Below is a comprehensive, real-world list of the skill sets and infrastructure considerations you’ll likely need to bring your ambitious system to life. I’ll break it down into major categories, then detail why each is important. Keep in mind, you don’t necessarily have to master every skill personally—some might be better handled by specialized teammates, or by using existing services/partnerships.
1. Core Programming & Software Engineering
- Programming Languages
- Python is the de facto standard for data science, machine learning, and rapidly prototyping AI-driven systems. You’ll use it for:
- Data ingestion & web scraping
- Writing and training (or fine-tuning) ML models
- Integrating with APIs (brokerage, social media, etc.)
- JavaScript (or TypeScript) might come into play if you build a web dashboard or user interface.
- Architecture & Design Patterns
- Understanding how to structure a modular application with microservices or well-defined layers (data ingestion, NLP, orchestration, UI).
- Knowledge of event-driven architectures (message queues, pub/sub) can help manage real-time data flows.
- Databases & Storage
- SQL for structured data (e.g., user profiles, trades).
- NoSQL or a document store (MongoDB, Elasticsearch) for large volumes of unstructured text/social media data.
- Potentially object storage (AWS S3, MinIO) for storing raw dumps or backups of large text corpora, training data, etc.
- API & Integration
- Experience building REST or GraphQL APIs if you want your data to be accessible by a web front-end or other services.
- Integrating with brokerage APIs (Alpaca, Interactive Brokers, Binance, etc.) for automated trading.
2. Data Engineering & Pipeline Management
- Data Ingestion & Scraping
- Familiarity with web scraping frameworks (Requests, BeautifulSoup, Selenium, Playwright) and with using official APIs (Twitter/X, Reddit, YouTube, etc.) to ingest real-time data.
- Knowledge of rate limits and API terms of service to avoid bans or legal issues.
- Real-Time Data Streaming
- Tools like Apache Kafka, RabbitMQ, or AWS Kinesis to handle continuous streams of social media data in near real-time.
- Experience with batch processing vs. stream processing (e.g., Spark Streaming, Flink) if you plan to scale up.
- ETL (Extract, Transform, Load)
- Designing workflows to clean, normalize, and store data.
- Potentially building multi-stage pipelines (e.g., raw scrape -> basic filtering -> advanced NLP -> storage -> alert system).
- Scaling & Optimization
- If your pipeline handles millions of messages or posts per day, you need to optimize ingestion and storage to prevent bottlenecks and skyrocketing costs.
3. ML/NLP & “LLM” Expertise
- Foundational NLP
- Text preprocessing (tokenization, stopwords, lemmatization) and basic machine learning (classification, sentiment analysis).
- Tools/libraries: spaCy, Hugging Face Transformers, NLTK, scikit-learn.
- LLM Usage or Fine-Tuning
- If you plan to build your own LLM from scratch, you’ll need:
- Access to high-end GPU clusters (e.g., A100 or H100 instances).
- Deep knowledge of transformer architectures, training pipelines, data curation, etc.
- Alternatively, many teams use pre-trained or hosted LLMs (OpenAI, Azure, Anthropic, or open-source LLaMA-2 with fine-tuning).
- Understanding prompt engineering: crafting effective prompts for summarization, classification, or “agent” tasks.
- Model Evaluation & Maintenance
- Continual updates: financial and social media language evolves quickly (new tickers, slang, acronyms).
- Periodic re-training or fine-tuning if you want your model to stay accurate and reduce “drift.”
- Specialized Finance Models
- For better accuracy on stock/crypto sentiment, you might fine-tune on specific corpora (e.g., financial news, earnings call transcripts).
- Agent Frameworks
- Tools like LangChain, Haystack, or custom frameworks for building multi-step reasoning agents.
- Understanding how to chain LLM calls, implement memory/state management, and set up “guardrails” to reduce hallucination.