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import os
import Quandl
import pandas
class QuandlInterface:
def __init__(self, api_key):
An interface for downloading data from Quandl
:param api_key: [YOUR API KEY] (taken from the .private.csv file)
self.api_key = api_key
def get_data_set(self, argument):
file_name = argument.to_string()
basepath = os.path.dirname(__file__)
path = os.path.abspath(os.path.join(basepath, os.pardir, "MarketData", file_name))
data_frame = pandas.read_csv(path)
data_frame = data_frame.set_index("Date")
return data_frame
data_frame = self.download_data_set(argument)
data_frame.to_csv(path, mode="w+")
return data_frame
def download_data_set(self, argument):
This method tries to fetch a data set from Quandl
:param argument: an argument object which contains the information to construct the request
:return: a pandas DataFrame containing the data
assert isinstance(argument, Argument)
data_frame = None
data_set_name =
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:
data_frame = data_frame.drop(d, axis=1)
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",
return data_frame
def get_data_sets(self, arguments):
This method just calls the get_data_set() method to download and join various data sets
:param arguments: a list of Argument objects
:return: a pandas DataFrame
# assert isinstance(arguments, [Argument])
combined_data_frame = None
for arg in arguments:
assert isinstance(arg, Argument)
arg_data_frame = self.get_data_set(arg)
new_columns = []
for i in range(len(arg_data_frame.columns)):
new_columns.append( + "_" + arg_data_frame.columns[i])
arg_data_frame.columns = new_columns
if combined_data_frame is None:
combined_data_frame = arg_data_frame
combined_data_frame = combined_data_frame.join(arg_data_frame)
combined_data_frame = combined_data_frame.dropna()
return combined_data_frame
class Argument:
def __init__(self, id, start, end, prefix=None, drop=None, rdiff="none", collapse="none"):
An Argument object which contains the information to construct a request to send to Quandl
:param id: the id of the data set
:param start: the start date
:param end: the end date
:param prefix: the database prefix
:param drop: the columns to drop from the dataframe
:param rdiff: the transformation to do (usually percentage change)
:param collapse: the frequency of data to download
""" = id
self.start = start
self.end = end
self.transformation = rdiff
self.collapse = collapse
self.prefix = prefix
# The default drop columns for Google Finance data
if drop is None:
drop = ["High", "Low", "Open", "Volume", "Adjusted Close", ""]
self.drop = drop
def to_string(self):
unique_id = "Cache"
unique_id += " id=" +
unique_id += " start=" + self.start
unique_id += " end=" + self.end
unique_id += " trans=" + self.transformation
unique_id += ".csv"
return unique_id.replace("\\", "-").replace("/", "-")