이 글은 영어로 작성된 이전 연구 노트입니다. 수치와 기간은 발행 당시 그대로입니다.
Welcome to Avalon QuantBrief, where we dissect sophisticated quantitative models to uncover actionable market insights. Today, we explore the Dynamic Factor Strength Strategy, an upgraded adaptation of the classic Dynamic Factor Timing model designed to capture factor momentum. While the strategy boasts an impressive historical return, its underlying volatility requires a careful look at the relationship between risk and reward.
📈 CAGR: 18.0%
📉 Max Drawdown: -24.1%
⚡ Sharpe Ratio: 0.97
💰 Total Return: 417.8%
🎯 Universe: US factor ETFs, defensive assets and cash-like funds
High Risk: Return Versus Risk
The headline result of this strategy is an impressive 18.0 percent CAGR, but it also experienced a significant 24.1 percent maximum drawdown. This substantial drawdown places the strategy firmly in the high-risk category. In this briefing, we will break down the rules, analyze the return path, and examine the loss periods before arriving at a practical conclusion.
The Core Mechanics of Factor Timing
The original strategy chooses among core US factor ETFs (momentum, quality, minimum volatility, value, growth and large-cap value). A market-trend check decides the regime: in uptrends it holds the strongest factors, and in downtrends it shifts to defensive assets to preserve capital.
Enhancing the Model: My Adapted Version
The adapted version makes three kinds of change: it ranks factors by risk-adjusted strength to favour stable trends over volatile spikes, splits the defensive allocation between long-term Treasuries and gold, and cuts equity exposure when market volatility runs high. The exact rules and parameters are not published.
Verified Backtest Results
The verified backtest numbers for this adapted strategy produced an 18.0 percent CAGR, a 24.1 percent maximum drawdown, a Sharpe ratio of 0.97, and a 417.8 percent total return. Please note that these are historical simulation results and do not constitute a forecast of future performance. When evaluating whether this strategy is usable for your portfolio, the drawdown figure is just as important as the headline return.
Analyzing Profit and Loss Regimes
Returns arrive unevenly over time, and analyzing the losing periods reveals whether the strategy can actually be held in practice. This backtest recorded 10 positive years and only 1 negative year, demonstrating strong consistency. To truly understand the strategy’s behavior, one must review the equity curve, drawdown depth, and recovery time together rather than relying solely on CAGR.
The Verdict: High Risk but Systematic
The final verdict on the Dynamic Factor Strength Strategy is high risk due to the observed 24.1 percent maximum drawdown. Any live implementation of this model would require careful position sizing to survive potential loss periods of this magnitude. Treat these findings strictly as research, compare them with alternative strategies, and never assume that past backtest results will repeat in the future.
⚠️ Not financial advice. This is for educational purposes only. Always do your own research before investing.
Go further on AvalonQuant
- Dynamic Factor Strength Strategy: re-run backtest — equity curve, max drawdown -24.1%, CAGR 18.0% (2016–2026), and how it ranks by drawdown.
- All strategy backtests — every strategy we re-ran, with equity curves and drawdowns side by side.
Backtests are hypothetical and do not guarantee future results. Not investment advice.
면책: 이 글은 과거 데이터에 대한 백테스트(가상 시뮬레이션)를 다룬 교육용 연구 자료이며 투자 권유가 아닙니다. 실제 거래 결과가 아니고 미래 수익을 보장하지 않습니다. 투자 판단과 손실 책임은 본인에게 있습니다.
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