代写Bastion Trading Data Python 程序
- 首页 >> C/C++编程 Bastion Trading Data & Python Assignment
Assessment Criteria
You can complete the task at your own pace, but we expect it to be submitted within 3 days from the time it was
assigned. Your submission will be evaluated based on the following criteria:
1. Exploratory Data Analysis & Insights
• Ability to identify trends, patterns, and relationships
• Use of appropriate statistical and mathematical techniques
• Clear explanation of findings
2. Methodology & Justification
• Thoughtful selection of techniques and models
• Justification for chosen approaches
• Effective handling of high-dimensional time-series data
3. Code Quality & Documentation
• Clean, readable, and well-structured code
• Proper documentation and comments
• Effective use of Jupyter Notebook features
4. Visualisation & Communication
• Clear and meaningful visual representations
• Concise explanation of insights
• Logical flow of the notebook
5. Python Programming Problem
• Accuracy of the solution
• Compliance with problem requirements
• Code efficiency and optimization
Confidentiality Notice
All interview details, including assessment tasks, are strictly confidential. Sharing or discussing them with any third
party—including other candidates, external entities, or on social media—is strictly prohibited. Any unauthorized
disclosure may result in disqualification and potential legal action.
CONFIDENTIAL
https://bastiontrading.com/
Annex 1: High-Dimensional Time-Series Data Analysis
Attached is an anonymised high-dimensional time-series dataset (TEST_Trader_Quant_dataset.csv). We would like you
to utilise and demonstrate your data analysis skills to extract meaningful insights.
This can include, but is not limited to:
• Identifying key features, trends, and patterns
• Detecting predictive or casual relationships between features using suitable mathematical and statistical
techniques
• Applying machine learning models (if applicable) to enhance insight generation
The depth of the analysis is up to you. No additional information will be provided.
Please ensure your work is presentable and professional. Remove unnecessary output cells and error logs, avoid
generating numerous unexplained graphs, and ensure all visualisations and conclusions are clearly supported by your
analysis. Submissions containing generic or LLM-generated content will be discounted.
Deliverables
A Jupyter Notebook exported as an HTML file containing:
• Code
• Findings
• Visualisations
• Conclusions
The exported file must be named using the following format: Firstname_Lastname.html (e.g., John_Doe.html)
Please submit only the .html file.
Annex 2: Python Programming Task
Attached is a Python programming problem (Python.zip). Please refer to the README file for detailed requirements
and expectations.
Deliverables
• A completed Python script that meets the outlined requirements
• Well-documented code following best practices
If your submission to Annex 2 includes multiple files, please compress them into a single ZIP folder named using the
Assessment Criteria
You can complete the task at your own pace, but we expect it to be submitted within 3 days from the time it was
assigned. Your submission will be evaluated based on the following criteria:
1. Exploratory Data Analysis & Insights
• Ability to identify trends, patterns, and relationships
• Use of appropriate statistical and mathematical techniques
• Clear explanation of findings
2. Methodology & Justification
• Thoughtful selection of techniques and models
• Justification for chosen approaches
• Effective handling of high-dimensional time-series data
3. Code Quality & Documentation
• Clean, readable, and well-structured code
• Proper documentation and comments
• Effective use of Jupyter Notebook features
4. Visualisation & Communication
• Clear and meaningful visual representations
• Concise explanation of insights
• Logical flow of the notebook
5. Python Programming Problem
• Accuracy of the solution
• Compliance with problem requirements
• Code efficiency and optimization
Confidentiality Notice
All interview details, including assessment tasks, are strictly confidential. Sharing or discussing them with any third
party—including other candidates, external entities, or on social media—is strictly prohibited. Any unauthorized
disclosure may result in disqualification and potential legal action.
CONFIDENTIAL
https://bastiontrading.com/
Annex 1: High-Dimensional Time-Series Data Analysis
Attached is an anonymised high-dimensional time-series dataset (TEST_Trader_Quant_dataset.csv). We would like you
to utilise and demonstrate your data analysis skills to extract meaningful insights.
This can include, but is not limited to:
• Identifying key features, trends, and patterns
• Detecting predictive or casual relationships between features using suitable mathematical and statistical
techniques
• Applying machine learning models (if applicable) to enhance insight generation
The depth of the analysis is up to you. No additional information will be provided.
Please ensure your work is presentable and professional. Remove unnecessary output cells and error logs, avoid
generating numerous unexplained graphs, and ensure all visualisations and conclusions are clearly supported by your
analysis. Submissions containing generic or LLM-generated content will be discounted.
Deliverables
A Jupyter Notebook exported as an HTML file containing:
• Code
• Findings
• Visualisations
• Conclusions
The exported file must be named using the following format: Firstname_Lastname.html (e.g., John_Doe.html)
Please submit only the .html file.
Annex 2: Python Programming Task
Attached is a Python programming problem (Python.zip). Please refer to the README file for detailed requirements
and expectations.
Deliverables
• A completed Python script that meets the outlined requirements
• Well-documented code following best practices
If your submission to Annex 2 includes multiple files, please compress them into a single ZIP folder named using the