File size: 5,580 Bytes
8a3e273 | 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 | import streamlit as st
import pandas as pd
from utils.stock_data import get_stock_data, format_number
from utils.recommendation_engine import analyze_stock
def recommendations_page():
st.title("AI Stock Recommendations")
# Load symbols
try:
symbols = pd.read_csv("attached_assets/symbol.csv", names=['Symbol'], skiprows=1)
symbols_list = symbols['Symbol'].dropna().tolist()
except Exception as e:
st.error(f"Error loading symbols: {str(e)}")
return
# Analysis parameters
min_confidence = st.selectbox("Minimum Confidence Level", ["Low", "Medium", "High"])
if st.button("Generate Recommendations"):
with st.spinner("Analyzing all NSE stocks..."):
recommendations = []
# Progress bar for analysis
progress_bar = st.progress(0)
total_stocks = len(symbols_list)
# Analyze each stock
for idx, symbol in enumerate(symbols_list):
df, error = get_stock_data(symbol, period='3mo', interval='1d')
if error:
continue
analysis = analyze_stock(symbol, df)
if analysis:
recommendations.append(analysis)
# Update progress
progress_bar.progress((idx + 1) / total_stocks)
if recommendations:
# Convert to DataFrame
rec_df = pd.DataFrame(recommendations)
# Filter by confidence
confidence_levels = {'Low': 0, 'Medium': 1, 'High': 2}
min_conf_level = confidence_levels[min_confidence]
rec_df = rec_df[rec_df['confidence'].map(lambda x: confidence_levels[x]) >= min_conf_level]
# Sort by technical score
rec_df = rec_df.sort_values('technical_score', ascending=False)
# Display recommendations
st.subheader("Stock Recommendations")
# Format DataFrame for display
display_df = rec_df.copy()
display_df['price_change'] = display_df['price_change'].round(2).astype(str) + '%'
display_df['technical_score'] = display_df['technical_score'].round(2)
# Generate recommendation basis
basis_list = []
for _, row in display_df.iterrows():
signals = []
if row['technical_score'] > 50:
signals.append("Strong technical indicators")
if 'RSI' in row['signal_summary'] and row['signal_summary']['RSI'] == 'Oversold':
signals.append("Oversold (RSI)")
if 'MACD' in row['signal_summary'] and row['signal_summary']['MACD'] == 'Buy':
signals.append("Bullish MACD crossover")
if 'Moving Average' in row['signal_summary'] and row['signal_summary']['Moving Average'] == 'Bullish':
signals.append("Above key moving averages")
basis_list.append(", ".join(signals) if signals else "Multiple factors")
display_df['Basis'] = basis_list
# Color-code recommendations
def color_recommendations(val):
if 'Strong Buy' in val:
return 'background-color: #9fff9c'
elif 'Buy' in val:
return 'background-color: #c8ffc6'
elif 'Strong Sell' in val:
return 'background-color: #ffc6c6'
elif 'Sell' in val:
return 'background-color: #ffdede'
return ''
# Display styled table
st.dataframe(
display_df[['symbol', 'recommendation', 'confidence', 'technical_score',
'price_change', 'last_price', 'Basis']]
.style
.apply(lambda x: [color_recommendations(val) for val in x], axis=1, subset=['recommendation'])
.format({'last_price': '₹{:.2f}'})
)
# Display analysis insights
st.subheader("Analysis Insights")
total_analyzed = len(recommendations)
buy_signals = len(rec_df[rec_df['recommendation'].isin(['Buy', 'Strong Buy'])])
sell_signals = len(rec_df[rec_df['recommendation'].isin(['Sell', 'Strong Sell'])])
col1, col2, col3 = st.columns(3)
col1.metric("Total Stocks Analyzed", total_analyzed)
col2.metric("Buy Signals", buy_signals)
col3.metric("Sell Signals", sell_signals)
# Display market sentiment
st.subheader("Market Sentiment")
buy_percentage = (buy_signals / total_analyzed) * 100
if buy_percentage > 60:
sentiment = "Bullish"
color = "green"
elif buy_percentage < 40:
sentiment = "Bearish"
color = "red"
else:
sentiment = "Neutral"
color = "gray"
st.markdown(f"Overall Market Sentiment: <span style='color: {color}'>{sentiment}</span>",
unsafe_allow_html=True)
else:
st.warning("No recommendations generated. Please try with different parameters.")
if __name__ == "__main__":
recommendations_page() |