Harnessing Volatility: Forex Trading In Uncertain Markets

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Harnessing Volatility: Forex Trading In Uncertain Markets

Harnessing Volatility: Forex Trading In Uncertain Markets

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Limiting Drawdowns By Adding Volatility Exposure To Your Portfolio

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A deep network-based trading and trend analysis system to monitor entry and exit points in the Forex Market

Asit Kumar Das Asit Kumar Das Scilit Preprints.org Google Scholar 1; Debahuti Mishra Debahuti Mishra Scilit Preprints.org Google Scholar 1; Kaberi Das Kaberi Das Scilit Preprints.org Google Scholar 2, Arup Kumar Mohanty Arup Kumar Mohanty Google Scholar 3; Mazin Abed Mohammed Mazin Abed Mohammed Scilit Preprints.org Google Scholar 4; Alaa S. Al-Waisy Alaa S. Al-Waisy Scilit Preprints.org Google Scholar 5; Seifedine Kadry Seifedine Jung Kadry Scilit Scholar and Google Preprints 6. Kim Jungeun Kim Scilit Preprints.org Google Scholar 7; *

Algorithmic Trading In Forex: Exploring The Opportunities And Challenges

Department of Computer Science and Information Technology; Siksha ‘O’ Anusandhan (assumed) University; Bhubaneswar 751030; Odisha, India

Received: 21 August 2022 / Revised: 3 September 2022 / Accepted: 28 September 2022 / Published: 4 October 2022

In the Forex market, Trend trading, where trend traders identify trends and attempt to capture profits by analyzing the momentum of an asset, is a great way to profit from market movements. When the price of a currency moves in any such direction; Up or down is called a trend. Analyzing this trend helps traders and investors find entry points or exit points that are less likely until the trend changes. In this paper, Empirical trade and trend analysis results are suggested by two-stage tests. First, Considering the blended learning paradigm and the extensive use of deep learning techniques, Vanilla-LSTM; Stacked-LSTM, Bidirectional-LSTM; CNN-LSTM; and variants of short-term-short-memory (LSTM) networks such as Conv. -LSTM is used to build effective investment trading systems for both short and long term time frames. Then, A deep network-based system used to obtain trends (uptrends and downtrends) of forecasted closing prices of currency pairs is proposed based on optimal forecasting networks measured using a few performance measures and Friedman’s non-restriction tests. . The observed trends are the Average Directional Index (ADX); rate of change (ROC); speed It has been compared with a few readily available technical indicators such as the Commodity Index (CCI) and the Moving Average Convergence Divergence (MACD). The forecasting ability of the proposed strategy for trend analysis can be summarized as follows: (a) AUD:INR achieves 99.7265% relative to the previous day for short-term forecasts and GBP:INR achieves 99.6582% for long-term forecasts. (b) Considering the trend analysis strategy with respect to due dates, AUD:INR achieves 98.2906% for short-term forecast days and USD:INR achieves trend forecasting accuracy of 96.0342%. A significant result of this article is the proposed trend estimation methodology. Average found during trajectory prediction; Attempts have been made to provide an environment to understand maximum and minimum unit increments/or decrements. Again, This deep learning based strategy will help investors and traders understand the ins and outs of this financial market.

Harnessing Volatility: Forex Trading In Uncertain Markets

In the Forex financial market, Forecasting and providing a platform to make accurate investment decisions helps investors, It is the most difficult matter for brokers and business houses. An understanding of the economic growth of markets and countries are potential inputs to analyze these markets and ensure that the assets invested in have measurable value and maximum return on investment. The main purpose of focusing on predicting or predicting currency exchange rates between countries is investors; To assist brokers and traders with automated systems that can generate informed or guided decisions to help maximize the return on investment. A well-developed trading strategy [1, 2] to. The most commonly used methods focus on: (a) purchasing power parity; The idea that the same asset should not be bought and sold in different markets in order to extract the best profit; A price that can exploit short-term variations; similar assets in different countries’ markets; (b) relative economic strength; It is basically used to measure a country’s economic strength and attract foreign investors. (c) econometric models that gather factors that influence exchange rate forecasting based on economic theories such as statistical and mathematical methods and consider variables that influence exchange rates [3, 4].

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The recent trend of digital medium of analyzing financial assets in business areas provides ample opportunities for the research community and the information technology industry to develop automated financial applications or models that understand financial market sentiments, make them accurate and smart. DECISIONS WITH EMERGING TECHNOLOGIES Although traditional forms of financial market analysis work well based on historical data, The short lifespan of these models limits their use and effectiveness. Understanding the patterns of the financial market for making decisions is also a motivation for researchers, and now they are studying the various patterns of this financial market through trend analysis. The forecasting process of the Forex currency market is a risky and tedious task. Therefore, Researchers need detailed insight into these practices when developing any trading and/or trend analysis model.

This study is based on historical data; This study, also known as consumption forecasting, aims to provide optimal decisions and monitoring models based on the current situation or environment and predictive analysis. This trend or usage estimate is daily; monthly It provides a solution to ‘what next’ for a specific time frame of investment either quarterly or annually. In short, Trend analysis examines financial market and economic conditions and identifies two types of trends; Uptrends or bull markets and downtrends or bear markets [2, 5, 6, 7] identifies two categories. Uptrends represent an increase in asset prices and growth over a period of time, and investors in this trend are eager to catch the investment wave to make a profit. On the contrary, Downtrends indicate the economic conditions of the market and lead investors to become more conservative and less interested in investing. To summarize, this trend analysis provides the direction of profit/loss for assets and simplifies the decision-making process for investors and businesses. Various tools and techniques of machine learning and deep learning algorithms play a key role in predicting the exchange price of each currency pair during the classification process, supervised learning techniques, With the help of market trends (fluctuations and trends), investors and traders make Forex transactions [5, 6, 8, 9] is expected to help make the right decisions.

The deep network has established a new paradigm of hybrid learning by exploiting the structural and functional capabilities of artificial intelligence and machine learning strategies in the field of financial market analysis. The robust underlying techniques of deep network based on mathematical and computational mechanisms have become a great challenge for researchers, and many are still trying to develop computational models based on this deep network. Therefore, The development of an application-specific network [10, 11, 12] is a challenge that needs to be addressed. A deep network demonstrates its ability to classify data sets or draw curves or straight lines through data points to predict a trend of increasing or decreasing value.

In general, Forecasting current currency exchange rates and identifying trends is a great opportunity to study this financial market and learn deep learning capabilities in relation to running multiple features with an embedded filtering architecture. In addition, The methods used in handling complex problems with unstructured data have shown their effectiveness to focus this research area. Motivated by the effectiveness of deep learning techniques; This study is a significant attempt to obtain a trend analysis strategy to identify volatility as well as currency pair trading prices.

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