/
marketdatagenerator.py
608 lines (471 loc) · 22.4 KB
/
marketdatagenerator.py
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__author__ = "saeedamen" # Saeed Amen
#
# Copyright 2016 Cuemacro
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on a "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#
# See the License for the specific language governing permissions and
# limitations under the License.
#
import copy
import datetime
import pandas as pd
from findatapy.market.ioengine import IOEngine
from findatapy.market.marketdatarequest import MarketDataRequest
from findatapy.timeseries import Filter, Calculations
from findatapy.util import DataConstants, LoggerManager, ConfigManager, \
SwimPool
constants = DataConstants()
class MarketDataGenerator(object):
"""Returns market data time series by directly calling market data sources.
At present it supports Bloomberg (bloomberg), Yahoo (yahoo),
Quandl (quandl), FRED (fred) etc. which are implemented in subclasses of
DataVendor class. This provides a common wrapper for all these
data sources.
"""
def __init__(self, data_vendor_dict={}):
self._config = ConfigManager().get_instance()
self._filter = Filter()
self._calculations = Calculations()
self._io_engine = IOEngine()
self._intraday_code = -1
self._days_expired_intraday_contract_download = -1
self._data_vendor_dict = data_vendor_dict
return
def set_intraday_code(self, code):
self._intraday_code = code
def get_data_vendor(self, md_request):
"""Loads appropriate data vendor class
Parameters
----------
md_request : MarketDataRequest
the data_source to use "bloomberg", "quandl", "yahoo", "google",
"fred" etc. we can also have forms like "bloomberg-boe" separated
by hyphens
Returns
-------
DataVendor
"""
logger = LoggerManager().getLogger(__name__)
data_source = md_request.data_source
data_engine = md_request.data_engine
# Special case for files (csv, h5, parquet or zip)
if ".csv" in str(data_source) or ".h5" in str(data_source) or \
".parquet" in str(data_source) or ".zip" in str(data_source) \
or data_engine is not None:
from findatapy.market.datavendorweb import DataVendorFlatFile
data_vendor = DataVendorFlatFile()
else:
try:
data_source = data_source.split("-")[0]
except:
logger.error("Was data data_source specified?")
return None
if data_source == "bloomberg":
try:
from findatapy.market.datavendorbbg import \
DataVendorBBGOpen
data_vendor = DataVendorBBGOpen()
except:
logger.warn("Bloomberg needs to be installed")
elif data_source == "quandl":
from findatapy.market.datavendorweb import DataVendorQuandl
data_vendor = DataVendorQuandl()
elif data_source == "eikon":
from findatapy.market.datavendorweb import DataVendorEikon
data_vendor = DataVendorEikon()
elif data_source == "ons":
from findatapy.market.datavendorweb import DataVendorONS
data_vendor = DataVendorONS()
elif data_source == "boe":
from findatapy.market.datavendorweb import DataVendorBOE
data_vendor = DataVendorBOE()
elif data_source == "dukascopy":
from findatapy.market.datavendorweb import DataVendorDukasCopy
data_vendor = DataVendorDukasCopy()
elif data_source == "fxcm":
from findatapy.market.datavendorweb import DataVendorFXCM
data_vendor = DataVendorFXCM()
elif data_source == "alfred":
from findatapy.market.datavendorweb import DataVendorALFRED
data_vendor = DataVendorALFRED()
elif data_source == "yahoo":
from findatapy.market.datavendorweb import DataVendorYahoo
data_vendor = DataVendorYahoo()
elif data_source in ["google", "fred", "oecd", "eurostat",
"edgar-index"]:
from findatapy.market.datavendorweb import DataVendorPandasWeb
data_vendor = DataVendorPandasWeb()
elif data_source == "bitcoincharts":
from findatapy.market.datavendorweb import \
DataVendorBitcoincharts
data_vendor = DataVendorBitcoincharts()
elif data_source == "poloniex":
from findatapy.market.datavendorweb import DataVendorPoloniex
data_vendor = DataVendorPoloniex()
elif data_source == "binance":
from findatapy.market.datavendorweb import DataVendorBinance
data_vendor = DataVendorBinance()
elif data_source == "bitfinex":
from findatapy.market.datavendorweb import DataVendorBitfinex
data_vendor = DataVendorBitfinex()
elif data_source == "gdax":
from findatapy.market.datavendorweb import DataVendorGdax
data_vendor = DataVendorGdax()
elif data_source == "kraken":
from findatapy.market.datavendorweb import DataVendorKraken
data_vendor = DataVendorKraken()
elif data_source == "bitmex":
from findatapy.market.datavendorweb import DataVendorBitmex
data_vendor = DataVendorBitmex()
elif data_source == "alphavantage":
from findatapy.market.datavendorweb import \
DataVendorAlphaVantage
data_vendor = DataVendorAlphaVantage()
elif data_source == "huobi":
from findatapy.market.datavendorweb import DataVendorHuobi
data_vendor = DataVendorHuobi()
elif data_source in self._data_vendor_dict:
data_vendor = self._data_vendor_dict[data_source]
elif data_source in md_request.data_vendor_custom:
data_vendor = md_request.data_vendor_custom[data_source]
else:
logger.warn(str(data_source) +
" is an unrecognized data source")
return data_vendor
def fetch_market_data(self, md_request):
"""Loads time series from specified data provider
Parameters
----------
md_request : MarketDataRequest
contains various properties describing time series to fetched,
including ticker, start & finish date etc.
Returns
-------
pandas.DataFrame
"""
logger = LoggerManager().getLogger(__name__)
# data_vendor = self.get_data_vendor(md_request.data_source)
# Check if tickers have been specified (if not load all of them for a
# category)
# also handle single tickers/list tickers
create_tickers = False
if md_request.vendor_tickers is not None \
and md_request.tickers is None:
md_request.tickers = md_request.vendor_tickers
tickers = md_request.tickers
if tickers is None:
create_tickers = True
elif isinstance(tickers, str):
if tickers == "": create_tickers = True
elif isinstance(tickers, list):
if tickers == []: create_tickers = True
if create_tickers:
md_request.tickers = ConfigManager().get_instance()\
.get_tickers_list_for_category(
md_request.category, md_request.data_source,
md_request.freq, md_request.cut)
if md_request.pretransformation is not None:
df_tickers = ConfigManager().get_instance()\
.get_dataframe_tickers()
df_tickers = df_tickers[
(df_tickers["category"] == md_request.category) &
(df_tickers["data_source"] == md_request.data_source) &
(df_tickers["freq"] == md_request.freq) &
(df_tickers["cut"] == md_request.cut)]
if "pretransformation" in df_tickers.columns:
md_request.pretransformation = \
df_tickers["pretransformation"].tolist()
# intraday or tick: only one ticker per cache file
if md_request.freq in ["intraday", "tick", "second", "hour",
"minute"]:
df_agg = self.download_intraday_tick(md_request)
# Daily: multiple tickers per cache file - assume we make one API call
# to vendor library
else:
df_agg = self.download_daily(md_request)
if "internet_load" in md_request.cache_algo:
logger.debug("Internet loading.. ")
if md_request.cache_algo == "cache_algo":
logger.debug(
"Only caching data in memory, do not return any time series.")
return
# Only return time series if specified in the algo
if "return" in md_request.cache_algo:
# Special case for events/events-dt which is not indexed like other
# tables (also same for downloading futures contracts dates)
if md_request.category is not None:
if "events" in md_request.category:
return df_agg
# Pad columns a second time (is this necessary to do here again?)
# TODO only do this for not daily data?
try:
if df_agg is not None:
df_agg = self._filter.filter_time_series(
md_request, df_agg, pad_columns=True)
df_agg = df_agg.dropna(how="all")
# Resample data using pandas if specified in the
# MarketDataRequest
if md_request.resample is not None:
if "last" in md_request.resample_how:
df_agg = df_agg.resample(
md_request.resample).last()
elif "first" in md_request.resample_how:
df_agg = df_agg.resample(
md_request.resample).first()
if "dropna" in md_request.resample_how:
df_agg = df_agg.dropna(how="all")
else:
logger.warn("No data returned for " + str(
md_request.tickers))
return df_agg
except Exception as e:
if df_agg is not None:
return df_agg
import traceback
logger.warn(
"No data returned for "
+ str(md_request.tickers) + ", " + str(e))
return None
def create_time_series_hash_key(self, md_request, ticker=None):
"""Creates a hash key for retrieving the time series
Parameters
----------
md_request : MarketDataRequest
contains various properties describing time series to fetched,
including ticker, start & finish date etc.
Returns
-------
str
"""
if (isinstance(ticker, list)):
ticker = ticker[0]
return self.create_cache_file_name(
MarketDataRequest().create_category_key(
md_request=md_request, ticker=ticker))
def download_intraday_tick(self, md_request):
"""Loads intraday time series from specified data provider
Parameters
----------
md_request : MarketDataRequest
contains various properties describing time series to fetched,
including ticker, start & finish date etc.
Returns
-------
pandas.DataFrame
"""
df_agg = None
calcuations = Calculations()
ticker_cycle = 0
df_group = []
# Single threaded version
# handle intraday ticker calls separately one by one
if len(md_request.tickers) == 1 or constants.market_thread_no[
"other"] == 1:
for ticker in md_request.tickers:
md_request_single = copy.copy(md_request)
md_request_single.tickers = ticker
if md_request.vendor_tickers is not None:
md_request_single.vendor_tickers = [
md_request.vendor_tickers[ticker_cycle]]
ticker_cycle = ticker_cycle + 1
df_single = self.fetch_single_time_series(
md_request)
# If the vendor doesn"t provide any data, don"t attempt to append
if df_single is not None:
if df_single.empty == False:
df_single.index.name = "Date"
df_single = df_single.astype("float32")
df_group.append(df_single)
# If you call for returning multiple tickers, be careful with
# memory considerations!
if df_group is not None:
df_agg = calcuations.join(df_group, how="outer")
return df_agg
else:
md_request_list = []
# Create a list of MarketDataRequests
for ticker in md_request.tickers:
md_request_single = copy.copy(md_request)
md_request_single.tickers = ticker
if md_request.vendor_tickers is not None:
md_request_single.vendor_tickers = [
md_request.vendor_tickers[ticker_cycle]]
ticker_cycle = ticker_cycle + 1
md_request_list.append(md_request_single)
return self.fetch_group_time_series(md_request_list)
def fetch_single_time_series(self, md_request):
md_request = MarketDataRequest(md_request=md_request)
# Only includes those tickers have not expired yet!
start_date = pd.Timestamp(md_request.start_date).date()
current_date = pd.Timestamp(datetime.datetime.utcnow().date())
tickers = md_request.tickers
vendor_tickers = md_request.vendor_tickers
expiry_date = pd.Timestamp(md_request.expiry_date)
config = ConfigManager().get_instance()
# In many cases no expiry is defined so skip them
for i in range(0, len(tickers)):
try:
expiry_date = config.get_expiry_for_ticker(
md_request.data_source, tickers[i])
except:
pass
if expiry_date is not None:
expiry_date = pd.Timestamp(expiry_date)
if not (pd.isna(expiry_date)):
# Use pandas Timestamp, a bit more robust with weird dates
# (can fail if comparing date vs datetime)
# if the expiry is before the start date of our download
# don"t bother downloading this ticker
if expiry_date < start_date:
tickers[i] = None
# Special case for futures-contracts which are intraday
# avoid downloading if the expiry date is very far in the
# past
# (we need this before there might be odd situations where
# we run on an expiry date, but still want to get
# data right till expiry time)
if md_request.category == "futures-contracts" \
and md_request.freq == "intraday" \
and self._days_expired_intraday_contract_download \
> 0:
if expiry_date + pd.Timedelta(
days=
self._days_expired_intraday_contract_download) \
< current_date:
tickers[i] = None
if vendor_tickers is not None and tickers[i] is None:
vendor_tickers[i] = None
md_request.tickers = [e for e in tickers if e != None]
if vendor_tickers is not None:
md_request.vendor_tickers = [e for e in vendor_tickers if
e != None]
df_single = None
if len(md_request.tickers) > 0:
df_single = self.get_data_vendor(
md_request).load_ticker(md_request)
if df_single is not None:
if df_single.empty == False:
df_single.index.name = "Date"
# Will fail for DataFrames which includes dates/strings
# eg. futures contract names
df_single = Calculations().convert_to_numeric_dataframe(
df_single)
if md_request.freq == "second":
df_single = df_single.resample("1s")
return df_single
def fetch_group_time_series(self, market_data_request_list):
logger = LoggerManager().getLogger(__name__)
df_agg = None
thread_no = constants.market_thread_no["other"]
if market_data_request_list[
0].data_source in constants.market_thread_no:
thread_no = constants.market_thread_no[
market_data_request_list[0].data_source]
if thread_no > 0:
pool = SwimPool().create_pool(
thread_technique=constants.market_thread_technique,
thread_no=thread_no)
# Open the market data downloads in their own threads and return
# the results
result = pool.map_async(self.fetch_single_time_series,
market_data_request_list)
df_group = result.get()
pool.close()
pool.join()
else:
df_group = []
for md_request in market_data_request_list:
df_group.append(
self.fetch_single_time_series(md_request))
# Collect together all the time series
if df_group is not None:
df_group = [i for i in df_group if i is not None]
if df_group is not None:
try:
df_agg = self._calculations.join(df_group,
how="outer")
# Force ordering to be the same!
# df_agg = df_agg[columns]
except Exception as e:
logger.warning(
"Possible overlap of columns? Have you specifed same "
"ticker several times: " + str(e))
return df_agg
def download_daily(self, md_request):
"""Loads daily time series from specified data provider
Parameters
----------
md_request : MarketDataRequest
contains various properties describing time series to fetched,
including ticker, start & finish date etc.
Returns
-------
pandas.DataFrame
"""
key = MarketDataRequest().create_category_key(
md_request=md_request)
is_key_overriden = False
for k in constants.override_multi_threading_for_categories:
if k in key:
is_key_overriden = True
break
# By default use other
thread_no = constants.market_thread_no["other"]
if str(md_request.data_source) in constants.market_thread_no:
thread_no = constants.market_thread_no[
md_request.data_source]
# Daily data does not include ticker in the key, as multiple tickers
# in the same file
if thread_no == 1 or ".csv" in str(md_request.data_source) or \
".h5" in str(
md_request.data_source) or ".parquet" in str(
md_request.data_source) \
or ".zip" in str(
md_request.data_source) or md_request.data_engine is not None:
# df_agg = data_vendor.load_ticker(md_request)
df_agg = self.fetch_single_time_series(md_request)
else:
md_request_list = []
# When trying your example "equitiesdata_example" I had a -1 result
# so it went out of the comming loop and I had errors in execution
group_size = max(
int(len(md_request.tickers) / thread_no - 1), 0)
if group_size == 0: group_size = 1
# Split up tickers into groups related to number of threads to call
for i in range(0, len(md_request.tickers), group_size):
md_request_single = copy.copy(md_request)
md_request_single.tickers = \
md_request.tickers[i:i + group_size]
if md_request.vendor_tickers is not None:
md_request_single.vendor_tickers = \
md_request.vendor_tickers[i:i + group_size]
if md_request.pretransformation is not None:
md_request_single.pretransformation = \
md_request.pretransformation[i:i + group_size]
md_request_list.append(md_request_single)
# Special case where we make smaller calls one after the other
if is_key_overriden:
df_list = []
for md in md_request_list:
df_list.append(self.fetch_single_time_series(md))
df_agg = self._calculations.join(df_list,
how="outer")
else:
df_agg = self.fetch_group_time_series(
md_request_list)
return df_agg
def refine_expiry_date(self, market_data_request):
# Expiry date
if market_data_request.expiry_date is None:
ConfigManager().get_instance().get_expiry_for_ticker(
market_data_request.data_source, market_data_request.ticker)
return market_data_request
def create_cache_file_name(self, filename):
return constants.folder_time_series_data + "/" + filename