Preface

This bi-weekly quantitative report (April 25 to May 12) analyzes the market trends of Bitcoin and Ethereum, utilizing key indicators such as long-short ratio, open interest, and funding rates. The report explores the application of the “Dense Moving Average Breakout Strategy” in the ETH/USDT market, detailing its logical framework and signal identification mechanism. Through systematic parameter optimization and backtesting, the strategy demonstrates robust performance in trend recognition and risk control, with a clear execution discipline. Overall, it outperforms a simple buy-and-hold approach for ETH, offering a practical framework for quantitative trading.

Summary

• Over the past two weeks, both BTC and ETH have risen, with ETH posting a larger gain and exhibiting more volatility.
• ETH’s long-short ratio has fluctuated significantly, indicating strong short-term trading activity and market speculation during the rally, with short positions not showing a clear retreat.
• BTC’s open interest has increased at a slower pace, while ETH saw a stronger surge in early May.
• The overall derivatives market experienced a wave of short liquidations in early May, followed by long liquidations on May 12, highlighting intensifying market divergence under high leverage.
• Quantitative analysis employed the “Dense Moving Average Breakout Strategy,” which achieved a return of up to 127% under optimal parameter settings.

Market Overview

1. Price Volatility Analysis of Bitcoin and Ethereum

Since mid-April, both BTC and ETH have shown a steady upward trend, maintaining a relatively synchronized pace through early May. During this period, BTC rose from around 78,000 USDT to nearly 105,000 USDT, while ETH surged from approximately 1,600 USDT to around 2,600 USDT. ETH’s increase was notably larger than BTC’s, demonstrating greater price elasticity. In early May, both assets experienced a sharp jump, likely driven by easing tariff policy concerns, with BTC entering a rebound phase.

BTC, with its higher price and lower volatility, exhibited a more stable trajectory; in contrast, ETH delivered a stronger and faster rally. Initially, the market lacked bullish expectations for ETH, causing it to lag behind. However, as May approached—with the upcoming Pectra upgrade and tariff relief—ETH saw renewed attention and a surge in trading volume, catching up in performance. This divergence indicates a renewed short-term focus on ETH’s allocation value within the market.[1][2]

Figure 1: BTC climbed to nearly 105,000 USDT, while ETH surged toward 2,600 USDT—showing a more aggressive gain and faster price reaction.

In terms of volatility, both BTC and ETH experienced significant changes in fluctuation patterns from early April to mid-May. In mid-April, BTC’s volatility frequently spiked, reflecting heightened market sentiment and sharp price adjustments. However, by late April and early May, volatility began to contract, suggesting a brief period of market stabilization.

In contrast, ETH’s volatility saw several sharp surges, particularly around its price breakout, at times even exceeding BTC’s. This indicates that ETH experienced more intense short-term swings during its upward movement. Overall, BTC’s volatility was more evenly distributed, whereas ETH’s volatility was concentrated at several critical moments, especially surrounding key price breakouts—suggesting ETH is more susceptible to momentum-driven capital flows.

Figure 2: BTC exhibited relatively consistent volatility, while ETH experienced multiple sharp spikes in volatility.

Overall, ETH demonstrated a larger price gain and more concentrated volatility shifts during this market cycle, indicating stronger price responsiveness at key moments. In contrast, BTC showed a more stable upward trend with a more evenly distributed volatility profile, reflecting its relative resilience amid market fluctuations.

Although both assets experienced a synchronized price surge, their volatility patterns and rhythms diverged significantly, highlighting distinct market characteristics and structural dynamics.

From a short-term trading perspective, monitoring BTC’s capital inflows and volatility shifts may serve as a key indicator of broader market risk appetite.

2. Analysis of Long-to-Short Ratio (LSR) for Bitcoin and Ethereum

The Long-to-Short Ratio (LSR) is a key indicator for measuring the relative volume of aggressive long versus short positions in the market. It is commonly used to assess market sentiment and the strength of prevailing trends. An LSR greater than 1 indicates that aggressive buy orders (longs) exceed aggressive sell orders (shorts), suggesting a bullish market bias.

According to data from Coinglass, both BTC and ETH have shown a clear upward price trend over the past two weeks. However, their LSR patterns reveal varying degrees of divergence. For BTC, the LSR saw a slight increase in the early phase of the rally but remained fluctuating around 1, even dropping below 1 around May 10. This suggests that, despite rising prices, short positions also increased—implying that some investors opted to hedge or open shorts at higher levels. The market has not formed a clear, one-sided bullish structure, and there remains some skepticism about the sustainability of the rally.[3]

In contrast, ETH’s LSR displayed more volatility. During its sharp rise from $2,000 to $2,600, the ratio did not climb steadily but instead experienced multiple sharp swings, including a notable drop around May 10. This indicates that ETH’s price surge was accompanied by intense short-term trading and market tug-of-war, with short positions persisting. Market sentiment remained divided throughout the rally.

Although BTC and ETH both saw significant price gains over the past two weeks, their LSRs did not show a sustained increase. On the contrary, the data reflects widespread caution and hedging activity at higher price levels, suggesting that the bullish momentum lacks clear structural support and that investor sentiment remains cautious.

Figure 3: BTC LSR declines amid volatility, indicating weakened bullish momentum at higher levels

Figure 4: ETH LSR shows high volatility, reflecting significant market sentiment divergence

3. Open Interest Analysis

According to data from Coinglass, both BTC and ETH open interest have shown an overall upward trend, indicating rising market participation and trading activity. BTC’s open interest climbed steadily from around $60 billion, experiencing some fluctuations but largely holding at elevated levels, eventually stabilizing in early May. ETH’s open interest rose from approximately $18 billion to nearly $24 billion, following a similar pattern to BTC but with a more stable trajectory. Notably, ETH saw a sharp increase in early May, signaling renewed capital inflows and active positioning during that period.[4]

Overall, the concurrent rise in open interest and prices for both assets confirms increasing market engagement and greater use of leverage. However, while BTC’s inflows leveled off after late April, ETH demonstrated stronger upward momentum in early May, suggesting a surge in derivative trading interest in ETH over the short term.

Figure 5: BTC open interest shows slower upward momentum, while ETH sees stronger surge in early May

4. Funding Rate

The funding rates for BTC and ETH have generally fluctuated slightly around 0%, frequently switching between positive and negative, indicating a relatively balanced battle between long and short positions. In late April, BTC experienced several instances of negative funding rates, with a notable dip to -0.025% around April 20, suggesting short positions were dominant at the time—potentially due to large-scale hedging activity. ETH showed a similar pattern during this period, though with slightly smaller fluctuations, indicating a temporary shift toward bearish sentiment without sustained pressure.[5][6]

As prices rose and open interest increased, the funding rates for both BTC and ETH gradually turned positive, stabilizing between 0% and 0.01%. This reflects growing bullish sentiment and active long positioning. However, the fact that funding rates did not spike sharply suggests that while leverage on long positions has increased, the market is not overheated, and sentiment remains cautiously optimistic.

Figure 6: BTC and ETH funding rates gradually turn positive and remain between 0% and 0.01%, reflecting growing bullish bias and active long positioning

5. Cryptocurrency Liquidation Chart

According to Coinglass data, since mid-April, the cryptocurrency market has experienced alternating waves of long and short liquidations, with short liquidations particularly prominent in early May. On May 8, short liquidations surged significantly, reaching $836 million in a single day, indicating a sharp price rally that forced many short positions to be liquidated.

On May 12, as market volatility intensified, long liquidations rose notably, with a daily total of $476 million, suggesting that some traders who entered long positions at higher levels were unable to withstand the pullback and were forcibly liquidated. This indicates that despite an overall bullish trend, short-term volatility remains high, and both longs and shorts have suffered at key inflection points. The derivatives market remains highly active and risk-concentrated.[7]

This pattern aligns with earlier observations of rising prices, increasing open interest, and funding rates turning positive, highlighting how shorts were wiped out during major price breakouts, giving bulls a temporary advantage. However, even in an uptrend, long positions can still face liquidation at local highs, particularly during periods of intensified volatility such as mid-May. This underscores the persistent volatility in the market, where high leverage and active risk hedging remain defining features of crypto derivatives trading.

Figure 7: Short liquidations surged on May 8, reaching $836 million in a single day

Quantitative Analysis – Dense Moving Average Breakout Strategy

(Disclaimer: All forecasts in this article are based on historical data and market trends and are for informational purposes only. They should not be considered investment advice or a guarantee of future market performance. Investors should carefully assess risks and make prudent decisions when engaging in related investments.)

1. Strategy Overview

The “Dense Moving Average Breakout Strategy” is a momentum-based approach that incorporates technical trend analysis. The strategy identifies potential directional market moves by observing the convergence of multiple short- to mid-term moving averages (e.g., 5-day, 10-day, 20-day) over a defined period. When these moving averages begin to align and cluster closely, it typically signals a consolidation phase, suggesting that the market is preparing for a breakout.

If the price decisively breaks above the clustered moving averages, it is interpreted as a bullish breakout signal. Conversely, a break below the moving average band indicates a bearish signal.

To enhance practicality and improve risk management, the strategy also integrates fixed-percentage take-profit and stop-loss mechanisms, allowing for timely entries and exits when trends emerge, balancing reward and risk. Overall, this strategy is designed to capture short- to mid-term trend opportunities, offering a disciplined and actionable trading framework.

2. Core Parameter Settings

3. Strategy Logic and Operational Mechanism

Entry Conditions

• Moving Average Convergence Check: Calculate the distance between the maximum and minimum values of six moving averages—SMA20, SMA60, SMA120, EMA20, EMA60, and EMA120. When the distance falls below a defined threshold (e.g., 1.5% of the price), it is considered a moving average convergence.
  The “threshold” refers to the critical value at which an effect is triggered, either as a minimum or maximum.

• Price Breakout Conditions:

  • If the current price breaks above the highest of the six moving averages, it is considered a bullish breakout signal and triggers a buy operation.
  • If the current price falls below the lowest of the six moving averages, it is considered a bearish breakout signal and triggers a sell operation.

Exit Conditions: Dynamic Take-Profit and Stop-Loss Mechanism

• Long Position Exit:

  • If the price falls below the lowest moving average at the time of entry, a stop-loss is triggered.
  • If the price rises by more than the distance between the entry price and the lowest MA × risk-reward ratio, a take-profit is triggered.

• Short Position Exit:

  • If the price rises above the highest moving average at the time of entry, a stop-loss is triggered.
  • If the price falls by more than the distance between the entry price and the highest MA × risk-reward ratio, a take-profit is triggered.

Example Chart

• Trade Signal Trigger
  The chart below shows the most recent entry signal triggered by the strategy on May 8, 2025, using the ETH/USDT 2-hour candlestick chart. As observed, the price broke upward after the six moving averages converged closely—meeting the strategy’s defined entry conditions. The system executed a buy order at the moment of breakout, successfully capturing the starting point of the subsequent rally.

Figure 8: Entry point illustration based on strategy conditions for ETH/USDT on May 8, 2025

• Trade Execution and Outcome
  The system exited the position automatically upon reaching the preset risk-reward ratio, in accordance with the dynamic take-profit mechanism effectively locking in the core portion of the price swing. Although there was additional upside afterward, the trade strictly followed strategy rules, demonstrating solid risk control and execution consistency. Incorporating a trailing stop or trend-following mechanism in the future could potentially extend profits during strong momentum phases.

Figure 9: Strategy exit point illustration for ETH/USDT on May 8, 2025

Through the above live example, we clearly demonstrated the strategy’s entry logic and dynamic take-profit mechanism triggered by moving average convergence and price breakout conditions. By leveraging the interaction between price and moving average structure, the strategy accurately captures the trend initiation point and exits automatically during subsequent fluctuations—securing core profit segments while maintaining effective risk control.

This case not only validates the practicality and execution discipline of the strategy, but also highlights its stability and risk management capability in real market conditions, laying a solid foundation for future parameter optimization and strategic refinement.

4. Practical Backtesting Example

Backtesting Parameter Setup

To identify the optimal parameter combinations, we conducted a systematic grid search across the following ranges:

• tp_sl_ratio: 3 to 14 (increment of 1)
• threshold: 1 to 19.9 (increment of 0.1)
  Using ETH/USDT as an example, the system tested 23,826 parameter combinations on 2-hour candlestick data from the past year. From these, the five best-performing sets were selected based on cumulative returns. Evaluation metrics included annualized return, Sharpe ratio, maximum drawdown, and ROMAD (Return Over Maximum Drawdown), providing a comprehensive assessment of strategy performance.

Figure 10: Performance comparison of the top five strategy parameter sets

Strategy Logic Explanation
The strategy triggers a buy signal when the system detects that the distance between the six moving averages has converged to within 1.4%, and the price breaks upward through the upper boundary of the moving averages. This structure aims to capture the moment a breakout is about to begin, entering the position at the current price and using the highest moving average at the time of the breakout as the reference point for dynamic take-profit, enhancing reward management.

The strategy uses the following settings:

• percentage_threshold = 1.4 (maximum allowed distance between the six moving averages)
• tp_sl_ratio = 10 (dynamic take-profit ratio)
• short_period = 6，long_period = 14 (moving average observation periods)

Performance and Results Analysis
The backtesting period spans from May 1, 2024, to May 12, 2025. During this timeframe, the selected parameter set delivered outstanding results, with an annualized return of 127.59%, maximum drawdown below 15%, and a ROMAD of 8.61%. These figures demonstrate the strategy’s strong capital appreciation potential along with effective downside risk control.

As shown in the chart, the strategy significantly outperformed a Buy and Hold approach for ETH over the past year (which returned -46.05%). Its performance was particularly notable during periods of heightened volatility or trend reversals, thanks to its robust take-profit and re-entry mechanisms. The drawdown control was clearly superior to passive holding.

We also conducted a cross-comparison of the top five parameter sets, with the current configuration achieving the best balance between return and stability, making it highly practical for real-world application. Looking ahead, the strategy could be further enhanced by integrating dynamic threshold adjustment, or incorporating volume and volatility filters, to improve adaptability in sideways markets and enable deployment across multiple assets and timeframes.

Figure 11: One-year cumulative return comparison of top five parameter strategies vs. ETH Buy and Hold

5. Strategy Summary

The “Dense Moving Average Breakout Strategy” is a trend-based momentum strategy designed around the dynamic convergence of multiple short- to mid-term moving averages. By detecting the compression of moving averages and corresponding price breakouts, the strategy aims to capture key inflection points just before market movements begin. It integrates structural price analysis with a dynamic take-profit mechanism to effectively participate in short- to mid-term trend swings while controlling downside risk.

In this backtest, we used ETH/USDT with 2-hour candlestick data and conducted a systematic grid search across 23,826 parameter combinations. The testing period spanned May 1, 2024, to May 12, 2025, from which the five best-performing parameter sets were selected based on return and risk control metrics. Performance evaluation was based on annualized return, maximum drawdown, Sharpe ratio, and ROMAD. The best-performing parameter combination was:percentage_threshold = 1.4 and tp_sl_ratio = 10.

Achieving an annualized return of 127.59%, maximum drawdown under 15%, and a ROMAD of 8.61%, far outperforming the ETH Buy and Hold benchmark over the same period (which returned -46.05%).

From the parameter distribution analysis, the top-performing strategies were concentrated in regions with low threshold values and moderate-to-high tp_sl_ratio. This suggests that early detection of tightly clustered moving averages, combined with a moderately relaxed take-profit setting, helps to capture full trend waves. In contrast, overly high threshold values or overly tight profit targets often led to frequent entries and premature exits, which diluted overall performance.

In summary, this strategy demonstrates high return efficiency and strong risk control within ETH’s medium-term price structure. The logic is robust and flexible across parameter variations, offering substantial real-world applicability. The parameter zones with threshold between 1.3 and 1.5 and tp_sl_ratio between 9 and 11 consistently delivered stronger performance across key metrics, reflecting the strategy’s ability to capture early trend momentum and sustain profitable swings. Furthermore, integrating volume filters and range-market exclusion mechanisms could enhance its adaptability and resilience across diverse market conditions, expanding its potential for multi-market deployment.

Conclusion

From April 25 to May 12, the cryptocurrency market exhibited a structural pattern of strong price movement amid cautious sentiment. BTC and ETH rose in tandem, with ETH showing a larger gain and greater volatility. However, long-short ratios and funding rates did not display a clear bullish bias, indicating limited enthusiasm for chasing the rally. Open interest continued to climb, with shorts liquidated en masse in early May, followed by long-facing forced liquidations on May 12—highlighting intensified market divergence under high leverage conditions. Overall, while prices strengthened, market sentiment and capital momentum remained unaligned, making risk control and timing critical for successful execution.

The quantitative analysis employed a “Dense Moving Average Breakout Strategy”, with systematic parameter optimization and performance evaluation using 2-hour ETH/USDT data. The strategy achieved an impressive annualized return of 127.59%, far outperforming the -46.05% return of ETH’s Buy and Hold strategy over the same period. By leveraging momentum structures and trend filtering, the strategy demonstrated strong trend-following capabilities and effective drawdown control.

However, in live trading, the strategy may still be impacted by choppy markets, extreme volatility, or signal failures. It is recommended to combine this strategy with additional quantitative factors and robust risk management mechanisms to enhance stability and adaptability ensuring rational judgment and caution in execution.


Reference:

1. Gate.io, https://www.gate.io/trade/BTC_USDT
2. Gate.io, https://www.gate.io/trade/ETH_USDT
3. Coinglass, https://www.coinglass.com/LongShortRatio
4. Coinglass, https://www.coinglass.com/BitcoinOpenInterest?utm_source=chatgpt.com
5. Gate.io, https://www.gate.io/futures_market_info/BTC_USD/capital_rate_history
6. Gate.io, https://www.gate.io/futures/introduction/funding-rate-history?from=USDT-M&contract=ETH_USDT
7. Coinglass, https://www.coinglass.com/pro/futures/Liquidations
8. Glassnode, https://studio.glassnode.com/charts/ba1ec93d-85f4-41fe-5606-798a2f30013a?s=1679144783&u=1742303183

Gate Reach is a comprehensive blockchain and cryptocurrency research platform that provides readers with in-depth content, including technical analysis, trending insights, market reviews, industry research, trend forecasts, and macroeconomic policy analysis.

Disclaimer
Investing in the cryptocurrency market involves high risk. Users are advised to conduct independent research and fully understand the nature of the assets and products before making any investment decisions. Gate.io is not responsible for any losses or damages arising from such investment decisions.