Thanks to this 100% online Postgraduate diplomaV, you will delve into the financial markets, evaluating the behavior of prices and short-term patterns, as well as the economic and financial fundamentals of companies” 

Why Study at TECH?

TECH is the world's largest 100% online business school. It is an elite business school, with a model based on the highest academic standards. A world-class center for intensive managerial skills education.   

TECH is a university at the forefront of technology, and puts all its resources at the student's disposal to help them achieve entrepreneurial success"

At TECH Global University

Innovation

The university offers an online learning model that balances the latest educational technology with the most rigorous teaching methods. A unique method with the highest international recognition that will provide students with the keys to develop in a rapidly-evolving world, where innovation must be every entrepreneur’s focus.

"Microsoft Europe Success Story", for integrating the innovative, interactive multi-video system. 

The Highest Standards

Admissions criteria at TECH are not economic. Students don't need to make a large investment to study at this university. However, in order to obtain a qualification from TECH, the student's intelligence and ability will be tested to their limits. The institution's academic standards are exceptionally high...  

95% of TECH students successfully complete their studies.

Networking

Professionals from countries all over the world attend TECH, allowing students to establish a large network of contacts that may prove useful to them in the future.  

100,000+ executives prepared each year, 200+ different nationalities.

Empowerment

Students will grow hand in hand with the best companies and highly regarded and influential professionals. TECH has developed strategic partnerships and a valuable network of contacts with major economic players in 7 continents.  

500+ collaborative agreements with leading companies.

Talent

This program is a unique initiative to allow students to showcase their talent in the business world. An opportunity that will allow them to voice their concerns and share their business vision. 

After completing this program, TECH helps students show the world their talent.

Multicultural Context

While studying at TECH, students will enjoy a unique experience. Study in a multicultural context. In a program with a global vision, through which students can learn about the operating methods in different parts of the world, and gather the latest information that best adapts to their business idea.

TECH students represent more than 200 different nationalities.  

Learn with the best

In the classroom, TECH’s teaching staff discuss how they have achieved success in their companies, working in a real, lively, and dynamic context. Teachers who are fully committed to offering a quality specialization that will allow students to advance in their career and stand out in the business world. 

Teachers representing 20 different nationalities. 

TECH strives for excellence and, to this end, boasts a series of characteristics that make this university unique:   

Analysis

TECH explores the student’s critical side, their ability to question things, their problem-solving skills, as well as their interpersonal skills.  

Academic Excellence

TECH offers students the best online learning methodology. The university combines the Relearning method (postgraduate learning methodology with the best international valuation) with the Case Study. Tradition and vanguard in a difficult balance, and in the context of the most demanding educational itinerary. 

TECH is the world’s largest online university. It currently boasts a portfolio of more than 10,000 university postgraduate programs. And in today's new economy, volume + technology = a ground-breaking price. This way, TECH ensures that studying is not as expensive for students as it would be at another university.   

At TECH, you will have access to the most rigorous and up-to-date case analyses in academia” 

Through technical analysis, entrepreneurs will be able to interpret charts and price patterns to forecast future market movemeTechnical Analysis, Fundamental Analysis and Algorithmic Tradingnts. In addition, fundamental analysis will complement this training by providing an in-depth understanding of the economic, financial and business factors that affect the value of assets. Algorithmic trading topics will also be included, where automated investment strategies and the use of algorithms to optimize order execution and maximize returns will be addressed. This multidimensional approach will enable graduates to develop practical and theoretical skills to trade effectively in an increasingly complex financial environment.

The Postgraduate diploma will provide you with comprehensive training in the financial arena, focusing on the tools and techniques essential for informed decision making in the markets”

Syllabus

Technical Analysis, Fundamental Analysis and Algorithmic Trading are three key approaches to decision making in the financial markets. Currently, emerging technologies, such as Artificial Intelligence and machine learning, are transforming these approaches, making them more sophisticated and accessible to institutional and retail investors.

Accordingly, TECH has developed a Postgraduate diploma that will offer a comprehensive education, enabling entrepreneurs to develop advanced strategies based on Artificial Intelligence, applicable in the field of financial services. In this sense, the different uses of AI for decision making will be examined, considering the associated risks and specific applications in areas such as portfolio management, identification of investment opportunities and automation of financial processes.

Likewise, professionals will focus on the use of advanced algorithmic trading techniques, which allow automating the purchase and sale of assets based on algorithms programmed to maximize performance. In addition, an in-depth analysis of the performance of applied strategies will be performed, using AI to improve predictive models, identify complex patterns and adjust operations to changing market conditions.

Therefore, TECH has created a comprehensive university program in a fully online format, making it easy for graduates to access educational materials from any electronic device with Internet access. This eliminates the obligation to travel to a physical space and follow established schedules. Additionally, the innovative Relearning methodology is applied, focused on the repetition of key concepts to guarantee a solid understanding of the content.

This Postgraduate diploma takes place over 6 months and is divided into 3 modules:

Module 1. Technical Analysis of Financial Markets with Artificial Intelligence
Module 2. Fundamental Analysis of Financial Markets with Artificial Intelligence
Module 3. Algorithmic Trading Strategies

Where, When and How is it Taught?

TECH offers the possibility of developing this Postgraduate diploma in Technical Analysis, Fundamental Analysis and Algorithmic Trading completely online. Throughout the 6 months of the educational program, the students will be able to access all the contents of this program at any time, allowing them to self-manage their study time. 

Module 1. Technical Analysis of Financial Markets with Artificial Intelligence

1.1. Analysis and Visualization of Technical Indicators with Plotly and Dash 

1.1.1. Implementation of Interactive Charts with Plotly 
1.1.2. Advanced Visualization of Time Series with Matplotlib 
1.1.3. Creating Real-Time Dynamic Dashboards with Dash

1.2. Optimization and Automation of Technical Indicators with Scikit-learn 

1.2.1. Automation of Indicators with Scikit-learn
1.2.2. Optimization of Technical Indicators 
1.2.3. Creating Personalized Indicators with Keras 

1.3. Financial Pattern Recognition with CNN 

1.3.1. Using CNN in TensorFlow to Identify Patterns in Charts 
1.3.2. Improving Recognition Models with Transfer Learning Techniques 
1.3.3. Validation of Recognition Models in Real-Time Markets

1.4. Quantitative Trading Strategies with QuantConnect 

1.4.1. Building Algorithmic Trading Systems with QuantConnect 
1.4.2. Backtesting Strategies with QuantConnect 
1.4.3. Integrating Machine Learning into Trading Strategies with QuantConnect

1.5. Algorithmic Trading with Reinforcement Learning Using TensorFlow

1.5.1. Reinforcement Learning for Trading 
1.5.2. Creating Trading Agents with TensorFlow Reinforcement Learning 
1.5.3. Simulating and Tuning Agents in OpenAI Gym

1.6. Time Series Modeling with LSTM in Keras for Price Forecasting 

1.6.1. Applying LSTM to Price Forecasting 
1.6.2. Implementing LSTM Models in Keras for Financial Time Series 
1.6.3. Optimization and Parameter Fitting in Time Series Models

1.7. Application of Explainable Artificial Intelligence (XAI) in Finance 

1.7.1. Applicability of XAI in Finances 
1.7.2. Applying LIME to Trading Models 
1.7.3. Using SHAP for Feature Contribution Analysis in AI Decisions 

1.8. High-Frequency Trading (HFT) Optimized with Machine Learning Models 

1.8.1. Developing ML Models for HFT 
1.8.2. Implementing HFT Strategies with TensorFlow 
1.8.3. Simulation and Evaluation of HFT in Controlled Environments 

1.9. Volatility Analysis Using Machine Learning 

1.9.1. Applying Intelligent Models to Predict Volatility 
1.9.2. Implementing Volatility Models with PyTorch 
1.9.3. Integrating Volatility Analysis into Portfolio Risk Management 

1.10. Portfolio Optimization with Genetic Algorithms 

1.10.1. Fundamentals of Genetic Algorithms for Investment Optimization in Markets 
1.10.2. Implementing Genetic Algorithms for Portfolio Selection 
1.10.3. Evaluation of Portfolio Optimization Strategies

Module 2. Fundamental Analysis of Financial Markets with Artificial Intelligence 

2.1. Predictive Financial Performance Modeling with Scikit-learn 

2.1.1. Linear and Logistic Regression for Financial Forecasting with Scikit-learn 
2.1.2. Using Neural Networks with TensorFlow to Forecast Revenues and Earnings 
2.1.3. Validating Predictive Models with Cross-Validation Using Scikit-learn 

2.2. Valuation of Companies with Deep Learning 

2.2.1. Automating the Discounted Cash Flows (DCF) Model with TensorFlow 
2.2.2. Advanced Valuation Models Using PyTorch 
2.2.3. Integration and Analysis of Multiple Valuation Models with Pandas

2.3. Analysis of Financial Statements with NLP Using ChatGPT 

2.3.1. Extracting Key Information from Annual Reports with ChatGPT 
2.3.2. Sentiment Analysis of Analyst Reports and Financial News with ChatGPT 
2.3.3. Implementing NLP Models with Chat GPT for Interpreting Financial Texts

2.4. Risk and Credit Analysis with Machine Learning 

2.4.1. Credit Scoring Models Using SVM and Decision Trees in Scikit-learn 
2.4.2. Credit Risk Analysis in Corporations and Bonds with TensorFlow 
2.4.3. Visualization of Risk Data with Tableau

2.5. Credit Analysis with Scikit-learn 

2.5.1. Implementing Credit Scoring Models 
2.5.2. Credit Risk Analysis with RandomForest in Scikit-learn 
2.5.3. Advanced Visualization of Credit Scoring Results with Tableau

2.6. ESG Sustainability Assessment with Data Mining Techniques 

2.6.1. ESG Data Mining Methods 
2.6.2. ESG Impact Modeling with Regression Techniques 
2.6.3. Applications of ESG Analysis in Investment Decisions

2.7. Sector Benchmarking with Artificial Intelligence Using TensorFlow and Power BI

2.7.1. Comparative Analysis of Companies Using AI 
2.7.2. Predictive Modeling of Sector Performance with TensorFlow 
2.7.3. Implementing Industry Dashboards with Power BI

2.8. Portfolio Management with AI Optimization 

2.8.1. Portfolio Optimization 
2.8.2. Use of Machine Learning Techniques for Portfolio Optimization with Scikit-Optimize 
2.8.3. Implementing and Evaluating the Effectiveness of Algorithms in Portfolio Management

2.9. Financial Fraud Detection with AI Using TensorFlow and Keras 

2.9.1. Basic Concepts and Techniques of Fraud Detection with AI 
2.9.2. Constructing Neural Network Detection Models in TensorFlow
2.9.3. Practical Implementation of Fraud Detection Systems in Financial Transactions

2.10. Analysis and Modeling in Mergers and Acquisitions with AI 

2.10.1. Using Predictive AI Models to Evaluate Mergers and Acquisitions 
2.10.2. Simulating Post-Merger Scenarios Using Machine Learning Techniques 
2.10.3. Evaluating the Financial Impact of M&A with Intelligent Models 

Module 3. Algorithmic Trading Strategies

3.1. Fundamentals of Algorithmic Trading

3.1.1. Algorithmic Trading Strategies 
3.1.2. Key Technologies and Platforms for the Development of Algorithmic Trading Algorithms 
3.1.3. Advantages and Challenges of Automated Trading versus Manual Trading

3.2. Design of Automated Trading Systems 

3.2.1. Structure and Components of an Automated Trading System 
3.2.2. Algorithm Programming: from the Idea to the Implementation 
3.2.3. Latency and Hardware Considerations in Trading Systems

3.3. Backtesting and Evaluation of Trading Strategies 

3.3.1. Methodologies for Effective Backtesting of Algorithmic Strategies 
3.3.2. Importance of Quality Historical Data in Backtesting 
3.3.3. Key Performance Indicators for Evaluating Trading Strategies

3.4. Optimizing Strategies with Machine Learning 

3.4.1. Applying Supervised Learning Techniques in Strategy Improvement 
3.4.2. Using Particle Swarm Optimization and Genetic Algorithms 
3.4.3. Challenges of Overfitting in Trading Strategy Optimization

3.5. High Frequency Trading (HFT) 

3.5.1. Principles and Technologies behind HFT 
3.5.2. Impact of HFT on Market Liquidity and Volatility 
3.5.3. Common HFT Strategies and Their Effectiveness

3.6. Order Execution Algorithms 

3.6.1. Types of Execution Algorithms and Their Practical Application 
3.6.2. Algorithms for Minimizing the Market Impact 
3.6.3. Using Simulations to Improve Order Execution

3.7. Arbitration Strategies in Financial Markets

3.7.1. Statistical Arbitrage and Price Merger in Markets 
3.7.2. Index and ETF Arbitrage 
3.7.3. Technical and Legal Challenges of Arbitrage in Modern Trading.

3.8. Risk Management in Algorithmic Trading 

3.8.1. Risk Measures for Algorithmic Trading 
3.8.2. Integrating Risk Limits and Stop-Loss in Algorithms 
3.8.3. Specific Risks of Algorithmic Trading and How to Mitigate Them

3.9. Regulatory Aspects and Compliance in Algorithmic Trading

3.9.1. Global Regulations Impacting Algorithmic Trading
3.9.2. Regulatory Compliance and Reporting in an Automated Environment 
3.9.3. Ethical Implications of Automated Trading

3.10. Future of Algorithmic Trading and Emerging Trends

3.10.1. Impact of Artificial Intelligence on the Future Development of Algorithmic Trading 
3.10.2. New Blockchain Technologies and Their Application in Algorithmic Trading 
3.10.3. Trends in Adaptability and Customization of Trading Algorithms

You will design predictive models that analyze patterns of behavior in the markets, anticipating movements and improving asset management, thanks to an extensive library of innovative multimedia resources”