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.

DATA MINING WITH SUPERALGORES

Old pots produce the best soup.

Science has always inspired indicator design. From physics to signal processing, many indicators use concepts from mechanical engineering, electronics, and probability. In Superalgos' Data Mining section, we've explored using thermodynamics and information theory to construct indicators and using statistical and probabilistic techniques like reduced normal law to take advantage of low probability events.

An asset's price is like a mechanical object revolving around its moving average. Using this approach, we could design an indicator using the oscillator's Total Energy. An oscillator's energy is finite and constant. Since we don't expect the price to follow the harmonic oscillator, this energy should deviate from the perfect situation, and the maximum of divergence may provide us valuable information on the price's moving average.

Definition of the Harmonic Oscillator in Few Words

Sinusoidal function describes a harmonic oscillator. The time-constant energy equation for a harmonic oscillator is:

With

Time saves energy.

In a mechanical harmonic oscillator, total energy equals kinetic energy plus potential energy. The formula for energy is the same for every kind of harmonic oscillator; only the terms of total energy must be adapted to fit the relevant units. Each oscillator has a velocity component (kinetic energy) and a position to equilibrium component (potential energy).

The Price Oscillator and the Energy Formula

Considering the harmonic oscillator definition, we must specify kinetic and potential components for our price oscillator. We define oscillator velocity as the rate of change and equilibrium position as the price's distance from its moving average.

Price kinetic energy:

It's like:

With

and

L is the number of periods for the rate of change calculation and P for the close price EMA calculation.

Total price oscillator energy =

Given that an asset's price can theoretically vary at a limitless speed and be endlessly far from its moving average, we don't expect this formula's outcome to be constrained. We'll normalize it using Z-Score for convenience of usage and readability, which also allows probabilistic interpretation.

Over 20 periods, we'll calculate E's moving average and standard deviation.

We calculated Z on BTC/USDT with L = 10 and P = 21 using Knime Analytics.

The graph is detrended. We added two horizontal lines at +/- 1.6 to construct a 94.5% probability zone based on reduced normal law tables. Price cycles to its moving average oscillate clearly. Red and green arrows illustrate where the oscillator crosses the top and lower limits, corresponding to the maximum/minimum price oscillation. Since the results seem noisy, we may apply a non-lagging low-pass or multipole filter like Butterworth or Laguerre filters and employ dynamic bands at a multiple of Z's standard deviation instead of fixed levels.

Kinetic Detrender Implementation in Superalgos

The Superalgos Kinetic detrender features fixed upper and lower levels and dynamic volatility bands.

The code is pretty basic and does not require a huge amount of code lines.

It starts with the standard definitions of the candle pointer and the constant declaration :

let candle = record.current
let len = 10
let P = 21
let T = 20
let up = 1.6
let low = 1.6

Upper and lower dynamic volatility band constants are up and low.

We proceed to the initialization of the previous value for EMA :

if (variable.prevEMA === undefined) {
    variable.prevEMA = candle.close
}

And the calculation of EMA with a function (it is worth noticing the function is declared at the end of the code snippet in Superalgos) :

variable.ema = calculateEMA(P, candle.close, variable.prevEMA)
//EMA calculation
function calculateEMA(periods, price, previousEMA) {
    let k = 2 / (periods + 1)
    return price * k + previousEMA * (1 - k)
}

The rate of change is calculated by first storing the right amount of close price values and proceeding to the calculation by dividing the current close price by the first member of the close price array:

variable.allClose.push(candle.close)
if (variable.allClose.length > len) {
    variable.allClose.splice(0, 1)
}
if (variable.allClose.length === len) {
    variable.roc = candle.close / variable.allClose[0]
} else {
    variable.roc = 1
}

Finally, we get energy with a single line:

variable.E = 1 / 2 * len * variable.roc + 1 / 2 * P * candle.close / variable.ema

The Z calculation reuses code from Z-Normalization-based indicators:

variable.allE.push(variable.E)
if (variable.allE.length > T) {
    variable.allE.splice(0, 1)
}
variable.sum = 0
variable.SQ = 0
if (variable.allE.length === T) {
    for (var i = 0; i < T; i++) {
        variable.sum += variable.allE[i]
    }
    variable.MA = variable.sum / T
for (var i = 0; i < T; i++) {
        variable.SQ += Math.pow(variable.allE[i] - variable.MA, 2)
    }
    variable.sigma = Math.sqrt(variable.SQ / T)
variable.Z = (variable.E - variable.MA) / variable.sigma
} else {
    variable.Z = 0
}
variable.allZ.push(variable.Z)
if (variable.allZ.length > T) {
    variable.allZ.splice(0, 1)
}
variable.sum = 0
variable.SQ = 0
if (variable.allZ.length === T) {
    for (var i = 0; i < T; i++) {
        variable.sum += variable.allZ[i]
    }
    variable.MAZ = variable.sum / T
for (var i = 0; i < T; i++) {
        variable.SQ += Math.pow(variable.allZ[i] - variable.MAZ, 2)
    }
    variable.sigZ = Math.sqrt(variable.SQ / T)
} else {
    variable.MAZ = variable.Z
    variable.sigZ = variable.MAZ * 0.02
}
variable.upper = variable.MAZ + up * variable.sigZ
variable.lower = variable.MAZ - low * variable.sigZ

We also update the EMA value.

variable.prevEMA = variable.EMA

Conclusion

We showed how to build a detrended oscillator using simple harmonic oscillator theory. Kinetic detrender's main line oscillates between 2 fixed levels framing 95% of the values and 2 dynamic levels, leading to auto-adaptive mean reversion zones.

Superalgos' Normalized Momentum data mine has the Kinetic detrender indication.

All the material here can be reused and integrated freely by linking to this article and Superalgos.

This post is informative and not financial advice. Seek expert counsel before trading. Risk using this material.

The Federal Funds Rate (FFR) is the target interest rate set by the Federal Reserve System (Fed)'s policy-making body (FOMC). This target is the rate at which the Fed suggests commercial banks borrow and lend their excess reserves overnight to each other.

The FOMC meets 8 times a year to set the target FFR. This is supposed to promote economic growth. The overnight lending market sets the actual rate based on commercial banks' short-term reserves. If the market strays too far, the Fed intervenes.

Banks must keep a certain percentage of their deposits in a Federal Reserve account. A bank's reserve requirement is a percentage of its total deposits. End-of-day bank account balances averaged over two-week reserve maintenance periods are used to determine reserve requirements.

If a bank expects to have end-of-day balances above what's needed, it can lend the excess to another institution.

The FOMC adjusts interest rates based on economic indicators that show inflation, recession, or other issues that affect economic growth. Core inflation and durable goods orders are indicators.

In response to economic conditions, the FFR target has changed over time. In the early 1980s, inflation pushed it to 20%. During the Great Recession of 2007-2009, the rate was slashed to 0.15 percent to encourage growth.

Inflation picked up in May 2022 despite earlier rate hikes, prompting today's 0.75 percent point increase. The largest increase since 1994. It might rise to around 3.375% this year and 3.1% by the end of 2024.

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We've all been there: You propose a suggestion to your team at a meeting, and most people appear on board, but a handful or small minority aren't. How can we achieve collective buy-in when we need to go forward but don't know how to deal with some team members' perceived intransigence?

Steps:

1. Investigate the divergent opinions: Begin by sincerely attempting to comprehend the viewpoint of your disagreeing coworkers. Maybe it makes sense to switch horses in the middle of the race. Have you completely overlooked a blind spot, such as a political concern that could arise as an unexpected result of proceeding? This is crucial to ensure that the person or people feel heard as well as to advance the goals of the team. Sometimes all individuals need is a little affirmation before they fully accept your point of view.

• It says a lot about you as a leader to be someone who always lets the perceived greatest idea win, regardless of the originating channel, if after studying and evaluating you see the necessity to align with the divergent position.

• If, after investigation and assessment, you determine that you must adhere to the original strategy, we go to Step 2.

2. Disagree and Commit: Jeff Bezos, CEO of Amazon, has had this experience, and Julie Zhuo describes how he handles it in her book The Making of a Manager.

It's OK to disagree when the team is moving in the right direction, but it's not OK to accidentally or purposefully damage the team's efforts because you disagree. Let the team know your opinion, but then help them achieve company goals even if they disagree. Unknown. You could be wrong in today's ever-changing environment.

So next time you have a team member who seems to be dissenting and you've tried the previous tactics, you may ask the individual in the meeting I understand you but I don't want us to leave without you on board I need your permission to commit to this approach would you give us your commitment?

Trigger warning — Andrew Tate is running a genius marketing campaign

Andrew Tate is a 2022 internet celebrity.

Kickboxing world champion became rich playboy with controversial views on gender roles.

Andrew's get-rich-quick scheme isn't new. His social media popularity is impressive.

He’s currently running one of the most genius marketing campaigns in history.

He pulls society's pendulum away from diversity and inclusion and toward diversion and exclusion. He's unstoppable.

Here’s everything you need to know about Andrew Tate. And how he’s playing chess while the world plays checkers.

Cobra Tate is the name he goes by.

American-born, English-raised entrepreneur Andrew Tate lives in Romania.

Romania? Says Andrew,

“I prefer a country in which corruption is available to everyone.”

Andrew was a professional kickboxer with the ring moniker Cobra before starting Hustlers University.

Before that, he liked chess and worshipped his father.

Emory Andrew Tate III is named after his grandmaster chess player father.

Emory was the first black-American chess champion. He was military, martial arts-trained, and multilingual. A superhuman.

He lived in his car to make ends meet.

Andrew and Tristan relocated to England with their mother when their parents split.

It was there that Andrew began his climb toward becoming one of the internet’s greatest villains.

Andrew fell in love with kickboxing.

Andrew spent his 20s as a professional kickboxer and reality TV star, featuring on Big Brother UK and The Ultimate Traveller.

These 3 incidents, along with a chip on his shoulder, foreshadowed Andrews' social media breakthrough.

• Chess

• Combat sports

• Reality television

A dangerous trio.

Andrew started making money online after quitting kickboxing in 2017 due to an eye issue.

Andrew didn't suddenly become popular.

Andrew's web work started going viral in 2022.

Due to his contentious views on patriarchy and gender norms, he's labeled the King of Toxic Masculinity. His most contentious views (trigger warning):

• “Women are intrinsically lazy.”

• “Female promiscuity is disgusting.”

• “Women shouldn’t drive cars or fly planes.”

• “A lot of the world’s problems would be solved if women had their body count tattooed on their foreheads.”

Andrew's two main beliefs are:

1. “These are my personal opinions based on my experiences.”

2. “I believe men are better at some things and women are better at some things. We are not equal.”

Andrew intentionally offends.

Andrew's thoughts began circulating online in 2022.

In July 2022, he was one of the most Googled humans, surpassing:

• Joe Biden

• Donald Trump

• Kim Kardashian

Andrews' rise is a mystery since no one can censure or suppress him. This is largely because Andrew nor his team post his clips.

But more on that later.

Andrew's path to wealth.

Andrew Tate is a self-made millionaire. His morality is uncertain.

Andrew and Tristan needed money soon after retiring from kickboxing.

“I owed some money to some dangerous people. I had $70K and needed $100K to stay alive.”

Andrews lost $20K on roulette at a local casino.

Andrew had one week to make $50,000, so he started planning. Andrew locked himself in a chamber like Thomas Edison to solve an energy dilemma.

He listed his assets.

• Physical strength (but couldn’t fight)

• a BMW (worth around $20K)

• Intelligence (but no outlet)

A lightbulb.

He had an epiphany after viewing a webcam ad. He sought aid from women, ironically. His 5 international girlfriends are assets.

Then, a lightbulb.

Andrew and Tristan messaged and flew 7 women to a posh restaurant. Selling desperation masked as opportunity, Andrew pitched his master plan:

A webcam business — with a 50/50 revenue split.

5 women left.

2 stayed.

Andrew Tate, a broke kickboxer, became Top G, Cobra Tate.

The business model was simple — yet sad.

Andrew's girlfriends moved in with him and spoke online for 15+ hours a day. Andrew handled ads and equipment as the women posed.

Andrew eventually took over their keyboards, believing he knew what men wanted more than women.

Andrew detailed on the Full Send Podcast how he emotionally manipulated men for millions. They sold houses, automobiles, and life savings to fuel their companionship addiction.

When asked if he felt bad, Andrew said,

“F*ck no.“

Andrew and Tristan wiped off debts, hired workers, and diversified.

Tristan supervised OnlyFans models.

Andrew bought Romanian casinos and MMA league RXF (Real Xtreme Fighting).

Pandemic struck suddenly.

Andrew couldn't run his 2 businesses without a plan. Another easy moneymaker.

He banked on Hustlers University.

The actual cause of Andrew's ubiquity.

On a Your Mom’s House episode Andrew's 4 main revenue sources:

1. Hustler’s University

2. Owning casinos in Romania

3. Owning 10% of the Romanian MMA league “RXF”

4. “The War Room” — a society of rich and powerful men

When the pandemic hit, 3/4 became inoperable.

So he expanded Hustlers University.

But what is Hustler’s University?

Andrew says Hustlers University teaches 18 wealth-building tactics online. Examples:

• Real estate

• Copywriting

• Amazon FBA

• Dropshipping

• Flipping Cryptos

How to swiftly become wealthy.

Lessons are imprecise, rudimentary, and macro-focused, say reviews. Invest wisely, etc. Everything is free online.

You pay for community. One unique income stream.

The only money-making mechanism that keeps the course from being a scam.

The truth is, many of Andrew’s students are actually making money. Maybe not from the free YouTube knowledge Andrew and his professors teach in the course, but through Hustler’s University’s affiliate program.

Affiliates earn 10% commission for each new student = $5.

Students can earn $10 for each new referral in the first two months.

Andrew earns $50 per membership per month.

This affiliate program isn’t anything special — in fact, it’s on the lower end of affiliate payouts. Normally, it wouldn’t be very lucrative.

But it has one secret weapon— Andrew and his viral opinions.

Andrew is viral. Andrew went on a media tour in January 2022 after appearing on Your Mom's House.

• The Full Send Podcast

• The Fresh & Fit Podcast

• “BFFs” by Barstool Sports

And many, many more…

He chatted with Twitch streamers. Hustlers University wanted more controversy (and clips).

Here’s the strategy behind Hustler’s University that has (allegedly) earned students upwards of $10K per month:

1. Make a social media profile with Andrew Tates' name and photo.

2. Post any of the online videos of Andrews that have gone viral.

3. Include a referral link in your bio.

Effectively simple.

Andrew's controversy attracts additional students. More student clips circulate as more join. Andrew's students earn more and promote the product as he goes viral.

A brilliant plan that's functioning.

At the beginning of his media tour, Hustler’s University had 5,000 students. 6 months in, and he now has over 100,000.

One income stream generates $5 million every month.

Andrew's approach is not new.

But it is different.

In the early 2010s, Tai Lopez dominated the internet.

His viral video showed his house.

“Here in my garage. Just bought this new Lamborghini.”

Tais' marketing focused on intellect, not strength, power, and wealth to attract women.

How reading quicker leads to financial freedom in 67 steps.

Years later, it was revealed that Tai Lopez rented the mansion and Lamborghini as a marketing ploy to build social proof. Meanwhile, he was living in his friend’s trailer.

Faked success is an old tactic.

Andrew is doing something similar. But with one major distinction.

Andrew outsources his virality — making him nearly impossible to cancel.

In 2022, authorities searched Andrews' estate over human trafficking suspicions. Investigation continues despite withdrawn charges.

Andrew's divisive nature would normally get him fired. Andrew's enterprises and celebrity don't rely on social media.

He doesn't promote or pay for ads. Instead, he encourages his students and anyone wishing to get rich quick to advertise his work.

Because everything goes through his affiliate program. Old saying:

“All publicity is good publicity.”

Final thoughts: it’s ok to feel triggered.

Tate is divisive.

His emotionally charged words are human nature. Andrews created the controversy.

It's non-personal.

His opinions are those of one person. Not world nor generational opinion.

Briefly:

• It's easy to understand why Andrews' face is ubiquitous. Money.

• The world wide web is a chessboard. Misdirection is part of it.

• It’s not personal, it’s business.

• Controversy sells

Sometimes understanding the ‘why’, can help you deal with the ‘what.’