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Where Do You Fall on the IQ Curve Above? Which Pepe Type Are You?

Trading psychology reveals the psychological responses traders have when faced with market events and various factors that influence trading decisions.

A trader’s mental state not only determines their trading decisions but also significantly impacts the development of their trading career. You may already know that the key to success lies not in high IQ, but in traits such as patience, perseverance, self-discipline, and a healthy mental state.

Under the same market conditions, different traders can react in completely different ways.

1. Trading Types

For example, when the price of Bitcoin ($BTC) drops significantly, some people will panic-sell, while others will choose to buy the dip, believing the price will rebound. Therefore, traders can generally be classified into the following types based on psychological traits:

1) Impulsive Traders

These traders lack a detailed plan and make decisions quickly, often ignoring the consequences. They are easily influenced by emotions, leading to potential massive losses.

2) Cautious traders

These traders thoroughly analyze the market and their financial situation before trading. They are usually emotionally stable and have good self-management skills. However, they can sometimes be too conservative and lack risk-taking tendencies, while calculated risks often bring higher returns.

3) Practical traders

Pragmatic traders combine risk-taking with cautious analysis. They understand how to manage risks and trade confidently. These traders are the ideal type: neither over-analyzing nor ignoring the necessary evaluation of whether each trade holds a positive expected value (+EV).

You might find yourself reflected in these types and reflect on how your psychological traits affect your trading outcomes.

Without a doubt, trading psychology is an essential part of successful trading.

2. Trading Biases

Trading biases are cognitive errors that traders may encounter during decision-making, which can significantly affect trading performance and outcomes.

Here are several common trading biases:

1) Confirmation Bias

Traders tend to look for information that supports their existing opinions while ignoring evidence that contradicts them. This bias can lead to poor decisions or overtrading.

For example, if you hold a significant amount of Ethereum ($ETH), you may frequently look for information on Crypto Twitter that supports “Ethereum as a good asset,” while avoiding the reasons Ethereum might not be the best choice. As a result, you are more likely to encounter content that aligns with your existing beliefs, rather than conducting a comprehensive and objective assessment.

Trading psychology not only helps you better understand the market but also helps you identify your own behavioral patterns, boosting your trading performance.

2) Availability bias

In cryptocurrency trading, availability bias is when investors base decisions on easily recalled or recently acquired information rather than thorough analysis. A typical example is when traders rush to buy a cryptocurrency because it is frequently mentioned on social media or news platforms, without considering its fundamentals.

For instance, a specific altcoin might become popular due to celebrity endorsements or viral memes on Twitter. Traders may overestimate its potential and heavily invest, even though the coin may lack solid technical foundations or practical use cases. This bias can lead to poor investment decisions because easily accessible information does not necessarily reflect the true value or long-term prospects of the asset. Another example is traders overreacting to recent market events. If Bitcoin’s price suddenly spikes, availability bias might make investors believe such rapid gains are common and achievable, leading to overly optimistic trades. This can result in chasing short-term trends while neglecting more stable long-term investment strategies.

3) Anchoring bias

In cryptocurrency trading, a classic example of anchoring bias is when an investor buys Bitcoin at $100,000 during a market peak. Even if market conditions change and the price falls significantly, they may still cling to the “anchor” price of $100,000. This psychological bias can lead to erroneous decisions:

• Hold on to the position when it becomes clear that you need to sell, hoping that the price will return to $100,000.
• Ignore new market information or analysis and only believe in your obsession with the $100,000 price.

Anchoring bias can lead to huge financial losses because traders fail to adapt to market changes and miss opportunities to cut losses or take profits at lower prices.

Another common anchoring bias is related to net worth figures. As a trader, you are exposed to profit and loss (PnL) fluctuations every day. Let’s say your crypto net worth is $100,000. If you lose $20,000, you can easily be trapped by the fact that your “account has shrunk” and find it difficult to get back to the original level. This mentality can lead you to adopt an overly defensive approach to the market, and even in trading opportunities that appear to have potential, you scale back your risk for fear of losing money again.

4) Loss aversion bias

Traders generally experience the pain of a loss more intensely than the pleasure of a profit. This often leads them to hold losing positions too long or prematurely close profitable positions.

The loss aversion bias is particularly evident in crypto trading. Let’s say a trader buys Bitcoin for $100,000, expecting the price to rise, but instead the price drops to $80,000. Although market indicators indicate that prices may continue to fall, traders are reluctant to take action in the hope that prices will return to their buying points.

This reluctance to cut losses stems from the psychological pain of realizing losses, even when the trend is clearly adverse.

Another manifestation is that when a coin rises by 10%, traders will sell quickly to lock in profits, fearing profit taking; but when a coin falls by 20%, they hesitate to sell, holding on to the illusion of a price rebound.

This behavior reflects that traders feel the pain of losses much more than the pleasure of equivalent gains. In volatile crypto markets, loss aversion can lead to:

• Hold underperforming assets for the long term.
• Missing out on other potential profit opportunities.
• Increased emotional stress and irrational decision-making.

Honestly, this is one of the classic traps I fall into daily. For example, I am currently shorting some weak altcoins. If I’ve made $10,000 profit but the price pulls back, reducing my profit to $5,000, I tend to hold on, thinking “I won’t close until I get back to $10,000,” even though the trade is still overall profitable. I believe many people can relate to this.

5) Overconfidence Bias

Traders often overestimate their knowledge and abilities, which can lead to excessive risk-taking and frequent trading. A typical example occurred during the 2021 Bitcoin bull market. Many traders were overly confident, believing they could predict market movements, so they leveraged their positions significantly, convinced that Bitcoin prices would continue to rise.

When Bitcoin’s price hit $60,000 in early 2021, many investors became overly optimistic due to the recent upward trend and believed the price would keep climbing. They ignored potential risks and market volatility. However, when the market eventually corrected and Bitcoin’s price dropped below $30,000 a few months later, these overconfident traders suffered significant losses.

6)Overconfidence Bias

Traders often overestimate their knowledge and abilities, leading to excessive risk-taking and frequent trading. A typical example occurred during the 2021 Bitcoin bull market. Many traders were overly confident in their ability to predict market trends, thus heavily leveraging their positions, believing that Bitcoin’s price would continue to rise.

When Bitcoin’s price broke $60,000 in early 2021, many investors became overly optimistic due to the recent price increase, convinced that the price would keep climbing. They ignored potential risks and the possibility of market fluctuations. However, when the market eventually corrected, and Bitcoin’s price fell below $30,000 a few months later, these overconfident traders suffered significant losses.

7) Fear and Greed

Fear and greed can lead traders to exit positions too early due to fear of losses or hold positions too long in an attempt to maximize profits.

This point is self-explanatory and intuitive.

8) Recent news bias

Traders tend to place more weight on recent events or information, overlooking long-term trends or historical data.

For example, you may overreact to short-term price fluctuations, making irrational decisions. Suppose Ethereum’s price drops significantly. Traders might think the downtrend will continue, quickly selling off their holdings and missing out on the market recovery. Consider how crypto Twitter (CT) reacts after a few days of decline—everyone says the market has ended, and you should have sold, but often the market will reverse.

9) Herd bias

Traders follow the crowd, making decisions based on others’ actions rather than their own analysis. This is very common in the crypto market and is a classic behavior pattern in crypto Twitter.

A typical example is Ethereum’s price performance from 2020 to 2021. From around $130 in early 2020 to a historic high of $4,859 in November 2021, Ethereum’s price surged an astonishing 3,756%.

This price surge reflects several herd behaviors:

FOMO (Fear of Missing Out): As Ethereum’s price continued to rise throughout 2020 and 2021, more and more investors piled in, not wanting to miss out on potential gains.

Market Sentiment: Bitcoin’s performance and institutional adoption drove positive sentiment across the entire crypto market, which spilled over to Ethereum.

Technological Advances: Ethereum’s transition to Ethereum 2.0 and the implementation of EIP-1559 (which introduced a burn mechanism for transaction fees) in August 2021 further sparked market interest.

DeFi Boom: As the main platform for decentralized finance (DeFi) applications, Ethereum saw a significant increase in demand and usage.

Institutional Interest: With the growth in institutional adoption and the introduction of Ethereum futures on the Chicago Mercantile Exchange (CME) in February 2021, Ethereum gained further credibility.

It’s important to note that after Ethereum reached its peak in November 2021, it experienced a significant correction in 2022, with its price dropping to around $900 in June, catching many investors off guard.

10) Frame effect

11) Framing Effect

The way information is presented can influence trading decisions. Traders may make different choices based on whether data is expressed positively or negatively.

For example, with Solana, here is a typical case of the framing effect in crypto trading:

• “Solana is up 10% in the past 24 hours, highlighting its strong ecosystem growth.”
• “Despite a 10% gain, Solana has failed to regain its all-time highs.”

Both sentences describe the same 10% increase, but the presentation is completely different. The first piece of news highlights positive information, potentially encouraging traders to buy or hold Solana, while the second piece focuses on potential shortcomings, possibly causing investors to hesitate or even sell.

This difference in framing can significantly impact traders’ decisions. For instance, after reading the first piece, traders may think Solana’s network growth is strong and be inclined to invest, while reading the second piece may cause traders to hesitate and exit even with the price increase.

12) Illusion of Control

Traders overestimate their influence over market outcomes, leading to excessive risk-taking.

For example, a trader might spend hours studying the price movements of a token called “Fartcoin” and believe they’ve discovered a perfect market timing strategy. Based on this “insight,” they might risk most of their portfolio, mistakenly thinking they can control the trade’s outcome.

This illusion of control is especially noticeable during bull markets. When the overall crypto market is on an upward trend, most tokens will rise accordingly. Traders may attribute success to their own abilities, rather than to the market’s overall trend. For instance, they might confidently say, “I knew this altcoin would go up 30% today because of my technical analysis,” but in reality, the increase may simply be the result of the overall market trend.

Personally, I don’t believe in technical analysis because it has repeatedly been proven that the true drivers of the market are news events, not the “invisible lines” you draw.

13) Clustering Illusion

Traders see patterns in random market data that don’t exist and develop faulty strategies.

A typical example is this: a crypto trader notices that the price of a certain coin has increased for five consecutive days. Based on this short-term pattern, he believes that a bullish trend has formed and decides to invest heavily in the asset. However, this five-day rise could be completely random and not represent any real trend.

This example reveals the core of the aggregation illusion:

• Traders see a pattern in a small sample of data (five days of price action).
• They attach meaning to the pattern while ignoring the broader market context or long-term data.
• Investment decisions are based on the assumption that this pattern will continue, without considering that market fluctuations may be random.

In the highly volatile environment of crypto trading, prices fluctuate wildly due to a variety of factors. Mistaking short-term random price movements for meaningful trends can lead to poor investment decisions.

Let’s be honest, we’ve all been there. But after all, our analysis must be based on some basis, right?

14) Negativity Bias

Traders tend to focus more on negative factors in trades or strategies, which may cause them to miss out on good opportunities.

For example: Suppose a trader has had an excellent performance over the past few months, with most trades being profitable. However, one day, due to negative regulatory news causing a market crash, they incur a significant loss. Despite the overall success, the trader begins to focus excessively on this negative experience, leading to:

• Being overly cautious, missing potential profit opportunities even when market conditions improve.
• Constantly expecting similar negative events to occur, thus prematurely selling positions or overusing stop-loss orders.
• Ignoring positive market indicators or favorable news and focusing solely on potential threats or adverse factors.

This bias also manifests itself in some traders selling assets they were previously bullish on. They may start fear-mongering (FUD) in an attempt to justify their decision and hope that the asset will not continue to rise (after all, they have already sold).

15) Self-Attribution Bias

Traders attribute successful trades to their own abilities and attribute failed trades to external factors, hindering learning and improvement.

A classic example: A trader buys Bitcoin at $80,000 and sells it at $105,000, making a significant profit. They attribute this success to their excellent market analysis and trading skills. However, when the same trader buys Ethereum at $3,500 and its price drops to $3,000, they blame market manipulation, unexpected regulatory news, or “whale” sell-offs.

We see this phenomenon on crypto Twitter (CT) almost every day (hint: it’s a daily occurrence!).

16) Hindsight Bias

Traders believe past events were more predictable than they actually were, leading to overconfidence in future predictions.

For example, a trader buys Solana (SOL) at $200 at the start of January 2025. By mid-January, the price rises to $250. Reflecting on this, the trader thinks, “I knew Solana would rise 25%. The market sentiment and technical indicators were so obvious.”

• Traders overestimate their ability to predict Solana’s price movements.
• They ignore the inherent volatility of the crypto market, especially for altcoins like Solana.
• They overlook external factors that contributed to the price increase, such as overall market conditions or specific news events.

This bias may lead to the following consequences:

• Overconfidence in future transactions
• Ignore potential risks or contrary indicators
• Failure to effectively diversify its crypto portfolio

3. The Issues I Experienced in Trading

These biases frequently emerged during my own trading journey as well. Being aware of their existence can help us better reflect on our trading behavior and improve our strategies.

1) Random Reinforcement

Sometimes, amateur traders (like myself) may make consecutive huge gains, while experienced traders face a series of losses. Although this is essentially a game of luck, traders may mistakenly believe that it’s due to their own ability—or conversely, they may severely doubt their competence, falling into the psychological trap of random reinforcement.

Random reinforcement is a destructive psychological phenomenon that is very common among traders. It can cause a trader to misunderstand their abilities, blur their judgment, and lead to overconfidence or extreme lack of confidence. The issue is that beginners may believe they have discovered an easy path to profits, while veterans might begin to question their skills, trading plans, or even their entire knowledge system of trading.

An example of a mistake I often make:

Let’s say I start my day by making a huge profit on $TIA. This could happen with any asset, but generally, if I begin with a large win, I become overly confident and more prone to frequent operations without a clear trading logic.

My thought process goes like this: “I’ve already made a lot—now I can take bigger risks. Even if I lose, it’s fine, since I’m betting with ‘free money’ I just earned.”

Can you see the flaw in that thinking?

Random reinforcement causes traders to ignore the randomness of the market and mistakenly believe that short-term success is entirely due to their own abilities. This leads to more high-risk decisions without a rigorous strategy. This mindset can result in:

• Ignore market risks and trade frequently.
• Overestimating your skills and ignoring potential warning signs.
• Experiencing significant losses when markets shift.

2) Fear of missing out (FOMO)

Everyone is familiar with FOMO. Social media, news, and herd mentality make us obsessed with the idea that “as long as you act now, you can make big money,” which is the beginning of panic trading. Trading driven by FOMO excludes rationality and sound reasoning.

To be honest, I feel this emotion almost every day on Crypto Twitter (CT). There’s always some token that might “fly to the moon.”

One reader once wrote to me:

“I haven’t taken a vacation since 2019 because I feel like if I leave for just a week, the market will skyrocket while I’m gone. I believe many people have had similar feelings and cannot fully enjoy life due to FOMO.”

It sounds sad, but I understand. Especially when I’m not fully engaged with the market, or during a bear market when I’ve closed positions, this feeling becomes particularly intense.

If you feel FOMO on green days… then on red days, you may already be out of ammo. If you must give in to FOMO, do it on a red day.

3) Revenge Trading

This type of trading is very harmful to a trader’s finances and often exacerbates losses.

Suppose you’ve had a good trading week and earned steady profits. However, over the weekend, you suddenly lose all of it—and more.

The next reaction is one of “revenge.”

The object of revenge is the market itself. So you try to quickly make up for the loss, frantically trading junk coins, often making unforgivable mistakes.

I define revenge trading as: After a losing trade, trying to recover losses through multiple low-quality trades.

suggestion:

• Prioritize quality, don’t rush for quick results. Finding good trading opportunities takes time, don’t be hasty because of emotions.
• Pause trading and re-evaluate your strategy. After suffering a loss, stop and reflect on what went wrong. Analyzing your trades to identify where you went wrong can help you avoid making the same mistakes in the future. Tools like CoinMarketMan and TradeStream can help you record and analyze trading data.

• Ask for help. If you find yourself having trouble breaking out of the cycle of losing money and revenge trading, consider finding a mentor or coach. They can provide valuable guidance and help you develop a more effective trading strategy.

4) Gambling Psychology

First, let’s admit that all of us have some degree of a gambler’s mentality.

The essence of trading is planning, strict discipline, and constant learning. However, some traders treat it as gambling. Traders with a gambling mindset usually do not consider building a sound trading strategy but instead act on a whim, relying on luck. They’re driven by the adrenaline rush of “winning the bet,” completely ignoring systematic operations.

This gambling mentality is very common among beginner traders and even some professional traders who desire to get rich without much effort.

Gambling psychology causes traders to make impulsive decisions without thorough consideration, ultimately leading to inevitable losses and emotional breakdowns.

5) Herd Instinct

Herd instinct is a key issue in the field of psychology. In trading, it often stems from the fear of failure. As a result, traders tend to rely on group decisions instead of conducting thorough market analysis. This reliance can lead to panic trading, irrational operations, and ultimately, financial losses.

To be a successful trader, you always need to monitor your mental state. This simple formula should be your guiding light in the trading journey: Rational Analysis > Herd Behavior.

An example of herd instinct:

Suppose Ansem posts a tweet about a new coin. Soon after, that token’s price starts to soar. Quickly, other opinion leaders in the crypto space begin to discuss the token too. Because the entire crowd is pouring in, you feel safe and follow the trend. However, if you’re not vigilant, you’re likely to suffer losses when the “dump” inevitably comes. That’s always how it goes.

Disclaimer:

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2. Disclaimer: The views and opinions expressed in this article represent only the author’s personal views and do not constitute any investment advice.

3. Other language versions of the article are translated by the Gate Learn team. The translated article may not be copied, distributed or plagiarized without mentioning Gate.io.