investing

Automated scripts for monitoring long-term stock trends

Installation

1. Clone this repository
2. Install the Python requirements
3. Run unit tests

git clone https://github.com/addisonklinke/investing.git
cd investing
pip install - r requirements.txt
python -m unittest discover -v

Configuration

Default configuration values are located in the file config/investing.defaults.yaml. To get started, run python launcher.py configure and follow the prompts. This will populate a new file called config/investing.yaml. Any values from the user-created config will take precedence over the default config at runtime.

You will need (free) accounts with the following websites to populate the API key fields

• Alpha Vantage: 20-year historical ticker prices
• Finnhub: alternative news and sentiment data
• Metals API: precious metals and foreign currencies

Currently, there is not a free API that can provide 20+ year historical data on gold prices. The best option as of this writing appears to be the Metals API listed above which can return a 5-day history (limited to 50 calls/month). Fortunately, the World Gold Council provides an XLSX file with daily prices going back to 1979.

By manually extracting the date and price columns from their "Daily" tab, you can save this under xau.csv in your configured save path. This will serve as a starting point which the Metals API can continue adding to automatically as time goes on. After saving the CSV, load and save it once through this package's Ticker class in order to apply consistent formatting

from investing.data import Ticker

xau = Ticker('xau')
xau.data.to_csv(xau.csv_path)

Usage

The launcher script provides the primary interface for running different workflows. To familiarize yourself with the available options, run python launcher.py -h

A typical sequence of commands would be

1. Configure a few different portfolios of tickers in the YAML file created above
2. Automate a cron job for python launcher.py daily_tickers. This will keep your local CSVs in-sync
3. Manually run the compare_performance or expected_return workflows for your own research

Disclaimer

Use at your own risk. Per the MIT license, this project does not provide ANY guarantees of proper functionality. Users should not make investment decisions without consulting a reliable, external source of information such as a professional financial planner.