TheDuckTrader is the flagship project I use to show how I work: hybrid funding decisioning, economics-aware carry research, exchange-aware execution, and runtime observability that makes strategy behavior inspectable instead of mysterious.
I am especially interested in roles across crypto trading infrastructure, quant engineering, exchange connectivity, and observability-first backend systems.
This site should not read like a vague product landing page. It should make it obvious where I fit: building the systems that let a strategy trade safely, explain itself, and survive real operational pressure.
I design execution-aware systems that track spot and perp legs, respect exchange constraints, and keep runtime state aligned with accounting state.
I turn strategy ideas into inspectable code paths with cost models, break-even logic, funding source comparisons, and evidence-driven tuning.
I treat dashboards, alerts, Telegram reports, and database truth as part of the product, not as optional afterthoughts.
The strongest signal I can send is not “I like crypto.” It is showing the kinds of problems I choose, the systems I build, and how I close the gap between theory and operations.
I added a hybrid funding source that anchors on realized funding and blends in predicted funding with guardrails, improving responsiveness without naively chasing raw noise.
The runtime now exposes break-even gap, cost components, basis contribution, and why-not-trading reasons so strategy flatness can be explained instead of guessed.
Grafana, Prometheus, Telegram, PDF reports, and DB-backed accounting were aligned so the system tells one coherent story under normal operation and under incident response.
Inventory cleanup actions were separated from strategy accounting so the platform does not misreport external cleanup as carry trades or false strategy P&L.
The project is intentionally presented as a serious testnet runtime with production-style discipline. That honesty is part of the signal: I would rather show a healthy, selective system than fake traction with bad trades.
The strategy is not funding-only. It evaluates funding, basis, cost, persistence, and risk guards before entering.
The system stays selective because the economics are still thin. That is exactly the behavior I want from a disciplined runtime.
BTC, ETH, SOL, ADA, XRP, and LINK, with runtime reports and dashboards focused on break-even gap and decision quality.
What matters to me is not just writing code, but building systems that are inspectable, testable, and operable by a team under real pressure.
If you are hiring for crypto trading infrastructure, quant engineering, exchange integrations, or observability-heavy backend work, this is the kind of environment where I can contribute quickly.
The strongest fit is where strategy, execution, reliability, and debugging all matter at the same time.