Quarterly Insight

“Even in a bruising market, skill finds a way.”

This inaugural quarterly brief distils what we learned by monitoring a select group of high‑performing leaderboard traders. The goal being to surface practical insights into execution and risk management.

Introduction

Over the quarter, we monitored more than 180 public leaderboard accounts across Binance and Hyperliquid. The aim isn’t to track thousands of traders, but to maintain a carefully curated and constantly updated list of top-performing ones. This is a complex task that requires monitoring various factors over time, such as consistency and trading behavior.

Data accuracy can’t always be guaranteed. For example, Binance traders can switch from public to private at any time, making it impossible to track all their trades. On Hyperliquid, strict rate limits create additional technical challenges in maintaining a fully synchronized historical view.

Despite these limitations, we dedicate significant effort at Mirrorly to ensure the data is as accurate and reliable as possible.

Overview

Across the sample we captured more than 22k distinct positions and more than 2.2 million orders. Slightly more than half of the traders (53%) finished the quarter with a positive realised P&L.

Trading activity among tracked accounts showed significant variation over the quarter. When grouped into five percentiles ranging from Very Low to Very High activity, the breakdown is as follows:

• Around 50% of traders averaged fewer than 12 trades per position.

• Approximately 25% averaged between 12 and 98 trades per position.

• The remaining 25% exhibited much higher activity, with an average of over 98 trades per position.

This spread highlights the diverse trading styles among the top performers we follow.

As expected, the three major large-cap tokens ($BTC, $ETH, and $SOL) accounted for approximately 79% of total notional trading volume. Outside of these, most of the remaining activity was concentrated in $XRP, $HYPE, and $TRUMP, with additional interest in $DOGE, $SUI, $ADA, and $LINK.

Q1 Market Drop: The Benchmark to Beat

Before evaluating how these traders performed in Q1, it’s useful to first look at overall market performance during the same period to establish a clearer basis for comparison.

Q1 was challenging. From the January high to the March close, $BTC fell 11.8%, $ETH 45.4%, and $SOL 34.2%. Those moves set a demanding benchmark for anyone trading on the long side.

Despite the market downturn, more than half of the traders we tracked ended the quarter with a positive return. From this group, we selected ten traders to showcase how a variety of trading styles can still succeed in extracting value from the market.

10 Traders, 10 Different Paths to Profit

The ten traders we chose posted gains ranging from about $900k to just above $9 million.

Their approaches differed significantly. Chatosil, for example, maintained a high win rate with a lower profit factor, whereas 0xb72 showed the opposite combination. Holding periods ran from minutes to weeks, signaling that consistency can arise from very different playbooks.

Profit per unit of exposure highlights the spread in style even more clearly. AccDwonOnlyPlsGibPts earned roughly $0.20 for every $1 of notional, translating into $4.3 million of profit. On the other hand, TRADERT22 generated a larger headline number ($7.4 million) but only after deploying far more capital, with a yield of about half a cent per notional dollar.

This isn’t to suggest one trader is better than the other. In the end, what matters is the bottom-line profit. The key takeaway is that each trader employs a strategy and execution style suited to their own strengths and preferences.

In this case, TRADERT22 trades with larger capital and holds positions for just a few hours on average, while AccDwonOnlyPlsGibPts holds trades for weeks. This difference is clearly reflected in performance metrics like ROI distribution. For TRADERT22, 50% of returns fall between -0.8% and +1.5%. For AccDwonOnlyPlsGibPts, that range spans from -1.2% to +20.5%. The higher variance and positive skew in ROI helps explain why AccDwonOnlyPlsGibPts achieves a greater profit per dollar invested.

Conclusion

Q1 2025 was marked by sharp corrections across major assets, yet more than half of the traders we tracked ended the quarter in profit. This highlights a key point: even in difficult market conditions, skill and discipline can prevail. Profitability isn’t purely a function of market direction, it often comes down to execution, risk management, and adaptation.

Just as important, there is no single blueprint for success. The traders featured in this report followed radically different approaches (from high-frequency scalping to longer-horizon conviction trades) yet all achieved strong results. The variation in holding periods, trade frequency, and ROI distributions confirms that there are countless paths to profitability, each shaped by the trader’s own strengths and strategy.

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Stay tuned for more insights in future editions.
Data analysis by @0xfab_eth.