The Three Major Trading Categories and Strategies Every Crypto Trader Must Know

Author: Cred

Compiled by: Saoirse, Foresight News

As an autonomous decision-making trader, categorizing trades is very useful.

Systematic trading and autonomous decision-making trading are not mutually exclusive or oppositional.

In extreme cases, one might be a fully automated trading system — always in an “on” state, managing every aspect of the trading process; or entirely intuitive speculation — with no rules and no fixed trading strategy.

From a technical perspective, as long as any degree of autonomous decision-making is exercised (for example, turning off automation or manually adjusting position balances), it can be classified as “autonomous decision-making behavior.” However, such a definition is too broad and lacks practical reference value.

In reality, my definition of “autonomous decision-making traders” may suit most readers, characterized by:

• Mainly executing trades manually;
• Analyzing primarily based on technical factors (including key price levels, charts, order flow, news catalysts, etc.);
• Subjectively judging whether a trading strategy is effective and worth participating in;
• Having autonomous decision-making authority over core trading elements: risk control, position size, entry points, stop-loss conditions, target prices, and trade management.

It’s important to clarify that “autonomous decision-making” should not be equated with “laziness.”

Some traders say: “Bro, look, no two trading strategies are exactly the same, so testing is pointless; every situation is different anyway.”

But excellent autonomous decision-making traders usually have detailed data on the markets they trade, develop trading manuals, set market condition filters, record trading logs to optimize performance, and so on.

When exercising autonomous decision-making, they at least follow a rough set of rules; with experience, these rules become more flexible, and the proportion of autonomous decision-making in their trading process gradually increases.

However, this flexible decision-making power is gained through accumulation, not something they inherently possess.

In any case, based on my experience and observations, most autonomous decision-making trading strategies with positive expected value (+EV) can be categorized into the following three distinct types (category names are my own):

• Incremental
• Convex
• Specialist

Each category is distinguished by three core dimensions:

• Risk-Reward Ratio (R:R)
• Probability of Success
• Frequency of Occurrence

(Note: Combining risk-reward ratio with success probability can roughly estimate the expected value of a trade, but I will not elaborate here. For simplicity, these three dimensions are used for understanding.)

Now, let’s analyze each of these three types of trades.

Incremental Trading

Core features: low risk-reward ratio, high success probability, medium occurrence frequency

This type of trading is crucial for maintaining normal account operation and market sensitivity.

They may not be “eye-catching” or suitable for social media bragging, but they are the “basic units” of trading — as long as you have some market advantage, the returns from these trades can achieve substantial compound growth.

Typical examples include: microstructure trading, order flow trading, intraday mean reversion, trades based on statistical regularities (such as intraday time effects, weekend effects, post-news effects), and range trading during low volatility periods.

The main risks faced by this type of trading are “advantage decay” and “market regime shifts.”

But these risks can be viewed as “necessary costs of trading”: intraday opportunities are inherently sporadic, and if you position yourself incorrectly during a regime shift, the cost can be very high (for example, understanding the risk of trend reversal during the fall of Gaddafi’s regime).

Incremental trading is highly practical: it can generate steady profits, and its occurrence frequency is high enough — smoothing profit and loss curves and providing valuable information about market trends and potential shifts.

Convex Trading

Core features: high risk-reward ratio, medium success probability, low occurrence frequency

Most trades based on higher timeframes (such as daily or weekly charts), especially those involving volatility surges or sudden market trend changes, fall into this category.

As the name suggests, these trades are infrequent, but once they occur, capturing part of the large fluctuations can yield substantial returns.

Typical examples include: breakout trades on higher timeframes, reversal trades after failed breakouts, trend continuation trades on higher timeframes, trades driven by major catalysts/news, trades involving extreme open interest or open contracts, and breakout trades following volatility compression.

The main risks include: false breakouts, long intervals between opportunities, and high management difficulty.

Again, these risks are “necessary costs of trading.”

Usually, engaging in these trades requires multiple attempts at the same strategy, experiencing several small losses before the strategy becomes effective (or it may never become effective). Additionally, these trades tend to have higher volatility and management complexity, making mistakes more likely — but this is also why they can deliver high returns.

In cryptocurrency trading, convex trades often contribute most to long-term profit and loss. Proper position sizing, capturing major trends, and seizing breakouts or trend reversals are key to avoiding fees eroding asset growth curves.

It can be said that the gains from convex trades can offset the trading fees, frequent trading costs, and volatility risks associated with incremental trades.

In simple terms, these are the “big hit” trades often talked about.

Professional Trading

Core features: high risk-reward ratio, high success probability, low occurrence frequency

This class of opportunities is rare and valuable — for example, recent perpetual contract market liquidations, stablecoin depegs, major tariff policy news (during periods of significant policy influence), trades driven by major catalysts, or markets with sharply increased volatility.

Typical examples include: capturing low timeframe entry points and expanding into higher timeframe swing trades, arbitrage when spot and derivatives prices diverge significantly, cross-exchange arbitrage exploiting large price differences, “cold” quotes offered at very low discounts, and providing liquidity in order-sparse markets to earn profits.

Participation in these trades usually requires meeting one of two conditions:

• The market experiences abnormal volatility or “breaks” (such as a sharp price drop or liquidity exhaustion)
• Combining high timeframe trading logic with low timeframe execution strategies to form a “snowball” effect of gains

The first condition is difficult because such opportunities are extremely rare; when they do occur, most traders are busy handling margin calls or managing existing positions, leaving little time for new opportunities. Additionally, exchange systems are often unstable during these moments, increasing operational difficulty.

The second condition is also challenging because high timeframe price movements often appear as high volatility and noise on low timeframe charts. This requires precise entry and stop-loss placement, as well as the ability to stick to low timeframe strategies during trend extensions on high timeframes and manage positions properly.

Main risks include: extremely high skill requirements, very low opportunity frequency, the possibility of missing opportunities due to being “busy surviving,” and execution risks such as slippage in order-sparse markets and liquidation risks.

These trades are very difficult, but successfully executing one can dramatically change a trader’s career.

It’s worth noting that the appeal of these trades is precisely what makes them risky.

Therefore, it’s wise for traders to reserve a portion of “crisis funds” — stablecoin capital that is not easily used — specifically for capturing these rare opportunities. This is a very prudent approach.

Conclusion

I recommend reviewing your trading logs or strategy manuals and trying to categorize your past trades into these three types. If you don’t yet have logs or manuals, this categorization framework can serve as a starting point.

Another valuable insight (derived through “elimination”) is that many trade categories are simply not worth investing time in. For example, “boring trades” — clearly falling into the “low risk-reward ratio, low success probability, high occurrence frequency” category — are an inefficient use of time and capital.

If you are an evolving trader, it’s advisable to focus most of your efforts on incremental trades: accumulating market data, building trading systems, optimizing strategies, and gradually gathering enough capital and experience before exploring other types.

You don’t need to be confined to just one category of trading forever.

A more valuable approach is to develop a strategy manual that balances all three types, and more importantly, set reasonable expectations for each type’s risk-reward ratio, success probability, occurrence frequency, potential risks, and strategy patterns.

For example, using a convex strategy but managing it with an incremental approach is a mistake; similarly, applying an incremental position sizing to a convex strategy is also a mistake (this is also my biggest weakness as a trader).

Therefore, clearly defining your trading types and adjusting accordingly is very important.

I have not set specific numerical standards for risk-reward ratio, success probability, or occurrence frequency because these metrics are heavily influenced by market conditions and vary significantly. For instance, during a hot bull run, convex opportunities might appear weekly; during a bear market, even incremental opportunities may be rare enough to be considered fortunate.