ForexAlert: AI-Powered Intelligent Forex Event Alert System

ForexAlert is an intelligent workflow system based on Python Flask, Supabase, and OpenAI, designed to address the pain points of forex traders in monitoring and analyzing high-impact economic events. The system automatically scrapes high-impact events from Forex Factory, generates AI-driven trading insights, and sends personalized email alerts according to users' time zones, helping traders prepare in advance and seize market opportunities.

In the forex trading market, the release of high-impact economic events (such as central bank interest rate decisions, non-farm payroll reports, and CPI data) often triggers sharp volatility. Traders need to grasp events and their potential impacts in advance to formulate strategies, but manual monitoring is time-consuming and prone to omissions. ForexAlert was created to solve this pain point, providing intelligent event monitoring, analysis, and alert services.

The core functions of the system include: 1. Intelligent event scraping and analysis: automatically obtain high-impact events from Forex Factory and call the OpenAI API to generate structured trading insights; 2. Personalized email push: send alerts according to the user-specified time zone and time, with key information highlighted in the email content; 3. Trading pair filtering: support users to filter events by their关注的 currency pairs and push relevant information accurately.

The tech stack consists of the following parts: 1. Backend framework: Python Flask, balancing development efficiency and deployment flexibility; 2. Data layer: Supabase (an open-source Firebase alternative), providing PostgreSQL database and authentication solutions; 3. AI capability: OpenAI API, converting raw event data into valuable trading insights; 4. Email service: Gmail API, ensuring email delivery rate and anti-spam performance.

The system workflow is divided into three links: 1. Data acquisition and parsing: regularly poll Forex Factory, scrape the event calendar, and extract fields such as type, currency pair, impact level, and release time; 2. AI analysis and insight generation: submit event data to the OpenAI model to generate reports containing event background, market expectation comparison, volatility direction, and trading suggestions; 3. Personalized delivery: filter events based on users' time zones and preferred trading pairs, and send responsive-designed emails via the Gmail API.

Suitable for three types of traders: 1. Intraday traders: need to accurately grasp event timing, and the system's regular reminders avoid missing key market movements; 2. Swing traders: focus on macro trends, and the background information from AI analysis helps judge trend directions; 3. Risk management-oriented traders: know high-volatility events in advance, adjust positions, or tighten stop-losses.

Project significance: represents a typical application of the 'agent workflow' in the fintech field, automating the 'monitoring-analysis-alert' process, improving timeliness (24/7 monitoring), consistency (avoiding subjective bias), and scalability (supporting access to more data sources). Future directions: add multilingual support, develop mobile applications, build a trader community, and implement historical backtesting functions.