Stock Market Analytics Zoomcamp

TODO List before the course starts:

• Subscribe to PythonInvest to receive analytics and future courses announcements.
• Register in DataTalks.Club's Slack
• Join the #course-stocks-analytics-zoomcamp channel
• Join the course Telegram channel with announcements
• The videos are published on PythonInvest's YouTube channel
• Frequently asked technical questions

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

• Module 1: Introduction and Data Sources
• Module 2: Working with the Data (in Pandas)
• Module 3: Analytical Modeling
• Module 4: Trading Strategy and Simulation
• Module 5: Deployment and Automation
• Project

Taking the course

2024 Cohort

• Start: 15 April 2024 (Monday) at 17:00 GMT

• Registration Form: LINK

• Cohort folder with homeworks and deadlines

• Pre-Launch stream with course overview and Q&A (streamed on 25th March 2024):

  • 
  • Slides

• Supplementary pre-read for the project selection:

  • Any recent financial news or analytics:

    • Weekly news coverage on PythonInvest's Financial News Feed: https://pythoninvest.com/
    • Analytical long-reads on PythonInvest's Blog: https://pythoninvest.com/blog
    • Simply Wall St Market Insights: https://simplywall.st/markets/insights
    • Investing on CNBC: https://www.cnbc.com/investing/
    • Unhedged podcast and articles: https://unhedged.ft.com/
    • Yahoo Finance: https://finance.yahoo.com/

  • Books:

    • The Trading Game: A Confession
    • Unknown Market Wizards (latest edition)
    • The Man Who Solved the Market
    • The Tao of Trading: How to Build Abundant Wealth in Any Market Condition

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.