Algorithmic trading

About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.

Intelligent stock trading system based on improved technical analysis and Echo State Network

Abstract: Stock trading system to assist decision-making is an emerging research area and has great commercial potentials. Successful trading operations should occur near the reversal points of price trends. Traditional technical analysis, which usually appears as various trading rules, does aim to look for peaks and bottoms of trends and is widely used in stock market. Unfortunately, it is not convenient to directly apply technical analysis since it depends on person's experience to select appropriate rules for individual share. In this paper, we enhance conventional technical analysis with Genetic Algorithms by learning trading rules from history for individual stock and then combine different rules together with Echo State Network to provide trading suggestions. Numerous experiments on S&P 500 components demonstrate that whether in bull or bear market, our system significantly outperforms buy-and-hold strategy. Especially in bear market where S&P 500 index declines a lot, our system still profits.

Traders' Long-Run Wealth in an Artificial Financial Market

Abstract: In this paper, we study the long-run wealth distribution of agents with different trading strategies in the framework of the Genoa Artificial Stock Market. The Genoa market is an agent-based simulated market able to reproduce the main stylised facts observed in financial markets, i.e., fat-tailed distribution of returns and volatility clustering. Various populations of traders have been introduced in a 'thermal bath' made by random traders who make random buy and sell decisions constrained by the available limited resources and depending on past price volatility. We study both trend following and trend contrarian behaviour; fundamentalist traders (i.e., traders believing that stocks have a fundamental price depending on factors external to the market) are also investigated. Results show that the strategy alone does not allow forecasting which population will prevail. Trading strategies yield different results in different market conditions. Generally, in a closed market (a market with no money creation process), we find that trend followers lose relevance and money to other populations of traders and eventually disappear, whereas in an open market (a market with money inflows), trend followers can survive, but their strategy is less profitable than the strategy of other populations.

Expiration-Day Effects of the All Ordinaries Share Price Index Futures: Empirical Evidence and Alternative Settlement Procedures

Abstract: Stock index futures were the most successful financial innovation of the 1980s. In spite of their widespread use internationally, they continue to be criticised for causing ‘aberrations’ in the stock market, particularly on expiration days when futures contracts are cash-settled. This paper examines expiration-day effects of the Sydney Futures Exchange’s All Ordinaries Share Price Index (SPI) futures and discusses alternative futures settlement procedures. Our investigations indicate that, while index stock trading volume is abnormally high near the close on expiration days, price movements are not different from those observed on other days. In other words, the SPI futures cash settlement at the close appears to have worked well through our sample period. This study also describes and analyses the two basic alternative cash settlement procedures—a single price settlement and an average price settlement.

Stock trading with cycles

Abstract: Research highlights? Reinforcement learning is used to formalize an automated process for determining stock cycles by tuningthe momentum and the average periods. ? The secondary and tertiary trends or short-term wave cycles are eliminated by a smoothing technique. ? The use of reinforcement learning (RL) as a non-arbitrage algorithmic trading system. ? Our study attempts to identify the change of a primary trend or a broad movement. ? Dynamic asset switching based on the detection of peaks and troughs within a portfolio of stock counters. Based on the principles of technical analysis, this paper proposes an artificial intelligence model, which employs the Adaptive Network Fuzzy Inference System (ANFIS) supplemented by the use of reinforcement learning (RL) as a non-arbitrage algorithmic trading system. The novel intelligent trading system is capable of identifying a change in a primary trend for trading and investment decisions. It dynamically determines the periods for momentum and moving averages using the RL paradigm and also appropriately shifting the cycle using ANFIS-RL to address the delay in the predicted cycle. This is used as a proxy to determine the best point in time to go LONG and visa versa for SHORT. When this is coupled with a group of stocks, we derive a simple form of "riding the cycles - waves". These are the derived features of the underlying stock movement. It provides a learning framework to trade on cycles. Initial experimental results are encouraging. Firstly, the proposed framework is able to outperform DENFIS and RSPOP in terms of true error and correlation. Secondly, based on the test trading with five US stocks, the proposed trading system is able to beat the market by about 50 percentage points over a period of 13years.