https://github.com/boyac/pytrading
Based on the concepts in "CIMTR" and others, swing trading
https://github.com/boyac/pytrading
equity fixed-income intraday-trading investment investment-portfolio multi-asset reits swing-trading systematic-trading-strategies trading trading-strategies
Last synced: over 1 year ago
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Based on the concepts in "CIMTR" and others, swing trading
- Host: GitHub
- URL: https://github.com/boyac/pytrading
- Owner: boyac
- License: other
- Created: 2018-03-24T11:31:06.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2025-02-23T10:47:07.000Z (over 1 year ago)
- Last Synced: 2025-02-23T11:29:35.657Z (over 1 year ago)
- Topics: equity, fixed-income, intraday-trading, investment, investment-portfolio, multi-asset, reits, swing-trading, systematic-trading-strategies, trading, trading-strategies
- Language: Jupyter Notebook
- Homepage:
- Size: 2.5 MB
- Stars: 11
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pyTrading
## Content
- use python 2.7
- systematic and swing trading, based on the concepts in "CIMTR" and others. As I'm getting busier each day, most scripts will remain as simple notes from readings
## Your Support
- You can contribute to the project by reporting bugs, suggesting enhancements, exchanging portfolio management experiences or
you can make a donation to this project:
## Formula
- Leveraged investment are amplified compared to an otherwise identical unleveraged investment. Gains are higher and losses are worse, so it is a high risk/high reward strategy. The return on a leveraged investment can be calculated as:
- R_li = R_ui + [(D/E) * (R_ui - c)]
- R_li = (Ending value with leverage - starting value with leverage - borrowing cost) / start value with leverage
-- R_li: leveraged return
-- R_ui: unleveraged return
-- D/E : borrowing ratio
-- c : cost of borrowing