mdubya

Live in production

• Webhook-driven trading server running a trend-following strategy across multiple perpetual-futures markets, managing positions across multiple accounts on more than one exchange from a single signal source.
• ATR-based risk management, tiered slippage, race-condition guards, automatic state recovery on restart. Per-account flip, force-close, and manual size adjustment, all gated behind an admin token.
• Live operator dashboard with a two-tier auth split: separate credentials for admins (write) versus read-only monitors, so a leaked monitor credential cannot place a trade.
• Production-hardened: built and shipped fixes for infinite exit loops from API verification failures, double-order race conditions, signal repainting, and an SDK-level hang that needed a custom stack-trace dump on detection.

Stack Python, async I/O, web framework + reverse proxy, systemd, embedded SQL

Live in production

• Avellaneda-Stoikov-style two-sided quoting with signal-aware directional skewing. Reads position state from the upstream trading server to align quotes with the active trend.
• Per-pair PnL kill switches (intentionally pair-level, not pooled across the book). Debounce logic so a single bad tick or zero-price read cannot force a trade.
• Hang-detector sidecar that triggers a stack-trace dump on the wedged process after a configurable log-silence threshold.
• Control API never exposed to the public internet; reachable only through a privately scoped tunnel from a separate ops host.

Stack Python, async I/O, systemd, restricted SSH transport

Live in production

• Code-review agents: scheduled review of the trading and market-maker codebases against a security-first rubric with tiered alerting. The first production run caught real bugs on day one and has continued to find regressions.
• Performance-monitor agent: regime-aware periodic recommendation reports. Pulls multi-month price and funding history, discovered and worked around two undocumented exchange-API pagination limits, runs k-means regime clustering on engineered features.
• Log-stream monitor agent: persistent live-log stream into the model with a structured pattern catalog across multiple primitive families (temporal, sustained-state, filesystem-watch, HTTP snapshot-delta) and a cross-pattern correlation layer that collapses paired signals into a single combined alert.
• Source code reaches the agent host only through one-way read-only mirrors, never through any external code-hosting system.

Stack agentic CLI (subscription auth), systemd timers, restricted SSH transport, chat-platform webhooks

Live in production

• Slash-command interface for controlling the agent fleet and trading systems: status reads, pause/resume, position queries, kill switch, watchlist CRUD.
• Routes over a restricted private tunnel to internal control APIs; nothing exposed to the public internet. Designed for fast phone use: space-separated args with smart defaults.
• Destructive actions (kill switch) require a typed-confirmation token, run server-side timeouts, and write to an audit log on every invocation.

Stack Python, restricted SSH transport, web framework

Live, auth-gated

• Computes and publishes a curated daily consumer-stress index from public macro series and a market-data watchlist.
• Cached client over a public chart endpoint, with handling for source-side outages and rate limits.
• Composite weights driven by what the historical data actually supports; calibration window chosen after discovering and excluding an anomalous early-period revision.

Stack Python, web framework, embedded SQL, public macro APIs

Live, auth-gated

• Operator dashboard over the market-maker process. Periodic snapshot timer writes to a local time-series store.
• Charts above raw state dumps: PnL curve, drawdown gauge, fills volume, cycle-health sparklines.
• Kill-switch UI with typed-confirmation dialog, server-side timeout, and a confirmation guard that prevents accidental fires.

Stack Python, web framework, embedded SQL, vanilla JS charts

Research tooling

• Hyperparameter-search framework for trading strategies with a multi-seed robustness check baked in (parallel optimization isn't deterministic even with a fixed seed; multi-seed catches that).
• Treats the pipeline itself as the deliverable: multiple candidate strategies run through it have failed walk-forward analysis, which is the point. The framework is the durable asset.
• A library of trading-platform strategies built in parallel against a consistent architecture: ATR-based risk, trend filters, webhook-ready alerting.

Stack Python, hyperparameter search, trading-platform scripting