ETH rebounds strongly, with a moving average breakout strategy annualized return of 127% | Gate Research Institute

2025-05-15 07:55:41

Preface

The quantitative bi-weekly report (from April 25 to May 12) analyzes the market trends of Bitcoin and Ethereum, comprehensively utilizing indicators such as long-short ratios, contract open interest, and funding rates. The quantitative section explores the application of the "dense moving average breakthrough strategy" in the ETH/USDT market, covering its logical structure and signal determination mechanisms. Through systematic parameter optimization and backtesting, the strategy demonstrates robust performance in trend identification and risk control, with clear execution discipline, overall outperforming the simple holding of ETH, providing a practical framework for quantitative trading.

Summary

In the past two weeks, BTC and ETH have risen in sync, with BTC increasing by about 34% and ETH rising by over 60%.
The long-short ratio of ETH fluctuates significantly, indicating that the rise of ETH is accompanied by strong short-term trading and market speculation, and short sellers have not clearly exited.
The increase in BTC contract positions is relatively slow, while ETH showed a stronger rise at the beginning of May.
The overall contract market saw shorts concentrated in liquidation at the beginning of May, while longs faced reverse liquidation on May 12, reflecting an intensification of market divergence under high leverage.
Quantitative analysis uses the "dense moving average breakout strategy", achieving a return rate of up to 127% under optimal parameter selection.

Market Overview

1. Analysis of Price Volatility of Bitcoin and Ethereum

BTC and ETH as a whole have shown a steady upward trend since mid-April and maintained a relatively consistent upward rhythm until early May. During this time, BTC rose from about 78,000 USDT to nearly 105,000 USDT, while ETH climbed significantly from around 1,600 USDT to around 2,600 USDT. It can be seen that ETH has risen significantly higher than BTC, showing stronger price elasticity, especially in early May, when the two jumped simultaneously, and it may be that with the slowdown of tariff policy, BTC has also come out of a wave of repair. BTC has a higher price, less volatility, and a relatively stable trend; ETH, on the other hand, rose more and reacted faster. Originally, the market lacked bullish expectations for ETH and its performance was relatively lagging behind, but after entering May, as the Pectra upgrade was approaching and the tariff policy was eased, ETH increased in volume. This round of changes reflects the market's renewed focus on the value of ETH allocation in the short term. 【1】【2】

Figure 1: BTC price rose to nearly 105,000 USDT, while ETH surged to around 2,600 USDT, with a more dramatic increase and response speed.

In terms of volatility, both BTC and ETH showed significant changes in overall fluctuation from early April to mid-May. In mid-April, BTC's volatility frequently peaked, indicating active market sentiment and sharp price adjustments; then, towards the end of April and early May, overall fluctuations tended to converge, reflecting a brief stabilization period in the market. However, before and after sharp price increases, ETH's volatility experienced multiple sudden surges, even temporarily exceeding that of BTC, indicating stronger short-term fluctuations accompanying its rise. Overall, BTC's volatility is relatively more even, while ETH's fluctuations are concentrated around several key points, especially before and after price breakthroughs, suggesting it is more susceptible to momentum-driven influences.

Figure 2: The volatility of BTC is relatively more even, while the volatility of ETH has experienced several sharp increases.

Overall, ETH has shown greater price increases and more concentrated volatility changes in this market movement, demonstrating its strong price responsiveness at critical moments; while BTC has displayed a relatively stable upward trend and a more dispersed volatility distribution, reflecting its relative stability during market fluctuations. Although both have jumped simultaneously during the price increase, their volatility characteristics and rhythms still show significant differences, reflecting different market characteristics and dynamic structures. In the short term, attention can be paid to the capital flow and volatility changes of BTC as important indicators of market risk appetite.

2. Analysis of Long-Short Ratio (LSR) between Bitcoin and Ethereum Trading Volume

The Long/Short Taker Size Ratio (LSR) is a key indicator to measure the volume of long and short taker trades in the market, and is usually used to determine market sentiment and trend strength. When the LSR is greater than 1, it means that the amount of active buying (taking long) in the market is greater than actively selling (taking short), indicating that the market is more inclined to go long and the sentiment is biased towards bullishness.

According to Coinglass data, the prices of BTC and ETH have shown a significant upward trend in the past two weeks, but in terms of LSR, the two have shown varying degrees of divergence. BTC's LSR rose slightly in the early days of the rally, but remained around 1 as a whole, and even fell below 1 around May 10, showing that even if the price continues to rise, the short trading volume in the market has also risen simultaneously, reflecting that some investors choose to lay out short orders or hedge operations at a high level, and the market has not formed an obvious unilateral long structure, and there is a certain amount of rising doubt.

In contrast, the long-short ratio of ETH fluctuates more dramatically. During the price breakout above 2,000 USDT and the rapid rise to 2,600 USDT, its LSR did not steadily increase but instead experienced multiple sharp oscillations, with a noticeable decline around May 10. This situation indicates that ETH's rise is accompanied by strong short-term trading and market games, with bears not significantly retreating, leading to a relatively divided market sentiment.

Despite the synchronized surge in BTC and ETH prices over the past two weeks, their long-short ratio has not shown a sustained increase. Instead, it reflects a general sentiment of wait-and-see and hedging in the market at high levels, with investors exhibiting a cautious attitude. The structural support behind the price increase still needs further verification.

Figure 3: The BTC long-short ratio is fluctuating downward, indicating a weakening of bullish momentum at high levels. !

Figure 4: The ETH long-short ratio fluctuates wildly, and market sentiment shows significant divergence. !

3. Contract Position Amount Analysis

According to Coinglass data, the contract positions for BTC and ETH have shown an overall upward trend, reflecting a continuous increase in market trading activity. The open interest for BTC has gradually risen from around 60 billion USD, and although there have been fluctuations, it has remained high overall and stabilized after early May. The open interest for ETH has increased from around 18 billion USD to nearly 24 billion USD, showing a similar trend to BTC but relatively stable, particularly with a noticeable jump in early May, indicating that funds were actively entering the market during this period.

Overall, the synchronized growth of the contract positions of both has corroborated the price increase, indicating a dual rise in market participation and leverage usage. However, the influx of funds into BTC has stabilized after the end of April, while ETH saw a stronger rise in early May, suggesting that ETH attracted more interest in contract trading in the short term.

Figure 5: The increase in BTC contract positions is relatively slow, while ETH showed a stronger rise at the beginning of May. !

4. Funding Rate

The funding rates of BTC and ETH as a whole remain around 0% and fluctuate slightly, showing frequent positive and negative switching, indicating that the market is relatively balanced in terms of long and short forces. In mid-to-late April, BTC had a number of times when the funding rate turned negative, especially around April 20, when it fell to -0.025%, indicating that the market was dominated by bears at that time, or there was large-scale short hedging. ETH had a similar move over the same period, but with a slightly smaller volatility, suggesting that while the market briefly turned bearish sentiment, it did not form a sustained suppression.

With the rise in prices and the increase in contract positions, both funding rates have gradually turned positive and are maintained between 0% and 0.01%, reflecting that the bulls are gradually gaining the upper hand, and the market tends to be actively building positions. However, overall, the funding rates have not continued to soar, indicating that although the sentiment for leveraged long positions has strengthened, it has not become overheated, and market sentiment remains in a moderately optimistic phase.

Figure 6: Both BTC and ETH funding rates are gradually turning positive and remain between 0% and 0.01%, reflecting the gradual dominance of bulls and the market's bias towards active positioning

5. Cryptocurrency Contract Liquidation Chart

According to Coinglass data, since mid-April, the cryptocurrency market has experienced a mixed situation of contract liquidations, with the amount of short liquidations being particularly significant in early May. Especially on May 8, the amount of short liquidations surged sharply, reaching a daily scale of 836 million dollars, indicating that the market price rose rapidly at that time, leading to a large number of short positions being forcibly closed.

On May 12, as the market volatility intensified, the amount of long liquidation increased significantly, reaching $476 million in a single day, indicating that some high-level long chasers failed to withstand volatility and suffered reverse liquidation. This phenomenon shows that despite the overall trend, there are still sharp fluctuations in the short-term market, with bears and bulls taking turns to suffer setbacks at key nodes, and the contract market is still highly active and risk-concentrated.

This trend corresponds to the previously mentioned price increase, the rise in contract positions, and the positive funding rate, reflecting that when the market breaks through key price levels, there is a phenomenon of concentrated short liquidations, forming a short-term bullish advantage. However, even in an upward trend, long positions may still face liquidation at local highs, especially when market fluctuations intensified in mid-May, putting long positions at significant risk, indicating that market volatility remains strong, and the characteristics of high leverage and risk hedging coexist in contract trading are still very evident.

Figure 7: The amount of short position liquidation surged significantly on May 8, reaching as high as 836 million USD in a single day. !

Quantitative Analysis - Moving Average Convergence Breakout Strategy

(Disclaimer: All predictions in this article are based on historical data and market trends, and are for reference only. They should not be considered as investment advice or guarantees of future market movements. Investors should fully consider risks and make cautious decisions when engaging in relevant investments.)

1. Strategy Overview

The "Moving Average Convergence Breakout Strategy" is a momentum strategy that combines technical trend judgment. The strategy identifies potential directional volatility in the market by observing the convergence of multiple short- to medium-term moving averages (such as 5-day, 10-day, 20-day, etc.) over a specific period. When multiple moving averages trend in unison and come closer together, it usually indicates that the market is in a consolidation phase, waiting for a breakout. If the price clearly breaks upwards above the moving average area at this time, it is considered a bullish signal; conversely, if the price breaks down below the moving average band, it is seen as a bearish signal.

In order to improve the practicality of the strategy and the effectiveness of risk control, this strategy also sets fixed ratios for take profit and stop loss mechanisms, ensuring timely entry and exit when trends emerge, balancing reward and risk control. The overall strategy is suitable for capturing medium to short-term trend markets and possesses a certain level of discipline and operability.

2. Core Parameter Settings

3. Strategy Logic and Operating Mechanism

Entry Conditions

Judgment of moving average intensity: Calculate the difference between the maximum and minimum values of the six moving averages of SMA20, SMA60, SMA120, EMA20, EMA60, and EMA120 (called the moving average distance), and when the distance is lower than the set threshold (such as 1.5% of the price), it is considered to be moving average intensive. Threshold refers to a critical value, which is the minimum or maximum value that an effect can produce.
Price Breakthrough Judgment:
When the current price crosses above the highest value of the six moving averages, it is regarded as a bullish breakout signal, triggering a buy operation.
When the current price breaks below the lowest value of the six moving averages, it is considered a bearish breakout signal, triggering a sell operation.

Entry Conditions: Dynamic Take Profit and Stop Loss Mechanism

Long Position Exit:
- If the price falls below the lowest moving average at the time of opening the position, trigger a stop loss;
Or if the price rises beyond "the distance between the opening price and the lowest moving average × profit-loss ratio", trigger the take profit.
Short Position Exit:
- If the price rises above the highest moving average at the time of opening, trigger the stop loss;
Or if the price drops more than "the distance between the opening price and the highest moving average × profit-loss ratio", trigger take profit.

Practical Example Diagram

Trading signal triggered The following chart shows the ETH/USDT 2-hour candlestick chart at the last entry trigger on May 8, 2025. It can be seen that after the price broke upwards following the high density of the six moving averages, it met the entry conditions set by the strategy. The system executed a buy operation at the price of the breakout, successfully capturing the starting point of the subsequent upward trend.

Figure 8: Actual entry position diagram when the strategy conditions for ETH/USDT are triggered (May 8, 2025)

Trading actions and results The system automatically exits based on a dynamic profit-taking mechanism once the preset profit-loss ratio is reached, effectively locking in the main segment gains. Although there may still be upward potential not accounted for, the overall operation adheres to strategic discipline, demonstrating good risk control and execution stability. If paired with a trailing stop or trend-following mechanism, it may further extend the profit potential in a strong market.

Figure 9: ETH/USDT Strategy Exit Position Diagram (May 8, 2025)

Through the above practical example, we intuitively presented the entry logic and dynamic profit-taking mechanism of the strategy when the moving averages are dense and the price breakthrough conditions are triggered. The strategy accurately captures the trend initiation point through the interaction between price and moving average structure, and automatically exits during subsequent fluctuations, locking in the main profit range while controlling risk. This case not only verifies the practicality and execution discipline of the strategy but also reflects its stability and risk control capability in the real market, laying the foundation for subsequent parameter optimization and strategy summary.

4. Practical Application Examples

Parameter Backtesting Settings To find the best combination of parameters, we conduct a systematic grid search over the following range:

tp_sl_ratio: 3 to 14 (step of 1)
threshold: 1 to 19.9 (step size of 0.1)

Taking ETH/USDT as an example, in the backtesting data of the 2-hour K-line over the past year, the system tested a total of 23,826 parameter combinations and selected the five groups with the best cumulative return performance. The evaluation criteria include annualized return rate, Sharpe ratio, maximum drawdown, and ROMAD (return to maximum drawdown ratio), used to comprehensively measure strategy performance.

Figure 10: Comparison Table of Performance of Five Optimal Strategies

Strategy Logic Explanation When the system detects that the distance between the six moving averages converges to within 1.4%, and the price breaks above the upper edge of the moving averages from below, it triggers a buy signal. This structure aims to capture the moment when the price is about to initiate a breakout, entering at the current price, and using the highest moving average at the time of the breakout as a reference benchmark for dynamic profit-taking, enhancing the ability to control returns.

The settings used in this strategy are as follows:

percentage_threshold = 1.4 (Maximum distance limit between six moving averages)
tp_sl_ratio = 10 (Dynamic Take Profit Margin Setting)
short_period = 6, long_period = 14 (moving average observation period)

Performance and Results Analysis The backtesting period is from May 1, 2024, to May 12, 2025. This set of parameters performed excellently during this period, achieving an annualized return rate of 127.59%, with a maximum drawdown of less than 15%. The ROMAD reached 8.61%, indicating that the strategy not only has a stable capital appreciation ability but also effectively compresses downside risk.

As shown in the chart, the overall performance of the strategy in the past year is significantly better than ETH's Buy and Hold strategy (-46.05%), especially in the stage of increased market volatility or trend reversal, showing a good take-profit and re-entry mechanism, and drawdown control is significantly better than passive holding.

We also conducted a horizontal comparison of the five groups of best-performing parameters. Currently, the combination achieves the best balance between reward and stability, with strong practical application value. In the future, we can further combine a dynamic threshold adjustment mechanism or incorporate trading volume and volatility screening logic to enhance adaptability in volatile markets and expand to multi-currency and multi-period strategy deployment.

Figure 11: Comparison of the cumulative return rates of five optimal parameter strategies and the ETH holding strategy over the past year.

5. Trading Strategy Summary

The "Moving Average Convergence Breakthrough Strategy" is a trend-based momentum strategy designed based on the dynamic aggregation state of multiple short to medium-term moving averages. By detecting the convergence of moving averages and price breakout behaviors, it captures key turning points before market initiation. This strategy integrates price structure judgment and a dynamic profit-taking mechanism, allowing effective participation in medium to short-term trend waves while controlling drawdowns.

In this backtest, we used ETH/USDT as the target and conducted systematic grid parameter optimization using 2-hour candlestick data, covering 23,826 sets of parameter combinations. The backtest period was from May 1, 2024, to May 12, 2025, resulting in the final selection of five sets of parameters with the best performance in terms of return and risk control. We analyzed their performance based on annualized return, maximum drawdown, Sharpe ratio, and ROMAD. The best strategy combination is: percentage_threshold = 1.4, tp_sl_ratio = 10, with an annualized return as high as 127.59%, a maximum drawdown controlled below 15%, and a ROMAD of 8.61%, showing far superior risk-adjusted performance compared to the ETH Buy and Hold benchmark (which was -46.05% during the same period).

Observing the parameter distribution, the best performance is mainly concentrated in the range of low threshold values and medium to high tp_sl_ratio. This indicates that detecting dense moving average structures in the early stages of market formation, while moderately relaxing the take-profit space, helps to capture complete market segments. In contrast, when the threshold is set too high or the take-profit ratio is too low, the strategy is more likely to fall into the problems of frequent entries and exits, and premature exits, which in turn lowers the overall return rate.

Overall, this strategy demonstrates high returns and risk control efficiency within the mid-term volatility structure of ETH. The strategy logic is stable and has parameter adaptability, showing significant practical potential. Based on the distribution characteristics of the backtest parameters, combinations where threshold ranges from 1.3 to 1.5 and tp_sl_ratio falls between 9 and 11 exhibit more stable performance in terms of returns and risk control across various performance indicators, reflecting the strategy's strong ability to capture early trend momentum and sustain segment profits. Additionally, by incorporating volume filtering and oscillation filtering mechanisms, it is expected to further enhance the strategy's adaptability and robustness in different market conditions, expanding its cross-market deployment potential.

Summary

From April 25 to May 12, the cryptocurrency market exhibited a structural characteristic of "price strongly rising, but sentiment remains cautious." BTC and ETH rose simultaneously, with ETH experiencing a larger increase and more volatility. The long-short ratio and funding rate did not show significant bullishness, indicating limited market enthusiasm for chasing prices. Contract positions continued to climb, with shorts facing concentrated liquidations in early May, followed by longs encountering reverse liquidations on May 12, reflecting an intensification of market divergence under high leverage. Overall, although prices strengthened, market sentiment and capital momentum have not aligned, making risk control and timing crucial for operations.

The quantitative analysis employs the "dense moving average breakout strategy" for systematic parameter optimization and performance evaluation. In the 2-hour level data of ETH/USDT, the strategy achieved an annualized return of 127.59%, far surpassing the -46.05% of the same period ETH Buy and Hold strategy. This strategy demonstrates good trend-following ability and drawdown control through momentum structure and trend filtering. However, in practical operations, it may still be affected by market fluctuations, extreme conditions, or signal failures. It is recommended to combine with other quantitative factors and risk control mechanisms to enhance the strategy's stability and adaptability, while making rational judgments and responding cautiously.

Reference materials:

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

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Last edited on 2025-05-15 08:11:28

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