This season's three best money moves.

“Millionaires are made in recessions.” — Time Capital

We're in a serious downturn, whether or not we're in a recession.

97% of business owners are decreasing costs by more than 10%, and all markets are down 30%.

If you know what you're doing and analyze the markets correctly, this is your chance to become a millionaire.

In any recession, there are always excellent possibilities to seize. Real estate, crypto, stocks, enterprises, etc.

What you do with your money could influence your future riches.

This article analyzes the three key markets, their circumstances for 2023, and how to profit from them.

Ways to make money on the stock market.

If you're conservative like me, you should invest in an index fund. Most of these funds are down 10-30% of ATH:

In earlier recessions, most money index funds lost 20%. After this downturn, they grew and passed the ATH in subsequent months.

Now is the greatest moment to invest in index funds to grow your money in a low-risk approach and make 20%.

If you want to be risky but wise, pick companies that will get better next year but are struggling now.

Even while we can't be 100% confident of a company's future performance, we know some are strong and will have a fantastic year.

Microsoft (down 22%), JPMorgan Chase (15.6%), Amazon (45%), and Disney (33.8%).

These firms give dividends, so you can earn passively while you wait.

So I consider that a good strategy to make wealth in the current stock market is to create two portfolios: one based on index funds to earn 10% to 20% profit when the corrections end, and the other based on individual stocks of popular and strong companies to earn 20%-30% return and dividends while you wait.

How to profit from the downturn in the real estate industry.

With rising mortgage rates, it's the worst moment to buy a home if you don't want to be eaten by banks. In the U.S., interest rates are double what they were three years ago, so buying now looks foolish.

Due to these rates, property prices are falling, but that won't last long since individuals will take advantage.

According to historical data, now is the ideal moment to buy a house for the next five years and perhaps forever.

If you can buy a house, do it. You can refinance the interest at a lower rate with acceptable credit, but not the house price.

Take advantage of the housing market prices now because you won't find a decent deal when rates normalize.

How to profit from the cryptocurrency market.

This is the riskiest market to tackle right now, but it could offer the most opportunities if done appropriately.

The most powerful cryptocurrencies are down more than 60% from last year: $68,990 for BTC and $4,865 for ETH.

If you focus on those two coins, you can make 30%-60% without waiting for them to return to their ATH, and they're low enough to be a solid investment.

I don't encourage trying other altcoins because the crypto market is in crisis and you can lose everything if you're greedy.

Still, the main Cryptos are a good investment provided you store them in an external wallet and follow financial gurus' security advice.

Last thoughts

We can't anticipate a recession until it ends. We can't forecast a market or asset's lowest point, therefore waiting makes little sense.

If you want to develop your wealth, assess the money prospects on all the marketplaces and initiate long-term trades.

Many millionaires are made during recessions because they don't fear negative figures and use them to scale their money.

What quarterly bank earnings reveal

Big banks know the economy best. Unless we’re talking about a housing crisis in 2007…

Banks are crucial to the U.S. economy. The Fed, communities, and investments exchange money.

An economy depends on money flow. Banks' views on the economy can affect their decision-making.

Most large banks released quarterly earnings and forward guidance last week. Others were pessimistic about the future.

What Makes Banks Confident

Bank of America's profit decreased 30% year-over-year, but they're optimistic about the economy. Comparatively, they're bullish.

Who banks serve affects what they see. Bank of America supports customers.

They think consumers' future is bright. They believe this for many reasons.

The average customer has decent credit, unless the system is flawed. Bank of America's new credit card and mortgage borrowers averaged 771. New-car loan and home equity borrower averages were 791 and 797.

2008's housing crisis affected people with scores below 620.

Bank of America and the economy benefit from a robust consumer. Major problems can be avoided if individuals maintain spending.

Reasons Other Banks Are Less Confident

Spending requires income. Many companies, mostly in the computer industry, have announced they will slow or freeze hiring. Layoffs are frequently an indication of poor times ahead.

BOA is positive, but investment banks are bearish.

Jamie Dimon, CEO of JPMorgan, outlined various difficulties our economy could confront.

But geopolitical tension, high inflation, waning consumer confidence, the uncertainty about how high rates have to go and the never-before-seen quantitative tightening and their effects on global liquidity, combined with the war in Ukraine and its harmful effect on global energy and food prices are very likely to have negative consequences on the global economy sometime down the road.

That's more headwinds than tailwinds.

JPMorgan, which helps with mergers and IPOs, is less enthusiastic due to these concerns. Incoming headwinds signal drying liquidity, they say. Less business will be done.

Final Reflections

I don't think we're done. Yes, stocks are up 10% from a month ago. It's a long way from old highs.

I don't think the stock market is a strong economic indicator.

Many executives foresee a 2023 recession. According to the traditional definition, we may be in a recession when Q2 GDP statistics are released next week.

Regardless of criteria, I predict the economy will have a terrible year.

Weekly layoffs are announced. Inflation persists. Will prices return to 2020 levels if inflation cools? Perhaps. Still expensive energy. Ukraine's war has global repercussions.

I predict BOA's next quarter earnings won't be as bullish about the consumer's strength.

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.

Most crypto is probably too.

The goals of Web 3 and the metaverse are admirable and attractive. Who doesn't want an internet owned by users? Who wouldn't want a digital realm where anything is possible? A better way to collaborate and visit pals.

Companies pursue profits endlessly. Infinite growth and revenue are expected, and if a corporation needs to sacrifice profits to safeguard users, the CEO, board of directors, and any executives will lose to the system of incentives that (1) retains workers with shares and (2) makes a company answerable to all of its shareholders. Only the government can guarantee user protections, but we know how successful that is. This is nothing new, just a problem with modern capitalism and tech platforms that a user-owned internet might remedy. Moxie, the founder of Signal, has a good articulation of some of these current Web 2 tech platform problems (but I forget the timestamp); thoughts on JRE aside, this episode is worth listening to (it’s about a bunch of other stuff too).

Moxie Marlinspike, founder of Signal, on the Joe Rogan Experience podcast.

Source: https://open.spotify.com/episode/2uVHiMqqJxy8iR2YB63aeP?si=4962b5ecb1854288

Web 3 champions are premature. There was so much spectacular growth during Web 2 that the next wave of founders want to make an even bigger impact, while investors old and new want a chance to get a piece of the moonshot action. Worse, crypto enthusiasts believe — and financially need — the fact of its success to be true, whether or not it is.

I’m doubtful that it will play out like current proponents say. Crypto has been the white-hot focus of SV’s best and brightest for a long time yet still struggles to come up any mainstream use case other than ‘buy, HODL, and believe’: a store of value for your financial goals and wishes. Some kind of the metaverse is likely, but will it be decentralized, mostly in VR, or will Meta (previously FB) play a big role? Unlikely.

METAVERSE

The metaverse exists already. Our digital lives span apps, platforms, and games. I can design a 3D house, invite people, use Discord, and hang around in an artificial environment. Millions of gamers do this in Rust, Minecraft, Valheim, and Animal Crossing, among other games. Discord's voice chat and Slack-like servers/channels are the present social anchor, but the interface, integrations, and data portability will improve. Soon you can stream YouTube videos on digital house walls. You can doodle, create art, play Jackbox, and walk through a door to play Apex Legends, Fortnite, etc. Not just gaming. Digital whiteboards and screen sharing enable real-time collaboration. They’ll review code and operate enterprises. Music is played and made. In digital living rooms, they'll watch movies, sports, comedy, and Twitch. They'll tweet, laugh, learn, and shittalk.

The metaverse is the evolution of our digital life at home, the third place. The closest analog would be Discord and the integration of Facebook, Slack, YouTube, etc. into a single, 3D, customizable hangout space.

I'm not certain this experience can be hugely decentralized and smoothly choreographed, managed, and run, or that VR — a luxury, cumbersome, and questionably relevant technology — must be part of it. Eventually, VR will be pragmatic, achievable, and superior to real life in many ways. A total sensory experience like the Matrix or Sword Art Online, where we're physically hooked into the Internet yet in our imaginations we're jumping, flying, and achieving athletic feats we never could in reality; exploring realms far grander than our own (as grand as it is). That VR is different from today's.

https://podcasts.google.com/feed/aHR0cHM6Ly9leHBvbmVudC5mbS9mZWVkLw/episode/aHR0cHM6Ly9leHBvbmVudC5mbS8_cD00MzM?hl=en&ved=2ahUKEwjH5u6r4rv2AhUjc98KHeybAP8QjrkEegQIChAF&ep=6

Ben Thompson released an episode of Exponent after Facebook changed its name to Meta. Ben was suspicious about many metaverse champion claims, but he made a good analogy between Oculus and the PC. The PC was initially far too pricey for the ordinary family to afford. It began as a business tool. It got so powerful and pervasive that it affected our personal life. Price continues to plummet and so much consumer software was produced that it's impossible to envision life without a home computer (or in our pockets). If Facebook shows product market fit with VR in business, through use cases like remote work and collaboration, maybe VR will become practical in our personal lives at home.

Before PCs, we relied on Blockbuster, the Yellow Pages, cabs to get to the airport, handwritten taxes, landline phones to schedule social events, and other archaic methods. It is impossible for me to conceive what VR, in the form of headsets and hand controllers, stands to give both professional and especially personal digital experiences that is an order of magnitude better than what we have today. Is looking around better than using a mouse to examine a 3D landscape? Do the hand controls make x10 or x100 work or gaming more fun or efficient? Will VR replace scalable Web 2 methods and applications like Web 1 and Web 2 did for analog? I don't know.

My guess is that the metaverse will arrive slowly, initially on displays we presently use, with more app interoperability. I doubt that it will be controlled by the people or by Facebook, a corporation that struggles to properly innovate internally, as practically every large digital company does. Large tech organizations are lousy at hiring product-savvy employees, and if they do, they rarely let them explore new things.

These companies act like business schools when they seek founders' results, with bureaucracy and dependency. Which company launched the last popular consumer software product that wasn't a clone or acquisition? Recent examples are scarce.

Web 3

Investors and entrepreneurs of Web 3 firms are declaring victory: 'Web 3 is here!' Web 3 is the future! Many profitable Web 2 enterprises existed when Web 2 was defined. The word was created to explain user behavior shifts, not a personal pipe dream.

Origins of Web 2: http://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html

One of these Web 3 startups may provide the connecting tissue to link all these experiences or become one of the major new digital locations. Even so, successful players will likely use centralized power arrangements, as Web 2 businesses do now. Some Web 2 startups integrated our digital lives. Rockmelt (2010–2013) was a customizable browser with bespoke connectors to every program a user wanted; imagine seeing Facebook, Twitter, Discord, Netflix, YouTube, etc. all in one location. Failure. Who knows what Opera's doing?

Silicon Valley and tech Twitter in general have a history of jumping on dumb bandwagons that go nowhere. Dot-com crash in 2000? The huge deployment of capital into bad ideas and businesses is well-documented. And live video. It was the future until it became a niche sector for gamers. Live audio will play out a similar reality as CEOs with little comprehension of audio and no awareness of lasting new user behavior deceive each other into making more and bigger investments on fool's gold. Twitter trying to buy Clubhouse for $4B, Spotify buying Greenroom, Facebook exploring live audio and 'Tiktok for audio,' and now Amazon developing a live audio platform. This live audio frenzy won't be worth their time or energy. Blind guides blind. Instead of learning from prior failures like Twitter buying Periscope for $100M pre-launch and pre-product market fit, they're betting on unproven and uncompelling experiences.

NFTs

NFTs are also nonsense. Take Loot, a time-limited bag drop of "things" (text on the blockchain) for a game that didn't exist, bought by rich techies too busy to play video games and foolish enough to think they're getting in early on something with a big reward. What gaming studio is incentivized to use these items? Who's encouraged to join? No one cares besides Loot owners who don't have NFTs. Skill, merit, and effort should be rewarded with rare things for gamers. Even if a small minority of gamers can make a living playing, the average game's major appeal has never been to make actual money - that's a profession.

No game stays popular forever, so how is this objective sustainable? Once popularity and usage drop, exclusive crypto or NFTs will fall. And if NFTs are designed to have cross-game appeal, incentives apart, 30 years from now any new game will need millions of pre-existing objects to build around before they start. It doesn’t work.

Many games already feature item economies based on real in-game scarcity, generally for cosmetic things to avoid pay-to-win, which undermines scaled gaming incentives for huge player bases. Counter-Strike, Rust, etc. may be bought and sold on Steam with real money. Since the 1990s, unofficial cross-game marketplaces have sold in-game objects and currencies. NFTs aren't needed. Making a popular, enjoyable, durable game is already difficult.

With NFTs, certain JPEGs on the internet went from useless to selling for $69 million. Why? Crypto, Web 3, early Internet collectibles. NFTs are digital Beanie Babies (unlike NFTs, Beanie Babies were a popular children's toy; their destinies are the same). NFTs are worthless and scarce. They appeal to crypto enthusiasts seeking for a practical use case to support their theory and boost their own fortune. They also attract to SV insiders desperate not to miss the next big thing, not knowing what it will be. NFTs aren't about paying artists and creators who don't get credit for their work.

South Park's Underpants Gnomes

NFTs are a benign, foolish plan to earn money on par with South Park's underpants gnomes. At worst, they're the world of hucksterism and poor performers. Or those with money and enormous followings who, like everyone, don't completely grasp cryptocurrencies but are motivated by greed and status and believe Gary Vee's claim that CryptoPunks are the next Facebook. Gary's watertight logic: if NFT prices dip, they're on the same path as the most successful corporation in human history; buy the dip! NFTs aren't businesses or museum-worthy art. They're bs.

Gary Vee compares NFTs to Amazon.com. vm.tiktok.com/TTPdA9TyH2

We grew up collecting: Magic: The Gathering (MTG) cards printed in the 90s are now worth over $30,000. Imagine buying a digital Magic card with no underlying foundation. No one plays the game because it doesn't exist. An NFT is a contextless image someone conned you into buying a certificate for, but anyone may copy, paste, and use. Replace MTG with Pokemon for younger readers.

When Gary Vee strongarms 30 tech billionaires and YouTube influencers into buying CryptoPunks, they'll talk about it on Twitch, YouTube, podcasts, Twitter, etc. That will convince average folks that the product has value. These guys are smart and/or rich, so I'll get in early like them. Cryptography is similar. No solid, scaled, mainstream use case exists, and no one knows where it's headed, but since the global crypto financial bubble hasn't burst and many people have made insane fortunes, regular people are putting real money into something that is highly speculative and could be nothing because they want a piece of the action. Who doesn’t want free money? Rich techies and influencers won't be affected; normal folks will.

Imagine removing every $1 invested in Bitcoin instantly. What would happen? How far would Bitcoin fall? Over 90%, maybe even 95%, and Bitcoin would be dead. Bitcoin as an investment is the only scalable widespread use case: it's confidence that a better use case will arise and that being early pays handsomely. It's like pouring a trillion dollars into a company with no business strategy or users and a CEO who makes vague future references.

New tech and efforts may provoke a 'get off my lawn' mentality as you approach 40, but I've always prided myself on having a decent bullshit detector, and it's flying off the handle at this foolishness. If we can accomplish a functional, responsible, equitable, and ethical user-owned internet, I'm for it.

Postscript:

I wanted to summarize my opinions because I've been angry about this for a while but just sporadically tweeted about it. A friend handed me a Dan Olson YouTube video just before publication. He's more knowledgeable, articulate, and convincing about crypto. It's worth seeing:

This post is a summary. See the original one here.

Product game mindsets? How do these vary from Project mindset?

1950s spawned the Iron Triangle. Project people everywhere know and live by it. In stakeholder meetings, it is used to stretch the timeframe, request additional money, or reduce scope.

Quality was added to this triangle as things matured.

Quality was intended to be transformative, but none of these principles addressed why we conduct projects.

Value and benefits are key.

Product value is quantified by ROI, revenue, profit, savings, or other metrics. For me, every project or product delivery is about value.

Most project managers, especially those schooled 5-10 years or more ago (thousands working in huge corporations worldwide), understand the world in terms of the iron triangle. What does that imply? They worry about:

a) enough time to get the thing done.

b) have enough resources (budget) to get the thing done.

c) have enough scope to fit within (a) and (b) >> note, they never have too little scope, not that I have ever seen! although, theoretically, this could happen.

Boom—iron triangle.

To make the triangle function, project managers will utilize formal governance (Steering) to move those things. Increase money, scope, or both if time is short. Lacking funds? Increase time, scope, or both.

In current product development, shifting each item considerably may not yield value/benefit.

Even terrible. This approach will fail because it deprioritizes Value/Benefit by focusing the major stakeholders (Steering participants) and delivery team(s) on Time, Scope, and Budget restrictions.

Pre-agile, this problem was terrible. IT projects failed wildly. History is here.

Value, or benefit, is central to the product method. Product managers spend most of their time planning value-delivery paths.

Product people consider risk, schedules, scope, and budget, but value comes first. Let me illustrate.

Imagine managing internal products in an enterprise. Your core customer team needs a rapid text record of a chat to fix a problem. The consumer wants a feature/features added to a product you're producing because they think it's the greatest spot.

Project-minded, I may say;

Ok, I have budget as this is an existing project, due to run for a year. This is a new requirement to add to the features we’re already building. I think I can keep the deadline, and include this scope, as it sounds related to the feature set we’re building to give the desired result”.

This attitude repeats Scope, Time, and Budget.

Since it meets those standards, a project manager will likely approve it. If they have a backlog, they may add it and start specking it out assuming it will be built.

Instead, think like a product;

What problem does this feature idea solve? Is that problem relevant to the product I am building? Can that problem be solved quicker/better via another route ? Is it the most valuable problem to solve now? Is the problem space aligned to our current or future strategy? or do I need to alter/update the strategy?

A product mindset allows you to focus on timing, resource/cost, feasibility, feature detail, and so on after answering the aforementioned questions.

The above oversimplifies because

Leadership in discovery

Project managers are facilitators of ideas. This is as far as they normally go in the ‘idea’ space.

Business Requirements collection in classic project delivery requires extensive upfront documentation.

Agile project delivery analyzes requirements iteratively.

However, the project manager is a facilitator/planner first and foremost, therefore topic knowledge is not expected.

I mean business domain, not technical domain (to confuse matters, it is true that in some instances, it can be both technical and business domains that are important for a single individual to master).

Product managers are domain experts. They will become one if they are training/new.

They lead discovery.

Product Manager-led discovery is much more than requirements gathering.

Requirements gathering involves a Business Analyst interviewing people and documenting their requests.

The project manager calculates what fits and what doesn't using their Iron Triangle (presumably in their head) and reports back to Steering.

If this requirements-gathering exercise failed to identify requirements, what would a project manager do? or bewildered by project requirements and scope?

They would tell Steering they need a Business SME or Business Lead assigning or more of their time.

Product discovery requires the Product Manager's subject knowledge and a new mindset.

How should a Product Manager handle confusing requirements?

Product Managers handle these challenges with their talents and tools. They use their own knowledge to fill in ambiguity, but they have the discipline to validate those assumptions.

To define the problem, they may perform qualitative or quantitative primary research.

They might discuss with UX and Engineering on a whiteboard and test assumptions or hypotheses.

Do Product Managers escalate confusing requirements to Steering/Senior leaders? They would fix that themselves.

Product managers raise unclear strategy and outcomes to senior stakeholders. Open talks, soft skills, and data help them do this. They rarely raise requirements since they have their own means of handling them without top stakeholder participation.

Discovery is greenfield, exploratory, research-based, and needs higher-order stakeholder management, user research, and UX expertise.

Product Managers also aid discovery. They lead discovery. They will not leave customer/user engagement to a Business Analyst. Administratively, a business analyst could aid. In fact, many product organizations discourage business analysts (rely on PM, UX, and engineer involvement with end-users instead).

The Product Manager must drive user interaction, research, ideation, and problem analysis, therefore a Product professional must be skilled and confident.

Creating vs. receiving and having an entrepreneurial attitude

Product novices and project managers focus on details rather than the big picture. Project managers prefer spreadsheets to strategy whiteboards and vision statements.

These folks ask their manager or senior stakeholders, "What should we do?"

They then elaborate (in Jira, in XLS, in Confluence or whatever).

They want that plan populated fast because it reduces uncertainty about what's going on and who's supposed to do what.

Skilled Product Managers don't only ask folks Should we?

They're suggesting this, or worse, Senior stakeholders, here are some options. After asking and researching, they determine what value this product adds, what problems it solves, and what behavior it changes.

Therefore, to move into Product, you need to broaden your view and have courage in your ability to discover ideas, find insightful pieces of information, and collate them to form a valuable plan of action. You are constantly defining RoI and building Business Cases, so much so that you no longer create documents called Business Cases, it is simply ingrained in your work through metrics, intelligence, and insights.

Product Management is not a free lunch.

Plateless.

Plates and food must be prepared.

In conclusion, Product Managers must make at least three mentality shifts:

1. You put value first in all things. Time, money, and scope are not as important as knowing what is valuable.

2. You have faith in the field and have the ability to direct the search. YYou facilitate, but you don’t just facilitate. You wouldn't want to limit your domain expertise in that manner.

3. You develop concepts, strategies, and vision. You are not a waiter or an inbox where other people can post suggestions; you don't merely ask folks for opinion and record it. However, you excel at giving things that aren't clearly spoken or written down physical form.

Learn or earn

In 2020, I started software engineering. My base wage has progressed as follows:

Amazon (2020): $112,000

Microsoft (2021): $123,000

Google (2022): $169,000

I didn't major in math, but those jumps appear more than a 7% wage increase. Here's a deeper look at the three.

The Three Categories of Compensation

Most software engineering compensation packages at IT organizations follow this format.

Minimum Salary

Base salary is pre-tax income. Most organizations give a base pay. This is paid biweekly, twice monthly, or monthly.

Recruiting Bonus

Sign-On incentives are one-time rewards to new hires. Companies need an incentive to switch. If you leave early, you must pay back the whole cost or a pro-rated amount.

Equity

Equity is complex and requires its own post. A company will promise to give you a certain amount of company stock but when you get it depends on your offer. 25% per year for 4 years, then it's gone.

If a company gives you $100,000 and distributes 25% every year for 4 years, expect $25,000 worth of company stock in your stock brokerage on your 1 year work anniversary.

Performance Bonus

Tech offers may include yearly performance bonuses. Depends on performance and funding. I've only seen 0-20%.

Engineers' overall compensation usually includes:

Base Salary + Sign-On + (Total Equity)/4 + Average Performance Bonus

Amazon: (TC: 150k)

Base Pay System

Amazon pays Seattle employees monthly on the first work day. I'd rather have my money sooner than later, even if it saves processing and pay statements.

The company upped its base pay cap from $160,000 to $350,000 to compete with other tech companies.

Performance Bonus

Amazon has no performance bonus, so you can work as little or as much as you like and get paid the same. Amazon is savvy to avoid promising benefits it can't deliver.

Sign-On Bonus

Amazon gives two two-year sign-up bonuses. First-year workers could receive $20,000 and second-year workers $15,000. It's probably to make up for the company's strange equity structure.

If you leave during the first year, you'll owe the entire money and a prorated amount for the second year bonus.

Equity

Most organizations prefer a 25%, 25%, 25%, 25% equity structure. Amazon takes a different approach with end-heavy equity:

• the first year, 5%

• 15% after one year.

• 20% then every six months

We thought it was constructed this way to keep staff longer.

Microsoft (TC: 185k)

Base Pay System

Microsoft paid biweekly.

Gainful Performance

My offer letter suggested a 0%-20% performance bonus. Everyone will be satisfied with a 10% raise at year's end.

But misleading press where the budget for the bonus is doubled can upset some employees because they won't earn double their expected bonus. Still barely 10% for 2022 average.

Sign-On Bonus

Microsoft's sign-on bonus is a one-time payout. The contract can require 2-year employment. You must negotiate 1 year. It's pro-rated, so that's fair.

Equity

Microsoft is one of those companies that has standard 25% equity structure. Except if you’re a new graduate.

In that case it’ll be

• 25% six months later

• 25% each year following that

New grads will acquire equity in 3.5 years, not 4. I'm guessing it's to keep new grads around longer.

Google (TC: 300k)

Base Pay Structure

Google pays biweekly.

Performance Bonus

Google's offer letter specifies a 15% bonus. It's wonderful there's no cap, but I might still get 0%. A little more than Microsoft’s 10% and a lot more than Amazon’s 0%.

Sign-On Bonus

Google gave a 1-year sign-up incentive. If the contract is only 1 year, I can move without any extra obligations.

Not as fantastic as Amazon's sign-up bonuses, but the remainder of the package might compensate.

Equity

We covered Amazon's tail-heavy compensation structure, so Google's front-heavy equity structure may surprise you.

Annual structure breakdown

• 33% Year 1

• 33% Year 2

• 22% Year 3

• 12% Year 4

The goal is to get them to Google and keep them there.

Final Thoughts

This post hopefully helped you understand the 3 firms' compensation arrangements.

There's always more to discuss, such as refreshers, 401k benefits, and business discounts, but I hope this shows a distinction between these 3 firms.