• Understanding Data-Driven Decisions and Initiating Data Extraction
  • Explore the philosophy behind making decisions based on data.
  • Delve into the landscape of potential personal investments.
  • Address questions about where to focus attention and considerations of risk and reward.
• Practical Setup: Colab and Initial Data Download
  • Guide you through setting up Colab for practical data analysis.
  • Download your initial financial data using Finance APIs.
• Essential Principles for API Selection
  • Considerations for selecting the right API for your data needs.
  • When it becomes necessary to consider payment options in the API selection process.
• Homework

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• The Core Libraries for Data Analysis in Python
  • Explore the core libraries: Numpy, Pandas, and Matplotlib (including Seaborn and Plotly Express).
• Understanding Data Types and Manipulation
  • Delve into various data types: numeric, string, and date categories.
  • Master the art of generating dummy variables for comprehensive analysis.
• Enhancing Datasets with Feature Generation Techniques
  • Derive additional features such as hour/day of the week, growth over different periods.
  • Incorporate technical indicators using the TaLib library.
  • Understand predictive elements, including future growth over a week, a month, or a year.
• Effective Data Cleaning Strategies
  • Learn strategies for cleaning and preparing data for analysis.
  • Acquire skills in joining multiple datasets for a holistic view.
• Thorough Descriptive Analysis
  • Conduct a comprehensive descriptive analysis of the dataset.
  • Explore correlations within the data to uncover meaningful insights.
• Homework

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• Framing Hypotheses and Unraveling Time-Series Predictions
• Heuristics and hand rules for practical predictions.
• Predicting time-series data: trends, seasonality, and remainder decomposition.
• Regression techniques for understanding data relationships.
• Binary classification to determine growth direction.
• [Optional] Example of neural networks in analytical modelling.
• Homework

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Moving Beyond Prediction into the realm of Trading Strategy and Simulation:

• [Optional] Explore screenshots of trading apps, guiding you on how to start—from downloading an app to placing a trade.
• Uncover key features of trading strategies, including considerations like trading fees, risk management, combining predictions, and timing of market entry.
• Delve into various strategy examples:
  • Single stock investment for a long-term approach.
  • Diversified portfolio optimisation for long investments in multiple stocks.
  • Market-neutral strategies, involving both long and short positions based on predictions.
  • Mean reversion strategy, driven by events.
  • Vertical stocks covering and pairs trading.
  • Exploration of "Penny" stocks and dividend strategies.
  • [Maybe - Advanced] Basic options strategy.
• Simulate the financial results based on predictions and the chosen strategy.
• Homework

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Streamlining Processes from Prediction to Action:

• Transition from Colab notebooks to Python files for improved deployment and execution.
• Establish persistent storage mechanisms, including files and potentially a simple SQLite database with an introduction to SQL.
• Explore automation techniques such as scheduling cron jobs for a series of .py files and consider data workflow solutions like Apache Airflow.
• Learn to generate predictions and execute trades systematically.
• [Maybe - Advanced] Implement automated email notifications containing predictions, trade details, and updates on profit/loss for the designated period.
• Homework

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Putting everything we learned to practice

• Week 1 and 2: working on your project
• Week 3: reviewing your peers

More details: will be shared in the coming weeks