Articles about algorithmic trading. How to properly create and validate trading strategies.
The basics: what it is, tools, data and how to create strategies
Complete guide: professional platforms (TradeStation, MultiCharts, AmiBroker), capital by market, technical requirements, and the reality of the industry.
Python vs C++, MetaTrader vs NinjaTrader, broker APIs and essential infrastructure. The complete guide to choosing your tech stack.
Data types (tick, OHLC), free vs premium providers, deadly biases like survivorship bias, and how to choose data for your backtest.
Setup, Trigger, Entry, Stop Loss, Take Profit and Position Sizing: the building blocks that separate winning strategies from losing ones.
The complete process: from generating ideas to coding. Academic sources, hypothesis formulation, edge and best practices.
Why backtests lie and how to measure real performance
Why your backtest lies: look-ahead bias, survivorship bias, overfitting, data snooping and slippage. Learn to detect the biases.
Sharpe Ratio, Profit Factor, Win Rate, Sortino, Calmar and Expectancy. What each metric means and what values are realistic vs suspicious.
Complete guide to risk-adjusted metrics. Formulas, interpretation, limitations and which to prioritize based on your trading profile.
Types of drawdown, how to calculate it, Ulcer Index, psychology of pain and kill switch protocol to protect your capital.
How to know if your strategy will work in live trading
The 5-step framework that separates profitable strategies from overfitting: statistics, optimization, Monte Carlo, Walk Forward and Live Tracking.
How to detect and avoid overfitting in your algorithmic trading strategies.
The most rigorous validation method: continuous optimization with out-of-sample data.
Upload your backtest and get professional analysis: advanced statistics, Monte Carlo, Walk Forward and more.
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