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import streamlit as st
import yfinance as yf
import pandas as pd
from prophet import Prophet
import plotly.graph_objs as go
import google.generativeai as genai
import numpy as np
# Streamlit app details
st.set_page_config(page_title="TechyTrade", layout="wide")
# Custom CSS
st.markdown("""
<style>
@import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;700&display=swap');
body {
background-color: #f4f4f9;
color: #333;
font-family: 'Montserrat', sans-serif;
}
.sidebar .sidebar-content {
background-color: #2c3e50;
color: white;
}
h1, h2, h3 {
color: #2980b9;
}
.css-1v3fvcr {
color: #2980b9 !important;
}
.css-17eq0hr {
font-family: 'Montserrat', sans-serif !important;
}
.css-2trqyj {
font-family: 'Montserrat', sans-serif !important;
}
</style>
""", unsafe_allow_html=True)
# Sidebar
with st.sidebar:
st.title("๐ TechyTrade")
ticker = st.text_input("Enter a stock ticker (e.g. AAPL) ๐ท๏ธ", "AAPL")
period = st.selectbox("Enter a time frame โณ", ("1D", "5D", "1M", "6M", "YTD", "1Y", "5Y"), index=2)
forecast_period = st.slider("Select forecast period (days) ๐ฎ", min_value=1, max_value=365, value=30)
st.write("Select Technical Indicators:")
sma_checkbox = st.checkbox("Simple Moving Average (SMA)")
ema_checkbox = st.checkbox("Exponential Moving Average (EMA)")
rsi_checkbox = st.checkbox("Relative Strength Index (RSI)")
macd_checkbox = st.checkbox("Moving Average Convergence Divergence (MACD)")
bollinger_checkbox = st.checkbox("Bollinger Bands")
google_api_key = st.text_input("Enter your Google API Key ๐", type="password")
button = st.button("Submit ๐")
# Load generative model
@st.cache_resource
def load_model(api_key):
genai.configure(api_key=api_key)
return genai.GenerativeModel('gemini-1.5-flash')
# Function to generate reasons using the generative model
def generate_reasons(fig, stock_info, price_info, biz_metrics, api_key):
model = load_model(api_key)
prompt = f"Based on the following stock price graph description:\n\n{fig}\n\n and the tables:\n\n{stock_info}\n\n and\n\n{price_info}\n\n and\n\n{biz_metrics}\n\n and analyze the trends and give recommendations and insights."
response = model.generate_content(prompt)
return response.text
# Function to format large numbers
def format_value(value):
suffixes = ["", "K", "M", "B", "T"]
suffix_index = 0
while value >= 1000 and suffix_index < len(suffixes) - 1:
value /= 1000
suffix_index += 1
return f"${value:.1f}{suffixes[suffix_index]}"
# Technical Indicators Functions
def calculate_sma(data, window):
return data.rolling(window=window).mean()
def calculate_ema(data, window):
return data.ewm(span=window, adjust=False).mean()
def calculate_rsi(data, window):
delta = data.diff()
gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
rs = gain / loss
return 100 - (100 / (1 + rs))
def calculate_macd(data, short_window=12, long_window=26, signal_window=9):
short_ema = calculate_ema(data, short_window)
long_ema = calculate_ema(data, long_window)
macd = short_ema - long_ema
signal = calculate_ema(macd, signal_window)
return macd, signal
def calculate_bollinger_bands(data, window):
sma = calculate_sma(data, window)
std = data.rolling(window=window).std()
upper_band = sma + (std * 2)
lower_band = sma - (std * 2)
return upper_band, lower_band
# If Submit button is clicked
if button:
if not ticker.strip():
st.error("Please provide a valid stock ticker.")
elif not google_api_key.strip():
st.error("Please provide a valid Google API Key.")
else:
try:
with st.spinner('Please wait...'):
# Retrieve stock data
stock = yf.Ticker(ticker)
info = stock.info
st.subheader(f"{ticker} - {info.get('longName', 'N/A')}")
# Plot historical stock price data
if period == "1D":
history = stock.history(period="1d", interval="1h")
elif period == "5D":
history = stock.history(period="5d", interval="1d")
elif period == "1M":
history = stock.history(period="1mo", interval="1d")
elif period == "6M":
history = stock.history(period="6mo", interval="1wk")
elif period == "YTD":
history = stock.history(period="ytd", interval="1mo")
elif period == "1Y":
history = stock.history(period="1y", interval="1mo")
elif period == "5Y":
history = stock.history(period="5y", interval="3mo")
# Create a plotly figure
fig = go.Figure()
fig.add_trace(go.Scatter(x=history.index, y=history['Close'], mode='lines', name='Close Price'))
# Add Technical Indicators
if sma_checkbox:
sma = calculate_sma(history['Close'], window=20)
fig.add_trace(go.Scatter(x=history.index, y=sma, mode='lines', name='SMA (20)'))
if ema_checkbox:
ema = calculate_ema(history['Close'], window=20)
fig.add_trace(go.Scatter(x=history.index, y=ema, mode='lines', name='EMA (20)'))
if rsi_checkbox:
rsi = calculate_rsi(history['Close'], window=14)
fig.add_trace(go.Scatter(x=history.index, y=rsi, mode='lines', name='RSI (14)', yaxis='y2'))
fig.update_layout(yaxis2=dict(title='RSI', overlaying='y', side='right'))
if macd_checkbox:
macd, signal = calculate_macd(history['Close'])
fig.add_trace(go.Scatter(x=history.index, y=macd, mode='lines', name='MACD'))
fig.add_trace(go.Scatter(x=history.index, y=signal, mode='lines', name='Signal Line'))
if bollinger_checkbox:
upper_band, lower_band = calculate_bollinger_bands(history['Close'], window=20)
fig.add_trace(go.Scatter(x=history.index, y=upper_band, mode='lines', name='Upper Band'))
fig.add_trace(go.Scatter(x=history.index, y=lower_band, mode='lines', name='Lower Band'))
fig.update_layout(
title=f"Historical Stock Prices for {ticker}",
xaxis_title="Date",
yaxis_title="Close Price",
hovermode="x unified"
)
st.plotly_chart(fig, use_container_width=True)
col1, col2, col3 = st.columns(3)
# Display stock information as a dataframe
country = info.get('country', 'N/A')
sector = info.get('sector', 'N/A')
industry = info.get('industry', 'N/A')
market_cap = info.get('marketCap', 'N/A')
ent_value = info.get('enterpriseValue', 'N/A')
employees = info.get('fullTimeEmployees', 'N/A')
stock_info = [
("Stock Info", "Value"),
("Country ", country),
("Sector ", sector),
("Industry ", industry),
("Market Cap ", format_value(market_cap)),
("Enterprise Value ", format_value(ent_value)),
("Employees ", employees)
]
df = pd.DataFrame(stock_info[1:], columns=stock_info[0])
col1.dataframe(df, width=400, hide_index=True)
# Display price information as a dataframe
current_price = info.get('currentPrice', 'N/A')
prev_close = info.get('previousClose', 'N/A')
day_high = info.get('dayHigh', 'N/A')
day_low = info.get('dayLow', 'N/A')
ft_week_high = info.get('fiftyTwoWeekHigh', 'N/A')
ft_week_low = info.get('fiftyTwoWeekLow', 'N/A')
price_info = [
("Price Info", "Value"),
("Current Price ", f"${current_price:.2f}"),
("Previous Close ", f"${prev_close:.2f}"),
("Day High ", f"${day_high:.2f}"),
("Day Low ", f"${day_low:.2f}"),
("52 Week High ", f"${ft_week_high:.2f}"),
("52 Week Low ", f"${ft_week_low:.2f}")
]
df = pd.DataFrame(price_info[1:], columns=price_info[0])
col2.dataframe(df, width=400, hide_index=True)
# Display business metrics as a dataframe
forward_eps = info.get('forwardEps', 'N/A')
forward_pe = info.get('forwardPE', 'N/A')
peg_ratio = info.get('pegRatio', 'N/A')
dividend_rate = info.get('dividendRate', 'N/A')
dividend_yield = info.get('dividendYield', 'N/A')
recommendation = info.get('recommendationKey', 'N/A')
biz_metrics = [
("Business Metrics", "Value"),
("EPS (FWD) ", f"{forward_eps:.2f}"),
("P/E (FWD) ", f"{forward_pe:.2f}"),
("PEG Ratio ", f"{peg_ratio:.2f}"),
("Div Rate (FWD) ", f"${dividend_rate:.2f}"),
("Div Yield (FWD) ", f"{dividend_yield * 100:.2f}%"),
("Recommendation ", recommendation.capitalize())
]
df = pd.DataFrame(biz_metrics[1:], columns=biz_metrics[0])
col3.dataframe(df, width=400, hide_index=True)
# Forecasting
st.subheader("Stock Price Forecast ๐ฎ")
df_forecast = history.reset_index()[['Date', 'Close']]
df_forecast['Date'] = pd.to_datetime(df_forecast['Date']).dt.tz_localize(None) # Remove timezone information
df_forecast.columns = ['ds', 'y']
m = Prophet(daily_seasonality=True)
m.fit(df_forecast)
future = m.make_future_dataframe(periods=forecast_period)
forecast = m.predict(future)
fig2 = go.Figure()
fig2.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Forecast'))
fig2.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], mode='lines', name='Upper Confidence Interval', line=dict(dash='dash')))
fig2.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], mode='lines', name='Lower Confidence Interval', line=dict(dash='dash')))
fig2.update_layout(
title=f"Stock Price Forecast for {ticker}",
xaxis_title="Date",
yaxis_title="Predicted Close Price",
hovermode="x unified"
)
st.plotly_chart(fig2, use_container_width=True)
# Generate reasons based on forecast
graph_description = f"The stock price forecast graph for {ticker} shows the predicted close prices along with the upper and lower confidence intervals for the next {forecast_period} days."
reasons = generate_reasons(fig, stock_info, price_info, biz_metrics, google_api_key)
st.subheader("Investment Analysis")
st.write(reasons)
except Exception as e:
st.exception(f"An error occurred: {e}")
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