script. It needs pip install yfinance pandas. If you’re new to time-series resampling, Finxter’s pandas DataFrame resample() guide explains the resample("ME").last() line that turns daily prices into month-end prices.

"""How often to rebalance a 60/40 portfolio? Backtest on real fund data, Dec 1986 to Sep 2026.
Stocks: VFINX (Vanguard 500 Index, S&P 500). Bonds: VBMFX (Vanguard Total Bond Market Index).
Yahoo Finance adjusted closes (dividends reinvested, fund fees included), month-ends.
Cash rate for Sharpe: FRED TB3MS (3-month T-bill).  pip install yfinance pandas"""
import pandas as pd

START, END, W = "1986-12-31", "2026-09-30", 0.60

def load():
    try:  # cached copies used for the article
        adj = pd.read_pickle("src/raw_yf.pkl")["Adj Close"][["VFINX", "VBMFX"]]
        tb = pd.read_csv("src/tb3ms.csv", parse_dates=[0], index_col=0).iloc[:, 0]
    except FileNotFoundError:
        import yfinance as yf
        adj = yf.download(["VFINX", "VBMFX"], start="1986-12-01", auto_adjust=False, progress=False)["Adj Close"]
        tb = pd.read_csv("https://fred.stlouisfed.org/graph/fredgraph.csv?id=TB3MS", parse_dates=[0], index_col=0).iloc[:, 0]
    px = adj.resample("ME").last().loc[START:END]
    tb.index = tb.index + pd.offsets.MonthEnd(0)
    rf = (tb / 100 / 12).reindex(px.index).ffill()
    return px.pct_change().dropna(), rf.iloc[1:]

def backtest(r, rule, w=W, band=0.05, start_value=10_000):
    """rule: 'never' | 'monthly' | 'quarterly' | 'annual' | 'band'. Rebalance at month-end after returns."""
    s, b = start_value * w, start_value * (1 - w)
    vals, wts, n_reb, turnover = [], [], 0, 0.0
    for d, (rs, rb) in r.iterrows():
        s *= 1 + rs; b *= 1 + rb; v = s + b; eq = s / v
        due = {"never": False, "monthly": True, "quarterly": d.month in (3, 6, 9, 12),
               "annual": d.month == 12, "band": abs(eq - w) > band}[rule]
        if due and abs(eq - w) > 1e-12:
            turnover += abs(eq - w) * v; n_reb += 1
            s, b = v * w, v * (1 - w); eq = w
        vals.append(v); wts.append(eq)
    return pd.DataFrame({"value": vals, "equity_w": wts}, index=r.index), n_reb, turnover

def stats(df, rf, n_reb, turnover):
    v = df.value; m = v.pct_change().fillna(v.iloc[0] / 10_000 - 1); yrs = len(v) / 12
    ex = m - rf.values
    return dict(end=v.iloc[-1], cagr=(v.iloc[-1] / 10_000) ** (1 / yrs) - 1, vol=m.std() * 12 ** 0.5,
                max_dd=(v / v.cummax() - 1).min(), dd_at=(v / v.cummax() - 1).idxmin(),
                sharpe=ex.mean() / m.std() * 12 ** 0.5, rebalances=n_reb,
                turnover_yr=turnover / v.mean() / yrs, max_eq=df.equity_w.max(), end_eq=df.equity_w.iloc[-1])

RULES = {"never": "Never (buy & hold)", "monthly": "Monthly", "quarterly": "Quarterly",
         "annual": "Once a year (Dec)", "band": "When off by 5 points"}

def run():
    r, rf = load()
    res = {k: backtest(r, k) for k in RULES}
    return r, rf, res, {k: stats(df, rf, n, t) for k, (df, n, t) in res.items()}

def rolling(r, rf, years=10):
    """CAGR and max drawdown of each rule in every rolling `years` window (monthly starts)."""
    n, rows = years * 12, []
    for i in range(0, len(r) - n + 1):
        sub = r.iloc[i:i + n]; row = {"start": sub.index[0]}
        for k in ("never", "monthly", "annual", "band"):
            v = backtest(sub, k)[0].value
            row[k] = (v.iloc[-1] / 10_000) ** (1 / years) - 1; row[k + "_dd"] = (v / v.cummax() - 1).min()
        rows.append(row)
    return pd.DataFrame(rows).set_index("start")

def napkin_cost(turnover_yr, cost=0.002, value=100_000):
    """ASSUMPTION: each dollar traded costs `cost` (spread, fees or tax drag). Annual drag in $ on `value`."""
    return turnover_yr * cost * value

if __name__ == "__main__":
    r, rf, res, S = run()
    print(f"60/40 VFINX/VBMFX, $10,000 on {START}, month-end data to {END} ({len(r)} months)")
    print(f"{'Rule':<22}{'End value':>11}{'CAGR':>7}{'Vol':>7}{'MaxDD':>8}{'Sharpe':>7}{'Trades':>7}{'Turn/yr':>8}{'Max stock%':>11}")
    for k, s in S.items():
        print(f"{RULES[k]:<22}{s['end']:>11,.0f}{s['cagr']:>7.2%}{s['vol']:>7.2%}{s['max_dd']:>8.1%}{s['sharpe']:>7.2f}"
              f"{s['rebalances']:>7}{s['turnover_yr']:>8.1%}{s['max_eq']:>11.1%}")
    w, a = res["never"][0].equity_w, res["annual"][0].equity_w
    print(f"\nBuy & hold ends at {S['never']['end_eq']:.1%} stocks; it entered the Oct 2007 peak at {w.loc['2007-10-31']:.1%} stocks.")
    print(f"Buy & hold first passed 70% stocks in {w[w > .7].index[0]:%b %Y} and 80% in {w[w > .8].index[0]:%b %Y}.")
    print(f"Annual rebalancing still drifted between {a.min():.1%} ({a.idxmin():%b %Y}) and {a.max():.1%} stocks between Decembers.")
    print(f"All five rules hit their worst drawdown in {S['never']['dd_at']:%b %Y}.")
    R = rolling(r, rf)
    print(f"\n{len(R)} rolling 10-year windows (starts {R.index[0]:%b %Y} to {R.index[-1]:%b %Y}):")
    for k in ("monthly", "band"):
        g = R[k] - R["annual"]
        print(f"  {RULES[k]} minus annual CAGR: median {g.median()*100:+.2f} pts, range {g.min()*100:+.2f} to {g.max()*100:+.2f}")
    g = R["never"] - R["annual"]; dd = R["never_dd"] - R["annual_dd"]
    print(f"  Never minus annual CAGR: median {g.median()*100:+.2f} pts; never had the deeper drawdown in {(dd < 0).mean():.0%} of windows")
    print(f"\nNapkin (assumption: 0.2% cost per dollar traded, $100,000 portfolio):")
    for k in ("monthly", "quarterly", "annual", "band"):
        print(f"  {RULES[k]:<22} {napkin_cost(S[k]['turnover_yr']):>6,.0f} $/yr")

The result: how often matters less than whether

Output of python3 rebalance.py:

60/40 VFINX/VBMFX, $10,000 on 1986-12-31, month-end data to 2026-09-30 (477 months)
Rule                    End value   CAGR    Vol   MaxDD Sharpe Trades Turn/yr Max stock%
Never (buy & hold)        442,046 10.00% 11.75%  -38.7%   0.61      0    0.0%      93.9%
Monthly                   304,242  8.97%  9.54%  -32.5%   0.63    477    9.2%      60.0%
Quarterly                 313,794  9.06%  9.50%  -32.0%   0.64    159    6.1%      63.6%
Once a year (Dec)         312,520  9.05%  9.53%  -30.8%   0.64     39    3.3%      67.9%
When off by 5 points      325,566  9.16%  9.62%  -31.9%   0.65     26    3.6%      65.0%

Buy & hold ends at 93.9% stocks; it entered the Oct 2007 peak at 78.6% stocks.
Buy & hold first passed 70% stocks in Nov 1995 and 80% in Dec 1998.
Annual rebalancing still drifted between 47.9% (Nov 2008) and 67.9% stocks between Decembers.
All five rules hit their worst drawdown in Feb 2009.

358 rolling 10-year windows (starts Jan 1987 to Oct 2016):
  Monthly minus annual CAGR: median -0.11 pts, range -0.38 to +0.12
  When off by 5 points minus annual CAGR: median +0.03 pts, range -0.35 to +0.31
  Never minus annual CAGR: median -0.09 pts; never had the deeper drawdown in 73% of windows

Napkin (assumption: 0.2% cost per dollar traded, $100,000 portfolio):
  Monthly                    18 $/yr
  Quarterly                  12 $/yr
  Once a year (Dec)           7 $/yr
  When off by 5 points        7 $/yr

The three calendar rules are almost identical: 8.97% to 9.06% a year, about 9.5% volatility, and Sharpe ratios of 0.63 to 0.64. Rebalancing 12 times more often bought you nothing. Monthly came out a hair behind because it held exactly 60% stocks all the time. The yearly portfolio averaged 60.6% stocks because it drifted a little between Decembers, and in a market where stocks win, that small extra share earns a bit more.

The 5-point band came out slightly ahead, with the highest end value ($325,566), the best Sharpe (0.65) and the fewest trades (26). That edge is small and depends on the period. The steadier way to say it: a band rule gets you yearly-level risk control with even fewer trades.

This matches Vanguard’s much longer study (1926 to 2009). Its monthly-rebalanced 60/40 needed 1,008 rebalancing events and its annual one 83, with average returns of 8.5% and 8.6%. Vanguard’s conclusion was that annual or semiannual monitoring, with rebalancing at 5% thresholds, is a reasonable balance for most broadly diversified stock and bond portfolios.

The real choice: rebalance or let it drift

The never-rebalanced portfolio ended with $442,046, about $116,500 more than the best rebalanced one. So why rebalance at all?

Because by the end it wasn’t a 60/40 portfolio anymore. Stocks grow faster than bonds, so with no rebalancing the stock share keeps climbing:

It passed 70% stocks in November 1995 and 80% in December 1998. It went into the October 2007 market peak at 78.6% stocks, so the 2008 crash hit far harder than a 60/40 should. The worst drawdown was −38.7%, against −30.8% for the yearly rebalancer. Volatility was 11.75% vs 9.53%. On a risk-adjusted basis buy and hold came last (Sharpe 0.61).

Vanguard found the same pattern over 1926 to 2009. Its never-rebalanced 60/40 drifted to a maximum of about 99% stocks and earned a higher average return (9.1% vs 8.5% for monthly), with more risk (14.4% vs 12.1% standard deviation). The extra return is mostly payment for holding more stock than you signed up for.

Rolling 10-year windows: no lucky period

One 40-year run could just be luck, so the script also tests every rolling 10-year window: 358 of them, starting each month from January 1987 to October 2016.

• Monthly minus yearly: median −0.11 percentage points a year, never more than +0.12 better. Monthly almost never wins, and when it does, it wins by a rounding error.
• 5-point band minus yearly: median +0.03 points, range −0.35 to +0.31. That’s a coin flip.
• Never minus yearly: median −0.09 points, with swings from about −0.6 to +1.6. Buy and hold also had the deeper drawdown in 73% of the windows.

So over a typical decade, never rebalancing didn’t even reliably earn more. It took more risk for an uncertain reward. The 40-year advantage comes from the drift compounding over a very long bull run in stocks.

What about trading costs?

At these frequencies, the cost of trades is small. Annual rebalancing traded about 3.3% of the portfolio per year, and monthly about 9.2%. The napkin math below rests on a labeled assumption that each traded dollar costs 0.2% (spread, commissions or tax drag). Then a $100,000 portfolio pays about $18 a year for monthly rebalancing and $7 for yearly.

Taxes are the cost that actually matters. In a taxable account, every rebalance sells winners and can trigger capital gains, which is why Vanguard says annual rebalancing is likely preferred when taxes or substantial costs are involved. Three practical fixes:

1. Rebalance with cash flows. Put new contributions, dividends and interest into whichever side is underweight. Vanguard specifically recommends this to cut costs.
2. Rebalance inside tax-advantaged accounts (401(k), IRA or your country’s equivalent) where selling doesn’t create a tax bill.
3. Use the 5-point band so you only trade when the drift is big enough to matter.

A simple rebalancing routine

1. Pick a date you’ll remember, for example your birthday or the first weekend of January.
2. Check your current weights. If you track holdings by hand, PortfolioTracker24 is a free browser tracker that keeps your data on your machine (no account, no broker connection), shows allocation by value, and exports Excel backups.
3. If stocks are more than 5 points off target, rebalance, starting with new cash and then tax-advantaged accounts.
4. If they’re inside the band, do nothing. That’s the rule working, not you being lazy.

To check whether your mix is really beating the index, see how to compare your portfolio to the S&P 500 (with Python). If you’re about to put a lump sum to work before your first rebalance, the backtest in lump sum vs DCA on the S&P 500 is the companion read.

Turn your backtest into something you can share

Once you’ve run this on your own funds, you have original analysis: real numbers, charts and a clear rule. That’s good material for a short guide, a newsletter lead magnet, or a small ebook. ImagineYourBook is an AI book-writing tool that turns a prompt or your notes into a complete ~10,000-word draft with its own cover, with Word and EPUB export on paid plans. Paste in your results table and rules, then edit the draft so every number matches your script. If you plan to sell it on Kindle, run the price through Finxter’s KDP royalty calculator in Python first.

Limitations

• One stock fund, one bond fund, US only. International stocks, small caps or other bond types will drift at different speeds.
• Month-end data. A band rule checked daily would trade at slightly different points. Vanguard notes that threshold-only rules need daily monitoring.
• No taxes or trading costs in the backtest itself; the cost section above is assumption-based napkin math.
• The past isn’t a forecast. Stocks returned much more than bonds over 1987 to 2026. In a period where bonds keep up, the drift (and the gap from never rebalancing) would be smaller. Not investment advice.

The takeaway

How often to rebalance a portfolio: once a year, or whenever an asset class drifts more than 5 points off target. Monthly and quarterly rebalancing only added trades in 40 years of real fund data. Never rebalancing turned a 60/40 into a 94/6 portfolio and gave it the deepest crash. Set one date a year and use new cash first. And if your backtest turns into a guide worth sharing, ImagineYourBook can turn your notes into a first draft: the $0 trial includes one 10,000-word book, requires a card, and becomes $29.99 a month unless you cancel before day 8.

Sources (checked October 27, 2026): Yahoo Finance: VFINX historical data (via yfinance) · Yahoo Finance: VBMFX historical data (via yfinance) · Yahoo Help: adjusted close · FRED: 3-Month Treasury Bill rate (TB3MS) · Vanguard: Best practices for portfolio rebalancing (PDF) · AAII Journal: Best practices for portfolio rebalancing (Vanguard summary) · yfinance on GitHub · PortfolioTracker24 (features checked on its site) · ImagineYourBook pricing and trial terms. Script: rebalance.py (shown above). Napkin math is labeled as assumptions. Not investment advice.

Be on the Right Side of Change

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