Fof基金量化投资研究助理
基金评价：利用Python及Wind API调取并处理基金持仓结构及原始业绩比较基准，对全市场基金重新分类并自拟基准，使用T-M/H-M/C-L模型进行业绩归因分析。对四类收益风险指标标准化处理，建立自动化基金量化评分系统，获得高分基金底池，自动跟踪基金各项评分、市场统计数据及可视化图表。策略模型：自主搭建回测框架，建立带趋势动量因子的风险平价模型RPMF（后期曾尝试加入PCA）。首先通过动量因子从高分基金底池中挑选每期调仓标的，根据风险平价模型计算资产风险贡献，使用梯度下降、SLSQP、信赖域算法最优化方法得到最终资产权重。实现自动输出回测结果、可视化图表及调仓明细。Fund Evaluation: Independently using Python and Wind API to establish a fund quantitative scoring model, combining the equity position and benchmark to establish a classification method and gave new benchmark, establishing quantitative scoring model with standardized processing based on four types of indicators, realizing automatic fund screening, saving all fund scoring processes in batches and outputting statistical data and visual charts, and exploring performance attribution analysis (mainly involving T-M/H-M/C-L models).Strategy Model: Independently build a Backtest Framework, establish a risk parity model (RPMF)(also tried PCA) with trend momentum factor, first obtain the target of each period from the high score funds through the momentum factor, calculate the asset risk contribution according to the risk parity model, calculate the final asset weight by adjusting the optimization method, and use SLSQP and trust region algorithms respectively, finally realized automatic output of results and visual charts and position adjustment details in each period.
