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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 = False # 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.experimental_get_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) |