Trend volume_breakout

Volume breakout

Detects unusual volume spikes exceeding N× the rolling average. Bullish: volume spike with close > open (buying pressure). Bearish: volume spike with close < open (selling pressure).

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
period Average volume lookback (days) 20 5–100
multiplier Volume multiplier threshold 2.0 1.5–5.0

Historical context

406,035 valid triggers on 3,716 distinct tickers between 2015-03-05 and 2026-04-22. Universe: us_only · mcap ≥ $100,000,000 · price ≥ $1 (3,719 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
+0.31%
vs random-date null: worse than random (pperm=1.000)
Bearish (negative alpha = signal right)
+0.20%
vs random-date null: beats random (pperm=0.005)

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 VOLUME_BREAKOUT fire in each regime?

The signal's bucket distribution is itself informative. If 50%+ of all VOLUME_BREAKOUT 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.

Volume breakout (volume_breakout) — 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.

Volume breakout (volume_breakout) — 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.

Volume breakout (volume_breakout) — 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% +0.06% +1.14% +3.13% +14.96%
Bench % +0.01% +0.21% +1.08% +2.98% +13.39%
Alpha % -0.12% -0.14% +0.10% +0.16% +1.55%
Median alpha -0.12% -0.37% -0.87% -2.28% -9.59%
Hit rate (α>0) 47.1% 46.6% 46.0% 44.1% 39.5%
p (naive) <0.001 <0.001 0.0042 0.0076 <0.001
p (HAC) <0.001 <0.001 0.0619 0.1715 0.0014
N 213,547 212,945 212,006 208,015 188,272
msci Stock % -0.11% +0.06% +1.14% +3.13% +14.96%
Bench % +0.06% +0.22% +0.96% +2.56% +11.03%
Alpha % -0.16% -0.15% +0.21% +0.62% +3.90%
Median alpha -0.17% -0.39% -0.77% -1.84% -7.16%
Hit rate (α>0) 46.1% 46.4% 46.4% 45.2% 42.0%
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 212,720 211,849 210,731 206,806 187,326
spxew Stock % -0.11% +0.06% +1.14% +3.13% +14.96%
Bench % +0.01% +0.16% +0.90% +2.26% +9.82%
Alpha % -0.12% -0.07% +0.31% +0.90% +5.37%
Median alpha -0.13% -0.30% -0.65% -1.51% -5.70%
Hit rate (α>0) 47.1% 47.2% 46.9% 45.9% 43.3%
p (naive) <0.001 <0.001 <0.001 <0.001 <0.001
p (HAC) <0.001 0.0006 <0.001 <0.001 <0.001
N 212,137 210,602 209,763 205,982 185,970
Distribution of all 20d alpha outcomes for this direction. Median and winsorized mean shown.
Volume breakout (volume_breakout) — 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.
Volume breakout (volume_breakout) — 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 -0.12% +0.01% [-0.04%, +0.38%] 1.000
1d msci -0.16% -0.02% [-0.07%, +0.36%] 1.000
1d spxew -0.12% -0.02% [-0.06%, +0.36%] 1.000
5d spx -0.14% +0.13% [+0.02%, +0.58%] 1.000
5d msci -0.15% +0.13% [+0.02%, +0.59%] 1.000
5d spxew -0.07% +0.15% [+0.04%, +0.61%] 1.000
20d spx +0.10% +0.41% [+0.23%, +0.96%] 1.000
20d msci +0.21% +0.53% [+0.35%, +1.08%] 1.000
20d spxew +0.31% +0.60% [+0.42%, +1.16%] 1.000
60d spx +0.16% +1.02% [+0.72%, +1.61%] 1.000
60d msci +0.62% +1.46% [+1.15%, +2.06%] 1.000
60d spxew +0.90% +1.69% [+1.39%, +2.30%] 1.000
252d spx +1.55% +3.30% [+2.70%, +4.20%] 1.000
252d msci +3.90% +5.58% [+5.00%, +6.50%] 1.000
252d spxew +5.37% +7.05% [+6.45%, +7.96%] 1.000

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

Six recent bullish VOLUME_BREAKOUT 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 VOLUME_BREAKOUT looks like when it works)
Weakest outcomes (what VOLUME_BREAKOUT looks like when it fails)
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 15,803 +0.83% +0.81% +0.06% 0.3558 +0.83% +0.64% +0.23% 0.0006 +0.83% +0.53% +0.37% <0.001
Trending + High vol Crisis selloff or parabolic rally 77,601 +1.16% +1.13% +0.03% 0.7517 +1.16% +1.00% +0.17% 0.0971 +1.16% +0.90% +0.27% 0.0084
Non-trending + Low vol Quiet chop, summer doldrums 18,560 +0.64% +0.73% -0.05% 0.3965 +0.64% +0.56% +0.11% 0.0662 +0.64% +0.46% +0.22% 0.0002
Non-trending + High vol Classical "whipsaw zone" for momentum 101,753 +1.33% +1.13% +0.25% 0.0002 +1.33% +1.04% +0.32% <0.001 +1.33% +0.98% +0.41% <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 68,351 -0.18% 0.0132 -0.01% 0.8501 +0.07% 0.3175
2020-2022 2020-01-01 → 2023-01-01 64,302 +0.05% 0.6433 +0.20% 0.0488 -0.08% 0.4523
2023-2026 2023-01-01 → 2099-01-01 81,070 +0.40% <0.001 +0.43% <0.001 +0.84% <0.001

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

Bench Metric 1d 5d 20d 60d 252d
spx Stock % -0.03% +0.18% +1.01% +3.46% +13.83%
Bench % +0.04% +0.24% +1.08% +3.47% +13.98%
Alpha % -0.07% -0.05% -0.05% +0.03% -0.22%
Median alpha -0.06% -0.20% -0.72% -1.91% -9.32%
Hit rate (α>0) 48.5% 48.1% 46.5% 44.9% 39.5%
p (naive) <0.001 0.0050 0.1262 0.6024 0.1189
p (HAC) <0.001 0.0141 0.2616 0.7588 0.5691
N 192,200 191,888 191,216 186,685 171,246
msci Stock % -0.03% +0.18% +1.01% +3.46% +13.83%
Bench % +0.03% +0.20% +0.89% +2.94% +11.60%
Alpha % -0.05% +0.01% +0.13% +0.54% +2.13%
Median alpha -0.06% -0.16% -0.55% -1.41% -6.83%
Hit rate (α>0) 48.6% 48.4% 47.2% 46.2% 42.1%
p (naive) <0.001 0.6262 <0.001 <0.001 <0.001
p (HAC) <0.001 0.6697 0.0025 <0.001 <0.001
N 191,675 191,063 189,503 185,555 170,411
spxew Stock % -0.03% +0.18% +1.01% +3.46% +13.83%
Bench % +0.06% +0.15% +0.78% +2.59% +10.23%
Alpha % -0.08% +0.02% +0.20% +0.79% +3.67%
Median alpha -0.07% -0.14% -0.50% -1.11% -5.57%
Hit rate (α>0) 48.2% 48.5% 47.5% 46.8% 43.4%
p (naive) <0.001 0.1732 <0.001 <0.001 <0.001
p (HAC) <0.001 0.2323 <0.001 <0.001 <0.001
N 190,316 188,957 188,546 184,156 168,585
Distribution of all 20d alpha outcomes for this direction. Median and winsorized mean shown.
Volume breakout (volume_breakout) — 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.
Volume breakout (volume_breakout) — 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 -0.07% +0.01% [-0.03%, +0.42%] 0.005
1d msci -0.05% -0.02% [-0.06%, +0.40%] 0.299
1d spxew -0.08% -0.02% [-0.06%, +0.40%] 0.005
5d spx -0.05% +0.13% [+0.00%, +0.59%] 0.005
5d msci +0.01% +0.13% [+0.00%, +0.59%] 0.050
5d spxew +0.02% +0.15% [+0.02%, +0.62%] 0.035
20d spx -0.05% +0.35% [+0.13%, +1.03%] 0.005
20d msci +0.13% +0.47% [+0.25%, +1.16%] 0.005
20d spxew +0.20% +0.55% [+0.33%, +1.23%] 0.005
60d spx +0.03% +0.77% [+0.38%, +1.45%] 0.005
60d msci +0.54% +1.22% [+0.82%, +1.91%] 0.005
60d spxew +0.79% +1.44% [+1.06%, +2.14%] 0.005
252d spx -0.22% +2.04% [+1.29%, +2.92%] 0.005
252d msci +2.13% +4.34% [+3.57%, +5.21%] 0.005
252d spxew +3.67% +5.76% [+5.01%, +6.67%] 0.005

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

Six recent bearish VOLUME_BREAKOUT 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 VOLUME_BREAKOUT looks like when it works)
Weakest outcomes (what VOLUME_BREAKOUT looks like when it fails)
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 14,160 +0.23% +0.69% -0.43% <0.001 +0.23% +0.45% -0.20% 0.0036 +0.23% +0.33% -0.09% 0.1952
Trending + High vol Crisis selloff or parabolic rally 67,464 +1.15% +1.20% -0.01% 0.9226 +1.15% +0.95% +0.20% 0.0169 +1.15% +0.77% +0.30% 0.0003
Non-trending + Low vol Quiet chop, summer doldrums 17,360 +0.28% +0.72% -0.42% <0.001 +0.28% +0.45% -0.17% 0.0020 +0.28% +0.40% -0.10% 0.0655
Non-trending + High vol Classical "whipsaw zone" for momentum 93,326 +1.22% +1.16% +0.11% 0.0696 +1.22% +1.00% +0.24% <0.001 +1.22% +0.96% +0.28% <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 59,792 +0.14% 0.0399 +0.41% <0.001 +0.29% <0.001
2020-2022 2020-01-01 → 2023-01-01 59,929 -0.20% 0.0128 +0.03% 0.6964 -0.16% 0.0432
2023-2026 2023-01-01 → 2099-01-01 72,591 -0.07% 0.3569 -0.01% 0.9267 +0.44% <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 +0.10% 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 Volume breakout 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 +0.25% / 20d on 101,753 historical triggers.
  • Best bearish setup: Non-trending + High vol — alpha +0.11% / 20d on 93,326 historical triggers.
  • Best era for bullish: 2023-2026 — alpha +0.40% / 20d.
  • Best era for bearish: 2015-2019 — alpha +0.14% / 20d.

3 · When it fails — common false positives

  • Weakest bullish cell: Non-trending + Low vol — alpha -0.05% / 20d on 18,560 triggers.
  • Weakest bearish cell: Trending + Low vol — alpha -0.43% / 20d on 14,160 triggers.
  • Worst era for bullish: 2015-2019 — alpha -0.18% / 20d.
  • Worst era for bearish: 2020-2022 — alpha -0.20% / 20d.

Signal-specific failure patterns

Bullish alpha is nearly zero and fails permutation null
Volume breakout bullish (day with volume > 2× 20d average, up-close) shows α=+0.10 at 20d vs SPX (p(HAC)=0.06, p_perm=1.000). At 60d α=+0.16 (non-significant). The point-estimate alpha is slightly positive but the permutation null says random-date firing would have done better. The signal does not provide information.
evidence: bullish vs SPX: 20d α=+0.10 p_perm=1.000; 60d α=+0.16 p_perm=1.000
Bearish is subtle — point-estimate small but p_perm significant
Bearish α_spx=−0.05 at 20d (p(HAC)=0.26 non-sig) but p_perm=0.005 significant. The small point-estimate combined with a significant left-tail p_perm means: observed is worse than random-date firing but by a tiny amount. Against SPXEW the point-estimate is +0.20 — signal wrong direction. Bearish volume breakouts on their own do not offer tradable alpha under Convention A.
evidence: bearish vs SPX: 20d α=−0.05 p_perm=0.005 (significant but tiny); vs SPXEW: α=+0.20 (wrong direction)
Sub-period shift: bullish flipped positive in 2023-2026
Bullish α: 2015-2019 −0.18, 2020-2022 +0.05, 2023-2026 +0.40. The signal has gotten BETTER recently. May be picking up momentum-driven AI-era breakouts that the 2015-2019 value-era market didn't reward. Interesting but only a 2-period trend — could reverse.
evidence: bullish 20d vs SPX: 2015-19 −0.18, 2020-22 +0.05, 2023-26 +0.40

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.

Complementary to price signals

Volume confirmation is a complementary principle to price signals, not a same-family redundancy. A price breakout or breakdown accompanied by above-average volume carries more weight in classical technical analysis than the same price move on thin volume (Murphy, Technical Analysis of the Financial Markets, 1999; Kirkpatrick & Dahlquist, Technical Analysis, 3rd ed. 2015). Combining volume_breakout with any price-based signal in the same direction adds weight rather than redundant evidence.

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.

  • Combine with price-level breakoutsVolume-confirmed breakouts are the textbook institutional accumulation signature. Pairing with new 20d or 52w highs would filter for real technical events rather than random volume spikes. This is the most natural rescue path.
  • Multi-day volume patternSignal is currently a single-day volume event. A 3-day sustained volume pattern (each day > 1.5× average) would reduce noise and might reveal cleaner alpha. Parameter tuning opportunity.

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 has strongly tradable alpha on its own under Convention A. The 2023+ bullish sub-period is intriguing but one sub-period is not conclusive. Best use: as a confirmation filter layered onto a structural breakout trigger (new_20d_high, new_52w_high). Entry open T+1.