Explore the vast range of titles available.

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.

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.

Investors and traders seek methods to identify reversal and continuation to better time their trades.This applies for virtually everyone, whether employing a swing trading strategy, engaging in options trading, or timing entry and exit to spot bull and bear reversals.

In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that the gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.

The recent financial network analysis approach reveals that the topologies of financial markets have an important influence on market dynamics. However, the majority of existing Finance Big Data networks are built as undirected networks without information on the influence directions among prices. Rather than understanding the correlations, this research applies the Granger causality test to build the Granger Causality Directed Network for 33 global major stock market indices. The paper further analyzes how the markets influence one another by investigating the directed edges in the different filtered networks. The network topology that evolves in different market periods is analyzed via a sliding window approach and Finance Big Data visualization. By quantifying the influences of market indices, 33 global major stock markets from the Granger causality network are ranked in comparison with the result based on PageRank centrality algorithm. Results reveal that the ranking lists are similar in both approaches where the U.S. indices dominate the top position followed by other American, European, and Asian indices. The lead-lag analysis reveals that there is lag effects among the global indices. The result sheds new insights on the influences among global stock markets with implications for trading strategy design, global portfolio management, risk management, and markets regulation.

• 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.

Risk Takers: Uses and Abuses of Financial Derivatives goes to the heart of the arcane and largely misunderstood world of derivative finance and makes it accessible to everyone—even novice readers. Marthinsen takes us behind the scenes, into the back alleyways of corporate finance and derivative trading, to provide a bird’s-eye view of the most shocking financial disasters of the past quarter century. The book draws on real-life stories to explain how financial derivatives can be used to create or to destroy value. In an approachable, non-technical manner, Marthinsen brings these financial derivatives situations to life, fully exploring the context of each event, evaluating their outcomes, and bridging the gap between theory and practice.

If you have experience in option trading, or a strong understanding of the options markets, but want to better understand how to trade given certain market conditions, this is the book for you.Many people have some knowledge of trading strategies, but have no idea how to pull it all together.