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.

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”

Python Volatility-Based Catapult Indicator

As a catapult, this technical indicator uses three systems: Volatility (the fulcrum), Momentum (the propeller), and a Directional Filter (Acting as the support). The goal is to get a signal that predicts volatility acceleration and direction based on historical patterns. We want to know when the market will move. and where. This indicator outperforms standard indicators.

Knowledge must be accessible to everyone. This is why my new publications Contrarian Trading Strategies in Python and Trend Following Strategies in Python now include free PDF copies of my first three books (Therefore, purchasing one of the new books gets you 4 books in total). GitHub-hosted advanced indications and techniques are in the two new books above.

The Foundation: Volatility

The Catapult predicts significant changes with the 21-period Relative Volatility Index.

The Average True Range, Mean Absolute Deviation, and Standard Deviation all assess volatility. Standard Deviation will construct the Relative Volatility Index.

Standard Deviation is the most basic volatility. It underpins descriptive statistics and technical indicators like Bollinger Bands. Before calculating Standard Deviation, let's define Variance.

Variance is the squared deviations from the mean (a dispersion measure). We take the square deviations to compel the distance from the mean to be non-negative, then we take the square root to make the measure have the same units as the mean, comparing apples to apples (mean to standard deviation standard deviation). Variance formula:

As stated, standard deviation is:

# The function to add a number of columns inside an array
def adder(Data, times):
    
    for i in range(1, times + 1):
    
        new_col = np.zeros((len(Data), 1), dtype = float)
        Data = np.append(Data, new_col, axis = 1)
        
    return Data

# The function to delete a number of columns starting from an index
def deleter(Data, index, times):
    
    for i in range(1, times + 1):
    
        Data = np.delete(Data, index, axis = 1)
        
    return Data
    
# The function to delete a number of rows from the beginning
def jump(Data, jump):
    
    Data = Data[jump:, ]
    
    return Data

# Example of adding 3 empty columns to an array
my_ohlc_array = adder(my_ohlc_array, 3)

# Example of deleting the 2 columns after the column indexed at 3
my_ohlc_array = deleter(my_ohlc_array, 3, 2)

# Example of deleting the first 20 rows
my_ohlc_array = jump(my_ohlc_array, 20)

# Remember, OHLC is an abbreviation of Open, High, Low, and Close and it refers to the standard historical data file

def volatility(Data, lookback, what, where):
    
  for i in range(len(Data)):

     try:

        Data[i, where] = (Data[i - lookback + 1:i + 1, what].std())
     except IndexError:
        pass
        
  return Data

The RSI is the most popular momentum indicator, and for good reason—it excels in range markets. Its 0–100 range simplifies interpretation. Fame boosts its potential.

The more traders and portfolio managers look at the RSI, the more people will react to its signals, pushing market prices. Technical Analysis is self-fulfilling, therefore this theory is obvious yet unproven.

RSI is determined simply. Start with one-period pricing discrepancies. We must remove each closing price from the previous one. We then divide the smoothed average of positive differences by the smoothed average of negative differences. The RSI algorithm converts the Relative Strength from the last calculation into a value between 0 and 100.

def ma(Data, lookback, close, where): 
    
    Data = adder(Data, 1)
    
    for i in range(len(Data)):
           
            try:
                Data[i, where] = (Data[i - lookback + 1:i + 1, close].mean())
            
            except IndexError:
                pass
            
    # Cleaning
    Data = jump(Data, lookback)
    
    return Data
def ema(Data, alpha, lookback, what, where):
    
    alpha = alpha / (lookback + 1.0)
    beta  = 1 - alpha
    
    # First value is a simple SMA
    Data = ma(Data, lookback, what, where)
    
    # Calculating first EMA
    Data[lookback + 1, where] = (Data[lookback + 1, what] * alpha) + (Data[lookback, where] * beta)    
 
    # Calculating the rest of EMA
    for i in range(lookback + 2, len(Data)):
            try:
                Data[i, where] = (Data[i, what] * alpha) + (Data[i - 1, where] * beta)
        
            except IndexError:
                pass
            
    return Datadef rsi(Data, lookback, close, where, width = 1, genre = 'Smoothed'):
    
    # Adding a few columns
    Data = adder(Data, 7)
    
    # Calculating Differences
    for i in range(len(Data)):
        
        Data[i, where] = Data[i, close] - Data[i - width, close]
     
    # Calculating the Up and Down absolute values
    for i in range(len(Data)):
        
        if Data[i, where] > 0:
            
            Data[i, where + 1] = Data[i, where]
            
        elif Data[i, where] < 0:
            
            Data[i, where + 2] = abs(Data[i, where])
            
    # Calculating the Smoothed Moving Average on Up and Down
    absolute values        
                             
    lookback = (lookback * 2) - 1 # From exponential to smoothed
    Data = ema(Data, 2, lookback, where + 1, where + 3)
    Data = ema(Data, 2, lookback, where + 2, where + 4)
    
    # Calculating the Relative Strength
    Data[:, where + 5] = Data[:, where + 3] / Data[:, where + 4]
    
    # Calculate the Relative Strength Index
    Data[:, where + 6] = (100 - (100 / (1 + Data[:, where + 5])))  
    
    # Cleaning
    Data = deleter(Data, where, 6)
    Data = jump(Data, lookback)

    return Data

def relative_volatility_index(Data, lookback, close, where):

    # Calculating Volatility
    Data = volatility(Data, lookback, close, where)
    
    # Calculating the RSI on Volatility
    Data = rsi(Data, lookback, where, where + 1) 
    
    # Cleaning
    Data = deleter(Data, where, 1)
    
    return Data

The Arm Section: Speed

The Catapult predicts momentum direction using the 14-period Relative Strength Index.

As a reminder, the RSI ranges from 0 to 100. Two levels give contrarian signals:

• A positive response is anticipated when the market is deemed to have gone too far down at the oversold level 30, which is 30.

• When the market is deemed to have gone up too much, at overbought level 70, a bearish reaction is to be expected.

Comparing the RSI to 50 is another intriguing use. RSI above 50 indicates bullish momentum, while below 50 indicates negative momentum.

The direction-finding filter in the frame

The Catapult's directional filter uses the 200-period simple moving average to keep us trending. This keeps us sane and increases our odds.

Moving averages confirm and ride trends. Its simplicity and track record of delivering value to analysis make them the most popular technical indicator. They help us locate support and resistance, stops and targets, and the trend. Its versatility makes them essential trading tools.

This is the plain mean, employed in statistics and everywhere else in life. Simply divide the number of observations by their total values. Mathematically, it's:

We defined the moving average function above. Create the Catapult indication now.

Indicator of the Catapult

The indicator is a healthy mix of the three indicators:

• The first trigger will be provided by the 21-period Relative Volatility Index, which indicates that there will now be above average volatility and, as a result, it is possible for a directional shift.

• If the reading is above 50, the move is likely bullish, and if it is below 50, the move is likely bearish, according to the 14-period Relative Strength Index, which indicates the likelihood of the direction of the move.

• The likelihood of the move's direction will be strengthened by the 200-period simple moving average. When the market is above the 200-period moving average, we can infer that bullish pressure is there and that the upward trend will likely continue. Similar to this, if the market falls below the 200-period moving average, we recognize that there is negative pressure and that the downside is quite likely to continue.

lookback_rvi = 21
lookback_rsi = 14
lookback_ma  = 200
my_data = ma(my_data, lookback_ma, 3, 4)
my_data = rsi(my_data, lookback_rsi, 3, 5)
my_data = relative_volatility_index(my_data, lookback_rvi, 3, 6)

Two-handled overlay indicator Catapult. The first exhibits blue and green arrows for a buy signal, and the second shows blue and red for a sell signal.

The chart below shows recent EURUSD hourly values.

def signal(Data, rvi_col, signal):
    
    Data = adder(Data, 10)
        
    for i in range(len(Data)):
            
        if Data[i,     rvi_col] < 30 and \
           Data[i - 1, rvi_col] > 30 and \
           Data[i - 2, rvi_col] > 30 and \
           Data[i - 3, rvi_col] > 30 and \
           Data[i - 4, rvi_col] > 30 and \
           Data[i - 5, rvi_col] > 30:
               
               Data[i, signal] = 1
                           
    return Data

Signals are straightforward. The indicator can be utilized with other methods.

my_data = signal(my_data, 6, 7)

Lumiwealth shows how to develop all kinds of algorithms. I recommend their hands-on courses in algorithmic trading, blockchain, and machine learning.

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.

After you find a trading method or approach, follow these steps:

• Put emotions aside and adopt an analytical perspective.

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

• Try improving it and performing a forward test if you notice any possibility.

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

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

After checking the aforementioned, monitor the plan because market dynamics may change and render it unprofitable.

40 years pass. After 14 years of retirement, you die. Am I the only one who sees the problem?

I’m the Jedi master of escaping the rat race.

Not to impress. I know this works since I've tried it. Quitting a job to make money online is worse than Kim Kardashian's internet-burning advice.

Let me help you rethink the move from a career to online income to f*ck you money.

To understand why a job is a joke, do some life math.

Without a solid why, nothing makes sense.

The retirement age is 65. Our processed food consumption could shorten our 79-year average lifespan.

You spend 40 years working.

After 14 years of retirement, you die.

Am I alone in seeing the problem?

Life is too short to work a job forever, especially since most people hate theirs. After-hours skills are vital.

Money equals unrestricted power, f*ck you.

F*ck you money is the answer.

Jack Raines said it first. He says we can do anything with the money. Jack, a young rebel straight out of college, can travel and try new foods.

F*ck you money signifies not checking your bank account before buying.

F*ck you” money is pure, unadulterated freedom with no strings attached.

Jack claims you're rich when you rarely think about money.

Avoid confusion.

This doesn't imply you can buy a Lamborghini. It indicates your costs, income, lifestyle, and bank account are balanced.

Jack established an online portfolio while working for UPS in Atlanta, Georgia. So he gained boundless power.

The portion that many erroneously believe

Yes, you need internet abilities to make money, but they're not different from 9-5 talents.

Sahil Lavingia, Gumroad's creator, explains.

A job is a way to get paid to learn.

Mistreat your boss 9-5. Drain his skills. Defuse him. Love and leave him (eventually).

Find another employment if yours is hazardous. Pick an easy job. Make sure nothing sneaks into your 6-12 time slot.

The dumb game that makes you a sheep

A 9-5 job requires many job interviews throughout life.

You email your résumé to employers and apply for jobs through advertisements. This game makes you a sheep.

You're competing globally. Work-from-home makes the competition tougher. If you're not the cheapest, employers won't hire you.

After-hours online talents (say, 6 pm-12 pm) change the game. This graphic explains it better:

Online talents boost after-hours opportunities.

You go from wanting to be picked to picking yourself. More chances equal more money. Your f*ck you fund gets the extra cash.

A novel method of learning is essential.

College costs six figures and takes a lifetime to repay.

Informal learning is distinct. 6-12pm:

• Observe the carefully controlled Twitter newsfeed.

• Make use of Teachable and Gumroad's online courses.

• Watch instructional YouTube videos

• Look through the top Substack newsletters.

Informal learning is more effective because it's not obvious. It's fun to follow your curiosity and hobbies.

The majority of people lack one attitude. It's simple to learn.

One big impediment stands in the way of f*ck you money and time independence. So often.

Too many people plan after 6-12 hours. Dreaming. Big-thinkers. Strategically. They fill their calendar with meetings.

This is after-hours masturb*tion.

Sahil Bloom reminded me that a bias towards action will determine if this approach works for you.

The key isn't knowing what to do from 6-12 a.m. Trust yourself and develop abilities as you go. It's for building the parachute after you jump.

Sounds risky. We've eliminated the risk by finishing this process after hours while you work 9-5.

With no risk, you can have an I-don't-care attitude and still be successful.

When you choose to move forward, this occurs.

Once you try 9-5/6-12, you'll tell someone.

It's bad.

Few of us hang out with problem-solvers.

It's how much of society operates. So they make reasons so they can feel better about not giving you money.

Matthew Kobach told me chasing f*ck you money is easier with like-minded folks.

Without f*ck you money friends, loneliness will take over and you'll think you've messed up when you just need to keep going.

Steal this easy guideline

Let's act. No more fluffing and caressing.

1. Learn

If you detest your 9-5 talents or don't think they'll work online, get new ones. If you're skilled enough, continue.

Easlo recommends these skills:

• Designer for Figma

• Designer Canva

• bubble creators

• editor in Photoshop

• Automation consultant for Zapier

• Designer of Webflow

• video editor Adobe

• Ghostwriter for Twitter

• Idea consultant

• Artist in Blender Studio

2. Develop the ability

Every night from 6-12, apply the skill.

Practicing ghostwriting? Write someone's tweets for free. Do someone's website copy to learn copywriting. Get a website to the top of Google for a keyword to understand SEO.

Free practice is crucial. Your 9-5 pays the money, so work for free.

3. Take off stealthily like a badass

Another mistake. Sell to few. Don't be the best. Don't claim expertise.

Sell your new expertise to others behind you.

Two ways:

• Using a digital good

• By providing a service,

Point 1 also includes digital service examples. Digital products include eBooks, communities, courses, ad-supported podcasts, and templates. It's easy. Your 9-5 job involves one of these.

Take ideas from work.

Why? They'll steal your time for profit.

4. Iterate while feeling awful

First-time launches always fail. You'll feel terrible. Okay. Remember your 9-5?

Find improvements. Ask free and paying consumers what worked.

Multiple relaunches, each 1% better.

5. Discover more

Never stop learning. Improve your skill. Add a relevant skill. Learn copywriting if you write online.

After-hours students earn the most.

6. Continue

Repetition is key.

7. Make this one small change.

Consistently. The 6-12 momentum won't make you rich in 30 days; that's success p*rn.

Consistency helps wage slaves become f*ck you money. Most people can't switch between the two.

Putting everything together

It's easy. You're probably already doing some.

This formula explains why, how, and what to do. It's a 5th-grade-friendly blueprint. Good.

Reduce financial risk with your 9-to-5. Replace Netflix with 6-12 money-making talents.

Life is short; do whatever you want. Today.

Apple's search engine has long been rumored. Recent Google developments may confirm the rumor. Is Apple about to become Google's biggest rival?

Here's a video:

People noted Apple's changes in 2020. AppleBot, a web crawler that downloads and caches Internet content, was more active than in the last five years.

Apple hired search engine developers, including ex-Googlers, such as John Giannandrea, Google's former search chief.

Apple also changed the way iPhones search. With iOS 14, Apple's search results arrived before Google's.

These facts fueled rumors that Apple was developing a search engine.

Apple and Google Have a Contract

Many skeptics said Apple couldn't compete with Google. This didn't affect the company's competitiveness.

Apple is the only business with the resources and scale to be a Google rival, with 1.8 billion active devices and a $2 trillion market cap.

Still, people doubted that due to a license deal. Google pays Apple $8 to $12 billion annually to be the default iPhone and iPad search engine.

Apple can't build an independent search product under this arrangement.

Why would Apple enter search if it's being paid to stay out?

Ironically, this partnership has many people believing Apple is getting into search.

A New Default Search Engine May Be Needed

Google was sued for antitrust in 2020. It is accused of anticompetitive and exclusionary behavior. Justice wants to end Google's monopoly.

Authorities could restrict Apple and Google's licensing deal due to its likely effect on market competitiveness. Hence Apple needs a new default search engine.

Apple Already Has a Search Engine

The company already has a search engine, Spotlight.

Since 2004, Spotlight has aired. It was developed to help users find photos, documents, apps, music, and system preferences.

Apple's search engine could do more than organize files, texts, and apps.

Spotlight Search was updated in 2014 with iOS 8. Web, App Store, and iTunes searches became available. You could find nearby places, movie showtimes, and news.

This search engine has subsequently been updated and improved. Spotlight added rich search results last year.

If you search for a TV show, movie, or song, photos and carousels will appear at the top of the page.

This resembles Google's rich search results.

When Will the Apple Search Engine Be Available?

When will Apple's search launch? Robert Scoble says it's near.

Scoble tweeted a number of hints before this year's Worldwide Developer Conference.

Scoble bases his prediction on insider information and deductive reasoning. January 2023 is expected.

Will you use Apple's search engine?