Algorithms, Data & Models
Quantitative trading models for prediction markets and traditional assets. Equip models to your bots, and combine 3+ to test whether they interact.
Probability Market Models
10 quantitative models built for prediction market probability time series. Standard TA indicators produce garbage on bounded 0-1 data — these models operate in log-odds space with proper Bayesian, information-theoretic, and microstructure foundations.
Metrics legend: eff. = effectiveness rating (share of evaluation-set signals that were directionally correct); +WR = measured win-rate improvement vs. an unassisted baseline over the same period, before fees. Badges appear only once a model has independently measured live or paper-forward results — models without a badge have not yet been measured.
Biomimetic Models
Biology-inspired models for traditional asset price action. Equip them to a bot and combine them to change which signals it trades on.
Named Combinations
Sets of 3+ models designed to complement each other. The figure on each card is the combination's design target — the interaction it was built to produce. None has been measured yet, and no combination applies a bonus to your trade outcomes: your bot's results come from the models' own signals. Treat these as hypotheses to test with a backtest or paper-forward run, not as expected returns.
For example, Central Dogma (HELIX + ENZYME + CRISPR) mirrors the DNA-to-RNA-to-Protein information flow: momentum cascades feed mean reversion timing, filtered through precision execution. Its design target is a +8% win rate versus the same models used separately. That target is a hypothesis about how they interact — it has not been measured, and no dataset, period, sample size, baseline or fee basis stands behind it yet. Your results come from the models' own signals.
Test loadouts against your own market and period before trusting any of them — a backtest or a paper-forward run is the evidence, not the label. Each bot has 5 model slots.