Quantitative Intelligence
Running While You Sleep.
LPPL criticality. Fokker-Planck drift-diffusion. Topological analysis. 2,000 Monte Carlo paths per symbol. Every 120 seconds.
46
Analysis Engines
2,000
Monte Carlo Sims
120s
Refresh Cycle
15min
Path Horizon
Criticality Detection
LPPL Criticality Detection
The Log-Periodic Power Law model detects regime transitions before they manifest in price. The characteristic oscillations with increasing frequency signal approach to the critical time tc.
ln p(t) = A + B(tc-t)m + C(tc-t)m·cos(ω·ln(tc-t)+φ)
Log-Periodic Power Law Fit
Forward Path Estimation
2,000 Paths Per Symbol
Fokker-Planck drift-diffusion equation solved via Monte Carlo simulation. Each path represents one possible future. The corridors represent probability.
Real-Time Output
Engine Signals
Every 120 seconds, 46 engines produce conviction scores across the entire watchlist. This is what the output looks like.
Architecture
Four-Phase Execution Pipeline
Parallel Data Ingestion
Every source fetched once per cycle. Zero redundancy.
46-Engine Parallel Compute
LPPL, Fokker-Planck, TDA, Wyckoff, Monte Carlo — all fire simultaneously.
9-Layer Synthesis
Pure functions compose outputs into conviction. Missing data lowers confidence, never blocks.
Forecast & Learn
Predictions persisted. Outcomes compared. Parameters self-calibrate.
Core Engines
The Mathematics Behind the Score
Peer-reviewed quantitative models. Rigorous. Explainable. Probabilistic.
Seismic Fault Lines
LPPLDetects structural criticality via Log-Periodic Power Law. Levenberg-Marquardt nonlinear optimization across rolling windows. Parameter validation: 0 < m < 1, 4 ≤ ω ≤ 15.
Probability Corridors
Fokker-PlanckForward path estimation via drift-diffusion SDE. 2,000 Monte Carlo sims per symbol. 5-node confidence corridors over 15-minute horizons.
Topological Integrity
TDAStructural health via persistent homology. Liquidity voids, cohesion decay, hidden fragility in market microstructure.
Pre-Breakout Fit
6-Factor WeightedCloseness-to-resistance (30%), range compression (25%), volume accumulation (20%), candle microstructure (15%), catalyst (10%) — minus 3 penalty functions.
Motion Oracle
Meta-EngineOrchestrates all sub-engines into unified forward forecast. Composes technical, structural, flow, sentiment signals into probability-weighted paths.
Wyckoff Accumulation
Phase Detection5-phase supply/demand absorption. Support defense frequency, higher-low convergence, volume climax patterns. Institutional accumulation detection.
Why Different
Not a Screener.
A Quantitative Research System.
Traditional screeners filter static attributes — P/E ratio, market cap, volume. By the time a stock passes those filters, the opportunity is already crowded.
This system runs 46 quantitative models simultaneously — stochastic calculus, topological analysis, nonlinear optimization, adaptive learning — producing probability-weighted conviction scores before setups become visible.
It learns from its own predictions. Every forecast is persisted, compared to outcome, and fed back. It doesn't guess. It converges.
Screeners
Static filters, lagging, crowded signals
Charting Tools
Manual pattern recognition, subjective bias
AI Chatbots
General knowledge, no real-time data pipeline
Signal Services
Black box, no transparency, no underlying math
DumpMoneyRises
46 engines. Peer-reviewed math. Adaptive. Probability-first.
See the market through
mathematics.
Free tier. No credit card. Full engine access.
Start FreeThen $29/mo for premium. Cancel anytime.