Introduction
This bi-weekly quantitative report (April 25 to May 12) analyzes Bitcoin and Ethereum market trends using key indicators like long/short ratios, open interest, and funding rates. The quantitative section explores the “Moving Average Dense Breakout Strategy” for ETH/USDT trading, detailing its logic and signal mechanisms. Through systematic parameter optimization and backtesting, the strategy demonstrates robust trend identification and risk management, outperforming simple ETH holding approaches.
Key Takeaways
- BTC and ETH surged simultaneously: BTC gained ~34% while ETH soared over 60%
- ETH’s volatile long/short ratio: Indicates strong short-term trading activity without clear bearish retreat
- Divergent open interest growth: ETH showed stronger contract interest surge in early May
- Leveraged market turbulence: Early May saw concentrated short liquidations, followed by long liquidations on May 12
- Quantitative strategy success: The optimized moving average breakout approach yielded 127% annualized returns
Market Overview
1. Bitcoin vs. Ethereum Volatility Analysis
BTC and ETH maintained steady upward trajectories since mid-April:
– BTC: 78,000 → 105,000 USDT
– ETH: 1,600 → 2,600 USDT
Notable observations:
– ETH displayed greater price elasticity (+62.5% vs BTC’s +34.6%)
– BTC exhibited more stable volatility patterns
– ETH’s price jumps coincided with Pectra upgrade anticipation and regulatory optimism
– Volatility spikes in ETH indicated stronger momentum trading activity
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2. Long/Short Ratio (LSR) Dynamics
BTC LSR:
– Fluctuated near neutral (1.0) throughout the rally
– Brief dips below 1.0 signaled persistent hedging activity
ETH LSR:
– Showed violent oscillations between 0.8-1.3
– Failed to establish sustained bullish dominance
– Revealed intense intra-rally competition between bulls and bears
3. Open Interest Trends
| Metric | BTC | ETH |
|---|---|---|
| Start OI | $60B | $18B |
| Peak OI | $63B | $24B |
| Growth Rate | +5% | +33% |
ETH’s sharper contract interest growth suggests:
– Stronger speculative participation
– Greater leverage utilization
– Heightened trader interest during breakout
4. Funding Rate Analysis
Both assets maintained balanced funding:
– Predominantly 0% to +0.01% range
– Brief negative periods indicated healthy correction phases
– Absence of extreme positive rates suggested measured optimism
5. Liquidation Patterns
Key liquidation events:
– May 8: $836M short liquidations (bullish momentum)
– May 12: $476M long liquidations (correction phase)
This whipsaw action demonstrates:
– High leverage vulnerability
– Continuous position flushing
– Necessity for disciplined risk management
Quantitative Strategy: Moving Average Dense Breakout
(Disclaimer: Past performance doesn’t guarantee future results. Conduct independent research before trading.)
1. Strategy Framework
This momentum strategy identifies:
– Convergence periods (multiple MAs within 1.5% range)
– Breakout signals (price crossing MA cluster boundaries)
– Dynamic profit-taking/stop-loss mechanisms
2. Core Parameters
| Parameter | Optimal Value |
|---|---|
| MA Types | SMA20/60/120, EMA20/60/120 |
| Convergence Threshold | 1.4% |
| Reward/Risk Ratio | 10:1 |
| Timeframe | 2-hour candles |
3. Trade Execution Logic
Entry Conditions:
1. MA cluster formation (6 MAs within 1.4% range)
2. Price breaks:
– Upper cluster boundary → Long
– Lower cluster boundary → Short
Exit Rules:
– Longs: Close below entry’s lowest MA OR 10x risk reward
– Shorts: Close above entry’s highest MA OR 10x risk reward
4. Backtest Performance (May 2024-May 2025)
| Metric | Strategy | Buy & Hold |
|---|---|---|
| Annualized Return | +127.59% | -46.05% |
| Max Drawdown | <15% | >60% |
| Risk-Adjusted Return (ROMAD) | 8.61 | -0.77 |
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5. Key Optimization Insights
- Best parameters clustered in 1.3-1.5% threshold range
- 9-11 reward/risk ratios showed optimal balance
- Early trend detection crucial for capturing full moves
- Overly tight thresholds increased whipsaw losses
Frequently Asked Questions
Q: How does this strategy handle ranging markets?
A: The MA convergence filter naturally reduces trade frequency during choppy periods, while the reward/risk ratio ensures favorable expectancy when breakouts occur.
Q: Why use both SMA and EMA?
A: Combining simple and exponential MAs provides balanced sensitivity – EMAs react faster to recent prices, while SMAs offer smoother reference points.
Q: What’s the minimum capital requirement?
A: While technically executable with small amounts, we recommend ≥$5,000 to properly implement position sizing and risk management.
Q: How often does this strategy trade?
A: The 2-hour timeframe typically generates 3-5 signals monthly, avoiding over-trading while capturing meaningful trends.
Q: Can this work for other cryptocurrencies?
A: Yes, though parameters may need adjustment based on each asset’s volatility profile. ETH’s medium volatility makes it particularly suitable.
Q: What are the tax implications?
A: Frequent trading may generate short-term capital gains. Consult a tax professional in your jurisdiction for specific guidance.
Conclusion
The analyzed period showcased ETH’s superior momentum characteristics compared to BTC, though both assets face increasing leverage-induced volatility. The Moving Average Dense Breakout Strategy demonstrates how systematic trend-following can outperform buy-and-hold approaches during both rallies and corrections.
Traders should note that: