Introduction

This bi-weekly quantitative report (Feb 17 - Mar 3, 2025) provides an in-depth analysis of recent trends and dynamics in the cryptocurrency market through multi-dimensional data analysis. The report examines key indicators such as volatility, long-short trading volume ratio, open interest, and funding rates for major cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH) while also analyzing liquidation events in the crypto derivatives market. The quantitative backtesting section also delves into the MACD indicator, evaluating its performance and backtested returns.

Abstract

• Over the past two weeks, Bitcoin has responded more sharply to market movements, while Ethereum has remained weak, showing little upward momentum but relatively stable volatility.
• The BTC long/short ratio fluctuated between 0.90 and 1.10, while ETH remained in the 0.85–1.05 range, suggesting that bullish momentum remains weak.
• BTC funding rate fluctuations have intensified, coupled with a decline in open interest and a rise in long liquidations. This could signal a period of deleveraging or short-term liquidity tightening, indicating increased caution among traders.
• Daily issuance of Solana meme coins has fallen to 40,000, a 65% drop from the January peak, reflecting cooling enthusiasm for speculative assets.
• A back-tested mean-reversion strategy using a standardized MACD indicator effectively identifies pullback opportunities after sharp price surges, providing traders with potential short signals.

Market Overview

1. Bitcoin & Ethereum Volatility Analysis

Volatility is measured using the standard deviation (STD) of daily returns, quantifying asset returns’ dispersion. A higher STD indicates greater price swings and increased market uncertainty, while a lower STD suggests more stable price movements.

Over the past two weeks, BTC exhibited greater volatility than ETH, reflecting a market cycle of downtrend consolidation, stabilization, and rapid rebound. Starting on February 23, BTC volatility surged, while ETH remained relatively stable, signaling a cautious market sentiment. As selling pressure intensified, BTC dropped below $80,000, and ETH reached a low of $2,100, triggering a wave of market panic.

Figure 1. BTC has exhibited higher volatility than ETH, reflecting stronger price fluctuations

However, in early March, volatility for both assets spiked significantly, with BTC experiencing the most pronounced swings. This sudden shift was likely triggered by U.S. President Donald Trump’s announcement to establish a U.S. Crypto Reserve, which reportedly includes BTC, ETH, SOL, XRP, and ADA as core assets. Following this news, market sentiment rebounded sharply, pushing BTC back above $90,000, while ETH recovered to around $2,500.

Figure 2. BTC surged past $90,000, whereas ETH only recovered to $2,500, indicating relative weakness.

Overall, BTC has demonstrated higher market sensitivity, while ETH remains weak, lacking strong upward momentum but showing lower volatility. If high volatility persists, the market may have further upside potential in the short term. Otherwise, choppy price action and consolidation remain key risks. [1][2]

2. Bitcoin and Ethereum Long/Short Taker Size Ratio (LSR) Analysis

The Long/Short Taker Size Ratio (LSR) is a key indicator used to measure the volume of aggressive buy (long) and sell (short) market orders, providing insight into market sentiment and trend strength. An LSR above 1 indicates that buy-side market orders (longs) exceed sell-side market orders (shorts), suggesting a more bullish sentiment.

According to Coinglass data, the BTC LSR has remained between 0.90 and 1.10, showing an inverse correlation with price movements. This suggests that traders tend to buy the dip when prices fall but are more cautious during rebounds. Meanwhile, ETH’s LSR has been more volatile, fluctuating between 0.85 and 1.05. Despite some recovery after recent declines, ETH has shown weaker rebound momentum, and even as its LSR improves, the market lacks strong upward conviction. This indicates higher uncertainty surrounding ETH’s price action, with slower capital inflows than BTC.

While BTC has shown some resilience amid choppy price action, its LSR suggests that underlying support remains intact. On the other hand, ETH continues to underperform, with investor sentiment remaining cautious. Traders should monitor whether market sentiment improves further, as this will be crucial in determining the next directional move.[3]

Figure 3. BTC Long/Short Ratio remains between 0.90 and 1.10, indicating a balanced market sentiment

Figure 4. ETH Long/Short Ratio has shown greater fluctuations, stabilizing between 0.85 and 1.05

3. Open Interest Analysis

According to Coinglass data, BTC futures open interest (OI) has sharply declined over the past two weeks, dropping below $51 billion. This could be attributed to leveraged position liquidations, market-driven deleveraging, or capital reallocations, reflecting a more cautious market sentiment. In early March, BTC open interest rebounded from its lows but remained below February’s peak, indicating that capital inflows are still relatively conservative.

In contrast, ETH open interest remained relatively stable and did not experience a significant drop during the market downturn in late February. This suggests that leverage positions in ETH were more cautiously managed. However, despite BTC’s recovery, ETH open interest only saw a modest increase, indicating a lack of strong confidence in ETH’s rebound and slower capital inflows compared to BTC.

BTC’s sharp fluctuations in open interest suggest a more active short-term trading environment, while ETH’s relatively stable open interest indicates that the ETH market is in a more wait-and-see mode. If BTC open interest continues to rise, it could provide momentum for further price gains. However, if capital inflows remain weak, the market may trade sideways.[4]

Figure 5. BTC open interest shows a stronger rebound, while ETH open interest remains subdued, reflecting lower confidence in an ETH recovery

4. Funding Rates Analysis

Over the past two weeks, BTC and ETH funding rates have experienced significant fluctuations, reflecting shifts in market leverage sentiment. While their funding rate trends were largely synchronized, there were notable periods of divergence. Additionally, BTC funding rate volatility was more pronounced than ETH’s, frequently dipping into negative territory. This suggests that short positions dominated the BTC derivatives market, leading to a bearish bias in market sentiment and causing funding rates to turn negative.

BTC funding rate volatility has intensified in the past two weeks, with multiple occurrences of negative funding. Combined with declining open interest and rising long liquidations, this could indicate a phase of market deleveraging or strengthening short-term bearish sentiment. For traders, funding rate fluctuations serve as a key signal of market capital positioning, potentially impacting short-term price movements and the overall leverage structure.[5][6]

Figure 6. BTC funding rate has shown greater volatility than ETH and has frequently turned negative

5. Cryptocurrency Contract Liquidation Trends

According to Coinglass data, the cryptocurrency derivatives market has experienced multiple large-scale liquidations over the past month. Between February 24 and March 3, the average daily liquidation volume across both long and short positions reached $732 million, representing a 42% increase from February 1 to February 17. Long positions dominated these liquidations, averaging $542 million per day, while short liquidations averaged $190 million daily. This pattern suggests that leveraged long positions face significantly higher liquidation risks during sharp market downturns.

During extreme market conditions, steep price drops often trigger long liquidations, worsening market liquidity and leading to a “liquidation cascade effect.” In contrast, short liquidations were relatively lower in magnitude but surged during sharp market rebounds. For instance, on March 2, short liquidations spiked. By analyzing funding rates, open interest, and liquidation trends, traders should remain cautious of rapid sentiment shifts that can heighten leverage risks. Proper position sizing and risk management are essential to avoid significant losses in highly volatile market environments. [7]

Figure 7. Between Feb 24 and Mar 3, the average daily liquidation volume in the derivatives market reached $732 million

6. Decline in Daily Issuance of Solana-Based Meme Coins

In January, the Solana meme coin market experienced a peak surge, with a wave of new projects emerging. Among them, the TRUMP token, launched by former U.S. President Donald Trump, garnered the most attention, driving increased activity in the ecosystem. However, due to rising risk-averse sentiment, tighter liquidity conditions, and evolving regulatory factors, many speculative meme coins failed to sustain investor interest, leading to a sharp decline in new issuances. As a result, the meme market on Solana has significantly cooled down, with daily new meme coin issuances dropping to 40,000—a 65% decrease from its January peak. This decline reflects diminishing enthusiasm for short-term speculative assets.

The slowdown in meme coin issuance has also affected overall trading activity on the Solana network. Gas fee revenue has declined sharply from $35 million (January 19) to $1.49 million (March 3), representing a 95% decline. Key network indicators have also shown significant decreases, including on-chain trading volume, active addresses, and Total Value Locked (TVL). These trends indicate a weakening liquidity cycle in the meme sector, with investors shifting toward a more risk-averse stance. [8][9]

Figure 10. Daily issuance of Solana meme coins has fallen to 40,000—a 65% drop from its January peak

Quantitative Analysis - Standardized MACD Strategy: Validating a Mean Reversion Trading Approach in the Crypto Market

(Disclaimer: All predictions in this article are based on historical data and market trends. They are for reference only and should not be considered investment advice or guarantees of future market movements. Investors should carefully consider risks and make informed decisions.)

This section introduces the Standardized MACD (MacNorm) indicator and its application in a mean reversion trading strategy through backtesting on the BTC/USDT trading pair. The Standardized MACD is an enhanced version of the traditional MACD, which normalizes MACD values within a fixed range (typically between -1 and +1). This adjustment makes the indicator more comparable across different market conditions, focusing on price movements’ relative strength and direction.

Core Components & Mechanism

The Standardized MACD indicator consists of two key components: the MacNorm Line and the Trigger Line. The MacNorm Line, which is the normalized main line, captures the relationship between short-term and long-term market momentum. When it is above 0, it indicates that short-term bullish momentum is stronger, whereas a value below 0 suggests that short-term bearish pressure dominates. The Trigger Line is a weighted moving average (WMA) of the MacNorm Line, serving as a signal line to confirm and filter trade signals. It moves more smoothly and typically lags behind the MacNorm Line. In the visualization, the red line represents the MacNorm Line (fast line), while the green line represents the Trigger Line (slow line), both constrained within a -1 to +1 range. The Standardized MACD employs several key parameters, each playing a critical role in its calculation and effectiveness as a trading tool.

Figure 11. Visualization of the MACD Indicator

1. Fast Moving Average (FastMA) Period
   This parameter defines the calculation period for the short-term moving average, which represents the short-term price trend. A smaller value makes the indicator more sensitive to price changes, allowing it to capture market movements quickly but at the cost of generating more false signals. This parameter affects the numerator or denominator in ratio calculations, influencing overall momentum assessment.

2. Slow Moving Average (SlowMA) Period
   This parameter controls the calculation period for the long-term moving average, representing the broader market trend. A larger value results in a smoother trend, reducing false signals and causing greater signal lag. The contrast between the fast and slow lines is the core of MACD, and this parameter determines the degree of “slowness” in trend analysis.

3. Trigger Line Period
   This determines the calculation of the weighted moving average (WMA) of the MacNorm Line, forming the Trigger Line. A smaller value makes the trigger line closely follow the MacNorm Line, generating more frequent but earlier signals. Conversely, a larger value results in a smoother signal line with fewer but potentially more reliable signals.

4. Normalization Period
   This is the lookback period used for standardization, where the MACD’s highest and lowest values within this period are identified to scale the result within a -1 to +1 range. A larger normalization period provides more stable standardization since it considers a broader price history, whereas a smaller period makes the standardization more dynamic but may lead to frequent indicator fluctuations.

5. Moving Average Type
   This parameter determines the method used to compute FastMA and SlowMA. The chosen method affects how price movements are weighted:

• Exponential Moving Average (EMA) assigns greater weight to recent prices, making it more responsive to market changes.
• Weighted Moving Average (WMA) applies a linear weight distribution over time.
• Simple Moving Average (SMA) gives equal weight to all prices, producing the smoothest trend.

Different moving average types influence indicator sensitivity and signal generation timing, affecting overall strategy effectiveness.

For a detailed breakdown of the calculation formulas, please refer to [10].

Practical Trading Strategy

The core logic of this trading strategy is based on the Mean Reversion theory, which assumes that prices tend to revert to their long-term average. The strategy utilizes the Standardized MACD indicator to identify excessive price deviations. Specifically, if the fast indicator (MacNorm) has remained at a high level (>0.995) for the past four periods but suddenly drops below the slow signal line (Trigger) in the current period, it signals that the price may have deviated too far from the mean, triggering a short-selling signal in anticipation of a return to the mean.

The holding period is fixed at N candlestick cycles (denoted as lag_N), and the backtesting period spans from March 3, 2024, to March 3, 2025, using 1-minute MACD signals. Transaction costs such as fees and slippage are not considered in this study.

Five core parameters define this strategy:

• Fast Moving Average Period (fast_length)
• Slow Moving Average Period (slow_length)
• Signal Line Smoothing Period (trigger_length)
• Normalization Lookback Period (norm_period)
• Moving Average Type (ma_type, set to EMA for this study)

To identify the optimal parameter combinations, we conducted backtests across the following ranges:

• Fast MA Period: 8, 10, 12, 14
• Slow MA Period: 16, 18, 21, 24
• Signal Smoothing Period: 9, 12, 15, 18
• Normalization Period: 5, 7, 9, 11
• Moving Average Type: EMA

To ensure strategy robustness, we applied two filtering criteria: a minimum win rate of 55% and a minimum of 50 trades per backtesting period. We then selected the top five parameter combinations with the highest average returns. This multi-layer filtering approach helps identify optimal parameters and reduces the risk of overfitting.

Figure 12. Cumulative returns based on the top five selected parameter sets, with equal-weighted allocation across strategies

Figure 13. Risk-Return Analysis – Sharpe Ratio

Figure 14. Total Return Performance

Trading Strategy Summary

Based on our backtesting analysis, we have identified five optimal parameter sets that delivered outstanding performance:

• Fast MA: 14, Slow MA: 18, Signal Smoothing: 18, Normalization Period: 7
• Fast MA: 14, Slow MA: 18, Signal Smoothing: 15, Normalization Period: 7
• Fast MA: 14, Slow MA: 21, Signal Smoothing: 18, Normalization Period: 5
• Fast MA: 14, Slow MA: 18, Signal Smoothing: 9, Normalization Period: 7
• Fast MA: 14, Slow MA: 18, Signal Smoothing: 12, Normalization Period: 7

These five optimized parameter sets were combined into a single composite trading strategy with equal weighting. Backtest results indicate that this strategy consistently generates stable returns regardless of the holding period. More importantly, as the holding period extends, the return curve shows a clear upward trend, suggesting that the strategy provides a notable advantage in long-term investment scenarios.

By analyzing the Sharpe ratio (risk-adjusted returns) and total return performance across different holding periods, we found that holding periods longer than 30 cycles consistently outperformed shorter holding periods in both metrics. This demonstrates that the strategy delivers higher cumulative returns over longer holding periods and achieves better risk management outcomes.

Conclusion

Between February 17 and March 3, the cryptocurrency market experienced significant volatility, with BTC exhibiting much higher price fluctuations than ETH. Driven by policy developments, BTC staged a rapid rebound. The long/short ratio analysis strongly supported BTC, whereas ETH lacked upward momentum. Futures open interest data revealed that BTC leveraged trading remained highly active, while ETH trading was more conservative. Funding rate volatility reflected intense long-short market battles, and liquidation data highlighted elevated market risks, particularly with heavy liquidation pressure on long positions. Additionally, the Solana meme coin market cooled significantly, with a sharp decline in daily new issuances, signaling a weakening speculative appetite.

From a quantitative analysis perspective, the standardized MACD-based mean reversion strategy effectively identified price pullbacks after excessive rallies, providing clear short-selling signals through backtesting and parameter optimization. However, no trading strategy is foolproof, and rapid market changes could impact its effectiveness. Traders should apply strategies cautiously and consider refining and optimizing them based on their risk tolerance and trading preferences.


References:

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. Solscan,https://solscan.io/analytics#tokens
9. DefiLlama,https://defillama.com/chain/solana?chainFees=false&tvl=true
10. Tradingview,https://tw.tradingview.com/script/zWnQDQOu-MACD-Normalized/

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