def _iex_aggregator(fn_name: str, **kwargs: object) -> dict:
agg = dict()
for chunk in util.chunk(symbols(remove='expired'), 100):
tickers = list(chunk)
fn = getattr(iex.Stock(tickers), fn_name)
retrieved = fn(**kwargs)
if type(retrieved) is list:
data = defaultdict(list)
for datum in retrieved:
data[datum['symbol']].append(datum)
elif type(retrieved) is dict:
data = {ticker: datum for ticker, datum in retrieved.items()}
else:
raise RuntimeError(
"Error: `%s' (retrieved) is neither dict nor list" % retrieved)
agg.update(data)
if fn_name == 'get_financials':
# Yes, iexfinance is returning this mess for financials, which we need to correct
return {
ticker: datum.get('financials', [None])[0]
for ticker, datum in agg.items()
}
else:
return {ticker: datum for ticker, datum in agg.items()}
def Evaluate(Name, **kwargs):
OverrideGrowthRate = kwargs.get('G', None)
#Get Stock from ticker Name
Company = Stock.Stock(Name)
eps = Company.get_earnings(last=1)
if (OverrideGrowthRate != None):
Results = (eps[0]['actualEPS'] *
(8.5 + 2.0 + 100.0 * OverrideGrowthRate) *
4.4) / USAAA20yrCorporateBondYield
return Results
else:
#inform on graham growth rate correction factors
#YoYGrowth = eps[0]['actualEPS']/eps[0]['yearAgo']
SurprisePercent = eps[0]['EPSSurpriseDollar'] / eps[0]['consensusEPS']
flow = Company.get_income_statement(period='annual', last=4)
RevGrowth = AvgRevenueGrowth(flow)
IncGrowth = AvgNetIncomeGrowth(flow)
GrowthRate = min(RevGrowth, IncGrowth)
#Corrections based on EPS GrowthYoY
#This is a judgement call and likely will need adjustment
#Will only bring Growth Rate down, never up to keep a reasonable margin of safety
if (SurprisePercent > 0):
GrowthRate = GrowthRate + SurprisePercent * 0.5
Result = (eps[0]['actualEPS'] *
(8.5 + 2.0 * GrowthRate) * 4.4) / USAAA20yrCorporateBondYield
return Result
def iex_stock(ticker):
global IEX_STOCKS
if ticker in IEX_STOCKS:
stock = IEX_STOCKS[ticker]
else:
stock = iex.Stock(ticker)
IEX_STOCKS[ticker] = stock
return stock
def get_chart(self, start=date(2019, 3, 1), end=datetime.now().date()):
quote = stocks.Stock(self.symbol).get_quote()
return stocks.get_historical_data(
self.symbol,
start=start,
end=end,
close_only=True,
output_format="pandas",
).close.append(
pd.Series(data=quote.get("latestPrice"),
index=[datetime.now().date()]))
def resolve_sectors(obj: Portfolio, info: GraphQLResolveInfo):
positions = obj.position_set.all()
sectors = []
for position in positions:
sectors.append({
"name": stocks.Stock(position.stock.symbol).get_sector(),
"share": position.quantity * position.price,
})
total = sum([sector.get("share") for sector in sectors])
sectors = [{
**sector, "share": sector.get("share") / total
} for sector in sectors]
return sectors
def buy_order(clean_input):
symbol = clean_input.get("symbol")
quote = stocks.Stock(symbol).get_quote()
portfolio = Portfolio.objects.first()
stock, created = Stock.objects.get_or_create(symbol=symbol,
name=quote.get("companyName"))
return Position.objects.create(
portfolio=portfolio,
stock=stock,
quantity=clean_input.get("quantity"),
opened=timezone.now(),
price=quote.get("latestPrice"),
long=True,
)
def makeLinearModel(ticker, start, end):
'''
Given a start, and ending date, we can calculate a linear regressionself.
'''
historicalData = iex.get_historical_data(ticker,
start,
end,
output_format='pandas')
historicalData = historicalData.values
Data_X = []
Data_Y = []
for i in range(len(historicalData) - 1):
Data_X.append([historicalData[i][0], historicalData[i][4]])
Data_Y.append([historicalData[i + 1][0]])
linear = LinearRegression()
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
Data_X = imputer.fit_transform(Data_X)
Data_Y = imputer.fit_transform(Data_Y)
linear.fit(Data_X, Data_Y)
stock = iex.Stock(ticker)
currentDay = [[stock.get_price(), stock.get_volume()]]
return (linear.predict(Data_X), linear.predict(currentDay))
def get_history(self):
history = self.get_balance()
for position in self.position_set.all():
prices = stocks.get_historical_data(
position.stock.symbol,
start=position.opened,
end=position.closed or timezone.now(),
close_only=True,
output_format="pandas",
)
quote = stocks.Stock(position.stock.symbol).get_quote()
values = prices.close
if not position.closed:
values = values.append(
pd.Series(data=quote.get("latestPrice"),
index=[datetime.now().date()]))
q = 1 if position.long else -1
history["close"] = history["close"].add(
position.quantity * values.diff().cumsum() * q,
fill_value=0,
)
return history
def informationMap(ticker):
'''
Input: Ticker (ie TSLA)
Output: A list of tuples, that contain all the basic informaiton of the stock.
'''
stock = iex.Stock(ticker)
financials = ['reportDate', 'totalRevenue', 'grossProfit', 'currentAssets']
infoMap = []
infoMap.append(('Company Name', stock.get_company_name()))
infoMap.append(('Sector', stock.get_sector()))
infoMap.append(('Market Cap', '$' + str(stock.get_market_cap())))
infoMap.append(('Volume', (stock.get_market_cap())))
infoMap.append(('Most Recent EPS', stock.get_latest_eps()))
for metric in financials:
if (metric == 'reportDate'):
infoMap.append((convert(metric),
' ' + str(stock.get_financials()[0][metric])))
else:
infoMap.append((convert(metric),
' $' + str(stock.get_financials()[0][metric])))
infoMap.append(('Beta', stock.get_beta()))
return infoMap
def randomForestModel(ticker, start, end):
'''
Given a start, and ending date, we can calculate a linear regressionself.
'''
historicalData = iex.get_historical_data(ticker,
start,
end,
output_format='pandas')
historicalData = historicalData.values
Data_X = []
Data_Y = []
for i in range(len(historicalData) - 1):
Data_X.append([historicalData[i][0], historicalData[i][4]])
Data_Y.append([historicalData[i + 1][0]])
Data_X = np.array(Data_X)
Data_Y = np.array(Data_Y)
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
Data_X = imputer.fit_transform(Data_X)
Data_Y = imputer.fit_transform(Data_Y)
forest = RandomForestRegressor(max_depth=10, n_estimators=100)
forest.fit(Data_X, Data_Y.ravel())
stock = iex.Stock(ticker)
currentDay = [[stock.get_price(), stock.get_volume()]]
return (forest.predict(Data_X), forest.predict(currentDay))
def GetData(name):
stock=Stock.Stock(str(name))
return stock.get_financials()
def get_iex_stock(ticker):
"""get_iex_stock(ticker) -> iexstocks.Stock
Gets a stock with the given ticker.
"""
return iexstocks.Stock(ticker, output_format='json', token=IEX_TOKEN)
#calculate Net Income growth last 3y
def AvgNetIncomeGrowth(Data):
FirstYear = Data[3]['netIncome']
LastYear = Data[0]['netIncome']
if (FirstYear != None and LastYear != None and FirstYear != 0):
rate = (LastYear / FirstYear) / 3
if (FirstYear > LastYear):
return rate * -1
else:
return rate
#Get latest earnings value
Company = Stock.Stock("MCD")
eps = Company.get_earnings(last=1)
#inform on graham growth rate correction factors
YoYGrowth = eps[0]['actualEPS'] / eps[0]['yearAgo']
SurprisePercent = eps[0]['EPSSurpriseDollar'] / eps[0]['consensusEPS']
flow = Company.get_income_statement(period='annual', last=4)
RevGrowth = AvgRevenueGrowth(flow)
IncGrowth = AvgNetIncomeGrowth(flow)
#To account for companies that are becoming
#more efficient while not increasing Revenue
GrowthRate = min(RevGrowth, IncGrowth)
#Corrections based on EPS GrowthYoY
def resolve_latest_price(obj: Stock, info: GraphQLResolveInfo):
quote = stocks.Stock(obj.symbol).get_quote()
return quote.get("latestPrice")
def CalcReturnOnTotalAssets(data):
EBIT = float(data[0]['grossProfit']) - float(data[0]['operatingExpense'])
ROTA = EBIT / float(data[0]['totalAssets'])
return round(ROTA * 100, 2)
#this is for long term ability to pay debts
def CalcDebtToEquityRatio(data):
output = float(data[0]['totalLiabilities']) / float(
data[0]['shareholderEquity'])
return round(output * 100, 2)
def CalcCurrentRatio(data):
output = float(data[0]['currentDebt']) / float(data[0]['currentAssets'])
return round(output * 100, 2)
stock = Stock.Stock("MCD")
d = stock.get_financials()
a = CalcReturnOnEquity(d)
b = CalcReturnOnTotalAssets(d)
c = CalcDebtToEquityRatio(d)
aa = CalcCurrentRatio(d)
def GetPrice(name):
if type(name) is str:
stock = Stock.Stock(name)
return stock.get_price()
