Reddit Options Trader (ROT)

A 165K-line financial intelligence platform that turns Reddit into structured options trade ideas. Built solo in 9 days.

The first financial intelligence MCP server in existence. Connect Claude, Cursor, or any MCP-compatible AI to live trading signals, sentiment analysis, and unusual options activity — in one line of config.

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MCP Server — Talk to Wall Street Through Your AI

ROT is the world's first financial intelligence MCP server. No other MCP server gives your AI real-time access to trading signals, sentiment data, unusual options activity, and sports betting intelligence — all sourced from Reddit, RSS, and social media, analyzed by a 9-stage AI pipeline.

Connect in 30 Seconds (Zero Install)

The server is hosted. No packages, no Docker, no API keys. Just add one block to your config:

Claude Desktop — Add to claude_desktop_config.json:

{
  "mcpServers": {
    "rot": {
      "url": "https://web-production-71423.up.railway.app/mcp"
    }
  }
}

Cursor / Windsurf / Any MCP Client — Point at:

https://web-production-71423.up.railway.app/mcp

That's it. Your AI can now query live financial intelligence.

What Your AI Can Do

Tool	What It Does
get_trending_tickers	See what Wall Street and Reddit are buzzing about right now
get_signals	Get AI-analyzed trading signals with strategies and confidence scores
get_sentiment	Bull/bear breakdown and net sentiment for any ticker
get_market_overview	30-day snapshot: win rate, signal volume, avg confidence
get_unusual_activity	Detect IV spikes, volume surges, and institutional positioning
get_sports_feed	Sports betting intel with line mover scores (0-100)
search_signals	Search all signals by keyword ("FDA approval", "earnings beat")

Try These Prompts

Once connected, just ask your AI in natural language:

• "What stocks are trending on Reddit right now?"
• "Show me bearish signals for TSLA"
• "What's the sentiment on NVDA?"
• "Are there any unusual options activity alerts?"
• "Search for signals about FDA approvals"
• "Give me a full market overview for the last 30 days"
• "What NFL injuries might move betting lines today?"

No API key needed. Every tool is free.

Full MCP documentation: MCP_README.md

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What Is This

ROT is a full-stack signal intelligence platform. It monitors Reddit in real time, detects emerging market events, extracts and validates tickers, enriches them with market data, scores credibility, reasons about conviction, and generates structured options trade ideas — complete with strike selection, expiry heuristics, and risk parameters.

This is not a trading bot. ROT is the intelligence layer that surfaces what matters before price fully reacts.

Live deployment: rot.up.railway.app

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The Numbers

CODEBASE
──────────────────────────────────────
Production Code:     58,869 lines  │  230 files
Test Code:           92,182 lines  │  201 files
Templates:           14,318 lines  │   64 files
Total Python:       151,116 lines  │  431 files
Grand Total:       ~165,000+ lines │  564 files

TESTING
──────────────────────────────────────
Test Functions:       6,916  (5,063 sync + 1,853 async)
Test-to-Prod Ratio:  1.57:1
External API Calls:  0  (fully mocked)
CI:                  Pytest on every push, 75% coverage floor

SECURITY
──────────────────────────────────────
CodeQL Alerts:         0  (425 fixed)
Dependabot Alerts:     0
Open CVEs:             0
TODO/FIXME Comments:   0
Bare except: pass:     0
Hardcoded Secrets:     0
Security Scanners:     5  (CodeQL, Bandit, pip-audit, TruffleHog, Dependabot)

INFRASTRUCTURE
──────────────────────────────────────
Database Tables:      33+
API Endpoints:       100+
MCP Tools:             7  (first financial MCP server)
MCP Resources:         2
Tier Gates:           35+
NLP Modules:          10
Background Loops:      8
Pipeline Stages:       9

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Why This Exists

In January 2026, Intercontinental Exchange — the $98.8B company that owns the NYSE — launched "Reddit Signals and Sentiment," selling structured Reddit market data to institutional investors.

ROT does the same thing for everyone else. ICE sells raw data feeds to hedge funds at institutional prices. ROT is the complete platform — ingestion, analysis, trade ideas, dashboard, alerts — at retail scale.

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Architecture

9-Stage Pipeline

Reddit/RSS → Trend Detection → NLP (10 modules) → Event Building → Market Enrichment
    → Credibility Scoring → Feedback Suppression → LLM Reasoning → Trade Ideas

Every stage runs continuously. Memory-bounded dedup (max 2,000). Circuit breaker on LLM (auto-disables after 3 failures, stub fallback). Full pipeline executes in seconds.

Security (Grade: A)

Independently audited at A (93-95/100) across two separate assessments.

• Authentication: JWT + API Key + Session Cookie (3 methods)
• Authorization: 5-tier hierarchy (Free → Pro → Premium → Ultra → Enterprise) + Admin. 35+ gate functions.
• SQL Injection: 100% parameterized queries. Field whitelist for dynamic updates.
• XSS Prevention: 3-layer defense — Jinja2 autoescape + nh3 Rust sanitizer + nonce-based CSP
• CSRF: Custom ASGI middleware with timing-safe HMAC comparison
• Security Headers: 6/6 — CSP, X-Frame-Options: DENY, nosniff, Referrer-Policy, Permissions-Policy, X-XSS-Protection
• Rate Limiting: Database-backed, multi-instance safe. Per-tier daily + burst limits. Brute-force protection.
• Security Logging: 10 SIEM-ready JSON event types. Global sanitizing filter. Request ID correlation.
• CI/CD: 5 automated scanners on every push — CodeQL, Bandit, pip-audit, TruffleHog, Dependabot

Test Suite

1.57:1 test-to-production ratio — more test code than production code.

6,916 tests across 201 files. Zero external API calls (everything mocked). Runs in CI on every push with coverage enforcement.

What the tests have found:

• A ghost endpoint — two health check routes existed, the minimal one shadowed the comprehensive one
• A tier gate design assumption that didn't match actual product behavior
• A CVE in cryptography caught and patched within hours of enabling dependency pinning
• A known pytest caplog fixture isolation issue triggered by extreme test density (23 tests for a single security logger module)
• A serialization bug where Evidence dataclass objects were silently failing to store, dropping 100% of signals

Key Design Patterns

Pattern	Implementation
Pipeline Orchestration	9-stage DAG with dedup and filtering
Circuit Breaker	LLM disabled after 3 failures, stub fallback, auto-recovery
Mixin Composition	16 DB mixins (231 methods) vs monolithic file
Dual-Path NLP	Custom 10-module engine with regex fallback
Query Cache	Async TTL with thundering-herd prevention (per-key locks)
Tier Gating	Returns dicts of flags, not exceptions. Admin bypasses all.
MCP Server	Remote HTTP/SSE, 7 tools + 2 resources, zero-config connection
Non-root Docker	gosu-based entrypoint with volume permission handling

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Core Capabilities

Real-Time Ingestion

• Reddit: PRAW streaming from r/wallstreetbets, r/stocks (hot, new, rising, top)
• RSS: 13+ feeds including Reuters Business, SEC 8-K filings
• Deduplication, freshness gating, persistence across restarts

Trend Detection

Momentum-based, not mention-based. Score velocity, comment velocity, engagement acceleration. Emits TrendCandidate objects when thresholds are exceeded.

Ticker Extraction & Validation

• $TSLA, bare TSLA, multi-ticker posts
• Aggressive filtering: macro noise, non-equities, slang, delisted symbols
• Alias normalization (SPXW → ^GSPC, TSMC → TSM)

Market Enrichment

Live data via yfinance with local caching. Price, market cap, volume, IV context.

Event Classification

Event types (earnings, squeeze, regulatory, product, macro), sentiment detection (bullish/bearish/mixed), time horizon inference (intraday through earnings window).

Credibility Scoring

ML scorer (GradientBoosting) + 12 heuristic factors. DD flair bonus, engagement quality, cross-post penalties, ticker focus, text depth. Transparent score breakdown.

LLM Reasoning (Optional)

Provider-agnostic — OpenAI, Anthropic, DeepSeek. Thesis synthesis, risk identification, context expansion. Circuit breaker with safe fallback.

Trade Idea Generation

Bull call spreads, bear put spreads, straddles. ATM ± 5% strike selection, weekly/monthly expiry heuristics, max loss calculation, quality scoring. Market cap and data availability gates.

Web Dashboard

FastAPI + Jinja2. Real-time signal feed (WebSockets), confidence bars, stance badges, trending tickers, signal detail pages with full reasoning and trade structure. Dark theme.

Alerts

Discord webhooks, email, Twitter. High-confidence signals only. Rich embeds with ticker, stance, confidence, strategy, option legs, risks, catalyst window.

Additional Systems

• MCP Server: World's first financial intelligence MCP server — 7 tools, 2 resources, zero-install remote connection
• Backtesting Engine: Monte Carlo simulation, walk-forward optimization, 12 modules
• Strategy Builder: Rule-based, ML optimizer, genetic algorithms, regime detection, marketplace
• Social Intelligence: Manipulation detection, bot detection, pump-dump patterns, coordination tracking
• Options Flow: Block/sweep/dark pool detection, IV analysis, Greek calculations
• Macro Events: FOMC calendar, earnings tracking, seasonal patterns, insider activity
• Gamification: Badges, leaderboards, progression system
• Enterprise Export: 9-step data lineage, scheduled exports, analytics

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Project Structure

src/rot/
├── app/          # Server, pipeline runner, background loops
├── ingest/       # Reddit + RSS ingestion (7 modules)
├── trend/        # Trend detection and ranking
├── nlp/          # 10-module NLP pipeline (500+ lexicon)
├── extract/      # Event builder (dual-path NLP/regex)
├── market/       # Trade builder, enrichment, validation
├── credibility/  # ML scorer + 12 heuristics
├── reasoner/     # LLM reasoning with circuit breaker
├── storage/      # 33+ tables, 16 DB mixins, migrations
├── web/          # FastAPI routes, auth, middleware, templates
├── strategy/     # ML, genetic, regime, marketplace
├── social/       # Manipulation, propagation, network analysis
├── flow/         # Options flow intelligence, Greeks
├── backtest/     # Monte Carlo, walk-forward, 12 modules
├── macro/        # FOMC, earnings, seasonal, insider
├── alerts/       # Discord, email, Twitter, webhook dispatch
├── agents/       # Autonomous trading agents (safety rails)
├── gamification/ # Badges, leaderboards, progression
├── export/       # Enterprise exports, 9-step lineage
├── core/         # Config, types, logging, sanitization
└── ...           # + affiliates, sports, integrations, analysis

tests/            # 201 files, 92,182 lines, 6,916 test functions
templates/        # 64 Jinja2 templates
docs/             # 17 documentation files

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Setup

# 1. Clone
git clone https://github.com/Mattbusel/Reddit-Options-Trader-ROT-.git
cd Reddit-Options-Trader-ROT-

# 2. Configure
cp .env.example .env
# Fill in: Reddit API credentials, optional LLM API key

# 3. Install
pip install -e ".[dev]"

# 4. Run
python -m rot.app.server
# Dashboard: http://localhost:8000/dashboard
# API docs:  http://localhost:8000/docs

# 5. Test
pytest tests/ -v

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Audit Results

Two independent AI-assisted audits, both scoring A overall:

Dimension	Score
Security	A to A+ (93-98/100)
Architecture	A (95/100)
Code Quality	A (94/100)
Test Suite	A (92-95/100)
Dependencies	A to A+
Documentation	A- to A
Overall	A (93-95/100)

Zero open recommendations. All P0, P1, P2, and P3 items from both audits have been closed.

Full audit reports available in docs/.

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Disclaimer

This project is for research and experimentation only. Nothing in this repository constitutes financial advice. ROT is a signal intelligence platform, not an execution engine.

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