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Trade.py
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Trade.py
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from app.data_sourcing import Data_Sourcing, data_update
from app.indicator_analysis import Indications
from app.graph import Visualization
from tensorflow.keras.models import load_model
import streamlit as st
import gc
gc.collect()
#data_update()
def main(app_data):
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()
st.plotly_chart(technical_analysis_fig, use_container_width = True)
if __name__ == '__main__':
import warnings
import gc
warnings.filterwarnings("ignore")
gc.collect()
action_model = load_model("models/action_prediction_model.h5")
price_model = load_model("models/price_prediction_model.h5")
app_data = Data_Sourcing()
main(app_data = app_data)