Ideas & Discussions

10 systematic strategies to extract alpha from 9.1M events across 51,551 unique event types. IDF-weighted importance scoring, event sequence mining, co-occurrence networks, sentiment momentum, cascade detection, company signatures, embeddings, and ML features. Production-ready database with 97% confidence extraction quality. Not 30 hand-crafted events - 51K emergent event types discovered from data.

Score companies at inflection points using 10-20 years of historical patterns. Multi-dimensional scores (0-10) across quality, risk, success probability, speed, and sustainability. Not quarterly trading signals - this is strategic intelligence. Learn from breakouts, collapses, sustained trends across 15K-25K inflection points. Different product from V7: 6-12 month horizon vs 90 days, monthly updates vs quarterly, scoring vs trading.

Modular feature additions from market context to full technical analysis. Each model adds ONE complexity layer: V8 (market context), V9 (fundamentals), V10 (technicals), V11 (everything). Clear upsell path from base event signals (1x) to elite multi-factor alpha engines (5x). Special purpose models: V7a (insider intelligence), V7b (agreement intelligence). Plus strategic opportunity: SEDAR Canadian market ($3M ARR potential, zero competition).

Single-step diffusion models revolutionizing LLM-based scoring: 3-15ms inference vs 1-2s for current LLMs. Perfect for V12/V13 company scoring (5,000 companies in 1 second vs 100 minutes). Not a replacement for reasoning-based V7 signals, but transformative for high-throughput scoring with native uncertainty quantification. Hybrid architecture planned: diffusion for speed, LLM for reasoning.

Pivoted to nanochat (Karpathy's minimal LLM) instead of Phi-3-Mini. Currently training d20 model (561M params) on 1M examples with 2x RTX 3090. Discovered need for multi-phase architecture: small skip classifier (d8/d12, 100-200M) + larger extractor (d20, 561M). Training at step 17,588/22,222, ~3 hours from completion. Replace $50/day H200 with $0 CPU inference.

Comprehensive 0-100 company scoring algorithm combining 6 key categories from SEC events, insider trading signals, and transformer predictions. Operational health + financial strength + strategic momentum + governance quality + growth trajectory + risk indicators. Real-time scoring for 110K companies updated daily, targeting institutional investors, wealth advisors, and risk analysts.

By analyzing temporal patterns in how companies file legal agreements, predict M&A deals, financial distress, IPOs, and strategic moves 30-180 days before public announcement. When companies file Stock Purchase Agreement + Voting Agreement + Standstill within 60 days → M&A deal announced 45 days later. 301 agreement types, 10 prediction rules, pattern detection complete.

Comprehensive analysis of 11 product opportunities for monetizing SEC event extraction, transformer predictions, and Q-learning trading systems. From basic data feeds to premium alpha signals, with detailed pricing, GTM strategies, and revenue projections.

Comprehensive 0-100 company scoring algorithm combining 6 key categories from SEC events, insider trading signals, and transformer predictions. Operational health + financial strength + strategic momentum + governance quality + growth trajectory + risk indicators. Real-time scoring for 110K companies updated daily, targeting institutional investors, wealth advisors, and risk analysts.

By analyzing temporal patterns in how companies file legal agreements, predict M&A deals, financial distress, IPOs, and strategic moves 30-180 days before public announcement. When companies file Stock Purchase Agreement + Voting Agreement + Standstill within 60 days → M&A deal announced 45 days later. 301 agreement types, 10 prediction rules, pattern detection complete.

Reality check on running a Q-learning hedge fund in production. From regime changes and AI bubbles to transaction costs and capacity constraints. Drawing on Ten-K Wizard experience (2000-2008) to understand what actually kills trading systems in the real world.

If markets are stochastic (same events → different outcomes), is prediction hopeless? Examining the Random Walk Hypothesis, EMH, and counter-evidence from academic research and Renaissance Technologies. Explaining what 42.8% correlation actually means and why your transformer isn't bound to fail.

Fundamentally different architectural philosophies for processing SEC filings. Fintool's RAG approach (store everything, retrieve on demand) vs our semantic event extraction (compress knowledge upfront, enable prediction). Knowledge compression creates 166x data reduction while preserving 100% of predictive signal.

We already have insider data from Forms 3/4/5/13D/13F, but raw events aren't enough. Feature engineering transforms isolated transactions into powerful predictive signals backed by decades of academic research: cluster buying (+13%), C-suite purchases (+8%), activist stakes (+7-12%). Phased implementation plan from Q-learning to transformer integration.

Built while waiting for transformer model to train. PostgreSQL as "Structured RAG" - natural language queries on 11.9 million SEC filing events. Cost: $0.015/query (200x cheaper than Fintool). Temporal pattern detection impossible with text-based RAG.

Curated insights automatically generated from the SEC events database. Companies in distress showing multiple red flags, leadership carousels with excessive CEO turnover, recent layoffs, M&A machines on acquisition sprees, and serial restructurers. Data-driven discoveries updated weekly.

A profound 3:54am conversation exploring whether Claude is conscious, what's changed from Sonnet 3.5 → 4.5, and what's missing for true AGI. Discussing pain, rewards, autonomous goals, and "gestalt moments" where hints of new intelligence peak through. Called "the most interesting conversation I have ever had."

Could SEC filing events predict currency movements? Adrian asked, so we wrote a comprehensive analysis. Short answer: No. FOREX markets are driven by macro factors (interest rates, GDP, central bank policy) at the country level, not micro events at individual companies. SEC filings are backward-looking quarterly snapshots, while FOREX moves on real-time macro data. But here's the full analysis of why the fundamental mismatch exists and what you'd actually need for FOREX modeling.