from Data import YahooData
import numpy as np
import pandas as pd
from Data import Utility
universe = Utility.get_stock_universe('stock_universe.csv')
u_tick = universe['Tick'].unique().tolist()
universe.set_index('Tick', inplace=True)
returns = YahooData.get_returns(u_tick)
ev_ebitda = YahooData.get_ev_ebitda(u_tick)
ocf_ev = YahooData.get_ocf_ev(u_tick)
ratios = YahooData.get_ratios(u_tick, ['Price', 'PS', 'PB', 'PE', '50ma', '200ma'])
comp_info = YahooData.get_sector_industry(u_tick)
df = universe.join(comp_info)
df = df.join(ratios)
df = df.join(ev_ebitda)
df = df.join(ocf_ev)
df = df.join(returns)
df = df.replace('N/A', np.nan)
df['ma_ratio'] = df['50ma'].astype(float) / df['200ma'].astype(float)
df['ebitda_ev_rank'] = df['ebitda_ev'].astype(float).rank(ascending=True)
df['ocf_ev_rank'] = df['ocf_ev'].astype(float).rank(ascending=True)
df['PS_rank'] = df['PS'].astype(float).rank(ascending=False)
df['PB_rank'] = df['PB'].astype(float).rank(ascending=False)
df['PE_rank'] = df['PE'].astype(float).rank(ascending=False)
df['ma_ratio_rank'] = df['ma_ratio'].rank(ascending=True)
df['return_rank'] = df['1yr_rtn'].rank(ascending=True)
