In 2025, the Algo Trend strategies did not deliver the returns originally expected based on their historical performance, and the gap deserves a clear explanation.
Algo Trend strategies are built on specific market behaviors: momentum, volatility expansion, and intraday price movement. In previous years (2018-2024), those conditions were present often enough to form the performance assumptions we shared. In 2025, the market behaved differently. Instead of sustained trends, most of the year was dominated by compressed volatility and prolonged consolidation phases. Price moved, but without the structure that trend-based systems rely on to generate consistent gains. As a result, the strategies faced more stop-loss sequences and fewer high-conviction momentum runs.
This report was not written to justify results after the fact, but to document how the strategies behaved, what conditions shaped those outcomes, and how expectations are being recalibrated going forward.
Its purpose is to explain how the Algo Trend strategies actually work, what market conditions they depend on, and why 2025 turned out to be a difficult environment for this type of approach. We also want to clarify what did work as intended – particularly on the risk and capital-protection side – and what we’re adjusting going forward.
If you’re using high-risk strategies, understanding why performance looks the way it does is just as important as the numbers themselves – and this summary will explain it in detail.
What defines high-risk strategies
High-risk strategies are not designed to produce smooth or predictable returns. Their goal is different: to capture specific market conditions when they appear — and accept periods of underperformance when they don’t.
All strategies in this category share a few core characteristics.
Active by nature. Capital is continuously repositioned based on short-term signals rather than long-term holding. This makes performance highly sensitive to market structure.
Results depend on how the market moves. It does not depend on whether it moves up or down. Strong trends, volatility expansion, and clean intraday momentum create opportunity. Choppy ranges, compressed volatility, and false breakouts do the opposite.
Drawdowns are part of the design. It’s not a failure. When conditions are unfavorable, the strategies prioritize risk limits and capital protection over forcing returns. This often leads to a sequence of small losses instead of one large one – which can look frustrating in the short term but is critical for long-term survivability.
They trade asymmetry. A relatively small number of favorable periods are expected to generate a disproportionate share of total returns. When those periods are delayed or absent (as was often the case in 2025) overall performance naturally suffers.
Understanding these mechanics is essential when evaluating high-risk strategies. Without this context, it’s easy to compare them to passive holding or to expect steady compounding – expectations they were never built to meet.
2025 Crypto Market Context
Overall, 2025 did not look like a bad year for crypto. Bitcoin moved significantly over the year and reached new price levels. In general, this creates the impression of a “trending” market.
However, for active strategies the internal structure of it mattered far more than the final price levels.
After a series of large liquidations early in the year, the market shifted into a different regime. For most of 2025, realized volatility steadily declined. Price continued to move, but those moves increasingly lacked follow-through. Short-term impulses appeared, but failed to develop into sustained trends. This distinction is important. A market can show large price changes over months while still being unfavorable for strategies that operate on shorter time horizons. Algo Trend strategies typically hold positions for one to two trading days. They rely on local volatility, continuation after breakouts, and intraday momentum. In 2025, these conditions were present less often and for shorter periods.
As a result, many signals behaved similarly: entries were triggered, initial movement appeared – and then price reverted back into the range. In this environment, strategies did not experience large, sudden losses. Instead, they faced a dense sequence of small stop-losses.
December 2025 illustrates this particularly well. Volatility became extremely compressed, with Bitcoin trading in a narrow range for extended periods. A similar setup earlier in the year resolved through a sharp breakout, allowing strategies to recover quickly. In December, that continuation never came. The absence of expansion led to repeated small losses without compensating momentum moves.
Crucially, this was not a case of models breaking or risk parameters failing. The challenge of 2025 was not excessive risk realization, but insufficient return realization. Risk controls functioned as designed, preventing deep drawdowns and tail events. What was missing was the market structure needed for trend-based strategies to express their edge.
In short, 2025 was defined by a mismatch between visible price movement and usable trading conditions. For momentum-driven, high-risk strategies, this was one of the more difficult environments in recent years – because of the absence of extreme volatility.
Algo Trend strategy snapshot
What it trades
Highly liquid crypto assets via perpetual futures, with primary exposure to BTC and ETH, complemented by other top-liquidity instruments.
What type of edge it uses
A quantitative trend-following edge focused on capturing asymmetric returns from short- to medium-term momentum and volatility expansion, using multiple independent rule-based models with strict risk limits.
What market conditions it prefers
Markets with expanding volatility and sustained directional movement. Performance typically degrades in low-volatility, range-bound environments where breakouts fail to follow through.
Algo Trend 2025 Performance Overview
USDT Algo Trend
The strategy closed the year slightly negative, with a cumulative result around −3.74%.
Performance over the year was uneven. Positive months were present, but gains were generally modest and failed to compound into a sustained upward move. Negative months were also limited in magnitude, yet frequent enough to offset recoveries.
The performance curve reflects a flat-to-choppy profile rather than a trending one: repeated recovery attempts followed by renewed pullbacks. Volatility of results remained controlled, with no sharp drawdowns, but the absence of extended profitable runs prevented meaningful upside realization.
Overall, 2025 for USDT Algo Trend is characterized by insufficient return realization relative to expectations for this strategy type.
BTC Algo Trend
In 2025, the BTC Algo Trend strategy closed the year with a negative result of around −10%.
Performance throughout the year was unstable and uneven. Several recovery phases emerged during the year (particularly in mid and late summer), but none of them developed into a sustained upward trend. Gains were short-lived and repeatedly offset by renewed pullbacks.
The performance curve shows a persistent downward bias with temporary rebounds, rather than a flat or oscillating profile. While individual positive months were present, they lacked sufficient follow-through to compensate for losses accumulated during periods of weak momentum.
Importantly, the year was not marked by extreme volatility or sharp drawdowns. Losses accumulated gradually through a series of unfavorable market phases, reflecting low efficiency of trend-following signals rather than excessive risk-taking.
Overall, 2025 for BTC Algo Trend was defined by prolonged underperformance in a market environment that failed to provide sustained directional moves, making it one of the more challenging years for this strategy type – which resulted in a negative performance.
What worked
Short volatility expansions and local breakouts
Best performance during brief periods of volatility expansion, when price broke out of ranges and showed at least limited follow-through. These phases were relatively rare in 2025, but when they occurred, the models were able to capture directional moves and generate positive months.
Impulse-driven market phases
Sudden momentum bursts (often triggered by news, liquidations, or positioning imbalances) remained the primary source of profits. Even in a generally difficult year, these short impulse windows accounted for most of the positive contribution.
Risk containment during unfavorable regimes
While not a source of profit, disciplined risk management worked as intended. Losses remained controlled, drawdowns developed gradually rather than abruptly, and no tail-risk events materialized throughout the year.
What didn’t work
This was the dominant part of the year.
Compressed, range-bound market regimes
Most of 2025 was characterized by low realized volatility and frequent false breakouts. Price moved, but without persistence. For trend-following and momentum systems, this environment is structurally unfavorable: signals appear, but continuation fails.
Frequent stop-loss sequences without recovery runs
Instead of large losses, the strategy experienced a dense series of small stop-outs. This is typical for low-volatility markets with noisy price action – expected returns cannot be realized because profitable trades do not have enough room to develop.
Why HODLing BTC looked better in hindsight
Over longer timeframes, BTC and other assets showed significant price appreciation. However, the strategy operates on much shorter horizons, typically holding positions for one to two trading days. Large multi-month price moves do not automatically translate into trading opportunities if intraday volatility and trend persistence are absent. As a result, passive holding benefited from this, while active strategies lacked the structure needed to compound gains during the same period.
Risk management behavior
Despite negative results in 2025, the strategy’s risk management behaved as designed.
Drawdown control
Losses accumulated gradually rather than through sharp drawdowns. There were no single events or clusters of trades that caused abrupt equity drops. Drawdowns remained within historically expected ranges and below stress levels observed during more volatile years.
Why capital was preserved
The strategy consistently reduced exposure when market conditions were unfavorable. Positions were entered with predefined risk limits and exited quickly when expected continuation failed to materialize. This prevented losses from escalating, even during prolonged periods of low volatility and repeated false signals. As a result, capital erosion remained limited despite extended underperformance.
Why the strategy did not “average down into disaster”
The models do not increase position size to compensate for losses and do not rely on mean exposure expansion to recover performance. There is no averaging down against adverse price movement. Each trade is independent, risk-defined, and sized conservatively. This design choice limits upside in weak regimes, but it also prevents scenarios where prolonged adverse conditions lead to uncontrolled drawdowns.
In short, 2025 tested the strategy’s ability to endure unfavorable markets rather than exploit favorable ones. While returns were negative, the absence of tail losses and structural breakdowns indicates that the strategy remained within its intended risk framework throughout the year.
What we took into account after 2025
2025 provided a large amount of new, high-quality data from a market regime that had been underrepresented in recent years. The key takeaway was not that the strategy failed, but that the realized opportunity set was materially different from the assumptions formed on previous cycles.
Based on this, we are revisiting how expected returns are framed and communicated.
First, we are recalculating expected performance ranges using the full 2025 dataset. These conditions will now be explicitly reflected in forward expectations.
Second, we are separating risk behavior from return realization more clearly. The strategy’s ability to control drawdowns and avoid tail risk was confirmed in 2025. What underperformed was the frequency and persistence of profitable regimes. Future expectations will reflect this distinction more explicitly.
Third, as part of the post-2025 review, we completed the December performance assessment with one of the USDT strategy managers. Based on this review, we decided to discontinue cooperation with that manager within the USDT Algo Trend structure.This decision was driven by performance dynamics and alignment, not by risk events or capital safety issues. The remaining managers continue to operate under the same risk framework and monitoring standards.
Finally, we are adjusting how we describe the strategy’s role within a portfolio. Algo Trend remains a high-risk, opportunity-driven strategy, but one whose payoff is market-dependent, not time-averaged. This matters for allocation sizing and for how results should be evaluated over time.
Expectations and framework for 2026
We do not base expectations for 2026 on forecasts or directional market views.
If the favourable conditions return, the strategy’s edge is positioned to re-express itself. If the market remains structurally compressed, returns may remain muted – even if headline prices continue to move over longer horizons.
For users, this means:
- results should be evaluated over full market cycles, not isolated months,
- underperformance does not imply structural failure,
- allocation to high-risk strategies should reflect tolerance for uneven return distribution.
The strategy enters the new year intact, risk-controlled, and better calibrated – but still dependent on the same fundamental requirement: the presence of usable market opportunity.
Conclusion
2025 was not a strong year for Algo Trend strategies in terms of realized returns. That outcome is visible in the numbers and does not require reinterpretation.
What the year did confirm is the nature of these strategies: they are designed to respond to opportunity. When market structure limits momentum and volatility expansion, performance becomes uneven and goes into negative – even when risk controls function correctly.