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Deep Nexus One (DN1) Research Repository for FX Prediction

Project Overview

This repository contains a foreign exchange (FX) trading prediction system developed in 2019. The project implements machine learning prediction models using an LSTM architecture with a regression output. Multiple models run in parallel and send signals to MT4.

Technical Architecture

Core Components

  • Prediction Models:

    • Implemented using TensorFlow and Keras
    • Long Short-Term Memory (LSTM) neural network architecture
    • Developed for 6 different currency pairs
    • Prediction frequency: Every 5 minutes

  • Backend Framework:

    • Flask web framework for serving models
    • Waitress WSGI server for deployment
    • Oanda Rest API integration for market data and trading execution

Technical Complexity

The project represented a significant technical challenge in:

  • Coordinating multiple LSTM neural network models
  • Integrating diverse technologies (TensorFlow, Keras, Flask, Waitress)
  • Serving multiple prediction models simultaneously
  • Executing trades across multiple currency pairs from a platform originally designed for single-pair trading

Key Technical Challenges Solved

  1. Multi-Model Management

    • Developed a custom .dll file to manage multiple prediction models and scripts
    • Implemented a timer function to orchestrate script execution across different currency pairs

  2. Platform Integration

    • Bridged prediction models with MetaTrader 4 (MT4) trading platform
    • Resolved complex compatibility issues between different programming environments
    • Managed communication protocols (largely involving TCP/IP networking)
    • Implemented asynchronous processing to overcome limitations of MT4

Technology Stack

  • Python
  • TensorFlow
  • Keras
  • Flask
  • Waitress
  • Oanda Rest API
  • MetaTrader 4 (mql4)

Deployment Considerations

  • Custom .dll file for script and model management
  • Periodic execution of prediction models
  • Seamless integration between machine learning predictions and trading platform

Limitations and Considerations

  • Research project from 2019
  • Developed for specific trading strategy and currency pairs
  • Requires careful review and potential updates for current market conditions
  • MT4 trade logic is built from a genetic algorithm and is sample logic only
  • Python 3.5 was used to build this project
  • Final production models are proprietary

Disclaimer

This is a research project and should not be considered financial advice. Trading involves significant financial risk.

License

http://www.apache.org/licenses/LICENSE-2.0

Contact

web@deepnexus.com

Repository initialized in 2024, based on research conducted in 2019.