1. Problem & Context
Modern financial traders face extreme data overload. Fragmented price action, conflicting social sentiment, and rapid liquidity shifts across centralized and decentralized exchanges make objective decision-making nearly impossible without automated quantitative telemetry.
Market Pilot was designed to consolidate dispersed market variables into a single high-fidelity, real-time dashboard powered by machine learning heuristics.
2. Architecture & Tech Stack
The system architecture is engineered for low latency and continuous 24/7 reliability:
Market Pilot Data Pipeline
[Exchange WebSocket Feeds] + [News/Twitter Sentiment Stream]
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[Python FastAPI Async Ingest Nodes]
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[Redis Pub/Sub Buffer & Time-Series DB]
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[ML Inference Workers (PyTorch + Scikit-Learn)]
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[High-Frequency WebSockets Push Server]
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[Lightweight React / Canvas Trading UI]
Python (FastAPI & PyTorch)
Node.js (WebSockets)
Redis Streams
PostgreSQL (TimescaleDB)
React & Canvas Overlays
3. Key Innovations
- Composite Order Flow Analysis: Aggregating market depth from multiple liquidity venues to detect hidden institutional iceberg orders.
- Real-Time Sentiment Vectors: Transformer-based NLP pipelines score macro financial news and social media chatter in under 120ms.
- Zero-Lag Canvas Rendering: Using hardware-accelerated HTML5 Canvas to plot thousands of tick candles without degrading browser frame rates.