The Merge cut Ethereum's energy use by 99.5%.

The Crypto community celebrated on September 15, 2022. This day, Ethereum Merged. The entire blockchain successfully merged with the Beacon chain, and it was so smooth you barely noticed.

Many have waited, dreaded, and longed for this day.

Some investors feared the network would break down, while others envisioned a seamless merging.

Speculators predict a successful Merge will lead investors to Ethereum. This could boost Ethereum's popularity.

What Has Changed Since The Merge

The merging transitions Ethereum mainnet from PoW to PoS.

PoW sends a mathematical riddle to computers worldwide (miners). First miner to solve puzzle updates blockchain and is rewarded.

The puzzles sent are power-intensive to solve, so mining requires a lot of electricity. It's sent to every miner competing to solve it, requiring duplicate computation.

PoS allows investors to stake their coins to validate a new transaction. Instead of validating a whole block, you validate a transaction and get the fees.

You can validate instead of mine. A validator stakes 32 Ethereum. After staking, the validator can validate future blocks.

Once a validator validates a block, it's sent to a randomly selected group of other validators. This group verifies that a validator is not malicious and doesn't validate fake blocks.

This way, only one computer needs to solve or validate the transaction, instead of all miners. The validated block must be approved by a small group of validators, causing duplicate computation.

PoS is more secure because validating fake blocks results in slashing. You lose your bet tokens. If a validator signs a bad block or double-signs conflicting blocks, their ETH is burned.

Theoretically, Ethereum has one block every 12 seconds, so a validator forging a block risks burning 1 Ethereum for 12 seconds of transactions. This makes mistakes expensive and risky.

What Impact Does This Have On Energy Use?

Cryptocurrency is a natural calamity, sucking electricity and eating away at the earth one transaction at a time.

Many don't know the environmental impact of cryptocurrencies, yet it's tremendous.

A single Ethereum transaction used to use 200 kWh and leave a large carbon imprint. This update reduces global energy use by 0.2%.

Ethereum will submit a challenge to one validator, and that validator will forward it to randomly selected other validators who accept it.

This reduces the needed computing power.

They expect a 99.5% reduction, therefore a single transaction should cost 1 kWh.

Carbon footprint is 0.58 kgCO2, or 1,235 VISA transactions.

This is a big Ethereum blockchain update.

I love cryptocurrency and Mother Earth.

The American who bought a meme cryptocurrency.

Cryptocurrency is the financial underground.

I love it. But there’s one thing I hate: scams. Over the last few years the Dogecoin cryptocurrency saw massive gains.

Glauber Contessoto overreacted. He shared his rags-to-riches cryptocurrency with the media.

He's only wealthy on paper. No longer Dogecoin millionaire.

Here's what he's doing now. It'll make you rethink cryptocurrency investing.

Strange beginnings

Glauber once had a $36,000-a-year job.

He grew up poor and wanted to make his mother proud. Tesla was his first investment. He bought GameStop stock after Reddit boosted it.

He bought whatever was hot.

He was a young investor. Memes, not research, influenced his decisions.

Elon Musk (aka Papa Elon) began tweeting about Dogecoin.

Doge is a 2013 cryptocurrency. One founder is Australian. He insists it's funny.

He was shocked anyone bought it LOL.

Doge is a Shiba Inu-themed meme. Now whenever I see a Shiba Inu, I think of Doge.

Elon helped drive up the price of Doge by talking about it in 2020 and 2021 (don't take investment advice from Elon; he's joking and gaslighting you).

Glauber caved. He invested everything in Doge. He borrowed from family and friends. He maxed out his credit card to buy more Doge. Yuck.

Internet dubbed him a genius. Slumdog millionaire and The Dogefather were nicknames. Elon pumped Doge on social media.

Good times.

From $180,000 to $1,000,000+

TikTok skyrocketed Doge's price.

Reddit fueled up. Influencers recommended buying Doge because of its popularity. Glauber's motto:

Scared money doesn't earn.

Glauber was no broke ass anymore.

His $180,000 Dogecoin investment became $1M. He championed investing. He quit his dumb job like a rebellious millennial.

A puppy dog meme captivated the internet.

Rise and fall

Whenever I invest in anything I ask myself “what utility does this have?”

Dogecoin is useless.

You buy it for the cute puppy face and hope others will too, driving up the price. All cryptocurrencies fell in 2021's second half.

Central banks raised interest rates, and inflation became a pain.

Dogecoin fell more than others. 90% decline.

Glauber’s Dogecoin is now worth $323K. Still no sales. His dog god is unshakeable. Confidence rocks. Dogecoin millionaire recently said...

“I should have sold some.”

Yes, sir.

He now avoids speculative cryptocurrencies like Dogecoin and focuses on Bitcoin and Ethereum.

I've long said this. Starbucks is building on Ethereum.

It's useful. Useful. Developers use Ethereum daily. Investing makes you wiser over time, like the Dogecoin millionaire.

When risk b*tch slaps you, humility follows, as it did for me when I lost money.

You have to lose money to make money. Few understand.

Dogecoin's omissions

You might be thinking Dogecoin is crap.

I'll take a contrarian stance. Dogecoin does nothing, but it has a strong community. Dogecoin dominates internet memes.

It's silly.

Not quite. The message of crypto that many people forget is that it’s a change in business model.

Businesses create products and services, then advertise to find customers. Crypto Web3 works backwards. A company builds a fanbase but sells them nothing.

Once the community reaches MVC (minimum viable community), a business can be formed.

Community members are relational versus transactional. They're invested in a cause and care about it (typically ownership in the business via crypto).

In this new world, Dogecoin has the most important feature.

Summary

While Dogecoin does have a community I still dislike it.

It's all shady. Anything Elon Musk recommends is a bad investment (except SpaceX & Tesla are great companies).

Dogecoin Millionaire has wised up and isn't YOLOing into more dog memes.

Don't follow the crowd or the hype. Investing is a long-term sport based on fundamentals and research.

Since Ethereum's inception, I've spent 10,000 hours researching.

Dogecoin will be the foundation of something new, like Pets.com at the start of the dot-com revolution. But I doubt Doge will boom.

Be safe!

Understanding your own energy depletion and restoration is how to truly optimize

Hack has meant many bad things for centuries. In the 1800s, a hack was a meager horse used to transport goods.

Modern usage describes a butcher or ax murderer's cleaver chop. The 1980s programming boom distinguished elegant code from "hacks". Both got you to your goal, but the latter made any programmer cringe and mutter about changing the code. From this emerged the hacker trope, the friendless anti-villain living in a murky hovel lit by the computer monitor, eating junk food and breaking into databases to highlight security system failures or steal hotdog money.

Now, start-a-billion-dollar-business-from-your-garage types have shifted their sights from app development to DIY biology, coining the term "bio-hack". This is a required keyword and meta tag for every fitness-related podcast, book, conference, app, or device.

Bio-hacking involves bypassing your body and mind's security systems to achieve a goal. Many biohackers' initial goals were reasonable, like lowering blood pressure and weight. Encouraged by their own progress, self-determination, and seemingly exquisite control of their biology, they aimed to outsmart aging and death to live 180 to 1000 years (summarized well in this vox.com article).

With this grandiose north star, the hunt for novel supplements and genetic engineering began.

Companies selling do-it-yourself biological manipulations cite lab studies in mice as proof of their safety and success in reversing age-related diseases or promoting longevity in humans (the goal changes depending on whether a company is talking to the federal government or private donors).

The FDA is slower than science, they say. Why not alter your biochemistry by buying pills online, editing your DNA with a CRISPR kit, or using a sauna delivered to your home? How about a microchip or electrical stimulator?

What could go wrong?

I'm not the neo-police, making citizen's arrests every time someone introduces a new plumbing gadget or extrapolates from animal research on resveratrol or catechins that we should drink more red wine or eat more chocolate. As a scientist who's spent her career asking, "Can we get better?" I've come to view bio-hacking as misguided, profit-driven, and counterproductive to its followers' goals.

We're creatures of nature. Despite all the new gadgets and bio-hacks, we still use Roman plumbing technology, and the best way to stay fit, sharp, and happy is to follow a recipe passed down since the beginning of time. Bacteria, plants, and all natural beings are rhythmic, with alternating periods of high activity and dormancy, whether measured in seconds, hours, days, or seasons. Nature repeats successful patterns.

During the Upstate, every cell in your body is naturally primed and pumped full of glycogen and ATP (your cells' energy currencies), as well as cortisol, which supports your muscles, heart, metabolism, cognitive prowess, emotional regulation, and general "get 'er done" attitude. This big energy release depletes your batteries and requires the Downstate, when your subsystems recharge at the cellular level.

Downstates are when you give your heart a break from pumping nutrient-rich blood through your body; when you give your metabolism a break from inflammation, oxidative stress, and sympathetic arousal caused by eating fast food — or just eating too fast; or when you give your mind a chance to wander, think bigger thoughts, and come up with new creative solutions. When you're responding to notifications, emails, and fires, you can't relax.

Downstates aren't just for consistently recharging your battery. By spending time in the Downstate, your body and brain get extra energy and nutrients, allowing you to grow smarter, faster, stronger, and more self-regulated. This state supports half-marathon training, exam prep, and mediation. As we age, spending more time in the Downstate is key to mental and physical health, well-being, and longevity.

When you prioritize energy-demanding activities during Upstate periods and energy-replenishing activities during Downstate periods, all your subsystems, including cardiovascular, metabolic, muscular, cognitive, and emotional, hum along at their optimal settings. When you synchronize the Upstates and Downstates of these individual rhythms, their functioning improves. A hard workout causes autonomic stress, which triggers Downstate recovery.

By choosing the right timing and type of exercise during the day, you can ensure a deeper recovery and greater readiness for the next workout by working with your natural rhythms and strengthening your autonomic and sleep Downstates.

Morning cardio workouts increase deep sleep compared to afternoon workouts. Timing and type of meals determine when your sleep hormone melatonin is released, ushering in sleep.

Rhythm isn't a hack. It's not a way to cheat the system or the boss. Nature has honed its optimization wisdom over trillions of days and nights. Stop looking for quick fixes. You're a whole system made of smaller subsystems that must work together to function well. No one pill or subsystem will make it all work. Understanding and coordinating your rhythms is free, easy, and only benefits you.

Dr. Sara C. Mednick is a cognitive neuroscientist at UC Irvine and author of The Power of the Downstate (HachetteGO)

The world's most prestigious vintage vehicle show included amazing autos and record-breaking sums.

This 1932 Duesenberg J Figoni Sports Torpedo earned Best of Show in 2022.

David Paul Morris (DPM)/Bloomberg

2022 Pebble Beach Concours d'Elegance winner was a pre-war roadster.

Lee Anderson's 1932 Duesenberg J Figoni Sports Torpedo won Best of Show at Pebble Beach Golf Links near Carmel, Calif., on Sunday. First American win since 2013.

Sandra Button, chairperson of the annual concours, said the car, whose chassis and body had been separated for years, "marries American force with European style." "Its resurrection story is passionate."

Pebble Beach Concours d'Elegance Auction

Since 1950, the Pebble Beach Concours d'Elegance has welcomed the world's most costly collectable vehicles for a week of parties, auctions, rallies, and high-roller meetings. The cold, dreary weather highlighted the automobiles' stunning lines and hues.

DPM/Bloomberg

A visitor photographs a 1948 Ferrari 166 MM Touring Barchetta. This is one of 25 Ferraris manufactured in the years after World War II. First shown at the 1948 Turin Salon. Others finished Mille Miglia and Le Mans, which set the tone for Ferrari racing for years.

DPM/Bloomberg

This year's frontrunners were ultra-rare pre-war and post-war automobiles with long and difficult titles, such a 1937 Talbot-Lago T150C-SS Figoni & Falaschi Teardrop Coupe and a 1951 Talbot-Lago T26 Grand Sport Stabilimenti Farina Cabriolet.

The hefty, enormous coaches inspire visions of golden pasts when mysterious saloons swept over the road with otherworldly style, speed, and grace. Only the richest and most powerful people, like Indian maharaja and Hollywood stars, owned such vehicles.

Antonio Chopitea, a Peruvian sugar tycoon, ordered a new Duesenberg in Paris. Hemmings says the two-tone blue beauty was moved to the US and dismantled in the 1960s. Body and chassis were sold separately and rejoined decades later in a three-year, prize-winning restoration.

The concours is the highlight of Monterey Car Week, a five-day Super Bowl for car enthusiasts. Early events included Porsche and Ferrari displays, antique automobile races, and new-vehicle debuts. Many auto executives call Monterey Car Week the "new auto show."

Many visitors were drawn to the record-breaking auctions.

A 1969 Porsche 908/02 auctioned for $4.185 million. Flat-eight air-cooled engine, 90.6-inch wheelbase, 1,320-pound weight. Vic Elford, Richard Attwood, Rudi Lins, Gérard Larrousse, Kurt Ahrens Jr., Masten Gregory, and Pedro Rodriguez drove it, according to Gooding.

DPM/Bloomberg

The 1931 Bentley Eight Liter Sports Tourer doesn't meet its reserve. Gooding & Co., the official auction house of the concours, made more than $105 million and had an 82% sell-through rate. This powerful open-top tourer is one of W.O. Bentley's 100 automobiles. Only 80 remain.

DPM/Bloomberg

The final auction on Aug. 21 brought in $456.1 million, breaking the previous high of $394.48 million established in 2015 in Monterey. “The week put an exclamation point on what has been an exceptional year for the collector automobile market,” Hagerty analyst John Wiley said.

Many cars that go unsold at public auction are sold privately in the days after. After-sales pushed the week's haul to $469 million on Aug. 22, up 18.9% from 2015's record.

In today's currencies, 2015's record sales amount to $490 million, Wiley noted. The dollar is degrading faster than old autos.

Still, 113 million-dollar automobiles sold. The average car sale price was $583,211, up from $446,042 last year, while multimillion-dollar hammer prices made up around 75% of total sales.

Industry insiders and market gurus expected that stock market volatility, the crisis in Ukraine, and the dollar-euro exchange rate wouldn't influence the world's biggest spenders.

Classic.com's CEO said there's no hint of a recession in an e-mail. Big sales and crowds.

Ticket-holders wore huge hats, flowery skirts, and other Kentucky Derby-esque attire. Coffee, beverages, and food are extra.

DPM/Bloomberg

Mercedes-Benz 300 SL Gullwing, 1955. Mercedes produced the two-seat gullwing coupe from 1954–1957 and the roadster from 1957–1963. It was once West Germany's fastest and most powerful automobile. You'd be hard-pressed to locate one for less $1 million.

DPM/Bloomberg

1955 Ferrari 410 Sport sold for $22 million at RM Sotheby's. It sold a 1937 Mercedes-Benz 540K Sindelfingen Roadster for $9.9 million and a 1924 Hispano-Suiza H6C Transformable Torpedo for $9.245 million. The family-run mansion sold $221.7 million with a 90% sell-through rate, up from $147 million in 2021. This year, RM Sotheby's cars averaged $1.3 million.

Not everyone saw such great benefits.

Gooding & Co., the official auction house of the concours, made more than $105 million and had an 82% sell-through rate. 1937 Bugatti Type 57SC Atalante, 1990 Ferrari F40, and 1994 Bugatti EB110 Super Sport were top sellers.

The 1969 Autobianchi A112 Bertone. This idea two-seater became a Hot Wheels toy but was never produced. It has a four-speed manual drive and an inline-four mid-engine arrangement like the Lamborghini Miura.

DPM/Bloomberg

1956 Porsche 356 A Speedster at Gooding & Co. The Porsche 356 is a lightweight, rear-engine, rear-wheel drive vehicle that lacks driving power but is loved for its rounded, Beetle-like hardtop coupé and open-top versions.

DPM/Bloomberg

Mecum sold $50.8 million with a 64% sell-through rate, down from $53.8 million and 77% in 2021. Its top lot, a 1958 Ferrari 250 GT 'Tour de France' Alloy Coupe, sold for $2.86 million, but its average price was $174,016.

Bonhams had $27.8 million in sales with an 88% sell-through rate. The same sell-through generated $35.9 million in 2021.

Gooding & Co. and RM Sotheby's posted all 10 top sales, leaving Bonhams, Mecum, and Hagerty-owned Broad Arrow fighting for leftovers. Six of the top 10 sellers were Ferraris, which remain the gold standard for collectable automobiles. Their prices have grown over decades.

Classic.com's Calle claimed RM Sotheby's "stole the show," but "BroadArrow will be a force to reckon with."

Although pre-war cars were hot, '80s and '90s cars showed the most appreciation and attention. Generational transition and new buyer profile."

2022 Pebble Beach Concours d'Elegance judges inspect 1953 Siata 208. The rounded coupe was introduced at the 1952 Turin Auto Show in Italy and is one of 18 ever produced. It sports a 120hp Fiat engine, five-speed manual transmission, and alloy drum brakes. Owners liked their style, but not their reliability.

DPM/Bloomberg

The Czinger 21 CV Max at Pebble Beach. Monterey Car Week concentrates on historic and classic automobiles, but modern versions like this Czinger hypercar also showed.

DPM/Bloomberg

The 1932 Duesenberg J Figoni Sports Torpedo won Best in Show in 2022. Lee and Penny Anderson of Naples, Fla., own the once-separate-chassis-from-body automobile.

DPM/Bloomberg

Python Heatmap Technical Indicator

Heatmaps provide an instant overview. They can be used with correlations or to predict reactions or confirm the trend in trading. This article covers RSI heatmap creation.

The Market System

Market regime:

• Bullish trend: The market tends to make higher highs, which indicates that the overall trend is upward.

• Sideways: The market tends to fluctuate while staying within predetermined zones.

• Bearish trend: The market has the propensity to make lower lows, indicating that the overall trend is downward.

Most tools detect the trend, but we cannot predict the next state. The best way to solve this problem is to assume the current state will continue and trade any reactions, preferably in the trend.

If the EURUSD is above its moving average and making higher highs, a trend-following strategy would be to wait for dips before buying and assuming the bullish trend will continue.

Indicator of Relative Strength

J. Welles Wilder Jr. introduced the RSI, a popular and versatile technical indicator. Used as a contrarian indicator to exploit extreme reactions. Calculating the default RSI usually involves these steps:

• Determine the difference between the closing prices from the prior ones.

• Distinguish between the positive and negative net changes.

• Create a smoothed moving average for both the absolute values of the positive net changes and the negative net changes.

• Take the difference between the smoothed positive and negative changes. The Relative Strength RS will be the name we use to describe this calculation.

• To obtain the RSI, use the normalization formula shown below for each time step.

The 13-period RSI and black GBPUSD hourly values are shown above. RSI bounces near 25 and pauses around 75. Python requires a four-column OHLC array for RSI coding.

import numpy as np
def add_column(data, times):
    
    for i in range(1, times + 1):
    
        new = np.zeros((len(data), 1), dtype = float)
        
        data = np.append(data, new, axis = 1)
    return data
def delete_column(data, index, times):
    
    for i in range(1, times + 1):
    
        data = np.delete(data, index, axis = 1)
    return data
def delete_row(data, number):
    
    data = data[number:, ]
    
    return data
def ma(data, lookback, close, position): 
    
    data = add_column(data, 1)
    
    for i in range(len(data)):
           
            try:
                
                data[i, position] = (data[i - lookback + 1:i + 1, close].mean())
            
            except IndexError:
                
                pass
            
    data = delete_row(data, lookback)
    
    return data
def smoothed_ma(data, alpha, lookback, close, position):
    
    lookback = (2 * lookback) - 1
    
    alpha = alpha / (lookback + 1.0)
    
    beta  = 1 - alpha
    
    data = ma(data, lookback, close, position)
    data[lookback + 1, position] = (data[lookback + 1, close] * alpha) + (data[lookback, position] * beta)
    for i in range(lookback + 2, len(data)):
        
            try:
                
                data[i, position] = (data[i, close] * alpha) + (data[i - 1, position] * beta)
        
            except IndexError:
                
                pass
            
    return data
def rsi(data, lookback, close, position):
    
    data = add_column(data, 5)
    
    for i in range(len(data)):
        
        data[i, position] = data[i, close] - data[i - 1, close]
     
    for i in range(len(data)):
        
        if data[i, position] > 0:
            
            data[i, position + 1] = data[i, position]
            
        elif data[i, position] < 0:
            
            data[i, position + 2] = abs(data[i, position])
            
    data = smoothed_ma(data, 2, lookback, position + 1, position + 3)
    data = smoothed_ma(data, 2, lookback, position + 2, position + 4)
    data[:, position + 5] = data[:, position + 3] / data[:, position + 4]
    
    data[:, position + 6] = (100 - (100 / (1 + data[:, position + 5])))
    data = delete_column(data, position, 6)
    data = delete_row(data, lookback)
    return data

Make sure to focus on the concepts and not the code. You can find the codes of most of my strategies in my books. The most important thing is to comprehend the techniques and strategies.

My weekly market sentiment report uses complex and simple models to understand the current positioning and predict the future direction of several major markets. Check out the report here:

Using the Heatmap to Find the Trend

RSI trend detection is easy but useless. Bullish and bearish regimes are in effect when the RSI is above or below 50, respectively. Tracing a vertical colored line creates the conditions below. How:

• When the RSI is higher than 50, a green vertical line is drawn.

• When the RSI is lower than 50, a red vertical line is drawn.

Zooming out yields a basic heatmap, as shown below.

Plot code:

def indicator_plot(data, second_panel, window = 250):
    fig, ax = plt.subplots(2, figsize = (10, 5))
    sample = data[-window:, ]
    for i in range(len(sample)):
        ax[0].vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1)  
        if sample[i, 3] > sample[i, 0]:
            ax[0].vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5)  
        if sample[i, 3] < sample[i, 0]:
            ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)  
        if sample[i, 3] == sample[i, 0]:
            ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)  
    ax[0].grid() 
    for i in range(len(sample)):
        if sample[i, second_panel] > 50:
            ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5)  
        if sample[i, second_panel] < 50:
            ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5)  
    ax[1].grid()
indicator_plot(my_data, 4, window = 500)

Call RSI on your OHLC array's fifth column. 4. Adjusting lookback parameters reduces lag and false signals. Other indicators and conditions are possible.

Another suggestion is to develop an RSI Heatmap for Extreme Conditions.

Contrarian indicator RSI. The following rules apply:

• Whenever the RSI is approaching the upper values, the color approaches red.

• The color tends toward green whenever the RSI is getting close to the lower values.

Zooming out yields a basic heatmap, as shown below.

Plot code:

import matplotlib.pyplot as plt
def indicator_plot(data, second_panel, window = 250):
    fig, ax = plt.subplots(2, figsize = (10, 5))
    sample = data[-window:, ]
    for i in range(len(sample)):
        ax[0].vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1)  
        if sample[i, 3] > sample[i, 0]:
            ax[0].vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5)  
        if sample[i, 3] < sample[i, 0]:
            ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)  
        if sample[i, 3] == sample[i, 0]:
            ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)  
    ax[0].grid() 
    for i in range(len(sample)):
        if sample[i, second_panel] > 90:
            ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5)  
        if sample[i, second_panel] > 80 and sample[i, second_panel] < 90:
            ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'darkred', linewidth = 1.5)  
        if sample[i, second_panel] > 70 and sample[i, second_panel] < 80:
            ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'maroon', linewidth = 1.5)  
        if sample[i, second_panel] > 60 and sample[i, second_panel] < 70:
            ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'firebrick', linewidth = 1.5) 
        if sample[i, second_panel] > 50 and sample[i, second_panel] < 60:
            ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5) 
        if sample[i, second_panel] > 40 and sample[i, second_panel] < 50:
            ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5) 
        if sample[i, second_panel] > 30 and sample[i, second_panel] < 40:
            ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'lightgreen', linewidth = 1.5)
        if sample[i, second_panel] > 20 and sample[i, second_panel] < 30:
            ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'limegreen', linewidth = 1.5) 
        if sample[i, second_panel] > 10 and sample[i, second_panel] < 20:
            ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'seagreen', linewidth = 1.5)  
        if sample[i, second_panel] > 0 and sample[i, second_panel] < 10:
            ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5)
    ax[1].grid()
indicator_plot(my_data, 4, window = 500)

Dark green and red areas indicate imminent bullish and bearish reactions, respectively. RSI around 50 is grey.

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.

When you find a trading strategy or technique, follow these steps:

• Put emotions aside and adopt a critical mindset.

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

• Try optimizing it and performing a forward test if you find any potential.

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

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

After checking the above, monitor the strategy because market dynamics may change and make it unprofitable.