I created this library to streamline the process of developing an Expert Advisor in MQL5. While MQL5 development can be complex, the same task is more straightforward in Python.

This project is part of a bigger one. I want to make Algo Trading Easy for you! You can find examples and more information in the documentation. easyT, easyTo trade, easyTo use!

Traditionally, volatility is modeled using parametric models. This project focuses on predicting EUR/USD volatility using more flexible, machine-learning methods.

This project is part of a bigger one. I want to make Algo Trading Easy for you! You can find examples and more information in the documentation. easyT, easyTo trade, easyTo use!

Python algorithm for trading the EUR/USD forex pair using a mean reversion strategy. The algorithm retrieves price data from OANDA's API, calculates the z-score of the closing prices, and executes a trade if the z-score is above a certain threshold (indicating an overbought condition) or below a certain threshold (indicating an oversold condition

Database Currency Pair

This is a modified mean reversion trading strategy that generates buy and sell signals based on the relative strength index (RSI) and moving average (MA) of the EUR/USD currency pair on a 1-minute timeframe.

LSTM model for EURUSD price forecasting, exploring techniques from 'Machine Learning Aplicado al Trading'.

This project develops and fine-tunes a TimeSeriesTransformer model to forecast EURUSD 5-minute closing prices, serving as a modern counterpart to a baseline LSTM model

This is a Forex data indicator that tries to give visual insights of forex trading pairs based on Trading Sessions and overlaps.

MQL Expert

1D-CNN that predicts the direction of the EURUSD pair.

Euro Macromechanica (EMM) Backtesting Ecosystem — metrics toolkit: methodology schema and metric calculator for the EMM backtest result.

stream EURUSD price from truefx.com and visualize it with matplotlib

Euro Macromechanica (EMM) Backtesting Ecosystem — EUR/USD M5 quant strategy backtest results across the full retail-broker trading era (since 2001; euro introduced 1999, cash 2002). Baseline 2003–Aug 2025; stress 2001–2002. Integrity: SHA-256, GPG, OTS; live run video proofs. Implemented a single M5 quantitative model—minimal yet self-sufficient.

Application of machine learning model, on datasets, to predict desired target variables.

Forex tick-by-tick EUR/USD data, free to use for your Forex machine learning stuffs..

EUR/USD ARIMA + GARCH volatility modeling with R (end-to-end)

Tests if EUR/USD daily moves are as random as coin flips using Monte Carlo and statistical tests (runs, Markov chains, Ljung–Box, Hurst, entropy). Results show returns look random, but volatility clusters, revealing structure beyond pure chance.

Euro Macromechanica (EMM) Backtest Ecosystem – Overview & Methodology web page