Why Python Has Taken Over Finance
summary
Argues that Python wins because concise code actually produces results: a few lines of pandas vectorized backtest builds positions, computes returns and plots performance; interactive exploration uses cufflinks interactive candlesticks and Seaborn from Jupyter; machine learning prototype uses TensorFlow DNN classifier (54% in‑sample accuracy) to predict up/down moves. Cites industry scale — large banks deploy Python widely and one reported 34 months with no daily loss after algorithmic trading and >$1tr volume yearly.
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chapters (9)
- 0:00Start
- 0:37Agenda
- 3:29What Is Financial Singularity
- 4:30Financial Singularities
- 7:15What Are the Driving Forces
- 15:52What Are the Benefits of Python for Finance
- 18:54Basis for a Trading Strategy
- 21:00Negative Correlation
- 25:36Prediction
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