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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"""
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__docformat__ = "numpy"
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import logging
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from typing import Tuple
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import pandas as pd
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from openbb_terminal.cryptocurrency.tools.tools_helpers import (
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calculate_hold_value,
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calculate_pool_value,
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)
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from openbb_terminal.decorators import log_start_end
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logger = logging.getLogger(__name__)
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@log_start_end(log=logger)
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def calculate_apy(apr: float, compounding_times: int) -> Tuple[pd.DataFrame, str]:
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"""Converts apr into apy
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Parameters
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----------
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apr: float
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value in percentage
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compounding_times: int
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number of compounded periods in a year
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Returns
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-------
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Tuple:
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- pd.DataFrame: dataframe with results
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- str: narrative version of results
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"""
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apy = (pow((1 + (apr / 100) / compounding_times), compounding_times) - 1) * 100
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apy_str = f"""
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An APR of {apr}% compounded {compounding_times} times per year equals to an APY of {round(apy,3)}%
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"""
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df = pd.DataFrame(
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{
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"Metric": [
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"APR",
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"Compounding Times",
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"APY",
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],
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"Value": [
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f"{apr}%",
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compounding_times,
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f"{round(apy, 3)}%",
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],
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}
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)
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return df, apy_str
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@log_start_end(log=logger)
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def calculate_il(
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price_changeA: float,
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price_changeB: float,
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proportion: float,
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initial_pool_value: float,
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) -> Tuple[pd.DataFrame, str]:
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"""Calculates Impermanent Loss in a custom liquidity pool
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Parameters
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----------
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price_changeA: float
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price change of crypto A in percentage
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price_changeB: float
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price change of crypto B in percentage
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proportion: float
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percentage of first token in pool
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initial_pool_value: float
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initial value that pool contains
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Returns
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-------
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Tuple:
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- pd.DataFrame: dataframe with results
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- str: narrative version of results
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"""
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pool_value = calculate_pool_value(price_changeA, price_changeB, proportion)
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hold_value = calculate_hold_value(price_changeA, price_changeB, proportion)
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il = abs(((pool_value / hold_value) - 1) * 100)
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hold_value = hold_value * initial_pool_value
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pool_value = pool_value * initial_pool_value
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il_str = f"""
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Ignoring fees/rewards and only accounting for impermanent loss:
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A change of {price_changeA}% in token A and {price_changeB}% in token B in
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a pool with a proportion of {proportion}/{100-proportion} and with an initial
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value of ${initial_pool_value} would result in an impermant loss of {round(il,2)}%
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If you just hold the tokens you would have ${round(hold_value,2)} whereas
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in the pool you would have ${round(pool_value,2)}
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"""
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df = pd.DataFrame(
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{
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"Metric": [
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"Price Change Token A",
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"Price Change Token B",
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"Initial Pool Value",
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"Proportion",
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"Impermanent Loss",
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"Hold Value",
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"Pool Value",
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],
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"Value": [
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f"{price_changeA}%",
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f"{price_changeB}%",
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f"${initial_pool_value}",
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f"{proportion}/{100-proportion}",
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f"{round(il,2)}%",
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f"${round(hold_value,2)}",
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f"${round(pool_value,2)}",
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],
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}
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)
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return df, il_str
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