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Patterns To Watch

All You Need is One Setup

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Written by Timothy Sykes
Updated 6/2/2022 5 min read

Last night I got a chance to do a LIVE Q&A on YouTube, my first ever!

It was pretty cool taking questions from people all over the world.

A lot of folks asked some really cool questions, especially about how to trade THIS market.

Right now, I’m teaching my students to focus on the morning panic setup.

This is a great setup for any trader no matter their skill or experience level.

Let me show you what I mean…

One Setup … Heck, One Stock…

In the month of May, I traded just one stock, Sysorex Inc. (OTC: SYSX) nine times for a net profit of $4,013 (all of which will go to charity).

Just in the week of May 24-31 I traded the stock five times for a $1,913 profit, including the one loss I took in all of nine trades.

EVERY morning, I walked into the EXACT same setup — the morning panic.

The first pattern I used to help break the million-dollar milestone was my Supernova.

I loved this pattern because it was easy to spot and trade.

But right now, I’m focused on the morning panic because that’s what the market is providing.

Just look at how many times SYSX dropped on a morning panic dip that offered a chance to take a long trade.

This setup is great because promoters keep jumping in to prop up the share price.

I don’t care why they want to do this. I just use the information to my advantage.

Now, this type of setup will eventually run its course.

And like any other pattern, it works better and shows up in some markets more than others.

But you don’t need to have more than one or two patterns and here’s why.

One Pattern Math

© Millionaire Media, LLC

Assume I’m starting out and I know nothing about trading.

I find one pattern that I practice until I get really good.

My win rate comes in around 65% and I get roughly 2 to 1 reward for every dollar I risk.

In probabilities and statistics, the amount of money I should make on average for each trade — assuming I take a lot of them — is what’s known as the ‘Expected Value.’

Expected Value is calculated as follows:

EV = (% Chance of Win x Reward) – (% Chance of Loss x Risk)

Using the numbers above, I get an expected value of $0.95 for every dollar I risk.

Think about that for a second.

That means if I can achieve those stats (on average), I’d come close to making twice what I risk on every trade.

However, it’s not that easy.

The trick is to actually hit those stats. And to do that, I need to become selective on my setups.

So let’s assume for a moment I start with a $10,000 account.

Assuming I risk 1% of my account each time, guess how many trades I would need to take to double my account … 72.

If you did that in a year, it translates to 1.385 trades per week.

If you got those stats over 477 trades, you’d have earned $1 million from a $10,000 account.

Sure, it would take a few years.

But as I explained in the live Q&A, patience is profits.

As SYSX shows, I’ve been able to ride one stock for a trade per day in the last week.

But you don’t need to force anything.

Trading isn’t about making $1 million in your first year. It’s about making $1 million.

The vast majority of my students don’t get there in their first year let alone within two.

And sometimes, it comes down to luck and what the markets throw at you.

But if you work one pattern over and over, fine-tuning your trading and improving your performance, it can make your account grow so much easier.

Now, if you’re looking for a pattern that’s been my bread and butter for more than a decade, then check out my Supernova Pattern.

What’s really cool about this pattern is that many traders have used it to craft their own strategies.

Click here to learn more about my Supernova Pattern.

—Tim


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* Results are not typical and will vary from person to person. Making money trading stocks takes time, dedication, and hard work. There are inherent risks involved with investing in the stock market, including the loss of your investment. Past performance in the market is not indicative of future results. Any investment is at your own risk. See Terms of Service here

The available research on day trading suggests that most active traders lose money. Fees and overtrading are major contributors to these losses.

A 2000 study called “Trading is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors” evaluated 66,465 U.S. households that held stocks from 1991 to 1996. The households that traded most averaged an 11.4% annual return during a period where the overall market gained 17.9%. These lower returns were attributed to overconfidence.

A 2014 paper (revised 2019) titled “Learning Fast or Slow?” analyzed the complete transaction history of the Taiwan Stock Exchange between 1992 and 2006. It looked at the ongoing performance of day traders in this sample, and found that 97% of day traders can expect to lose money from trading, and more than 90% of all day trading volume can be traced to investors who predictably lose money. Additionally, it tied the behavior of gamblers and drivers who get more speeding tickets to overtrading, and cited studies showing that legalized gambling has an inverse effect on trading volume.

A 2019 research study (revised 2020) called “Day Trading for a Living?” observed 19,646 Brazilian futures contract traders who started day trading from 2013 to 2015, and recorded two years of their trading activity. The study authors found that 97% of traders with more than 300 days actively trading lost money, and only 1.1% earned more than the Brazilian minimum wage ($16 USD per day). They hypothesized that the greater returns shown in previous studies did not differentiate between frequent day traders and those who traded rarely, and that more frequent trading activity decreases the chance of profitability.

These studies show the wide variance of the available data on day trading profitability. One thing that seems clear from the research is that most day traders lose money .

Millionaire Media 66 W Flagler St. Ste. 900 Miami, FL 33130 United States (888) 878-3621 This is for information purposes only as Millionaire Media LLC nor Timothy Sykes is registered as a securities broker-dealer or an investment adviser. No information herein is intended as securities brokerage, investment, tax, accounting or legal advice, as an offer or solicitation of an offer to sell or buy, or as an endorsement, recommendation or sponsorship of any company, security or fund. Millionaire Media LLC and Timothy Sykes cannot and does not assess, verify or guarantee the adequacy, accuracy or completeness of any information, the suitability or profitability of any particular investment, or the potential value of any investment or informational source. The reader bears responsibility for his/her own investment research and decisions, should seek the advice of a qualified securities professional before making any investment, and investigate and fully understand any and all risks before investing. Millionaire Media LLC and Timothy Sykes in no way warrants the solvency, financial condition, or investment advisability of any of the securities mentioned in communications or websites. In addition, Millionaire Media LLC and Timothy Sykes accepts no liability whatsoever for any direct or consequential loss arising from any use of this information. This information is not intended to be used as the sole basis of any investment decision, nor should it be construed as advice designed to meet the investment needs of any particular investor. Past performance is not necessarily indicative of future returns.

Citations for Disclaimer

Barber, Brad M. and Odean, Terrance, Trading is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors. Available at SSRN: “Day Trading for a Living?”

Barber, Brad M. and Lee, Yi-Tsung and Liu, Yu-Jane and Odean, Terrance and Zhang, Ke, Learning Fast or Slow? (May 28, 2019). Forthcoming: Review of Asset Pricing Studies, Available at SSRN: “https://ssrn.com/abstract=2535636”

Chague, Fernando and De-Losso, Rodrigo and Giovannetti, Bruno, Day Trading for a Living? (June 11, 2020). Available at SSRN: “https://ssrn.com/abstract=3423101”