from terms import terms, Engine

portfolio = Engine(
    start_date = "2018-06-09", 
    portfolio = ["BABA", "PDD", "KO", "AMD","^IXIC"], 
    weights = [0.2, 0.2, 0.2, 0.2, 0.2],  # equal weighting is set by default
    benchmark = ["SPY"]  # SPY is set by default
)

terms(portfolio)

A portfolio can be rebalanced for either a specific time period or for specific dates using the rebalance option.

Time periods available for rebalancing are 2y, 1y, 6mo, quarterly, monthly

from terms import terms, Engine

portfolio = Engine(
    start_date = "2018-06-09", 
    portfolio = ["BABA", "PDD", "KO", "AMD","^IXIC"], 
    weights = [0.2, 0.2, 0.2, 0.2, 0.2],  # equal weighting is set by default
    benchmark = ["SPY"],  # SPY is set by default
    rebalance = "1y"
)

terms(portfolio)

You can rebalance a portfolio by specifying a list of custom dates.
⚠️ When using custom dates, the first date of the list must correspond with the start_date and the last element should correspond to the end_date which is today"s date by default.

from terms import terms, Engine

portfolio = Engine(
    start_date = "2018-06-09", 
    portfolio = ["BABA", "PDD", "KO", "AMD","^IXIC"], 
    weights = [0.2, 0.2, 0.2, 0.2, 0.2],  # equal weighting is set by default
    benchmark = ["SPY"],  # SPY is set by default
    rebalance = ["2018-06-09", "2019-01-01", "2020-01-01", "2021-01-01"]
)

terms(portfolio)

The default optimizer is equal weighting. You can specify custom weights, if desired.

from terms import terms, Engine

portfolio = Engine(
    start_date = "2018-01-01",
    portfolio = ["BABA", "PDD", "KO", "AMD","^IXIC"], 
    weights = [0.1, 0.3, 0.15, 0.25, 0.2],   # custom weights
    rebalance = "1y"  # rebalance every year
)

terms(portfolio)

You can also use the built-in optimizers. There are 4 optimizers available:

• "EF": Global Efficient Frontier Example
• "MEANVAR": Mean-Variance Example
• "HRP": Hierarchical Risk Parity Example
• "MINVAR": Minimum-Variance Example

from terms import terms, Engine

portfolio = Engine(
    start_date = "2018-01-01",
    portfolio = ["BABA", "PDD", "KO", "AMD","^IXIC"],
    optimizer = "EF",
    rebalance = "1y"  # rebalance every year
)

portfolio.weights

Output:

[0.0, 0.0, 0.0348, 0.9652, 0.0]

We can see that the allocation has been optimized.

3 Risk Managers are available:

• Max Drawdown: {"Max Drawdown" : -0.3} Example
• Take Profit: {"Take Profit" : 0.4} Example
• Stop Loss: {"Stop Loss" : -0.2} Example

from terms import terms, Engine

portfolio = Engine(
    start_date = "2018-01-01",
    portfolio= ["BABA", "PDD", "KO", "AMD","^IXIC"], 
    optimizer = "EF",
    rebalance = "1y",  # rebalance every year
    risk_manager = {"Max Drawdown" : -0.2}  # Stop the investment when the drawdown becomes superior to -20%
)

terms(portfolio)

You can use the get_report() function of terms to generate a tearsheet, and then download this as a PDF document.

from terms import get_report, Engine

portfolio = Engine(
      start_date = "2018-01-01",
      portfolio = ["BABA", "PDD", "KO", "AMD","^IXIC"],
      optimizer = "EF",
      rebalance = "1y", #rebalance every year
      risk_manager = {"Stop Loss" : -0.2}
)

get_report(portfolio)

Output:

terms uses GitHub to host its source code. Learn more about the Github flow.

For larger changes (e.g., new feature request, large refactoring), please open an issue to discuss first.

• If you wish to create a new Issue, then click here to create a new issue.

Smaller improvements (e.g., document improvements, bugfixes) can be handled by the Pull Request process of GitHub: pull requests.

• To contribute to the code, you will need to do the following:

• Fork terms - Click the Fork button at the upper right corner of this page.

• Clone your own fork. E.g., git clone https://github.com/ssantoshp/terms.git
  If your fork is out of date, then will you need to manually sync your fork: Synchronization method

• Create a Pull Request using your fork as the compare head repository.

You contributions will be reviewed, potentially modified, and hopefully merged into terms.

Thanks goes to these wonderful people (emoji key):

Brendan Glancy 💻 🐛

Renan Lopes 💻 🐛

Mark Thebault 💻

Diego Alvarez 💻🐛

Rakesh Bhat 💻

Anh Le 🐛

Tony Zhang 💻

Ikko Ashimine ✒️

QuantNomad 📹

Buckley ✒️💻

Adam Nelsson 💻

Ranjan Grover 🐛💻

This project follows the all-contributors specification. Contributions of any kind are welcome!

This library has also been made possible because of the work of these incredible people:

• Ran Aroussi for the Quantstats library
• Robert Martin for the PyPortfolioOpt

You are welcome to contact us by email at [email protected] or in terms"s discussion space

MIT