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Leptokurtic
Detecting Crashes with Fat-Tail Statistics
We built fatcrash, a Rust+Python toolkit with 15 crash detection methods: LPPLS, DFA, EVT, Hill, Kappa, Hurst, GSADF, momentum/reversal, price velocity, and more. Tested on 96 drawdowns across BTC, SPY, Gold, 23 forex pairs, and equity crises with honest precision/recall/F1 metrics. Plus: which methods transfer to revenue and profit data.
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The Edge of Chaos
Reflexivity by the Numbers
Everyone agrees markets react to themselves. The question is how much. A statistical tool built for earthquakes turns that vague idea into a single number: the fraction of market activity that is the market reacting to its own moves rather than to outside news. The number turns out to be close to the level where a chain reaction would run away.
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The Edge of Chaos
Faster Than Exponential: Can You See a Crash Coming?
The last essay said big cascades are built in and the trigger tells you nothing. Didier Sornette disagrees, at least about the biggest ones. He argues a bubble grows faster than exponentially toward a finite-time singularity, leaves a telltale wobble on the way up, and that this makes some crashes partly foreseeable. This is the optimistic case, and its limits.