Combining Technical Indicators For Profitable Trading In Boston

Combining Technical Indicators For Profitable Trading In Boston – Testing for the Rayleigh Distribution: A New Test with Comparisons to Tests for Exponentiality Based on Transformed Data

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Combining Technical Indicators For Profitable Trading In Boston

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Received: 24 February 2022 / Revised: 18 March 2022 / Accepted: 30 March 2022 / Published: 1 April 2022

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This research is the first attempt to create machine learning (ML) algorithmic systems that will be able to automatically trade precious metals. The algorithm uses three forecasting methods: Linear Regression (LR), Darwas Box (DB), and Bollinger Bands (BB). Our data consists of 20 years of daily price data for five precious metals futures: gold, silver, copper, platinum, and palladium. We find that the current daily returns of all the examined precious metals are negatively autocorrelated with their prior day returns and identify lagged interdependencies between the examined metals. Silver futures prices were found to be the best forecast by our systems and platinum the worst. Additionally, our system predicts price uptrends better than downtrends for all examined technologies and commodities. Linear regression was found to be the best technique for predicting silver and gold price trends, while the Bollinger Bands technique was the best fit for palladium forecasting.

The use of artificial intelligence (AI) in financial asset price forecasting and trading has become more and more frequent as the volume and speed of new financial data flows has increased dramatically. Algorithms are used to analyze simultaneous multi-source data. Those systems are developed by market experts and are usually applied to the stock and currency markets. The following research develops and tests such an AI system and applies it to the precious metals futures market. Precious metals have always been perceived by investors as a hedging tool against inflation (eg, [1]) or stock market crashes. In the following research, we designed, optimized and tested three algorithmic trading systems suitable for precious metal futures trading. Our long-term data enables us to test our system’s performance over changing economic conditions. The technical analysis approach used here, commonly used by practitioners to trade stocks and foreign exchange, relies on historical data to predict future prices. We used the Particle Swarm Optimization (PSO) algorithm as our primary optimization tool because of its ability to handle multi-objective optimization simultaneously.

Many researchers have tried to prove the ability of such algorithmic trading systems to achieve abnormal returns for stocks, currencies and indices. However, many researchers focus on stocks and foreign exchange and partially neglected commodity futures and precious metal futures in particular. The following research aims to fill that gap with an insight into three algorithmic trading strategies that were programmed according to the peculiarities of the precious metal financial markets. We use 20 years of daily futures data corresponding to five major precious metals, including gold, silver, copper, platinum, and palladium, to test three algorithmic trading strategies: linear regression (LR), Darwas Box (DB), and Bollinger Band (BB). We followed [2], which concluded that LR and DB can help traders predict Bitcoin short-term price trends. Our 20 years of data were split into 10 years of training and optimization and 10 years of testing commercial results. We have found that it is possible to predict the short-term price trends of precious metals. Silver futures prices were found to be the best predicted by our system, and platinum was the worst. Our system predicts price uptrends better than downtrends for all tested technologies and commodities. Linear regression was found to be the best technique for forecasting silver and gold prices, while the Bollinger Bands technique was the best fit for palladium forecasting.

Our system is based on pattern recognition which is a developing AI field that helps us understand various chaotic phenomena. [3] argued that the use of Bayesian methods was greatly enhanced by the development of a range of approximation algorithms such as variational Bayes and expectation propagation. An important foundation for learning input-output mappings from a set of examples was presented by [4]. They developed a theoretical framework for inference mechanisms based on regularization networks that are closely related to pattern recognition. Their methods include task-dependent clustering and dimensionality reduction. Other researchers provided insight into the mathematical concepts behind forecasting methods that are based on probability derivatives. [5] provided a joint introduction to Gaussian processes (GP) and relevance vector machines (RVM developed by [6]). They found that RVMs allow the selection of more general basis functions, while the behavior of the predictive variance is generally unfavorable. [7] examined GP and RVM models and concluded that probabilistic models can produce predictive distributions rather than point forecasts.

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Most researchers who have attempted to explain precious metal prices have done so by relating the stock market to the precious metal market. [8] explained that precious metal futures have higher returns when investor sentiment is pessimistic rather than optimistic. [9] argued that the price of precious metals and their volatility are driven by economic uncertainty and shocks in investors’ risk appetite which is prevalent in the stock market. Other researchers focused on the correlation between the prices of the major precious metals. [10] showed that precious metals were strongly correlated with each other in the last decade. [11] document that weekly changes in traders’ positions have volatile effects on subsequent conditional volatility in the gold, silver, and palladium futures markets.

Other researchers correlated the prices of precious metals with each other and with other commodities. [12] investigated spillover effects in six commodity futures markets and found that both gold and silver are information transmitters in other commodity futures markets. [13] have investigated the impact of oil price changes on precious metals prices. They identified precious metals as safe havens against falling oil prices.

Earlier researchers also tried to build AI systems to predict the prices of precious metals. [14] proposed a model that combines adaptive neuro-fuzzy inference systems and genetic algorithms. [15] discovered hidden patterns governing the evolution of systems. In contrast to these attempts to predict precious metals prices, we have designed algorithmic trading systems and tested their ability to predict precious metals prices.

Our data consists of 20 years of daily data of open-close, high-low prices of five precious metals futures. We used a lagged multivariate stepwise regression model to examine lagged relationships between the daily returns of the examined precious metals, including autocorrelations, as described in Equation (1).

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The results of this model enabled us to better understand the short-term autocorrelations of returns and the lagged dependence between precious metals price movements and helped us design our trading systems.

We designed our algorithmic trading system to report actual trading results: Net Profit (NP), Percentage of Profitable Trades (PP) of all trades, and Profit Factor (PF). NP is the dollar value of the total net profit generated by the trading system, PP is the percentage of winning trades out of the entire set of trades generated by the system, and PF is the total profit divided by the total loss. We programmed three algorithmic systems based on three modern commercial technical tools and changed their configuration until we got the maximum profit in terms of NP and PF. The designed system is based on three methods: Linear Regression, Darwas Box, and Bollinger Bands which are well-known techniques commonly used to analyze investment opportunities for stock and currency traders. We then optimized NP and PF by changing the setups behind our systems and dividing system performance into long and short positions.

The complexity of our systems requires multi-objective optimization formulations. We chose Particle Swarm Optimization (PSO) developed by Kennedy and Eberhart ([ 16 , 17 ]) as our primary optimization method. This method enabled us to train the system initially and test it later. The 20 years of our examined period were divided into two distinct periods, 10 years of training and adaptation and 10 years of

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