st.set_page_config(layout = "wide")

indication = 'Predicted'

st.sidebar.subheader('Asset:')

asset_options = sorted(['Cryptocurrency', 'Index Fund', 'Forex', 'Futures & Commodities', 'Stocks'])

asset = st.sidebar.selectbox('', asset_options, index = 4)

if asset in ['Index Fund', 'Forex', 'Futures & Commodities', 'Stocks']:

exchange = 'Yahoo! Finance'

app_data.exchange_data(exchange)

if asset == 'Stocks':

st.sidebar.subheader(f'Stock Index:')

stock_indexes = app_data.stock_indexes

market = st.sidebar.selectbox('', stock_indexes, index = 11)

app_data.market_data(market)

assets = app_data.stocks

asset = f'{market} Companies'

elif asset == 'Index Fund':

assets = app_data.indexes

elif asset == 'Futures & Commodities':

assets = app_data.futures

elif asset == 'Forex':

assets = app_data.forex

st.sidebar.subheader(f'{asset}:')

equity = st.sidebar.selectbox('', assets)

if asset == 'Futures & Commodities':

currency = 'USD'

market = None

elif asset == 'Index Fund':

currency = 'Pts'

market = None

elif asset == 'Forex':

currency = app_data.df_forex[(app_data.df_forex['Currencies'] == equity)]['Currency'].unique()[0]

market = app_data.df_forex[(app_data.df_forex['Currencies'] == equity)]['Market'].unique()[0]

elif asset == f'{market} Companies':

currency = app_data.df_stocks[((app_data.df_stocks['Company'] == equity) & (app_data.df_stocks['Index Fund'] == market))]['Currency'].unique()[0]

asset = 'Stock'

st.sidebar.subheader('Interval:')

interval = st.sidebar.selectbox('', ('5 Minute', '15 Minute', '30 Minute', '1 Hour', '1 Day', '1 Week'), index = 4)

volitility_index = 0

elif asset in ['Cryptocurrency']:

exchange = 'Binance'

app_data.exchange_data(exchange)

markets = app_data.markets

st.sidebar.subheader('Market:')

market = st.sidebar.selectbox('', markets, index = 3)

app_data.market_data(market)

assets = app_data.assets

currency = app_data.currency

st.sidebar.subheader('Crypto:')

equity = st.sidebar.selectbox('', assets)

st.sidebar.subheader('Interval:')

interval = st.sidebar.selectbox('', ('1 Minute', '3 Minute', '5 Minute', '15 Minute', '30 Minute', '1 Hour', '6 Hour', '12 Hour', '1 Day', '1 Week'), index = 8)

volitility_index = 2

label = asset

st.sidebar.subheader('Trading Volatility:')

risk = st.sidebar.selectbox('', ('Low', 'Medium', 'High'), index = volitility_index)

st.title(f'Automated Technical Analysis.')

st.subheader(f'{label} Data Sourced from {exchange}.')

st.info(f'Predicting...')

future_price = 1

analysis = Visualization(exchange, interval, equity, indication, action_model, price_model, market)

analysis_day = Indications(exchange, '1 Day', equity, market)

requested_date = analysis.df.index[-1]

current_price = float(analysis.df['Adj Close'][-1])

change = float(analysis.df['Adj Close'].pct_change()[-1]) * 100

requested_prediction_price = float(analysis.requested_prediction_price)

requested_prediction_action = analysis.requested_prediction_action

risks = {'Low': [analysis_day.df['S1'].values[-1], analysis_day.df['R1'].values[-1]],

'Medium': [analysis_day.df['S2'].values[-1], analysis_day.df['R2'].values[-1]],

'High': [analysis_day.df['S3'].values[-1], analysis_day.df['R3'].values[-1]],}

buy_price = float(risks[risk][0])

sell_price = float(risks[risk][1])

if change > 0:

change_display = f'A **{float(change):,.2f}%** gain'

elif change < 0:

change_display = f'A **{float(change):,.2f}%** loss'

else:

change_display = 'UNCH'

if exchange == 'Yahoo! Finance':

current_price = f'{float(current_price):,.2f}'

requested_prediction_price = f'{float(requested_prediction_price):,.2f}'

buy_price = f'{float(buy_price):,.2f}'

sell_price = f'{float(sell_price):,.2f}'

else:

current_price = f'{float(current_price):,.8f}'

requested_prediction_price = f'{float(requested_prediction_price):,.8f}'

buy_price = f'{float(buy_price):,.8f}'

sell_price = f'{float(sell_price):,.8f}'

if analysis.requested_prediction_action == 'Hold':

present_statement_prefix = 'off from taking any action with'

present_statement_suffix = ' at this time'

else:

present_statement_prefix = ''

present_statement_suffix = ''

accuracy_threshold = {analysis.score_action: 75., analysis.score_price: 75.}

confidence = dict()

for score, threshold in accuracy_threshold.items():

if float(score) >= threshold:

confidence[score] = f'*({score}% confident)*'

else:

confidence[score] = ''

forcast_prefix = int(interval.split()[0]) * future_price

if forcast_prefix > 1:

forcast_suffix = str(interval.split()[1]).lower() + 's'

else:

forcast_suffix = str(interval.split()[1]).lower()

asset_suffix = 'price'

st.markdown(f'**Prediction Date & Time (UTC):** {str(requested_date)}.')

st.markdown(f'**Current Price:** {currency} {current_price}.')

st.markdown(f'**{interval} Price Change:** {change_display}.')

st.markdown(f'**Recommended Trading Action:** You should **{requested_prediction_action.lower()}** {present_statement_prefix} this {label.lower()[:6]}{present_statement_suffix}. {str(confidence[analysis.score_action])}')

st.markdown(f'**Estimated Forecast Price:** The {label.lower()[:6]} {asset_suffix} for **{equity}** is estimated to be **{currency} {requested_prediction_price}** in the next **{forcast_prefix} {forcast_suffix}**. {str(confidence[analysis.score_price])}')

if requested_prediction_action == 'Hold':

st.markdown(f'**Recommended Trading Margins:** You should consider buying more **{equity}** {label.lower()[:6]} at **{currency} {buy_price}** and sell it at **{currency} {sell_price}**.')

prediction_fig = analysis.prediction_graph(asset)

st.success(f'Historical {label[:6]} Price Action.')

st.plotly_chart(prediction_fig, use_container_width = True)

technical_analysis_fig = analysis.technical_analysis_graph()