def __init__(self, conn, k, account):
self.conn = conn
self.k = k
self.pairs = account.asset_pair.keys()
self.pred = dict()
self.diff = dict()
self.price = dict()
self.simulate = True
# Get Configuration Values for Trader from JSON File
# This is required in case, we want ot optimize the algorithms later on.
trader_name = hf.get_tader_name(self)
self.constant = hf.get_trader_config()[trader_name]
#Calculate the predicted change
self.predict_change()
def __init__(self, account):
self.account = account
self.queries = db_queries.DbQueries()
self.pairs = account.asset_pair.keys()
self.diff = dict()
self.price = dict()
# Get Configuration Values for Trader from JSON File
# This is required in case, we want ot optimize the algorithms later on.
trader_name = hf.get_tader_name(self)
self.constant = hf.get_trader_config()[trader_name]
self.keep = min(0.01, self.constant["delta"])
# Calculate the predicted change
self.run_trader()
self.keep_back(dt.datetime.strptime("2016-01-01", "%Y-%m-%d"))
def __init__(self, account):
self.account = account
self.queries = db_queries.DbQueries()
self.pairs = account.asset_pair.keys()
self.diff = dict()
self.price = dict()
# Get Configuration Values for Trader from JSON File
# This is required in case, we want ot optimize the algorithms later on.
trader_name = hf.get_tader_name(self)
self.constant = hf.get_trader_config()[trader_name]
self.keep = min(0.01,self.constant["delta"])
# Calculate the predicted change
self.run_trader()
self.keep_back(dt.datetime.strptime("2016-01-01","%Y-%m-%d"))
def write_new_trader(self):
hf.save_trader_config(self.constant, hf.get_tader_name(self))
def write_new_trader(self):
hf.save_trader_config(self.constant, hf.get_tader_name(self))