Data & research7 min read
Beginner
Data engineering
The data-quality checklist for trading research
Bad timestamps, duplicated bars, missing sessions, and inconsistent symbols can create an edge that exists only in the dataset.
Read articleBack to knowledge
Topic / Data engineering
Data engineering
Collecting, cleaning, aligning, and versioning the data behind a research process.
6 guides
Explore this topic.
Data & research7 min read
Intermediate
Data engineering
Bars, ticks, and choosing data resolution
Match the data resolution to the strategy's decisions so the backtest does not invent precision the live system cannot use.
Read articleData & research6 min read
Intermediate
Data engineering
Missing data, outliers, and bad timestamps
A practical process for finding the data defects that silently change signals, session filters, and backtest outcomes.
Read articleEngineering7 min read
Beginner
Python & tooling
A practical Python research project structure
Keep data, experiments, reports, and production candidates separate so your research can be reproduced months later.
Read articleData & research8 min read
Advanced
Machine learning
Feature engineering without leakage
Create useful features from trading data while keeping the future out of the input and the live pipeline reproducible.
Read articleMT5 setup7 min read
Beginner
MT5 setup
Broker symbol specifications that change your EA
Tick value, point size, volume steps, trading sessions, and stop levels can differ across brokers even when the symbol name looks familiar.
Read articleDiscuss data engineering
Ask a setup question, share a backtest, or compare notes with other algorithmic traders. Use a display name; your email remains private.
Create an account or sign in above to join the conversation.
Loading discussion…