shadowwatch 0.1.0

Behavioral intelligence for your application

Shadow Watch

"Like a shadow — always there, never seen."

Behavioral intelligence for your application. Add passive behavioral biometrics, personalization, and fraud detection with zero user friction.

What It Does

Shadow Watch silently learns user behavior patterns and uses them for:

• 🔐 Security: Behavioral biometric authentication (detects account takeovers)
• 🎯 Personalization: Auto-generates interest profiles based on activity
• 🤖 Intent Prediction: Understands what users care about without asking
• 🚨 Fraud Detection: Flags suspicious behavior before damage happens

Installation

# Basic installation
pip install shadowwatch

# With Redis support (recommended for production)
pip install shadowwatch[redis]

Quick Start

from shadowwatch import ShadowWatch

# Initialize with your database
sw = ShadowWatch(
    database_url="postgresql+asyncpg://user:pass@localhost/db",
    license_key="SW-TRIAL-XXXX-XXXX-XXXX"  # Get trial at shadowwatch.dev
)

# Track user activity (silent, no UI)
await sw.track(
    user_id=123,
    entity_id="AAPL",
    action="view"
)

# Get user profile
profile = await sw.get_profile(user_id=123)
# Returns: {"total_items": 42, "fingerprint": "a7f9e2c4...", "library": [...]}

# Verify login (trust score)
trust = await sw.verify_login(
    user_id=123,
    request_context={
        "ip": "192.168.1.1",
        "user_agent": "...",
        "library_fingerprint": "..."  # From client cache
    }
)
# Returns: {"trust_score": 0.85, "risk_level": "low", "action": "allow"}

How It Works

1. Silent Tracking: Every user action (views, searches, trades) is logged
2. Interest Scoring: Actions aggregate into weighted interest scores
3. Fingerprinting: Top interests generate a unique behavioral fingerprint
4. Trust Calculation: Fingerprint mismatch = suspicious login attempt

Use Cases

Fintech Apps

• Detect account takeover attempts
• Behavioral 2FA (no user friction)
• Portfolio-aware personalization

E-commerce

• Predict purchase intent
• Fraud detection
• Product recommendations

Trading Platforms

• Smart watchlists
• Pattern-based alerts
• Risk profiling

Features

✅ Zero User Friction - Works silently, no prompts
✅ Passive Authentication - Behavioral biometric layer
✅ Auto-Generated Profiles - based on actual behavior
✅ Investment Priority - Trades weighted 10x higher than views
✅ Self-Hosted - Runs on YOUR infrastructure
✅ Privacy-First - Your data never leaves your servers

Database Setup

Shadow Watch uses 3 tables:

# Create tables (SQLAlchemy)
from shadowwatch.models import Base
async with engine.begin() as conn:
    await conn.run_sync(Base.metadata.create_all)

Tables created:

• shadow_watch_activity_events (raw events)
• shadow_watch_interests (aggregated scores)
• shadow_watch_library_versions (snapshots)

Pricing

Tier	Price	Events/Month	Support
Trial	Free (30 days)	10,000	Email
Startup	$500/month	100,000	Priority
Growth	$1,500/month	1,000,000	Slack
Enterprise	Custom	Unlimited	Dedicated

For trial license mail To: [e-mail]

Documentation

• Getting Started Guide (coming soon)
• API Reference (coming soon)
• Integration Guides (coming soon)

Comparison

Feature	Traditional 2FA	Shadow Watch
User Friction	High (SMS, app)	Zero (passive)
Hackable	Yes (SIM swap)	No (behavioral)
Setup Time	Weeks	5 lines of code
Personalization	None	Auto-generated

License

MIT License - see LICENSE

Author

Built by Tanishq during development of QuantForge Terminal

Questions?

• Email: [e-mail]
• GitHub Issues: github.com/Tanishq1030/Shadow_Watch/issues

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"Always there. Never seen. Forever watching." 🌑