Python Market Data, Gradient Boosting, and MQL5 Signal Integration
Summary
The article describes a Python and MetaTrader 5 workflow that retrieves minute bars directly from the terminal, stores history in Parquet, engineers technical and statistical features, trains a classifier, and serves predictions to an MQL5 Expert Advisor through a Flask API. The feature set includes price-spike normalization, MACD, RSI, ATR, moving-average envelopes, and a trend estimate. Labels classify price movement over a forward horizon as buy, sell, or wait; model selection uses time-ordered cross-validation and randomized hyperparameter search.
The EA polls for predictions, displays signal and confidence information, and can draw levels or place orders with automated trading enabled. The document includes implementation detail and an example response, but the supplied text is truncated and does not provide a complete evaluation of predictive or live trading performance. Its claims about efficient operation and risk handling should therefore be treated as design goals rather than demonstrated results.
Key ideas
- Python can retrieve MetaTrader 5 market bars directly and maintain an incremental historical store.
- The proposed features combine momentum, volatility, price extremes, and a trend estimate.
- A Gradient Boosting classifier predicts buy, sell, or wait labels from forward price changes.
- TimeSeriesSplit is used to preserve temporal ordering during model selection.
- An MQL5 EA can poll a Flask service to display predictions and optionally execute trades.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.