[PDF] TSFDC: A trading strategy based on forecasting directional change | Semantic Scholar (2024)

Two DC based trading algorithms are designed: TA1 takes advantage of the AOL and T2 takes profit with a more conservative criteria, and the results suggest that in most scenarios, the algorithms are able to generate a positive outcome.

Results show that the GA-optimized strategies are able to generate new and profitable trading strategies, significantly outperforming the individual DC-based strategies, as well as a buy and sell benchmark.

Intelligent DBA overcomes the weaknesses of DBA as it embraces an original order size management and risk management modules and can provide significantly greater returns than DBA.

The accuracy of the FDCN-ML approach is evaluated by training and comparing the effectiveness of tree-based ML and ensemble models (Decision Tree, Random Forest, XGBoost and Voting Classifier) and proves that the directional changes of stocks are predictable.

This thesis pioneers a new method for regime change detection under the DC framework and showed that normal and abnormal regimes can becharacterised using DC indicators, which could be used for effective market tracking, which potentially lays the foundation for a practical financial early warning system.

Reinforcement Learning (RL) can achieve optimal dynamic algorithmic trading by considering the price time-series as its environment and a representation of the environment states using the Directional Change (DC) event approach with a dynamic DC threshold is proposed.

A stock trading signal filtering model based on Bagging, Random Forest and Logistic Regression is proposed, and the comparison of the experimental results shows that the Bagaging-RF-LR model is effective and has a good classification effect on the Trading signal filtering problem.

An adaptive intelligent Directional Change pattern recognition model with Reinforcement Learning is proposed, so called DCRL model, which is an alternative intelligent approach that samples price time-series using an event-based time interval and RL.

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The results show that the proposed contrarian trading strategy is profitable with Alpha over than 10 in some cases, however, counting the bid and ask prices can decrease considerably the profits under particular settings.

This paper addresses the problem of forecasting trend's direction in the foreign exchange market under the DC framework and proposes one single independent variable to make the forecast, confirming that directional changes are predictable, and the identified independent variable is useful for forecasting under theDC framework.

An innovative fuzzy logic approach is proposed which identifies and categorizes technical rules performance across different regions in the trend and volatility space and dynamically prioritizes higher performing regions at an intraday level and adapts money management policies with the objective to maximize global risk-adjusted performance.

A parsimonious method for building a new type of investment strategy that not only generates profits, but also provides liquidity to financial markets and does not have a priori restrictions on the amount of assets that are managed.

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