Blog
Articles, guides and insights on algorithmic trading, backtesting strategies, quantitative analysis, and data quality.
1–9 of 130 articles
Tick vs Minute Data: Which One Your Backtest Needs
Tick data records every trade or quote; minute bars compress them into OHLCV. See what each keeps, what it loses, and which one fits your backtest.
Volatility Regime Detection: Methods, Inputs and Pitfalls
How to detect volatility regimes: the inputs, the models (HMM, GARCH, clustering, change points), how to avoid look-ahead bias, and where detectors fail.
MT5 Custom Symbols: Create, Import and Test Your Data
How to create a custom symbol in MT5, set it up before importing, fix time shift and time zone problems, and backtest it in the Strategy Tester.
Position Sizing Backtesting: How to Test Size Rules Properly
Learn how to backtest position sizing: fixed, percent-of-equity and volatility methods, margin limits, costs, and the data quality that keeps results honest.
Rolling Window Backtest: How to Build One That Holds Up
Learn how a rolling window backtest works, how to pick window length and step, avoid look-ahead bias, and read results across overlapping periods.
Why 'MetaTrader Robot' Is Trending Again: A Practitioner's Guide to Evaluating EAs in 2026
Search interest in MetaTrader robots is spiking alongside demand for intraday data and EURUSD strategies. Here's a grounded, data-driven framework for evaluating any EA before you ever consider running it on a live account.
Intraday Seasonality: How to Find, Test and Trade It
Intraday seasonality explained for quants: the U-shape, closing momentum, why patterns fade after costs, and how to test time-of-day effects on clean minute data.
Dataset Version Control for Quant Traders: A Practical Guide
How to version datasets so every backtest can be reproduced: tools, folder layout, hashes, changelogs and the market-data traps that break results.
Survivorship Bias in Backtesting: How to Spot and Fix It
Survivorship bias makes backtests look better than reality. See how much it inflates returns, real examples, and a checklist to test your data.