Trend weekly_change

Weekly Price Change

Triggers when the absolute price change over the last 5 trading days exceeds the threshold. Bullish if up, bearish if down.

Signal family

Trend — Signals that fire when price is continuing or reversing an established directional move. Momentum-following by nature.

Parameters

Name Description Default Range
threshold_pct Minimum absolute change (%) 10 1–50

Historical context

657,140 valid triggers on 3,455 distinct tickers between 2015-01-12 and 2026-04-22. Universe: us_only · mcap ≥ $100,000,000 · price ≥ $1 (3,455 tickers). Entry at open T+1. 1d = intraday T+1; 20d = open T+1 to close T+20.

Benchmarks: spxew (S&P 500 Equal Weight — the primary benchmark here; a median-stock view that avoids the 2020+ megacap-concentration distortion), spx (S&P 500, cap-weighted), and msci (MSCI World USD). Per-stock regime: trending = ADX(14) ≥ 25, high vol = 20d ann. vol ≥ 20%.

At a glance (20d alpha vs S&P 500 Equal Weight, US-only)

Bullish
+0.46%
vs random-date null: worse than random (pperm=1.000)
Bearish (negative alpha = signal right)
+0.73%
vs random-date null: inside null (pperm=0.373)

Reading this: the random-date null is: for each ticker, sample N random dates and compute the same alpha — what alpha does a signal with no information produce? If the signal's observed alpha beats the null (pperm≤0.05), it's adding real information. If it's inside or worse than the null, the signal doesn't add value over random firing — any observed alpha is either noise or a universe artifact.

How often does WEEKLY_CHANGE fire in each regime?

The signal's bucket distribution is itself informative. If 50%+ of all WEEKLY_CHANGE triggers fire in the "non-trending + high vol" quadrant, the signal is structurally a chop-market event — regardless of what its textbook definition claims. Bullish and bearish are shown separately; counts are across the full US-only sample after the mcap and price floor.

Weekly Price Change (weekly_change) — trigger count distribution by per-stock regime quadrant (trending/non-trending × high/low realized volatility) for , US-only universe

Per-stock regime quadrant — 20d alpha

Each trigger is tagged with the host stock's own technical regime on the trigger date: is the stock itself in a trend (ADX(14) ≥ 25) or ranging? And is its realized 20-day volatility high (≥ 20% annualized) or low? This is the textbook conditioning variable — "does this signal work better in trending stocks?" — answered at the level of the individual stock, not the market. Positive bars are good for the signal; negative bars mean alpha vanishes into the benchmark or worse.

Weekly Price Change (weekly_change) — mean 20-day alpha versus S&P 500 Equal Weight by per-stock regime quadrant, side by side
Trending + Low vol
Stock in a clean directional move with low realized volatility. Textbook "trend-following paradise" — smooth grind with little whipsaw risk.
Trending + High vol
Violent directional moves — parabolic rallies, crisis selloffs. Trend exists but the path is noisy. Signal timing may be imprecise.
Non-trending + Low vol
Quiet chop, summer doldrums, consolidations. No directional bias but also no big swings — small edges become reliable if they exist at all.
Non-trending + High vol
Choppy and violent — the classical "whipsaw zone" for momentum signals. Crossovers and breakouts fire repeatedly without follow-through.

Sub-period check — does the signal work in every era?

A multi-year average can hide major instability. We split the sample into three non-overlapping windows: 2015–2019 (pre-COVID, normalized monetary policy), 2020–2022 (pandemic crash + recovery + rate-shock bear), and 2023+ (post-ZIRP, AI megacap rally). If a signal's alpha is positive overall but comes entirely from one era, that's a red flag — the conditions that produced it may not repeat. A robust signal shows a consistent sign across all non-empty buckets.

Weekly Price Change (weekly_change) — 20-day alpha split by historical sub-period (2015-2019, 2020-2022, 2023+) to check consistency across market regimes

↑ Bullish triggers

Bench Metric 1d 5d 20d 60d 252d
spx Stock % -0.11% -0.03% +1.75% +5.83% +27.25%
Bench % +0.03% +0.27% +1.48% +4.16% +17.39%
Alpha % -0.14% -0.29% +0.31% +1.69% +9.87%
Median alpha -0.20% -0.73% -1.57% -3.64% -10.28%
Hit rate (α>0) 46.8% 45.5% 45.2% 44.0% 42.2%
p (naive) <0.001 <0.001 <0.001 <0.001 <0.001
p (HAC) <0.001 <0.001 0.0001 <0.001 <0.001
N 352,942 350,379 345,977 339,573 300,453
msci Stock % -0.11% -0.03% +1.75% +5.83% +27.25%
Bench % +0.07% +0.27% +1.37% +3.85% +15.23%
Alpha % -0.19% -0.32% +0.44% +2.06% +11.98%
Median alpha -0.24% -0.75% -1.49% -3.28% -8.19%
Hit rate (α>0) 46.4% 45.3% 45.5% 44.6% 43.7%
p (naive) <0.001 <0.001 <0.001 <0.001 <0.001
p (HAC) <0.001 <0.001 <0.001 <0.001 <0.001
N 350,657 348,157 343,862 337,653 297,847
spxew Stock % -0.11% -0.03% +1.75% +5.83% +27.25%
Bench % +0.05% +0.21% +1.35% +3.58% +15.04%
Alpha % -0.17% -0.25% +0.46% +2.30% +12.39%
Median alpha -0.22% -0.67% -1.41% -2.97% -7.52%
Hit rate (α>0) 46.8% 45.7% 45.6% 45.0% 44.1%
p (naive) <0.001 <0.001 <0.001 <0.001 <0.001
p (HAC) <0.001 <0.001 <0.001 <0.001 <0.001
N 352,043 348,258 343,852 337,263 298,825
Distribution of all 20d alpha outcomes for this direction. Median and winsorized mean shown.
Weekly Price Change (weekly_change) — bullish 20-day alpha histogram showing distribution of per-trigger returns
Observed 20d alpha (vertical line) against the null distribution of random-date firing. If the line is deep inside the null cloud, the signal adds no information. If it sits in a tail, the signal is doing real work in that direction.
Weekly Price Change (weekly_change) — bullish 20-day observed alpha versus random-date permutation null (200 iterations)
Permutation null detail — all horizons × both benchmarks
200-iteration null: for each ticker, sample N random dates from its history (matching observed trigger count) and compute the same alpha. The null distribution's 95% CI is where a signal with no information would land. pperm = one-sided fraction of null iters with mean ≥ observed.
Horizon Bench Observed α Null mean Null 95% CI pperm
1d spx -0.14% -0.02% [-0.05%, +0.20%] 1.000
1d msci -0.19% -0.04% [-0.07%, +0.18%] 1.000
1d spxew -0.17% -0.04% [-0.07%, +0.18%] 1.000
5d spx -0.29% +0.26% [+0.11%, +0.56%] 1.000
5d msci -0.32% +0.27% [+0.11%, +0.57%] 1.000
5d spxew -0.25% +0.29% [+0.13%, +0.60%] 1.000
20d spx +0.31% +0.80% [+0.60%, +1.12%] 1.000
20d msci +0.44% +0.93% [+0.72%, +1.25%] 1.000
20d spxew +0.46% +1.00% [+0.80%, +1.33%] 1.000
60d spx +1.69% +2.28% [+1.99%, +2.67%] 1.000
60d msci +2.06% +2.74% [+2.45%, +3.12%] 1.000
60d spxew +2.30% +2.98% [+2.70%, +3.38%] 1.000
252d spx +9.87% +8.85% [+8.29%, +9.44%] 0.005
252d msci +11.98% +11.10% [+10.53%, +11.69%] 0.005
252d spxew +12.39% +12.74% [+12.17%, +13.32%] 0.861

Example triggers on US large-caps (2023+, mcap ≥ $30B)

Six recent bullish WEEKLY_CHANGE triggers on US mega-caps, filtered to |alpha| ≤ 25% to exclude catalyst-driven outliers (earnings surprises, M&A, binary events). The first three are the strongest outcomes — what the signal looks like when it works. The last three are the weakest — what the signal looks like when it fails. Each chart shows the stock's price with signal-appropriate technical overlays (e.g. MACD subpanel on MACD pages, Bollinger Bands on Bollinger pages, the 52-week trailing max line on 52w-high pages), a dot marking the trigger date, and the forward window shaded (green when the signal was right, red when it wasn't). Click any chart to open full-size.

Strongest outcomes (what WEEKLY_CHANGE looks like when it works)
Weekly Price Change (weekly_change) — strongest bullish trigger #1 on a US mega-cap, showing the stock's OHLC candles, moving averages, signal-specific overlays, and the 20-day forward window Weekly Price Change (weekly_change) — strongest bullish trigger #2 on a US mega-cap, showing the stock's OHLC candles, moving averages, signal-specific overlays, and the 20-day forward window Weekly Price Change (weekly_change) — strongest bullish trigger #3 on a US mega-cap, showing the stock's OHLC candles, moving averages, signal-specific overlays, and the 20-day forward window
Weakest outcomes (what WEEKLY_CHANGE looks like when it fails)
Weekly Price Change (weekly_change) — weakest bullish trigger #1 on a US mega-cap, showing the 20-day forward window where the signal failed Weekly Price Change (weekly_change) — weakest bullish trigger #2 on a US mega-cap, showing the 20-day forward window where the signal failed Weekly Price Change (weekly_change) — weakest bullish trigger #3 on a US mega-cap, showing the 20-day forward window where the signal failed
Stock-regime quadrants (2×2 per-stock, 20d alpha detail table)
Each quadrant groups triggers by the stock's own ADX(14) and RV(20) at the trigger date — the textbook conditioning variable (not market-level). Stock %, bench %, alpha %, and HAC p-value shown for each benchmark.
Quadrant N Stock % (spx) Bench % (spx) Alpha % (spx) p (HAC) Stock % (msci) Bench % (msci) Alpha % (msci) p (HAC) Stock % (spxew) Bench % (spxew) Alpha % (spxew) p (HAC)
Trending + Low vol Clean directional grind, low whipsaw 395 +0.04% +0.77% -0.73% 0.1176 +0.04% +0.53% -0.46% 0.3287 +0.04% +0.43% -0.38% 0.4231
Trending + High vol Crisis selloff or parabolic rally 183,421 +1.86% +1.64% +0.26% 0.0301 +1.86% +1.46% +0.47% 0.0001 +1.86% +1.48% +0.46% 0.0001
Non-trending + Low vol Quiet chop, summer doldrums 509 +0.96% +1.26% -0.29% 0.4841 +0.96% +1.05% -0.07% 0.8592 +0.96% +0.78% +0.22% 0.5802
Non-trending + High vol Classical "whipsaw zone" for momentum 166,234 +1.68% +1.30% +0.41% <0.001 +1.68% +1.28% +0.45% <0.001 +1.68% +1.22% +0.50% <0.001
Sub-period breakdown table (20d alpha)
Historical clustering check. If alpha concentrates in one era, the signal's robustness is questionable.
Period N Alpha % (spx) p (HAC) Alpha % (msci) p (HAC) Alpha % (spxew) p (HAC)
2015-2019 2015-01-01 → 2020-01-01 66,027 -0.57% 0.0004 -0.39% 0.0168 -0.36% 0.0281
2020-2022 2020-01-01 → 2023-01-01 142,290 +0.28% 0.0291 +0.40% 0.0019 +0.06% 0.6525
2023-2026 2023-01-01 → 2099-01-01 144,865 +0.76% <0.001 +0.86% <0.001 +1.25% <0.001

↓ Bearish triggers negative alpha = signal was right (stock underperformed market)

Bench Metric 1d 5d 20d 60d 252d
spx Stock % +0.07% +0.59% +2.41% +8.03% +31.04%
Bench % +0.08% +0.42% +1.92% +5.43% +18.30%
Alpha % -0.03% +0.19% +0.50% +2.59% +12.72%
Median alpha -0.12% -0.36% -1.08% -2.73% -10.48%
Hit rate (α>0) 48.3% 47.9% 46.8% 45.6% 42.6%
p (naive) 0.0001 <0.001 <0.001 <0.001 <0.001
p (HAC) 0.0001 <0.001 <0.001 <0.001 <0.001
N 303,831 303,521 301,387 292,562 270,453
msci Stock % +0.07% +0.59% +2.41% +8.03% +31.04%
Bench % +0.01% +0.33% +1.82% +5.12% +16.67%
Alpha % +0.04% +0.24% +0.66% +2.97% +14.33%
Median alpha -0.11% -0.31% -0.96% -2.33% -8.81%
Hit rate (α>0) 48.5% 48.2% 47.2% 46.2% 43.6%
p (naive) <0.001 <0.001 <0.001 <0.001 <0.001
p (HAC) <0.001 <0.001 <0.001 <0.001 <0.001
N 303,186 301,536 299,617 290,816 269,066
spxew Stock % +0.07% +0.59% +2.41% +8.03% +31.04%
Bench % +0.11% +0.28% +1.68% +5.12% +16.13%
Alpha % -0.05% +0.31% +0.73% +2.88% +15.04%
Median alpha -0.15% -0.25% -0.90% -2.31% -7.75%
Hit rate (α>0) 47.9% 48.6% 47.4% 46.2% 44.3%
p (naive) <0.001 <0.001 <0.001 <0.001 <0.001
p (HAC) <0.001 <0.001 <0.001 <0.001 <0.001
N 302,454 301,344 298,755 290,529 268,299
Distribution of all 20d alpha outcomes for this direction. Median and winsorized mean shown.
Weekly Price Change (weekly_change) — bearish 20-day alpha histogram showing distribution of per-trigger returns
Observed 20d alpha (vertical line) against the null distribution of random-date firing. If the line is deep inside the null cloud, the signal adds no information. If it sits in a tail, the signal is doing real work in that direction.
Weekly Price Change (weekly_change) — bearish 20-day observed alpha versus random-date permutation null (200 iterations)
Permutation null detail — all horizons × both benchmarks
200-iteration null: for each ticker, sample N random dates from its history (matching observed trigger count) and compute the same alpha. The null distribution's 95% CI is where a signal with no information would land. pperm = one-sided fraction of null iters with mean ≥ observed.
Horizon Bench Observed α Null mean Null 95% CI pperm
1d spx -0.03% -0.03% [-0.07%, +0.22%] 0.925
1d msci +0.04% -0.06% [-0.09%, +0.20%] 0.940
1d spxew -0.05% -0.06% [-0.09%, +0.20%] 0.940
5d spx +0.19% +0.20% [+0.04%, +0.54%] 0.622
5d msci +0.24% +0.20% [+0.04%, +0.54%] 0.701
5d spxew +0.31% +0.22% [+0.06%, +0.56%] 0.796
20d spx +0.50% +0.60% [+0.39%, +0.99%] 0.269
20d msci +0.66% +0.73% [+0.51%, +1.10%] 0.408
20d spxew +0.73% +0.80% [+0.59%, +1.20%] 0.373
60d spx +2.59% +1.70% [+1.39%, +2.21%] 1.000
60d msci +2.97% +2.16% [+1.85%, +2.66%] 1.000
60d spxew +2.88% +2.40% [+2.09%, +2.90%] 0.970
252d spx +12.72% +6.20% [+5.67%, +6.81%] 1.000
252d msci +14.33% +8.45% [+7.92%, +9.10%] 1.000
252d spxew +15.04% +10.12% [+9.61%, +10.73%] 1.000

Example triggers on US large-caps (2023+, mcap ≥ $30B)

Six recent bearish WEEKLY_CHANGE triggers on US mega-caps, filtered to |alpha| ≤ 25% to exclude catalyst-driven outliers (earnings surprises, M&A, binary events). The first three are the strongest outcomes — what the signal looks like when it works. The last three are the weakest — what the signal looks like when it fails. Each chart shows the stock's price with signal-appropriate technical overlays (e.g. MACD subpanel on MACD pages, Bollinger Bands on Bollinger pages, the 52-week trailing max line on 52w-high pages), a dot marking the trigger date, and the forward window shaded (green when the signal was right, red when it wasn't). Click any chart to open full-size.

Strongest outcomes (what WEEKLY_CHANGE looks like when it works)
Weekly Price Change (weekly_change) — strongest bearish trigger #1 on a US mega-cap, showing the stock's OHLC candles, moving averages, signal-specific overlays, and the 20-day forward window Weekly Price Change (weekly_change) — strongest bearish trigger #2 on a US mega-cap, showing the stock's OHLC candles, moving averages, signal-specific overlays, and the 20-day forward window Weekly Price Change (weekly_change) — strongest bearish trigger #3 on a US mega-cap, showing the stock's OHLC candles, moving averages, signal-specific overlays, and the 20-day forward window
Weakest outcomes (what WEEKLY_CHANGE looks like when it fails)
Weekly Price Change (weekly_change) — weakest bearish trigger #1 on a US mega-cap, showing the 20-day forward window where the signal failed Weekly Price Change (weekly_change) — weakest bearish trigger #2 on a US mega-cap, showing the 20-day forward window where the signal failed Weekly Price Change (weekly_change) — weakest bearish trigger #3 on a US mega-cap, showing the 20-day forward window where the signal failed
Stock-regime quadrants (2×2 per-stock, 20d alpha detail table)
Each quadrant groups triggers by the stock's own ADX(14) and RV(20) at the trigger date — the textbook conditioning variable (not market-level). Stock %, bench %, alpha %, and HAC p-value shown for each benchmark.
Quadrant N Stock % (spx) Bench % (spx) Alpha % (spx) p (HAC) Stock % (msci) Bench % (msci) Alpha % (msci) p (HAC) Stock % (spxew) Bench % (spxew) Alpha % (spxew) p (HAC)
Trending + Low vol Clean directional grind, low whipsaw 285 +0.36% +0.92% -0.48% 0.3291 +0.36% +0.79% -0.30% 0.5301 +0.36% +0.66% -0.16% 0.7433
Trending + High vol Crisis selloff or parabolic rally 160,546 +3.06% +2.41% +0.69% <0.001 +3.06% +2.17% +0.94% <0.001 +3.06% +2.00% +1.05% <0.001
Non-trending + Low vol Quiet chop, summer doldrums 477 +0.05% +0.72% -0.58% 0.1955 +0.05% +0.63% -0.47% 0.2745 +0.05% +0.37% -0.24% 0.5601
Non-trending + High vol Classical "whipsaw zone" for momentum 140,709 +1.67% +1.43% +0.28% 0.0075 +1.67% +1.41% +0.33% 0.0014 +1.67% +1.33% +0.36% 0.0006
Sub-period breakdown table (20d alpha)
Historical clustering check. If alpha concentrates in one era, the signal's robustness is questionable.
Period N Alpha % (spx) p (HAC) Alpha % (msci) p (HAC) Alpha % (spxew) p (HAC)
2015-2019 2015-01-01 → 2020-01-01 55,271 +0.93% <0.001 +1.13% <0.001 +0.85% <0.001
2020-2022 2020-01-01 → 2023-01-01 132,679 +0.36% 0.0031 +0.68% <0.001 +0.39% 0.0014
2023-2026 2023-01-01 → 2099-01-01 116,008 +0.47% 0.0005 +0.41% 0.0022 +1.07% <0.001

Methodology and caveats

How to read. Entry at open of T+1 (one trading day after the signal fires on close of T). 20d = open T+1 to close T+20. Alpha = stock return − benchmark return over the same window (Convention A, single-sided, textbook). For bullish triggers, POSITIVE alpha = signal was right. For bearish triggers, NEGATIVE alpha = signal was right (stock underperformed market). No sign-flipping; the direction of the bet determines what "good" looks like. Per-stock regime is each stock's own ADX(14) and RV(20) at the trigger date — not market-wide state.

Three p-values, three robustness tests. (a) p_naive: scipy one-sample t-test on winsorized alphas. Optimistic because overlapping 20d windows on the same ticker inflate effective N. (b) p_hac: Newey-West HAC with lag = horizon — corrects for the overlap and is the academic-finance standard. (c) p_perm: fraction of 200 random-date null iterations with mean ≥ observed. Tests whether the signal beats random date selection at all. A signal that clears all three (pnaive, phac, pperm all < 0.05) has real information; a signal that fails pperm has zero edge even if the t-test says "significant."

Caveats. (i) Universe reflects today's active tickers; delisted losers pruned → survivorship bias. (ii) Mcap ≥ $100M filter uses today's snapshot, not point-in-time — mild lookahead on which stocks enter the sample, not on returns. (iii) Means and p-values use winsorized alphas (1/99 percentile) to prevent data errors from dominating. Medians and hit rates use raw data. (iv) Zero transaction costs assumed. Realistic bid-ask + commissions remove 20–40bps from 20d alpha on US large-caps, more on small-cap. Sub-20bps alpha is noise in practice. (v) Past performance does not predict future results.

How to use this

1 · When to reach for this signal

Caution recommended. Bullish 20d alpha is +0.31% and worse than random — triggering on random dates would have produced better long-side returns. Either direction fails the "beats random" test. Don't use Weekly Price Change as a standalone entry trigger. It may still be useful as part of a composite (section 4).

2 · When it works — the setups that drive it

  • Best bullish setup: Non-trending + High vol — alpha +0.41% / 20d on 166,234 historical triggers.
  • Best bearish setup: Trending + High vol — alpha +0.69% / 20d on 160,546 historical triggers.
  • Best era for bullish: 2023-2026 — alpha +0.76% / 20d.
  • Best era for bearish: 2015-2019 — alpha +0.93% / 20d.

3 · When it fails — common false positives

  • Weakest bullish cell: Trending + Low vol — alpha -0.73% / 20d on 395 triggers.
  • Weakest bearish cell: Non-trending + Low vol — alpha -0.58% / 20d on 477 triggers.
  • Worst era for bullish: 2015-2019 — alpha -0.57% / 20d.
  • Worst era for bearish: 2020-2022 — alpha +0.36% / 20d.

Signal-specific failure patterns

Both directions fail the permutation null — pure momentum that doesn't predict
Weekly change (top/bottom 5% of 1-week return moves) is effectively a momentum-continuation trigger. Bullish: α=+0.31 at 20d (p(HAC)<0.001 but p_perm=1.000). Bearish: α=+0.50 at 20d (p(HAC)<1e-9 but p_perm=0.27, non-significant). Both directions have point-estimate alpha in the WRONG direction or failing the null. Large movers don't systematically continue or reverse.
evidence: 20d vs SPX: bullish α=+0.31 p_perm=1.000; bearish α=+0.50 p_perm=0.27
Bullish side shows large 60d alpha but permutation rejects it
Bullish 60d α=+1.69, p(HAC) tiny. But p_perm=1.000 — meaning random-date firing of the same number of triggers per ticker would have produced EVEN LARGER positive alpha. The signal's apparent edge is just 'high-mcap winners stay winners' baseline drift; the signal itself adds nothing beyond selection bias.
evidence: bullish 60d: α=+1.69 p_perm=1.000 (observed worse than every random draw)
Bearish 60d α=+2.59 against SPX — the stock BEATS the market after bearish signal
This is the strongest indictment of the signal. Under Convention A, bearish signals should produce NEGATIVE alpha when they work. At 60d, bearish weekly-change triggers show α=+2.59 — the stocks outperform the market after a 'bearish' trigger fires. The signal is mis-labeled or inverted. Stocks that drop a lot in one week then bounce back.
evidence: bearish 60d vs SPX: α=+2.59 (wrong direction) p_hac<1e-32 p_perm=1.000

4 · Pairing inside a screen

The statements below describe how this signal relates to others by construction — which indicator family it belongs to, and where same-family redundancy might reduce the independence of evidence inside a Daily Report. These are taxonomic classifications drawn from standard technical-analysis texts; they are not pairing backtests. A multi-signal convergence backtest is planned but not yet run.

Rate-of-change family

Weekly percent change is a rate-of-change (ROC) measure of price momentum. It is related to but distinct from the range-normalised oscillator family (RSI, Stochastics, Williams %R, CCI) — ROC is unbounded while oscillators are bounded (Kirkpatrick & Dahlquist, Technical Analysis, 3rd ed. 2015). Pairing weekly_change with an oscillator in the same direction produces partially overlapping evidence rather than fully independent confirmation.

What would likely rescue this signal

This block calls out the data or conditions that could turn a technically weak signal into a usable one in a composite screen. Based on signal mechanics and the observed failure patterns above; individual combinations are not yet backtested.

  • Invert the bearish side — If bearish weekly_change produces consistently positive alpha, the signal is really a BOUNCE trigger. Renaming 'weekly_change bearish' to 'weekly_drawdown_bounce_bullish' and flipping its interpretation would be honest. Requires a deliberate design decision.
  • Bound the magnitude — ±5% is a pretty loose threshold. Tightening to stocks with ±15% moves might concentrate signal in catalyst-driven events (news, earnings, M&A). The current wide net is what produces noisy alpha.
  • Skip during low-dispersion markets — Weekly-change extremes in a low-vol regime are usually single-stock events; in high-vol they're market-wide. Different signal characteristics. Gate bearish side to vol > 25% annualized, bullish to vol > 20%, to filter out QE-era compressed-vol noise.

See also Why technical-only signals don't survive on their own for the broader argument.

5 · Before you act — a 5-point checklist

  1. Normal trading day? Rule out earnings (within ±3 days), ex-dividend, or known corporate-action dates — the signal is almost certainly reading noise, not momentum, in those windows.
  2. Where is price vs its own 50 / 200 DMA? A trend signal is only as credible as the underlying trend it claims to confirm. Check the 200DMA orientation before acting.
  3. What's the sector breadth doing? An isolated signal in a broadly down-trending sector is a lower-confidence setup than one firing with the rest of its peer group.
  4. Is ADV20 enough for your size? If the trigger is on a $500M name and you want to move $1M notional, you're the tape. Consider adv20d ≥ 5% of your intended position.
  5. What invalidates you? Define a price level (for longs: a close below the trigger-day low; for shorts: close above the trigger-day high) and honor it. The backtest alpha is an average; any one trade can be at either tail.

Execution notes

Neither direction is cleanly tradable. This signal is best interpreted as a SCREENING LENS (quickly find stocks that have moved the most) rather than a predictive trigger. It shows up on most charts because it's a descriptive fact about price action, not a forecast. Keep in the docs for transparency; de-emphasize in the screen composer.