How did his money blind me?

Like most fledgling entrepreneurs, I wanted a mentor. I met as many nearby folks with "entrepreneur" in their LinkedIn biographies for coffee.

These meetings taught me a lot, and I'd suggest them to any new creator. Attention! Meeting with many experienced entrepreneurs means getting contradictory advice. One entrepreneur will tell you to do X, then the next one you talk to may tell you to do Y, which are sometimes opposites. You'll have to chose which suggestion to take after the chats.

I experienced this. Same afternoon, I had two coffee meetings with experienced entrepreneurs. The first meeting was with a billionaire entrepreneur who took his company public.

I met him in a swanky hotel lobby and ordered a drink I didn't pay for. As a fledgling entrepreneur, money was scarce.

During the meeting, I demoed the software I'd built, he liked it, and we spent the hour discussing what features would make it a success. By the end of the meeting, he requested I include a killer feature we both agreed would attract buyers. The feature was complex and would require some time. The billionaire I was sipping coffee with in a beautiful hotel lobby insisted people would love it, and that got me enthusiastic.

The second meeting was with a young entrepreneur who had recently raised a small amount of investment and looked as eager to pitch me as I was to pitch him. I forgot his name. I mostly recall meeting him in a filthy coffee shop in a bad section of town and buying his pricey cappuccino. Water for me.

After his pitch, I demoed my app. When I was done, he barely noticed. He questioned my customer acquisition plan. Who was my client? What did they offer? What was my plan? Etc. No decent answers.

After our meeting, he insisted I spend more time learning my market and selling. He ignored my questions about features. Don't worry about features, he said. Customers will request features. First, find them.

Putting your faith in results over relevance

Problems plagued my afternoon. I met with two entrepreneurs who gave me differing advice about how to proceed, and I had to decide which to pursue. I couldn't decide.

Ultimately, I followed the advice of the billionaire.

Obviously.

Who wouldn’t? That was the guy who clearly knew more.

A few months later, I constructed the feature the billionaire said people would line up for.

The new feature was unpopular. I couldn't even get the billionaire to answer an email showing him what I'd done. He disappeared.

Within a few months, I shut down the company, wasting all the time and effort I'd invested into constructing the killer feature the billionaire said I required.

Would follow the struggling entrepreneur's advice have saved my company? It would have saved me time in retrospect. Potential consumers would have told me they didn't want what I was producing, and I could have shut down the company sooner or built something they did want. Both outcomes would have been better.

Now I know, but not then. I favored achievement above relevance.

Success vs. relevance

The millionaire gave me advice on building a large, successful public firm. A successful public firm is different from a startup. Priorities change in the last phase of business building, which few entrepreneurs reach. He gave wonderful advice to founders trying to double their stock values in two years, but it wasn't beneficial for me.

The other failing entrepreneur had relevant, recent experience. He'd recently been in my shoes. We still had lots of problems. He may not have achieved huge success, but he had valuable advice on how to pass the closest hurdle.

The money blinded me at the moment. Not alone So much of company success is defined by money valuations, fundraising, exits, etc., so entrepreneurs easily fall into this trap. Money chatter obscures the value of knowledge.

Don't base startup advice on a person's income. Focus on what and when the person has learned. Relevance to you and your goals is more important than a person's accomplishments when considering advice.

Internet's fantastic, right?

We've never had a better way to share our creativity.

I can now draw on my iPad and tweet or Instagram it to thousands. I may get some likes.

With such a great, free tool, you're not alone.

Millions more bright-eyed artists are sharing their work online.

The issue is getting innovative work noticed, not sharing it.

In a world where creators want attention, attention is valuable.

We build for attention.

Attention helps us establish a following, make money, get notoriety, and make a difference.

Most of us require attention to stay sane while creating wonderful things.

I know how hard it is to work hard and receive little views.

How do we receive more attention, more often, in a sea of talent?

Advertising and celebrity endorsements are options. These may work temporarily.

To attract true, organic, and long-term attention, you must create in high quality, high volume, and consistency.

Adapting Steve Martin's Be so amazing, they can't ignore you (with a mention to Dan Norris in his great book Create or Hate for the reminder)

Create a lot.

Eventually, your effort will gain traction.

Traction shows your work's influence.

Traction is when your product sells more. Traction is exponential user growth. Your work is shared more.

No matter how good your work is, it will always have minimal impact on the world.

Your work can eventually dent or puncture. Daily, people work to dent.

To achieve this tipping point, you must consistently produce exceptional work.

Expect traction after hundreds of outputs.

Dilbert creator Scott Adams says repetition persuades. If you don't stop, you can persuade practically anyone with anything.

Volume lends believability. So make more.

I worked as an illustrator for at least a year and a half without any recognition. After 150 illustrations on iStockphoto, my work started selling.

With 350 illustrations on iStock, I started getting decent client commissions.

Producing often will improve your craft and draw attention.

It's the only way to succeed. More creation means better results and greater attention.

Austin Kleon says you can improve your skill in relative anonymity before you become famous. Before obtaining traction, generate a lot and become excellent.

Most artists, even excellent ones, don't create consistently enough to get traction.

It may hurt. For makers who don't love and flow with their work, it's extremely difficult.

Your work must bring you to life.

To generate so much that others can't ignore you, decide what you'll accomplish every day (or most days).

Commit and be patient.

Prepare for zero-traction.

Anticipating this will help you persevere and create.

My online guru Grant Cardone says: Anything worth doing is worth doing every day.

Do.

His success was unforeseeable.

2014 saw Facebook's $2.3 billion purchase of Oculus VR.

19-year-old Palmer Luckey founded Oculus. He quit journalism school. His parents worried about his college dropout.

Facebook bought Oculus VR in less than 2 years.

Palmer Luckey started Anduril Industries. Palmer has raised $385 million with Anduril.

The Oculus journey began in a trailer

Palmer Luckey, 19, owned the trailer.

Luckey had his trailer customized. The trailer had all six of Luckey's screens. In the trailer's remaining area, Luckey conducted hardware tests.

At 16, he became obsessed with virtual reality. Virtual reality was rare at the time.

Luckey didn't know about VR when he started.

Previously, he liked "portabilizing" mods. Hacking ancient game consoles into handhelds.

In his city, fewer portabilizers actively traded.

Luckey started "ModRetro" for other portabilizers. Luckey was exposed to VR headsets online.

Luckey:

“Man, ModRetro days were the best.”

Palmer Luckey used VR headsets for three years. His design had 50 prototypes.

Luckey used to work at the Long Beach Sailing Center for minimum salary, servicing diesel engines and cleaning boats.

Luckey worked in a USC Institute for Creative Technologies mixed reality lab in July 2011. (ICT).

Luckey cleaned the lab, did reports, and helped other students with VR projects.

Luckey's lab job was dull.

Luckey chose to work in the lab because he wanted to engage with like-minded folks.

By 2012, Luckey had a prototype he hoped to share globally. He made cheaper headsets than others.

Luckey wanted to sell an easy-to-assemble virtual reality kit on Kickstarter.

He realized he needed a corporation to do these sales legally. He started looking for names. "Virtuality," "virtual," and "VR" are all taken.

Hence, Oculus.

If Luckey sold a hundred prototypes, he would be thrilled since it would boost his future possibilities.

John Carmack, legendary game designer

Carmack has liked sci-fi and fantasy since infancy.

Carmack loved imagining intricate gaming worlds.

His interest in programming and computer science grew with age.

He liked graphics. He liked how mismatching 0 and 1 might create new colors and visuals.

Carmack played computer games as a teen. He created Shadowforge in high school.

He founded Id software in 1991. When Carmack created id software, console games were the best-sellers.

Old computer games have weak graphics. John Carmack and id software developed "adaptive tile refresh."

This technique smoothed PC game scrolling. id software launched 3-D, Quake, and Doom using "adaptive tile refresh."

These games made John Carmack a gaming star. Later, he sold Id software to ZeniMax Media.

How Palmer Luckey met Carmack

In 2011, Carmack was thinking a lot about 3-D space and virtual reality.

He was underwhelmed by the greatest HMD on the market. Because of their flimsiness and latency.

His disappointment was partly due to the view (FOV). Best HMD had 40-degree field of view.

Poor. The best VR headset is useless with a 40-degree FOV.

Carmack intended to show the press Doom 3 in VR. He explored VR headsets and internet groups for this reason.

Carmack identified a VR enthusiast in the comments section of "LEEP on the Cheap." "PalmerTech" was the name.

Carmack approached PalmerTech about his prototype. He told Luckey about his VR demos, so he wanted to see his prototype.

Carmack got a Rift prototype. Here's his May 17 tweet.

John Carmack tweeted an evaluation of the Luckey prototype.

Dan Newell, a Valve engineer, and Mick Hocking, a Sony senior director, pre-ordered Oculus Rift prototypes with Carmack's help.

Everyone praised Luckey after Carmack demoed Rift.

Palmer Luckey received a job offer from Sony.

• It was a full-time position at Sony Computer Europe.

• He would run Sony’s R&D lab.

• The salary would be $70k.

Who is Brendan Iribe?

Brendan Iribe started early with Startups. In 2004, he and Mike Antonov founded Scaleform.

Scaleform created high-performance middleware. This package allows 3D Flash games.

In 2011, Iribe sold Scaleform to Autodesk for $36 million.

How Brendan Iribe discovered Palmer Luckey.

Brendan Iribe's friend Laurent Scallie.

Laurent told Iribe about a potential opportunity.

Laurent promised Iribe VR will work this time. Laurent introduced Iribe to Luckey.

Iribe was doubtful after hearing Laurent's statements. He doubted Laurent's VR claims.

But since Laurent took the name John Carmack, Iribe thought he should look at Luckey Innovation. Iribe was hooked on virtual reality after reading Palmer Luckey stories.

He asked Scallie about Palmer Luckey.

Iribe convinced Luckey to start Oculus with him

First meeting between Palmer Luckey and Iribe.

The Iribe team wanted Luckey to feel comfortable.

Iribe sought to convince Luckey that launching a company was easy. Iribe told Luckey anyone could start a business.

Luckey told Iribe's staff he was homeschooled from childhood. Luckey took self-study courses.

Luckey had planned to launch a Kickstarter campaign and sell kits for his prototype. Many companies offered him jobs, nevertheless.

He's considering Sony's offer.

Iribe advised Luckey to stay independent and not join a firm. Iribe asked Luckey how he could raise his child better. No one sees your baby like you do?

Iribe's team pushed Luckey to stay independent and establish a software ecosystem around his device.

After conversing with Iribe, Luckey rejected every job offer and merger option.

Iribe convinced Luckey to provide an SDK for Oculus developers.

After a few months. Brendan Iribe co-founded Oculus with Palmer Luckey. Luckey trusted Iribe and his crew, so he started a corporation with him.

Crowdfunding

Brendan Iribe and Palmer Luckey launched a Kickstarter.

Gabe Newell endorsed Palmer's Kickstarter video.

Gabe Newell wants folks to trust Palmer Luckey since he's doing something fascinating and answering tough questions.

Mark Bolas and David Helgason backed Palmer Luckey's VR Kickstarter video.

Luckey introduced Oculus Rift during the Kickstarter campaign. He introduced virtual reality during press conferences.

Oculus' Kickstarter effort was a success. Palmer Luckey felt he could raise $250,000.

Oculus raised $2.4 million through Kickstarter. Palmer Luckey's virtual reality vision was well-received.

Mark Zuckerberg's Oculus discovery

Brendan Iribe and Palmer Luckey hired the right personnel after a successful Kickstarter campaign.

Oculus needs a lot of money for engineers and hardware. They needed investors' money.

Series A raised $16M.

Next, Andreessen Horowitz partner Brain Cho approached Iribe.

Cho told Iribe that Andreessen Horowitz could invest in Oculus Series B if the company solved motion sickness.

Mark Andreessen was Iribe's dream client.

Marc Andreessen and his partners gave Oculus $75 million.

Andreessen introduced Iribe to Zukerberg. Iribe and Zukerberg discussed the future of games and virtual reality by phone.

Facebook's Oculus demo

Iribe showed Zuckerberg Oculus.

Mark was hooked after using Oculus. The headset impressed him.

The whole Facebook crew who saw the demo said only one thing.

“Holy Crap!”

This surprised them all.

Mark Zuckerberg was impressed by the team's response. Mark Zuckerberg met the Oculus team five days after the demo.

First meeting Palmer Luckey.

Palmer Luckey is one of Mark's biggest supporters and loves Facebook.

Oculus Acquisition

Zuckerberg wanted Oculus.

Brendan Iribe had requested for $4 billion, but Mark wasn't interested.

Facebook bought Oculus for $2.3 billion after months of drama.

After selling his company, how does Palmer view money?

Palmer loves the freedom money gives him. Money frees him from small worries.

Money has allowed him to pursue things he wouldn't have otherwise.

“If I didn’t have money I wouldn’t have a collection of vintage military vehicles…You can have nice hobbies that keep you relaxed when you have money.”

He didn't start Oculus to generate money. His virtual reality passion spanned years.

He didn't have to lie about how virtual reality will transform everything until he needed funding.

The company's success was an unexpected bonus. He was merely passionate about a good cause.

After Oculus' $2.3 billion exit, what changed?

Palmer didn't mind being rich. He did similar things.

After Facebook bought Oculus, he moved to Silicon Valley and lived in a 12-person shared house due to high rents.

Palmer might have afforded a big mansion, but he prefers stability and doing things because he wants to, not because he has to.

“Taco Bell is never tasted so good as when you know you could afford to never eat taco bell again.”

Palmer's leadership shifted.

Palmer changed his leadership after selling Oculus.

When he launched his second company, he couldn't work on his passions.

“When you start a tech company you do it because you want to work on a technology, that is why you are interested in that space in the first place. As the company has grown, he has realized that if he is still doing optical design in the company it’s because he is being negligent about the hiring process.”

Once his startup grows, the founder's responsibilities shift. He must recruit better firm managers.

Recruiting talented people becomes the top priority. The founder must convince others of their influence.

A book that helped me write this:

The History of the Future: Oculus, Facebook, and the Revolution That Swept Virtual Reality — Blake Harris

*This post is a summary. Read the full article here.

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.

When you stop blogging for a few weeks, your views and profits plummet.

Because you're writing fascinating posts for others. Everyone's done ithat…

If I keep writing, the graph should maintain velocity, you could say. If I wrote more, it could rise.

However, entertaining pieces still tend to roller coaster and jump.

this type of writing is like a candle. They burn out and must be replaced. You must continuously light new ones to maintain the illumination.

When you quit writing, your income stops.

A substitute

Instead of producing amusing articles, try solving people's issues. You should answer their search questions.

Here's what happens when you answer their searches.

My website's Google analytics. As a dentist, I answer oral health questions.

This chart vs. Medium is pretty glaring, right?

As of yesterday, it was averaging 15k page views each day.

How much would you make on Medium with 15k daily views?

Evergreen materials

In SEO, this is called evergreen content.

Your content is like a lush, evergreen forest, and by green I mean Benjamins.

Do you have knowledge that you can leverage? Why not help your neighbors and the world?

Answer search inquiries and help others. You'll be well rewarded.

This is better than crafting candle-like content that fizzles out quickly.

Is beauty really ephemeral like how flowers bloom? Nah, I prefer watching forests grow instead (: