upgraded stock market simulator

app.py

@@ -3,10 +3,25 @@ import yfinance as yf
 import pandas as pd
 import plotly.graph_objs as go
 import numpy as np
+from plotly.subplots import make_subplots
+import os
 
-isPswdValid = False
+from langchain.embeddings import GooglePalmEmbeddings
+from langchain.llms import GooglePalm
+
+from langchain.document_loaders import UnstructuredURLLoader  #load urls into docoument-loader
+from langchain.chains.question_answering import load_qa_chain
+from langchain.indexes import VectorstoreIndexCreator #vectorize db index with chromadb
+from langchain.text_splitter import CharacterTextSplitter #text splitter
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import UnstructuredPDFLoader  #load pdf
+from langchain.agents import create_pandas_dataframe_agent
+import google.generativeai as palm
+
+isPswdValid = True # Set to True to temporarily disable password checking
+palm_api_key = st.secrets["PALM_API_KEY"]
 try:
-    pswdVal = st.experimental_get_query_params()['pwd'][0]
+    pswdVal = st.query_params()['pwd'][0]
     if pswdVal==st.secrets["PSWD"]:
         isPswdValid = True
 except:
@@ -15,6 +30,13 @@ except:
 if not isPswdValid:
     st.write("Invalid Password")
 else:
+    # Initialize language model
+    api_key = palm_api_key # put your API key here
+    os.environ["GOOGLE_API_KEY"] = palm_api_key
+    palm.configure(api_key=palm_api_key)
+    llm = GooglePalm()
+    llm.temperature = 0.1
+
     # Set the Streamlit app title and icon
     st.set_page_config(page_title="Stock Analysis", page_icon="📈")
 
@@ -29,31 +51,120 @@ else:
         stock_data = yf.download(ticker_symbol, start=start_date, end=end_date)
     except Exception as e:
         st.error("Error fetching stock data. Please check the ticker symbol and date range.")
+    df = stock_data
+    df.reset_index(inplace=True)  # Reset index to ensure 'Date' becomes a column
+    
+    # Create figure with secondary y-axis
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+
+    # include candlestick with rangeselector
+    fig.add_trace(go.Candlestick(x=df['Date'], # Except date, query all other data using Symbol
+                    open=df['Open'][ticker_symbol], high=df['High'][ticker_symbol],
+                    low=df['Low'][ticker_symbol], close=df['Close'][ticker_symbol]),
+                secondary_y=True)
 
-    # Display basic stock information
-    st.header(f"Stock Analysis for {ticker_symbol}")
-    st.subheader("Basic Stock Information")
-    st.write(stock_data.tail())
-
-    # Plot a candlestick chart
-    st.subheader("Candlestick Chart")
-    fig = go.Figure(data=[go.Candlestick(x=stock_data.index,
-                open=stock_data['Open'],
-                high=stock_data['High'],
-                low=stock_data['Low'],
-                close=stock_data['Close'])])
-    fig.update_layout(title=f'{ticker_symbol} Candlestick Chart', xaxis_title='Date', yaxis_title='Price')
+    # include a go.Bar trace for volumes
+    fig.add_trace(go.Bar(x=df['Date'], y=df['Volume'][ticker_symbol]),
+                secondary_y=False)
+
+    fig.layout.yaxis2.showgrid=False
     st.plotly_chart(fig)
 
-    # Calculate basic statistics
-    st.subheader("Basic Statistics")
-    st.write(f"**Average Closing Price**: ${np.mean(stock_data['Close']):.2f}")
-    st.write(f"**Minimum Closing Price**: ${np.min(stock_data['Close']):.2f}")
-    st.write(f"**Maximum Closing Price**: ${np.max(stock_data['Close']):.2f}")
-    st.write(f"**Total Volume Traded**: {np.sum(stock_data['Volume']):,} shares")
-
-    # Add a text summary
-    st.subheader("Stock Summary")
-    st.write("This is a brief summary of the stock's performance.")
-    st.write("You can add more in-depth analysis and insights here.")
-    # You can use external libraries or APIs for more advanced analysis
+    # Technical Indicators
+    st.header("Technical Indicators")
+
+    # Moving Averages
+    st.subheader("Moving Averages")
+    df['SMA_20'] = df['Close'][ticker_symbol].rolling(window=20).mean()
+    df['SMA_50'] = df['Close'][ticker_symbol].rolling(window=50).mean()
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df['Date'], y=df['Close'][ticker_symbol], mode='lines', name='Close Price'))
+    fig.add_trace(go.Scatter(x=df['Date'], y=df['SMA_20'], mode='lines', name='20-Day SMA'))
+    fig.add_trace(go.Scatter(x=df['Date'], y=df['SMA_50'], mode='lines', name='50-Day SMA'))
+    fig.update_layout(title="Moving Averages", xaxis_title="Date", yaxis_title="Price (USD)")
+    st.plotly_chart(fig)
+
+    # RSI (Manual Calculation)
+    st.subheader("Relative Strength Index (RSI)")
+    window_length = 14
+
+    # Calculate the daily price changes
+    delta = df['Close'][ticker_symbol].diff()
+
+    # Separate gains and losses
+    gain = delta.where(delta > 0, 0)
+    loss = -delta.where(delta < 0, 0)
+
+    # Calculate the average gain and average loss
+    avg_gain = gain.rolling(window=window_length, min_periods=1).mean()
+    avg_loss = loss.rolling(window=window_length, min_periods=1).mean()
+
+    # Calculate the RSI
+    rs = avg_gain / avg_loss
+    df['RSI'] = 100 - (100 / (1 + rs))
+
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df['Date'], y=df['RSI'], mode='lines', name='RSI'))
+    fig.add_hline(y=70, line_dash="dash", line_color="red", annotation_text="Overbought")
+    fig.add_hline(y=30, line_dash="dash", line_color="green", annotation_text="Oversold")
+    fig.update_layout(title="RSI Indicator", xaxis_title="Date", yaxis_title="RSI")
+    st.plotly_chart(fig)
+
+    # Volume Analysis
+    st.subheader("Volume Analysis")
+    fig = go.Figure()
+    fig.add_trace(go.Bar(x=df['Date'], y=df['Volume'][ticker_symbol], name='Volume'))
+    fig.update_layout(title="Volume Analysis", xaxis_title="Date", yaxis_title="Volume")
+    st.plotly_chart(fig)
+
+    loader = []
+    index = VectorstoreIndexCreator(
+            embedding=GooglePalmEmbeddings(),
+            text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(loader)
+    chain = RetrievalQA.from_chain_type(llm=llm,
+                            chain_type="stuff",
+                            retriever=index.vectorstore.as_retriever(),
+                            input_key="question")
+
+    # Additional Insights
+    st.header("In-depth Analysis")
+    # Prepare text for PaLM
+    chatTextStr = f"""
+    Analyze the following stock data for patterns, trends, and insights.
+    Provide a detailed summary of key market movements.
+    """
+
+    # Initializing the agent
+    agent = create_pandas_dataframe_agent(llm, df[['Date', 'Open', 'High', 'Low', 'Close']].tail(10), verbose=False)
+    answer = agent.run(chatTextStr)
+
+    # # Query PaLM API
+    # try:
+    #     response = palm.generate_text(
+    #         prompt=chatTextStr,
+    #         temperature=0.1,
+    #         max_output_tokens=500
+    #     )
+    #     st.write(response.result)
+    # except Exception as e:
+    #     st.error(f"Error using Google PaLM API: {e}")
+
+    st.markdown("""
+        Google Gemini API analysis:
+        {answer}
+    """)
+
+    # User Interaction
+    st.header("Custom Analysis")
+    start_date = st.date_input("Select start date:", value=pd.to_datetime("2024-01-01"))
+    end_date = st.date_input("Select end date:", value=pd.to_datetime("2024-09-30"))
+
+    # Ensure all dates are timezone-naive
+    df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize(None)
+    start_date = pd.to_datetime(start_date).tz_localize(None)
+    end_date = pd.to_datetime(end_date).tz_localize(None)
+
+    # Filter the DataFrame based on the date range
+    filtered_df = df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]
+
+    st.write(filtered_df)

requirements.txt

@@ -2,3 +2,6 @@ streamlit
 yfinance
 pandas
 plotly
+google-generativeai
+langchain==0.0.310
+chromadb==0.4.14