Welcome 👋 This is where I've uploaded the assignments I've completed for the Datatalks Stock Market Analytics Zoomcamp 2025 course.
- 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.
- 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.
- 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.
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.
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.
Putting everything we learned to practice
- Week 1 and 2: working on your project
- Week 3: reviewing your peers
Valeria Q.M
