//How It Works
The methodology, technology, and scale behind DAITIQ's prediction engines.
// Methodology
01
Data Collection
Automated pipelines pull data from 20+ sources including sports APIs, financial feeds, and government databases. 200+ cron jobs keep everything fresh.
02
Feature Engineering
Raw data is transformed into predictive features through domain-specific engineering. Rolling averages, momentum indicators, and proprietary signals.
03
Model Training
XGBoost, Ridge Regression, and ensemble methods trained on historical data. Models retrain daily to adapt to changing patterns.
04
Prediction & Validation
Predictions are generated, backtested, and paper-traded before going live. Continuous accuracy monitoring ensures model health.
// Tech Stack
Languages
PythonTypeScriptSQL
ML / AI
XGBoostRidge RegressionTensorFlowMonte CarloBlack-Scholes
Data & Storage
PostgreSQLNumPySciPyPandas
Backend & Infra
FlaskAWS LightsailStreamlitGitHub Actions
Frontend
Next.jsReactTailwind CSSFramer Motion
// By the Numbers
500+
Scripts in Production
200+
Automated Cron Jobs
20+
Data Sources
7
Sports Covered
500+
Tickers Tracked
6
ML Models Active