Choose.

DNA makes us distinct.

We act alike. Most people follow the same road, ignoring differences. We remain quiet about our uniqueness for fear of exclusion (family, social background, religion). We live a more or less imposed life.

Off the beaten path, we stand out from the others. We obey without realizing we're sewing a shroud. We're told to do as everyone else and spend 40 years dreaming of a golden retirement and regretting not living.

“One of the greatest regrets in life is being what others would want you to be, rather than being yourself.” - Shannon L. Alder

Others dare. Again, few are creative; most follow the example of those who establish a business for the sake of entrepreneurship. To live.

They pick a potential market and model their MVP on an existing solution. Most mimic others, alter a few things, appear to be original, and end up with bland products, adding to an already crowded market.

SaaS, PaaS, etc. followed suit. It's reduced pricing, profitability, and product lifespan.

As competitors become more aggressive, their profitability diminishes, making life horrible for them and their employees. They fail to innovate, cut costs, and close their company.

Few of them look happy and fulfilled.

How did they do it?

The answer is unsettlingly simple.

They are themselves.

• They start their company, propelled at first by a passion or maybe a calling.

• Then, at their own pace, they create it with the intention of resolving a dilemma.

• They assess what others are doing and consider how they might improve it.

• In contrast to them, they respond to it in their own way by adding a unique personal touch. Therefore, it is obvious.

Originals, like their DNA, can't be copied. Or if they are, they're poorly printed. Originals are unmatched. Artist-like. True collectors only buy Picasso paintings by the master, not forgeries, no matter how good.

Imaginative people are constantly ahead. Copycats fall behind unless they innovate. They watch their competition continuously. Their solution or product isn't sexy. They hope to cash in on their copied product by flooding the market.

They're mostly pirates. They're short-sighted, unlike creators.

Creators see further ahead and have no rivals. They use copiers to confirm a necessity. To maintain their individuality, creators avoid copying others. They find copying boring. It's boring. They oppose plagiarism.

It's thrilling and inspiring.

It will also make them more able to withstand their opponents' tension. Not to mention roadblocks. For creators, impediments are games.

Others fear it. They race against the clock and fear threats that could interrupt their momentum since they lack inventiveness and their product has a short life cycle.

Creators have time on their side. They're dedicated. Clearly. Passionate booksellers will have their own bookstore. Their passion shows in their book choices. Only the ones they love.

The copier wants to display as many as possible, including mediocre authors, and will cut costs. All this to dominate the market. They're digging their own grave.

The bookseller is just one example. I could give you tons of them.

Closing remarks

Entrepreneurs might follow others or be themselves. They risk exhaustion trying to predict what their followers will do.

It's true.

Life offers choices.

Being oneself or doing as others do, with the possibility of regretting not expressing our uniqueness and not having lived.

“Be yourself; everyone else is already taken”. Oscar Wilde

The choice is yours.

People don’t believe in influencers anymore; they need people like you.

Social media influencers have dominated for years. We've seen videos, images, and articles of *famous* individuals unwrapping, reviewing, and endorsing things.

This industry generates billions. This year, marketers spent $2.23 billion on Instagram, $1 million on Youtube, and $775 million on Tiktok. This marketing has helped start certain companies.

Influencers are dying, so ordinary people like us may take over this billion-dollar sector. Why?

Why influencers are perishing

Most influencers lie to their fans, especially on Instagram. Influencers' first purpose was to make their lives so flawless that others would want to buy their stuff.

In 2015, an Australian influencer with 600,000 followers went viral for revealing all her photos and everything she did to seem great before deleting her account.

“I dramatically edited the pictures, I manipulated the environements, and made my life look perfect in social media… I remember I obsessively checked the like count for a full week since uploading it, a selfie that now has close to 2,500 likes. It got 5 likes. This was when I was so hungry for social media validation … This was the reason why I quit social media: for me, personally, it consumed me. I wasn’t living in a 3D world.”

Influencers then lost credibility.

Influencers seem to live in a bubble, separate from us. Thanks to self-popularity love's and constant awareness campaigns, people find these people ridiculous.

Influencers are praised more for showing themselves as natural and common than for showing luxuries and lies.

Little by little, they are dying, making room for a new group to take advantage of this multi-million dollar business, which gives us (ordinary people) a big opportunity to grow on any content creation platform we want.

Why this is your chance to develop on any platform for creating content

In 2021, I wrote “Not everyone who talks about money is a Financial Advisor, be careful of who you take advice from,”. In it, I warned that not everyone with a large following is a reputable source of financial advice.

Other writers hated this post and said I was wrong.

People don't want Jeff Bezos or Elon Musk's counsel, they said. They prefer to hear about their neighbor's restroom problems or his closest friend's terrible business.

Real advice from regular folks.

And I found this was true when I returned to my independent YouTube channel and had more than 1000 followers after having abandoned it with fewer than 30 videos in 2021 since there were already many personal finance and travel channels and I thought mine wasn't special.

People appreciated my videos because I was a 20-something girl trying to make money online, and they believed my advice more than that of influencers with thousands of followers.

I think today is the greatest time to grow on any platform as an ordinary person. Normal individuals give honest recommendations about what works for them and look easier to make because they have the same options as us.

Nobody cares how a millionaire acquired a Lamborghini unless it's entertaining. Education works now. Real counsel from average people is replicable.

Many individuals don't appreciate how false influencers seem (unreal bodies and excessive surgery and retouching) since it makes them feel uneasy.

That's why body-positive advertisements have been so effective, but they've lost ground in places like Tiktok, where the audience wants more content from everyday people than influencers living amazing lives. More people will relate to your content if you appear genuine.

Last thoughts

Influencers are dwindling. People want more real people to give real advice and demonstrate an ordinary life.

People will enjoy anything you tell about your daily life as long as you provide value, and you can build a following rapidly if you're honest.

This is a millionaire industry that is getting more expensive and will go with what works, so stand out immediately.

During the dot-com bust, I started my internet career. People used the internet intermittently to check email, plan travel, and do research. The average internet user spent 30 minutes online a day, compared to 7 today. To use the internet, you had to "log on" (most people still used dial-up), unlike today's always-on, high-speed mobile internet. In 2001, Amazon's market cap was $2.2B, 1/500th of what it is today. A study asked Americans if they'd adopt broadband, and most said no. They didn't see a need to speed up email, the most popular internet use. The National Academy of Sciences ranked the internet 13th among the 100 greatest inventions, below radio and phones. The internet was a cool invention, but it had limited uses and wasn't a good place to build a business. 

A small but growing movement of developers and founders believed the internet could be more than a read-only medium, allowing anyone to create and publish. This is web 2. The runner up name was read-write web. (These terms were used in prominent publications and conferences.) 

Web 2 concepts included letting users publish whatever they want ("user generated content" was a buzzword), social graphs, APIs and mashups (what we call composability today), and tagging over hierarchical navigation. Technical innovations occurred. A seemingly simple but important one was dynamically updating web pages without reloading. This is now how people expect web apps to work. Mobile devices that could access the web were niche (I was an avid Sidekick user). 

The contrast between what smart founders and engineers discussed over dinner and on weekends and what the mainstream tech world took seriously during the week was striking. Enterprise security appliances, essentially preloaded servers with security software, were a popular trend. Many of the same people would talk about "serious" products at work, then talk about consumer internet products and web 2. It was tech's biggest news. Web 2 products were seen as toys, not real businesses. They were hobbies, not work-related. 

There's a strong correlation between rich product design spaces and what smart people find interesting, which took me some time to learn and led to blog posts like "The next big thing will start out looking like a toy" Web 2's novel product design possibilities sparked dinner and weekend conversations. Imagine combining these features. What if you used this pattern elsewhere? What new product ideas are next? This excited people. "Serious stuff" like security appliances seemed more limited. 

The small and passionate web 2 community also stood out. I attended the first New York Tech meetup in 2004. Everyone fit in Meetup's small conference room. Late at night, people demoed their software and chatted. I have old friends. Sometimes I get asked how I first met old friends like Fred Wilson and Alexis Ohanian. These topics didn't interest many people, especially on the east coast. We were friends. Real community. Alex Rampell, who now works with me at a16z, is someone I met in 2003 when a friend said, "Hey, I met someone else interested in consumer internet." Rare. People were focused and enthusiastic. Revolution seemed imminent. We knew a secret nobody else did. 

My web 2 startup was called SiteAdvisor. When my co-founders and I started developing the idea in 2003, web security was out of control. Phishing and spyware were common on Internet Explorer PCs. SiteAdvisor was designed to warn users about security threats like phishing and spyware, and then, using web 2 concepts like user-generated reviews, add more subjective judgments (similar to what TrustPilot seems to do today). This staged approach was common at the time; I called it "Come for the tool, stay for the network." We built APIs, encouraged mashups, and did SEO marketing. 

Yahoo's 2005 acquisitions of Flickr and Delicious boosted web 2 in 2005. By today's standards, the amounts were small, around $30M each, but it was a signal. Web 2 was assumed to be a fun hobby, a way to build cool stuff, but not a business. Yahoo was a savvy company that said it would make web 2 a priority. 

As I recall, that's when web 2 started becoming mainstream tech. Early web 2 founders transitioned successfully. Other entrepreneurs built on the early enthusiasts' work. Competition shifted from ideation to execution. You had to decide if you wanted to be an idealistic indie bar band or a pragmatic stadium band. 

Web 2 was booming in 2007 Facebook passed 10M users, Twitter grew and got VC funding, and Google bought YouTube. The 2008 financial crisis tested entrepreneurs' resolve. Smart people predicted another great depression as tech funding dried up. 

Many people struggled during the recession. 2008-2011 was a golden age for startups. By 2009, talented founders were flooding Apple's iPhone app store. Mobile apps were booming. Uber, Venmo, Snap, and Instagram were all founded between 2009 and 2011. Social media (which had replaced web 2), cloud computing (which enabled apps to scale server side), and smartphones converged. Even if social, cloud, and mobile improve linearly, the combination could improve exponentially. 

This chart shows how I view product and financial cycles. Product and financial cycles evolve separately. The Nasdaq index is a proxy for the financial sentiment. Financial sentiment wildly fluctuates. 

Next row shows iconic startup or product years. Bottom-row product cycles dictate timing. Product cycles are more predictable than financial cycles because they follow internal logic. In the incubation phase, enthusiasts build products for other enthusiasts on nights and weekends. When the right mix of technology, talent, and community knowledge arrives, products go mainstream. (I show the biggest tech cycles in the chart, but smaller ones happen, like web 2 in the 2000s and fintech and SaaS in the 2010s.) 

Tech has changed since the 2000s. Few tech giants dominate the internet, exerting economic and cultural influence. In the 2000s, web 2 was ignored or dismissed as trivial. Entrenched interests respond aggressively to new movements that could threaten them. Creative patterns from the 2000s continue today, driven by enthusiasts who see possibilities where others don't. Know where to look. Crypto and web 3 are where I'd start. 

Today's negative financial sentiment reminds me of 2008. If we face a prolonged downturn, we can learn from 2008 by preserving capital and focusing on the long term. Keep an eye on the product cycle. Smart people are interested in things with product potential. This becomes true. Toys become necessities. Hobbies become mainstream. Optimists build the future, not cynics.

Full article is available here

11 science-backed ways to improve health, happiness, healing, relaxation, and mental clarity.

Vagus nerve is the main parasympathetic nervous system component.

It helps us rest and digest by slowing and stabilizing a resting heart rate, slowing and stabilizing the breath, promoting digestion, improving recovery and healing times, producing saliva, releasing endorphins and hormones like dopamine, oxytocin, and serotonin, and boosting the immune, digestive, and cardiovascular systems.

The vagus nerve sends anti-inflammatory signals to other parts of the body and is located behind the tongue, in the throat, neck, heart, lungs, abdomen, and brainstem.

Vagus means wandering in Latin. So, it's bold.

Here are 11 proven ways to boost health, happiness, and the vagus nerve.

1. Extend

“Yoga stimulates different nerves in your body, especially the vagus nerve that carries information from the brain to most of the body’s major organs, slows everything down and allows self-regulation. It’s the nerve that is associated with the parasympathetic system and emotions like love, joy, and compassion.” — Deepak Chopra

Stretching doesn't require a yoga background.

Listen to your body and ease into simple poses. This connects the mind and body.

If you're new to yoga or don't have access to an in-person class, try Yoga with Adrienne. Over 600 YouTube videos give her plenty of material.

2. Inhale

Because inhaling and exhaling activate the autonomic nervous system, we can breathe to relax.

Exhaling activates the parasympathetic nervous system (rest and digest). One inhales stress, the other exhales it.

So, faster or more intense breathing increases stress. Slower breathing relaxes us.

Breathe slowly, smoothly, and less.

Rhythmic breathing helps me relax.

What to do is as follows:

1. Take 4 smooth, forceless nose breaths.

2. Exhale smoothly and forcefully for 4 seconds

3. Don't pause at the inhale or exhale.

4. Continue for 5 minutes/40 breaths

5. Hold your breath as long as comfortable.

6. Breathe normally.

If four seconds is too long, try breathing in and out for two seconds, or in and out for three seconds, until your breath naturally relaxes. Once calmer, extend your breath.

Any consistent rhythm without force is good. Your heart will follow your lead and become coherent.

3. Chant/Hum

Singing, chanting, or humming activate the vagus nerve through the back of the throat.

Humming emits nitric oxide.

Nitric oxide improves blood circulation, blood flow, heart health, and blood pressure.

Antiviral, antibacterial, anti-inflammatory, antioxidant, and antimicrobial properties kill viruses and bacteria in the nose and throat.

Gargling water stimulates the vagus nerve.

Simple ways to heal, boost energy, and boost mood are often the healthiest. They're free and can be done anywhere.

4. Have more fun

Laughing stimulates the throat muscles, activating the vagus nerve. What's not to like? It releases dopamine.

Take time to enjoy life. Maybe it's a book, podcast, movie, socializing with friends, or laughing yoga.

Follow your bliss, as Joseph Campbell says.

Laugh at yourself

Actually. Really.

Gagging activates vagus nerve-connected muscles. Some doctors use the gag reflex to test the vagus nerve.

Grossness isn't required. While brushing, gag quickly. My girlfriend's brother always does it.

I'm done brushing when I gag, he says.

6. Take in the outdoors

Nature relaxes body and mind. Better if you can walk barefoot.

Earthing is associated with hippies dancing in daisies.

Science now supports hippies.

7. Enter some chilly water.

The diving reflex activates the vagus nerve when exposed to cold water.

The diving reflex involves holding your breath in cold water. Cold showers work best.

Within minutes of being in cold water, parasympathetic nervous system activity, which calms the body, increases.

8. Workout

Exercise increases dopamine, blood circulation, and breathing. So we feel energized, calm, and well-rested.

After resting, the parasympathetic nervous system engages.

It's worth waiting for, though.

9. Play music with brainwaves

Brainwave music harmonizes brainwave activity, boosts productivity and mental clarity, and promotes peace and relaxation by stimulating the vagus nerve.

Simply play a song.

My favorite.

10. Make gentle eyes

Eyes, like breath, often reflect inner state. Sharp, dilated, focused eyes indicate alertness.

Soft, open eyes reflect relaxation and ease. Soft eyes relax the nervous system.

This practice reduces stress, anxiety, and body tension. It's a quick and effective way to enter a calm, peaceful state.

Wild animals can be hunted one minute and graze the next.

Put it into action:

Relax while seated.

Gaze at a distant object

Use peripheral vision while looking straight ahead

Without moving your eyes, look up and down. Connect side spaces to your vision.

Focus on everything as your eyes soften.

Keep breathing

Stay as long as you like

11. Be intimate

We kiss, moan, and breathe deeper during love. We get dopamine, oxytocin, serotonin, and vagus nerve stimulation.

Why not?

To sum up

Here are 11 vagus nerve resets:

1. Stretch

2. Breathe

3. Hum/Chant

4. More humor

5. Amuse yourself

6. Spend time outdoors

7. Leap into chilly water

8. Exercise

9. Play music with brainwaves.

10. Make gentle eyes.

11. Be intimate

If these words have inspired you, try my favorite breathwork technique. Combining breathing, chanting, and brainwave music. Win-win-win :)

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.