Mainstream economics haven't noticed.

What come to mind when I say the word "economics"?

Probably GDP, unemployment, and inflation.

If you've ever watched the news or listened to an economist, they'll use data like these to defend a political goal.

The issue is that these statistics are total bunk.

I'm being provocative, but I mean it:

• The economy is not measured by GDP.

• How many people are unemployed is not counted in the unemployment rate.

• Inflation is not measured by the CPI.

All orthodox economists' major economic statistics are either wrong or falsified.

Government institutions create all these stats. The administration wants to reassure citizens the economy is doing well.

GDP does not reflect economic expansion.

GDP measures a country's economic size and growth. It’s calculated by the BEA, a government agency.

The US has the world's largest (self-reported) GDP, growing 2-3% annually.

If GDP rises, the economy is healthy, say economists.

Why is the GDP flawed?

GDP measures a country's yearly spending.

The government may adjust this to make the economy look good.

GDP = C + G + I + NX

C = Consumer Spending

G = Government Spending

I = Investments (Equipment, inventories, housing, etc.)

NX = Exports minus Imports

GDP is a country's annual spending.

The government can print money to boost GDP. The government has a motive to increase and manage GDP.

Because government expenditure is part of GDP, printing money and spending it on anything will raise GDP.

They've done this. Since 1950, US government spending has grown 8% annually, faster than GDP.

In 2022, government spending accounted for 44% of GDP. It's the highest since WWII. In 1790-1910, it was 3% of GDP.

Who cares?

The economy isn't only spending. Focus on citizens' purchasing power or quality of life.

Since GDP just measures spending, the government can print money to boost GDP.

Even if Americans are poorer than last year, economists can say GDP is up and everything is fine.

How many people are unemployed is not counted in the unemployment rate.

The unemployment rate measures a country's labor market. If unemployment is high, people aren't doing well economically.

The BLS estimates the (self-reported) unemployment rate as 3-4%.

Why is the unemployment rate so high?

The US government surveys 100k persons to measure unemployment. They extrapolate this data for the country.

They come into 3 categories:

• Employed

People with jobs are employed … duh.

• Unemployed

People who are “jobless, looking for a job, and available for work” are unemployed

• Not in the labor force

The “labor force” is the employed + the unemployed.

The unemployment rate is the percentage of unemployed workers.

Problem is unemployed definition. You must actively seek work to be considered unemployed.

You're no longer unemployed if you haven't interviewed in 4 weeks.

This shit makes no goddamn sense.

Why does this matter?

You can't interview if there are no positions available. You're no longer unemployed after 4 weeks.

In 1994, the BLS redefined "unemployed" to exclude discouraged workers.

If you haven't interviewed in 4 weeks, you're no longer counted in the unemployment rate.

If unemployment were measured by total unemployed, it would be 25%.

Because the government wants to keep the unemployment rate low, they modify the definition.

If every US resident was unemployed and had no job interviews, economists would declare 0% unemployment. Excellent!

Inflation is not measured by the CPI.

The BLS measures CPI. This month was the highest since 1981.

CPI measures the cost of a basket of products across time. Food, energy, shelter, and clothes are included.

A 9.1% CPI means the basket of items is 9.1% more expensive.

What is the CPI problem?

Here's a more detailed explanation of CPI's flaws.

In summary, CPI is manipulated to be understated.

Housing costs are understated to manipulate CPI. Housing accounts for 33% of the CPI because it's the biggest expense for most people.

This signifies it's the biggest CPI weight.

Rather than using actual house prices, the Bureau of Labor Statistics essentially makes shit up. You can read more about the process here.

Surprise! It’s bullshit

The BLS stated Shelter's price rose 5.5% this month.

House prices are up 11-21%. (Source 1, Source 2, Source 3)

Rents are up 14-26%. (Source 1, Source 2)

Why is this important?

If CPI included housing prices, it would be 12-15 percent this month, not 9.1 percent.

9% inflation is nuts. Your money's value halves every 7 years at 9% inflation.

Worse is 15% inflation. Your money halves every 4 years at 15% inflation.

If everyone realized they needed to double their wage every 4-5 years to stay wealthy, there would be riots.

Inflation drains our money's value so the government can keep printing it.

The Solution

Most individuals know the existing system doesn't work, but can't explain why.

People work hard yet lag behind. The government lies about the economy's data.

In reality:

• GDP has been down since 2008

• 25% of Americans are unemployed

• Inflation is actually 15%

People might join together to vote out kleptocratic politicians if they knew the reality.

Having reliable economic data is the first step.

People can't understand the situation without sufficient information. Instead of immigrants or billionaires, people would blame liar politicians.

Here’s the vision:

A decentralized, transparent, and global dashboard that tracks economic data like GDP, unemployment, and inflation for every country on Earth.

Government incentives influence economic statistics.

ShadowStats has already started this effort, but the calculations must be transparent, decentralized, and global to be effective.

If interested, email me at trevorstark02@gmail.com.

Here are some links to further your research:

1. MIT Billion Prices Project

2. 1729 Decentralized Inflation Dashboard Project

3. Balaji Srinivasan on “Fiat Information VS. Crypto Information”

Python Volatility-Based Catapult Indicator

As a catapult, this technical indicator uses three systems: Volatility (the fulcrum), Momentum (the propeller), and a Directional Filter (Acting as the support). The goal is to get a signal that predicts volatility acceleration and direction based on historical patterns. We want to know when the market will move. and where. This indicator outperforms standard indicators.

Knowledge must be accessible to everyone. This is why my new publications Contrarian Trading Strategies in Python and Trend Following Strategies in Python now include free PDF copies of my first three books (Therefore, purchasing one of the new books gets you 4 books in total). GitHub-hosted advanced indications and techniques are in the two new books above.

The Foundation: Volatility

The Catapult predicts significant changes with the 21-period Relative Volatility Index.

The Average True Range, Mean Absolute Deviation, and Standard Deviation all assess volatility. Standard Deviation will construct the Relative Volatility Index.

Standard Deviation is the most basic volatility. It underpins descriptive statistics and technical indicators like Bollinger Bands. Before calculating Standard Deviation, let's define Variance.

Variance is the squared deviations from the mean (a dispersion measure). We take the square deviations to compel the distance from the mean to be non-negative, then we take the square root to make the measure have the same units as the mean, comparing apples to apples (mean to standard deviation standard deviation). Variance formula:

As stated, standard deviation is:

# The function to add a number of columns inside an array
def adder(Data, times):
    
    for i in range(1, times + 1):
    
        new_col = np.zeros((len(Data), 1), dtype = float)
        Data = np.append(Data, new_col, axis = 1)
        
    return Data

# The function to delete a number of columns starting from an index
def deleter(Data, index, times):
    
    for i in range(1, times + 1):
    
        Data = np.delete(Data, index, axis = 1)
        
    return Data
    
# The function to delete a number of rows from the beginning
def jump(Data, jump):
    
    Data = Data[jump:, ]
    
    return Data

# Example of adding 3 empty columns to an array
my_ohlc_array = adder(my_ohlc_array, 3)

# Example of deleting the 2 columns after the column indexed at 3
my_ohlc_array = deleter(my_ohlc_array, 3, 2)

# Example of deleting the first 20 rows
my_ohlc_array = jump(my_ohlc_array, 20)

# Remember, OHLC is an abbreviation of Open, High, Low, and Close and it refers to the standard historical data file

def volatility(Data, lookback, what, where):
    
  for i in range(len(Data)):

     try:

        Data[i, where] = (Data[i - lookback + 1:i + 1, what].std())
     except IndexError:
        pass
        
  return Data

The RSI is the most popular momentum indicator, and for good reason—it excels in range markets. Its 0–100 range simplifies interpretation. Fame boosts its potential.

The more traders and portfolio managers look at the RSI, the more people will react to its signals, pushing market prices. Technical Analysis is self-fulfilling, therefore this theory is obvious yet unproven.

RSI is determined simply. Start with one-period pricing discrepancies. We must remove each closing price from the previous one. We then divide the smoothed average of positive differences by the smoothed average of negative differences. The RSI algorithm converts the Relative Strength from the last calculation into a value between 0 and 100.

def ma(Data, lookback, close, where): 
    
    Data = adder(Data, 1)
    
    for i in range(len(Data)):
           
            try:
                Data[i, where] = (Data[i - lookback + 1:i + 1, close].mean())
            
            except IndexError:
                pass
            
    # Cleaning
    Data = jump(Data, lookback)
    
    return Data
def ema(Data, alpha, lookback, what, where):
    
    alpha = alpha / (lookback + 1.0)
    beta  = 1 - alpha
    
    # First value is a simple SMA
    Data = ma(Data, lookback, what, where)
    
    # Calculating first EMA
    Data[lookback + 1, where] = (Data[lookback + 1, what] * alpha) + (Data[lookback, where] * beta)    
 
    # Calculating the rest of EMA
    for i in range(lookback + 2, len(Data)):
            try:
                Data[i, where] = (Data[i, what] * alpha) + (Data[i - 1, where] * beta)
        
            except IndexError:
                pass
            
    return Datadef rsi(Data, lookback, close, where, width = 1, genre = 'Smoothed'):
    
    # Adding a few columns
    Data = adder(Data, 7)
    
    # Calculating Differences
    for i in range(len(Data)):
        
        Data[i, where] = Data[i, close] - Data[i - width, close]
     
    # Calculating the Up and Down absolute values
    for i in range(len(Data)):
        
        if Data[i, where] > 0:
            
            Data[i, where + 1] = Data[i, where]
            
        elif Data[i, where] < 0:
            
            Data[i, where + 2] = abs(Data[i, where])
            
    # Calculating the Smoothed Moving Average on Up and Down
    absolute values        
                             
    lookback = (lookback * 2) - 1 # From exponential to smoothed
    Data = ema(Data, 2, lookback, where + 1, where + 3)
    Data = ema(Data, 2, lookback, where + 2, where + 4)
    
    # Calculating the Relative Strength
    Data[:, where + 5] = Data[:, where + 3] / Data[:, where + 4]
    
    # Calculate the Relative Strength Index
    Data[:, where + 6] = (100 - (100 / (1 + Data[:, where + 5])))  
    
    # Cleaning
    Data = deleter(Data, where, 6)
    Data = jump(Data, lookback)

    return Data

def relative_volatility_index(Data, lookback, close, where):

    # Calculating Volatility
    Data = volatility(Data, lookback, close, where)
    
    # Calculating the RSI on Volatility
    Data = rsi(Data, lookback, where, where + 1) 
    
    # Cleaning
    Data = deleter(Data, where, 1)
    
    return Data

The Arm Section: Speed

The Catapult predicts momentum direction using the 14-period Relative Strength Index.

As a reminder, the RSI ranges from 0 to 100. Two levels give contrarian signals:

• A positive response is anticipated when the market is deemed to have gone too far down at the oversold level 30, which is 30.

• When the market is deemed to have gone up too much, at overbought level 70, a bearish reaction is to be expected.

Comparing the RSI to 50 is another intriguing use. RSI above 50 indicates bullish momentum, while below 50 indicates negative momentum.

The direction-finding filter in the frame

The Catapult's directional filter uses the 200-period simple moving average to keep us trending. This keeps us sane and increases our odds.

Moving averages confirm and ride trends. Its simplicity and track record of delivering value to analysis make them the most popular technical indicator. They help us locate support and resistance, stops and targets, and the trend. Its versatility makes them essential trading tools.

This is the plain mean, employed in statistics and everywhere else in life. Simply divide the number of observations by their total values. Mathematically, it's:

We defined the moving average function above. Create the Catapult indication now.

Indicator of the Catapult

The indicator is a healthy mix of the three indicators:

• The first trigger will be provided by the 21-period Relative Volatility Index, which indicates that there will now be above average volatility and, as a result, it is possible for a directional shift.

• If the reading is above 50, the move is likely bullish, and if it is below 50, the move is likely bearish, according to the 14-period Relative Strength Index, which indicates the likelihood of the direction of the move.

• The likelihood of the move's direction will be strengthened by the 200-period simple moving average. When the market is above the 200-period moving average, we can infer that bullish pressure is there and that the upward trend will likely continue. Similar to this, if the market falls below the 200-period moving average, we recognize that there is negative pressure and that the downside is quite likely to continue.

lookback_rvi = 21
lookback_rsi = 14
lookback_ma  = 200
my_data = ma(my_data, lookback_ma, 3, 4)
my_data = rsi(my_data, lookback_rsi, 3, 5)
my_data = relative_volatility_index(my_data, lookback_rvi, 3, 6)

Two-handled overlay indicator Catapult. The first exhibits blue and green arrows for a buy signal, and the second shows blue and red for a sell signal.

The chart below shows recent EURUSD hourly values.

def signal(Data, rvi_col, signal):
    
    Data = adder(Data, 10)
        
    for i in range(len(Data)):
            
        if Data[i,     rvi_col] < 30 and \
           Data[i - 1, rvi_col] > 30 and \
           Data[i - 2, rvi_col] > 30 and \
           Data[i - 3, rvi_col] > 30 and \
           Data[i - 4, rvi_col] > 30 and \
           Data[i - 5, rvi_col] > 30:
               
               Data[i, signal] = 1
                           
    return Data

Signals are straightforward. The indicator can be utilized with other methods.

my_data = signal(my_data, 6, 7)

Lumiwealth shows how to develop all kinds of algorithms. I recommend their hands-on courses in algorithmic trading, blockchain, and machine learning.

Summary

To conclude, my goal is to contribute to objective technical analysis, which promotes more transparent methods and strategies that must be back-tested before implementation. Technical analysis will lose its reputation as subjective and unscientific.

After you find a trading method or approach, follow these steps:

• Put emotions aside and adopt an analytical perspective.

• Test it in the past in conditions and simulations taken from real life.

• Try improving it and performing a forward test if you notice any possibility.

• Transaction charges and any slippage simulation should always be included in your tests.

• Risk management and position sizing should always be included in your tests.

After checking the aforementioned, monitor the plan because market dynamics may change and render it unprofitable.

When everyone laughs at him, he's powerless.

1-2-3: Fold your hands and chuckle heartily. Repeat until you're really laughing.

We're laughing at Russia's modern-day shortcomings, if you hadn't guessed.

Watch Good Fellas' laughing scene on YouTube. Ray Liotta, Joe Pesci, and others laugh hysterically in a movie. Laugh at that scene, then think of Putin's macho guy statement on February 24 when he invaded Ukraine. It's cathartic to laugh at his expense.

Right? It makes me feel great that he was convinced the military action will be over in a week. I love reading about Putin's morning speech. Many stupid people on Earth supported him. Many loons hailed his speech historic.

Russia preys on the weak. Strong Ukraine overcame Russia. Ukraine's right. As usual, Russia is in the wrong.

A so-called thought leader recently complained on Russian TV that the West no longer fears Russia, which is why Ukraine is kicking Russia's ass.

Let's simplify for this Russian intellectual. Except for nuclear missiles, the West has nothing to fear from Russia. Russia is a weak, morally-empty country whose DNA has degraded to the point that evolution is already working to flush it out.

The West doesn't fear Russia since he heads a prominent Russian institution. Russian universities are intellectually barren. I taught at St. Petersburg University till June (since February I was virtually teaching) and was astounded by the lack of expertise.

Russians excel in science, math, engineering, IT, and anything that doesn't demand critical thinking or personal ideas.

Reflecting on many of the high-ranking individuals from around the West, Satanovsky said: “They are not interested in us. We only think we’re ‘big politics’ for them but for those guys we’re small politics. “We’re small politics, even though we think of ourselves as the descendants of the Russian Empire, of the USSR. We are not the Soviet Union, we don’t have enough weirdos and lunatics, we practically don’t have any (U.S. Has Stopped Fearing Us).”

Professor Dmitry Evstafiev, president of the Institute of the Middle East, praised Nikita Khrushchev's fiery nature because he made the world fear him, which made the Soviet Union great. If the world believes Putin is crazy, then Russia will be great, says this man. This is crazy.

Evstafiev covered his cowardice by saluting Putin. He praised his culture and Ukraine patience. This weakling professor ingratiates himself to Putin instead of calling him a cowardly, demonic shithead.

This is why we don't fear Russia, professor. Because you're all sycophantic weaklings who sold your souls to a Leningrad narcissist. Putin's nothing. He lacks intelligence. You've tied your country's fate and youth's future to this terrible monster. Disgraceful!

How can you loathe your country's youth so much to doom them to decades or centuries of ignominy? My son is half Russian and must now live with this portion of him.

We don't fear Russia because you don't realize that it should be appreciated, not frightened. That would need lobotomizing tens of millions of people like you.

Sadman. You let a Leningrad weakling castrate you and display your testicles. He shakes the container, saying, "Your balls are mine."

Why is Russia not feared?

Your self-inflicted national catastrophe is hilarious. Sadly, it's laugh-through-tears.

We're opening up Maki through a series of Medium posts. We'll describe what Maki is building and how. We'll explain how we built a SaaS in 100 days. This isn't a step-by-step guide to starting a business, but a product philosophy to help you build quickly.

Focus on end-users.

This may seem obvious, but it's important to talk to users first. When we started thinking about Maki, we interviewed 100 HR directors from SMBs, Next40 scale-ups, and major Enterprises to understand their concerns. We initially thought about the future of employment, but most of their worries centered on Recruitment. We don't have a clear recruiting process, it's time-consuming, we recruit clones, we don't support diversity, etc. And as hiring managers, we couldn't help but agree.

Co-create your product with your end-users.

We went to the drawing board, read as many books as possible (here, here, and here), and when we started getting a sense for a solution, we questioned 100 more operational HR specialists to corroborate the idea and get a feel for our potential answer. This confirmed our direction to help hire more objectively and efficiently.

Back to the drawing board, we designed our first flows and screens. We organized sessions with certain survey respondents to show them our early work and get comments. We got great input that helped us build Maki, and we met some consumers. Obsess about users and execute alongside them.

Don’t shoot for the moon, yet. Make pragmatic choices first.

Once we were convinced, we began building. To launch a SaaS in 100 days, we needed an operating principle that allowed us to accelerate while still providing a reliable, secure, scalable experience. We focused on adding value and outsourced everything else. Example:

Concentrate on adding value. Reuse existing bricks.

When determining which technology to use, we looked at our strengths and the future to see what would last. Node.js for backend, React for frontend, both with typescript. We thought this technique would scale well since it would attract more talent and the surrounding mature ecosystem would help us go quicker.

We explored for ways to bootstrap services while setting down strong foundations that might support millions of users. We built our backend services on NestJS so we could extend into microservices later. Hasura, a GraphQL APIs engine, automates Postgres data exposing through a graphQL layer. MUI's ready-to-use components powered our design-system. We used well-maintained open-source projects to speed up certain tasks.

We outsourced important components of our platform (Auth0 for authentication, Stripe for billing, SendGrid for notifications) because, let's face it, we couldn't do better. We choose to host our complete infrastructure (SQL, Cloud run, Logs, Monitoring) on GCP to simplify our work between numerous providers.

Focus on your business, use existing bricks for the rest. For the curious, we'll shortly publish articles detailing each stage.

Most importantly, empower people and step back.

We couldn't have done this without the incredible people who have supported us from the start. Since Powership is one of our key values, we provided our staff the power to make autonomous decisions from day one. Because we believe our firm is its people, we hired smart builders and let them build.

Nicolas left Spendesk to create scalable interfaces using react-router, react-queries, and MUI. JD joined Swile and chose Hasura as our GraphQL engine. Jérôme chose NestJS to build our backend services. Since then, Justin, Ben, Anas, Yann, Benoit, and others have followed suit.

If you consider your team a collective brain, you should let them make decisions instead of directing them what to do. You'll make mistakes, but you'll go faster and learn faster overall.

Invest in great talent and develop a strong culture from the start. Here's how to establish a SaaS in 100 days.

Research on preventing disease, extending life, and caring for your body from the inside out

Grapefruit and pomegranates aren't on the list, so ignore that. Mostly, I enjoyed the visual, but those fruits are healthful, too.

15 (or 17 if you consider the photo) different foods a day sounds like a lot. If you're not used to it  — it is.

These lists don't aim for perfection. Instead, use this article and the science below to eat more of these foods. If you can eat 5 foods one day and 5 the next, you're doing well. This list should be customized to your requirements and preferences.

“Every time you eat or drink, you are either feeding disease or fighting it” -Heather Morgan.

The 15 Foods That You Should Consume Daily and Why:

1. Dark/Red Berries

(blueberries, blackberries, acai, goji, cherries, strawberries, raspberries)

The 2010 Global Burden of Disease Study is the greatest definitive analysis of death and disease risk factors in history. They found the primary cause of both death, disability, and disease inside the United States was diet.

Not eating enough fruit, and specifically berries, was one of the best predictors of disease (1).

What's special about berries? It's their color! Berries have the most antioxidants of any fruit, second only to spices. The American Cancer Society found that those who ate the most berries were less likely to die of cardiovascular disease.

2. Beans

Soybeans, black beans, kidney beans, lentils, split peas, chickpeas.

Beans are one of the most important predictors of survival in older people, according to global research (2).

For every 20 grams (2 tablespoons) of beans consumed daily, the risk of death is reduced by 8%.

Soybeans and soy foods are high in phytoestrogen, which reduces breast and prostate cancer risks. Phytoestrogen blocks the receptors' access to true estrogen, mitigating the effects of weight gain, dairy (high in estrogen), and hormonal fluctuations (3).

3. Nuts

(almonds, walnuts, pecans, pistachios, Brazil nuts, cashews, hazelnuts, macadamia nuts)

Eating a handful of nuts every day reduces the risk of chronic diseases like heart disease and diabetes. Nuts also reduce oxidation, blood sugar, and LDL (bad) cholesterol, improving arterial function (4).

Despite their high-fat content, studies have linked daily nut consumption to a slimmer waistline and a lower risk of obesity (5).

4. Flaxseed

(milled flaxseed)

2013 research found that ground flaxseed had one of the strongest anti-hypertensive effects of any food. A few tablespoons (added to a smoothie or baked goods) lowered blood pressure and stroke risk 23 times more than daily aerobic exercise (6).

Flax shouldn't replace exercise, but its nutritional punch is worth adding to your diet.

5. Other seeds

(chia seeds, hemp seeds, pumpkin seeds, sesame seeds, fennel seeds)

Seeds are high in fiber and omega-3 fats and can be added to most dishes without being noticed.

When eaten with or after a meal, chia seeds moderate blood sugar and reduce inflammatory chemicals in the blood (7). Overall, a great daily addition.

6. Dates

Dates are one of the world's highest sugar foods, with 80% sugar by weight. Pure cake frosting is 60%, maple syrup is 66%, and cotton-candy jelly beans are 70%.

Despite their high sugar content, dates have a low glycemic index, meaning they don't affect blood sugar levels dramatically. They also improve triglyceride and antioxidant stress levels (8).

Dates are a great source of energy and contain high levels of dietary fiber and polyphenols, making 3-10 dates a great way to fight disease, support gut health with prebiotics, and satisfy a sweet tooth (9).

7. Cruciferous Veggies

(broccoli, Brussel sprouts, horseradish, kale, cauliflower, cabbage, boy choy, arugula, radishes, turnip greens)

Cruciferous vegetables contain an active ingredient that makes them disease-fighting powerhouses. Sulforaphane protects our brain, eyesight, against free radicals and environmental hazards, and treats and prevents cancer (10).

Unless you eat raw cruciferous vegetables daily, you won't get enough sulforaphane (and thus, its protective nutritional benefits). Cooking destroys the enzyme needed to create this super-compound.

If you chop broccoli, cauliflower, or turnip greens and let them sit for 45 minutes before cooking them, the enzyme will have had enough time to work its sulforaphane magic, allowing the vegetables to retain the same nutritional value as if eaten raw. Crazy, right? For more on this, see What Chopping Your Vegetables Has to Do with Fighting Cancer.

8. Whole grains

(barley, brown rice, quinoa, oats, millet, popcorn, whole-wheat pasta, wild rice)

Whole-grains are one of the healthiest ways to consume your daily carbs and help maintain healthy gut flora.

This happens when fibre is broken down in the colon and starts a chain reaction, releasing beneficial substances into the bloodstream and reducing the risk of Type 2 Diabetes and inflammation (11).

9. Spices

(turmeric, cumin, cinnamon, ginger, saffron, cloves, cardamom, chili powder, nutmeg, coriander)

7% of a person's cells will have DNA damage. This damage is caused by tiny breaks in our DNA caused by factors like free-radical exposure.

Free radicals cause mutations that damage lipids, proteins, and DNA, increasing the risk of disease and cancer. Free radicals are unavoidable because they result from cellular metabolism, but they can be avoided by consuming anti-oxidant and detoxifying foods.

Including spices and herbs like rosemary or ginger in our diet may cut DNA damage by 25%. Yes, this damage can be improved through diet. Turmeric worked better at a lower dose (just a pinch, daily). For maximum free-radical fighting (and anti-inflammatory) effectiveness, use 1.5 tablespoons of similar spices (12).

10. Leafy greens

(spinach, collard greens, lettuce, other salad greens, swiss chard)

Studies show that people who eat more leafy greens perform better on cognitive tests and slow brain aging by a year or two (13).

As we age, blood flow to the brain drops due to a decrease in nitric oxide, which prevents blood vessels from dilatation. Daily consumption of nitrate-rich vegetables like spinach and swiss chard may prevent dementia and Alzheimer's.

11. Fermented foods

(sauerkraut, tempeh, kombucha, plant-based kefir)

Miso, kimchi, and sauerkraut contain probiotics that support gut microbiome.

Probiotics balance the good and bad bacteria in our bodies and offer other benefits. Fermenting fruits and vegetables increases their antioxidant and vitamin content, preventing disease in multiple ways (14).

12. Sea vegetables

(seaweed, nori, dulse flakes)

A population study found that eating one sheet of nori seaweed per day may cut breast cancer risk by more than half (15).

Seaweed and sea vegetables may help moderate estrogen levels in the metabolism, reducing cancer and disease risk.

Sea vegetables make up 30% of the world's edible plants and contain unique phytonutrients. A teaspoon of these super sea-foods on your dinner will help fight disease from the inside out.

13. Water

I'm less concerned about whether you consider water food than whether you drink enough. If this list were ranked by what single item led to the best health outcomes, water would be first.

Research shows that people who drink 5 or more glasses of water per day have a 50% lower risk of dying from heart disease than those who drink 2 or less (16).

Drinking enough water boosts energy, improves skin, mental health, and digestion, and reduces the risk of various health issues, including obesity.

14. Tea

All tea consumption is linked to a lower risk of stroke, heart disease, and early death, with green tea leading for antioxidant content and immediate health benefits.

Green tea leaves may also be able to interfere with each stage of cancer formation, from the growth of the first mutated cell to the spread and progression of cancer in the body. Green tea is a quick and easy way to support your long-term and short-term health (17).

15. Supplemental B12 vitamin

B12, or cobalamin, is a vitamin responsible for cell metabolism. Not getting enough B12 can have serious consequences.

Historically, eating vegetables from untreated soil helped humans maintain their vitamin B12 levels. Due to modern sanitization, our farming soil lacks B12.

B12 is often cited as a problem only for vegetarians and vegans (as animals we eat are given B12 supplements before slaughter), but recent studies have found that plant-based eaters have lower B12 deficiency rates than any other diet (18).

Article Sources:

1. The Global Burden of Disease Study 2010 (GBD 2010)

2. I. Darmadi-Blackberry, M. Wahlqvist, A. Kouris-Blazos, et al. Legumes: the most important dietary predictor of survival in older people of different ethnicities. Asia Pac J Clin Nutr. 2004;13(2):217–20.

3. Guha N, Kwan ML, Quesenberry CP Jr, Weltzien EK, Castillo AL, Caan BJ. Soy isoflavones and risk of cancer recurrence in a cohort of breast cancer survivors: the Life After Cancer Epidemiology study. Breast Cancer Res Treat. 2009 Nov;118(2):395–405.

4. Y. Bao, J. Han, F. B. Hu, E. L. Giovannucci, M. J. Stampfer, W. C. Willett, C. S. Fuchs. Association of nut consumption with total and cause-specific mortality. N. Engl. J. Med. 2013 369(21):2001–2011.

5. V. Vadivel, C. N. Kunyanga, H. K. Biesalski. Health benefits of nut consumption with special reference to body weight control. Nutrition 2012 28(11–12):1089–1097.

6. D Rodriguez-Leyva, W Weighell, A L Edel,R LaVallee, E Dibrov,R Pinneker, T G Maddaford, B Ramjiawan, M Aliani, R Guzman R, G N Pierce. Potent antihypertensive action of dietary flaxseed in hypertensive patients. Hypertension. 2013 Dec;62(6):1081–9. doi: 10.1161/HYPERTENSIONAHA.113.02094.

7. Vuksan V, Jenkins AL, Dias AG, Lee AS, Jovanovski E, Rogovik AL, Hanna A. Reduction in postprandial glucose excursion and prolongation of satiety: possible explanation of the long-term effects of whole grain Salba (Salvia Hispanica L.). Eur J Clin Nutr. 2010 Apr;64(4):436–8. doi: 10.1038/ejcn.2009.159. Epub 2010 Jan 20. PMID: 20087375.

8. W. Rock, M. Rosenblat, H. Borochov-Neori, N. Volkova, S. Judeinstein, M. Elias, and M. Aviram. Effects of date (Phoenix dactylifera L., Medjool or Hallawi Variety) consumption by healthy subjects on serum glucose and lipid levels and on serum oxidative status: a pilot study. J. Agric. Food. Chem., 57(17):8010{8017, 2009.

9. Eid N, Enani S, Walton G, et al. The impact of date palm fruits and their component polyphenols, on gut microbial ecology, bacterial metabolites and colon cancer cell proliferation. J Nutr Sci. 2014;3:e46.

10. Li Y, Zhang T, Korkaya H, Liu S, Lee HF, Newman B, Yu Y, Clouthier SG, Schwartz SJ, Wicha MS, Sun D. Sulforaphane, a Dietary Component of Broccoli/Broccoli Sprouts, Inhibits Breast Cancer Stem Cells. Clin Cancer Res. 2010 May 1;16(9):2580–90.

11. Lappi J, Kolehmainen M, Mykkänen H, Poutanen K. Do large intestinal events explain the protective effects of whole grain foods against type 2 diabetes? Crit Rev Food Sci Nutr. 2013;53(6):631–40.

12. S. S. Percival, J. P. V. Heuvel, C. J. Nieves, C. Montero, A. J. Migliaccio, J. Meadors. Bioavailability of Herbs and Spices in Humans as Determined by ex vivo Inflammatory Suppression and DNA Strand Breaks. J Am Coll Nutr. 2012 31(4):288–294.

13. Nurk E, Refsum H, Drevon CA, et al. Cognitive performance among the elderly in relation to the intake of plant foods. The Hordaland Health Study. Br J Nutr. 2010;104(8):1190–201.

14. Melini, F.; Melini, V.; Luziatelli, F.; Ficca, A.G.; Ruzzi, M. Health-Promoting Components in Fermented Foods: An Up-to-Date Systematic Review. Nutrients2019, 11, 1189.

15. H. Funahashi, T. Imai, T. Mase, M. Sekiya, K. Yokoi, H. Hayashi, A. Shibata, T. Hayashi, M. Nishikawa, N. Suda, Y. Hibi, Y. Mizuno, K. Tsukamura, A. Hayakawa, S. Tanuma. Seaweed prevents breast cancer? Jpn. J. Cancer Res. 2001 92(5):483–487.

16. Chan J, Knutsen SF, Blix GG, Lee JW, Fraser GE. Water, other fluids, and fatal coronary heart disease: the Adventist Health Study. Am J Epidemiol. 2002 May 1;155(9):827–33. doi: 10.1093/aje/155.9.827. PMID: 11978586.

17. Fujiki H, Imai K, Nakachi K, Shimizu M, Moriwaki H, Suganuma M. Challenging the effectiveness of green tea in primary and tertiary cancer prevention. J Cancer Res Clin Oncol. 2012 Aug;138(8):1259–70.

18. Damayanti, D., Jaceldo-Siegl, K., Beeson, W. L., Fraser, G., Oda, K., & Haddad, E. H. (2018). Foods and Supplements Associated with Vitamin B12Biomarkers among Vegetarian and Non-Vegetarian Participants of the Adventist Health Study-2 (AHS-2) Calibration Study. Nutrients, 10(6), 722. doi:10.3390/nu10060722