StaxBacktester

Signal services live in Discord chat: unstructured alert messages, updates, and exits scattered across channels. Subscribers get marketing win rates but no way to verify performance, no fee accounting, and no tooling to test whether a different exit strategy would do better on the same signals.

I was following one of these workflows myself and wanted the truth. So I built the instrument.

Regex alert parsers turn Discord messages into structured signals and trades. From there the platform tracks P&L automatically, models per-trade fees, and amortizes the subscription cost itself into net performance, down to a cost-per-day-traded figure.

The analytics layer goes deeper than win rates: milestone funnels (how often signals reach +10%, +25%, +50%), MAE drawdown profiles, stop-loss sensitivity, reversal statistics, and per-indicator signal quality scoring.

A configurable backtesting engine replays the full signal history under different exit strategies: fixed targets, trailing stops, tiered take-profits, and an Optimal Day Simulator that searches for the best-performing configuration over a chosen window.

The platform does what no signal service will do for you: it models whether the service clears its own subscription cost, and shows which exit discipline would have performed best on identical signals.

It runs as a Dockerized multi-user app with JWT auth, a documented REST API, and a professional README covering the API reference and data model.