Generating long-term trading system rules using a genetic algorithm based on analyzing historical data

In current times, trading success depends on choosing a correct strategy. Algorithmic trading is often based on technical analysis - an approach where the values of one or several technical indicators are translated into buy or sell signals. Thus, every trader's main challenge is the choice and use of the most fitting trading rules. In our work, we suggest an evolutionary algorithm for generating and selecting the most fitting trading rules for interday trading, which are presented in the form of binary decision trees. A distinctive feature of this approach is the interpretation of the evaluation of the current state of technical indicators with the help of dynamic ranges that are recalculated on a daily basis. This allows to create long-term trading rules. We demonstrate the effectiveness of this system for the Top-5 stocks of the United States IT sector and discuss the ways to improve it

Algorithmic trading for online portfolio selection under limited market liquidity

We propose an optimal intraday trading algorithm to reduce overall transaction costs by absorbing price shocks when an online portfolio selection (OPS) method rebalances its portfolio. Having considered the real-time data of limit order books (LOB), the trading algorithm optimally splits a sizeable market order into a number of consecutive market orders to minimize the overall transaction costs, including both the liquidity costs and the proportional transaction costs. The proposed trading algorithm, compatible with any OPS methods, optimizes the number of intraday trades and finds an optimal intraday trading path. Backtesting results from the historical LOB data of NASDAQ-traded stocks show that the proposed trading algorithm significantly reduces the overall transaction costs when market liquidity is limited

Algorithmic Trading for Online Portfolio Selection under Limited Market Liquidity

We propose an optimal intraday trading algorithm to reduce overall transaction costs through absorbing price shocks when an online portfolio selection (OPS) rebalances its portfolio. Having considered the real-time data of limit order books (LOB), the trading algorithm optimally splits a sizeable market order into a number of consecutive market orders to minimise the overall transaction costs, including both the market impact costs and the proportional transaction costs. The proposed trading algorithm, compatible to any OPS methods, optimises the number of intraday trades as well as finds an optimal intraday trading path. Backtesting results from the historical LOB data of NASDAQ-traded stocks show that the proposed trading algorithm significantly reduces the overall transaction costs in an environment of limited market liquidity

Machine learning in quantitative finance

This thesis consists of the three chapters. Chapter 1 aims to decrease the time complexity of multi-output relevance vector regression from O(VM^3) to O(V^3+M^3), where V is the number of output dimensions, M is the number of basis functions, and V<M. The experimental results demonstrate that the proposed method is more competitive than the existing method, with regard to computation time. MATLAB codes are available at http://www.mathworks.com/matlabcentral/fileexchange/49131. The performance of online (sequential) portfolio selection (OPS), which rebalances a portfolio in every period (e.g. daily or weekly) in order to maximise the portfolio's expected terminal wealth in the long run, has been overestimated by the ideal assumption of unlimited market liquidity (i.e. no market impact costs). Therefore, a new transaction cost factor model that considers market impact costs, estimated from limit order book data, as well as proportional transaction costs (e.g. brokerage commissions or transaction taxes in a fixed percentage) is proposed in Chapter 2 for both measuring OPS performance in a more practical way and developing a new OPS method. Backtesting results from the historical limit order book data of NASDAQ-traded stocks show both the performance deterioration of OPS by the market impact costs and the superiority of the proposed OPS method in the environment of limited market liquidity. MATLAB codes are available at http://www.mathworks.com/matlabcentral/fileexchange/56496. Chapter 3 proposes an optimal intraday trading strategy to absorb the shock to the stock market when an online portfolio selection algorithm rebalances a portfolio. It considers real-time data of limit order books and splits a very large market order into a number of consecutive market orders to minimise overall transaction costs, consisting of market impact costs as well as proportional transaction costs. To be specific, it optimises both the number of intraday tradings and an intraday trading path for a multi-asset portfolio. Backtesting results from the historical limit order book data of NASDAQ-traded stocks show the superiority of the proposed trading algorithm in the environment of limited market liquidity. MATLAB codes are available at http://www.mathworks.com/matlabcentral/fileexchange/62503

Gated Bayesian Networks for Algorithmic Trading

Gated Bayesian networks (GBNs) are a recently introduced extension of Bayesian networks that aims to model dynamical systems consisting of several distinct phases. In this paper, we present an algorithm for semi-automatic learning of GBNs. We use the algorithm to learn GBNs that output buy and sell decisions for use in algorithmic trading systems. We show how using the learnt GBNs can substantially lower risks towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strategy buy-and-hold.Funding agencies: Center for Industrial Information Technology, Linkoping University (CENIIT) [09.01]; Swedish Research Council [2010-4808]</p

Bayesian Optimisation of Gated Bayesian Networks for Algorithmic Trading

Gated Bayesian networks (GBNs) are an extension of Bayesian networks that aim to model systems that have distinct phases. In this paper, we aim to use GBNs to output buy and sell decisions for use in algorithmic trading systems. These systems may have several parameters that require tuning, and assessing the performance of these systems as a function of their parameters cannot be expressed in closed form, and thus requires simulation. Bayesian optimisation has grown in popularity as a means of global optimisation of parameters where the objective function may be costly or a black box. We show how algorithmic trading using GBNs, supported by Bayesian optimisation, can lower risk towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strategy buy-and-hold