From Trading Idea to Rule Set: What a Strategy Really Needs
A trading idea is not yet a rule set. Learn what building blocks a complete strategy needs and where gaps often get filled without you noticing.
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Insights on strategy development, backtesting and algorithmic trading. Education, not investment advice.
Robustness Tests for Trading Strategies
Robustness tests for trading strategies: the types StrategyQuant offers, how an example funnel links them, and what the results actually show.
Generate Trading Strategies Automatically
How StrategyQuant generates trading strategies automatically, from the first random run to robustness testing, and why only a few candidates survive.
Overfitting in Trading Strategies: When a Backtest Looks Too Good
Overfitting in trading strategies can't be proven by one metric alone. Here's how to check your backtest in StrategyQuant X, with the method's real limits.
In-Sample vs Out-of-Sample Trading: Why It Matters
In-sample and out-of-sample trading data explained: what separates them, what the OOS/IS ratio in StrategyQuant X shows, and where its limits are.
Strategy Correlation in Portfolio: How It Cuts Risk
Two solid strategies can still make a weak portfolio if they correlate. Here's how to check strategy correlation in your portfolio with StrategyQuant X.
Algo trading: How strategies are built and tested
Quantitative trading explained: two ways to build a strategy, why optimization comes before robustness testing, and what Monte Carlo analysis can show.
Expert Advisor: What an EA Is and How It Works
What is an expert advisor? Learn how an EA is built from a StrategyQuant strategy, no coding required, and tested in MetaTrader.
Trading Strategy: What It Is and How It's Built
What makes up a trading strategy? Learn the building blocks behind algorithmic trading strategies and how automated generation differs from manual design.
Monte Carlo Simulation in Trading: What It Shows
Learn what a Monte Carlo simulation in trading is, what it changes about a backtest, and where the method reaches its limits.
Python in Algorithmic Trading: When Do You Need to Code?
Do you need to code to use StrategyQuant? Here's how algo trading Python fits as an optional add-on to the no-code core.
Drawdown in Trading: What It Measures and What It Doesn't
Drawdown in trading explained simply: what this metric measures, how StrategyQuant X calculates it, and what it can't predict.
Calculate Position Size: Shape Your Risk Profile
Calculate position size: why the model you choose changes not just returns but the whole risk profile of your strategy.
Systematic Trading: Decision Before the Trade
Systematic trading means rules are set and tested before the trade, whether you apply them manually or with automation.
Stock backtesting: Understanding the Stockpicker engine
Stock backtesting works differently than testing a single symbol. Learn the limits of the Stockpicker engine in StrategyQuant X.
Automated Trading: Where the Automation Actually Stops
Where does automation in StrategyQuant X actually stop? See what setup, licensing, hardware, and oversight still need your attention.
Algo Trading Programming: Write Your Own Rules in StrategyQuant X
What does algo trading programming really involve? Learn how to build, test and automate your own rules in StrategyQuant X.
Algo Trading Without Coding: Where the Line Really Is
Do you need to code for algorithmic trading? See exactly where the no-code path ends, where code comes back in, and what it still requires from you.
AI Trading Strategies: What Machine Learning Really Means
See what 'AI trading strategies' really means in StrategyQuant X: a rule search plus a prompt-based assistant, not a market predictor.
What Is Walk-Forward Analysis? A Plain-Language Guide
Learn what walk-forward analysis is, how it works in StrategyQuant, and what its results can and cannot tell you about a trading strategy.