Built. Shipped. Measured.

A production autonomous trading platform that fuses AI reasoning with institutional risk discipline. Not a signal bot, a decision system that knows when NOT to trade.

Most ‘AI trading bots’ optimize entries and ignore psychology. The harder problem is regime awareness, conviction sizing, and emotional discipline under uncertainty. EdgeQuant is built around that harder problem.

Six-layer architecture: Universe Filter → Regime Classifier → Multi-model Prediction Engine → A+ Gate (14 typed skip reasons) → Risk Engine (cooldowns, sector caps, heat limits) → Execution & Telemetry. An agentic layer (Hedge Fund Engine, Highest Conviction, Whale Activity, Institutional Memo, Multi-Horizon Forecast) feeds an accuracy tracker that measures the system on its own predictions.

Autonomous agent that reads every earnings call transcript, extracts forward guidance signals, and scores them BULLISH / BEARISH / NEUTRAL before the market opens.

Earnings calls move stocks 5-20%. The signal is in the language, not the numbers. Retail reads headlines. Institutions read transcripts. AI can read all 500 S&P companies the same night.

SEC EDGAR pulls transcripts → Claude, ChatGPT, Gemini extract guidance tone, management confidence, raised/lowered language → scores each call → outputs a ranked watchlist before market open.

Autonomous agent that analyzes every IPO filing (S-1), scores quality across 20 signals, and predicts first-year performance against a historical comp set.

80% of IPOs underperform in year one. The signals are in the S-1, revenue quality, insider lockups, VC backing, use-of-proceeds language. Most investors never read them.

SEC EDGAR pulls new S-1 filings → Claude, ChatGPT, Gemini extract 20 quality signals → scores TIER_A/B/C → builds a comparable set from historical IPOs → outputs conviction score plus written thesis.

Autonomous macro regime detection agent that tells portfolio managers when the market environment has fundamentally changed, and what to do about it.

Most losses happen when investors use the wrong playbook for the regime, buying growth in a rate-hike cycle, holding bonds in inflation. Regime shifts are slow but detectable.

FRED pulls 30 macro indicators daily → Claude, ChatGPT, Gemini synthesize cross-asset signals → classifies regime (Risk-On/Off, Inflationary, Recessionary, etc.) → compares to prior regimes → outputs portfolio adjustment recommendations.

Autonomous agent that detects divergence between institutional analyst sentiment and retail social sentiment, and trades the gap.

When Wall Street upgrades a stock and retail is still bearish, that is an asymmetric setup. The gap between institutional and retail sentiment closes predictably.

Finnhub pulls analyst ratings → LunarCrush pulls social sentiment → Claude, ChatGPT, Gemini measure divergence score → flags high-divergence setups → feeds trading signals.

Autonomous agent that monitors portfolios 24/7 for tax-loss harvesting opportunities and executes within IRS wash-sale rules, with substitute-security recommendations.

Tax-loss harvesting adds 1-2% in annual after-tax return on average. Brokers offer it quarterly. Daily monitoring captures 3-5x more opportunities. Wash-sale violations cost investors millions every year.

Reads portfolio positions → monitors unrealized losses in real time → identifies harvest candidates → checks the 30-day wash-sale window → suggests substitute securities → alerts advisor for approval.

Autonomous agent that monitors real estate market conditions across 50 US metros and signals regime shifts, buyer's market, seller's market, correction risk.

Real estate is the largest asset class but data is fragmented. Mortgage rates, inventory, days-on-market, price cuts, all public, none synthesized. Buyers and sellers make $500k decisions on gut feel.

Zillow and Redfin market data scraped + mortgage rate data from FRED + local employment data → Claude synthesizes per-metro → scores market regime → outputs buy/wait/sell signal per metro.

Autonomous agent that reads your portfolio holdings and generates a personalized institutional-quality investment memo every week.

Retail investors get generic market commentary. Institutional investors get research tailored to their positions. AI closes that gap at $50/month instead of $50k/year.

User inputs portfolio → Claude, ChatGPT, Gemini pull relevant earnings, analyst reports, macro signals, and insider activity per holding → synthesizes into a personalized weekly memo → flags risks and opportunities specific to those positions.

Autonomous agent that maps real-time liquidity conditions across crypto markets, stop clusters, liquidation cascade zones, smart-money positioning.

80% of crypto retail losses happen at liquidity events, stop hunts, liquidation cascades, wash trades. Institutions know where these levels are. Retail does not.

CoinGlass pulls open interest, funding rates, and liquidation heatmaps → Binance and Bybit pull order book depth → Claude, ChatGPT, Gemini synthesize the liquidity landscape → scores risk zones → outputs danger zones and opportunity zones per asset.