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.

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.

Mainstream economics haven't noticed.

What come to mind when I say the word "economics"?

Probably GDP, unemployment, and inflation.

If you've ever watched the news or listened to an economist, they'll use data like these to defend a political goal.

The issue is that these statistics are total bunk.

I'm being provocative, but I mean it:

• The economy is not measured by GDP.

• How many people are unemployed is not counted in the unemployment rate.

• Inflation is not measured by the CPI.

All orthodox economists' major economic statistics are either wrong or falsified.

Government institutions create all these stats. The administration wants to reassure citizens the economy is doing well.

GDP does not reflect economic expansion.

GDP measures a country's economic size and growth. It’s calculated by the BEA, a government agency.

The US has the world's largest (self-reported) GDP, growing 2-3% annually.

If GDP rises, the economy is healthy, say economists.

Why is the GDP flawed?

GDP measures a country's yearly spending.

The government may adjust this to make the economy look good.

GDP = C + G + I + NX

C = Consumer Spending

G = Government Spending

I = Investments (Equipment, inventories, housing, etc.)

NX = Exports minus Imports

GDP is a country's annual spending.

The government can print money to boost GDP. The government has a motive to increase and manage GDP.

Because government expenditure is part of GDP, printing money and spending it on anything will raise GDP.

They've done this. Since 1950, US government spending has grown 8% annually, faster than GDP.

In 2022, government spending accounted for 44% of GDP. It's the highest since WWII. In 1790-1910, it was 3% of GDP.

Who cares?

The economy isn't only spending. Focus on citizens' purchasing power or quality of life.

Since GDP just measures spending, the government can print money to boost GDP.

Even if Americans are poorer than last year, economists can say GDP is up and everything is fine.

How many people are unemployed is not counted in the unemployment rate.

The unemployment rate measures a country's labor market. If unemployment is high, people aren't doing well economically.

The BLS estimates the (self-reported) unemployment rate as 3-4%.

Why is the unemployment rate so high?

The US government surveys 100k persons to measure unemployment. They extrapolate this data for the country.

They come into 3 categories:

• Employed

People with jobs are employed … duh.

• Unemployed

People who are “jobless, looking for a job, and available for work” are unemployed

• Not in the labor force

The “labor force” is the employed + the unemployed.

The unemployment rate is the percentage of unemployed workers.

Problem is unemployed definition. You must actively seek work to be considered unemployed.

You're no longer unemployed if you haven't interviewed in 4 weeks.

This shit makes no goddamn sense.

Why does this matter?

You can't interview if there are no positions available. You're no longer unemployed after 4 weeks.

In 1994, the BLS redefined "unemployed" to exclude discouraged workers.

If you haven't interviewed in 4 weeks, you're no longer counted in the unemployment rate.

If unemployment were measured by total unemployed, it would be 25%.

Because the government wants to keep the unemployment rate low, they modify the definition.

If every US resident was unemployed and had no job interviews, economists would declare 0% unemployment. Excellent!

Inflation is not measured by the CPI.

The BLS measures CPI. This month was the highest since 1981.

CPI measures the cost of a basket of products across time. Food, energy, shelter, and clothes are included.

A 9.1% CPI means the basket of items is 9.1% more expensive.

What is the CPI problem?

Here's a more detailed explanation of CPI's flaws.

In summary, CPI is manipulated to be understated.

Housing costs are understated to manipulate CPI. Housing accounts for 33% of the CPI because it's the biggest expense for most people.

This signifies it's the biggest CPI weight.

Rather than using actual house prices, the Bureau of Labor Statistics essentially makes shit up. You can read more about the process here.

Surprise! It’s bullshit

The BLS stated Shelter's price rose 5.5% this month.

House prices are up 11-21%. (Source 1, Source 2, Source 3)

Rents are up 14-26%. (Source 1, Source 2)

Why is this important?

If CPI included housing prices, it would be 12-15 percent this month, not 9.1 percent.

9% inflation is nuts. Your money's value halves every 7 years at 9% inflation.

Worse is 15% inflation. Your money halves every 4 years at 15% inflation.

If everyone realized they needed to double their wage every 4-5 years to stay wealthy, there would be riots.

Inflation drains our money's value so the government can keep printing it.

The Solution

Most individuals know the existing system doesn't work, but can't explain why.

People work hard yet lag behind. The government lies about the economy's data.

In reality:

• GDP has been down since 2008

• 25% of Americans are unemployed

• Inflation is actually 15%

People might join together to vote out kleptocratic politicians if they knew the reality.

Having reliable economic data is the first step.

People can't understand the situation without sufficient information. Instead of immigrants or billionaires, people would blame liar politicians.

Here’s the vision:

A decentralized, transparent, and global dashboard that tracks economic data like GDP, unemployment, and inflation for every country on Earth.

Government incentives influence economic statistics.

ShadowStats has already started this effort, but the calculations must be transparent, decentralized, and global to be effective.

If interested, email me at trevorstark02@gmail.com.

Here are some links to further your research:

1. MIT Billion Prices Project

2. 1729 Decentralized Inflation Dashboard Project

3. Balaji Srinivasan on “Fiat Information VS. Crypto Information”

Orion's red supergiant Betelgeuse erupted. This is astronomers' most magnificent occurrence.

Betelgeuse, a supergiant star in Orion, garnered attention in 2019 for its peculiar appearance. It continued to dim in 2020.

The star was previously thought to explode as a supernova. Studying the event has revealed what happened to Betelgeuse since it happened.

Astronomers saw that the star released a large amount of material, causing it to lose a section of its surface.

They have never seen anything like this and are unsure what caused the star to release so much material.

According to Harvard-Smithsonian Center for Astrophysics astrophysicist Andrea Dupre, astronomers' data reveals an unexplained mystery.

They say it's a new technique to examine star evolution. The James Webb telescope revealed the star's surface features.

Corona flares are stellar mass ejections. These eruptions change the Sun's outer atmosphere.

This could affect power grids and satellite communications if it hits Earth.

Betelgeuse's flare ejected four times more material than the Sun's corona flare.

Astronomers have monitored star rhythms for 50 years. They've seen its dimming and brightening cycle start, stop, and repeat.

Monitoring Betelgeuse's pulse revealed the eruption's power.

Dupre believes the star's convection cells are still amplifying the blast's effects, comparing it to an imbalanced washing machine tub.

The star's outer layer has returned to normal, Hubble data shows. The photosphere slowly rebuilds its springy surface.

Dupre noted the star's unusual behavior. For instance, it’s causing its interior to bounce.

This suggests that the mass ejections that caused the star's surface to lose mass were two separate processes.

Researchers hope to better understand star mass ejection with the James Webb Space Telescope.

A fraud, free-market, antifragility tale

Crypto's only rival is public opinion.

In less than a week, mainstream media, bloggers, and TikTokers turned on FTX's founder.

While some were surprised, almost everyone with a keyboard and a Twitter account predicted the FTX collapse. These financial oracles should have warned the 1.2 million people Sam Bankman-Fried duped.

After happening, unexpected events seem obvious to our brains. It's a bug and a feature because it helps us cope with disasters and makes our reasoning suck.

Nobody predicted the FTX debacle. Bloomberg? Politicians. Non-famous. No cryptologists. Who?

When FTX imploded, taking billions of dollars with it, an outrage bomb went off, and the resulting shockwave threatens the crypto market's existence.

As someone who lost more than $78,000 in a crypto scam in 2020, I can only understand people’s reactions.  When the dust settles and rationality returns, we'll realize this is a natural occurrence in every free market.

What specifically occurred with FTX? (Skip if you are aware.)

FTX is a cryptocurrency exchange where customers can trade with cash. It reached #3 in less than two years as the fastest-growing platform of its kind.

FTX's performance helped make SBF the crypto poster boy. Other reasons include his altruistic public image, his support for the Democrats, and his company Alameda Research.

Alameda Research made a fortune arbitraging Bitcoin.

Arbitrage trading uses small price differences between two markets to make money. Bitcoin costs $20k in Japan and $21k in the US. Alameda Research did that for months, making $1 million per day.

Later, as its capital grew, Alameda expanded its trading activities and began investing in other companies.

Let's now discuss FTX.

SBF's diabolic master plan began when he used FTX-created FTT coins to inflate his trading company's balance sheets. He used inflated Alameda numbers to secure bank loans.

SBF used money he printed himself as collateral to borrow billions for capital. Coindesk exposed him in a report.

One of FTX's early investors tweeted that he planned to sell his FTT coins over the next few months. This would be a minor event if the investor wasn't Binance CEO Changpeng Zhao (CZ).

The crypto space saw a red WARNING sign when CZ cut ties with FTX. Everyone with an FTX account and a brain withdrew money. Two events followed. FTT fell from $20 to $4 in less than 72 hours, and FTX couldn't meet withdrawal requests, spreading panic.

SBF reassured FTX users on Twitter. Good assets.

He lied.

SBF falsely claimed FTX had a liquidity crunch. At the time of his initial claims, FTX owed about $8 billion to its customers. Liquidity shortages are usually minor. To get cash, sell assets. In the case of FTX, the main asset was printed FTT coins.

Sam wouldn't get out of trouble even if he slashed the discount (from $20 to $4) and sold every FTT. He'd flood the crypto market with his homemade coins, causing the price to crash.

SBF was trapped. He approached Binance about a buyout, which seemed good until Binance looked at FTX's books.

Binance's tweet ended SBF, and he had to apologize, resign as CEO, and file for bankruptcy.

Bloomberg estimated Sam's net worth to be zero by the end of that week. 0!

But that's not all. Twitter investigations exposed fraud at FTX and Alameda Research. SBF used customer funds to trade and invest in other companies.

Thanks to the Twitter indie reporters who made the mainstream press look amateurish. Some Twitter detectives didn't sleep for 30 hours to find answers. Others added to existing threads. Memes were hilarious.

One question kept repeating in my bald head as I watched the Blue Bird. Sam, WTF?

Then I understood.

SBF wanted that FTX becomes a bank.

Think about this. FTX seems healthy a few weeks ago. You buy 2 bitcoins using FTX. You'd expect the platform to take your dollars and debit your wallet, right?

No. They give I-Owe-Yous.

FTX records owing you 2 bitcoins in its internal ledger but doesn't credit your account. Given SBF's tricks, I'd bet on nothing.

What happens if they don't credit my account with 2 bitcoins? Your money goes into FTX's capital, where SBF and his friends invest in marketing, political endorsements, and buying other companies.

Over its two-year existence, FTX invested in 130 companies. Once they make a profit on their purchases, they'll pay you and keep the rest.

One detail makes their strategy dumb. If all FTX customers withdraw at once, everything collapses.

Financially savvy people think FTX's collapse resembles a bank run, and they're right. SBF designed FTX to operate like a bank.

You expect your bank to open a drawer with your name and put $1,000 in it when you deposit $1,000. They deposit $100 in your drawer and create an I-Owe-You for $900. What happens to $900?

Let's sum it up: It's boring and headache-inducing.

When you deposit money in a bank, they can keep 10% and lend the rest. Fractional Reserve Banking is a popular method. Fractional reserves operate within and across banks.

Fractional reserve banking generates $10,000 for every $1,000 deposited. People will pay off their debt plus interest.

As long as banks work together and the economy grows, their model works well.

SBF tried to replicate the system but forgot two details. First, traditional banks need verifiable collateral like real estate, jewelry, art, stocks, and bonds, not digital coupons. Traditional banks developed a liquidity buffer. The Federal Reserve (or Central Bank) injects massive cash into troubled banks.

Massive cash injections come from taxpayers. You and I pay for bankers' mistakes and annual bonuses. Yes, you may think banking is rigged. It's rigged, but it's the best financial game in 150 years. We accept its flaws, including bailouts for too-big-to-fail companies.

Anyway.

SBF wanted Binance's bailout. Binance said no, which was good for the crypto market.

Free markets are resilient.

Nassim Nicholas Taleb coined the term antifragility.

“Some things benefit from shocks; they thrive and grow when exposed to volatility, randomness, disorder, and stressors and love adventure, risk, and uncertainty. Yet, in spite of the ubiquity of the phenomenon, there is no word for the exact opposite of fragile. Let us call it antifragile. Antifragility is beyond resilience or robustness. The resilient resists shocks and stays the same; the antifragile gets better.”

The easiest way to understand how antifragile systems behave is to compare them with other types of systems.

• Glass is like a fragile system. It snaps when shocked.

• Similar to rubber, a resilient system. After a stressful episode, it bounces back.

• A system that is antifragile is similar to a muscle. As it is torn in the gym, it gets stronger.

Time-changed things are antifragile. Culture, tech innovation, restaurants, revolutions, book sales, cuisine, economic success, and even muscle shape. These systems benefit from shocks and randomness in different ways, but they all pay a price for antifragility.

Same goes for the free market and financial institutions. Taleb's book uses restaurants as an example and ends with a reference to the 2008 crash.

“Restaurants are fragile. They compete with each other. But the collective of local restaurants is antifragile for that very reason. Had restaurants been individually robust, hence immortal, the overall business would be either stagnant or weak and would deliver nothing better than cafeteria food — and I mean Soviet-style cafeteria food. Further, it [the overall business] would be marred with systemic shortages, with once in a while a complete crisis and government bailout.”

Imagine the same thing with banks.

Independent banks would compete to offer the best services. If one of these banks fails, it will disappear. Customers and investors will suffer, but the market will recover from the dead banks' mistakes.

This idea underpins a free market. Bitcoin and other cryptocurrencies say this when criticizing traditional banking.

The traditional banking system's components never die. When a bank fails, the Federal Reserve steps in with a big taxpayer-funded check. This hinders bank evolution. If you don't let banking cells die and be replaced, your financial system won't be antifragile.

The interdependence of banks (centralization) means that one bank's mistake can sink the entire fleet, which brings us to SBF's ultimate travesty with FTX.

FTX has left the cryptocurrency gene pool.

FTX should be decentralized and independent. The super-star scammer invested in more than 130 crypto companies and linked them, creating a fragile banking-like structure. FTX seemed to say, "We exist because centralized banks are bad." But we'll be good, unlike the centralized banking system.

FTX saved several companies, including BlockFi and Voyager Digital.

FTX wanted to be a crypto bank conglomerate and Federal Reserve. SBF wanted to monopolize crypto markets. FTX wanted to be in bed with as many powerful people as possible, so SBF seduced politicians and celebrities.

Worst? People who saw SBF's plan flaws praised him. Experts, newspapers, and crypto fans praised FTX. When billions pour in, it's hard to realize FTX was acting against its nature.

Then, they act shocked when they realize FTX's fall triggered a domino effect. Some say the damage could wipe out the crypto market, but that's wrong.

Cell death is different from body death.

FTX is out of the game despite its size. Unfit, it fell victim to market natural selection.

Next?

The challengers keep coming. The crypto economy will improve with each failure.

Free markets are antifragile because their fragile parts compete, fostering evolution. With constructive feedback, evolution benefits customers and investors.

FTX shows that customers don't like being scammed, so the crypto market's health depends on them. Charlatans and con artists are eliminated quickly or slowly.

Crypto isn't immune to collapse. Cryptocurrencies can go extinct like biological species. Antifragility isn't immortality. A few more decades of evolution may be enough for humans to figure out how to best handle money, whether it's bitcoin, traditional banking, gold, or something else.

Keep your BS detector on. Start by being skeptical of this article's finance-related claims. Even if you think you understand finance, join the conversation.

We build a better future through dialogue. So listen, ask, and share. When you think you can't find common ground with the opposing view, remember:

Sam Bankman-Fried lied.

I shouldn't generalize, yet this is true.

Spending is simpler than earning.

Prove me wrong, but with home debt at $145k in 2020 and individual debt at $67k, people don't have their priorities straight.

Where does this loan originate?

Under-50 Americans owed $7.86 trillion in Q4 20T. That's more than the US's 3-trillion-dollar deficit.

Here’s a breakdown:
🏡 Mortgages/Home Equity Loans = $5.28 trillion (67%)
🎓 Student Loans = $1.20 trillion (15%)
🚗 Auto Loans = $0.80 trillion (10%)
💳 Credit Cards = $0.37 trillion (5%)
🏥 Other/Medical = $0.20 trillion (3%)

Images.google.com

At least the Fed and government can explain themselves with their debt balance which includes:

-Providing stimulus packages 2x for Covid relief

-Stabilizing the economy

-Reducing inflation and unemployment

-Providing for the military, education and farmers

No American should have this much debt.

Don’t get me wrong. Debt isn’t all the same. Yes, it’s a negative number but it carries different purposes which may not be all bad.

Good debt: Use those funds in hopes of them appreciating as an investment in the future

-Student loans
-Business loan
-Mortgage, home equity loan
-Experiences

Paying cash for a home is wasteful. Just if the home is exceptionally uncommon, only 1 in a million on the market, and has an incredible bargain with numerous bidders seeking higher prices should you do so.

To impress the vendor, pay cash so they can sell it quickly. Most people can't afford most properties outright. Only 15% of U.S. homebuyers can afford their home. Zillow reports that only 37% of homes are mortgage-free.

People have clearly overreached.

Ignore appearances.

5% down can buy a 10-bedroom mansion.

Not paying in cash isn't necessarily a negative thing given property prices have increased by 30% since 2008, and throughout the epidemic, we've seen work-from-homers resort to the midwest, avoiding pricey coastal cities like NYC and San Francisco.

By no means do I think NYC is dead, nothing will replace this beautiful city that never sleeps, and now is the perfect time to rent or buy when everything is below average value for people who always wanted to come but never could. Once social distance ends, cities will recover. 24/7 sardine-packed subways prove New York isn't designed for isolation.

When buying a home, pay 20% cash and the balance with a mortgage. A mortgage must be incorporated into other costs such as maintenance, brokerage fees, property taxes, etc. If you're stuck on why a home isn't right for you, read here. A mortgage must be paid until the term date. Whether its a 10 year or 30 year fixed mortgage, depending on interest rates, especially now as the 10-year yield is inching towards 1.25%, it's better to refinance in a lower interest rate environment and pay off your debt as well since the Fed will be inching interest rates up following the 10-year eventually to stabilize the economy, but I believe that won't be until after Covid and when businesses like luxury, air travel, and tourism will get bashed.

Bad debt: I guess the contrary must be true. There is no way to profit from the loan in the future, therefore it is just money down the drain.

-Luxury goods
-Credit card debt
-Fancy junk
-Vacations, weddings, parties, etc.

Credit cards and school loans are the two largest risks to the financial security of those under 50 since banks love to compound interest to affect your credit score and make it tougher to take out more loans, not that you should with that much debt anyhow. With a low credit score and heavy debt, banks take advantage of you because you need aid to pay more for their services. Paying back debt is the challenge for most.

Choose Not Chosen

As a financial literacy advocate and blogger, I prefer not to brag, but I will now. I know what to buy and what to avoid. My parents educated me to live a frugal, minimalist stealth wealth lifestyle by choice, not because we had to.

That's the lesson.

The poorest person who shows off with bling is trying to seem rich.

Rich people know garbage is a bad investment. Investing in education is one of the best long-term investments. With information, you can do anything.

Good with money shun some items out of respect and appreciation for what they have.

Less is more.

Instead of copying the Joneses, use what you have. They may look cheerful and stylish in their 20k ft home, yet they may be as broke as OJ Simpson in his 20-bedroom mansion.

Let's look at what appears good to follow and maintain your wealth.

#1: Quality comes before quantity

Being frugal doesn't entail being cheap and cruel. Rich individuals care about relationships and treating others correctly, not impressing them. You don't have to be rich to be good with money, although most are since they don't live the fantasy lifestyle.

Underspending is appreciating what you have.

Many people believe organic food is the same as washing chemical-laden produce. Hopefully. Organic, vegan, fresh vegetables from upstate may be more expensive in the short term, but they will help you live longer and save you money in the long run.

Consider. You'll save thousands a month eating McDonalds 3x a day instead of fresh seafood, veggies, and organic fruit, but your life will be shortened. If you want to save money and die early, go ahead, but I assume we all want to break the world record for longest person living and would rather spend less. Plus, elderly people get tax breaks, medicare, pensions, 401ks, etc. You're living for free, therefore eating fast food forever is a terrible decision.

With a few longer years, you may make hundreds or millions more in the stock market, spend more time with family, and just live.

Folks, health is wealth.

Consider the future benefit, not simply the cash sign. Cheapness is useless.

Same with stuff. Don't stock your closet with fast-fashion you can't wear for years. Buying inexpensive goods that will fail tomorrow is stupid.

Investing isn't only in stocks. You're living. Consume less.

#2: If you cannot afford it twice, you cannot afford it once

I learned this from my dad in 6th grade. I've been lucky to travel, experience things, go to a great university, and conduct many experiments that others without a stable, decent lifestyle can afford.

I didn't live this way because of my parents' paycheck or financial knowledge.

Saving and choosing caused it.

I always bring cash when I shop. I ditch Apple Pay and credit cards since I can spend all I want on even if my account bounces.

Banks are nasty. When you lose it, they profit.

Cash hinders banks' profits. Carrying a big, hefty wallet with cash is lame and annoying, but it's the best method to only spend what you need. Not for vacation, but for tiny daily expenses.

Physical currency lets you know how much you have for lunch or a taxi.

It's physical, thus losing it prevents debt.

If you can't afford it, it will harm more than help.

#3: You really can purchase happiness with money.

If used correctly, yes.

Happiness and satisfaction differ.

It won't bring you fulfillment because you must work hard on your own to help others, but you can travel and meet individuals you wouldn't otherwise meet.

You can meet your future co-worker or strike a deal while waiting an hour in first class for takeoff, or you can meet renowned people at a networking brunch.

Seen a pattern here?

Your time and money are best spent on connections. Not automobiles or firearms. That’s just stuff. It doesn’t make you a better person.

Be different if you've earned less. Instead of trying to win the lotto or become an NFL star for your first big salary, network online for free.

Be resourceful. Sign up for LinkedIn, post regularly, and leave unengaged posts up because that shows power.

Consistency is beneficial.

I did that for a few months and met amazing people who helped me get jobs. Money doesn't create jobs, it creates opportunities.

Resist social media and scammers that peddle false hopes.

Choose wisely.

#4: Avoid gushing over titles and purchasing trash.

As Insider’s Hillary Hoffower reports, “Showing off wealth is no longer the way to signify having wealth. In the US particularly, the top 1% have been spending less on material goods since 2007.”

I checked my closet. No brand comes to mind. I've never worn a brand's logo and rotate 6 white shirts daily. I have my priorities and don't waste money or effort on clothing that won't fit me in a year.

Unless it's your full-time work, clothing shouldn't be part of our mornings.

Lifestyle of stealth wealth. You're so fulfilled that seeming homeless won't hurt your self-esteem.

That's self-assurance.

Extroverts aren't required.

That's irrelevant.

Showing off won't win you friends.

They'll like your personality.

#5: Time is the most valuable commodity.

Being rich doesn't entail working 24/7 M-F.

They work when they are ready to work.

Waking up at 5 a.m. won't make you a millionaire, but it will inculcate diligence and tenacity in you.

You have a busy day yet want to exercise. You can skip the workout or wake up at 4am instead of 6am to do it.

Emotion-driven lazy bums stay in bed.

Those that are accountable keep their promises because they know breaking one will destroy their week.

Since 7th grade, I've worked out at 5am for myself, not to impress others. It gives me greater energy to contribute to others, especially on weekends and holidays.

It's a habit that I have in my life.

Find something that you take seriously and makes you a better person.

As someone who is close to becoming a millionaire and has encountered them throughout my life, I can share with you a few important differences that have shaped who we are as a society based on the weekends:

-Read

-Sleep

-Best time to work with no distractions

-Eat together

-Take walks and be in nature

-Gratitude

-Major family time

-Plan out weeks

-Go grocery shopping because health = wealth

#6. Perspective is Important

Timing the markets will slow down your career. Professors preach scarcity, not abundance. Why should school teach success? They give us bad advice.

If you trust in abundance and luck by attempting and experimenting, growth will come effortlessly. Passion isn't a term that just appears. Mistakes and fresh people help. You can get money. If you don't think it's worth it, you won't.

You don’t have to be wealthy to be good at money, but most are for these reasons.  Rich is a mindset, wealth is power. Prioritize your resources. Invest in yourself, knowing the toughest part is starting.

Thanks for reading!