Thanks to this comprehensive 100% online university program, you will master Algorithmic Trading, market structure, and the programming of financial strategies”

The global financial sector is undergoing a profound digital transformation. Therefore, the increasing sophistication of markets, the abundance of data, and the need to execute trades with pinpoint precision have placed Algorithmic Trading at the heart of investment strategies. In this way, those aiming to stand out in this field must not only master the fundamentals of the market but also the technological tools that enable the automation and optimization of investment decisions. This constant dynamism demands ongoing updates and specialization to understand the risks and seize the opportunities presented by these new operational paradigms.

In light of this, where mastering technology and quantitative strategy has become essential for competitiveness in financial markets, the Algorithmic Trading program at TECH emerges. This comprehensive program is designed to provide professionals with the theoretical knowledge and practical tools necessary to understand, develop, and implement the related algorithms. In doing so, they will be prepared for the future of automated investments.

The academic path will cover fundamental topics such as a global overview of financial markets, operating instruments and structures, risks, regulation, and market microstructure and its influence. Additionally, emphasis will be placed on order types and execution, financial intermediaries, macroeconomic factors impacting the market, and the latest innovations such as Digitalization, Blockchain, Cryptocurrencies, and Asset Tokenization.

At the same time, this university program offers a 100% online methodology, providing the flexibility needed for professionals to balance their academic development with their work and personal obligations. As such, the degree content is available 24/7, accessible from any device with an internet connection. Finally, the learning process will be reinforced through the implementation of the Relearning method, which facilitates the assimilation of key concepts through repetition.

You will be trained to master the automation of investments and data analysis in financial markets through this comprehensive academic path”

This Master's Degree in Algorithmic Trading contains the most complete and up-to-date program on the market. The most important features include:

• The development of practical case studies presented by experts in Algorithmic Trading
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an internet connection

TECH will provide you with a cutting-edge teaching methodology, designed to help you master the complexities of programming and strategy in financial markets”

The program includes teachers who are professionals in the field of Algorithmic Trading, sharing the expertise from their work, along with renowned specialists from leading firms and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide an immersive learning experience designed to prepare for real-life situations.

This program is designed around Problem-Based Learning, whereby the student must try to solve the different professional practice situations that arise throughout the program. For this purpose, the professional will be assisted by an innovative interactive video system created by renowned and experienced experts.

A 100% online postgraduate degree that allows you to train at any time and from anywhere, adapting to your lifestyle while specializing in Algorithmic Trading”

The vast array of academic resources will help you consolidate your theoretical knowledge in Algorithmic Trading”

The educational resources that make up this program have been developed by a select group of experts in Financial Markets and Algorithmic Development. As a result, the syllabus will delve into the market microstructure, from order types to Market Makers, providing professionals with a deep understanding of market dynamics. Additionally, the syllabus will explore the most advanced Algorithmic strategies, including Momentum, Trend Following, Market Making, and Statistical Arbitrage. In this way, graduates will be able to design and implement robust and efficient Trading systems.

Through a comprehensive syllabus, you will learn to design cutting-edge algorithms and master Artificial Intelligence for financial decision-making, transforming the market microstructure to your advantage”

Module 1. Algorithmic Trading in Financial Markets

1.1. Global Overview of Financial Markets

1.1.1. Elements of a Financial System
1.1.2. History and Evolution of Financial Markets
1.1.3. Types of Financial Markets
1.1.4. Participants in the Markets
1.1.5. Trading Robots as Market Participants

1.2. Financial Instruments for Trading

1.2.1. Stocks, Bonds, and Derivatives
1.2.2. Spot and Futures Markets
1.2.3. ETFs and Other Investment Vehicles

1.3. Market Structure and Functioning

1.3.1. Trading Hours and Mechanisms
1.3.2. Organized and OTC Markets
1.3.3. Price Formation

1.4. Market Microstructure and Its Influence on Trading

1.4.1. Market Depth and Liquidity
1.4.2. Spread and Transaction Costs
1.4.3. Role of Market Makers

1.5. Risks in Financial Markets

1.5.1. Market, Credit, and Liquidity Risks
1.5.2. Systemic Risk
1.5.3. Risk Management and Hedging

1.6. Regulation and Standards

1.6.1. European and Global Regulations
1.6.2. Market Supervision
1.6.3. Investor Protection

1.7. Order Types and Execution

1.7.1. Market and Limit Orders
1.7.2. Stop Loss and Take Profit Orders
1.7.3. Trailing Stop Orders
1.7.4. Order Programming in Algorithmic Trading

1.8. Financial Intermediaries

1.8.1. Banks, Brokers, and Hedge Funds
1.8.2. Investment Funds and ETFs
1.8.3. Trading Platforms

1.9. Macroeconomic Factors in the Markets

1.9.1. Monetary and Fiscal Policy
1.9.2. Key Economic Indicators
1.9.3. Impact of News and Events

1.10. Innovation in Financial Markets

1.10.1. Digitalization and Blockchain
1.10.2. Cryptocurrencies and DeFi
1.10.3. Tokenization of Assets

Module 2. Stock Market Analysis in Algorithmic Trading

2.1. Evaluation of Stock Market Analysis in Algorithmic Trading

2.1.1. Technical Analysis vs. Fundamental Analysis
2.1.2. Market Efficiency Theory
2.1.3. Principles of Trading Based on Analysis

2.2. Fundamental Analysis of Companies

2.2.1. Economic and Financial Diagnosis
2.2.2. Financial Statements and Key Ratios
2.2.3. Company Valuation by Static Methods
2.2.4. External Factors Affecting Stocks

2.3. Company Valuation

2.3.1. Market Consensus
2.3.2. Valuation by Multiples
2.3.3. Valuation by Dividend Discount
2.3.4. Valuation by Discounted Cash Flow
2.3.5. Use of AI and Company Valuation Bots

2.4. Technical Analysis: Basic Principles for Trading

2.4.1. Types of Charts and Their Interpretation
2.4.2. Volume and Trend
2.4.3. Key Technical Indicators

2.5. Japanese Candlestick Patterns

2.5.1. Individual Candles and Combinations
2.5.2. Reversal and Continuation Patterns
2.5.3. Applications in Trading

2.6. Advanced Technical Indicators to Implement in Algorithmic Trading

2.6.1. RSI, MACD, and Bollinger Bands
2.6.2. Oscillators and Moving Averages
2.6.3. Configuration and Application

2.7. Technical Analysis Strategies to Implement in Trading

2.7.1. Trend Trading
2.7.2. Range Trading
2.7.3. Trading with Volume

2.8. Intermarket Analysis and Correlations

2.8.1. Relationship Between Financial Assets
2.8.2. Commodities, Currencies, and Equities
2.8.3. Hedging and Diversification

2.9. Order Flow Analysis

2.9.1. Level 2 and Order Book
2.9.2. Market Depth and VWAP
2.9.3. Tape Reading

2.10. Limitations of Stock Market Analysis

2.10.1. Biases and Common Mistakes
2.10.2. Market Manipulation
2.10.3. Real Applications and Context

Module 3. Algorithmic Trading in Psychology and Decision Making

3.1. The Importance of Psychology in Trading

3.1.1. Emotional Impact on Decisions
3.1.2. Common Cognitive Biases
3.1.3. Emotional Control in Volatile Markets

3.2. Cognitive Biases in Trading

3.2.1. Anchoring Effect and Loss Aversion
3.2.2. Overconfidence and Excessive Trading
3.2.3. Herd Effect and Confirmation Bias

3.3. Emotional Management in Trading

3.3.1. Strategies to Stay Calm
3.3.2. Resilience and Discipline
3.3.3. Mindfulness Techniques and Stress Control

3.4. Decision Making in Uncertainty

3.4.1. Rational vs. Emotional Analysis
3.4.2. How to Assess Probabilities
3.4.3. Decision-Making Methods

3.5. Developing a Professional and/or Automated Trading Mindset

3.5.1. Planning and Discipline
3.5.2. Learning and Continuous Improvement
3.5.3. Psychological Preparation for Trading

3.6. Managing Psychological Risk

3.6.1. Impact of Drawdown on the Trader
3.6.2. Handling Consecutive Losses
3.6.3. Avoiding Revenge Trading
3.6.4. Is There Psychological Risk in Algorithmic Trading?

3.7. Strategies to Prevent Mental Burnout

3.7.1. How to Avoid Burnout
3.7.2. Importance of Breaks
3.7.3. Disconnection Techniques
3.7.4. Automation

3.8. Psychology of Money and Risk Aversion

3.8.1. Relationship between Risk and Return
3.8.2. Personal Risk Tolerance
3.8.3. Financial Goal Assessment

3.9. Neuroscience Applied to Trading

3.9.1. Brain Function in Decision Making
3.9.2. Dopamine and Trading Addiction
3.9.3. How to Train the Mind for Success

3.10. Common Psychological Errors and How to Avoid Them

3.10.1. Lack of Patience and Overtrading
3.10.2. Not Following the Trading Plan
3.10.3. How to Maintain Discipline

Module 4. Fundamentals of Algorithmic Trading

4.1. Philosophy of Algorithmic Trading

4.1.1. Advantages of Algorithmic Trading over Manual Trading
4.1.2. Evolution and Adoption in the Markets
4.1.3. Differences with Discretionary Trading

4.2. Intraday Algorithmic Strategies

4.2.1. Characteristics of Intraday Investment Strategies
4.2.2. Advanced Study of Intraday Strategies
4.2.3. Profitability and Risk of These Strategies

4.3. Swing Algorithmic Strategies

4.3.1. Characteristics of Continuous Investment
4.3.2. Advanced Study of Continuous Trading Systems
4.3.3. Profitability and Risk of These Strategies

4.4. Architecture of an Algorithmic Trading System

4.4.1. Key Components
4.4.2. Data Flow and Execution
4.4.3. Integration with Market APIs

4.5. Data Sources in Algorithmic Trading

4.5.1. Historical and Real-Time Data
4.5.2. Data Quality and Cleansing
4.5.3. Free and Paid Sources

4.6. Latency and Speed in Algorithmic Trading

4.6.1. Importance of Fast Execution
4.6.2. Factors Affecting Latency
4.6.3. Co-location and High-Frequency Trading

4.7. Performance Metrics

4.7.1. Metrics Based on Profitability
4.7.2. Drawdown Analysis
4.7.3. Metrics Based on Hit Rate
4.7.4. Metrics Based on Risk Management

4.8. Backtesting and Strategy Validation

4.8.1. Backtesting Methods
4.8.2. Avoiding Overfitting
4.8.3. Performance Evaluation

4.9. Infrastructure and Hardware for Algorithmic Trading

4.9.1. Dedicated Servers vs. Cloud Computing
4.9.2. Networks and Connectivity
4.9.3. Security and Maintenance

4.10. Limitations and Challenges of Algorithmic Trading

4.10.1. Complexity and Costs
4.10.2. Risks of Technical Failures
4.10.3. Adaptability to Changing Conditions

Module 5. Typology, Logic, and Design of Algorithmic Trading Strategies

5.1. Momentum and Trend Following Strategies

5.1.1. Identifying Trends
5.1.2. Indicators and Filters
5.1.3. Implementation in Code

5.2. Mean Reversion Strategies

5.2.1. Mean Reversion Investment
5.2.2. Application in Different Markets
5.2.3. Statistical Models

5.3. Statistical Arbitrage and Pairs Trading

5.3.1. Identifying Correlated Pairs
5.3.2. Cointegration Models
5.3.3. Execution and Risk Management

5.4. Market Making and Liquidity Provision

5.4.1. How Market Makers Operate
5.4.2. Strategies to Capture the Spread
5.4.3. Risks and Optimization

5.5. Volume-Based and Order Flow Strategies

5.5.1. Order Flow Analysis
5.5.2. Impact of Volume on Price
5.5.3. Identifying Opportunities

5.6. Event and News-Based Strategies

5.6.1. Trading on Macroeconomic Events
5.6.2. Sentiment Analysis in News
5.6.3. Automation of News-Based Trading

5.7. High-Frequency Trading (HFT) Strategies

5.7.1. Characteristics of HFT
5.7.2. Ultra-Fast Execution Algorithms
5.7.3. Technological Requirements

5.8. Hybrid Strategies and Combinations

5.8.1. Integrating Multiple Strategies
5.8.2. Algorithmic Portfolio Management
5.8.3. Diversification and Risk Control

5.9. Optimization and Adaptation of Strategies

5.9.1. Parameter Adjustment
5.9.2. Machine Learning in Optimization
5.9.3. Adaptability to Market Changes

5.10. Ethical and Regulatory Considerations

5.10.1. Regulations on Algorithmic Trading
5.10.2. Market Manipulation Issues
5.10.3. Ethics in the Use of Financial Algorithms

Module 6. Quantitative Analysis and Machine Learning in Algorithmic Trading

6.1. Fundamentals of Quantitative Analysis

6.1.1. Key Characteristics of Quantitative Analysis
6.1.2. Probabilistic Models in Trading
6.1.3.  Use of Statistics in Financial Markets

6.2. Mathematical Models Applied to Trading

6.2.1. Time Series Models
6.2.2. Regression and Correlations
6.2.3. Volatility Models

6.3. Machine Learning in Algorithmic Trading

6.3.1. Advanced Understanding of Machine Learning
6.3.2. Supervised Learning Algorithms
6.3.3. Unsupervised Learning Algorithms
6.3.4. Reinforcement Learning Algorithms
6.3.5. Benefits and Risks

6.4. Neural Networks and Deep Learning in Algorithmic Trading

6.4.1. Applications of Neural Networks
6.4.2. Price Prediction Models
6.4.3. Limitations and Challenges

6.5. Advanced Backtesting with Machine Learning

6.5.1. Evaluation of Predictive Models
6.5.2. Cross-Validation
6.5.3. Avoiding Overfitting

6.6. Optimization of Strategies with Artificial Intelligence

6.6.1. Genetic Algorithms
6.6.2. Reinforcement in Trading
6.6.3. AutoML in Finance

6.7. Risk Factors in Quantitative Models

6.7.1. Biases in Data
6.7.2. Overfitting and Noisy Data
6.7.3. Model Robustness

6.8. Implementation of ML Strategies in Real Environments

6.8.1. Deployment in Production
6.8.2. Model Monitoring
6.8.3. Adapting to Market Changes

6.9. Use of Alternative Data in Trading

6.9.1. Social Media and Market Sentiment
6.9.2. Satellite and Alternative Data
6.9.3. Other Sentiment Indicators

6.10. Ethics and Regulation in the Use of AI in Trading

6.10.1. Algorithmic Biases
6.10.2. Emerging Regulations
6.10.3. Responsibility in Decision Making

Module 7. Programming and Development of Algorithms in Trading

7.1. Fundamentals of Programming for Trading

7.1.1. Most Common Programming Languages (Python, R, etc.)
7.1.2. Development Environments and Tools
7.1.3. Version Control

7.2. Financial Data Manipulation with Python

7.2.1. Essential Libraries (Pandas, NumPy, etc.)
7.2.2. Loading and Processing Historical Data
7.2.3. Analysis and Visualization

7.3. Automation of Trading Strategies

7.3.1. Developing Scripts for Automated Execution
7.3.2. Broker APIs and Market Connections
7.3.3. Automation of Analysis and Reporting

7.4. Design of Custom Indicators

7.4.1. Creating Custom Technical Indicators
7.4.2. Combining Multiple Signals
7.4.3. Implementation in Code

7.5. Development of Trading Bots

7.5.1. Architecture of a Trading Bot
7.5.2. Order Execution and Management
7.5.3. Simulation of Trades

7.6. Testing and Debugging Algorithms

7.6.1. Identifying Common Errors
7.6.2. Debugging Tools
7.6.3. Unit Testing and Quality Control

7.7. Use of Databases in Algorithmic Trading

7.7.1. SQL vs. NoSQL in Trading
7.7.2. Efficient Storage of Historical Data
7.7.3. Query Optimization

7.8. Integration with Market Data APIs

7.8.1. APIs with Brokers and Data Feeders
7.8.2. Real-Time Data Extraction and Updates
7.8.3. Web Scraping and Alternative Data Sources

7.9. Infrastructure and Deployment of Algorithms

7.9.1. Local Servers vs. Cloud Computing
7.9.2. Deployment in Major Clouds (AWS, Google Cloud, Azure)
7.9.3. Security and Maintenance

7.10. Optimization and Scalability of Algorithms

7.10.1. Code Performance Improvement
7.10.2. Parallelization and Distributed Processing
7.10.3. Latency Management and Execution Times

Module 8. Implementation, Development, and Monitoring of Algorithmic Trading Strategies

8.1. From Development to Live Market Execution

8.1.1. Transition Process from Backtesting to Live Trading
8.1.2. Testing in Simulated Environments
8.1.3. Final Adjustments and Calibrations

8.2. Selecting a Broker and Execution Platform

8.2.1. Brokers for Algorithmic Trading
8.2.2. Differences Between ECN, STP, and Market Maker
8.2.3. Commissions and Hidden Costs

8.3. Implementation of Automated Execution Systems

8.3.1. Types of Execution (Market, Limit, Stop)
8.3.2. Smart Order Routing Algorithms
8.3.4. Impact of Slippage on Strategies

8.4. Monitoring and Adjusting Strategies

8.4.1. Real-Time Performance Evaluation
8.4.2. Algorithmic Efficiency Indicators
8.4.3. Adjustments on the Fly

8.5. Risk Management in Strategy Execution

8.5.1. Loss and Exposure Control
8.5.2. Dynamic Leverage Adjustment
8.5.3. Identifying Execution Failures

8.6. Use of Dedicated Servers for Execution

8.6.1. Co-location and Low Latency Servers
8.6.2. Hardware and Software Considerations
8.6.3. Costs and Benefits

8.7. Costs and Benefits

8.7.1. Handling Emergencies and System Failures
8.7.2. Contingency Plans
8.7.3. Automation of Alerts and Notifications

8.8. Performance Metrics Evaluation

8.8.1. Risk-Adjusted Profitability
8.8.2. Drawdowns and Volatility
8.8.3. Analysis of Key Metrics (Sharpe, Sortino, Calmar)

8.9. Continuous Strategy Optimization

8.9.1. Machine Learning in Strategy Adjustment
8.9.2. Periodic Review of Models
8.9.3. Avoiding Over-Optimization

8.10. Regulatory Aspects of Algorithmic Execution

8.10.1. Regulations on Automated Trading
8.10.2. Transparency and Audit Requirements
8.10.3. Compliance Standards (MiFID, SEC, ESMA)

Module 9. Risk Analysis in Algorithmic Trading

9.1. The Importance of Risk Management in Trading

9.1.1. Types of Risk in Financial Markets
9.1.2. Importance of Risk Control
9.1.3. Quantitative vs. Qualitative Approaches

9.2. Market Risk and Volatility

9.2.1. Factors Influencing Volatility
9.2.2. Calculation and Use of Value at Risk (VaR)
9.2.3. Volatility Prediction Models

9.3. Liquidity and Implementation Risk

9.3.1. Liquidity and Execution Risk
9.3.2. Impact of Liquidity on Trading
9.3.3. Order Book Analysis

9.4. Credit and Counterparty Risk

9.4.1. Importance of Counterparty Risk
9.4.2. Evaluating Broker Solvency
9.4.3. Preventing Default Risk

9.5. Operational Risk in Algorithmic Trading

9.5.1. Technical Failures and Execution Errors
9.5.2. Risks Associated with Data and Market Feeds
9.5.3. Mitigation Strategies

9.6. Systemic Risk and Financial Crises

9.6.1. Crisis Trigger Factors
9.6.2. Domino Effect in Markets
9.6.3. Hedging Strategies in Crises

9.7. Managing Drawdown and Loss Control

9.7.1. Evaluating Drawdowns in Strategies
9.7.2. Loss Reduction Techniques
9.7.3. Psychology of Risk and Loss Aversion

9.8. Diversification and Portfolio Management

9.8.1. Diversification Across Strategies and Markets
9.8.2. Asset Correlations
9.8.3. Using Portfolio Optimization Models

9.9. Risk Management Tools and Software

9.9.1. Specialized Platforms
9.9.2. Adverse Scenario Simulation
9.9.3. Evaluation of Key Metrics

9.10. Regulatory Framework and Compliance in Risk Management

9.10.1. International Risk Regulations
9.10.2. Regulatory Requirements for Funds and Traders
9.10.3. Transparency and Auditing in Risk Management

Module 10. Taxation of Algorithmic Trading

10.1. The Importance of Taxation in Trading

10.1.1. Tax Obligations of Traders
10.1.2. Differences Between the Taxation of Individuals and Companies
10.1.3. Tax Regime for Derivatives and Cryptocurrencies

10.2. Taxation of Gains and Losses in Trading

10.2.1. Tax Calculation on Profits
10.2.2. Loss Deductions
10.2.3. Differences According to Country of Residence

10.3. Taxation of Algorithmic Trading vs. Discretionary Trading

10.3.1. Differences in Taxation
10.3.2. Legal Aspects of Automated Trading
10.3.3. Tax Control on Financial Algorithms

10.4. Tax Havens and International Regulation

10.4.1. Use of Offshore Companies
10.4.2. International Regulations Against Tax Evasion
10.4.3. Legal Implications

10.5. Transparency and Auditing in Algorithmic Trading

10.5.1. Financial Reporting Requirements
10.5.2. Audits in Investment Funds
10.5.3. Data Protection Regulation

10.6. Sustainability in Financial Markets

10.6.1. ESG Investment and Sustainable Criteria
10.6.2. Trading Algorithms with a Positive Impact
10.6.3. Regulations on Sustainable Finance

10.7. Cryptocurrencies and Taxation

10.7.1. Taxation of Digital Assets
10.7.2. Emerging Regulations
10.7.3. Security and Regulatory Compliance

10.8. Environmental Impact of Algorithmic Trading

10.8.1. Energy Consumption in HFT (High-Frequency Trading)
10.8.2. Sustainable Alternatives
10.8.3. Environmental Regulations

10.9. Tax Strategies for Professional Traders

10.9.1. Tax Optimization
10.9.2. Tax Planning
10.9.3. Use of Legal Structures

10.10. Ethics in Algorithmic Trading and Social Responsibility

10.10.1. Social Impact of Financial Markets
10.10.2. Transparency and Governance
10.10.3. Ethical Standards in Algorithm Development

You will become an architect of financial systems, applying quantitative analysis and Machine Learning to optimize your investment decisions”