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.”

Understanding your own energy depletion and restoration is how to truly optimize

Hack has meant many bad things for centuries. In the 1800s, a hack was a meager horse used to transport goods.

Modern usage describes a butcher or ax murderer's cleaver chop. The 1980s programming boom distinguished elegant code from "hacks". Both got you to your goal, but the latter made any programmer cringe and mutter about changing the code. From this emerged the hacker trope, the friendless anti-villain living in a murky hovel lit by the computer monitor, eating junk food and breaking into databases to highlight security system failures or steal hotdog money.

Now, start-a-billion-dollar-business-from-your-garage types have shifted their sights from app development to DIY biology, coining the term "bio-hack". This is a required keyword and meta tag for every fitness-related podcast, book, conference, app, or device.

Bio-hacking involves bypassing your body and mind's security systems to achieve a goal. Many biohackers' initial goals were reasonable, like lowering blood pressure and weight. Encouraged by their own progress, self-determination, and seemingly exquisite control of their biology, they aimed to outsmart aging and death to live 180 to 1000 years (summarized well in this vox.com article).

With this grandiose north star, the hunt for novel supplements and genetic engineering began.

Companies selling do-it-yourself biological manipulations cite lab studies in mice as proof of their safety and success in reversing age-related diseases or promoting longevity in humans (the goal changes depending on whether a company is talking to the federal government or private donors).

The FDA is slower than science, they say. Why not alter your biochemistry by buying pills online, editing your DNA with a CRISPR kit, or using a sauna delivered to your home? How about a microchip or electrical stimulator?

What could go wrong?

I'm not the neo-police, making citizen's arrests every time someone introduces a new plumbing gadget or extrapolates from animal research on resveratrol or catechins that we should drink more red wine or eat more chocolate. As a scientist who's spent her career asking, "Can we get better?" I've come to view bio-hacking as misguided, profit-driven, and counterproductive to its followers' goals.

We're creatures of nature. Despite all the new gadgets and bio-hacks, we still use Roman plumbing technology, and the best way to stay fit, sharp, and happy is to follow a recipe passed down since the beginning of time. Bacteria, plants, and all natural beings are rhythmic, with alternating periods of high activity and dormancy, whether measured in seconds, hours, days, or seasons. Nature repeats successful patterns.

During the Upstate, every cell in your body is naturally primed and pumped full of glycogen and ATP (your cells' energy currencies), as well as cortisol, which supports your muscles, heart, metabolism, cognitive prowess, emotional regulation, and general "get 'er done" attitude. This big energy release depletes your batteries and requires the Downstate, when your subsystems recharge at the cellular level.

Downstates are when you give your heart a break from pumping nutrient-rich blood through your body; when you give your metabolism a break from inflammation, oxidative stress, and sympathetic arousal caused by eating fast food — or just eating too fast; or when you give your mind a chance to wander, think bigger thoughts, and come up with new creative solutions. When you're responding to notifications, emails, and fires, you can't relax.

Downstates aren't just for consistently recharging your battery. By spending time in the Downstate, your body and brain get extra energy and nutrients, allowing you to grow smarter, faster, stronger, and more self-regulated. This state supports half-marathon training, exam prep, and mediation. As we age, spending more time in the Downstate is key to mental and physical health, well-being, and longevity.

When you prioritize energy-demanding activities during Upstate periods and energy-replenishing activities during Downstate periods, all your subsystems, including cardiovascular, metabolic, muscular, cognitive, and emotional, hum along at their optimal settings. When you synchronize the Upstates and Downstates of these individual rhythms, their functioning improves. A hard workout causes autonomic stress, which triggers Downstate recovery.

By choosing the right timing and type of exercise during the day, you can ensure a deeper recovery and greater readiness for the next workout by working with your natural rhythms and strengthening your autonomic and sleep Downstates.

Morning cardio workouts increase deep sleep compared to afternoon workouts. Timing and type of meals determine when your sleep hormone melatonin is released, ushering in sleep.

Rhythm isn't a hack. It's not a way to cheat the system or the boss. Nature has honed its optimization wisdom over trillions of days and nights. Stop looking for quick fixes. You're a whole system made of smaller subsystems that must work together to function well. No one pill or subsystem will make it all work. Understanding and coordinating your rhythms is free, easy, and only benefits you.

Dr. Sara C. Mednick is a cognitive neuroscientist at UC Irvine and author of The Power of the Downstate (HachetteGO)

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.

And tips for anyone trying.

We see the same story every few years.

BREAKING NEWS: [Platform X] launched a social network. With Facebook's reputation down, the new startup bets millions will switch.

Despite the excitement surrounding each new platform (Diaspora, Ello, Path, MeWe, Minds, Vero, etc.), no major exodus occurred.

Snapchat and TikTok attracted teens with fresh experiences (ephemeral messaging and rapid-fire videos). These features aren't Facebook, even if Facebook replicated them.

Facebook's core is simple: you publish items (typically text/images) and your friends (generally people you know IRL) can discuss them.

It's cool. Sometimes I don't want to, but sh*t. I like it.

Because, well, I like many folks I've met. I enjoy keeping in touch with them and their banter.

I dislike Facebook's corporation. I've been cautiously optimistic whenever a Facebook-killer surfaced.

None succeeded.

Why? Two causes, I think:

People couldn't switch quickly enough, which is reason #1

Your buddies make a social network social.

Facebook started in self-contained communities (college campuses) then grew outward. But a new platform can't.

If we're expected to leave Facebook, we want to know that most of our friends will too.

Most Facebook-killers had bottlenecks. You have to waitlist or jump through hoops (e.g. setting up a server).

Same outcome. Upload. Chirp.

After a week or two of silence, individuals returned to Facebook.

Reason #2: The fundamental experience was different.

Even when many of our friends joined in the first few weeks, it wasn't the same.

There were missing features or a different UX.

Want to reply with a meme? No photos in comments yet. (Trying!)

Want to tag a friend? Nope, sorry. 2019!

Want your friends to see your post? You must post to all your friends' servers. Good luck!

It's difficult to introduce a platform with 100% of the same features as one that's been there for 20 years, yet customers want a core experience.

If you can't, they'll depart.

The causes that led to the causes

Having worked on software teams for 14+ years, I'm not surprised by these challenges. They are a natural development of a few tech sector meta-problems:

Lean startup methodology

Silicon Valley worships lean startup. It's a way of developing software that involves testing a stripped-down version with a limited number of people before selecting what to build.

Billion people use Facebook's functions. They aren't tested. It must work right away*

*This may seem weird to software people, but it's how non-software works! You can't sell a car without wheels.

2. Creativity

Startup entrepreneurs build new things, not copies. I understand. Reinventing the wheel is boring.

We know what works. Different experiences raise adoption friction. Once millions have transferred, more features (and a friendlier UX) can be implemented.

3. Cost scaling

True. Building a product that can sustain hundreds of millions of users in weeks is expensive and complex.

Your lifeboats must have the same capacity as the ship you're evacuating. It's required.

4. Pure ideologies

People who work on Facebook-alternatives are (understandably) critical of Facebook.

They build an open-source, fully-distributed, data-portable, interface-customizable, offline-capable, censorship-proof platform.

Prioritizing these aims can prevent replicating the straightforward experience users expect. Github, not Facebook, is for techies only.

What about the business plan, though?

Facebook-killer attempts have followed three models.

1. Utilize VC funding to increase your user base, then monetize them later. (If you do this, you won't kill Facebook; instead, Facebook will become you.)

2. Users must pay to utilize it. (This causes a huge bottleneck and slows the required quick expansion, preventing it from seeming like a true social network.)

3. Make it a volunteer-run, open-source endeavor that is free. (This typically denotes that something is cumbersome, difficult to operate, and is only for techies.)

Wikipedia is a fourth way.

Wikipedia is one of the most popular websites and a charity. No ads. Donations support them.

A Facebook-killer managed by a good team may gather millions (from affluent contributors and the crowd) for their initial phase of development. Then it might sustain on regular donations, ethical transactions (e.g. fees on commerce, business sites, etc.), and government grants/subsidies (since it would essentially be a public utility).

When you're not aiming to make investors rich, it's remarkable how little money you need.

If you want to build a Facebook competitor, follow these tips:

1. Drop the lean startup philosophy. Wait until you have a finished product before launching. Build it, thoroughly test it for bugs, and then release it.

2. Delay innovating. Wait till millions of people have switched before introducing your great new features. Make it nearly identical for now.

3. Spend money climbing. Make sure that guests can arrive as soon as they are invited. Never keep them waiting. Make things easy for them.

4. Make it accessible to all. Even if doing so renders it less philosophically pure, it shouldn't require technical expertise to utilize.

5. Constitute a nonprofit. Additionally, develop community ownership structures. Profit maximization is not the only strategy for preserving valued assets.

Last thoughts

Nobody has killed Facebook, but Facebook is killing itself.

The startup is burying the newsfeed to become a TikTok clone. Meta itself seems to be ditching the platform for the metaverse.

I wish I was happy, but I'm not. I miss (understandably) removed friends' postings and remarks. It could be a ghost town in a few years. My dance moves aren't TikTok-worthy.

Who will lead? It's time to develop a social network for the people.

Greetings if you're working on it. I'm not a company founder, but I like to help hard-working folks.

ERC165 (or EIP-165) is a standard utilized by various open-source smart contracts like Open Zeppelin or Aavegotchi.

What's it? You must implement? Why do we need it? I'll describe the standard and answer any queries.

What is ERC165

ERC165 detects and publishes smart contract interfaces. Meaning? It standardizes how interfaces are recognized, how to detect if they implement ERC165, and how a contract publishes the interfaces it implements. How does it work?

Why use ERC165? Sometimes it's useful to know which interfaces a contract implements, and which version.

Identifying interfaces

An interface function's selector. This verifies an ABI function. XORing all function selectors defines an interface in this standard. The following code demonstrates.

// SPDX-License-Identifier: UNLICENCED
pragma solidity >=0.8.0 <0.9.0;

interface Solidity101 {
    function hello() external pure;
    function world(int) external pure;
}

contract Selector {
    function calculateSelector() public pure returns (bytes4) {
        Solidity101 i;
        return i.hello.selector ^ i.world.selector;
        // Returns 0xc6be8b58
    }

    function getHelloSelector() public pure returns (bytes4) {
        Solidity101 i;
        return i.hello.selector;
        // Returns 0x19ff1d21
    }

    function getWorldSelector() public pure returns (bytes4) {
        Solidity101 i;
        return i.world.selector;
        // Returns 0xdf419679
    }
}

This code isn't necessary to understand function selectors and how an interface's selector can be determined from the functions it implements.

Run that sample in Remix to see how interface function modifications affect contract function output.

Contracts publish their implemented interfaces.

We can identify interfaces. Now we must disclose the interfaces we're implementing. First, import IERC165 like so.

pragma solidity ^0.4.20;

interface ERC165 {
    /// @notice Query if a contract implements an interface
    /// @param interfaceID The interface identifier, as specified in ERC-165
    /// @dev Interface identification is specified in ERC-165. 
    /// @return `true` if the contract implements `interfaceID` and
    ///  `interfaceID` is not 0xffffffff, `false` otherwise
    function supportsInterface(bytes4 interfaceID) external view returns (bool);
}

We still need to build this interface in our smart contract. ERC721 from OpenZeppelin is a good example.

// SPDX-License-Identifier: MIT
// OpenZeppelin Contracts (last updated v4.5.0) (token/ERC721/ERC721.sol)

pragma solidity ^0.8.0;

import "./IERC721.sol";
import "./extensions/IERC721Metadata.sol";
import "../../utils/introspection/ERC165.sol";
// ...

contract ERC721 is Context, ERC165, IERC721, IERC721Metadata {
  // ...

  function supportsInterface(bytes4 interfaceId) public view virtual override(ERC165, IERC165) returns (bool) {
    return
      interfaceId == type(IERC721).interfaceId ||
      interfaceId == type(IERC721Metadata).interfaceId ||
      super.supportsInterface(interfaceId);
  }
  
  // ...
}

I deleted unnecessary code. The smart contract imports ERC165, IERC721 and IERC721Metadata. The is keyword at smart contract declaration implements all three.

Kind (interface).

Note that type(interface).interfaceId returns the same as the interface selector.

We override supportsInterface in the smart contract to return a boolean that checks if interfaceId is the same as one of the implemented contracts.

Super.supportsInterface() calls ERC165 code. Checks if interfaceId is IERC165.

function supportsInterface(bytes4 interfaceId) public view virtual override returns (bool) {
    return interfaceId == type(IERC165).interfaceId;
}

So, if we run supportsInterface with an interfaceId, our contract function returns true if it's implemented and false otherwise. True for IERC721, IERC721Metadata, andIERC165.

Conclusion

I hope this post has helped you understand and use ERC165 and why it's employed.

Have a great day, thanks for reading!

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.