def __init__(self, scope, q, alpha, reward, discount, quotes, bankroll,
log=None):
self.logger = log
self.actions = ACTIONS # BUY, SELL, DO_NOTHING
Indicators.__init__(self, log)
Learning.__init__(self, q, alpha, reward, discount, self.state, \
self.actions)
Order.__init__(self, scope, bankroll, log)
self.num_trades = 0
self.performance = 1
self.volume = max(self.performance, 1)
self.logger = log
self.status = {'status':'','action':''}
self.quotes = quotes
self.states = None
def __init__(self,
scope,
q,
alpha,
reward,
discount,
quotes,
bankroll,
log=None):
self.logger = log
self.scope = scope
self.actions = ACTIONS
Indicators.__init__(self, log)
Order.__init__(self, scope, bankroll, log)
Learning.__init__(self, q, alpha, reward, discount, self.state, \
self.actions)
self.num_trades = 0
self.performance = 1
self.volume = max(self.performance, 1)
self.logger = log
self.status = {'status': 'idle', 'action': ''}
self.quotes = quotes
self.states = None
def __init__(self,args):
self.checkpoint_dir = '/data/tf/ckpts'
self.log_dir = '/data/tf/logs'
self.result_dir = 'results'
self._attrs = ['generator','rule','rule_apply', 'order', 'subsample', 'ratio'] #'gan', ,'parse'
Learning.__init__(self,
model=args.model, dataset=args.dataset,
model_path=args.model_path,data_path=args.data_path,
generator=args.generator, rule=args.rule,
gan=args.gan, parse=args.parse,
rule_apply=args.rule_apply,order=args.order,
subsample=args.subsample, ratio=args.ratio,
reload=args.reload, beam=args.beam)
tf.reset_default_graph()
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth=True
tf_config.intra_op_parallelism_threads=15
tf_config.inter_op_parallelism_threads=15
self.sess = tf.InteractiveSession(config=tf_config)
