This research investigates the ability of genetic programming (GP) to build profitable trading strategies for the Foreign Exchange Market (FX) of three major currency pairs (EURUSD, USDCHF and EURCHF) using one hour prices from 2008 to 2011. We recognize that such environments are likely to be non-stationary. Thus, we do not require a single training partition to capture all likely future behav...

User community plays an important role in online trading platforms. It provides users a place to communicate, to solve problems and to exchange information with each other on an online trading platform which faced a bilateral market —seller and buyer participants. Beginning with the incentive strategy, this paper studies the influence of material and spiritual incentive strategies on online tra...

In this paper we propose a financial trading system whose strategy is developed by means of an artificial neural network approach based on a recurrent reinforcement learning algorithm. In general terms, this kind of approach consists in specifying a trading policy based on some predetermined investor’s measure of profitability, and in setting the financial trading system while using it. In part...

Outline Motivation / algorithmic pairs trading Model setup Detection of local mean-reversion Adaptive estimation 1. RLS with gradient variable forgetting factor 2. RLS with Gauss-Newton variable forgetting factor 3. RLS with beta-Bernoulli forgetting factor Trading strategy Pepsi and Coca Cola example Introduction Statistical arbitrage. Algorithmic pairs trading market neutral trading. Buy low,...

User community plays an important role in online trading platforms. It provides users a place to communicate, to solve problems and to exchange information with each other on an online trading platform which faced a bilateral market —seller and buyer participants. Beginning with the incentive strategy, this paper studies the influence of material and spiritual incentive strategies on online tra...

In this paper, we examine the use of GARCH models, Neural Network Regression (NNR), Recurrent Neural Network (RNN) regression and model combinations for forecasting and trading currency volatility, with an application to the GBP/USD and USD/JPY exchange rates. Both the results of the NNR/RNN models and the model combination results are benchmarked against the simpler GARCH alternative. The idea...

Allocation and exchange of many discrete resources – such as kidneys or school seats – is conducted via direct mechanisms without monetary transfers. A primary concern in designing such mechanisms is the coordinated strategic behavior of market participants and its impact on resulting allocations. To assess the impact of this implementation constraint, we construct the full class of group domin...

In contrast to empirical evidence, standard theories conclude that transaction costs only have a second order effect on liquidity premia. In this paper, we show that if one incorporates the well-established fact that market volatility during trading periods is significantly higher than during nontrading periods, then transaction costs have a first order effect that is much greater than that fou...

The paper concerns an approximate dynamic programming. It deals with a class of tasks, where the optimal strategy on a shorter horizon is close to the global optimal strategy. This property leads to a new, specific, design of the Bellman function estimation. The paper introduces the proposed approach and provides an illustrative example performed on the futures trading data.

Recent studies in financial markets suggest that technical analysis can be a very useful tool in predicting the trend. Trading systems are widely used for market assessment. This paper employs a genetic algorithm to evolve an optimized stock market trading system. Our proposed system can decide a trading strategy for each day and produce a high profit for each stock. Our decision-making model i...