Computational Quantitative Finance
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Computational Quantitative Finance
· Students are required to implement a trading strategy and evaluate its performance relative to a random walk strategy.
· Students can choose one trading strategy covered in term 1, a strategy from the literature or implement their own.
· Students are required to collect data for their chosen trading strategy from one of the following sources:
§ Refinitiv
§ Bloomberg
§ Capital IQ
§ WRDS (including Datastream)
§ yfinance
· The report should contain the followings:
o Details of the random walk model for asset returns, including critique of relevant stochastic models.
o Details of the chosen trading strategy, including critique of the chosen strategy with reference to relevant literature.
o Implementation details such as trade entry, exit, risk control and transaction frequency & costs.
o Financial performance; average return, Sharpe ratio, Maximum Drawdown, etc.
o Historical simulation of the trading strategy using in and out of sample data.
o Critical evaluation of the results.
· Details and critique of trading strategies should be written in Markdown.
· Analysis and Evaluation of code output should be written in Markdown.
· Explanation about code should be written within code cells with the ‘#’ notation.
· The report should be written in a Jupyter Notebook and submitted as a ‘.ipynb’ file.
Overall word limit: 1500
MARKING GUIDELINES
Performance in the summative assessment for this module is judged against the following criteria:
· Relevance to question(s)
· Organisation, structure and presentation
· Depth of understanding
· Analysis and discussion
· Use of sources and referencing
· Overall conclusions
2024-02-06