How to limit Pandas memory usage
Pandas is a powerful library for data analysis in Python. It is widely used in data science and data engineering. However, it can be memory-intensive, especially when working with large datasets. In this post, we will explore how to limit Pandas memory usage and improve it by 80% on an example trading dataset.
Plotting cumulative orderbook in Python
Simple function for plotting cumulative orderbook in Python with Python and Plotly
Integrating Custom Trading Indicators with Pandas
How to implement common trading indicators using Pandas/NumPy and extend Pandas objects with custom methods for computing those indicators for seamless financial analysis.
Working with missing data in time-series processing
Missing data often cannot be avoided, especially when working with time series. If we however handle them properly, we can minimize their impact on results. Here is how to do it using Pandas library and its NaN handling machinery.
Building HFT algos with hftbacktest and Lake
Come back later for: Backtest high-sharpe HFT strategies with hftbacktest integrated with Lake
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