def update( self):
""" Grab most recent prices from on/offline and append them to
our exchange data structures.
"""
#######################################################
# -- SIMULATION MODE -- #
#######################################################
# simulation mode. pull most recent price from our logs and
# append if different
if self.live == False:
#######################################################
# -- REAL TIME SIMULATION MODE -- #
#######################################################
# if warp is false, we will pretend this is realtime and
# grab prices from our logs using our timedelta
if self.warp == False:
# calculate our timedelta from NOW!!
adjusted_t = datetime.datetime.today() - self.delta
# Get our last prices from the logs
last_gox , last_ltc , last_ltc_depth = self._offline_prices( adjusted_t)
# make sure we got a timeseries object back, otherwise we
# hit the end of the log
if( type(last_gox) != pd.Series or
type(last_ltc) != pd.Series or
type(last_ltc_depth) != pd.Series):
if self.verbose: print "[!]", "End of log."
return False
# we have values, so add them to each coin
else:
# give coins new price changes ... them bitches'll do the rest
self.gox.add( last_gox[0], last_gox.name)
# bid-ask avg for LTC only
ba = pl2.bid_ask( last_ltc_depth[0])
self.ltc.add( last_ltc[0], last_ltc.name, ba=ba)
return True
#######################################################
# -- FAST MODE -- #
#######################################################
# otherwise, we'll grab our next price from the index
else:
# r we about to do something stupid? (hit end of the f*****g log)
if self.logrange_n >= len(self.logrange):
if self.verbose: print "[!]", "End of log."
return False
# NO!
else:
# get our next date in our time index & grab the prices
t = self.logrange[self.logrange_n]
if self._debug:
print "\n_update"
print "t:", t
print "logrange:", self.logrange_n
last_gox, last_ltc, last_ltc_depth = self._offline_prices( t)
# get LTC market data (bid ask)
ba = pl2.bid_ask( last_ltc_depth[0])
# upd8 fuk'n coinz
if self._debug:
print "\n_update"
print "\nltc"
print "last_ltc:", last_ltc[0], last_ltc.name
print "ba:", ba
self.ltc.add( last_ltc[0], last_ltc.name, ba=ba)
if self._debug:
print "\ngox"
print "last_gox:", last_gox[0], last_gox.name
self.gox.add( last_gox[0], last_gox.name)
# increment for the next f*****g time
self.logrange_n += 1
return True
def update(self):
""" Grab most recent prices from on/offline and append them to
our exchange data structures.
"""
#######################################################
# -- SIMULATION MODE -- #
#######################################################
# simulation mode. pull most recent price from our logs and
# append if different
if self.live == False:
#######################################################
# -- REAL TIME SIMULATION MODE -- #
#######################################################
# if warp is false, we will pretend this is realtime and
# grab prices from our logs using our timedelta
if self.warp == False:
# calculate our timedelta from NOW!!
adjusted_t = datetime.datetime.today() - self.delta
# Get our last prices from the logs
last_gox, last_ltc, last_ltc_depth = self._offline_prices(
adjusted_t)
# make sure we got a timeseries object back, otherwise we
# hit the end of the log
if (type(last_gox) != pd.Series or type(last_ltc) != pd.Series
or type(last_ltc_depth) != pd.Series):
if self.verbose: print "[!]", "End of log."
return False
# we have values, so add them to each coin
else:
# give coins new price changes ... them bitches'll do the rest
self.gox.add(last_gox[0], last_gox.name)
# bid-ask avg for LTC only
ba = pl2.bid_ask(last_ltc_depth[0])
self.ltc.add(last_ltc[0], last_ltc.name, ba=ba)
return True
#######################################################
# -- FAST MODE -- #
#######################################################
# otherwise, we'll grab our next price from the index
else:
# r we about to do something stupid? (hit end of the f*****g log)
if self.logrange_n >= len(self.logrange):
if self.verbose: print "[!]", "End of log."
return False
# NO!
else:
# get our next date in our time index & grab the prices
t = self.logrange[self.logrange_n]
if self._debug:
print "\n_update"
print "t:", t
print "logrange:", self.logrange_n
last_gox, last_ltc, last_ltc_depth = self._offline_prices(
t)
# get LTC market data (bid ask)
ba = pl2.bid_ask(last_ltc_depth[0])
# upd8 fuk'n coinz
if self._debug:
print "\n_update"
print "\nltc"
print "last_ltc:", last_ltc[0], last_ltc.name
print "ba:", ba
self.ltc.add(last_ltc[0], last_ltc.name, ba=ba)
if self._debug:
print "\ngox"
print "last_gox:", last_gox[0], last_gox.name
self.gox.add(last_gox[0], last_gox.name)
# increment for the next f*****g time
self.logrange_n += 1
return True
def __init__( self, **kwargs):
""" Set up our data structures and determine whether we're in
live or simulated mode.
time_str : (default "5min") time-frame to analyze on ... this
controls the length of each "bar" or period, can be
any pandas-recognized string, (10s, 10min, 1h, 1d, etc)
live : live or simulated mode (whether or not to read from
filename or from the web), defaults to False (simulated)
filename : name of log file to read in simulated mode ... interpreted
as ./logs/filename ... file must be in this dir
warp : whether or not to use our timedelta or just next value
for each update() ... so we can do all calculations
as fast as possible, defaults to False ("realtime")
debug : whether or not to spit out debugging info
sample_secs : if in warp-mode, N-seconds to sample on (the shorter
N, the more often we are "checking" the price and
the more iterations it will take to complete a series)
instant : (default False) Setting this to true will make Data
send the lastprice series to the Coins to calculate all
in faster, one-pass mode
ltc_opts : dict structure on what to do with LTC data ... see coin for
options from kwargs (default is same as GOX...)
Here's an example of a fully loaded options dict
{ "debug": False,
"relative": False,
"calc_rolling": False,
"rolling": { self.time_str : { 5: pd.DataFrame(),
25: pd.DataFrame(),
50: pd.DataFrame() } },
"calc_mid": False,
"calc_ohlc": True,
"ohlc": { self.time_str : pd.DataFrame() },
"calc_indicators": True,
"indicators":{ "RSI" : { "data": pd.DataFrame(), "n":14 },
"ROC" : { "data": pd.DataFrame(), "n":20 },
"AMA" : { "data": pd.DataFrame(), "n":10, "fn":2.5, "sn":30 },
"CCI" : { "data": pd.DataFrame(), "n":20 },
"FRAMA": { "data": pd.DataFrame(), "n":10 },
"RVI2" : { "data": pd.DataFrame(), "n":14, "s":10 },
"MACD" : { "data": pd.DataFrame(), "f":12, "s":26, "m":9 },
"ADX" : { "data": pd.DataFrame(), "n":14 },
"ELI" : { "data": pd.DataFrame(), "n":14 },
"TMI" : { "data": pd.DataFrame(), "nb":10, "nf":5} }
"calc_std": True,
"std": { 10: pd.DataFrame(), 50: pd.DataFrame(), 100: pd.DataFrame() },
"calc_crt": True,
"crt": { 1: pd.DataFrame(), 2: pd.DataFrame(),
3: pd.DataFrame(), 5: pd.DataFrame(),
8: pd.DataFrame() },
"instant": False,
"time_str": self.time_str }
gox_opts : dict structure on what to do with GOX BTC data ... see coin for
options from kwargs (default: everything disabled but OHLC ... )
{ "debug": False,
"relative": False,
"calc_rolling": False,
"rolling": False,
"calc_mid": False,
"calc_ohlc": True,
"ohlc": { self.time_str : pd.DataFrame() },
"calc_indicators": False,
"calc_std": False,
"std": False,
"calc_crt": False,
"crt": False,
"instant": False,
"time_str": self.time_str }
pickled_data : (default False) if this is set to a data structure,
from pickle'd pandas csv data structure, it'll take
it from here instead of from disk. Faster on multiple
iterations.
verbose : (default False) whether or not to print out shit
"""
self.live = kwargs.get("live", False)
self.filename = kwargs.get("filename", "test.csv")
self.warp = kwargs.get( "warp", True)
self._debug = kwargs.get( "debug", False)
self.sample_secs = kwargs.get( "sample_secs", 5)
self.instant = kwargs.get( "instant", False)
self.time_str = kwargs.get( "time_str", "5min")
self.verbose = kwargs.get( "verbose", False)
# default LTC options
def_ltc = { "debug": False,
"relative": False,
"calc_rolling": False,
"rolling": False,
"calc_mid": False,
"calc_ohlc": True,
"ohlc": { self.time_str : pd.DataFrame() },
"calc_indicators": False,
"indicators": False,
"calc_std": False,
"std": False,
"calc_crt": False,
"crt": False,
"instant": False,
"time_str": self.time_str }
self.ltc_opts = kwargs.get( "ltc_opts", def_ltc)
# default gox options
def_gox = { "debug": False,
"relative": False,
"calc_rolling": False,
"rolling": False,
"calc_mid": False,
"calc_ohlc": True,
"ohlc": { self.time_str : pd.DataFrame() },
"calc_indicators": False,
"indicators": False,
"calc_std": False,
"std": False,
"calc_crt": False,
"crt": False,
"instant": False,
"time_str": self.time_str }
self.gox_opts = kwargs.get( "gox_opts", def_gox)
self.pickled_data = kwargs.get( "pickled_data", False)
if self.verbose:
print "[*]", "Online" if self.live else "Offline", "mode initiated"
print "[*]", "Simulated" if not self.warp else "Speed", "mode initiated"
# if we're running simulated, set up price logs so we can query them
# in realtime as if they were actual price changes
if self.live == False:
# did we supply a pre-parsed pandas CSV data struct?
if self.pickled_data != False:
if self.verbose:
print "[*]", "Loading supplied pickle!"
data = self.pickled_data
# nope ... load from disk!
else:
# loading from CSV takes a long time, lets prepare a pickle of the
# loaded CSV if we haven't already done so, if we have then load it
filename_pick = os.path.realpath( os.path.join( "logs", self.filename+".pickle"))
if os.path.exists( filename_pick):
if self.verbose:
print "[*]", "Loading csv pickle from %s" % filename_pick
f = open( filename_pick, "rb")
data = cPickle.load( f)
f.close()
else:
filename_csv = os.path.realpath( os.path.join( "logs", self.filename))
if self.verbose: print "[*] Loading %s" % filename_csv
data = pl2.load2( filename_csv)
if self.verbose: print "[*] Generating pickle for next time to %s" % filename_pick
f = open( filename_pick, "wb")
cPickle.dump( data, f)
f.close()
# load our time-series dataframe from csv using pandas library
self._gox_offline = data["gox"]
self._ltc_offline = data["ltc"]
self._ltc_depth_offline = data["ltc_depth"]
# if we're running in non-simulated offline mode, where we just
# want to run through our historical price data as quickly as
# possible, then we build a range of dates that we will walk through
if self.warp == True:
# get our start and end points in our timerange
start = max( [ self._gox_offline.index[0], self._ltc_offline.index[0]])
end = max( [ self._gox_offline.index[-1], self._ltc_offline.index[-1]])
# our list of total dates to run through
# jump to N-seconds intervals (self.sample_secs)
if self.verbose:
print "[*]","Building daterange"
self.logrange = self._daterange( start, end, self.sample_secs)
# we're going to need to iterate through this one at a time ...
# get new values, calculate indicators, train, repeat, so we'll
# need to keep track of where we are
self.logrange_n = 0
if self.verbose:
print "[*] Dates from", start, "to", end
# otherwise we pretend we're live (slow so we can watch it IRT)
else:
# find out which has the earliest starting date. We will use
# this to calculate our timedelta. In the future when we want
# to check the price, we will use this delta compared to current
# time to grab the proper simulated price
# (we use max here so we don't get any initial NaN prices if possible)
self.delta = datetime.datetime.today() - max( [ self._gox_offline.index[0],
self._ltc_offline.index[0]])
if self.verbose: print "[*] Timedelta: %s" % self.delta
#####################################
# #
# C O I N S #
# #
#####################################
# prepare instant if necessary
if self.instant:
# seed prices with midprice
if self.ltc_opts["calc_mid"]:
filename = os.path.realpath( os.path.join( "logs",
self.filename+".midprices.pickle"))
# if midprices pickle doesn't exist, we need to generate it ... this is slow as f**k
# so we really want to have this preloaded
if os.path.exists( filename):
if self.verbose: print "[*]", "Loading midprices from %s" % filename
f = open( filename, "rb")
bas = cPickle.load( f)
else:
if self.verbose: print "[*]","Calculating midprices ..."
bas = [ pl2.bid_ask(self._ltc_depth_offline.ix[i][0],
avg=True) for i in xrange( len( self._ltc_depth_offline))]
f = open( filename, "wb")
if self.verbose: print "[*]", "Saving midprices to %s" % filename
cPickle.dump( bas, f)
self.ltc_opts["instant"] = pd.DataFrame( {"lastprice":bas},
index=[self._ltc_depth_offline.index])
# otherwise hand it lastprice
else:
self.ltc_opts["instant"] = self._ltc_offline
self.ltc = Coin( debug=self.ltc_opts["debug"],
relative=self.ltc_opts["relative"],
calc_rolling=self.ltc_opts["calc_rolling"],
rolling=self.ltc_opts["rolling"],
calc_mid=self.ltc_opts["calc_mid"],
calc_ohlc=self.ltc_opts["calc_ohlc"],
ohlc=self.ltc_opts["ohlc"],
calc_indicators=self.ltc_opts["calc_indicators"],
indicators=self.ltc_opts["indicators"],
calc_std=self.ltc_opts["calc_std"],
std=self.ltc_opts["std"],
calc_crt=self.ltc_opts["calc_crt"],
crt=self.ltc_opts["crt"],
instant=self.ltc_opts["instant"],
time_str=self.ltc_opts["time_str"],
verbose=self.verbose)
# for gox, all I want to calculate is the EMA of the last prices ...
# I chose last price, not mid, because I think that a lot of people
# are trading based on the last price ticker, not where the market
# really is.
# prepare instant if necessary
# prepare instant if necessary
if self.instant:
# seed prices with midprice
if self.gox_opts["calc_mid"]:
if self.verbose: print "[*]","Calculating midprices ..."
bas = [ pl2.bid_ask(self._gox_depth_offline.ix[i][0], avg=True) for i in xrange( len( self._gox_depth_offline))]
self.gox_opts["instant"] = pd.DataFrame( {"lastprice":bas}, index=[self._gox_depth_offline.index])
# otherwise hand it lastprice
else:
self.gox_opts["instant"] = self._gox_offline
self.gox = Coin( debug=self.gox_opts["debug"],
relative=self.gox_opts["relative"],
calc_rolling=self.gox_opts["calc_rolling"],
rolling=self.gox_opts["rolling"],
calc_mid=self.gox_opts["calc_mid"],
calc_ohlc=self.gox_opts["calc_ohlc"],
ohlc=self.gox_opts["ohlc"],
calc_indicators=self.gox_opts["calc_indicators"],
indicators=self.gox_opts["indicators"],
calc_std=self.gox_opts["calc_std"],
std=self.gox_opts["std"],
calc_crt=self.gox_opts["calc_crt"],
crt=self.gox_opts["crt"],
instant=self.gox_opts["instant"],
time_str=self.gox_opts["time_str"],
verbose=self.verbose)
def __init__(self, **kwargs):
""" Set up our data structures and determine whether we're in
live or simulated mode.
time_str : (default "5min") time-frame to analyze on ... this
controls the length of each "bar" or period, can be
any pandas-recognized string, (10s, 10min, 1h, 1d, etc)
live : live or simulated mode (whether or not to read from
filename or from the web), defaults to False (simulated)
filename : name of log file to read in simulated mode ... interpreted
as ./logs/filename ... file must be in this dir
warp : whether or not to use our timedelta or just next value
for each update() ... so we can do all calculations
as fast as possible, defaults to False ("realtime")
debug : whether or not to spit out debugging info
sample_secs : if in warp-mode, N-seconds to sample on (the shorter
N, the more often we are "checking" the price and
the more iterations it will take to complete a series)
instant : (default False) Setting this to true will make Data
send the lastprice series to the Coins to calculate all
in faster, one-pass mode
ltc_opts : dict structure on what to do with LTC data ... see coin for
options from kwargs (default is same as GOX...)
Here's an example of a fully loaded options dict
{ "debug": False,
"relative": False,
"calc_rolling": False,
"rolling": { self.time_str : { 5: pd.DataFrame(),
25: pd.DataFrame(),
50: pd.DataFrame() } },
"calc_mid": False,
"calc_ohlc": True,
"ohlc": { self.time_str : pd.DataFrame() },
"calc_indicators": True,
"indicators":{ "RSI" : { "data": pd.DataFrame(), "n":14 },
"ROC" : { "data": pd.DataFrame(), "n":20 },
"AMA" : { "data": pd.DataFrame(), "n":10, "fn":2.5, "sn":30 },
"CCI" : { "data": pd.DataFrame(), "n":20 },
"FRAMA": { "data": pd.DataFrame(), "n":10 },
"RVI2" : { "data": pd.DataFrame(), "n":14, "s":10 },
"MACD" : { "data": pd.DataFrame(), "f":12, "s":26, "m":9 },
"ADX" : { "data": pd.DataFrame(), "n":14 },
"ELI" : { "data": pd.DataFrame(), "n":14 },
"TMI" : { "data": pd.DataFrame(), "nb":10, "nf":5} }
"calc_std": True,
"std": { 10: pd.DataFrame(), 50: pd.DataFrame(), 100: pd.DataFrame() },
"calc_crt": True,
"crt": { 1: pd.DataFrame(), 2: pd.DataFrame(),
3: pd.DataFrame(), 5: pd.DataFrame(),
8: pd.DataFrame() },
"instant": False,
"time_str": self.time_str }
gox_opts : dict structure on what to do with GOX BTC data ... see coin for
options from kwargs (default: everything disabled but OHLC ... )
{ "debug": False,
"relative": False,
"calc_rolling": False,
"rolling": False,
"calc_mid": False,
"calc_ohlc": True,
"ohlc": { self.time_str : pd.DataFrame() },
"calc_indicators": False,
"calc_std": False,
"std": False,
"calc_crt": False,
"crt": False,
"instant": False,
"time_str": self.time_str }
pickled_data : (default False) if this is set to a data structure,
from pickle'd pandas csv data structure, it'll take
it from here instead of from disk. Faster on multiple
iterations.
verbose : (default False) whether or not to print out shit
"""
self.live = kwargs.get("live", False)
self.filename = kwargs.get("filename", "test.csv")
self.warp = kwargs.get("warp", True)
self._debug = kwargs.get("debug", False)
self.sample_secs = kwargs.get("sample_secs", 5)
self.instant = kwargs.get("instant", False)
self.time_str = kwargs.get("time_str", "5min")
self.verbose = kwargs.get("verbose", False)
# default LTC options
def_ltc = {
"debug": False,
"relative": False,
"calc_rolling": False,
"rolling": False,
"calc_mid": False,
"calc_ohlc": True,
"ohlc": {
self.time_str: pd.DataFrame()
},
"calc_indicators": False,
"indicators": False,
"calc_std": False,
"std": False,
"calc_crt": False,
"crt": False,
"instant": False,
"time_str": self.time_str
}
self.ltc_opts = kwargs.get("ltc_opts", def_ltc)
# default gox options
def_gox = {
"debug": False,
"relative": False,
"calc_rolling": False,
"rolling": False,
"calc_mid": False,
"calc_ohlc": True,
"ohlc": {
self.time_str: pd.DataFrame()
},
"calc_indicators": False,
"indicators": False,
"calc_std": False,
"std": False,
"calc_crt": False,
"crt": False,
"instant": False,
"time_str": self.time_str
}
self.gox_opts = kwargs.get("gox_opts", def_gox)
self.pickled_data = kwargs.get("pickled_data", False)
if self.verbose:
print "[*]", "Online" if self.live else "Offline", "mode initiated"
print "[*]", "Simulated" if not self.warp else "Speed", "mode initiated"
# if we're running simulated, set up price logs so we can query them
# in realtime as if they were actual price changes
if self.live == False:
# did we supply a pre-parsed pandas CSV data struct?
if self.pickled_data != False:
if self.verbose:
print "[*]", "Loading supplied pickle!"
data = self.pickled_data
# nope ... load from disk!
else:
# loading from CSV takes a long time, lets prepare a pickle of the
# loaded CSV if we haven't already done so, if we have then load it
filename_pick = os.path.realpath(
os.path.join("logs", self.filename + ".pickle"))
if os.path.exists(filename_pick):
if self.verbose:
print "[*]", "Loading csv pickle from %s" % filename_pick
f = open(filename_pick, "rb")
data = cPickle.load(f)
f.close()
else:
filename_csv = os.path.realpath(
os.path.join("logs", self.filename))
if self.verbose: print "[*] Loading %s" % filename_csv
data = pl2.load2(filename_csv)
if self.verbose:
print "[*] Generating pickle for next time to %s" % filename_pick
f = open(filename_pick, "wb")
cPickle.dump(data, f)
f.close()
# load our time-series dataframe from csv using pandas library
self._gox_offline = data["gox"]
self._ltc_offline = data["ltc"]
self._ltc_depth_offline = data["ltc_depth"]
# if we're running in non-simulated offline mode, where we just
# want to run through our historical price data as quickly as
# possible, then we build a range of dates that we will walk through
if self.warp == True:
# get our start and end points in our timerange
start = max(
[self._gox_offline.index[0], self._ltc_offline.index[0]])
end = max(
[self._gox_offline.index[-1], self._ltc_offline.index[-1]])
# our list of total dates to run through
# jump to N-seconds intervals (self.sample_secs)
if self.verbose:
print "[*]", "Building daterange"
self.logrange = self._daterange(start, end, self.sample_secs)
# we're going to need to iterate through this one at a time ...
# get new values, calculate indicators, train, repeat, so we'll
# need to keep track of where we are
self.logrange_n = 0
if self.verbose:
print "[*] Dates from", start, "to", end
# otherwise we pretend we're live (slow so we can watch it IRT)
else:
# find out which has the earliest starting date. We will use
# this to calculate our timedelta. In the future when we want
# to check the price, we will use this delta compared to current
# time to grab the proper simulated price
# (we use max here so we don't get any initial NaN prices if possible)
self.delta = datetime.datetime.today() - max(
[self._gox_offline.index[0], self._ltc_offline.index[0]])
if self.verbose: print "[*] Timedelta: %s" % self.delta
#####################################
# #
# C O I N S #
# #
#####################################
# prepare instant if necessary
if self.instant:
# seed prices with midprice
if self.ltc_opts["calc_mid"]:
filename = os.path.realpath(
os.path.join("logs", self.filename + ".midprices.pickle"))
# if midprices pickle doesn't exist, we need to generate it ... this is slow as f**k
# so we really want to have this preloaded
if os.path.exists(filename):
if self.verbose:
print "[*]", "Loading midprices from %s" % filename
f = open(filename, "rb")
bas = cPickle.load(f)
else:
if self.verbose: print "[*]", "Calculating midprices ..."
bas = [
pl2.bid_ask(self._ltc_depth_offline.ix[i][0], avg=True)
for i in xrange(len(self._ltc_depth_offline))
]
f = open(filename, "wb")
if self.verbose:
print "[*]", "Saving midprices to %s" % filename
cPickle.dump(bas, f)
self.ltc_opts["instant"] = pd.DataFrame(
{"lastprice": bas}, index=[self._ltc_depth_offline.index])
# otherwise hand it lastprice
else:
self.ltc_opts["instant"] = self._ltc_offline
self.ltc = Coin(debug=self.ltc_opts["debug"],
relative=self.ltc_opts["relative"],
calc_rolling=self.ltc_opts["calc_rolling"],
rolling=self.ltc_opts["rolling"],
calc_mid=self.ltc_opts["calc_mid"],
calc_ohlc=self.ltc_opts["calc_ohlc"],
ohlc=self.ltc_opts["ohlc"],
calc_indicators=self.ltc_opts["calc_indicators"],
indicators=self.ltc_opts["indicators"],
calc_std=self.ltc_opts["calc_std"],
std=self.ltc_opts["std"],
calc_crt=self.ltc_opts["calc_crt"],
crt=self.ltc_opts["crt"],
instant=self.ltc_opts["instant"],
time_str=self.ltc_opts["time_str"],
verbose=self.verbose)
# for gox, all I want to calculate is the EMA of the last prices ...
# I chose last price, not mid, because I think that a lot of people
# are trading based on the last price ticker, not where the market
# really is.
# prepare instant if necessary
# prepare instant if necessary
if self.instant:
# seed prices with midprice
if self.gox_opts["calc_mid"]:
if self.verbose: print "[*]", "Calculating midprices ..."
bas = [
pl2.bid_ask(self._gox_depth_offline.ix[i][0], avg=True)
for i in xrange(len(self._gox_depth_offline))
]
self.gox_opts["instant"] = pd.DataFrame(
{"lastprice": bas}, index=[self._gox_depth_offline.index])
# otherwise hand it lastprice
else:
self.gox_opts["instant"] = self._gox_offline
self.gox = Coin(debug=self.gox_opts["debug"],
relative=self.gox_opts["relative"],
calc_rolling=self.gox_opts["calc_rolling"],
rolling=self.gox_opts["rolling"],
calc_mid=self.gox_opts["calc_mid"],
calc_ohlc=self.gox_opts["calc_ohlc"],
ohlc=self.gox_opts["ohlc"],
calc_indicators=self.gox_opts["calc_indicators"],
indicators=self.gox_opts["indicators"],
calc_std=self.gox_opts["calc_std"],
std=self.gox_opts["std"],
calc_crt=self.gox_opts["calc_crt"],
crt=self.gox_opts["crt"],
instant=self.gox_opts["instant"],
time_str=self.gox_opts["time_str"],
verbose=self.verbose)