Skip to content

Latest commit

 

History

History
96 lines (61 loc) · 3.22 KB

README.md

File metadata and controls

96 lines (61 loc) · 3.22 KB
Raw

Forex Trading using Deep Neural Networks

This repository contains a deep neural network (DNN) model for predicting forex commodity price movements and a trading bot that uses the model with the OANDA API for live trading. The bot is designed to trade forex pairs such as USD/JPY, leveraging historical candlestick data and machine learning to make informed trading decisions.

Note: This project is for educational purposes only, and caution should be exercised when/if live trading.

Table of Contents

  1. Overview
  2. Features
  3. Setup Instructions
  4. Usage
  5. Model Details
  6. Backtesting and Performance
  7. Live Trading
  8. Example

Overview

This project combines financial data analysis, machine learning, and automated trading. The DNN model predicts price direction using engineered features and a custom architecture. The trading bot integrates the model with the OANDA API for real-time trading.

Features

  • Data Collection: Uses OANDA API to fetch historical candlestick data.
  • Feature Engineering: Includes MACD, EMA, and custom lagged features.
  • DNN Model: Binary classification for price movement direction.
  • Backtesting: Evaluates strategy performance on historical data.
  • Live Trading: Executes trades in real-time based on model predictions.

Setup Instructions

Prerequisites

  • Python 3.8+
  • OANDA Practice Account (API access)
  • Required Python packages: 
    pip install tensorflow keras matplotlib pandas numpy oandapyV20

Usage

Training the Model

  1. Use the model.py script to train the DNN model.
  2. Model parameters and training results are saved in the models/ directory.

Running the Trading Bot

  1. Use the model_inference.py script for live trading.
  2. Adjust the instrument and parameters in the script as per your model's training setup.

Model Details

  • Inputs: Historical price data, MACD, EMA, SMA, Volume, Min, Max, lagged features.
  • Architecture: Fully connected layers with dropout and optional L1 regularization.
  • Optimizer: Adam with a learning rate of 1e-5.
  • Loss Function: Binary cross-entropy.
  • Outputs: Probability of upward price movement.

Backtesting and Performance

  • Backtesting is implemented in model.py using a trading strategy based on model probabilities.
  • Results include cumulative returns and strategy visualization.

Live Trading

  • The trading bot fetches real-time data using the OANDA API.
  • Executes long/short positions based on model predictions.
  • Handles trading parameters like granularity, thresholds, and lot sizes.

Example

A Model was trained on the USD/JPY instrument with 15-minute candles. Historical data was collected from 2024-01-01, with features EMA_200, MACD_line, SIGNAL_line, min and max. Results were as follows:

image

model_e_returns

Across the randomised two-month period, the model achieved a net return (before fees) of 3.398%, marginally exceeding buy-hold returns.