yfinance
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Supervised machine learning – Linear regression
Investment decisions demand a blend of precision, speed, and foresight. Quantitative developers play a pivotal role in enabling financial firms to stay ahead by designing predictive models that extract actionable insights from vast amounts of data. Leveraging Python’s capabilities, one…
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Supervised machine learning – Decision trees
Quantitative developers are pivotal in leveraging data to make informed investment decisions. Among the array of analytical tools available, decision trees are a powerful, interpretable, and versatile method for solving financial problems. Their ability to model decisions based on structured…
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Portfolio optimisation – Linear algebra
Making investment decisions based on intuition or limited data can often lead to suboptimal outcomes. Quantitative analysis revolutionises how investors and portfolio managers approach asset allocation, enabling precise, data-backed strategies that optimise returns while controlling risks. One example of this…
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Statistical arbitrage – Cointegration – Pairs
Statistical arbitrage (or stat-arb) has become a powerful strategy for quantitative traders to exploit price discrepancies between financial instruments. By harnessing Python’s robust data analysis and statistical libraries, quantitative developers can implement sophisticated trading strategies, including pairs trading, to find and profit from…
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Supervised machine learning – Gaussian Naïve Bayes
In quantitative finance, having access to precise data analytics and advanced predictive models is key. With its versatile ecosystem, Python has emerged as an indispensable tool for quantitative developers seeking to optimise investment strategies and forecast market movements. One such…