Investment portfolio trading based on Markov chain and fuzzy logic

In the present paper, a trading strategy is proposed for a portfolio composed of shares in the stock exchange. The proposed strategy is based mainly on three blocks: 1) a K-means clustering algorithm is used to determine and learn the internal hidden patterns in the time series of stock market prices, 2) a pattern predictor is performed based on a simple Markov chain, and 3) a fuzzy inference system take the decision to trade based on the estimation. The fuzzy inference system is composed of the rules provided by an expert trader. The performance of the trading algorithm is validated through simulations using real prices of the Mexican stock exchange

Investigating the Predictability of a Chaotic Time-Series Data using Reservoir Computing, Deep-Learning and Machine- Learning on the Short-, Medium- and Long-Term Pricing of Bitcoin and Ethereum.

This study will investigate the predictability of a Chaotic time-series data using Reservoir computing (Echo State Network), Deep-Learning(LSTM) and Machine- Learning(Linear, Bayesian, ElasticNetCV , Random Forest, XGBoost Regression and a machine learning Neural Network) on the short (1-day out prediction), medium (5-day out prediction) and long-term (30-day out prediction) pricing of Bitcoin and Ethereum Using a range of machine learning tools, to perform feature selection by permutation importance to select technical indicators on the individual cryptocurrencies, to ensure the datasets are the best for predictions per cryptocurrency while reducing noise within the models. The predictability of these two chaotic time-series is then compared to evaluate the models to find the best fit model. The models are fine-tuned, with hyperparameters, design of the network within the LSTM and the reservoir size within the Echo State Network being adjusted to improve accuracy and speed. This research highlights the effect of the trends within the cryptocurrency and its effect on predictive models, these models will then be optimized with hyperparameter tuning, and be evaluated to compare the models across the two currencies. It is found that the datasets for each cryptocurrency are different, due to the different permutation importance, which does not affect the overall predictability of the models with the short and medium-term predictions having the same models being the top performers. This research confirms that the chaotic data although can have positive results for shortand medium-term prediction, for long-term prediction, technical analysis basedprediction is not sufficient

A Gated Recurrent Unit Approach to Bitcoin Price Prediction

In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. in this study, we investigate a framework with a set of advanced machine learning methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that gated recurring unit (GRU) model with recurrent dropout performs better better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.Comment: 8 figures, 16 page

Algorithmic trading with cryptocurrencies - does twitter sentiment impact short-term price fluctuations in bitcoin

Since its inception in 2009, Bitcoin has gained popularity and importance in financial markets. The Bitcoin price is highly volatile entailing high risk and chances of high returns for traders. This work is part of a work project, which performs a holistic approach to build an intra day Bitcoin trading algorithm based on predictive analysis of Machine Learning models. This part performs a Sentiment Analysis on Twitter data, showing a Granger causal relationship between the extracted Sentiment and the Bitcoin price

Forecasting power of neural networks in cryptocurrency domain : Forecasting the prices of Bitcoin, Ethereum and Cardano with Gated Recurrent Unit and Long Short-Term Memory

Machine learning has developed substantially during the past decades and more emphasis has gone to deeper machine learning methods, i.e., artificial neural networks, computer-based networks seeking to mimic how the human brain functions. The groundwork for ANN research was established already in the 1940s and the advancement of ANNs has been ex-tensive. Price prediction of different financial assets is a broadly studied field, as researchers have been trying to create models to predict the volatile and noisy environment of financial markets. Also, ANNs have been placed for these hard prediction tasks, as their advantage is the ability to find non-linear patterns in uncertain and volatile setting. Cryptocurrencies have made their way to the common audience in the past years. After Nakamoto (2008) presented the first proposal for an electronic cash system, Bitcoin, the number of different cryptocurrencies has exceeded over 8 000. Also, the market capitaliza-tion of all cryptocurrencies has grown rapidly, in November 2021 the aggregate market capi-talization topped 3 000 billion U.S. dollars. Cryptocurrencies are not a small concept for closed groups of tech-people, but a phenomenon that concerns also in the governmental level. This study utilizes recurrent neural networks, GRU and LSTM, in the prediction task regarding cryptocurrencies. In addition to trading data, this study uses Google trend-based popularity score to try to better the ANNs accuracy. In addition to the sole prediction task, the study compares the two used RNN architectures and presents the performance and accuracy with selected performance measures. The results show that recurrent neural networks have potential in prediction tasks in the cryptocurrency domain. The constructed models were able to find coherent trends in the price fluctuations but the average differences on actual and predicted prices were compara-tively high, with the introduced simple RNN models. On average, the LSTM model was able to predict the cryptocurrency prices more accurately, but the GRU model showed also great evidence of prediction accuracy in the domain. All in all, the cryptocurrency prediction task is a hard task due to its volatile nature, but his study shows great evidence for ANNs ability to predict cryptocurrency prices. Considering the findings, further research could be applied to more optimized and complex ANN models as the models used in the study were relatively simple one-layer models.Koneoppiminen on kehittynyt erittäin paljon viimeisten vuosikymmenten aikana, painottuen enemmän syvempien koneoppimisen metodien, kuten keinotekoisten neuroverkkojen (ANN), kehitykseen. Keinotekoiset neuroverkot ovat tietokoneeseen perustuvia verkkoja, jotka pyrkivät jäljittelemään ihmisaivojen toimintaa. Keinotekoisten neuroverkkojen tutki-mus on alkanut jo 1940-luvulla, josta lähtien kyseisten verkkojen kehitys on ollut nopeaa. Eri omaisuuslajien hintakehityksen ennustaminen on laajasti tutkittu alue, kun tutkijat ovat yrit-täneet luoda malleja, joilla he ovat pyrkineet ennustamaan epävarmaa rahoitusmarkkinaym-päristöä. Keinotekoiset neuroverot on valjastettu tähän vaikeaan tehtävän, koska niiden selkeänä etuna on kyky löytää epälineaarisia yhteyksiä epävarmassa ja epävakaassa ympäris-tössä. Viime vuosien aikana kryptovaluutat ovat yleistyneet huomattavasti, niin yksityissijoittajien kun institutionaalisten sijoittajien joukossa. Sen jälkeen, kun Nakamoto (2008) esitteli en-simmäisen ehdotuksen käteisen ja valuutan sähköisestä järjestelmästä, kryptovaluuttojen lukumäärä on kasvanut yli 8 000 yksittäiseen valuuttaan. Samaan aikaan kryptovaluuttojen yhteenlaskettu markkina-arvo on kasvanut räjähdysmäisesti, marraskuussa 2021 kokonais-markkina-arvo kasvoi yli 3 000 miljardiin Yhdysvaltojen dollariin. Nykyään kryptovaluutat eivät ole vain konsepti suljetuille teknologiasta kiinnostuneille ryhmille, vaan ilmiö, joka vaikuttaa myös valtiollisella tasolla. Tämä tutkimus hyödyntää toistuvia neuroverkkoja (recurrent neural networks), GRU ja LSTM, kryptovaluuttojen hintakehityksen ennustamisessa. Kaupankäyntitietojen lisäksi, tut-kimuksessa käytetään Googlen hakutiedusteluihn perustuvaa Google Trend suosiomittaria, neuroverkkojen tarkkuuden parantamiseksi. Kryptovaluuttojen hintakehityksen ennustami-sen lisäksi, tutkimuksessa verrataan kahta RNN-rakennetta ja esitellään molempien verkko-jen tarkkuutta sekä verrataan sitä valituilla tarkkuusmittareilla. Tutkimuksen tulokset osoittavat, että yksinkertaisilla RNN-rakenteilla on selkeää potentiaalia kryptovaluuttojen hintakehityksen ennustamisessa. Tutkimuksessa luodut mallit löytävät johdonmukaisia ja selkeitä trendejä, mutta keskimääräiset erotukset todellisilla ja ennuste-tuilla hinnoilla oli suhteellisesti korkeat. Tutkituista malleista LSTM-malli tuottaa keskimäärin tarkempia ennusteita kuin GRU-malli, mutta erot mallien tarkkuuksissa ovat pienet. Kokonai-suudessaan kryptovaluuttojen hintojen ennustaminen on vaikea tehtävä kryptovaluut-tamarkkinan epävakaan luonteen johdosta, tämä tutkimus kuitenkin osoittaa näyttöä keino-tekoisten neuroverkkojen kyvystä ennustaa kryptovaluuttojen hintoja. Ottaen huomioon tutkimuksen löydökset, lisätutkimusta voisi soveltaa tarkemmin optimoituihin ja kompleksi-simpiin keinotekoisiin neuroverkkoihin, sillä tässä tutkimuksessa käytetyt mallit olivat suh-teellisen yksinkertaisia

Massively Scalable Inverse Reinforcement Learning in Google Maps

Optimizing for humans' latent preferences is a grand challenge in route recommendation, where globally-scalable solutions remain an open problem. Although past work created increasingly general solutions for the application of inverse reinforcement learning (IRL), these have not been successfully scaled to world-sized MDPs, large datasets, and highly parameterized models; respectively hundreds of millions of states, trajectories, and parameters. In this work, we surpass previous limitations through a series of advancements focused on graph compression, parallelization, and problem initialization based on dominant eigenvectors. We introduce Receding Horizon Inverse Planning (RHIP), which generalizes existing work and enables control of key performance trade-offs via its planning horizon. Our policy achieves a 16-24% improvement in global route quality, and, to our knowledge, represents the largest instance of IRL in a real-world setting to date. Our results show critical benefits to more sustainable modes of transportation (e.g. two-wheelers), where factors beyond journey time (e.g. route safety) play a substantial role. We conclude with ablations of key components, negative results on state-of-the-art eigenvalue solvers, and identify future opportunities to improve scalability via IRL-specific batching strategies

Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting

International audienceIn this work, we introduce a new modeling and inferential tool for dynamical processing of time series. The approach is called recurrent dictionary learning (RDL). The proposed model reads as a linear Gaussian Markovian state-space model involving two linear operators, the state evolution and the observation matrices, that we assumed to be unknown. These two unknown operators (that can be seen interpreted as dictionaries) and the sequence of hidden states are jointly learnt via an expectation-maximization algorithm. The RDL model gathers several advantages, namely online processing, probabilistic inference, and a high model expressiveness which is usually typical of neural networks. RDL is particularly well suited for stock forecasting. Its performance is illustrated on two problems: next day forecasting (regression problem) and next day trading (classification problem), given past stock market observations. Experimental results show that our proposed method excels over state-of-the-art stock analysis models such as CNN-TA, MFNN, and LSTM