# Load historical prices and volumes from MOEX for liquidity report

import requests
import apimoex
import pandas as pd
import urllib.request, json 

def load_prices(securities, 
                start, 
                end):
    result = []
    for security in securities:
            with urllib.request.urlopen(f'https://iss.moex.com/iss/securities.json?q={security}') as url:
                data = json.loads(url.read().decode())
                ticker = data['securities']['data'][0][1]
    
            with requests.Session() as session:
                data = apimoex.find_securities(session, 
                                               security, 
                                               columns = None)
                df = pd.DataFrame(data)
                board = df.at[0, 'primary_boardid']
                engine = 'futures' if board == 'RFUD' else 'stock'
                market = 'forts' if board == 'RFUD' else 'shares'
                start_date = start
                end_date = end
                data = apimoex.get_board_history(session, 
                                                 security = ticker, 
                                                 start = start_date, 
                                                 end = end_date, 
                                                 columns = ['SECID', 
                                                            'TRADEDATE', 
                                                            'HIGH', 
                                                            'LOW', 
                                                            'CLOSE', 
                                                            'VOLUME'], 
                                                 board = board, 
                                                 market = market, 
                                                 engine = engine)
                result.extend(data[-10:])
  
    df = pd.DataFrame(result)
    df.set_index('SECID', inplace=True)
    # To save to .csv uncomment
    # df.to_csv(f'{security}_{start_date}-{end_date}.txt',sep='\t', index=False)
    # To print as a plain text uncomment
    # print(df.to_string())
    return df


# ============================================================================== #
# Set initial data:
securities  = ['CHMF', 
               'TATNP', 
               'PHOR', 
               'BRM2', 
               'RU0007661625']
start       = '2022-04-01'  #YYYY-MM-DD
end         = '2022-04-30'  #YYYY-MM-DD

load_prices(securities, start, end)