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Stock Market Analytics Zoomcamp

TODO List before the course starts:

(Short) Syllabus (published on PythonInvest's website)

Taking the course

2024 Cohort

Self-paced mode

All the materials of the course are freely available, so that you can take the course at your own pace

  • Follow the suggested syllabus (see below) week by week
  • You don't need to fill in the registration form. Just start watching the videos and join Slack
  • Check FAQ if you have problems
  • If you can't find a solution to your problem in FAQ, ask for help in Slack

(Detailed) Syllabus

Module 1: Introduction and Data Sources

  • 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

More details

Module 2: Working with the Data (in Pandas)

  • 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

More details

Module 3: Analytical Modeling

  • 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

More details

Module 4: Trading Strategy and Simulation

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

More details

Module 5: Deployment and Automation

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

More details

Project

Putting everything we learned to practice

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

Asking for help in Slack

The best way to get support is to use DataTalks.Club's Slack. Join the #course-stocks-analytics-zoomcamp channel.

To make discussions in Slack more organized:

  • Read the DataTalks.Club community guidelines

  • Follow these recommendations when asking for help in Slack:

    • Before posting a question, try to Google it and Check Course's FAQ (Frequently asked technical questions) first
    • DO NOT use screenshots, especially don’t take pictures from a phone.
    • DO NOT tag instructors, it may discourage others from helping you.
    • Copy and paste errors; if it’s long, just post it in a reply to your thread.
    • Use ``` for formatting your code.
    • Use the same thread for the conversation (that means replying to your own thread).
    • DO NOT create multiple posts to discuss the issue.
    • You may create a new post if the issue reemerges down the road. Be sure to describe what has changed in the environment.
    • Provide additional information in the same thread of the steps you have taken for resolution.