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If your StrategyQuant data is set up wrong, your entire trading system is doomed from the start. This chapter breaks down the most common setup mistakes—like pip size, point value, and JPY pair errors—that cause fake-perfect equity curves. Learn how to configure everything correctly so your strategies are realistic, accurate, and built to last. Skip…
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Embarking on the path of algorithmic trading success means more than chasing flashy ideas — it’s about discipline, data, and direction. In Chapter 1, we unveil the “No-Nonsense” workflow: a systematic process used by top quant firms to build diversified portfolios of robust strategies. Rather than searching for a mythical holy grail, you’ll learn to…
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When developing Forex strategies for prop firm challenges, backtesting with both In-Sample and Out-of-Sample (OOS) data is essential to avoid overfitting. Using tools like StrategyQuant, ensure your strategy trains on data from multiple periods, such as 2009–2022, and validates on OOS data like 2018–2022. Test your strategy on recent market conditions as well as older…
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Artificial intelligence is revolutionizing trading in the world’s most popular financial market. This article explains how AI-driven trading bots work and why they outperform traditional rule-based Expert Advisor (EA) robots. While EAs follow static strategies, AI models learn from vast data, adapt to changing market conditions, and continuously improve their predictions. We highlight key differences…