Research and
trading guides.
Set up a market scan, build a trading bot, or learn how to read a backtest. Practical guides with examples you can use.
- AI Market Scanner: Set Up Stock and Crypto ResearchSet up an AI market scanner for stocks and crypto. Choose markets, sources, schedules and reports, then decide when your agent reviews or acts on a finding.
- AI Trading Agents: Research, Market Scans and AutomationLearn how AI trading agents research markets, explain findings and automate trades. Set permissions, evaluate strategies and choose when to review an action.
- A Pine Script Alternative: Backtest Without CodeBacktest a trading strategy without writing Pine Script. Describe the rules, review the generated Python, and inspect the historical trades and assumptions.
- What Is Backtesting? A Plain Guide for TradersBacktesting means testing a trading idea against past market data before taking it into live markets. Here's how it works, why it matters, and where it misleads.
- What Is Maximum Drawdown? (With a Worked Example)Maximum drawdown is the largest peak-to-trough fall in an account before a new high. Here's the formula, a worked example, and why it decides if a strategy is livable.
- CAGR, Explained Simply (Compound Annual Growth Rate)CAGR is the smooth yearly rate that gets you from a starting value to an ending value over several years. Here's the formula, a worked example, and why traders use it.
- What Is the Sharpe Ratio? A Clear GuideThe Sharpe ratio measures return earned per unit of risk. Here's the formula, a worked example, and why a strategy with a lower return can still be the better one.
- What Is Profit Factor? (Trading Metric Explained)Profit factor is gross profit divided by gross loss. Above 1 means a strategy made money. Here's the formula, a worked example, and how to read it without being fooled.
- Win Rate vs. Expectancy: Which Actually Matters?Expectancy is the average amount you can expect to make per trade. It's why a strategy can win less than half its trades and still be profitable. Formula and example inside.
- Overfitting: Why a Great Backtest Can LieOverfitting is when a strategy is tuned so tightly to past data that it captures noise, not signal - and fails live. Here's how to spot it and avoid being fooled.
- Survivorship & Look-Ahead Bias in BacktestingSurvivorship bias and look-ahead bias are two ways a backtest quietly cheats - using winners-only data or future information. Here's how each works and how to avoid them.
- Why Backtests Don't Match Live TradingA strategy can ace its backtest and lose money live. Here are the real reasons - costs, slippage, overfitting, regime change - and how to close the gap before you trade.
- How to Backtest a Dip-Buying Strategy (Step by Step)A step-by-step guide to backtesting a buy-the-dip strategy: defining the rule, choosing data, reading the results, and avoiding the common mistakes.
- How to Build a Crypto Trading Bot Without CodingBuild a crypto trading bot without coding: define your rules, review the AI-generated strategy, backtest, paper trade, and connect Discord or a signal bot.
- How to Backtest a Trading Strategy Without CodingYou don't need Python to backtest a trading idea anymore. Here's how to test a strategy just by describing it - no code, no spreadsheets - and what to watch out for.
- How to Backtest a Trading Strategy With AIUse AI to turn trading rules into a Python backtest. Learn how to review the generated code, historical data, trades, and costs before interpreting the result.
- How to Backtest a Trading Strategy With ChatGPTBacktest a trading strategy with ChatGPT: define the rules, prepare historical data, run the Python code, and review the trades, timing, and cost assumptions.
- Free Crypto Backtester: How to Test Strategies at No CostLooking for a free way to backtest crypto strategies? Here's what to expect from free backtesting tools, the trade-offs to watch, and how to test an idea just by describing it.
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