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4 AI Strategies to End Self-Destructive Trading with XGPT

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Written by Timothy Sykes
Updated 12/15/2023 8 min read

Why do more than 90% of traders lose money if we all look at the same charts, read the same news, trade the same stocks?

It’s our own minds that get in the way.

It’s the psychological traps and biases that lead us down a path of self-sabotage.

That’s what makes AI tools like XGPT so powerful.

This game changing tool redefines the rules by allowing us to take emotions out of the equation.

Then, it goes a step further by sniffing out lucrative trading opportunities within the noise.

Take the case of ticker symbol CCCC: XGPT flagged it as a top pick, and rightly so. The stock leaped from $2.75 to $8.37, offering multiple winning moments for traders.

Discover how XGPT not only shields you from costly errors but also opens the door to massive trading opportunities…

#1: Overcome Emotional Trading with Data-Driven Decisions

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One of the biggest challenges traders face is emotional decision-making. Even I fall victim to it from time to time.

Heck, it happened to me when I traded CCCC.

I was dead right when I initially took the trade on Tuesday morning.

However, when I traded it in the afternoon, I let my emotions get the best of me. And despite the stock surging by the close, I talked myself out of the position and ended up taking a small loss.

If a trader like myself with over $7.5 million in career trading profits can stumble and make emotional decisions…how bad do you think it can get for new and less experienced traders?

For some, they blow out their accounts before they can ever figure out how to control their emotions.

How XGPT Solves This Issue:

XGPT has no emotions.

It makes decisions based on data, trends, and statistical probabilities…

Not on fear or excitement.

For example, on Tuesday afternoon XGPT sent out an alert for CCC at $2.75.

XGPT has been on fire!

On Wednesday the stock hit a high of $6.03…

Most traders including myself would be reluctant to trade CCCC after such a monster move…

But XGPT alerted CCCC again on Wednesday afternoon…

CCCC hit a high of $8.37 yesterdayreaching XGPT’s profit target. 

#2: Breaking The Cycle of Overtrading

Overtrading often stems from the urge to recover losses quickly or the misconception that more trades equals more profits.

This horrible habit increases exposure to risk and can lead to significant losses.

How XGPT Solves This Issue:

XGPT scans the entire market for you, and spits out an automated report each trading day. It highlights the top stocks based on a proprietary ranking system.

For example, Tuesday and Wednesday’s top pick was CCCC. A stock that experienced a monster move…surging by more than 200%

Not only does XGPT find the stocks with the highest potential…but it also lays out a game plan on how to potentially trade them.

This is critical for traders who suffer from overtrading because it can help them maintain a balance, ensuring that each trade aligns with a well-thought-out strategy rather than impulsive reactions.

#3: Eliminating Bias and Improving Decision Quality

how to read level 2 trading
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Human traders are prone to biases like confirmation biases or overconfidence, which can skew their perception and decision-making process.

Take short sellers in this market as an example. They are probably 99% right on their thesis…put it’s their overconfidence that gets them destroyed.

How XGPT Solves This Issue:

XGPT operates without bias. 

It processes information and recognizes patterns based on objective analysis, not subjective beliefs or past experiences. This unbiased approach can lead to more consistent and reliable trading decisions.

#4: Consistent Application of Risk Management Principles

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One of the reasons why short sellers are getting destroyed in this market isn’t because they have bad ideas…

It’s because they are stubborn and lack any type of risk management skill.

They often ignore stop-loss orders or risk more than they should on a single trade because they feel like they will eventually be right.

How XGPT Solves This Issue:

XGPT comes with its own set of risk management rules…

It clearly defines levels of support and resistance. As well as, what area you should look to cut losses.

This consistency ensures that risk parameters are always followed, protecting traders from unexpected market swings and emotional decision-making.

Transform Your Trading with XGPT: Exclusive Presentation! 🌟

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Step into the future of trading with XGPT’s AI-driven insights.

This isn’t just another tool; it’s a complete paradigm shift in how you approach the market.

📈 Harness Advanced Pattern Recognition

🤖 Embrace Unbiased, Data-Driven Decisions

📊 Develop AI-Informed Trading Strategies

🚀 Navigate Real-Time Market Dynamics

📰 Analyze Deep Market Sentiments

 

Join me for an exclusive presentation where we unveil how XGPT has been changing the game.

Discover its role in identifying high-potential trades like CCCC and learn how it combats common trader challenges such as emotional decision-making, overtrading, and bias.

Ready to see how XGPT can revolutionize your trading experience? 🚀

👉 Sign Up for Our Not-to-Miss XGPT Presentation Now! 👈

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Timothy Sykes

Tim Sykes is a penny stock trader and teacher who became a self-made millionaire by the age of 22 by trading $12,415 of bar mitzvah money. After becoming disenchanted with the hedge fund world, he established the Tim Sykes Trading Challenge to teach aspiring traders how to follow his trading strategies. He’s been featured in a variety of media outlets including CNN, Larry King, Steve Harvey, Forbes, Men’s Journal, and more. He’s also an active philanthropist and environmental activist, a co-founder of Karmagawa, and has donated millions of dollars to charity. Read More

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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 (205) 851-0506 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”