Mean reversion stochastics

Stochastics (Slow)

Slow Stochastics with %K/%D crossover. Bullish: %K crosses above %D while %K is in the oversold zone (below oversold+10, i.e. 30 at default settings). Bearish: %K crosses below %D while %K is in the overbought zone (above overbought−10, i.e. 70 at default settings).

Signal family

Mean reversion — Oscillator-based signals that fire at overbought or oversold extremes — typically fade the prevailing move.

Parameters

Name Description Default Range
k_period %K period 14 5–50
d_period %D period 3 2–10
slow_period Slow smoothing period 3 1–10
overbought Overbought level 80 60–90
oversold Oversold level 20 10–40

Historical context

4,114,229 triggers on 24,242 tickers, 1995-09-06 → 2026-05-01. Universe: global — all covered exchanges (mcap ≥ $100,000,000, price ≥ $1). Long-only convention: BUY at open T+1, hold the horizon, compare to S&P 500 Equal Weight over the same window.

Methodology footnotes

Benchmarks shown in the detail tables: spxew (S&P 500 Equal Weight — primary, median-stock view, avoids the 2020+ megacap-concentration distortion), spx (S&P 500 cap-weighted, distorted post-2020), msci (MSCI World USD). Per-stock regime tags: trending = ADX(14) ≥ 25, high vol = 20d realized annualized vol ≥ 20%. 1d return = intraday T+1 open→close; 20d = open T+1 to close T+20.

At a glance — alpha vs S&P 500 Equal Weight, global universe

Holding-period sensitivity. Bullish columns: positive = signal worked (long the trigger beat the index). Bearish columns: negative = signal worked (the flagged stock underperformed).

Horizon Bullish α Bearish α
5-day -0.01% -0.02%
20-day -0.06% +0.23%
60-day +0.19% +0.51%
1-year +1.71% +2.72%
Random-date null check (20-day): Bullish: worse than random (p=1.000).
Bearish: worse than random (p=1.000).

Where does STOCHASTICS actually fire?

The bucket distribution often reveals what the signal really is, regardless of its textbook label. Heavy concentration in "non-trending + high vol" = it's mostly a chop-market event. Heavy in "trending + low vol" = it picks up the smooth grinds. Read the chart before the alpha numbers — context shapes everything that follows.

Stochastics (Slow) (stochastics) — trigger count distribution by per-stock regime quadrant (trending/non-trending × high/low realized volatility) for bullish and bearish triggers, global universe

Does it work in every regime?

Trigger alpha split by the host stock's own regime on the trigger date — trending or ranging, high-vol or low-vol. The 20d alpha you'd actually capture if you took the trade. Bars matching your direction's "right" sign (positive for bullish, negative for bearish) = the signal worked in that regime; opposite sign = avoid it there. A signal with one strong-positive bar and three flat ones isn't a "20d alpha" signal — it's a "20d alpha when the stock is X" signal.

Stochastics (Slow) (stochastics) — mean 20-day alpha versus S&P 500 Equal Weight by per-stock regime quadrant, bullish and bearish triggers 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.

Does it work in every era?

A multi-year average can hide major instability. The sample splits into three windows: 2015–2019 (pre-COVID), 2020–2022 (pandemic + 2022 bear), and 2023+ (post-ZIRP + AI megacap rally). All three matching your direction's "right" sign = the signal is durable. One era doing all the work = a regime-specific edge that may not repeat. The bigger the variance across eras, the smaller the position you should run.

Stochastics (Slow) (stochastics) — 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
spxew Stock % -0.01% +0.23% +1.07% +3.12% +12.46%
Bench % +0.03% +0.21% +1.13% +2.88% +10.62%
Alpha % -0.05% -0.01% -0.06% +0.19% +1.71%
Median alpha -0.11% -0.25% -0.91% -2.04% -7.13%
Hit rate (α>0) 47.3% 47.4% 45.5% 44.4% 41.3%
p (naive) <0.001 0.0007 <0.001 <0.001 <0.001
p (HAC) <0.001 0.0009 <0.001 <0.001 <0.001
N 2,033,103 1,963,226 1,944,521 1,896,574 1,719,279
spx Stock % -0.01% +0.23% +1.07% +3.12% +12.46%
Bench % +0.01% +0.25% +1.36% +3.45% +14.40%
Alpha % -0.03% -0.05% -0.28% -0.33% -1.97%
Median alpha -0.09% -0.30% -1.16% -2.64% -10.93%
Hit rate (α>0) 47.7% 46.9% 44.3% 42.9% 37.5%
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 2,045,426 1,978,528 1,973,097 1,916,943 1,741,632
msci Stock % -0.01% +0.23% +1.07% +3.12% +12.46%
Bench % +0.05% +0.27% +1.19% +3.07% +12.07%
Alpha % -0.05% -0.06% -0.15% +0.07% +0.06%
Median alpha -0.12% -0.32% -1.04% -2.27% -8.77%
Hit rate (α>0) 46.9% 46.6% 44.8% 43.8% 39.6%
p (naive) <0.001 <0.001 <0.001 <0.001 0.1118
p (HAC) <0.001 <0.001 <0.001 0.0153 0.6646
N 2,032,904 1,961,767 1,948,252 1,901,959 1,722,180
Observed 20-day lift (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 the right tail, the signal is doing real work in that direction; in the left tail it ran inverted — random dates served the bullish case better than its own triggers did.
Stochastics (Slow) (stochastics) — bullish 20-day observed lift versus random-date permutation null (200 iterations)
Permutation null detail — all horizons × each benchmark
200-iteration null: for each ticker, sample N random dates from its history (matching observed trigger count) and compute the same alpha. Both observed and null are baseline-centered per ticker (each ticker's own baseline alpha is subtracted), which removes the universe-selection lift that all surviving names share, so the comparison is about the trigger's timing. It does not put the null at zero: that baseline is a median, while a random date's expectation is the ticker's mean, so the null settles at the gap between the two — the right-skew of the ticker's own alpha distribution, positive for equities. Read observed minus null, not the absolute position of either column — which is also why these figures do not match the α columns in the tables above (those are raw, uncentered means). pperm = one-sided fraction of null iters with mean in the "signal was right" tail (right for bullish, left for bearish), so it runs from a floor of 0.005 (trigger dates beat every random draw in the claimed direction) to a 1.000 ceiling (every random draw served the claimed direction better — a reliably inverted signal, not an absent one). The floor is 1/(iterations + 1) and moves with the iteration count; the ceiling does not. Either end says the result was reliable, not that it was large: the gap between the line and the cloud can be a fraction of a percent and still reach an endpoint, and the transaction-cost floor in the caveats below would swallow a gap that small. Read the size off the α and hit-rate columns, the reliability off pperm.
Horizon Bench Observed lift Null mean Null 95% CI pperm
1d spxew +0.10% +0.08% [+0.08%, +0.09%] 0.005
1d spx +0.09% +0.09% [+0.09%, +0.09%] 0.005
1d msci +0.10% +0.09% [+0.09%, +0.10%] 0.005
5d spxew +0.40% +0.37% [+0.36%, +0.38%] 0.005
5d spx +0.40% +0.39% [+0.38%, +0.40%] 0.010
5d msci +0.40% +0.39% [+0.38%, +0.40%] 0.328
20d spxew +1.13% +1.21% [+1.19%, +1.23%] 1.000
20d spx +1.14% +1.24% [+1.22%, +1.25%] 1.000
20d msci +1.16% +1.25% [+1.24%, +1.27%] 1.000
60d spxew +2.67% +2.59% [+2.56%, +2.62%] 0.005
60d spx +2.88% +2.66% [+2.62%, +2.68%] 0.005
60d msci +2.87% +2.68% [+2.65%, +2.71%] 0.005
252d spxew +5.39% +5.01% [+4.96%, +5.06%] 0.005
252d spx +5.75% +5.35% [+5.29%, +5.40%] 0.005
252d msci +5.54% +5.30% [+5.24%, +5.35%] 0.005

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

Six recent bullish STOCHASTICS triggers on US mega-caps. Top three: the signal's best outcomes. Bottom three: the worst. Extreme outliers (|α| > 25%) excluded. The three best and three worst are still tail outcomes by construction — read them as the range, not the typical result.

Strongest outcomes (what STOCHASTICS looks like when it works)
Stochastics (Slow) (stochastics) — 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 Stochastics (Slow) (stochastics) — 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 Stochastics (Slow) (stochastics) — 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 STOCHASTICS looks like when it fails)
Stochastics (Slow) (stochastics) — weakest bullish trigger #1 on a US mega-cap, showing the 20-day forward window where the signal failed Stochastics (Slow) (stochastics) — weakest bullish trigger #2 on a US mega-cap, showing the 20-day forward window where the signal failed Stochastics (Slow) (stochastics) — 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 % (spxew) Bench % (spxew) Alpha % (spxew) p (HAC) Stock % (spx) Bench % (spx) Alpha % (spx) p (HAC) Stock % (msci) Bench % (msci) Alpha % (msci) p (HAC)
Trending + Low vol Clean directional grind, low whipsaw 133,739 +0.24% +0.83% -0.53% <0.001 +0.24% +1.11% -0.83% <0.001 +0.24% +0.95% -0.68% <0.001
Trending + High vol Crisis selloff or parabolic rally 752,486 +1.71% +1.25% +0.37% <0.001 +1.71% +1.54% +0.12% <0.001 +1.71% +1.32% +0.29% <0.001
Non-trending + Low vol Quiet chop, summer doldrums 229,833 +0.22% +0.80% -0.55% <0.001 +0.22% +1.07% -0.83% <0.001 +0.22% +0.95% -0.70% <0.001
Non-trending + High vol Classical "whipsaw zone" for momentum 993,591 +0.91% +1.12% -0.20% <0.001 +0.91% +1.30% -0.38% <0.001 +0.91% +1.17% -0.27% <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 % (spxew) p (HAC) Alpha % (spx) p (HAC) Alpha % (msci) p (HAC)
2015-2019 2015-01-01 → 2020-01-01 627,269 -0.27% <0.001 -0.37% <0.001 -0.17% <0.001
2020-2022 2020-01-01 → 2023-01-01 641,953 -0.34% <0.001 -0.11% <0.001 +0.05% 0.0109
2023-2026 2023-01-01 → 2099-01-01 839,960 +0.32% <0.001 -0.34% <0.001 -0.28% <0.001

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

Bench Metric 1d 5d 20d 60d 252d
spxew Stock % +0.00% +0.19% +0.86% +2.65% +12.64%
Bench % +0.04% +0.21% +0.69% +2.19% +9.97%
Alpha % -0.05% -0.02% +0.23% +0.51% +2.72%
Median alpha -0.08% -0.26% -0.63% -1.64% -5.54%
Hit rate (α>0) 47.8% 47.2% 46.8% 45.3% 42.9%
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 1,925,994 1,850,329 1,840,350 1,802,708 1,586,223
spx Stock % +0.00% +0.19% +0.86% +2.65% +12.64%
Bench % +0.03% +0.23% +0.88% +3.01% +13.75%
Alpha % -0.03% -0.04% +0.01% -0.33% -1.19%
Median alpha -0.07% -0.30% -0.88% -2.51% -9.59%
Hit rate (α>0) 47.9% 46.7% 45.5% 43.0% 38.4%
p (naive) <0.001 <0.001 0.0665 <0.001 <0.001
p (HAC) <0.001 <0.001 0.1907 <0.001 <0.001
N 1,945,624 1,882,506 1,858,286 1,826,403 1,608,203
msci Stock % +0.00% +0.19% +0.86% +2.65% +12.64%
Bench % +0.05% +0.21% +0.77% +2.57% +11.29%
Alpha % -0.04% -0.01% +0.14% +0.12% +1.21%
Median alpha -0.09% -0.27% -0.76% -2.07% -7.16%
Hit rate (α>0) 47.5% 47.0% 46.1% 44.1% 41.1%
p (naive) <0.001 0.0008 <0.001 <0.001 <0.001
p (HAC) <0.001 0.0011 <0.001 0.0001 <0.001
N 1,931,291 1,866,321 1,854,966 1,816,289 1,600,011
Observed 20-day lift (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 the left tail, the signal is doing real work in that direction; in the right tail it ran inverted — random dates served the bearish case better than its own triggers did.
Stochastics (Slow) (stochastics) — bearish 20-day observed lift versus random-date permutation null (200 iterations)
Permutation null detail — all horizons × each benchmark
200-iteration null: for each ticker, sample N random dates from its history (matching observed trigger count) and compute the same alpha. Both observed and null are baseline-centered per ticker (each ticker's own baseline alpha is subtracted), which removes the universe-selection lift that all surviving names share, so the comparison is about the trigger's timing. It does not put the null at zero: that baseline is a median, while a random date's expectation is the ticker's mean, so the null settles at the gap between the two — the right-skew of the ticker's own alpha distribution, positive for equities. Read observed minus null, not the absolute position of either column — which is also why these figures do not match the α columns in the tables above (those are raw, uncentered means). pperm = one-sided fraction of null iters with mean in the "signal was right" tail (right for bullish, left for bearish), so it runs from a floor of 0.005 (trigger dates beat every random draw in the claimed direction) to a 1.000 ceiling (every random draw served the claimed direction better — a reliably inverted signal, not an absent one). The floor is 1/(iterations + 1) and moves with the iteration count; the ceiling does not. Either end says the result was reliable, not that it was large: the gap between the line and the cloud can be a fraction of a percent and still reach an endpoint, and the transaction-cost floor in the caveats below would swallow a gap that small. Read the size off the α and hit-rate columns, the reliability off pperm.
Horizon Bench Observed lift Null mean Null 95% CI pperm
1d spxew +0.08% +0.07% [+0.06%, +0.07%] 1.000
1d spx +0.08% +0.08% [+0.07%, +0.08%] 0.279
1d msci +0.09% +0.08% [+0.08%, +0.09%] 1.000
5d spxew +0.30% +0.31% [+0.30%, +0.32%] 0.005
5d spx +0.33% +0.32% [+0.31%, +0.33%] 0.721
5d msci +0.34% +0.33% [+0.32%, +0.34%] 1.000
20d spxew +1.11% +1.01% [+0.99%, +1.03%] 1.000
20d spx +1.13% +1.04% [+1.02%, +1.06%] 1.000
20d msci +1.14% +1.05% [+1.04%, +1.07%] 1.000
60d spxew +2.13% +2.21% [+2.19%, +2.24%] 0.005
60d spx +2.04% +2.28% [+2.26%, +2.31%] 0.005
60d msci +2.07% +2.30% [+2.27%, +2.33%] 0.005
252d spxew +3.95% +4.32% [+4.27%, +4.38%] 0.005
252d spx +4.13% +4.67% [+4.62%, +4.72%] 0.005
252d msci +4.20% +4.62% [+4.57%, +4.67%] 0.005

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

Six recent bearish STOCHASTICS triggers on US mega-caps. Top three: the signal's best outcomes. Bottom three: the worst. Extreme outliers (|α| > 25%) excluded. The three best and three worst are still tail outcomes by construction — read them as the range, not the typical result.

Strongest outcomes (what STOCHASTICS looks like when it works)
Stochastics (Slow) (stochastics) — 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 Stochastics (Slow) (stochastics) — 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 Stochastics (Slow) (stochastics) — 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 STOCHASTICS looks like when it fails)
Stochastics (Slow) (stochastics) — weakest bearish trigger #1 on a US mega-cap, showing the 20-day forward window where the signal failed Stochastics (Slow) (stochastics) — weakest bearish trigger #2 on a US mega-cap, showing the 20-day forward window where the signal failed Stochastics (Slow) (stochastics) — 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 % (spxew) Bench % (spxew) Alpha % (spxew) p (HAC) Stock % (spx) Bench % (spx) Alpha % (spx) p (HAC) Stock % (msci) Bench % (msci) Alpha % (msci) p (HAC)
Trending + Low vol Clean directional grind, low whipsaw 179,306 +0.47% +0.33% +0.20% <0.001 +0.47% +0.64% -0.14% <0.001 +0.47% +0.50% +0.01% 0.6054
Trending + High vol Crisis selloff or parabolic rally 813,969 +1.05% +0.73% +0.39% <0.001 +1.05% +0.94% +0.15% <0.001 +1.05% +0.81% +0.28% <0.001
Non-trending + Low vol Quiet chop, summer doldrums 239,088 +0.53% +0.43% +0.12% <0.001 +0.53% +0.69% -0.15% <0.001 +0.53% +0.54% +0.00% 0.9117
Non-trending + High vol Classical "whipsaw zone" for momentum 772,198 +0.92% +0.82% +0.15% <0.001 +0.92% +0.95% +0.01% 0.6608 +0.92% +0.86% +0.11% <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 % (spxew) p (HAC) Alpha % (spx) p (HAC) Alpha % (msci) p (HAC)
2015-2019 2015-01-01 → 2020-01-01 616,082 -0.15% <0.001 -0.40% <0.001 -0.21% <0.001
2020-2022 2020-01-01 → 2023-01-01 585,138 +0.28% <0.001 +0.49% <0.001 +0.61% <0.001
2023-2026 2023-01-01 → 2099-01-01 802,918 +0.51% <0.001 +0.00% 0.8372 +0.07% 0.0002

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: one-sided fraction of 200 random-date null iterations falling in the “signal was right” tail (mean ≥ observed for bullish; mean ≤ observed for bearish). 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 not beaten random timing whatever the t-test says — and because the test is one-sided, a pperm up at its 1.000 ceiling is not "no edge" but inverted edge: every random draw served the claimed direction better than the trigger dates did.

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

Not a standalone entry trigger at 20 days. Bullish 20d alpha is -0.06% — worse than random : firing on random dates would have done better. Bearish 20d alpha is +0.23% — worse than random : firing on random dates would have done better. Fires are screening context inside a composite (section 4), not entries.

These verdicts are 20-day holds vs S&P 500 Equal Weight. Longer horizons can differ in either direction — check the permutation detail tables below before extrapolating.

2 · When it works — the setups that drive it

  • Best bullish setup: Trending + High vol — alpha +0.37% / 20d on 752,486 historical triggers.
  • Least-bad bearish cell: Non-trending + Low vol — alpha +0.12% / 20d on 239,088 triggers — still wrong-signed; no bearish cell produced negative alpha.
  • Best era for bullish: 2023-2026 — alpha +0.32% / 20d on 839,960 triggers.
  • Best era for bearish: 2015-2019 — alpha -0.15% / 20d on 616,082 triggers.

3 · When it fails — common false positives

  • Weakest bullish cell: Non-trending + Low vol — alpha -0.55% / 20d on 229,833 triggers.
  • Weakest bearish cell: Trending + High vol — alpha +0.39% / 20d on 813,969 triggers.
  • Worst era for bullish: 2020-2022 — alpha -0.34% / 20d on 641,953 triggers.
  • Worst era for bearish: 2023-2026 — alpha +0.51% / 20d on 802,918 triggers.

Signal-specific failure patterns

Double smoothing means the cross prints well after the turn
The trigger runs on slow Stochastics: fast %K (close positioned inside the 14-day high-low range) is smoothed once into fast %D (3-day SMA), which becomes slow %K, then smoothed again into slow %D (another 3-day SMA). The crossover of these two smoothed lines is what fires. Two rounds of averaging suppress whipsaw but guarantee lag: in a fast V-shaped reversal the bullish cross prints days after the actual low, with part of the bounce already spent. Whether what remains after that lag beats the benchmark is exactly what the at-a-glance table and the permutation-null line above measure — read them for the current answer.
Oscillator pinning in strong trends
In a sustained trend the oscillator pins near its bound: a strong uptrend keeps slow %K elevated, so bearish crosses in the overbought zone are frequently just routine pullbacks inside an ongoing advance, and in a grinding downtrend bullish crosses out of the oversold zone re-fire repeatedly against the trend. Nothing in the trigger checks trend direction. This is the timeless structural weakness of zone-based oscillator crosses; the live tables on this page adjudicate whether either side has carried forward information despite it.
The zone condition is wider than the textbook bands
The trigger does not require the oscillator to be inside the classic 20/80 extremes at the moment of the cross — it requires slow %K below oversold+10 (30 at default settings) for bullish and above overbought−10 (70 at defaults) for bearish. That tolerance exists because the smoothed lines often cross just after leaving the extreme zone, but it also admits crossovers that never actually reached a true extreme, a mechanically noisier population than textbook stochastic signals. Behaviour can also differ sharply across liquidity regimes, so check the sub-period rows in the tables above before extrapolating any single era.

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. Measured pair results — same-day co-fires put through the pair backtest — follow under “Measured pairings” below.

Oscillator-family redundancy

Stochastics belongs to the momentum-oscillator family alongside RSI, Williams %R, and CCI — each normalises recent price against a short lookback window into a bounded range (Murphy, Technical Analysis of the Financial Markets, 1999; Pring, Technical Analysis Explained, 5th ed. 2014; Kirkpatrick & Dahlquist, Technical Analysis, 3rd ed. 2015). The overlap with Williams %R is exact by construction — raw %K equals %R plus 100 over the same 14-day lookback — so the two differ only in smoothing and trigger logic, not in underlying information. Stacking two or more family members in the same direction within a single Daily Report produces correlated rather than independent evidence.

Measured pairings — Bonferroni survivors

Beyond the literature pairings above, these are the same-day co-fire combinations involving Stochastics (Slow) that cleared the pair backtest's Bonferroni cut on the full 2016–2026 sample (549 pairs × 5 horizons = 2,745 hypotheses), on universes filtered to ADV ≥ $5M, price ≥ $5 and market cap ≥ $100M. That cut is two-sided: it asks only whether the co-fire's α is reliably different from zero, in either direction, so a pair can survive by reliably underperforming — 3 of the 34 rows below do exactly that (negative full-sample α). The same run holds out 2023+: the Test columns are that held-out window, printed for every row with enough held-out co-fires to measure, so a survivor that did not repeat out of sample is visible rather than hidden. All α figures here are for holding the stock long after the co-fire — no shorting assumed, and no sign flip for bearish legs. So positive α means the co-fire was followed by outperformance and negative α by underperformance, whichever way either leg points — a bearish leg does not flip the reading. Survivors are rare by design — absence of a pair here means it did not clear the cut, not that it was untested. Ranked by held-out (2023+) α. Historical tendencies, not recommendations.

US (NYSE / NASDAQ / AMEX)

Pair (same-day co-fire, long) Full α Test α (2023+) Test N p_perm test
stochastics bearish + weekly_change bullish +0.52% +1.06% 9,020 0.002
bollinger bullish + stochastics bullish +0.43% +0.47% 13,468 0.002
cci bullish + stochastics bullish +0.30% +0.45% 18,538 0.002
stochastics bullish + williams_r bullish +0.20% +0.32% 31,815 0.002
fresh_52w_low bearish + stochastics bullish -1.31% -1.85% 619 0.002
hh_hl_structure bullish + stochastics bearish -2.69% -2.74% 82 0.006

6 of this universe's 18 surviving pairs involve this signal · α vs ^SPXEW.

Europe — 4 surviving pairs
Pair (same-day co-fire, long) Full α Test α (2023+) Test N p_perm test
rsi bearish + stochastics bearish +0.41% +0.48% 3,779 0.002
stochastics bearish + williams_r bearish +0.36% +0.39% 11,446 0.002
cci bearish + stochastics bearish +0.37% +0.34% 6,544 0.002
bollinger bullish + stochastics bullish +0.46% +0.24% 3,957 0.066

4 of this universe's 20 surviving pairs involve this signal · α vs ^STOXX.

Hong Kong — 4 surviving pairs
Pair (same-day co-fire, long) Full α Test α (2023+) Test N p_perm test
bollinger bearish + stochastics bearish +1.13% +1.76% 1,904 0.002
rsi bearish + stochastics bearish +1.19% +1.72% 1,276 0.002
stochastics bearish + weekly_change bullish +1.29% +1.18% 1,528 0.004
stochastics bearish + williams_r bearish +0.53% +0.73% 3,861 0.002

4 of this universe's 23 surviving pairs involve this signal · α vs ^HSI.

China A-shares — 20 surviving pairs
Pair (same-day co-fire, long) Full α Test α (2023+) Test N p_perm test
stochastics bullish + volume_breakout bearish +3.81% +7.21% 954 0.002
stochastics bullish + weekly_change bearish +4.27% +5.57% 14,105 0.002
hh_hl_streak bullish + stochastics bearish +2.33% +3.77% 3,278 0.002
stochastics bullish + volume_breakout bullish +2.02% +3.56% 3,345 0.002
hh_hl_structure bearish + stochastics bearish +1.60% +2.12% 1,705 0.002
new_20d_low bearish + stochastics bullish +1.37% +1.91% 16,120 0.002
stochastics bearish + vwap_cross bullish +1.19% +1.80% 903 0.002
stochastics bearish + volume_breakout bullish +0.51% +1.67% 8,439 0.002
bearish_trend_breakout bullish + stochastics bearish +1.13% +1.52% 2,364 0.002
bollinger bullish + stochastics bullish +1.55% +1.44% 29,183 0.002
macd bullish + stochastics bullish +0.66% +1.36% 5,230 0.002
rsi bearish + stochastics bearish +0.70% +1.21% 15,894 0.002
stochastics bearish + williams_r bearish +0.52% +1.01% 57,784 0.002
bollinger bearish + stochastics bearish +0.25% +0.88% 27,307 0.002
rsi bullish + stochastics bullish +1.60% +0.81% 14,370 0.002
new_20d_high bullish + stochastics bearish +0.41% +0.78% 14,587 0.002
stochastics bullish + williams_r bullish +0.51% +0.67% 65,651 0.002
cci bullish + stochastics bullish +0.35% +0.56% 35,021 0.002
cci bearish + stochastics bearish +0.22% +0.47% 25,460 0.002
hh_hl_structure bullish + stochastics bearish -4.64% -5.03% 63 0.002

20 of this universe's 138 surviving pairs involve this signal · α vs 83188.HK.

China A-share survivor α runs large but skews toward small-caps, where trading costs and thin liquidity claim a large share of any measured edge — screening context, not a capturable spread.

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.

  • Pair with a trend filter — Because the trigger is trend-blind, conditioning on trend is the natural refinement — for example, taking bearish crosses only below a declining 50DMA. The filter separates with-trend fires from countertrend ones and discards most of the trigger set, which is the point. Testable within the platform's own screens.
  • Let the table pick the holding period — Smoothed-oscillator crosses can look very different at 20 versus 60 days, and the pattern shifts across regimes. Compare the horizon columns in the at-a-glance table before deciding between time stops and price stops — extend holds only if the current data shows the effect compounding with horizon.

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 mean-reversion signal firing against the long-term trend (e.g. oversold in a clean uptrend) is much more reliable than one firing with it.
  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

The trigger is a one-day cross event on the slow lines: slow %K crossing above slow %D with slow %K below 30 for bullish, crossing below slow %D with slow %K above 70 for bearish (default 14/3/3 settings). The backtest behind the tables above measures entry at the open on T+1 after a trigger on the close of T. Treat tradability as direction-conditional and empirical: if a side clears the random-date null in the current tables at your horizon, it can serve as a screen tile in that direction; otherwise treat its fires as context about where the stock sits in its two-week range. Pick the holding horizon from the horizon columns in the live table rather than from any remembered figure. Historical tendency, not a recommendation.