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In the rapid promotion of China’s electricity spot market, a large number of electricity retailers and large consumers participate in power trading, of which medium- and long-term power trading accounts for a large proportion. In the electricity spot market, the previous medium- and long-term transactions need to be closely combined with the current spot market transaction settlement rules. This paper analyzes the trading strategy of large retailers in the power market. In order to effectively reduce the total electricity cost, it is necessary to optimize the medium- and long-term transactions based on three aspects: electricity quantity and benchmark price decisions of medium- and long-term contracts, the daily electricity decomposition method in the day-ahead (DA) market, and the daily load curve decomposition strategy. According to load history characteristics that are extracted by the X12 method, daily electricity is decomposed from the medium- and long-term electricity quantity in the DA market. This paper introduces three methods of decomposing the daily load curve and proves that the particle swarm algorithm is the best method for effectively minimizing the cost in the DA market. Through analyzing the total electricity cost change pattern, we prove that the basic component of decision making is the relative relationship between the electricity price of medium- and long-term contracts and the equivalent kWh price of medium- and long-term electricity in the DA market, which is determined by the decomposition daily curve method. If the equivalent kilowatt-hour price obtained by the decomposition method in the DA market is greater than the electricity price of medium- and long-term contracts, the larger the electrical energy of medium- and long-term contracts, the lower the costs. Based on the above principles, electricity retailers can carry out planning for medium- and long-term transactions, as well as the decomposition and declaration of the daily electricity quantities and daily load curves.

In the financial market, commodity prices change over time, yielding profit opportunities. Various trading strategies have been proposed to yield good earnings. Pairs trading is one such critical, widely-used strategy with good effect. Given two highly correlated paired target stocks, the strategy suggests buying one when its price falls behind, selling it when its stock price converges, and operating the other stock inversely. In the existing approach, the genetic Bollinger Bands and correlation-coefficient-based pairs trading strategy (GBCPT) utilizes optimization technology to determine the parameters for correlation-based candidate pairs and discover Bollinger Bands-based trading signals. The correlation coefficients are used to calculate the relationship between two stocks through their historical stock prices, and the Bollinger Bands are indicators composed of the moving averages and standard deviations of the stocks. In this paper, to achieve more robust and reliable trading performance, AGBCPT, an advanced GBCPT algorithm, is proposed to take into account volatility and more critical parameters that influence profitability. It encodes six critical parameters into a chromosome. To evaluate the fitness of a chromosome, the encoded parameters are utilized to observe the trading pairs and their trading signals generated from Bollinger Bands. The fitness value is then calculated by the average return and volatility of the long and short trading pairs. The genetic process is repeated to find suitable parameters until the termination condition is met. Experiments on 44 stocks selected from the Taiwan 50 Index are conducted, showing the merits and effectiveness of the proposed approach.

A practitioner's guide to finding alpha in fixed income trading in emerging markets Emerging fixed income markets are both large and fast growing. China, currently the second largest economy in the world, is predicted to overtake the United States by 2030. Chinese fixed income markets are worth more than $11 trillion USD and are being added to global fixed income indices starting in 2019. Access for foreigners to the Indian fixed income market, valued at almost 1trn USD, is also becoming easier - a trend repeated in emerging markets around the world. The move to include large Emerging Market (EM) fixed income markets into non-EM benchmarks requires non-EM specialists to understand EM fixed income. Trading Fixed Income in Emerging Markets examines the principle drivers for EM fixed income investing. This timely guide suggests a more systematic approach to EM fixed income trading with a focus on practical trading rules on how to generate alpha, assisting EM practitioners to limit market-share losses to passive investment vehicles.  The definitive text on trading EM fixed income, this book is heavily data-driven - every trading rule is thoroughly back-tested over the last 10+ years. Case studies help readers identify and benefit from market regularities, while discussions of the business cycle and typical EM events inform and optimise trading strategies. Topics include portfolio construction, how to apply ESG principles to EM and the future of EM investing in the realm of Big Data and machine learning. Written by practitioners for practitioners, this book: Provides effective, immediately-accessible tools Covers all three fixed income asset classes: EMFX, EM local rates and EM credit Thoroughly analyses the impact of the global macro cycle on EM investing Examines the influence of the financial rise of China and its fixed income markets Includes case studies of trades that illustrate how markets typically behave in certain situations  The first book of its kind, Trading Fixed Income in Emerging Markets: A Practitioner's Guide is an indispensable resource for EM fund managers, analysts and strategists, sell-side professionals in EM and non-EM specialists considering activity in emerging markets.

• Biological market theory provides a conceptual framework to analyse trade strategies in symbiotic partnerships. A key prediction of biological market theory is that individuals can influence resource value – meaning the amount a partner is willing to pay for it – by mediating where and when it is traded. The arbuscular mycorrhizal symbiosis, characterised by roots and fungi trading phosphorus and carbon, shows many features of a biological market. However, it is unknown if or how fungi can control phosphorus value when exposed to abrupt changes in their trade environment. • We mimicked an economic ‘crash’, manually severing part of the fungal network (Rhizophagus irregularis) to restrict resource access, and an economic ‘boom’ through phosphorus additions. We quantified trading strategies over a 3-wk period using a recently developed technique that allowed us to tag rock phosphate with fluorescing quantum dots of three different colours. • We found that the fungus: compensated for resource loss in the ‘crash’ treatment by transferring phosphorus from alternative pools closer to the host root (Daucus carota); and stored the surplus nutrients in the ‘boom’ treatment until root demand increased. • By mediating from where, when and how much phosphorus was transferred to the host, the fungus successfully controlled resource value.

This paper investigates if and to what point it is possible to trade on news sentiment and if deep learning (DL), given the current hype on the topic, would be a good tool to do so. DL is built explicitly for dealing with significant amounts of data and performing complex tasks where automatic learning is a necessity. Thanks to its promise to detect complex patterns in a dataset, it may be appealing to those investors that are looking to improve their trading process. Moreover, DL and specifically LSTM seem a good pick from a linguistic perspective too, given its ability to “remember” previous words in a sentence. After having explained how DL models are built, we will use this tool for forecasting the market sentiment using news headlines. The prediction is based on the Dow Jones industrial average by analyzing 25 daily news headlines available between 2008 and 2016, which will then be extended up to 2020. The result will be the indicator used for developing an algorithmic trading strategy. The analysis will be performed on two specific cases that will be pursued over five time-steps and the testing will be developed in real-world scenarios.

This paper proposes a novel genetic algorithm to optimize recommendations from multiple trading strategies derived from the Directional Changes (DC) paradigm. DC is an event-based approach that differs from the traditional physical time data, which employs fixed time intervals and uses a physical time scale. The DC method records price movements when specific events occur instead of using fixed intervals. The determination of these events relies on a threshold, which captures significant changes in price of a given asset. This work employs eight trading strategies that are developed based on directional changes. These strategies were profiled using varying values of thresholds to provide a comprehensive analysis of their effectiveness. In order to optimize and prioritize the conflicting recommendations given by the different trading strategies under different DC thresholds, we are proposing a novel genetic algorithm (GA). To analyze the GA’s trading performance, we utilize 200 stocks listed on the New York Stock Exchange. Our findings show that it can generate highly profitable trading strategies at very low risk levels. The GA is also able to statistically and significantly outperform other DC-based trading strategies, as well as 8 financial trading strategies that are based on technical indicators such as aroon, exponential moving average, and relative strength index, and also buy-and-hold. The proposed GA is also able to outperform the trading performance of 7 market indices, such as the Dow Jones Industrial Average, and the Standard & Poors (S&P) 500.

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF (ticker symbol: SPY) based on 60 financial and economic features. DNNs and traditional artificial neural networks (ANNs) are then deployed over the entire preprocessed but untransformed dataset, along with two datasets transformed via principal component analysis (PCA), to predict the daily direction of future stock market index returns. While controlling for overfitting, a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000. Moreover, a set of hypothesis testing procedures are implemented on the classification, and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset, as well as several other hybrid machine learning algorithms. In addition, the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested, including in a comparison against two standard benchmarks.

What do hedge funds really do? These lightly regulated funds continually innovate new investing and trading strategies to take advantage of temporary mispricing of assets (when their market price deviates from their intrinsic value). These techniques are shrouded in mystery, which permits hedge fund managers to charge exceptionally high fees. While the details of each fund's approach are carefully guarded trade secrets, this book draws the curtain back on the core building blocks of many hedge fund strategies.

In recent years, research on algorithmic trading based on machine learning has been increasing. One challenge faced is getting an accurate representation of the stock market environment from multi-type data. Most existing algorithmic trading studies analyze the stock market based on a relatively single data source. However, with the complicated stock market environment, different types of data reflect the changes in the stock market from different perspectives, and how to obtain the temporal features of different types of data and integrate them to obtain a deeper representation of the stock market environment are still problems to be solved. To tackle these problems, in this study, we combine deep learning and reinforcement learning (RL) and propose a multi-type data fusion framework with deep reinforcement learning (MSF-DRL) that integrates stock data, technical indicators and candlestick charts, in which technical indicators can reduce the impact of noise in stock data. In the process of learning trading strategies under the MSF-DRL framework, the temporal features of stock data and technical indicators are extracted through a long short-term memory (LSTM) network, and a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) are successively used to extract the features of the candlestick chart. The fused features are used as the input of the RL module, which makes trading decisions on this basis. To verify the effectiveness of the MSF-DRL framework, we conducted comparative experiments on datasets composed of Chinese stocks and some stocks of the S&P 500 stock market index. Compared with the other trading strategies, our trading strategy can obtain more profits and a higher Sharpe ratio.

With the development of the Internet, information on the stock market has gradually become transparent, and stock information is easy to obtain. For investors, investment performance depends on the amount of capital and effective trading strategies. The analysis tool commonly used by investors and securities analysts is technical analysis (TA). Technical analysis is the study of past and current financial market information, and a large amount of statistical data is used to predict price trends and determine trading strategies. Technical indicators (TIs) are a type of technical analysis that summarizes possible future trends of stock prices based on historical statistical data to assist investors in making decisions. The stock price trend is a typical time series data with special characteristics such as trend, seasonality, and periodicity. In recent years, time series deep neural networks (DNNs) have demonstrated their powerful performance in machine translation, speech processing, and natural language processing fields. This research proposes the concept of attention-based BiLSTM (AttBiLSTM) applied to trading strategy design and verified the effectiveness of a variety of TIs, including stochastic oscillator, RSI, BIAS, W%R, and MACD. This research also proposes two trading strategies that suitable for DNN, combining with TIs and verifying their effectiveness. The main contributions of this research are as follows: (1) As our best knowledge, this is the first research to propose the concept of applying TIs to the LSTM-attention time series model for stock price prediction. (2) This study introduces five well-known TIs, which reached a maximum of 68.83% in the accuracy of stock trend prediction. (3) This research introduces the concept of exporting the probability of the deep model to the trading strategy. On the backtest of TPE0050, the experimental results reached the highest return on investment of 42.74%. (4) This research concludes from an empirical point of view that technical analysis combined with time series deep neural network has significant effects in stock price prediction and return on investment.