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Trading Analytics & Self-Learning System
A data-driven analytics and self-learning module built for an automated Telegram trading system.
The system continuously records trading signals and completed trades, analyzes historical performance, and provides real-time statistics directly through Telegram.
It tracks win rate, total and average PnL, best and worst trades, signal distribution, confidence scores, and trading performance over time.
Key features:
Automated trade history tracking
Real-time PnL calculation
Win rate monitoring
BUY / SELL / HOLD signal statistics
Confidence score analysis
Historical performance evaluation
PostgreSQL data storage
Telegram reporting interface
Automated signal logging
This module was developed as part of a larger Python-based automated market analysis and demo trading platform.
@Денис Нижегородов Tracking confidence scores alongside win rate and PnL should expose useful calibration gaps. Are the PostgreSQL records also segmented by market regime or signal type?
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I’m currently building a RAG system for an e-commerce company, basically giving an AI assistant access to the company’s own knowledge and product information so it can give useful, context-aware responses instead of just generating random answers.
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→ Actually be useful in a real business workflow
Still working on it, but I’m excited about where it’s going.
This is the kind of AI automation I enjoy building, systems that solve actual business problems, not just AI demos.
Building something similar? Let’s talk.
I’ve built something similar for Purchaser, an e-commerce marketplace for automotive and industrial products.
I built a RAG-powered AI agent inside Slack that gives the team access to Purchaser’s product and spare-parts database. Instead of opening Excel and manually searching...