Esempio n. 1
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    def __init__(self, account):
        self.account = account
        self.diff = dict()
        self.dbq = dbq.DbQueries()
        self.hf = hf.HelperFunctions(self.account.asset_pairs, self.dbq)

        # Get Configuration Values for Trader from JSON File
        # This is required in case, we want ot optimize the algorithms later on.
        trader_name = 'MaDBTrader'
        self.constant = hf.get_trader_config()[trader_name]
Esempio n. 2
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    def __init__(self, account):
        self.account = account
        self.diff = dict()
        self.dbq = dbq.DbQueries()
        self.hf = hf.HelperFunctions(self.account.asset_pairs, self.dbq)

        # Get Configuration Values for Trader from JSON File
        # This is required in case, we want ot optimize the algorithms later on.
        trader_name = 'MaDBTrader'
        self.constant = hf.get_trader_config()[trader_name]
Esempio n. 3
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    def __init__(self, k, account):
        self.k = k
        self.account = account
        self.pairs = account.asset_pair.keys()
        #self.pred = dict()
        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 = 'mas_trader'
        self.constant = hf.get_trader_config()[trader_name]

        #Calculate the predicted change
        self.run_trader()
Esempio n. 4
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    def __init__(self, k, account):
        self.k = k
        self.account = account
        self.pairs = account.asset_pair.keys()
        #self.pred = dict()
        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 = 'mas_trader'
        self.constant = hf.get_trader_config()[trader_name]

        #Calculate the predicted change
        self.run_trader()
Esempio n. 5
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    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()
Esempio n. 6
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    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"))
Esempio n. 7
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    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"))