pandas
-
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…
-
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…
-
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, the Kalman filter is now extensively used in finance to smooth out noise in time…
-
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…