Tag: time series data
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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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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 mispricings. Python libraries Python has emerged as the go-to programming language for quantitative finance due to…