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| import streamlit as st | |
| import pandas as pd | |
| import plotly.graph_objects as go | |
| from data.loader import load_from_hf, seed_dataset_from_scratch, sync_incremental_data, X_EQUITY_TICKERS, FI_TICKERS | |
| from engine.trend_engine import run_trend_module | |
| st.set_page_config(layout="wide", page_title="P2 Strategy Suite") | |
| if 'master_data' not in st.session_state: | |
| st.session_state.master_data = load_from_hf() | |
| with st.sidebar: | |
| st.header("🗂️ Configuration") | |
| if st.session_state.master_data is None: | |
| if st.button("🚀 Seed Database"): | |
| st.session_state.master_data = seed_dataset_from_scratch() | |
| st.rerun() | |
| else: | |
| st.success(f"DB Last Entry: {st.session_state.master_data.index.max().date()}") | |
| if st.button("🔄 Sync New Data"): | |
| updated_df, status_code = sync_incremental_data(st.session_state.master_data) | |
| st.session_state.master_data = updated_df | |
| messages = { | |
| "success": "✅ Data refreshed", | |
| "already_current": "ℹ️ Already up-to-date", | |
| "no_new_data_yet": "⏳ Market not yet closed", | |
| "api_failure": "❌ Connection/API issue", | |
| "error": "❌ Critical Error" | |
| } | |
| st.session_state.sync_status = messages.get(status_code, "❓ Unknown Status") | |
| st.rerun() | |
| if 'sync_status' in st.session_state: | |
| st.sidebar.info(st.session_state.sync_status) | |
| st.divider() | |
| option = st.selectbox("Universe Selection", ("Option A - FI Trend", "Option B - Equity Trend")) | |
| sub_option = st.selectbox("Conviction Strategy", | |
| ("All Trending ETFs", "3 Highest Conviction", "1 Highest Conviction")) | |
| start_yr = st.slider("OOS Start Year", 2008, 2026, 2018) | |
| vol_target = st.slider("Risk Target (%)", 5, 20, 12) / 100 | |
| run_btn = st.button("🚀 Run Analysis", use_container_width=True, type="primary") | |
| if st.session_state.master_data is not None: | |
| if run_btn: | |
| is_fi = "Option A" in option | |
| univ = FI_TICKERS if is_fi else X_EQUITY_TICKERS | |
| bench = "AGG" if is_fi else "SPY" | |
| results = run_trend_module(st.session_state.master_data[univ], | |
| st.session_state.master_data[bench], | |
| st.session_state.master_data['SOFR_ANNUAL'], | |
| vol_target, start_yr, sub_option) | |
| st.title(f"📊 {option}: {sub_option}") | |
| m1, m2, m3, m4 = st.columns(4) | |
| m1.metric("Annual Return", f"{results['ann_ret']:.1%}") | |
| m2.metric("Sharpe Ratio", f"{results['sharpe']:.2f}") | |
| m3.metric("Max Drawdown", f"{results['max_dd']:.1%}") | |
| m4.metric("Current SOFR", f"{results['current_sofr']:.2%}") | |
| fig = go.Figure() | |
| fig.add_trace(go.Scatter(x=results['equity_curve'].index, y=results['equity_curve'], name='Strategy')) | |
| fig.add_trace(go.Scatter(x=results['bench_curve'].index, y=results['bench_curve'], name=f'Benchmark ({bench})')) | |
| fig.update_layout(title="Out-of-Sample Performance", template="plotly_dark") | |
| st.plotly_chart(fig, use_container_width=True) | |
| st.divider() | |
| col_l, col_r = st.columns([1, 1.5]) | |
| with col_l: | |
| st.subheader(f"🎯 Target Allocation: {results['next_day']}") | |
| weights = results['current_weights'][results['current_weights'] > 0.0001].to_dict() | |
| weights['CASH (SOFR)'] = results['cash_weight'] | |
| st.table(pd.DataFrame.from_dict(weights, orient='index', columns=['Weight']).style.format("{:.2%}")) | |
| with col_r: | |
| st.subheader("📚 Methodology: Zarattini & Antonacci") | |
| st.markdown("Strategy uses 50/200 SMA filters, conviction ranking, and 60-day volatility targeting.") | |
| else: | |
| st.info("💡 Adjust settings and click 'Run Analysis'.") | |