matplotlib
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Fractal structure
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 structures within asset price movements, has become a powerful tool for recognising these turning points.…
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Statistical arbitrage – cointegration
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 mispricings. Python libraries Python has emerged as the go-to programming language for quantitative finance due to…
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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 exchange financial data feed that includes bid and ask volumes typically provides a time series…
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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, trading volumes, and economic indicators. For quantitative analysts, the ability to apply robust statistical models…