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-zoomcampchannel - 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
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Start: 15 April 2024 (Monday) at 17:00 GMT
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Registration Form: LINK
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Cohort folder with homeworks and deadlines
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Pre-Launch stream with course overview and Q&A (streamed on 25th March 2024):
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Supplementary pre-read for the project selection:
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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/
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Books (note: these are affiliate links to Amazon):
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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
- 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
- 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
- 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
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
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
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
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:
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Read the DataTalks.Club community guidelines
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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.