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Cross-sectional momentum only uses the order, so train on the order: learn-to-rank beats regress-then-rank in equities. But the 3.40 Sharpe is frictionless daily demo; weekly it is 0.54.
Crypto time series momentum only pays when the market return is weighted by trading volume, not manipulable market cap: 0.94%/day, Sharpe 2.17. Equal-weight the same signal and it loses 1.19%/day.
In low-signal economics and finance, the do-nothing zero forecast is Bayes-optimal. Ridge can beat it, Lasso cannot for any penalty, and signal weakness, not sparsity, is why.
The return you predict matters more than the 147 features you feed the model. Demeaning, standardizing, or ranking the target moves monthly alpha ~0.9pp; feature transforms move it ~0.4pp.
Landolfi's percentile-rank momentum: rank moves against their own sign-consistent past, gate them with a hysteresis band to kill churn, validate with a walk-forward bundle. Steal the plumbing, doubt the crypto curve.
Polymarket prices do converge to the truth over 90 days. But they are far more volatile than stocks or crypto, overprice longshots (slope 1.13 > 1), and lean "yes." The edge: bet favorites, early.
Decentralized prediction markets, cut into eight swappable stages. Seven are engineering trade-offs. The eighth, resolution, is where a handful of token wallets can settle a market against the truth.
CryptoMamba, a compact Mamba SSM, forecasts Bitcoin's next close and trades $100 into $262. But it never runs the naive or buy-and-hold baselines, on one bull year of daily bars.
A 14B model trained with outcome-only RL matched o1 and beat it on calibration. The lesson: drop GRPO's variance scaling, block leakage, and the win is honest probabilities, not beating the market.
When the game is against you, betting small is slow suicide. Bold play, swinging your whole stack at the target, maximizes your chance of hitting it. Optimal bet size flips with the sign of your edge.
Hyperliquid runs fully on-chain, so a node captures every order event: wallet IDs, counterparty inventory, and the ~89% of orders that are rejected and invisible in LOBSTER-style data.
Two economists shocked 817 prediction markets by 5 points each. Sixty days later the shove was still there. Prices revert, but slowly, partly, and cheaply beaten in thin markets.