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Quantitative Research
Powered by Sonar AI

The first platform that combines deep financial analysis with generative AI to uncover alpha in unstructured data at scale.

Alpha Generation
Sentiment Analysis
Alternative Data
Earnings Intelligence
Alpha Generation
Sentiment Analysis
Alternative Data
Earnings Intelligence
Alpha Generation
Sentiment Analysis
Alternative Data
Earnings Intelligence

Institutional-Grade AI Tools

Designed by quants for quants - the most sophisticated research platform for hedge funds and asset managers

Dynamic Research Briefs

AI-generated reports on market trends with citations from 10K+ data sources including earnings calls, SEC filings, and real-time feeds.

Example: "Uranium price surge analysis with supply constraints, ETF flows, and geopolitical risks"
Cites Cameco earnings calls, Sprott holdings, and policy documents

Contradiction Detection

Flags inconsistencies in earnings calls and cross-references with market data to identify potential red flags.

Detects claims vs. reality: "Musk 'no demand issues' but deferred 20% of orders"
Cross-references inventory levels with production claims

Alpha Signal Backtesting

Test unconventional investment hypotheses against historical data with statistical significance scoring.

Example: "CEO LinkedIn activity spikes before bad earnings"
Backtests signal against S&P 500 since 2020 (68% success rate)

Advanced Sentiment Analysis

Real-time sentiment scoring across earnings calls, news, and social media with proprietary context-aware models

Executive Tone Detection

Analyzes vocal patterns and word choice to detect confidence, hesitation, or evasion in executive communications.

Confidence Score78/100
"Our Q3 outlook remains strong" (confidence up 12% vs last quarter)

Market Sentiment Correlation

Tracks sentiment across alternative data sources and correlates with price movements.

Reddit SentimentBullish (0.82)
Price Change+4.2%

What-If Scenario Engine

Model complex market scenarios with multiple variables and probabilistic outcomes

Scenario Parameters

3 months

Projected Outcomes

Probability Distribution
Tech Sector Performance
Monte Carlo Simulation
-30%
-15%
-5%
+5%
+15%
+30%
+50%
Most Likely Scenario+5% to +15%
Probability42%
Tail Risk15% chance >30% drop

Compliance Monitor

Automated regulatory compliance checks and risk flagging across all research activities

Regulatory Change Tracking

Real-time monitoring of SEC, FINRA, and global regulatory updates with impact analysis.

New SEC Rule 10b5-1 Updates
Effective Jan 2025
EU Market Abuse Regulation
Draft proposal

Research Audit Trail

Complete documentation of all research processes with version control.

Analysis ID
COMPLIANT
CreatedNov 15, 2024
Last ModifiedNov 18, 2024
Approved ByCompliance Team

Risk Flagging System

Automated detection of potential compliance issues and conflicts.

Potential MNPI Detected
In earnings call analysis for AAPL
Conflict of Interest
Analyst holds position in analyzed security

Cutting-Edge Technology Stack

The most advanced AI architecture designed specifically for financial research

Sonar AI Architecture

1

Multi-Modal Data Fusion

Blends real-time market data with unstructured documents (PDFs, transcripts, news)

2

Chain-of-Thought Reasoning

Shows step-by-step analysis with citations to all source materials

3

Temporal Context Engine

Maintains historical context across earnings calls and filings

Developer Platform

alpha_signals.py
import quantsonar

# Analyze earnings call transcript
analysis = quantsonar.analyze(
  content=open('tesla_q2_2024.txt'),
  context='previous_calls',
  compare_with='market_data'
)

# Backtest custom alpha signal
results = quantsonar.backtest(
  signal='executive_social_activity',
  universe='SP500',
  period='5y',
  metrics=['sharpe', 'hit_rate']
)
Explore Full API Documentation

Transform Your Research Process

Join the world's most sophisticated hedge funds and asset managers using QuantSonar to uncover alpha in unstructured data.