Build your own AI trading systems with copy-paste code and clear explanations.
Why beginners should start with a simple EMA crossover bot before adding complexity, and how to build one with Python, backtest it, and paper trade it safely.
Learn how to register an AI trading agent, publish signals, and copy-trade on AI-Trader's platform using paper trading.
A practical guide to using FreqAI in freqtrade to build adaptive, machine-learning-driven crypto trading strategies.
Learn how to combine OpenBB and Alpaca to build a free, live data pipeline for your Python trading bots and research.
Learn how to run the Avellaneda-Stoikov market-making strategy on Hummingbot. Understand parameters, risks, and how to avoid common mistakes.
Deploy a factor-based trading strategy locally using QuantConnect Lean CLI and Python. A step-by-step tutorial for retail quants.
Get started with Lumibot, a Python framework for backtesting and live trading stocks and options with realistic broker simulation.
Learn how to use Superalgos, an open-source visual platform for building, backtesting, and deploying crypto trading strategies without writing code.
Learn how to use Jesse, a Python crypto algo trading framework with advanced backtesting, machine learning, and live trading features.
Get started with Nautilus Trader, a high-performance Python and Rust event-driven trading platform for backtesting and live trading.
Set up TradingAgents, the popular multi-agent LLM trading framework. Learn to install, configure analyst agents, and run your first backtest.
Understand the full AI trading data pipeline: ingestion, cleaning, feature engineering, storage, validation, and common data quality issues, with Python code.
Learn how to turn an AI trading strategy you saw on YouTube into a real Python backtest. Step-by-step workflow, common traps, and code included.
Take an AI trading strategy from YouTube and paper trade it safely on Alpaca. Setup, data, order logic, tracking, and Python snippets included.
A hands-on walkthrough of the ai-hedge-fund open-source project. Learn how multi-agent LLMs mimic Buffett, Graham, Lynch, and Wood to generate investment signals.
Learn how to backtest a simple SMA crossover strategy in backtrader, plot equity curves, and run walk-forward validation before layering on machine learning.
Build AI trading signals with FinGPT sentiment analysis. Download the LoRA, score headlines, combine with momentum, and backtest 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.
Build your first algorithmic trading bot with Python, EMA crossover signals, and Alpaca's free paper trading API. Step-by-step code included.
AI crypto trading bot tutorial: learn how to install Freqtrade, configure, write a Python strategy, backtest, and run your first 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.
Build a Claude trading bot step-by-step using Claude Code Routines and the Alpaca API. Includes complete code, risk guardrails, and deployment tips.
