BTC–ZEC Daily Correlation Study September 2026 · quant-lab
Quant-Lab Research Note · Cross-Asset Dependence · Status: complete

BTC & ZEC: An Opposite-Correlation Illusion?

A daily-return correlation study of BTCUSDT and ZECUSDT for September 2026, set against the 2025–2026 record, with ETHUSDT as a control. The suspected strong inverse relationship does not survive contact with returns.

Instruments — BTCUSDT · ZECUSDT · ETHUSDT Binance USD-M perpetual Daily bars, 2025-01-01 → 2026-09-22 quant-lab research

AbstractExecutive summary

Question. Do BTCUSDT and ZECUSDT candles show a strong opposite (negative) correlation in September 2026?

Answer. No. On daily returns — the statistically meaningful object — the September relationship is positive but weak: Pearson +0.34 (n = 21, p = 0.13), with a 95% confidence interval that spans zero, [−0.11, +0.67]. Thirteen of twenty-one days moved the same way. The only large negative number in the data is the full-history correlation of price levels (−0.60) — and that is a textbook spurious artifact: BTC fell −8.9% while ZEC rose +2,699% over the window, so two non-stationary trending series with opposite slopes correlate negatively. In September itself, when both rose, the level correlation is positive (+0.49).

Verdict

There is no evidence of a strong inverse relationship between BTC and ZEC. Returns co-move weakly and positively; the negative coefficient lives entirely in the levels of two price series that drifted apart.

Headline numbers

September 2026 returns, ZEC vs BTC+0.34 (n.s.)
Full-history returns, ZEC vs BTC+0.44
Full-history levels, ZEC vs BTC−0.60 (spurious)
September levels, ZEC vs BTC+0.49
September control, ETH vs BTC+0.93
September cumulative, BTC / ZEC+11.3% / +96.5%
Days same / opposite sign (Sep)13 / 8
ZEC daily vol vs BTC (Sep)3.5×

§ 1Question and hypothesis

The working suspicion is that BTCUSDT and ZECUSDT candles moved inversely during September 2026 — a strong negative correlation. If true, it would be economically useful: a hedge, a pairs trade, or a rotation signal.

H. The daily returns (and perhaps the price paths) of BTC and ZEC are strongly negatively correlated in September 2026. Hypothesis under test

We test it directly, distinguish price levels from price changes, and place September inside the full 2025–2026 record so the month can be judged against its own history. ETHUSDT enters as a control asset that should, under any sensible crypto market-factor model, correlate positively with BTC — if it does not, the method is broken rather than the market.

§ 2Data and method

2.1 Data

Daily (1d) klines from Binance Public Data bulk archives, futures USD-M perpetual, normalized to canonical Parquet by the lab and validated cleanly. Three symbols are aligned on the UTC day by inner join: BTCUSDT, ZECUSDT and ETHUSDT.

Table 1 — Sample Binance USD-M perpetual, daily candles
windowcandlesreturnsfirstlastmissing days
September 202622212026-09-012026-09-220 (month incomplete)
Full history6306292025-01-012026-09-220

2.2 Definitions

returns
daily log returns, rt = ln(Pt / Pt−1), computed close-to-close at 00:00 UTC.
levels
the log price series ln Pt itself, used only to demonstrate the spurious-correlation trap.
correlation
Pearson by default; Spearman and Kendall reported for robustness.
uncertainty
Fisher-z 95% interval and a 20,000-draw bootstrap percentile interval.
beta
OLS slope of ZEC (or ETH) on BTC, with Newey–West HAC standard errors.

The stationarity contrast is decisive and is reported in Table 2: the price levels are indistinguishable from unit-root processes, while the returns are stationary.

Table 2 — Augmented Dickey–Fuller, full history
seriesBTC pZEC pETH preading
log price levels0.581.000.46non-stationary
log returns0.000.000.00stationary

Correlating non-stationary levels can produce arbitrarily large coefficients unrelated to any co-movement of changes. The levels result is shown for diagnosis, not as evidence of dependence.

§ 3Levels versus returns

The distinction is the whole story. Over the full 2025–2026 window the two assets moved in opposite directions at the trend level: BTC ended −8.9% (and spent much of 2026 falling) while ZEC compounded +2,699%. Two series with opposite drift trace out a large negative levels correlation.

Figure 1. Full-history indexed prices (log scale, start = 100). ZEC is an extreme idiosyncratic trend; BTC and ETH are flat-to-negative. Correlating these levels yields −0.60 for BTC–ZEC — a property of the slopes, not of daily co-movement.

Table 3 — The level/return sign flip
measureSeptember 2026Full historycomment
log levels, BTC–ZEC+0.49−0.60sign flips with drift
log returns, BTC–ZEC+0.34+0.44positive in both
log returns, BTC–ETH (control)+0.93+0.85market factor intact

In September both assets rose, so even their levels correlate positively (+0.49). There is no window in which a negative levels coefficient coincides with negative return co-movement — the negative number is a trend artifact that reverses sign the moment the drifts align.

§ 4September results

4.1 The two paths

ZEC nearly doubled in three weeks (+96.5%) while BTC rose +11.3% and ETH +13.9%. ZEC’s move is real but idiosyncratic, punctuated by double-digit daily swings in both directions.

Figure 2. September 2026 indexed prices (start = 100). Both assets rose; ZEC’s path is driven by idiosyncratic jumps (e.g. +15.5% on 03 Sep, +18.6% on 16 Sep) rather than by BTC.

4.2 Return correlation

Table 4 — Daily return correlation, September 2026 (n = 21)
statisticZEC vs BTCETH vs BTCreading
Pearson r+0.342+0.930ZEC weak, ETH strong
Pearson p (2-sided)0.130<0.001ZEC not significant
Spearman ρ+0.484+0.949rank agreement moderate
Kendall τ+0.314——
R²0.1170.865BTC explains ~12% of ZEC
Fisher 95% CI[−0.11, +0.67][+0.83, +0.97]ZEC interval spans 0
Bootstrap 95% CI[−0.05, +0.70]—consistent with 0
Same-direction days13 / 21 (62%)20 / 21 (95%)majority co-move

Figure 3. September daily log returns. Left: ZEC versus BTC — a shallow positive fit (r = +0.34) with the eight opposite-sign days marked. Right: the ETH control, tightly aligned along the market factor.

4.3 Beta and alpha

Regressing ZEC returns on BTC returns gives a positive beta, not a negative one, but with a very wide interval:

Table 5 — OLS with Newey–West HAC standard errors
regressionalpha (daily)betabeta 95% CIR²
ZEC ~ BTC, September+2.60% (p=0.09)+1.20 (p=0.16)[−0.48, +2.88]0.12
ZEC ~ BTC, full history+0.55% (p=0.04)+1.35 (p<0.001)[+1.07, +1.63]0.20
ETH ~ BTC, full history−0.01% (n.s.)+1.36 (p<0.001)[+1.25, +1.46]0.72

The positive alpha is the elephant: ZEC delivered returns unrelated to BTC. Over the full record that alpha is ~0.55%/day with p=0.04 and a beta above one — high volatility, high idiosyncratic drift, low explanatory power.

4.4 The September tape

Eight of twenty-one sessions moved in opposite directions. They cluster late in the month and are the likely source of the “opposite correlation” impression.

§ 5Stability and conditioning

5.1 Rolling correlation

A trailing 30-day correlation over the full record never turns negative. It ranges from +0.06 (30 Nov 2025) to +0.84 (4 Feb 2025), averaging +0.51. September’s trailing average is +0.58 — on the high side of the asset’s own history, not the low.

Figure 4. Rolling 30-day return correlation, BTC–ZEC (red) and BTC–ETH control (blue). The zero line is never crossed by either pair. Shaded region: September 2026.

5.2 Monthly correlation

Across 21 calendar months the BTC–ZEC return correlation is never negative. The weakest month is November 2025 (+0.06); September 2026 (+0.36) ranks in the lower third but remains positive.

ZEC vs BTChighlighted = Sep 2026ETH vs BTC (control)

Figure 5. Monthly return correlation. ZEC (red bars) versus the ETH market-factor control (blue dots). September 2026 is highlighted. No negative month exists.

5.3 Lead-lag and conditioning

Cross-correlation is essentially zero at every non-zero lag: the relationship is purely contemporaneous, so neither asset predicts the other.

Figure 6. corr(ZECt, BTCt+k) by lag k. Only k=0 is material (+0.44, full sample); all |k|≥1 are within ±0.05. September (hollow) is noise-dominated at n≤21.

Conditioning on BTC’s direction does not rescue the hypothesis. Pooled over the full history, ZEC’s correlation with BTC is +0.26 on BTC-up days and +0.25 on BTC-down days — symmetric and positive. In September the tiny sub-samples split to −0.01 (11 up days) and +0.45 (10 down days), a difference produced by sampling noise, not by an inversion.

5.4 Influence and outliers

ZEC’s most extreme sessions are idiosyncratic: on 1 Oct 2025 it rose +62.9% while BTC added +3.9%; on 8 Oct 2025 it added +39.5% against +1.6% for BTC. These are ZEC-specific repricings, and they pull the full-sample correlation down, not up.

Leave-one-out on September confirms how fragile a 21-point estimate is: dropping 3 Sep raises the correlation to +0.52, while dropping 21 Sep lowers it to +0.24. The full-history estimate is far steadier (+0.43 to +0.46).

Table 6 — ZEC’s largest daily moves vs BTC same-day
dateZEC returnBTC returnETH return
2025-10-01+62.9%+3.9%+4.8%
2025-10-08+39.5%+1.6%+1.7%
2026-06-04−26.0%−0.4%−2.4%
2026-08-21+29.1%+7.0%+7.8%
2025-10-11+29.0%−1.9%−2.0%
2026-04-07+27.4%+4.4%+6.1%

§ 6Why the illusion appears

  1. Levels, not returns, were being read. BTC trended down and ZEC trended violently up across 2025–2026, so their log-price levels correlate at −0.60. That is the one genuinely large negative coefficient in the data — and it is exactly the spurious-regression trap. Both series fail an ADF test for stationarity.
  2. September contradicts even that artifact. When the two happened to rise together the level correlation flipped to +0.49. A “relationship” whose sign follows the drifts is not a relationship.
  3. Returns are weakly positive. The meaningful object gives +0.34 in September and +0.44 over the full record — positive in every month on record.
  4. ZEC is idiosyncratic, not inverse. Its September was +96.5% against BTC’s +11.3%, with ~12% of its variance explained by BTC and 3.5× the daily volatility. Big independent moves are easy to mistake for opposition.
  5. The eye weights recent, salient pairs. The final two sessions were opposite, and they dominate the visual impression. A 21-day sample with an 8-opposite/13-same split is unremarkable — the binomial probability of 8 or fewer same-sign days is not extreme, and the correlation interval spans zero.
Control check

The ETH–BTC control behaves exactly as a market factor should (+0.93 in September, +0.85 full). The method is sound; the market simply does not contain a BTC–ZEC inversion.

§ 7Limitations

§ 8Conclusion

The suspected strong opposite correlation between BTCUSDT and ZECUSDT is not supported.

Recommendation

Do not trade a BTC–ZEC inverse thesis. If the goal is a hedge or pairs relationship, work with returns (never levels) over a longer sample, size for ZEC’s ~3.5× volatility, and treat its alpha as an idiosyncratic trend that may reverse. A proper next step is a rolling, HAC-corrected beta/hedge-ratio study on at least a year of hourly data.

§ 9Reproducibility

Build the data

# history (monthly archives)
quantlab data build binance --market futures --symbol BTCUSDT --interval 1d \
    --start 2025-01-01 --end 2026-08-31 --granularity monthly
quantlab data build binance --market futures --symbol ZECUSDT --interval 1d \
    --start 2025-01-01 --end 2026-08-31 --granularity monthly
quantlab data build binance --market futures --symbol ETHUSDT --interval 1d \
    --start 2025-01-01 --end 2026-08-31 --granularity monthly
# September 2026 (daily archives)
quantlab data build binance --market futures --symbol BTCUSDT --interval 1d \
    --start 2026-09-01 --end 2026-09-23 --granularity daily
quantlab data build binance --market futures --symbol ZECUSDT --interval 1d \
    --start 2026-09-01 --end 2026-09-23 --granularity daily
quantlab data build binance --market futures --symbol ETHUSDT --interval 1d \
    --start 2026-09-01 --end 2026-09-23 --granularity daily
quantlab data validate --symbol ZECUSDT --dataset bars --interval 1d

Run the correlation study

uv run python scripts/correlation_report.py \
    --start 2025-01-01 --end 2026-09-23 --sept-start 2026-09-01 --window 30 \
    --out experiments/reports/btc_zec_correlation

Artifacts

pathcontents
experiments/reports/btc_zec_correlation/summary.jsonall statistics, rolling series, monthly table
experiments/reports/btc_zec_correlation/series.csvaligned prices and log returns
scripts/correlation_report.pyanalysis source

Pearson/Spearman/Kendall via SciPy; Fisher and bootstrap intervals computed directly; HAC OLS and ADF via statsmodels.