Gate Research Institute: BTC and ETH remain in low-range consolidation, with moving average breakout strategies capturing structural market trends

##Market Overview

To systematically present the current capital behavior and trading structure changes in the cryptocurrency market, this report approaches from five key dimensions: Bitcoin and Ethereum price volatility, Long/Short Trading Ratio (LSR), contract open interest, funding rates, and market liquidation data. These five indicators cover price trends, market sentiment, and risk conditions, providing a comprehensive reflection of current market trading intensity and structural features. The following will sequentially analyze the latest changes of each indicator since December 9:

###1. Bitcoin and Ethereum Price Volatility Analysis

According to CoinGecko data, from December 9 to December 22, BTC and ETH showed a pattern of weak recovery after oscillating downward, with price centers significantly lower than previous highs, and market sentiment remaining cautious. BTC surged temporarily then quickly fell back, entering a range of repeated oscillations, with multiple rebounds constrained by overhead resistance, failing to form an effective trend reversal, mainly showing technical correction within lower ranges; ETH’s movement was highly synchronized with BTC but with a steeper downward slope, often breaking short-term support levels during declines, with a lagging rebound pace, and overall strength slightly weaker than BTC.【1】【2】【3】

Structurally, after a clear volume-driven decline in the middle segment, both formed a brief stabilization at low levels and entered a sideways consolidation zone, but rebound heights were limited, indicating buying power remains weak. ETH experienced technical rebounds near lows but did not change the overall weak pattern, with weak capital inflow; BTC performed slightly steadier but also lacked sustainability in rebounds, mostly driven by short-seller covering.

Overall, BTC and ETH have not yet broken free from the consolidation structure after mid-term corrections, with the market still mainly oscillating weakly. In the absence of new funds and clear trend signals, short-term rebound space is limited, and price movements mainly fluctuate within ranges. Only after key resistance levels are effectively broken can a structural shift be confirmed.

Figure 1: BTC and ETH fall back from highs into weak consolidation zones, with slow rebound slopes, with the overall trend still mainly oscillating and recovering

BTC and ETH’s short-term volatility still show high-frequency oscillations, with multiple sharp spikes of rapid increases, but there are clear differences in their volatility intensities. Measured on the left axis, BTC volatility fluctuates frequently but with limited peak amplitudes, indicating market sentiment remains restrained during price swings; in contrast, ETH volatility spikes more significantly, with multiple high peaks, reflecting more intense short-term capital inflows and outflows, and higher emotional sensitivity than BTC.

Structurally, ETH volatility enlargements often correspond to rapid price drops or surges, indicating a high proportion of speculation and short-term trading; BTC volatility, while rising in sync, remains controlled, mainly reflecting range battles near key price levels rather than uncontrolled emotional swings.

Overall, the market remains in an active but directionless phase. ETH’s high volatility suggests short-term risks and opportunities coexist, while BTC’s relatively low volatility indicates dominant funds remain cautious. If both volatility measures rise simultaneously with increased trading volume, it could signal the start of a new directional trend.

Figure 2: BTC volatility remains controlled with smaller peaks; ETH volatility frequently enlarges, with more sensitive short-term sentiment reactions

###2. Bitcoin and Ethereum Long/Short Trading Scale Ratio (LSR) Analysis Long/Short Taker Size Ratio (LSR) measures the proportion of active long and short funds in the market. An LSR greater than 1 indicates a bullish market dominated by long orders; below 1 indicates a bearish bias. This indicator reflects trading sentiment strength and momentum changes.

According to Coinglass data, from December 9 to December 22, the LSR of BTC and ETH hovered around 1 without sustained unilateral bias, indicating leverage funds mainly engaged in short-term trading and range battles, and the market has yet to form a consensus on direction. Currently, LSR reflects frequent sentiment shifts rather than trend-based position accumulation.【5】

Structurally, BTC’s LSR fluctuates within a relatively narrow range, mostly between 0.95–1.05, showing cautious attitude from dominant funds; ETH’s LSR exhibits larger swings, often dropping near 0.90 and rising to around 1.05, indicating a higher proportion of short-term leverage and emotional funds, with greater sensitivity to price fluctuations than BTC.

Overall, BTC and ETH’s LSR are more suitable as sentiment indicators in a ranging market. When LSR rises, it often corresponds to a temporary rebound; when it dips to lows, it may accompany short-term stabilization, but before reaching extreme values, the market likely remains in range consolidation, favoring contrarian and range trading strategies.

Figure 3: BTC’s LSR remains between 0.95–1.05, indicating cautious dominant fund attitude

Figure 4: ETH’s LSR fluctuates more widely, reflecting higher short-term leverage and emotional fund participation, with greater sensitivity to price swings than BTC

###3. Contract Open Interest Analysis

According to Coinglass data, combining BTC and ETH’s open interest and price performance over the past two weeks, the overall market remains in a de-leveraged correction phase. During rebounds, open interest did not show sustained expansion, indicating price volatility is mainly driven by existing funds rather than new trend-driven positions, with market risk appetite still low.【6】

Structurally, BTC’s open interest remained relatively stable, with only mild recovery after declines, reflecting cautious attitude from main funds, preferring risk control over leverage expansion; ETH’s open interest spiked during phase rebounds but quickly fell back, indicating high short-term participation but low stability, with more emotional trading.

Overall, with current open interest not showing significant volume growth and funding rates and LSR not forming trend signals, BTC and ETH are more likely to maintain range-bound oscillations. Only when prices rise with continuous open interest expansion and leverage structure stabilizes can a new trend be confirmed.

Figure 5: BTC’s open interest remains relatively stable, with mild recovery after declines, reflecting cautious main fund attitude

Figure 6: ETH’s open interest briefly expands during rebounds but quickly falls back, indicating active short-term funds but unstable positions

###4. Funding Rates

BTC and ETH funding rates during the observation period show high-frequency oscillations, mainly fluctuating around zero with rapid shifts, indicating market sentiment swings and lack of directional consensus. BTC funding rates often switch from positive to negative and back, with significant amplitude, reflecting continuous leverage adjustments during price oscillations and corrections, with overall risk appetite remaining cautious.【7】【8】

ETH’s funding rate trends roughly mirror BTC but with more volatile short-term fluctuations, turning negative more easily during corrections and quickly recovering to near zero, indicating higher frequency of short-term capital inflows and outflows, and greater sensitivity to price changes. Compared to BTC, ETH’s funding rate amplitude is slightly higher, with more trading-driven features.

Overall, current funding rates remain in a state of “no clear direction, rapid rotation,” reflecting a market dominated by short-term speculation, with no stable trend leadership. If funding rates can sustain above zero with rising volume, it may signal market stabilization or rebound; if negative, watch for increasing bearish pressure.

Figure 7: BTC and ETH funding rates oscillate around zero with high frequency, sentiment swings, and unclear market direction

###5. Cryptocurrency Contract Liquidation Charts

According to Coinglass data, from the distribution of liquidations, the market over the past two weeks shows frequent two-way liquidations but no continuous unilateral squeezes. Liquidation scales mostly stay at medium-low levels, with occasional significant spikes, indicating price fluctuations are mainly driven by leverage adjustments within oscillations rather than large-scale squeezes caused by trend moves.【9】

Structurally, long liquidations significantly enlarge during multiple price declines, reflecting high-pressure long positions during rebounds, with insufficient leverage stability; short liquidations occur mainly during short-term rebounds or rapid surges but are limited in persistence, indicating short-term betting rather than continuous short squeeze. The liquidation rhythm shows “rapid release—quick fall,” with frequent leverage entry and exit.

Overall, the liquidation structure aligns with previous signals from funding rates, LSR, and open interest, indicating the market remains in a de-leveraged correction phase. Before sustained large-scale liquidations in one direction, the market is likely to stay in range; only when liquidation concentrates unilaterally and resonates with price breakouts can a new trend be confirmed.

Figure 8: Liquidation scale remains at medium-low levels, with occasional spikes, mainly driven by leverage adjustments within oscillations rather than trend-driven squeezes

In the current weak oscillating environment, BTC and ETH are in low-range consolidation, with limited rebound momentum, multiple volatility enlargements, and high market caution and sensitivity at key levels. The long/short ratio, funding rates, and open interest all show high-frequency oscillations and low levels, reflecting uncertain leverage directions and low risk appetite, with the overall market still in a de-leveraged correction phase.

In this context, trading focus shifts to precise identification of trend reversals and structural breakouts. Future analysis will focus on the performance of moving average breakout strategies during weak oscillations and trend initiations, assessing their effectiveness in filtering noise, improving entry/exit discipline, and reducing emotional trading risks.

Quantitative Analysis – Moving Average Dense Breakout Strategy

(Disclaimer: All predictions in this article are based on historical data and market trend analysis, for reference only, not investment advice or guarantees of future market movements. Investors should conduct thorough research and consider risks carefully before investing.)

###1. Strategy Overview

The “Moving Average Dense Breakout Strategy” is a momentum approach combining technical trend judgment. It observes the convergence of multiple short- and medium-term moving averages (e.g., 5, 10, 20 days) over a specific period to identify potential upcoming directional volatility. When multiple moving averages converge and approach each other, it usually indicates a consolidation phase awaiting a breakout. If the price then clearly breaks above the moving average zone, it signals a bullish entry; if it breaks below, it signals a bearish entry.

To enhance practicality and risk control, the strategy also incorporates fixed profit-taking and stop-loss mechanisms, ensuring timely entries and exits when trends emerge, balancing returns and risk management. The overall approach aims to capture medium-short-term trend moves with disciplined and operational simplicity.

###2. Core Parameter Settings

###3. Strategy Logic and Operation Mechanism

####Entry Conditions

• Moving average convergence:
  • Calculate the maximum and minimum of SMA20, SMA60, SMA120, EMA20, EMA60, EMA120 (called the moving average distance). When the distance is below a set threshold (e.g., 1.5% of price), it indicates dense moving averages.
  • Threshold is a critical value representing the minimum or maximum effect level.
• Price breakout judgment:
  • When the current price crosses above the highest of the six moving averages, it triggers a bullish breakout signal, initiating a buy.
  • When the current price drops below the lowest of the six moving averages, it triggers a bearish breakout signal, initiating a sell.

####Exit Conditions: Dynamic profit and loss mechanisms

• Long position exit:
  • If the price falls below the lowest moving average at entry, trigger stop-loss;
  • Or if the price rises beyond the “entry price and lowest moving average distance × risk-reward ratio,” trigger take-profit.
• Short position exit:
  • If the price rises above the highest moving average at entry, trigger stop-loss;
  • Or if the price falls below the “entry price and highest moving average distance × risk-reward ratio,” trigger take-profit.

####Practical Example Chart

• Trade signal trigger

The following chart shows the strategy triggered entry on April 22, 2025, on the 4-hour K-line of SUI/USDT. It is evident that after dense moving averages, the price broke upward, meeting the entry conditions. The system executed a buy at the breakout, successfully capturing the start of the subsequent rally.

Figure 9: Actual entry point illustration of SUI/USDT strategy conditions triggered (April 22, 2025)

• Trade action and result

The system automatically exits based on the dynamic profit-taking mechanism after reaching the preset profit-loss ratio, effectively locking in major gains. Although further upside remained, the operation followed the strategy discipline, demonstrating good risk control and execution stability. Combining with trailing stops or trend-following mechanisms could further extend profit potential in strong trends.

Figure 10: SUI/USDT strategy exit point illustration (April 25, 2025)

The above practical example vividly demonstrates the entry logic and dynamic profit-taking mechanism when the strategy is triggered by dense moving averages and price breakouts. By linking price and moving average structures, it accurately captures trend initiation points and automatically exits during subsequent fluctuations, locking in main profits while controlling risks. This case validates the strategy’s practicality and discipline, and its stability and risk control in real markets, laying a foundation for parameter optimization and strategy summarization.

###4. Practical Application Example

####Parameter Backtest Settings To find the optimal parameter combination, a systematic grid search was conducted over the following ranges:

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

Taking DOGE/USDT as an example, in backtest data of the past year’s 4-hour K-line, the system tested 23,826 parameter combinations, selecting the top five with the best cumulative returns. Evaluation metrics include annualized return, Sharpe ratio, maximum drawdown, and ROMAD (return-to-max drawdown ratio), providing a comprehensive performance assessment.

Figure 11: Performance comparison table of the five best strategies

####Strategy Logic Explanation Using DOGE as an example, when the system detects the six moving averages’ distance converging within 2.2%, and the price breaks upward through the upper edge of the moving averages, it triggers a buy signal. This structure aims to catch the imminent breakout, entering at the current price, and using the breakout of the highest moving average as a dynamic take-profit reference to enhance return control.

The settings used are:

• percentage_threshold = 2.2 (max distance of six moving averages)
• tp_sl_ratio = 6 (dynamic take-profit setting)
• short_period = 6, long_period = 14 (moving average observation periods)

####Performance and Result Analysis Backtest from October 1, 2024, to December 17, 2025, shows this parameter set performed excellently, with an annualized return of 93.04%, maximum drawdown below 10%, and ROMAD of 9.32, indicating stable capital growth and effective downside risk reduction.

This article also compares the top five parameter sets, which currently achieve the best balance between return and stability, with strong practical value. Future improvements could include dynamic threshold adjustments or incorporating volume and volatility filters to enhance adaptability in oscillating markets and extend to multi-asset, multi-timeframe strategies.

Figure 12: Performance comparison of the top five strategies over the past year

###5. Trading Strategy Summary

The dense moving average trend strategy uses the divergence of highly converged short- and medium-term moving averages as the core entry signal, combined with staged position increases and dynamic profit-taking, aiming to steadily accumulate gains after trend confirmation. Backtest results over the past year show that DOGE, ADA, and SOL triggered trend moves after multiple dense moving averages, with cumulative returns showing a clear stepwise upward pattern, with ADA and DOGE’s gains exceeding 100% at times, and SOL maintaining steady growth, demonstrating good trend-following ability in high-volatility and moderate-trend assets; XRP and TRX tend to be more range-bound but still contribute stable positive returns during clear trend phases, keeping overall portfolio returns expanding.

From the curve structure, this strategy performs especially well in environments of market rotation and oscillation transitioning into trends. Moving averages as a filtering mechanism effectively reduce chasing high risks, allowing the strategy to hold gains through multiple pullbacks, with cumulative returns gradually rising rather than recovering from large drawdowns. However, in rapid trending rallies, the need for confirmation causes entry points to be relatively conservative, missing some initial gains, resulting in slightly lower elasticity compared to extreme beta assets.

From a long-term risk-return allocation perspective, the moving average trend strategy is suitable as an aggressive or enhanced return component within a portfolio, but should be complemented with low-volatility, drawdown-controlled strategies to balance overall risk. For example, Gate Quantitative Fund’s core neutral arbitrage, hedging, and capital efficiency strategies can provide smoother returns across market cycles. While trend strategies offer higher profit potential, they also involve higher drawdowns and frequent position adjustments. Combining both can improve long-term compounding while controlling overall volatility.

##Summary

From December 9 to December 22, 2025, the overall crypto market remains in a post-correction weak oscillation phase, with declining fund participation and risk appetite. BTC and ETH prices have repeatedly consolidated after high-level declines, with weak rebound continuity, showing no signs of trend recovery; short-term volatility spikes multiple times, reflecting high market sensitivity at key levels, with emotional stability still low. The long/short trading ratio hovers around 1, indicating a lack of clear active fund direction; funding rates frequently switch between positive and negative, further confirming leverage funds mainly engage in short-term adjustments, with no established bullish consensus.

From derivatives structure, BTC and ETH’s contract open interest, after significant deleveraging, has not effectively recovered, remaining at low levels, with little new leverage entering, indicating the overall leverage system remains in a cautious, observing state. Liquidation distribution shows concentrated long liquidations during declines, while short liquidations are limited, indicating downward risks are not fully cleared but no large-scale squeeze has formed. Overall, the market remains in a weak consolidation phase, with chips adjusting and sentiment oscillating; before clear fund inflows or volume-price resonance signals, short-term trends may still weaken further or trigger localized liquidations.

In the current oscillating, weak market environment, the moving average breakout strategy remains practically viable. Backtests show DOGE, ADA, and SOL, after moving averages shift from convergence to divergence, exhibit clear stepwise upward returns; XRP and TRX, though less volatile, still contribute stable positive gains during trend phases, demonstrating consistent trend capture ability across different assets. The overall return structure emphasizes steady accumulation rather than reliance on extreme market moves.

However, in rapid trending rallies, the strategy’s reliance on moving average confirmation may cause slow entries or early stop-losses, missing some initial gains. Combining volatility filters, moving average slopes, or volume indicators could improve trend initiation recognition and cycle adaptability. Compared to this, Gate Quantitative Fund’s core neutral arbitrage and hedging strategies focus on drawdown control and smoothing, suitable as a stable underlying allocation, complementing trend strategies.

References:

1. CoinGecko, https://www.coingecko.com/
2. Gate, https://www.gate.com/trade/BTC_USDT
3. Gate, https://www.gate.com/trade/ETH_USDT
4. Sosovalue, https://sosovalue.com/assets/etf/us-btc-spot?from=moved
5. Coinglass, https://www.coinglass.com/LongShortRatio
6. Coinglass, https://www.coinglass.com/BitcoinOpenInterest?utm_source=chatgpt.com
7. Gate, https://www.gate.com/futures_market_info/BTC_USD/capital_rate_history
8. Gate, https://www.gate.com/futures/introduction/funding-rate-history?from=USDT-M&contract=ETH_USDT
9. Coinglass, https://www.coinglass.com/pro/futures/Liquidations
10. Gate, https://www.gate.com/institution/quant-fund

Gate Research Institute is a comprehensive blockchain and cryptocurrency research platform, providing in-depth content including technical analysis, hot insights, market reviews, industry research, trend forecasts, and macroeconomic policy analysis.

Disclaimer
Investing in cryptocurrencies involves high risks. Users should conduct independent research and fully understand the nature of assets and products before making any investment decisions. Gate is not responsible for any losses or damages caused by such investment decisions.