Open Python live trading engine posted

Why it matters

An open-source GitHub repo called “Quantum Edge Terminal” says it packages a live trading stack in public, from market data to order execution. (github.com) The repository sits under GitHub user bullishoptionstrat-hub as `openclaw-vs2`, with a `quantum-edge-terminal` directory and a Phase 8 deployment summary posted within the last two days. (github.com 1) (github.com 2) That summary says the system totals 5,900-plus lines of code, 22 components, and eight modules, with named pieces for `MarketDataStreamer`, `LiveBrokerConnector`, `DeploymentGateController`, `PerformanceMonitor`, and `ProductionRunner`. (github.com) For non-specialists, a live trading engine is the layer between a strategy idea and a real brokerage account. It has to ingest prices, generate signals, size positions, check risk, send orders, and keep records when markets move in real time. (github.com 1) (github.com 2) The repo’s own documents say it uses Alpaca for real-time bars and broker connectivity, and lists Python 3.11, FastAPI, pandas, NumPy, and scikit-learn in its stack. A separate infrastructure file also lists a Node.js backend, Next.js frontend, PostgreSQL, Redis, and Docker. (github.com 1) (github.com 2) The notable detail is not just signal generation. An audit document says the project added execution tracking for signal timestamps, order acknowledgments, fill times, slippage in basis points, rejection reasons, partial fills, and a kill switch for execution failures. (github.com) That focus reflects a common gap in retail quant projects: many backtest well, but fewer show what happens after an order hits a broker. The repo’s audit file explicitly frames that problem as the difference between paper assumptions and live fills. (github.com) The codebase also appears broader than a single Python script. The top-level README view shows Pine Script files, backtest scripts, validation documents, deployment guides, and interface code alongside the trading modules. (github.com) The repo does not, by itself, prove profitable live trading. What it does provide is a public example of how one builder says a production-style trading workflow should be wired together. (github.com) (github.com) In that sense, the post is less a strategy reveal than a systems blueprint: a public map of the plumbing needed to take a model from backtest to broker. (github.com)

Key numbers

• The repository’s Phase 8 summary says the system spans 5,900-plus lines, 22 components, and Alpaca-linked modules for live bars, order routing, and alerts.
• (github.com) The repository sits under GitHub user bullishoptionstrat-hub as openclaw-vs2, with a quantum-edge-terminal directory and a Phase 8 deployment summary posted within the last two days.
• (github.com 1) (github.com 2) That summary says the system totals 5,900-plus lines of code, 22 components, and eight modules, with named pieces for MarketDataStreamer, LiveBrokerConnector, DeploymentGateController, PerformanceMonitor, and ProductionRunner.
• (github.com 1) (github.com 2) The repo’s own documents say it uses Alpaca for real-time bars and broker connectivity, and lists Python 3.11, FastAPI, pandas, NumPy, and scikit-learn in its stack.

What happens next

• A separate infrastructure file also lists a Node.js backend, Next.js frontend, PostgreSQL, Redis, and Docker.

Sources

• github.com
• github.com 1
• github.com 2
• github.com 2
• github.com

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