## More on Lifestyle

Michael Le

2 years ago

## Union LA x Air Jordan 2 “Future Is Now” PREVIEW

With the help of Virgil Abloh and Union LA‘s Chris Gibbs, it's now clear that Jordan Brand intended to bring the Air Jordan 2 back in 2022.

The “Future Is Now” collection includes two colorways of MJ's second signature as well as an extensive range of apparel and accessories.

“We wanted to juxtapose what some futuristic gear might look like after being worn and patina'd,”

Union stated on the collaboration's landing page.

“You often see people's future visions that are crisp and sterile. We thought it would be cool to wear it in and make it organic...”

The classic co-branding appears on short-sleeve tees, hoodies, and sweat shorts/sweat pants, all lightly distressed at the hems and seams.

Also, a filtered black-and-white photo of MJ graces the adjacent long sleeves, labels stitch into the socks, and the Jumpman logo adorns the four caps.

Liner jackets and flight pants will also be available, adding reimagined militaria to a civilian ensemble.

The Union LA x Air Jordan 2 (Grey Fog and Rattan) shares many of the same beats. Vintage suedes show age, while perforations and detailing reimagine Bruce Kilgore's design for the future.

The “UN/LA” tag across the modified eye stays, the leather patch across the tongue, and the label that wraps over the lateral side of the collar complete the look.

The footwear will also include a Crater Slide in the “Grey Fog” color scheme.

## BUYING

On 4/9 and 4/10 from 9am-3pm, Union LA will be giving away a pair of Air Jordan 2s at their La Brea storefront (110 S. LA BREA AVE. LA, CA 90036). The raffle is only open to LA County residents with a valid CA ID. You must enter by 11:59pm on 4/10 to win. Winners will be notified via email.

Architectural Digest

1 year ago

## Take a look at The One, a Los Angeles estate with a whopping 105,000 square feet of living area.

The interiors of the 105,000-square-foot property, which sits on a five-acre parcel in the wealthy Los Angeles suburb of Bel Air and is suitably titled The One, have been a well guarded secret. We got an intimate look inside this world-record-breaking property, as well as the creative and aesthetic geniuses behind it.

The estate appears to float above the city, surrounded on three sides by a moat and a 400-foot-long running track. Completed over eight years—and requiring 600 workers to build—the home was designed by architect Paul McClean and interior designer Kathryn Rotondi, who were enlisted by owner and developer Nile Niami to help it live up to its standard.

"This endeavor seemed both exhilarating and daunting," McClean says. However, the home's remarkable location and McClean's long-standing relationship with Niami persuaded him to "build something unique and extraordinary" rather than just take on the job.

And McClean has more than delivered.

The home's main entrance leads to a variety of meeting places with magnificent 360-degree views of the Pacific Ocean, downtown Los Angeles, and the San Gabriel Mountains, thanks to its 26-foot-high ceilings. There is water at the entrance area, as well as a sculpture and a bridge. "We often employ water in our design approach because it provides a sensory change that helps you acclimatize to your environment," McClean explains.

Niami wanted a neutral palette that would enable the environment and vistas to shine, so she used black, white, and gray throughout the house.

McClean has combined the home's inside with outside "to create that quintessential L.A. lifestyle but on a larger scale," he says, drawing influence from the local environment and history of Los Angeles modernism. "We separated the entertaining spaces from the living portions to make the house feel more livable. The former are on the lowest level, which serves as a plinth for the rest of the house and minimizes its apparent mass."

The home's statistics, in addition to its eye-catching style, are equally impressive. There are 42 bathrooms, 21 bedrooms, a 5,500-square-foot master suite, a 30-car garage gallery with two car-display turntables, a four-lane bowling alley, a spa level, a 30-seat movie theater, a "philanthropy wing (with a capacity of 200) for charity galas, a 10,000-square-foot sky deck, and five swimming pools.

Rotondi, the creator of KFR Design, collaborated with Niami on the interior design to create different spaces that flow into one another despite the house's grandeur. "I was especially driven to 'wow factor' components in the hospitality business," Rotondi says, citing top luxury hotel brands such as Aman, Bulgari, and Baccarat as sources of inspiration. Meanwhile, the home's color scheme, soft textures, and lighting are a nod to Niami and McClean's favorite Tom Ford boutique on Rodeo Drive.

The house boasts an extraordinary collection of art, including a butterfly work by Stephen Wilson on the lower level and a Niclas Castello bespoke panel in black and silver in the office, thanks to a cooperation between Creative Art Partners and Art Angels. There is also a sizable collection of bespoke furniture pieces from byShowroom.

A house of this size will never be erected again in Los Angeles, thanks to recently enacted city rules, so The One will truly be one of a kind. "For all of us, this project has been such a long and instructive trip," McClean says. "It was exciting to develop and approached with excitement, but I don't think any of us knew how much effort and time it would take to finish the project."

Will Lockett

1 year ago

## There Is A New EV King in Town

McMurtry Spéirling outperforms Tesla in speed and efficiency.

EVs were ridiculously slow for decades. However, the 2008 Tesla Roadster revealed that EVs might go extraordinarily fast. The Tesla Model S Plaid and Rimac Nevera are the fastest-accelerating road vehicles, despite combustion-engined road cars dominating the course. A little-known firm beat Tesla and Rimac in the 0-60 race, beat F1 vehicles on a circuit, and boasts a 350-mile driving range. The McMurtry Spéirling is completely insane.

Mat Watson of CarWow, a YouTube megastar, was recently handed a Spéirling and access to Silverstone Circuit (view video above). Mat ran a quarter-mile on Silverstone straight with former F1 driver Max Chilton. The little pocket-rocket automobile touched 100 mph in 2.7 seconds, completed the quarter mile in 7.97 seconds, and hit 0-60 in 1.4 seconds. When looking at autos quickly, 0-60 times can seem near. The Tesla Model S Plaid does 0-60 in 1.99 seconds, which is comparable to the Spéirling. Despite the meager statistics, the Spéirling is nearly 30% faster than Plaid!

My vintage VW Golf 1.4s has an 8.8-second 0-60 time, whereas a BMW Z4 3.0i is 30% faster (with a 0-60 time of 6 seconds). I tried to beat a Z4 off the lights in my Golf, but the Beamer flew away. If they challenge the Spéirling in a Model S Plaid, they'll feel as I did. Fast!

Insane quarter-mile drag time. Its road car record is 7.97 seconds. A Dodge Demon, meant to run extremely fast quarter miles, finishes so in 9.65 seconds, approximately 20% slower. The Rimac Nevera's 8.582-second quarter-mile record was miles behind drag racing. This run hampered the Spéirling. Because it was employing gearing that limited its top speed to 150 mph, it reached there in a little over 5 seconds without accelerating for most of the quarter mile! McMurtry can easily change the gearing, making the Spéirling run quicker.

McMurtry did this how? First, the Spéirling is a tiny single-seater EV with a 60 kWh battery pack, making it one of the lightest EVs ever. The 1,000-hp Spéirling has more than one horsepower per kg. The Nevera has 0.84 horsepower per kg and the Plaid 0.44.

However, you cannot simply construct a car light and power it. Instead of accelerating, it would spin. This makes the Spéirling a fan car. Its huge fans create massive downforce. These fans provide the Spéirling 2 tonnes of downforce while stationary, so you could park it on the ceiling. Its fast 0-60 time comes from its downforce, which lets it deliver all that power without wheel spin.

It also possesses complete downforce at all speeds, allowing it to tackle turns faster than even race vehicles. Spéirlings overcame VW IDRs and F1 cars to set the Goodwood Hill Climb record (__read more here__). The Spéirling is a dragstrip winner and track dominator, unlike the Plaid and Nevera.

The Spéirling is astonishing for a single-seater. Fan-generated downforce is more efficient than wings and splitters. It also means the vehicle has very minimal drag without the fan. The Spéirling can go 350 miles per charge (WLTP) or 20-30 minutes at full speed on a track despite its 60 kWh battery pack. The G-forces would hurt your neck before the battery died if you drove around a track for longer. The Spéirling can charge at over 200 kW in about 30 minutes. Thus, driving to track days, having fun, and returning is possible. Unlike other high-performance EVs.

Tesla, Rimac, or Lucid will struggle to defeat the Spéirling. They would need to build a fan automobile because adding power to their current vehicle would make it uncontrollable. The EV and automobile industries now have a new, untouchable performance king.

## You might also like

Sofien Kaabar, CFA

1 year ago

## Innovative Trading Methods: The Catapult Indicator

Python Volatility-Based Catapult Indicator

As a catapult, this technical indicator uses three systems: Volatility (the fulcrum), Momentum (the propeller), and a Directional Filter (Acting as the support). The goal is to get a signal that predicts volatility acceleration and direction based on historical patterns. We want to know when the market will move. and where. This indicator outperforms standard indicators.

Knowledge must be accessible to everyone. This is why my new publications Contrarian Trading Strategies in Python and Trend Following Strategies in Python now include free PDF copies of my first three books (Therefore, purchasing one of the new books gets you 4 books in total). GitHub-hosted advanced indications and techniques are in the two new books above.

# The Foundation: Volatility

The Catapult predicts significant changes with the 21-period Relative Volatility Index.

The Average True Range, Mean Absolute Deviation, and Standard Deviation all assess volatility. Standard Deviation will construct the Relative Volatility Index.

Standard Deviation is the most basic volatility. It underpins descriptive statistics and technical indicators like Bollinger Bands. Before calculating Standard Deviation, let's define Variance.

Variance is the squared deviations from the mean (a dispersion measure). We take the square deviations to compel the distance from the mean to be non-negative, then we take the square root to make the measure have the same units as the mean, comparing apples to apples (mean to standard deviation standard deviation). Variance formula:

As stated, standard deviation is:

```
# The function to add a number of columns inside an array
def adder(Data, times):
for i in range(1, times + 1):
new_col = np.zeros((len(Data), 1), dtype = float)
Data = np.append(Data, new_col, axis = 1)
return Data
# The function to delete a number of columns starting from an index
def deleter(Data, index, times):
for i in range(1, times + 1):
Data = np.delete(Data, index, axis = 1)
return Data
# The function to delete a number of rows from the beginning
def jump(Data, jump):
Data = Data[jump:, ]
return Data
# Example of adding 3 empty columns to an array
my_ohlc_array = adder(my_ohlc_array, 3)
# Example of deleting the 2 columns after the column indexed at 3
my_ohlc_array = deleter(my_ohlc_array, 3, 2)
# Example of deleting the first 20 rows
my_ohlc_array = jump(my_ohlc_array, 20)
# Remember, OHLC is an abbreviation of Open, High, Low, and Close and it refers to the standard historical data file
def volatility(Data, lookback, what, where):
for i in range(len(Data)):
try:
Data[i, where] = (Data[i - lookback + 1:i + 1, what].std())
except IndexError:
pass
return Data
```

The RSI is the most popular momentum indicator, and for good reason—it excels in range markets. Its 0–100 range simplifies interpretation. Fame boosts its potential.

The more traders and portfolio managers look at the RSI, the more people will react to its signals, pushing market prices. Technical Analysis is self-fulfilling, therefore this theory is obvious yet unproven.

RSI is determined simply. Start with one-period pricing discrepancies. We must remove each closing price from the previous one. We then divide the smoothed average of positive differences by the smoothed average of negative differences. The RSI algorithm converts the Relative Strength from the last calculation into a value between 0 and 100.

```
def ma(Data, lookback, close, where):
Data = adder(Data, 1)
for i in range(len(Data)):
try:
Data[i, where] = (Data[i - lookback + 1:i + 1, close].mean())
except IndexError:
pass
# Cleaning
Data = jump(Data, lookback)
return Data
def ema(Data, alpha, lookback, what, where):
alpha = alpha / (lookback + 1.0)
beta = 1 - alpha
# First value is a simple SMA
Data = ma(Data, lookback, what, where)
# Calculating first EMA
Data[lookback + 1, where] = (Data[lookback + 1, what] * alpha) + (Data[lookback, where] * beta)
# Calculating the rest of EMA
for i in range(lookback + 2, len(Data)):
try:
Data[i, where] = (Data[i, what] * alpha) + (Data[i - 1, where] * beta)
except IndexError:
pass
return Datadef rsi(Data, lookback, close, where, width = 1, genre = 'Smoothed'):
# Adding a few columns
Data = adder(Data, 7)
# Calculating Differences
for i in range(len(Data)):
Data[i, where] = Data[i, close] - Data[i - width, close]
# Calculating the Up and Down absolute values
for i in range(len(Data)):
if Data[i, where] > 0:
Data[i, where + 1] = Data[i, where]
elif Data[i, where] < 0:
Data[i, where + 2] = abs(Data[i, where])
# Calculating the Smoothed Moving Average on Up and Down
absolute values
lookback = (lookback * 2) - 1 # From exponential to smoothed
Data = ema(Data, 2, lookback, where + 1, where + 3)
Data = ema(Data, 2, lookback, where + 2, where + 4)
# Calculating the Relative Strength
Data[:, where + 5] = Data[:, where + 3] / Data[:, where + 4]
# Calculate the Relative Strength Index
Data[:, where + 6] = (100 - (100 / (1 + Data[:, where + 5])))
# Cleaning
Data = deleter(Data, where, 6)
Data = jump(Data, lookback)
return Data
```

```
def relative_volatility_index(Data, lookback, close, where):
# Calculating Volatility
Data = volatility(Data, lookback, close, where)
# Calculating the RSI on Volatility
Data = rsi(Data, lookback, where, where + 1)
# Cleaning
Data = deleter(Data, where, 1)
return Data
```

# The Arm Section: Speed

The Catapult predicts momentum direction using the 14-period Relative Strength Index.

As a reminder, the RSI ranges from 0 to 100. Two levels give contrarian signals:

A positive response is anticipated when the market is deemed to have gone too far down at the oversold level 30, which is 30.

When the market is deemed to have gone up too much, at overbought level 70, a bearish reaction is to be expected.

Comparing the RSI to 50 is another intriguing use. RSI above 50 indicates bullish momentum, while below 50 indicates negative momentum.

# The direction-finding filter in the frame

The Catapult's directional filter uses the 200-period simple moving average to keep us trending. This keeps us sane and increases our odds.

Moving averages confirm and ride trends. Its simplicity and track record of delivering value to analysis make them the most popular technical indicator. They help us locate support and resistance, stops and targets, and the trend. Its versatility makes them essential trading tools.

This is the plain mean, employed in statistics and everywhere else in life. Simply divide the number of observations by their total values. Mathematically, it's:

We defined the moving average function above. Create the Catapult indication now.

# Indicator of the Catapult

The indicator is a healthy mix of the three indicators:

The first trigger will be provided by the 21-period Relative Volatility Index, which indicates that there will now be above average volatility and, as a result, it is possible for a directional shift.

If the reading is above 50, the move is likely bullish, and if it is below 50, the move is likely bearish, according to the 14-period Relative Strength Index, which indicates the likelihood of the direction of the move.

The likelihood of the move's direction will be strengthened by the 200-period simple moving average. When the market is above the 200-period moving average, we can infer that bullish pressure is there and that the upward trend will likely continue. Similar to this, if the market falls below the 200-period moving average, we recognize that there is negative pressure and that the downside is quite likely to continue.

```
lookback_rvi = 21
lookback_rsi = 14
lookback_ma = 200
my_data = ma(my_data, lookback_ma, 3, 4)
my_data = rsi(my_data, lookback_rsi, 3, 5)
my_data = relative_volatility_index(my_data, lookback_rvi, 3, 6)
```

Two-handled overlay indicator Catapult. The first exhibits blue and green arrows for a buy signal, and the second shows blue and red for a sell signal.

The chart below shows recent EURUSD hourly values.

```
def signal(Data, rvi_col, signal):
Data = adder(Data, 10)
for i in range(len(Data)):
if Data[i, rvi_col] < 30 and \
Data[i - 1, rvi_col] > 30 and \
Data[i - 2, rvi_col] > 30 and \
Data[i - 3, rvi_col] > 30 and \
Data[i - 4, rvi_col] > 30 and \
Data[i - 5, rvi_col] > 30:
Data[i, signal] = 1
return Data
```

Signals are straightforward. The indicator can be utilized with other methods.

`my_data = signal(my_data, 6, 7)`

Lumiwealth shows how to develop all kinds of algorithms. I recommend their hands-on courses in algorithmic trading, blockchain, and machine learning.

# Summary

To conclude, my goal is to contribute to objective technical analysis, which promotes more transparent methods and strategies that must be back-tested before implementation. Technical analysis will lose its reputation as subjective and unscientific.

After you find a trading method or approach, follow these steps:

Put emotions aside and adopt an analytical perspective.

Test it in the past in conditions and simulations taken from real life.

Try improving it and performing a forward test if you notice any possibility.

Transaction charges and any slippage simulation should always be included in your tests.

Risk management and position sizing should always be included in your tests.

After checking the aforementioned, monitor the plan because market dynamics may change and render it unprofitable.

Ezra Reguerra

2 years ago

## Yuga Labs’ Otherdeeds NFT mint triggers backlash from community

Unhappy community members accuse Yuga Labs of fraud, manipulation, and favoritism over Otherdeeds NFT mint.

Following the Otherdeeds NFT mint, disgruntled community members took to Twitter to criticize Yuga Labs' handling of the event.

Otherdeeds NFTs were a huge hit with the community, selling out almost instantly. Due to high demand, the launch increased Ethereum gas fees from 2.6 ETH to 5 ETH.

But the event displeased many people. Several users speculated that the mint was “planned to fail” so the group could advertise launching its own blockchain, as the team mentioned a chain migration in one tweet.

Others like Mark Beylin tweeted that he had "sold out" on all Ape-related NFT investments after Yuga Labs "revealed their true colors." Beylin also advised others to assume Yuga Labs' owners are “bad actors.”

Some users who failed to complete transactions claim they lost ETH. However, Yuga Labs promised to refund lost gas fees.

CryptoFinally, a Twitter user, claimed Yuga Labs gave BAYC members better land than non-members. Others who wanted to participate paid for shittier land, while BAYCS got the only worthwhile land.

The Otherdeed NFT drop also increased Ethereum's burn rate. Glassnode and Data Always reported nearly 70,000 ETH burned on mint day.

Katherine Kornei

1 year ago

## The InSight lander from NASA has recorded the greatest tremor ever felt on Mars.

The magnitude 5 earthquake was responsible for the discharge of energy that was 10 times greater than the previous record holder.

Any Martians who happen to be reading this should quickly learn how to duck and cover.

NASA's Jet Propulsion Laboratory in Pasadena, California, reported that on May 4, the planet Mars was shaken by an earthquake of around magnitude 5, making it the greatest Marsquake ever detected to this point. The shaking persisted for more than six hours and unleashed more than ten times as much energy as the earthquake that had previously held the record for strongest.

The event was captured on record by the InSight lander, which is operated by the United States Space Agency and has been researching the innards of Mars ever since it touched down on the planet in 2018 (SN: 11/26/18). The epicenter of the earthquake was probably located in the vicinity of Cerberus Fossae, which is located more than 1,000 kilometers away from the lander.

The surface of Cerberus Fossae is notorious for being broken up and experiencing periodic rockfalls. According to geophysicist Philippe Lognonné, who is the lead investigator of the Seismic Experiment for Interior Structure, the seismometer that is onboard the InSight lander, it is reasonable to assume that the ground is moving in that area. "This is an old crater from a volcanic eruption."

Marsquakes, which are similar to earthquakes in that they give information about the interior structure of our planet, can be utilized to investigate what lies beneath the surface of Mars (SN: 7/22/21). And according to Lognonné, who works at the Institut de Physique du Globe in Paris, there is a great deal that can be gleaned from analyzing this massive earthquake. Because the quality of the signal is so high, we will be able to focus on the specifics.