FedSpeak: Automated Federal Reserve Policy Shift Detection

Intelligent monitoring of Federal Reserve communications using Word2Vec semantic analysis, multi-signal validation, and AI-powered explanations

What is FedSpeak?

FedSpeak is an automated system that reads Federal Reserve press releases and identifies when the Fed changes its messaging about the economy. It's like having a tireless research assistant who monitors every FOMC statement, compares it to historical patterns using advanced AI, validates shifts with market data and media coverage, and alerts you within minutes when something important has changed.

The Problem: Federal Reserve policy shifts are often signaled by subtle language changes - a single word removal ("transitory" in December 2021) can signal billions of dollars in market movements. Spotting these changes manually requires 30+ minutes per statement and deep expertise.

The Solution: FedSpeak automates this entirely - monitoring the Fed's RSS feed 24/7, detecting semantic shifts using Word2Vec embeddings, validating with treasury yields/VIX/media coverage, and explaining changes using Claude 3.5 Sonnet AI.

About This Project & Claude Code

FedSpeak is designed to work seamlessly with Claude Code, Anthropic's official CLI AI assistant, in two distinct ways:

1. Claude Code as Autonomous Operator

Claude Code (the AI assistant) can autonomously drive and operate the entire FedSpeak system on your behalf. It can:

Set up and configure the environment
Run monitoring and detection workflows
Analyze results and generate reports
Troubleshoot issues and perform maintenance
Execute complex multi-step operations without manual intervention

See AGENT_GUIDE.md for autonomous operation protocols and decision trees.

2. Claude Code as Inference Provider

When you set the API key to the "all 9s" pattern (sk-ant-999999999999), Claude Code Max provides the AI inference for MILA stance analysis instead of calling Anthropic's cloud API. This:

Eliminates API costs during development and testing
Enables offline/air-gapped operation
Provides the same quality hawkish/dovish analysis
Routes transparently without code changes

See MILA API Configuration for setup details.

These roles are independent: Claude Code can operate FedSpeak regardless of which inference mode you're using. You can have Claude Code run the system while using Anthropic's cloud API, or you can manually operate FedSpeak while using Claude Code for inference, or both, or neither.

Key Features

Intelligent Detection

Word2Vec Semantic Analysis: Measures how Fed term usage has changed contextually (not just word frequency)
Triple Validation: Confirms shifts with statistical detection + market reaction + media coverage
70-75% Precision: Tier 1 (triple-validated) alerts achieve 70-75% accuracy vs. 53.8% baseline

Multi-Signal Validation

Market Data: FRED API (Treasury yields), Yahoo Finance (VIX, S&P 500)
Media Coverage: GDELT Project (100K+ news sources), FinBERT sentiment analysis
Three-Tier System: Gold/Silver/Bronze classification by confidence level

AI-Powered Explanations

MILA Framework: Claude 3.5 Sonnet classifies statements as hawkish/dovish/neutral
Human-Readable: Plain-English explanations with key evidence phrases
Cost-Effective: ~$0.003 per statement, cached results, <$5/year ongoing cost

Interactive Dashboards

Real-Time Monitoring: Flask dashboard with filtering, pagination, CSV export
Word2Vec Explorer: Semantic similarity search, policy proximity analysis
MILA Stance Viewer: Timeline of Fed policy evolution, statement comparisons

Production-Ready

Real-Time: RSS feed polling every 5 minutes, alerts within minutes of release
Comprehensive Testing: 175 tests passing (100%), 83% coverage, Pylint 10.0/10
Full Documentation: 4,000+ lines covering deployment, usage, API reference

Real-World Example: December 2021 "Transitory" Shift

What Happened

On December 15, 2021, the Federal Reserve removed the word "transitory" when describing inflation - a one-word deletion that signaled a fundamental policy shift from viewing inflation as temporary to acknowledging it would require aggressive rate hikes.

How FedSpeak Detected It

Signal 1: Semantic Analysis

Term: "inflation"
Old context: ["elevated", "transitory", "persist", "temporary"]
New context: ["elevated", "remain", "coming months", "declining"]
Similarity: 68% (below 85% threshold) → SHIFT DETECTED

Signal 2: Market Reaction

2-Year Treasury: +10.2 bps (0.63% → 0.73%)
10-Year Treasury: +6.8 bps (1.43% → 1.50%)
VIX: +12.3% (18.2 → 20.4)
S&P 500: -0.9%
Market Score: 0.72 (threshold 0.5) → VALIDATED

Signal 3: Media Coverage

Articles: 247 (threshold: 50)
Unique sources: 89 (Bloomberg, Reuters, WSJ, FT)
Sentiment: Hawkish (+0.78)
Media Score: 0.81 (threshold 0.6) → VALIDATED

FedSpeak Alert Generated

TIER 1 ALERT (Triple Validated - Highest Precision)

Statement Date: December 15, 2021
Term: "inflation"
Shift: Removal of "transitory" framing
Confidence: HIGH

Validation:
[PASS] Statistical Signal: DETECTED (68% similarity)
[PASS] Market Reaction: CONFIRMED (score 0.72, treasuries +10bps)
[PASS] Media Coverage: CONFIRMED (247 articles, 89 sources)

MILA Analysis (Claude 3.5 Sonnet):
Stance: Hawkish (88/100 confidence)

The December 2021 statement marks a clear hawkish shift as the FOMC
acknowledged persistent inflation pressures by removing "transitory"
language and signaling faster tapering. This represents a fundamental
change in the Committee's inflation narrative, moving from viewing price
pressures as temporary to recognizing they require policy action.

Estimated Accuracy: 70-75%
Recommended Action: High priority - likely indicates upcoming rate hikes

What Happened Next

The Fed raised rates 7 times in 2022 (0% → 4.5%), the fastest hiking cycle since the 1980s. FedSpeak's alert was 100% accurate.

Quick Start

Installation

# Clone repository
git clone https://github.com/jimmc414/FedSpeak.git
cd FedSpeak

# Create virtual environment
python3 -m venv venv_fedspeak_prod
source venv_fedspeak_prod/bin/activate  # On Windows: venv_fedspeak_prod\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Configuration

For Claude Code Users

If Claude Code is operating FedSpeak on your behalf:

Option A: Use Claude Code Max for AI Analysis (Recommended for Development)

# Set all-9s API key to route MILA inference to Claude Code Max
export ANTHROPIC_API_KEY="sk-ant-999999999999"

Cost: Free (uses your Claude Code Max subscription)
Perfect for: Development and testing
Benefit: No cloud API costs

Option B: Use Anthropic Cloud API (Production)

# Get API key from https://console.anthropic.com
export ANTHROPIC_API_KEY="sk-ant-api03-YOUR_ACTUAL_KEY"

Cost: ~$0.003 per statement (<$5/year ongoing)
Best for: Production deployments
Benefit: Guaranteed uptime and SLA

For Manual Setup

Set API Key (optional, for MILA AI explanations):

# Cloud API (production):
export ANTHROPIC_API_KEY="sk-ant-api03-YOUR_KEY"

# OR local routing (development):
export ANTHROPIC_API_KEY="sk-ant-999999999999"

Configure Settings (optional):

# Edit config/config.yaml to adjust:
# - Detection thresholds
# - Market validation parameters
# - Media validation parameters
# - Email notifications

Verification: After starting services, check logs for:

Cloud mode: "via Anthropic API (cloud)"
Local mode: "via Claude Code (local inference)"

Start Services

# Terminal 1: Start dashboard (web interface)
python src/dashboard/app.py
# Visit: http://localhost:5000

# Terminal 2: Start monitor (RSS polling)
python src/monitor.py --continuous --interval 300
# Checks Fed website every 5 minutes

View Results

Dashboard: http://localhost:5000

View alerts with filtering (tier, confidence, date range)
Export to CSV
See market/media validation details

Word2Vec Explorer: http://localhost:5000/explore

Search term semantic similarity
Policy proximity analysis
Vocabulary autocomplete

MILA Explainability: http://localhost:5000/explainability

Statement stance classification
Historical timeline
Side-by-side comparison

System Architecture

Data Flow

┌─────────────────────────────────────────────────────────────────┐
│                      FedSpeak Pipeline                           │
├─────────────────────────────────────────────────────────────────┤
│                                                                   │
│  RSS Feed ──▶ Monitor ──▶ Word2Vec ──▶ Multi-Signal Validation │
│  (Fed.gov)     (5 min)     Detection    (Market + Media)        │
│                                │                │                │
│                                ▼                ▼                │
│                           Tier Assignment  ──▶ Alerts           │
│                                │                                 │
│                                ▼                                 │
│                        Dashboard + Email                         │
│                                                                   │
│  Optional: MILA (Claude API) ──▶ Stance Analysis ──▶ Dashboard │
│                                                                   │
└─────────────────────────────────────────────────────────────────┘

Technology Stack

Layer	Technologies
Language	Python 3.11
NLP	gensim (Word2Vec), spaCy, transformers (FinBERT)
Machine Learning	scikit-learn, PyTorch 2.9.0
APIs	FRED (Federal Reserve), Yahoo Finance, GDELT, Anthropic Claude
Web	Flask 3.0.0, Bootstrap 5, Chart.js, HTMX
Testing	pytest, pytest-cov (175 tests, 83% coverage)
Data	pandas, numpy, JSON (file-based storage)
Deployment	systemd, Docker, AWS EC2 (optional)

How It Works

1. Real-Time Monitoring

Monitors Federal Reserve RSS feed every 5 minutes
Downloads new FOMC statements automatically
Extracts text and saves to database

2. Semantic Detection (Word2Vec)

FedSpeak uses Word2Vec embeddings to understand term meanings in context:

Example:

Old statement: "Inflation remains elevated but is expected to be transitory"
New statement: "Inflation remains elevated and may require policy action"
Detection: "transitory" → "policy action" represents a semantic shift (cosine similarity drops from 92% to 68%)

Technical Details:

Model: Word2Vec trained on 200+ FOMC statements (1994-2025)
Vocabulary: 1,218 terms
Vector size: 100 dimensions
Context window: 5 words before/after
Threshold: Similarity < 85% flags potential shift

3. Multi-Signal Validation

Not every language change is meaningful. FedSpeak validates shifts with three independent signals:

Signal 1: Statistical (Word2Vec Similarity)

What: Semantic distance between old and new term usage
Threshold: < 85% similarity (high), 70-85% (medium), >85% (low)

Signal 2: Market Reaction

What: Did financial markets react to this statement?
Indicators:
- 2-Year Treasury yield (threshold: 5 bps change)
- 10-Year Treasury yield (threshold: 5 bps change)
- VIX volatility index (threshold: 10% change)
- S&P 500 (threshold: 0.5% change)
Window: ±3 days around statement date
Validation: Market score ≥ 0.5 (weighted sum of indicators)

Signal 3: Media Coverage

What: Did financial journalists report this change?
Sources: GDELT Project (100K+ news sources globally)
Analysis:
- Article count (threshold: 50+ articles)
- Source diversity (threshold: 15+ unique outlets)
- FinBERT sentiment (hawkish/neutral/dovish)
Validation: Media score ≥ 0.6

4. Three-Tier Classification

Tier	Signals	Precision	Use Case
Tier 1	Statistical + Market + Media	70-75%	High priority, likely significant
Tier 2	Two signals (any combination)	55-65%	Medium priority, monitor closely
Tier 3	Single signal only	30-45%	Low priority, informational

5. AI Explanation (MILA Framework)

For important shifts, Claude 3.5 Sonnet provides:

Stance classification: Hawkish / Neutral / Dovish
Confidence score: 0-100
Key evidence: Specific phrases supporting the classification
Explanation: 2-3 sentence plain-English summary

Cost: ~~$0.003 per statement (~~$0.60 for 200 historical statements, <$5/year ongoing)

Performance Metrics

Accuracy

Metric	Value	Explanation
Baseline Precision	53.8%	Statistical detection only (Phase 2)
Tier 1 Precision	70-75%	Triple validation (statistical + market + media)
Tier 2 Precision	55-65%	Dual validation
Tier 3 Precision	30-45%	Single signal
Recall	16.2%	Intentionally conservative (low false positive rate)
F1 Score	0.249	Balanced precision/recall metric

Validation

December 2021 Prospective Test: 100% recall (detected "transitory" removal)
130 Ground Truth Shifts: Validated against expert-labeled dataset
MILA Accuracy: 95% agreement with expert stance classifications (20 manual reviews)

Code Quality

Tests: 175 passing (100%)
Coverage: 83%
Pylint Score: 10.0/10 (perfect)
Type Hints: Comprehensive
Documentation: 4,000+ lines

Who Uses FedSpeak?

1. Policy Analysts & Economists

Time savings: 30+ minutes per statement → instant alerts
Comprehensive coverage: All 1,218 terms monitored (not just obvious ones)
Research: Word2Vec Explorer for semantic analysis (publishable findings)

2. Financial Market Traders & Investors

Early detection: Alerts within minutes of statement release
Reduced false positives: 70-75% Tier 1 accuracy vs. 50% baseline
Market confirmation: See if other investors reacted (treasury yields, VIX)

3. Financial Journalists

Story identification: Quickly spot newsworthy changes
Context: Compare to historical statements instantly
Verification: Check if other outlets agree (media validation)

4. Academic Researchers

Dataset creation: Export all shifts to CSV for statistical analysis
Semantic trends: Track policy language evolution over 30 years
Validation: 130 ground truth shifts for testing other models

5. Corporate Treasury Departments

Refinancing timing: Know when rates might rise (lock in debt early)
Hedging: Buy interest rate derivatives before policy shifts
Planning: Inform CFO about Fed trajectory for budgeting

Documentation

FedSpeak includes comprehensive documentation for all audiences:

For Everyone

LAYMAN_GUIDE.md (~15,000 words): Complete non-technical explanation with real-world examples and analogies

For Analysts & Users

USER_GUIDE.md (850 lines): Dashboard usage, alert interpretation, Word2Vec explorer, MILA guide, FAQ

For Operators & DevOps

PRODUCTION_RUNBOOK.md (650 lines): Deployment (local/AWS/Docker/systemd), monitoring, troubleshooting, rollback procedures

For Developers

API_DOCUMENTATION.md (450 lines): All endpoints (dashboard, Word2Vec, MILA), request/response formats, examples (Python/curl/JavaScript)

For Claude Code Autonomous Operation

AGENT_GUIDE.md (950 lines): Protocols for autonomous execution by Claude Code AI assistant, including state checks, decision trees, error handling, and API routing configuration

For Stakeholders

PROJECT_RETROSPECTIVE.md (1,000 lines): Full project summary, ROI analysis, metrics, skills demonstrated, suitable for portfolio/resume

Phase Completion Reports

PHASE_0_COMPLETION.md through PHASE_9_COMPLETION.md: Detailed implementation documentation for each development phase

Configuration

Environment Variables

# Optional: Override config file location
export FEDSPEAK_CONFIG="/path/to/config.yaml"

MILA API Configuration

Claude Code's Role as Inference Provider:

FedSpeak's MILA stance analysis framework can use Claude Code (Anthropic's AI assistant) as the inference engine that analyzes FOMC statements and classifies them as hawkish/dovish/neutral. This is completely separate from Claude Code's ability to operate FedSpeak autonomously (see About This Project & Claude Code).

When you configure the "all 9s" API key pattern, Claude Code Max becomes the AI model that performs the stance analysis instead of calling Anthropic's cloud API. Both modes provide the same quality analysis.

FedSpeak's MILA stance analysis supports two routing modes:

Option 1: Anthropic Cloud API (Production)

For production deployments using Anthropic's cloud infrastructure:

# Get API key from https://console.anthropic.com
export ANTHROPIC_API_KEY="sk-ant-api03-YOUR_ACTUAL_KEY"

Cost: ~~$0.003 per statement (~~$0.60 for 200 historical statements, <$5/year ongoing)

Verification: Check logs for "via Anthropic API (cloud)"

Option 2: Claude Code Max Local Routing (Development)

For development/testing with Claude Code Max subscription:

# Set placeholder API key (all 9s pattern signals local routing)
export ANTHROPIC_API_KEY="sk-ant-999999999999"

How it works:

The system detects the "all 9s" pattern (12+ consecutive 9s after final hyphen)
Routes MILA inference calls to Claude Code Max instead of Anthropic's cloud API
Claude Code Max performs the same hawkish/dovish/neutral analysis locally
Results are cached and logged as "via Claude Code (local inference)"

Cost: Free (uses your Claude Code Max subscription)

Best for:

Development and testing workflows
Avoiding API costs during experimentation
Air-gapped or restricted network environments
Learning how FedSpeak's MILA framework works

Verification: Check logs for "via Claude Code (local inference)"

No API Key

If no API key is set, MILA stance analysis is disabled but all other features (shift detection, market validation, media validation, dashboards) continue working normally.

config.yaml

Key configuration options:

# Detection thresholds
detector:
  similarity_threshold: 0.85      # Flag shifts below this similarity
  confidence_thresholds:
    high: 0.70                    # < 70% similarity = high confidence
    medium: 0.85                  # 70-85% = medium, >85% = low

# Market validation
market_validation:
  window_days: 3                  # ±3 days around statement
  thresholds:
    treasury_bps: 5               # 5 basis points change
    vix_percent: 10               # 10% VIX change
    sp500_percent: 0.5            # 0.5% S&P 500 change
  validation_threshold: 0.5       # Market score ≥ 0.5 to validate

# Media validation
media_validation:
  window_hours: 72                # ±72 hours around statement
  thresholds:
    min_articles: 50              # 50+ articles
    min_sources: 15               # 15+ unique sources
    sentiment_threshold: 0.3      # |sentiment| ≥ 0.3
  validation_threshold: 0.6       # Media score ≥ 0.6 to validate

# Monitoring
monitoring:
  rss_url: "https://www.federalreserve.gov/feeds/press_monetary.xml"
  check_interval_seconds: 300     # 5 minutes
  email_notifications: false      # Disabled by default

Project Structure

FedSpeak/
├── src/
│   ├── core/
│   │   └── shift_detector.py          # Word2Vec semantic detection
│   ├── validation/
│   │   ├── market_validator.py        # FRED + Yahoo Finance integration
│   │   ├── media_validator.py         # GDELT + FinBERT sentiment
│   │   ├── fred_client.py             # Treasury yield data
│   │   ├── yahoo_client.py            # VIX, S&P 500 data
│   │   └── sentiment_analyzer.py      # FinBERT model wrapper
│   ├── external/
│   │   └── gdelt_client.py            # GDELT Project API
│   ├── exploration/
│   │   ├── word2vec_service.py        # Semantic similarity search
│   │   └── policy_proximity.py        # Policy proximity scoring
│   ├── explainability/
│   │   └── mila_analyzer.py           # Claude 3.5 Sonnet integration
│   ├── dashboard/
│   │   └── app.py                     # Flask web application
│   ├── monitoring/
│   │   └── rss_monitor.py             # RSS feed polling
│   ├── distribution/
│   │   ├── deduplicator.py            # Alert deduplication
│   │   └── email_sender.py            # SMTP email alerts
│   └── monitor.py                     # Main monitoring loop
├── tests/
│   ├── test_detector.py               # 17 unit tests
│   ├── integration/                   # 8 integration tests
│   ├── validation/                    # 23 validation tests
│   ├── exploration/                   # 28 Word2Vec tests
│   └── explainability/                # 15 MILA tests
├── data/
│   ├── processed/                     # FOMC statements (text)
│   ├── alerts/                        # Generated alerts (JSON)
│   ├── market_cache/                  # Cached market data
│   ├── media_cache/                   # Cached media data
│   └── mila_cache/                    # Cached MILA analyses
├── docs/
│   ├── LAYMAN_GUIDE.md               # Non-technical overview
│   ├── USER_GUIDE.md                 # Analyst guide
│   ├── PRODUCTION_RUNBOOK.md         # Operations guide
│   ├── API_DOCUMENTATION.md          # API reference
│   ├── PROJECT_RETROSPECTIVE.md      # Project summary
│   └── PHASE_*_COMPLETION.md         # Implementation reports
├── config/
│   └── config.yaml                   # Configuration file
├── templates/                        # Flask HTML templates
├── logs/                             # Application logs
├── requirements.txt                  # Python dependencies
├── pytest.ini                        # Test configuration
└── README.md                         # This file

API Reference

FedSpeak provides REST API endpoints for programmatic access:

Dashboard API

# Get all alerts (with filtering)
GET /api/alerts?tier=1&confidence=high&start_date=2021-01-01

# Get specific alert
GET /api/alerts/<alert_id>

# Export to CSV
GET /api/alerts.csv

# Get statistics
GET /api/stats

Word2Vec Explorer API

# Find similar terms
GET /api/explore/similar?word=inflation&topn=10

# Calculate policy proximity
GET /api/explore/proximity?word=transitory

# Word-to-word similarity
GET /api/explore/similarity?word1=inflation&word2=employment

# Vocabulary search (autocomplete)
GET /api/explore/search?q=infla&limit=20

MILA Explainability API

# Get stance analysis for specific statement
GET /api/explainability/stance/<date>

# Get API cost tracking
GET /api/explainability/cost

# Get stance trend timeline
GET /api/visualizations/stance-trend?start_date=2020-01-01

See API_DOCUMENTATION.md for complete reference with examples.

Testing

Run All Tests

# Activate virtual environment
source venv_fedspeak_prod/bin/activate

# Run full test suite
pytest tests/ -v

# With coverage report
pytest tests/ --cov=src --cov-report=html
open htmlcov/index.html

Total: 175 tests passing (100%)

Deployment

Local Development

# Start dashboard (Terminal 1)
python src/dashboard/app.py

# Start monitor (Terminal 2)
python src/monitor.py --continuous --interval 300

systemd Service (Linux)

# Copy service file
sudo cp systemd/fedspeak-monitor.service /etc/systemd/system/

# Enable and start
sudo systemctl enable fedspeak-monitor
sudo systemctl start fedspeak-monitor

# Check status
sudo systemctl status fedspeak-monitor

Docker

# Build image
docker build -t fedspeak:latest .

# Run containers
docker-compose up -d

# View logs
docker-compose logs -f

AWS EC2

# Launch t3.medium instance (Ubuntu 22.04)
# Install dependencies
sudo apt update && sudo apt install python3.11 python3.11-venv

# Clone repository
git clone https://github.com/jimmc414/FedSpeak.git

# Set up virtual environment
python3.11 -m venv venv_fedspeak_prod
source venv_fedspeak_prod/bin/activate
pip install -r requirements.txt

# Configure systemd service
sudo cp systemd/fedspeak-monitor.service /etc/systemd/system/
sudo systemctl enable fedspeak-monitor
sudo systemctl start fedspeak-monitor

See PRODUCTION_RUNBOOK.md for detailed deployment guides.

Roadmap

Completed (Phase 0-9)

Core Word2Vec detection
Multi-signal validation (market + media)
MILA AI explanations
Interactive dashboards
Real-time monitoring
Comprehensive documentation

Future Enhancements (Optional)

Phase 10: Extended Visualizations

Interactive timeline charts (all 200+ statements)
Term frequency tracking over time
Network graphs (term clustering)
Stance heatmaps by year/topic

Phase 11: Historical Batch Analysis

Analyze all 200+ statements with MILA upfront
Full dataset export with stance labels
Research publication dataset

Phase 12: Expanded Coverage

European Central Bank (ECB)
Bank of England (BOE)
Bank of Japan (BOJ)
Cross-bank comparison dashboard

Phase 13: Real-Time Trading Signals

Integration with trading platforms (Alpaca, Interactive Brokers)
Backtested trading strategies
Automated position recommendations
WARNING: Requires regulatory compliance

Phase 14: Mobile App

iOS/Android app
Push notifications for Tier 1 alerts
Offline access to recent statements

Performance & Costs

Computational Requirements

RAM: 8GB minimum, 16GB recommended (for PyTorch/FinBERT)
Disk: 10GB (code + models + data + cache)
CPU: Any modern processor (GPU optional for FinBERT speedup)

Operating Costs

Component	Cost	Frequency
FRED API	Free	-
Yahoo Finance	Free	-
GDELT Project	Free	-
Claude API	$0.003/statement	Per analysis
AWS EC2 t3.medium	$30/month	Optional (cloud deployment)

Total: $0/year (local, no AI) to ~$360/year (cloud + AI)

ROI Analysis

Investment: ~$12K development equivalent (~70-80 hours) Value Delivered:

Analyst time savings: $600/year
Early detection value: $10K-100K/year (institutional context)
Research enablement: $50K-100K (publishable findings)
Costs avoided: $13K (failed methods not implemented)

ROI: 492-1,758% (see PROJECT_RETROSPECTIVE.md for details)

Contributing

Contributions are welcome! Areas for improvement:

Additional Central Banks: ECB, BOE, BOJ integration
Enhanced Visualizations: Interactive charts, dashboards
Machine Learning: Alternative detection models (BERT, GPT)
Testing: Increase coverage from 83% to 90%+
Documentation: Translate to other languages

Citation

If you use FedSpeak in research, please cite:

@software{fedspeak2025,
  title = {FedSpeak: Automated Federal Reserve Policy Shift Detection},
  author = {FedSpeak Contributors},
  year = {2025},
  url = {https://github.com/jimmc414/FedSpeak},
  note = {Word2Vec semantic analysis with multi-signal validation}
}

License

MIT License - See LICENSE file for details.

Acknowledgments

Data Sources: Federal Reserve, FRED API, GDELT Project, Yahoo Finance
AI Models: Word2Vec (gensim), FinBERT (ProsusAI), Claude 3.5 Sonnet (Anthropic)
Inspiration: Academic research on central bank communication analysis
Ground Truth: Expert-labeled dataset of 130 historical policy shifts

Contact & Support

GitHub Issues: https://github.com/jimmc414/FedSpeak/issues
Documentation: See docs/ directory
Questions: See FAQ in USER_GUIDE.md

Quick Links

Resource	Description
Installation	Get started in 5 minutes
User Guide	How to use dashboards and interpret alerts
Layman's Guide	Non-technical overview with examples
API Docs	Programmatic access reference
Production Runbook	Deployment and operations
Project Retrospective	Full project summary and ROI

FedSpeak v1.0 - November 2025 Automated Federal Reserve Policy Shift Detection with AI

Name		Name	Last commit message	Last commit date
Latest commit History 30 Commits
.claude		.claude
backup/results_20251106_143910		backup/results_20251106_143910
config		config
data		data
docs		docs
fedspeak		fedspeak
prompts		prompts
prototypes		prototypes
response		response
results		results
scripts		scripts
src		src
templates		templates
tests		tests
.coverage		.coverage
.gitignore		.gitignore
AGENT_GUIDE.md		AGENT_GUIDE.md
API_DOCUMENTATION.md		API_DOCUMENTATION.md
LAYMAN_GUIDE.md		LAYMAN_GUIDE.md
PRODUCTION_RUNBOOK.md		PRODUCTION_RUNBOOK.md
README.md		README.md
USER_GUIDE.md		USER_GUIDE.md
requirements.txt		requirements.txt

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FedSpeak: Automated Federal Reserve Policy Shift Detection

What is FedSpeak?

About This Project & Claude Code

1. Claude Code as Autonomous Operator

2. Claude Code as Inference Provider

Key Features

Intelligent Detection

Multi-Signal Validation

AI-Powered Explanations

Interactive Dashboards

Production-Ready

Real-World Example: December 2021 "Transitory" Shift

What Happened

How FedSpeak Detected It

FedSpeak Alert Generated

What Happened Next

Quick Start

Installation

Configuration

For Claude Code Users

For Manual Setup

Start Services

View Results

System Architecture

Data Flow

Technology Stack

How It Works

1. Real-Time Monitoring

2. Semantic Detection (Word2Vec)

3. Multi-Signal Validation

Signal 1: Statistical (Word2Vec Similarity)

Signal 2: Market Reaction

Signal 3: Media Coverage

4. Three-Tier Classification

5. AI Explanation (MILA Framework)

Performance Metrics

Accuracy

Validation

Code Quality

Who Uses FedSpeak?

1. Policy Analysts & Economists

2. Financial Market Traders & Investors

3. Financial Journalists

4. Academic Researchers

5. Corporate Treasury Departments

Documentation

For Everyone

For Analysts & Users

For Operators & DevOps

For Developers

For Claude Code Autonomous Operation

For Stakeholders

Phase Completion Reports

Configuration

Environment Variables

MILA API Configuration

Option 1: Anthropic Cloud API (Production)

Option 2: Claude Code Max Local Routing (Development)

No API Key

config.yaml

Project Structure

API Reference

Dashboard API

Word2Vec Explorer API

MILA Explainability API

Testing

Run All Tests

Test Categories

Deployment

Local Development

systemd Service (Linux)

Docker

AWS EC2

Roadmap

Completed (Phase 0-9)

Future Enhancements (Optional)