2025 Trading Guide: Three Essential Trading Categories and Strategies Every Trader Must See
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2025 Trading Guide: Three Essential Trading Categories and Strategies Every Trader Must See
Clearly identify the type of transaction you are participating in and make corresponding adjustments.
Navigating Web3 tides with focused insightsAuthor: Cred
Translation: Saoirse, Foresight News
As a discretionary trader, categorizing trades is highly useful.
Systematic trading and discretionary trading are not binary opposites or mutually exclusive.
In extreme cases, on one end lies fully automated trading systems—operating continuously in an “on” state, managing every aspect of the trading process; on the other end lies pure speculative gambling—entirely ruleless, with no fixed strategy whatsoever.
Technically speaking, any exercise of discretion—such as disabling an automated system or manually adjusting position balance—could be classified as "discretionary behavior," but such a definition is too broad to be practically meaningful.
In practice, my definition of a "discretionary trader" likely applies to most readers, and includes these core characteristics:
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Primarily executing trades manually;
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Analysis focused on technicals (including key levels, charts, order flow, news catalysts, etc.);
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Subjectively judging whether a trade setup is valid and worth participating in;
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Exercising full discretion over core trading elements: risk management, position sizing, entry points, stop-loss conditions, target levels, and trade management.
It's important to clarify that "discretionary" should not equate to "lazy."
Some traders say: "Bro, no two trades are ever exactly alike, so testing is useless anyway since every situation is different."
Yet skilled discretionary traders typically gather detailed market data, develop trading playbooks, set market regime filters, keep trade journals to optimize performance, and so on.
When exercising discretion, they at least follow a rough framework of rules. As experience accumulates, these rules become more flexible, and the role of discretion in the trading process increases accordingly.
But this flexibility is earned through experience—not granted by default.
Based on my experience and observation, most positive expected value (+EV) discretionary trading strategies fall clearly into one of the following three categories (names coined by me):
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Incremental
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Convex
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Specialist
The key distinguishing dimensions for each category are three:
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Risk-reward ratio (R:R)
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Probability of success
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Frequency of occurrence
(Note: Combining risk-reward ratio and probability allows rough estimation of a trade’s expected value, though we won’t delve into that here—these three dimensions suffice for simplified understanding.)
Let’s now examine each of these three types in turn.
Incremental Trading
Core traits: Low risk-reward ratio, high success probability, medium frequency
This type of trading is crucial for maintaining account stability and staying attuned to the market.
These trades may not be flashy or suitable for social media bragging, but they form the trader’s “baseline”—as long as there’s a slight edge, incremental trades can generate substantial compounding returns over time.
Typical examples include: market microstructure trades, order flow trades, intraday mean reversion trades, statistically based trades (e.g., intraday timing effects, weekend effect, post-news reaction), range-bound trades during low volatility periods, etc.
The main risks faced by this category are “edge decay” and “sudden market regime shifts.”
However, these risks can be seen as “necessary costs of doing business”: intraday opportunities naturally come and go, and being on the wrong side during a regime shift often carries a high cost (consider the case of Gaddafi’s fall as an analogy for the dangers of逆势 trading during trend reversals).
Incremental trading is highly practical: it typically generates steady profits and occurs frequently enough to smooth the P&L curve while providing valuable market signals and insights into potential trends.
Convex Trading
Core traits: High risk-reward ratio, medium success probability, low frequency
Most higher-timeframe trades (e.g., daily, weekly)—especially those centered around rising volatility or sudden trend shifts—fall into this category.
As the name suggests, such setups occur infrequently, but when they do, capturing even a portion of the large move can yield substantial returns.
Typical examples include: higher-timeframe breakout trades, reversal trades after failed breakouts, trend continuation trades on higher timeframes, major catalyst/news-driven trades, funding rate and open interest extreme trades, volatility compression breakout trades, etc.
Main risks include: false breakouts, long intervals between opportunities, and difficult trade management.
Again, these are “necessary costs of the trade.”
Typically, traders may need to attempt the same setup multiple times, enduring several small losses before finally catching a winning trade (which may never come). Additionally, these trades tend to have higher volatility and are harder to manage, increasing the likelihood of execution errors—but this is precisely why they offer high returns.
In crypto trading, convex trades are often the primary drivers of long-term profitability. Proper position sizing, riding major trends, and capturing breakouts or trend reversals are essential for overcoming fee drag on the equity curve.
In essence, profits from convex trades can offset the fee burn, frequent trading costs, and volatility risk incurred in incremental trading.
Colloquially, these are what people call “home run trades.”
Specialist Trading
Core traits: High risk-reward ratio, high success probability, low frequency
This is a “once-in-a-lifetime” premium opportunity—for example, recent cascading liquidations in perpetual markets, stablecoin depeg events, critical tariff policy news (during periods of high policy impact), major catalyst-driven trades, or sharp volatility expansions.
Typical examples include: identifying low-timeframe entries and scaling into higher-timeframe swings, arbitrage during large spot-derivative price divergences, cross-exchange arbitrage during extreme spreads, executing “orphaned quotes” at deeply discounted prices, and providing liquidity in illiquid markets to capture rewards.
Participating in such trades usually requires meeting one of the following two conditions:
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Market exhibits abnormal volatility or “fracture” (e.g., price crash, liquidity drought)
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Perfect alignment of higher-timeframe logic with lower-timeframe execution, creating a “snowballing” return
The challenge with the first condition is that such opportunities are extremely rare. When they do arise, most traders are preoccupied with margin calls and managing existing positions, leaving little bandwidth for new setups. Moreover, exchange systems often suffer instability during such events, further increasing execution difficulty.
The challenge with the second condition lies in the fact that high-timeframe moves often appear as high-noise, high-volatility patterns on lower timeframes. This demands precise entry and stop placement, along with the ability to stick to low-timeframe tactics while managing positions throughout the extension of a high-timeframe trend.
Main risks include: extremely high skill requirements, very low frequency, risk of missing the opportunity due to being “in survival mode,” and execution risks (e.g., slippage in thin books, liquidation exposure).
These trades are exceptionally difficult, but successfully capturing just one could transform a trader’s career.
Notably, the very source of their appeal is also the root of their risk.
Therefore, it’s wise to maintain a dedicated “crisis capital pool”—a reserve of stablecoins not easily touched—specifically allocated for seizing these rare opportunities.
Conclusion
I recommend reviewing your trade journal or playbook and attempting to classify past trades into these three categories. If you don’t yet have a journal or playbook, this framework offers a solid starting point.
Another valuable insight—arrived at via elimination—is that many trade types aren’t worth pursuing. For instance, “boring trades”—clearly falling into the “low R:R, low probability, high frequency” quadrant—represent inefficient use of time and capital.
If you’re a developing trader, focus most of your effort on incremental trading: use it to accumulate market data, build a trading system, refine execution, and grow both capital and experience—then gradually explore other trade types.
You don’t need to remain confined to one category forever.
A more valuable approach is to create a playbook that accounts for all three types, and more importantly, to set realistic expectations for each regarding risk-reward, success probability, frequency, risks, and structural form.
For example, applying incremental-style trade management to a convex strategy is a mistake. Likewise, sizing positions for a convex trade using incremental trade standards is also wrong (this, incidentally, is my own biggest weakness as a trader).
Thus, clearly identifying the type of trade you're engaging in—and adjusting accordingly—is crucial.
I haven’t defined specific numerical thresholds for risk-reward, probability, or frequency because these vary dramatically with market conditions. For instance, during a hot bull market, convex opportunities might arise weekly; in contrast, during a stagnant market, even spotting an incremental opportunity may feel like a blessing.
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