def request_Quandl(symbol, tableName, cols, digits):
con = connect_to_DB()
create_table(tableName, con)
str_ = """SELECT * FROM `""" +tableName+ """` ORDER BY DATE_TIME DESC LIMIT 1;"""
print str_
data = pd.read_sql_query(str_, con=con, index_col='DATE_TIME')
if empty(data) == 0:
#execute query with conenction
quandl_data = q.get(symbol, returns='pandas', authtoken=token, sort_order='asc')
if empty(quandl_data) == 1:
print quandl_data.tail()
print "Data length: %s" % len(quandl_data)
insert_data(quandl_data, cols, digits, con)
else:
last_date = str(data.index[0].year)+"-"+str(data.index[0].month)+"-"+str(data.index[0].day+1)
print "Last date "+last_date
quandl_data = q.get(symbol, returns='pandas', authtoken=token, trim_start=last_date, sort_order='asc')
if empty(quandl_data) == 1:
print quandl_data.tail()
print "Data length: %s" % len(quandl_data)
insert_data(quandl_data, cols, digits, con)
def index():
if request.method=='POST':
if request.form.get('stocks',None):
if request.form.get('symbols',None).find('#')==-1:
try:
sdata=[ql.get('WIKI/'+request.form.get('symbols',None),trim_start=get_start_date(),authtoken=q_toke)]
names=[request.form.get('symbols',None)]
except:
flash("Sorry invalid name, please try another ticker symbol.")
return render_template('index.html')
else:
try:
sdata=[ql.get('WIKI/'+u,trim_start=get_start_date(),authtoken=q_toke)\
for u in request.form.get('symbols',None).rsplit('#')[:]]
names=request.form.get('symbols',None).rsplit('#')[:]
except:
flash("Sorry invalid name, please try another ticker symbol.")
return render_template('index.html')
reqs=['adjc','adjo','c','o']
r2ql=dict(zip(reqs,['Adj. Close','Adj. Open','Close','Open']))
checked=[]
#gather checked values
for r in [request.form.get(n,None) for n in reqs]:
if r:
checked.append(r2ql[r])
#make bokeh plot with the data
script, div = make_bokeh_html(sdata,names,checked)
return render_template('stocks.html',script=script,div=div)
return render_template('index.html')
def load(self):
# If this is the first time of getting the data, send queries to Quandl website and download data there.
# Then, store the date in local directories as csv files
# If the data has already been downloaded, load the local csv.
fund_code = self.fund_code
index_code = self.index_code
start_date = self.start_date
fund_file_name = "../data/" + fund_code.replace("/", "_") + ".csv"
index_file_name = "../data/" + index_code.replace("/", "_") + ".csv"
if path.isfile(fund_file_name): # file exists at local
fund_data = pd.io.parsers.read_csv(fund_file_name, index_col=0) # the csv file has to be parsed to have date as index
else: # download from Quandl and save it to local as csv file
fund_data = Quandl.get(fund_code, trim_start=start_date, authtoken="XANwfFd9CmdoE3PdFzRg")
fund_data.to_csv(fund_file_name)
if path.isfile(index_file_name):
index_data = pd.io.parsers.read_csv(index_file_name, index_col=0) # the csv file has to be parsed to have date as index
else:
index_data = Quandl.get(index_code, trim_start=start_date, authtoken="XANwfFd9CmdoE3PdFzRg")
index_data.to_csv(index_file_name)
# rename columns so that the two dataframes don't share any common names
index_data.columns = map(''.join, zip([index_code+'_']*index_data.shape[1], list(index_data.columns))) # rename the columns with index_code as prefix
fund_data.columns = map(''.join, zip([fund_code+'_']*fund_data.shape[1], list(fund_data.columns))) # rename the columns with fund_code as prefix
# join the two data frames by date
self.data = fund_data.join(index_data, how='inner')
if index_code+'_Adjusted Close' not in self.data.columns: # if no adjusted close, copy close to make name uniform across differnet data
self.data[index_code+'_Adjusted Close'] = self.data[index_code+'_Close']
def getCommodityPrice():
#get crude oil data
oilData = Quandl.get("OPEC/ORB", authtoken="xxxxxxxxxxxxxxxx")
print(oilData.tail())
print(oilData.head())
oilData.to_csv('quandlcrudeoilData.csv')
#get gold data.
goldData = Quandl.get("BUNDESBANK/BBK01_WT5511",
authtoken="xxxxxxxxxxxxxxxx")
print(goldData.tail())
print(goldData.head())
goldData.to_csv('quandlgoldData.csv')
#get copper data
copperData = Quandl.get("LSE/COPA", authtoken="xxxxxxxxxxxxxxxx")
print(copperData.tail())
print(copperData.head())
copperData.to_csv('quandlcopperData.csv')
#get silver data
silverData = Quandl.get("LBMA/SILVER", authtoken="xxxxxxxxxxxxxxxx")
print(silverData.tail())
print(silverData.head())
silverData.to_csv('quandlsilverData.csv')
#get bitcoin pricing data.
bitcoinData = Quandl.get("BAVERAGE/USD", authtoken="xxxxxxxxxxxxxxxx")
print(bitcoinData.tail())
print(bitcoinData.head())
bitcoinData.to_csv('bitcoindata.csv')
time.sleep(86400) #sleep for a day
def extractData(self):
#------------------------------
#-- extract rawdata from quandl
#------------------------------
print "extracting data from quandl ... \n"
# get data
dataGbpAud = Quandl.get("BOE/XUDLADS", trim_start=self.startDate)
dataEurGbp = Quandl.get("BOE/XUDLSER", trim_start=self.startDate)
print "preparing data ... \n"
dataGbpAud.columns = ['gbpaud']
dataGbpEur = 1 / dataEurGbp
dataGbpEur.columns = ['gbpeur']
dataExchange = dataGbpEur.merge(dataGbpAud
, how='inner'
, left_index=True
, right_index=True
)
print "extracting rawdata ... \n"
self.rawData = []
# files with data
for index, row in dataExchange.iterrows():
rowval = ExchangeRates(period=index,gbpeur=row['gbpeur'],gbpaud=row['gbpaud'])
self.rawData.append(rowval)
print "rawdata extract complete. \n"
def request_Quandl(symbol, tableName, cols, digits):
con = connect_to_DB()
create_table(tableName, con)
str_ = """SELECT * FROM `""" + tableName + """` ORDER BY DATE_TIME DESC LIMIT 1;"""
print str_
data = pd.read_sql_query(str_, con=con, index_col='DATE_TIME')
if empty(data) == 0:
#execute query with conenction
quandl_data = q.get(symbol,
returns='pandas',
authtoken=token,
sort_order='asc')
if empty(quandl_data) == 1:
print quandl_data.tail()
print "Data length: %s" % len(quandl_data)
insert_data(quandl_data, cols, digits, con)
else:
last_date = str(data.index[0].year) + "-" + str(
data.index[0].month) + "-" + str(data.index[0].day + 1)
print "Last date " + last_date
quandl_data = q.get(symbol,
returns='pandas',
authtoken=token,
trim_start=last_date,
sort_order='asc')
if empty(quandl_data) == 1:
print quandl_data.tail()
print "Data length: %s" % len(quandl_data)
insert_data(quandl_data, cols, digits, con)
def update_dataset_data(engine, dataset, api_key):
"""
Updates data for each code in the data set, retrieving new entries since the last update.
"""
update_code = sql.text(
'UPDATE ' + dataset + '_CODES '
'SET [Start Date] = :start, [End Date] = :end, [Last Updated] = :updated '
'WHERE Code = :code')
conn = engine.connect()
# retrieve the current code table
code_table = load_sql_data(engine, dataset + '_CODES', date_columns=['Start Date', 'End Date', 'Last Updated'])
# TESTING ONLY
code_table = code_table.iloc[:10, :]
# fetch the first code's data to create the data frame
data = Quandl.get(code_table['API Code'].iloc[0], rows=1, authtoken=api_key)
data = data.reset_index()
data = data.rename(columns=lambda x: x[0] + x[1:].lower())
data['Code'] = code_table['Code'].iloc[0]
data = data.iloc[0:0]
# iterate over each code and append the returned data
counter = 0
for index, row in code_table.iterrows():
if row['Last Updated'] == datetime(1900, 1, 1):
# this is a new code so we need to pull all historical data
print('Loading historical data for new company {0}...'.format(row['API Code']))
code_data = Quandl.get(row['API Code'], authtoken=api_key)
else:
# incremental update from the current end date for the code
code_data = Quandl.get(row['API Code'], trim_start=str(row['End Date']), authtoken=api_key)
# concat new data to the total set of new records
code_data = code_data.reset_index()
code_data = code_data.rename(columns=lambda x: x[0] + x[1:].lower())
code_data['Code'] = row['Code']
data = pd.concat([data, code_data])
# update the code table
min_date = code_data['Date'].min().to_datetime()
max_date = code_data['Date'].max().to_datetime()
current_date = datetime.now()
conn.execute(update_code, start=min_date, end=max_date, updated=current_date, code=row['Code'])
counter += 1
if counter % 100 == 0:
print('Sleeping for one minute to avoid hitting API call limits...')
time.sleep(60)
# move the code column to the beginning
columns = data.columns.tolist()
columns = [columns[-1]] + columns[:-1]
data = data[columns]
save_sql_data(engine, data, dataset, exists='append', index=False)
print(dataset + ' code data updated successfully.')
def test_api_calls(api_key):
"""
Test function with examples of calls using the Quandl API via python wrapper.
"""
data = Quandl.get('WIKI/AAPL', authtoken=api_key)
data_new = Quandl.get('WIKI/AAPL', rows=5, sort_order='desc', authtoken=api_key)
data_range = Quandl.get('WIKI/AAPL', trim_start='2015-01-01', trim_end='2015-01-01', authtoken=api_key)
data_multiple = Quandl.get(['WIKI/AAPL', 'WIKI/MSFT'], authtoken=api_key)
def getQuandle(what):
"""
Wrapper around Quandl requests, using authtoken only if available
"""
if authtoken:
return Quandl.get(what, authtoken=authtoken)
else:
return Quandl.get(what)
def loadQuandlData():
quandl_id = "hThEeCQhKC2oWz2hSB4x"
it = Quandl.get(['GOOG/NASDAQ_AAPL','GOOG/NASDAQ_GOOGL','GOOG/NASDAQ_MSFT', 'GOOG/NASDAQ_FB','GOOG/NYSE_ORCL'], trim_start='1998-01-01', authtoken=quandl_id)
financial = Quandl.get(['GOOG/NYSE_DB','GOOG/NYSE_GS','GOOG/NYSE_SAN'], authtoken=quandl_id)
industrial = Quandl.get(['GOOG/NYSE_SI', 'GOOG/NYSE_GE'], authtoken=quandl_id)
closing_it = it[['GOOG.NASDAQ_AAPL - Close','GOOG.NASDAQ_GOOGL - Close','GOOG.NASDAQ_MSFT - Close','GOOG.NASDAQ_FB - Close','GOOG.NYSE_ORCL - Close']]
closing_financial = financial[['GOOG.NYSE_DB - Close','GOOG.NYSE_GS - Close','GOOG.NYSE_SAN - Close']]
closing_industrial = industrial[['GOOG.NYSE_SI - Close','GOOG.NYSE_GE - Close']]
closing_it.to_csv('Data/Closing_it.csv', sep=';')
closing_financial.to_csv('Data/Closing_financial.csv', sep=';')
closing_industrial.to_csv('Data/Closing_industrial.csv', sep=';')
def fetchSpread(CONTRACT, M1, M2, ST_YEAR, END_YEAR, CONT_YEAR1, CONT_YEAR2, ST_DATE, END_DATE, BUCK_PRICE,
STARTFROMZERO, years):
startdate = datetime.strptime(ST_DATE, '%Y-%m-%d %H:%M:%S')
enddate = datetime.strptime(END_DATE, '%Y-%m-%d %H:%M:%S')
totalSpread = pd.Series()
lastValue = 0
for i in years:
year = str(i)
price = str(BUCK_PRICE)
filename = CONTRACT + M1 + M2 + year + ST_DATE + END_DATE + price
filename = re.sub('[/ ]', '_', filename)
filename = re.sub('[:]', '.', filename)
cont1 = str(CONTRACT) + str(M1) + str(i + CONT_YEAR1)
cont2 = str(CONTRACT) + str(M2) + str(i + CONT_YEAR2)
print('==============')
print("contract1: " + cont1)
print("contract2: " + cont2)
startDate = startdate.replace(year=ST_YEAR - 2000 + i)
endDate = enddate.replace(year=END_YEAR - 2000 + i)
print('==============')
print('Trim start: ', startDate.strftime('%Y-%m-%d'))
print('Trim end: ', endDate.strftime('%Y-%m-%d'))
print('==============')
if not checkIfCached(filename):
data1 = q.get(cont1, authtoken=AUTH_TOKEN, trim_start=startDate, trim_end=endDate)
data2 = q.get(cont2, authtoken=AUTH_TOKEN, trim_start=startDate, trim_end=endDate)
spread = (data1 - data2).Settle * BUCK_PRICE
if spread.size == 0:
print('!!!!!!!!!!!!*****WARNING****!!!!!!!!!!!!')
print('No data available for contracts %s, %s. Skiping period from %s to %s.' % (
cont1, cont2, startDate.strftime('%Y-%m-%d'), endDate.strftime('%Y-%m-%d')))
print('!!!!!!!!!!!!*****WARNING****!!!!!!!!!!!!')
continue
else:
if math.isnan(spread[0]):
spread = spread.fillna(method='bfill')
#replace NaN value with a previous one
spread = spread.fillna(method='pad')
#remove row with NAN value
# spread = spread.dropna()
writeCacheToFile(filename, spread)
else:
print("Loading cached data from file: %s !" % filename)
cache = readCacheFromFile(filename)
spread = cache
if STARTFROMZERO:
delta = lastValue - spread[0]
spread = spread + delta
totalSpread = totalSpread.append(spread)
lastValue = totalSpread[-1]
if totalSpread.size == 0:
sys.exit(-1)
return totalSpread
def readLast5DaysStockPrice():
with open('stock_features.csv', 'w') as csvfile:
fieldnames = ['Stock', 'Adj_Close_1','High_1', 'Low_1'
,'Adj_Close_2','High_2', 'Low_2','VolChg2_1','IndexChg2_1'
,'Adj_Close_3','High_3', 'Low_3','VolChg3_2','IndexChg3_2'
,'Adj_Close_4','High_4', 'Low_4','VolChg4_3','IndexChg4_3'
,'Adj_Close_5','High_5', 'Low_5','VolChg5_4','IndexChg5_4']
writer = csv.DictWriter(csvfile, lineterminator='\n', fieldnames=fieldnames)
writer.writeheader()
dow_code = 'BCB/UDJIAD1'
dji_index = []
j = 0
while (j < 10):
ticker = Share(stock_dict.get(j))
i = 0
stock_price = []
current_date = date.today()
fmt = '%Y-%m-%d'
while( i < 5):
if (ticker.get_historical(current_date.strftime(fmt), current_date.strftime(fmt)) == []):
current_date = current_date - timedelta(days=1)
else:
stock_price += ticker.get_historical(current_date.strftime(fmt), current_date.strftime(fmt))
if(j == 0):
if(i == 0 and Quandl.get(dow_code, trim_start=current_date, trim_end=current_date, authtoken="T246AaoCUiwSyz1C4Vfe").values.tolist() == []):
dji_index.append(get_latest_dji())
else:
dji_index.append(Quandl.get(dow_code, trim_start=current_date, trim_end=current_date, authtoken="T246AaoCUiwSyz1C4Vfe").values.tolist()[0][0])
current_date = current_date - timedelta(days=1)
i = i + 1
AbVolChg2_1 = int(stock_price[1].get('Volume')) - int(stock_price[0].get('Volume'))
VolChg2_1 = log(AbVolChg2_1,2) if AbVolChg2_1 > 0 else -1*log(fabs(AbVolChg2_1),2)
AbVolChg3_2 = int(stock_price[2].get('Volume')) - int(stock_price[1].get('Volume'))
VolChg3_2 = log(AbVolChg3_2,2) if AbVolChg3_2 > 0 else -1*log(fabs(AbVolChg3_2),2)
AbVolChg4_3 = int(stock_price[3].get('Volume')) - int(stock_price[2].get('Volume'))
VolChg4_3 = log(AbVolChg4_3,2) if AbVolChg4_3 > 0 else -1*log(fabs(AbVolChg4_3),2)
AbVolChg5_4 = int(stock_price[4].get('Volume')) - int(stock_price[3].get('Volume'))
VolChg5_4 = log(AbVolChg5_4,2) if AbVolChg5_4 > 0 else -1*log(fabs(AbVolChg5_4),2)
writer.writerow({'Stock': stock_dict.get(j)
,'Adj_Close_1' : stock_price[0].get('Adj_Close'),'High_1' : stock_price[0].get('High'),'Low_1' : stock_price[0].get('Low')
,'Adj_Close_2' : stock_price[1].get('Adj_Close'),'High_2' : stock_price[1].get('High'),'Low_2' : stock_price[1].get('Low')
,'VolChg2_1': VolChg2_1,'IndexChg2_1': (float(dji_index[1]) - float(dji_index[0]))
,'Adj_Close_3' : stock_price[2].get('Adj_Close'),'High_3' : stock_price[2].get('High'),'Low_3' : stock_price[2].get('Low')
,'VolChg3_2': VolChg3_2,'IndexChg3_2': (dji_index[2] - dji_index[1])
,'Adj_Close_4' : stock_price[3].get('Adj_Close'),'High_4' : stock_price[3].get('High'),'Low_4' : stock_price[3].get('Low')
,'VolChg4_3': VolChg4_3,'IndexChg4_3': (dji_index[3] - dji_index[2])
,'Adj_Close_5' : stock_price[4].get('Adj_Close'),'High_5' : stock_price[4].get('High'),'Low_5' : stock_price[4].get('Low')
,'VolChg5_4': VolChg5_4,'IndexChg5_4': (dji_index[4] - dji_index[3])
})
j = j+1
def info(symbol):
query_params = request.args
trim_start = query_params.get('start_date') or '2015-12-01'
trim_end = query_params.get('end_date') or '2015-12-31'
datasets = Quandl.search(symbol, authtoken=keys['quandl'], verbose=False)
code = datasets[0][u'code']
data = Quandl.get(code,
authtoken=keys['quandl'],
collapse='daily',
trim_start=trim_start,
trim_end=trim_end)
return data.to_html()
def get_data_set(self, data_set, start, end, drop=None, collapse="daily", transform="None"):
"""
Method for downloading one data set from Quandl
:param data_set: the data set code
:param start: the start date
:param end: the end date
:param drop: which columns to drop
:param collapse: frequency of the data
:param transform: any data transformations from quandl
:return: the data set as a pandas data frame
"""
if drop is None:
drop = []
if self.proxy:
# If we are running behind the proxy set it up
os.environ['HTTP_PROXY'] = "http://" + self.username + ":" + self.password + "@" + self.server
# Check if the dataframe has been downloaded already in this session
hash_val = hash(data_set + str(start) + str(end) + str(transform))
if self.memoized_data.__contains__(hash_val):
return self.memoized_data[hash_val]
else:
try:
print("\tDownloading", data_set)
# Otherwise download the data frame from scratch
if transform is not "None":
downloaded_data_frame = Quandl.get(data_set, authtoken=self.token, trim_start=start,
trim_end=end, collapse=collapse, transformation=transform)
else:
downloaded_data_frame = Quandl.get(data_set, authtoken=self.token, trim_start=start,
trim_end=end, collapse=collapse)
# Remove any unnecessary columns and rename the columns
# print downloaded_data_frame.columns
updated_column_labels = []
for column_label in downloaded_data_frame.columns:
if column_label in drop:
downloaded_data_frame = downloaded_data_frame.drop([column_label], axis=1)
else:
updated_column_labels.append(data_set + "_" + column_label)
downloaded_data_frame.columns = updated_column_labels
self.memoized_data[hash_val] = downloaded_data_frame
return downloaded_data_frame
except Quandl.DatasetNotFound:
print("Exception - DataSetNotFound", data_set)
except Quandl.CodeFormatError:
print("Exception - CallFormatError", data_set)
except Quandl.DateNotRecognized:
print("Exception - DateNotRecognized", data_set)
except Quandl.ErrorDownloading:
print("Exception - ErrorDownloading", data_set)
except Quandl.ParsingError:
print("Exception - ParsingError", data_set)
except:
print("Some other error occurred")
def main():
actually_query = True
query_format = "WIKI/%s"
sp500 = read_sp500()
for c in sp500:
query = query_format % c.stripped_ticker()
#print("query: %s" % query)
#continue
if actually_query:
'''
data = Quandl.get(query, collapse='daily', trim_start='2015-10-01', trim_end='2015-10-31', limit=31, authtoken='ZbyrpahXoQapzxxLR8Qe')
print(data.index)
sum = 0
for i,r in data.iterrows():
print(r['Volume'])
sum += r['Volume']
print("sum: %d\n" % sum)
'''
try:
volume_data = Quandl.get(query,
collapse='daily',
trim_start='2015-10-01',
trim_end='2015-10-31',
row=1,
limit=1,
sort_order="desc",
transformation="cumul",
authtoken='ZbyrpahXoQapzxxLR8Qe')
close_data = Quandl.get(query,
collapse='monthly',
trim_start='2015-09-01',
trim_end='2015-10-31',
row=1,
limit=1,
sort_order="desc",
transformation="rdiff",
authtoken='ZbyrpahXoQapzxxLR8Qe')
if 'Volume' in volume_data and len(volume_data['Volume']) > 0:
c.stock_volume = volume_data['Volume'][0]
else:
c.stock_volume = 'NaN'
if 'Close' in close_data and len(close_data['Close']) > 0:
c.stock_close = close_data['Close'][0]
else:
c.stock_close = '0.0'
except Quandl.DatasetNotFound as e:
c.stock_volume = 'DatasetNotFound'
c.stock_close = '0.0'
except Quandl.ErrorDownloading as e:
c.stock_volume = 'ErrorDownloading'
c.stock_close = '0.0'
print("%s" % (c))
def load_historical_data(engine, dataset, api_key, mode='normal'):
"""
Creates a new data table for the provided data set and loads historical data for each code into the table.
"""
update_code = sql.text(
'UPDATE ' + dataset + '_CODES '
'SET [Start Date] = :start, [End Date] = :end, [Last Updated] = :updated '
'WHERE Code = :code')
conn = engine.connect()
# retrieve the current code table
code_table = load_sql_data(engine, dataset + '_CODES', date_columns=['Start Date', 'End Date', 'Last Updated'])
# TESTING ONLY
code_table = code_table.iloc[:10, :]
# fetch the first code's data to create the data frame
data = Quandl.get(code_table['API Code'].iloc[0], rows=1, authtoken=api_key)
data = data.reset_index()
data = data.rename(columns=lambda x: x[0] + x[1:].lower())
data['Code'] = code_table['Code'].iloc[0]
data = data.iloc[0:0]
# iterate over each code and append the returned data
counter = 0
for index, row in code_table.iterrows():
code_data = Quandl.get(row['API Code'], authtoken=api_key)
code_data = code_data.reset_index()
code_data = code_data.rename(columns=lambda x: x[0] + x[1:].lower())
code_data['Code'] = row['Code']
data = pd.concat([data, code_data])
# update the code table
min_date = code_data['Date'].min().to_datetime()
max_date = code_data['Date'].max().to_datetime()
current_date = datetime.now()
conn.execute(update_code, start=min_date, end=max_date, updated=current_date, code=row['Code'])
counter += 1
if counter % 100 == 0:
print('Sleeping for one minute to avoid hitting API call limits...')
time.sleep(60)
# move the code column to the beginning
columns = data.columns.tolist()
columns = [columns[-1]] + columns[:-1]
data = data[columns]
save_sql_data(engine, data, dataset, exists='replace', index=False)
print(dataset + ' historical data loaded successfully.')
def __init__ (self, job_queue, trim_start, trim_end, calc_params, authtoken, fp):
self.job_queue = job_queue
self.trim_start = trim_start
self.trim_end = trim_end
self.calc_params = calc_params
self.authtoken = authtoken
self.index_data = ql.get("YAHOO/INDEX_SPY", column=6, trim_start=self.trim_start,
trim_end=self.trim_end, authtoken=self.authtoken)
self.index_data = self.index_data["Adjusted Close"]
self.rfr_data = ql.get("USTREASURY/YIELD", column=1, trim_start=self.trim_start,
trim_end=self.trim_end, authtoken=self.authtoken)
self.rfr_data = self.rfr_data["1 Mo"]*0.01
self.fp = fp
threading.Thread.__init__ (self)
def get(content):
if bool(content):
return Quandl.get(
content["dataCode"],
trim_start=content["startDate"],
trim_end=content["finishDate"],
collapse=content["timeRange"],
transformation="rdiff",
returns=content["dataType"],
) # Warning: Uses Internet Connection
else:
return Quandl.get(
"YAHOO/AAPL", trim_start="2001-01-01", trim_end="2010-01-01", collapse="annual", returns="pandas"
)
def get_market_value(stock='all', start='', end='', size=0):
filename = 'WIKI_tickers.csv'
if (stock == 'all'):
f = open(filename, 'r')
f.readline()
for line in f:
stock_value = line.replace('"', '').replace('\n', '').split(',', 1)
print "%-10s : %s" % (stock_value[0], stock_value[1])
else:
data = None
if (start == '' and end == ''): data = Quandl.get(stock)
else: data = Quandl.get(stock, trim_start=start, trim_end=end)
if (size == 0): print data
else: print data.head(size).to_string()
def get_market_value(stock='all', start='', end='', size=0):
filename = 'WIKI_tickers.csv'
if (stock == 'all'):
f = open(filename, 'r')
f.readline()
for line in f:
stock_value = line.replace('"','').replace('\n','').split(',', 1)
print "%-10s : %s" % (stock_value[0], stock_value[1])
else:
data = None
if (start == '' and end == ''): data = Quandl.get(stock)
else: data = Quandl.get(stock, trim_start=start, trim_end=end)
if (size == 0): print data
else: print data.head(size).to_string()
def test_api_calls(api_key):
"""
Test function with examples of calls using the Quandl API via python wrapper.
"""
data = Quandl.get('WIKI/AAPL', authtoken=api_key)
data_new = Quandl.get('WIKI/AAPL',
rows=5,
sort_order='desc',
authtoken=api_key)
data_range = Quandl.get('WIKI/AAPL',
trim_start='2015-01-01',
trim_end='2015-01-01',
authtoken=api_key)
data_multiple = Quandl.get(['WIKI/AAPL', 'WIKI/MSFT'], authtoken=api_key)
def get(content):
if bool(content):
return Quandl.get(
content['dataCode'],
trim_start=content['startDate'],
trim_end=content['finishDate'],
collapse=content['timeRange'],
transformation='rdiff',
returns=content['dataType']) # Warning: Uses Internet Connection
else:
return Quandl.get('YAHOO/AAPL',
trim_start='2001-01-01',
trim_end='2010-01-01',
collapse='annual',
returns='pandas')
def get_stock(self, ticker, start, end, drop=None, collapse="daily", transform="rdiff"):
"""
:param ticker: ticker
:param start: start-date
:param end: end-date
:param drop: columns in data-frame to drop
:param collapse: frequency of data
:param transform: rdiff = simple returns
"""
quandl_args = "GOOG/" + ticker
if drop is None:
drop = ["Open", "High", "Low", "Volume"]
hash_val = str(ticker) + "_" + str(start) + "_" + str(end)
try:
cached_data = pandas.read_csv("cache/" + str(hash_val))
cached_data = cached_data.set_index("Date")
return cached_data
except IOError:
try:
# print("Downloading", data_set)
# Otherwise download the data frame from scratch
if transform is not "None":
downloaded_data_frame = Quandl.get(quandl_args, authtoken=self.token, trim_start=start,
trim_end=end, collapse=collapse, transformation=transform)
else:
downloaded_data_frame = Quandl.get(quandl_args, authtoken=self.token, trim_start=start,
trim_end=end, collapse=collapse)
# Remove any unnecessary columns and rename the columns
# print downloaded_data_frame.columns
updated_column_labels = []
for column_label in downloaded_data_frame.columns:
if column_label in drop:
downloaded_data_frame = downloaded_data_frame.drop([column_label], axis=1)
else:
updated_column_labels.append(quandl_args + "_" + column_label)
downloaded_data_frame.columns = updated_column_labels
downloaded_data_frame.to_csv("cache/" + str(hash_val))
return downloaded_data_frame
except Quandl.DatasetNotFound:
print("Exception - DataSetNotFound", quandl_args)
except Quandl.CodeFormatError:
print("Exception - CallFormatError", quandl_args)
except Quandl.DateNotRecognized:
print("Exception - DateNotRecognized", quandl_args)
except Quandl.ErrorDownloading:
print("Exception - ErrorDownloading", quandl_args)
except Quandl.ParsingError:
print("Exception - ParsingError", quandl_args)
def grabCountryStats(self, indicators):
numToPlot = self.numToPlot
dne = dict()
f = open(indicators[0], 'r+')
for line in f:
dne[line.replace('\n', '')] = 0
for country in self.gdpLowToHigh:
if numToPlot <= 0:
break
if country in dne.keys():
pass
else:
#print countryStats[country]['name'] + '\n'
for wwdiIndicator in indicators:
try:
requestCode = 'WWDI/' + country + '_' + wwdiIndicator
data = q.get(requestCode, authtoken=quandlToken)
# pick the latest date, and first value
value = data.tail(1).iloc[0, 0]
countryStats[country][wwdiIndicator] = value
numToPlot -= 1
except q.DatasetNotFound as e:
print "Dataset not found for: " + self.countryStats[
country]['name']
f.write(country + '\n')
break
except IndexError as e:
print e
except q.ErrorDownloading as e:
print e
def draw(tick):
#import numpy as np
mydata = Quandl.get("WIKI/" + tick)
#markers_on = np.array(['2013-02-26','2015-01-26','2016-02-26', '2016-04-01'], dtype='datetime64')
#df3 = pd.DataFrame(markers_on)
#df4 = df3.set_index(0)
#df5 = df4.join(mydata,how='left')
#df6 = df5['Adj. Close']
#mynewdata = mydata.join(df6,how="left",lsuffix='_OG',rsuffix='_Mark')
#get trading start
def tradedetails(tradetype,tradevalue,minprice,maxprice,isofficer,ceo,cfo,isdir,is10,isother,stock):
hf = pd.read_html("http://openinsider.com/screener?fd=0&fdr=&td=0&tdr=&s="+ stock + "&o=&t="+ tradetype + "&minprice=" + str(minprice) + "&maxprice=" + str(maxprice) + "&v="+ str(tradevalue) +"&isofficer=" + str(isofficer) + "&isceo=" + str(ceo) + "&iscfo=" + str(cfo) + "&isdirector=" + str(isdir) + "&istenpercent=" + str(is10) + "&isother=" + str(isother) + "&sicMin=&sicMax=&sortcol=1&maxresults=1000")
return hf[5]
def convertdate(x):
return x[5:7] + "/" + x[8:10] + "/" + x[0:4]
def converttime(x):
return x[11:]
def convertnumber(x):
return x.replace("+","").replace("$","").replace(",","")
def cleandataframe(df):
df['Trade Date'] = df['Trade Date'].apply(convertdate)
df['Filing Time'] = df['Filing Date'].apply(converttime)
df['Filing Date'] = df['Filing Date'].apply(convertdate)
#df['Shares Traded'] = df['Shares Traded'].apply(convertnumber)
df['Value Traded'] = df['Value Traded'].apply(convertnumber)
#df['Shares Owned'] = df['Shares Owned'].apply(convertnumber)
return df
def cleanerdataframe(df):
df['Trade Date'] = df['Trade Date'].apply(convertdate)
df['Filing Time'] = df['Filing Date'].apply(converttime)
df['Filing Date'] = df['Filing Date'].apply(convertdate)
df['Shares Traded'] = df['Shares Traded'].apply(convertnumber)
df['Value Traded'] = df['Value Traded'].apply(convertnumber)
#df['Shares Owned'] = df['Shares Owned'].apply(convertnumber)
return df
detail = tradedetails("p",25000,"","",0,1,1,0,0,0,tick)
pd.to_datetime(detail['Trade Date'])
detail = detail.set_index('Trade Date')
newdetail = detail.join(mydata,how='left')
df6 = newdetail['Adj. Close']
mynewdata = mydata.join(df6,how="left",lsuffix='_OG',rsuffix='_Mark')
#get trading end
plt.plot(mynewdata['Adj. Close_OG'])
plt.plot(mynewdata['Adj. Close_Mark'],marker='o',color='r', markersize=11)
plt.show()
def get_futures_contract_dataset(exchange, contract, start=None, end=None,
auth_token=None):
'''
Synopsis
--------
Fetches dataset for the given futures contract from Quandl.
Parameters
----------
exchange: Name of futures exchange. String.
contract: Name of futures contract (e.g., 'SPZ2015'). String.
start: Optional start date for dataset. Ignores all data timestamped
before this date. Example: 2015-01-01. String.
end: Optional end date for dataset. Ignores all data timestamped
after this date. Example: 2016-01-01. String. Cannot be before
the start date.
auth_token: Quandl auth token. String.
Returns
-------
Pandas DataFrame
'''
template = '{exchange}/{contract}'
quandl_code = template.format(exchange=exchange, contract=contract)
dataset = Quandl.get(
quandl_code,
trim_start=start,
trim_end=end,
authtoken=auth_token)
# Quandl returns futures datasets as a pandas DataFrame with a
# "Date" column as its index; sets this as a proper column to
# make aggregating data easier
dataset = dataset.reset_index()
return dataset
def download_data_set(self, argument):
assert isinstance(argument, Argument)
data_frame = None
try:
data_set_name = argument.id
if argument.prefix is not None:
data_set_name = argument.prefix + data_set_name
data_frame = Quandl.get(data_set_name, authtoken=self.api_key,
trim_start=argument.start, trim_end=argument.end,
transformation=argument.transformation, collapse=argument.collapse)
assert isinstance(data_frame, pandas.DataFrame)
for d in argument.drop:
try:
data_frame = data_frame.drop(d, axis=1)
except:
continue
except Quandl.DatasetNotFound:
print("Data set not found")
except Quandl.ErrorDownloading:
print("Error downloading")
except Quandl.ParsingError:
print("Parsing error")
except Quandl.WrongFormat:
print("Wrong format")
except Quandl.CallLimitExceeded:
print("Call limit exceeded")
except Quandl.CodeFormatError:
print("Code format error")
except Quandl.MissingToken:
print("Missing token")
if data_frame is None:
raise Exception("Data Set Not Initialized", argument.id)
else:
return data_frame
def run(self):
while True:
qcode = "N/A"
try:
qcode = self.job_queue.get()
if qcode:
data = ql.get(qcode,
column=11,
trim_start=self.trim_start,
trim_end=self.trim_end,
authtoken=self.authtoken)
mpt = MPTStats(data["Adj. Close"], self.index_data,
self.rfr_data)
result = mpt.calculate(self.calc_params["calc_date"],\
freq=self.calc_params["freq"], span=self.calc_params["span"])
ticker = qcode.split("/")[1]
csv = self._to_csv(result)
with self._lock:
self.fp.write(ticker + "," +
str(self.calc_params["calc_date"]) +
"," + csv + "\n")
except Exception as e:
_logger.exception("Error %s for qcode %s" % (str(e), qcode))
finally:
self.job_queue.task_done()
def get_quandl_earnings(date, use_cache=True):
ls_spr = get_index_symbols(date, 'spr', use_cache).index.values
# Derive surprise dates for each equity
for i, s_equity in enumerate(ls_spr):
s_equity_path = make_dir(date) + '/' + s_equity + '.csv'
if os.path.isfile(s_equity_path):
continue
df_earnings = Quandl.get("ZES/"+s_equity.replace('.','_'), authtoken=quandl_token) # get surprise date from Quandl
ls_earnings_dates = df_earnings.index.values
# derive close price from Yahoo
while True:
try:
dt_start = ls_earnings_dates[0] - np.timedelta64(1, 'W')
break
except IndexError:
pass
dt_end = ls_earnings_dates[-1] + np.timedelta64(1, 'W')
df_close = web.DataReader(s_equity.replace('.','/'), 'yahoo', dt_start, dt_end)['Adj Close']
ls_close_dates = list(df_close.index)
# append before/after close into df_earnings
ls_annouce_hour = []
ls_prev_close = []
ls_next_close = []
for dt_date in ls_earnings_dates:
i_annouce_hour = int(str(dt_date)[11:13])
ls_annouce_hour.append(i_annouce_hour)
while True:
try:
i_date = ls_close_dates.index(dt_date)
break
except ValueError:
if i_annouce_hour > 16:
dt_date -= np.timedelta64(1,'D')
elif i_annouce_hour < 9:
dt_date += np.timedelta64(1,'D')
else:
raise ValueError('Earning Annouce Hour between 9am and 4pm')
if i_annouce_hour > 16:
ls_prev_close.append(df_close[i_date])
ls_next_close.append(df_close[i_date+1])
elif i_annouce_hour < 9:
ls_prev_close.append(df_close[i_date-1])
ls_next_close.append(df_close[i_date])
else:
raise ValueError('Earning Annouce Hour between 9am and 4pm')
df_earnings['ANNOUCE_HOUR'] = ls_annouce_hour
df_earnings['BEFORE_CLOSE'] = ls_prev_close
df_earnings['AFTER_CLOSE'] = ls_next_close
# save into csv
df_earnings.to_csv(s_equity_path, date_format='%Y%m%d')
def risk_free(start, end, date_index):
# Get CDI Daily Rate
risk_free = Quandl.get("BCB/12", trim_start=start, trim_end=end)
risk_free.name = 'risk_free'
risk_free.rename(columns={'Value': 'cdi'}, inplace=True)
return risk_free.ix[date_index, :].dropna()
def put(self):
stock_sticker = "WIKI/" + request.form['stocksticker']
stockdata = Quandl.get(stock_sticker,
returns="numpy",
trim_start="2015-01-01",
trim_end="2015-01-31")
return {"stockdata": list(stockdata.Close)}
def get_daily_historic_data_quandl(**kwargs):
ticker = kwargs["ticker"]
quandl_database_4ticker = get_quandl_database_4ticker(ticker)
quandl_input = {"authtoken": "zwBtPkKDycmg5jmYvK_s"}
if "date_from" in kwargs.keys():
date_from_string = cu.convert_datestring_format(
{"date_string": str(kwargs["date_from"]), "format_from": "yyyymmdd", "format_to": "yyyy-mm-dd"}
)
quandl_input["trim_start"] = date_from_string
if "date_to" in kwargs.keys():
date_to_string = cu.convert_datestring_format(
{"date_string": str(kwargs["date_to"]), "format_from": "yyyymmdd", "format_to": "yyyy-mm-dd"}
)
quandl_input["trim_end"] = date_to_string
try:
data_out = qndl.get(quandl_database_4ticker + "/" + ticker, **quandl_input)
success = True
except:
print("Error Loading " + quandl_database_4ticker + "/" + ticker + ": " + str(sys.exc_info()[0]))
success = False
data_out = pd.DataFrame()
new_column_names = ["Open Interest" if x == "Prev. Day Open Interest" else x for x in data_out.columns]
data_out.columns = new_column_names
return {"success": success, "data_out": data_out}
def load_quandl_prices(dataset, start, transform='rdiff'):
cache_file = '../data/prices-cache.csv'
if os.path.exists(cache_file):
print 'Loading prices from cache'
return pd.read_csv(cache_file, index_col=[0, 1], parse_dates=True)
else:
prices = pd.DataFrame()
quandl_auth = 'pdRRzNygCjs_YY5Y2MVe'
for index, row in dataset.iterrows():
print 'Downloading prices for', row['Ticker']
all_price_data = Quandl.get(row['Code'],
trim_start=start,
transformation=transform,
authtoken=quandl_auth)
close_price_data = all_price_data[['Close']]
close_price_data['Ticker'] = row['Ticker']
close_price_data['CloseClass'] = 'Neutral'
close_price_data.loc[close_price_data['Close'] > 0,
'CloseClass'] = 'Gain'
close_price_data.loc[close_price_data['Close'] < 0,
'CloseClass'] = 'Loss'
prices = prices.append(close_price_data)
prices.index = pd.MultiIndex.from_arrays(
[prices.index, prices['Ticker']])
prices.drop('Ticker', axis=1, inplace=True)
prices.to_csv(cache_file)
return prices
def get_index(self, index='XU030', price_type='Close',
price_frequency='daily'):
px = Quandl.get("GOOG/INDEXIST_%s" % index,
collapse=price_frequency, trim_start=self.start_date,
trim_end=self.end_date,
authtoken="Vbxts9YuGDArmJJbie4e")
return px[price_type]
def gdp_data():
df = Quandl.get("BCB/4385", trim_start="1975-01-01", authtoken=api_key)
df["Value"] = (df["Value"] - df["Value"][0]) / df["Value"][0] * 100.0
df = df.resample('M')
df.rename(columns={'Value': 'GDP'}, inplace=True)
df = df['GDP']
return df
def short_interest(x): x = x.capitalize() symbol = 'SI/'+ x + '_SI' sym = q.get(symbol, authtoken = auth) if "Days To Cover" in sym.columns: dtc = "Days To Cover" sint = sym[dtc] else: dtc = "Days to Cover" sint = sym[dtc] sint = sint[-24:] median = sint.median() recent = sint[-1] avg = 100*(recent-median)/median avg = round(avg,2) if avg > 0: avg = str(avg) return 'The short interest in '+ x.upper() + " rose " + avg +'%' else: avg = str(avg) return 'The short interest in '+ x.upper() + " fell " + avg + '%' std = sint.std() cstd = (recent - median)/std cstd = abs(cstd) cstd = round(cstd,2) cstd = str(cstd) print "This is a " +cstd + " standard deviation move"
def getStockPrices(ticker, frequency="monthly", update=False):
"""
Gets the closing prices for a given stock ticker at a given frequency
:param ticker: Name of the company's ticker
:param frequency: Frequency of returned time series. See Quandl.get()'s collapse param.
:param update: Always updates instead of using cache if true
:return: Pandas dataframe representing time series of ticker's closing prices, -1 for
connection errors
"""
name = ticker + "_" + frequency # Name of data in cache
prices = None
# If there is no cached version of the pickle, or update flag is on, download price data and cache it
if update or not util.pickleExists(name):
try:
prices = Quandl.get(STOCK_DATA_SOURCE + ticker,
collapse=frequency,
authtoken="xx_T2u2fsQ_MjyZjTb6E")
util.savePickle(prices, name)
# Catch various connection errors
except:
return -1
# Otherwise, use most recent cache entry
else:
prices = util.getMostRecentPickle(name)
# Return closing prices
return prices.get("Close")
def quandl_download(ds_code, ds_path):
"""
Executes Quandl API call for downloading whole dataset and saves it to given path.
"""
print("Downloading dataset: {0}".format(ds_code))
ds_data = Quandl.get(ds_code, authtoken=auth_token)
ds_data.to_csv(ds_path)
def getDatasets(self):
print "In get datasets"
datasets = Quandl.search(query=self.symbol,
source="SEC",
prints=False,
authtoken=self.key)
self.rawData = datasets
def get_stock_data(symbol, start_date=None, end_date=None, db_code="WIKI"):
"""
Get OHLC stock data from Quandl for a single stock
:param symbol: string
:param start_date: datetime
:param end_date: datetime
:param db_code: Quandl database code
:return: DataFrame of stock data from start_date to end_date
"""
if start_date is None:
start_date = dt(year=1990, month=1, day=1)
if end_date is None:
end_date = dt.today()
if start_date is not None and end_date is not None:
assert start_date < end_date, "Start date is later than end date."
log.info("Loading symbol: {}".format(symbol))
quandl_code = db_code + "/" + symbol
symbol_data = qd.get(quandl_code,
returns="pandas",
trim_start=start_date,
trim_end=end_date)
return symbol_data
def web_download(contract, start, end):
df = Quandl.get(contract,
trim_start=start,
trim_end=end,
returns="pandas",
authtoken=simple.get_quandl_auth())
return df
def Pivots(symb,symbec='STK',eday=None):
if eday== None:
eday = datetime.now(timezone('US/Eastern'))
bday=eday-timedelta(days=10)
symbDict={'INDU':'^DJI','COMP':'^IXIC','SPX':'^GSPC','RUT':'^RUT'}
if symb in symbDict.keys():
symb=symbDict[symb]
if symb=='^DJI':
Data = Quandl.get("YAHOO/INDEX_DJI", authtoken='977FGLXLZ1wsdKt8DgUH',
trim_start="%d-%02d-%02d" %(bday.year,bday.month,bday.day),
trim_end="%d-%02d-%02d" %(eday.year,eday.month,eday.day))[0:1]
H=Data['High'].values[-1]
L=Data['Low'].values[-1]
C=Data['Close'].values[-1]
else:
Data = yf.getHistoricData(symb,bday.timetuple()[0:3],eday.timetuple()[0:3])[['high','low','close','adj_close']] # get data from yahoo finance
H=Data['high'].values[-1]
L=Data['low'].values[-1]
C=Data['close'].values[-1]
P = (H + L + C) / 3
#P = (H + L + C + C) / 4
#P = (H + L + O + O) / 4
R1 = 2*P-L
S1 = 2*P-H
R2 = P+(H-L)
S2 = P-(H-L)
R3 = R1+(H-L)
S3 = S1-(H-L)
return [round(R3,1),round(R2,1),round(R1,1),round(P,1),round(S1,1),round(S2,1),round(S3,1)]
def fetch_quandl(self, code, **kwargs):
'''
Quandl entry point in datafeed object
_____________________________________
Parameters
code: str
quandl data code
kwargs: dict
keyword args passed to quandl call
_____________________________________
Return:
data: pandas.dataframe or numpy array
returned from quandl call
'''
log.debug('Fetching QuanDL data (%s)' % code)
# This way you can use your credentials even if
# you didn't provide them to the constructor
if 'authtoken' in kwargs:
self.quandl_key = kwargs.pop('authtoken')
# Harmonization: Quandl call start_date trim_start
if 'start_date' in kwargs:
kwargs['trim_start'] = kwargs.pop('start_date')
if 'end_date' in kwargs:
kwargs['trim_end'] = kwargs.pop('end_date')
try:
data = Quandl.get(code, authtoken=self.quandl_key, **kwargs)
data.index = data.index.tz_localize(pytz.utc)
except:
log.error('** Fetching %s from Quandl' % code)
data = pd.DataFrame()
return data
def download_time_series(self,
vendor_ticker,
pretty_ticker,
start_date,
source,
csv_file=None):
if source == 'Quandl':
import Quandl
# Quandl requires API key for large number of daily downloads
# https://www.quandl.com/help/api
spot = Quandl.get(
vendor_ticker) # Bank of England's database on Quandl
spot = pandas.DataFrame(data=spot['Value'], index=spot.index)
spot.columns = [pretty_ticker]
elif source == 'Bloomberg':
from bbg_com import HistoricalDataRequest
req = HistoricalDataRequest([vendor_ticker], ['PX_LAST'],
start=start_date)
req.execute()
spot = req.response_as_single()
spot.columns = [pretty_ticker]
elif source == 'CSV':
dateparse = lambda x: pandas.datetime.strptime(x, '%Y-%m-%d')
# in case you want to use a source other than Bloomberg/Quandl
spot = pandas.read_csv(csv_file,
index_col=0,
parse_dates=0,
date_parser=dateparse)
return spot
def load(*args, **kwargs):
"""Load data from Quandl into a dataframe, modify column names and
check for non-numeric values."""
# Grab the Quandl token
# token = os.environ.get('QUANDL_TOKEN')
# if token is None:
token = raw_input("Enter Quandl token: ")
ticker = raw_input("Enter Quandl ticker symbol (or hit Enter for default of YAHOO/INDEX_GSPC): ")
if len(ticker) < 1:
ticker = 'YAHOO/INDEX_GSPC'
print(ticker)
start_date = raw_input("Enter start date as YYYY-MM-DD (or hit ENTER for default of 1990-01-01): ")
if len(start_date) < 1:
start_date = '1990-01-01'
print(start_date)
# Call Quandl module, trim input by default from 1990 forward
print('Pulling Quandl data...')
df = Quandl.get(ticker, authtoken=token, trim_start=start_date)
# Get the column labels
# old_columns = list(df.columns.values)
# Use the ticker symbol as our new prefix
# ticker_tag = ticker.split('_')[-1] + '_'
# Drop spaces and concatenate
# new_labels = [ticker_tag + i.replace(' ', '') for i in old_columns]
# Create a dictionary of old and new column labels
# new_columns = dict(zip(old_columns, new_labels))
# Rename the columns using our dictionary
# df = df.rename(columns=new_columns)
nulls = df[~df.applymap(np.isreal).all(1)]
# Check for non-numeric values
if len(nulls) > 0:
raise ValueError('Dataframe contains non-numeric values')
row_count = len(df)
print('%d rows loaded into dataframe.' % row_count)
return df
def store_ETF_data(self, tickers) :
settings = Settings()
db = settings.get_mongo_db('Quandl', local=True)
coll = db['ETF']
for t in tickers:
# find the ticker
dates = coll.find_one({'name':t}, sort=[('Date', -1)])
if (dates is None):
print 'max_date is none'
mdate = None
else :
mdate = pd.to_datetime(dates['Date']*1000*1000).strftime(format='%Y-%m-%d')
print 'downloadng {t}'.format(t=t)
df = Quandl.get(t, trim_start=mdate)
df['name'] = t
df = df.reset_index()
if mdate is None :
df_a = df
else :
df_a = df[df['Date'] > mdate]
if len(df_a) == 0:
print "data for {ticker} is already updated till {date}".format(ticker=t, date=mdate)
continue
records = json.loads(df_a.T.to_json()).values()
#print records
print "uploading {t}".format(t=t)
coll.insert_many(records)
def hit_quandl(symbol, start, end):
"""Gets adjusted close data for a stock."""
import os
quandl_token = os.environ['QUANDL_TOKEN']
price = Quandl.get("YAHOO/{}.6".format(symbol), trim_start=start,
trim_end=end, authtoken=quandl_token)
return price.rename(columns={'Adjusted Close': symbol})
def SMA(symb,intvs,symbec='STK',eday=None):
if eday==None:
eday = datetime.now(timezone('US/Eastern'))
bday=eday-timedelta(days=365)
symbDict={'INDU':'^DJI','COMP':'^IXIC','SPX':'^GSPC','RUT':'^RUT'}
if symb in symbDict.keys():
symb=symbDict[symb]
if symb=='^DJI':
Data = Quandl.get("YAHOO/INDEX_DJI", authtoken='977FGLXLZ1wsdKt8DgUH',
trim_start="%d-%02d-%02d" %(bday.year,bday.month,bday.day),
trim_end="%d-%02d-%02d" %(eday.year,eday.month,eday.day))
sums=list()
for n in intvs:
if len(Data.index)<n: #no enough data
sums.append(-1)
else:
sums.append(round(Data['Adjusted Close'][-n-1:-1].mean(),1))
else:
Data = yf.getHistoricData(symb,bday.timetuple()[0:3],eday.timetuple()[0:3])[['adj_close']] # get data from yahoo finance
print Data.tail()
sums=list()
for n in intvs:
if len(Data.index)<n: #no enough data
sums.append(-1)
else:
sums.append(round(Data['adj_close'][-n-1:-1].mean(),1))
return sums
def readData(self, lookupTicker, source, start, end):
'''Read the data - assumes start and end are datetime.date objects'''
try:
lookupTicker = str(lookupTicker)
if source == 'Quandl':
#use Quandl reader
start = str(start)
end = str(end)
data = Quandl.get(lookupTicker,
authtoken = self.quandlAuthToken,
trim_start = start,
trim_end= end)
else:
#use pandas.io DataReader
data = DataReader(lookupTicker, source , start, end)
data = data.reset_index()
logging.info("Read ticker {}".format(lookupTicker))
except:
logging.error("importData: Can't read ticker {}".format(lookupTicker))
raise
else:
return data
def all_dbmetafrmSRC(pglmt=10000, src="YALE", authtkn=AUTHTOKEN):
"""
all_dbmetafrmSRC = all_dbmetafrmSRC(pglmt=10000,src="YALE",authtkn=AUTHTOKEN)
all_dbmetafrmSRC, i.e. all database Metadata from a Source, returns all the "metadata", the information describing a databse, for all databases from a single source, specified by a string that is the Quandl code
INPUTS:
int pglmt: (default=10000), page numberS to search
str sre: (default="YALE", chosen as an example), the Quandl code for the SOURCE or "src" to search from
str authtkn: (default="AUTHTOKEN", that's globally declared at the beginning of the .py code), your authentication code with Quandl, you should find that under API-key in your Quandl account and copy and paste that into the code
"""
metalst = []
counter = 1
while (counter <= pglmt):
resp = Quandl.search(query="*",
source=src,
page=counter,
authtoken=authtkn)
if len(resp) != 0:
metalst = metalst + resp
counter += 1
else:
break
if counter > pglmt:
print "Counter limit, pglmt, was reached; try a higher pglmt, i.e. page limit \n"
return metalst
def get_leanhog_contract_data(years):
print("Loading contracts from Quandl web service....")
print(' loading years: {0}'.format(years))
recordList = []
columnList = []
columnRenameDict = {}
for year in years:
for month in all_months:
s = "CME/LN{0}{1}".format(month, year)
recordList.append(s)
t = "CME.LN{0}{1} - Settle".format(month, year)
tx = "LN{0}{1}".format(month, year)
# May contracts didn't start until 2008
if not ((month == 'K') and (year < 2008)):
columnList.append(t)
columnRenameDict.update({t: tx})
df = q.get(recordList, authtoken="dXzByDoZicZy-WFvPyTf")
# take out any of the columns that Quandl didn't return that were expected
# possibly a contract year that hasn't started
columnList = [x for x in columnList if x in df.columns]
df = df[columnList]
df.rename(columns=columnRenameDict, inplace=True)
return df
def load_prices_quandl(codes, start_date=None, end_date=None, field_selector='CLOSE'):
if not os.path.isdir(os.path.abspath(_CACHE_LOCATION)):
os.makedirs(os.path.abspath(_CACHE_LOCATION))
datasets_encoded = quote_plus('@'.join(codes))
cache_path = os.path.abspath(os.sep.join([_CACHE_LOCATION, datasets_encoded]))
if os.path.isfile(cache_path):
logging.info('reading datasets from local cache')
quandl_df = pandas.read_pickle(cache_path)
else:
quandl_df = Quandl.get(codes, authtoken=sensitive('quandl_token')).dropna()
quandl_df.to_pickle(cache_path)
filtered = quandl_df
if start_date is not None and end_date is not None:
filtered = quandl_df.ix[start_date:end_date]
elif start_date is not None and end_date is None:
filtered = quandl_df.ix[start_date::]
elif start_date is None and end_date is not None:
filtered = quandl_df.ix[::end_date]
selected = filtered
if field_selector is not None:
pattern = ' - ' + field_selector.upper()
selected = filtered.loc[:, filtered.columns.str.upper().str.endswith(pattern)]
return selected
def createChart(t,d):
ticker = t
trading_days = int(d)
try:
df = Quandl.get('WIKI/' + ticker, authtoken=q_key)[-trading_days:]
ptitle = ticker + ' Closing Price: last ' + str(trading_days) + ' trading days'
max_split = max(df['Split Ratio'])
if max_split > 1:
split_date = np.argmax(df['Split Ratio'])
df['Multiply'] = [split_date > d for d in df.index]
df['AdjClose'] = [df.Close[x]*(1 - (max_split - 1)/max_split*df.Multiply[x].sum()) for x in np.arange(len(df))]
df['Close'] = df['AdjClose']
output_file("stockplot.html", title='Close Plot')
p = figure(x_axis_type = 'datetime')
p.line(df.index, df['Close'], color='black')
p.title = ptitle
p.xaxis.axis_label = 'Date'
p.yaxis.axis_label = 'Price'
save(p)
st = df['Close'][0]
ed = df['Close'][-1]
cdgr = round((ed-st)/st*100.,2)
script, div = components(p)
p_dict = {'script':script, 'div':div, 'cdgr':cdgr}
except Quandl.Quandl.DatasetNotFound:
p_dict = {'script':None, 'div':None, 'cdgr':None}
return p_dict
def mortgage_30y():
df = Quandl.get('FMAC/MORTG', trim_start="1975-01-01", authtoken=api_key)
df['Value'] = (df['Value'] - df['Value'][0]) / df['Value'][0] * 100.0
df.columns = ['M30']
df = df.resample('D').mean()
df = df.resample('M').mean()
return df
def dl_quandl(query="USD", authtoken=None):
count = 0
for name, code in name_to_code(query=query, authtoken=authtoken).items():
curve = Quandl.get(code, authtoken=authtoken)
curve.to_csv(name + ".csv")
count += 1
print ("Got %d curves" % count)
def fetch_data(self, qcode, start_date, end_date, column=None,
transformation=None):
if not self._is_open:
self.open_cache()
import Quandl as ql
fetch = False
data = None
try:
if self._db.has_key(qcode):
cache = self._db[qcode]
if (cache[0]>start_date) or (cache[1]<end_date):
fetch = True
else:
data = cache[2]
else:
fetch = True
except Exception as e:
logging.debug("Error happened with db")
fetch = True
try:
if fetch:
trim_start = self._format_date( start_date )
trim_end = self._format_date(end_date)
data = ql.get(qcode, column=column, trim_start=trim_start,
trim_end=trim_end, authtoken=self._authtoken,
transformation=transformation)
if data is not None:
self._db[qcode] = (start_date, end_date, data)
except Exception as e:
logging.exception("Exception fetching %s : %s"%(qcode, str(e)))
#data = self._snap_to_interval(data,start_date, end_date)
return data.ix[start_date:end_date]
def download_datasets(collection):
"""
Downloads datasets of a given collection. New files are fetched in full,
existing files are appended only the new entries since last update.
"""
print("Downloading datasets for {0}".format(collection))
collection_path = os.path.join("collections/{}".format(collection))
collection_metadata = pandas.read_csv(os.path.join(collection_path, "metadata.csv"))
executor = concurrent.futures.ThreadPoolExecutor(max_workers=5)
# TODO: DUPLICATES
# Iterate over datasets in collection
for idx, row in collection_metadata.iterrows():
#print("Dataset {0} last updated at {1}".format(row[0], row[5]))
ds_code = row[0]
ds_name = ds_code.split("/")[1]
ds_path = os.path.join(collection_path, ds_name+".csv")
if not os.path.exists(ds_path):
executor.submit(quandl_download, ds_code, ds_path)
else:
ds_data = pandas.read_csv(ds_path)
last_update = pandas.Timestamp(ds_data.iloc[-1][0])
last_change = pandas.Timestamp(row[3])
#print("Last update {0}: {1}, last change: {2}".format(ds_name, last_update, last_change))
if last_change > last_update:
ds_diff = Quandl.get(ds_code, trim_start=last_update+pandas.DateOffset(days=1), authtoken=auth_token)
if not ds_diff.empty:
print("Updating dataset: {0}".format(ds_code))
ds_file = open(ds_path, "a")
ds_file.write(ds_diff.to_csv(header=False, index_names=False))
ds_file.close()
