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dashboard.data.pool_metrics

np​

pd​

math​

random​

PoolMetrics Objects​

class PoolMetrics()

__init__​

def __init__(prices)

Initialize the PoolMetrics class with a series of prices.

Arguments:

  • prices: pandas Series of price data

calc_mean_drift​

def calc_mean_drift()

Calculate mean drift based on log returns and price changes.

Returns:

tuple (return_drift, price_drift, price_drift_pct)

calc_mean_volatility​

def calc_mean_volatility()

Calculate mean volatility based on log returns and price changes.

Returns:

tuple (return_volatility, price_volatility, price_volatility_pct)

calc_all_mean_metrics​

def calc_all_mean_metrics()

Calculate all mean metrics: mean drift and mean volatility.

Returns:

tuple (return_drift, price_drift, price_drift_pct, return_volatility, price_volatility, price_volatility_pct)

random_bm​

@staticmethod
def random_bm(mu, sigma)

Generate a random number using Box-Muller transform.

Arguments:

  • mu: Mean
  • sigma: Standard deviation

Returns:

Random number

calc_imp_loss​

@staticmethod
def calc_imp_loss(lower_limit, upper_limit, px, alpha)

Calculate impermanent loss.

Arguments:

  • lower_limit: Lower price limit
  • upper_limit: Upper price limit
  • px: Current price
  • alpha: Alpha parameter

Returns:

Impermanent loss

calc_exp_imp_loss​

def calc_exp_imp_loss(range_perc, mu, sigma, alpha)

Calculate expected impermanent loss.

Arguments:

  • range_perc: Range percentage
  • mu: Mean
  • sigma: Standard deviation
  • alpha: Alpha parameter

Returns:

Expected impermanent loss

calc_iv​

def calc_iv(range_perc, mu, alpha)

Calculate implied volatility.

Arguments:

  • range_perc: Range percentage
  • mu: Mean
  • alpha: Alpha parameter

Returns:

Implied volatility