Abstract
AbstractIn the past, most strategies were mainly designed to focus on stocks or futures as the trading target. However, due to the enormous number of companies in the market, it is not easy to select a set of stocks or futures for investment. By investigating each company’s financial situation and the trend of the overall financial market, people can invest precisely in the market and choose to go long or short. Moreover, how to determine the position size of the transaction is also a problematic issue. In the past, many money management theories were based on the Kelly criterion. And they put a certain percentage of their total funds into the market for trading. Nonetheless, three massive problems cannot be overcome. First, futures are leveraged transactions, and extra funds must be deposited as margin. It causes that the position size is hard to be estimated by the Kelly criterion. The second point is that the trading strategy is difficult to determine the winning rate in the financial market and cannot be brought into the Kelly criterion to calculate the optimal fraction. Last, the financial data are always massive. A big data technique should be applied to resolve this issue and enhance the performance of the framework to reveal knowledge in the financial data. Therefore, in this paper, a concept of converting the original futures trading strategy into options trading is proposed. An LSTM (long short-term memory)-based framework is proposed to predict the profit probability of the original futures strategy and convert the corresponding daily take-profit and stop-loss points according to the delta value of the options. Finally, the proposed framework brings the results into the Kelly criterion to get the optimal fraction of options trading. The final research results show that options trading is closer to the optimal fraction calculated by the Kelly criterion than futures trading. If the original futures trading strategy can profit, the benefits after converting to options trading can be further superior.
Highlights
For many experts, scholars, and even traders, it is fascinating to research and develop profitable trading strategies or optimize already existing trading strategies to maximize the overall profitable growth
After we use options trading to replace the original futures trading strategy, another problem is that the trading strategy we have developed is difficult to estimate the winning rate, which cannot be brought into the Kelly criterion for calculation
The result will be converted to an options trading strategy, which is used to compare with the original futures trading strategy
Summary
Scholars, and even traders, it is fascinating to research and develop profitable trading strategies or optimize already existing trading strategies to maximize the overall profitable growth. To use the Kelly criterion in the financial market, we need to know the odds and winning probability of the trading strategy in advance. We decided to use LSTM to estimate our trading strategy’s winning rate and, at the same time, using fixed take-profit and stop-loss points to fix the odds and bring it into the Kelly criterion to calculate the position size. If we can convert the futures strategy with good performance into options as the trading target and control the position size, it can save a lot of time in developing strategies. 3. Due to the proposed LSTM-based prediction framework and the conversion process, the new generated options trading strategy can effectively enhance the total profit
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