Development Of Trading Systems Research Articles

A multi-model approach to the development of algorithmic trading systems for the Forex market

In the decade passed, considerable affords were made to develop effective trading systems based on different assumptions concerned with the market nature, methods for data processing and uncertainty modeling. Such systems are often so sophisticated that they can be applied only by their authors. Another limitation of them is concerned with the focus on the development of a universal single best model. Besides, any model works well only in limited time periods and fails when noticeable changes in the market behavior occur. Then a major revision or the development of a new model is inevitable. Unfortunately, usually this needs too much time. Therefore, in this paper, to avoid the above problems, the simple multi-model approach to the development of trading systems in the Forex market is proposed. It is based on some working hypotheses, which are justified in this paper. The first of them is based on the observation that the Forex is the aggregation of numerous streams (strategies) provided by the broad trades community. Therefore, we can expect that even a very simple model based on the particular trading idea or ideas may catch such a string to be profitable, at least during a small period. If we have developed a set of such simple models optimized for different currency pairs, in each trading period we can use the model providing maximal profit for a certain currency pair. The profitability of the proposed approach is illustrated by the trading results obtained on the symbols EURUSD,GBPUSD, AUDUSD and USDJPY for the timeframes H1 and H4 with the use of the Meta Trader 4 platform.

Modelling of stock market security price Dynamics Using market microstructure Data

In modern electronic stock exchanges there is an opportunity to analyze event driven market microstructure data. This data is highly informative and describes physical price formation which makes it possible to find complex patterns in price dynamics. It is very time consuming and hard to find this kind of patterns by handcrafted rules. However, modern machine learning models are able to solve such issues automatically by learning price behavior which is always changing. The present study presents profitable trading system based on a machine learning model and market microstructure data. Data for the research was collected from Moscow stock exchange MICEX and represents a limit order book change log and all market trades of a liquid security for a certain period. Logistic regression model was used and compared to neural network models with different configuration. According to the study results logistic regression model has almost the same prediction quality as neural network models have but also has a high speed of response which is very important for stock market trading. The developed trading system has medium frequency of deals submission that lets it to avoid expensive infrastructure which is usually needed in high-frequency trading systems. At the same time, the system uses the potential of high quality market microstructure data to the full extent. This paper describes the entire process of trading system development including feature engineering, models behavior comparison and creation of trading strategy with testing on historical data.

Know Your System! Turning Data Mining from Bias to Benefit Through System Parameter Permutation

The goal of this paper is to assist the trader in answering two questions: 1) What is a reasonable performance estimate of the long-run edge of the trading system? and, 2) What worst-case contingencies must be tolerated in short-run performance in order to achieve the long-run expectation? With this information, the trader can make probabilistic, data-driven decisions on whether to allocate capital to the system and once actively trading, whether the system is broken and should cease trading.Traditional trading system development leads not only to positively biased performance estimates due to the data mining bias, but much valuable information is lost in the process. The result for many traders is frustration due to poor realized trading system performance that does not live up to expectations. This paper explores how to leverage the optimization inherent in typical system development via a method named System Parameter Permutation (SPP) and to extract information that enables realistic contingency planning based on probabilities. Many traders and system developers go to great lengths to avoid the effects of randomness in trading results, knowing the large impact it may cause. In contrast, SPP embraces randomness as a tool to help uncover what may probabilistically be expected from a trading system in the future. The method is simple to apply yet very effective. The method is applied to an example rotational trading system based on relative momentum and the results are compared to traditional out-of-sample testing. The example shows how SPP fully leverages available historical data to enable deep understanding of potential risks and rewards prior to allocating capital to a trading system.

Kernel price pattern trading

A new approach to algorithmic trading system development is presented. This approach, Kernel Price Pattern Trading (KPPT P ), allows the practitioner to link the performance of a learned classifier...

The Formal and Systematic Specification of Market Structures and Trading Services

:This paper supplies methodological support for trading system development. It provides a formal framework for the systematic definition of market structures and the specification of trading services. Market structure describes the interaction among traders, and trading services identifies the trading system interface and the description of the system behavior. The framework provides guidance in applying “trace specifications” as a formal description technique for both market structure and trading services. It also details how to formally assess market structure and trading service properties. Moreover, the framework introduces concepts for building market structure taxonomies that support the systematic search and development of alternative forms of markets. The taxonomies are collections of trace specifications for market structures with specialization and extension relationships defined between them. Improvements in market quality and efficiency should result from such a disciplined and systematic approach to market structure and trading services definition and implementation.