Discussing the article: "Price Action Analysis Toolkit Development (Part 35): Training and Deploying Predictive Models"
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Check out the new article: Price Action Analysis Toolkit Development (Part 35): Training and Deploying Predictive Models.
Historical data is far from “trash”—it’s the foundation of any robust market analysis. In this article, we’ll take you step‑by‑step from collecting that history to using it to train a predictive model, and finally deploying that model for live price forecasts. Read on to learn how!
In this article, we transition from using a simple script to developing a full Expert Advisor (EA) to allow continuous monitoring and real-time communication with a Python backend—something a standalone script cannot efficiently handle. The Spike Detector EA on MetaTrader 5 operates in a client-server setup, where it serves as the client and a Python Flask server acts as the backend. The EA continuously observes the formation of new candlesticks. At defined intervals, it collects a configured number of historical candles (OHLCV data and timestamps), serializes this into JSON format, and sends it via an HTTP POST request to the Python server.
The Python backend, which typically contains either a machine learning model or rule-based logic, analyzes the incoming market data and returns a signal: BUY, SELL, CLOSE, or WAIT. Upon receiving this response, the EA interprets the signal and reacts accordingly—drawing arrows on the chart, opening trades, or closing existing positions—based on the user’s settings. This feedback loop allows MetaTrader to extend its native capabilities with external analytical intelligence in real time, effectively combining MetaTrader 5's execution engine with Python's processing power.
Author: Christian Benjamin