Upload app.py

app.py

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+from stocks import *
+from functions import *
+from datetime import datetime
+import streamlit as st
+
+st.set_page_config(layout="wide")
+
+st.title("Tech Stocks Trading Assistant")
+
+left_column, right_column = st.columns(2)
+
+with left_column:
+
+    all_tickers = {
+                "Apple":"AAPL", 
+                "Microsoft":"MSFT", 
+                "Nvidia":"NVDA", 
+                "Paypal":"PYPL",
+                "Amazon":"AMZN",
+                "Spotify":"SPOT",
+                "Twitter":"TWTR",
+                "AirBnB":"ABNB",
+                "Uber":"UBER",
+                "Google":"GOOG"
+                }
+
+    st.subheader("Technical Analysis Methods")
+    option_name = st.selectbox('Choose a stock:', all_tickers.keys())
+    option_ticker = all_tickers[option_name]
+    execution_timestamp = datetime.now()
+    'You selected: ', option_name, "(",option_ticker,")"
+    'Last execution:', execution_timestamp
+
+    s = Stock_Data()
+    t = s.Ticker(tick=option_ticker)  
+
+    m = Models()
+
+    with st.spinner('Loading stock data...'):
+
+        technical_analysis_methods_outputs = {
+            'Technical Analysis Method': [
+                'Bollinger Bands (20 days & 2 stand. deviations)', 
+                'Bollinger Bands (10 days & 1.5 stand. deviations)', 
+                'Bollinger Bands (50 days & 3 stand. deviations)', 
+                'Moving Average Convergence Divergence (MACD)'
+                ],
+                'Outlook': [
+                    m.bollinger_bands_20d_2std(t),         
+                    m.bollinger_bands_10d_1point5std(t), 
+                    m.bollinger_bands_50d_3std(t), 
+                    m.MACD(t)
+                    ],
+                'Timeframe of Method': [
+                    "Medium-term",         
+                    "Short-term", 
+                    "Long-term", 
+                    "Short-term"
+                    ]
+                }
+
+        df = pd.DataFrame(technical_analysis_methods_outputs)
+
+
+    def color_survived(val):
+        color = ""
+        if (val=="Sell" or val=="Downtrend and sell signal" or val=="Downtrend and no signal"):
+            color="#EE3B3B"
+        elif (val=="Buy" or val=="Uptrend and buy signal" or val=="Uptrend and no signal"):
+            color="#3D9140"
+        else:
+            color="#CD950C"
+        return f'background-color: {color}'
+    
+    
+    st.table(df.sort_values(['Timeframe of Method'], ascending=False).
+               reset_index(drop=True).style.applymap(color_survived, subset=['Outlook']))
+    
+with right_column:
+
+    st.subheader("FinBERT-based Sentiment Analysis")
+
+    with st.spinner("Connecting with www.marketwatch.com..."):
+        st.plotly_chart(m.finbert_headlines_sentiment(t)["fig"])
+        "Current sentiment:", m.finbert_headlines_sentiment(t)["current_sentiment"], "%"
+
+    st.subheader("LSTM-based 7-day stock price prediction model")
+
+    with st.spinner("Compiling LSTM model.."):
+        st.plotly_chart(m.LSTM_7_days_price_predictor(t))
+