rh_checker.py

from typing import List, Any, Union

import robin_stocks as r
import pandas as pd
import datetime as dt
from datetime import date
import pandas_datareader as web
import time
from config import usr, pswd

earliest_date = ""

# year_today = int(today.split('-')[0])
# month_today = int(today.split('-')[1])
# day_today = int(today.split('-')[2])

##Getting the Most recent trading day from today
lastBusDay = dt.datetime.today()
shift = dt.timedelta(max(1, (lastBusDay.weekday() + 6) % 7 - 3))
lastBusDay = lastBusDay - shift

last_trading_year = lastBusDay.year
last_trading_month = lastBusDay.month
last_trading_day = lastBusDay.day

lastBusDay = dt.datetime(last_trading_year, last_trading_month, last_trading_day)
print(lastBusDay)


# def is_market_open():
#     t = time.localtime()
#     current_time = time.strftime("%H:%M:%S", t)
#     print(current_time)
#     if


def login():
    r.login(usr, pswd)
    print("Logged in!")


def get_shares(ticker):
    positions_data = r.get_open_stock_positions()
    for item in positions_data:
        item['symbol'] = r.get_symbol_by_url(item['instrument'])
        if item['symbol'] == ticker:
            print("{} shares of {}".format(item['quantity'], item['symbol']))


def get_open_positions():
    positions_data = r.get_open_stock_positions()
    with open('portfolio.txt', 'w') as f:
        for item in positions_data:
            item['symbol'] = r.get_symbol_by_url(item['instrument'])
            print("{} | {}".format(item['symbol'], item['average_buy_price']))
            f.write(item['symbol'] + '\n')


def get_Net():
    my_stocks = r.build_holdings()
    profits, losses = 0, 0
    for key, value in my_stocks.items():
        change = float(value['equity_change'])
        print("{} | {}".format(key, change))
        if change > 0:
            profits += change
        else:
            losses += change
    print("Profits: " + str(profits))
    print("Losses: " + str(losses))


def quote_category():
    earnings = r.get_all_stocks_from_market_tag('most-popular-under-25')
    ###'biopharmaceutical', 'upcoming-earnings', 'most-popular-under-25', and 'technology'
    for stock in earnings:
        print("{} | {}".format(stock['symbol'], stock['last_trade_price']))
    # r.get_all_stocks_from_market_tag('technology')  # get all tech tags


def get_all_stocks():
    portfolio = pd.DataFrame(columns=['Ticker', 'Quantity', 'Average_Price', 'Date'])
    list = r.get_all_positions(info=None)
    for entry in list:
        quantity = entry['quantity']
        if quantity == '0.00000000':
            continue
        instrument = entry['instrument']
        avg_price = entry['average_buy_price']
        date = entry['created_at'].split('T')[0]
        ticker = r.stocks.get_symbol_by_url(instrument)
        portfolio = portfolio.append(
            pd.Series([ticker, quantity, avg_price, date], index=['Ticker', 'Quantity', 'Average_Price', 'Date']),
            ignore_index=True)
    # portfolio = portfolio.set_index('Ticker')
    # portfolio = portfolio.set_index(pd.DatetimeIndex(portfolio['Ticker'].values))
    global earliest_date
    earliest_date = portfolio['Date'].min()
    return portfolio


def portfolio_to_txt():
    portfolio = get_all_stocks()
    f = open('portfolio.txt', 'w')
    f.write("Ticker | Quantity | Average Price | Date Bought")
    f.write("\n")
    vals = portfolio.values
    for entry in vals:
        f.write(str(entry) + "\n")
    f.close()


def add_ticker(df):
    df['ticker'] = df[['instrument']].apply(lambda i: r.get_symbol_by_url(i.item()), axis=1)


def get_positions():
    position_data = r.get_all_positions()
    df = pd.DataFrame(position_data)
    add_ticker(df)
    df['quantity'] = df['quantity'].astype(float)
    df['average_buy_price'] = df['average_buy_price'].astype(float)
    df = df[['ticker', 'quantity', 'average_buy_price', 'created_at']]
    df = df[df.quantity != 0]
    return df


def get_trades():
    trades = r.get_all_stock_orders()
    df = pd.DataFrame(trades)
    add_ticker(df)
    df = df[['ticker', 'average_price', 'price', 'quantity', 'type', 'side', 'executions']]
    df.average_price = df.average_price.astype(float)
    df.price = df.price.astype(float)
    df.quantity = df.quantity.astype(float)
    return df


def get_SP500(earliest_day):
    year = int(earliest_day.split('-')[0])
    month = int(earliest_day.split('-')[1])
    day = int(earliest_day.split('-')[2])
    start = dt.datetime(year, month, day)
    end = dt.datetime(last_trading_year, last_trading_month, last_trading_day)
    df = web.DataReader('^GSPC', 'yahoo', start, end)
    df.to_csv('GSPC.csv')


def calculate_rate_return_SP500(date):
    df = pd.read_csv('GSPC.csv')
    df = df.set_index(pd.DatetimeIndex(df['Date'].values))
    close = df.loc[date]['Close']
    close_today = df[lastBusDay]['Close']
    return close / close_today - 1


def compare_performance():
    portfolio = get_all_stocks()
    values = portfolio.values
    for entry in values:
        # print(entry)
        ticker = entry[0]
        date = entry[3]
        print(ticker + " " + calculate_rate_return_SP500(date))


# def calculate_rate_return_SP500(ticker):

def main():
    login()
    # positions = get_positions()
    # positions.to_csv('portfolio.csv')
    positions = get_trades()
    positions.to_csv('portfolio.csv')
    # quote_category()
    # get_portfolio()
    # get_shares('NIO')
    # NIOData = [item for item in positions_data if item['symbol'] == 'NIO']
    # sellQuantity = float(NIOData['quantity'])//2.0
    # print(sellQuantity)
    # r.order_sell_limit('TSLA',sellQuantity,200.00)
    # profile = r.build_user_profile()
    # print(profile)
    # my_stocks = r.build_holdings()
    # for key, value in my_stocks.items():
    #     print(key, value)
    # get_Net()
    # print(r.get_all_positions(info=None))
    print(get_all_stocks())
    # portfolio_to_txt()
    get_SP500(earliest_date)
    compare_performance()
    # with open('list.txt', 'r') as f:
    #     lines = f.readlines()[1:]
    #     for line in lines:
    #         print(line[3])
    #         rr = calculate_rate_return_SP500(line[3])
    #         print(line[0] + "Return of: " + rr)


if __name__ == '__main__':
    main()