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Psychology Of Profitable Trading: Boston Traders’ Insights – Testing for the Rayleigh Distribution: A New Test with Comparisons to Tests for Exponentiality Based on Transformed Data
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Psychology Of Profitable Trading: Boston Traders’ Insights
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Inverse Head And Shoulders: What The Pattern Means In Trading
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Received: February 24, 2022 / Revised: March 18, 2022 / Accepted: March 30, 2022 / Published: April 1, 2022
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This research is the first attempt to create machine learning (ML) algorithmic systems that would be able to automatically trade precious metals. The algorithm uses three forecasting methodologies: linear regression (LR), Darvas boxes (DB), and Bollinger bands (BB). Our data consists of 20 years of daily prices on five precious metal futures: gold, silver, copper, platinum and palladium. We found that all of the current daily returns of the examined precious metals are negatively autocorrelated to their previous day and identified lagged interdependencies between the examined metals. Silver futures prices were found to be the best predicted by our systems, and platinum the worst. Moreover, our system is better at predicting price uptrends than downtrends for all examined techniques and commodities. Linear regression has been found to be the best technique for forecasting silver and gold price trends, while the Bollinger band technique best suits palladium forecasting.
The use of artificial intelligence (AI) in financial asset price forecasting and trading has become more and more common as the amount and speed of the flow of new financial data has increased dramatically. Algorithms are used to analyze simultaneous multi-source data. These systems are developed by market experts and are usually applied to 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 hedge against inflation (see, for example, [1]) or stock market crashes. In the following research, we designed, optimized and tested three algorithmic trading systems suitable for trading precious metal futures. Our long time data allows us to test the performance of our system over changing economic conditions. The technical analysis approach used here, commonly used by practitioners to trade stocks and foreign exchanges, relies on historical data to predict future prices. We used the particle swarm optimization (PSO) algorithm as our primary optimization tool due to its ability to handle multi-objective optimization simultaneously.
Many researchers have tried to demonstrate the ability of such algorithmic trading systems to achieve extraordinary returns for stocks, currencies and indices. However, many researchers focus on stocks and foreign exchange and partially neglected commodity futures and especially precious metal futures. The following research aims to fill that gap with an understanding of three algorithmic trading strategies that have been programmed according to the uniqueness of the precious metals 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), Darvas boxes (DB) and Bollinger bands ( BB). We followed [2], which concluded that LR and DB could help traders predict Bitcoin short-term price trends. Our 20 years of data was divided into 10 years of training and optimization and 10 years of testing the business results. We have found that it is possible to predict short-term price trends of precious metals. Silver futures prices were found to be best predicted by our systems, and platinum was the worst. Our system is better at predicting price uptrends than downtrends for all techniques and commodities examined. Linear regression has been found to be the best technique for forecasting silver and gold prices, while the Bollinger band technique is best suited to palladium forecasting.
Our system is based on pattern recognition, which is a developing AI field that helps us understand different chaotic phenomena. [3] argued that the applicability of Bayesian methods was greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. An important foundation for learning input-output mapping from a set of examples was presented by [4]. They developed a theoretical framework for the approximation method based on regulatory networks that are closely related to pattern recognition. Their methodologies included task-dependent clustering and dimensionality reduction. Other researchers have provided an understanding of the mathematical concepts behind forecasting methods that are based on probabilistic derivatives. [5] provided a joint introduction to Gaussian processes (GP) and significant vector machines (RVM-developed by [6]). They found that RVMs allow the selection of more general basis functions, while the behavior of predictive variance is generally counterintuitive. [7] examined the GP and RVM models and concluded that probabilistic models could produce forecast distributions instead of point forecasts.
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Most researchers who have tried to explain precious metal prices have done so by linking 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 is driven by shocks originating from the economic uncertainty and risk appetite of investors who dominate the stock market. Other researchers have focused on the interrelationships between the prices of the major precious metals. [10] showed that precious metals were strongly correlated with each other in the last decade. [11] documented that weekly changes in traders’ positions have a destabilizing effect on subsequent conditional volatility in gold, silver, and palladium futures markets.
Other researchers have linked precious metal prices to each other and other commodities. [12] examined spillovers among six commodity futures markets and found that both gold and silver are information transmitters to other commodity futures markets. [13] examined the effect of oil price changes on precious metals prices. They identified the safe nature of precious metals against an oil price drop.
Previous researchers have also tried to build AI systems to predict prices of precious metals. [14] proposed a model that combines the adaptive neuro-fuzzy inference system and a genetic algorithm. [15] discovered hidden patterns governing the evolution of systems. Unlike these attempts to predict precious metals prices, we 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 metal futures. We used a lagged multi-dimensional step model to examine lagged correlations between the daily return 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 short-term autocorrelations of returns and lagged dependencies between the stock prices and helped us design our trading systems.
We designed our algorithmic trading system to report the actual trading results: net profit (NP), percentage of profitable trades of all trades (PP), and the profit factor (PF). NP is the dollar value of the total net profit generated by the trading system, PP is the percentage number of winning trades out of the total set of trades generated by the system, and PF is defined as gross profits divided by gross losses. We programmed three algorithmic systems based on three complex trading technical tools and changed their configuration until we achieved maximum profit in terms of NP and PF. The designed systems are based on three methodologies: linear regression, Darvas boxes and Bollinger bands, which are well-known technical formations that are usually used to analyze investment opportunities for stock and currency traders. We then optimized NP and PF by changing the settings behind our systems and dividing the performance of the system into long and short positions.
The complexity of our systems requires multi-objective optimization formulas. We chose particle swarm optimization (PSO), developed by Kennedy and Eberhart ([16, 17]) as our main optimization method. This methodology enabled us to train the system in the initial period and test it in the last period. The 20 years of our examined period were divided into two distinct periods, 10 years of training and optimization and 10 years of
