def test_get_balances(mock_get):
"""This function tests the get account balances method."""
qtrade = Questrade(token_yaml="access_token.yml")
balances = qtrade.get_account_balances(123)
assert len(balances) == 4
assert list(balances.keys()) == [
"perCurrencyBalances",
"combinedBalances",
"sodPerCurrencyBalances",
"sodCombinedBalances",
]
with pytest.raises(Exception):
_ = qtrade.get_account_positions(987)
class QuestradeBot:
def __init__(self, acctNum):
# Initialize Questrade Instance
if path.exists("./access_token.yml"):
#print("first try in questrade")
self.qtrade = Questrade(token_yaml='./access_token.yml')
try:
acct_list = self.qtrade.get_account_id()
#print(acctNum in acct_list)
assert acctNum in acct_list
except:
#print("first if statement")
self.qtrade.refresh_access_token(from_yaml=True)
self.qtrade = Questrade(token_yaml='./access_token.yml')
try:
assert acctNum in self.qtrade.get_account_id()
except:
#print("yml file removed!")
remove("./access_token.yml")
# get new access code
access_code = new_access_code()
self.qtrade = Questrade(access_code=access_code)
else:
#print("no yml file exist")
access_code = new_access_code()
self.qtrade = Questrade(access_code=access_code)
try:
accts_list = self.qtrade.get_account_id()
except:
while not isinstance(accts_list, list):
#print("in while loop")
access_code = new_access_code()
self.qtrade = Questrade(access_code=access_code)
self.acctNum = acctNum
def get_acct_id(self):
return self.qtrade.get_account_id()
def get_ticker_info(self, symbol: str):
return self.qtrade.ticker_information(symbol)
def get_acct_positions(self):
return self.qtrade.get_account_positions(self.acctNum)
def _get_account_activities(self):
return self.qtrade.get_account_activities(self.acctNum)
def get_usd_total_equity(self):
balance = self.get_account_balance_summary()
return balance.loc['USD', 'Total_Equity']
def get_usd_total_mv(self):
balance = self.get_account_balance_summary()
return balance.loc['USD', 'Market_Value']
def get_cad_total_equity(self):
balance = self.get_account_balance_summary()
return balance.loc['CAD', 'Total_Equity']
def get_cad_total_mv(self):
balance = self.get_account_balance_summary()
return balance.loc['CAD', 'Market_Value']
def get_usd_total_cost(self):
positions = self.get_acct_positions()
total_cost = 0
for pos in positions:
curr_cost = pos['totalCost']
total_cost += curr_cost
return total_cost
def get_account_balance_summary(self):
bal = self.qtrade.get_account_balances(self.acctNum)
data = {
'Currency': [],
'Cash': [],
'Market_Value': [],
'Total_Equity': [],
'Cash (%)': [],
'Investment (%)': []
}
for x in bal['perCurrencyBalances']:
data['Currency'].append(x['currency'])
data['Cash'].append(x['cash'])
data['Market_Value'].append(x['marketValue'])
data['Total_Equity'].append(x['totalEquity'])
if x['totalEquity'] != 0:
data['Cash (%)'].append(
round(100 * x['cash'] / x['totalEquity'], 2))
data['Investment (%)'].append(
round(100 * x['marketValue'] / x['totalEquity'], 2))
else:
data['Cash (%)'].append(0)
data['Investment (%)'].append(0)
df = pd.DataFrame(data)
df.set_index('Currency', inplace=True)
#print(tabulate(df, headers='keys'))
return df
def get_investment_summary(self):
# p&l
position_data = {
'Symbol': [],
'Description': [],
'Currency': [],
'Quantities': [],
'Market Value': [],
'Return (%)': [],
'Portfolio (%)': []
}
total_market_value = self.get_usd_total_mv()
total_costs = 0
positions = self.qtrade.get_account_positions(self.acctNum)
for position in positions:
# handle daily execution for closeQuantity
if position['openQuantity'] != 0:
symbol = position['symbol']
description = self.qtrade.ticker_information(
symbol)['description']
qty = position['openQuantity']
cmv = position['currentMarketValue']
currency = self.qtrade.ticker_information(symbol)['currency']
cost = position['totalCost']
change = round(100 * (cmv - cost) / cost, 2)
total_costs = total_costs + cost
position_data['Symbol'].append(symbol)
position_data['Description'].append(description)
position_data['Currency'].append(currency)
position_data['Quantities'].append(qty)
position_data['Market Value'].append(cmv)
position_data['Return (%)'].append(change)
position_data['Portfolio (%)'].append(
round(100 * (cmv / total_market_value), 2))
portfolio = pd.DataFrame(position_data)
portfolio.set_index('Symbol', inplace=True)
#portfolio.index.name = None
#print(tabulate(portfolio))
return portfolio
def get_historical_dividend_income(self, period):
# identify the first date for creation
endDate = dt.date.today().strftime("%Y-%m-%d")
startDate = dt.date.today() - dt.timedelta(days=period)
dtrange = pd.date_range(startDate, endDate, freq='d')
months = pd.Series(dtrange.month)
starts, ends = months.ne(months.shift(1)), months.ne(months.shift(-1))
startEndDates = pd.DataFrame({
'month_starting_date':
dtrange[starts].strftime('%Y-%m-%d'),
'month_ending_date':
dtrange[ends].strftime('%Y-%m-%d')
})
dateList = startEndDates.values.tolist()
output = {}
total_div_earned = 0
for date in dateList:
start = date[0]
end = date[1]
activities = self.qtrade.get_account_activities(
self.acctNum, start, end)
monthly_div = 0
for activity in activities:
if activity['type'] == 'Dividends':
monthly_div = monthly_div + activity['netAmount']
output[dt.datetime.strptime(
start, "%Y-%m-%d").strftime("%Y-%m")] = monthly_div
total_div_earned = total_div_earned + monthly_div
monthly_div_df = pd.DataFrame.from_dict(
output, orient='index', columns=['Monthly_Dividend_Income'])
return monthly_div_df
def _monthly_return(self, assets):
monthly_prices = pd.DataFrame()
for asset in assets:
monthly_prices[asset] = yf.download(asset,
start=dt.datetime(2018, 1, 1),
end=dt.datetime.today(),
interval='1mo',
progress=False)['Adj Close']
monthly_returns = monthly_prices.pct_change()
monthly_returns.dropna(inplace=True)
return monthly_returns
def _cumulative_returns(self, assets, weights):
prices = pd.DataFrame()
for symbol in assets:
prices[symbol] = yf.download(symbol,
start=dt.datetime(2018, 1, 1),
end=dt.datetime.today(),
interval='1mo',
progress=False)['Adj Close']
prices.dropna(inplace=True)
monthly_returns = prices.pct_change()
monthly_returns = monthly_returns.shift(-1)
monthly_returns['port'] = monthly_returns.dot(weights)
cum_returns = np.exp(np.log1p(monthly_returns['port']).cumsum())[:-1]
return cum_returns
def _cagr(self, assets, weights):
cum_ret = self._cumulative_returns(assets, weights)
first_value = cum_ret[0]
last_value = cum_ret[-1]
years = len(cum_ret.index) / 12
cagr = (last_value / first_value)**(1 / years) - 1
return cagr
def _mdd(self, assets, weights):
cum_ret = self._cumulative_returns(assets, weights)
previous_peaks = cum_ret.cummax()
drawdown = (cum_ret - previous_peaks) / previous_peaks
port_mdd = drawdown.min()
return port_mdd
def calculate_portfolio_performance(self):
BM_assets = ['SPY', 'IEF']
BM_weights = np.array([0.6, 0.4])
BM_cagr = round(self._cagr(BM_assets, BM_weights) * 100, 2)
BM_mdd = round(self._mdd(BM_assets, BM_weights) * 100, 2)
investments = self.get_investment_summary()
port_assets = list(investments.index)
port_weights = np.array(list(investments['Portfolio (%)'] / 100))
port_cagr = round(self._cagr(port_assets, port_weights) * 100, 2)
port_mdd = round(self._mdd(port_assets, port_weights) * 100, 2)
stat = {
'Portfolio': ['BenchMark', 'CurrentPortfolio'],
'CAGR (%)': [BM_cagr, port_cagr],
'MDD (%)': [BM_mdd, port_mdd]
}
stat_df = pd.DataFrame(stat)
stat_df.set_index('Portfolio', inplace=True)
return stat_df
def strategy_allocation(self):
# cash allocation
# total equity - cash = allocatable amount
total_equity = self.get_usd_total_equity()
total_mv = self.get_usd_total_mv()
curr_cash = total_equity - total_mv
print(curr_cash)
target_cash = total_equity * (self.cash_rate / 100)
if target_cash < curr_cash:
# invest more from curr_cash
invest_amount = curr_cash - target_cash
print("invest more from curr_cash")
else:
# sell some from investment to increase curr_cash
new_market_value = total_mv - (target_cash - curr_cash)
print("sell some from investment to increase curr_cash")