Pattern double_bottom_breakdown

Double Bottom Breakdown

Bearish: two troughs test the same support level, then price breaks down below. Tolerance is normalized by daily volatility (z-scores). Requires minimum 8% rally between troughs.

Signal family

Pattern — Formal chart-pattern detectors (double tops / bottoms, failed breakouts, HH/HL structure).

Parameters

Name Description Default Range
peak_order Peak detection window 15 5–25
tolerance_zscore Tolerance (z-scores of daily vol) 1.5 0.5–3.0
min_separation Min days between troughs 25 10–60
max_separation Max days between troughs 252 60–504

Historical context

17,616 valid triggers on 3,154 distinct tickers between 2015-08-11 and 2026-04-21. Universe: us_only · mcap ≥ $100,000,000 · price ≥ $1 (3,159 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)

Bearish (negative alpha = signal right)
+0.14%
vs random-date null: inside null (pperm=0.289)

Double Bottom Breakdown is a single-direction signal — only the bearish side is meaningful. (The trigger condition only describes one side of the move.)

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 DOUBLE_BOTTOM_BREAKDOWN fire in each regime?

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

Double Bottom Breakdown (double_bottom_breakdown) — 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.

Double Bottom Breakdown (double_bottom_breakdown) — 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.

Double Bottom Breakdown (double_bottom_breakdown) — 20-day alpha split by historical sub-period (2015-2019, 2020-2022, 2023+) to check consistency across market regimes

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

Bench Metric 1d 5d 20d 60d 252d
spx Stock % +0.18% -0.14% +1.11% +3.81% +13.30%
Bench % +0.17% +0.05% +1.39% +4.50% +15.57%
Alpha % -0.02% -0.20% -0.22% -0.68% -2.23%
Median alpha -0.01% -0.11% -0.28% -1.70% -10.84%
Hit rate (α>0) 49.6% 49.1% 48.9% 45.8% 38.4%
p (naive) 0.5110 0.0002 0.0308 0.0001 <0.001
p (HAC) 0.5116 0.0002 0.0407 0.0020 0.0468
N 17,607 17,593 17,495 17,005 16,054
msci Stock % +0.18% -0.14% +1.11% +3.81% +13.30%
Bench % +0.06% -0.10% +1.16% +4.03% +13.76%
Alpha % +0.13% -0.05% +0.02% -0.17% -0.44%
Median alpha +0.08% +0.03% -0.02% -1.17% -9.10%
Hit rate (α>0) 51.2% 50.2% 49.9% 47.1% 40.4%
p (naive) <0.001 0.3440 0.8392 0.3260 0.3322
p (HAC) <0.001 0.3445 0.8484 0.4368 0.6934
N 17,555 17,487 17,355 16,871 15,947
spxew Stock % +0.18% -0.14% +1.11% +3.81% +13.30%
Bench % +0.17% -0.17% +0.91% +3.95% +12.16%
Alpha % -0.00% -0.00% +0.14% -0.21% +1.13%
Median alpha -0.03% +0.00% +0.01% -1.34% -7.49%
Hit rate (α>0) 49.3% 50.0% 50.0% 46.5% 41.4%
p (naive) 0.8702 0.9843 0.1665 0.2249 0.0125
p (HAC) 0.8704 0.9844 0.1990 0.3301 0.2968
N 17,472 17,365 17,238 16,806 15,846
Distribution of all 20d alpha outcomes for this direction. Median and winsorized mean shown.
Double Bottom Breakdown (double_bottom_breakdown) — 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.
Double Bottom Breakdown (double_bottom_breakdown) — 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 -0.02% -0.03% [-0.08%, +0.02%] 0.657
1d msci +0.13% -0.05% [-0.10%, -0.00%] 1.000
1d spxew -0.00% -0.05% [-0.10%, -0.00%] 0.965
5d spx -0.20% +0.10% [-0.13%, +0.68%] 0.010
5d msci -0.05% +0.11% [-0.13%, +0.69%] 0.209
5d spxew -0.00% +0.13% [-0.12%, +0.71%] 0.348
20d spx -0.22% +0.21% [-0.23%, +1.16%] 0.040
20d msci +0.02% +0.33% [-0.10%, +1.29%] 0.194
20d spxew +0.14% +0.40% [-0.04%, +1.36%] 0.289
60d spx -0.68% +0.31% [-0.46%, +2.13%] 0.005
60d msci -0.17% +0.77% [-0.02%, +2.59%] 0.005
60d spxew -0.21% +0.98% [+0.20%, +2.79%] 0.005
252d spx -2.23% -0.36% [-2.06%, +1.59%] 0.010
252d msci -0.44% +1.96% [+0.31%, +3.87%] 0.005
252d spxew +1.13% +3.30% [+1.62%, +5.24%] 0.005

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

Six recent bearish DOUBLE_BOTTOM_BREAKDOWN 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 DOUBLE_BOTTOM_BREAKDOWN looks like when it works)
Weakest outcomes (what DOUBLE_BOTTOM_BREAKDOWN 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 802 +0.65% +1.02% -0.40% 0.0801 +0.65% +0.70% -0.12% 0.5937 +0.65% +0.78% -0.13% 0.5791
Trending + High vol Crisis selloff or parabolic rally 6,931 +0.73% +1.30% -0.50% 0.0047 +0.73% +1.03% -0.25% 0.1587 +0.73% +0.65% +0.03% 0.8474
Non-trending + Low vol Quiet chop, summer doldrums 644 +0.21% +0.62% -0.49% 0.1638 +0.21% +0.32% -0.22% 0.5258 +0.21% +0.43% -0.23% 0.4972
Non-trending + High vol Classical "whipsaw zone" for momentum 9,239 +1.52% +1.50% +0.06% 0.7085 +1.52% +1.34% +0.26% 0.0961 +1.52% +1.15% +0.29% 0.0630
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 4,627 +0.72% <0.001 +1.05% <0.001 +0.50% 0.0018
2020-2022 2020-01-01 → 2023-01-01 5,992 -1.03% <0.001 -0.74% 0.0002 -0.62% 0.0018
2023-2026 2023-01-01 → 2099-01-01 6,997 -0.12% 0.5150 +0.00% 0.9965 +0.59% 0.0012

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

Neutral signal. Bullish 20d alpha —, bearish -0.22%. Neither direction beats random date selection (pperm not significant either way). Observed alpha is likely noise or universe drift rather than information about the trigger. Useful for context, not for standalone entries.

2 · When it works — the setups that drive it

  • Best bearish setup: Non-trending + High vol — alpha +0.06% / 20d on 9,239 historical triggers.
  • Best era for bearish: 2015-2019 — alpha +0.72% / 20d.

3 · When it fails — common false positives

  • Weakest bearish cell: Trending + High vol — alpha -0.50% / 20d on 6,931 triggers.
  • Worst era for bearish: 2020-2022 — alpha -1.03% / 20d.

Signal-specific failure patterns

Bearish alpha exists but is fragile and period-dependent
Double-bottom breakdown bearish α=−0.22 at 20d (p(HAC)=0.04 borderline-sig, p_perm=0.04). 60d deepens to −0.68 (p=0.002, p_perm=0.005). The 60d edge is clearer than 20d. Signal does identify persistent weakness when support breaks, but the period dispersion is dramatic.
evidence: bearish 20d α=−0.22 p_perm=0.04; 60d α=−0.68 p_perm=0.005
2015-2019 was an INVERTED regime
Sub-period bearish 20d: 2015-2019 α=+0.72 (signal inverted — breakdowns bounced strongly), 2020-2022 α=−1.03 (signal works cleanly), 2023-2026 α=−0.12 (marginal). The 2015-2019 window was a stealth-bounce regime where breakdowns got bought. Subsequent periods are more bearish.
evidence: bearish 20d by period: +0.72, −1.03, −0.12
Similar to fresh_52w_low's post-2020 edge pattern
Both fresh_52w_low and double_bottom_breakdown show the same temporal pattern: weak or inverted pre-2020, strong edge in the 2020-2022 window, modest in 2023-2026. Suggests the underlying phenomenon is regime-dependent — narrow-breadth concentrated markets produce persistent relative weakness in breakdown names, broad-breadth markets don't.

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.

Reversal-pattern family

Double bottom and double top are canonical two-swing reversal patterns (Edwards & Magee, Technical Analysis of Stock Trends, 11th ed. 2018; Bulkowski, Encyclopedia of Chart Patterns, 3rd ed. 2021; Lo, Mamaysky, and Wang, "Foundations of Technical Analysis", Journal of Finance 55(4), 2000). A completed double-bottom breakdown and a failed-double-bottom signal on the same stock fire in sequence rather than concurrently — they represent different stages of the same pattern.

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.

  • Volume filterBreakdowns on weak volume often reverse as squeezes; 1.5-2× average volume gate should concentrate the alpha.
  • Hold to 60dThe signal is stronger at 60d than 20d. Time stop at 60 trading days captures more.
  • Regime filter on market breadthEdge is in narrow-breadth regimes (2020+). Gate on 'market breadth < 55%' to enable the signal only when the regime favors it.

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? Pattern signals carry their own structural context; check that the implied support/resistance levels have historical relevance, not just the most-recent 3-month range.
  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

Tradable at 60d under Convention A. 20d signal is borderline significant; 60d is clear. Entry open T+1. Skip the 2015-2019 regime if you're calibrating on period sub-samples — it's inverted relative to current behavior. Recent 2023-2026 alpha is only −0.12, so don't oversize.