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This article has been updated Abstract In 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. Moreover, how to determine the minutely binary options predictions 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.
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Nonetheless, minutely binary options predictions massive problems cannot be overcome. First, futures are leveraged transactions, and extra funds must be deposited as margin.
Trading strategy: 1-Minute Breaks
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.
Convert index trading to option strategies via LSTM architecture
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 Fibonacci retracement levels options.
Finally, the proposed framework brings the results into the Kelly criterion to get the optimal fraction of options trading.
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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.
The strategy in detail The 1-Minute Breaks strategy is suitable for all instruments and is traded on a 1-minute chart.
Introduction 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. In many trading strategies, most of the trading targets are stocks and futures, and stocks represent the ownership of a company. Also, many people directly use stock index futures as the investment target, because as long as the overall stock market trend is judged, enter the market to go long or short.
Many trading strategies are based on common strategies or ideas and further extended, such as moving average trading strategies based on the average price over a while [ 101318 ]; or because of trading at the opening the trading volume and price fluctuations are larger than other periods, there are also many trading strategies based on this, such as the open range breakthrough ORB trading strategy [ 1535 ], first formulate a period as the observation minutely binary options predictions, if only when there is a breakthrough up or down before minutely binary options predictions the market.
After deciding the target and strategy of the transaction, one of the most important factors in the financial market is money management [ 363738 ].
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Many traders are more concerned about choosing the investment target and the time to enter and exit, and ignore the control of the position size, making the final result loss or performance not as expected.
The foundation of money management was derived from the Kelly criterion [ 17 ] proposed by John Larry Kelly at Bell Labs inbut it was originally a formula proposed to study the probability of noise occurring in communications. It was until Edward O.
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Thorp put the Kelly criterion in other fields, such as blackjack for playing cards, sports lottery tickets, and the stock market [ 33 ]. The Kelly criterion can be used in situations such as gambling that can be played unlimited times and has a fixed probability of winning and odds [ 41 ], in the process of repeated betting to maximize the growth of his assets [ 20303442 ]. However, there is a gap between the financial market and the gambling [ 16 ], so many scholars have begun to explore how to use the Kelly formula in a more general situation [ 4691143 ].
To use the Kelly criterion in the financial market, we need to know the odds and winning probability of the trading strategy in advance.
Although the odds of the trading strategy can be controlled by formulating the take-profit and stop-loss point, due to the futures are leveraged transactions, we have to deposit some money as a margin to operate. So if we want to calculate the position size according to the Kelly criterion, we cannot reach the size of the position calculated by the Kelly criterion when using futures trading.
Therefore, we propose an architecture to convert the original futures trading strategy to using the options as the target of the transaction.
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At the same time, because the options premium unit is small, the position size can be controlled more accurately to achieve the optimal betting fraction by the Kelly criterion. After we use options trading to replace the original futures trading strategy, another problem is that the minutely binary options predictions strategy we have developed is difficult to estimate the minutely binary options predictions rate, which cannot be brought into the Kelly criterion for calculation.
Many studies have combined financial transactions with machine learning to estimate stock prices or the direction of ups and downs [ 719233132 ]. To further improve the accuracy of prediction, artificial neural networks and deep learning have also been used in financial markets [ 122545 ].
The study also pointed out that because of the time series characteristics in the financial market, each time the price is affected by the long-term or short-term [ 3 ]. Thus, the long short-term memory LSTM algorithm [ 14 ] has many different applications in the financial market and also more accurate than traditional machine-learning methods in the past [ 527 ].
Due to the difference between the final profit and loss results of using futures and options under the same trading strategy, the original futures trading strategy cannot be converted into options trading directly.
In the futures, according to the characteristics of the option, different options retest strategies can be used in combination with the Kelly formula to achieve better performance [ 3940 ]. However, 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.
In this paper, a LSTM-based framework is proposed to convert a futures strategy to a options strategy.
The major contributions of our work are listed as follows: 1. We first proposed a conversion process from a futures trading strategy to a new options trading strategy.
It causes a futures trading strategy can be applied in options transaction directly. A win rate prediction system-based LSTM network is proposed to apply the proposed method into Kelly formula.
Due to the proposed LSTM-based prediction framework and the conversion process, the new generated options trading strategy can effectively enhance the total profit. Preliminaries and related work Futures trading strategy at market opening Futures trading price fluctuations and volume at opening periods are much bigger than other periods.
For instance, Fig. We discover that the first half time of differences of high and low is more than 60 points, but the second half time of differences is less than 30 points, namely the first half time is twice as much as the second half time. Not only the price fluctuations but also the trading volume is much bigger than the second half time except for the last closing period.
86# 1 min scalping with Bollinger bands and RSI
We discover that the first half time of differences of high and low minutely binary options predictions even more than 80 points, but the second half time of differences is less than 40 points. The price fluctuations and the trading volume are lager in the first half time than the second half no matter in bull market or bear market.
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So, there are many trading strategies based on the opening period. For example, the opening range breakout strategy is a famous and classic case that many people know.
One of the basic operations is shown in Fig.