Algorithmic Trading for Trading Firms and Asset Management Companies
Challenges:
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.
Solution:
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.
Business Impact:
The implementation of Python-based trading algorithms led to significant business improvements.
- Trading speed was enhanced by 40%, enabling quicker responses to market changes.
- Back testing optimization improved strategy performance by 25%, ensuring more effective trading decisions.
- In the end, these changes have brought a noticeable increase in trading profits by about 20%
Thus, emphasizing the advantages of the use of Python in algorithmic trading.
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