# pypme – Python package for PME (Public Market Equivalent) calculation
Based on the [Modified PME
method](https://en.wikipedia.org/wiki/Public_Market_Equivalent#Modified_PME).
## Example
```python
from pypme import verbose_xpme
from datetime import date
pmeirr, assetirr, df = verbose_xpme(
dates=[date(2015, 1, 1), date(2015, 6, 12), date(2016, 2, 15)],
cashflows=[-10000, 7500],
prices=[100, 120, 100],
pme_prices=[100, 150, 100],
)
```
Will return `0.5525698793027238` and `0.19495150355969598` for the IRRs and produce this
dataframe:
Notes:
- The `cashflows` are interpreted from a transaction account that is used to buy from an
asset at price `prices`.
- The corresponding prices for the PME are `pme_prices`.
- The `cashflows` is extended with one element representing the remaining value, that's
why all the other lists (`dates`, `prices`, `pme_prices`) need to be exactly 1 element
longer than `cashflows`.
## Variants
- `xpme`: Calculate PME for unevenly spaced / scheduled cashflows and return the PME IRR
only. In this case, the IRR is always annual.
- `verbose_xpme`: Calculate PME for unevenly spaced / scheduled cashflows and return
vebose information.
- `pme`: Calculate PME for evenly spaced cashflows and return the PME IRR only. In this
case, the IRR is for the underlying period.
- `verbose_pme`: Calculate PME for evenly spaced cashflows and return vebose
information.
- `tessa_xpme` and `tessa_verbose_xpme`: Use live price information via the tessa
library. See below.
## tessa examples – using tessa to retrieve PME prices online
Use `tessa_xpme` and `tessa_verbose_xpme` to get live prices via the [tessa
library](https://github.com/ymyke/tessa) and use those prices as the PME. Like so:
```python
from datetime import datetime, timezone
from pypme import tessa_xpme
common_args = {
"dates": [
datetime(2012, 1, 1, tzinfo=timezone.utc),
datetime(2013, 1, 1, tzinfo=timezone.utc)
],
"cashflows": [-100],
"prices": [1, 1],
}
print(tessa_xpme(pme_ticker="LIT", **common_args)) # source will default to "yahoo"
print(tessa_xpme(pme_ticker="bitcoin", pme_source="coingecko", **common_args))
print(tessa_xpme(pme_ticker="SREN.SW", pme_source="yahoo", **common_args))
```
Note that the dates need to be timezone-aware for these functions.
## Garbage in, garbage out
Note that the package will only perform essential sanity checks and otherwise just works
with what it gets, also with nonsensical data. E.g.:
```python
from pypme import verbose_pme
pmeirr, assetirr, df = verbose_pme(
cashflows=[-10, 500], prices=[1, 1, 1], pme_prices=[1, 1, 1]
)
```
Results in this df and IRRs of 0:
## Other noteworthy libraries
- [tessa](https://github.com/ymyke/tessa): Find financial assets and get their price history without worrying about different APIs or rate limiting.
- [strela](https://github.com/ymyke/strela): A python package for financial alerts.
## References
- [Google Sheet w/ reference calculation](https://docs.google.com/spreadsheets/d/1LMSBU19oWx8jw1nGoChfimY5asUA4q6Vzh7jRZ_7_HE/edit#gid=0)
- [Modified PME on Wikipedia](https://en.wikipedia.org/wiki/Public_Market_Equivalent#Modified_PME)
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"description": "# pypme \u2013 Python package for PME (Public Market Equivalent) calculation\n\nBased on the [Modified PME\nmethod](https://en.wikipedia.org/wiki/Public_Market_Equivalent#Modified_PME).\n\n## Example\n\n```python\nfrom pypme import verbose_xpme\nfrom datetime import date\n\npmeirr, assetirr, df = verbose_xpme(\n dates=[date(2015, 1, 1), date(2015, 6, 12), date(2016, 2, 15)],\n cashflows=[-10000, 7500],\n prices=[100, 120, 100],\n pme_prices=[100, 150, 100],\n)\n```\n\nWill return `0.5525698793027238` and `0.19495150355969598` for the IRRs and produce this\ndataframe:\n\n\n\nNotes:\n- The `cashflows` are interpreted from a transaction account that is used to buy from an\n asset at price `prices`.\n- The corresponding prices for the PME are `pme_prices`.\n- The `cashflows` is extended with one element representing the remaining value, that's\n why all the other lists (`dates`, `prices`, `pme_prices`) need to be exactly 1 element\n longer than `cashflows`.\n\n## Variants\n\n- `xpme`: Calculate PME for unevenly spaced / scheduled cashflows and return the PME IRR\n only. In this case, the IRR is always annual.\n- `verbose_xpme`: Calculate PME for unevenly spaced / scheduled cashflows and return\n vebose information.\n- `pme`: Calculate PME for evenly spaced cashflows and return the PME IRR only. In this\n case, the IRR is for the underlying period.\n- `verbose_pme`: Calculate PME for evenly spaced cashflows and return vebose\n information.\n- `tessa_xpme` and `tessa_verbose_xpme`: Use live price information via the tessa\n library. See below.\n\n## tessa examples \u2013 using tessa to retrieve PME prices online\n\nUse `tessa_xpme` and `tessa_verbose_xpme` to get live prices via the [tessa\nlibrary](https://github.com/ymyke/tessa) and use those prices as the PME. Like so:\n\n```python\nfrom datetime import datetime, timezone\nfrom pypme import tessa_xpme\n\ncommon_args = {\n \"dates\": [\n datetime(2012, 1, 1, tzinfo=timezone.utc), \n datetime(2013, 1, 1, tzinfo=timezone.utc)\n ],\n \"cashflows\": [-100],\n \"prices\": [1, 1],\n}\nprint(tessa_xpme(pme_ticker=\"LIT\", **common_args)) # source will default to \"yahoo\"\nprint(tessa_xpme(pme_ticker=\"bitcoin\", pme_source=\"coingecko\", **common_args))\nprint(tessa_xpme(pme_ticker=\"SREN.SW\", pme_source=\"yahoo\", **common_args))\n```\n\nNote that the dates need to be timezone-aware for these functions.\n\n\n## Garbage in, garbage out\n\nNote that the package will only perform essential sanity checks and otherwise just works\nwith what it gets, also with nonsensical data. E.g.:\n\n```python\nfrom pypme import verbose_pme\n\npmeirr, assetirr, df = verbose_pme(\n cashflows=[-10, 500], prices=[1, 1, 1], pme_prices=[1, 1, 1]\n)\n```\n\nResults in this df and IRRs of 0:\n\n\n\n\n## Other noteworthy libraries\n\n- [tessa](https://github.com/ymyke/tessa): Find financial assets and get their price history without worrying about different APIs or rate limiting.\n- [strela](https://github.com/ymyke/strela): A python package for financial alerts.\n\n\n## References\n\n- [Google Sheet w/ reference calculation](https://docs.google.com/spreadsheets/d/1LMSBU19oWx8jw1nGoChfimY5asUA4q6Vzh7jRZ_7_HE/edit#gid=0)\n- [Modified PME on Wikipedia](https://en.wikipedia.org/wiki/Public_Market_Equivalent#Modified_PME)\n",
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