mirror of
https://github.com/777r1NTR/FX-QUANT-SCAN.git
synced 2026-08-22 04:48:06 +00:00
93 lines
3.9 KiB
Python
93 lines
3.9 KiB
Python
import streamlit as st
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import pandas as pd
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from datetime import datetime
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from core.zone_transition import get_zone_transitions_today
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TAB_NAME = "📈 Zone Transitions"
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def render():
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st.header("📈 Zone Transitions (Past 24h)")
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# Add a refresh button
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col1, col2 = st.columns([1, 4])
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with col1:
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refresh_data = st.button("🔄 Refresh Data", help="Scan for new zone transitions")
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# Check if it's weekend
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if datetime.now().weekday() >= 5:
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st.warning("⏸️ Markets are closed on weekends. Showing cached data if available.")
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# Get live data or use cached data
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with st.spinner("Scanning tickers for zone transitions..."):
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if refresh_data or 'zone_transitions_cache' not in st.session_state:
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df_transitions = get_zone_transitions_today()
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st.session_state.zone_transitions_cache = df_transitions
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else:
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df_transitions = st.session_state.zone_transitions_cache
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if df_transitions.empty:
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st.info("No zone transitions detected in the last 24 hours.")
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# Optionally show historical data from CSV
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st.subheader("📋 Historical Data")
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show_historical = st.checkbox("Show historical transitions from log file")
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if show_historical:
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try:
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import os
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log_file = "reports/zone_transition_log.csv"
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if os.path.exists(log_file):
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df_historical = pd.read_csv(log_file)
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# Standardize column names
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if 'Date' in df_historical.columns and 'Timestamp' not in df_historical.columns:
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df_historical = df_historical.rename(columns={'Date': 'Timestamp'})
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if 'Timestamp' in df_historical.columns:
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df_historical['Timestamp'] = pd.to_datetime(df_historical['Timestamp'])
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df_historical = df_historical.sort_values(by='Timestamp', ascending=False)
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# Show last 50 transitions
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st.dataframe(df_historical.head(50), use_container_width=True)
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st.caption(f"Showing last 50 of {len(df_historical)} total historical transitions")
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else:
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st.info("No historical data file found.")
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except Exception as e:
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st.error(f"Could not load historical data: {e}")
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else:
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st.success(f"✅ {len(df_transitions)} transitions found in the last 24 hours!")
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# Display the fresh data
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st.dataframe(df_transitions, use_container_width=True)
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# Add some analytics
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if len(df_transitions) > 0:
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col1, col2, col3 = st.columns(3)
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with col1:
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unique_tickers = df_transitions['Ticker'].nunique()
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st.metric("🏷️ Active Tickers", unique_tickers)
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with col2:
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most_active = df_transitions['Ticker'].value_counts().iloc[0] if len(df_transitions) > 0 else 0
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st.metric("🔥 Max Transitions", most_active)
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with col3:
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latest_time = df_transitions['Timestamp'].max()
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hours_ago = (datetime.now() - latest_time.replace(tzinfo=None)).total_seconds() / 3600
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st.metric("⏰ Latest Transition", f"{hours_ago:.1f}h ago")
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# Ticker breakdown
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st.subheader("📊 Transitions by Ticker")
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ticker_counts = df_transitions['Ticker'].value_counts()
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st.bar_chart(ticker_counts)
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# Download button
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csv = df_transitions.to_csv(index=False).encode("utf-8")
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st.download_button(
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"📥 Download Current Transitions",
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csv,
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file_name=f"zone_transitions_{datetime.now().strftime('%Y%m%d_%H%M')}.csv",
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mime="text/csv"
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)
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