Start with a market idea in plain English. Build the smallest useful workflow, make every rule visible, and add complexity only when the evidence justifies it.
FIG. 01 · Level 01
Research prompts, checklists, spreadsheets, and tools that work without programming.
FIG. 02 · Level 02
TradingView scripts and small automations generated with AI and explained line by line.
FIG. 03 · Level 03
Market data, backtesting, broker APIs, logs, alerts, and explicit risk controls.
FIG. 04 · Level 04
Research and execution agents with structured decisions, permissions, and kill switches.
A step-by-step tutorial on downloading the FinGPT sentiment LoRA, scoring financial headlines, combining sentiment with a momentum filter, and backtesting the results honestly.
A hands-on guide to building your first reinforcement-learning trading bot using FinRL, including environment setup, PPO and SAC training, and comparison to buy-and-hold.
A step-by-step Freqtrade tutorial for beginners. Learn how to install, configure, write a Python strategy, backtest, and run your first crypto trading bot in dry-run mode.
A hands-on guide to building a zero-cost quantitative research stack using OpenBB, Python, and pandas. Pull real market data, build signals, and avoid common pitfalls.
A hands-on guide to installing the QuantConnect Lean CLI, creating your first algorithm project, running local backtests, and paper trading from your own machine.
Step-by-step tutorial to build an autonomous trading agent using Claude Code Routines and the Alpaca API. Includes complete code, guardrails, and deployment tips.