Automated Trading Research Articles

The Quality Trading Coefficient. General Formula to Qualify a Trade and a Trading Methodology

Trading the financial markets is a wide activity nowadays. There are several indicators to measure this activity. The drawdown, the profit factor and the trading efficiency are some of them. This paper will present a different one: the quality trading coefficient. The new formula will indicate several particular aspects originating from the human psychological emotions and fears during transactions on the way to make profit. If the trade was fast and the price moved continuous to the profit target level, the quality trading coefficient will have a positive value close to one. When most of the time the trading position was on loss and the profit was made on the last part of the trade time, the coefficient will have a positive value more close to zero. If the trade is closed on losses, the coefficient will have a negative value tends to minus one if the market goes against the direction of the trade on the entire interval. Measuring this coefficient for all trades made with a trading strategy we can compare and categorize the strategies for any market and for any time interval. The quality trading coefficient became a new optimization criterion for the trading strategies used in automated trading systems.

Take Profit and Stop Loss Trading Strategies Comparison in Combination with an MACD Trading System

A lot of strategies for Take Profit and Stop Loss functionalities have been propounded and scrutinized over the years. In this paper, we examine various strategies added to a simple MACD automated trading system and used on selected assets from Forex, Metals, Energy, and Cryptocurrencies categories and afterwards, we compare and contrast their results. We conclude that Take Profit strategies based on faster take profit signals on MACD are not better than a simple MACD strategy and of the different Stop Loss strategies based on ATR, the sliding and variable ATR window has the best results for a period of 12 and a multiplier of 6. For the first time, to the best of our knowledge, we implement a combination of an adaptive MACD Expert Advisor that uses back-tested optimized parameters per asset with price levels defined by the ATR indicator, used to set limits for Stop Loss.

Reliable Signals Based on Fisher Transform for Algorithmic Trading

Abstract Trading and investment on financial markets are common activities today. A very high number of investors, companies, public or private funds are buying and selling every day with a single purpose: the profit. The common questions for any market participant are: when to buy, when to sell and when is better to stay away from the market risk. In order to answer all these questions, many trading strategies are used to establish the best moments to entry or to exit the trades. Due to the large price volatility, a significant part of the trades is set up automatically today by computers using algorithmic trading procedures. For this particular field, special aspects must be met in order to automate the trading process. This paper presents one of these mathematical models used in automated trading systems, a method based on the Fisher transform. A general form of this method will be presented, the functional parameters and the way to optimize them in order to reduce the risk. It will be also suggested a method to build reliable trading signals with the Fisher function in order to be automated. Three different trading signal types will be explained together with the significance of the functional parameters in the price field. A code sample will be included in this paper to prove the simplicity of this method. Real results obtained with the Fisher trading signals will be also presented, compared and analyzed in order to show how this method can be implemented in algorithmic trading.

Do small caps generate above average returns in the Brazilian stock market?

Some studies suggest that low capitalization stocks have great potential to provide returns above the market average, although some indicate that investment in small caps should be avoided, since they are market anomalies with low liquidity. This article develops a method based on an automated trading system (ATS) applied to the Brazilian stock market, and investigates the relevance of small caps to the investor. The study indicates that, in the case of the Brazilian stock exchange, although there is a possibility of high returns the profitability of technical analysis of small caps is similar to strategies using blue chips.

A hybrid financial trading support system using multi-category classifiers and random forest

This study presents a decision support system for algorithmic trading in the financial market that uses a new hybrid approach for making automatic trading decision. The hybrid approach integrates weighted multicategory generalized eigenvalue support vector machine (WMGEPSVM) and random forest (RF) algorithms (named RF-WMGEPSVM) to generate “Buy/Hold/Sell” signals. The WMGEPSVM technique has an advantage of handling the unbalanced data set effectively. The input variables are generated from a number of technical indicators and oscillators that are widely used in industry by professional financial experts. Selection of relevant input variables can enhance the predictive capability of the prediction algorithms. RF technique is employed to discover the optimal feature subset from a large set of technical indicators. The proposed hybrid system is tested using “walk forward” approach for its capability of taking an automatic trading decision on daily data of five index futures, viz., NASDAQ, DOW JONES, S&P 500, NIFTY 50 and NIFTY BANK. RF-WMGEPSVM achieves the notable improvement over the buy/hold strategy and other predictive models contemplated in this study. It is also observed that combining WMGEPSVM with RF further improves the results. Empirical results confirm the effectiveness of RF-WMGEPSVM in the real market scenarios having bullish, bearish or flat trend.

Finding attractive technical patterns in cryptocurrency markets

The cryptographic currency market is an emerging venue for traders looking to diversify their investments. We investigate the use of genetic programming (GP) for finding attractive technical patterns in a cryptocurrency market. We decompose the problem of automatic trading into two parts, mining useful signals and applying them to trading strategies, and focus our attention on the former. Extensive experiments are performed to analyze the factors that affect the quality of the solutions found by the proposed GP system. With the introduction of domain knowledge through extended function sets and the inclusion of diversity preserving mechanism, we show that the proposed GP system successfully finds attractive technical patterns. Out-of-sample performance of the patterns indicates that the GP consistently finds signals that are profitable and frequent. A trading simulation with the generated patterns suggests that the captured signals are indeed useful for portfolio optimization.

Examination of the profitability of technical analysis based on moving average strategies in BRICS

In this paper, we investigated the profitability of technical analysis as applied to the stock markets of the BRICS member nations. In addition, we searched for evidence that technical analysis and fundamental analysis can complement each other in these markets. To implement this research, we created a comprehensive portfolio containing the assets traded in the markets of each BRICS member. We developed an automated trading system that simulated transactions in this portfolio using technical analysis techniques. Our assessment updated the findings of previous research by including more recent data and adding South Africa, the latest member included in BRICS. Our results showed that the returns obtained by the automated system, on average, exceeded the value invested. There were groups of assets from each country that performed well above the portfolio average, surpassing the returns obtained using a buy and hold strategy. The returns from the sample portfolio were very strong in Russia and India. We also found that technical analysis can help fundamental analysis identify the most dynamic companies in the stock market.

Regressed Profits on Total Trades, Winners, Size, and Intraday Drawdown of Commodities, Equities, Fixed Income, and Trade Duration Exit Days

This paper describes Business Intelligence Systems and Engineering of Profits from trading. This paper discusses a system (The System) that includes regressed profits on Total Trades, Winners, Size, and Intraday drawdown of Commodities, Equities, Fixed Income, and Trade duration Exit Days. The System uses the SP including a variety of traded securities. The System applies trading portfolio theories, such as asset diversification, and extends these theories to human resource management diversification. The System integrates an Automated Trading System (ATS), of algorithmic trading, with a Simple Moving Average (SMA) trading strategy. This paper also tests several null hypotheses on predictor independent variables and their coefficient of determination; rejecting the null hypotheses that these determinations are equal to zero. Furthermore, this paper links constructs to SMA and Trend-Following Ordinary Least Square (OLS), to the World Wide Web (WWW), and to High Frequency Trading (HTF). Additionally, this paper discusses topping the WWW’s Search Engine Results page (SERP) and discusses Search Engine Optimization (SEO). The System includes Mobile Phones (smart phones and tablets), Audit Counter Party Audio/Video (AV), a surveillance approach to IP (Internet Protocol) both fixed and mobile, and wired and wireless AV cameras. The System also includes wearable cameras; such as Google-Glass. Additionally, The System includes screen-sharing broadcasting, archiving shots, and webcams. The System also implements Google Plus’s Hangouts on Air (G+ HOA), as well as Voice over IP (VOIP) phone video conferencing, which is similar to Skype. Software robots (BOTs) and classroom Instructor’s Lecture Videos, which are recorded with the instructor’s tablet, supplement The System. Furthermore, The System deals with Quantitative Robotic Algorithmic Automated Securities (e.g., Stocks, Equities, Treasuries, Commodities, and Futures). Additionally The System includes Trading System Strategy, internet security, forensic accounting, and expert witness testimony, point of view (POV) surveillance, fraud examination, and web programming. The System helps identify misinformation such as “cooking the books” from Toxic Paper Sub-Prime Mortgages, CDS, and CSOs Material Misstatements of the Financial Services Industry; such as the Crisis Challenges and counterparty surveillance of collateralized debt obligations.

Evaluation of the performance of automated trading systems based on non-parametric methods

Evaluation of the performance of automated trading systems based on non-parametric methods

Automatic trading method based on piecewise aggregate approximation and multi-swarm of improved self-adaptive particle swarm optimization with validation

Financial time series represent the stock prices over time and exhibit behavior similar to a data stream. Many works report on the use of data mining techniques to predict the future direction of stock prices and to discover patterns in the time series data to provide decision support for trading operations. Traditional optimization methods do not take into account the possibility that the function to be optimized, namely, the final financial balance for operations considering some stock, may have multiple peaks, i.e., be represented by multimodal functions. However, multimodality is a known feature of real-world financial time series optimization problems. To deal with this issue, this article proposes the PAA-MS-IDPSO-V approach (Piecewise Aggregate Approximation - Multi-Swarm of Improved Self-adaptive Particle Swarm Optimization with Validation). The proposed method aims to find patterns in financial time series to support investment decisions. The approach uses multi-swarms to obtain a better particle initialization for the final optimization phase since it aims to tackle multimodal problems. Furthermore, it uses a validation set with early stopping to avoid overfitting. The patterns discovered by the method are used together with investment rules to support decisions and thus help investors to maximize the profit in their operations in the stock market. The experiments reported in this paper compare the results obtained by the proposed model with the Buy-and-Hold, PAA-IDPSO approaches and another approach found in the literature. We report on experiments conducted with S&P100 index stocks and using the Friedman Non-Parametric Test with the Nemenyi post-hoc Test both with 95% confidence level. The results show that the proposed model outperformed the competing methods and was able to considerably reduce the variance for all stocks.

A meta-grammatical evolutionary process for portfolio selection and trading

This study presents the implementation of an automated trading system that uses three critical analyses to determine time-decisions and portfolios for investment. The approach is based on a meta-grammatical evolution methodology that combines technical, fundamental and macroeconomic analysis on a hybrid top-down paradigm. First, the method provides a low-risk portfolio by analyzing countries and industries. Next, aiming to focus on the most robust companies, the system filters the portfolio by analyzing their economic variables. Finally, the system analyzes prices and volumes to optimize investment decisions during a given period. System validation involves a series of experiments in the European financial markets, which are reflected with a data set of over nine hundred companies. The final solutions have been compared with static strategies and other evolutionary implementations and the results show the effectiveness of the proposal.

A strategy based on mean reverting property of markets and applications to foreign exchange trading with trailing stops

We propose a strategy for automated trading, outline theoretical justification of the profitability of this strategy, and overview the backtesting results in application to foreign currencies tradi...

A hybrid automated trading system based on multi-objective grammatical evolution

A hybrid automated trading system based on multi-objective grammatical evolution

Examining the profitability of automatic trading strategies with a focus on trend indicators

In this paper, we examine the usefulness of a much-noticed trend indicator which is called the 1-2-3 indicator. Applying trend-following trading strategies which are based on this trend indicator t...

AN EMPIRICAL STUDY ON THE DYNAMIC RELATIONSHIP BETWEEN CRUDE OIL PRICES AND NIGERA STOCK MARKET

In this paper, we have examined the crude oil price on the performance of Nigerian stock exchange and exchange rate act as the plausible countercyclical tool .we have applied the different models and collected the results that crude oil prices have direct impact on the stock exchange of Nigeria. The Nigeria stock exchange is regulated by the Securities and Exchange Commission .Nigeria stock exchange has the automated trading system. The basic facility of Nigeria trading system is (ATS),it is helpful to remote trading system.Consequently, most of the investorsdo trade with the method of ATS.This study is also proving that Nigeria stock exchange has influenced on the performance of the economy, Impact of oil crisis on the Nigeria stock exchange, Impact of crude oil crisis on the development of country, Effect of exchange rate policy on the performance of Nigeria stock exchange.

Evaluation of on-line trading systems: Markov-switching vs time-varying parameter models

Automatic trading systems, to support the decisions of investors in financial markets, are increasingly used nowadays. Such systems process data on-line and provide signals of buy and sell in correspondence of pits and peaks of the market. Real-time detection of turning points in financial time series is a challenging issue and can only be performed with sequential methods. This paper considers non-linear and non-stationary dynamic models used in statistics and econometrics, and evaluates their performance. In particular, it compares Markov switching (MS) regression and time-varying parameter (TVP) methods; the latter extend moving-average (MA) techniques which are widely used by traders. The novel approach of this paper is to select the coefficients of the detection methods by optimizing the profit objective functions of the trading activity, using statistical estimates as initial values. The paper also develops a sequential approach, based on sliding windows, to cope with the time-variability of MS coefficients. An extensive application to the daily Standard & Poor 500 index (the world's leading indicator of stock values) in the period 1999–2015, provides evidence in favor of models with a few parameters. This seems a natural consequence of the complexity of the gain maximization problem, which usually admits multiple local solutions. Directions for further research are represented by multivariate detection methods and the development of recursive algorithms for gain optimization.

Efficient Utilization Condition of MACD on Stock Market and Nontrend Status Detecting Indicatior

Background/Objectives: In the trend stock market, when low frequency components determine wave form of stock price, MACD can be useful for predicting stock price. The MA and MACD occurs time delay and whipsaw problem. In order to use the MACD efficiently, we propose indicators are derived from the power measurement of the price waveform. Methods/Statistical Analysis: The proposed method differentiates trend market against nontrend market. In this method, given stock signal is converted into the sum of many frequency components by using the fourier transform and the power of low frequency components is measured. Findings: When power values increase rapidly it is highly likely that market is in trend market and when the values linearly decrease then the inflection point in stock price waveform can be occurred. Therefore, once appropriate reference line is set up, one can determine that trend market is in progress if values increase passing the reference line, that opposite trend market after inflection point is in progress if values decrease passing the reference line using power calculation. Improvements/Applications: The market is determined as 'trend market' if the power value is larger enough, and it is remarkably helpful and particularly useful at automatic trading systems.

Developing a rule change trading system for the futures market using rough set analysis

Many technical indicators have been selected as input variables in order to develop an automated trading system that determines buying and selling trading decision using optimal trading rules within the futures market. However, optimal technical trading rules alone may not be sufficient for real-world application given the endlessly changing futures market. In this study, a rule change trading system (RCTS) that consists of numerous trading rules generated using rough set analysis is developed in order to cover diverse market conditions. To change the trading rules, a rule change mechanism based on previous trading results is proposed. Simultaneously, a genetic algorithm is employed with the objective function of maximizing the payoff ratio to determine the thresholds of market timing for both buying and selling in the futures market. An empirical study of the proposed system was conducted in the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. The proposed trading system yields profitable results as compared to both the buy-and-hold strategy, and a system not utilizing a genetic algorithm for maximizing the payoff ratio.

AN EMPIRICAL STUDY ON THE DYNAMIC RELATIONSHIP BETWEEN CRUDE OIL PRICES AND NIGERA STOCK MARKET

In this paper, we have examined the crude oil price on the performance of Nigerian stock exchange and exchange rate act as the plausible countercyclical tool .we have applied the different models and collected the results that crude oil prices have direct impact on the stock exchange of Nigeria. The Nigeria stock exchange is regulated by the Securities and Exchange Commission .Nigeria stock exchange has the automated trading system. The basic facility of Nigeria trading system is (ATS),it is helpful to remote trading system .Consequently, most of the investors do trade with the method of ATS .This study is also proving that Nigeria stock exchange has influenced on the performance of the economy. Impact of oil crisis on the Nigeria stock exchange.) Impact of crude oil crisis on the development of country. Effect of exchange rate policy on the performance of Nigeria stock exchange.

An Empirical Study on the Dynamic Relationship between Crude Oil Prices and Nigeria Stock Market

In this paper, we have examined the crude oil price on the performance of Nigerian stock exchange and exchange rate act as the plausible countercyclical tool. We have applied the different models and collected the results that crude oil prices have direct impact on the stock exchange of Nigeria. The Nigeria stock exchange is regulated by the Securities and Exchange Commission. Nigeria stock exchange has the automated trading system. The basic facility of Nigeria trading system is (ATS), it is helpful to remote trading system. Consequently, most of the investors do trade with the method of ATS. This study is also proving that Nigeria stock exchange has influenced on the performance of the economy. Impact of oil crisis on the Nigeria stock exchange. Impact of crude oil crisis on the development of country. Effect of exchange rate policy on the performance of Nigeria stock exchange.