def predictFuture(m1,m2,old_pred,writeToFile=False):
actual,latest_p = util.getCurrentData(label=True)
actual = np.array(util.reduceCurrent(actual)).reshape(1,12)
pred = util.augmentValue(net.predict(actual)[0],m1,m2)
pred = float(int(pred[0]*100)/100)
if writeToFile:
f = open("results","a")
f.write("[{}] Actual:{}$ Last Prediction:{}$ Next 9m:{}$\n".format(time.strftime("%H:%M:%S"),latest_p,old_pred,pred))
f.close()
print("[{}] Actual:{}$ Last Prediction:{}$ Next 9m:{}$".format(time.strftime("%H:%M:%S"),latest_p,old_pred,pred))
return latest_p,pred
def predictFuture(m1,m2,old_pred,writeToFile=False):
actual,latest_p = util.getCurrentData(label=True)
actual = np.array(util.reduceCurrent(actual)).reshape(1,12)
pred = util.augmentValue(net.predict(actual)[0],m1,m2)
pred = float(int(pred[0]*100)/100)
cex = util.getCEXData()
slope,nrmse = predict.getslope(False)
if writeToFile:
f = open("results","a")
f.write("[{}] Actual:{}$ Last Prediction:{}$ Next {}sec:{}$\n".format(time.strftime("%H:%M:%S"),latest_p,old_pred,wait_time,pred))
f.close()
c = conn.cursor()
c.execute("INSERT INTO predict(actual,last,target,cex_ask,slope,nrmse) VALUES (?,?,?,?,?,?)",(latest_p,old_pred,pred,cex["ask"],slope,nrmse))
conn.commit()
print("[{}] Actual:{}$ Last Prediction:{}$ Next {}sec:{}$ Slope:{}$ NRMSE:{}$\n".format(time.strftime("%H:%M:%S"),latest_p,old_pred,wait_time,pred,slope,nrmse))
return latest_p,pred
def predictFuture(m1,
m2,
old_pred,
window_data,
prev_closep,
writeToFile=False):
actual, latest_p, ctime = util.getCurrentData(label=True)
if net_type == 'RNN':
actual = np.array(util.reduceCurrent(actual)).reshape(1, 12)
pred = util.augmentValue(net.predict(actual)[0], m1, m2)
pred = float(int(pred[0] * 100) / 100)
if writeToFile:
f = open("results", "a")
f.write("[{}] Actual:{}$ Last Prediction:{}$ Next 9m:{}$\n".format(
time.strftime("%H:%M:%S"), latest_p, old_pred, pred))
f.close()
print("[{}] Actual:{}$ Last Prediction:{}$ Next 9m:{}$".format(
time.strftime("%H:%M:%S"), latest_p, old_pred, pred))
return latest_p, pred, 0, 0, ctime
else:
for i in xrange(1, len(window_data)):
window_data[i - 1][:] = window_data[i][:]
window_data[len(window_data) - 1][:] = actual
actual = np.array(window_data).reshape(1, window, 12)
pred = (net.predict(actual)[0])
real = util.convert_close_to_label(prev_closep, latest_p)
if writeToFile:
f = open("results", "a")
f.write(
"[{}] Actual:{}$, Last actual value {}, Last Prediction:{}$ Next 9m:{}\n"
.format(time.strftime("%H:%M:%S"), latest_p, prev_closep,
old_pred, np.argmax(pred)))
f.close()
print(
"[{}] Actual:{}$, Last actual value {}, Last Prediction:{}$ Next 9m:{}"
.format(time.strftime("%H:%M:%S"), latest_p, prev_closep, old_pred,
label[np.argmax(pred)]))
return np.argmax(real), np.argmax(pred), window_data, latest_p, ctime
data = util.reduceMatRows(data)
labels,m1,m2 =util.reduceVector(labels,getVal=True)
print("{} chunk loaded!\n".format(len(labels)),end="")
if args.run is not None:
#Loading weights
w_name = args.model
net.load_weights(w_name)
epochs = "run"
print("Starting main loop...")
hip = 0
reals,preds = [],[]
for i in range(len(data)-40,len(data)):
x = np.array(data[i]).reshape(1,12)
predicted = util.augmentValue(net.predict(x)[0],m1,m2)[0]
real = util.augmentValue(labels[i],m1,m2)
preds.append(predicted)
reals.append(real)
while True:
try:
real,hip = predictFuture(m1,m2,hip,writeToFile=True)
reals.append(real)
preds.append(hip)
time.sleep(wait_time)
except KeyboardInterrupt:
### PLOTTING
chart(reals,preds,show=False)
print("Chart saved!")
s = input("Type yes to close the program: ")
