Dylan Tamayo
In this project, I created a pipeline for collecting weather data from multiple sources using API calls, Python modules, and web scraping; tested different machine learning models to forecast weather temperature; and automated API trading on the Kalshi market based on predicted values.
Explanation of contents of Common Task folder:
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collect_data.py: pipeline for collecting data from sources
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create_order.py: pipeline for consolidating source data, making forecast, and submitting Kalshi order
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ML_models.ipynb: Jupyter notebook comparing different machine learning models
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results/
- common_task_report.ipynb: main report of findings
- common_task_results.csv: predicted and actual maximum temperature for Weeks 1-3
- historical_predictions.csv: predicted and actual maximum temperature for historical data
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weather_forecast/: contains csv generated by collect_data.py
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KalshiClientsBaseV2.py: Kalshi API code for connecting to exchange client
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city_config.json: information for each city used in data pipelines
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logins.json: login information for Kalshi account and API keys (needs to be filled in by user)
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common_task_env.yml: conda environment used for task