Trend ma_crossover

Moving Average Crossover

Golden Cross (bullish): fast MA crosses above slow MA. Death Cross (bearish): fast MA crosses below slow MA.

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
fast Fast MA period 50 5–200
slow Slow MA period 200 20–500

Historical context

229,035 triggers on 21,536 tickers, 1989-03-31 → 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% +0.03%
20-day +0.20% -0.05%
60-day +0.16% +0.24%
1-year +3.55% +0.85%
Random-date null check (20-day): Bullish: beats random (p=0.005).
Bearish: beats random (p=0.015).

Where does MA_CROSSOVER 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.

Moving Average Crossover (ma_crossover) — 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.

Moving Average Crossover (ma_crossover) — 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.

Moving Average Crossover (ma_crossover) — 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 % +0.00% +0.14% +0.69% +2.02% +13.37%
Bench % +0.03% +0.16% +0.62% +1.88% +9.82%
Alpha % -0.04% -0.01% +0.20% +0.16% +3.55%
Median alpha -0.10% -0.33% -0.96% -2.47% -6.05%
Hit rate (α>0) 47.7% 46.6% 45.4% 43.2% 42.4%
p (naive) <0.001 0.4653 <0.001 0.0151 <0.001
p (HAC) <0.001 0.4655 <0.001 0.0415 <0.001
N 109,789 105,519 105,348 103,501 91,322
spx Stock % +0.00% +0.14% +0.69% +2.02% +13.37%
Bench % +0.01% +0.22% +0.99% +3.01% +14.40%
Alpha % -0.02% -0.07% -0.21% -1.01% -1.20%
Median alpha -0.09% -0.41% -1.38% -3.74% -11.18%
Hit rate (α>0) 47.7% 45.8% 43.4% 40.1% 37.3%
p (naive) 0.0297 0.0003 <0.001 <0.001 <0.001
p (HAC) 0.0305 0.0003 <0.001 <0.001 0.0169
N 110,777 107,245 106,920 104,949 92,545
msci Stock % +0.00% +0.14% +0.69% +2.02% +13.37%
Bench % +0.04% +0.22% +0.84% +2.59% +11.78%
Alpha % -0.04% -0.05% -0.06% -0.55% +1.35%
Median alpha -0.12% -0.40% -1.23% -3.31% -8.69%
Hit rate (α>0) 47.1% 46.0% 44.1% 41.2% 39.9%
p (naive) <0.001 0.0050 0.1036 <0.001 <0.001
p (HAC) <0.001 0.0051 0.1097 <0.001 0.0070
N 110,459 106,960 106,702 104,528 92,127
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.
Moving Average Crossover (ma_crossover) — 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 +0.09% +0.08% [+0.06%, +0.09%] 0.020
1d spx +0.09% +0.08% [+0.07%, +0.10%] 0.139
1d msci +0.10% +0.09% [+0.07%, +0.10%] 0.149
5d spxew +0.38% +0.34% [+0.30%, +0.38%] 0.025
5d spx +0.37% +0.36% [+0.32%, +0.40%] 0.249
5d msci +0.38% +0.36% [+0.33%, +0.40%] 0.204
20d spxew +1.31% +1.12% [+1.04%, +1.18%] 0.005
20d spx +1.12% +1.15% [+1.07%, +1.22%] 0.746
20d msci +1.16% +1.16% [+1.09%, +1.23%] 0.577
60d spxew +2.28% +2.38% [+2.24%, +2.50%] 0.905
60d spx +1.87% +2.45% [+2.31%, +2.57%] 1.000
60d msci +1.89% +2.47% [+2.34%, +2.59%] 1.000
252d spxew +6.02% +4.62% [+4.41%, +4.89%] 0.005
252d spx +5.41% +5.01% [+4.79%, +5.27%] 0.005
252d msci +5.55% +4.96% [+4.76%, +5.23%] 0.005

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

Six recent bullish MA_CROSSOVER 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 MA_CROSSOVER looks like when it works)
Moving Average Crossover (ma_crossover) — 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 Moving Average Crossover (ma_crossover) — 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 Moving Average Crossover (ma_crossover) — 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 MA_CROSSOVER looks like when it fails)
Moving Average Crossover (ma_crossover) — weakest bullish trigger #1 on a US mega-cap, showing the 20-day forward window where the signal failed Moving Average Crossover (ma_crossover) — weakest bullish trigger #2 on a US mega-cap, showing the 20-day forward window where the signal failed Moving Average Crossover (ma_crossover) — 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 10,037 +0.30% +0.39% -0.05% 0.5184 +0.30% +0.86% -0.52% <0.001 +0.30% +0.67% -0.32% <0.001
Trending + High vol Crisis selloff or parabolic rally 57,317 +0.97% +0.59% +0.54% <0.001 +0.97% +1.00% +0.06% 0.3158 +0.97% +0.84% +0.23% 0.0002
Non-trending + Low vol Quiet chop, summer doldrums 10,996 +0.42% +0.49% -0.02% 0.7301 +0.42% +0.87% -0.41% <0.001 +0.42% +0.71% -0.25% <0.001
Non-trending + High vol Classical "whipsaw zone" for momentum 35,850 +0.54% +0.74% -0.09% 0.1860 +0.54% +1.04% -0.41% <0.001 +0.54% +0.90% -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 % (spxew) p (HAC) Alpha % (spx) p (HAC) Alpha % (msci) p (HAC)
2015-2019 2015-01-01 → 2020-01-01 27,578 -0.72% <0.001 -1.08% <0.001 -0.80% <0.001
2020-2022 2020-01-01 → 2023-01-01 36,368 +0.49% <0.001 +0.66% <0.001 +0.72% <0.001
2023-2026 2023-01-01 → 2099-01-01 50,218 +0.48% <0.001 -0.37% <0.001 -0.21% 0.0006

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

Bench Metric 1d 5d 20d 60d 252d
spxew Stock % +0.00% +0.30% +1.32% +3.70% +12.54%
Bench % +0.05% +0.24% +1.39% +3.50% +11.73%
Alpha % -0.06% +0.03% -0.05% +0.24% +0.85%
Median alpha -0.09% -0.20% -0.80% -1.72% -7.00%
Hit rate (α>0) 47.6% 47.7% 45.9% 44.9% 41.0%
p (naive) <0.001 0.1289 0.1097 <0.001 <0.001
p (HAC) <0.001 0.1325 0.1132 0.0007 0.0165
N 110,300 106,246 104,802 102,404 92,156
spx Stock % +0.00% +0.30% +1.32% +3.70% +12.54%
Bench % +0.03% +0.29% +1.56% +3.91% +14.95%
Alpha % -0.03% -0.02% -0.22% -0.20% -2.36%
Median alpha -0.07% -0.27% -1.00% -2.31% -10.55%
Hit rate (α>0) 48.0% 47.1% 44.9% 43.3% 37.4%
p (naive) <0.001 0.3064 <0.001 0.0006 <0.001
p (HAC) <0.001 0.3120 <0.001 0.0033 <0.001
N 111,434 107,675 106,456 103,697 93,782
msci Stock % +0.00% +0.30% +1.32% +3.70% +12.54%
Bench % +0.06% +0.29% +1.38% +3.48% +12.49%
Alpha % -0.06% -0.01% -0.06% +0.26% -0.12%
Median alpha -0.11% -0.27% -0.85% -1.83% -8.16%
Hit rate (α>0) 47.4% 47.0% 45.5% 44.6% 39.8%
p (naive) <0.001 0.4279 0.0495 <0.001 0.4394
p (HAC) <0.001 0.4323 0.0524 0.0001 0.7317
N 110,535 106,722 105,468 103,175 92,761
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.
Moving Average Crossover (ma_crossover) — 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 +0.06% +0.08% [+0.06%, +0.09%] 0.050
1d spx +0.08% +0.08% [+0.07%, +0.10%] 0.184
1d msci +0.08% +0.09% [+0.07%, +0.11%] 0.169
5d spxew +0.38% +0.34% [+0.31%, +0.37%] 0.995
5d spx +0.37% +0.36% [+0.32%, +0.39%] 0.821
5d msci +0.38% +0.36% [+0.33%, +0.40%] 0.781
20d spxew +1.00% +1.10% [+1.02%, +1.17%] 0.015
20d spx +1.06% +1.13% [+1.06%, +1.20%] 0.025
20d msci +1.10% +1.15% [+1.08%, +1.22%] 0.080
60d spxew +2.41% +2.36% [+2.24%, +2.47%] 0.771
60d spx +2.70% +2.43% [+2.30%, +2.55%] 1.000
60d msci +2.74% +2.45% [+2.33%, +2.57%] 1.000
252d spxew +4.17% +4.58% [+4.32%, +4.77%] 0.005
252d spx +4.94% +4.97% [+4.73%, +5.18%] 0.328
252d msci +4.86% +4.92% [+4.69%, +5.10%] 0.279

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

Six recent bearish MA_CROSSOVER 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 MA_CROSSOVER looks like when it works)
Moving Average Crossover (ma_crossover) — 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 Moving Average Crossover (ma_crossover) — 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 Moving Average Crossover (ma_crossover) — 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 MA_CROSSOVER looks like when it fails)
Moving Average Crossover (ma_crossover) — weakest bearish trigger #1 on a US mega-cap, showing the 20-day forward window where the signal failed Moving Average Crossover (ma_crossover) — weakest bearish trigger #2 on a US mega-cap, showing the 20-day forward window where the signal failed Moving Average Crossover (ma_crossover) — 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 8,610 +0.21% +0.81% -0.52% <0.001 +0.21% +1.05% -0.80% <0.001 +0.21% +0.94% -0.68% <0.001
Trending + High vol Crisis selloff or parabolic rally 42,788 +2.19% +1.98% +0.17% 0.0063 +2.19% +2.21% -0.04% 0.5391 +2.19% +1.94% +0.22% 0.0004
Non-trending + Low vol Quiet chop, summer doldrums 13,530 +0.38% +0.70% -0.29% <0.001 +0.38% +0.91% -0.51% <0.001 +0.38% +0.82% -0.41% <0.001
Non-trending + High vol Classical "whipsaw zone" for momentum 49,902 +1.03% +1.13% -0.08% 0.1217 +1.03% +1.25% -0.20% <0.001 +1.03% +1.12% -0.09% 0.0619
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 29,173 -0.41% <0.001 -0.55% <0.001 -0.40% <0.001
2020-2022 2020-01-01 → 2023-01-01 37,771 -0.42% <0.001 -0.19% 0.0027 +0.08% 0.1981
2023-2026 2023-01-01 → 2099-01-01 47,844 +0.50% <0.001 -0.03% 0.5612 +0.05% 0.3571

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

Use Moving Average Crossover bullish as a long-side screening tile. Bullish 20d alpha is +0.20% and beats random (permutation test, 200 iterations). Bearish 20d alpha is -0.05% and beats random , but sits below the ~20bps cost floor from the caveats — screening context, not a standalone edge.

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 +0.54% / 20d on 57,317 historical triggers.
  • Best bearish setup: Trending + Low vol — alpha -0.52% / 20d on 8,610 historical triggers.
  • Best era for bullish: 2020-2022 — alpha +0.49% / 20d on 36,368 triggers.
  • Best era for bearish: 2020-2022 — alpha -0.42% / 20d on 37,771 triggers.

3 · When it fails — common false positives

  • Weakest bullish cell: Non-trending + High vol — alpha -0.09% / 20d on 35,850 triggers.
  • Weakest bearish cell: Trending + High vol — alpha +0.17% / 20d on 42,788 triggers.
  • Worst era for bullish: 2015-2019 — alpha -0.72% / 20d on 27,578 triggers.
  • Worst era for bearish: 2023-2026 — alpha +0.50% / 20d on 47,844 triggers.

Signal-specific failure patterns

Small alpha at best, and not regime-stable
50DMA crossing above (golden cross) or below (death cross) the 200DMA produces small alpha against equal-weight at the 20-day horizon in the current run — see the at-a-glance table and the permutation rows for which side, if either, clears the random-date test. For the bullish side, point-estimates against cap-weighted SPX are notably worse than against equal-weight — which is why popular golden-cross-doesn't-work takes built on cap-weighted backtests overstate the case on the median stock.
Sub-period dispersion is large
Pre-COVID, COVID era, and post-2023 windows produce different signs and magnitudes for both directions. The signals headline alpha is an average across very different regimes — do not read it as a stationary edge. Position size should reflect the era-by-era variance shown on the periods chart.
Lag is structural, not solvable by parameter tuning
By the time the 50DMA crosses the 200DMA, the underlying trend has been building for weeks. This is not a noise problem — it is a definition problem. Faster crossover parameters (20/50, 50/150) reduce lag but also reduce signal-to-noise. Use the existing 50/200 as a regime confirmation tool, not a timing tool.

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.

Trend-follower family

Moving-average crossover signals are derived from the intersection of two price moving averages at different lookbacks and are classified as trend-following (Murphy, Technical Analysis of the Financial Markets, 1999; Kirkpatrick & Dahlquist, Technical Analysis, 3rd ed. 2015). This construction overlaps with MACD, which is the difference of two EMAs (Appel, Technical Analysis: Power Tools for Active Investors, 2005); stacking MA crossover with MACD in the same direction produces correlated rather than independent evidence.

Measured pairings — Bonferroni survivors

Beyond the literature pairings above, these are the same-day co-fire combinations involving Moving Average Crossover 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 — every row below happens to be positive on the full sample, but that is not what the test asked. 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.

China A-shares

Pair (same-day co-fire, long) Full α Test α (2023+) Test N p_perm test
ma_crossover bearish + weekly_change bullish +3.90% +7.89% 249 0.002
ma_crossover bearish + weekly_change bearish +4.75% +6.71% 584 0.002
failed_double_bottom bullish + ma_crossover bearish +4.44% +5.28% 38 0.054
bollinger bullish + ma_crossover bearish +1.64% +3.62% 428 0.002
hh_hl_streak bullish + ma_crossover bearish +2.54% +3.44% 244 0.002
ma_crossover bullish + macd bearish +2.20% +2.49% 436 0.002
cci bearish + ma_crossover bullish +1.65% +2.10% 743 0.002
ma_crossover bearish + new_20d_low bearish +1.19% +1.38% 1,372 0.002

8 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.

  • Use as confirmation filter, not entry trigger — The signals natural home is as a regime gate: take MACD bullish trades only when the 50DMA is above the 200DMA, or take RSI bearish only during death-cross regime. Layered onto other triggers, MA crossover improves precision without its own weak short-horizon returns doing damage.
  • Volume confirmation for bearish death cross — Death crosses on expanding volume are more decisive than on contracting volume (the latter is often a late-stage rollover that is about to bounce). A filter requiring volume above 1.2x the 20d average during the week the cross prints would sharpen the bearish signal.
  • Faster MA parameters — A formal parameter grid search (20/50, 50/150, 50/200, 100/200) would reveal whether the lag issue can be reduced by going faster without losing too much signal-to-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

MA crossover is a lagging structural indicator — not a timing signal. Best use: regime gate that tells you which side of the market structure favors your other triggers. The 1-year alpha numbers are the headline; the 20-day numbers are noise around a structural state. Entry open T+1 if traded directly.

See a live screen of this signal

Curated Daily Reports that screen for this signal, refreshed at the open, midday and close — free to view, no account needed: