class TheHood:
"""
A wrapper for producing the kinds of transactions and calls on my Robinhood
portfolio I'm looking for.
"""
def __init__(self, credentials: str) -> None:
self._ext_equity = 0.0
# Set up connection to Robinhood's API.
self._rh = Robinhood()
self._rh.login(**read_credentials(credentials))
@property
def extended_hours_equity(self) -> float:
"""
Keep track of the extended equity and prevent its setting to NoneType.
Returns:
The current extended equity.
"""
return self._ext_equity
@extended_hours_equity.setter
def extended_hours_equity(self, new_equity: Union[float, None]) -> None:
"""
Keep track of the extended equity and prevent its setting to NoneType.
Returns:
The current extended equity.
"""
if type(new_equity) is not float:
pass
else:
self._ext_equity = new_equity
@retry
def total_dollar_equity(self) -> Tuple[float, float, float]:
"""
Get values that explain the current monetary value of my account.
Returns:
A tuple containing today's closing equity in my account, followed by the
previous day's closing value and the current extended / after-hours value.
"""
self.extended_hours_equity = self._rh.extended_hours_equity()
return self._rh.equity(), self._rh.equity_previous_close(
), self.extended_hours_equity
@retry
def account_potential(self) -> float:
"""
Get the total account potential for comparison against the total account value.
I define account potential as the sum of all stocks' current worth plus the
absolute value of any losses.
Returns:
A float representing the account potential.
"""
stocks = self._rh.securities_owned()['results']
potential_sum = float(self._rh.portfolios()['withdrawable_amount'])
for stock in stocks:
# Make quantity a float as the API may change when I buy fractional shares.
quantity = float(stock['quantity'])
buy_price = float(stock['average_buy_price'])
potential_sum += quantity * buy_price
return potential_sum
@retry
def dividend_payments(self, since: str = '') -> float:
"""
If there are dividend payments, I want to graph a sum in Grafana.
Args:
since: the date since we should allow the summation of dividends. For
instance, you may wish to set this to the past year.
Returns:
A float representing the sum of dividend payments to my account.
"""
dividends: Dict = self._rh.dividends()['results']
dividend_sum = 0.0
for dividend in dividends:
if dividend['state'] == 'paid':
if since and not (datetime.fromisoformat(
dividend['paid_at'][:-1]) >
datetime.fromisoformat(since)):
continue
dividend_sum += float(dividend['amount'])
return dividend_sum
def watcher():
global graph_msg, graph_min, graph_max
rh = Robinhood()
rh.login(username=rh_user, password=rh_pass, qr_code=rh_qr)
raw_result = rh.positions()
result = raw_result['results']
shares_total = []
port_msg = f"Your portfolio ({rh.get_account()['account_number']}):\n"
loss_output = 'Loss:'
profit_output = 'Profit:'
loss_total = []
profit_total = []
graph_msg = None # initiates a variable graph_msg as None for looped condition below
n = 0
n_ = 0
for data in result:
share_id = str(data['instrument'].split('/')[-2])
buy = round(float(data['average_buy_price']), 2)
shares_count = int(data['quantity'].split('.')[0])
if shares_count != 0:
n = n + 1
n_ = n_ + shares_count
else:
continue
raw_details = rh.get_quote(share_id)
share_name = raw_details['symbol']
call = raw_details['instrument']
share_full_name = loads(get(call).text)['simple_name']
total = round(shares_count * float(buy), 2)
shares_total.append(total)
current = round(float(raw_details['last_trade_price']), 2)
current_total = round(shares_count * current, 2)
difference = round(float(current_total - total), 2)
if difference < 0:
loss_output += (
f'\n{share_full_name}:\n{shares_count} shares of {share_name} at ${buy} Currently: ${current}\n'
f'Total bought: ${total} Current Total: ${current_total}'
f'\nLOST ${-difference}\n')
loss_total.append(-difference)
else:
profit_output += (
f'\n{share_full_name}:\n{shares_count} shares of {share_name} at ${buy} Currently: ${current}\n'
f'Total bought: ${total} Current Total: ${current_total}'
f'\nGained ${difference}\n')
profit_total.append(difference)
if graph_min and graph_max:
graph_min = float(graph_min)
graph_max = float(graph_max)
if difference > graph_max or difference < -graph_min:
time_now = datetime.now()
metrics = time_now - timedelta(days=7)
numbers = []
historic_data = (rh.get_historical_quotes(
share_name, '10minute', 'week'))
historical_values = historic_data['results'][0]['historicals']
for close_price in historical_values:
numbers.append(round(float(close_price['close_price']), 2))
fig, ax = plt.subplots()
if difference > graph_max:
plt.title(
f"Stock Price Trend for {share_full_name}\nShares: {shares_count} Profit: ${difference}"
)
elif difference < graph_min:
plt.title(
f"Stock Price Trend for {share_full_name}\nShares: {shares_count} LOSS: ${-difference}"
)
plt.xlabel(
f"1 Week trend with 10 minutes interval from {metrics.strftime('%m-%d %H:%M')} to "
f"{time_now.strftime('%m-%d %H:%M')}")
plt.ylabel('Price in USD')
ax.plot(numbers, linewidth=1.5)
if not path.isdir('img'):
mkdir('img')
fig.savefig(f"img/{share_full_name}.png", format="png")
plt.close(
) # close plt to avoid memory exception when more than 20 graphs are generated
# stores graph_msg only if a graph is generated else graph_msg remains None
if not graph_msg: # used if not to avoid storing the message repeatedly
graph_msg = f"Attached are the graphs for stocks which exceeded a profit of " \
f"${graph_max} or deceeded a loss of ${graph_min}"
elif not graph_msg: # used elif not to avoid storing the message repeatedly
graph_msg = "Add the env variables for <graph_min> and <graph_max> to include a graph of previous " \
"week's trend."
lost = round(fsum(loss_total), 2)
gained = round(fsum(profit_total), 2)
port_msg += f'The below values will differ from overall profit/loss if shares were purchased ' \
f'with different price values.\nTotal Profit: ${gained}\nTotal Loss: ${lost}\n'
net_worth = round(float(rh.equity()), 2)
output = f'Total number of stocks purchased: {n}\n'
output += f'Total number of shares owned: {n_}\n\n'
output += f'Current value of your total investment is: ${net_worth}\n'
total_buy = round(fsum(shares_total), 2)
output += f'Value of your total investment while purchase is: ${total_buy}\n'
total_diff = round(float(net_worth - total_buy), 2)
if total_diff < 0:
output += f'Overall Loss: ${total_diff}'
else:
output += f'Overall Profit: ${total_diff}'
yesterday_close = round(float(rh.equity_previous_close()), 2)
two_day_diff = round(float(net_worth - yesterday_close), 2)
output += f"\n\nYesterday's closing value: ${yesterday_close}"
if two_day_diff < 0:
output += f"\nCurrent Dip: ${two_day_diff}"
else:
output += f"\nCurrent Spike: ${two_day_diff}"
if not graph_msg: # if graph_msg was not set above
graph_msg = f"You have not lost more than ${graph_min} or gained more than " \
f"${graph_max} to generate a graph."
return port_msg, profit_output, loss_output, output, graph_msg
