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Mubashar Bashir@Mobi124
1
Events attended
1
Submissions made
Pakistan
2+ years of experience
About me
🚀 Agentic AI Engineer passionate about building intelligent systems that think, plan, and act autonomously. Currently exploring the frontier of multi-agent architectures, LLM pipelines, and AI-powered automation. 💡 I build AI apps that solve real problems — from RAG-based knowledge systems to autonomous agents using LangChain, CrewAI, and the Anthropic Claude API. 🛠️ Stack: Python | LangChain | CrewAI | FastAPI | Claude API | OpenAI | Vector DBs | HuggingFace 🎯 Always hacking, always shipping. Let's build the future together.
🤝 Top Collaborators
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🤓 Latest Submissions
APEX Trader Autonomous Multi-Agent Trading System
APEX Trader is a production-grade autonomous AI trading system built on a multi-agent architecture where five specialized agents work in a coordinated pipeline to analyze, validate, and execute cryptocurrency trades autonomously. The system consists of five agents: the Fundamental Agent analyzes NVT ratios, exchange net flows, and fair value models; the Technical Agent processes EMA crossovers, RSI, MACD, Bollinger Bands, and volume confirmation signals; the Sentiment Agent evaluates Fear & Greed Index and social sentiment scores; the Risk Agent enforces position sizing rules, portfolio heat limits, and R:R ratio thresholds; and the Backtester Agent validates every signal against historical win rates and Sharpe ratios before approval. Each trade requires multi-agent consensus above a configurable confidence threshold (72% day trading / 78% swing trading) before execution. Both day trading (5m–15m timeframes) and swing trading (4h–1D timeframes) with dynamically adjusted parameters — risk per trade, stop-loss placement, take-profit scaling, and trailing stops — all tuned to expert-level specifications. The APEX self-learning mechanism (evaluate-agent.py) continuously trains on closed trade P&L data, adjusting confidence thresholds autonomously. A self-healing daemon runs 24/7 with automatic error recovery and cooldown logic. The real-time dashboard (built on React/Next.js at port 3201 with a FastAPI backend at port 3202) provides a fully redesigned Agent Analysis Log where every stakeholder — trader, risk manager, operator, executive — gets layered information: trade identity, agent pipeline status, per-agent reasoning, strategy prediction with entry/SL/TP targets, course of action, and contextual RSS news feed — all grouped by trade, pair, or date. The project demonstrates how agentic AI systems can move beyond single-model decision making into coordinated multi-agent architectures that are transparent, auditable, and continuously self-improving.
12 Apr 2026
