def range_volatility(ticker, components):
# Parse and check: find components matching dates, ensure start and end are present, ensure dates are valid
today, dates = current_date(), []
for each in components:
if PATTERNS['valid_date'].match(each):
dates.append(each)
if len(dates)!=2:
return {"message": Response.vol_required_dates(ticker)}
for each in dates:
if each > today:
return {"message": Response.invalid_date(each)}
try:
date = datetime.datetime.strptime(each, '%Y-%m-%d')
except ValueError:
return {"message": Response.invalid_date(each)}
# Volatility Calculation
dates = sorted(dates)
start, end = dates[0], dates[1]
try:
quotes = data.DataReader(ticker, 'google')['Close'].loc[start:end]
if len(quotes) < 10:
return {"message": Response.vol_range_size(ticker)}
except Exception:
return {"message": Response.data_notfound(ticker)}
logreturns = np.log(quotes / quotes.shift(1))
vol = round(np.sqrt(252*logreturns.var()), 5)
return {"message" : Response.range_vol(ticker, start, end, vol)}
def get_fred(symbol, components):
symbol = symbol.upper()
try:
df = data.get_data_fred(symbol)
except Exception:
return {"message":Response.fred_notfound(symbol)}
today, dates = current_date(), []
for each in components:
if PATTERNS['valid_date'].match(each):
dates.append(each)
# No dates: get most recent value
if not dates:
df.dropna()
last_value = df.tail(1)[symbol][0]
last_value = ('%.3f' % last_value)
return {"message": Response.basic_fred(symbol, last_value)}
# Clean dates
if len(dates)>2:
return {"message": Response.too_many_dates(symbol)}
for each in dates:
if each > today:
return {"message": Response.invalid_date(each)}
try:
date = datetime.datetime.strptime(each, '%Y-%m-%d')
except ValueError:
return {"message": Response.invalid_date(each)}
# Return price data for one day
if len(dates)==1:
date = dates[0]
ts = pd.DatetimeIndex.asof(df.index, date)
if pd.isnull(ts):
return {"message": Response.fred_date_notfound(symbol, date)}
value = df.loc[ts][symbol]
if pd.isnull(value):
return {"message": Response.fred_date_notfound(symbol, date)}
return {"message": Response.date_fred(symbol, date, value)}
# If 2 dates are entered, returned the range during the given period
else:
dates = sorted(dates)
start, end = dates[0], dates[1]
df = df.loc[start:end]
high = ('%.3f' % df[symbol].max())
low = ('%.3f' % df[symbol].min())
return {"message": Response.fred_data_range(symbol, start, end, high, low)}
def historical_data(ticker, components):
# Prices adjusted for splits
today, dates = current_date(), []
for each in components:
if PATTERNS['valid_date'].match(each):
dates.append(each)
if not dates:
return {"message": Response.missing_dates(ticker)}
# Validate dates
for each in dates:
if each > today:
return {"message": Response.invalid_date(each)}
try:
date = datetime.datetime.strptime(each, '%Y-%m-%d')
except ValueError:
return {"message": Response.invalid_date(each)}
# Validate ticker and fetch data
try:
quotes = data.get_data_google(ticker)
except Exception:
return {"message": Response.data_notfound(ticker)}
# Return price data for one day
if len(dates)==1:
date = dates[0]
try:
quote = quotes.loc[date]
except KeyError:
return {"message": Response.no_data_for_date(date)}
return {"message": Response.historical_price(
ticker, date, quote['Open'], quote['High'], quote['Low'], quote['Close'], int(quote['Volume']))}
# If 2 dates are entered, returned the range during the given period
elif len(dates)==2:
dates = sorted(dates)
start, end = dates[0], dates[1]
quotes = quotes.loc[start:end]
high = round(quotes['High'].max(),2)
low = round(quotes['Low'].min(),2)
return {"message": Response.historical_range(ticker, start, end, high, low)}
else:
return {"message": Response.too_many_dates(ticker)}
