60/40 Portfolio With a Trend Filter: What Actually Changes?

Short answer

Adding a 200-day moving-average trend filter to each leg of a classic 60/40 portfolio (SPY 60% + AGG 40%, monthly rebalanced) reduced the maximum drawdown from −21.8% to −17.4% over the 2011–2026 evaluation window — but compound growth fell from 9.5% to 3.1% per year. The filter spent about 44% of the window in cash and produced repeated buy-high/sell-low whipsaws in 2015–16 and 2022–23. The drawdown insurance is real (it clearly helped in 2022); the price is much larger than the benefit in this configuration.

Why this question matters

“Add a trend filter” is one of the most common DIY improvements suggested for a 60/40 portfolio. The pitch is intuitive: when the stock market is below its 200-day average, why stay fully invested? Before trusting that intuition, it should be tested as a controlled experiment: same baseline, same window, same costs, one changed variable — and with the failure periods and cash exposure shown, not just the CAGR.

Hypothesis

A per-asset 200-day MA trend gate should cut the worst drawdowns of a 60/40 portfolio by moving a leg to cash when its own trend is down. The open question is the price: how much return, cash exposure and turnover change in exchange for the drawdown insurance.

Baseline and changed variable

In the trend-filtered variant each leg is evaluated independently: when a leg closes below its own 200-day moving average it moves to cash at the next monthly rebalance, and returns to its target weight only when it closes back above. A leg that is out of trend does not transfer its weight to the other leg — the freed weight sits in cash. This is the engine’s documented “trend filter + cash / risk-off” semantics.

Warm-up is explicit: the data source serves US daily prices from 2010-01-01 (shared data contract), so a 200-day MA cannot be computed until late 2010. Both variants run from 2010-01-01, but every reported metric, table and chart covers only the evaluation window starting 2011-01-03, with NAV normalized to 1 at that point. This keeps the comparison free of indicator warm-up bias.

Exact rules

• Universe: SPY (SPDR S&P 500 ETF) and AGG (iShares Core US Aggregate Bond ETF), daily adjusted closes.
• Baseline: hold 60% SPY / 40% AGG; rebalance on the first trading day of each month when a leg deviates from target by ≥ 3 percentage points.
• Variant: same rebalancing, plus a trend gate per leg — a leg is in trend when its close is above its 200-day simple moving average; out of trend means its target weight becomes 0 (cash) on the next rebalance day.
• Entries and exits are evaluated only on monthly rebalance days. An exit is therefore deferred up to one full period after a trend breaks (documented engine behavior, chosen to keep turnover low).
• Long-only, unlevered. Cash earns 0% in the engine. No leverage, no derivatives, no shorting.
• No slippage or commissions in the engine run; fee sensitivity is discussed in Limitations.
• Both variants run from 2010-01-01 (warm-up). Metrics are reported for the evaluation window 2011-01-03 → 2026-05-01 only.

Results and evidence

The metric grid, comparison table, annual returns and charts below are rendered directly from the study’s evidence file (real engine runs). Annual returns make the pattern clear: the filter helped in 2022 (−7.2% vs baseline −16.2%) and shrank the COVID drawdown, then gave back far more than it saved — 2017 +0.4% vs +14.4%, 2023 −9.5% vs +17.8%, 2025 +6.0% vs +14.0%.

Where the strategy failed

• Bond-leg whipsaw: AGG was below its own 200-day MA for 31% of trading days in the evaluation window. Long stretches in 2013, 2016–17 and 2022–23 kept the ballast out of the portfolio. The filter repeatedly removes the asset a 60/40 relies on for stability.
• Equity whipsaw 2015–16: SPY crossed its 200-day MA repeatedly; the portfolio averaged roughly 45–56% cash in 2015–16 and missed the 2016–17 recovery. 2017 returned +0.4% vs baseline +14.4% — and held about 92% cash that year.
• Cash drag: the variant held ~44% cash on average across the evaluation window — about 95% in 2022, 67% in 2023, 54% in 2025. Cash earns 0% in this engine.
• Never recovered from 2022: the variant’s deepest drawdown episode started 2022-01-05 and was still open at the data cutoff (2026-05-01, −17.4%). The plain 60/40 recovered its 2022 drawdown by January 2024.
• Month-end execution: entries and exits are evaluated only at monthly rebalances, so the filter systematically buys after a rally has begun and sells after a decline has already happened — visible in the 2023 trade record above.

Limitations and counter-evidence

Counter-evidence — the filter did provide real insurance: it cut the 2022 calendar-year loss from −16.2% to −7.2%, reduced the COVID drawdown from −21.8% to −9.9%, and lowered volatility from 10.5% to 6.2%. Those benefits are real. The question this study answers is the price: a 6.4pp annual cost and ~44% average cash for a 4.4pp drawdown improvement.

• Data constraint: the shared data source serves US daily prices from 2010-01-01 only, so the MA(200) warm-up consumes 2010 and the evaluation window starts 2011-01-03. A longer history (including 2008) could change relative performance; this study cannot observe it.
• Single evaluation window: 2011–2026 contains no 2008-style equity collapse and no sustained 2000s bond bull. Relative performance is window-dependent; a different window can change the ranking of these two portfolios.
• This tests one filter design (MA200, per-asset, monthly execution, cash as the fallback). It is not a test of equity-only filters, T-bill fallbacks, slope filters, or daily execution — all of which are related research.
• Month-end execution is an engine design choice. A filter that exits immediately on a trend break (daily evaluation) would behave differently — including likely higher turnover.
• Cash earns 0% in the engine. Holding T-bills instead of cash would add a small return and change the comparison modestly.
• $0 commission assumption. The variant trades ~1.7× more (74 vs 44 trades) and has ~20× the dollar turnover, so real fees and spreads hit the filtered variant much harder than the baseline.
• Dividends are reinvested in the backtest (adjusted prices). SPY (1993) and AGG (2003) predate the window, so there is no ETF inception bias here.
• Backtest ≠ proof. Validation means executable, not trustworthy. Nothing here is investment advice.

Verdict

Reproducibility & assumptions

The rules, evidence and failure analysis above are the reader-facing research. This section preserves the audit trail needed to reproduce the experiment without making engine implementation details part of the main argument.

{
  "code": "research_6040_baseline",
  "name": "Research: 60/40 Portfolio Baseline (SPY 60% + AGG 40%)",
  "description": "Classic 60/40 stock/bond portfolio with monthly rebalancing and a 3% deviation threshold. Baseline leg of the '60/40 Portfolio With a Trend Filter' study. Same data window (2010-01-01 to 2026-05-01), symbols and costs as the trend-filter variant; the only changed variable is the 200-day trend gate.",
  "strategy": {
    "name": "TargetWeightStrategy",
    "params": {
      "weights": {
        "SPY": 0.6,
        "AGG": 0.4
      },
      "rebalance_frequency": "m",
      "rebalance_threshold": 0.03
    }
  },
  "capital_strategy": {
    "name": "RebalancingCapitalStrategy",
    "params": {
      "initial_capital": 100000
    }
  },
  "symbols": [
    {
      "symbol": "SPY",
      "name": "SPDR S&P 500 ETF"
    },
    {
      "symbol": "AGG",
      "name": "iShares Core US Aggregate Bond ETF"
    }
  ],
  "commission": 0,
  "start_date": "2010-01-01",
  "end_date": "2026-05-01",
  "currency": "USD",
  "market": "US"
}

{
  "code": "research_6040_trend_ma200",
  "name": "Research: 60/40 Portfolio with 200-Day Trend Filter (SPY 60% + AGG 40%)",
  "description": "60/40 stock/bond portfolio where each leg exits to cash when its price closes below its 200-day moving average, and returns to target weight when back above. Monthly rebalancing with a 3% deviation threshold, identical symbols/costs/window to the baseline.",
  "strategy": {
    "name": "TargetWeightTrendFilterStrategy",
    "params": {
      "weights": {
        "SPY": 0.6,
        "AGG": 0.4
      },
      "signal_symbols": {},
      "ma_period": 200,
      "use_slope_filter": false,
      "slope_lookback": 20,
      "rebalance_frequency": "m",
      "rebalance_threshold": 0.03
    }
  },
  "capital_strategy": {
    "name": "RebalancingCapitalStrategy",
    "params": {
      "initial_capital": 100000
    }
  },
  "symbols": [
    {
      "symbol": "SPY",
      "name": "SPDR S&P 500 ETF"
    },
    {
      "symbol": "AGG",
      "name": "iShares Core US Aggregate Bond ETF"
    }
  ],
  "commission": 0,
  "start_date": "2010-01-01",
  "end_date": "2026-05-01",
  "currency": "USD",
  "market": "US"
}

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Related research

• Dual Momentum: does an absolute-momentum gate reduce drawdown?Both studies ask whether a defensive trend/momentum gate buys meaningful drawdown protection after accounting for its opportunity cost.
• Trend filter on the equity leg only (T-bill fallback)Planned follow-up: gates only SPY and parks cash in T-bills, the design most articles call “60/40 with a trend filter”.
• MA200 filter with slope confirmationPlanned robustness check: requires a rising MA to re-enter, which should reduce whipsaw entries.