Research
Fifteen phases.
One coherent system.
Each layer adds compounding intelligence. What cannot be replicated is the calibration, the integration, and the signal combinations that emerge over time.
01
Ph. 01–02
Data & Feature Engineering
data_manager
Cached market data pipeline for 20-asset universe. Efficient storage with automatic freshness checking.
feature_engine
68+ technical and microstructure features. Captures market memory, volatility regimes, and hidden structure in price data.
03
Ph. 03–04
Model Suite & Signal Generation
ensemble
Four core models combined via rolling Sharpe attribution. Weights shift automatically as models gain or lose predictive power.
regime_detector
Four-state market classification. Signal blend adjusts per regime — momentum strategies run in trending markets, mean-reversion in ranging ones.
meta_labeling
A secondary filter that learns when to trust the primary signal and when to stay out. Structurally reduces overfitting.
dynamic_weighting
Signals with negative recent Sharpe are automatically disabled. The system self-curates its own signal set over time.
05
Ph. 05–08
Risk & Execution
tail_risk
Stress-tested against five historical crisis scenarios. Tail losses modelled using extreme value theory — not normal distribution assumptions.
correlation_risk
Time-varying correlation model. Automatically reduces leverage when cross-asset correlations spike — a hallmark of crisis periods.
smart_routing
Orders scheduled to minimise market impact. Trade size calibrated to daily volume so the system avoids moving prices against itself.
compliance
Seven pre-trade checks run before every order. Immutable audit trail with complete lineage from signal to fill.
ml_exit_engine
Six independent exit signals — ensemble reversal, momentum exhaustion, volatility regime shift, time decay, profit protection, and macro deterioration — combine into a single adaptive threshold. Targets Profit Factor 1.40+.
sanity_check
Eight validation layers run before every order: concentration limits, liquidity floors, regime alignment, macro score gates, drawdown halts, position sizing bounds, duplicate detection, and kill-switch check. No trade clears without passing all eight.
09
Ph. 09–11
Advanced Alpha & Alternative Data
microstructure
Estimates the probability that institutional, informed money is actively trading in an asset. High readings trigger position reduction.
insider_signal
Parses live SEC regulatory filings. Cluster buying by multiple insiders within a short window generates a strong directional signal.
earnings_nlp
Earnings call transcripts scored for linguistic confidence and uncertainty. Management tone often leads price action by days.
tda_features
Applies mathematical topology to price sequences. Detects structural shifts in market behaviour before they become visible in conventional indicators.
alt_data_bundle
Six independent alternative data sources — Supply Chain (AIS vessel tracking, freight indices, UN Comtrade), EDGAR insider intelligence, Ghost Factory (EIA electricity proxy), Google Trends brand interest, Reddit/Stocktwits social sentiment, and GitHub developer activity for NVDA, MSFT, GOOGL, META — combined into a single normalised score per asset.
13
Ph. 13–15
Self-Improving Intelligence
gene_pool
Discovers new alpha formulae by evolving mathematical expressions over market data. No human specifies what to look for.
auto_researcher
Weekly loop: discover → validate → check for overlap with existing signals → integrate if it genuinely adds new information.
shadow_mode
New system versions run silently alongside production. Statistical test gates live promotion. Automatic rollback on underperformance.
nas_search
Model architecture is evolved, not designed. Each run produces a different neural network — the result cannot be reproduced without running the search.