def get_historical_volume(historical, company, scaler):
historical_volume = [] #is a dynamic array (list) for python
average_volume = []
for i in range(len(historical)):
x = float(historical[i]['Volume'])
historical_volume.append(x)
average_volume.append(float(company.get_avg_daily_volume()))
scaled_historical_volume = scale.scale(historical_volume, scaler)
scaled_average_volume = scale.scale(average_volume, scaler)
return historical_volume, average_volume, scaled_historical_volume, scaled_average_volume
def get_historical_volume(historical, company, scaler):
historical_volume = [] #is a dynamic array (list) for python
average_volume = []
for i in range(len(historical)):
x = float(historical[i]['Volume'])
historical_volume.append(x)
average_volume.append(float(company.get_avg_daily_volume()))
scaled_historical_volume = scale.scale(historical_volume, scaler)
scaled_average_volume = scale.scale(average_volume, scaler)
return historical_volume, average_volume, scaled_historical_volume, scaled_average_volume
def get_change(historical, scaler):
change = []
change.append(0)
for i in range(len(historical)-1):
x = float(historical[i+1]["Close"]) - float(historical[i]['Close'])
change.append(x)
scaled_change = scale.scale(change, scaler)
return change, scaled_change
def get_change(historical, scaler):
change = []
change.append(0)
for i in range(len(historical)-1):
x = float(historical[i+1]["Close"]) - float(historical[i]['Close'])
change.append(x)
scaled_change = scale.scale(change, scaler)
return change, scaled_change
def get_historical_low(historical, scaler):
days_low = []
for i in range(len(historical)):
x = float(historical[i]['Low'])
days_low.append(x)
scaled_low = scale.scale(days_low, scaler)
return days_low, scaled_low
def get_historical_low(historical, scaler):
days_low = []
for i in range(len(historical)):
x = float(historical[i]['Low'])
days_low.append(x)
scaled_low = scale.scale(days_low, scaler)
return days_low, scaled_low
def get_historical_high(historical, scaler):
days_high = []
for i in range(len(historical)):
x = float(historical[i]['High'])
days_high.append(x)
scaled_high = scale.scale(days_high, scaler)
return days_high, scaled_high
def get_historical_opening(historical, scaler):
opening = []
for i in range(len(historical)):
x = float(historical[i]['Open'])
opening.append(x)
scaled_opening = scale.scale(opening, scaler)
return opening, scaled_opening
def get_historical_closing(historical, scaler):
closing = []
for i in range(len(historical)):
x = float(historical[i]['Adj_Close'])
closing.append(x)
scaled_closing = scale.scale(closing, scaler)
return closing, scaled_closing
def get_historical_high(historical, scaler):
days_high = []
for i in range(len(historical)):
x = float(historical[i]['High'])
days_high.append(x)
scaled_high = scale.scale(days_high, scaler)
return days_high, scaled_high
def get_historical_opening(historical, scaler):
opening = [] #is a dynamic array (list) for python
for i in range(len(historical)):
x = float(historical[i]['Open'])
opening.append(x)
scaled_opening = scale.scale(opening, scaler)
return opening, scaled_opening
def get_back_trading_day(historical, index, scaler):
op = float(historical[-index]["Open"])
h = float(historical[-index]['High'])
l = float(historical[-index]['Low'])
v = float(historical[-index]['Volume'])
ch = h - l
ths = np.array((op, h, l, v))
sc_ths = scale.scale(ths, scaler)
#print index, ". ", historical[-index]['Date']
return sc_ths
def get_historical_opening(historical, scaler):
opening = [] #is a dynamic array (list) for python
for i in range(len(historical)):
x = float(historical[i]['Open'])
opening.append(x)
scaled_opening = scale.scale(opening, scaler)
return opening, scaled_opening
def get_historical_closing(historical, scaler):
#same for closing
closing = []
for i in range(len(historical)):
x = float(historical[i]['Adj_Close'])
closing.append(x)
scaled_closing = scale.scale(closing, scaler)
return closing, scaled_closing
def get_trading_day(company, scaler, useSpread, useVolume):
opening_price = float(company.get_open())
todays_volume = float(company.get_volume())
high = float(company.get_days_high())
low = float(company.get_days_low())
avg_volume = float(company.get_avg_daily_volume())
change = float(company.get_change())
if useSpread is False and useVolume is False:
today = np.array((opening_price, high, low))
elif useSpread is True and useVolume is False:
today = np.array((opening_price, high, low, change))
elif useSpread is False and useVolume is True:
today = np.array((opening_price, high, low, todays_volume))
else:
today = np.array((opening_price, high, low, change, todays_volume))
scaled_today = scale.scale(today, scaler)
return today, scaled_today
def get_trading_day(company, scaler, useSpread, useVolume):
opening_price = float(company.get_open())
todays_volume = float(company.get_volume())
high = float(company.get_days_high())
low = float(company.get_days_low())
avg_volume = float(company.get_avg_daily_volume())
change = float(company.get_change())
if useSpread is False and useVolume is False:
today = np.array((opening_price, high, low))
elif useSpread is True and useVolume is False:
today = np.array((opening_price, high, low, change))
elif useSpread is False and useVolume is True:
today = np.array((opening_price, high, low, todays_volume))
else:
today = np.array((opening_price, high, low, change, todays_volume))
scaled_today = scale.scale(today, scaler)
return today, scaled_today
def test_normalize_scale(self): #scales two points about their midpoint
from normalize import scale
self.assertEqual(scale(2.4, (64, 34), (36, 34)), ((83, 34), (16, 34)))
receivers.append(int(comps[2]))
if int(comps[1]) > lastNodeID:
lastNodeID = int(comps[1])
if int(comps[2]) > lastNodeID:
lastNodeID = int(comps[2])
#Just for test
#if comps[0] == '10':
# break
nodes = list(range(lastNodeID+1))
# Scale the data
edges = scale(edges, normMethod)
edges = get_edges_from_list(edges)
graph = {
"nodes": nodes,
"edges": edges,
"senders": senders,
"receivers": receivers
}
seed = 2
rand = np.random.RandomState(seed=seed)
print("Convert dictionary to graph")
graph = dict_to_graph(graph)
print("Shortest path")
graph = add_shortest_path(rand, graph)