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.

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.

Cryptoland rollercoaster

I've lost money in crypto.

Unimportant.

The real issue: I didn’t understand how.

I'm surrounded with winners. To learn more, I created my own NFTs, currency, and DAO.

Web3 is a hilltop castle. Everything is valuable, decentralized, and on-chain.

The castle is Disneyland: beautiful in images, but chaotic with lengthy lines and kids spending too much money on dressed-up animals.

When the throng and businesses are gone, Disneyland still has enchantment.

The Real Story of Web3

NFTs

Scarcity. Scarce NFTs. That's their worth.

Skull. Rare-looking!

Nonsense.

Bored Ape Yacht Club vs. my NFTs?

Marketing.

BAYC is amazing, but not for the reasons people believe. Apecoin and Otherside's art, celebrity following, and innovation? Stunning.

No other endeavor captured the zeitgeist better. Yet how long did you think it took to actually mint the NFTs?

1 hour? Maybe a week for the website?

Minting NFTs is incredibly easy. Kid-friendly. Developers are rare. Think about that next time somebody posts “DevS dO SMt!?”

NFTs will remain popular. These projects are like our Van Goghs and Monets. Still, be wary. It still uses exclusivity and wash selling like the OG art market.

Not all NFTs are art-related.

Soulbound and anonymous NFTs could offer up new use cases. Property rights, privacy-focused ID, open-source project verification. Everything.

NFTs build online trust through ownership.

We just need to evolve from the apes first.

NFTs' superpower is marketing until then.

Crypto currency

What the hell is a token?

99% of people are clueless.

So I invested in both coins and tokens. Same same. Only that they are not.

Coins have their own blockchain and developer/validator community. It's hard.

Creating a token on top of a blockchain? Five minutes.

Most consumers don’t understand the difference, creating an arbitrage opportunity: pretend you’re a serious project without having developers on your payroll.

Few market sites help. Take a look. See any tokens?

There's a hint one click deeper.

Some tokens are legitimate. Some coins are bad investments.

Tokens are utilized for DAO governance and DApp payments. Still, know who's behind a token. They might be 12 years old.

Coins take time and money. The recent LUNA meltdown indicates that currency investing requires research.

DAOs

Decentralized Autonomous Organizations (DAOs) don't work as you assume.

Yes, members can vote.

A productive organization requires more.

I've observed two types of DAOs.

• Total decentralization total dysfunction

• Centralized just partially. Community-driven.

A core team executes the DAO's strategy and roadmap in successful DAOs. The community owns part of the organization, votes on decisions, and holds the team accountable.

DAOs are public companies.

Amazing.

A shareholder meeting's logistics are staggering. DAOs may hold anonymous, secure voting quickly. No need for intermediaries like banks to chase up every shareholder.

Successful DAOs aren't totally decentralized. Large-scale voting and collaboration have never been easier.

And that’s all that matters.

Scale, speed.

My Web3 learnings

Disneyland is enchanting. Web3 too.

In a few cycles, NFTs may be used to build trust, not clout. Not speculating with coins. DAOs run organizations, not themselves.

Finally, some final thoughts:

• NFTs will be a very helpful tool for building trust online. NFTs are successful now because of excellent marketing.

• Tokens are not the same as coins. Look into any project before making a purchase. Make sure it isn't run by three 9-year-olds piled on top of one another in a trench coat, at the very least.

• Not entirely decentralized, DAOs. We shall see a future where community ownership becomes the rule rather than the exception once we acknowledge this fact.

Crypto Disneyland is a rollercoaster with loops that make you sick.

Always buckle up.

Have fun!

My kids redesigned the internet because it lacks inventiveness.

Internet today is bland. Everything is generic: fonts, layouts, pages, and visual language. Microtypography is messy.

Web design today seems dictated by technical and ideological constraints rather than creativity and ideas. Text and graphics are in containers on every page. All design is assumed.

Ironically, web technologies can design a lot. We can execute most designs. We make shocking, evocative websites. Experimental typography, generating graphics, and interactive experiences are possible.

Even designer websites use containers in containers. Dribbble and Behance, the two most popular creative websites, are boring. Lead image.

How did this happen?

Several reasons. WordPress and other blogging platforms use templates. These frameworks build web pages by combining graphics, headlines, body content, and videos. Not designs, templates. These rules combine related data types. These platforms don't let users customize pages beyond the template. You filled the template.

Templates are content-neutral. Thus, the issue.

Form should reflect and shape content, which is a design principle. Separating them produces content containers. Templates have no design value.

One of the fundamental principles of design is a deep and meaningful connection between form and content.

Web design lacks imagination for many reasons. Most are pragmatic and economic. Page design takes time. Large websites lack the resources to create a page from scratch due to the speed of internet news and the frequency of new items. HTML, JavaScript, and CSS continue to challenge web designers. Web design can't match desktop publishing's straightforward operations.

Designers may also be lazy. Mobile-first, generic, framework-driven development tends to ignore web page visual and contextual integrity.

How can we overcome this? How might expressive and avant-garde websites look today?

Rediscovering the past helps design the future.

'90s-era web design

At the University of the Arts Bremen's research and development group, I created my first website 23 years ago. Web design was trendy. Young web. Pages inspired me.

We struggled with HTML in the mid-1990s. Arial, Times, and Verdana were the only web-safe fonts. Anything exciting required table layouts, monospaced fonts, or GIFs. HTML was originally content-driven, thus we had to work against it to create a page.

Experimental typography was booming. Designers challenged the established quo from Jan Tschichold's Die Neue Typographie in the twenties to April Greiman's computer-driven layouts in the eighties. By the mid-1990s, an uncommon confluence of technological and cultural breakthroughs enabled radical graphic design. Irma Boom, David Carson, Paula Scher, Neville Brody, and others showed it.

Early web pages were dull compared to graphic design's aesthetic explosion. The Web Design Museum shows this.

Nobody knew how to conduct browser-based graphic design. Web page design was undefined. No standards. No CMS (nearly), CSS, JS, video, animation.

Now is as good a time as any to challenge the internet’s visual conformity.

In 2018, everything is browser-based. Massive layouts to micro-typography, animation, and video. How do we use these great possibilities? Containerized containers. JavaScript-contaminated mobile-first pages. Visually uniform templates. Web design 23 years later would disappoint my younger self.

Our imagination, not technology, restricts web design. We're too conformist to aesthetics, economics, and expectations.

Crisis generates opportunity. Challenge online visual conformity now. I'm too old and bourgeois to develop a radical, experimental, and cutting-edge website. I can ask my students.

I taught web design at the Potsdam Interface Design Programme in 2017. Each team has to redesign a website. Create expressive, inventive visual experiences on the browser. Create with contemporary web technologies. Avoid usability, readability, and flexibility concerns. Act. Ignore Erwartungskonformität.

The class outcome pleased me. This overview page shows all results. Four diverse projects address the challenge.

1. ZKM by Frederic Haase and Jonas Köpfer

Frederic and Jonas began their experiments on the ZKM website. The ZKM is Germany's leading media art exhibition location, but its website remains conventional. It's useful but not avant-garde like the shows' art.

Frederic and Jonas designed the ZKM site's concept, aesthetic language, and technical configuration to reflect the museum's progressive approach. A generative design engine generates new layouts for each page load.

ZKM redesign.

2. Streem by Daria Thies, Bela Kurek, and Lucas Vogel

Street art magazine Streem. It promotes new artists and societal topics. Streem includes artwork, painting, photography, design, writing, and journalism. Daria, Bela, and Lucas used these influences to develop a conceptual metropolis. They designed four neighborhoods to reflect magazine sections for their prototype. For a legible city, they use powerful illustrative styles and spatial typography.

Streem makeover.

3. Medium by Amelie Kirchmeyer and Fabian Schultz

Amelie and Fabian structured. Instead of developing a form for a tale, they dissolved a web page into semantic, syntactical, and statistical aspects. HTML's flexibility was their goal. They broke Medium posts into experimental typographic space.

Medium revamp.

4. Hacker News by Fabian Dinklage and Florian Zia

Florian and Fabian made Hacker News interactive. The social networking site aggregates computer science and IT news. Its voting and debate features are extensive despite its simple style. Fabian and Florian transformed the structure into a typographic timeline and network area. News and comments sequence and connect the visuals. To read Hacker News, they connected their design to the API. Hacker News makeover.

Communication is not legibility, said Carson. Apply this to web design today. Modern websites must be legible, usable, responsive, and accessible. They shouldn't limit its visual palette. Visual and human-centered design are not stereotypes.

I want radical, generative, evocative, insightful, adequate, content-specific, and intelligent site design. I want to rediscover web design experimentation. More surprises please. I hope the web will appear different in 23 years.

Update: this essay has sparked a lively discussion! I wrote a brief response to the debate's most common points: Creativity vs. Usability

NASA's Artemis 1 mission will help return people to the Moon after a half-century break. The mission is a shakedown cruise for NASA's Space Launch System and Orion Crew Capsule.

The spaceship will visit the Moon, deploy satellites, and enter orbit. NASA wants to practice operating the spacecraft, test the conditions people will face on the Moon, and ensure a safe return to Earth.

We asked Jack Burns, a space scientist at the University of Colorado Boulder and former member of NASA's Presidential Transition Team, to describe the mission, explain what the Artemis program promises for space exploration, and reflect on how the space program has changed in the half-century since humans last set foot on the moon.

What distinguishes Artemis 1 from other rockets?

Artemis 1 is the Space Launch System's first launch. NASA calls this a "heavy-lift" vehicle. It will be more powerful than Apollo's Saturn V, which transported people to the Moon in the 1960s and 1970s.

It's a new sort of rocket system with two strap-on solid rocket boosters from the space shuttle. It's a mix of the shuttle and Saturn V.

The Orion Crew Capsule will be tested extensively. It'll spend a month in the high-radiation Moon environment. It will also test the heat shield, which protects the capsule and its occupants at 25,000 mph. The heat shield must work well because this is the fastest capsule descent since Apollo.

This mission will also carry miniature Moon-orbiting satellites. These will undertake vital precursor science, including as examining further into permanently shadowed craters where scientists suspect there is water and measuring the radiation environment to see long-term human consequences.

Artemis 1 will launch, fly to the Moon, place satellites, orbit it, return to Earth, and splash down in the ocean. NASA.

What's Artemis's goal? What launches are next?

The mission is a first step toward Artemis 3, which will lead to the first human Moon missions since 1972. Artemis 1 is unmanned.

Artemis 2 will have astronauts a few years later. Like Apollo 8, it will be an orbital mission that circles the Moon and returns. The astronauts will orbit the Moon longer and test everything with a crew.

Eventually, Artemis 3 will meet with the SpaceX Starship on the Moon's surface and transfer people. Orion will stay in orbit while the lunar Starship lands astronauts. They'll go to the Moon's south pole to investigate the water ice there.

Artemis is reminiscent of Apollo. What's changed in 50 years?

Kennedy wanted to beat the Soviets to the Moon with Apollo. The administration didn't care much about space flight or the Moon, but the goal would place America first in space and technology.

You live and die by the sword if you do that. When the U.S. reached the Moon, it was over. Russia lost. We planted flags and did science experiments. Richard Nixon canceled the program after Apollo 11 because the political goals were attained.

Large rocket with two boosters between two gates

NASA's new Space Launch System is brought to a launchpad. NASA

50 years later... It's quite different. We're not trying to beat the Russians, Chinese, or anyone else, but to begin sustainable space exploration.

Artemis has many goals. It includes harnessing in-situ resources like water ice and lunar soil to make food, fuel, and building materials.

SpaceX is part of this first journey to the Moon's surface, therefore the initiative is also helping to develop a lunar and space economy. NASA doesn't own the Starship but is buying seats for astronauts. SpaceX will employ Starship to transport cargo, private astronauts, and foreign astronauts.

Fifty years of technology advancement has made getting to the Moon cheaper and more practical, and computer technology allows for more advanced tests. 50 years of technological progress have changed everything. Anyone with enough money can send a spacecraft to the Moon, but not humans.

Commercial Lunar Payload Services engages commercial companies to develop uncrewed Moon landers. We're sending a radio telescope to the Moon in January. Even 10 years ago, that was impossible.

Since humans last visited the Moon 50 years ago, technology has improved greatly.

What other changes does Artemis have in store?

The government says Artemis 3 will have at least one woman and likely a person of color. 

I'm looking forward to seeing more diversity so young kids can say, "Hey, there's an astronaut that looks like me. I can do this. I can be part of the space program.”