import pandas as pd
import math
from datetime import datetime
import hvplot.pandas
import math
import numpy as np
import time
from streamz import Stream

b_stocks = pd.read_pickle('../data/b_stocks.pkl')
p_stocks = pd.read_pickle('../data/p_stocks.pkl')
p_profile = pd.read_pickle('../data/p_profile.pkl')
b_profile = pd.read_pickle('../data/b_profile.pkl')

# start stream here
dates = b_stocks.date.unique()
b_stocks[b_stocks.date == dates[1]]

def add(prev_df, new_df):
    merged_df = pd.concat([prev_df, new_df])
    merged_df.sort_values(by=['date'], inplace=True)
    merged_df['pct'] = merged_df.groupby('ticker')['close'].pct_change()

    # remove prev_df
    merged_df = merged_df[~merged_df.isin(prev_df)].dropna()
    return merged_df

source = Stream()
source.accumulate(add).sink(print)
source.emit(b_stocks[b_stocks.date == dates[0]])
source.emit(b_stocks[b_stocks.date == dates[1]])
source.emit(b_stocks[b_stocks.date == dates[2]])