Overview

A multi-user algorithmic trading platform designed to execute automated trading strategies across multiple markets with strict time, concurrency, and reliability constraints.

The system was built to handle simultaneous user logins, real-time market data, and automated trade execution with minimal latency during live market hours.

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Problem Statement

Manual trading and semi-automated setups fail under:

• Market open load (9:15 AM NSE spike)
• Concurrent user execution
• Strategy timing precision
• Operational reliability

The goal was to build a server-side automated trading system that:

• Logs in multiple users programmatically
• Executes strategies in parallel
• Minimizes human intervention
• Handles failures gracefully during live markets

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Architecture & Design

• Backend Core: Python-based orchestration layer
• API Layer: FastAPI for internal services and control endpoints
• Concurrency Model:
  • Multithreading for I/O-bound broker calls
  • Multiprocessing for CPU-heavy strategy evaluation
• Market Data:
  • Amibroker integration for charting and signal generation
• Execution Layer:
  • Broker API integrations (Kotak Neo, Binance, MT5)
• Frontend:
  • Lightweight React dashboard for monitoring and control
• Communication:
  • WebSockets for live updates and bot state visibility

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Key Features

• Market Open Automation

  • System auto-starts at market open (9:15 AM)
  • Performs parallel login for all configured users
  • Activates strategy execution pipelines

• Multi-Broker Strategy Support

  • NSE Options (Kotak Neo)
  • Crypto Trading (Binance)
  • Forex Trading (MT5)

• Concurrent Trade Execution

  • Parallel order placement per user
  • Isolated execution contexts to prevent cross-user interference

• Risk & Loss Control

  • Strategy-based exit conditions
  • Automated stop mechanisms to minimize drawdowns

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Engineering Challenges Solved

• Handling race conditions during simultaneous order placement
• Managing shared vs isolated memory between concurrent tasks
• Ensuring broker API reliability under burst traffic
• Synchronizing strategy signals with execution timing
• Preventing cascading failures during partial outages

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Technologies & Concepts

• Python (core orchestration)
• FastAPI (internal APIs)
• AsyncIO, Multithreading, Multiprocessing
• WebSockets (real-time updates)
• Pandas (data processing & indicators)
• Broker API integrations
• React (monitoring UI)

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My Role

Sole Developer

• Designed system architecture
• Implemented concurrency model
• Integrated broker APIs
• Built backend services and automation logic
• Developed monitoring dashboard
• Deployed and maintained live trading instances