The Same Methodology Used by Institutional Desks
Traditional prediction models assume markets behave consistently. They don't. Crypto markets cycle through distinct regimes—trending, consolidating, panicking—each requiring different analytical approaches. Institutional quant teams have known this for decades. Now you do too.
Vega's architecture is regime-first: we classify the current market state, then deploy models specialized for that regime. This is exactly how sophisticated trading operations approach markets—and it's been inaccessible to retail traders until now.
The Vega Process
Four steps that power institutional-grade predictions
Data Ingestion
We process real-time and historical data from multiple sources
Data Sources
- •Binance order books (20 levels, sub-second updates)
- •Futures data (funding rates, open interest, liquidations)
- •Macro indicators (DXY, VIX, S&P 500, Treasury yields)
- •Dominance metrics (BTC.D, ETH.D, altcoin market share)
- •Long/short positioning (Bitfinex margin data)
Feature Engineering
Our feature engineering pipeline transforms raw market data into actionable signals. From technical indicators and volume patterns to order flow imbalance and whale tracking—all updated in real-time.
Regime Classification
Our EMA/MACD hybrid classifier analyzes price structure, momentum consistency, and volatility to determine current regime
In Q4 2025, we detected 16 crisis events including the November altcoin correction (10 tokens, avg -21% drawdown). Historic backtests also flag FTX and LUNA collapses.
Five Market Regimes
Advanced Features
- ✓Hysteresis to prevent flickering
- ✓Transition risk quantification
- ✓Position sizing adjustments during unstable periods
- ✓Real-time regime change detection
Regime-Specific Predictions
We don't use one model for all conditions. We train 25 models per symbol (5 regimes × 5 horizons)
Trending Regimes
LightGBM models for impulse conditions
Range-Bound Regimes
Ridge regression for chop conditions
Crisis Conditions
Heuristic models for high volatility
Five Time Horizons
Ensemble & Risk Adjustment
Predictions from multiple horizons are blended with regime-dependent weights
Final Outputs
- •Predicted return with confidence score
- •Recommended position size via Kelly criterion
- •Maximum position size based on VaR limits
- •Regime context and transition risk
- •Optimized TP/SL per asset class
Adaptive Weighting
During stable regimes, we trust short-term signals more. During transitions, we weight longer horizons higher to avoid whipsaws.
Example: In bull impulse with low transition risk, 24h predictions get 40% weight. In uncertain chop with high transition risk, 30d predictions get 50% weight.
Asset-Class Optimized Exits
TP/SL parameters calibrated to each asset's behavior—not one-size-fits-all
BTC behaves differently than altcoins. Our exit parameters are optimized per sector using walk-forward validation—the same methodology we use for our prediction models. No curve-fitting, no cherry-picking.
Volatility Adaptive: Exit parameters automatically adjust based on current market volatility. Low-vol environments use tighter targets; high-vol environments expand to capture larger moves.
Walk-Forward Validation
No cherry-picking, no curve-fitting
All models are validated using walk-forward methodology with expanding windows. We don't cherry-pick good periods—performance metrics reflect what you would have experienced in real-time deployment.
Quality Control: Models that fail to meet thresholds are retrained or deprecated. You always see model vintage and validation statistics.
See the System in Action
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