"""
The ``invariants`` module calculates the market invariants for various securities and
returns an usage dataframe of those invariants.
Currently Implemented:
- Stocks
- Forex
Future plans include fixed-income and derivatives market invariants.
"""
import numpy as np
import pandas as pd
import warnings
def stock_invariants(prices, no_assets):
"""
Calculates stock price invariants, which are the compounded returns
:param prices: stock prices data of the various tickers
:type prices: pd Dataframe
:param no_assets: number of assets in data
:type no_assets: int
:return: dataframe of stock invariants
:rtype: pd.Dataframe
"""
if not isinstance(prices, pd.DataFrame):
warnings.warn("prices are not a pd Dataframe", RuntimeWarning)
asset_ret = pd.DataFrame()
for j in range(no_assets):
returns = []
for i in range(1, len(prices)):
log_ret = np.log(prices.iloc[i, j] / prices.iloc[i-1, j])
returns.append(log_ret)
asset_ret = pd.concat([pd.DataFrame(returns), asset_ret], axis=1, ignore_index=True)
return asset_ret
def forex_invariants(prices, no_assets):
"""
Calculates forex price invariants, which are the compounded returns
:param prices: stock prices data of the various tickers
:type prices: pd Dataframe
:param no_assets: number of assets in data
:type no_assets: int
:return: dataframe of stock invariants
:rtype: pd.Dataframe
"""
if not isinstance(prices, pd.DataFrame):
warnings.warn("prices are not a pd Dataframe", RuntimeWarning)
asset_ret = pd.DataFrame()
for j in range(no_assets):
returns = []
for i in range(1, len(prices)):
log_ret = np.log(prices.iloc[i, j] / prices.iloc[i-1, j])
returns.append(log_ret)
asset_ret = pd.concat([pd.DataFrame(returns), asset_ret], axis=1, ignore_index=True)
return asset_ret
