WTF is wrong with you people?

When I was laid off as editor of my town's daily newspaper, I went silent on social media. I knew it was coming and had been quietly removing personal items each day, but the pain was intense.

I posted a day later. I didn't bad-mouth GateHouse Media but expressed my sadness at leaving the newspaper industry, pride in my accomplishments, and hope for success in another industry.

Normal job-loss response.

What do you recognize as abnormal?

The bullshit I’ve been reading from laid-off folks on LinkedIn.

If you're there, you know. Many Twitter or Facebook/Meta employees recently lost their jobs.

Well, many of them did not “lose their job,” actually. They were “impacted by the layoffs” at their former employer. I keep seeing that phrase.

Why don’t they want to actually say it? Why the euphemism?

Many are excited about the opportunities ahead. The jobless deny being sad.

They're ecstatic! They have big plans.

Hope so. Sincerely! Being laid off stinks, especially if, like me, your skills are obsolete. It's worse if, like me, you're too old to start a new career. Ageism exists despite denials.

Nowadays, professionalism seems to demand psychotic levels of fake optimism.

Why? Life is unpredictable. That's indisputable. You shouldn't constantly complain or cry in public, but you also shouldn't pretend everything's great.

It makes you look psychotic, not positive. It's like saying at work:

“I was impacted by the death of my spouse of 20 years this week, and many of you have reached out to me, expressing your sympathy. However, I’m choosing to remember the amazing things we shared. I feel confident that there is another marriage out there for me, and after taking a quiet weekend trip to reset myself, I’ll be out there looking for the next great marital adventure! #staypositive #available #opentolove

Also:

“Now looking for our next #dreamhome after our entire neighborhood was demolished by a wildfire last night. We feel so lucky to have lived near so many amazing and inspirational neighbors, all of whom we will miss as we go on our next housing adventure. The best house for us is yet to come! If you have a great neighborhood you’d recommend, please feel free to reach out and touch base with us! #newhouse #newneighborhood #newlife

Admit it. That’s creepy.

The constant optimism makes me feel sick to my stomach.

Viscerally.

I hate fakes.

Imagine a fake wood grain desk. Wouldn't it be better if the designer accepted that it's plastic and went with that?

Real is better but not always nice. When something isn't nice, you don't have to go into detail, but you also shouldn't pretend it's great.

How to announce your job loss to the world.

Do not pretend to be happy, but don't cry and drink vodka all afternoon.

Say you loved your job, and that you're looking for new opportunities.

Yes, if you'll miss your coworkers. Otherwise, don't badmouth. No bridge-burning!

Please specify the job you want. You may want to pivot.

Alternatively, try this.

You could always flame out.

If you've pushed yourself too far into toxic positivity, you may be ready to burn it all down. If so, make it worthwhile by writing something like this:

Well, I was shitcanned by the losers at #Acme today. That bitch Linda in HR threw me under the bus just because she saw that one of my “friends” tagged me in some beach pics on social media after I called in sick with Covid. The good thing is I will no longer have to watch my ass around that #asspincher Ron in accounting, but I’m sad that I will no longer have a cushy job with high pay or access to the primo office supplies I’ve been sneaking home for the last five years. (Those gel pens were the best!) I am going to be taking some time off to enjoy my unemployment and hammer down shots of Jägermeister but in about five months I’ll be looking for anything easy with high pay and great benefits. Reach out if you can help! #officesupplies #unemploymentrocks #drinkinglikeagirlboss #acmesucks

It beats the fake positivity.

Homeownership isn't the only life pattern.

Want to irritate people?

My party trick is to say I used to own a house but no longer do.

I no longer wish to own a home, not because I lost it or because I'm moving.

It was a long-term plan. It was more deliberate than buying a home. Many people are committed for this reason.

Poppycock.

Anyone who told me that owning a house (or striving to do so) is a must is wrong.

Because, URGH.

One pattern for life is to own a home, but there are millions of others.

You can afford to buy a home? Go, buddy.

You think you need 1,000 square feet (or more)? You think it's non-negotiable in life?

Nope.

It's insane that society forces everyone to own real estate, regardless of income, wants, requirements, or situation. As if this trade brings happiness, stability, and contentment.

Take it from someone who thought this for years: drywall isn't happy. Living your way brings contentment.

That's in real estate. It may also be renting a small apartment in a city that makes your soul sing, but you can't afford the downpayment or mortgage payments.

Living or traveling abroad is difficult when your life savings are connected to something that eats your money the moment you sign.

#vanlife, which seems like torment to me, makes some people feel alive.

I've seen co-living, vacation rental after holiday rental, living with family, and more work.

Insisting that home ownership is the only path in life is foolish and reduces alternative options.

How little we question homeownership is a disgrace.

No one challenges a homebuyer's motives. We congratulate them, then that's it.

When you offload one, you must answer every question, even if you have a loose screw.

• Why do you want to sell?

• Do you have any concerns about leaving the market?

• Why would you want to renounce what everyone strives for?

• Why would you want to abandon a beautiful place like that?

• Why would you mismanage your cash in such a way?

• But surely it's only temporary? RIGHT??

Incorrect questions. Buying a property requires several inquiries.

• The typical American has $4500 saved up. When something goes wrong with the house (not if, it’s never if), can you actually afford the repairs?

• Are you certain that you can examine a home in less than 15 minutes before committing to buying it outright and promising to pay more than twice the asking price on a 30-year 7% mortgage?

• Are you certain you're ready to leave behind friends, family, and the services you depend on in order to acquire something?

• Have you thought about the connotation that moving to a suburb, which more than half of Americans do, means you will be dependent on a car for the rest of your life?

Plus:

Are you sure you want to prioritize home ownership over debt, employment, travel, raising kids, and daily routines?

Homeownership entails that. This ex-homeowner says it will rule your life from the time you put the key in the door.

This isn't questioned. We don't question enough. The holy home-ownership grail was set long ago, and we don't challenge it.

Many people question after signing the deeds. 70% of homeowners had at least one regret about buying a property, including the expense.

Exactly. Tragic.

Homes are different from houses

We've been fooled into thinking home ownership will make us happy.

Some may agree. No one.

Bricks and brick hindered me from living the version of my life that made me most comfortable, happy, and steady.

I'm spending the next month in a modest apartment in southern Spain. Even though it's late November, today will be 68 degrees. My spouse and I will soon meet his visiting parents. We'll visit a Sherry store. We'll eat, nap, walk, and drink Sherry. Writing. Jerez means flamenco.

That's my home. This is such a privilege. Living a fulfilling life brings me the contentment that buying a home never did.

I'm happy and comfortable knowing I can make almost all of my days good. Rejecting home ownership is partly to blame.

I'm broke like most folks. I had to choose between home ownership and comfort. I said, I didn't find them together.

Feeling at home trumps owning brick-and-mortar every day.

The following is the reality of what it's like to turn the American Dream around.

Leaving the housing market.

Sometimes I wish I owned a home.

I miss having my own yard and bed. My kitchen, cookbooks, and pizza oven are missed.

But I rarely do.

Someone else's life plan pushed home ownership on me. I'm grateful I figured it out at 35. Many take much longer, and some never understand homeownership stinks (for them).

It's confusing. People will think you're dumb or suicidal.

If you read what I write, you'll know. You'll realize that all you've done is choose to live intentionally. Find a home beyond four walls and a picket fence.

Miss? As I said, they're not home. If it were, a pizza oven, a good mattress, and a well-stocked kitchen would bring happiness.

No.

If you can afford a house and desire one, more power to you.

There are other ways to discover home. Find calm and happiness. For fun.

For it, look deeper than your home's foundation.

Looking back into the past can help you understand what's happening today and why.

"Creator economy" conjures up images of originality, sincerity, and passion. Where do Michelangelos and da Vincis push advancement with their gifts without battling for bread and proving themselves posthumously? 

Creativity has been as long as humanity, but it's just recently become a new economic paradigm. We even talk about Web3 now.

Let's examine the creative economy's history to better comprehend it. What brought us here? Looking back can help you understand what's happening now.

No yawning, I promise 😉.

Creator Economy's history

Long, uneven transition to creator economy. Let's examine the economic and societal changes that led us there.

1. Agriculture to industry

Mid-18th-century Industrial Revolution led to shift from agriculture to manufacturing. The industrial economy lasted until World War II.

The industrial economy's principal goal was to provide more affordable, accessible commodities.

Unlike today, products were scarce and inaccessible.

To fulfill its goals, industrialization triggered enormous economic changes, moving power from agrarians to manufacturers. Industrialization brought hard work, rivalry, and new ideas connected to production and automation. Creative thinkers focused on that then.

It doesn't mean music, poetry, or painting had no place back then. They weren't top priority. Artists were independent. The creative field wasn't considered a different economic subdivision.

2. The consumer economy

Manufacturers produced more things than consumers desired after World War II. Stuff was no longer scarce.

The economy must make customers want to buy what the market offers.

The consumer economic paradigm supplanted the industrial one. Customers (or consumers) replaced producers as the new economic center.

Salesmen, marketing, and journalists also played key roles (TV, radio, newspapers, etc.). Mass media greatly boosted demand for goods, defined trends, and changed views regarding nearly everything.

Mass media also gave rise to pop culture, which focuses on mass-market creative products. Design, printing, publishing, multi-media, audio-visual, cinematographic productions, etc. supported pop culture.

The consumer paradigm generated creative occupations and activities, unlike the industrial economy. Creativity was limited by the need for wide appeal.

Most creators were corporate employees.

Creating a following and making a living from it were difficult.

Paul Saffo said that only journalists and TV workers were known. Creators who wished to be known relied on producers, publishers, and other gatekeepers. To win their favor was crucial. Luck was the best tactic.

3. The creative economy

Consumer economy was digitized in the 1990s. IT solutions transformed several economic segments. This new digital economy demanded innovative, digital creativity.

Later, states declared innovation a "valuable asset that creates money and jobs." They also introduced the "creative industries" and the "creative economy" (not creator!) and tasked themselves with supporting them. Australia and the UK were early adopters.

Individual skill, innovation, and intellectual property fueled the creative economy. Its span covered design, writing, audio, video material, etc. The creative economy required IT-powered activity.

The new challenge was to introduce innovations to most economic segments and meet demand for digital products and services.

Despite what the title "creative economy" may imply, it was primarily oriented at meeting consumer needs. It didn't provide inventors any new options to become entrepreneurs. Instead of encouraging innovators to flourish on their own, the creative economy emphasized "employment-based creativity."

4. The creator economy

Next, huge IT platforms like Google, Facebook, YouTube, and others competed with traditional mainstream media.

During the 2008 global financial crisis, these mediums surpassed traditional media. People relied on them for information, knowledge, and networking. That was a digital media revolution. The creator economy started there.

The new economic paradigm aimed to engage and convert clients. The creator economy allowed customers to engage, interact, and provide value, unlike the consumer economy. It gave them instruments to promote themselves as "products" and make money.

Writers, singers, painters, and other creators have a great way to reach fans. Instead of appeasing old-fashioned gatekeepers (producers, casting managers, publishers, etc.), they can use the platforms to express their talent and gain admirers. Barriers fell.

It's not only for pros. Everyone with a laptop and internet can now create.

2022 creator economy:

Since there is no academic description for the current creator economy, we can freestyle.

The current (or Web2) creator economy is fueled by interactive digital platforms, marketplaces, and tools that allow users to access, produce, and monetize content.

No entry hurdles or casting in the creative economy. Sign up and follow platforms' rules. Trick: A platform's algorithm aggregates your data and tracks you. This is the payment for participation.

The platforms offer content creation, design, and ad distribution options. This is platforms' main revenue source.

The creator economy opens many avenues for creators to monetize their work. Artists can now earn money through advertising, tipping, brand sponsorship, affiliate links, streaming, and other digital marketing activities.

Even if your content isn't digital, you can utilize platforms to promote it, interact and convert your audience, and more. No limits. However, some of your income always goes to a platform (well, a huge one).

The creator economy aims to empower online entrepreneurship by offering digital marketing tools and reducing impediments.

Barriers remain. They are just different. Next articles will examine these.

Why update the creator economy for Web3?

I could address this question by listing the present creator economy's difficulties that led us to contemplate a Web3 upgrade.

I don't think these difficulties are the main cause. The mentality shift made us see these challenges and understand there was a better reality without them.

Crypto drove this thinking shift. It promoted disintermediation, independence from third-party service providers, 100% data ownership, and self-sovereignty. Crypto has changed the way we view everyday things.

Crypto's disruptive mission has migrated to other economic segments. It's now called Web3. Web3's creator economy is unique.

Here's the essence of the Web3 economy:

• Eliminating middlemen between creators and fans.

• 100% of creators' data, brand, and effort.

• Business and money-making transparency.

• Authentic originality above ad-driven content.

In the next several articles, I'll explain. We'll also discuss the creator economy and Web3's remedies.

Final thoughts

The creator economy is the organic developmental stage we've reached after all these social and economic transformations.

The Web3 paradigm of the creator economy intends to allow creators to construct their own independent "open economy" and directly monetize it without a third party.

If this approach succeeds, we may enter a new era of wealth creation where producers aren't only the products. New economies will emerge.

This article is a summary. To read the full post, click here.

Do you want to build and launch your own software company? To do this, all you need is a product that solves a problem.

Coming up with profitable ideas is not that easy. But you’re in luck because you got me!

I’ll give you the idea for free. All you need to do is execute it properly.

If you’re ready, let’s jump right into it! Starting with the problem.

Problem

Youtube has many creators. Every day, they think of new ways to entertain or inform us.

They work hard to make videos. Many of their efforts go to waste. They limit their revenue and reach.

Solution

Content repurposing solves this problem.

One video can become several TikToks. Creating YouTube videos from a podcast episode.

Or, one video might become a blog entry.

By turning videos into blog entries, Youtubers may develop evergreen SEO content, attract a new audience, and reach a non-YouTube audience.

Many YouTube creators want this easy feature.

Let's build it!

Implementation

We identified the problem, and we have a solution. All that’s left to do is see how it can be done.

Monitoring new video uploads

First, watch when a friend uploads a new video. Everything should happen automatically without user input.

YouTube Webhooks make this easy. Our server listens for YouTube Webhook notifications.

After publishing a new video, we create a conversion job.

Creating a Blog Post from a Video

Next, turn a video into a blog article.

To convert, we must extract the video's audio (which can be achieved by using FFmpeg on the server).

Once we have the audio channel, we can use speech-to-text.

Services can accomplish this easily.

• Speech-to-text on Google

• Google Translate

• Deepgram

Deepgram's affordability and integration make it my pick.

After conversion, the blog post needs formatting, error checking, and proofreading.

After this, a new blog post will appear in our web app's dashboard.

Completing a blog post

After conversion, users must examine and amend their blog posts.

Our application dashboard would handle all of this. It's a dashboard-style software where users can:

• Link their Youtube account

• Check out the converted videos in the future.

• View the conversions that are ongoing.

• Edit and format converted blog articles.

It's a web-based app.

It doesn't matter how it's made but I'd choose Next.js.

Next.js is a React front-end standard. Vercel serverless functions could conduct the conversions.

This would let me host the software for free and reduce server expenditures.

Taking It One Step Further

SaaS in a nutshell. Future improvements include integrating with WordPress or Ghost.

Our app users could then publish blog posts. Streamlining the procedure.

MVPs don't need this functionality.

Final Thoughts

Repurposing content helps you post more often, reach more people, and develop faster.

Many agencies charge a fortune for this service. Handmade means pricey.

Content creators will go crazy if you automate and cheaply solve this problem.

Just execute this idea!

“I think if I had time to do something else, I would be so excited to go after this company right now.”

Sam Altman, CEO of Open AI, recently discussed AI's present and future.

Open AI is important. They're creating the cyberpunk and sci-fi worlds.

They use the most advanced algorithms and data sets.

GPT-3...sound familiar? Open AI built most copyrighting software. Peppertype, Jasper AI, Rytr. If you've used any, you'll be shocked by the quality.

Open AI isn't only GPT-3. They created DallE-2 and Whisper (a speech recognition software released last week).

What will they do next? What's the next great chance?

Sam Altman, CEO of Open AI, recently gave a lecture about the next trillion-dollar AI opportunity.

Who is the organization behind Open AI?

Open AI first. If you know, skip it.

Open AI is one of the earliest private AI startups. Elon Musk, Greg Brockman, and Rebekah Mercer established OpenAI in December 2015.

OpenAI has helped its citizens and AI since its birth.

They have scary-good algorithms.

Their GPT-3 natural language processing program is excellent.

The algorithm's exponential growth is astounding. GPT-2 came out in November 2019. May 2020 brought GPT-3.

Massive computation and datasets improved the technique in just a year. New York Times said GPT-3 could write like a human.

Same for Dall-E. Dall-E 2 was announced in April 2022. Dall-E 2 won a Colorado art contest.

Open AI's algorithms challenge jobs we thought required human innovation.

So what does Sam Altman think?

The Present Situation and AI's Limitations

During the interview, Sam states that we are still at the tip of the iceberg.

So I think so far, we’ve been in the realm where you can do an incredible copywriting business or you can do an education service or whatever. But I don’t think we’ve yet seen the people go after the trillion dollar take on Google.

He's right that AI can't generate net new human knowledge. It can train and synthesize vast amounts of knowledge, but it simply reproduces human work.

“It’s not going to cure cancer. It’s not going to add to the sum total of human scientific knowledge.”

But the key word is yet.

And that is what I think will turn out to be wrong that most surprises the current experts in the field.

Reinforcing his point that massive innovations are yet to come.

But where?

The Next $1 Trillion AI Company

Sam predicts a bio or genomic breakthrough.

There’s been some promising work in genomics, but stuff on a bench top hasn’t really impacted it. I think that’s going to change. And I think this is one of these areas where there will be these new $100 billion to $1 trillion companies started, and those areas are rare.

Avoid human trials since they take time. Bio-materials or simulators are suitable beginning points.

AI may have a breakthrough. DeepMind, an OpenAI competitor, has developed AlphaFold to predict protein 3D structures.

It could change how we see proteins and their function. AlphaFold could provide fresh understanding into how proteins work and diseases originate by revealing their structure. This could lead to Alzheimer's and cancer treatments. AlphaFold could speed up medication development by revealing how proteins interact with medicines.

Deep Mind offered 200 million protein structures for scientists to download (including sustainability, food insecurity, and neglected diseases).

Being in AI for 4+ years, I'm amazed at the progress. We're past the hype cycle, as evidenced by the collapse of AI startups like C3 AI, and have entered a productive phase.

We'll see innovative enterprises that could replace Google and other trillion-dollar companies.

What happens after AI adoption is scary and unpredictable. How will AGI (Artificial General Intelligence) affect us? Highly autonomous systems that exceed humans at valuable work (Open AI)

My guess is that the things that we’ll have to figure out are how we think about fairly distributing wealth, access to AGI systems, which will be the commodity of the realm, and governance, how we collectively decide what they can do, what they don’t do, things like that. And I think figuring out the answer to those questions is going to just be huge. — Sam Altman CEO

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.