wilson
量化工程师
ETrade Inc · 3年经验
日薪：
￥1000/8h
地点：
其他 全区
时间：
下班后周六周日可工作日远程
信用行为
0
接单数
0
评价数
0
收藏数
技术能力
• Built models and designed automated algorithms for farm insurance pricing through Python with 5 team members.
• Applied seasonal time series model to predict farm product prices based on past data to determine strike prices.
• Optimized diversified efficient portfolios of a set of farm insurance and simulated efficient frontier using Monte
Carlo process with one million iterations, delivering insights and strategies of investment to clients.
• Created real-time interactive visualization to help clients customize own portfolios and calculate possible return.
• Applied seasonal time series model to predict farm product prices based on past data to determine strike prices.
• Optimized diversified efficient portfolios of a set of farm insurance and simulated efficient frontier using Monte
Carlo process with one million iterations, delivering insights and strategies of investment to clients.
• Created real-time interactive visualization to help clients customize own portfolios and calculate possible return.
项目经验
Analyzed the influence of the number of cars produced every year on Dow Jones U.S. Auto Manufacturers Index using
multiple machine learning methods.
• Collected vehicle production amount from companies' annual reports and DJUSAT index, along with some other
macroeconomic index from Bloomberg; built models by using machine learning techniques like Logistic Regression, Lasso, SVM, Gradient Boosting and compared the results;
• Tested the significance of production amounts, especially the amounts of famous brands.
Investigate the SQ3R study system's effectiveness Jan. 2019-Mar. 2019
• Analyzed the relationship between the time students spent on the SQ3R study system and students' academic performance.
• Cleaned panel data, including dealing with missing data, checking the existence of overlaps and gaps in time frames, examining the identical of constant variables for every ID.
• Analyzed the cleaned data, and reached the conclusion through summarized tables.
Investigate the Performance of Online Learning Models in Security Market July. 2017-Sep. 2017
• Built online learning models with 300 stocks included in CSI300 index and compared the performance of securities portfolio
managed by models with the real CSI index values.
• Collected CSI300 index and its 300 A-share stock prices between Jan. 1 and June 30 from Wind; used online gradient decent
method to build decision models and manage the portfolios; compared the return of the portfolios using OGD method and the corresponding return of real CSI index and obtained the conclusion that OGD performed slightly better than the market.
multiple machine learning methods.
• Collected vehicle production amount from companies' annual reports and DJUSAT index, along with some other
macroeconomic index from Bloomberg; built models by using machine learning techniques like Logistic Regression, Lasso, SVM, Gradient Boosting and compared the results;
• Tested the significance of production amounts, especially the amounts of famous brands.
Investigate the SQ3R study system's effectiveness Jan. 2019-Mar. 2019
• Analyzed the relationship between the time students spent on the SQ3R study system and students' academic performance.
• Cleaned panel data, including dealing with missing data, checking the existence of overlaps and gaps in time frames, examining the identical of constant variables for every ID.
• Analyzed the cleaned data, and reached the conclusion through summarized tables.
Investigate the Performance of Online Learning Models in Security Market July. 2017-Sep. 2017
• Built online learning models with 300 stocks included in CSI300 index and compared the performance of securities portfolio
managed by models with the real CSI index values.
• Collected CSI300 index and its 300 A-share stock prices between Jan. 1 and June 30 from Wind; used online gradient decent
method to build decision models and manage the portfolios; compared the return of the portfolios using OGD method and the corresponding return of real CSI index and obtained the conclusion that OGD performed slightly better than the market.
wilson
量化工程师 · 3年经验 · ETrade Inc
0
累计接单
0
用户评价
0
被收藏
信用行为
0
接单数
0
评价数
0
收藏数
技术能力
• Built models and designed automated algorithms for farm insurance pricing through Python with 5 team members.
• Applied seasonal time series model to predict farm product prices based on past data to determine strike prices.
• Optimized diversified efficient portfolios of a set of farm insurance and simulated efficient frontier using Monte
Carlo process with one million iterations, delivering insights and strategies of investment to clients.
• Created real-time interactive visualization to help clients customize own portfolios and calculate possible return.
• Applied seasonal time series model to predict farm product prices based on past data to determine strike prices.
• Optimized diversified efficient portfolios of a set of farm insurance and simulated efficient frontier using Monte
Carlo process with one million iterations, delivering insights and strategies of investment to clients.
• Created real-time interactive visualization to help clients customize own portfolios and calculate possible return.
项目经验
Analyzed the influence of the number of cars produced every year on Dow Jones U.S. Auto Manufacturers Index using
multiple machine learning methods.
• Collected vehicle production amount from companies' annual reports and DJUSAT index, along with some other
macroeconomic index from Bloomberg; built models by using machine learning techniques like Logistic Regression, Lasso, SVM, Gradient Boosting and compared the results;
• Tested the significance of production amounts, especially the amounts of famous brands.
Investigate the SQ3R study system's effectiveness Jan. 2019-Mar. 2019
• Analyzed the relationship between the time students spent on the SQ3R study system and students' academic performance.
• Cleaned panel data, including dealing with missing data, checking the existence of overlaps and gaps in time frames, examining the identical of constant variables for every ID.
• Analyzed the cleaned data, and reached the conclusion through summarized tables.
Investigate the Performance of Online Learning Models in Security Market July. 2017-Sep. 2017
• Built online learning models with 300 stocks included in CSI300 index and compared the performance of securities portfolio
managed by models with the real CSI index values.
• Collected CSI300 index and its 300 A-share stock prices between Jan. 1 and June 30 from Wind; used online gradient decent
method to build decision models and manage the portfolios; compared the return of the portfolios using OGD method and the corresponding return of real CSI index and obtained the conclusion that OGD performed slightly better than the market.
multiple machine learning methods.
• Collected vehicle production amount from companies' annual reports and DJUSAT index, along with some other
macroeconomic index from Bloomberg; built models by using machine learning techniques like Logistic Regression, Lasso, SVM, Gradient Boosting and compared the results;
• Tested the significance of production amounts, especially the amounts of famous brands.
Investigate the SQ3R study system's effectiveness Jan. 2019-Mar. 2019
• Analyzed the relationship between the time students spent on the SQ3R study system and students' academic performance.
• Cleaned panel data, including dealing with missing data, checking the existence of overlaps and gaps in time frames, examining the identical of constant variables for every ID.
• Analyzed the cleaned data, and reached the conclusion through summarized tables.
Investigate the Performance of Online Learning Models in Security Market July. 2017-Sep. 2017
• Built online learning models with 300 stocks included in CSI300 index and compared the performance of securities portfolio
managed by models with the real CSI index values.
• Collected CSI300 index and its 300 A-share stock prices between Jan. 1 and June 30 from Wind; used online gradient decent
method to build decision models and manage the portfolios; compared the return of the portfolios using OGD method and the corresponding return of real CSI index and obtained the conclusion that OGD performed slightly better than the market.
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