Skip to content

Instantly share code, notes, and snippets.

@liberium

liberium/README.md

Created December 2, 2025 08:11
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save liberium/5383dbc8f395f0add68e9971e36c713e to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save liberium/5383dbc8f395f0add68e9971e36c713e to your computer and use it in GitHub Desktop.
Download ZIP
Raw
README.md

Hyper Scalper

Professional-grade trading terminal built from the ground up to highlight potentially interesting market situations, deliver trading opportunities and signals, trading financial instruments on wide range of markets. It fuses a high-performance, real-time data visualization in web app with a robust, event-driven back end to provide traders with a critical edge.

This repository contains the full codebase, from the Next.js frontend to the Python-based data ingestion and trading services. The project is architected for future integration of AI-driven analysis and trade automation, aiming to evolve from a powerful manual trading tool into a comprehensive, intelligent automatic trading system.

Core Features

Market Scanning

  • 📢 Real-Time Market Insights: Back-end scanners produce market insights (situations, opportunities, signals) in real time and stream them to the user.

Manual Trading

  • 📈 Multi-Chart Visualisation: Synchronized charts with key technical indicators (RSI, Volume, VWMA).
  • ⚡ High-Resolution Market Depth: A CScalp-inspired UI with a real-time order book (DOM), live ticks, and dynamic volume clusters.
  • ⌨️ Rapid Order Execution: Place orders directly from the chart or DOM using keyboard shortcuts and mouse clicks, with semi-automated risk management.
  • A high-fidelity Market Replay Engine for backtesting manual and algorithmic startegies.
  • An AI Mentor to analyze trading patterns and optimise trading strategy.

Semi-Automatic Trading

  • Receive trading signals from AI Market Analyser.

Automatic Trading

  • Strategy execution by AI Trader trained on market data and user trades.

Architecture

Hyper Scalper is built as a modular monolith, following Domain-Driven Design principles and employing a functional-reactive, event-driven architecture. This approach ensures clear separation of concerns, scalability, and efficient real-time data streaming.

Key Principles:

  • Asynchronous Communication: Bounded contexts (modules) communicate asynchronously via events over a message broker (Kafka). This decouples services and ensures resilience.
  • Immutable Data Streams: Pipelines are designed as compositions of functions that operate on immutable data streams, promoting predictable and testable logic.

The backend is structured into distinct modules, each representing a bounded context:

  • Ingestion Context: Connects to data sources (e.g., Telegram, exchange WebSockets) and normalizes raw inputs into canonical event types.
  • Detection Context: Processes market data streams to detect and emit MarketSituation events.
    • Example: “Volume spike on TUTUSDT on binance (usdt_perps): 6.31M in 14m, anomaly 10%.”
  • Enrichment Context: Subscribes to MarketSituation events and enhances them with external context like news or symbol metadata.
  • Opportunity Context: Consumes enriched situations and news events to build and emit TradingOpportunity events.
    • Example: “Gap-up continuation possible on TUTUSDT 5m; momentum aligned; watch pullback to VWAP.”
  • Signal Context: Consumes TradingOpportunity events to generate and emit actionable TradingSignal events.
    • Example: “Long TUTUSDT @ 0.0701; SL 0.0689; TP1 0.0716; TP2 0.0730.”
  • Delivery Context: Subscribes to all MarketInsight events (situations, opportunities, signals) to stream them to the UI via WebSockets and persist them to storage.
  • Orchestration Context: Manages and schedules the end-to-end data pipelines (scanners).

Data Storage

All market data, user trading history, and configuration are persistently stored in a TimescaleDB instance, optimized for time-series data.

API & Communication

RESTful endpoints provide access to resources like market data, orders, and positions. Real-time market insights (situations, opportunities, signals) are delivered to the frontend via WebSockets.

Web App

Developed with Next.js and TypeScript, the web application provides a rich, interactive user interface. It features synchronized multi-chart layouts, a high-resolution market depth (DOM) with live trades and volume clusters, and a real-time signal feed. It is designed for rapid order execution and visual management of trades.

Tech Stack

Web App: TypeScript 5, Next.js, Tailwind CSS, TradingView Lightweight Charts Back End: Python 3.13, FastAPI, Pydantic, Telethon, CCXT, Kafka, TimescaleDB

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment