File size: 7,416 Bytes
2c5028f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | import streamlit as st
import plotly.graph_objects as go
import yfinance as yf
from transformers import pipeline
class AIDashboard:
def __init__(self):
self.sentiment_model = pipeline(
"sentiment-analysis",
model="yiyanghkust/finbert-tone"
)
def render(self):
st.title("🧠 AI Stock Research Lab")
tab1, tab2, tab3 = st.tabs([
"📰 News Sentiment",
"📈 Technical Analysis",
"💬 AI Chat Analyst"
])
with tab1:
self.news_sentiment_tab()
with tab2:
self.technical_analysis_tab()
with tab3:
self.ai_chat_tab()
def news_sentiment_tab(self):
st.subheader("Financial News Sentiment Analysis")
# Input for multiple stocks
symbols = st.text_input("Enter stock symbols (comma-separated):",
"AAPL, MSFT, NVDA, TSLA")
if st.button("Analyze News Sentiment"):
symbol_list = [s.strip() for s in symbols.split(',')]
for symbol in symbol_list[:5]: # Limit to 5
with st.expander(f"📊 {symbol} News Analysis"):
try:
ticker = yf.Ticker(symbol)
news = ticker.news[:3] # Get 3 latest news
if news:
total_score = 0
for item in news:
title = item.get('title', 'No title')
st.write(f"**Headline**: {title}")
# Analyze sentiment
result = self.sentiment_model(title[:512])
sentiment = result[0]['label']
score = result[0]['score']
total_score += score if sentiment == 'Positive' else -score
# Display sentiment
if sentiment == 'Positive':
st.success(f"✅ Positive ({score:.2%})")
elif sentiment == 'Negative':
st.error(f"❌ Negative ({score:.2%})")
else:
st.info(f"📊 Neutral ({score:.2%})")
# Overall sentiment
avg_sentiment = total_score / len(news)
st.metric("Overall Sentiment Score", f"{avg_sentiment:.2%}")
else:
st.warning("No recent news available")
except Exception as e:
st.error(f"Error analyzing {symbol}: {str(e)}")
def technical_analysis_tab(self):
st.subheader("AI-Powered Technical Analysis")
symbol = st.text_input("Stock Symbol:", "AAPL")
period = st.selectbox("Time Period", ["1mo", "3mo", "6mo", "1y"])
if st.button("Generate AI Analysis"):
try:
# Get data
ticker = yf.Ticker(symbol)
hist = ticker.history(period=period)
if len(hist) > 0:
# Create interactive chart
fig = go.Figure(data=[go.Candlestick(
x=hist.index,
open=hist['Open'],
high=hist['High'],
low=hist['Low'],
close=hist['Close'],
name='Price'
)])
# Add moving averages
hist['SMA_20'] = hist['Close'].rolling(window=20).mean()
hist['SMA_50'] = hist['Close'].rolling(window=50).mean()
fig.add_trace(go.Scatter(
x=hist.index,
y=hist['SMA_20'],
name='20-Day MA',
line=dict(color='orange', width=2)
))
fig.add_trace(go.Scatter(
x=hist.index,
y=hist['SMA_50'],
name='50-Day MA',
line=dict(color='blue', width=2)
))
fig.update_layout(
title=f"{symbol} Technical Analysis",
yaxis_title="Price ($)",
xaxis_title="Date",
template="plotly_dark"
)
st.plotly_chart(fig, use_container_width=True)
# AI-generated insights
current_price = hist['Close'].iloc[-1]
sma_20 = hist['SMA_20'].iloc[-1]
sma_50 = hist['SMA_50'].iloc[-1]
st.subheader("🤖 AI Technical Insights")
if current_price > sma_20 and current_price > sma_50:
st.success("**BULLISH SIGNAL**: Price above both moving averages")
st.write("AI Recommendation: Consider buying on pullbacks")
elif current_price < sma_20 and current_price < sma_50:
st.error("**BEARISH SIGNAL**: Price below both moving averages")
st.write("AI Recommendation: Consider selling or waiting")
else:
st.warning("**NEUTRAL/MIXED SIGNALS**")
st.write("AI Recommendation: Hold and monitor")
except Exception as e:
st.error(f"Error: {str(e)}")
def ai_chat_tab(self):
st.subheader("💬 AI Stock Analyst Chat")
# Simple chat interface
user_question = st.text_input("Ask about any stock or trading strategy:")
if user_question:
# Simple response logic (enhance with actual LLM)
responses = {
"buy": "Based on technical analysis, consider buying when price is above 50-day moving average with increasing volume.",
"sell": "Consider selling if stock breaks below key support levels or shows bearish divergence.",
"hold": "Hold if fundamentals remain strong despite short-term volatility.",
"portfolio": "For your portfolio, focus on diversification and risk management."
}
question_lower = user_question.lower()
if "buy" in question_lower:
st.info(responses["buy"])
elif "sell" in question_lower:
st.info(responses["sell"])
elif "hold" in question_lower:
st.info(responses["hold"])
elif "portfolio" in question_lower:
st.info(responses["portfolio"])
else:
st.info("AI Analysis: Consider both technical and fundamental factors before making investment decisions.") |