teaches learners how to evaluate, compare, and use modern AI platforms for stock screening, sentiment analysis, and automated financial research. The course explores tools across TradingView, FinRL, QuantConnect, ChatGPT, MT5, and enterprise AutoML systems, giving participants hands-on experience building screening models and AI-driven workflows. With a strong focus on signal generation, NLP interpretation, platform selection, and toolchain integration, learners gain practical capabilities for real-market analysis. Graduates leave able to design full AI pipelines, benchmark platforms, and deploy insight-driven stock screening systems with transparency and confidence.
Growth
Skills & learning objectives
There are 9 modules in this class
- The Rise of AI in Financial Trading and AnalysisUnlock this class to watch3m 31s
- Key Functions of AI in Market Analysis: Signal Generation, Forecasting, Sentiment MiningUnlock this class to watch3m 56s
- Human vs. Machine Reasoning in Trading EnvironmentsUnlock this class to watch2m 35s
- Framework for Evaluating AI Platforms: Transparency, Accuracy, CustomizabilityUnlock this class to watch2m 33s
- Comparison Matrix: AI Tool vs. Use Case vs. Required Skills vs. CostUnlock this class to watch2m 52s
- TradingView with LLM Plugins: Building Smart Dashboards and AlertsUnlock this class to watch2m 26s
- FinChat, Perplexity AI, and Bard: Conversational Interfaces for Chart ReadingUnlock this class to watch2m 27s
- Pattern Recognition AI: Tickeron, TrendSpider, and ChartMillUnlock this class to watch2m 37s
- Real-Time Use Case: Setting Up a Multi-Signal TradingView WorkspaceUnlock this class to watch2m 32s
- Limits of Interactive Tools: Black-Box Warnings and Hallucination RisksUnlock this class to watch2m 14s
- FinRL and ElegantTrader: Reinforcement Learning for Dynamic PortfoliosUnlock this class to watch3m 31s
- Backtrader vs. Zipline: Historical Strategy Testing EnvironmentsUnlock this class to watch3m 7s
- XGBoost, CatBoost, and AutoTS: Tabular Models for Alpha ExtractionUnlock this class to watch3m 20s
- Pipeline Design: From Data Ingestion to Backtest Reports in JupyterUnlock this class to watch2m 56s
- Case Study: Daily Stock Screener with Custom ML Models (Python)Unlock this class to watch2m 43s
- Quantitative Performance Metrics & Portfolio Risk DashboardsUnlock this class to watch2m 45s
- QuantConnect (LEAN): Algorithmic Research on Institutional DataUnlock this class to watch2m 3s
- MetaTrader 5 (MT5) and Python Bridge: From Indicators to ExecutionUnlock this class to watch1m 52s
- QuantRocket and Interactive Brokers: Data Handling + Live OrdersUnlock this class to watch1m 40s
- Alpaca, Tradier, and OANDA APIs: Broker-Agnostic Strategy DeploymentUnlock this class to watch1m 36s
- Case Study: Building and Executing a Momentum Strategy in QuantConnectUnlock this class to watch1m 39s
- Platform Considerations: Cost, Brokerage Support, and Strategy ComplexityUnlock this class to watch2m 16s
- FinBERT and VADER: Comparing Pretrained Models for Earnings CallsUnlock this class to watch1m 54s
- Newsflow Integration: AlphaSense, RavenPack, and Custom API ParsingUnlock this class to watch1m 59s
- Reddit, Twitter, and StockTwits Data: Signal vs. Noise in Retail SentimentUnlock this class to watch1m 58s
- Design Walkthrough: LLM-Powered Sentiment Dashboard for U.S. EquitiesUnlock this class to watch2m 9s
- Ethical Risks: Manipulation, Bias in Language Models, and MisinformationUnlock this class to watch1m 41s
- H2O.ai and DataRobot: Enterprise-Scale AutoML for Price PredictionUnlock this class to watch3m 26s
- Google AutoML & Vertex AI: Quick Prototyping for Financial Time SeriesUnlock this class to watch2m 31s
- Akkio, Obviously AI, and Orange: No-Code Tools for Trading InsightsUnlock this class to watch2m 35s
- Comparison Table: Flexibility vs. Simplicity vs. Model AccuracyUnlock this class to watch2m 3s
- Use Case: Deploying a No-Code Signal Model on Currency DataUnlock this class to watch1m 49s
- Model Interpretability and Explainability: SHAP, LIME, and Attention MapsUnlock this class to watch3m 4s
- IEX Cloud, Polygon.io, Alpha Vantage: Real-Time and Historical Market Data APIsUnlock this class to watch4m 57s
- Combining OpenAI with Market APIs: ChatGPT for Earnings Recaps and Risk AlertsUnlock this class to watch3m 18s
- Toolchains: Connecting GPT, TradingView, and Alpaca for Event-Driven TradingUnlock this class to watch2m 18s
- Risks and Controls: Latency, Rate Limits, and Automation HazardsUnlock this class to watch2m 29s
- Security and Access Control ChecklistUnlock this class to watch3m 6s
- Designing a Tool Stack: Aligning Goals with Platform CapabilitiesUnlock this class to watch2m 55s
- Deployment Options: Cloud Dashboards, Browser Apps, and IDE PipelinesUnlock this class to watch2m 50s
- Compliance and Monitoring: Broker Audits, Logging, and Model RiskUnlock this class to watch2m 8s
- Ethical AI Use in Trading: Attribution, Overfitting, and Fair AccessUnlock this class to watch2m 7s
- Data Governance Tools and Version ControlUnlock this class to watch1m 42s
- Capstone 1: Build an End-to-End Trading Pipeline with Sentiment + Price ModelsUnlock this class to watch1m 50s
- Capstone 2: Compare Two Platforms (FinRL vs. QuantConnect)Unlock this class to watch1m 24s
- Capstone 3: Design an Interactive Trading Interface with NLP FeedbackUnlock this class to watch2m 39s
- ClosingUnlock this class to watch1m 16s
This class is part of the following certifications
Certified Portfolio & Fund Manager (CPFM)
63 ClassesUnlimited interactive exercises
Unlock Certificate
Certificate in AI-Powered Financial Analysis (CAFA)
56 ClassesUnlimited interactive exercises
Unlock Certificate
ClassFAQs
The course aims to teach learners how to apply AI, machine learning, and NLP to equity screening and investment research, combining structured financial data with unstructured text to build intelligent stock-screening systems.
The course is designed for analysts, traders, data scientists, and investment professionals seeking to integrate AI-enhanced research tools, sentiment models, and automated screening into their equity-analysis workflows.
The module covers tokenization, embeddings, topic modeling, entity extraction, and sentiment scoring applied to filings, earnings-call transcripts, and financial news.
The course explains LLM-based screeners, research-assistant agents, prompt engineering, hybrid factor-LLM screening, and automated thesis generation for investment research.
Learners will learn to validate ranking models, detect bias and drift, apply explainability tools, implement governance and compliance standards, and build a production-style AI stock-screening dashboard.
Explore
More from trading
Reviews
What our learners say
4.6
0 reviews