I pitched 10 brands per workday for a total of 200.

How did I do?

It was difficult.

I've never pitched so much.

What did this challenge teach me?

• the superiority of quality over quantity

• When you need help, outsource

• Don't disregard burnout in order to complete a challenge because it exists.

First, pitching brands for brand deals requires quality. Find firms that align with your brand to expose to your audience.

If you associate with any company, you'll lose audience loyalty. I didn't lose sight of that, but I couldn't resist finishing the task.

Outsourcing.

Delegating work to teammates is effective.

I wish I'd done it.

Three people can pitch 200 companies a month significantly faster than one.

One person does research, one to two do outreach, and one to two do follow-up and negotiating.

Simple.

In 2022, I'll outsource everything.

Burnout.

I felt this, so I slowed down at the end of the month.

Thanksgiving week in November was slow.

I was buying and decorating for Christmas. First time putting up outdoor holiday lights was fun.

Much was happening.

I'm not perfect.

I'm being honest.

The Outcomes

Less than 50 brands pitched.

Result: A deal with 3 brands.

I hoped for 4 brands with reaching out to 200 companies, so three with under 50 is wonderful.

That’s a 6% conversion rate!

Whoo-hoo!

I needed 2%.

Here's a screenshot from one of the deals I booked.

These companies fit my company well. Each campaign is different, but I've booked $2,450 in brand work with a couple of pending transactions for December and January.

$2,450 in brand work booked!

How did I do? You tell me.

Is this something you’d try yourself?

SaaS spending is 40/40/20. 40% of operating expenses should be R&D, 40% sales and marketing, and 20% G&A. We wanted to see the statistics behind the rules of thumb. Since October 2017, 73 SaaS startups have gone public. Perhaps the rule of thumb should be 30/50/20. The data is below.

30/50/20. R&D accounts for 26% of opex, sales and marketing 48%, and G&A 22%. We think R&D/S&M/G&A should be 30/50/20.

There are outliers. There are exceptions to rules of thumb. Dropbox spent 45% on R&D whereas Zoom spent 13%. Zoom spent 73% on S&M, Dropbox 37%, and Bill.com 28%. Snowflake spent 130% of revenue on S&M, while their EBITDA margin is -192%.

G&A shouldn't stand out. Minimize G&A spending. Priorities should be product development and sales. Cloudflare, Sendgrid, Snowflake, and Palantir spend 36%, 34%, 37%, and 43% on G&A.

Another myth is that COGS is 20% of revenue. Median and averages are 29%.

Where is the profitability? Data-driven operating income calculations were simplified (Revenue COGS R&D S&M G&A). 20 of 73 IPO businesses reported operational income. Median and average operating income margins are -21% and -27%.

As long as you're growing fast, have outstanding retention, and marquee clients, you can burn cash since recurring income that doesn't churn is a valuable annuity.

The data was compelling overall. 30/50/20 is the new 40/40/20 for more established SaaS enterprises, unprofitability is alright as long as your business is expanding, and COGS can be somewhat more than 20% of revenue.

In the early 2000s, I began developing SaaS. I helped launch an internet fax service that delivered faxes to email inboxes. Back then, it saved consumers money and made the procedure easier.

Google AdWords was young then. Anyone might establish a new website, spend a few hundred dollars on keywords, and see dozens of new paying clients every day. That's how we launched our new SaaS, and these clients stayed for years. Our early ROI was sky-high.

Changing times

The situation changed dramatically after 15 years. Our paid advertising cost $200-$300 for every new customer. Paid advertising takes three to four years to repay.

Fortunately, we still had tens of thousands of loyal clients. Good organic rankings gave us new business. We needed less sponsored traffic to run a profitable SaaS firm.

Is it still possible?

Since selling our internet fax firm, I've dreamed about starting a SaaS company. One I could construct as a lone developer and progressively grow a dedicated customer base, as I did before in a small team.

It seemed impossible to me. Solo startups couldn't afford paid advertising. SEO was tough. Even the worst SaaS startup ideas attracted VC funding. How could I compete with startups that could hire great talent and didn't need to make money for years (or ever)?

The One and Only Way to Learn

After years of talking myself out of SaaS startup ideas, I decided to develop and launch one. I needed to know if a solitary developer may create a SaaS app in 2022.

Thus, I did. I invented webwriter.ai, an AI-powered writing tool for website content, from hero section headlines to blog posts, this year. I soft-launched an MVP in July.

Considering the Issue

Now that I've developed my own fully capable SaaS app for site builders and developers, I wonder if it's still possible. Can webwriter.ai be successful?

I know webwriter.ai's proposal is viable because Jasper.ai and Grammarly are also AI-powered writing tools. With competition comes validation.

To Win, Differentiate

To compete with well-funded established brands, distinguish to stand out to a portion of the market. So I can speak directly to a target user, unlike larger competition.

I created webwriter.ai to help web builders and designers produce web content rapidly. This may be enough differentiation for now.

Budget-Friendly Promotion

When paid search isn't an option, we get inventive. There are more tools than ever to promote a new website.

• Organic Results

• on social media (Twitter, Instagram, TikTok, LinkedIn)

• Marketing with content that is compelling

• Link Creation

• Listings in directories

• references made in blog articles and on other websites

• Forum entries

The Beginning of the Journey

As I've labored to construct my software, I've pondered a new mantra. Not sure where that originated from, but I like it. I'll live by it and teach my kids:

“Do the work.”

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.

ZERO GRACE/ZERO MALICE

Big companies and wealthy people make stupid mistakes too.

Your ancestors kept snakes and drank bad water. You (probably) don't because you've learnt from their failures via instinct+, the ultimate life-lessons streaming network in your head. Instincts foretell the future. If you approach a lion, it'll eat you. Our society's nuanced/complex decisions have surpassed instinct. Human growth depends on how we handle these issues. 80% of people believe they are above-average drivers, yet few believe they make many incorrect mistakes that make them risky. Stupidity hurts others like death. Basic Laws of Human Stupidity by Carlo Cipollas:

1. Everyone underestimates the prevalence of idiots in our society.

2. Any other trait a person may have has no bearing on how likely they are to be stupid.

3. A dumb individual is one who harms someone without benefiting themselves and may even lose money in the process.

4. Non-dumb people frequently underestimate how destructively powerful stupid people can be.

5. The most dangerous kind of person is a moron.

Professor Cippola defines stupid as bad for you and others. We underestimate the corporate world's and seemingly successful people's ability to make bad judgments that harm themselves and others. Success is an intoxication that makes you risk-aggressive and blurs your peripheral vision.

Stupid companies and decisions:

Big Dumber

Big-company bad ideas have more bulk and inertia. The world's most valuable company recently showed its board a VR headset. Jony Ive couldn't destroy Apple's terrible idea in 2015. Mr. Ive said that VR cut users off from the outer world, made them seem outdated, and lacked practical uses. Ives' design team doubted users would wear headsets for lengthy periods.

VR has cost tens of billions of dollars over a decade to prove nobody wants it. The next great SaaS startup will likely come from Florence, not Redmond or San Jose.

Apple Watch and Airpods have made the Cupertino company the world's largest jewelry maker. 10.5% of Apple's income, or $38 billion, comes from wearables in 2021. (seven times the revenue of Tiffany & Co.). Jewelry makes you more appealing and useful. Airpods and Apple Watch do both.

Headsets make you less beautiful and useful and promote isolation, loneliness, and unhappiness among American teenagers. My sons pretend they can't hear or see me when on their phones. VR headsets lack charisma.

Coinbase disclosed a plan to generate division and tension within its workplace weeks after Apple was pitched $2,000 smokes. The crypto-trading platform is piloting a program that rates staff after every interaction. If a coworker says anything you don't like, you should tell them how to improve. Everyone gets a 110-point scorecard. Coworkers should evaluate a person's rating while deciding whether to listen to them. It's ridiculous.

Organizations leverage our superpower of cooperation. This encourages non-cooperation, period. Bridgewater's founder Ray Dalio designed the approach to promote extreme transparency. Dalio has 223 billion reasons his managerial style works. There's reason to suppose only a small group of people, largely traders, will endure a granular scorecard. Bridgewater has 20% first-year turnover. Employees cry in bathrooms, and sex scandals are settled by ignoring individuals with poor believability levels. Coinbase might take solace that the stock is 80% below its initial offering price.

Poor Stupid

Fools' ledgers are valuable. More valuable are lists of foolish rich individuals.

Robinhood built a $8 billion corporation on financial ignorance. The firm's median account value is $240, and its stock has dropped 75% since last summer. Investors, customers, and society lose. Stupid. Luna published a comparable list on the blockchain, grew to $41 billion in market cap, then plummeted.

A podcast presenter is recruiting dentists and small-business owners to invest in Elon Musk's Twitter takeover. Investors pay a 7% fee and 10% of the upside for the chance to buy Twitter at a 35% premium to the current price. The proposal legitimizes CNBC's Trade Like Chuck advertising (Chuck made $4,600 into $460,000 in two years). This is stupid because it adds to the Twitter deal's desperation. Mr. Musk made an impression when he urged his lawyers to develop a legal rip-cord (There are bots on the platform!) to abandon the share purchase arrangement (for less than they are being marketed by the podcaster). Rolls-Royce may pay for this list of the dumb affluent because it includes potential Cullinan buyers.

Worst company? Flowcarbon, founded by WeWork founder Adam Neumann, operates at the convergence of carbon and crypto to democratize access to offsets and safeguard the earth's natural carbon sinks. Can I get an ayahuasca Big Gulp?

Neumann raised $70 million with their yogababble drink. More than half of the consideration came from selling GNT. Goddess Nature Token. I hope the company gets an S-1. Or I'll start a decentralized AI Meta Renewable NFTs company. My Community Based Ebitda coin will fund the company. Possible.

Stupidity inside oneself

This weekend, I was in NYC with my boys. My 14-year-old disappeared. He's realized I'm not cool and is mad I let the charade continue. When out with his dad, he likes to stroll home alone and depart before me. Friends told me hell would return, but I was surprised by how fast the eye roll came.

Not so with my 11-year-old. We went to The Edge, a Hudson Yards observation platform where you can see the city from 100 storeys up for $38. This is hell's seventh ring. Leaning into your boys' interests is key to engaging them (dad tip). Neither loves Crossfit, WW2 history, or antitrust law.

We take selfies on the Thrilling Glass Floor he spots. Dad, there's a bar! Coke? I nod, he rushes to the bar, stops, runs back for money, and sprints back. Sitting on stone seats, drinking Atlanta Champagne, he turns at me and asks, Isn't this amazing? I'll never reach paradise.

Later that night, the lads are asleep and I've had two Zacapas and Cokes. I SMS some friends about my day and how I feel about sons/fatherhood/etc. How I did. They responded and approached. The next morning, I'm sober, have distance from my son, and feel ashamed by my texts. Less likely to impulsively share my emotions with others. Stupid again.