📢 The "Top Traders Monthly Report - April '25'' is out now! 🚀

Featuring curated, consistently profitable traders from the Binance and Hyperliquid Leaderboards.

We'll reveal key stats and insights that distinguish top traders in this competitive arena.

Let's explore the trading elites! 💪

Top 3 Traders of the Month - Overview

April 2025 highlights its top three traders of the month as follows:

🏆 TRADERT22 (now ApexTraderAlex2) from Binance

🥈 BonkGuyJr. from Hyperliquid

🥉 Sky from Hyperliquid

April opened with sharp losses across the majors, with $BTC down ~10% and both $ETH and $SOL dropping over 20% by April 10. But the market quickly rebounded. By month-end, $SOL and $BTC had not only recovered but posted strong gains of +20.4% and +15.9%, respectively. $ETH, while bouncing back, lagged behind, ending the month roughly flat.

Many of the traders we track at Mirrorly adapted well to the market reversal. This month’s top three performers secured profits ranging from $420k to $936k.

All of them trade on short timeframes, with average holding periods under 16 hours. A key trait they share is cutting losses quickly while letting winners run, as seen in the gap between their average winning and losing trade durations.

TRADERT22 and Sky both operate with a ~50% win rate and profit factors between 1.5 and 1.9. In contrast, BonkGuyJr has a lower win rate of 28.3% but a higher profit factor of 3.0. This highlights the typical inverse relationship between win rate and profit factor, where improving one often comes at the cost of the other.

Trader Spotlight: TRADERT22

TRADERT22’s April activity shows a tendency to trade heavily in one direction at a time, but without a consistent bias toward either long or short. His positions were large, with notional exposure swinging between -$20 million and +$15 million.

Given the size of his trades, he focused primarily on major tokens like $BTC and $ETH. However, he also took selective positions in lower-cap tokens such as $GUN, $INIT, and $MUBARAK.

TRADERT22 generated the bulk of his profits from $TRUMP (+$260k), followed by $BTC, $ETH, and $PEOPLE, each contributing around +$180k. These four symbols alone accounted for $800k, or 85% of his total PnL.

Trader Spotlight: BonkGuyJr.

Like TRADERT22, BonkGuyJr. traded in one direction at a time, frequently switching sides, with massive notional exposure ranging from -$20 million to +$30 million. His PnL curve was more volatile, likely due to a lower win rate that led to more frequent losses while waiting for larger wins to materialize.

He began the month in a $500k drawdown, which he quickly recovered by April 10, pushing his PnL to +$1 million. That marked his monthly peak, after which he gradually gave back nearly half of the gains, ending April with a profit of around +$500k.

BonkGuyJr.’s most traded symbols were $BTC, $ETH, and $SOL, but he also took positions in several memecoins, including $CHILLGUY, $FARTCOIN, and $BONK.

Despite trading a broad set of symbols, it was $BTC that ultimately drove his profitability, delivering $640k in gains, roughly 5.8 times more than what he made from $ETH.

Trader Spotlight: Sky

With 78 positions closed in April, Sky was the most selective in terms of symbol choice, trading only six assets, over 90% of which were in $BTC. He also had the shortest average holding period among the top traders, keeping positions open for less than five hours on average, with notional exposure ranging from -$5 million to +$7.5 million.

Sky started the month strong, reaching nearly $300k in profit by April 9, but gave it all back in a sharp drawdown between April 9–10. He quickly recovered, maintaining a stable PnL between $350k and $400k for most of the month as his activity decreased. In the final week of April, he broke above the $400k mark and closed the month at $420k.

Like the other top performers, Sky did not hedge his positions, trading either fully long or fully short throughout the month.

As shown below, Sky traded exclusively $BTC for the first 20 days of April, with a single exception for a long on $TRX at the very start of the month. It wasn’t until the final 10 days that he added positions in $AAVE, $DOGE, $ETH, and $SUI to his book.

Out of the six symbols traded, he turned a profit on all but one, recording a small $6.2k loss on $TRX. The majority of his gains came from $BTC, which accounted for $310k, roughly 74% of his total profits.

Case Study: Proof That a Slight Edge Works

This case illustrates how even a modest edge can consistently generate profits. You don’t need sky-high win rates or exceptional risk-to-reward ratios to be successful. TRADERT22 is a clear example.

In April, he closed 91 positions with a 47.25% win rate and a profit factor of 1.5, good enough to walk away with $936k in profits. Let’s focus on $BTC, one of his most traded assets. He executed 34 trades on $BTC, accounting for 37% of his total activity. In the chart below, green arrows indicate new long positions, red arrows new shorts.

To better understand his approach, we examine the PnL of each individual trade and the cumulative PnL over time. Most gains and losses fall within a range of -$150k to +$150k, suggesting a disciplined risk management strategy. With an average position size of $10M, this implies he risks roughly 1.5% per trade.

Looking closer at the distribution of outcomes, the 25th and 75th percentiles for his $BTC trades are -$60.4k and +$76.5k, respectively, implying an average risk-to-reward ratio of 1.27.

That figure might seem low, but it’s more than enough. A risk-to-reward of 1.27 only requires a breakeven win rate of about 44%. With an actual win rate of 47.3% overall and 50% specifically on $BTC, TRADERT22’s edge was small but sufficient to turn a strong profit.

Conclusion

April’s market reversal created a challenging yet rewarding backdrop for active traders, and the top performers featured in this report demonstrated how disciplined execution and tight risk control can outperform even in volatile conditions. Across the board, TRADERT22, BonkGuyJr., and Sky each applied distinct trading styles, yet shared a common thread: short-term positioning, clear sizing limits, and directional conviction without overcomplication. Their strategies underline that consistent profitability doesn’t require extraordinary win rates, just a repeatable edge and sound risk management.

The data also reinforces a key lesson for copy traders and aspiring professionals alike: success often lies in mastering execution, even in a few symbols, rather than chasing every opportunity. $BTC, unsurprisingly, remained the centerpiece for all three traders, but it was their ability to cut losses early and let winners run that defined their edge.

Top Traders Report - April ‘25