Algo & Quant
Advanced6 min read
Algorithmic Trading Guide: From Idea to Running System
Automation does not create an edge, it enforces one. Most of the work is engineering and risk control rather than signal discovery.
13 in-depth guides
Algorithmic and Quantitative Trading Guides
Automation does not create an edge, it enforces one. These guides cover the engineering and statistics needed to run a strategy as code without fooling yourself.
Advanced guides
Assumes comfort with statistics, code, or derivatives.
Algo & Quant
Advanced5 min read
Alpha Research: Finding and Validating a Signal
Alpha research is the discipline of measuring whether a feature predicts returns at all, before any of it becomes a strategy.
Algo & Quant
Advanced5 min read
Broker APIs for Automated Trading: What to Check Before You Build
The API you build on determines what your system can do and how it fails. Most of the important properties are not in the marketing material.
Algo & Quant
Advanced5 min read
Execution Algorithms: TWAP, VWAP, and Getting Filled Well
Execution is the part of trading where a small improvement is guaranteed to help, because every strategy pays these costs on every trade.
Algo & Quant
Advanced5 min read
Factor Investing Explained: The Documented Return Drivers
Factors are the systematic characteristics that have explained differences in returns. Knowing them tells you what your strategy is actually exposed to.
Algo & Quant
Advanced5 min read
High-Frequency Trading Explained: What It Is and Is Not
High-frequency trading is an infrastructure business. The strategies are simple; the barrier is measured in microseconds and millions of dollars.
Algo & Quant
Advanced5 min read
How to Build a Trading Bot: A Practical Walkthrough
Building a bot is mostly software engineering. The trading logic is often the smallest and simplest part of the system.
Algo & Quant
Advanced6 min read
Machine Learning for Trading: Where It Helps and Where It Fails
Financial data has a low signal-to-noise ratio and a non-stationary distribution, which is close to the worst case for machine learning.
Algo & Quant
Advanced5 min read
Market Data Guide: Types, Sources, and What You Actually Need
Most traders buy data they do not need and skip the data quality checks they do. This guide covers both sides.
Algo & Quant
Advanced5 min read
Portfolio Optimisation: Turning Signals Into Positions
Optimisation converts forecasts into positions. Its central difficulty is that the inputs are estimated with error, and optimisers amplify error.
Algo & Quant
Advanced5 min read
Python for Trading: Tools, Structure, and Pitfalls
Python is the default language for trading research because of its data ecosystem. The traps are subtle and mostly involve accidentally using future data.
Algo & Quant
Advanced5 min read
Quantitative Trading Explained: What Quants Actually Do
Quantitative trading is the application of statistical method to markets. The distinguishing feature is not mathematics but the discipline of hypothesis testing.
Algo & Quant
Advanced6 min read
Strategy Development Process: From Hypothesis to Live Trading
A development process is what stops you from trading the first idea that produced a good backtest. Most of its value is in what it rejects.
Every guide in this category
- Algorithmic Trading Guide: From Idea to Running System
- Alpha Research: Finding and Validating a Signal
- Broker APIs for Automated Trading: What to Check Before You Build
- Execution Algorithms: TWAP, VWAP, and Getting Filled Well
- Factor Investing Explained: The Documented Return Drivers
- High-Frequency Trading Explained: What It Is and Is Not
- How to Build a Trading Bot: A Practical Walkthrough
- Machine Learning for Trading: Where It Helps and Where It Fails
- Market Data Guide: Types, Sources, and What You Actually Need
- Portfolio Optimisation: Turning Signals Into Positions
- Python for Trading: Tools, Structure, and Pitfalls
- Quantitative Trading Explained: What Quants Actually Do
- Strategy Development Process: From Hypothesis to Live Trading
Other categories
Trading Foundations
Plain-English foundations for new traders: what a trading strategy is, how markets work, order types, timeframes, and how to build a first rule-based plan.
Trading Strategy Types
In-depth guides to every major trading strategy type, from trend following and mean reversion to pairs trading, arbitrage, market making, and event-driven trading.
Indicators & Signals
How technical indicators are calculated, what they actually measure, and how to turn moving averages, RSI, MACD, ATR, and volume tools into tested trading rules.
Chart Patterns & Price Action
Candlestick patterns, classical chart patterns, support and resistance, and price action structure, each with objective definitions you can actually code and backtest.
Strategies by Asset Class
The strategies that actually fit each market: stocks, ETFs, options, futures, forex, crypto, commodities, bonds, and index products, with costs, hours, and leverage compared.
Risk & Money Management
Position sizing, stop losses, drawdown control, correlation risk, and the maths of ruin, explained so you can size trades with a formula instead of a feeling.
Backtesting & Validation
Backtesting methodology that survives contact with live markets: look-ahead bias, survivorship bias, walk-forward analysis, Monte Carlo testing, and realistic cost modelling.
Market Mechanics & Execution
Order books, matching engines, order types, spreads, slippage, market makers, settlement, and the trading session structure that decides what your fill really costs.
Trading Psychology & Process
The behavioural side of trading: loss aversion, revenge trading, overconfidence, journaling, routine design, and the process controls that keep rules intact under stress.