def main():
args = parser.parse_args()
source = args.SOURCE
symbol = args.SYMBOL
alias = args.alias
share = bb.Share(source, symbol, alias = alias)
def predict(request):
# print(request.body)
# s = json.dumps(request.body)
# data = json.loads(s)
# print(data['years'])
# print(request.body[len(request.body)-1])
os.environ["BULBEA_QUANDL_API_KEY"] = 'AENaz-R8uBmUxQsYrLzD'
quandl.ApiConfig.api_key = 'AENaz-R8uBmUxQsYrLzD'
now = datetime.datetime.now()
my_json = request.body.decode('utf8').replace("'", '"')
data = json.loads(my_json)
# dataFrameJSON = json.dumps(data["data"], indent=4, sort_keys=True)
print(data["CompanyName"])
df = pd.read_json(data["data"], orient='split')
# print(dataFrameJSON)
# print(type(df))
dates = []
prices = []
lst_for_result = []
predictionYear = 0
predictionType = data["pred_type"]
if (predictionType == 'Years'):
predictionYear = now.year + int(data["years"])
# print(df.index)
for i in df.index:
# print(str(i).split()[0].split('-')[2])
dates.append(int(str(i).split()[0].split('-')[0]))
for j in df['Close']:
prices.append(float(str(j)))
if (predictionType == 'Months'):
predictionYear = int(data["months"])
# print(df.index)
for i in df.index:
# print(str(i).split()[0].split('-')[2])
dates.append(int(str(i).split()[0].split('-')[1]))
for j in df['Close']:
prices.append(float(str(j)))
# predictionYear = now.year + int(data["years"])
# for i in df.index:
# # print(str(i).split()[0].split('-')[2])
# dates.append(int(str(i).split()[0].split('-')[0]))
# for j in df['Close']:
# prices.append(float(str(j)))
dates = np.reshape(dates, (len(dates), 1))
svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
svr_rbf.fit(dates, prices)
predictedValue = svr_rbf.predict(predictionYear)
share = bb.Share(source='NSE', ticker=data["CompanyName"])
Xtrain, Xtest, ytrain, ytest = split(share, 'Close', normalize=True)
Xtrain = pd.DataFrame(Xtrain)
Xtest = pd.DataFrame(Xtest)
ytrain = pd.DataFrame(ytrain)
ytest = pd.DataFrame(ytest)
Xtrain = Xtrain.dropna(axis=0, how='any')
Xtest = Xtest.dropna(axis=0, how='any')
ytrain = ytrain.dropna(axis=0, how='any')
ytest = ytest.dropna(axis=0, how='any')
Xtrain = Xtrain.as_matrix()
Xtest = Xtest.as_matrix()
ytrain = ytrain.as_matrix()
ytest = ytest.as_matrix()
Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], Xtrain.shape[1], 1))
Xtest = np.reshape(Xtest, (Xtest.shape[0], Xtest.shape[1], 1))
rnn = RNN([1, 100, 100, 1]) # number of neurons in each layer
rnn.fit(Xtrain, ytrain)
p = rnn.predict(Xtest)
mse = mean_squared_error(ytest, p)
print(mse)
fig, ax = plt.subplots()
ax.plot(ytest)
ax.plot(p)
# ax.scatter(dates, prices, color='black', label='Data')
# ax.plot(dates, svr_rbf.predict(dates), color='red', label='RBF Model')
# ax.set_xticklabels(dates, rotation=90, ha='left', fontsize=10)
dictRepPredictionGraph = mpld3.fig_to_dict(fig)
plt.savefig("/Users/rjmac/Desktop/prediction.png")
result = {}
result['predictedValue'] = predictedValue[0]
result['yearPredicted'] = predictionYear
with open("/Users/rjmac/Desktop/prediction.png", "rb") as img:
st = base64.b64encode(img.read())
print(type(st))
strng = st.decode("utf-8")
result['predictionGraph'] = strng
# lst_for_result.append(result)
json_data = json.dumps(result)
return HttpResponse(json_data, content_type='application/json')
def save(self, file):
return self.model.save(file)
def load(self, file):
return load_model(file)
# Saving a file in python
# file = "f.txt"
# path = os.path.join("test/", file)
# file = open(path, "w")
stock_list = [
'ADM', 'FLS', 'ADI', 'ADBE', 'CB', 'FAST', 'ABBV', 'SJM', 'DHI', 'ACN',
'AAP', 'ZTS', 'SIG', 'CME', 'XOM', 'CMCSA', 'ABC', 'ABT', 'JBHT', 'DHR',
'GOOGL', 'AAL', 'XLNX', 'MMC', 'RRC', 'ROST', 'GPC', 'AAPL', 'DLTR', 'WM'
]
for i in stock_list:
stock = bb.Share('Wiki', i)
Xtrain, Xtest, ytrain, ytest = split(stock, 'Close', normalize=True)
Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], Xtrain.shape[1], 1))
Xtest = np.reshape(Xtest, (Xtest.shape[0], Xtest.shape[1], 1))
rnn = RNN([1, 100, 100, 1]) # number of neurons in each layer
rnn.fit(Xtrain, ytrain)
rnn.save("models/" + i + ".h5")
import bulbea as bb
share = bb.Share('YAHOO', 'GOOGL')
a=1
def on_click_search(self):
database = self.frame.entry[App.Frame.ENTRY_DATABASE].get()
symbol = self.frame.entry[App.Frame.ENTRY_SYMBOL].get()
share = bb.Share(database, symbol)
axes = share.plot(['Close'])
import os os.environ["BULBEA_QUANDL_API_KEY"] = 'VGjjpcct_DscJ4Fa8DCP' import bulbea as bb share = bb.Share(source='SSE', ticker='MSFT', provider='alphavantage') # share = bb.Share(source='SSE', ticker='AMZ', provider='quandl') share.groupDataByAttribute() '''print(share.data)''' from bulbea.learn.evaluation import split import numpy as np Xtrain, Xtest, ytrain, ytest = split(share, '4. close', normalize=True) # Xtrain, Xtest, ytrain, ytest = split(share, 'Last', normalize=True) Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], Xtrain.shape[1], 1)) Xtest = np.reshape(Xtest, (Xtest.shape[0], Xtest.shape[1], 1)) from bulbea.learn.models import RNN rnn = RNN([1, 100, 100, 1]) # number of neurons in each layer rnn.fit(Xtrain, ytrain) from sklearn.metrics import mean_squared_error p = rnn.predict(Xtest) mean_squared_error(ytest, p) import matplotlib.pyplot as pplt pplt.plot(ytest)
def __openStock(self, symbol, source='WIKI'):
self.stock = bb.Share(source='WIKI', ticker=symbol)
features = ["SMA", "RSI"] # Financial indicators of choice trainStart = 0 # Start of training period trainEnd = 1000 # End of training period testStart = 1001 # Start of testing period testEnd = 5000 # End of testing period buyThreshold = 0.1 # Confidence threshold for predicting buy (default = 0.65) sellThreshold = 0.65 # Confidence threshold for predicting sell (default = 0.65) continuedTraining = False # Continue training during testing period? (default = false) # Initialize backtester backtest = Backtest(features, trainStart, trainEnd, testStart, testEnd, buyThreshold, sellThreshold, continuedTraining) # gets share data provider = 'alphavantage' share = bb.Share(source='SSE', ticker='MSFT', provider=provider) df = pd.DataFrame(share.data) df = df.transpose() df.columns = ['high','low','open','dropme1','close','dropme2', 'dropme3', 'dropme4'] df = df.drop(['dropme1','dropme2','dropme3', 'dropme4'], axis=1) df['date'] = pd.to_datetime(df.index, infer_datetime_format=True) df['high'] = pd.to_numeric(df['high']) df['low'] = pd.to_numeric(df['low']) df['open'] = pd.to_numeric(df['open']) df['close'] = pd.to_numeric(df['close']) cols = df.columns.tolist() cols = cols[-1:] + cols[:-1] df = df[cols] df['SMA'] = talib.SMA(df["close"]) df['RSI'] = talib.RSI(df["close"]) # df['MACD'] = talib.MACD(df["close"])[0]
models = load_model_helper(coin_name)
# In[4]:
from coinmarketcap_draw import coinmarketcap_data
# In[5]:
data = coinmarketcap_data(coin_name)
# In[6]:
import bulbea as bb
figsize = (20, 15)
get_ipython().magic('matplotlib inline')
share = bb.Share("123", '123', data=data)
# In[7]:
share.plot(figsize=figsize)
# ## Convert the data to hourly
# In[8]:
from datetime import timedelta
import pandas as pd
def convert_with_n_hour_gap(data, n):
times = data.index.copy()
import bulbea as bb
share = bb.Share('WIKI', 'GOOGL')
from bulbea.learn.evaluation import split
Xtrain, Xtest, ytrain, ytest = split(share, 'Close', normalize=True)
import numpy as np
import bulbea as bb
#import matplotlib.pyplot as pplt
from bulbea.learn.evaluation import split
from bulbea.learn.models import RNN
from sklearn.metrics import mean_squared_error
if __name__ == '__main__':
if len(sys.argv) != 5:
print(
"Usage : python update.py [currency_1] [currency_2] [start_date] [end_date], date format : YYYY-MM-DD"
)
sys.exit(0)
#Get initial object share
share = bb.Share(source=sys.argv[1],
ticker=sys.argv[2],
start=sys.argv[3],
end=sys.argv[4])
#Get the data and save to .csv file
share.update()
print(share.data.head())
share.save(filename='{sym}_{start}_{end}.csv'.format(
sym='{cur1}{cur2}'.format(cur1=sys.argv[1], cur2=sys.argv[2]),
start=sys.argv[3],
end=sys.argv[4]))
#Pre-process
Xtrain, Xtest, ytrain, ytest = split(share, 'Close', normalize=True)
Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], Xtrain.shape[1], 1))
Xtest = np.reshape(Xtest, (Xtest.shape[0], Xtest.shape[1], 1))
