| Name | moneycounter JSON |
| Version |
1.3.1
JSON |
| download |
| home_page | |
| Summary | Portfolio Analytics Utilities |
| upload_time | 2023-05-03 16:50:49 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
| license | MIT |
| keywords |
risk
fifo
money
investments
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Money Counter
*Portfolio analytics utilities*
This is the beginning of a work in progress.
I expect it will be in pretty good shape early in
2023 and then evolve from there.
This is a supporting package for a larger project I am working on and should be useful to others as is.
### Installation
[PyPI Page](https://pypi.org/search/?q=moneycounter)
```shell
$ pip install moneycounter
```
### Prerequisite Trades Data Frame
A trades dataframe has these columns:
`columns = Index(['dt', 'q', 'p', 'cs', 't', 'a'], dtype='object')`
It must be ordered by dt.
Where:
| Column | Description |
|:------:|:-----------------------------------------------:|
| `dt` | execution time as datetime.datetime |
| `q` | quantity traded, signed with negative as a sale |
| `p` | execution price |
| `cs` | contract size, typically 1.0 |
| `t` | ticker |
| `a` | account |
### Example Calculations
```python
from datetime import date
from moneycounter import pnl, realized_gains, wap_calc
# Calculate realized, unrealized and total pnl from trades dataframe.
realized, unrealized, total = pnl(df, price=price)
# Calculate weighted average price of open positions from trades data frame.
wap = wap_calc(df)
# Calculate realized gains from trades data frame.
realized = realized_gains(df)
$` \phi = c * Q * (p - p_wap) `$
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"description": "# Money Counter\n*Portfolio analytics utilities*\n\n\n\nThis is the beginning of a work in progress.\nI expect it will be in pretty good shape early in\n2023 and then evolve from there.\n\nThis is a supporting package for a larger project I am working on and should be useful to others as is.\n\n### Installation\n\n[PyPI Page](https://pypi.org/search/?q=moneycounter)\n\n```shell\n$ pip install moneycounter \n```\n\n### Prerequisite Trades Data Frame\n\nA trades dataframe has these columns:\n\n`columns = Index(['dt', 'q', 'p', 'cs', 't', 'a'], dtype='object')`\n\nIt must be ordered by dt.\n\nWhere:\n\n| Column | Description |\n|:------:|:-----------------------------------------------:|\n| `dt` | execution time as datetime.datetime |\n| `q` | quantity traded, signed with negative as a sale |\n| `p` | execution price |\n| `cs` | contract size, typically 1.0 |\n| `t` | ticker |\n| `a` | account |\n\n\n### Example Calculations\n\n```python\nfrom datetime import date\nfrom moneycounter import pnl, realized_gains, wap_calc\n\n# Calculate realized, unrealized and total pnl from trades dataframe.\nrealized, unrealized, total = pnl(df, price=price)\n\n# Calculate weighted average price of open positions from trades data frame.\nwap = wap_calc(df)\n\n# Calculate realized gains from trades data frame.\nrealized = realized_gains(df)\n\n$` \\phi = c * Q * (p - p_wap) `$\n",
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