class Stock(object):
def __init__(self, ticker, fundamental_frequency='q'):
self.ticker= ticker
self.zacks_earnings_cal = get_zacks_earnings_calendar(ticker)
self.yquery = Ticker(ticker)
self.returns_data = self.yquery.history(adj_ohlc=True,
start=(datetime.today() \
-timedelta(days = 365*3)
).strftime('%Y-%m-%d'),
end = datetime.today().strftime('%Y-%m-%d')
).droplevel(0).rename(columns={'high': 'PriceHigh',
'volume': 'Volume',
'open': 'PriceOpen',
'low': 'PriceLow',
'close': 'PriceClose'
})
self.financial_data = self.get_all_financial_data(fundamental_frequency=fundamental_frequency)
try:
self.ratings_data = get_finviz_fundamentals_ratings(ticker)
except:
self.ratings_data = None
try:
self.insider_trading_data = get_finviz_inside_trading(ticker)
except:
self.insider_trading_data = None
try:
self.news_data = get_finviz_news(ticker)
except:
self.news_data = None
def get_all_financial_data (self, fundamental_frequency='q'):
df = self.yquery.all_financial_data( frequency = fundamental_frequency)
df['Quarter'] = df['asOfDate'].apply(lambda x: as_of_date_to_quarter(x))
df = pd.merge(df, self.zacks_earnings_cal, how = 'left', on = 'Quarter')\
.set_index('ReleaseDate').drop(['asOfDate', 'periodType', 'Quarter'], axis = 1)
return df
def get_fundamental_ts (self, item):
ts = self.financial_data[item]
date_idx = pd.date_range(self.returns_data.index[0], self.returns_data.index[-1])
ts = ts.reindex(index = date_idx).shift(1).ffill()
ts = ts.loc[self.returns_data.index]
return ts
def __getitem__(self, item):
if item in self.returns_data.columns:
return self.returns_data[item]
elif item in self.financial_data.columns:
return self.get_fundamental_ts(item)
else:
raise KeyError('Item not Found!')
def query_yahoofinance(input):
"""Get financial data from Yahoo! Finance
:param input: list of firm tickers for which data should be retrieved
:return : None (store returned data as CSV)
"""
query = Ticker(input)
print("Run query .all_financial_data()")
try:
# query all financial data
df_fin_raw = query.all_financial_data()
# store raw data
stamp = h.timestamp_now()
h.make_csv(df_fin_raw, stamp+'yahoofinance.csv', '../../data/raw', header=True, index=True)
except Exception as e:
print(e)
class MagicFormula:
def __init__(self, ticker, last_open, debug_level=0):
self.debug = debug_level
self.ticker = ticker
self.roic = None
self.ebit = None
self.working_cap = None
self.net_PPE = None
self.ev = None
self.ey = None
self.price_12m = None
self.price_6m = None
self.price_now = None
self.change_12m = None
self.change_6m = None
self.market_cap = None
self.cap_category = None
self._last_trade_date = last_open
### These next three lines are the time consuming part. Once we have this info, we can derive the rest of the
### info (except for the change in price over time).
self._yq = Ticker(ticker)
self._all_financial_data = self._yq.all_financial_data()
self._valuation_measures = self._yq.valuation_measures
try: # enterprise value
last_ev = self._valuation_measures[
self._valuation_measures['periodType'] == '3M'].tail(1)
self.ev = float(last_ev.EnterpriseValue)
except:
self.debug_writer(2,
"WARNING: Failed to get enterprise value for ")
try: # market cap
last_market_cap = self._valuation_measures[
self._valuation_measures['periodType'] == '3M'].tail(1)
self.market_cap = float(last_market_cap.MarketCap)
except:
self.debug_writer(2, "WARNING: Failed to get market cap for ")
try: # working cap
self.working_cap = self._all_financial_data.WorkingCapital[-1]
except:
self.debug_writer(2, "WARNING: Failed to get working capital for ")
try: # net plant, property, and equipment
self.net_PPE = self._all_financial_data.NetPPE[-1]
except:
self.debug_writer(2,
"WARNING: Failed to failed to get net PPE for ")
try: # expenses before interest and taxes
self.ebit = self._all_financial_data.EBIT[-1]
except:
self.debug_writer(2, "WARNING: Failed to get EBIT for ")
try: # ticker price from last year
date_12m = self._last_trade_date - relativedelta(years=1)
history = self._yq.history("1d", "1d", date_12m,
date_12m + relativedelta(days=1))
self.price_12m = float(history.close)
except:
self.debug_writer(2,
"WARNING: Failed to get last year's price for ")
try: # ticker price from 6m ago
date_6m = self._last_trade_date - relativedelta(days=182)
history = self._yq.history("1d", "1d", date_6m,
date_6m + relativedelta(days=1))
self.price_6m = float(history.close)
except:
self.debug_writer(2,
"WARNING: Failed to get price from 6m ago for ")
try: # last closing price
history = self._yq.history("1d", "1d",
self._last_trade_date).tail(1)
self.price_now = float(history.close)
except:
self.debug_writer(
2, "WARNING: Failed to get last trading day price for ")
### Run calculations
# ROIC (Return on invested capital) = EBIT / (net working capital + net PPE)
if self.ebit and self.working_cap and self.net_PPE:
self.roic = self.ebit / (self.working_cap + self.net_PPE)
else:
self.debug_writer(
2,
"WARNING: Could not calculate return on invested capital for ")
# Earnings yield = EBIT / Enterprise value
if self.ebit and self.ev:
self.ey = self.ebit / self.ev
else:
self.debug_writer(
2, "WARNING: Could not calculate enterprise yield for ")
# 12m change in price
if self.price_now and self.price_12m and self.price_12m != 0:
self.change_12m = ((self.price_now - self.price_12m) /
self.price_12m)
else:
self.debug_writer(
2,
"WARNING: Could not calculate change in previous 12m price for "
)
# 6m change in price
if self.price_now and self.price_6m and self.price_6m != 0:
self.change_6m = ((self.price_now - self.price_6m) / self.price_6m)
else:
self.debug_writer(
2,
"WARNING: Could not calculate change in previous 6m price for "
)
### Categorize market cap for easy excel grouping
if self.market_cap:
self.cap_category = "nano"
if self.market_cap >= 50000000:
self.cap_category = "micro"
if self.market_cap >= 300000000:
self.cap_category = "small"
if self.market_cap >= 2000000000:
self.cap_category = "medium"
if self.market_cap >= 10000000000:
self.cap_category = "large"
if self.market_cap >= 200000000000:
self.cap_category = "mega"
self.debug_writer(3, "INFO: Finished getting info for ")
def debug_writer(self, message_debug_level, message):
if self.debug >= message_debug_level:
print(message, self.ticker)
def __str__(self):
return [
str(self._last_trade_date),
str(self.ticker),
str(self.roic),
str(self.ey),
str(self.price_12m),
str(self.price_6m),
str(self.price_now),
str(self.change_12m),
str(self.change_6m),
str(self.market_cap),
str(self.cap_category),
str(self.ebit),
str(self.working_cap),
str(self.net_PPE),
str(self.ev)
]
def __repr__(self):
return [
self._last_trade_date, self.ticker, self.roic, self.ey,
self.price_12m, self.price_6m, self.price_now, self.change_12m,
self.change_6m, self.market_cap, self.cap_category, self.ebit,
self.working_cap, self.net_PPE, self.ev
]
