Trading Rules Research Articles

ATS – Algorithmic Trading System (Gold)

In this paper, we use trading system to gold currency for using Algorithmic Trading System. Algorithms are used in algorithmic trading to carry out trades by following a predetermined set of rules and a trend. The business can create money at an unhumanely high pace of repetition. The described sets of trading rules that are transmitted to the programmes are dependent on time, significance, magnitude, or any other mathematical paradigm. Algo-trading offers the trader more than just lucrative opportunities. By eliminating the impact of human emotions on trading, increases market liquidity and improves trade accuracy. Our project seeks to advance this change in the marketplaces of the future by offering a practical and effective way to get beyond the problems associated with manual by creating an automated trading bot that uses both its own algorithms and user methods for day-to-day trading.

Enhancing the profitability of lottery strategies

Recent studies show that lottery strategies, buying non-lottery type stocks and shorting lottery-type stocks, earn positive returns on average. This study examines whether the profitability of lottery strategies is predictable, and, more importantly, whether such predictability is exploited to enhance their performance. As a predictor, we employ the speculation sentiment index recently developed by Davies (forthcoming) based on observable trading activity in the leveraged Exchange Traded Funds market. We find that the profitability of lottery strategies is predictable by the lagged speculation sentiment index both in sample and out of sample. We propose active trading rules that are implementable in real time and dynamically switch the long and short legs on the lottery strategies exploiting the predictive power of speculation sentiment. The proposed dynamic strategies significantly outperform the passive strategies, yielding significant economic gains for investors with certainty equivalent return gains of 7.41%–26.35% and increases in annualized Sharpe ratios of 0.37–1.15. • The profitability of lottery strategies is predicable both in- and out-of-sample. • Such predictability is exploited to enhance the performance of lottery strategies. • The dynamic lottery strategies significantly outperform the standard strategies.

Changes to the trading calendar and the day of the week effect in returns and volatility of the Saudi Stock Exchange

Until June 29, 2013, the trading days of the Saudi Stock Exchange (TADAWUL) were from Sunday to Wednesday. From June 29, 2013, TADAWUL changed trading days and started trading from Sunday to Thursday. This paper investigates whether this change has impacted the day-of-the-week effect on returns and volatility of the Saudi Stock Exchange. After estimating several GARCH-type models, the EGARCH (2,2) model was selected for the analysis. The study found that the stock return on the week’s first trading day (Saturday) was positive during the previous trading calendar.In contrast, the current trading calendar observed a positive stock return on the last trading day of the week (Thursday). Further, a negative volatility exists at the end of the week during the previous trading calendar. At the beginning of the week, there is a high degree of positive volatility during the current trading calendar. These results indicate that the behavior of stock returns is different between the two trading calendar regimes. In addition, the behavioral patterns on other trading days suggest that the Saudi stock market does not conform to the weak form of the efficient market hypothesis. The above findings indicate that investors in the Saudi stock market could devise trading rules to predict the market index and earn abnormal returns consistently.

Profitability of Ichimoku-Based Trading Rule in Vietnam Stock Market in the Context of the COVID-19 Outbreak.

Ichimoku Kinkohyo or Ichimoku Cloud Chart is one of the most popular technical indicators used by traders all over the world. However, its profitability is heavily influenced by the market environment, to which it is applied. Furthermore, the COVID-19 outbreak may have an impact on the market environment as well as the performance of all technical indicators. This study is the first to look into the profitability of Ichimoku-based trading rules in the Vietnamese stock market in the context of the COVID-19 outbreak. More particularly, the COVID-19 outbreak has a positive influence on the performance of this strategy when considering the entire market as well as a variety of industries including real estate industry, food and beverage industry, resource industry, and automotive and electronic components industry. Compared to the pre-pandemic period, the return on investment obtained per each transaction using the Ichimoku-based strategy increased by roughly 8-9% in the pandemic period. Compared to the Buy-and-hold method, the Ichimoku-based strategy could slightly increase Accumulated return while posing a lower risk. The findings indicate that the Ichimoku-based strategy is applicable to the Vietnam stock market, regardless of the adverse effects of the pandemic on the industries.

Machine Learning and the Stock Market

AbstractPractitioners allocate substantial resources to technical analysis whereas academic theories of market efficiency rule out technical trading profitability. We study this long-standing puzzle by applying a diverse set of machine learning algorithms. The results show that an investor can find profitable technical trading rules using past prices, and that this out-of-sample profitability decreases through time, showing that markets have become more efficient over time. In addition, we find that the evolutionary genetic algorithm’s attitude in not shying away from erroneous predictions gives it an edge in building profitable strategies compared to the strict loss-minimization-focused machine learning algorithms.

Investment and trading strategies in the maritime sector: an application to the secondhand containership market

As shipping market players operate in a competitive and volatile business environment, their highly capital-intensive investment decisions to target value-generating projects are exposed to critical risks. Therefore, the paper investigates the efficiency of investment strategies based on the combination of technical trading rules and fundamental analysis in selling and purchasing ships in the container shipping market. Using historical datasets of second-hand vessel prices and time-charter rates from October 1996 to June 2021, long-run cointegrating implications and short-run causality spillover effects are examined for three groups of containerships, distinguished by their transportation capacity, viz. 725 TEUs, 1700 TEUs, and 3500 TEUs. In addition, the moving average trading rules are used to indicate the timing of investment or divestment decisions through the analysis period. The results for vessel prices and earnings for 3500 TEU containerships appeared to be more volatile compared to smaller ships (725 TEUs and 1700 TEUs), while time-charter earnings are seen to exert an impact on second-hand prices across all vessel types. Moreover, due to higher volatility, the trading strategies based on price-earning ratios significantly outperform the buy-and-hold strategies for the 3500 TEU and the 1700 TEU containerships. On the contrary, the decision to buy-and-hold smaller container ships (725 TEU) yields higher profits than the active sale and purchase strategy. The insights provided in this paper can be used by multiple stakeholders, such as liner operators, investors, lessors, and researchers.

Technical trading rule profitability in currencies: It’s all about momentum

Recent academic and practitioner attention has focused on currency momentum. In this paper we replicate technical trading rules to assess their relationship with momentum. We find the effectiveness of technical trading rules falls significantly over time, with the mean Sharpe Ratio of our sample of portfolios falling from 0.66 in our in-sample period to 0.06 out-of-sample. Further, the returns do not survive modest transaction costs out-of-sample. We identify time series momentum as the single common factor driving returns across the range of strategies. Any abnormal return generated by technical trading rules is fully explained by time series momentum.

Can exchange-traded funds be profitably traded with the trading range breakout technical trading rule?

This study investigates whether the trading range breakout technical trading rule can be applied in exchange-traded funds. Using the samples of crude oil exchange-traded funds, namely USO, USL, UCO, and DBO, that track West Texas intermediate (WTI), the results indicate that the trading range breakout technical trading rule outperforms the benchmark buy-and-hold strategy. In addition, a volatility-based trading range breakout technical trading rule is proposed to enhance trading performance. The results suggest that the proposed TGARCH-MA can be used as a technical trading rule to assist investors and fund managers interested in using the trading range breakout in exchange-traded funds.

Improved Segregated Advancement (I-SA): a new method for solving full triangular fuzzy transportation problems

ABSTRACT In this paper, triangular fuzzy numbers (TFN) are used to represent the uncertainty of data in the transportation problems (TP), which are referred to as fuzzy transportation problems (FTP). The main issues in the FTP are the lack of information, error of the basic trading rules, incomplete fuzzy data, and the limitations of the existing fuzzy-ranking functions which is failed to compare two TFN. Segregated advancement (SA) is a separation approach where the TFN represented by low, middle, and upper are solved part by part. The flaw of SA is that it uses the classical algorithms which are NWC, LCM, and VAM. Therefore, we proposed the improvement based on the combination of total ratio cost matrix and total difference method by column without using classical ranking functions. The first and second examples illustrate the existing methods without using the SA approach. The results show that the proposed method obtained the optimal solution of FTP, whereas the existing methods produced infeasible solution. The third example presents the limitation of existing methods along with the SA approach. The results show that the proposed method is capable in solving the third example whereas the existing approach failed to solve the said example.

Research on quantitative trading strategy based on prediction and dynamic programming model

Today, the digital currency market represented by bitcoin has become an important part of the capital market. As the COVID-19 pandemic has increased volatility in capital markets in the past two years, effective trend forecasting and risk-averse investment strategies can bring substantial profits to companies and individuals and reduce the possibility of financial risks. To achieve the above objectives, we established the following models: (1)price prediction models of gold and bitcoin; (2)trading market risk judgment model; (3)revenue valuation model based on dynamic programming. This paper uses ARIMA and LSTM neural network to establish the prediction model. After checking the historical data, the best parameter fitting values of ARIMA model were obtained by using the exhaustive method according to BIC criterion, which were ARIMA (4,1,3) (gold) and ARIMA (2,1,2) (bitcoin) respectively. Then LSTM recurrent neural network is used to modify the forecast results, and the price forecast graph with high fitting degree with the real data is obtained. Then, this paper combines the technical indicators commonly used in the financial market to form a market risk judgment model. On the basis of the eight laws of buying and selling, the weight index is introduced to put forward the bull and bear market judgment rules of gold and bitcoin trading, and the tendency distribution map of the market environment in the past five years is obtained. At the same time, the buying risk coefficient is quantified by combining the maximum retraction rate. On the basis of the first two sub-models, this paper establishes a revenue valuation model based on the idea of dynamic programming. By setting the policy trigger threshold to control the risk, the problem that local optimal solution may appear in the model is improved.

Value for Equity Index Options: Expected—Not Realized—Volatility and the Distribution of Forecasts

Models of option returns neglect the distribution of expected asset volatility, unfortunately for not only derivatives traders but also investors who monitor options as <i>fear gauges</i>. Six common GARCH (generalized autoregressive conditional heteroskedasticity) models afford estimates of the physical, rather than the risk-neutral, distribution of anticipated—instead of historical—volatility, as well as of volatility disagreement. This value specification covering nine global equity indexes and five expiries from 1 to 12 months fits implied volatilities closely, with sizeable and robust error-correction speeds out of sample, all else equal. Exploratory backtests of delta-neutral trading rules produce high Sharpe ratios and alphas, with modest drawdowns and skew.

What drives trend-following profits in stocks? The role of the trading signals’ volatility

ABSTRACT We document the influence of stock volatility on trend-following profits for a global sample of 1618 blue-chip stocks, across 43911 evaluation subperiods (2004–2018). We use the price sensitivity of trend signals (i.e. signal volatility) to isolate the detrimental impact of high stock volatility manifested through excessive/inefficient trading. Despite its almost null correlation with the stocks’ mean-variance characteristics, the signal volatility greatly complements them in explaining the time series variation in trend-following excess returns. The results hold for both the buy and sell excess returns, are robust across stock markets, and conserve after considering explicit and implicit trading costs. Investors can use ex postsignal volatility estimates as a valid criterion to choose across potential trading rules, according to their specific levels of risk aversion and transaction costs.

The overall and time-varying efficiency test for the carbon market in China

This paper analyzes the overall and time-varying efficiency of China's carbon market, which is crucial for carbon risk management and policymaking. Due to the severe illiquidity problems other pilots face, we first select Shenzhen, Guangdong, and Hubei pilots out of the eight carbon pilots for our study based on the statistical analysis. We then test the overall efficiency of the markets using the LM, CD, and Wild-Bootstrap variance ratio test. The sliding window method is combined with the above three variance ratio test methods to validate the time-varying efficiency. The results prove that the overall efficiency of the three carbon pilots has not yet reached a weak form. And we observe that the short-term efficiency is time-varying and may be weak-form. The study further found Hubei to be the most efficient carbon pilot, followed by Guangdong and Shenzhen. Finally, this paper conducts a qualitative analysis on the low efficiency from five aspects namely carbon trading rules, market liquidity, price volatility, trading volume, and types of trading products. Several conclusions and recommendations are proposed for policymakers and investors.

An Evaluation of the Regional Comprehensive Economic Partnership Agreement: Market Access and Trading Rules

AbstractThe Regional Comprehensive Economic Partnership (RCEP) agreement was signed on November 15, 2020. This marks the formal conclusion of the world's largest free trade agreement in terms of population and economic scale. The RCEP agreement covers new liberalization commitments in goods, services, investment, and movement of natural persons, and addresses some emerging behind‐the‐border trade issues, to forge more transparent, open, and inclusive trade rules. Its aim is to build a comprehensive, modern, inclusive, and high‐quality free trade agreement. This paper summarizes the framework and highlights of the RCEP agreement, measures the extent of tariff reduction from various perspectives, makes a quantitative assessment of the level of service trade liberalization of the member states adopting positive list commitments, and makes an in‐depth analysis of trade rules and provisions in the RCEP agreement. Furthermore, this paper also makes a comprehensive comparison of main provisions among the Comprehensive and Progressive Agreement for Trans‐Pacific Partnership, RCEP, and US‐Mexico‐Canada Agreement. The paper concludes with policy recommendations for China to promote the implementation of the RCEP agreement.

COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTING DIAMOND PRICES BASED ON EXPLORATORY DATA ANALYSIS

Diamonds are a unique commodity whose socially generated notions significantly influence perceived value. To study how a diamond's physical attributes might predict its price, a massive dataset of loose diamonds scraped from an online diamond store is subjected to data mining, which reveals that diamond weight, color, and clarity are the most influential determinants of diamond pricing. Therefore, submit a proposal for an Exploratory Data Analysis that includes a component that analyses various parts of news articles using LASSO Regression, ElasticNet Regression, and Random Forest Regression. This system is trained on past data to forecast diamond prices while retaining an easily interpretable trading approach concerning rule complexity. The suggested strategy beats cutting-edge methods for prediction accuracy and interpretability, such as extreme learning machines using deep learning. Our data indicate that the news impact factor is crucial for forecasting. Demonstrate that the suggested system outperforms the average yearly return while offering a set of language trading rules that are interpretable. This has substantial repercussions for investors. A significant degree of subjectivity in diamond pricing may result from diamond dealers' price concealment techniques.

Timing Profits to Realize Gains with $1,000: Evidence from Five-Year Prices of Bitcoin and Gold

Quantitative investment methods use mathematical models to estimate the price trends of financial assets based on their historical movements and to accurately choose the best time to trade, enhancing returns and their stability. In this paper, we construct quantitative investment models and strategies and practice them in a five-year portfolio of cash, gold and bitcoin, achieving considerable returns. To form a complete investment strategy, two main models are required, namely, one to forecast prices of gold and bitcoin, and one to decide on specific trading operations. To forecast prices, ARIMA-GARCH models group is firstly built. In the method of price forecasting using time series analysis, the ARIMA time series model can grasp the dynamic pattern and trend of prices. However, according to the empirical data, it is found that it is difficult to reflect the price volatility characteristics using ARIMA model forecasting. Therefore, we consider the conditional heteroskedasticity GARCH model to reflect the volatility of the time series in practice. In this section, we use the data given in the topic to construct a hybrid model ARIMA (4,1,4)-GARCH (1,1) by combining the ARIMA model with the GARCH model to forecast the price of gold and the price of bitcoin. After forecasting of the future prices of gold and bitcoin, we apply a dynamic program model to design the specific daily investment strategies based on the prediction. We divide the investment strategies into whether to trade a certain asset and how much to trade. In the specific discussion of each case, we introduce qualifying conditions from the perspective of risk and trading rules, and determine the amount of trading by solving the objective function under the qualifying conditions to form a complete investment strategy. Setting a 10% cap on our gold position and a 90% cap on our bitcoin position without changing transaction fees, our portfolio ends up at $37370.91 after 5 years, with an ideal ROI of 3637.09%, an IRR of 205.19%. For complementary, we conduct sensitivity analysis. To study the impact of transaction costs on total returns, we change the transaction costs of gold and bitcoin 1% at a time simultaneously and calculate the change in expected returns, while using two models, the normalized coefficient method and the shapley value decomposition method, to determine the sensitivity of the transaction costs of both to total returns. It is finally shown that the higher the transaction cost, the lower the return; bitcoin’s transaction cost is more sensitive and contributes more. At the end of the paper, we present evidence that our model provides a better strategy by proving the ARIMA-GARCH model has the ability to forecast prices more accurately.

Stock Portfolio Optimization Using a Combined Approach of Multi Objective Grey Wolf Optimizer and Machine Learning Preselection Methods.

The present paper deals with optimizing the stock portfolio of active companies listed on the Tehran Stock Exchange based on the forecast price. This paper is based on a combination of different filtering methods such as optimization of trading rules based on technical analysis (ROC, SMA, EMA, WMA, and MACD at six levels—Very Very Weak (VVW), Very Weak (VW), Weak (W), Strong (S), Very Strong (VS), and Very Very Strong (VVS)), Markov Chains, and Machine Learning (Random Forest and Support Vector Machine) Filter stock exchanges and provide buy signals between 2011 and 2020. In proportion to each combination of filtering methods, a buy signal is issued and based on the mean-variance (M-V) model, the stock portfolio is optimized based on increasing the portfolio return and minimizing the stock portfolio risk. Based on this, out of 480 companies listed on the Tehran Stock Exchange, 85 active companies have been selected and stock portfolio optimization is based on two algorithms, MOGWO and NSGA II. The analysis results show that the use of SVM learning machine leads to minor correlation error than the random forest method. Therefore, this method was used to predict stock prices. Based on the results, it was observed that if the shares of companies are filtered, the risk of transactions decreases, and the return on the stock portfolio increases. Also, if two filtering methods are applied simultaneously, the stock portfolio returns slightly and the risk increases. In the analysis, MOGWO algorithm has obtained 133.13% stock return rate with a risk of 3.346%, while the stock portfolio returns in NSGA II algorithm 107.73, with a risk of 1.459%. Comparison of solution methods shows that the MOGWO algorithm has high efficiency in stock portfolio optimization.

Market Structure and Instability Artifacts in Heterogeneous Agent Models: Lessons from Implicit Discretizations of Stiff Equations

We consider a standard heterogeneous agent model (HAM) that is widely used to analyze price developments in financial markets. The model is linear in log-prices and, in its basic setting, populated by fundamentalists and chartists. As the number of fundamentalists increases and exceeds a specific threshold, oscillations occur whose amplitude might even grow exponentially over time. From an economic perspective to adequately interpret such instability results it is indispensable to ensure that the characteristics and specific building blocks of the HAM are not at odds with the underlying structure of financial markets, in particular the specific trading rules. We expect that in markets with (almost) only fundamentalist traders prices might in the most extreme case oscillate, but never explode. In addition, if limit orders are available, prices should converge monotonically. Finally, if price bubbles occur in financial markets with fundamentalist traders, they should only result from the interactions between fundamentalists and the other traders, e.g., chartists, but not from fundamentalists’ decisions alone. From a mathematical perspective we show that the instability result common to the standard approach can be related to a “hidden” explicit discretization of a stiff ordinary differential equation contained in the model. Replacing this explicit discretization by an implicit one improves the model as it removes this artifact, bringing the model’s prediction in line with standard theory. The refined model still allows for price overshoots, bubbles, and crashes. However, in the implicit model these instabilities are caused by chartists and not by an unintended artifact.

Blockchain-Based Smart Contract for P2P Energy Trading in a Microgrid Environment

The purpose of this study is to propose a method of peer-to-peer (P2P) energy trading that allow prosumers with energy deficiency to buy energy from prosumers with excess energy in a microgrid system. The proposed method solves the problems associated with lack of trust in P2P energy trading and utilized the blockchain technology that made it impossible to tamper with data. The data is referred as transaction generated by using blockchain. A blockchain based smart contract execute the trading and payment rules without the intermediaries. Thus, the security and fairness of energy trading are significantly enhanced compared to conventional database technology. Without the third-party intervention, a miner that is selected among the participants in the microgrid environment process all the transaction generated from buying and selling energy. The smart contract consists of two main components; bidding and settlement module, payment module. The smart contract deployed in blockchain test network to test the interaction of smart contract. From the simulation, the proposed method is validated using realistic data with the Ethereum Virtual Machine (EVM). The method will be expected to be useful to designers who need to integrate renewable energy in a microgrid system.

High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method

Futures price-movement-direction forecasting has always been a significant and challenging subject in the financial market. In this paper, we propose a combination approach that integrates the XGBoost (eXtreme Gradient Boosting), SMOTE (Synthetic Minority Oversampling Technique), and NSGA-II (Non-dominated Sorting Genetic Algorithm-II) methods. We applied the proposed approach on the direction prediction and simulation trading of rebar futures, which are traded on the Shanghai Futures Exchange. Firstly, the minority classes of the high-frequency rebar futures price change magnitudes are oversampled using the SMOTE algorithm to overcome the imbalance problem of the class data. Then, XGBoost is adopted to construct a multiclassification model for the price-movement-direction prediction. Next, the proposed approach employs NSGA-II to optimize the parameters of the pre-designed trading rule for trading simulation. Finally, the price-movement direction is predicted, and we conducted the high-frequency trading based on the optimized XGBoost model and the trading rule, with the classification and trading performances empirically evaluated by four metrics over four testing periods. Meanwhile, the LIME (Local Interpretable Model-agnostic Explanations) is applied as a model explanation approach to quantify the prediction contributions of features to the forecasting samples. From the experimental results, we found that the proposed approach performed best in terms of direction prediction accuracy, profitability, and return–risk ratio. The proposed approach could be beneficial for decision-making of the rebar traders and related companies engaged in rebar futures trading.