Gate Research: BTC and ETH Both Retreat, Trend Strategies Become Primary Source of Yield

Executive Summary

• In June, BTC and ETH declined by 20.43% and 21.67% respectively. The overall market continued its weak adjustment, with price centers moving lower, and ETH continued to underperform BTC.

• The derivatives market continued deleveraging. Open interest for BTC and ETH perpetual contracts decreased by 25.76% and 26.31% respectively. Long liquidations were significantly higher than short liquidations. Funding rates were generally neutral. The price decline primarily reflected weakening spot selling pressure and risk appetite simultaneously.

• June's market was suitable for trend-following and breakout confirmation strategies. Parameter backtesting showed that the moving average convergence breakout strategy generally outperformed buy-and-hold, making it more suitable for capturing directional market moves.

• Based on net returns, drawdown, and number of trades, AAVE USDT was the best practical case for June. The strategy achieved a net return of 60.2%, compared to a buy-and-hold return of 3.76%, with a maximum drawdown of 12.9%.

• For July, continue monitoring the moving average convergence breakout strategy, incorporating volume confirmation and BTC trend filters to enhance signal quality and reduce the risk of false breakouts against the trend.

In June 2026, mainstream crypto assets continued their weak consolidation and downward spread. BTC opened the month at $73,684.1, closed at $58,632.4, with a monthly return of -20.43%, a monthly high of $74,090.8, a low of $58,106.9, and a range of 27.51%. ETH's monthly return was -21.67%, with a maximum drawdown of -21.88%. Structurally, BTC experienced a rapid decline in early June before entering a low-level recovery. A mid-month rebound failed to re-open upside space, and prices fell again at month-end. ETH's relative weakness was more pronounced, lacking price elasticity and facing pressure during liquidity contractions.

The derivatives market showed no stable recovery in the open interest of major contracts. The nominal open interest for BTC USDT perpetuals dropped from $5.19B to $3.85B, a monthly change of -25.76%; ETH's monthly change was -26.31%. In the liquidation structure, long liquidation amounts were significantly higher than short liquidations, indicating that forced position reduction during the decline remained the dominant force. Funding rates remained slightly positive or close to neutral for most of the time; the price decline was not driven by extreme short-side crowding but by the trend effect resulting from the simultaneous weakening of spot selling pressure and risk appetite.

From a quantitative strategy perspective, this month was suitable for trend-following and breakout confirmation. This article uses 4-hour candlesticks from Gate exchange to conduct parameter grid backtesting on 29 valid USDT spot trading pairs. Screening criteria were: monthly Gate spot trading volume above $50 million, at least 2 trades per month, strategy maximum drawdown not exceeding 20%, and total one-way cost and slippage estimated at 0.08%. Based on net return, drawdown, and number of trades, the best practical case for June was the AAVE USDT moving average convergence breakout strategy: monthly net return of 60.2%, buy-and-hold return of 3.76%, maximum drawdown of -12.9%, 4 trades, win rate of 75%, and profit factor of 9.63.

1. Market Overview

The core features of the June market were a downward shift in price centers, insufficient rebound sustainability, and a convergence of trading concentration towards BTC and a few large-cap assets. BTC and ETH remained the most important bellwethers. BTC's monthly range reached 27.51%, with an annualized realized volatility of approximately 43.55%; ETH's monthly range was 34.29%, with an annualized realized volatility of about 65.43%. When major assets experience significant drawdowns simultaneously, cross-coin diversification offers limited protection for net value in the short term, requiring strict adherence to position direction and exit discipline at the strategy level.

In terms of trading volume, the highest spot trading volume samples on Gate in June were concentrated in high-liquidity assets like BTC, ETH, SOL, XRP, and DOGE. High trading volume has two implications. First, backtest signals are closer to executable real-market conditions. Second, during periods of increased volatility within the month, rising volume is often accompanied by simultaneous passive stop-losses and active position adjustments, making it easier for trend strategies to capture continuous price ranges.

2. BTC and ETH Structural Observations

BTC's June price action can be divided into three phases. The first phase, from June 1st to June 6th, saw prices rapidly decline from the month's opening area, with daily candles weakening consecutively and contract long liquidations expanding simultaneously. The second phase, from June 7th to June 18th, saw BTC recover within the low range. A local rebound triggered short covering, but the price failed to sustainably re-establish the month's earlier highs. The third phase was late June, where BTC lost mid-month support again, closing near the low for the month, indicating capital still favored reducing risk exposure.

ETH's performance was weaker than BTC's. ETH's June return was -21.67%, a relative gap of -1.25% compared to BTC. In weak months, ETH typically requires on-chain activity, ecosystem capital, or expanding risk appetite for additional support; these factors did not offset the macroeconomic risks and market deleveraging pressure this month. Strategically, ETH is better suited as a risk thermometer rather than a standalone offensive asset: when ETH cannot strengthen relative to BTC, the Beta risk for altcoin portfolios needs to be reduced.

The relationship between volume and volatility is also noteworthy. BTC's trading volume expanded significantly during the initial decline and the late-month pullback, suggesting the price decline was not merely a low-liquidity slide but was accompanied by real turnover. If BTC subsequently enters a low-volatility consolidation phase, the moving average convergence strategy will wait for the moving average ribbons to converge before judging the breakout direction. If prices continue along the downtrend channel, short-cycle trend models may still outperform mean-reversion strategies.

3. Derivatives Market: Open Interest, Liquidations, and Funding Rates

The signals from derivatives data were relatively consistent. This was passive de-risking following the decline. Total BTC long liquidations were $329.4M, while short liquidations were $144.9M; total ETH long liquidations were $314.8M, while short liquidations were $193.4M. The high proportion of long liquidations means leveraged longs were forced to exit as prices fell, also transmitting sentiment to spot prices.

Funding rates did not show extreme negative values, indicating the market was not in a one-sided short-side frenzy. Funding rates were close to neutral or slightly positive for most of the time, suggesting that some capital still attempted to buy the dip or maintain long positions during the price weakness. Only if funding rates turn significantly negative while prices stop making new lows would conditions be closer to a short-term rebound setup. Such a strong reflexive structure did not form this month.

A period where the account long/short ratio is above 1 does not equate to bullishness. In a weak market, a rising long/short ratio can sometimes result from retail traders' contrarian long positions. Without accompanying OI expansion and price increases, this can easily become a source of future liquidation pressure. The account long/short ratio for BTC and DOGE was high on some trading days, yet prices failed to recover sustainably; such divergences need to be incorporated into risk control.

4. Quantitative Analysis: Moving Average Convergence Breakout Strategy

4.1 Strategy Logic

This report continues the core idea of moving average convergence breakout. When multiple short-to-medium-term moving averages gradually converge, prices are in a compressed state before a directional choice. When prices break out above the upper edge of the moving average ribbon, it suggests bulls are regaining control. When prices break down below the lower edge, it indicates a higher probability of the bearish trend continuing. This strategy does not predict turning points but waits for prices to signal direction after the moving average ribbon converges.

This article uses six moving averages to form the ribbon, consisting of three sets of SMAs and EMAs. The parameter grid includes four sets of periods: (6,18,54), (8,24,72), (12,36,108), (20,60,120). Thresholds include 1.2%, 1.8%, 2.2%, 3%, 4%. Dynamic take-profit multipliers include 3, 4, 6, 8. It uses 4-hour candlesticks, with data from May 1st to May 31st used for indicator warm-up, and June 1st to June 30th included in performance calculation.

Entry rules are as follows:

• Ribbon Width = (Highest value of six MAs - Lowest value of six MAs) / Closing Price;

• When the ribbon width is below the threshold, it is considered moving average convergence;

• If the closing price breaks above the upper edge of the ribbon from below, go long at the open of the next 4H candle;

• If the closing price breaks down below the lower edge of the ribbon from above, go short at the open of the next 4H candle;

• Stop loss: Go long and then close below the lower edge of the ribbon; Go short and then close above the upper edge of the ribbon;

• Take profit: When profit reaches "Entry Ribbon Width × Take Profit Multiplier", close the position at the open of the next 4H candle;

• Any open positions at month-end are forcibly closed at the closing price of the last 4H candle of the month.

The backtest cost assumption is a 0.08% deduction for each position change, covering transaction costs and slippage. This assumption does not represent Gate's actual fee schedule and is only used for uniform comparison across different trading pairs and parameter combinations. The strategy does not use leverage, and capital utilization is calculated at 100%. Buy-and-hold returns are calculated using the first daily open and last daily close of the same trading pair for June.

4.2 Sample and Screening

The candidate pool includes 29 valid Gate USDT trading pairs: BTC, ETH, SOL, XRP, DOGE, BNB, ADA, TRX, LINK, AVAX, BCH, LTC, DOT, NEAR, UNI, AAVE, ICP, ETC, ATOM, FIL, OP, ARB, SUI, WLD, INJ, PEPE, SHIB, ONDO, HBAR, among others.

To prevent a single accidental signal from becoming the best sample, this article restricts the practical case application to: monthly Gate spot trading volume above $50 million, at least 2 trades within June, strategy maximum drawdown not exceeding 20%, and position exposure not exceeding 95%. The purpose of this rule is not to pursue the theoretically highest return but to identify strategy combinations executable in a live June environment.

4.3 Best Practical Case for June: AAVE USDT

Based on the screening rules above, the best case for June is AAVE USDT. This trading pair had a monthly spot trading volume of $108.2M, a monthly buy-and-hold return of 3.76%, an intra-month range of 72.28%, and a maximum drawdown of -24.02%. The optimal strategy parameters were: Moving Average Periods (8, 24, 72), Convergence Threshold 4%, Dynamic Take-Profit Multiplier 8.

Backtest results show that the NAV curve for AAVE USDT exhibited a step-like change in June. The strategy did not predict the direction at the start of the month but waited for a breakout signal after the moving average ribbon converged. This characteristic allowed it to avoid some invalid oscillations and retain positions when prices showed a continuous direction. Compared to buy-and-hold, the strategy's return differential was 56.44%, with the maximum drawdown controlled at -12.9%, indicating that the month's returns primarily came from directional switches and dynamic take-profit. This sample is not just a replay of the spot price but also possesses the basic conditions for expressing long/short direction through perpetual contracts.

From the trade details, the strategy's best-performing phases were concentrated during the rapid price departure from the moving average ribbon. Short signals contributed more in the downward month, while long signals primarily functioned as rebound confirmation. If only spot longs were allowed, the strategy's returns for the month would have been significantly lower. If executing with perpetual contracts, additional attention is needed for funding rates, liquidation prices, and position limits.

4.4 Sources of Strategy Returns

The effectiveness of the moving average convergence breakout strategy this month primarily came from three types of market structures.

First, prices transitioned from narrow-range consolidation to directional expansion multiple times. The moving average convergence condition divides the market into "waiting" and "executing" states, reducing frequent trading during erratic fluctuations. The strategy only assumes directional risk when prices leave the moving average ribbon.

Second, in the weak market, the downward segments were more continuous. Many high-Beta trading pairs in June did not immediately recover after a single day's decline but continued to fall over several 4H candles. Trend strategies have a higher probability of achieving positive expectations in such an environment compared to mean reversion.

Third, dynamic take-profit reduced profit retracement. Fixed take-profit can lead to premature exit during volatility expansion, while pure moving average stop-loss might return realized profits to the market. The strategy in this article uses the "Entry Ribbon Width × Multiplier" method, making the take-profit target varies with the compression level at entry. The tighter the moving averages are, the smaller the take-profit distance after a breakout; the wider the ribbon, the larger the trend space the strategy allows.

The drawbacks of the strategy are also clear. Moving average confirmation inherently lags, unable to capture the very start of a trend. When prices reverse sharply, short positions might stop out near the upper edge of the ribbon. If the market enters a directionless wide-range oscillation, the moving average ribbon will repeatedly converge and diverge, and trading costs will erode returns. Therefore, this strategy is suitable as a trend enhancement module and is not suitable as a standalone all-weather configuration.

5. Portfolio Perspective: Combining Trend Enhancement and Neutral Strategies

The June sample shows that trend strategies can play both a defensive and offensive role in declining months. Short signals can hedge spot Beta, while long signals can capture low-level bounces, but their return distribution is not smooth. If the moving average convergence breakout strategy is used for portfolio management, it is better suited as an enhancement module, paired with low-correlation strategies.

One executable portfolio framework is as follows:

• Core position uses BTC, ETH, or stablecoin yield strategies as a low-turnover base layer;

• The trend enhancement module is activated only after moving average convergence and breakout, remaining in cash at other times;

• Risk budget for a single trading pair does not exceed 10%-15% of portfolio equity;

• Set lower single-loss limits for high-Beta altcoins;

• If both BTC and ETH break down their daily short-to-medium-term moving averages, reduce the weight of long signals;

• When funding rates are continuously and significantly positive but prices fail to make new highs, avoid chasing longs;

• When funding rates turn negative, prices stop making new lows, and OI stabilizes and rises, then increase the weight of rebound signals.

The focus of this framework is to place strategy signals within a risk budget, rather than directly extrapolating a specific backtest result. The best case for June is representative, but it does not guarantee replicating the same returns in July. The vitality of a trend strategy comes from discipline: not trading when there is no convergence breakout, exiting when stop-losses are triggered, and realizing profits when dynamic take-profit targets are hit.

6. Risk Warnings and Subsequent Observations

Going forward, three types of indicators need close observation.

First, whether BTC can reclaim the mid-June rebound range. If BTC can only consolidate at low levels, the sustainability of altcoin bounces will be limited. If BTC breaks out with high volume and drives the ETH/BTC pair to recover, the quality of long