This master thesis deals with issue of trade on commodity market, especially the gold. It uses the artificial intelligence resources, more accurate non-linear auregressive neural network. The purpose is the prediction of the gold prices by indicators which has impact on the gold.

This work deals with the usage of neural network for the purpose of stock market prediction. A basic stock market theory and trading approaches are mentioned at the beginning of this work. Then neural networks and their application are discussed with their deeper description. Similar approaches are referred and finally two new prediction systems are designed. These systems are utilized by proposed trading model and tested on selected data. The results are compared to human and random trading models and new development steps are devised at the end of this work.

Stock market prediction has been a hot topic lately due to advances in computer technology and economics. One economic theory, called Efficient Market Hypothesis (EMH), states that all known information is already factored into the prices which makes it impossible to predict the stock market. Despite the EMH, many researchers have been successful in predicting the stock market using neural networks on historical data. This thesis investigates stock prediction using both linear regression and neural networks (NN), with a twist. The inputs to the proposed methods are a number of profit predictions calculated with stochastic methods such as generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA). By contrast the traditional approach was instead to use raw data as inputs. The proposed methods show superior result in yielding profit: at best 1.1% in the Swedish market and 4.6% in the American market. The neural network yielded more profit than the linear regression model, which is reasonable given its ability to find nonlinear patterns. The historical data was used with different window sizes. This gives a good understanding of the window size impact on the prediction performance.

Due to the unpredictability of the stock market,forecasting stock prices is a challenging task. In this project,we will investigate the performance of the machine learningalgorithm LSTM for stock market prediction. The algorithmwill be based only on historical numerical data and technicalindicators for IBM and FORD. Furthermore, the denoising anddimension reduction algorithm, PCA, is applied to the stockdata, to examine if the performance of forecasting the stockprice is greater than the initial model. A second method, transferlearning, is applied by training the model on the IBM datasetand then applying it on the FORD dataset, and vice versa, toevaluate if the results will improve. The results show that whenthe PCA algorithm is applied to the dataset separately, and incombination with transfer learning, the performance is greater incomparison to the initial model. Moreover, the transfer learningmodel is inconsistent as the performance is worse for FORD inrespect to the initial model, but better for IBM. Thus, concerningthe results when forecasting stock prices using related tools, it issuggested to use trial and error to identify which of the modelsthat performs the optimally. / Att förutse aktiekurser är en utmanande uppgift. Detta beror på aktiemarknadens oförutsägbarhet. Därför kommer vi i detta projekt att undersöka prestandan för maskininlärnings algoritmen LSTMs prognosförmåga för aktie priser. Algoritmen baseras endast på historisk numerisk data och tekniska indikatorer for företagen IBM och FORD. Vidare tillämpas brus minskande och dimension reducerande algorithmen, PCA, på aktiedata för att undersöka om prestandan för att förutse aktie priser är bättre än den ursprungliga modellen. En andra metod, transfer learning, tillämpas genom att träna modellen på IBM data och sedan använda den på FORD data, och vice versa, för att utvärdera om resultaten kommer att förbättras. Resultaten visar, när PCA-algoritmen tillämpas på aktiedata separat, och i kombination med transfer learning är prestandan bättre jämfört med bas modellen. Vidare kan vi inte dra slutsatser om transfer learning då prestandan är sämre för FORD med avseende på bas modellen, men bättre för IBM. I hänsyn till resultaten så föreslås det att man tillämpar modellerna för att identifiera vilken som är mest optimal när man arbetar i ett relaterat ämnesområde. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

The work deals with an automatic trading system and adaptive training. Is used both technical and automatic fundamental analyses, therefore as inputs to the neural network is used historical data exchanges and text data from reports. It explains the basics of trading, technical analysis and technical terms. The work deals with technical and fundamental analysis. It contains a description of algorithmic nature, program implementation and experiment with developed trading system. The selected strategy is compared to other approaches.

This thesis aims to investigate the feasibility of using a Markovian approach toforecast short-term stock market movements. To assist traders in making soundtrading decisions, this study proposes a Markovian model using a selection ofthe latest closing prices. Assuming that each time step in the one-minute timeframe of the stock market is stochastically independent, the model eliminates theimpact of fundamental analysis and creates a feasible Markov model. The modeltreats the stock price’s movement as entirely randomly generated, which allowsfor a more simplified model that can be implemented with ease. The modelis intended to serve as a starting ground for more advanced technical tradingstrategies and act as useful guidance for a short-term trader when combinedwith other resources. The creation of the model involves Laplace smoothing toensure there are no zero-probabilities and calculating the steady-state probabilityvector of the smoothed matrix to determine the predicted direction of the nexttime step. The model will reset daily, reducing the impact of fundamental factorsoccurring outside trading hours and reducing the risk of carrying over bias fromprevious trading day. Any open positions will hence be closed at the end of theday. The study’s purpose is to research and test if a simple forecasting modelbased on Markov chains can serve as a useful tool for forecasting stock prices atshort time intervals. The result of the study shows that a Markov-based tradingstrategy is more profitable than a simple buy-and-hold strategy and that theprediction accuracy of the Markov model is relatively high. / Denna avhandling syftar till att undersöka möjligheten att använda en markoviskmetod för att förutsäga kortsiktiga rörelser på aktiemarknaden. För att hjälpaaktörer på aktiemarknaden att fatta välgrundade handelsbeslut föreslår dennastudie en markovisk modell för att förutsäga nästa stängningspris baserat påde senaste stängningspriserna. Modellen antar att varje tidssteg i ett en-minuts intervall på aktiemarknaden är stokastiskt oberoende, vilket eliminerarpåverkan från fundamental analys och skapar förutsättningen för en genomförbarmarkov-modell. Modellen behandlar aktieprisets rörelse som helt slumpmässigtgenererat, vilket möjliggör en mer förenklad modell som kan implementeraspå marknaden. Modellen är avsedd att tjäna som en utgångspunkt förmer avancerade tekniska handelsalgoritmer och fungera som en användbarvägledning för en akitehandlare med kort tidshorisont i kombination med andraresurser. Skapandet av modellen inkluderar använding av Laplace-jämning föratt säkerställa att det inte finns nollsannolikheter samt beräknandet av denstationära sannolikhetsvektorn för den jämnade matrisen i syfte att bestämmaden förutsedda riktningen för nästa tidssteg. Modellen kommer att återställasdagligen, vilket minskar påverkan från de fundamentala faktorer som inträffarutanför handelstiderna och ser till att bias inte överförs till nästa börsdag. Dettainnebär att alla öppna positioner stängs vid dagens slut. Studiens syfte är attforska och testa om en enkel prognosmodell baserad på Markovkedjor kan varaanvändbar som ett verktyg för att förutsäga aktiepriser vid korta tidsintervall.Resultatet från studien visar på att en markov-baserad trading strategi är merlönsam än en enkel köp-och-behåll strategi och att prediktionernas träffsäkerhetfrån en markov modell är relativt höga.

Proteins are considered the primary actors in living organisms. Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. PPI identification has been addressed by various experimental methods such as the yeast two-hybrid, mass spectrometry, and protein microarrays, to mention a few. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. Therefore a sequence-based framework called ProtInteract is developed to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequential pattern by extracting uncorrelated attributes and more expressive descriptors. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction. Three different scenarios formulate the prediction task. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The present study makes two significant contributions to the field of protein-protein interaction (PPI) prediction. Firstly, it addresses the computational challenges posed by the high dimensionality of protein datasets through the use of dimensionality reduction techniques, which extract highly informative sequence attributes. Secondly, the proposed framework, ProtInteract, utilises this information to identify the interaction characteristics of a protein based on its amino acid configuration. ProtInteract encodes the protein's primary structure into a lower-dimensional vector space, thereby reducing the computational complexity of PPI prediction. Our results provide evidence of the proposed framework's accuracy and efficiency in predicting protein-protein interactions.

L’objectif de cette recherche était d’évaluer l’efficacité du paramètre de classification pour prédire suivre les tendances boursières. Les méthodes traditionnelles basées sur la prévision, qui ciblent l’immédiat pas de temps suivant, rencontrent souvent des défis dus à des données non stationnaires, compromettant le modèle précision et stabilité. En revanche, notre approche de classification prédit une évolution plus large du cours des actions avec des mouvements sur plusieurs pas de temps, visant à réduire la non-stationnarité des données. Notre ensemble de données, dérivé de diverses actions du NASDAQ-100 et éclairé par plusieurs indicateurs techniques, a utilisé un mélange d'experts composé d'un mécanisme de déclenchement souple et d'une architecture basée sur les transformateurs. Bien que la méthode principale de cette expérience ne se soit pas révélée être aussi réussie que nous l'avions espéré et vu initialement, la méthodologie avait la capacité de dépasser toutes les lignes de base en termes de performance dans certains cas à quelques époques, en démontrant le niveau le plus bas taux de fausses découvertes tout en ayant un taux de rappel acceptable qui n'est pas zéro. Compte tenu de ces résultats, notre approche encourage non seulement la poursuite des recherches dans cette direction, dans lesquelles un ajustement plus précis du modèle peut être mis en œuvre, mais offre également aux personnes qui investissent avec l'aide de l'apprenstissage automatique un outil différent pour prédire les tendances boursières, en utilisant un cadre de classification et un problème défini différemment de la norme. Il est toutefois important de noter que notre étude est basée sur les données du NASDAQ-100, ce qui limite notre l’applicabilité immédiate du modèle à d’autres marchés boursiers ou à des conditions économiques variables. Les recherches futures pourraient améliorer la performance en intégrant les fondamentaux des entreprises et effectuer une analyse du sentiment sur l'actualité liée aux actions, car notre travail actuel considère uniquement indicateurs techniques et caractéristiques numériques spécifiques aux actions. / The objective of this research was to evaluate the classification setting's efficacy in predicting stock market trends. Traditional forecasting-based methods, which target the immediate next time step, often encounter challenges due to non-stationary data, compromising model accuracy and stability. In contrast, our classification approach predicts broader stock price movements over multiple time steps, aiming to reduce data non-stationarity. Our dataset, derived from various NASDAQ-100 stocks and informed by multiple technical indicators, utilized a Mixture of Experts composed of a soft gating mechanism and a transformer-based architecture. Although the main method of this experiment did not prove to be as successful as we had hoped and seen initially, the methodology had the capability in surpassing all baselines in certain instances at a few epochs, demonstrating the lowest false discovery rate while still having an acceptable recall rate. Given these results, our approach not only encourages further research in this direction, in which further fine-tuning of the model can be implemented, but also offers traders a different tool for predicting stock market trends, using a classification setting and a differently defined problem. It's important to note, however, that our study is based on NASDAQ-100 data, limiting our model's immediate applicability to other stock markets or varying economic conditions. Future research could enhance performance by integrating company fundamentals and conducting sentiment analysis on stock-related news, as our current work solely considers technical indicators and stock-specific numerical features.