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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("📊 TechyTrade") | |
| ticker = st.text_input("Enter a stock ticker (e.g. AAPL) 🏷️", "AAPL") | |
| period = st.selectbox("Enter a time frame ⏳", ("1D", "5D", "1M", "6M", "YTD", "1Y", "5Y"), index=2) | |
| forecast_period = st.slider("Select forecast period (days) 🔮", 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 🔑", type="password") | |
| button = st.button("Submit 🚀") | |
| # Load generative model | |
| 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"${value:.1f}{suffixes[suffix_index]}" | |
| # Technical Indicators Functions | |
| def calculate_sma(data, window): | |
| return data.rolling(window=window).mean() | |
| def calculate_ema(data, window): | |
| return data.ewm(span=window, adjust=False).mean() | |
| def calculate_rsi(data, window): | |
| delta = data.diff() | |
| gain = (delta.where(delta > 0, 0)).rolling(window=window).mean() | |
| loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean() | |
| rs = gain / loss | |
| return 100 - (100 / (1 + rs)) | |
| def calculate_macd(data, short_window=12, long_window=26, signal_window=9): | |
| short_ema = calculate_ema(data, short_window) | |
| long_ema = calculate_ema(data, long_window) | |
| macd = short_ema - long_ema | |
| signal = calculate_ema(macd, signal_window) | |
| return macd, signal | |
| def calculate_bollinger_bands(data, window): | |
| sma = calculate_sma(data, window) | |
| std = data.rolling(window=window).std() | |
| upper_band = sma + (std * 2) | |
| lower_band = sma - (std * 2) | |
| return upper_band, lower_band | |
| # If Submit button is clicked | |
| def safe_float_format(value, prefix="$", suffix="", is_percentage=False): | |
| try: | |
| if value is None or isinstance(value, str): | |
| return "N/A" | |
| if is_percentage: | |
| value *= 100 | |
| return f"{prefix}{value:.2f}{suffix}" | |
| except: | |
| return "N/A" | |
| 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 ", safe_float_format(current_price)), | |
| ("Previous Close ", safe_float_format(prev_close)), | |
| ("Day High ", safe_float_format(day_high)), | |
| ("Day Low ", safe_float_format(day_low)), | |
| ("52 Week High ", safe_float_format(ft_week_high)), | |
| ("52 Week Low ", safe_float_format(ft_week_low)), | |
| ] | |
| 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) ", safe_float_format(forward_eps, prefix="")), | |
| ("P/E (FWD) ", safe_float_format(forward_pe, prefix="")), | |
| ("PEG Ratio ", safe_float_format(peg_ratio, prefix="")), | |
| ("Div Rate (FWD) ", safe_float_format(dividend_rate)), | |
| ("Div Yield (FWD) ", safe_float_format(dividend_yield, prefix="", suffix="%", is_percentage=True)), | |
| ("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 🔮") | |
| 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}") | |