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Justin Kuepper

Justin Kuepper

3 years ago

Day Trading Introduction

Historically, only large financial institutions, brokerages, and trading houses could actively trade in the stock market. With instant global news dissemination and low commissions, developments such as discount brokerages and online trading have leveled the playing—or should we say trading—field. It's never been easier for retail investors to trade like pros thanks to trading platforms like Robinhood and zero commissions.

Day trading is a lucrative career (as long as you do it properly). But it can be difficult for newbies, especially if they aren't fully prepared with a strategy. Even the most experienced day traders can lose money.

So, how does day trading work?

Day Trading Basics

Day trading is the practice of buying and selling a security on the same trading day. It occurs in all markets, but is most common in forex and stock markets. Day traders are typically well educated and well funded. For small price movements in highly liquid stocks or currencies, they use leverage and short-term trading strategies.

Day traders are tuned into short-term market events. News trading is a popular strategy. Scheduled announcements like economic data, corporate earnings, or interest rates are influenced by market psychology. Markets react when expectations are not met or exceeded, usually with large moves, which can help day traders.

Intraday trading strategies abound. Among these are:

  • Scalping: This strategy seeks to profit from minor price changes throughout the day.
  • Range trading: To determine buy and sell levels, range traders use support and resistance levels.
  • News-based trading exploits the increased volatility around news events.
  • High-frequency trading (HFT): The use of sophisticated algorithms to exploit small or short-term market inefficiencies.

A Disputed Practice

Day trading's profit potential is often debated on Wall Street. Scammers have enticed novices by promising huge returns in a short time. Sadly, the notion that trading is a get-rich-quick scheme persists. Some daytrade without knowledge. But some day traders succeed despite—or perhaps because of—the risks.

Day trading is frowned upon by many professional money managers. They claim that the reward rarely outweighs the risk. Those who day trade, however, claim there are profits to be made. Profitable day trading is possible, but it is risky and requires considerable skill. Moreover, economists and financial professionals agree that active trading strategies tend to underperform passive index strategies over time, especially when fees and taxes are factored in.

Day trading is not for everyone and is risky. It also requires a thorough understanding of how markets work and various short-term profit strategies. Though day traders' success stories often get a lot of media attention, keep in mind that most day traders are not wealthy: Many will fail, while others will barely survive. Also, while skill is important, bad luck can sink even the most experienced day trader.

Characteristics of a Day Trader

Experts in the field are typically well-established professional day traders.
They usually have extensive market knowledge. Here are some prerequisites for successful day trading.

Market knowledge and experience

Those who try to day-trade without understanding market fundamentals frequently lose. Day traders should be able to perform technical analysis and read charts. Charts can be misleading if not fully understood. Do your homework and know the ins and outs of the products you trade.

Enough capital

Day traders only use risk capital they can lose. This not only saves them money but also helps them trade without emotion. To profit from intraday price movements, a lot of capital is often required. Most day traders use high levels of leverage in margin accounts, and volatile market swings can trigger large margin calls on short notice.

Strategy

A trader needs a competitive advantage. Swing trading, arbitrage, and trading news are all common day trading strategies. They tweak these strategies until they consistently profit and limit losses.

Strategy Breakdown:

Type | Risk | Reward

Swing Trading | High | High
Arbitrage | Low | Medium
Trading News | Medium | Medium
Mergers/Acquisitions | Medium | High

Discipline

A profitable strategy is useless without discipline. Many day traders lose money because they don't meet their own criteria. “Plan the trade and trade the plan,” they say. Success requires discipline.

Day traders profit from market volatility. For a day trader, a stock's daily movement is appealing. This could be due to an earnings report, investor sentiment, or even general economic or company news.

Day traders also prefer highly liquid stocks because they can change positions without affecting the stock's price. Traders may buy a stock if the price rises. If the price falls, a trader may decide to sell short to profit.

A day trader wants to trade a stock that moves (a lot).

Day Trading for a Living

Professional day traders can be self-employed or employed by a larger institution.

Most day traders work for large firms like hedge funds and banks' proprietary trading desks. These traders benefit from direct counterparty lines, a trading desk, large capital and leverage, and expensive analytical software (among other advantages). By taking advantage of arbitrage and news events, these traders can profit from less risky day trades before individual traders react.

Individual traders often manage other people’s money or simply trade with their own. They rarely have access to a trading desk, but they frequently have strong ties to a brokerage (due to high commissions) and other resources. However, their limited scope prevents them from directly competing with institutional day traders. Not to mention more risks. Individuals typically day trade highly liquid stocks using technical analysis and swing trades, with some leverage. 

Day trading necessitates access to some of the most complex financial products and services. Day traders usually need:

Access to a trading desk

Traders who work for large institutions or manage large sums of money usually use this. The trading or dealing desk provides these traders with immediate order execution, which is critical during volatile market conditions. For example, when an acquisition is announced, day traders interested in merger arbitrage can place orders before the rest of the market.

News sources

The majority of day trading opportunities come from news, so being the first to know when something significant happens is critical. It has access to multiple leading newswires, constant news coverage, and software that continuously analyzes news sources for important stories.

Analytical tools

Most day traders rely on expensive trading software. Technical traders and swing traders rely on software more than news. This software's features include:

  • Automatic pattern recognition: It can identify technical indicators like flags and channels, or more complex indicators like Elliott Wave patterns.

  • Genetic and neural applications: These programs use neural networks and genetic algorithms to improve trading systems and make more accurate price predictions.

  • Broker integration: Some of these apps even connect directly to the brokerage, allowing for instant and even automatic trade execution. This reduces trading emotion and improves execution times.

  • Backtesting: This allows traders to look at past performance of a strategy to predict future performance. Remember that past results do not always predict future results.

Together, these tools give traders a competitive advantage. It's easy to see why inexperienced traders lose money without them. A day trader's earnings potential is also affected by the market in which they trade, their capital, and their time commitment.

Day Trading Risks

Day trading can be intimidating for the average investor due to the numerous risks involved. The SEC highlights the following risks of day trading:

Because day traders typically lose money in their first months of trading and many never make profits, they should only risk money they can afford to lose.
Trading is a full-time job that is stressful and costly: Observing dozens of ticker quotes and price fluctuations to spot market trends requires intense concentration. Day traders also spend a lot on commissions, training, and computers.
Day traders heavily rely on borrowing: Day-trading strategies rely on borrowed funds to make profits, which is why many day traders lose everything and end up in debt.
Avoid easy profit promises: Avoid “hot tips” and “expert advice” from day trading newsletters and websites, and be wary of day trading educational seminars and classes. 

Should You Day Trade?
As stated previously, day trading as a career can be difficult and demanding.

  • First, you must be familiar with the trading world and know your risk tolerance, capital, and goals.
  • Day trading also takes a lot of time. You'll need to put in a lot of time if you want to perfect your strategies and make money. Part-time or whenever isn't going to cut it. You must be fully committed.
  • If you decide trading is for you, remember to start small. Concentrate on a few stocks rather than jumping into the market blindly. Enlarging your trading strategy can result in big losses.
  • Finally, keep your cool and avoid trading emotionally. The more you can do that, the better. Keeping a level head allows you to stay focused and on track.
    If you follow these simple rules, you may be on your way to a successful day trading career.

Is Day Trading Illegal?

Day trading is not illegal or unethical, but it is risky. Because most day-trading strategies use margin accounts, day traders risk losing more than they invest and becoming heavily in debt.

How Can Arbitrage Be Used in Day Trading?

Arbitrage is the simultaneous purchase and sale of a security in multiple markets to profit from small price differences. Because arbitrage ensures that any deviation in an asset's price from its fair value is quickly corrected, arbitrage opportunities are rare.

Why Don’t Day Traders Hold Positions Overnight?

Day traders rarely hold overnight positions for several reasons: Overnight trades require more capital because most brokers require higher margin; stocks can gap up or down on overnight news, causing big trading losses; and holding a losing position overnight in the hope of recovering some or all of the losses may be against the trader's core day-trading philosophy.

What Are Day Trader Margin Requirements?

Regulation D requires that a pattern day trader client of a broker-dealer maintain at all times $25,000 in equity in their account.

How Much Buying Power Does Day Trading Have?

Buying power is the total amount of funds an investor has available to trade securities. FINRA rules allow a pattern day trader to trade up to four times their maintenance margin excess as of the previous day's close.

The Verdict

Although controversial, day trading can be a profitable strategy. Day traders, both institutional and retail, keep the markets efficient and liquid. Though day trading is still popular among novice traders, it should be left to those with the necessary skills and resources.

More on Economics & Investing

Liam Vaughan

Liam Vaughan

3 years ago

Investors can bet big on almost anything on a new prediction market.

Kalshi allows five-figure bets on the Grammys, the next Covid wave, and future SEC commissioners. Worst-case scenario

On Election Day 2020, two young entrepreneurs received a call from the CFTC chairman. Luana Lopes Lara and Tarek Mansour spent 18 months trying to start a new type of financial exchange. Instead of betting on stock prices or commodity futures, people could trade instruments tied to real-world events, such as legislation, the weather, or the Oscar winner.

Heath Tarbert, a Trump appointee, shouted "Congratulations." "You're competing with 1840s-era markets. I'm sure you'll become a powerhouse too."

Companies had tried to introduce similar event markets in the US for years, but Tarbert's agency, the CFTC, said no, arguing they were gambling and prone to cheating. Now the agency has reversed course, approving two 24-year-olds who will have first-mover advantage in what could become a huge new asset class. Kalshi Inc. raised $30 million from venture capitalists within weeks of Tarbert's call, his representative says. Mansour, 26, believes this will be bigger than crypto.

Anyone who's read The Wisdom of Crowds knows prediction markets' potential. Well-designed markets can help draw out knowledge from disparate groups, and research shows that when money is at stake, people make better predictions. Lopes Lara calls it a "bullshit tax." That's why Google, Microsoft, and even the US Department of Defense use prediction markets internally to guide decisions, and why university-linked political betting sites like PredictIt sometimes outperform polls.

Regulators feared Wall Street-scale trading would encourage investors to manipulate reality. If the stakes are high enough, traders could pressure congressional staffers to stall a bill or bet on whether Kanye West's new album will drop this week. When Lopes Lara and Mansour pitched the CFTC, senior regulators raised these issues. Politically appointed commissioners overruled their concerns, and one later joined Kalshi's board.

Will Kanye’s new album come out next week? Yes or no?

Kalshi's victory was due more to lobbying and legal wrangling than to Silicon Valley-style innovation. Lopes Lara and Mansour didn't invent anything; they changed a well-established concept's governance. The result could usher in a new era of market-based enlightenment or push Wall Street's destructive tendencies into the real world.

If Kalshi's founders lacked experience to bolster their CFTC application, they had comical youth success. Lopes Lara studied ballet at the Brazilian Bolshoi before coming to the US. Mansour won France's math Olympiad. They bonded over their work ethic in an MIT computer science class.

Lopes Lara had the idea for Kalshi while interning at a New York hedge fund. When the traders around her weren't working, she noticed they were betting on the news: Would Apple hit a trillion dollars? Kylie Jenner? "It was anything," she says.

Are mortgage rates going up? Yes or no?

Mansour saw the business potential when Lopes Lara suggested it. He interned at Goldman Sachs Group Inc., helping investors prepare for the UK leaving the EU. Goldman sold clients complex stock-and-derivative combinations. As he discussed it with Lopes Lara, they agreed that investors should hedge their risk by betting on Brexit itself rather than an imperfect proxy.

Lopes Lara and Mansour hypothesized how a marketplace might work. They settled on a "event contract," a binary-outcome instrument like "Will inflation hit 5% by the end of the month?" The contract would settle at $1 (if the event happened) or zero (if it didn't), but its price would fluctuate based on market sentiment. After a good debate, a politician's election odds may rise from 50 to 55. Kalshi would charge a commission on every trade and sell data to traders, political campaigns, businesses, and others.

In October 2018, five months after graduation, the pair flew to California to compete in a hackathon for wannabe tech founders organized by the Silicon Valley incubator Y Combinator. They built a website in a day and a night and presented it to entrepreneurs the next day. Their prototype barely worked, but they won a three-month mentorship program and $150,000. Michael Seibel, managing director of Y Combinator, said of their idea, "I had to take a chance!"

Will there be another moon landing by 2025?

Seibel's skepticism was rooted in America's historical wariness of gambling. Roulette, poker, and other online casino games are largely illegal, and sports betting was only legal in a few states until May 2018. Kalshi as a risk-hedging platform rather than a bookmaker seemed like a good idea, but convincing the CFTC wouldn't be easy. In 2012, the CFTC said trading on politics had no "economic purpose" and was "contrary to the public interest."

Lopes Lara and Mansour cold-called 60 Googled lawyers during their time at Y Combinator. Everyone advised quitting. Mansour recalls the pain. Jeff Bandman, a former CFTC official, helped them navigate the agency and its characters.

When they weren’t busy trying to recruit lawyers, Lopes Lara and Mansour were meeting early-stage investors. Alfred Lin of Sequoia Capital Operations LLC backed Airbnb, DoorDash, and Uber Technologies. Lin told the founders their idea could capitalize on retail trading and challenge how the financial world manages risk. "Come back with regulatory approval," he said.

In the US, even small bets on most events were once illegal. Under the Commodity Exchange Act, the CFTC can stop exchanges from listing contracts relating to "terrorism, assassination, war" and "gaming" if they are "contrary to the public interest," which was often the case.

Will subway ridership return to normal? Yes or no?

In 1988, as academic interest in the field grew, the agency allowed the University of Iowa to set up a prediction market for research purposes, as long as it didn't make a profit or advertise and limited bets to $500. PredictIt, the biggest and best-known political betting platform in the US, also got an exemption thanks to an association with Victoria University of Wellington in New Zealand. Today, it's a sprawling marketplace with its own subculture and lingo. PredictIt users call it "Rules Cuck Panther" when they lose on a technicality. Major news outlets cite PredictIt's odds on Discord and the Star Spangled Gamblers podcast.

CFTC limits PredictIt bets to $850. To keep traders happy, PredictIt will often run multiple variations of the same question, listing separate contracts for two dozen Democratic primary candidates, for example. A trader could have more than $10,000 riding on a single outcome. Some of the site's traders are current or former campaign staffers who can answer questions like "How many tweets will Donald Trump post from Nov. 20 to 27?" and "When will Anthony Scaramucci's role as White House communications director end?"

According to PredictIt co-founder John Phillips, politicians help explain the site's accuracy. "Prediction markets work well and are accurate because they attract people with superior information," he said in a 2016 podcast. “In the financial stock market, it’s called inside information.”

Will Build Back Better pass? Yes or no?

Trading on nonpublic information is illegal outside of academia, which presented a dilemma for Lopes Lara and Mansour. Kalshi's forecasts needed to be accurate. Kalshi must eliminate insider trading as a regulated entity. Lopes Lara and Mansour wanted to build a high-stakes PredictIt without the anarchy or blurred legal lines—a "New York Stock Exchange for Events." First, they had to convince regulators event trading was safe.

When Lopes Lara and Mansour approached the CFTC in the spring of 2019, some officials in the Division of Market Oversight were skeptical, according to interviews with people involved in the process. For all Kalshi's talk of revolutionizing finance, this was just a turbocharged version of something that had been rejected before.

The DMO couldn't see the big picture. The staff review was supposed to ensure Kalshi could complete a checklist, "23 Core Principles of a Designated Contract Market," which included keeping good records and having enough money. The five commissioners decide. With Trump as president, three of them were ideologically pro-market.

Lopes Lara, Mansour, and their lawyer Bandman, an ex-CFTC official, answered the DMO's questions while lobbying the commissioners on Zoom about the potential of event markets to mitigate risks and make better decisions. Before each meeting, they would write a script and memorize it word for word.

Will student debt be forgiven? Yes or no?

Several prediction markets that hadn't sought regulatory approval bolstered Kalshi's case. Polymarket let customers bet hundreds of thousands of dollars anonymously using cryptocurrencies, making it hard to track. Augur, which facilitates private wagers between parties using blockchain, couldn't regulate bets and hadn't stopped users from betting on assassinations. Kalshi, by comparison, argued it was doing everything right. (The CFTC fined Polymarket $1.4 million for operating an unlicensed exchange in January 2022. Polymarket says it's now compliant and excited to pioneer smart contract-based financial solutions with regulators.

Kalshi was approved unanimously despite some DMO members' concerns about event contracts' riskiness. "Once they check all the boxes, they're in," says a CFTC insider.

Three months after CFTC approval, Kalshi announced funding from Sequoia, Charles Schwab, and Henry Kravis. Sequoia's Lin, who joined the board, said Tarek, Luana, and team created a new way to invest and engage with the world.

The CFTC hadn't asked what markets the exchange planned to run since. After approval, Lopes Lara and Mansour had the momentum. Kalshi's March list of 30 proposed contracts caused chaos at the DMO. The division handles exchanges that create two or three new markets a year. Kalshi’s business model called for new ones practically every day.

Uncontroversial proposals included weather and GDP questions. Others, on the initial list and later, were concerning. DMO officials feared Covid-19 contracts amounted to gambling on human suffering, which is why war and terrorism markets are banned. (Similar logic doomed ex-admiral John Poindexter's Policy Analysis Market, a Bush-era plan to uncover intelligence by having security analysts bet on Middle East events.) Regulators didn't see how predicting the Grammy winners was different from betting on the Patriots to win the Super Bowl. Who, other than John Legend, would need to hedge the best R&B album winner?

Event contracts raised new questions for the DMO's product review team. Regulators could block gaming contracts that weren't in the public interest under the Commodity Exchange Act, but no one had defined gaming. It was unclear whether the CFTC had a right or an obligation to consider whether a contract was in the public interest. How was it to determine public interest? Another person familiar with the CFTC review says, "It was a mess." The agency didn't comment.

CFTC staff feared some event contracts could be cheated. Kalshi wanted to run a bee-endangerment market. The DMO pushed back, saying it saw two problems symptomatic of the asset class: traders could press government officials for information, and officials could delay adding the insects to the list to cash in.

The idea that traders might manipulate prediction markets wasn't paranoid. In 2013, academics David Rothschild and Rajiv Sethi found that an unidentified party lost $7 million buying Mitt Romney contracts on Intrade, a now-defunct, unlicensed Irish platform, in the runup to the 2012 election. The authors speculated that the trader, whom they dubbed the “Romney Whale,” may have been looking to boost morale and keep donations coming in.

Kalshi said manipulation and insider trading are risks for any market. It built a surveillance system and said it would hire a team to monitor it. "People trade on events all the time—they just use options and other instruments. This brings everything into the open, Mansour says. Kalshi didn't include election contracts, a red line for CFTC Democrats.

Lopes Lara and Mansour were ready to launch kalshi.com that summer, but the DMO blocked them. Product reviewers were frustrated by spending half their time on an exchange that represented a tiny portion of the derivatives market. Lopes Lara and Mansour pressed politically appointed commissioners during the impasse.

Tarbert, the chairman, had moved on, but Kalshi found a new supporter in Republican Brian Quintenz, a crypto-loving former hedge fund manager. He was unmoved by the DMO's concerns, arguing that speculation on Kalshi's proposed events was desirable and the agency had no legal standing to prevent it. He supported a failed bid to allow NFL futures earlier this year. Others on the commission were cautious but supportive. Given the law's ambiguity, they worried they'd be on shaky ground if Kalshi sued if they blocked a contract. Without a permanent chairman, the agency lacked leadership.

To block a contract, DMO staff needed a majority of commissioners' support, which they didn't have in all but a few cases. "We didn't have the votes," a reviewer says, paraphrasing Hamilton. By the second half of 2021, new contract requests were arriving almost daily at the DMO, and the demoralized and overrun division eventually accepted defeat and stopped fighting back. By the end of the year, three senior DMO officials had left the agency, making it easier for Kalshi to list its contracts unimpeded.

Today, Kalshi is growing. 32 employees work in a SoHo office with big windows and exposed brick. Quintenz, who left the CFTC 10 months after Kalshi was approved, is on its board. He joined because he was interested in the market's hedging and risk management opportunities.

Mid-May, the company's website had 75 markets, such as "Will Q4 GDP be negative?" Will NASA land on the moon by 2025? The exchange recently reached 2 million weekly contracts, a jump from where it started but still a small number compared to other futures exchanges. Early adopters are PredictIt and Polymarket fans. Bets on the site are currently capped at $25,000, but Kalshi hopes to increase that to $100,000 and beyond.

With the regulatory drawbridge down, Lopes Lara and Mansour must move quickly. Chicago's CME Group Inc. plans to offer index-linked event contracts. Kalshi will release a smartphone app to attract customers. After that, it hopes to partner with a big brokerage. Sequoia is a major investor in Robinhood Markets Inc. Robinhood users could have access to Kalshi so that after buying GameStop Corp. shares, they'd be prompted to bet on the Oscars or the next Fed commissioner.

Some, like Illinois Democrat Sean Casten, accuse Robinhood and its competitors of gamifying trading to encourage addiction, but Kalshi doesn't seem worried. Mansour says Kalshi's customers can't bet more than they've deposited, making debt difficult. Eventually, he may introduce leveraged bets.

Tension over event contracts recalls another CFTC episode. Brooksley Born proposed regulating the financial derivatives market in 1994. Alan Greenspan and others in the government opposed her, saying it would stifle innovation and push capital overseas. Unrestrained, derivatives grew into a trillion-dollar industry until 2008, when they sparked the financial crisis.

Today, with a midterm election looming, it seems reasonable to ask whether Kalshi plans to get involved. Elections have historically been the biggest draw in prediction markets, with 125 million shares traded on PredictIt for 2020. “We can’t discuss specifics,” Mansour says. “All I can say is, you know, we’re always working on expanding the universe of things that people can trade on.”

Any election contracts would need CFTC approval, which may be difficult with three Democratic commissioners. A Republican president would change the equation.

Sam Hickmann

Sam Hickmann

3 years ago

What is this Fed interest rate everybody is talking about that makes or breaks the stock market?

The Federal Funds Rate (FFR) is the target interest rate set by the Federal Reserve System (Fed)'s policy-making body (FOMC). This target is the rate at which the Fed suggests commercial banks borrow and lend their excess reserves overnight to each other.

The FOMC meets 8 times a year to set the target FFR. This is supposed to promote economic growth. The overnight lending market sets the actual rate based on commercial banks' short-term reserves. If the market strays too far, the Fed intervenes.

Banks must keep a certain percentage of their deposits in a Federal Reserve account. A bank's reserve requirement is a percentage of its total deposits. End-of-day bank account balances averaged over two-week reserve maintenance periods are used to determine reserve requirements.

If a bank expects to have end-of-day balances above what's needed, it can lend the excess to another institution.

The FOMC adjusts interest rates based on economic indicators that show inflation, recession, or other issues that affect economic growth. Core inflation and durable goods orders are indicators.

In response to economic conditions, the FFR target has changed over time. In the early 1980s, inflation pushed it to 20%. During the Great Recession of 2007-2009, the rate was slashed to 0.15 percent to encourage growth.

Inflation picked up in May 2022 despite earlier rate hikes, prompting today's 0.75 percent point increase. The largest increase since 1994. It might rise to around 3.375% this year and 3.1% by the end of 2024.

Wayne Duggan

Wayne Duggan

3 years ago

What An Inverted Yield Curve Means For Investors

The yield spread between 10-year and 2-year US Treasury bonds has fallen below 0.2 percent, its lowest level since March 2020. A flattening or negative yield curve can be a bad sign for the economy.

What Is An Inverted Yield Curve? 

In the yield curve, bonds of equal credit quality but different maturities are plotted. The most commonly used yield curve for US investors is a plot of 2-year and 10-year Treasury yields, which have yet to invert.

A typical yield curve has higher interest rates for future maturities. In a flat yield curve, short-term and long-term yields are similar. Inverted yield curves occur when short-term yields exceed long-term yields. Inversions of yield curves have historically occurred during recessions.

Inverted yield curves have preceded each of the past eight US recessions. The good news is they're far leading indicators, meaning a recession is likely not imminent.

Every US recession since 1955 has occurred between six and 24 months after an inversion of the two-year and 10-year Treasury yield curves, according to the San Francisco Fed. So, six months before COVID-19, the yield curve inverted in August 2019.

Looking Ahead

The spread between two-year and 10-year Treasury yields was 0.18 percent on Tuesday, the smallest since before the last US recession. If the graph above continues, a two-year/10-year yield curve inversion could occur within the next few months.

According to Bank of America analyst Stephen Suttmeier, the S&P 500 typically peaks six to seven months after the 2s-10s yield curve inverts, and the US economy enters recession six to seven months later.

Investors appear unconcerned about the flattening yield curve. This is in contrast to the iShares 20+ Year Treasury Bond ETF TLT +2.19% which was down 1% on Tuesday.

Inversion of the yield curve and rising interest rates have historically harmed stocks. Recessions in the US have historically coincided with or followed the end of a Federal Reserve rate hike cycle, not the start.

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Vanessa Karel

Vanessa Karel

3 years ago

10 hard lessons from founding a startup.

Here is the ugly stuff, read this if you have a founder in your life or are trying to become one. Your call.

#1 You'll try to talk yourself to sleep, but it won't always work.

As founders, we're all driven. Good and bad, you're restless. Success requires resistance and discipline. Your startup will be on your mind 24/7, and not everyone will have the patience to listen to your worries, ideas, and coffee runs. You become more self-sufficient than ever before.

#2 No one will understand what you're going through unless they've been a founder.

Some of my closest friends don't understand the work that goes into starting a business, and we can't blame them.

#3 You'll feel alienated.

Your problems aren't common; calling your bestie won't help. You must search hard for the right resources. It alienates you from conversations you no longer relate to. (No 4th of July, no long weekends!)

#4 Since you're your "own boss," people assume you have lots of free time.

Do you agree? I was on a webinar with lots of new entrepreneurs, and one woman said, "I started my own business so I could have more time for myself." This may be true for some lucky people, and you can be flexible with your schedule. If you want your business to succeed, you'll probably be its slave for a while.

#5 No time for illness or family emergencies.

Both last month. Oh, no! Physically and emotionally withdrawing at the worst times will give you perspective. I learned this the hard way because I was too stubborn to postpone an important interview. I thought if I rested all day and only took one call, I'd be fine. Nope. I had a fever and my mind wasn't as sharp, so my performance and audience interaction suffered. Nope. Better to delay than miss out.

Oh, and setting a "OoO" makes you cringe.

#6 Good luck with your mental health, perfectionists.

When building a startup, it's difficult to accept that there won't be enough time to do everything. You can't make them all, not perfectly. You must learn to accept things that are done but not perfect.

#7 As a founder, you'll make mistakes, but you'll want to make them quickly so you can learn.

Hard lessons are learned quicker. You'll need to pivot and try new things often; some won't work, and it's best to discover them sooner rather than later.

#8 Pyramid schemes abound.

I didn't realize how bad it was until I started a company. You must spy and constantly research. As a founder, you'll receive many emails from people claiming to "support" you. Be wary and keep your eyes open. When it's too good to be true. Some "companies" will try to get you to pay for "competitions" to "pitch at events." Don't do it.

#9 Keep your competitor research to a minimum.

Actually, competition is good. It means there's a market for those solutions. However, this can be mentally exhausting too. Learn about their geography and updates, but that's it.

#10 You'll feel guilty taking vacation.

I don't know what to say, but I no longer enjoy watching TV, and that's okay. Pay attention to things that enrich you, bring you joy, and have fun. It boosts creativity.

Being a startup founder may be one of the hardest professional challenges you face, but it's also a great learning experience. Your passion will take you places you never imagined and open doors to opportunities you wouldn't have otherwise. You'll meet amazing people. No regrets, no complaints. It's a roller coaster, but the good days are great.

Miss anything? Comment below

Zuzanna Sieja

Zuzanna Sieja

3 years ago

In 2022, each data scientist needs to read these 11 books.

Non-technical talents can benefit data scientists in addition to statistics and programming.

As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.

Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.

Ready? Let’s dive in.

Best books for data scientists

1. The Black Swan

Author: Nassim Taleb

First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.

Three characteristics define a black swan event:

  • It is erratic.

  • It has a significant impact.

  • Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.

People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.

Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.

Try multiple tactics and models because you may find the answer.

2. High Output Management

Author: Andrew Grove

Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.

That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.

Five lessons:

  • Every action is a procedure.

  • Meetings are a medium of work

  • Manage short-term goals in accordance with long-term strategies.

  • Mission-oriented teams accelerate while functional teams increase leverage.

  • Utilize performance evaluations to enhance output.

So — if the above captures your imagination, it’s well worth getting stuck in.

3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers

Author: Ben Horowitz

Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.

Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.

It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.

Find suggestions on:

  • create software

  • Run a business.

  • Promote a product

  • Obtain resources

  • Smart investment

  • oversee daily operations

This book will help you cope with tough times.

4. Obviously Awesome: How to Nail Product Positioning

Author: April Dunford

Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.

How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.

You'll learn:

  • Select the ideal market for your products.

  • Connect an audience to the value of your goods right away.

  • Take use of three positioning philosophies.

  • Utilize market trends to aid purchasers

5. The Mom test

Author: Rob Fitzpatrick

The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.

Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.

6. Introduction to Machine Learning with Python: A Guide for Data Scientists

Authors: Andreas C. Müller, Sarah Guido

Now, technical documents.

This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.

Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.

If you know machine learning or artificial neural networks, skip this.

7. Python Data Science Handbook: Essential Tools for Working with Data

Author: Jake VanderPlas

Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.

Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.

The only thing missing is a way to apply your learnings.

8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Author: Wes McKinney

The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.

The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.

9. Data Science from Scratch

Author: Joel Grus

Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.

The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.

Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.

10. Machine Learning Yearning

Author: Andrew Ng

Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.

The book delivers knowledge and teaches how to apply it, so you'll know how to:

  • Determine the optimal course of action for your ML project.

  • Create software that is more effective than people.

  • Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.

  • Identifying machine learning system flaws

Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.

11. Deep Learning with PyTorch Step-by-Step

Author: Daniel Voigt Godoy

The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.

It comprises four parts:

  1. Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)

  2. Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)

  3. Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)

  4. Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)

We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.

Is every data scientist a humanist?

Even as a technological professional, you can't escape human interaction, especially with clients.

We hope these books will help you develop interpersonal skills.

Jari Roomer

Jari Roomer

3 years ago

5 ways to never run out of article ideas

Perfectionism is the enemy of the idea muscle. " — James Altucher

Photo by Paige Cody on Unsplash

Writer's block is a typical explanation for low output. Success requires productivity.

In four years of writing, I've never had writer's block. And you shouldn't care.

You'll never run out of content ideas if you follow a few tactics. No, I'm not overpromising.


Take Note of Ideas

Brains are strange machines. Blank when it's time to write. Idiot. Nothing. We get the best article ideas when we're away from our workstation.

  • In the shower

  • Driving

  • In our dreams

  • Walking

  • During dull chats

  • Meditating

  • In the gym

No accident. The best ideas come in the shower, in nature, or while exercising.

(Your workstation is the worst place for creativity.)

The brain has time and space to link 'dots' of information during rest. It's eureka! New idea.

If you're serious about writing, capture thoughts as they come.

Immediately write down a new thought. Capture it. Don't miss it. Your future self will thank you.

As a writer, entrepreneur, or creative, letting ideas slide is bad.

I recommend using Evernote, Notion, or your device's basic note-taking tool to capture article ideas.

It doesn't matter whatever app you use as long as you collect article ideas.

When you practice 'idea-capturing' enough, you'll have an unending list of article ideas when writer's block hits.


High-Quality Content

More books, films, Medium pieces, and Youtube videos I consume, the more I'm inspired to write.

What you eat shapes who you are.

Celebrity gossip and fear-mongering news won't help your writing. It won't help you write regularly.

Instead, read expert-written books. Watch documentaries to improve your worldview. Follow amazing people online.

Develop your 'idea muscle' Daily creativity takes practice. The more you exercise your 'idea muscles,' the easier it is to generate article ideas.

I've trained my 'concept muscle' using James Altucher's exercise.


Write 10 ideas daily.

Write ten book ideas every day if you're an author. Write down 10 business ideas per day if you're an entrepreneur. Write down 10 investing ideas per day.

Write 10 article ideas per day. You become a content machine.

It doesn't state you need ten amazing ideas. You don't need 10 ideas. Ten ideas, regardless of quality.

Like at the gym, reps are what matter. With each article idea, you gain creativity. Writer's block is no match for this workout.


Quit Perfectionism

Perfectionism is bad for writers. You'll have bad articles. You'll have bad ideas. OK. It's creative.

Writing success requires prolificacy. You can't have 'perfect' articles.

Perfectionism is the enemy of the idea muscle. Perfectionism is your brain trying to protect you from harm.” — James Altucher

Vincent van Gogh painted 900 pieces. The Starry Night is the most famous.

Thomas Edison invented 1093 things, but not all were as important as the lightbulb or the first movie camera.

Mozart composed nearly 600 compositions, but only Serenade No13 became popular.

Always do your best. Perfectionism shouldn't stop you from working. Write! Publicize. Make. Even if imperfect.


Write Your Story

Living an interesting life gives you plenty to write about. If you travel a lot, share your stories or lessons learned.

Describe your business's successes and shortcomings.

Share your experiences with difficulties or addictions.

More experiences equal more writing material.

If you stay indoors, perusing social media, you won't be inspired to write.

Have fun. Travel. Strive. Build a business. Be bold. Live a life worth writing about, and you won't run out of material.