Gate Research: BTC and ETH Both Retreat, Trend Strategies Become the Main Source of Returns

Summary

• In June, BTC and ETH fell by 20.43% and 21.67% respectively. The overall market continued its weak adjustment, with the price center moving lower. 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 remained generally neutral. The price decline primarily reflected a simultaneous weakening of spot selling pressure and risk appetite.

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

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

• In July, traders can continue to monitor the moving average confluence breakout strategy, incorporating volume confirmation and BTC trend filters to improve signal quality and reduce the risk of false breakouts during counter-trend movements.

In June 2026, major crypto assets continued their weak consolidation and the weakness spread further. BTC opened the month at $73,684.1 and closed at $58,632.4, recording a monthly return of -20.43%. Its monthly high was $74,090.8, low was $58,106.9, resulting in a range of 27.51%. ETH recorded a monthly return of -21.67% and a maximum drawdown of -21.88%. Structurally, BTC experienced a rapid decline in early June followed by a low-level recovery. The mid-month rebound failed to reopen upward space, and prices fell again towards the end of the month. ETH's relative weakness was more pronounced, with insufficient price elasticity, leading to increased pressure during liquidity contraction.

On the derivatives side, open interest in major contracts did not form a stable recovery. The nominal value of BTC USDT perpetual open interest dropped from $5.19B to $3.85B, a monthly change of -25.76%. ETH's open interest nominal value changed by -26.31% monthly. In the liquidation structure, the amount of long liquidations significantly exceeded short liquidations, with passive deleveraging during the decline being the dominant force. Funding rates remained slightly positive or near neutral for most of the period. The price decline was not driven by extreme short crowding but by the trend effect resulting from the simultaneous weakening of spot selling pressure and risk appetite.

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

1. Market Overview

The core characteristics of the June market were a lower price center, insufficient rebound sustainability, and a convergence of trading volume 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 approximately 65.43%. When major assets experience significant drawdowns simultaneously, cross-coin diversification offers limited protection for net asset value in the short term. Strategically, strict adherence to position direction and exit discipline is required.

In terms of trading volume, the highest spot trading volume in June on Gate was concentrated in high-liquidity assets like BTC, ETH, SOL, XRP, and DOGE. High trading volume has two implications. First, backtest signals are closer to a real executable environment. Second, during periods of increased volatility, rising volume usually coincides with both 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 trajectory can be divided into three phases. The first phase, from June 1st to June 6th, saw prices rapidly decline from the start-of-month range, with daily charts weakening consecutively and long liquidations in the futures market expanding simultaneously. The second phase, from June 7th to June 18th, saw BTC recover within the lower range. The local rebound triggered short covering, but prices failed to reclaim the start-of-month highs. The third phase, in late June, saw BTC lose mid-month support again, closing near the monthly low, indicating capital's continued preference for reducing risk exposure.

ETH underperformed BTC. ETH's monthly return in June was -21.67%, with a relative gap of -1.25% compared to BTC. During weak months, ETH often requires on-chain activity, ecosystem capital, or expanding risk appetite to provide additional support. This month, these factors did not offset the macro risks and market deleveraging pressure. 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 of altcoin portfolios should be lowered.

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

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

Signals from derivatives data were relatively consistent, indicating passive risk reduction following the decline. Total BTC long liquidations amounted to $329.4M, while short liquidations totaled $144.9M. Total ETH long liquidations amounted to $314.8M, while short liquidations totaled $193.4M. The higher proportion of long liquidations means leveraged longs were forced to exit during the price decline, which also transmitted sentiment to spot prices.

Funding rates did not turn extremely negative, indicating the market was not overly crowded on the short side. Funding rates remained near neutral or slightly positive for most of the time, implying some capital still attempted to buy the dip or maintain long positions during the weakness. A significant turn towards negative funding rates, combined with prices failing to make new lows, would be a stronger condition for short-term bounces. This strong reflexive structure did not form this month.

A long/short account ratio above 1 does not equate to bullishness. In a weak market, a rising long/short ratio can sometimes result from retail traders going against the trend. Without OI expansion and price increases to confirm, this can easily become a source of subsequent liquidation pressure. The long/short account ratio for BTC and DOGE was high on certain trading days, yet prices failed to sustain a recovery. Such divergences need to be incorporated into risk control measures.

4. Quantitative Analysis: Moving Average Confluence Breakout Strategy

4.1 Strategy Logic

This report adheres to the core concept of moving average confluence breakout. When multiple short-to-medium-term moving averages converge, price action is in a compressed state before a directional decision. A price breakout above the upper band of the moving averages suggests buyers are regaining control. A price breakdown below the lower band indicates a higher probability of a continuing downtrend. This strategy does not predict turning points but waits for price to signal direction after the moving average band converges.

This article uses six moving averages to form the band, comprising three sets of SMAs and EMAs. The parameter grid includes four period groups: (6,18,54), (8,24,72), (12,36,108), (20,60,120). Threshold values include 1.2%, 1.8%, 2.2%, 3%, and 4%. Dynamic take-profit multiples include 3, 4, 6, and 8. Using 4-hour candlesticks, data from May 1st to May 31st is used for indicator warm-up, while June 1st to June 30th is used for performance evaluation.

Entry rules are as follows:

• Moving Average Band Width = (Max of 6 MAs - Min of 6 MAs) / Close Price;

• When the MA Band Width is below the threshold, the MAs are considered to be in confluence;

• Close price breaks above the upper band: Go long at the open of the next 4H candle;

• Close price breaks below the lower band: Go short at the open of the next 4H candle;

• Stop-loss: If long, exit when price breaks below the lower band; if short, exit when price breaks above the upper band;

• Take-profit: When profit reaches (`MA Band Width at Entry` × Take-Profit Multiple), close position at the open of the next 4H candle;

• Positions still open near month-end are force-closed at the closing price of the last 4H candle.

The backtest cost hypothesis is a 0.08% deduction per position change, covering trading costs and slippage. This hypothesis does not represent Gate's actual fee schedule but is 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 opening price of the first daily candle in June and the closing price of the last daily candle for the same trading pair.

4.2 Sample and Screening

The candidate pool includes 29 active 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.

To avoid an accidental single signal becoming the best sample, this paper restricts practical cases based on: monthly Gate spot trading volume above $50 million, at least 2 trades in June, a strategy maximum drawdown not exceeding 20%, and position exposure not exceeding 95%. The purpose of this rule is not to pursue the theoretical highest return but to find a strategy combination executable in the live market during June.

4.3 Best Practical Case for June: AAVE USDT

According to 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%, a monthly range of 72.28%, and a maximum drawdown of -24.02%. The optimal strategy parameters were: MA period (8, 24, 72), MA confluence threshold of 4%, and dynamic take-profit multiple of 8.

Backtest results show that the equity curve for AAVE USDT exhibited a step-like change in June. The strategy did not predict the direction at the beginning of the month but waited for a breakout signal after the MA band converged. This characteristic allowed it to avoid some ineffective range-bound movements and retain positions during continuous price direction. Compared to buy-and-hold, the strategy outperformed by 56.44%, with a maximum drawdown controlled at -12.9%. This indicates that this month's returns primarily came from directional shifts and dynamic profit-taking. This sample is not just a replay of spot prices; it also possesses the fundamental conditions for expressing long and short directions using perpetual contracts.

From the trade details, the strategy's best-performing periods were concentrated after prices rapidly broke away from the MA band. Short signals contributed more during the bearish month, while long signals functioned more to confirm bounces. If only spot long positions were allowed, the strategy's return for the month would have been significantly lower. If executed using perpetual contracts, additional attention must be paid to funding rates, liquidation prices, and position limits.

4.4 Sources of Strategy Returns

The effectiveness of the MA confluence breakout strategy this month primarily stems from three types of market structures.

First, prices transitioned from narrow range-bound trading to directional expansion multiple times. The MA confluence condition divides the market into "waiting" and "executing" states, reducing frequent trading in choppy movements. The strategy only assumes directional risk when price leaves the MA band.

Second, the declining segments were more continuous during the weak market. Many high-beta trading pairs in June did not experience a single-day drop followed by an immediate recovery but declined consecutively over several 4-hour candles. Trend strategies are more likely to have a positive expectation in such environments compared to mean reversion strategies.

Third, dynamic profit-taking reduced the giving back of profits. Fixed take-profit levels can lead to premature exits during expanding volatility, while pure MA-based stop-losses might return realized profits to the market. This paper's strategy uses the method of `MA Band Width at Entry` × Multiple, making the profit target variable based on the degree of compression at entry. The tighter the MA band, the smaller the take-profit distance after the breakout. If the MA band is slightly wider, the strategy allows for a larger trend space.

The strategy's shortcomings are also clear. MA confirmation is inherently lagging, unable to capture the very beginning of a trend. In cases of rapid price reversals, short positions might be stopped out near the upper band. If the market enters a wide, directionless range-bound phase, the MA band will repeatedly converge and diverge, and transaction costs will erode returns. Therefore, this strategy is suitable as a trend-enhancement module, not as a standalone all-weather allocation.

5. Portfolio Perspective: Combining Trend Enhancement with Neutral Strategies

The June samples illustrate 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. However, its return distribution is not smooth. If the MA confluence breakout strategy is used for portfolio management, it is better suited as an enhancement module paired with low-correlation strategies.

A possible executable portfolio framework is as follows:

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

• Trend enhancement module: Activate only after MA confluence breakout; otherwise, remain flat;

• Risk budget for a single trading pair: Not to exceed 10%-15% of portfolio equity;

• Set lower per-trade loss limits for high-beta altcoins;

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

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

• When funding rates turn negative, prices fail to make new lows, and OI stabilizes and recovers, then increase the weight of bounce signals.

The key of this framework is to place strategy signals within a risk budget, rather than directly extrapolating a single backtest result. The June best case is representative but does not mean the same return can be replicated in July. The vitality of a trend strategy comes from discipline: not trading when there is no confluence breakout, exiting when a stop-loss is triggered, and taking profits when the dynamic target is reached.

6. Risk Warning and Subsequent Observations

Going forward, three types of indicators need close monitoring.

First, whether BTC can reclaim its mid-June rebound range. If BTC can only trade sideways at lower levels, the sustainability of altcoin rebounds will be limited. If BTC breaks out upwards with volume and drives ETH