Learn the concepts, inspect the tools, reproduce the builds, and test every idea before capital is involved.
A sober look at what AI trading can actually deliver for retail traders, what it cannot fix, and how to build realistic expectations before spending money.
Learn how to spot AI trading scams before you lose money. We break down the most common warning signs with real examples from 2025 and 2026.
We cut through the hype around AI trading bots. Here's what actually works for retail traders in 2026, what doesn't, and how to test tools without blowing up your account.
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.
New to AI trading? Learn what AI trading really is, how it works, common risks, and how to choose your first tool without getting scammed.
A practical 60-day checklist that helps algorithmic traders move from paper trading to live capital safely, with readiness checks, sizing rules, and kill switches.
Discover practical AI day trading strategies, including momentum, mean reversion, and breakout setups, plus the tools and risk rules you need to survive.
A practical, no-hype guide to validating AI trading strategies: avoid overfitting, use walk-forward and out-of-sample testing, account for costs, and spot repainting indicators.
Compare the top AI trading platforms for stocks, crypto, and forex. See pricing, key features, and who each platform is best for.
A hands-on comparison of four popular algorithmic trading stacks. Learn which platform fits your skill level, budget, asset classes, and migration path.
A hands-on review of the most popular open-source AI trading projects on GitHub, including TradingAgents, OpenBB, freqtrade, FinRL, and QuantConnect Lean.
We tested the most popular AI trading tools for stocks and crypto. Here's an honest comparison of Trade Ideas, TrendSpider, Tickeron, and more.
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.
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.
Build your first algorithmic trading bot with Python, EMA crossover signals, and Alpaca's free paper trading API. No hype, no paid tools, just honest step-by-step code.
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.