pandas
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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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Macroeconomic decision-making
Macroeconomic events are pivotal in influencing market dynamics. Understanding and responding to these events can make the difference between profit and loss for investment professionals. Python, with its extensive ecosystem and user-friendly capabilities, has become a go-to tool for quantitative…
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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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Absorptions
Leveraging Python and its powerful libraries like pandas and matplotlib provides a significant advantage in analysing financial data, especially when interpreting data feeds from centralised exchanges that include bid and ask data. The pandas library is paramount for managing and manipulating structured data in financial markets. A centralised…
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Kalman filter
As a quantitative analyst in financial markets, it is crucial to deploy models that can efficiently manage missing or noisy data and extract meaningful trends. The Kalman filter is one of the most robust tools for this purpose. Originating from control theory,…
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Time series analysis
Understanding time series data is crucial for making informed investment decisions in financial markets and the quantum analysis space. Time series analysis allows financial professionals to model and forecast market movements, identify trends, and detect underlying patterns in asset prices,…
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Martingale properties
Martingale properties lie at the core of modern financial theory and quantitative analysis. These properties describe stochastic processes where the conditional expected value of the next observation, given the current and past values, equals the current observation. In simple terms, martingales represent “fair games” where…
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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…