Stop Paying for Trading Bots! Build a $0 Local AI Trading Agent with Google Gemma 4 (Zero API Fees)

Welcome to the definitive masterclass on building an autonomous, 100% local AI Trading Coach from scratch!

In this comprehensive 4-in-1 course, Abhishek from MBStudio walks you step-by-step through architecting a privacy-first, zero-API-cost financial agent powered by Google's cutting-edge Gemma 4 (12B) running directly on Apple Silicon with Apple MLX.

Unlike toy tutorials that rely on hardcoded mock prices and cloud LLM subscriptions, every single module in this masterclass interacts with 100% LIVE market telemetry—scraping real-time Yahoo Finance gainers, purging penny-stock volatility, grounding reasoning with Serper web search, enforcing type-safe Pydantic contracts, and running comparative valuation models locally on your Mac's Metal GPU.

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⚠️ MANDATORY FINANCIAL & LEGAL DISCLAIMER (PLEASE READ CAREFULLY):
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This video, the code repository, and the "AI Trading Coach" agent are strictly for EDUCATIONAL, EXPERIMENTAL, AND SOFTWARE ENGINEERING PURPOSES ONLY. I am a software engineer and AI educator—I am NOT a licensed financial advisor, certified broker, or fiduciary. DO NOT constitute investment advice, stock recommendations, or financial endorsement and may be incorrect.
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GET THE COMPLETE SOURCE CODE & NOTEBOOKS (FREE GITHUB REPO):
👉 https://github.com/MysteryByte....s-Labs/agentic_ai/tr
(Includes all 4 interactive Jupyter notebooks, MLX inference runners, and Pydantic schemas)

💬 JOIN OUR DEVELOPER COMMUNITY:
👉 MysteryBytes Studio: https://mysterybytesstudio.xom

⏱️ TIMESTAMPS / TABLE OF CONTENTS:
CHAPTER 1: LOCAL INFERENCE WITH GEMMA 4 & APPLE MLX
00:39 - 4-Stage Tutorial Roadmap
03:00 - Loading Gemma 4 (12B QAT) into Unified Memory
05:32 - Full-Sequence Table Inference on Apple Metal GPU
06:05 - Rich Markdown Display & Chapter 1 Wrap-Up

CHAPTER 2: TYPE-SAFE CONTRACTS & LIVE WEB GROUNDING
06:46 - Welcome to Chapter 2: Moving Beyond Toy Weather Tools
08:00 - Why Production Agents Require Pydantic Contracts
09:00 - Defining Type-Safe Pydantic Financial Models
11:45 - Google Serper Real-Time Telemetry Retrieval
12:45 - Dynamic Temporal Grounding (Solving Date Hallucination)
13:47 - Orchestrating the Full Closed-Loop Agent (run_agent)
14:40 - Live Scenario 1: Real-Time Market Analysis (AAPL)
15:30 - Live Scenario 2: Fundamental Valuation Modeling (TSLA)

📈 CHAPTER 3: LIVE MARKET SCREENER & VALUATION ENGINE
16:48 - Welcome to Chapter 3: Institutional Financial Valuation
17:50 - Architectural Overview: 4-Method Quantitative Pipeline
18:40 - Environment Verification & Gemma 4 MLX Initialization
19:20 - Method 1: Live Market Screener (Yahoo Finance)
20:00 - The Penny Stock Purge: Strict Risk & Volatility Filtering
20:45 - Method 2: Live Stock Telemetry & Market Multiples
21:40 - Method 3: Financial Valuation Engine (P/E, EV/EBITDA, P/B)
22:40 - Method 4: Gemma 4 Executive Investment Research Memo
23:45 - Full Pipeline Run: Synthesizing Top 3 Market Opportunities

🤖 CHAPTER 4: CAPSTONE: AUTONOMOUS "AI TRADING COACH"
24:42 - Welcome to Chapter 4: Building StockAgent & Legal Disclaimer
25:35 - StockAgent 3-Phase Execution Architecture
26:50 - Pydantic Guardrails: Enforcing Risk Boundaries in Code
27:55 - Master Routing Contract (StockAgentDecision)
28:25 - Real-Time Yahoo Finance Web Scraper & Penny Stock Purge
29:50 - Assembling the Autonomous Agent Loop (run_stock_agent)
30:16 - Live Test 1: Active Market Screener (SpaceX, NVDA, NU)
31:02 - Live Test 2: High-Momentum Breakout Strategy
31:39 - Live Test 3: Conceptual Risk Evaluation (Why Purge Penny Stocks?)

💡 KEY TAKEAWAYS & WHAT YOU WILL MASTER:
1. Zero-Cost Inference: Run dozens of reasoning and tool-calling loops without worrying about per-token API bills.
2. Live Data Grounding: How combining real-time web scraping with local SLMs eliminates cutoff date hallucinations.
5. Autonomous Agent Architecture: How to structure state loops, tool routing, and agent self-reflection.
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🎙️ AUTHOR & PRODUCTION DISCLOSURE:
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• Produced by: MysteryBytes Studio
• Notebook Author & Execution: All interactive Jupyter notebooks, custom agent architectures, and code executions shown in this video were designed, built, and run by Abhishek Saxena.
• Audio & Voiceover: The accompanying spoken narration and audio walkthrough were produced by MysteryBytes Studio using AI Text-to-Speech (TTS) technology.
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