new stock market

.gitattributes

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README.md

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----
-title: Stockmarketplatform
-emoji: ⚡
-colorFrom: blue
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Stockmarketplatform
+emoji: ⚡
+colorFrom: blue
+colorTo: blue
+sdk: streamlit
+sdk_version: 1.26.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,174 +1,127 @@
-import streamlit as st
-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
-
-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.query_params()['pwd'][0]
-    if pswdVal==st.secrets["PSWD"]:
-        isPswdValid = True
-except:
-    pass
-
-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="📈")
-
-    # Create a Streamlit sidebar for user input
-    st.sidebar.title("Stock Analysis")
-    ticker_symbol = st.sidebar.text_input("Enter Stock Ticker Symbol:", value='AAPL')
-    start_date = st.sidebar.date_input("Start Date", pd.to_datetime('2024-01-01'))
-    end_date = st.sidebar.date_input("End Date", pd.to_datetime('2024-10-01'))
-
-    # Fetch stock data from Yahoo Finance
-    try:
-        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)
-
-    # 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)
-
-    # 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)
-    
-    from langchain.document_loaders import DataFrameLoader
-
-    loader = DataFrameLoader(df)
-    index = VectorstoreIndexCreator(embedding=GooglePalmEmbeddings()).from_loaders([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)
+import streamlit as st
+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
+
+from langchain_openai import ChatOpenAI
+
+
+isPswdValid = True # Set to True to temporarily disable password checking
+OPEN_ROUTER_KEY = st.secrets["OPEN_ROUTER_KEY"]
+OPEN_ROUTER_MODEL = "meta-llama/llama-3.1-70b-instruct:free"
+
+try:
+    pswdVal = st.query_params()['pwd'][0]
+    if pswdVal==st.secrets["PSWD"]:
+        isPswdValid = True
+except:
+    pass
+
+if not isPswdValid:
+    st.write("Invalid Password")
+else:
+    # Initialize language model
+    llm = ChatOpenAI(model=OPEN_ROUTER_MODEL, temperature=0.1, openai_api_key=OPEN_ROUTER_KEY, openai_api_base="https://openrouter.ai/api/v1")
+
+    # Set the Streamlit app title and icon
+    st.set_page_config(page_title="Stock Analysis", page_icon="📈")
+
+    # Create a Streamlit sidebar for user input
+    st.sidebar.title("Stock Analysis")
+    ticker_symbol = st.sidebar.text_input("Enter Stock Ticker Symbol:", value='AAPL')
+    start_date = st.sidebar.date_input("Start Date", pd.to_datetime('2024-01-01'))
+    end_date = st.sidebar.date_input("End Date", pd.to_datetime('2024-10-01'))
+
+    # Fetch stock data from Yahoo Finance
+    try:
+        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
+    
+    # Technical Indicators
+    st.header("Stock Price Chart")
+
+    # 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)
+
+    # 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)
+
+    # 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)
+
+    # 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.
+    """
+
+    answer = llm.predict(f'''
+    I have yfinance data below on {ticker_symbol} symbol:
+
+    {str(df[['Date', 'Open', 'High', 'Low', 'Close']].tail(30))}
+
+    {chatTextStr}
+    ''')
+    st.write(answer)

requirements.txt

@@ -1,7 +1,4 @@
-streamlit
-yfinance
-pandas
-plotly
-google-generativeai
-langchain==0.0.310
-chromadb==0.4.14
+yfinance
+pandas
+plotly
+langchain_openai==0.2.9