Abstract
In this chapter we discuss the benefits of using Python to analyse financial markets. We discuss the parallels between the stages involved in solving a generalised data science problem, and the specific case of developing trading strategies. We outline the general stages of developing a trading strategy. We briefly describe how open source Python libraries finmarketpy, findatapy and chartpy aim to tackle each of these specific stages. In particular, we discuss how abstraction can be used to help generate clean code for developing trading strategies, without the low level details of data collection and data visualisation. Later, we give Python code examples to show how we can download market data, analyse it, and how to present the results using visualisations. We also give an example of how to implement a backtest for a simple trend following trading strategy in Python using finmarketpy.
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References
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Amen, S. (2017). Using Python to Analyse Financial Markets. In: Ehrhardt, M., Günther, M., ter Maten, E. (eds) Novel Methods in Computational Finance. Mathematics in Industry(), vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-61282-9_29
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DOI: https://doi.org/10.1007/978-3-319-61282-9_29
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