Develop a strategy that helps predict economic cycles and implement a sector rotation investment strategy.
This project leverages leading economic indicators to create a model that aids portfolio managers in determining when to be Overweight, Underweight, or Neutral in pro-cyclical assets. The goal is to generate an investment strategy that shifts between pro-cyclical and anti-cyclical sectors based on the economic cycle.
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Project Definition and Scope:
- Introduction to the project, including software and terminology.
- Exploratory Data Analysis (EDA) of economic indicators and their percentage changes.
- Graphical comparison and analysis of economic indicators versus the S&P 500.
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Model Proposal:
- Develop a model for the sector rotation strategy using the given economic indicators.
- Generate a discretized output (Y) based on the performance of the S&P 500.
- Download historical data for the benchmark and financial assets.
- Classify assets based on their pro-cyclical or anti-cyclical nature and their beta.
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Model Training and Validation:
- Train and validate the proposed model.
- Optimize and generalize the parameters of the proposed model.
- Identify expected trends (Overweight, Underweight, Neutral).
- Select the investment strategy.
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Strategy Implementation:
- Implement the sector rotation investment strategy using the algorithm developed in the previous phase.
- Perform dynamic backtesting with random asset selection.
- Analyze the performance metrics of the strategy.
- Compare the strategy against a benchmark.
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Final Deliverables:
- Final corrections and adjustments.
- Presentation of results in a paper format.
- Final project presentation.
To run this project, you need to set up a virtual environment and install the necessary dependencies.
python -m venv env- On Windows:
env\Scripts\activate- On macOS and Linux:
source env/bin/activatepip install -r requirements.txtYou're now ready to run the project! =)