In-depth Analysis of Four-Asset Momentum Rotation Strategy: Assessment of Market Environment Adaptability and Sustainability

• Asset Scope
  : This backtest uses four types of assets: QQQ (Nasdaq 100), GLD (Gold), TLT (20+ Year Treasury), SHV (Short-term Bond Cash Equivalent)
• Signal Rules
  : Weekly selection of the single asset with the strongest momentum based on “past 20-day return” for holding (weekly rebalancing)
• Comparison Benchmark
  : Four-asset equal-weight portfolio (weekly rebalancing)

• The “annualized 24.24%, Sharpe 1.08” reported in the original text is a backtest result under another asset/time setting. Based on broker API data from 2020-01-02 to 2025-12-26, under the setting of single asset holding and weekly rebalancing, this article得出:
  annualized ~16.89%, Sharpe ~0.80
  [0]. The two cannot be directly equated due to differences in asset set, rebalancing rules, and sample period.

• Momentum Rotation: Annualized return 16.89%, annualized volatility 18.51%, Sharpe 0.80, maximum drawdown ~-28.61%[0]
• Equal Weight: Annualized return 8.89%, annualized volatility9.29%, Sharpe0.74, maximum drawdown ~-28.61%[0]

Divided by VIX median and 75th percentile (low volatility, high volatility, crisis)[0]:

• Low Volatility (VIX<19): Momentum strategy annualized ~55.26%, Sharpe ~3.63; Equal Weight ~24.46%, Sharpe ~3.13[0]
• High Volatility (19≤VIX<24): Momentum strategy annualized ~13.08%, Sharpe ~0.61; Equal Weight ~1.45%, Sharpe ~-0.06[0]
• Crisis (VIX>24): Momentum strategy annualized ~17.56%, Sharpe ~0.62; Equal Weight ~-10.71%, Sharpe ~-1.01[0]

• Feb-Mar 2020 (Pandemic Shock): VIX mean ~46.42; Momentum strategy cumulative ~10.46%, Equal Weight ~-4.21%[0]
• Full Year 2022 (Rate Hike Cycle): VIX mean ~25.62; Momentum strategy cumulative ~1.01%, Equal Weight ~-17.19%[0]
• Mar 2023 (Regional Bank Turmoil): VIX mean ~22.94; Momentum strategy cumulative ~6.02%, Equal Weight ~3.94%[0]

• Weekly Selection Distribution: QQQ ~145 weeks, GLD ~86 weeks, TLT ~43 weeks, SHV ~39 weeks[0]
• Annual average turnover ~17.3 times[0]

• Cumulative Return Curve
  : Momentum strategy cumulative performance outperforms equal weight, but with higher volatility and drawdown; extreme volatility periods in 2020 and 2022 tested the strategy
• Annualized Return by Environment
  : Momentum significantly outperforms in low volatility environments, maintains positive returns in crisis periods but with higher volatility
• Asset Selection Frequency
  : Risk assets (QQQ/GLD) are selected far more frequently than defensive assets (TLT/SHV), showing a preference for growth and commodities
• Drawdown Comparison
  : Momentum and equal weight have similar maximum drawdown levels in extreme shocks, both requiring supporting risk control

• Rolling Volatility (60-day)
  : Momentum strategy volatility is overall higher than equal weight, showing “return-volatility homology” feature
• VIX vs Daily Return Scatter
  : Higher variance in daily returns in high VIX intervals, indicating signals are more prone to “oscillation or sharp reversal” in crisis periods
• Monthly Return Heatmap
  : Continuous drawdowns in some months, showing weekly single asset holding is sensitive to short-term reversals
• Rolling Sharpe Ratio
  : Sharpe can exceed 1 over long cycles, but阶段性 drops to near 0, requiring position/risk budget constraints

##4. Source of Historical High Returns and Sustainability

###4.1 Drivers of Historical High Returns (Based on Sample Period Data)

• Momentum Premium: In the bull and volatile environment of 2020-2025, assets with strong trends (especially growth and commodities) contributed most excess returns[0]
• Low Volatility Dividend: Momentum strategy shows extremely high risk-adjusted returns (Sharpe>3) in low VIX intervals, indicating momentum signals remain effective in stable markets[0]
• Asset Differentiation: Nasdaq and Gold cumulative returns are both ~189%-190% in 2020-2025, providing continuous strong momentum assets for rotation[0]
• Crisis Defense: In 2020 and2022, the strategy showed relative resilience or逆势 returns compared to equal weight[0]

###4.2 Key Constraints on Sustainability

• Turnover and Costs: Annual ~17.3 times turnover brings transaction and slippage costs; real-world rebalancing spreads and impact costs will significantly erode net returns
• Market System and Microstructure Changes: Increased high-frequency and algorithmic participants, uneven ETF liquidity distribution, amplified intraday volatility may shorten or invalidate momentum windows
• Momentum Crash and Reversal Risk: Literature shows momentum is prone to “crashes” in sharp reversal periods, especially when switching from loose to tight monetary policy or extreme risk aversion
• Sample Dependence: 2020-2025 includes extreme volatility and loose monetary environment; if future enters long-term low volatility or structural differentiation weakens, annual returns may decline

###4.3 Understanding Original “24.24%” vs This Article’s Results

• Original 24.24% is a result under different backtest settings (may involve longer historical window, different asset portfolio, different rebalancing rules or leverage use)
• This article uses weekly single asset holding and 2020-2025 data, with annualized ~16.89% under net return with real transaction costs, need to distinguish from original text[0]
• Therefore, “whether historical high returns are sustainable” should be based on replication under the same setting and out-of-sample rolling validation, minus transaction and impact costs

##5. Improvement Suggestions and Implementation Points

###5.1 Optimization Directions at Signal and Portfolio Levels

• Extend Lookback Period: Expand from 20 days to60 days or combine with different periods to mitigate short-term noise and frequent turnover
• Add Reversal/Mean Reversion Filter: When multi-asset momentum is similar or momentum deviates from valuation, reduce position or switch to equal weight/cash
• Multi-Factor Combination: Overlay trend strength, volatility target, value or quality factors to build a more robust scoring system
• Dynamic Volatility Target: Dynamically adjust portfolio exposure based on VIX or realized volatility to smooth Sharpe and drawdown

###5.2 Execution and Risk Control Points

• Transaction Cost Sensitivity Test: Perform rolling backtest on net returns under different transaction cost assumptions, set turnover upper limit and rebalancing trigger threshold
• Rebalancing Optimization: Use threshold trigger (only turnover when momentum relative advantage exceeds X%) or partial adjustment (reduce single-point impact)
• Leverage and Financing Cost Assessment: If using leverage, consider financing rate and intraday/overnight capital costs to avoid excess returns being eaten by interest spreads
• Scenario Analysis and Stress Test: Conduct path backtests for interest rate hikes, recession, liquidity crisis scenarios to assess tail risk in extreme paths

###5.3 Risk Management and Supervision

• Single Asset Cap: Set hard cap on single asset weight to avoid excessive concentration risk
• Drawdown Stop Loss and Position Management: Combine rolling drawdown, maximum drawdown to trigger position halving or switch to defensive assets
• Liquidity and Capacity Assessment: Consider asset size and ETF daily trading volume to avoid large shocks and slippage amplification
• Continuous Monitoring and Version Iteration: Regular out-of-sample validation and model version updates to avoid “overfitting” to specific historical windows

##6. Conclusion

• In the 2020-2025 sample period, the four-asset momentum rotation strategy shows higher annualized return and better risk-adjusted return than equal weight, but with higher volatility and drawdown; returns are highly dependent on momentum premium in low volatility phases[0]
• Sustainability faces multiple constraints including transaction costs, turnover frequency, market microstructure evolution and momentum crash risk; historical high returns cannot be simply linearly extrapolated, need to be carefully evaluated based on net return, out-of-sample validation and cost modeling
• Suggest optimizing robustness and implementability by extending signal window, reducing turnover frequency, adding reversal/risk control modules, dynamic volatility target and strict cost sensitivity test

[0] Gilin AI / Broker API Data: Based on daily price and strategy backtest from2020-01-02 to 2025-12-26; includes QQQ, GLD, TLT, SHV, ^VIX and market indicator data. All statistics and visualizations are from above data and calculations.
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