Learn Algorithmic Trading
Algorithmic Trading Fundamentals
Learn algorithmic trading by doing. Use SMA and RSI indicators inside portfolio strategies with Aurora, define trading conditions, build a first buy strategy, run portfolio backtests, and learn how paper deployment works.
Difficulty: BEGINNER
- Explain how indicators, conditions, strategies, and portfolio backtests fit together
- Use a 10-day SMA and an RSI indicator inside portfolio drafts through Aurora
- Define SMA-to-price and RSI threshold conditions inside SPY strategies
- Create a paper portfolio and a first AMZN buy strategy
- Run a buy-and-hold backtest and compare a strategy with a SPY benchmark
- Describe paper deployment and the checks to make before considering live trading
AI Agents from Scratch
A practical, ground-up introduction to AI agents. Understand the ReAct loop, tools, orchestration, memory, and evaluation, then build real agents using Aurora Trading Agent platform as your lab.
Difficulty: BEGINNER
- Explain the difference between a bare language model, a chatbot, and an AI agent
- Describe the ReAct loop (Thought → Action → Observation) and why it makes agents agentic
- Design tool-calling workflows and understand why tools, function calling, and MCP servers are all the same core concept
- Use autonomy controls (whitelists, approvals) to safely run agents in production
- Connect memory, scheduling, and subagents into a full autonomous workflow
- Build traces and evaluators (including LLM judges) to measure agent quality
Your First Software Engineering Job
Build a resume from your own work, get actionable feedback, prepare a Codex job-search prompt, and practice technical interviews with a simple plan.
Difficulty: BEGINNER
- Create an editable resume using your education, experience, and projects
- Use a transparent rubric to prioritize resume improvements
- Prepare a truthful, reviewable job-application workflow with Codex
- Organize LeetCode practice and explain a solution under a timer