Python
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Binomial options pricing model
The ability to price derivatives accurately and efficiently is paramount. The binomial pricing model is one of the most widely used methods for option pricing, providing a structured approach to evaluating potential outcomes under various market conditions. With its powerful…
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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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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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Price modelling – Itô’s Lemma – Geometric Brownian Motion
Quantitative analysts constantly seek robust methods to model and predict asset price movements. One such powerful tool is Itô’s Lemma, a cornerstone of stochastic calculus. By leveraging this mathematical framework alongside Python, quants can enhance their analytical toolkit, making strides in…
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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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Market structure – Fractal microstructures
In financial markets, the ability to accurately and timely identify turning points in floating assets such as stocks, commodities, or forex pairs can spell the difference between profit and loss. Fractal analysis, a technical approach focusing on repetitive patterns and…
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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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Supervised machine learning – Support vector machine
Machine learning has become an indispensable tool for tackling complex problems in quantitative analysis. Among the arsenal of algorithms, Support Vector Machines (SVM) stand out for their robustness in classification and regression tasks. With powerful libraries like sklearn and Matplotlib, Python empowers quant developers to build, evaluate,…