Genius or lucky?

From the title, you might think I'm selling advertising for a financial influencer, a dubious trading site, or a training organization to attract clients. I'm suspicious. Better safe than sorry.

But not here.

Jake Freeman, 20, made $110 million in a month, according to the Financial Times. At 18, he ran for president. He made his name in markets, not politics. Two years later, he's Wall Street's prince. Interview requests flood the prodigy.

Jake Freeman bought 5 million Bed Bath & Beyond Group shares for $5.5 in July 2022 and sold them for $27 a month later. He thought the stock might double. Since speculation died down, he sold well. The stock fell 40.5% to 11 dollars on Friday, 19 August 2022. On August 22, 2022, it fell 16% to $9.

Smallholders have been buying the stock for weeks and will lose heavily if it falls further. Bed Bath & Beyond is the second most popular stock after Foot Locker, ahead of GameStop and Apple.

Jake Freeman earned $110 million thanks to a significant stock market flurry.

Online broker customers aren't the only ones with jitters. By June 2022, Ken Griffin's Citadel and Stephen Mandel's Lone Pine Capital held nearly a third of the company's capital. Did big managers sell before the stock plummeted?

Recent stock movements (derivatives) and rumors could prompt a SEC investigation.

Jake Freeman wrote to the board of directors after his investment to call for a turnaround, given the company's persistent problems and short sellers. The bathroom and kitchen products distribution group's stock soared in July 2022 due to renewed buying by private speculators, who made it one of their meme stocks with AMC and GameStop.

Second-quarter 2022 results and financial health worsened. He didn't celebrate his miraculous operation in a nightclub. He told a British newspaper, "I'm shocked." His parents dined in New York. He returned to Los Angeles to study math and economics.

Jake Freeman founded Freeman Capital Management with his savings and $25 million from family, friends, and acquaintances. They are the ones who are entitled to the $110 million he raised in one month. Will his investors pocket and withdraw all or part of their profits or will they trust the young prodigy for new stunts on Wall Street?

His operation should attract new clients. Well-known hedge funds may hire him.

Jake Freeman didn't listen to gurus or former traders. At 17, he interned at a quantitative finance and derivatives hedge fund, Volaris. At 13, he began investing with his pharmaceutical executive uncle. All countries have increased their Google searches for the young trader in the last week.

Naturally, his success has inspired resentment.

His success stirs jealousy, and he's attacked on social media. On Reddit, people who lost money on Bed Bath & Beyond, Jake Freeman's fortune, are mourning.

Several conspiracy theories circulate about him, including that he doesn't exist or is working for a Taiwanese amusement park.

If all 20 million American students had the same trading skills, they would have generated $1.46 trillion. Jake Freeman is unique. Apprentice traders' careers are often short, disillusioning, and tragic.

Two years ago, 20-year-old Robinhood client Alexander Kearns committed suicide after losing $750,000 trading options. Great traders start young. Michael Platt of BlueCrest invested in British stocks at age 12 under his grandmother's supervision and made a £30,000 fortune. Paul Tudor Jones started trading before he turned 18 with his uncle. Warren Buffett, at age 10, was discussing investments with Goldman Sachs' head. Oracle of Omaha tells all.

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.

Recent investigations have shown that generative AI can boost hackers and misinformation spreaders.

Since its inception in late November 2022, OpenAI's ChatGPT has entertained and assisted many online users in writing, coding, task automation, and linguistic translation. Given this versatility, it is maybe unsurprising but nonetheless regrettable that fraudsters and mis-, dis-, and malinformation (MDM) spreaders are also considering ChatGPT and related AI models to streamline and improve their operations.

Malign actors may benefit from ChatGPT, according to a WithSecure research. ChatGPT promises to elevate unlawful operations across many attack channels. ChatGPT can automate spear phishing attacks that deceive corporate victims into reading emails from trusted parties. Malware, extortion, and illicit fund transfers can result from such access.

ChatGPT's ability to simulate a desired writing style makes spear phishing emails look more genuine, especially for international actors who don't speak English (or other languages like Spanish and French).

This technique could let Russian, North Korean, and Iranian state-backed hackers conduct more convincing social engineering and election intervention in the US. ChatGPT can also create several campaigns and various phony online personas to promote them, making such attacks successful through volume or variation. Additionally, image-generating AI algorithms and other developing techniques can help these efforts deceive potential victims.

Hackers are discussing using ChatGPT to install malware and steal data, according to a Check Point research. Though ChatGPT's scripts are well-known in the cyber security business, they can assist amateur actors with little technical understanding into the field and possibly develop their hacking and social engineering skills through repeated use.

Additionally, ChatGPT's hacking suggestions may change. As a writer recently indicated, ChatGPT's ability to blend textual and code-based writing might be a game-changer, allowing the injection of innocent content that would subsequently turn out to be a malicious script into targeted systems. These new AI-powered writing- and code-generation abilities allow for unique cyber attacks, regardless of viability.

OpenAI fears ChatGPT usage. OpenAI, Georgetown University's Center for Security and Emerging Technology, and Stanford's Internet Observatory wrote a paper on how AI language models could enhance nation state-backed influence operations. As a last resort, the authors consider polluting the internet with radioactive or misleading data to ensure that AI language models produce outputs that other language models can identify as AI-generated. However, the authors of this paper seem unaware that their "solution" might cause much worse MDM difficulties.

Literally False News

The public argument about ChatGPTs content-generation has focused on originality, bias, and academic honesty, but broader global issues are at stake. ChatGPT can influence public opinion, troll individuals, and interfere in local and national elections by creating and automating enormous amounts of social media material for specified audiences.

ChatGPT's capacity to generate textual and code output is crucial. ChatGPT can write Python scripts for social media bots and give diverse content for repeated posts. The tool's sophistication makes it irrelevant to one's language skills, especially English, when writing MDM propaganda.

I ordered ChatGPT to write a news piece in the style of big US publications declaring that Ukraine is on the verge of defeat in its fight against Russia due to corruption, desertion, and exhaustion in its army. I also gave it a fake reporter's byline and an unidentified NATO source's remark. The outcome appears convincing:

Worse, terrible performers can modify this piece to make it more credible. They can edit the general's name or add facts about current wars. Furthermore, such actors can create many versions of this report in different forms and distribute them separately, boosting its impact.

In this example, ChatGPT produced a news story regarding (fictional) greater moviegoer fatality rates:

Editing this example makes it more plausible. Dr. Jane Smith, the putative author of the medical report, might be replaced with a real-life medical person or a real victim of this supposed medical hazard.

Can deceptive texts be found? Detecting AI text is behind AI advancements. Minor AI-generated text alterations can upset these technologies.

Some OpenAI individuals have proposed covert methods to watermark AI-generated literature to prevent its abuse. AI models would create information that appears normal to humans but would follow a cryptographic formula that would warn other machines that it was AI-made. However, security experts are cautious since manually altering the content interrupts machine and human detection of AI-generated material.

How to Prepare

Cyber security and IT workers can research and use generative AI models to fight spear fishing and extortion. Governments may also launch MDM-defence projects.

In election cycles and global crises, regular people may be the most vulnerable to AI-produced deceit. Until regulation or subsequent technical advances, individuals must recognize exposure to AI-generated fraud, dating scams, other MDM activities.

A three-step verification method of new material in suspicious emails or social media posts can help identify AI content and manipulation. This three-step approach asks about the information's distribution platform (is it reliable? ), author (is the reader familiar with them? ), and plausibility given one's prior knowledge of the topic.

Consider a report by a trusted journalist that makes shocking statements in their typical manner. AI-powered fake news may be released on an unexpected platform, such as a newly created Facebook profile. However, if it links to a known media source, it is more likely to be real.

Though hard and subjective, this verification method may be the only barrier against manipulation for now.

AI language models:

How to Recognize an AI-Generated Article ChatGPT, the popular AI-powered chatbot, can and likely does generate medium.com-style articles.

AI-Generated Text Detectors Fail. Do This. Online tools claim to detect ChatGPT output. Even with superior programming, I tested some of these tools. pub

Why Original Writers Matter Despite AI Language Models Creative writers may never be threatened by AI language models.

We love round numbers, don't we? $100,000 is a frequent freelancing milestone. You feel like six figures means you're doing something properly.

You've most likely already conquered initial freelancing challenges like finding clients, setting fair pricing, coping with criticism, getting through dry spells, managing funds, etc.

You think I must be doing well. Last month, my freelance income topped $100,000.

That may not sound impressive considering I've been freelancing for 2.75 years, but I made 30% of that in the previous four months, which is crazy.

Here are the things I wish I'd known during the early days of self-employment that would have helped me hit $100,000 faster.

1. The Volatility of Freelancing Will Stabilize.

Freelancing is risky. No surprise.

Here's an example.

October 2020 was my best month, earning $7,150. Between $4,004 in September and $1,730 in November. Unsteady.

Freelancing is regrettably like that. Moving clients. Content requirements change. Allocating so much time to personal pursuits wasn't smart, but yet.

Stabilizing income takes time. Consider my rolling three-month average income since I started freelancing. My three-month average monthly income. In February, this metric topped $5,000. Now, it's in the mid-$7,000s, but it took a while to get there.

Finding freelance gigs that provide high pay, high volume, and recurring revenue is difficult. But it's not impossible.

TLDR: Don't expect a steady income increase at first. Be patient.

2. You Have More Value Than You Realize.

Writing is difficult. Assembling words, communicating a message, and provoking action are a puzzle.

People are willing to pay you for it because they can't do what you do or don't have enough time.

Keeping that in mind can have huge commercial repercussions.

When talking to clients, don't tiptoe. You can ignore ridiculous deadlines. You don't have to take unmanageable work.

You solve an issue, so make sure you get rightly paid.

TLDR: Frame services as problem-solutions. This will let you charge more and set boundaries.

3. Increase Your Prices.

I studied hard before freelancing. I read articles and watched videos about writing businesses.

I didn't want to work for pennies. Despite this clarity, I had no real strategy to raise my rates.

I then luckily stumbled into higher-paying work. We discussed fees and hours with a friend who launched a consulting business. It's subjective and speculative because value isn't standardized. One company may laugh at your charges. If your solution helps them create a solid ROI, another client may pay $200 per hour.

When he told me he charged his first client $125 per hour, I thought, Why not?

A new-ish client wanted to discuss a huge forthcoming project, so I raised my rates. They knew my worth, so they didn't blink when I handed them my new number.

TLDR: Increase rates periodically (e.g., every 6 or 12 months). Writing skill develops with practice. You'll gain value over time.

4. Remember Your Limits.

If you can squeeze additional time into a day, let me know. I can't manipulate time yet.

We all have time and economic limits. You could theoretically keep boosting rates, but your prospect pool diminishes. Outsourcing and establishing extra revenue sources might boost monthly revenues.

I've devoted a lot of time to side projects (hopefully extra cash sources), but I've only just started outsourcing. I wish I'd tried this earlier.

If you can discover good freelancers, you can grow your firm without sacrificing time.

TLDR: Expand your writing network immediately. You'll meet freelancers who understand your daily grind and locate reference sources.

5. Every Action You Take Involves an Investment. Be Certain to Select Correctly.

Investing in stocks or crypto requires paying money, right?

In business, time is your currency (and maybe money too). Your daily habits define your future. If you spend time collecting software customers and compiling content in the space, you'll end up with both. So be sure.

I only spend around 50% of my time on client work, therefore it's taken me nearly three years to earn $100,000. I spend the remainder of my time on personal projects including a freelance book, an investment newsletter, and this blog.

Why? I don't want to rely on client work forever. So, I'm working on projects that could pay off later and help me live a more fulfilling life.

TLDR: Consider the long-term impact of your time commitments, and don't overextend. You can only make so many "investments" in a given time.

6. LinkedIn Is an Endless Mine of Gold. Use It.

Why didn't I use LinkedIn earlier?

I designed a LinkedIn inbound lead strategy that generates 12 leads a month and a few high-quality offers. As a result, I've turned down good gigs. Wish I'd begun earlier.

If you want to create a freelance business, prioritize LinkedIn. Too many freelancers ignore this site, missing out on high-paying clients. Build your profile, post often, and interact.

TLDR: Study LinkedIn's top creators. Once you understand their audiences, start posting and participating daily.

For 99% of People, Freelancing is Not a Get-Rich-Quick Scheme.

Here's a list of things I wish I'd known when I started freelancing.

1. Although it is erratic, freelancing eventually becomes stable.

2. You deserve respect and discretion over how you conduct business because you have solved an issue.

3. Increase your charges rather than undervaluing yourself. If necessary, add a reminder to your calendar. Your worth grows with time.

4. In order to grow your firm, outsource jobs. After that, you can work on the things that are most important to you.

5. Take into account how your present time commitments may affect the future. It will assist in putting things into perspective and determining whether what you are doing is indeed worthwhile.

6. Participate on LinkedIn. You'll get better jobs as a result.

If I could give my old self (and other freelancers) one bit of advice, it's this:

Despite appearances, you're making progress.

Each job. Tweets. Newsletters. Progress. It's simpler to see retroactively than in the moment.

Consistent, intentional work pays off. No good comes from doing nothing. You must set goals, divide them into time-based targets, and then optimize your calendar.

Then you'll understand you're doing well.

Want to learn more? I’ll teach you.

Many developers do it, but I don't approve.

Aren't many programmers part-time? Many work full-time but also freelance. If the job agreement allows it, I see no problem.

Tech businesses' policies vary. I have a friend in Google, Germany. According to his contract, he couldn't do an outside job. Google owns any code he writes while employed.

I was shocked. Later, I found that different Google regions have different policies.

A corporation can normally establish any agreement before hiring you. They're negotiable. When there's no agreement, state law may apply. In court, law isn't so simple.

I won't delve into legal details. Instead, let’s talk about the incident.

How he was discovered

In one month, he missed two deadlines. His boss was frustrated because the assignment wasn't difficult to miss twice. When a team can't finish work on time, they all earn bad grades.

He annoyed the whole team. One team member (anonymous) told the project manager he worked on side projects during office hours. He may have missed deadlines because of this.

The project manager was furious. He needed evidence. The manager caught him within a week. The manager told higher-ups immediately.

The company wanted to set an example

Management could terminate him and settle the problem. But the company wanted to set an example for those developers who breached the regulation.

Because dismissal isn't enough. Every organization invests heavily in developer hiring. If developers depart or are fired after a few months, the company suffers.

The developer spent 10 months there. The employer sacked him and demanded ten months' pay. Or they'd sue him.

It was illegal and unethical. The youngster paid the fine and left the company quietly to protect his career.

Right or wrong?

Is the developer's behavior acceptable? Let's discuss developer malpractice.

During office hours, may developers work on other projects? If they're bored during office hours, they might not. Check the employment contract or state law.

If there's no employment clause, check country/state law. Because you can't justify breaking the law. Always. Most employers own their employees' work hours unless it's a contractual position.

If the company agrees, it's fine.

I also oppose companies that force developers to work overtime without pay.

Most states and countries have laws that help companies and workers. Law supports employers in this case. If any of the following are true, the company/employer owns the IP under California law.

• using the business's resources

• any equipment, including a laptop used for business.

• company's mobile device.

• offices of the company.

• business time as well. This is crucial. Because this occurred in the instance of my junior.

Company resources are dangerous. Because your company may own the product's IP.  If you have seen the TV show Silicon Valley, you have seen a similar situation there, right?

Conclusion

Simple rule. I avoid big side projects. I work on my laptop on weekends for side projects. I'm safe. But I also know that my company might not be happy with that.

As an employee, I suppose I can. I can make side money. I won't promote it, but I'll respect their time, resources, and task. I also sometimes work extra time to finish my company’s deadlines.