Backtesting & Strategy Validation

This article will provide a step-by-step guide to backtesting the Turtle Trading system.

A practical guide to combining the KST indicator with other technical analysis tools to filter signals, improve accuracy, and build a more resilient trading strategy.

Discover why Steve Clark champions simplicity in trading system design, and how this philosophy contributes to more robust and reliable trading outcomes.

An examination of Kristjan Kullamägi’s insistence on thousands of hours of manual backtesting and historical chart study as the foundation for building unshakeable trading conviction.

Walk-forward optimization is a effective technique for building resilient trading systems that can withstand the test of time. It is a process of continuous improvement that helps to ensure that your

Walk-forward optimization is a effective technique for developing robust trading strategies that can adapt to changing market conditions. This is especially true for the Nasdaq-100 (NQ) futures contra

This article will walk through a real-world case study of a walk-forward optimization of a trading strategy. The strategy is a simple moving average crossover system, and it will be applied to the SPY

Walk-forward optimization is an essential tool for traders of individual stocks like Apple (AAPL), as it helps in developing strategies that are robust to the ever-changing market dynamics and news-dr

Traditional backtesting, while a useful first step, has a major flaw: it is static. A strategy that is optimized on a single historical data set is unlikely to perform well in the dynamic and ever-cha

Walk-forward optimization is the gold standard for testing and validating intraday trading strategies. It provides a robust framework for assessing a strategy's performance in a way that closely mimic

The foreign exchange market (Forex) is the largest and most liquid financial market in the world, and the EUR/USD is the most traded currency pair. This high liquidity and constant flow of information

Backtesting and Optimizing Range Bar Strategies for Consistent Profitability Range bar charts present an alternative to time-based charts by creating bars based on price movement rather than elapsed time....

Learn how to properly backtest a trading strategy based on a market internals dashboard. This guide covers data collection, analysis, and interpretation of backtesting results.

Learn how to refine your intraday trading setups by analyzing your trade journal. This article focuses on Tracking Win Rate for Setup Optimization.

A comprehensive guide to backtesting trading strategies to find a statistical edge.

A step-by-step guide to designing, backtesting, and automating an Initial Balance trading system, enabling a more disciplined and statistically robust approach to trading.

Focusing on the performance tuning of time-series databases for live trading, covering indexing strategies, query optimization, and caching mechanisms.

The Dunning-Kruger Effect in Algorithmic Trading: Overfitting and Model Delusion In algorithmic trading, the interplay between competence and confidence is important, yet often misjudged. The Dunning-Kruger effect—wherein traders with limited skill overestimate their ability—manifests prominently in systematic strategy development, particularly through overfitting and model delusion. Understanding the statistical and psychological...

Move beyond simple profit maximization to create more resilient trading strategies. This article details the construction of fitness functions that balance risk and return, covering metrics like Sharpe Ratio, Calmar Ratio, and Sortino Ratio, and the use of Pareto fronts to visualize trade-offs.

Traditional backtesting, while a fundamental component of quantitative strategy development, suffers from a important flaw: it is inherently static. A model is trained and tested on a fixed historical dataset, and its performance is evaluated based on this single pass....

A important examination of the biggest pitfall of using GAs in trading. This article discusses the causes of overfitting, details methods for detection like walk-forward analysis, and provides practical tips for prevention.

A balanced comparison of GAs with other common optimization methods in quantitative finance. This article compares and contrasts GAs with grid search, random search, and gradient-based methods, and provides guidance on when to choose GAs over other techniques.

Traditional backtesting relies on historical data to evaluate the performance of a trading strategy. While this approach is valuable, it has its limitations. The historical record represents only one possible path that the market could have taken. A strategy that...

For quantitative and systematic traders, the cloud offers a effective new edge. This article explores how the cloud's massive computational power is being used to develop and backtest sophisticated alpha signals at a scale that was previously unimaginable.

## The Trader's Time Machine: Backtesting with WebSocket Data Every quantitative trading strategy is born from an idea, a hypothesis about how markets behave. But an idea alone is worthless.

Address the important issue of overfitting when building HMMs. This article will cover techniques for model selection (e.g., AIC, BIC), cross-validation, and out-of-sample testing to ensure the robustness of the HMM and prevent curve-fitting.

The article presents a detailed framework for backtesting AUD/NZD trading strategies that accounts for the unique economic relationship between Australia and New Zealand, focusing on interest rate differentials, commodity influences, and central bank policy divergences. It emphasizes the importance of high-quality data, dynamic transaction cost modeling, and walk-forward validation to ensure realistic and robust strategy performance. The framework enables traders to capture Trans-Tasman trading

A profitable backtest can be dangerously misleading if it doesn't accurately account for gap risk and slippage. This article provides a practical guide for traders and quants on how to properly backtest strategies that involve overnight or weekend holding. Learn about the importance of using high-quality data, modeling realistic slippage, and stress-testing your strategy against historical and simulated gap events.

Algorithmic trading, at its core, is the process of using computer programs to execute trading strategies. For the retail trader, Python has become the language of choice for this endeavor...

Setting a daily loss limit is often a process guided by heuristics and rules of thumb—1% of the account, twice the average daily win, and so on. While these are...