Construction and Evaluation of Trading Systems: Warsaw Index Futures

Witkowska, Dorota; Marcinkiewicz, Edyta

doi:10.1007/s11294-004-7496-7

Cited by 12 publications

(10 citation statements)

References 7 publications

(3 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The highest percentage of winning trades equal to 82.6% is reached for the SVM approach for three class classification when predicting for 20 days ahead with the input window length equal to 15 or 20 days, and for 25 days ahead with the input window length equal to 20 days. These results are comparable to the highest winning rate of 86.55% obtained by Winkowska and Marcinkiewicz in [29] for ANN. The pattern, found for the prediction accuracy, is clearly reproduced for the winning rate.…”

Section: Winning Ratesupporting

confidence: 87%

See 1 more Smart Citation

Forecasting price movements using technical indicators: Investigating the impact of varying input window length

et al. 2017

View full text Add to dashboard Cite

-The creation of a predictive system that correctly forecasts future changes of a stock price is crucial for investment management and algorithmic trading. The use of technical analysis for financial forecasting has been successfully employed by many researchers. Input window length is a time frame parameter required to be set when calculating many technical indicators. This study explores how the performance of the predictive system depends on a combination of a forecast horizon and an input window length for forecasting variable horizons. Technical indicators are used as input features for machine learning algorithms to forecast future directions of stock price movements. The dataset consists of ten years daily price time series for fifty stocks. The highest prediction performance is observed when the input window length is approximately equal to the forecast horizon. This novel pattern is studied using multiple performance metrics: prediction accuracy, winning rate, return per trade and Sharpe ratio.

show abstract

Section: Winning Ratesupporting

confidence: 87%

“…Values of 1, 3, 5, 7, 10, 15, 20, 25 and 30 trading days are chosen for analysis. There has been a lot of interest in oneday-ahead forecasting which remains an area of active research [20], [28], [29]. Therefore, the smallest horizon is set to one trading day.…”

Section: Forecast Horizonmentioning

confidence: 99%

Forecasting price movements using technical indicators: Investigating the impact of varying input window length

et al. 2017

View full text Add to dashboard Cite

show abstract

“…This message is the result of one technical analysis indicator or a combination of two or more indicators. The message indicates to the technical analyst a course of action whether to buy, sell, or hold [21].…”

Section: In the Spring Web Layermentioning

confidence: 99%

“…The more information investor has, the more accurate is his prediction. Analysis of stock exchange trends is not easy, but economic studies provide many mathematical models for stock exchange data processing and prediction [2,3,12,13,21]. Also efficient software tools allow for stock exchange data presentation and forecasting trends with a certain probability [22].…”

Section: Introductionmentioning

confidence: 99%

Intelligent information system for stock exchange data processing and presentation

Paluch¹,

Jackowska-Strumiłło²

2015

2015 8th International Conference on Human System Interaction (HSI)

View full text Add to dashboard Cite

The paper describes the architecture of the information expert system designed and dedicated to stock exchange data processing, analyzing and presentation. The system uses Artificial Neural Networks (ANN) combined with technical analysis and fractal analysis to predict future price of stock exchange assets. It also enables selection of companies, which assets will be increased. The selection of companies is based on comparing algorithms implementing Hopfield network. The system displays a list of the companies with the highest expected profit sorted in descending order.

show abstract

“…Most systems generate trading rules using neural networks where their main inputs are technical analysis indicators. 1 This approach has been applied to forecast and trade S&P 500 index futures [58,15], the Warsaw stock price index 20 (WIG20) futures [60], and Korea stock index 200 (KOSPI 200) futures [38]. Hamid and Iqbal [33] forecast the volatility of S&P 500 index futures using 16 futures indexes and 3 spot prices.…”

Section: Introductionmentioning

confidence: 99%

Model calibration and automated trading agent for Euro futures

Creamer

2012

Quantitative Finance

View full text Add to dashboard Cite

We explored the application of a machine learning method, Logitboost, to automatically calibrate a trading model using different versions of the same technical analysis indicators. This approach takes advantage of boosting's feature selection capability to select an optimal combination of technical indicators and design a new set of trading rules. We tested this approach with high frequency data of the Dow Jones EURO STOXX 50 Index Futures (FESX) and the DAX Futures (FDAX) for March 2009. Our method was implemented with different learning algorithms and outperformed a combination of the same group of technical analysis indicators using the parameters typically recommended by practitioners.We incorporated this method of model calibration in a trading agent that relies on a layered structure consisting of the machine learning algorithm described above, an online learning utility, a trading strategy, and a risk management overlay. The online learning layer combines the output of several experts and suggests a short or long position. If the expected position is positive (negative), the trading agent sends a buy (sell) limit order at prices slightly lower (higher) than the bid price at the top of the buy (sell) order book less (plus) transaction costs. If the order is not 100% filled within a fixed period (i.e. 1 minute) of being issued, the existent limit orders are cancelled, and limit orders are reissued according to the new experts' forecast. As part of its risk management capability, the trading agent eliminates any weak trading signal.The trading agent algorithm generated positive returns for the two major European index futures (FESX and FDAX) and outperformed a buy and hold strategy.

show abstract

Construction and Evaluation of Trading Systems: Warsaw Index Futures

Cited by 12 publications

References 7 publications

Forecasting price movements using technical indicators: Investigating the impact of varying input window length

Forecasting price movements using technical indicators: Investigating the impact of varying input window length

Intelligent information system for stock exchange data processing and presentation

Model calibration and automated trading agent for Euro futures

Contact Info

Product

Resources

About