mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-28 13:01:38 +00:00
a12e0946da
* started crypto tools menu * allow zero float * added obb script Co-authored-by: Chavithra <chavithra@gmail.com> Co-authored-by: didierlopes.eth <dro.lopes@campus.fct.unl.pt> Co-authored-by: minhhoang1023 <40023817+minhhoang1023@users.noreply.github.com>
119 lines
3.3 KiB
Python
119 lines
3.3 KiB
Python
"""Tools Model"""
|
|
__docformat__ = "numpy"
|
|
|
|
import logging
|
|
from typing import Tuple
|
|
|
|
import pandas as pd
|
|
|
|
from openbb_terminal.cryptocurrency.tools.tools_helpers import (
|
|
calculate_hold_value,
|
|
calculate_pool_value,
|
|
)
|
|
from openbb_terminal.decorators import log_start_end
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@log_start_end(log=logger)
|
|
def calculate_apy(apr: float, compounding_times: int) -> Tuple[pd.DataFrame, str]:
|
|
"""Converts apr into apy
|
|
|
|
Parameters
|
|
----------
|
|
apr: float
|
|
value in percentage
|
|
compounding_times: int
|
|
number of compounded periods in a year
|
|
|
|
Returns
|
|
-------
|
|
Tuple:
|
|
- pd.DataFrame: dataframe with results
|
|
- str: narrative version of results
|
|
"""
|
|
apy = (pow((1 + (apr / 100) / compounding_times), compounding_times) - 1) * 100
|
|
apy_str = f"""
|
|
An APR of {apr}% compounded {compounding_times} times per year equals to an APY of {round(apy,3)}%
|
|
"""
|
|
df = pd.DataFrame(
|
|
{
|
|
"Metric": [
|
|
"APR",
|
|
"Compounding Times",
|
|
"APY",
|
|
],
|
|
"Value": [
|
|
f"{apr}%",
|
|
compounding_times,
|
|
f"{round(apy, 3)}%",
|
|
],
|
|
}
|
|
)
|
|
return df, apy_str
|
|
|
|
|
|
@log_start_end(log=logger)
|
|
def calculate_il(
|
|
price_changeA: float,
|
|
price_changeB: float,
|
|
proportion: float,
|
|
initial_pool_value: float,
|
|
) -> Tuple[pd.DataFrame, str]:
|
|
"""Calculates Impermanent Loss in a custom liquidity pool
|
|
|
|
Parameters
|
|
----------
|
|
price_changeA: float
|
|
price change of crypto A in percentage
|
|
price_changeB: float
|
|
price change of crypto B in percentage
|
|
proportion: float
|
|
percentage of first token in pool
|
|
initial_pool_value: float
|
|
initial value that pool contains
|
|
|
|
Returns
|
|
-------
|
|
Tuple:
|
|
- pd.DataFrame: dataframe with results
|
|
- str: narrative version of results
|
|
"""
|
|
pool_value = calculate_pool_value(price_changeA, price_changeB, proportion)
|
|
hold_value = calculate_hold_value(price_changeA, price_changeB, proportion)
|
|
il = abs(((pool_value / hold_value) - 1) * 100)
|
|
hold_value = hold_value * initial_pool_value
|
|
pool_value = pool_value * initial_pool_value
|
|
il_str = f"""
|
|
Ignoring fees/rewards and only accounting for impermanent loss:
|
|
|
|
A change of {price_changeA}% in token A and {price_changeB}% in token B in
|
|
a pool with a proportion of {proportion}/{100-proportion} and with an initial
|
|
value of ${initial_pool_value} would result in an impermant loss of {round(il,2)}%
|
|
If you just hold the tokens you would have ${round(hold_value,2)} whereas
|
|
in the pool you would have ${round(pool_value,2)}
|
|
"""
|
|
df = pd.DataFrame(
|
|
{
|
|
"Metric": [
|
|
"Price Change Token A",
|
|
"Price Change Token B",
|
|
"Initial Pool Value",
|
|
"Proportion",
|
|
"Impermanent Loss",
|
|
"Hold Value",
|
|
"Pool Value",
|
|
],
|
|
"Value": [
|
|
f"{price_changeA}%",
|
|
f"{price_changeB}%",
|
|
f"${initial_pool_value}",
|
|
f"{proportion}/{100-proportion}",
|
|
f"{round(il,2)}%",
|
|
f"${round(hold_value,2)}",
|
|
f"${round(pool_value,2)}",
|
|
],
|
|
}
|
|
)
|
|
return df, il_str
|