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2.31.7

soban-saleem

soban saleem@soban-saleem

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Events attended

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🤝 Top Collaborators

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Mubashar Bashir

🚀 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.

🤓 Latest Submissions

    APEX Trader Autonomous Multi-Agent Trading System

    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.

    Hackathon link

    12 Apr 2026

👌 Attended Hackathons

    AI Trading Agents

    AI Trading Agents

    ⏱️ Design, build, and deploy trustless AI trading agents. 📅 March 30 – April 12, 2026 🌐 Build AI trading agents that execute strategies using ERC-8004 or Kraken CLI. 🤝 Build solo or in teams to create autonomous trading, risk, yield, or protection agents. 💰 $55,000 prize pool, allocated and shared via Surge and Kraken. Your team should have registered project at early.surge.xyz in order to be eligible for the prize. Get access in your team profile or our discord 🔑 Access to early.surge.xyz If prompted for login credentials, use: • Username: admin • Password: JBRv2xWG7AzwVrLz88