We apply quantitative analysis to astronomical cycles that have tracked civilisational transitions for millennia. Not astrology as prediction. Pattern recognition as a timing overlay.
Each note includes full methodology, raw data, statistical tests, and honest interpretation. Code is open source.
Statistical analysis of whether major market disruptions cluster around Jupiter-Saturn conjunction windows. 40 years of S&P 500 data, 33 conjunctions, Monte Carlo baseline, bootstrap confidence intervals.
Finding: Consistent 2.14x signal at 30-90 day windows. Both conjunction eras (2000-2001, 2020) coincide with major regime shifts. Signal suggestive but not yet statistically significant due to small sample size.
Tempora is a research venture that tests whether ancient astronomical timing frameworks contain statistically valid signals for modern pattern recognition.
Astronomical cycles that have been tracked for millennia may correlate with periods of heightened structural change. Not because planets cause events, but because long cycles create useful timing frameworks for understanding when regime transitions are more likely.
Swiss Ephemeris for astronomical computation. Binomial testing, Monte Carlo baselines, and bootstrap confidence intervals for statistical validation. Every finding is published with full methodology. Null and inconclusive results are reported honestly.
Python, pyswisseph, scipy, numpy, pandas. Vedic sidereal (Lahiri ayanamsha) coordinate system. Open source computation engine on GitHub. Reproducible by anyone.
A 2x signal that needs more data is more credible than a 10x claim that doesn't hold up. We present findings as they are, not as we wish they were. Rigour builds trust. Trust builds a research institution.
New analysis notes, data visualisations, and methodology updates. No spam, no predictions, no hype.