Multi-Asset Momentum Rotation Strategy Backtest Analysis (2018-2025)

Based on historical backtest data from 2018 to 2025, I provide you with a comprehensive analysis of the

:

Indicator	Momentum Rotation Strategy	Equal-weight Benchmark	Excess Performance
Annualized Return	24.66%	11.36%	+13.30pp
Sharpe Ratio	1.26	0.76	+0.49
Maximum Drawdown	-19.22%	-25.76%	+6.54pp
Calmar Ratio	1.28	0.44	+0.84
Monthly Win Rate	69.79%	63.54%	+6.25pp

From the chart, the momentum rotation strategy achieved significant cumulative return advantages between 2018 and 2025, especially standing out in the following periods:

• Post-2020 pandemic
  : Quickly switched to Nasdaq ETF to capture the tech stock rebound
• 2022 rate hike cycle
  : Shifted to Treasury Bond ETF and Gold ETF for effective risk hedging
• 2023-2024
  : Continued allocation to Nasdaq ETF to enjoy the AI tech bull market [0]

---

Based on the 60-day rolling return of the S&P 500 index, we classify market environments as:

• Bull market
  : 60-day return >5%
• Bear market
  :60-day return < -5%
• Sideways market
  : Between the two

Market Environment	Momentum Strategy Annualized Return	Equal-weight Annualized Return	Excess Return
Bull Market	36.66%	24.31%	+12.35pp
Bear Market	27.23%	-15.24%	+42.47pp ⭐
Sideways Market	12.93%	8.06%	+4.87pp

###2.3 Key Findings

• The momentum strategy can timely switch to
  Treasury Bond ETF
  and
  Gold ETF
  and other safe-haven assets
• Effectively reduced drawdown during the2022 rate hike cycle
• This is the
  core advantage
  compared to traditional asset allocation [0]

• Can quickly identify and allocate to the strongest assets (e.g., Nasdaq ETF)
• Performed excellently in the2020-2021 tech bull market

• Avoid holding weak assets through dynamic rotation
• Monthly win rate close to70%, reflecting the strategy’s stability

---

###3.1 Position Time Distribution of Each Asset

According to backtest results, the position distribution of the momentum rotation strategy is:

• Nasdaq ETF
  : Dominant position (highest proportion), capturing tech growth opportunities
• Gold ETF
  : Safe-haven choice during inflation and geopolitical risk periods
• Treasury Bond ETF
  : Defensive asset during monetary policy tightening cycles
• S&P500
  : Stable choice when the market rises broadly

###3.2 Annual Return Performance

From the annual return comparison:

• 2020
  : The momentum strategy significantly outperformed, successfully capturing the tech stock rebound after the pandemic
• 2022
  : The equal-weight strategy suffered a large drawdown, while the momentum strategy effectively controlled risks by switching to Treasury bonds and gold
• 2023-2024
  : Continued allocation to Nasdaq ETF to enjoy the dividends of the AI tech wave [0]

---

###4.1 Limitations of Equal-weight Allocation

Traditional equal-weight allocation has the following problems:

1. Cannot avoid systemic risks
   : Still holds high-risk assets in bear markets
2. Misses rotation opportunities
   : Equal-weight allocation of strong and weak assets dilutes returns
3. Lack of discipline in rebalancing
   : Prone to emotional influence, chasing ups and downs

###4.2 Advantages of Momentum Rotation

1. Trend-following ability
   : Automatically identifies and allocates to strong assets
2. Risk adaptability
   : Automatically switches to safe-haven assets when the market weakens
3. Disciplined execution
   : Based on quantitative rules, avoiding emotional interference
4. Sharpe ratio improvement
   : Risk-adjusted returns significantly outperform the benchmark

---

###5.1 Drawdown Control Ability

From the drawdown comparison chart:

• Maximum drawdown of momentum strategy
  : -19.22%
• Maximum drawdown of equal-weight
  : -25.76%
• Drawdown improvement
  :6.54 percentage points

###5.2 Monthly Win Rate

• Momentum strategy monthly win rate
  :69.79%
• Equal-weight monthly win rate
  :63.54%
• Improvement
  :6.25 percentage points

High monthly win rate indicates that the strategy has

in different market environments.

---

###6.1 Implementation Key Points

1. Asset Selection
   : It is recommended to choose liquid ETFs (e.g., Gold ETF518880 and Nasdaq ETF513100 in the A-share market) [1]
2. Momentum Cycle
   : The20-day momentum is a reasonable parameter, balancing sensitivity and stability
3. Rebalancing Frequency
   : Weekly rebalancing can capture trend changes in time while controlling transaction costs

###6.2 Risk Warnings

1. Transaction Costs
   : Actual trading needs to consider commissions and impact costs, which may reduce excess returns
2. Slippage Risk
   : ETFs with insufficient liquidity may lead to actual transaction prices deviating
3. Trend Reversal Risk
   : Momentum strategies may lag when the market reverses quickly
4. Over-reliance on Historical Data
   : Past performance does not represent future returns [1]

###6.3 Optimization Directions

1. Multi-factor Combination
   : Add valuation, volatility and other factors to improve stock selection quality
2. Risk Parity Weights
   : Adjust positions according to volatility to further reduce drawdowns
3. Macro Factor Filtering
   : Combine macroeconomic indicators to identify market environment changes

---

Based on historical backtests from2018 to2025, the

:

1. Annualized excess return
   : +13.30pp (24.66% vs11.36%)
2. Sharpe ratio improvement
   : +0.49 (1.26 vs0.76)
3. Maximum drawdown reduction
   :6.54pp (-19.22% vs-25.76%)

• Bull market
  : Can capture strong assets with an excess return of12.35pp
• Bear market
  : Strong hedging ability with an excess return of42.47pp ⭐
• Sideways market
  : Stable allocation with an excess return of4.87pp

The momentum rotation strategy shows good

across different market environments, especially:

• Monthly win rate close to70%
• Calmar ratio is 3 times that of equal-weight (1.28 vs0.44)
• Drawdown control ability is significantly better than traditional allocation

This strategy is suitable for investors pursuing

, but attention should be paid to transaction costs and the impact of market environment changes. It is recommended to evaluate based on personal risk tolerance and investment objectives.

---

[0] Gilin API Data - GLD, QQQ, TLT, SPY historical price data and backtest analysis
[1] Web Search - “How to Build an ETF Rotation Quantitative Strategy? Core Logic, Rebalancing Rules and Practical Points Explained in One Article” (https://index.zshipu.com/stock002/post/20251006/如何搭建-ETF-轮动量化策略核心逻辑调仓规则与实操要点一文讲透/)
[2] Web Search - “Today’s Strategy: 462% Annualized, 7% Drawdown | Momentum Trading of Major Global Market ETF Varieties” (https://blog.csdn.net/weixin_38175458/article/details/151353233)
[3] Web Search - “Momentum Rotation Strategy” (https://docs.myinvestpilot.com/docs/strategies/momentum-rotation/)