Case Study
Signal to Alpha
Turning unstructured trading conversations into a reliable, automated options-trading system
Options trade callouts often arrive as inconsistent Discord messages. A trader has to interpret each message, verify the contract, place the order, manage risk, and reconstruct the outcome later. That process is slow, error-prone, and difficult to evaluate systematically.
I built Signal to Alpha to turn that fragmented workflow into one cohesive platform — from callout ingestion through execution, position management, and performance analysis.
The Challenge
The system needed to:
- Interpret inconsistent, abbreviated human language
- Avoid placing trades when the parser was uncertain
- Integrate brokers with different authentication and data models
- Stream live prices without relying on frontend polling
- Apply risk controls consistently during live market conditions
- Recover safely from restarts and incomplete broker data
- Produce trustworthy records for later analysis
In a trading system, incorrect automation is worse than no automation. The architecture therefore had to treat uncertainty, failure, and operator control as first-class concerns.
What I Built
A full-stack platform built with Next.js, FastAPI, PostgreSQL, and broker APIs.
- A four-stage parsing pipeline that classifies, extracts, normalizes, and scores Discord trade callouts
- Confidence-based routing that sends ambiguous alerts to human review instead of automatically executing them
- A unified execution layer for Schwab and Webull
- Encrypted broker credentials and paper-trading support
- Automated strategies with shared risk controls
- Persistent WebSocket connections that distribute normalized live-price events to connected clients
- Position tracking, trade journaling, analytics, and AI-assisted trade critique
- Offline market replay for testing strategies without live broker or market access
Key Engineering Decisions
Make uncertainty visible
Every parsed alert receives a confidence score. High-confidence alerts can proceed automatically; incomplete or ambiguous messages enter a review queue. This preserves automation without pretending that natural-language parsing is infallible.
Separate strategy logic from broker execution
Strategies emit normalized trading intentions rather than broker-specific requests. Execution adapters translate those intentions for each broker, allowing the system to add or replace integrations without rewriting the strategy engine.
Design failure paths before enabling automation
Risk-sensitive functionality ships behind dormant feature flags and explicit promotion checklists. Historical-data corrections use dry runs, stable report digests, environment checks, verified backups, and post-operation audits before any production record is modified.
Test the system as a lifecycle
The validation suite replays synthetic market data through the real strategy engine and verifies the entire path: market data → signal → order → fill → protection → exit → realized P&L. Restart simulations also verify that legitimate signals survive process recovery without creating duplicates.
Result
Signal to Alpha now supports the complete workflow from an unstructured Discord callout to execution, live position monitoring, risk-managed exits, and performance analysis.
The trading engine is backed by an automated suite spanning deterministic market replay, restart recovery, paper-trading lifecycle validation, and PostgreSQL migration testing.
What it demonstrates