摘要
量化择时策略是量化投资和量化交易的核心策略,需要考虑到特征因子的含义和资产价格的涨跌幅度。基于此,使用XGBoost、LightGBM等树类模型提取了分类指标,构建了考虑到止盈止损区间的量化交易模型,并用于沪铜期货的量化投资交易分析。实证结果表明,该方法能有效预测资产价格的涨跌幅度,且使用机器学习可解释性分析得到的解释结果具有解释能力,符合实际情况。
Quantitative timing strategy is the core strategy of quantitative investment and quantitative trading, which needs to take into account the meaning of characteristic factors and the rise and fall of asset prices. If so, we use XGBoost, LightGBM and other tree models to extract classification indicators, build a quantitative trading model considering the stop-loss interval, and use it to analyze the quantitative investment and trading of Shanghai Copper Futures. The empirical results show that this method can effectively predict the rise and fall of asset prices, and the interpretation results obtained by machine learning interpretability analysis have explanatory power, which is consistent with the actual situation.
作者
吴青山
WU Qing-shan(School of Economics,Guizhou University,Guiyang 550025,China)
出处
《经济研究导刊》
2023年第2期83-85,共3页
Economic Research Guide
关键词
机器学习
量化投资
期货
machine learning
quantitative investment
futures
