def index():
# Get Ethereum gas data
gas_data = get_gas_data()
print(
f"Last Block: {gas_data['last_block']} | Safe Gas Price: {gas_data['safe_gas']} | Propose Gas Price: {gas_data['propose_gas']}"
)
# Get Ethereum price data
eth_data = get_eth_price()
print(f"Ethereum Price: {eth_data['eth_usd']} | Time: {eth_data['time']}")
# Get stock market price data for Vanguard Total Stock Market ETF (VTI)
stock_data = get_stock_data("vti")
print(f"{stock_data['ticker']}: {stock_data['price']}")
# Get bond market price data for Vanguard Total Bond Market ETF (BND)
bond_data = get_stock_data("bnd")
print(f"{bond_data['ticker']}: {bond_data['price']}")
# Render the index page template
return render_template("index.html",
meta=meta,
gas=gas_data,
eth_data=eth_data,
stock_data=stock_data,
bond_data=bond_data)
import functions as DLmodels
n_steps_in, n_steps_out = 7, 1
interval = '1wk'
samples_test = 1
all_MAPE = []
ML_Techniques = ['LSTM', 'BidirectionalLSTM', 'convLSTM1D', 'convLSTM2D']
for stock in cs.stocks_codigo[int(sys.argv[1]):int(sys.argv[1]) +
len(cs.stocks_codigo)]:
print(stock)
(flag, symbol) = (True, stock)
if flag:
dataframe = DLmodels.get_stock_data(symbol, interval)
dataframe = dataframe[[
'Open', 'High', 'Low', 'Close', 'Volume', 'HighLoad', 'Change',
'Adj Close'
]]
dataframe = DLmodels.clean_dataset(dataframe)
scaler = StandardScaler()
dataset = scaler.fit_transform(dataframe.ffill().values)
scaler_filename = 'scalers/' + stock + '-' + interval + '.save'
scaler = pickle.load(open(scaler_filename, 'rb'))
dataset = scaler.fit_transform(dataframe.iloc[:, :].ffill().values)
n_features = dataset.shape[1]
n_steps_in, n_steps_out = 50, 3
epochs = 1000
verbose=0
save = True
update = True
samples_test = 15
interval='1d'
main_df, cor = DLmodels.get_correlation_stock_matrix(cs.stocks_codigo)
datagrouped = DLmodels.data_grouped_foreign_stock(cs.foreign_stocks, interval)
for stock in cs.stocks_codigo[int(sys.argv[1]):int(sys.argv[1]) + len(cs.stocks_codigo)]:
print(stock)
dataframe = DLmodels.get_stock_data(stock, interval)
if len(dataframe) < 200:
continue
dataframe['Moving_av']= dataframe['Adj Close'].rolling(window=20,min_periods=0).mean()
i=1
upper_volatility=[dataframe.iloc[0]['Moving_av']]
lower_volatility=[dataframe.iloc[0]['Moving_av']]
while i<len(dataframe):
upper_volatility.append(dataframe.iloc[i-1]['Moving_av']+3/100*dataframe.iloc[i-1]['Moving_av'])
lower_volatility.append(dataframe.iloc[i-1]['Moving_av']-3/100*dataframe.iloc[i-1]['Moving_av'])
i+=1
dataframe['Upper_volatility']=upper_volatility
dataframe['Lower_volatility']=lower_volatility
# App layout
################################################################################
# Sidebar
st.sidebar.markdown(
"Look up ticker symbols [here](https://finance.yahoo.com/lookup)")
ticker_symbol = st.sidebar.text_input('Stock ticker symbol', value='AAPL')
start_date = st.sidebar.date_input("Start day", datetime.date(2010, 8, 14))
end_date = st.sidebar.date_input("End day", datetime.date(2020, 8, 14))
# Main Window
st.title("Stock Price Chart")
# Function calls to get data and make image
df_ticker = functions.get_stock_data(ticker_symbol, start_date, end_date)
stock_prices = functions.prepare_data(df_ticker['Close'])
fig = functions.make_picture(stock_prices,
img=img,
x_width_image=x_width_image,
horizon_height=horizon_height)
st.pyplot(fig=fig, bbox_inches='tight')
time.sleep(
1) # workaound, see https://github.com/streamlit/streamlit/issues/1294
plt.close(fig)
gc.collect()
st.markdown(
"Suggestions [welcome](https://github.com/dhaitz/stock-art). Image source: [mamunurpics](https://www.pexels.com/@mamunurpics). Inspired by [stoxart](https://www.stoxart.com)."
)
samples_test = 15
interval = '1d'
all_MAPE = []
ML_Techniques = ['LSTM', 'BidirectionalLSTM', 'convLSTM1D', 'convLSTM2D']
data_trend_pt_br = pd.read_csv('./logs/trends_pt-BR.csv')
data_trend_en_us = pd.read_csv('./logs/trends_en-US.csv')
geo_us = 'en_us'
geo_br = 'pt-BR'
for stock in cs.stocks_codigo[int(sys.argv[1]):int(sys.argv[1]) +
len(cs.stocks_codigo)]:
print(stock)
df_target = DLmodels.get_stock_data(stock, interval)
df_target['Moving_av'] = df_target['Adj Close'].rolling(
window=20, min_periods=0).mean()
i = 1
upper_volatility = [df_target.iloc[0]['Moving_av']]
lower_volatility = [df_target.iloc[0]['Moving_av']]
while i < len(df_target):
upper_volatility.append(df_target.iloc[i - 1]['Moving_av'] +
3 / 100 * df_target.iloc[i - 1]['Moving_av'])
lower_volatility.append(df_target.iloc[i - 1]['Moving_av'] -
3 / 100 * df_target.iloc[i - 1]['Moving_av'])
i += 1
df_target['Upper_volatility'] = upper_volatility
