|
|
|
|
|
import streamlit as st |
|
|
import requests |
|
|
from model import fetch_data, calculate_indicators, calculate_support_resistance |
|
|
import os |
|
|
from dotenv import load_dotenv |
|
|
|
|
|
|
|
|
load_dotenv() |
|
|
|
|
|
|
|
|
|
|
|
API_TOKEN = os.getenv('HF_API_KEY') |
|
|
API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3.1-8B-Instruct" |
|
|
|
|
|
if not API_TOKEN: |
|
|
raise ValueError("HF_API_KEY not found in environment variables. Please set it in .env file or Hugging Face Spaces secrets.") |
|
|
|
|
|
def generate_prompt(ticker, start_date, end_date): |
|
|
"""Fetch data, calculate indicators, and prepare the prompt.""" |
|
|
data = fetch_data(ticker, start_date, end_date) |
|
|
|
|
|
if data is None: |
|
|
return "No data available for the given ticker and date range.", None |
|
|
|
|
|
data = calculate_indicators(data) |
|
|
support, resistance = calculate_support_resistance(data) |
|
|
|
|
|
|
|
|
highest_close = data['Close'].max() |
|
|
lowest_close = data['Close'].min() |
|
|
average_close = data['Close'].mean() |
|
|
average_volume = data['Volume'].mean() |
|
|
highest_volume = data['Volume'].max() |
|
|
lowest_volume = data['Volume'].min() |
|
|
daily_returns = data['Close'].pct_change().dropna() |
|
|
volatility = daily_returns.std() |
|
|
|
|
|
recent_trend = "uptrend" if data['Close'].iloc[-1] > data['Close'].iloc[0] else "downtrend" if data['Close'].iloc[-1] < data['Close'].iloc[0] else "sideways" |
|
|
|
|
|
|
|
|
summary = { |
|
|
'latest_close': data['Close'].iloc[-1], |
|
|
'SMA_50': data['SMA_50'].iloc[-1], |
|
|
'EMA_50': data['EMA_50'].iloc[-1], |
|
|
'RSI': data['RSI'].iloc[-1], |
|
|
'MACD': data['MACD'].iloc[-1], |
|
|
'MACD_Signal': data['MACD_Signal'].iloc[-1], |
|
|
'Bollinger_High': data['Bollinger_High'].iloc[-1], |
|
|
'Bollinger_Low': data['Bollinger_Low'].iloc[-1], |
|
|
'ATR': data['ATR'].iloc[-1], |
|
|
'OBV': data['OBV'].iloc[-1], |
|
|
'Support': support, |
|
|
'Resistance': resistance, |
|
|
'Highest_Close': highest_close, |
|
|
'Lowest_Close': lowest_close, |
|
|
'Average_Close': average_close, |
|
|
'Average_Volume': average_volume, |
|
|
'Highest_Volume': highest_volume, |
|
|
'Lowest_Volume': lowest_volume, |
|
|
'Volatility': volatility, |
|
|
'Recent_Trend': recent_trend, |
|
|
'Percentage_Change': (data['Close'].iloc[-1] - data['Close'].iloc[-2]) / data['Close'].iloc[-2] * 100 if len(data) > 1 else 0 |
|
|
} |
|
|
|
|
|
prompt = f""" |
|
|
Analyze the following stock data for {ticker} and provide a buy/sell recommendation: |
|
|
Latest Close Price: {summary['latest_close']} |
|
|
SMA 50: {summary['SMA_50']} |
|
|
EMA 50: {summary['EMA_50']} |
|
|
RSI: {summary['RSI']} |
|
|
MACD: {summary['MACD']} |
|
|
MACD Signal: {summary['MACD_Signal']} |
|
|
Bollinger Bands High: {summary['Bollinger_High']} |
|
|
Bollinger Bands Low: {summary['Bollinger_Low']} |
|
|
ATR: {summary['ATR']} |
|
|
OBV: {summary['OBV']} |
|
|
Support Level: {summary['Support']} |
|
|
Resistance Level: {summary['Resistance']} |
|
|
Highest Close Price: {summary['Highest_Close']} |
|
|
Lowest Close Price: {summary['Lowest_Close']} |
|
|
Average Close Price: {summary['Average_Close']} |
|
|
Average Volume: {summary['Average_Volume']} |
|
|
Highest Volume: {summary['Highest_Volume']} |
|
|
Lowest Volume: {summary['Lowest_Volume']} |
|
|
Volatility: {summary['Volatility']} |
|
|
Recent Trend: {summary['Recent_Trend']} |
|
|
Percentage Change: {summary['Percentage_Change']}% |
|
|
""" |
|
|
|
|
|
return prompt, summary |
|
|
|
|
|
def get_recommendation(prompt): |
|
|
"""Get stock recommendation from Hugging Face API.""" |
|
|
headers = { |
|
|
"Authorization": f"Bearer {API_TOKEN}", |
|
|
"Content-Type": "application/json" |
|
|
} |
|
|
payload = {"inputs": prompt} |
|
|
|
|
|
response = requests.post(API_URL, headers=headers, json=payload) |
|
|
response.raise_for_status() |
|
|
result = response.json() |
|
|
|
|
|
return result[0]['generated_text'].strip() |
|
|
|
|
|
def display_recommendation(ticker, start_date, end_date): |
|
|
"""Fetch data, generate prompt, get recommendation, and display it in a nice format.""" |
|
|
prompt, summary = generate_prompt(ticker, start_date, end_date) |
|
|
|
|
|
if summary is None: |
|
|
st.error(prompt) |
|
|
return |
|
|
|
|
|
try: |
|
|
recommendation = get_recommendation(prompt) |
|
|
except Exception as e: |
|
|
st.error(f"An error occurred while getting recommendation: {e}") |
|
|
return |
|
|
|
|
|
|
|
|
st.markdown(f"### Stock Analysis & Recommendation for {ticker}") |
|
|
|
|
|
st.markdown(f""" |
|
|
<div style='border:2px solid #4CAF50; padding: 15px; border-radius: 10px;'> |
|
|
<table style='width:100%; border-collapse: collapse;'> |
|
|
<tr> |
|
|
<th style='text-align: left;'>Indicator</th> |
|
|
<th style='text-align: left;'>Value</th> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Latest Close Price</td> |
|
|
<td>{summary['latest_close']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>SMA 50</td> |
|
|
<td>{summary['SMA_50']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>EMA 50</td> |
|
|
<td>{summary['EMA_50']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RSI</td> |
|
|
<td>{summary['RSI']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>MACD</td> |
|
|
<td>{summary['MACD']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>MACD Signal</td> |
|
|
<td>{summary['MACD_Signal']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Bollinger Bands High</td> |
|
|
<td>{summary['Bollinger_High']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Bollinger Bands Low</td> |
|
|
<td>{summary['Bollinger_Low']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>ATR</td> |
|
|
<td>{summary['ATR']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>OBV</td> |
|
|
<td>{summary['OBV']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Support Level</td> |
|
|
<td>{summary['Support']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Resistance Level</td> |
|
|
<td>{summary['Resistance']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Highest Close Price</td> |
|
|
<td>{summary['Highest_Close']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Lowest Close Price</td> |
|
|
<td>{summary['Lowest_Close']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Average Close Price</td> |
|
|
<td>{summary['Average_Close']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Average Volume</td> |
|
|
<td>{summary['Average_Volume']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Highest Volume</td> |
|
|
<td>{summary['Highest_Volume']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Lowest Volume</td> |
|
|
<td>{summary['Lowest_Volume']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Volatility</td> |
|
|
<td>{summary['Volatility']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Recent Trend</td> |
|
|
<td>{summary['Recent_Trend']}</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Percentage Change</td> |
|
|
<td>{summary['Percentage_Change']}%</td> |
|
|
</tr> |
|
|
</table> |
|
|
</div> |
|
|
""", unsafe_allow_html=True) |
|
|
|
|
|
st.write('AI Recommendation:') |
|
|
st.write(recommendation) |
|
|
|
