hack2skill · MJ-NJ · Aug 24, 2023 · Aug 24, 2023 · Aug 24, 2023 · Aug 24, 2023
diff --git a/Error/Book-Recomendation/setup.py b/Error/Book-Recomendation/setup.py
@@ -0,0 +1,33 @@
+import requests, webbrowser
+import json
+from bs4 import BeautifulSoup
+import re
+from urllib.parse import urlparse
+import pandas as  pd
+import os
+
+def get_stock_market_books(level):
+  if level == 1:
+    books = ["1. The Intelligent Investor", "2. The Little Book of Common Sense Investing", "3. A Random Walk Down Wall Street"]
+  elif level == 2:
+    books = ["1. How to Make Money in Stocks", "2. Stock Investing For Dummies", "3. Market Wizards"]
+  elif level == 3:
+    books = ["1. The New Market Wizards", "2. The Art of Contrary Thinking", "3. Reminiscences of a Stock Operator"]
+  else:
+    raise ValueError("Invalid level. Please choose 1, 2, or 3.")
+  return books
+
+def main():
+  print("Select the level that you would consider yourself to be:")
+  print("1: Beginner")
+  print("2: Intermediate")
+  print("3: Advanced")
+  level = int(input("Enter the level of the stock market books you want: "))
+  print("Here are the books we recommend:")
+  books = get_stock_market_books(level)
+
+  for book in books:
+    print(book)
+
+if __name__ == "__main__":
+  main()
diff --git a/Error/Market_Insights/Market_Insights.py b/Error/Market_Insights/Market_Insights.py
@@ -0,0 +1,244 @@
+
+from matplotlib.pyplot import axis
+import streamlit as st  
+import pandas as pd  
+import yfinance as yf  
+import datetime  
+from datetime import date
+from plotly import graph_objs as go  
+from plotly.subplots import make_subplots
+from prophet import Prophet  
+from prophet.plot import plot_plotly
+import time  
+from streamlit_option_menu import option_menu  
+
+st.set_page_config(layout="wide", initial_sidebar_state="expanded")
+
+def add_meta_tag():
+    meta_tag = """
+        <head>
+            <meta name="google-site-verification" content="QBiAoAo1GAkCBe1QoWq-dQ1RjtPHeFPyzkqJqsrqW-s" />
+        </head>
+    """
+    st.markdown(meta_tag, unsafe_allow_html=True)
+
+
+add_meta_tag()
+
+
+today = date.today()  
+st.write('''# I.N.V.E.S.T. ''') 
+st.sidebar.image("Error\Market_Insights\Market_Insights\Images\invest.png", width=250,
+                 use_column_width=False)  # logo
+st.sidebar.write('''# I.N.V.E.S.T. ''')
+with st.sidebar: 
+        selected = option_menu("Utilities", ["Stocks Performance Comparison", "Real-Time Stock Price", "Stock Prediction"])
+
+start = st.sidebar.date_input(
+    'Start', datetime.date(2022, 1, 1)) 
+end = st.sidebar.date_input('End', datetime.date.today())  
+
+
+stock_df = pd.read_csv("Error\Market_Insights\Market_Insights\StockData.csv")
+
+
+if(selected == 'Stocks Performance Comparison'):  
+    st.subheader("Stocks Performance Comparison")
+    tickers = stock_df["Company Name"]
+    dropdown = st.multiselect('Pick your assets', tickers)
+
+    with st.spinner('Loading...'):  
+        time.sleep(2)
+
+
+    dict_csv = pd.read_csv('Error\Market_Insights\Market_Insights\StockData.csv', header=None, index_col=0).to_dict()[1]  # read csv file
+    symb_list = []  
+    for i in dropdown:  
+        val = dict_csv.get(i)  
+        symb_list.append(val)  
+
+    def relativeret(df): 
+        rel = df.pct_change()  
+        cumret = (1+rel).cumprod() - 1 
+        cumret = cumret.fillna(0)  
+        return cumret  
+
+    if len(dropdown) > 0:  
+        df = relativeret(yf.download(symb_list, start, end))[
+            'Adj Close']  
+        raw_df = relativeret(yf.download(symb_list, start, end))
+        raw_df.reset_index(inplace=True) 
+
+        closingPrice = yf.download(symb_list, start, end)[
+            'Adj Close']  
+        volume = yf.download(symb_list, start, end)['Volume']
+
+        st.subheader('Raw Data {}'.format(dropdown))
+        st.write(raw_df) 
+        chart = ('Line Chart', 'Area Chart', 'Bar Chart')  
+
+        dropdown1 = st.selectbox('Pick your chart', chart)
+        with st.spinner('Loading...'):  
+            time.sleep(2)
+
+        st.subheader('Relative Returns {}'.format(dropdown))
+
+        if (dropdown1) == 'Line Chart':  
+            st.line_chart(df)  
+            st.write("### Closing Price of {}".format(dropdown))
+            st.line_chart(closingPrice)  
+
+            st.write("### Volume of {}".format(dropdown))
+            st.line_chart(volume)  
+
+        elif (dropdown1) == 'Area Chart':  
+            st.area_chart(df)  
+            st.write("### Closing Price of {}".format(dropdown))
+            st.area_chart(closingPrice)  
+            st.write("### Volume of {}".format(dropdown))
+            st.area_chart(volume)  
+
+        elif (dropdown1) == 'Bar Chart':  
+            st.bar_chart(df)  
+            st.write("### Closing Price of {}".format(dropdown))
+            st.bar_chart(closingPrice) 
+
+            st.write("### Volume of {}".format(dropdown))
+            st.bar_chart(volume)  
+
+        else:
+            st.line_chart(df, width=1000, height=800,
+                          use_container_width=False)  
+            st.write("### Closing Price of {}".format(dropdown))
+            st.line_chart(closingPrice) 
+
+            st.write("### Volume of {}".format(dropdown))
+            st.line_chart(volume)  
+
+    else:  
+        st.write('Please select atleast one asset')  
+
+
+elif(selected == 'Real-Time Stock Price'):  
+    st.subheader("Real-Time Stock Price")
+    tickers = stock_df["Company Name"]  
+    a = st.selectbox('Pick a Company', tickers)
+
+    with st.spinner('Loading...'):  
+            time.sleep(2)
+
+    dict_csv = pd.read_csv('Error\Market_Insights\Market_Insights\StockData.csv', header=None, index_col=0).to_dict()[1]  # read csv file
+    symb_list = []  
+
+    val = dict_csv.get(a)  
+    symb_list.append(val) 
+
+    if "button_clicked" not in st.session_state:  
+        st.session_state.button_clicked = False  
+
+    def callback():  
+        st.session_state.button_clicked = True  
+    if (
+        st.button("Search", on_click=callback)  
+        or st.session_state.button_clicked  
+    ):
+        if(a == ""):  
+            st.write("Click Search to Search for a Company")
+            with st.spinner('Loading...'): 
+             time.sleep(2)
+        else:  
+            data = yf.download(symb_list, start=start, end=end)
+            data.reset_index(inplace=True)  
+            st.subheader('Raw Data of {}'.format(a))  
+            st.write(data) 
+
+            def plot_raw_data(): 
+                fig = go.Figure()  
+                fig.add_trace(go.Scatter( 
+                    x=data['Date'], y=data['Open'], name="stock_open")) 
+                fig.add_trace(go.Scatter( 
+                    x=data['Date'], y=data['Close'], name="stock_close")) 
+                fig.layout.update( 
+                    title_text='Line Chart of {}'.format(a) , xaxis_rangeslider_visible=True) 
+                st.plotly_chart(fig)  
+
+            def plot_candle_data():  
+                fig = go.Figure()  
+                fig.add_trace(go.Candlestick(x=data['Date'], 
+                                             open=data['Open'],
+                                             high=data['High'], 
+                                             low=data['Low'], 
+                                             close=data['Close'], name='market data')) 
+                fig.update_layout(  
+                    title='Candlestick Chart of {}'.format(a), 
+                    yaxis_title='Stock Price', 
+                    xaxis_title='Date') 
+                st.plotly_chart(fig) 
+
+            chart = ('Candle Stick', 'Line Chart')  
+            dropdown1 = st.selectbox('Pick your chart', chart)
+            with st.spinner('Loading...'): 
+             time.sleep(2)
+            if (dropdown1) == 'Candle Stick':  
+                plot_candle_data()  
+            elif (dropdown1) == 'Line Chart':  
+                plot_raw_data()  
+            else: 
+                plot_candle_data() 
+
+
+elif(selected == 'Stock Prediction'):  
+    st.subheader("Stock Prediction")
+
+    tickers = stock_df["Company Name"]  
+    a = st.selectbox('Pick a Company', tickers)
+    with st.spinner('Loading...'):  
+             time.sleep(2)
+    dict_csv = pd.read_csv('Error\Market_Insights\Market_Insights\StockData.csv', header=None, index_col=0).to_dict()[1]  
+    symb_list = []  
+    val = dict_csv.get(a)  
+    symb_list.append(val)  
+    if(a == ""):  
+        st.write("Enter a Stock Name") 
+    else:  
+        data = yf.download(symb_list, start=start, end=end)
+        data.reset_index(inplace=True)  
+        st.subheader('Raw Data of {}'.format(a))  
+        st.write(data)  
+
+        def plot_raw_data():  
+            fig = go.Figure()  
+            fig.add_trace(go.Scatter( 
+                x=data['Date'], y=data['Open'], name="stock_open")) 
+            fig.add_trace(go.Scatter(
+                x=data['Date'], y=data['Close'], name="stock_close")) 
+            fig.layout.update( 
+                title_text='Time Series Data of {}'.format(a), xaxis_rangeslider_visible=True) 
+            st.plotly_chart(fig) 
+
+        plot_raw_data()  
+        n_years = st.slider('Years of prediction:', 1, 4)
+        period = n_years * 365 
+
+
+        df_train = data[['Date', 'Close']]
+        df_train = df_train.rename(
+            columns={"Date": "ds", "Close": "y"})  
+
+        m = Prophet()  
+        m.fit(df_train)  
+        future = m.make_future_dataframe(
+            periods=period) 
+        forecast = m.predict(future) 
+
+
+        st.subheader('Forecast Data of {}'.format(a))  
+        st.write(forecast)  
+
+        st.subheader(f'Forecast plot for {n_years} years')  
+        fig1 = plot_plotly(m, forecast)  
+        st.plotly_chart(fig1)  
+
+        st.subheader("Forecast components of {}".format(a))  
+        fig2 = m.plot_components(forecast)  
+        st.write(fig2)  
diff --git a/...ts/Market_Insights/Images/237e57514b5e59a375c74748a055d4cd-removebg-preview.png b/...ts/Market_Insights/Images/237e57514b5e59a375c74748a055d4cd-removebg-preview.png
diff --git a/Error/Market_Insights/Market_Insights/Images/invest.png b/Error/Market_Insights/Market_Insights/Images/invest.png