Quantitative Analysis · Data Science · Machine Learning

Author archive for quant

  • Why PnL Correlation Fails for Same-Market Strategy Portfolios

    PnL Correlation vs. Overlap Detection – A Better Way to Think About Diversification? When building a portfolio of trading...

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  • Swap Fees – The Hidden Cost That Breaks Trading Systems

    When traders talk about costs, the conversation almost always revolves around commissions and spreads. Systematic traders may go a...

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  • Why Most Machine-Learning Trading Strategies Fail

    For many quantitative traders, the journey begins with a sense of optimism: you build a machine-learning classifier, train it...

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  • Adaptive Position Sizing in Algorithmic Trading

    Introduction One of the most important challenges in algorithmic trading is controlling losses during drawdowns while maintaining upside potential....

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  • The Diminishing Benefits of Diversification

    Diversification is a core principle in portfolio management, aimed at reducing risk by spreading investments across multiple instruments. However,...

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  • Reducing Position Sizing During Drawdowns

    Managing risk is paramount in live trading, especially when your equity curve enters a drawdown phase. One of the...

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  • Skewness in Trading Strategy PnL Distributions

    In trading, analyzing the distribution of profit and loss (PnL) can offer valuable insights into the performance characteristics and...

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  • Reward: Risk vs. Accuracy in Trading Strategies

    In the world of trading and investing, two crucial metrics that determine the effectiveness and profitability of a trading...

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  • Precision vs. Recall in ML Classification

    In financial machine learning, particularly when predicting between three classes like “buy,” “sell,” and “hold,” the choice between optimizing...

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  • Calculating Value-at-Risk using Historical Simulation

    The Historical Simulation Method is a straightforward approach to calculate Value at Risk (VaR) by using historical market data...

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