High-frequency trading (HFT) is proving to be really difficult for trading firms and asset management companies due to the deployment of trading strategies within milliseconds in the case of a single tick variance of the price. Spending time developing rapidly deployable complex algorithms is a potential economy, but can become lost time as the opportunity may pass, or performance may not be optimal. Also, the execution of a large volume of datasets with low latency is itself an effective trading strategy but very hard to implement.
In order to solve these problems, the python language was used to implement effective strategies of high-frequency trading. Using libraries like NumPy, pandas, and TA-Lib, firms were able to streamline the development and back testing processes. The use of Apache Storm and Redis for data processing ensured that real-time data could be handled effectively, reducing latency. To do this, Zipline was used to recreate a historical period's trading strategy. The speed of Python, combined with its numerous tools, proved to be very useful in meeting the requirements of algorithmic trading.
The implementation of Python-based trading algorithms led to significant business improvements.
Thus, emphasizing the advantages of the use of Python in algorithmic trading.