We are building a high-performance, low-latency trading engine designed for microstructure-based execution strategies in a high-tax (STT) environment.
This is NOT a basic retail trading bot.
This system requires advanced system-level engineering, multi-core CPU architecture control, shared memory communication, and real-time observability dashboard.
The focus of this project is minimizing latency between signal generation and order execution while maintaining regulatory compliance (Order-to-Trade Ratio constraints).
The developer must understand low-level performance optimization, concurrency architecture, and Linux system behavior.
Core Technical Requirements Python Version (Mandatory)
The engine must use:
Python 3.13 Free-Threaded build (3.13t)
NOT standard Python 3.10–3.12
Reason: Standard Python uses the Global Interpreter Lock (GIL), which blocks true parallelism. In low-latency systems, a 1–2ms delay caused by GIL contention is unacceptable.
Multi-Core Architecture with CPU Core Pinning
The engine must:
Assign specific modules to specific CPU cores
Use os.sched_setaffinity (Linux only)
Prevent OS core migration (avoid context switching)
Modules include:
Sentinel (Risk & OTR monitoring)
Sonar (Market entropy / regime detection)
Oracle (Signal calculation loop)
Execution Engine (Order placement)
The goal is to eliminate unpredictable latency spikes caused by OS scheduling and cache invalidation.
Inter-Process Communication
Standard Python queues are NOT acceptable.
Communication must use:
multiprocessing.shared_memory
Memory-mapped buffers
Lock-free ring buffer architecture
Reason: Standard queues introduce locking and object allocation overhead, increasing latency.
The target is sub-millisecond internal communication between signal generator and execution engine.
Latency Measurement
The system must measure:
End-to-end order placement latency
Round-trip time (RTT)
Module processing time
Using:
time.perf_counter_ns()
Latency histogram logging
This data must be streamed to the dashboard.
Order Execution Logic
The system should:
Prefer passive limit orders
Include 200ms cancel logic
Manage Order-to-Trade Ratio (OTR)
Implement controlled order flooding logic (compliant with broker rules)
This is not a simple market order bot.
FRONTEND REQUIREMENTS (React Dashboard)
The frontend is NOT a trading UI.
It is a real-time monitoring and control cockpit.
Preferred stack:
React (Vite or Next.js)
WebSocket for live streaming
Lightweight charting (Canvas or WebGL-based)
Required Dashboard Modules Sentinel Panel
Real-time RTT graph
20ms lockdown threshold indicator
CPU usage per pinned core
Emergency status
Sonar Panel
Market regime indicator (Attack / Veto mode)
Entropy score display
Zero-trust gate status
Oracle Panel
Weighted Order Book Imbalance (WOBI) heatmap
Liquidity imbalance %
Signal strength score
Must use high-performance rendering (Canvas, not heavy SVG).
Execution Panel
Net Expected Value (NEV)
Fill rate %
Cancel rate
Order-to-Trade Ratio (OTR) status
Emergency Kill Switch
Dashboard must include:
Global kill switch
Sends signal to monitoring service
Monitoring service writes flag to shared memory
Engine halts immediately
Dashboard must NOT communicate directly with broker API.
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