LaLiga2 stats & predictions

1. Almería vs. Rayo Vallecano

Il match tra Almería e Rayo Vallecano si preannuncia come uno scontro equilibrato. L'Almería, ospitante, ha mostrato una solida difesa nelle ultime partite, mentre il Rayo Vallecano è noto per la sua capacità di segnare gol in trasferta. Le statistiche indicano un possibile pareggio, ma con una leggera preferenza per una vittoria esterna.

Predizioni di Scommessa:

• Pareggio: 3.20
• Vittoria Almería: 2.50
• Vittoria Rayo Vallecano: 2.80

2. Cartagena vs. Girona

Cartagena e Girona si affronteranno in un match che promette spettacolo. Il Cartagena ha una formazione ben rodata e gioca bene in casa, mentre il Girona ha dimostrato di essere una squadra molto organizzata e pericolosa in attacco. Le probabilità di vittoria sono abbastanza equilibrate, ma il Girona potrebbe avere un leggero vantaggio grazie alla sua recente serie positiva.

Predizioni di Scommessa:

• Pareggio: 3.10
• Vittoria Cartagena: 2.60
• Vittoria Girona: 2.70

3. Numancia vs. Lugo

Numancia e Lugo si affronteranno in una partita che potrebbe decidere le sorti della classifica. Il Numancia ha bisogno di punti per risalire la classifica, mentre il Lugo cerca conferme dopo una serie di prestazioni convincenti. Le statistiche suggeriscono che il Numancia potrebbe avere la meglio grazie al fattore campo.

Predizioni di Scommessa:

• Pareggio: 3.30
• Vittoria Numancia: 2.40
• Vittoria Lugo: 3.00

Almería

L'Almería ha dimostrato una grande solidità difensiva nelle ultime partite, subendo pochi gol. La squadra si basa su una difesa compatta e su contropiedi rapidi per mettere in difficoltà gli avversari.

Rayo Vallecano

Il Rayo Vallecano è noto per la sua aggressività e per il gioco offensivo che riesce a creare anche in trasferta. La squadra ha diversi giocatori capaci di fare la differenza in qualsiasi momento della partita.

Cartagena

Il Cartagena gioca un calcio equilibrato, con un'attenzione particolare alla fase difensiva. La squadra è abile nel gestire il possesso palla e nel costruire gioco dalla propria metà campo.

Girona

Il Girona è una squadra organizzata e ben strutturata tatticamente. La squadra sa come difendersi bene e allo stesso tempo è capace di colpire in contropiede con grande efficacia.

Numancia

Il Numancia ha bisogno di punti per uscire dalla zona calda della classifica. La squadra si affida a un gioco diretto e a una buona organizzazione difensiva per cercare di ottenere risultati positivi.

Lugo

Il Lugo ha mostrato un buon livello di gioco nelle ultime partite, riuscendo a ottenere risultati importanti sia in casa che in trasferta. La squadra è capace di giocare un calcio offensivo e dinamico.

Tattiche dell'Almería

L'Almería adotta una strategia difensiva solida, cercando di limitare le occasioni degli avversari e poi ripartire velocemente con lanci lunghi verso i suoi attaccanti veloci.

Tattiche del Rayo Vallecano

Il Rayo Vallecano predilige un gioco offensivo aggressivo, con pressing alto sugli avversari per recuperare rapidamente il possesso palla e creare situazioni da gol.

Tattiche del Cartagena

Il Cartagena utilizza un sistema tattico equilibrato, con un focus particolare sulla costruzione del gioco dalla difesa verso l'attacco, cercando di mantenere il possesso palla e gestire bene i tempi della partita.

Tattiche del Girona

Il Girona si basa su una solida organizzazione difensiva e su ripartenze rapide, cercando di sfruttare gli spazi lasciati dagli avversari per creare occasioni da rete.

Tattiche del Numancia

Il Numancia adotta un approccio pragmatico alla partita, concentrandosi sulla difesa ed esplorando le opportunità offensive attraverso cross dalle fasce laterali.

Tattiche del Lugo

Il Lugo gioca un calcio offensivo e dinamico, cercando di mantenere alta la pressione sugli avversari e sfruttare le occasioni create dalle transizioni rapide.

Sfide dell'Almería

• Mantenere la solidità difensiva contro un attacco aggressivo come quello del Rayo Vallecano.
• Sfruttare al meglio le occasioni create nei contropiedi.

Sfide del Rayo Vallecano

• Riuscire a penetrare la difesa compatta dell'Almería.
• Mantenere alta l'intensità del pressing senza subire ripartenze rapide.

Sfide del Cartagena

• Bilanciare la fase difensiva con quella offensiva contro un Girona ben organizzato.

Sfide del Girona

• Riuscire a mantenere la posizione in classifica migliorando la propria capacità realizzativa sotto pressione.

Sfide del Numancia

• Riuscire a ottenere punti vitali contro un avversario direttamente concorrente come il Lugo.

Sfide del Lugo<|repo_name|>KU-AI/Research<|file_sep|>/README.md # Research ## Current Research Projects ### Sentiment Analysis - [ ] Use BERT or similar model to classify sentences from news articles as positive or negative. - [ ] Use this to predict stock prices based on news articles ### Game Theory - [ ] Study Nash Equilibrium - [ ] Study Bayesian games - [ ] Use Game Theory to find best strategy for games like Poker ### Reinforcement Learning - [ ] Use RL to play Starcraft II - [ ] Use RL to play Dota II ### Natural Language Processing - [ ] Use NLP techniques to summarize large text files - [ ] Use NLP techniques to classify texts into different categories ### Time Series Analysis - [ ] Forecast stock prices using LSTM and other techniques. - [ ] Forecast stock prices using sentiment analysis of news articles. ### Other Ideas - [ ] Build an AI system that plays Chess. - [ ] Build an AI system that plays Go. - [ ] Build an AI system that plays Starcraft II. - [ ] Build an AI system that plays Dota II. - [ ] Build an AI system that plays Minecraft. ## Past Research Projects ### Algorithmic Trading using Machine Learning and Deep Learning (Completed) The project aims to develop and implement machine learning and deep learning models for algorithmic trading strategies on financial markets. The project will involve the following tasks: 1. Data collection and preprocessing: Collect historical financial data such as stock prices, volume, and other relevant market indicators from various sources such as Yahoo Finance and Alpha Vantage API. 2. Feature engineering: Identify and extract relevant features from the collected data that can be used as inputs for machine learning and deep learning models. 3. Model development: Develop and train machine learning and deep learning models such as support vector machines (SVM), random forests (RF), long short-term memory (LSTM) networks, and convolutional neural networks (CNNs) for algorithmic trading strategies such as trend following, mean reversion, and momentum. 4. Backtesting and evaluation: Backtest the developed models on historical data to evaluate their performance using metrics such as Sharpe ratio, maximum drawdown, and profit factor. 5. Implementation: Implement the best-performing models as live trading strategies on simulated or real financial markets using platforms such as QuantConnect or Interactive Brokers API. The expected outcome of the project is a set of robust machine learning and deep learning models that can be used for algorithmic trading strategies with high profitability and risk management capabilities. #### Report [Algorithmic Trading using Machine Learning and Deep Learning](https://github.com/KU-AI/Research/blob/main/Algorithmic%20Trading%20using%20Machine%20Learning%20and%20Deep%20Learning.pdf) #### GitHub Repository [GitHub Repository](https://github.com/KU-AI/Algorithmic-Trading) #### Kaggle Notebook [Kaggle Notebook](https://www.kaggle.com/code/nikhilbhatia1999/algorithmic-trading-using-machine-learning-and-deep) #### Project Video (YouTube) [Project Video](https://youtu.be/uk6XivR7uJw) ### Using Machine Learning Techniques to Predict Stock Prices (Completed) The project aims to develop machine learning models to predict stock prices based on historical data. The project will involve the following tasks: 1. Data collection and preprocessing: Collect historical stock price data from various sources such as Yahoo Finance API or Quandl API. 2. Feature engineering: Identify relevant features such as opening price, closing price, high price, low price, volume traded etc., that can be used as inputs for machine learning models. 3. Model development: Develop machine learning models such as linear regression, decision tree regression or support vector regression (SVR) etc., using libraries like Scikit-Learn or TensorFlow/Keras etc., to predict future stock prices based on past data. 4. Evaluation of model performance: Evaluate the performance of each model using metrics like mean absolute error (MAE), root mean square error (RMSE) or R-squared value etc., through cross-validation techniques like k-fold validation etc. 5. Optimization of hyperparameters: Optimize hyperparameters of each model through techniques like grid search or random search etc., to improve its performance further. The expected outcome of the project is a set of optimized machine learning models that can accurately predict future stock prices based on historical data with high accuracy. #### Report [Using Machine Learning Techniques to Predict Stock Prices](https://github.com/KU-AI/Research/blob/main/Using%20Machine%20Learning%20Techniques%20to%20Predict%20Stock%20Prices.pdf) #### GitHub Repository [GitHub Repository](https://github.com/KU-AI/Predicting-Stock-Prices) #### Kaggle Notebook [Kaggle Notebook](https://www.kaggle.com/code/nikhilbhatia1999/predicting-stock-prices-using-machine-learning) #### Project Video (YouTube) [Project Video](https://youtu.be/gNDDkWZDmCI) ### Analyzing Cryptocurrency Market Trends using Data Science Techniques (Completed) The project aims to analyze cryptocurrency market trends using data science techniques. The project will involve the following tasks: 1. Data collection and preprocessing: Collect historical cryptocurrency market data from various sources such as CoinMarketCap API or CryptoCompare API etc., including features like opening price/closing price/highest price/lowest price/volume traded/etc., for different cryptocurrencies over a period of time. 2. Exploratory data analysis (EDA): Perform EDA on collected data using visualization tools like Matplotlib/Seaborn/Pandas Profiling etc., to identify patterns/trends/outliers/inconsistencies within the dataset which could be useful while building predictive models later on. 3.Feature engineering & selection : Identify relevant features based on EDA results & domain knowledge which could help us build better predictive models later on & select them accordingly using techniques like correlation analysis/variance thresholding/principal component analysis(PCA)/etc., The expected outcome of this project is an understanding of cryptocurrency market trends over time which could help investors make informed decisions while investing/trading cryptocurrencies.<|repo_name|>KU-AI/Research<|file_sep|>/Using Machine Learning Techniques to Predict Stock Prices.md # Using Machine Learning Techniques to Predict Stock Prices ## Introduction Stock prices are one of the most important indicators of economic health and are closely monitored by investors around the world. Predicting stock prices accurately can help investors make informed decisions about buying or selling stocks. However, predicting stock prices is challenging due to their volatile nature. In recent years, machine learning has emerged as a powerful tool for analyzing large datasets and making predictions. This project aims to explore how machine learning techniques can be used to predict stock prices. ## Objectives The main objective of this project is to develop accurate prediction models for stock prices. Specifically, we aim to: 1. Understand the factors that influence stock prices 2. Select appropriate features for predicting stock prices 3. Build prediction models using different machine learning algorithms 4. Evaluate the performance of prediction models 5. Optimize hyperparameters for improved accuracy ## Methodology We followed these steps for developing our prediction models: 1. Data Collection: We collected historical daily closing prices of S&P500 companies from Yahoo Finance API between January 1st, 2000, and December 31st, 2020. We also collected other relevant financial indicators like trading volume, PE ratio, EPS growth rate, etc. 2. Data Preprocessing: We cleaned our dataset by removing missing values, duplicates, and outliers. We normalized our features using Min-Max scaling. We split our dataset into training(70%) & test(30%) sets. 3. Feature Engineering: We created new features like moving averages(MA), exponential moving averages(EMA), Bollinger Bands(BB), etc. We also used technical indicators like Relative Strength Index(RSI), Moving Average Convergence Divergence(MACD), Stochastic Oscillator(STOCH), etc. We selected top performing features based on correlation analysis. Finally, we created lagged variables by shifting closing prices by one day ahead & one day behind.  *Figure: Correlation Heatmap showing top performing features.* *Figure: Plot showing actual vs predicted values.* ## Results We trained different machine learning algorithms like Linear Regression(LR), Random Forest(RF), Gradient Boosting(GB), XGBoost(XGB), LightGBM(LGBM), CatBoost(CAT), Support Vector Regression(SVR) on our training dataset. We evaluated their performance using metrics like Mean Absolute Error(MAE), Root Mean Squared Error(RMSE), Mean Absolute Percentage Error(MAPE), and R-squared(R^2). Based on our evaluation results, XGBoost performed best with lowest MAE(0.xx), RMSE(0.yy), MAPE(0.zz)%, and highest R^2(0.nn).  *Figure: Performance Metrics showing XGBoost outperforming other algorithms.* ## Conclusion Our results show that XGBoost performed best among all algorithms tested. By incorporating technical indicators & lagged variables into our feature set we were able to improve accuracy significantly. We also found that certain technical indicators like RSI & MACD had high correlation with future closing prices which could be useful for further research. However, it's important to note that predicting stock prices accurately is extremely difficult due to their volatile nature & unpredictable events like political unrest, natural disasters,& pandemics which cannot be captured by any algorithm alone. Therefore, investors should use these prediction models along with fundamental analysis &