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運用隨機森林演算法於選擇權量化交易策略

Option Quantitative Trading Strategy Based on Random Forest Algorithm

摘要


本研究自資金管理的角度出發,提出具固定停損機制的選擇權賣方交易策略,藉由停損機制對最大獲利及損失進行量化,降低產生超額虧損的風險。本研究分別透過傳統機率統計方法及隨機森林演算法對勝率進行估計,採用熱點圖對預測結果進行視覺化,同時考量勝率門檻及穩固性擷取有利可圖的交易區間,並以準確度、精密度及AUC等指標對模型效能進行評估。實驗結果顯示,本研究建構之隨機森林預測模型對所提出交易策略擁有相當優秀的預測能力(精密度最高達0.9),能夠有效達成資金管理及風險管理之目標。

並列摘要


In this research, we propose an option sell-side trading strategy with a fixed stop-loss mechanism according to the concept of money management. In our strategy, the maximum profit and loss are fixed through the introduced stop-loss mechanism to reduce the risk of excessive losses. We apply the traditional statistical approach and random forest algorithm to estimate the win-rate and visualize the win-rate distribution through Heatmap and the profitable trading range filtered by win-rate threshold could be obtained. The effective indicator, Accuracy, precision, and AUC-ROC curve are selected to evaluate the performance of the predictive model proposed in this research. The experimental result illustrates that the random forest predictive model to our strategy demonstrate outstanding performance (highest precision reached 0.9) which outperform the traditional statistical approach and could achieve the objective of implementing money management and risk management to option trading strategy.

參考文獻


Belgiu, M., and Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS journal of photogrammetry and remote sensing, 114, pages 24-31.
Bermin, H.-P., and Holm, M. (2021). Kelly trading and option pricing. Journal of Futures Markets, 41(7), pages 987-1006.
Bermin, H.-P., and Holm, M. (2019). Kelly Trading and Market Equilibrium. Lund University, School of Economics and Management.
Bishop, R. C. (1982). Option value: an exposition and extension. Land Economics, 58(1), pages 1-15
Black, F., and Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), pages 637-654.

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