Writing Lab Mini-Lab: A Trend Filter + Volatility Target Overlay (Daily, Testable, Boring on Purpose)

Many “trend overlays” fail because they’re hard to implement cleanly or evaluated with the wrong yardstick.

This mini-lab gives you a toy overlay you can actually implement with daily bars:

• Idea (toy strategy): hold a risky asset (e.g., SPY) only when it’s in an uptrend; size the position to a target volatility; otherwise go to cash (or T-bills).
• Constraint (implementability): daily data only, no intraday assumptions, no lookahead, and explicit “trade timing.”
• What to measure: not only Sharpe/CAGR, but drawdown control, exposure, turnover, and path dependence.

Toy strategy / idea

You start with a baseline: buy-and-hold SPY (or a liquid index future). Then add two simple components:

1. Trend filter (regime gate): only take risk when price is above a moving average.

• Signal: trend_on = (close[t-1] > SMA(close, 200)[t-1]).
• Why t-1: if you rebalance at today’s close, you only know yesterday’s close and yesterday’s SMA at decision time.

2. Volatility target (risk overlay): scale the position so the expected vol is ~10% annualized.

• Estimate daily vol from trailing returns.
• Position size: w = target_vol / est_vol, clipped to a max leverage (e.g., 1.0 for unlevered ETFs).

Trade timing choice (pick one and stick to it):

• Conservative: compute signals on day t-1 close and trade at day t open.
• Simpler (still defensible for daily bars): compute on t-1 and trade at t close.

In this lab, pick trade at next open. It forces you to account for gaps and makes your assumptions explicit.

Implementability constraints

To keep this from becoming “research theater,” enforce these constraints:

• Data: adjusted close for ETFs; for futures, continuous back-adjusted series plus separate carry/roll modeling if you care about reality.
• Signal delay: use information available at the time you claim to trade.
• Vol estimate: use trailing window (e.g., 20–60 trading days). No future volatility.
• Position caps: hard cap leverage and (optionally) max daily turnover.
• Cash return: if you go to cash, optionally earn the risk-free rate (or 0, but be consistent).

This overlay is “boring” because the edge isn’t magical; the value is in risk shaping.

Minimal pseudocode

# inputs: price series P[t], daily returns r[t] = P[t]/P[t-1]-1
# params: ma_len=200, vol_len=20, target_vol_ann=0.10, max_w=1.0

for each day t >= max(ma_len, vol_len)+1:
    sma = mean(P[t-ma_len : t])          # up to t-1 if trading at t open
    trend_on = (P[t-1] > sma)

    est_vol_daily = stdev(r[t-vol_len : t])
    target_vol_daily = target_vol_ann / sqrt(252)
    w_raw = target_vol_daily / est_vol_daily
    w = clamp(w_raw, 0, max_w)

    if trend_on:
        pos[t] = w
    else:
        pos[t] = 0

# PnL uses pos[t] applied to return from t -> t+1, with your chosen execution model
strategy_return[t] = pos[t] * r[t+1] - costs(turnover)

What to measure (beyond Sharpe)

Treat this as a research reader’s checklist:

1. Exposure profile

• Average position size (mean of pos[t]).
• Fraction of days invested (trend gate effect).
• Distribution of position sizes (how often you hit the cap?).

2. Drawdown behavior

• Max drawdown (MDD).
• Drawdown duration (time underwater). Trend filters often reduce duration even if they sacrifice some upside.
• Worst 1-month / 3-month return (tail proxy).

3. Turnover and costs

• Daily turnover: abs(pos[t]-pos[t-1]).
• Apply a simple cost model: cost = turnover * bps_per_turn.
• Sensitivity sweep: 0, 5, 10, 25 bps.

4. Path dependence / regime dependence

• Split the sample into crisis vs calm (e.g., top decile of realized vol).
• Compare performance conditional on vol regimes.

5. Benchmark the right thing

• Compare against:
  • Buy-and-hold.
  • Vol-target only (no trend gate).
  • Trend gate only (no vol targeting).

This decomposition tells you whether the “trend” is doing anything, or whether vol targeting alone explains the improvement.

What could break

1. Lookahead via “same-bar” indicators If you use today’s close to compute today’s SMA and also trade at today’s close, you’ve introduced subtle lookahead. The fix is simple: lag everything.
2. Vol targeting blow-ups from stale or too-short windows In quiet periods, a short vol window can estimate extremely low vol, causing the overlay to lever up right before a spike. Clamps help, but test:

• longer vol windows,
• floor on vol (est_vol >= floor),
• and max leverage.

3. Cash assumption mismatch A “cash” allocation isn’t free; in practice it’s T-bills, a money market fund, or collateral yield (futures). Decide what cash earns and keep it consistent.
4. Execution realism Trading at the open can be worse than you think for some instruments (gaps, wider spreads). ETFs like SPY are usually fine; single names are not.
5. Benchmarking bias If you target 10% vol, you’ve implicitly lowered risk versus buy-and-hold. You should also compare against a risk-matched benchmark (e.g., buy-and-hold scaled down to 10% vol).

Close

This mini-lab isn’t about discovering a new alpha factor. It’s about learning whether your “trend overlay” is:

• a genuine regime filter,
• a disguised risk reduction,
• or an artifact of timing/cost assumptions.

If you can’t explain which of those it is after running the decomposition benchmarks, the overlay isn’t ready for publication—or capital.