[ 'wage_us_yoy', '(wage_us_weekly_nonsupervisory/lag(wage_us_weekly_nonsupervisory,12)-1)*100' ],
[ 'wti_yoy', '(avg(wti_spot,M)/lag(avg(wti_spot,M),12)-1)*100' ],
[ 'zillow_median_sale_price_yoy', '(zillow_median_sale_price/lag(zillow_median_sale_price,12)-1)*100' ],
[ 'ng_yoy', '(ng_hh_spot/lag(ng_hh_spot,12)-1)*100' ],
[ 'gdp_us_yoy', '(gdp_us/lag(gdp_us,4)-1)*100' ],
]
processed_list = [
[ 'wage_lag_15m', 'mlag(wage_us_yoy,15)' ],
[ 'zillow_median_sale_price_15m', 'mlag(zillow_median_sale_price_yoy,15)' ],
]
df = DataFrameClient('localhost',8086,'root','root')
if({'name':'Econ'} not in df.get_list_database()):
df.create_database('Econ')
df.switch_database('Econ')
#add all items in interested list
start = timeit.default_timer()
for item in interest_list:
if item[0] in df.get_list_series():
df.delete_series(item[0])
results = influx_Fred.interpret(item[1])
results = results.replace(to_replace='NaN',value='.')
results = DataFrame({'value':results['value']})
df.write_points({item[0]:results})
print item
print 'total time in seconds: %.2f' % (timeit.default_timer() - start)
#add all items in processed list
start = timeit.default_timer()
'''
Created on Jul 8, 2015
@author: shaunz
'''
from QuandlAPI import QuandlAPI
from influxdb.influxdb08 import DataFrameClient
from QuandlTicker import Quandl_ticker_list
import timeit
quandl = QuandlAPI()
df = DataFrameClient('localhost', 8086, 'root', 'root')
if({'name':'Quandl'} not in df.get_list_database()):
df.create_database('Quandl')
df.switch_database('Quandl')
for series in df.get_list_series():
df.delete_series(series)
start = timeit.default_timer()
for item in Quandl_ticker_list:
results = quandl.get_series(item[1],item[2],item[3])
results = results.replace(to_replace='NaN',value='.')
print item
df.write_points({item[0]:results})
print 'total time in seconds: %.2f' % (timeit.default_timer() - start)
class InfluxDB(object):
'''
Connect to influxdb and pull/write data
'''
def __init__(self, db_name=None):
self.url = 'localhost'
self.port = 8086
self.user = '******'
self.password = '******'
self.db_list = [ 'FRED', 'Quandl', 'Econ', 'ChinaData' ]
self.db = DataFrameClient(self.url, self.port, self.user, self.password)
if(db_name != None):
self.db_name = db_name
self.db.switch_database(db_name)
def _search_db(self,series_name):
'''
Search the db name for a series name
'''
for db in self.db_list:
temp_db = DataFrameClient(self.url, self.port, self.user, self.password, db)
if series_name in temp_db.get_list_series():
return db
return None
def query(self,series_name,db_name=None):
'''
Query a particular series
------
series_name: str
name of the series, e.g. "CPI_US"
------
return a pandas DataFrame with NaN representing missing values
------
'''
if(db_name != None):
self.db.switch_database(db_name)
results = self.db.query('SELECT * FROM %s' % series_name)
else:
db_name = self._search_db(series_name)
self.db.switch_database(db_name)
results = self.db.query('SELECT * FROM %s' % series_name)
if(results['value'].str.contains('.').isnull().sum()!=len(results)):
results.loc[results['value']=='.','value'] = None
return results.astype(float)
def _is_num(self, s):
'''
Determine if a string is a number
'''
try:
float(s)
return True
except ValueError:
return False
def _include(self,expression_list,c):
'''
check whether expression_list include basic operator c, and give the index of first occurrence
------
c: str
the basic operator or parentheses c
------
'''
for index, item in enumerate(expression_list):
if(type(item)==type('string')):
if(item == c):
return index
return -1
def _get_index(self,expression_list, op_1, op_2):
'''
Get the index of first occurrence of op_1 OR op_2, assume expression_list includes op_1 OR op_2
'''
index_1 = self._include(expression_list, op_1)
index_2 = self._include(expression_list, op_2)
if(min(index_1,index_2)==-1):
return(max(index_1,index_2))
else:
return(min(index_1,index_2))
def _close_parentheses(self,expression):
'''
Find the closing parentheses to the first opening (
------
expression: str
str contains an opening (
------
'''
layer = 0
for i, char in enumerate(expression):
if(char=='('):
layer = layer + 1
elif(char==')'):
layer = layer - 1
if(layer == 0):
return i
return -1
def _break_expression(self, expression, operators, functions):
'''
Break the expression into logical components
'''
expression = expression.replace(' ','')
#empty string
if(len(expression)==0):
return [expression]
# interpret functions
for func in functions:
if func in expression:
func_start = expression.find(func)
func_end = func_start + self._close_parentheses(expression[func_start:])
if(expression[func_start-1] in operators or func_start == 0):
return (self._break_expression(expression[:func_start], operators, functions)
+[expression[func_start:func_end+1]]
+self._break_expression(expression[func_end+1:], operators, functions))
# then deal with the time series, operators and numbers
results = []
current_expression = ''
for char in expression:
if(char in operators):
results.append(current_expression)
results.append(char)
current_expression = ''
else:
current_expression = current_expression + char
results.append(current_expression)
results[:] = [item for item in results if item != '']
return results
def _eval_func(self, func):
'''
Evaluate the function
'''
function = func[:func.index('(')]
if 'lag' == function: #lag the time series by a number of periods. lag(*series*,i) where i is number of period
index_start = func.index('(')
index_mid = len(func)-func[::-1].index(',')-1
index_end = len(func)-func[::-1].index(')')-1
try:
series = self.query(func[index_start+1:index_mid])
except:
series = self.interpret(func[index_start+1:index_mid])
periods = float(func[index_mid+1:index_end])
return series.shift(periods)
elif 'mlag' == function: # shift the time stamp by a number of months. mlag(*series*,i) where i is number of months
index_start = func.index('(')
index_mid = len(func)-func[::-1].index(',')-1
index_end = len(func)-func[::-1].index(')')-1
try:
series = self.query(func[index_start+1:index_mid])
except:
series = self.interpret(func[index_start+1:index_mid])
periods = float(func[index_mid+1:index_end])
return series.tshift(periods,freq='M').tshift(1,freq='D')
elif 'avg' == function: #taking the average of time series
index_start = func.index('(')
index_mid = len(func)-func[::-1].index(',')-1
index_end = len(func)-func[::-1].index(')')-1
try:
series = self.query(func[index_start+1:index_mid])
except:
series = self.interpret(func[index_start+1:index_mid])
freq = func[index_mid+1:index_end]
series = series.resample(freq, how='mean')
if 'M' in freq:
return series.tshift(-1,freq='M').tshift(1,freq='D')
else:
return series
elif 'anticum' == function: #reverse the cumulative common to China stats
index_start = func.index('(')
index_mid = len(func)-func[::-1].index(',')-1
index_end = len(func) - func[::-1].index(')')-1
new_series = {}
try:
series = self.query(func[index_start+1:index_mid])
except:
series = self.interpret(func[index_start+1:index_mid])
freq = func[index_mid+1:index_end]
if(freq=='M'):
freq_m = 1
elif(freq=='Q'):
freq_m = 3
for item in series.iterrows():
if item[0].month != freq_m:
prev_date = item[0] + DateOffset(months=-1*freq_m)
prev_date = prev_date.to_period('M').to_timestamp('M')
monthly = item[1]['value']-series.loc[prev_date]
new_series[item[0]] = monthly['value']
else:
new_series[item[0]] = item[1]['value']
return DataFrame({'value':Series(new_series)})
else:
message = '%s not defined' % func
raise ValueError(message)
def _convert_expressions(self, expression, operators):
'''
Convert expressions to data series after they are broken down
'''
expression[:] = [item for item in expression if item != '']
converted_results = []
for item in expression:
if '(' in item and ')' in item:
converted_results.append(self._eval_func(item))
elif item in operators:
converted_results.append(item)
elif self._is_num(item):
converted_results.append(float(item))
else:
converted_results.append(self.query(item))
return converted_results
def _calculate(self, expression_list):
'''
Calculate a list of expression elements
'''
e_list = expression_list
if(len(e_list)==1):
return e_list
if(self._include(e_list, '^') != -1):
position = self._include(e_list, '^')
eval_result = pow(e_list[position-1],e_list[position+1])
return self._calculate(e_list[:position-1] + [eval_result] + e_list[position+2:])
elif(self._include(e_list, '*') != -1 or self._include(e_list, '/') != -1):
position = self._get_index(e_list, '*', '/')
if(e_list[position]=='*'):
eval_result = e_list[position-1] * e_list[position+1]
else:
eval_result = e_list[position-1] / e_list[position+1]
return self._calculate(e_list[:position-1] + [eval_result] + e_list[position+2:])
elif(self._include(e_list, '+') != -1 or self._include(e_list, '-') != -1):
position = self._get_index(e_list, '+', '-')
if(e_list[position]=='+'):
eval_result = e_list[position-1] + e_list[position+1]
else:
eval_result = e_list[position-1] - e_list[position+1]
return self._calculate(e_list[:position-1] + [eval_result] + e_list[position+2:])
else:
raise ValueError('cannot recognize operators in the expression')
def _parentheses(self, expression_list):
'''
Iterate through parentheses, always interpret the first closing parentheses )
'''
e_list = expression_list
if(self._include(e_list, '(') != -1 and self._include(e_list, ')') != -1):
close_index = self._include(e_list, ')')
sub = e_list[:close_index][::-1]
open_index = close_index - self._include(sub, '(') - 1
eval_result = self._calculate(e_list[(open_index+1):close_index])
new_list = e_list[:open_index] + eval_result + e_list[close_index+1:]
return(self._parentheses(new_list))
else:
if(self._include(e_list, '(') != -1 or self._include(e_list, ')') != -1):
raise ValueError('unmatched parentheses, check the expression')
else:
return(self._calculate(e_list))
def interpret(self, expression):
'''
Interpret an expression
'''
operators = ['^','+','-','*','/','(',')']
functions = ['lag', 'mlag', 'avg', 'anticum']
broken_expression = self._break_expression(expression, operators, functions)
interp_expression = self._convert_expressions(broken_expression, operators)
results = self._parentheses(interp_expression)[0]
results = results.loc[isnull(results['value']) != True]
return results
'''
Created on Jul 2, 2015
@author: shaunz
'''
from influxdb.influxdb08 import DataFrameClient
from pandas import DataFrame
from FredAPI import FredLink
from FredLink.FredTicker import Fred_ticker_list
import timeit
fred = FredLink()
df = DataFrameClient('localhost', 8086, 'root', 'root')
if({'name':'FRED'} not in df.get_list_database()):
df.create_database('FRED')
df.switch_database('FRED')
for series in df.get_list_series():
df.delete_series(series)
start = timeit.default_timer()
for item in Fred_ticker_list:
results = fred.get_series(item[1])
results = results.replace(to_replace='NaN',value='.')
data = DataFrame({'value': results})
print item
df.write_points({item[0]:data})
print 'total time in seconds: %.2f' % (timeit.default_timer() - start)
