def generate_top_news():
start_time = time.time()
r.login(username, password)
# Get list of stocks in watchlist and positions.
stocks_dict = {}
watchlist = r.get_watchlist_by_name()
positions = r.get_current_positions()
stocks = watchlist + positions
# Get stock info (e.g. ticker, name) for each stock.
with ThreadPoolExecutor(max_workers=20) as executor:
stock_info_futures = [
executor.submit(r.request_get, stock['instrument'])
for stock in stocks
]
for stock_info_future in as_completed(stock_info_futures):
stock_info = stock_info_future.result()
ticker = stock_info['symbol']
stock = Stock(stock_info['simple_name'], ticker)
stocks_dict[ticker] = stock
print("--- %s seconds --- (Got all tickers)" % (time.time() - start_time))
# Get top news for all stocks (prioritized by num_clicks).
# TODO: Consider prioritizing by date also.
news_heap = []
news_dict = {}
with ThreadPoolExecutor(max_workers=20) as executor:
news_futures_to_ticker = {
executor.submit(r.get_news, stock.ticker): stock.ticker
for stock in stocks_dict.values()
}
for news_future in as_completed(news_futures_to_ticker):
ticker = news_futures_to_ticker[news_future]
news = news_future.result()
if not news:
continue
recent_news = news[0]
# Rank news by views. Need to retain metadata for ticker, source, title, and views.
num_clicks = recent_news['num_clicks']
source = recent_news['api_source']
title = recent_news['title']
# (-views, ticker, source, title)
heapq.heappush(news_heap, (-num_clicks, ticker, source, title))
print("--- %s seconds --- (Got all news)" % (time.time() - start_time))
# TTS for top news.
news_aggregate = []
for news in news_heap[:5]:
ticker, source, title = news[1], news[2], news[3]
stock_news_string = "Top news for stock " + stocks_dict[
ticker].name + ", "
news_source_string = "from " + source + ": "
news_aggregate.append(stock_news_string + news_source_string + title +
"\n")
print("--- %s seconds --- (End)" % (time.time() - start_time))
return ''.join(news_aggregate)
def get_portfolio_symbols():
"""
Returns: the symbol for each stock in your portfolio as a list of strings
"""
symbols = []
holdings_data = r.get_current_positions()
for item in holdings_data:
if not item:
continue
instrument_data = r.get_instrument_by_url(item.get('instrument'))
symbol = instrument_data['symbol']
symbols.append(symbol)
return symbols
def get_modified_holdings():
""" Retrieves the same dictionary as r.build_holdings, but includes data about
when the stock was purchased, which is useful for the read_trade_history() method
in tradingstats.py
Returns:
the same dict from r.build_holdings, but with an extra key-value pair for each
position you have, which is 'bought_at': (the time the stock was purchased)
"""
holdings = r.build_holdings()
holdings_data = r.get_current_positions()
for symbol, dict in holdings.items():
bought_at = get_position_creation_date(symbol, holdings_data)
bought_at = str(pd.to_datetime(bought_at))
holdings[symbol].update({'bought_at': bought_at})
return holdings
def sell_old_stocks():
divies = [[x.get('instrument'), x.get('record_date')] for x in r.get_dividends()]
positions_data = r.get_current_positions()
for position in positions_data:
for d in divies:
if position.get('instrument') in d[0]:
about = r.request_get(position['instrument'])
div_diff = (datetime.strptime(d[1], '%Y-%m-%d') - today).days
if div_diff <= 0:
stock_dict = r.stocks.get_stock_quote_by_symbol(about.get('symbol'))
last_trade_price = float(stock_dict.get('last_trade_price'))
if last_trade_price / float(position['average_buy_price']) > 1.005:
if float(position.get('shares_held_for_sells')) == 0:
if easygui.ccbox(msg=about.get('symbol') + '\n' + d[1] + '\nQuantity: ' + str(position['quantity']) + '\nPurchase $' + str(float(position['average_buy_price'])) + '\nCurrent price $' + str(last_trade_price) + '\nProfit per $' + str(float(last_trade_price) - float(position['average_buy_price'])) + '\nTotal win $' + str((float(last_trade_price) - float(position['average_buy_price']))*float(position['quantity'])) + '\nROI ' + str(100 - (float(last_trade_price) / float(position['average_buy_price']))*-100) + '%' ):
r.order_sell_limit(about.get('symbol'), position['quantity'], round(last_trade_price*1.0035, 2))
print('selling ', about.get('symbol'))
else:
stock_dict = r.stocks.get_stock_quote_by_symbol(about.get('symbol'))
last_trade_price = float(stock_dict.get('last_trade_price'))
if last_trade_price / float(position['average_buy_price']) > 1.015:
if float(position.get('shares_held_for_sells')) == 0:
if easygui.ccbox(msg='NO DIVVIE SALE\n' +about.get('symbol') + '\n' + d[1] + '\nQuantity: ' + str(position['quantity']) + '\nPurchase $' + str(float(position['average_buy_price'])) + '\nCurrent price $' + str(last_trade_price) + '\nProfit per $' + str(float(last_trade_price) - float(position['average_buy_price'])) + '\nTotal win $' + str((float(last_trade_price) - float(position['average_buy_price'])) * float(position['quantity'])) + '\nROI ' + str(100 - (float(last_trade_price) / float(position['average_buy_price'])) * -100) + '%'):
r.order_sell_limit(about.get('symbol'), position['quantity'], round(last_trade_price * 1.0035, 2))
print('selling ', about.get('symbol'))
#----------------------------
# helpers
def time_stamp():
utc_now = pytz.utc.localize(datetime.utcnow())
est_now = utc_now.astimezone(pytz.timezone("America/New_York"))
dt_string = est_now.strftime("%b-%d, %Y: %H:%M:%S")
print(dt_string)
return dt_string
def buy(quantity):
price = 147.87
twilio.send_msg("Bought " + str(quantity) + "shares @ $" + str(price))
#----------------------------
# MODELS:
TA_List = np.full(23500,-1)
portfolio = robin.get_current_positions()
ms_stocks = portfolio['MSFT']['quantity']
# Linear_reg_list = []
#----------------------------
# CONFIG
funds = 0
permitted = False
stock_symbol = "MSFT"
#-------
# log in
robin.login(config.rh_usr_name, config.rh_password)
# get current holdings
# {
import wget
import json
import stocks as s
'''
Robinhood portfolio script
'''
#!!! Fill out username and password
username = ''
password = ''
#!!!
login = r.login(username, password)
mypos = r.get_current_positions()
for counter in mypos:
mystring = counter['instrument']
myr = requests.get(mystring)
try:
jdata = (json.loads(myr.content))
except ValueError as e:
#print(" ")
continue
except:
continue
try:
print("Name:" + str(jdata['simple_name']))
except ValueError as f:
API documentation
http://www.robin-stocks.com/en/latest/functions.html#logging-in-and-out
"""
import robin_stocks as r
from cred_util import load_cred
real_buy_cell = False
#%%
########## info require login #############
cred = load_cred('../../credential/ini.json')
login = r.login(cred['robinhood']['username'], cred['robinhood']['password'])
## get all you current position
positions_data = r.get_current_positions()
## simple function to get all holdings
my_stocks = r.build_holdings()
# get some user profile data
profile_info = r.profiles.load_account_profile()
print(profile_info['crypto_buying_power'])
## load portfolio info
portfolio = r.profiles.load_portfolio_profile()
print(portfolio['last_core_portfolio_equity'])
##get stock information
stock = r.stocks.find_instrument_data('tesla')
stock_sym = stock[0]['symbol']
def put_in_stop_loss_orders_all(
access_token=None,
username=None,
password=None,
portfolio=None,
column="Close",
alpha=0.5,
sigmas=2,
):
"""
Accesses Robinhood account and put in stop loss sell orders for all securities in a portfolio.
Stop loss price is calculated as exponentially weighted average price and standard deviation.
Parameters
==========
username : Robinhood username
password : Robinhood account password
portfolio : portfolio name
free_stock : include a free stock not captured by transaction history (see below)
Returns
=======
"""
import time
if not access_token:
if username is None:
username = getpass("Username: "******"Password: "******"instrument"]) == r.get_name_by_symbol(ticker)
][0]
# assert that portfolio positions are the same as returned from API
if (portfolio.positions_df.Quantity[ticker] >
0) & (portfolio.positions_df.Quantity[ticker] == float(
stock_data["quantity"])):
portfolio.securities[ticker].latest_price = float(
r.get_latest_price([ticker])[0])
portfolio.securities[ticker].stop_loss_price = round(
get_stop_loss_price(
security=portfolio.securities[ticker],
column=column,
alpha=alpha,
sigmas=sigmas,
),
2,
)
quantity = float(stock_data["quantity"])
# print out summary
print(
ticker,
quantity,
portfolio.securities[ticker].latest_price,
portfolio.securities[ticker].last_price_ewm,
portfolio.securities[ticker].stop_loss_price,
portfolio.securities[ticker].stop_loss_price -
portfolio.securities[ticker].last_price_ewm,
100 * (portfolio.securities[ticker].stop_loss_price -
portfolio.securities[ticker].latest_price) /
portfolio.securities[ticker].latest_price,
)
# put in the order
r.order_sell_stop_loss(
ticker, quantity, portfolio.securities[ticker].stop_loss_price)
# Wait for a few seconds to not get thrown off Robinhood
time.sleep(1)
else:
print(
"Disagreement: ",
ticker,
portfolio.positions_df.Quantity[ticker],
float(stock_data["quantity"]),
)
