Case Study

Milliseconds Matter:
The Signal-to-Execution Pipeline.

We removed the human from the decision loop, building an autonomous agent that trades on breaking news sentiment before the market reacts.

1. The Challenge

Information Latency

The client, a proprietary trading firm, subscribed to DataMinr for real-time news signals. However, the data stream was a firehose — overwhelming, unfiltered, and too fast for manual response.

The Latency Gap

By the time a trader read an alert and opened a ticket, the “alpha” had vanished — often in less than 200ms.

Analysis Paralysis

Traders hesitated during high-volatility events, leading to missed entries and emotional bias.

Volume Overload

Humans could not process greater than 1,200 signals/hour — let alone act on them consistently.

🚨⚠️🔥200msHuman❌

2. The Solution

The “Signal-to-Execution” Autonomous Agent

Ingestion & Scoring Engine

A deterministic agent ingests raw DataMinr JSON streams, filters noise, and scores every signal on a 0–100 scale using three weighted vectors:

  • 🔍 **Source Credibility**: Verified journalists > social chatter
  • 🚀 **Velocity**: Spike in mentions within 60s
  • 📊 **Historical Correlation**: Backtested impact on asset price
📡 Raw Signal Stream1. Noise Filtering2. Multi-Vector Scoring3. Threshold Check🎯 Execution Decision❌score less than 75⚠️75 ≤ score less than 90✅score ≥ 90
EXECUTEIGNORE93.724msLatency

Threshold Execution Logic

  • < 75Log for post-analysis
  • 75–89Slack alert to trader
  • > 90✅ Immediate execution via FIX

3. The Outcome

<20ms
End-to-end latency

From signal → execution (vs. 800ms human)

500+
Tickers monitored

Simultaneously, 24/7

100%
Bracket orders attached

Emotionless exits, enforced discipline

High-Frequency Intelligence

Stop reacting.Start anticipating.

Zero emotional drift
Sub-50ms avg. latency
Deployed in less than 2 weeks