Multi-Factor Composite
Ranks assets across multiple factors: Value (price relative to long-term average), Momentum (recent returns), and Quality (low volatility, low drawdown). Combines factor scores with configurable weights to select top-ranked assets.
At a glance
What the backtest shows
- Beat buy-and-hold on 1 of 1 markets by annual return over 5 years.
- Had a smaller maximum drawdown than buy-and-hold on 1 of 1 markets.
- Best market by Sharpe ratio: SPY (Sharpe 0.75, CAGR 10.8%).
Equity curve
Score and risk profile
Each bar is a percentile against every strategy on the same market and window (100 = best).
How it held up in sell-offs (SPY)
| Period | Strategy | Buy & hold |
|---|---|---|
| Q4 2018 sell-off | 0.0% | -13.9% |
| COVID crash (Feb–Mar 2020) | -23.4% | -25.5% |
| 2022 bear market | -6.5% | -9.1% |
| Market above its 200-day average (annualized) · 67% | +15.7% | +20.1% |
| Market below its 200-day average (annualized) · 33% | +6.7% | +8.7% |
Sell-off dates follow the US stock market. Above/below the 200-day average uses the previous day's position, so there is no look-ahead.
Out-of-sample check (SPY)
| Check | Value |
|---|---|
| Chosen parameters on their 3-year training windows (average Sharpe) | 1.31 |
| Same parameters in the following 6 months, when traded (Sharpe) | 1.18 |
| Default parameters over the same months (Sharpe) | 1.07 |
| Parameter re-selections / changes | 12 / 7 |
| Parameter settings with a positive Sharpe (full history) | 9 of 9 |
| Sharpe across settings: worst / median / best | 0.79 / 0.90 / 1.16 |
Out-of-sample periods start on 2020-11-09. Training-window figures are flattered by picking the best of 9 settings; the out-of-sample figure is what the strategy actually earned. A large drop, or a result that depends on one setting, points to overfitting. How walk-forward testing works.
Results by market (5 years)
| Market | CAGR | Buy & hold CAGR | Sharpe | Max drawdown | Buy & hold max DD | Volatility | Arena Score |
|---|---|---|---|---|---|---|---|
| SPYSPDR S&P 500 | 10.8% | 9.1% | 0.75 | -19.4% | -25.1% | 15.0% | 83 |
Strategy metrics use daily returns without trading costs. Buy-and-hold figures are for the same market and window. Rows are sorted by Sharpe ratio.
SPY across time windows
Rules
Entry
Allocate to assets with highest composite score
Exit
Monthly rebalance based on updated scores
Indicators
Value: Price / 252-day MA (lower = better value) Momentum: 6-month return Quality: Inverse volatility + low drawdown Composite z-score
Inputs
Value_Lookback = 252 Mom_Lookback = 126 Vol_Lookback = 60 Weights = [0.3, 0.4, 0.3]
Position sizing
Proportional to factor scores
Risk controls
Volatility adjustment
Strategy evidence
- SPY · Inspect evidence 2026-11-10
Only available, validated bundles appear below. Missing evidence is not a passing validation result.
Evidence coverage
1 / 1 declared strategy–market pairs validated
Notes
- Monthly decisions use the preceding completed observation at the first observed new-month session; this changes trading month-end timing too.
- Partial-month terminal rows do not trigger rebalancing; targets carry unchanged between monthly decisions.
- Factor diversification reduces single-factor risk
- Momentum tends to be strongest factor
- Quality provides downside protection
Embed this score
Add the live badge to a blog post, README or forum signature. It updates with the daily data.
<a href="https://quantarenas.com/strategy/factor-05-multi-factor-composite/"><img src="https://quantarenas.com/badge/factor-05-multi-factor-composite.svg" alt="Multi-Factor Composite Arena Score on Quant Arena" height="20"></a>
[](https://quantarenas.com/strategy/factor-05-multi-factor-composite/)
Backtests are historical simulations on daily data and do not include commissions or slippage. They do not guarantee future results and are not investment advice. See the methodology for how every strategy is tested.