Insights Stocks: Smart Money Signals
An automated trade-signal platform that ingests insider and congressional disclosures, runs nine quant models per ticker, and pushes scored, ranked calls to a React dashboard and Slack.
- Role
- Solo: PM + Engineering
- Stack
- Python / FastAPI · React / TypeScript
- Models
- 9 quant + institutional bias controls
- Status
- Production, private beta
9
Quant models ensembled
4 / 9
Model-agreement gate
BH-FDR
False-discovery control
18
API routes
Problem
Most signal tools confuse you with confidence.
Retail trade-signal apps tend to be one of two things: a wrapper around a single model with a confident UI, or a firehose of indicators you have to interpret yourself. Both quietly leak survivorship and selection bias, and the raw inputs, insider and congressional buys, are noisy on their own. I wanted a third thing: an ensemble that is honest about uncertainty and only fires when several independent models agree.
User stories
Who it's for.
- Self-directed investor: give me a short, vetted shortlist with a single score I can act on, not a 50-row table I have to decode.
- Smart-money follower: tell me when insiders and Congress are actually buying something worth attention, and ping me on Slack when conviction is high.
- Quant-curious analyst: let me drill into every model's output and a backtest, so I can see why a call fired before I trust it.
Solution
Ingest, enrich, model, score, alert.
The platform pulls insider trades from SEC EDGAR Form 4 filings and congressional disclosures from Capitol Trades over a rolling 14-day window, deduplicates them, and enriches each ticker with fundamentals via yfinance (P/E, market cap, momentum, RSI, drawdown) plus a macro regime read from FRED (yield curve, CPI, VIX, M2). Each ticker then runs through nine quantitative models, Monte Carlo, Hidden Markov regime, GARCH, Fama-French 5-factor, event study, copula tail risk, Bayesian decay, mean-variance, and options flow, which an ensemble combines into a single 0-100 score and recommendation. An 18-route FastAPI backend feeds a React dashboard and posts high-conviction calls to Slack: a message a real person can act on, not a 50-row table.
Product sense
False-positive control is the product.
What separates this from a typical ML side build is the layer that decides when not to fire a signal:
- Benjamini-Hochberg FDR correction across the full ticker universe, so the more you test the harder it is to claim a hit.
- 4-of-9 agreement gate: at least four models must independently concur before a buy or sell is issued, removing roughly 30% of spurious signals.
- Granger causality, conformal prediction intervals, and adversarial validation to confirm a source predicts returns and to catch distribution shift.
- Deflated Sharpe ratio and isotonic calibration to keep the backtest honest.
The reason I'm proud of this build isn't the model count. It's that the guardrails were a product decision before they were a math decision.