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!

Three options exist. The third one is the quickest and most fruitful.

You've mastered crypto trading and want to become a broker.

So you may wonder: Where to begin?

If so, keep reading.

Today I'll compare three different approaches to becoming a cryptocurrency trader.

What are cryptocurrency brokers, and how do they vary from stockbrokers?

A stockbroker implements clients' market orders (retail or institutional ones).

Brokerage firms are regulated, insured, and subject to regulatory monitoring.

Stockbrokers are required between buyers and sellers. They can't trade without a broker. To trade, a trader must open a broker account and deposit money. When a trader shops, he tells his broker what orders to place.

Crypto brokerage is trade intermediation with cryptocurrency.

In crypto trading, however, brokers are optional.

Crypto exchanges offer direct transactions. Open an exchange account (no broker needed) and make a deposit.

Question:

Since crypto allows DIY trading, why use a broker?

Let's compare cryptocurrency exchanges vs. brokers.

Broker versus cryptocurrency exchange

Most existing crypto exchanges are basically brokers.

Examine their primary services:

• connecting purchasers and suppliers

• having custody of clients' money (with the exception of decentralized cryptocurrency exchanges),

• clearance of transactions.

Brokerage is comparable, don't you think?

There are exceptions. I mean a few large crypto exchanges that follow the stock exchange paradigm. They outsource brokerage, custody, and clearing operations. Classic exchange setups are rare in today's bitcoin industry.

Back to our favorite “standard” crypto exchanges. All-in-one exchanges and brokers. And usually, they operate under a broker or a broker-dealer license, save for the exchanges registered somewhere in a free-trade offshore paradise. Those don’t bother with any licensing.

What’s the sense of having two brokers at a time?

Better liquidity and trading convenience.

The crypto business is compartmentalized.

We have CEXs, DEXs, hybrid exchanges, and semi-exchanges (those that aggregate liquidity but do not execute orders on their sides). All have unique regulations and act as sovereign states.

There are about 18k coins and hundreds of blockchain protocols, most of which are heterogeneous (i.e., different in design and not interoperable).

A trader must register many accounts on different exchanges, deposit funds, and manage them all concurrently to access global crypto liquidity.

It’s extremely inconvenient.

Crypto liquidity fragmentation is the largest obstacle and bottleneck blocking crypto from mass adoption.

Crypto brokers help clients solve this challenge by providing one-gate access to deep and diverse crypto liquidity from numerous exchanges and suppliers. Professionals and institutions need it.

Another killer feature of a brokerage may be allowing clients to trade crypto with fiat funds exclusively, without fiat/crypto conversion. It is essential for professional and institutional traders.

Who may work as a cryptocurrency broker?

Apparently, not anyone. Brokerage requires high-powered specialists because it involves other people's money.

Here's the essentials:

• excellent knowledge, skills, and years of trading experience

• high-quality, quick, and secure infrastructure

• highly developed team

• outstanding trading capital

• High-ROI network: long-standing, trustworthy connections with customers, exchanges, liquidity providers, payment gates, and similar entities

• outstanding marketing and commercial development skills.

What about a license for a cryptocurrency broker? Is it necessary?

Complex question.

If you plan to play in white-glove jurisdictions, you may need a license. For example, in the US, as a “money transmitter” or as a CASSP (crypto asset secondary services provider) in Australia.

Even in these jurisdictions, there are no clear, holistic crypto brokerage and licensing policies.

Your lawyer will help you decide if your crypto brokerage needs a license.

Getting a license isn't quick. Two years of patience are needed.

How can you turn into a cryptocurrency broker?

Finally, we got there! 🎉

Three actionable ways exist:

1. To kickstart a regulated stand-alone crypto broker

2. To get a crypto broker franchise, and

3. To become a liquidity network broker.

Let's examine each.

1. Opening a regulated cryptocurrency broker

It's difficult. Especially If you're targeting first-world users.

You must comply with many regulatory, technical, financial, HR, and reporting obligations to keep your organization running. Some are mentioned above.

The licensing process depends on the products you want to offer (spots or derivatives) and the geographic areas you plan to service. There are no general rules for that.

In an overgeneralized way, here are the boxes you will have to check:

• capital availability (usually a large amount of capital c is required)

• You will have to move some of your team members to the nation providing the license in order to establish an office presence there.

• the core team with the necessary professional training (especially applies to CEO, Head of Trading, Assistant to Head of Trading, etc.)

• insurance

• infrastructure that is trustworthy and secure

• adopted proper AML/KYC/financial monitoring policies, etc.

Assuming you passed, what's next?

I bet it won’t be mind-blowing for you that the license is just a part of the deal. It won't attract clients or revenue.

To bring in high-dollar clientele, you must be a killer marketer and seller. It's not easy to convince people to give you money.

You'll need to be a great business developer to form successful, long-term agreements with exchanges (ideally for no fees), liquidity providers, banks, payment gates, etc. Persuade clients.

It's a tough job, isn't it?

I expect a Quora-type question here:

Can I start an unlicensed crypto broker?

Well, there is always a workaround with crypto!

You can register your broker in a free-trade zone like Seychelles to avoid US and other markets with strong watchdogs.

This is neither wise nor sustainable.

First, such experiments are illegal.

Second, you'll have trouble attracting clients and strategic partners.

A license equals trust. That’s it.

Even a pseudo-license from Mauritius matters.

Here are this method's benefits and downsides.

Cons first.

• As you navigate this difficult and expensive legal process, you run the risk of missing out on business prospects. It's quite simple to become excellent compliance yet unable to work. Because your competitors are already courting potential customers while you are focusing all of your effort on paperwork.

• Only God knows how long it will take you to pass the break-even point when everything with the license has been completed.

• It is a money-burning business, especially in the beginning when the majority of your expenses will go toward marketing, sales, and maintaining license requirements. Make sure you have the fortitude and resources necessary to face such a difficult challenge.

Pros

• It may eventually develop into a tool for making money. Because big guys who are professionals at trading require a white-glove regulated brokerage. You have every possibility if you work hard in the areas of sales, marketing, business development, and wealth. Simply put, everything must align.

Launching a regulated crypto broker is analogous to launching a crypto exchange. It's ROUGH. Sure you can take it?

2. Franchise for Crypto Broker (Crypto Sub-Brokerage)

A broker franchise is easier and faster than becoming a regulated crypto broker. Not a traditional brokerage.

A broker franchisee, often termed a sub-broker, joins with a broker (a franchisor) to bring them new clients. Sub-brokers market a broker's products and services to clients.

Sub-brokers are the middlemen between a broker and an investor.

Why is sub-brokering easier?

• less demanding qualifications and legal complexity. All you need to do is keep a few certificates on hand (each time depends on the jurisdiction).

• No significant investment is required

• there is no demand that you be a trading member of an exchange, etc.

As a sub-broker, you can do identical duties without as many rights and certifications.

What about the crypto broker franchise?

Sub-brokers aren't common in crypto.

In most existing examples (PayBito, PCEX, etc.), franchises are offered by crypto exchanges, not brokers. Though we remember that crypto exchanges are, in fact, brokers, do we?

Similarly:

• For a commission, a franchiser crypto broker receives new leads from a crypto sub-broker.

See above for why enrolling is easy.

Finding clients is difficult. Most crypto traders prefer to buy-sell on their own or through brokers over sub-broker franchises.

3. Broker of the Crypto Trading Network (or a Network Broker)

It's the greatest approach to execute crypto brokerage, based on effort/return.

Network broker isn't an established word. I wrote it for clarity.

Remember how we called crypto liquidity fragmentation the current crypto finance paradigm's main bottleneck?

Where there's a challenge, there's progress.

Several well-funded projects are aiming to fix crypto liquidity fragmentation. Instead of launching another crypto exchange with siloed trading, the greatest minds create trading networks that aggregate crypto liquidity from desynchronized sources and enable quick, safe, and affordable cross-blockchain transactions. Each project offers a distinct option for users.

Crypto liquidity implies:

• One-account access to cryptocurrency liquidity pooled from network participants' exchanges and other liquidity sources

• compiled price feeds

• Cross-chain transactions that are quick and inexpensive, even for HFTs

• link between participants of all kinds, and

• interoperability among diverse blockchains

Fast, diversified, and cheap global crypto trading from one account.

How does a trading network help cryptocurrency brokers?

I’ll explain it, taking Yellow Network as an example.

Yellow provides decentralized Layer-3 peer-to-peer trading.

• trade across chains globally with real-time settlement and

• Between cryptocurrency exchanges, brokers, trading companies, and other sorts of network members, there is communication and the exchange of financial information.

Have you ever heard about ECN (electronic communication network)? If not, it's an automated system that automatically matches buy and sell orders. Yellow is a decentralized digital asset ECN.

Brokers can:

• Start trading right now without having to meet stringent requirements; all you need to do is integrate with Yellow Protocol and successfully complete some KYC verification.

• Access global aggregated crypto liquidity through a single point.

• B2B (Broker to Broker) liquidity channels that provide peer liquidity from other brokers. Orders from the other broker will appear in the order book of a broker who is peering with another broker on the market. It will enable a broker to broaden his offer and raise the total amount of liquidity that is available to his clients.

• Select a custodian or use non-custodial practices.

Comparing network crypto brokerage to other types:

• A licensed stand-alone brokerage business is much more difficult and time-consuming to launch than network brokerage, and

• Network brokerage, in contrast to crypto sub-brokerage, is scalable, independent, and offers limitless possibilities for revenue generation.

Yellow Network Whitepaper. has more details on how to start a brokerage business and what rewards you'll obtain.

Final thoughts

There are three ways to become a cryptocurrency broker, including the non-conventional liquidity network brokerage. The last option appears time/cost-effective.

Crypto brokerage isn't crowded yet. Act quickly to find your right place in this market.

Choose the way that works for you best and see you in crypto trading.

Discover Web3 & DeFi with Yellow Network!

Yellow, powered by Openware, is developing a cross-chain P2P liquidity aggregator to unite the crypto sector and provide global remittance services that aid people.

Join the Yellow Community and plunge into this decade's biggest product-oriented crypto project.

• Observe Yellow Twitter

• Enroll in Yellow Telegram

• Visit Yellow Discord.

• On Hacker Noon, look us up.

Yellow Network will expose development, technology, developer tools, crypto brokerage nodes software, and community liquidity mining.

Amazon's Rings of Power is the most costly TV series ever made. This is merely a down payment towards Amazon's grand goal.

Here's a video:

Amazon bought J.R.R. Tolkien's fantasy novels for $250 million in 2017. This agreement allows Amazon to create a Tolkien series for Prime Video.

The business spent years developing and constructing a Lord of the Rings prequel. Rings of Power premiered on September 2, 2022.

It drew 25 million global viewers in 24 hours. Prime Video's biggest debut.

An Exorbitant Budget

The most expensive. First season cost $750 million to $1 billion, making it the most costly TV show ever.

Jeff Bezos has spent years looking for the next Game of Thrones, a critically and commercially successful original series. Rings of Power could help.

Why would Amazon bet $1 billion on one series?

It's Not Just About the Streaming War

It's simple to assume Amazon just wants to win. Since 2018, the corporation has been fighting Hulu, Netflix, HBO, Apple, Disney, and NBC. Each wants your money, talent, and attention. Amazon's investment goes beyond rivalry.

Subscriptions Are the Bait

Audible, Amazon Music, and Prime Video are subscription services, although the company's fundamental business is retail. Amazon's online stores contribute over 50% of company revenue. Subscription services contribute 6.8%. The company's master plan depends on these subscriptions.

Streaming videos on Prime increases membership renewals. Free trial participants are more likely to join. Members buy twice as much as non-members.

Amazon Studios doesn't generate original programming to earn from Prime Video subscriptions. It aims to retain and attract clients.

Amazon can track what you watch and buy. Its algorithm recommends items and services. Mckinsey says you'll use more Amazon products, shop at Amazon stores, and watch Amazon entertainment.

In 2015, the firm launched the first season of The Man in the High Castle, a dystopian alternate history TV series depicting a world ruled by Nazi Germany and Japan after World War II.

This $72 million production earned two Emmys. It garnered 1.15 million new Prime users globally.

When asked about his Hollywood investment, Bezos said, "A Golden Globe helps us sell more shoes."

Selling more footwear

Amazon secured a deal with DirecTV to air Thursday Night Football in restaurants and bars. First streaming service to have exclusive NFL games.

This isn't just about Thursday night football, says media analyst Ritchie Greenfield. This sells t-shirts. This may be a ticket. Amazon does more than stream games.

The Rings of Power isn't merely a production showcase, either. This sells Tolkien's fantasy novels such Lord of the Rings, The Hobbit, and The Silmarillion.

This tiny commitment keeps you in Amazon's ecosystem.

I apologize if this sounds basic, but throughout my career, I've always been clear that a company's activities are shaped by its founder(s)' morality.

I consider Palantir, owned by PayPal founder Peter Thiel, evil. He got $5 billion tax-free by hacking a statute to help middle-class savings. That may appear clever, but I think it demonstrates a shocking lack of solidarity with society. As a result of this and other things he has said and done, I early on dismissed Peter Thiel as someone who could contribute anything positive to society, and events soon proved me right: we are talking about someone who clearly considers himself above everyone else and who does not hesitate to set up a company, Palantir, to exploit the data of the little people and sell it to the highest bidder, whoever that is and whatever the consequences.

The German courts have confirmed my warnings concerning Palantir. The problem is that politicians love its surveillance tools because they think knowing more about their constituents gives them power. These are ideal for dictatorships who want to snoop on their populace. Hence, Silicon Valley's triumphalist dialectic has seduced many governments at many levels and collected massive volumes of data to hold forever.

Dangerous company. There are many more. My analysis of the moral principles that disclose company management changed my opinion of Facebook, now Meta, and anyone with a modicum of interest might deduce when that happened, a discovery that leaves you dumbfounded. TikTok was easy because its lack of morality was revealed early when I saw the videos it encouraged minors to post and the repercussions of sharing them through its content recommendation algorithm. When you see something like this, nothing can convince you that the firm can change its morals and become good. Nothing. You know the company is awful and will fail. Speak it, announce it, and change it. It's like a fingerprint—unchangeable.

Some of you who read me frequently make its Facebook today jokes when I write about these firms, and that's fine: they're my moral standards, those of an elderly professor with thirty-five years of experience studying corporations and discussing their cases in class, but you don't have to share them. Since I'm writing this and don't have to submit to any editorial review, that's what it is: when you continuously read a person, you have to assume that they have moral standards and that sometimes you'll agree with them and sometimes you won't. Morality accepts hierarchies, nuances, and even obsessions. I know not everyone shares my opinions, but at least I can voice them. One day, one of those firms may sue me (as record companies did some years ago).

Palantir is incredibly harmful. Limit its operations. Like Meta and TikTok, its business strategy is shaped by its founders' immorality. Such a procedure can never be beneficial.

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.