Strategy7 min read
Beginner
Research basics
How to define a trading hypothesis
Turn a vague market belief into a precise, falsifiable question that an algorithmic research process can test.
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Topic / Research hygiene
Research hygiene
Avoiding false discoveries with clear hypotheses, baselines, and honest validation.
15 guides
Explore this topic.
Strategy6 min read
Beginner
Strategy design
Use indicators as measurements, not magic signals
Indicators transform price and volume into measurements. Learn how to use them without confusing a formula with an edge.
Read articleData & research6 min read
Beginner
Performance analysis
Win rate, profit factor, and payoff ratio
Read common trading metrics together so one attractive number cannot hide an unstable or expensive system.
Read articleStrategy8 min read
Advanced
Strategy research
Regime awareness without overfitting
Use market conditions to understand where a system works without building a fragile switch that only recognises the past.
Read articleData & 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 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 articleData & research8 min read
Intermediate
Research hygiene
Look-ahead bias and data snooping
The fastest way to improve a backtest is often to accidentally use information that would not have been known at the time.
Read articleData & research7 min read
Intermediate
Validation
How to design an honest out-of-sample test
Freeze the rules, protect the holdout, and evaluate the strategy in a way that resembles how it will face the future.
Read articleEngineering8 min read
Intermediate
Python & tooling
Build reproducible backtests with Python
A reproducible backtest gives the same answer from the same inputs and makes disagreements easier to diagnose.
Read articleEngineering6 min read
Beginner
Engineering discipline
Version control for strategy research
Use commits, experiment notes, and configuration hashes to know exactly which code produced a trading result.
Read articleData & research8 min read
Intermediate
Machine learning
Machine learning for trading: a realistic starting point
ML can help with classification, ranking, or regime description, but it does not remove the need for clean data and honest validation.
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 articleData & research8 min read
Advanced
Machine learning
Walk-forward validation for machine-learning models
Use rolling training and validation windows to test whether an ML pipeline adapts without learning from the future.
Read articleData & research6 min read
Intermediate
Machine learning
When not to use machine learning
Sometimes a clear rule, better data, or better execution solves the problem more reliably than a model.
Read articleEngineering6 min read
Beginner
Process
A safe process for changing strategy parameters
Treat every input change as a new experiment so a live system does not drift away from the evidence that justified it.
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