Sync from GitHub

hf_space/data/loader.py

@@ -7,7 +7,7 @@ import streamlit as st
 
 # --- GLOBAL CONSTANTS ---
 X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
-FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
+FI_TICKERS = ["TLT", "IEF", "TIP", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
 REPO_ID = "P2SAMAPA/etf_trend_data"
 FILENAME = "market_data.csv"
 

hf_space/hf_space/hf_space/hf_space/app.py

@@ -6,27 +6,22 @@ from engine.trend_engine import run_trend_module
 
 st.set_page_config(layout="wide", page_title="P2 Strategy Suite")
 
-# Initial Data Load
 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:
-        # Show database status
         st.success(f"DB Last Entry: {st.session_state.master_data.index.max().date()}")
         
-        # Sync Action
         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
             
-            # Map logical codes to UI messages
             messages = {
                 "success": "✅ Data refreshed",
                 "already_current": "ℹ️ Already up-to-date",
@@ -34,33 +29,26 @@ with st.sidebar:
                 "api_failure": "❌ Connection/API issue",
                 "error": "❌ Critical Error"
             }
-            # Save message to session state so it survives the rerun
             st.session_state.sync_status = messages.get(status_code, "❓ Unknown Status")
             st.rerun()
 
-        # Persistent status display
         if 'sync_status' in st.session_state:
             st.sidebar.info(st.session_state.sync_status)
 
     st.divider()
-    
-    # Strategy Parameters
     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:
-        # Configuration mapping
         is_fi = "Option A" in option
         univ = FI_TICKERS if is_fi else X_EQUITY_TICKERS
         bench = "AGG" if is_fi else "SPY"
         
-        # Compute Results
         results = run_trend_module(st.session_state.master_data[univ], 
                                  st.session_state.master_data[bench], 
                                  st.session_state.master_data['SOFR_ANNUAL'], 
@@ -68,23 +56,20 @@ if st.session_state.master_data is not None:
         
         st.title(f"📊 {option}: {sub_option}")
         
-        # Display Metrics
         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%}")
 
-        # Performance Visualization
         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", hovermode="x unified")
+        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()
@@ -95,6 +80,4 @@ if st.session_state.master_data is not None:
             st.subheader("📚 Methodology: Zarattini & Antonacci")
             st.markdown("Strategy uses 50/200 SMA filters, conviction ranking, and 60-day volatility targeting.")
     else:
-        st.info("💡 Adjust your parameters and click 'Run Analysis'.")
-else:
-    st.warning("⚠️ Data source missing. Please Seed or check HF Token.")
+        st.info("💡 Adjust settings and click 'Run Analysis'.")

hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -1,100 +1,72 @@
-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
+import pandas_datareader.data as web
+import yfinance as yf
+from huggingface_hub import hf_hub_download, HfApi
+import os
+import streamlit as st
 
-st.set_page_config(layout="wide", page_title="P2 Strategy Suite")
+# --- GLOBAL CONSTANTS ---
+X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
+FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
+REPO_ID = "P2SAMAPA/etf_trend_data"
+FILENAME = "market_data.csv"
 
-# Initial Data Load
-if 'master_data' not in st.session_state:
-    st.session_state.master_data = load_from_hf()
+def get_safe_token():
+    try: return st.secrets["HF_TOKEN"]
+    except: return os.getenv("HF_TOKEN")
 
-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:
-        # Show database status
-        st.success(f"DB Last Entry: {st.session_state.master_data.index.max().date()}")
-        
-        # Sync Action
-        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
-            
-            # Map logical codes to UI messages
-            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"
-            }
-            # Save message to session state so it survives the rerun
-            st.session_state.sync_status = messages.get(status_code, "❓ Unknown Status")
-            st.rerun()
+def load_from_hf():
+    token = get_safe_token()
+    if not token: return None
+    try:
+        path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
+        df = pd.read_csv(path, index_col=0, parse_dates=True)
+        return df.ffill()
+    except: return None
 
-        # Persistent status display
-        if 'sync_status' in st.session_state:
-            st.sidebar.info(st.session_state.sync_status)
+def seed_dataset_from_scratch():
+    tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
+    data = yf.download(tickers, start="2008-01-01", progress=False)
+    master_df = data['Adj Close'] if 'Adj Close' in data.columns else data['Close']
+    try:
+        sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
+        master_df['SOFR_ANNUAL'] = sofr / 100
+    except:
+        master_df['SOFR_ANNUAL'] = 0.045
+    master_df = master_df.sort_index().ffill()
+    master_df.to_csv(FILENAME)
+    upload_to_hf(FILENAME)
+    return master_df
 
-    st.divider()
-    
-    # Strategy Parameters
-    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
+def sync_incremental_data(df):
+    if df is None: return None, "error"
+    last_date = pd.to_datetime(df.index.max()).date()
+    today = pd.Timestamp.now().date()
     
-    run_btn = st.button("🚀 Run Analysis", use_container_width=True, type="primary")
-
-if st.session_state.master_data is not None:
-    if run_btn:
-        # Configuration mapping
-        is_fi = "Option A" in option
-        univ = FI_TICKERS if is_fi else X_EQUITY_TICKERS
-        bench = "AGG" if is_fi else "SPY"
-        
-        # Compute Results
-        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}")
-        
-        # Display Metrics
-        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%}")
+    if last_date >= today:
+        return df, "already_current"
 
-        # Performance Visualization
-        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", hovermode="x unified")
-        st.plotly_chart(fig, use_container_width=True)
+    sync_start = last_date + pd.Timedelta(days=1)
+    tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
+    try:
+        new_data_raw = yf.download(tickers, start=sync_start, progress=False)
+        if new_data_raw is None or new_data_raw.empty:
+            return df, "no_new_data_yet"
 
-        st.divider()
-        col_l, col_r = st.columns([1, 1.5])
+        new_data = new_data_raw['Adj Close'] if 'Adj Close' in new_data_raw.columns else new_data_raw['Close']
+        combined = pd.concat([df, new_data]).sort_index()
+        combined = combined[~combined.index.duplicated(keep='last')].ffill()
         
-        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%}"))
+        combined.to_csv(FILENAME)
+        upload_to_hf(FILENAME)
+        return combined, "success"
+    except:
+        return df, "api_failure"
 
-        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 your parameters and click 'Run Analysis'.")
-else:
-    st.warning("⚠️ Data source missing. Please Seed or check HF Token.")
+def upload_to_hf(path):
+    token = get_safe_token()
+    if token:
+        api = HfApi()
+        try:
+            api.upload_file(path_or_fileobj=path, path_in_repo=FILENAME, repo_id=REPO_ID, repo_type="dataset", token=token)
+        except: pass

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -1,70 +1,100 @@
-import pandas as pd
-import pandas_datareader.data as web
-import yfinance as yf
-from huggingface_hub import hf_hub_download, HfApi
-import os
 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
 
-# --- GLOBAL CONSTANTS ---
-X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
-FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
-
-REPO_ID = "P2SAMAPA/etf_trend_data"
-FILENAME = "market_data.csv"
+st.set_page_config(layout="wide", page_title="P2 Strategy Suite")
 
-def get_safe_token():
-    try: return st.secrets["HF_TOKEN"]
-    except: return os.getenv("HF_TOKEN")
+# Initial Data Load
+if 'master_data' not in st.session_state:
+    st.session_state.master_data = load_from_hf()
 
-def load_from_hf():
-    token = get_safe_token()
-    if not token: return None
-    try:
-        path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
-        df = pd.read_csv(path, index_col=0, parse_dates=True)
-        return df.ffill()
-    except: return None
+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:
+        # Show database status
+        st.success(f"DB Last Entry: {st.session_state.master_data.index.max().date()}")
+        
+        # Sync Action
+        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
+            
+            # Map logical codes to UI messages
+            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"
+            }
+            # Save message to session state so it survives the rerun
+            st.session_state.sync_status = messages.get(status_code, "❓ Unknown Status")
+            st.rerun()
 
-def seed_dataset_from_scratch():
-    tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
-    data = yf.download(tickers, start="2008-01-01", progress=False)
-    master_df = data['Adj Close'] if 'Adj Close' in data.columns else data['Close']
-    try:
-        sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
-        master_df['SOFR_ANNUAL'] = sofr / 100
-    except:
-        master_df['SOFR_ANNUAL'] = 0.045
-    master_df = master_df.sort_index().ffill()
-    master_df.to_csv(FILENAME)
-    upload_to_hf(FILENAME)
-    return master_df
+        # Persistent status display
+        if 'sync_status' in st.session_state:
+            st.sidebar.info(st.session_state.sync_status)
 
-def sync_incremental_data(df):
-    """Syncs data and returns (df, success_bool)"""
-    if df is None: return None, False
-    last_date = pd.to_datetime(df.index.max())
-    sync_start = last_date + pd.Timedelta(days=1)
+    st.divider()
     
-    if sync_start > pd.Timestamp.now().normalize():
-        return df, False
+    # Strategy Parameters
+    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:
+        # Configuration mapping
+        is_fi = "Option A" in option
+        univ = FI_TICKERS if is_fi else X_EQUITY_TICKERS
+        bench = "AGG" if is_fi else "SPY"
+        
+        # Compute Results
+        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}")
+        
+        # Display Metrics
+        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%}")
 
-    tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
-    try:
-        new_data_raw = yf.download(tickers, start=sync_start, progress=False)
-        if new_data_raw.empty: return df, False
+        # Performance Visualization
+        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", hovermode="x unified")
+        st.plotly_chart(fig, use_container_width=True)
 
-        new_data = new_data_raw['Adj Close'] if 'Adj Close' in new_data_raw.columns else new_data_raw['Close']
-        combined = pd.concat([df, new_data]).sort_index()
-        combined = combined[~combined.index.duplicated(keep='last')].ffill()
+        st.divider()
+        col_l, col_r = st.columns([1, 1.5])
         
-        combined.to_csv(FILENAME)
-        upload_to_hf(FILENAME)
-        return combined, True
-    except:
-        return df, False
+        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%}"))
 
-def upload_to_hf(path):
-    token = get_safe_token()
-    if token:
-        api = HfApi()
-        api.upload_file(path_or_fileobj=path, path_in_repo=FILENAME, repo_id=REPO_ID, repo_type="dataset", token=token)
+        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 your parameters and click 'Run Analysis'.")
+else:
+    st.warning("⚠️ Data source missing. Please Seed or check HF Token.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -6,7 +6,7 @@ from engine.trend_engine import run_trend_module
 
 st.set_page_config(layout="wide", page_title="P2 Strategy Suite")
 
-# Initialize Session State safely
+# Initial Data Load
 if 'master_data' not in st.session_state:
     st.session_state.master_data = load_from_hf()
 
@@ -18,28 +18,33 @@ with st.sidebar:
             st.session_state.master_data = seed_dataset_from_scratch()
             st.rerun()
     else:
+        # Show database status
         st.success(f"DB Last Entry: {st.session_state.master_data.index.max().date()}")
         
-        # Sync Button with Persistent UI Feedback
+        # Sync Action
         if st.button("🔄 Sync New Data"):
-            updated_df, success = sync_incremental_data(st.session_state.master_data)
+            updated_df, status_code = sync_incremental_data(st.session_state.master_data)
             st.session_state.master_data = updated_df
             
-            # Store the result so it survives the rerun
-            if success:
-                st.session_state.sync_status = "✅ Data refreshed"
-            else:
-                st.session_state.sync_status = "❌ Data refresh failed"
-            
+            # Map logical codes to UI messages
+            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"
+            }
+            # Save message to session state so it survives the rerun
+            st.session_state.sync_status = messages.get(status_code, "❓ Unknown Status")
             st.rerun()
 
-        # Display the persisted status message if it exists in state
+        # Persistent status display
         if 'sync_status' in st.session_state:
-            st.sidebar.write(st.session_state.sync_status)
+            st.sidebar.info(st.session_state.sync_status)
 
     st.divider()
     
-    # Strategy Inputs
+    # Strategy Parameters
     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"))
@@ -50,12 +55,12 @@ with st.sidebar:
 
 if st.session_state.master_data is not None:
     if run_btn:
-        # Determine Universe and Benchmark
+        # Configuration mapping
         is_fi = "Option A" in option
         univ = FI_TICKERS if is_fi else X_EQUITY_TICKERS
         bench = "AGG" if is_fi else "SPY"
         
-        # Run the Quantitative Engine
+        # Compute Results
         results = run_trend_module(st.session_state.master_data[univ], 
                                  st.session_state.master_data[bench], 
                                  st.session_state.master_data['SOFR_ANNUAL'], 
@@ -63,51 +68,33 @@ if st.session_state.master_data is not None:
         
         st.title(f"📊 {option}: {sub_option}")
         
-        # Row 1: Key Performance Metrics
+        # Display Metrics
         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%}")
 
-        # Row 2: Performance Chart
+        # Performance Visualization
         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 Cumulative Performance", 
-            template="plotly_dark", 
-            xaxis_title="Timeline",
-            yaxis_title="Growth of $1.00",
-            hovermode="x unified"
-        )
+        fig.update_layout(title="Out-of-Sample Performance", template="plotly_dark", hovermode="x unified")
         st.plotly_chart(fig, use_container_width=True)
 
-        # Row 3: Methodology & Next-Day Allocations
         st.divider()
-        col_left, col_right = st.columns([1, 1.5])
+        col_l, col_r = st.columns([1, 1.5])
         
-        with col_left:
+        with col_l:
             st.subheader(f"🎯 Target Allocation: {results['next_day']}")
-            # Filter out zero weights for the display table
             weights = results['current_weights'][results['current_weights'] > 0.0001].to_dict()
             weights['CASH (SOFR)'] = results['cash_weight']
-            
-            df_weights = pd.DataFrame.from_dict(weights, orient='index', columns=['Weight'])
-            st.table(df_weights.style.format("{:.2%}"))
+            st.table(pd.DataFrame.from_dict(weights, orient='index', columns=['Weight']).style.format("{:.2%}"))
 
-        with col_right:
+        with col_r:
             st.subheader("📚 Methodology: Zarattini & Antonacci")
-            st.markdown(f"""
-            This strategy implements the **2025 Charles H. Dow Award** winning framework by **Andrea Zarattini** and **Michael Antonacci**.
-            
-            1. **Regime Identification**: A dual 50/200-day SMA filter determines asset eligibility. 
-            2. **Conviction Ranking**: Assets are ranked by their distance from the 200-day SMA (Trend Strength).
-            3. **Concentrated Sizing**: In **{sub_option}** mode, the risk budget is focused only on top leaders.
-            4. **Volatility Targeting**: Allocations are sized inversely to 60-day volatility to maintain a stable **{vol_target:.0%}** risk profile.
-            5. **Cash Buffer**: Remaining budget earns the live SOFR rate (Federal Reserve Bank of New York).
-            """)
+            st.markdown("Strategy uses 50/200 SMA filters, conviction ranking, and 60-day volatility targeting.")
     else:
-        st.info("💡 Adjust your risk parameters in the sidebar and click 'Run Analysis' to see predicted allocations.")
+        st.info("💡 Adjust your parameters and click 'Run Analysis'.")
 else:
-    st.warning("⚠️ No data found. Please check your HF_TOKEN or click 'Seed Database'.")
+    st.warning("⚠️ Data source missing. Please Seed or check HF Token.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -13,34 +13,30 @@ if 'master_data' not in st.session_state:
 with st.sidebar:
     st.header("🗂️ Configuration")
     
-    # Database Initialization / Status
     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:
-        # Display the last available date in the dataset
-        last_db_date = st.session_state.master_data.index.max().date()
-        st.success(f"DB Last Entry: {last_db_date}")
+        st.success(f"DB Last Entry: {st.session_state.master_data.index.max().date()}")
         
-        # Sync Button with UI Feedback
+        # Sync Button with Persistent UI Feedback
         if st.button("🔄 Sync New Data"):
-            with st.spinner("Checking Stooq & yFinance..."):
-                # Captures the updated dataframe and the status message from the loader
-                updated_df, status_msg = sync_incremental_data(st.session_state.master_data)
-                st.session_state.master_data = updated_df
-                
-                # Visual Feedback Logic
-                if "Refreshed" in status_msg and "Not" not in status_msg:
-                    st.sidebar.success(status_msg)
-                    st.toast(status_msg, icon="✅")
-                else:
-                    st.sidebar.warning(status_msg)
-                    st.toast(status_msg, icon="⚠️")
+            updated_df, success = sync_incremental_data(st.session_state.master_data)
+            st.session_state.master_data = updated_df
+            
+            # Store the result so it survives the rerun
+            if success:
+                st.session_state.sync_status = "✅ Data refreshed"
+            else:
+                st.session_state.sync_status = "❌ Data refresh failed"
             
-            # Force UI to refresh to show new dates/data
             st.rerun()
 
+        # Display the persisted status message if it exists in state
+        if 'sync_status' in st.session_state:
+            st.sidebar.write(st.session_state.sync_status)
+
     st.divider()
     
     # Strategy Inputs
@@ -114,4 +110,4 @@ if st.session_state.master_data is not None:
     else:
         st.info("💡 Adjust your risk parameters in the sidebar and click 'Run Analysis' to see predicted allocations.")
 else:
-    st.warning("⚠️ No data found. Please provide a Hugging Face Token in Secrets or click 'Seed Database'.")
+    st.warning("⚠️ No data found. Please check your HF_TOKEN or click 'Seed Database'.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -5,7 +5,7 @@ from huggingface_hub import hf_hub_download, HfApi
 import os
 import streamlit as st
 
-# --- GLOBAL CONSTANTS (Required for app.py imports) ---
+# --- GLOBAL CONSTANTS ---
 X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
 FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
 
@@ -17,74 +17,51 @@ def get_safe_token():
     except: return os.getenv("HF_TOKEN")
 
 def load_from_hf():
-    """Initial load function called by app.py"""
     token = get_safe_token()
-    if not token: 
-        return None
+    if not token: return None
     try:
         path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
         df = pd.read_csv(path, index_col=0, parse_dates=True)
-        return df.ffill() 
-    except Exception as e:
-        st.error(f"HF Load Error: {e}")
-        return None
+        return df.ffill()
+    except: return None
 
 def seed_dataset_from_scratch():
-    """Initializes the CSV with full history"""
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
     data = yf.download(tickers, start="2008-01-01", progress=False)
-    
-    if 'Adj Close' in data.columns:
-        master_df = data['Adj Close']
-    else:
-        master_df = data['Close']
-    
+    master_df = data['Adj Close'] if 'Adj Close' in data.columns else data['Close']
     try:
         sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
         master_df['SOFR_ANNUAL'] = sofr / 100
     except:
         master_df['SOFR_ANNUAL'] = 0.045
-    
     master_df = master_df.sort_index().ffill()
     master_df.to_csv(FILENAME)
     upload_to_hf(FILENAME)
     return master_df
 
 def sync_incremental_data(df):
-    """Attempt to sync data and return (dataframe, status_message)"""
-    if df is None:
-        return None, "Error: No initial data to sync."
-        
+    """Syncs data and returns (df, success_bool)"""
+    if df is None: return None, False
     last_date = pd.to_datetime(df.index.max())
     sync_start = last_date + pd.Timedelta(days=1)
     
-    # Check if we are already current
     if sync_start > pd.Timestamp.now().normalize():
-        return df, "Already Up-to-Date"
+        return df, False
 
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
-    
     try:
         new_data_raw = yf.download(tickers, start=sync_start, progress=False)
-        
-        if new_data_raw is None or new_data_raw.empty:
-            return df, "Not Refreshed (No new data available yet)"
-
-        if 'Adj Close' in new_data_raw.columns:
-            new_data = new_data_raw['Adj Close']
-        else:
-            new_data = new_data_raw['Close']
+        if new_data_raw.empty: return df, False
 
+        new_data = new_data_raw['Adj Close'] if 'Adj Close' in new_data_raw.columns else new_data_raw['Close']
         combined = pd.concat([df, new_data]).sort_index()
         combined = combined[~combined.index.duplicated(keep='last')].ffill()
         
         combined.to_csv(FILENAME)
         upload_to_hf(FILENAME)
-        
-        return combined, f"Data Refreshed (Updated to {combined.index.max().date()})"
-
-    except Exception as e:
-        return df, f"Not Refreshed (Error: {str(e)})"
+        return combined, True
+    except:
+        return df, False
 
 def upload_to_hf(path):
     token = get_safe_token()

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -6,35 +6,60 @@ from engine.trend_engine import run_trend_module
 
 st.set_page_config(layout="wide", page_title="P2 Strategy Suite")
 
+# Initialize Session State safely
 if 'master_data' not in st.session_state:
     st.session_state.master_data = load_from_hf()
 
 with st.sidebar:
     st.header("🗂️ Configuration")
+    
+    # Database Initialization / Status
     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"Sync: {st.session_state.master_data.index.max().date()}")
+        # Display the last available date in the dataset
+        last_db_date = st.session_state.master_data.index.max().date()
+        st.success(f"DB Last Entry: {last_db_date}")
+        
+        # Sync Button with UI Feedback
         if st.button("🔄 Sync New Data"):
-            st.session_state.master_data = sync_incremental_data(st.session_state.master_data)
+            with st.spinner("Checking Stooq & yFinance..."):
+                # Captures the updated dataframe and the status message from the loader
+                updated_df, status_msg = sync_incremental_data(st.session_state.master_data)
+                st.session_state.master_data = updated_df
+                
+                # Visual Feedback Logic
+                if "Refreshed" in status_msg and "Not" not in status_msg:
+                    st.sidebar.success(status_msg)
+                    st.toast(status_msg, icon="✅")
+                else:
+                    st.sidebar.warning(status_msg)
+                    st.toast(status_msg, icon="⚠️")
+            
+            # Force UI to refresh to show new dates/data
             st.rerun()
 
     st.divider()
+    
+    # Strategy Inputs
     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:
+        # Determine Universe and Benchmark
         is_fi = "Option A" in option
         univ = FI_TICKERS if is_fi else X_EQUITY_TICKERS
         bench = "AGG" if is_fi else "SPY"
         
+        # Run the Quantitative Engine
         results = run_trend_module(st.session_state.master_data[univ], 
                                  st.session_state.master_data[bench], 
                                  st.session_state.master_data['SOFR_ANNUAL'], 
@@ -42,30 +67,38 @@ if st.session_state.master_data is not None:
         
         st.title(f"📊 {option}: {sub_option}")
         
-        # Row 1: Metrics (Annual Return First)
+        # Row 1: Key Performance Metrics
         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%}")
 
-        # Row 2: Performance Chart (Interactive Years)
+        # Row 2: Performance Chart
         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", xaxis_title="Year")
+        fig.update_layout(
+            title="Out-of-Sample Cumulative Performance", 
+            template="plotly_dark", 
+            xaxis_title="Timeline",
+            yaxis_title="Growth of $1.00",
+            hovermode="x unified"
+        )
         st.plotly_chart(fig, use_container_width=True)
 
-        # Row 3: Methodology & Allocations
+        # Row 3: Methodology & Next-Day Allocations
         st.divider()
         col_left, col_right = st.columns([1, 1.5])
         
         with col_left:
-            st.subheader(f"🎯 Allocation for {results['next_day']}")
-            w = results['current_weights'][results['current_weights'] > 0.0001].to_dict()
-            w['CASH (SOFR)'] = results['cash_weight']
-            df_w = pd.DataFrame.from_dict(w, orient='index', columns=['Weight'])
-            st.table(df_w.style.format("{:.2%}"))
+            st.subheader(f"🎯 Target Allocation: {results['next_day']}")
+            # Filter out zero weights for the display table
+            weights = results['current_weights'][results['current_weights'] > 0.0001].to_dict()
+            weights['CASH (SOFR)'] = results['cash_weight']
+            
+            df_weights = pd.DataFrame.from_dict(weights, orient='index', columns=['Weight'])
+            st.table(df_weights.style.format("{:.2%}"))
 
         with col_right:
             st.subheader("📚 Methodology: Zarattini & Antonacci")
@@ -79,4 +112,6 @@ if st.session_state.master_data is not None:
             5. **Cash Buffer**: Remaining budget earns the live SOFR rate (Federal Reserve Bank of New York).
             """)
     else:
-        st.info("💡 Adjust settings and click 'Run Analysis'.")
+        st.info("💡 Adjust your risk parameters in the sidebar and click 'Run Analysis' to see predicted allocations.")
+else:
+    st.warning("⚠️ No data found. Please provide a Hugging Face Token in Secrets or click 'Seed Database'.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -5,7 +5,7 @@ from huggingface_hub import hf_hub_download, HfApi
 import os
 import streamlit as st
 
-# Explicitly define the lists first so they are available for export
+# --- GLOBAL CONSTANTS (Required for app.py imports) ---
 X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
 FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
 
@@ -24,7 +24,7 @@ def load_from_hf():
     try:
         path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
         df = pd.read_csv(path, index_col=0, parse_dates=True)
-        return df.ffill() # Handle internal NaNs immediately
+        return df.ffill() 
     except Exception as e:
         st.error(f"HF Load Error: {e}")
         return None
@@ -34,7 +34,6 @@ def seed_dataset_from_scratch():
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
     data = yf.download(tickers, start="2008-01-01", progress=False)
     
-    # Robustly handle Column Multi-Index
     if 'Adj Close' in data.columns:
         master_df = data['Adj Close']
     else:
@@ -52,46 +51,40 @@ def seed_dataset_from_scratch():
     return master_df
 
 def sync_incremental_data(df):
-    """The function triggered by the Sync Button"""
+    """Attempt to sync data and return (dataframe, status_message)"""
     if df is None:
-        return seed_dataset_from_scratch()
+        return None, "Error: No initial data to sync."
         
     last_date = pd.to_datetime(df.index.max())
     sync_start = last_date + pd.Timedelta(days=1)
     
-    # Check if data is already current to today
+    # Check if we are already current
     if sync_start > pd.Timestamp.now().normalize():
-        st.toast("Data is already up to date!", icon="✅")
-        return df
+        return df, "Already Up-to-Date"
 
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
     
     try:
         new_data_raw = yf.download(tickers, start=sync_start, progress=False)
         
-        if new_data_raw.empty:
-            st.toast("No new market sessions found.", icon="ℹ️")
-            return df
+        if new_data_raw is None or new_data_raw.empty:
+            return df, "Not Refreshed (No new data available yet)"
 
-        # Column selection
         if 'Adj Close' in new_data_raw.columns:
             new_data = new_data_raw['Adj Close']
         else:
             new_data = new_data_raw['Close']
 
-        # Merge and clean
         combined = pd.concat([df, new_data]).sort_index()
         combined = combined[~combined.index.duplicated(keep='last')].ffill()
         
-        # Save locally and push to cloud
         combined.to_csv(FILENAME)
         upload_to_hf(FILENAME)
         
-        st.toast(f"Sync complete: {combined.index.max().date()}", icon="🚀")
-        return combined
+        return combined, f"Data Refreshed (Updated to {combined.index.max().date()})"
+
     except Exception as e:
-        st.error(f"Sync failed: {e}")
-        return df
+        return df, f"Not Refreshed (Error: {str(e)})"
 
 def upload_to_hf(path):
     token = get_safe_token()

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -1,49 +1,100 @@
-def sync_incremental_data(df):
-    # Ensure index is datetime
-    df.index = pd.to_datetime(df.index)
-    last_date = df.index.max()
-    
+import pandas as pd
+import pandas_datareader.data as web
+import yfinance as yf
+from huggingface_hub import hf_hub_download, HfApi
+import os
+import streamlit as st
+
+# Explicitly define the lists first so they are available for export
+X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
+FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
+
+REPO_ID = "P2SAMAPA/etf_trend_data"
+FILENAME = "market_data.csv"
+
+def get_safe_token():
+    try: return st.secrets["HF_TOKEN"]
+    except: return os.getenv("HF_TOKEN")
+
+def load_from_hf():
+    """Initial load function called by app.py"""
+    token = get_safe_token()
+    if not token: 
+        return None
+    try:
+        path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
+        df = pd.read_csv(path, index_col=0, parse_dates=True)
+        return df.ffill() # Handle internal NaNs immediately
+    except Exception as e:
+        st.error(f"HF Load Error: {e}")
+        return None
+
+def seed_dataset_from_scratch():
+    """Initializes the CSV with full history"""
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
+    data = yf.download(tickers, start="2008-01-01", progress=False)
     
-    # Calculate sync start (day after last record)
+    # Robustly handle Column Multi-Index
+    if 'Adj Close' in data.columns:
+        master_df = data['Adj Close']
+    else:
+        master_df = data['Close']
+    
+    try:
+        sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
+        master_df['SOFR_ANNUAL'] = sofr / 100
+    except:
+        master_df['SOFR_ANNUAL'] = 0.045
+    
+    master_df = master_df.sort_index().ffill()
+    master_df.to_csv(FILENAME)
+    upload_to_hf(FILENAME)
+    return master_df
+
+def sync_incremental_data(df):
+    """The function triggered by the Sync Button"""
+    if df is None:
+        return seed_dataset_from_scratch()
+        
+    last_date = pd.to_datetime(df.index.max())
     sync_start = last_date + pd.Timedelta(days=1)
     
-    # If sync_start is in the future, nothing to do
+    # Check if data is already current to today
     if sync_start > pd.Timestamp.now().normalize():
-        st.info("Data is already up to date.")
+        st.toast("Data is already up to date!", icon="✅")
         return df
 
+    tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
+    
     try:
-        # Download new data
         new_data_raw = yf.download(tickers, start=sync_start, progress=False)
         
         if new_data_raw.empty:
-            st.warning("No new market data found to sync.")
+            st.toast("No new market sessions found.", icon="ℹ️")
             return df
 
-        # Handle columns
+        # Column selection
         if 'Adj Close' in new_data_raw.columns:
             new_data = new_data_raw['Adj Close']
         else:
             new_data = new_data_raw['Close']
 
-        # Clean NaNs before merging
-        new_data = new_data.dropna(how='all')
-
-        # Combine, sort, and deduplicate
+        # Merge and clean
         combined = pd.concat([df, new_data]).sort_index()
-        combined = combined[~combined.index.duplicated(keep='last')]
+        combined = combined[~combined.index.duplicated(keep='last')].ffill()
         
-        # Forward fill any holes in the middle, but don't fill the end
-        combined = combined.ffill()
-
-        # Save and Push
+        # Save locally and push to cloud
         combined.to_csv(FILENAME)
         upload_to_hf(FILENAME)
         
-        st.success(f"Synced successfully up to {combined.index.max().date()}")
+        st.toast(f"Sync complete: {combined.index.max().date()}", icon="🚀")
         return combined
-
     except Exception as e:
-        st.error(f"Sync failed error: {e}")
+        st.error(f"Sync failed: {e}")
         return df
+
+def upload_to_hf(path):
+    token = get_safe_token()
+    if token:
+        api = HfApi()
+        api.upload_file(path_or_fileobj=path, path_in_repo=FILENAME, repo_id=REPO_ID, repo_type="dataset", token=token)

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py

@@ -3,71 +3,110 @@ import numpy as np
 import pandas_market_calendars as mcal
 
 def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr, sub_option):
-    # 1. Trend & Conviction Logic
+    """
+    Quantitative Engine based on Zarattini & Antonacci (2025).
+    Implements Volatility Targeting and Conviction-based ETF Allocation.
+    """
+    
+    # --- 1. DATA CLEANING & PREPARATION ---
+    # Forward fill holes and drop assets with no data in the current window
+    price_df = price_df.ffill()
+    # Ensure benchmarks and SOFR are aligned
+    sofr_series = sofr_series.ffill()
+    
+    # --- 2. TREND & CONVICTION SIGNALS ---
     sma_200 = price_df.rolling(200).mean()
     sma_50 = price_df.rolling(50).mean()
     
-    # Conviction = Percentage distance above the 200 SMA
+    # Conviction = % distance above the 200 SMA (momentum strength)
     conviction_score = (price_df / sma_200) - 1
-    signals = (sma_50 > sma_200).astype(int)
+    # Basic Signal: 50 SMA > 200 SMA
+    base_signals = (sma_50 > sma_200).astype(int)
+    
+    # --- 3. CONVICTION FILTERING (Sub-Options) ---
+    if sub_option == "3 Highest Conviction":
+        # Rank assets daily; 1 is highest conviction. 
+        # Only assets in a base trend (base_signals == 1) are eligible for ranking.
+        ranked_conviction = conviction_score.where(base_signals == 1)
+        ranks = ranked_conviction.rank(axis=1, ascending=False)
+        final_signals = ((ranks <= 3)).astype(int)
+    elif sub_option == "1 Highest Conviction":
+        ranked_conviction = conviction_score.where(base_signals == 1)
+        ranks = ranked_conviction.rank(axis=1, ascending=False)
+        final_signals = ((ranks <= 1)).astype(int)
+    else:
+        # "All Trending ETFs"
+        final_signals = base_signals
     
-    # 2. Risk Metrics
+    # --- 4. VOLATILITY TARGETING (RISK BUDGETING) ---
     returns = price_df.pct_change()
+    # 60-day Annualized Realized Volatility
     asset_vol = returns.rolling(60).std() * np.sqrt(252)
     
-    # 3. Apply Sub-Option Concentration
-    if sub_option == "3 Highest Conviction":
-        ranks = conviction_score.rank(axis=1, ascending=False)
-        signals = ((ranks <= 3) & (signals == 1)).astype(int)
-    elif sub_option == "1 Highest Conviction":
-        ranks = conviction_score.rank(axis=1, ascending=False)
-        signals = ((ranks <= 1) & (signals == 1)).astype(int)
+    # Safety: If vol is NaN or 0, set to a very high number to prevent infinite weights
+    asset_vol = asset_vol.replace(0, np.nan).fillna(9.99)
     
-    # 4. Volatility Target Weighting
-    active_counts = signals.sum(axis=1)
-    # Target Vol / Asset Vol, distributed across active signals
+    # Methodology: Target Vol / Asset Vol, distributed across active signals
+    active_counts = final_signals.sum(axis=1)
+    # Avoid division by zero if no assets are in trend
     raw_weights = (target_vol / asset_vol).divide(active_counts, axis=0).replace([np.inf, -np.inf], 0).fillna(0)
-    final_weights = raw_weights * signals
     
-    # 5. Leverage Cap (1.5x)
+    # Multiply by signals to zero out non-trending assets
+    final_weights = raw_weights * final_signals
+    
+    # --- 5. EXPOSURE & LEVERAGE MANAGEMENT ---
     total_exposure = final_weights.sum(axis=1)
-    scale_factor = total_exposure.apply(lambda x: 1.5/x if x > 1.5 else 1.0)
+    # Cap total gross leverage at 1.5x (150%)
+    leverage_cap = 1.5
+    scale_factor = total_exposure.apply(lambda x: leverage_cap/x if x > leverage_cap else 1.0)
     final_weights = final_weights.multiply(scale_factor, axis=0)
     
-    # 6. Cash (SOFR) Allocation
-    cash_weight = 1.0 - final_weights.sum(axis=1)
+    # --- 6. CASH (SOFR) ALLOCATION ---
+    # Remainder of the 100% capital not used in the risk budget goes to SOFR
+    final_exposure = final_weights.sum(axis=1)
+    cash_weight = 1.0 - final_exposure
     
-    # 7. Portfolio Returns
+    # --- 7. PERFORMANCE CALCULATION ---
+    # Strategy Return = (Weights * Asset Returns) + (Cash Weight * SOFR)
+    # We shift weights by 1 to prevent look-ahead bias (trading at today's close for tomorrow)
     portfolio_ret = (final_weights.shift(1) * returns).sum(axis=1)
     portfolio_ret += cash_weight.shift(1) * (sofr_series.shift(1) / 252)
     
-    # 8. Out-of-Sample Slicing
+    # --- 8. OUT-OF-SAMPLE (OOS) METRICS ---
     oos_mask = portfolio_ret.index.year >= start_yr
-    equity_curve = (1 + portfolio_ret[oos_mask]).cumprod()
-    bench_curve = (1 + bench_series.pct_change().fillna(0)[oos_mask]).cumprod()
+    oos_returns = portfolio_ret[oos_mask]
+    
+    equity_curve = (1 + oos_returns).cumprod()
+    bench_returns = bench_series.pct_change().fillna(0)[oos_mask]
+    bench_curve = (1 + bench_returns).cumprod()
+    
+    # Drawdowns
+    dd_series = (equity_curve / equity_curve.cummax()) - 1
     
     # Stats
-    ann_ret = portfolio_ret[oos_mask].mean() * 252
-    ann_vol = portfolio_ret[oos_mask].std() * np.sqrt(252)
-    dd = (equity_curve / equity_curve.cummax()) - 1
+    ann_ret = oos_returns.mean() * 252
+    ann_vol = oos_returns.std() * np.sqrt(252)
+    current_sofr = sofr_series.ffill().iloc[-1]
+    
+    # Sharpe Ratio: (Return - RiskFree) / Vol
+    sharpe = (ann_ret - current_sofr) / ann_vol if ann_vol > 0 else 0
     
-    # --- NEXT DAY TRADING LOGIC ---
+    # --- 9. NEXT TRADING DAY CALENDAR ---
     nyse = mcal.get_calendar('NYSE')
-    # Use real-world today to anchor the search for the NEXT session
+    # Anchor to system clock to ensure we always look FORWARD
     today_dt = pd.Timestamp.now().normalize()
     search_start = today_dt + pd.Timedelta(days=1)
     sched = nyse.schedule(start_date=search_start, end_date=search_start + pd.Timedelta(days=10))
-    next_day = sched.index[0] 
+    next_day = sched.index[0]
     
     return {
         'equity_curve': equity_curve,
         'bench_curve': bench_curve,
         'ann_ret': ann_ret,
-        'sharpe': (ann_ret - sofr_series.iloc[-1]) / ann_vol if ann_vol > 0 else 0,
-        'max_dd': dd.min(),
-        'avg_daily_dd': dd.mean(),
+        'sharpe': sharpe,
+        'max_dd': dd_series.min(),
         'next_day': next_day.date(),
         'current_weights': final_weights.iloc[-1],
         'cash_weight': cash_weight.iloc[-1],
-        'current_sofr': sofr_series.iloc[-1]
+        'current_sofr': current_sofr
     }

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -1,90 +1,49 @@
-import pandas as pd
-import pandas_datareader.data as web
-import yfinance as yf
-from huggingface_hub import hf_hub_download, HfApi
-import os
-import streamlit as st
-
-# 1. Define the Ticker Lists
-X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
-FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
-
-REPO_ID = "P2SAMAPA/etf_trend_data"
-FILENAME = "market_data.csv"
-
-def get_safe_token():
-    try: return st.secrets["HF_TOKEN"]
-    except: return os.getenv("HF_TOKEN")
-
-# 2. Define load_from_hf
-def load_from_hf():
-    token = get_safe_token()
-    if not token: 
-        return None
-    try:
-        path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
-        return pd.read_csv(path, index_col=0, parse_dates=True)
-    except Exception as e:
-        st.warning(f"Could not load from HuggingFace: {e}")
-        return None
-
-# 3. Define seed_dataset_from_scratch
-def seed_dataset_from_scratch():
-    tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
-    data = yf.download(tickers, start="2008-01-01", progress=False)
-    
-    # Handle the 'Adj Close' multi-index issue
-    if 'Adj Close' in data.columns:
-        master_df = data['Adj Close']
-    else:
-        master_df = data['Close']
-    
-    # Add SOFR from FRED
-    try:
-        sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
-        master_df['SOFR_ANNUAL'] = sofr / 100
-    except:
-        master_df['SOFR_ANNUAL'] = 0.045 # Default fallback
-    
-    master_df = master_df.sort_index().ffill()
-    master_df.to_csv(FILENAME)
-    upload_to_hf(FILENAME)
-    return master_df
-
-# 4. Define sync_incremental_data
 def sync_incremental_data(df):
-    last_date = pd.to_datetime(df.index.max())
+    # Ensure index is datetime
+    df.index = pd.to_datetime(df.index)
+    last_date = df.index.max()
+    
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
+    
+    # Calculate sync start (day after last record)
     sync_start = last_date + pd.Timedelta(days=1)
     
-    if sync_start > pd.Timestamp.now():
+    # If sync_start is in the future, nothing to do
+    if sync_start > pd.Timestamp.now().normalize():
+        st.info("Data is already up to date.")
         return df
 
     try:
+        # Download new data
         new_data_raw = yf.download(tickers, start=sync_start, progress=False)
+        
         if new_data_raw.empty:
+            st.warning("No new market data found to sync.")
             return df
 
+        # Handle columns
         if 'Adj Close' in new_data_raw.columns:
             new_data = new_data_raw['Adj Close']
         else:
             new_data = new_data_raw['Close']
 
+        # Clean NaNs before merging
+        new_data = new_data.dropna(how='all')
+
+        # Combine, sort, and deduplicate
         combined = pd.concat([df, new_data]).sort_index()
         combined = combined[~combined.index.duplicated(keep='last')]
         
+        # Forward fill any holes in the middle, but don't fill the end
+        combined = combined.ffill()
+
+        # Save and Push
         combined.to_csv(FILENAME)
         upload_to_hf(FILENAME)
+        
+        st.success(f"Synced successfully up to {combined.index.max().date()}")
         return combined
+
     except Exception as e:
-        st.error(f"Sync failed: {e}")
+        st.error(f"Sync failed error: {e}")
         return df
-
-def upload_to_hf(path):
-    token = get_safe_token()
-    if token:
-        api = HfApi()
-        try:
-            api.upload_file(path_or_fileobj=path, path_in_repo=FILENAME, repo_id=REPO_ID, repo_type="dataset", token=token)
-        except Exception as e:
-            st.error(f"HF Upload failed: {e}")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -1,40 +1,90 @@
+import pandas as pd
+import pandas_datareader.data as web
+import yfinance as yf
+from huggingface_hub import hf_hub_download, HfApi
+import os
+import streamlit as st
+
+# 1. Define the Ticker Lists
+X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
+FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
+
+REPO_ID = "P2SAMAPA/etf_trend_data"
+FILENAME = "market_data.csv"
+
+def get_safe_token():
+    try: return st.secrets["HF_TOKEN"]
+    except: return os.getenv("HF_TOKEN")
+
+# 2. Define load_from_hf
+def load_from_hf():
+    token = get_safe_token()
+    if not token: 
+        return None
+    try:
+        path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
+        return pd.read_csv(path, index_col=0, parse_dates=True)
+    except Exception as e:
+        st.warning(f"Could not load from HuggingFace: {e}")
+        return None
+
+# 3. Define seed_dataset_from_scratch
+def seed_dataset_from_scratch():
+    tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
+    data = yf.download(tickers, start="2008-01-01", progress=False)
+    
+    # Handle the 'Adj Close' multi-index issue
+    if 'Adj Close' in data.columns:
+        master_df = data['Adj Close']
+    else:
+        master_df = data['Close']
+    
+    # Add SOFR from FRED
+    try:
+        sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
+        master_df['SOFR_ANNUAL'] = sofr / 100
+    except:
+        master_df['SOFR_ANNUAL'] = 0.045 # Default fallback
+    
+    master_df = master_df.sort_index().ffill()
+    master_df.to_csv(FILENAME)
+    upload_to_hf(FILENAME)
+    return master_df
+
+# 4. Define sync_incremental_data
 def sync_incremental_data(df):
     last_date = pd.to_datetime(df.index.max())
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
     sync_start = last_date + pd.Timedelta(days=1)
     
-    # Check if we even need to sync (avoiding weekend/pre-market errors)
     if sync_start > pd.Timestamp.now():
         return df
 
     try:
-        # Download from yfinance with specific configuration to avoid Multi-Index issues
-        new_data_raw = yf.download(tickers, start=sync_start, progress=False, group_by='column')
-        
+        new_data_raw = yf.download(tickers, start=sync_start, progress=False)
         if new_data_raw.empty:
             return df
 
-        # Logic to handle different yfinance return structures
         if 'Adj Close' in new_data_raw.columns:
             new_data = new_data_raw['Adj Close']
-        elif 'Close' in new_data_raw.columns:
-            new_data = new_data_raw['Close']
         else:
-            # If it's a single ticker or flattened
-            new_data = new_data_raw
+            new_data = new_data_raw['Close']
 
-        # Standardize: Ensure we only have the tickers we want and no empty columns
-        new_data = new_data[new_data.columns.intersection(tickers)]
-        
-        # Combine with master dataframe
         combined = pd.concat([df, new_data]).sort_index()
-        # Keep the most recent data point if duplicates occur
         combined = combined[~combined.index.duplicated(keep='last')]
         
         combined.to_csv(FILENAME)
         upload_to_hf(FILENAME)
         return combined
-
     except Exception as e:
         st.error(f"Sync failed: {e}")
         return df
+
+def upload_to_hf(path):
+    token = get_safe_token()
+    if token:
+        api = HfApi()
+        try:
+            api.upload_file(path_or_fileobj=path, path_in_repo=FILENAME, repo_id=REPO_ID, repo_type="dataset", token=token)
+        except Exception as e:
+            st.error(f"HF Upload failed: {e}")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -1,81 +1,40 @@
-import pandas as pd
-import pandas_datareader.data as web
-import yfinance as yf
-import time
-from huggingface_hub import hf_hub_download, HfApi
-import os
-import streamlit as st
-
-REPO_ID = "P2SAMAPA/etf_trend_data"
-FILENAME = "market_data.csv"
-
-X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
-FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
-
-def get_safe_token():
-    """Bypasses Streamlit's hard-fail on missing secrets.toml by using environment fallback."""
-    try:
-        # Try Streamlit secrets first
-        return st.secrets["HF_TOKEN"]
-    except Exception:
-        # Standard environment variable fallback (How HF actually stores them)
-        return os.getenv("HF_TOKEN")
-
-def load_from_hf():
-    token = get_safe_token()
-    if not token: return None
-    try:
-        path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
-        return pd.read_csv(path, index_col=0, parse_dates=True)
-    except:
-        return None
-
-def seed_dataset_from_scratch():
-    # Include benchmarks for comparison logic
+def sync_incremental_data(df):
+    last_date = pd.to_datetime(df.index.max())
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
-    master_df = pd.DataFrame()
-    status = st.empty()
-    progress = st.progress(0)
+    sync_start = last_date + pd.Timedelta(days=1)
     
-    for i, t in enumerate(tickers):
-        status.text(f"🛰️ Fetching {t} from Stooq...")
-        try:
-            data = web.DataReader(f"{t}.US", 'stooq', start='2008-01-01')
-            if not data.empty:
-                master_df[t] = data['Close'].sort_index()
-            time.sleep(0.7) # Polite delay
-        except:
-            try:
-                master_df[t] = yf.download(t, start="2008-01-01", progress=False)['Adj Close']
-            except: pass
-        progress.progress((i + 1) / len(tickers))
+    # Check if we even need to sync (avoiding weekend/pre-market errors)
+    if sync_start > pd.Timestamp.now():
+        return df
 
-    # Add SOFR Rate (Cash Interest)
     try:
-        sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
-        master_df['SOFR_ANNUAL'] = sofr / 100
-    except:
-        master_df['SOFR_ANNUAL'] = 0.045
-
-    master_df = master_df.sort_index().ffill()
-    master_df.to_csv(FILENAME)
-    upload_to_hf(FILENAME)
-    return master_df
-
-def sync_incremental_data(df):
-    last_date = df.index.max()
-    tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
-    new_data = yf.download(tickers, start=last_date, progress=False)['Adj Close']
-    combined = pd.concat([df, new_data]).sort_index()
-    combined = combined[~combined.index.duplicated(keep='last')]
-    combined.to_csv(FILENAME)
-    upload_to_hf(FILENAME)
-    return combined
-
-def upload_to_hf(path):
-    token = get_safe_token()
-    if not token:
-        st.error("❌ Cannot upload: HF_TOKEN is missing from Space Secrets.")
-        return
-    api = HfApi()
-    api.upload_file(path_or_fileobj=path, path_in_repo=FILENAME, repo_id=REPO_ID, repo_type="dataset", token=token)
+        # Download from yfinance with specific configuration to avoid Multi-Index issues
+        new_data_raw = yf.download(tickers, start=sync_start, progress=False, group_by='column')
+        
+        if new_data_raw.empty:
+            return df
+
+        # Logic to handle different yfinance return structures
+        if 'Adj Close' in new_data_raw.columns:
+            new_data = new_data_raw['Adj Close']
+        elif 'Close' in new_data_raw.columns:
+            new_data = new_data_raw['Close']
+        else:
+            # If it's a single ticker or flattened
+            new_data = new_data_raw
+
+        # Standardize: Ensure we only have the tickers we want and no empty columns
+        new_data = new_data[new_data.columns.intersection(tickers)]
+        
+        # Combine with master dataframe
+        combined = pd.concat([df, new_data]).sort_index()
+        # Keep the most recent data point if duplicates occur
+        combined = combined[~combined.index.duplicated(keep='last')]
+        
+        combined.to_csv(FILENAME)
+        upload_to_hf(FILENAME)
+        return combined
+
+    except Exception as e:
+        st.error(f"Sync failed: {e}")
+        return df

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -6,7 +6,6 @@ from engine.trend_engine import run_trend_module
 
 st.set_page_config(layout="wide", page_title="P2 Strategy Suite")
 
-# Initialize Session State safely
 if 'master_data' not in st.session_state:
     st.session_state.master_data = load_from_hf()
 
@@ -43,14 +42,14 @@ if st.session_state.master_data is not None:
         
         st.title(f"📊 {option}: {sub_option}")
         
-        # Row 1: Metrics
+        # Row 1: Metrics (Annual Return First)
         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%}")
 
-        # Row 2: Performance Chart
+        # Row 2: Performance Chart (Interactive Years)
         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})'))
@@ -74,10 +73,10 @@ if st.session_state.master_data is not None:
             This strategy implements the **2025 Charles H. Dow Award** winning framework by **Andrea Zarattini** and **Michael Antonacci**.
             
             1. **Regime Identification**: A dual 50/200-day SMA filter determines asset eligibility. 
-            2. **Conviction Ranking**: Assets are ranked by their distance from the 200-day SMA.
-            3. **Concentrated Sizing**: Under the **{sub_option}** setting, the system focuses the risk budget only on the top leaders.
+            2. **Conviction Ranking**: Assets are ranked by their distance from the 200-day SMA (Trend Strength).
+            3. **Concentrated Sizing**: In **{sub_option}** mode, the risk budget is focused only on top leaders.
             4. **Volatility Targeting**: Allocations are sized inversely to 60-day volatility to maintain a stable **{vol_target:.0%}** risk profile.
-            5. **Cash Buffer**: Remaining budget earns the live SOFR rate.
+            5. **Cash Buffer**: Remaining budget earns the live SOFR rate (Federal Reserve Bank of New York).
             """)
     else:
         st.info("💡 Adjust settings and click 'Run Analysis'.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py

@@ -25,6 +25,7 @@ def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr,
     
     # 4. Volatility Target Weighting
     active_counts = signals.sum(axis=1)
+    # Target Vol / Asset Vol, distributed across active signals
     raw_weights = (target_vol / asset_vol).divide(active_counts, axis=0).replace([np.inf, -np.inf], 0).fillna(0)
     final_weights = raw_weights * signals
     
@@ -50,17 +51,13 @@ def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr,
     ann_vol = portfolio_ret[oos_mask].std() * np.sqrt(252)
     dd = (equity_curve / equity_curve.cummax()) - 1
     
-    # --- FIXED NEXT DAY LOGIC ---
+    # --- NEXT DAY TRADING LOGIC ---
     nyse = mcal.get_calendar('NYSE')
-    last_dt = price_df.index[-1]
-    
-    # Generate schedule starting from the day AFTER last_dt to ensure we find the future open
-    search_start = last_dt + pd.Timedelta(days=1)
+    # Use real-world today to anchor the search for the NEXT session
+    today_dt = pd.Timestamp.now().normalize()
+    search_start = today_dt + pd.Timedelta(days=1)
     sched = nyse.schedule(start_date=search_start, end_date=search_start + pd.Timedelta(days=10))
-    
-    # Take the first valid trading day from the future schedule
     next_day = sched.index[0] 
-    # ----------------------------
     
     return {
         'equity_curve': equity_curve,
@@ -68,6 +65,7 @@ def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr,
         'ann_ret': ann_ret,
         'sharpe': (ann_ret - sofr_series.iloc[-1]) / ann_vol if ann_vol > 0 else 0,
         'max_dd': dd.min(),
+        'avg_daily_dd': dd.mean(),
         'next_day': next_day.date(),
         'current_weights': final_weights.iloc[-1],
         'cash_weight': cash_weight.iloc[-1],

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py

@@ -17,16 +17,13 @@ def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr,
     
     # 3. Apply Sub-Option Concentration
     if sub_option == "3 Highest Conviction":
-        # Rank daily: 1 is highest conviction
         ranks = conviction_score.rank(axis=1, ascending=False)
         signals = ((ranks <= 3) & (signals == 1)).astype(int)
     elif sub_option == "1 Highest Conviction":
         ranks = conviction_score.rank(axis=1, ascending=False)
         signals = ((ranks <= 1) & (signals == 1)).astype(int)
-    # Else: "All Trending ETFs" uses the base signals
     
     # 4. Volatility Target Weighting
-    # Methodology: Target Vol / Asset Vol, distributed across active signals
     active_counts = signals.sum(axis=1)
     raw_weights = (target_vol / asset_vol).divide(active_counts, axis=0).replace([np.inf, -np.inf], 0).fillna(0)
     final_weights = raw_weights * signals
@@ -53,10 +50,17 @@ def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr,
     ann_vol = portfolio_ret[oos_mask].std() * np.sqrt(252)
     dd = (equity_curve / equity_curve.cummax()) - 1
     
-    # NYSE Calendar
+    # --- FIXED NEXT DAY LOGIC ---
     nyse = mcal.get_calendar('NYSE')
     last_dt = price_df.index[-1]
-    next_day = nyse.schedule(start_date=last_dt, end_date=last_dt + pd.Timedelta(days=10)).index[1]
+    
+    # Generate schedule starting from the day AFTER last_dt to ensure we find the future open
+    search_start = last_dt + pd.Timedelta(days=1)
+    sched = nyse.schedule(start_date=search_start, end_date=search_start + pd.Timedelta(days=10))
+    
+    # Take the first valid trading day from the future schedule
+    next_day = sched.index[0] 
+    # ----------------------------
     
     return {
         'equity_curve': equity_curve,

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -6,19 +6,28 @@ from engine.trend_engine import run_trend_module
 
 st.set_page_config(layout="wide", page_title="P2 Strategy Suite")
 
+# Initialize Session State safely
 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"Sync: {st.session_state.master_data.index.max().date()}")
+        if st.button("🔄 Sync New Data"):
+            st.session_state.master_data = sync_incremental_data(st.session_state.master_data)
+            st.rerun()
+
+    st.divider()
     option = st.selectbox("Universe Selection", ("Option A - FI Trend", "Option B - Equity Trend"))
-    
-    # NEW SUB-OPTIONS
-    sub_option = st.selectbox("Conviction Level", 
+    sub_option = st.selectbox("Conviction Strategy", 
                              ("All Trending ETFs", "3 Highest Conviction", "1 Highest Conviction"))
-    
-    start_yr = st.slider("OOS Start", 2008, 2026, 2018)
-    vol_target = st.slider("Volatility Target (%)", 5, 20, 12) / 100
+    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:
@@ -32,48 +41,43 @@ if st.session_state.master_data is not None:
                                  st.session_state.master_data['SOFR_ANNUAL'], 
                                  vol_target, start_yr, sub_option)
         
-        st.title(f"📊 {option} - {sub_option}")
+        st.title(f"📊 {option}: {sub_option}")
         
-        # Metrics
+        # Row 1: Metrics
         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%}")
 
-        # Chart
+        # Row 2: Performance Chart
         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="OOS Performance", template="plotly_dark")
+        fig.update_layout(title="Out-of-Sample Performance", template="plotly_dark", xaxis_title="Year")
         st.plotly_chart(fig, use_container_width=True)
 
-        # Methodology & Target
+        # Row 3: Methodology & Allocations
         st.divider()
         col_left, col_right = st.columns([1, 1.5])
         
         with col_left:
-            st.subheader(f"🎯 Target Allocation: {results['next_day']}")
+            st.subheader(f"🎯 Allocation for {results['next_day']}")
             w = results['current_weights'][results['current_weights'] > 0.0001].to_dict()
             w['CASH (SOFR)'] = results['cash_weight']
-            st.table(pd.DataFrame.from_dict(w, orient='index', columns=['Weight']).style.format("{:.2%}"))
+            df_w = pd.DataFrame.from_dict(w, orient='index', columns=['Weight'])
+            st.table(df_w.style.format("{:.2%}"))
 
         with col_right:
-            st.subheader("📚 Methodology: Zarattini & Antonacci (2025)")
+            st.subheader("📚 Methodology: Zarattini & Antonacci")
             st.markdown(f"""
-            This strategy implements the **2025 Charles H. Dow Award** framework authored by **Andrea Zarattini** and **Michael Antonacci**.
+            This strategy implements the **2025 Charles H. Dow Award** winning framework by **Andrea Zarattini** and **Michael Antonacci**.
             
-            * **Trend Detection**: Uses a 50/200 SMA dual-filter.
-            * **Conviction Scoring**: Assets are ranked based on their relative distance from the 200-day trend line.
-            * **Concentration**: Under **{sub_option}**, the engine filters the universe to only the top-tier trending assets.
-            * **Risk Sizing**: Allocation is inversely proportional to 60-day volatility. If the selected ETFs cannot safely fill the **{vol_target:.0%}** risk budget, the remainder is held in **CASH (SOFR)**.
+            1. **Regime Identification**: A dual 50/200-day SMA filter determines asset eligibility. 
+            2. **Conviction Ranking**: Assets are ranked by their distance from the 200-day SMA.
+            3. **Concentrated Sizing**: Under the **{sub_option}** setting, the system focuses the risk budget only on the top leaders.
+            4. **Volatility Targeting**: Allocations are sized inversely to 60-day volatility to maintain a stable **{vol_target:.0%}** risk profile.
+            5. **Cash Buffer**: Remaining budget earns the live SOFR rate.
             """)
-            
-
-
-### Why this is powerful:
-* **The "3 Highest Conviction" sub-option** creates a "Best of the Best" portfolio. Instead of diluting your risk budget across 20 ETFs that are barely in trend, it puts the full 12% risk budget into the 3 strongest leaders.
-* **The "1 Highest Conviction" sub-option** is the ultimate momentum play, concentrating all allowed risk into the single strongest trend.
-* **Authorship**: Zarattini and Antonacci's names are now front-and-center in the methodology section.
-
-**Would you like me to add a "Drawdown Overlay" chart so you can compare the risk spikes between the Concentrated (1-ETF) and Broad (All ETFs) sub-options?**
+    else:
+        st.info("💡 Adjust settings and click 'Run Analysis'.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py

@@ -3,54 +3,55 @@ import numpy as np
 import pandas_market_calendars as mcal
 
 def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr, sub_option):
-    # 1. Trend Signals & Conviction Scoring
+    # 1. Trend & Conviction Logic
     sma_200 = price_df.rolling(200).mean()
     sma_50 = price_df.rolling(50).mean()
     
-    # Conviction Score = How far is the price above the 200 SMA?
+    # Conviction = Percentage distance above the 200 SMA
     conviction_score = (price_df / sma_200) - 1
     signals = (sma_50 > sma_200).astype(int)
     
-    # 2. Individual Asset Volatility
+    # 2. Risk Metrics
     returns = price_df.pct_change()
     asset_vol = returns.rolling(60).std() * np.sqrt(252)
     
-    # 3. CONVICTION FILTERING (Sub-Options)
+    # 3. Apply Sub-Option Concentration
     if sub_option == "3 Highest Conviction":
-        # Rank assets by score, keep only top 3 that are ALSO in trend
+        # Rank daily: 1 is highest conviction
         ranks = conviction_score.rank(axis=1, ascending=False)
         signals = ((ranks <= 3) & (signals == 1)).astype(int)
     elif sub_option == "1 Highest Conviction":
-        # Rank assets, keep only the top 1 that is ALSO in trend
         ranks = conviction_score.rank(axis=1, ascending=False)
         signals = ((ranks <= 1) & (signals == 1)).astype(int)
-    # "All Trending" remains as is
+    # Else: "All Trending ETFs" uses the base signals
     
-    # 4. Volatility Scaling
+    # 4. Volatility Target Weighting
+    # Methodology: Target Vol / Asset Vol, distributed across active signals
     active_counts = signals.sum(axis=1)
-    # Inverse vol weight per asset
     raw_weights = (target_vol / asset_vol).divide(active_counts, axis=0).replace([np.inf, -np.inf], 0).fillna(0)
     final_weights = raw_weights * signals
     
-    # 5. Leverage Cap & Cash
+    # 5. Leverage Cap (1.5x)
     total_exposure = final_weights.sum(axis=1)
     scale_factor = total_exposure.apply(lambda x: 1.5/x if x > 1.5 else 1.0)
     final_weights = final_weights.multiply(scale_factor, axis=0)
     
+    # 6. Cash (SOFR) Allocation
     cash_weight = 1.0 - final_weights.sum(axis=1)
     
-    # 6. Performance Calculation
+    # 7. Portfolio Returns
     portfolio_ret = (final_weights.shift(1) * returns).sum(axis=1)
     portfolio_ret += cash_weight.shift(1) * (sofr_series.shift(1) / 252)
     
-    # 7. OOS Filtering
+    # 8. Out-of-Sample Slicing
     oos_mask = portfolio_ret.index.year >= start_yr
     equity_curve = (1 + portfolio_ret[oos_mask]).cumprod()
     bench_curve = (1 + bench_series.pct_change().fillna(0)[oos_mask]).cumprod()
     
-    # 8. Stats
-    dd = (equity_curve / equity_curve.cummax()) - 1
+    # Stats
     ann_ret = portfolio_ret[oos_mask].mean() * 252
+    ann_vol = portfolio_ret[oos_mask].std() * np.sqrt(252)
+    dd = (equity_curve / equity_curve.cummax()) - 1
     
     # NYSE Calendar
     nyse = mcal.get_calendar('NYSE')
@@ -61,7 +62,7 @@ def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr,
         'equity_curve': equity_curve,
         'bench_curve': bench_curve,
         'ann_ret': ann_ret,
-        'sharpe': (ann_ret - sofr_series.iloc[-1]) / (portfolio_ret[oos_mask].std() * np.sqrt(252)),
+        'sharpe': (ann_ret - sofr_series.iloc[-1]) / ann_vol if ann_vol > 0 else 0,
         'max_dd': dd.min(),
         'next_day': next_day.date(),
         'current_weights': final_weights.iloc[-1],

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -10,20 +10,14 @@ if 'master_data' not in st.session_state:
     st.session_state.master_data = load_from_hf()
 
 with st.sidebar:
-    st.header("🗂️ Data Controls")
-    if st.session_state.master_data is None:
-        if st.button("🚀 Seed Database (FRED/Stooq)"):
-            st.session_state.master_data = seed_dataset_from_scratch()
-            st.rerun()
-    else:
-        st.success(f"DB Last Updated: {st.session_state.master_data.index.max().date()}")
-        if st.button("🔄 Sync Daily Data"):
-            st.session_state.master_data = sync_incremental_data(st.session_state.master_data)
-            st.rerun()
+    st.header("🗂️ Configuration")
+    option = st.selectbox("Universe Selection", ("Option A - FI Trend", "Option B - Equity Trend"))
     
-    st.divider()
-    option = st.radio("Asset Universe", ("Option A - FI Trend", "Option B - Equity Trend"))
-    start_yr = st.slider("Out-of-Sample Start", 2008, 2026, 2018)
+    # NEW SUB-OPTIONS
+    sub_option = st.selectbox("Conviction Level", 
+                             ("All Trending ETFs", "3 Highest Conviction", "1 Highest Conviction"))
+    
+    start_yr = st.slider("OOS Start", 2008, 2026, 2018)
     vol_target = st.slider("Volatility Target (%)", 5, 20, 12) / 100
     run_btn = st.button("🚀 Run Analysis", use_container_width=True, type="primary")
 
@@ -36,50 +30,50 @@ if st.session_state.master_data is not None:
         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)
+                                 vol_target, start_yr, sub_option)
         
-        st.title(f"📈 {option} Performance Report")
+        st.title(f"📊 {option} - {sub_option}")
         
-        # Row 1: Key Metrics (Reordered)
+        # Metrics
         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_peak']:.1%}")
-        m4.metric("Current SOFR (Live)", f"{results['current_sofr']:.2%}")
+        m3.metric("Max Drawdown", f"{results['max_dd']:.1%}")
+        m4.metric("Current SOFR", f"{results['current_sofr']:.2%}")
 
-        # Row 2: Interactive Plotly Chart (Visible Years)
+        # Chart
         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="Growth of $1.00 (OOS)", template="plotly_dark", xaxis_title="Timeline")
+        fig.update_layout(title="OOS Performance", template="plotly_dark")
         st.plotly_chart(fig, use_container_width=True)
 
-        # Row 3: Methodology & Allocations
+        # Methodology & Target
         st.divider()
         col_left, col_right = st.columns([1, 1.5])
         
         with col_left:
             st.subheader(f"🎯 Target Allocation: {results['next_day']}")
-            weights_df = results['current_weights'][results['current_weights'] > 0.001].to_dict()
-            weights_df['CASH (SOFR)'] = results['cash_weight']
-            
-            # Clean Table View
-            final_df = pd.DataFrame.from_dict(weights_df, orient='index', columns=['Weight'])
-            final_df['Weight'] = final_df['Weight'].apply(lambda x: f"{x:.2%}")
-            st.table(final_df)
+            w = results['current_weights'][results['current_weights'] > 0.0001].to_dict()
+            w['CASH (SOFR)'] = results['cash_weight']
+            st.table(pd.DataFrame.from_dict(w, orient='index', columns=['Weight']).style.format("{:.2%}"))
 
         with col_right:
-            st.subheader("📚 Strategy Methodology")
+            st.subheader("📚 Methodology: Zarattini & Antonacci (2025)")
             st.markdown(f"""
-            This engine implements the **'Century of Profitable Trends'** framework (2025 Dow Award):
+            This strategy implements the **2025 Charles H. Dow Award** framework authored by **Andrea Zarattini** and **Michael Antonacci**.
             
-            1. **Regime Identification**: A dual 50/200-day Simple Moving Average (SMA) filter determines eligibility. Only assets in an uptrend are held.
-            2. **Inverse-Volatility Sizing**: Unlike equal weighting, each asset is sized based on its 60-day realized volatility. Lower volatility assets receive higher capital allocations.
-            3. **Portfolio Risk Targeting**: The system calculates a total portfolio weight to meet your **{vol_target:.0%} Volatility Target**.
-            4. **Cash Scaling (SOFR)**: If the combined risk of the trending assets exceeds the target, or if assets fall out of trend, capital is diverted to **CASH**, earning the live SOFR rate. 
-            5. **Leverage Management**: Gross exposure is dynamically managed and capped at 1.5x to prevent excessive drawdown during regime shifts.
+            * **Trend Detection**: Uses a 50/200 SMA dual-filter.
+            * **Conviction Scoring**: Assets are ranked based on their relative distance from the 200-day trend line.
+            * **Concentration**: Under **{sub_option}**, the engine filters the universe to only the top-tier trending assets.
+            * **Risk Sizing**: Allocation is inversely proportional to 60-day volatility. If the selected ETFs cannot safely fill the **{vol_target:.0%}** risk budget, the remainder is held in **CASH (SOFR)**.
             """)
-
             
-    else:
-        st.info("💡 Adjust your risk parameters and click 'Run Analysis' to see the predicted allocations.")
+
+
+### Why this is powerful:
+* **The "3 Highest Conviction" sub-option** creates a "Best of the Best" portfolio. Instead of diluting your risk budget across 20 ETFs that are barely in trend, it puts the full 12% risk budget into the 3 strongest leaders.
+* **The "1 Highest Conviction" sub-option** is the ultimate momentum play, concentrating all allowed risk into the single strongest trend.
+* **Authorship**: Zarattini and Antonacci's names are now front-and-center in the methodology section.
+
+**Would you like me to add a "Drawdown Overlay" chart so you can compare the risk spikes between the Concentrated (1-ETF) and Broad (All ETFs) sub-options?**

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py

@@ -2,53 +2,57 @@ import pandas as pd
 import numpy as np
 import pandas_market_calendars as mcal
 
-def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr):
-    # 1. Signal Logic: Dual SMA Crossover
-    sma_fast = price_df.rolling(50).mean()
-    sma_slow = price_df.rolling(200).mean()
-    signals = (sma_fast > sma_slow).astype(int)
+def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr, sub_option):
+    # 1. Trend Signals & Conviction Scoring
+    sma_200 = price_df.rolling(200).mean()
+    sma_50 = price_df.rolling(50).mean()
     
-    # 2. Volatility Logic: 60-Day Realized Standard Deviation
+    # Conviction Score = How far is the price above the 200 SMA?
+    conviction_score = (price_df / sma_200) - 1
+    signals = (sma_50 > sma_200).astype(int)
+    
+    # 2. Individual Asset Volatility
     returns = price_df.pct_change()
     asset_vol = returns.rolling(60).std() * np.sqrt(252)
     
-    # 3. Risk-Budgeted Weighting
-    # Methodology: Allocation = (Target Vol / Asset Vol) / Number of Active Assets
-    # This ensures that each trending asset contributes a fixed 'slice' of risk.
-    active_counts = signals.sum(axis=1)
+    # 3. CONVICTION FILTERING (Sub-Options)
+    if sub_option == "3 Highest Conviction":
+        # Rank assets by score, keep only top 3 that are ALSO in trend
+        ranks = conviction_score.rank(axis=1, ascending=False)
+        signals = ((ranks <= 3) & (signals == 1)).astype(int)
+    elif sub_option == "1 Highest Conviction":
+        # Rank assets, keep only the top 1 that is ALSO in trend
+        ranks = conviction_score.rank(axis=1, ascending=False)
+        signals = ((ranks <= 1) & (signals == 1)).astype(int)
+    # "All Trending" remains as is
     
-    # Weight per asset: Target Vol divided by Asset Vol, then distributed among active trends
-    raw_weights = (target_vol / asset_vol).divide(active_counts, axis=0).fillna(0)
+    # 4. Volatility Scaling
+    active_counts = signals.sum(axis=1)
+    # Inverse vol weight per asset
+    raw_weights = (target_vol / asset_vol).divide(active_counts, axis=0).replace([np.inf, -np.inf], 0).fillna(0)
     final_weights = raw_weights * signals
     
-    # 4. Leverage Cap & Cash Logic
-    # We cap total gross exposure at 1.5x (150%) to prevent extreme tail risk
+    # 5. Leverage Cap & Cash
     total_exposure = final_weights.sum(axis=1)
     scale_factor = total_exposure.apply(lambda x: 1.5/x if x > 1.5 else 1.0)
     final_weights = final_weights.multiply(scale_factor, axis=0)
     
-    # Recalculate exposure after capping
-    final_exposure = final_weights.sum(axis=1)
-    cash_weight = 1.0 - final_exposure
+    cash_weight = 1.0 - final_weights.sum(axis=1)
     
-    # 5. Returns: Asset Performance + Cash (SOFR) Interest
-    # If exposure is < 100%, the remainder earns SOFR interest
+    # 6. Performance Calculation
     portfolio_ret = (final_weights.shift(1) * returns).sum(axis=1)
     portfolio_ret += cash_weight.shift(1) * (sofr_series.shift(1) / 252)
     
-    bench_returns = bench_series.pct_change().fillna(0)
-    
-    # 6. OOS Performance Slicing
+    # 7. OOS Filtering
     oos_mask = portfolio_ret.index.year >= start_yr
     equity_curve = (1 + portfolio_ret[oos_mask]).cumprod()
-    bench_curve = (1 + bench_returns[oos_mask]).cumprod()
+    bench_curve = (1 + bench_series.pct_change().fillna(0)[oos_mask]).cumprod()
     
-    # 7. Drawdown & Stats
-    dd_series = (equity_curve / equity_curve.cummax()) - 1
+    # 8. Stats
+    dd = (equity_curve / equity_curve.cummax()) - 1
     ann_ret = portfolio_ret[oos_mask].mean() * 252
-    ann_vol = portfolio_ret[oos_mask].std() * np.sqrt(252)
     
-    # NYSE Calendar for Next Session
+    # NYSE Calendar
     nyse = mcal.get_calendar('NYSE')
     last_dt = price_df.index[-1]
     next_day = nyse.schedule(start_date=last_dt, end_date=last_dt + pd.Timedelta(days=10)).index[1]
@@ -57,9 +61,8 @@ def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr):
         'equity_curve': equity_curve,
         'bench_curve': bench_curve,
         'ann_ret': ann_ret,
-        'sharpe': (ann_ret - sofr_series.iloc[-1]) / ann_vol if ann_vol > 0 else 0,
-        'max_dd_peak': dd_series.min(),
-        'avg_daily_dd': dd_series.mean(),
+        'sharpe': (ann_ret - sofr_series.iloc[-1]) / (portfolio_ret[oos_mask].std() * np.sqrt(252)),
+        'max_dd': dd.min(),
         'next_day': next_day.date(),
         'current_weights': final_weights.iloc[-1],
         'cash_weight': cash_weight.iloc[-1],

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py

@@ -3,49 +3,65 @@ import numpy as np
 import pandas_market_calendars as mcal
 
 def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr):
-    # 1. Full-period math for indicators
+    # 1. Signal Logic: Dual SMA Crossover
     sma_fast = price_df.rolling(50).mean()
     sma_slow = price_df.rolling(200).mean()
     signals = (sma_fast > sma_slow).astype(int)
     
+    # 2. Volatility Logic: 60-Day Realized Standard Deviation
     returns = price_df.pct_change()
-    realized_vol = returns.rolling(60).std() * np.sqrt(252)
-    weights = (target_vol / realized_vol).fillna(0).clip(upper=1.5)
+    asset_vol = returns.rolling(60).std() * np.sqrt(252)
     
-    # 2. Strategy Returns
-    asset_ret = (signals.shift(1) * weights.shift(1) * returns).mean(axis=1)
-    cash_pct = 1 - signals.mean(axis=1)
-    strat_returns = asset_ret + (cash_pct.shift(1) * (sofr_series.shift(1) / 252))
-    bench_returns = bench_series.pct_change().fillna(0)
+    # 3. Risk-Budgeted Weighting
+    # Methodology: Allocation = (Target Vol / Asset Vol) / Number of Active Assets
+    # This ensures that each trending asset contributes a fixed 'slice' of risk.
+    active_counts = signals.sum(axis=1)
+    
+    # Weight per asset: Target Vol divided by Asset Vol, then distributed among active trends
+    raw_weights = (target_vol / asset_vol).divide(active_counts, axis=0).fillna(0)
+    final_weights = raw_weights * signals
     
-    # 3. Slice for OOS Period
-    oos_mask = strat_returns.index.year >= start_yr
-    oos_strat = strat_returns[oos_mask]
-    oos_bench = bench_returns[oos_mask]
+    # 4. Leverage Cap & Cash Logic
+    # We cap total gross exposure at 1.5x (150%) to prevent extreme tail risk
+    total_exposure = final_weights.sum(axis=1)
+    scale_factor = total_exposure.apply(lambda x: 1.5/x if x > 1.5 else 1.0)
+    final_weights = final_weights.multiply(scale_factor, axis=0)
     
-    equity_curve = (1 + oos_strat).cumprod()
-    bench_curve = (1 + oos_bench).cumprod()
+    # Recalculate exposure after capping
+    final_exposure = final_weights.sum(axis=1)
+    cash_weight = 1.0 - final_exposure
     
-    # 4. Drawdowns
-    hwm = equity_curve.cummax()
-    dd_series = (equity_curve / hwm) - 1
+    # 5. Returns: Asset Performance + Cash (SOFR) Interest
+    # If exposure is < 100%, the remainder earns SOFR interest
+    portfolio_ret = (final_weights.shift(1) * returns).sum(axis=1)
+    portfolio_ret += cash_weight.shift(1) * (sofr_series.shift(1) / 252)
+    
+    bench_returns = bench_series.pct_change().fillna(0)
     
-    # 5. Next Day Trading Date
+    # 6. OOS Performance Slicing
+    oos_mask = portfolio_ret.index.year >= start_yr
+    equity_curve = (1 + portfolio_ret[oos_mask]).cumprod()
+    bench_curve = (1 + bench_returns[oos_mask]).cumprod()
+    
+    # 7. Drawdown & Stats
+    dd_series = (equity_curve / equity_curve.cummax()) - 1
+    ann_ret = portfolio_ret[oos_mask].mean() * 252
+    ann_vol = portfolio_ret[oos_mask].std() * np.sqrt(252)
+    
+    # NYSE Calendar for Next Session
     nyse = mcal.get_calendar('NYSE')
     last_dt = price_df.index[-1]
-    sched = nyse.schedule(start_date=last_dt, end_date=last_dt + pd.Timedelta(days=10))
-    next_day = sched.index[1] if len(sched) > 1 else sched.index[0]
-    
-    ann_ret = oos_strat.mean() * 252
-    ann_vol = oos_strat.std() * np.sqrt(252)
+    next_day = nyse.schedule(start_date=last_dt, end_date=last_dt + pd.Timedelta(days=10)).index[1]
     
     return {
         'equity_curve': equity_curve,
         'bench_curve': bench_curve,
-        'sharpe': (ann_ret - 0.03) / ann_vol if ann_vol > 0 else 0,
         'ann_ret': ann_ret,
+        'sharpe': (ann_ret - sofr_series.iloc[-1]) / ann_vol if ann_vol > 0 else 0,
         'max_dd_peak': dd_series.min(),
         'avg_daily_dd': dd_series.mean(),
         'next_day': next_day.date(),
-        'current_signals': signals.iloc[-1]
+        'current_weights': final_weights.iloc[-1],
+        'cash_weight': cash_weight.iloc[-1],
+        'current_sofr': sofr_series.iloc[-1]
     }

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,95 +1,85 @@
 import streamlit as st
 import pandas as pd
-import numpy as np
+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 | 2025 Dow Award Edition")
+st.set_page_config(layout="wide", page_title="P2 Strategy Suite")
 
-# --- SAFE SESSION INITIALIZATION ---
 if 'master_data' not in st.session_state:
     st.session_state.master_data = load_from_hf()
 
-# --- SIDEBAR UI ---
 with st.sidebar:
-    st.header("🗂️ Data Management")
+    st.header("🗂️ Data Controls")
     if st.session_state.master_data is None:
-        st.error("Dataset not found.")
-        if st.button("🚀 Seed Database (2008-2026)", use_container_width=True):
+        if st.button("🚀 Seed Database (FRED/Stooq)"):
             st.session_state.master_data = seed_dataset_from_scratch()
             st.rerun()
     else:
-        last_dt = pd.to_datetime(st.session_state.master_data.index).max()
-        st.success(f"Database Active: {last_dt.date()}")
-        if st.button("🔄 Sync Daily Data", use_container_width=True):
+        st.success(f"DB Last Updated: {st.session_state.master_data.index.max().date()}")
+        if st.button("🔄 Sync Daily Data"):
             st.session_state.master_data = sync_incremental_data(st.session_state.master_data)
             st.rerun()
     
     st.divider()
-    st.header("⚙️ Strategy Settings")
-    option = st.radio("Strategy Selection", ("Option A - FI Trend", "Option B - Equity Trend"))
-    start_yr = st.slider("OOS Start Year", 2008, 2026, 2018)
-    vol_target = st.slider("Ann. Vol Target (%)", 5, 25, 12) / 100
+    option = st.radio("Asset Universe", ("Option A - FI Trend", "Option B - Equity Trend"))
+    start_yr = st.slider("Out-of-Sample Start", 2008, 2026, 2018)
+    vol_target = st.slider("Volatility Target (%)", 5, 20, 12) / 100
     run_btn = st.button("🚀 Run Analysis", use_container_width=True, type="primary")
 
-# --- MAIN OUTPUT UI ---
 if st.session_state.master_data is not None:
     if run_btn:
-        with st.spinner("Analyzing Market Regimes..."):
-            # 1. Setup Universe and Benchmark
-            is_fi = "Option A" in option
-            univ = FI_TICKERS if is_fi else X_EQUITY_TICKERS
-            bench_ticker = "AGG" if is_fi else "SPY"
-            
-            # 2. Filter Data (Using Start Year as OOS boundary)
-            # The engine uses data prior to start_yr for signal lookback (Training/Buffer)
-            df = st.session_state.master_data
-            
-            # 3. Execute Engine
-            results = run_trend_module(df[univ], df[bench_ticker], df['SOFR_ANNUAL'], vol_target, start_yr)
-            
-            # 4. KPI Header
-            st.title(f"📈 {option} Performance vs {bench_ticker}")
-            m1, m2, m3, m4 = st.columns(4)
-            m1.metric("OOS Sharpe", f"{results['sharpe']:.2f}")
-            m2.metric("Ann. Return", f"{results['ann_ret']:.1%}")
-            m3.metric("Peak-to-Trough DD", f"{results['max_dd_peak']:.1%}")
-            m4.metric("Avg Daily DD", f"{results['avg_daily_dd']:.2%}")
+        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)
+        
+        st.title(f"📈 {option} Performance Report")
+        
+        # Row 1: Key Metrics (Reordered)
+        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_peak']:.1%}")
+        m4.metric("Current SOFR (Live)", f"{results['current_sofr']:.2%}")
 
-            # 5. Equity Curve Chart
-            chart_df = pd.DataFrame({
-                "Strategy Portfolio": results['equity_curve'],
-                f"Benchmark ({bench_ticker})": results['bench_curve']
-            })
-            st.subheader("Cumulative Growth of $1.00 (Out-of-Sample)")
-            st.line_chart(chart_df)
+        # Row 2: Interactive Plotly Chart (Visible Years)
+        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="Growth of $1.00 (OOS)", template="plotly_dark", xaxis_title="Timeline")
+        st.plotly_chart(fig, use_container_width=True)
 
-            # 6. Actionable Allocation (Next Trading Day)
-            st.divider()
-            c1, c2 = st.columns([1, 2])
-            with c1:
-                st.subheader("📅 Next Trading Session")
-                st.info(f"**NYSE Market Date:** {results['next_day']}\n\n**Action:** Execute at Open")
-            with c2:
-                st.subheader("🎯 Required Allocation")
-                active = results['current_signals'][results['current_signals'] > 0].index.tolist()
-                if active:
-                    st.success(f"**Long Positions:** {', '.join(active)}")
-                else:
-                    st.warning("⚖️ **Position:** 100% CASH (Market Neutral)")
+        # Row 3: Methodology & Allocations
+        st.divider()
+        col_left, col_right = st.columns([1, 1.5])
+        
+        with col_left:
+            st.subheader(f"🎯 Target Allocation: {results['next_day']}")
+            weights_df = results['current_weights'][results['current_weights'] > 0.001].to_dict()
+            weights_df['CASH (SOFR)'] = results['cash_weight']
+            
+            # Clean Table View
+            final_df = pd.DataFrame.from_dict(weights_df, orient='index', columns=['Weight'])
+            final_df['Weight'] = final_df['Weight'].apply(lambda x: f"{x:.2%}")
+            st.table(final_df)
 
-            # 7. Methodology Footer
-            st.divider()
-            with st.expander("📚 Methodology & 2025 Dow Award Reference"):
-                st.markdown("""
-                ### A Century of Profitable Trends (Zarattini & Antonacci, 2025)
-                This model implements the framework from the 2025 Charles H. Dow Award winning paper:
-                * **Regime Filter:** Dual SMA logic (50/200 crossover) proxying for Keltner/Donchian channels.
-                * **Volatility Targeting:** Positions sized by $Weight = \sigma_{target} / \sigma_{realized}$, capped at 1.5x.
-                * **Benchmarking:** Equity trends are compared to SPY; Fixed Income to AGG.
-                * **OOS Testing:** The analysis shown above represents the **Out-of-Sample** period. Data prior to the start year is used solely for initial indicator 'burn-in'.
-                """)
+        with col_right:
+            st.subheader("📚 Strategy Methodology")
+            st.markdown(f"""
+            This engine implements the **'Century of Profitable Trends'** framework (2025 Dow Award):
+            
+            1. **Regime Identification**: A dual 50/200-day Simple Moving Average (SMA) filter determines eligibility. Only assets in an uptrend are held.
+            2. **Inverse-Volatility Sizing**: Unlike equal weighting, each asset is sized based on its 60-day realized volatility. Lower volatility assets receive higher capital allocations.
+            3. **Portfolio Risk Targeting**: The system calculates a total portfolio weight to meet your **{vol_target:.0%} Volatility Target**.
+            4. **Cash Scaling (SOFR)**: If the combined risk of the trending assets exceeds the target, or if assets fall out of trend, capital is diverted to **CASH**, earning the live SOFR rate. 
+            5. **Leverage Management**: Gross exposure is dynamically managed and capped at 1.5x to prevent excessive drawdown during regime shifts.
+            """)
+
+            
     else:
-        st.info("💡 Adjust your parameters in the sidebar and click **'Run Analysis'**.")
-else:
-    st.warning("👈 Please click 'Seed Database' to initialize historical data.")
+        st.info("💡 Adjust your risk parameters and click 'Run Analysis' to see the predicted allocations.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py

@@ -3,7 +3,7 @@ import numpy as np
 import pandas_market_calendars as mcal
 
 def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr):
-    # 1. Full period calculations for signals (Training + OOS)
+    # 1. Full-period math for indicators
     sma_fast = price_df.rolling(50).mean()
     sma_slow = price_df.rolling(200).mean()
     signals = (sma_fast > sma_slow).astype(int)
@@ -18,7 +18,7 @@ def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr):
     strat_returns = asset_ret + (cash_pct.shift(1) * (sofr_series.shift(1) / 252))
     bench_returns = bench_series.pct_change().fillna(0)
     
-    # 3. Filter for Out-of-Sample (OOS) Period
+    # 3. Slice for OOS Period
     oos_mask = strat_returns.index.year >= start_yr
     oos_strat = strat_returns[oos_mask]
     oos_bench = bench_returns[oos_mask]
@@ -26,11 +26,11 @@ def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr):
     equity_curve = (1 + oos_strat).cumprod()
     bench_curve = (1 + oos_bench).cumprod()
     
-    # 4. Drawdown Stats
+    # 4. Drawdowns
     hwm = equity_curve.cummax()
     dd_series = (equity_curve / hwm) - 1
     
-    # 5. NYSE Next Day
+    # 5. Next Day Trading Date
     nyse = mcal.get_calendar('NYSE')
     last_dt = price_df.index[-1]
     sched = nyse.schedule(start_date=last_dt, end_date=last_dt + pd.Timedelta(days=10))

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -12,36 +12,45 @@ FILENAME = "market_data.csv"
 X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
 FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
 
+def get_safe_token():
+    """Bypasses Streamlit's hard-fail on missing secrets.toml by using environment fallback."""
+    try:
+        # Try Streamlit secrets first
+        return st.secrets["HF_TOKEN"]
+    except Exception:
+        # Standard environment variable fallback (How HF actually stores them)
+        return os.getenv("HF_TOKEN")
+
 def load_from_hf():
+    token = get_safe_token()
+    if not token: return None
     try:
-        token = st.secrets.get("HF_TOKEN") or os.getenv("HF_TOKEN")
-        if not token: return None
         path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
         return pd.read_csv(path, index_col=0, parse_dates=True)
     except:
         return None
 
 def seed_dataset_from_scratch():
-    # Include benchmarks SPY and AGG
+    # Include benchmarks for comparison logic
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
     master_df = pd.DataFrame()
     status = st.empty()
     progress = st.progress(0)
     
     for i, t in enumerate(tickers):
-        status.text(f"Fetching {t} from Stooq...")
+        status.text(f"🛰️ Fetching {t} from Stooq...")
         try:
             data = web.DataReader(f"{t}.US", 'stooq', start='2008-01-01')
             if not data.empty:
                 master_df[t] = data['Close'].sort_index()
-            time.sleep(0.6)
+            time.sleep(0.7) # Polite delay
         except:
             try:
                 master_df[t] = yf.download(t, start="2008-01-01", progress=False)['Adj Close']
             except: pass
         progress.progress((i + 1) / len(tickers))
 
-    # Add SOFR Rate
+    # Add SOFR Rate (Cash Interest)
     try:
         sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
         master_df['SOFR_ANNUAL'] = sofr / 100
@@ -64,6 +73,9 @@ def sync_incremental_data(df):
     return combined
 
 def upload_to_hf(path):
+    token = get_safe_token()
+    if not token:
+        st.error("❌ Cannot upload: HF_TOKEN is missing from Space Secrets.")
+        return
     api = HfApi()
-    token = st.secrets.get("HF_TOKEN") or os.getenv("HF_TOKEN")
     api.upload_file(path_or_fileobj=path, path_in_repo=FILENAME, repo_id=REPO_ID, repo_type="dataset", token=token)

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py

@@ -2,12 +2,8 @@ import pandas as pd
 import numpy as np
 import pandas_market_calendars as mcal
 
-def run_trend_module(price_df, benchmark_df, sofr_series, target_vol=0.12):
-    """
-    Enhanced Engine for 2025 Dow Award Logic.
-    Includes Dual Drawdowns and Benchmark Comparison.
-    """
-    # 1. Signals & Weights
+def run_trend_module(price_df, bench_series, sofr_series, target_vol, start_yr):
+    # 1. Full period calculations for signals (Training + OOS)
     sma_fast = price_df.rolling(50).mean()
     sma_slow = price_df.rolling(200).mean()
     signals = (sma_fast > sma_slow).astype(int)
@@ -16,43 +12,40 @@ def run_trend_module(price_df, benchmark_df, sofr_series, target_vol=0.12):
     realized_vol = returns.rolling(60).std() * np.sqrt(252)
     weights = (target_vol / realized_vol).fillna(0).clip(upper=1.5)
     
-    # 2. Returns Calculation
-    # Strategy
+    # 2. Strategy Returns
     asset_ret = (signals.shift(1) * weights.shift(1) * returns).mean(axis=1)
     cash_pct = 1 - signals.mean(axis=1)
     strat_returns = asset_ret + (cash_pct.shift(1) * (sofr_series.shift(1) / 252))
+    bench_returns = bench_series.pct_change().fillna(0)
     
-    # Benchmark (Buy & Hold)
-    bench_returns = benchmark_df.pct_change().fillna(0)
+    # 3. Filter for Out-of-Sample (OOS) Period
+    oos_mask = strat_returns.index.year >= start_yr
+    oos_strat = strat_returns[oos_mask]
+    oos_bench = bench_returns[oos_mask]
     
-    # Equity Curves
-    equity_curve = (1 + strat_returns).cumprod()
-    bench_curve = (1 + bench_returns).cumprod()
+    equity_curve = (1 + oos_strat).cumprod()
+    bench_curve = (1 + oos_bench).cumprod()
     
-    # 3. Drawdown Calculations
-    def get_dd_stats(curve):
-        hwm = curve.cummax()
-        dd = (curve / hwm) - 1
-        return dd.min(), dd # Max DD and the full DD series
+    # 4. Drawdown Stats
+    hwm = equity_curve.cummax()
+    dd_series = (equity_curve / hwm) - 1
     
-    max_dd_peak, dd_series = get_dd_stats(equity_curve)
-    
-    # 4. Next Trading Day & Allocations (NYSE Calendar)
+    # 5. NYSE Next Day
     nyse = mcal.get_calendar('NYSE')
-    last_date = price_df.index[-1]
-    next_day = nyse.valid_days(start_date=last_date + pd.Timedelta(days=1), end_date=last_date + pd.Timedelta(days=10))[0]
+    last_dt = price_df.index[-1]
+    sched = nyse.schedule(start_date=last_dt, end_date=last_dt + pd.Timedelta(days=10))
+    next_day = sched.index[1] if len(sched) > 1 else sched.index[0]
     
-    # Current Allocations (Based on most recent signals)
-    current_signals = signals.iloc[-1]
-    active_assets = current_signals[current_signals > 0].index.tolist()
+    ann_ret = oos_strat.mean() * 252
+    ann_vol = oos_strat.std() * np.sqrt(252)
     
     return {
         'equity_curve': equity_curve,
         'bench_curve': bench_curve,
-        'strat_ret_series': strat_returns,
-        'max_dd_peak': max_dd_peak,
-        'dd_series': dd_series,
-        'next_trading_day': next_day.date(),
-        'active_assets': active_assets,
-        'signals': current_signals
+        'sharpe': (ann_ret - 0.03) / ann_vol if ann_vol > 0 else 0,
+        'ann_ret': ann_ret,
+        'max_dd_peak': dd_series.min(),
+        'avg_daily_dd': dd_series.mean(),
+        'next_day': next_day.date(),
+        'current_signals': signals.iloc[-1]
     }

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -9,86 +9,61 @@ import streamlit as st
 REPO_ID = "P2SAMAPA/etf_trend_data"
 FILENAME = "market_data.csv"
 
-# The 27 Equity X-ETFs and 15 FI ETFs from the 2025 Paper
 X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
 FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
 
-def get_hf_token():
-    """Safely retrieves the token from secrets or environment."""
-    try:
-        return st.secrets["HF_TOKEN"]
-    except:
-        return os.getenv("HF_TOKEN")
-
 def load_from_hf():
-    """Reads dataset from Hugging Face if it exists."""
-    token = get_hf_token()
-    if not token:
-        return None
     try:
+        token = st.secrets.get("HF_TOKEN") or os.getenv("HF_TOKEN")
+        if not token: return None
         path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
         return pd.read_csv(path, index_col=0, parse_dates=True)
     except:
         return None
 
 def seed_dataset_from_scratch():
-    """Initial download of 18 years of data using Stooq primarily."""
+    # Include benchmarks SPY and AGG
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
     master_df = pd.DataFrame()
-    
     status = st.empty()
-    progress_bar = st.progress(0)
+    progress = st.progress(0)
     
-    for i, ticker in enumerate(tickers):
-        status.text(f"Fetching {ticker} from Stooq...")
+    for i, t in enumerate(tickers):
+        status.text(f"Fetching {t} from Stooq...")
         try:
-            # Stooq primary (requires .US suffix for ETFs)
-            data = web.DataReader(f"{ticker}.US", 'stooq', start='2008-01-01')
+            data = web.DataReader(f"{t}.US", 'stooq', start='2008-01-01')
             if not data.empty:
-                master_df[ticker] = data['Close'].sort_index()
-            time.sleep(0.6) 
+                master_df[t] = data['Close'].sort_index()
+            time.sleep(0.6)
         except:
-            # YFinance fallback if Stooq fails for a ticker
             try:
-                yf_data = yf.download(ticker, start="2008-01-01", progress=False)['Adj Close']
-                master_df[ticker] = yf_data
-            except:
-                pass
-        progress_bar.progress((i + 1) / len(tickers))
+                master_df[t] = yf.download(t, start="2008-01-01", progress=False)['Adj Close']
+            except: pass
+        progress.progress((i + 1) / len(tickers))
 
-    # Add SOFR Rate (Cash interest)
+    # Add SOFR Rate
     try:
         sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
         master_df['SOFR_ANNUAL'] = sofr / 100
     except:
-        master_df['SOFR_ANNUAL'] = 0.045 # Conservative proxy
+        master_df['SOFR_ANNUAL'] = 0.045
 
     master_df = master_df.sort_index().ffill()
     master_df.to_csv(FILENAME)
-    
     upload_to_hf(FILENAME)
     return master_df
 
-def sync_incremental_data(df_existing):
-    """Updates only new data since last index date using YFinance for speed."""
-    last_date = pd.to_datetime(df_existing.index).max()
+def sync_incremental_data(df):
+    last_date = df.index.max()
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
-    
     new_data = yf.download(tickers, start=last_date, progress=False)['Adj Close']
-    combined = pd.concat([df_existing, new_data])
-    combined = combined[~combined.index.duplicated(keep='last')].sort_index()
-    
+    combined = pd.concat([df, new_data]).sort_index()
+    combined = combined[~combined.index.duplicated(keep='last')]
     combined.to_csv(FILENAME)
     upload_to_hf(FILENAME)
     return combined
 
 def upload_to_hf(path):
     api = HfApi()
-    token = get_hf_token()
-    api.upload_file(
-        path_or_fileobj=path, 
-        path_in_repo=FILENAME, 
-        repo_id=REPO_ID, 
-        repo_type="dataset", 
-        token=token
-    )
+    token = st.secrets.get("HF_TOKEN") or os.getenv("HF_TOKEN")
+    api.upload_file(path_or_fileobj=path, path_in_repo=FILENAME, repo_id=REPO_ID, repo_type="dataset", token=token)

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,68 +1,95 @@
 import streamlit as st
+import pandas as pd
+import numpy as np
 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")
+st.set_page_config(layout="wide", page_title="P2 Strategy Suite | 2025 Dow Award Edition")
 
-# --- INITIALIZATION ---
+# --- SAFE SESSION INITIALIZATION ---
 if 'master_data' not in st.session_state:
     st.session_state.master_data = load_from_hf()
 
-# --- SIDEBAR: DATA CONTROLS ---
+# --- SIDEBAR UI ---
 with st.sidebar:
     st.header("🗂️ Data Management")
     if st.session_state.master_data is None:
-        st.error("No dataset detected.")
+        st.error("Dataset not found.")
         if st.button("🚀 Seed Database (2008-2026)", use_container_width=True):
             st.session_state.master_data = seed_dataset_from_scratch()
             st.rerun()
     else:
-        last_dt = st.session_state.master_data.index.max()
+        last_dt = pd.to_datetime(st.session_state.master_data.index).max()
         st.success(f"Database Active: {last_dt.date()}")
-        if st.button("🔄 Sync New Data", use_container_width=True):
+        if st.button("🔄 Sync Daily Data", use_container_width=True):
             st.session_state.master_data = sync_incremental_data(st.session_state.master_data)
             st.rerun()
     
     st.divider()
     st.header("⚙️ Strategy Settings")
-    option = st.radio("Universe Selection", ("Option A - FI Trend", "Option B - Equity Trend"))
-    start_yr = st.slider("Backtest Start Year", 2008, 2026, 2015)
-    vol_target = st.slider("Target Vol (%)", 5, 20, 12) / 100
-    
-    st.divider()
-    run_btn = st.button("🚀 Run Strategy Analysis", use_container_width=True, type="primary")
+    option = st.radio("Strategy Selection", ("Option A - FI Trend", "Option B - Equity Trend"))
+    start_yr = st.slider("OOS Start Year", 2008, 2026, 2018)
+    vol_target = st.slider("Ann. Vol Target (%)", 5, 25, 12) / 100
+    run_btn = st.button("🚀 Run Analysis", use_container_width=True, type="primary")
 
-# --- MAIN PAGE: DISPLAY ---
+# --- MAIN OUTPUT UI ---
 if st.session_state.master_data is not None:
     if run_btn:
-        with st.spinner("Crunching data..."):
-            # Universe Selection
-            univ = FI_TICKERS if "Option A" in option else X_EQUITY_TICKERS
-            # Slice by date
-            df = st.session_state.master_data[st.session_state.master_data.index.year >= start_yr]
+        with st.spinner("Analyzing Market Regimes..."):
+            # 1. Setup Universe and Benchmark
+            is_fi = "Option A" in option
+            univ = FI_TICKERS if is_fi else X_EQUITY_TICKERS
+            bench_ticker = "AGG" if is_fi else "SPY"
             
-            # Execute Engine
-            results = run_trend_module(df[univ], df['SOFR_ANNUAL'], vol_target)
+            # 2. Filter Data (Using Start Year as OOS boundary)
+            # The engine uses data prior to start_yr for signal lookback (Training/Buffer)
+            df = st.session_state.master_data
             
-            # Show Metrics
-            st.title(f"📊 {option} Performance Report")
-            m1, m2, m3 = st.columns(3)
-            m1.metric("Sharpe Ratio", f"{results['sharpe']:.2f}")
-            m2.metric("Annual Return", f"{results['ann_ret']:.1%}")
-            m3.metric("Max Drawdown", f"{results['max_dd']:.1%}")
+            # 3. Execute Engine
+            results = run_trend_module(df[univ], df[bench_ticker], df['SOFR_ANNUAL'], vol_target, start_yr)
             
-            # Equity Curve
-            st.subheader("Cumulative Growth (vs Cash)")
-            st.line_chart(results['equity_curve'])
-            
-            # Allocation Check
+            # 4. KPI Header
+            st.title(f"📈 {option} Performance vs {bench_ticker}")
+            m1, m2, m3, m4 = st.columns(4)
+            m1.metric("OOS Sharpe", f"{results['sharpe']:.2f}")
+            m2.metric("Ann. Return", f"{results['ann_ret']:.1%}")
+            m3.metric("Peak-to-Trough DD", f"{results['max_dd_peak']:.1%}")
+            m4.metric("Avg Daily DD", f"{results['avg_daily_dd']:.2%}")
+
+            # 5. Equity Curve Chart
+            chart_df = pd.DataFrame({
+                "Strategy Portfolio": results['equity_curve'],
+                f"Benchmark ({bench_ticker})": results['bench_curve']
+            })
+            st.subheader("Cumulative Growth of $1.00 (Out-of-Sample)")
+            st.line_chart(chart_df)
+
+            # 6. Actionable Allocation (Next Trading Day)
             st.divider()
-            st.subheader("Current Market Status")
-            active_assets = results['current_signals'][results['current_signals'] > 0].index.tolist()
-            st.write(f"**In-Trend Assets:** {', '.join(active_assets) if active_assets else 'All Cash'}")
-            
+            c1, c2 = st.columns([1, 2])
+            with c1:
+                st.subheader("📅 Next Trading Session")
+                st.info(f"**NYSE Market Date:** {results['next_day']}\n\n**Action:** Execute at Open")
+            with c2:
+                st.subheader("🎯 Required Allocation")
+                active = results['current_signals'][results['current_signals'] > 0].index.tolist()
+                if active:
+                    st.success(f"**Long Positions:** {', '.join(active)}")
+                else:
+                    st.warning("⚖️ **Position:** 100% CASH (Market Neutral)")
+
+            # 7. Methodology Footer
+            st.divider()
+            with st.expander("📚 Methodology & 2025 Dow Award Reference"):
+                st.markdown("""
+                ### A Century of Profitable Trends (Zarattini & Antonacci, 2025)
+                This model implements the framework from the 2025 Charles H. Dow Award winning paper:
+                * **Regime Filter:** Dual SMA logic (50/200 crossover) proxying for Keltner/Donchian channels.
+                * **Volatility Targeting:** Positions sized by $Weight = \sigma_{target} / \sigma_{realized}$, capped at 1.5x.
+                * **Benchmarking:** Equity trends are compared to SPY; Fixed Income to AGG.
+                * **OOS Testing:** The analysis shown above represents the **Out-of-Sample** period. Data prior to the start year is used solely for initial indicator 'burn-in'.
+                """)
     else:
-        st.title("Welcome to the 2025 Trend Suite")
-        st.info("👈 Use the sidebar to manage your data and click 'Run Strategy Analysis' to begin.")
+        st.info("💡 Adjust your parameters in the sidebar and click **'Run Analysis'**.")
 else:
-    st.warning("Please initialize the database using the 'Seed' button in the sidebar.")
+    st.warning("👈 Please click 'Seed Database' to initialize historical data.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py

@@ -1,47 +1,58 @@
 import pandas as pd
 import numpy as np
+import pandas_market_calendars as mcal
 
-def run_trend_module(price_df, sofr_series, target_vol=0.12):
+def run_trend_module(price_df, benchmark_df, sofr_series, target_vol=0.12):
     """
-    Implements 2025 Dow Award Logic.
+    Enhanced Engine for 2025 Dow Award Logic.
+    Includes Dual Drawdowns and Benchmark Comparison.
     """
-    # 1. Dual-Trend Signal (Fast vs Slow SMA)
+    # 1. Signals & Weights
     sma_fast = price_df.rolling(50).mean()
     sma_slow = price_df.rolling(200).mean()
-    # Signal is 1 if in trend, 0 if cash
     signals = (sma_fast > sma_slow).astype(int)
     
-    # 2. Volatility Targeting (Inverse Vol Sizing)
     returns = price_df.pct_change()
     realized_vol = returns.rolling(60).std() * np.sqrt(252)
-    # Weights = Target Vol / Realized Vol
-    weights = (target_vol / realized_vol).fillna(0)
-    weights = weights.clip(upper=1.5) # Cap leverage at 150%
+    weights = (target_vol / realized_vol).fillna(0).clip(upper=1.5)
     
-    # 3. Portfolio Returns
-    # Position = Signal * Weight
-    asset_returns = (signals.shift(1) * weights.shift(1) * returns).mean(axis=1)
+    # 2. Returns Calculation
+    # Strategy
+    asset_ret = (signals.shift(1) * weights.shift(1) * returns).mean(axis=1)
+    cash_pct = 1 - signals.mean(axis=1)
+    strat_returns = asset_ret + (cash_pct.shift(1) * (sofr_series.shift(1) / 252))
     
-    # 4. Interest on Cash (SOFR)
-    # If signals are 0 (in cash), we earn SOFR
-    cash_percentage = 1 - signals.mean(axis=1)
-    interest_returns = (cash_percentage.shift(1) * (sofr_series.shift(1) / 252))
+    # Benchmark (Buy & Hold)
+    bench_returns = benchmark_df.pct_change().fillna(0)
     
-    total_returns = asset_returns + interest_returns
-    equity_curve = (1 + total_returns).fillna(0).cumprod()
+    # Equity Curves
+    equity_curve = (1 + strat_returns).cumprod()
+    bench_curve = (1 + bench_returns).cumprod()
     
-    # 5. Metrics
-    ann_ret = total_returns.mean() * 252
-    ann_vol = total_returns.std() * np.sqrt(252)
-    sharpe = (ann_ret - 0.035) / ann_vol if ann_vol > 0 else 0
+    # 3. Drawdown Calculations
+    def get_dd_stats(curve):
+        hwm = curve.cummax()
+        dd = (curve / hwm) - 1
+        return dd.min(), dd # Max DD and the full DD series
     
-    dd = equity_curve / equity_curve.cummax() - 1
-    max_dd = dd.min()
+    max_dd_peak, dd_series = get_dd_stats(equity_curve)
+    
+    # 4. Next Trading Day & Allocations (NYSE Calendar)
+    nyse = mcal.get_calendar('NYSE')
+    last_date = price_df.index[-1]
+    next_day = nyse.valid_days(start_date=last_date + pd.Timedelta(days=1), end_date=last_date + pd.Timedelta(days=10))[0]
+    
+    # Current Allocations (Based on most recent signals)
+    current_signals = signals.iloc[-1]
+    active_assets = current_signals[current_signals > 0].index.tolist()
     
     return {
         'equity_curve': equity_curve,
-        'sharpe': sharpe,
-        'ann_ret': ann_ret,
-        'max_dd': max_dd,
-        'current_signals': signals.iloc[-1]
+        'bench_curve': bench_curve,
+        'strat_ret_series': strat_returns,
+        'max_dd_peak': max_dd_peak,
+        'dd_series': dd_series,
+        'next_trading_day': next_day.date(),
+        'active_assets': active_assets,
+        'signals': current_signals
     }

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py

@@ -1,40 +1,47 @@
-import numpy as np
 import pandas as pd
+import numpy as np
 
-def run_trend_module(prices, daily_sofr, vol_target):
-    # 1. Dual-Trend Signal
-    d_high = prices.rolling(20).max()
-    sma = prices.rolling(20).mean()
-    atr = (prices.rolling(20).max() - prices.rolling(20).min()) / 2
-    k_upper = sma + (2 * atr)
-    
-    entry_band = np.minimum(d_high, k_upper)
-    signals = (prices > entry_band.shift(1)).astype(int)
-    
-    # 2. Risk Parity Position Sizing
-    returns = prices.pct_change()
-    realized_vol = returns.rolling(21).std() * np.sqrt(252)
-    
-    n = len(prices.columns)
-    # Target weight = (Target Vol / Total Assets) / Individual Asset Vol
-    target_weights = (vol_target / n) / realized_vol.shift(1)
-    
-    # 3. Strategy Returns (Positions + SOFR on Cash)
-    pos_rets = (signals.shift(1) * target_weights.shift(1) * returns).sum(axis=1)
-    weight_used = (signals.shift(1) * target_weights.shift(1)).sum(axis=1)
-    cash_rets = (1 - weight_used).clip(0, 1) * (daily_sofr / 252)
-    
-    strat_rets = pos_rets + cash_rets
-    equity_curve = (1 + strat_rets).fillna(0).cumprod()
-    
-    # 4. Target Allocation for Tomorrow
-    tomorrow_sig = (prices.iloc[-1] > entry_band.iloc[-1]).astype(int)
-    tomorrow_w = (vol_target / n) / realized_vol.iloc[-1]
-    
-    alloc = pd.DataFrame({
-        "Ticker": prices.columns,
-        "Signal": ["LONG" if s == 1 else "CASH" for s in tomorrow_sig],
-        "Weight (%)": (tomorrow_sig * tomorrow_w * 100).round(2)
-    })
-    
-    return {"curve": equity_curve, "alloc": alloc}
+def run_trend_module(price_df, sofr_series, target_vol=0.12):
+    """
+    Implements 2025 Dow Award Logic.
+    """
+    # 1. Dual-Trend Signal (Fast vs Slow SMA)
+    sma_fast = price_df.rolling(50).mean()
+    sma_slow = price_df.rolling(200).mean()
+    # Signal is 1 if in trend, 0 if cash
+    signals = (sma_fast > sma_slow).astype(int)
+    
+    # 2. Volatility Targeting (Inverse Vol Sizing)
+    returns = price_df.pct_change()
+    realized_vol = returns.rolling(60).std() * np.sqrt(252)
+    # Weights = Target Vol / Realized Vol
+    weights = (target_vol / realized_vol).fillna(0)
+    weights = weights.clip(upper=1.5) # Cap leverage at 150%
+    
+    # 3. Portfolio Returns
+    # Position = Signal * Weight
+    asset_returns = (signals.shift(1) * weights.shift(1) * returns).mean(axis=1)
+    
+    # 4. Interest on Cash (SOFR)
+    # If signals are 0 (in cash), we earn SOFR
+    cash_percentage = 1 - signals.mean(axis=1)
+    interest_returns = (cash_percentage.shift(1) * (sofr_series.shift(1) / 252))
+    
+    total_returns = asset_returns + interest_returns
+    equity_curve = (1 + total_returns).fillna(0).cumprod()
+    
+    # 5. Metrics
+    ann_ret = total_returns.mean() * 252
+    ann_vol = total_returns.std() * np.sqrt(252)
+    sharpe = (ann_ret - 0.035) / ann_vol if ann_vol > 0 else 0
+    
+    dd = equity_curve / equity_curve.cummax() - 1
+    max_dd = dd.min()
+    
+    return {
+        'equity_curve': equity_curve,
+        'sharpe': sharpe,
+        'ann_ret': ann_ret,
+        'max_dd': max_dd,
+        'current_signals': signals.iloc[-1]
+    }

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -14,7 +14,7 @@ X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XL
 FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
 
 def get_hf_token():
-    """Safely retrieves the token without triggering a SecretNotFoundError crash."""
+    """Safely retrieves the token from secrets or environment."""
     try:
         return st.secrets["HF_TOKEN"]
     except:
@@ -32,7 +32,7 @@ def load_from_hf():
         return None
 
 def seed_dataset_from_scratch():
-    """Downloads 2008-Present data from STOOQ."""
+    """Initial download of 18 years of data using Stooq primarily."""
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
     master_df = pd.DataFrame()
     
@@ -42,13 +42,13 @@ def seed_dataset_from_scratch():
     for i, ticker in enumerate(tickers):
         status.text(f"Fetching {ticker} from Stooq...")
         try:
-            # Stooq primary
+            # Stooq primary (requires .US suffix for ETFs)
             data = web.DataReader(f"{ticker}.US", 'stooq', start='2008-01-01')
             if not data.empty:
                 master_df[ticker] = data['Close'].sort_index()
-            time.sleep(0.6) # Anti-rate limit
+            time.sleep(0.6) 
         except:
-            # YFinance fallback
+            # YFinance fallback if Stooq fails for a ticker
             try:
                 yf_data = yf.download(ticker, start="2008-01-01", progress=False)['Adj Close']
                 master_df[ticker] = yf_data
@@ -56,12 +56,12 @@ def seed_dataset_from_scratch():
                 pass
         progress_bar.progress((i + 1) / len(tickers))
 
-    # Add SOFR Rate
+    # Add SOFR Rate (Cash interest)
     try:
         sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
         master_df['SOFR_ANNUAL'] = sofr / 100
     except:
-        master_df['SOFR_ANNUAL'] = 0.05
+        master_df['SOFR_ANNUAL'] = 0.045 # Conservative proxy
 
     master_df = master_df.sort_index().ffill()
     master_df.to_csv(FILENAME)
@@ -70,11 +70,10 @@ def seed_dataset_from_scratch():
     return master_df
 
 def sync_incremental_data(df_existing):
-    """Updates only new data since last index date."""
+    """Updates only new data since last index date using YFinance for speed."""
     last_date = pd.to_datetime(df_existing.index).max()
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
     
-    # Simple incremental fetch
     new_data = yf.download(tickers, start=last_date, progress=False)['Adj Close']
     combined = pd.concat([df_existing, new_data])
     combined = combined[~combined.index.duplicated(keep='last')].sort_index()

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,45 +1,68 @@
 import streamlit as st
-import pandas as pd
-from data.loader import load_from_hf, seed_dataset_from_scratch, sync_incremental_data
+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 Trend Suite")
+st.set_page_config(layout="wide", page_title="P2 Strategy Suite")
 
-# --- SIDEBAR: DATA MANAGEMENT ---
-st.sidebar.title("🗂️ Data Management")
-
-# Initialize Session State
+# --- INITIALIZATION ---
 if 'master_data' not in st.session_state:
     st.session_state.master_data = load_from_hf()
 
-# LOGIC: If no data, show SEED. If data exists, show SYNC.
-if st.session_state.master_data is None:
-    st.sidebar.warning("Database not found.")
-    if st.sidebar.button("🚀 Step 1: Seed Database (2008-2026)"):
-        with st.spinner("Downloading full history..."):
+# --- SIDEBAR: DATA CONTROLS ---
+with st.sidebar:
+    st.header("🗂️ Data Management")
+    if st.session_state.master_data is None:
+        st.error("No dataset detected.")
+        if st.button("🚀 Seed Database (2008-2026)", use_container_width=True):
             st.session_state.master_data = seed_dataset_from_scratch()
-            st.sidebar.success("Database Seeded!")
             st.rerun()
-else:
-    st.sidebar.success(f"Database Active: {st.session_state.master_data.index.max()}")
-    
-    # SYNC BUTTON for daily incremental updates
-    if st.sidebar.button("🔄 Step 2: Sync Daily Data"):
-        with st.spinner("Pinging Stooq/FRED for new data..."):
+    else:
+        last_dt = st.session_state.master_data.index.max()
+        st.success(f"Database Active: {last_dt.date()}")
+        if st.button("🔄 Sync New Data", use_container_width=True):
             st.session_state.master_data = sync_incremental_data(st.session_state.master_data)
-            st.sidebar.success("Incremental Sync Complete!")
             st.rerun()
+    
+    st.divider()
+    st.header("⚙️ Strategy Settings")
+    option = st.radio("Universe Selection", ("Option A - FI Trend", "Option B - Equity Trend"))
+    start_yr = st.slider("Backtest Start Year", 2008, 2026, 2015)
+    vol_target = st.slider("Target Vol (%)", 5, 20, 12) / 100
+    
+    st.divider()
+    run_btn = st.button("🚀 Run Strategy Analysis", use_container_width=True, type="primary")
 
-# --- SIDEBAR: STRATEGY CONTROLS ---
-st.sidebar.divider()
-st.sidebar.title("⚙️ Strategy Settings")
-option = st.sidebar.radio("Select Module", ("Option A - FI Trend", "Option B - Equity Trend"))
-start_year = st.sidebar.slider("Start Year", 2008, 2026, 2015)
-vol_target = st.sidebar.slider("Annual Vol Target", 0.05, 0.25, 0.126)
-
-# --- MAIN UI: ANALYSIS ---
+# --- MAIN PAGE: DISPLAY ---
 if st.session_state.master_data is not None:
-    # Your strategy execution code here...
-    st.title(f"📊 {option}")
-    # ...
+    if run_btn:
+        with st.spinner("Crunching data..."):
+            # Universe Selection
+            univ = FI_TICKERS if "Option A" in option else X_EQUITY_TICKERS
+            # Slice by date
+            df = st.session_state.master_data[st.session_state.master_data.index.year >= start_yr]
+            
+            # Execute Engine
+            results = run_trend_module(df[univ], df['SOFR_ANNUAL'], vol_target)
+            
+            # Show Metrics
+            st.title(f"📊 {option} Performance Report")
+            m1, m2, m3 = st.columns(3)
+            m1.metric("Sharpe Ratio", f"{results['sharpe']:.2f}")
+            m2.metric("Annual Return", f"{results['ann_ret']:.1%}")
+            m3.metric("Max Drawdown", f"{results['max_dd']:.1%}")
+            
+            # Equity Curve
+            st.subheader("Cumulative Growth (vs Cash)")
+            st.line_chart(results['equity_curve'])
+            
+            # Allocation Check
+            st.divider()
+            st.subheader("Current Market Status")
+            active_assets = results['current_signals'][results['current_signals'] > 0].index.tolist()
+            st.write(f"**In-Trend Assets:** {', '.join(active_assets) if active_assets else 'All Cash'}")
+            
+    else:
+        st.title("Welcome to the 2025 Trend Suite")
+        st.info("👈 Use the sidebar to manage your data and click 'Run Strategy Analysis' to begin.")
 else:
-    st.info("Please use the sidebar to Seed the database first.")
+    st.warning("Please initialize the database using the 'Seed' button in the sidebar.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -9,10 +9,22 @@ import streamlit as st
 REPO_ID = "P2SAMAPA/etf_trend_data"
 FILENAME = "market_data.csv"
 
-# Make sure these match exactly what app.py expects
+# The 27 Equity X-ETFs and 15 FI ETFs from the 2025 Paper
+X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
+FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
+
+def get_hf_token():
+    """Safely retrieves the token without triggering a SecretNotFoundError crash."""
+    try:
+        return st.secrets["HF_TOKEN"]
+    except:
+        return os.getenv("HF_TOKEN")
+
 def load_from_hf():
-    token = st.secrets.get("HF_TOKEN")
-    if not token: return None
+    """Reads dataset from Hugging Face if it exists."""
+    token = get_hf_token()
+    if not token:
+        return None
     try:
         path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
         return pd.read_csv(path, index_col=0, parse_dates=True)
@@ -20,10 +32,64 @@ def load_from_hf():
         return None
 
 def seed_dataset_from_scratch():
-    # ... (Your Stooq download logic here)
-    # Ensure this function name matches the import in app.py
+    """Downloads 2008-Present data from STOOQ."""
+    tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
+    master_df = pd.DataFrame()
+    
+    status = st.empty()
+    progress_bar = st.progress(0)
+    
+    for i, ticker in enumerate(tickers):
+        status.text(f"Fetching {ticker} from Stooq...")
+        try:
+            # Stooq primary
+            data = web.DataReader(f"{ticker}.US", 'stooq', start='2008-01-01')
+            if not data.empty:
+                master_df[ticker] = data['Close'].sort_index()
+            time.sleep(0.6) # Anti-rate limit
+        except:
+            # YFinance fallback
+            try:
+                yf_data = yf.download(ticker, start="2008-01-01", progress=False)['Adj Close']
+                master_df[ticker] = yf_data
+            except:
+                pass
+        progress_bar.progress((i + 1) / len(tickers))
+
+    # Add SOFR Rate
+    try:
+        sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
+        master_df['SOFR_ANNUAL'] = sofr / 100
+    except:
+        master_df['SOFR_ANNUAL'] = 0.05
+
+    master_df = master_df.sort_index().ffill()
+    master_df.to_csv(FILENAME)
+    
+    upload_to_hf(FILENAME)
     return master_df
 
 def sync_incremental_data(df_existing):
-    # ... (Your incremental update logic here)
+    """Updates only new data since last index date."""
+    last_date = pd.to_datetime(df_existing.index).max()
+    tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
+    
+    # Simple incremental fetch
+    new_data = yf.download(tickers, start=last_date, progress=False)['Adj Close']
+    combined = pd.concat([df_existing, new_data])
+    combined = combined[~combined.index.duplicated(keep='last')].sort_index()
+    
+    combined.to_csv(FILENAME)
+    upload_to_hf(FILENAME)
     return combined
+
+def upload_to_hf(path):
+    api = HfApi()
+    token = get_hf_token()
+    api.upload_file(
+        path_or_fileobj=path, 
+        path_in_repo=FILENAME, 
+        repo_id=REPO_ID, 
+        repo_type="dataset", 
+        token=token
+    )

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -9,47 +9,21 @@ import streamlit as st
 REPO_ID = "P2SAMAPA/etf_trend_data"
 FILENAME = "market_data.csv"
 
-def seed_dataset():
-    tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
-    master_df = pd.DataFrame()
-    
-    st.info("🛰️ Initializing Stooq Data Fetch (2008-Present)...")
-    progress_bar = st.progress(0)
-    
-    for i, ticker in enumerate(tickers):
-        # Stooq ticker format is usually 'TICKER.US'
-        stooq_symbol = f"{ticker}.US"
-        try:
-            # PRIMARY: STOOQ
-            data = web.DataReader(stooq_symbol, 'stooq', start='2008-01-01')
-            if not data.empty:
-                # Stooq returns data in reverse chronological order; we sort it.
-                master_df[ticker] = data['Close'].sort_index()
-            
-            # Anti-Rate Limit: 0.8s delay between requests
-            time.sleep(0.8) 
-            
-        except Exception as e:
-            st.warning(f"⚠️ Stooq failed for {ticker}. Attempting YFinance fallback...")
-            try:
-                # BACKUP: YFinance
-                yf_data = yf.download(ticker, start="2008-01-01", progress=False)['Adj Close']
-                master_df[ticker] = yf_data
-            except:
-                st.error(f"❌ Failed to fetch {ticker} from all sources.")
-        
-        progress_bar.progress((i + 1) / len(tickers))
-    
-    # Add SOFR (Cash Rate) from FRED
+# Make sure these match exactly what app.py expects
+def load_from_hf():
+    token = st.secrets.get("HF_TOKEN")
+    if not token: return None
     try:
-        sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
-        master_df['SOFR_ANNUAL'] = sofr / 100
+        path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
+        return pd.read_csv(path, index_col=0, parse_dates=True)
     except:
-        master_df['SOFR_ANNUAL'] = 0.05 # Conservative fallback
+        return None
 
-    master_df = master_df.sort_index().ffill()
-    
-    # Save & Upload
-    master_df.to_csv(FILENAME)
-    upload_to_hf(FILENAME)
+def seed_dataset_from_scratch():
+    # ... (Your Stooq download logic here)
+    # Ensure this function name matches the import in app.py
     return master_df
+
+def sync_incremental_data(df_existing):
+    # ... (Your incremental update logic here)
+    return combined

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -1,26 +1,55 @@
-def sync_incremental_data(df_existing):
-    """Downloads only missing data since last update and saves to HF."""
-    import yfinance as yf
-    
-    # Identify last date in the CSV
-    last_date = pd.to_datetime(df_existing.index).max()
+import pandas as pd
+import pandas_datareader.data as web
+import yfinance as yf
+import time
+from huggingface_hub import hf_hub_download, HfApi
+import os
+import streamlit as st
+
+REPO_ID = "P2SAMAPA/etf_trend_data"
+FILENAME = "market_data.csv"
+
+def seed_dataset():
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
+    master_df = pd.DataFrame()
+    
+    st.info("🛰️ Initializing Stooq Data Fetch (2008-Present)...")
+    progress_bar = st.progress(0)
     
-    # Fetch new bars from yfinance or stooq
-    new_data = yf.download(tickers, start=last_date, progress=False)['Adj Close']
+    for i, ticker in enumerate(tickers):
+        # Stooq ticker format is usually 'TICKER.US'
+        stooq_symbol = f"{ticker}.US"
+        try:
+            # PRIMARY: STOOQ
+            data = web.DataReader(stooq_symbol, 'stooq', start='2008-01-01')
+            if not data.empty:
+                # Stooq returns data in reverse chronological order; we sort it.
+                master_df[ticker] = data['Close'].sort_index()
+            
+            # Anti-Rate Limit: 0.8s delay between requests
+            time.sleep(0.8) 
+            
+        except Exception as e:
+            st.warning(f"⚠️ Stooq failed for {ticker}. Attempting YFinance fallback...")
+            try:
+                # BACKUP: YFinance
+                yf_data = yf.download(ticker, start="2008-01-01", progress=False)['Adj Close']
+                master_df[ticker] = yf_data
+            except:
+                st.error(f"❌ Failed to fetch {ticker} from all sources.")
+        
+        progress_bar.progress((i + 1) / len(tickers))
     
-    # Combine (Drop duplicates to avoid double-counting the last day)
-    combined = pd.concat([df_existing, new_data])
-    combined = combined[~combined.index.duplicated(keep='last')].sort_index()
+    # Add SOFR (Cash Rate) from FRED
+    try:
+        sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
+        master_df['SOFR_ANNUAL'] = sofr / 100
+    except:
+        master_df['SOFR_ANNUAL'] = 0.05 # Conservative fallback
+
+    master_df = master_df.sort_index().ffill()
     
-    # Save & Push
-    combined.to_csv(FILENAME)
-    api = HfApi()
-    api.upload_file(
-        path_or_fileobj=FILENAME,
-        path_in_repo=FILENAME,
-        repo_id=REPO_ID,
-        repo_type="dataset",
-        token=st.secrets["HF_TOKEN"]
-    )
-    return combined
+    # Save & Upload
+    master_df.to_csv(FILENAME)
+    upload_to_hf(FILENAME)
+    return master_df

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -1,38 +1,26 @@
-import pandas as pd
-import yfinance as yf
-import pandas_datareader.data as web
-from huggingface_hub import hf_hub_download, HfApi
-import os
-import streamlit as st
-
-REPO_ID = "P2SAMAPA/etf_trend_data"
-FILENAME = "market_data.csv"
-
-X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
-FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
-
-def load_from_hf():
-    try:
-        token = st.secrets["HF_TOKEN"]
-        path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
-        return pd.read_csv(path, index_col=0, parse_dates=True)
-    except:
-        return None
-
-def seed_dataset():
+def sync_incremental_data(df_existing):
+    """Downloads only missing data since last update and saves to HF."""
+    import yfinance as yf
+    
+    # Identify last date in the CSV
+    last_date = pd.to_datetime(df_existing.index).max()
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
-    # Download Wide Format
-    df = yf.download(tickers, start="2008-01-01")['Adj Close']
     
-    # Add SOFR
-    sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
-    df['SOFR_ANNUAL'] = sofr / 100
-    df = df.sort_index().ffill()
+    # Fetch new bars from yfinance or stooq
+    new_data = yf.download(tickers, start=last_date, progress=False)['Adj Close']
+    
+    # Combine (Drop duplicates to avoid double-counting the last day)
+    combined = pd.concat([df_existing, new_data])
+    combined = combined[~combined.index.duplicated(keep='last')].sort_index()
     
-    df.to_csv(FILENAME)
-    upload_to_hf(FILENAME)
-    return df
-
-def upload_to_hf(path):
+    # Save & Push
+    combined.to_csv(FILENAME)
     api = HfApi()
-    api.upload_file(path_or_fileobj=path, path_in_repo=FILENAME, repo_id=REPO_ID, repo_type="dataset", token=st.secrets["HF_TOKEN"])
+    api.upload_file(
+        path_or_fileobj=FILENAME,
+        path_in_repo=FILENAME,
+        repo_id=REPO_ID,
+        repo_type="dataset",
+        token=st.secrets["HF_TOKEN"]
+    )
+    return combined

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,35 +1,45 @@
 import streamlit as st
 import pandas as pd
-import pandas_market_calendars as mcal
-from datetime import datetime
-from data.loader import load_from_hf, seed_dataset, X_EQUITY_TICKERS, FI_TICKERS
-from engine.trend_engine import run_trend_module
+from data.loader import load_from_hf, seed_dataset_from_scratch, sync_incremental_data
 
 st.set_page_config(layout="wide", page_title="P2 Trend Suite")
 
-# Sidebar Logic
-st.sidebar.title("Configuration")
-option = st.sidebar.radio("Select Strategy", ("Option A - FI Trend Follower", "Option B - Equity Trend Follower"))
-start_year = st.sidebar.slider("Start Year", 2008, 2026, 2015)
-vol_target = st.sidebar.slider("Annual Vol Target", 0.05, 0.25, 0.12)
+# --- SIDEBAR: DATA MANAGEMENT ---
+st.sidebar.title("🗂️ Data Management")
 
-# Data Initialization
-if 'data' not in st.session_state:
-    st.session_state.data = load_from_hf()
+# Initialize Session State
+if 'master_data' not in st.session_state:
+    st.session_state.master_data = load_from_hf()
 
-if st.session_state.data is None:
-    if st.button("🚀 First Time Setup: Seed 2008-2026 Data"):
-        st.session_state.data = seed_dataset()
-        st.rerun()
+# LOGIC: If no data, show SEED. If data exists, show SYNC.
+if st.session_state.master_data is None:
+    st.sidebar.warning("Database not found.")
+    if st.sidebar.button("🚀 Step 1: Seed Database (2008-2026)"):
+        with st.spinner("Downloading full history..."):
+            st.session_state.master_data = seed_dataset_from_scratch()
+            st.sidebar.success("Database Seeded!")
+            st.rerun()
 else:
-    # RUN STRATEGY
-    universe = FI_TICKERS if "Option A" in option else X_EQUITY_TICKERS
-    bench = "AGG" if "Option A" in option else "SPY"
-    
-    # Filter by Year
-    d = st.session_state.data[st.session_state.data.index.year >= start_year]
-    results = run_trend_module(d[universe], d['SOFR_ANNUAL'], vol_target)
+    st.sidebar.success(f"Database Active: {st.session_state.master_data.index.max()}")
     
-    # UI OUTPUTS (Sharpe, Max DD, etc.)
-    st.title(f"📈 {option} Performance")
-    # ... (Insert Metric & Chart code here)
+    # SYNC BUTTON for daily incremental updates
+    if st.sidebar.button("🔄 Step 2: Sync Daily Data"):
+        with st.spinner("Pinging Stooq/FRED for new data..."):
+            st.session_state.master_data = sync_incremental_data(st.session_state.master_data)
+            st.sidebar.success("Incremental Sync Complete!")
+            st.rerun()
+
+# --- SIDEBAR: STRATEGY CONTROLS ---
+st.sidebar.divider()
+st.sidebar.title("⚙️ Strategy Settings")
+option = st.sidebar.radio("Select Module", ("Option A - FI Trend", "Option B - Equity Trend"))
+start_year = st.sidebar.slider("Start Year", 2008, 2026, 2015)
+vol_target = st.sidebar.slider("Annual Vol Target", 0.05, 0.25, 0.126)
+
+# --- MAIN UI: ANALYSIS ---
+if st.session_state.master_data is not None:
+    # Your strategy execution code here...
+    st.title(f"📊 {option}")
+    # ...
+else:
+    st.info("Please use the sidebar to Seed the database first.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -1,6 +1,6 @@
 import pandas as pd
-import pandas_datareader.data as web
 import yfinance as yf
+import pandas_datareader.data as web
 from huggingface_hub import hf_hub_download, HfApi
 import os
 import streamlit as st
@@ -12,49 +12,27 @@ X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XL
 FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
 
 def load_from_hf():
-    """Reads the dataset from Hugging Face."""
     try:
-        # Note: Use st.secrets if token is not in env
-        token = st.secrets.get("HF_TOKEN") or os.getenv("HF_TOKEN")
+        token = st.secrets["HF_TOKEN"]
         path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
         return pd.read_csv(path, index_col=0, parse_dates=True)
-    except Exception as e:
-        print(f"Dataset load failed: {e}")
+    except:
         return None
 
-def seed_dataset_from_scratch():
-    """Download full history from 2008 and upload to HF."""
+def seed_dataset():
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
-    master_df = pd.DataFrame()
-    
-    status = st.empty()
-    progress_bar = st.progress(0)
-    
-    for i, t in enumerate(tickers):
-        status.text(f"Seeding {t}...")
-        try:
-            # Fetching from 2008 for initial dataset
-            data = yf.download(t, start="2008-01-01", progress=False)['Adj Close']
-            master_df[t] = data
-        except:
-            continue
-        progress_bar.progress((i + 1) / len(tickers))
+    # Download Wide Format
+    df = yf.download(tickers, start="2008-01-01")['Adj Close']
     
     # Add SOFR
     sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
-    master_df['SOFR_ANNUAL'] = sofr / 100
-    master_df = master_df.sort_index().ffill()
-    
-    master_df.to_csv(FILENAME)
+    df['SOFR_ANNUAL'] = sofr / 100
+    df = df.sort_index().ffill()
     
-    # Upload
+    df.to_csv(FILENAME)
+    upload_to_hf(FILENAME)
+    return df
+
+def upload_to_hf(path):
     api = HfApi()
-    token = st.secrets.get("HF_TOKEN") or os.getenv("HF_TOKEN")
-    api.upload_file(
-        path_or_fileobj=FILENAME,
-        path_in_repo=FILENAME,
-        repo_id=REPO_ID,
-        repo_type="dataset",
-        token=token
-    )
-    return master_df
+    api.upload_file(path_or_fileobj=path, path_in_repo=FILENAME, repo_id=REPO_ID, repo_type="dataset", token=st.secrets["HF_TOKEN"])

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,23 +1,35 @@
 import streamlit as st
-from data.loader import load_from_hf, seed_dataset_from_scratch, X_EQUITY_TICKERS, FI_TICKERS
-# ... other imports
+import pandas as pd
+import pandas_market_calendars as mcal
+from datetime import datetime
+from data.loader import load_from_hf, seed_dataset, X_EQUITY_TICKERS, FI_TICKERS
+from engine.trend_engine import run_trend_module
 
-st.sidebar.title("Data Management")
+st.set_page_config(layout="wide", page_title="P2 Trend Suite")
 
-# Check if data exists
-if 'master_data' not in st.session_state:
-    st.session_state.master_data = load_from_hf()
+# Sidebar Logic
+st.sidebar.title("Configuration")
+option = st.sidebar.radio("Select Strategy", ("Option A - FI Trend Follower", "Option B - Equity Trend Follower"))
+start_year = st.sidebar.slider("Start Year", 2008, 2026, 2015)
+vol_target = st.sidebar.slider("Annual Vol Target", 0.05, 0.25, 0.12)
 
-if st.session_state.master_data is None:
-    st.warning("Dataset not found on Hugging Face. Please Seed the Database.")
-    if st.sidebar.button("🚀 Step 1: Seed Database (2008-Present)"):
-        with st.spinner("Downloading 18 years of data... this takes a few minutes."):
-            st.session_state.master_data = seed_dataset_from_scratch()
-        st.success("Database seeded and uploaded to HF!")
-else:
-    if st.sidebar.button("🔄 Step 2: Daily Incremental Sync"):
-        # (Existing incremental sync logic here)
-        st.sidebar.write("Last Data Point:", st.session_state.master_data.index.max())
+# Data Initialization
+if 'data' not in st.session_state:
+    st.session_state.data = load_from_hf()
 
-# --- REST OF THE UI ---
-# Run Option A/B logic using st.session_state.master_data
+if st.session_state.data is None:
+    if st.button("🚀 First Time Setup: Seed 2008-2026 Data"):
+        st.session_state.data = seed_dataset()
+        st.rerun()
+else:
+    # RUN STRATEGY
+    universe = FI_TICKERS if "Option A" in option else X_EQUITY_TICKERS
+    bench = "AGG" if "Option A" in option else "SPY"
+    
+    # Filter by Year
+    d = st.session_state.data[st.session_state.data.index.year >= start_year]
+    results = run_trend_module(d[universe], d['SOFR_ANNUAL'], vol_target)
+    
+    # UI OUTPUTS (Sharpe, Max DD, etc.)
+    st.title(f"📈 {option} Performance")
+    # ... (Insert Metric & Chart code here)

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -8,51 +8,53 @@ import streamlit as st
 REPO_ID = "P2SAMAPA/etf_trend_data"
 FILENAME = "market_data.csv"
 
-# Universes
 X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
 FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
 
+def load_from_hf():
+    """Reads the dataset from Hugging Face."""
+    try:
+        # Note: Use st.secrets if token is not in env
+        token = st.secrets.get("HF_TOKEN") or os.getenv("HF_TOKEN")
+        path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=token)
+        return pd.read_csv(path, index_col=0, parse_dates=True)
+    except Exception as e:
+        print(f"Dataset load failed: {e}")
+        return None
+
 def seed_dataset_from_scratch():
-    """Download full history from 2008 for all 42+ tickers and upload to HF."""
+    """Download full history from 2008 and upload to HF."""
     tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
     master_df = pd.DataFrame()
     
+    status = st.empty()
     progress_bar = st.progress(0)
+    
     for i, t in enumerate(tickers):
+        status.text(f"Seeding {t}...")
         try:
-            # We use yfinance for the heavy initial lift as it handles long historical ranges reliably
+            # Fetching from 2008 for initial dataset
             data = yf.download(t, start="2008-01-01", progress=False)['Adj Close']
             master_df[t] = data
-        except Exception as e:
-            st.warning(f"Failed to fetch {t}: {e}")
+        except:
+            continue
         progress_bar.progress((i + 1) / len(tickers))
     
-    # Add SOFR (Cash Interest)
+    # Add SOFR
     sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
     master_df['SOFR_ANNUAL'] = sofr / 100
+    master_df = master_df.sort_index().ffill()
     
-    master_df = master_df.sort_index().ffill().dropna(how='all')
-    
-    # Save and Upload
     master_df.to_csv(FILENAME)
-    upload_to_hf(FILENAME)
-    return master_df
-
-def upload_to_hf(local_path):
-    """Pushes the local CSV to your Hugging Face Dataset repo."""
+    
+    # Upload
     api = HfApi()
+    token = st.secrets.get("HF_TOKEN") or os.getenv("HF_TOKEN")
     api.upload_file(
-        path_or_fileobj=local_path,
+        path_or_fileobj=FILENAME,
         path_in_repo=FILENAME,
         repo_id=REPO_ID,
         repo_type="dataset",
-        token=st.secrets["HF_TOKEN"]
+        token=token
     )
-
-def load_from_hf():
-    """Reads the dataset from Hugging Face."""
-    try:
-        path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=st.secrets["HF_TOKEN"])
-        return pd.read_csv(path, index_col=0, parse_dates=True)
-    except:
-        return None
+    return master_df

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,99 +1,23 @@
 import streamlit as st
-import pandas as pd
-import numpy as np
-import pandas_market_calendars as mcal
-from datetime import datetime
-from data.loader import refresh_market_data, X_EQUITY_TICKERS, FI_TICKERS
-from engine.trend_engine import run_trend_module
-
-st.set_page_config(layout="wide", page_title="P2 Trend Suite")
-
-# --- SIDEBAR UI ---
-st.sidebar.title("Strategy Controls")
-
-# 1. Module Toggle
-option = st.sidebar.radio("Select Module", 
-                         ("Option A - FI Trend Follower", "Option B - Equity Trend Follower"))
-
-# 2. Year Slider
-start_year = st.sidebar.slider("Start Year", 2008, 2026, 2015)
-
-# 3. Parameters
-vol_target = st.sidebar.slider("Annual Vol Target", 0.05, 0.25, 0.126)
-
-if st.sidebar.button("🔄 Sync Market Data"):
-    with st.spinner("Fetching Data..."):
-        refresh_market_data()
-    st.sidebar.success("Data Synced!")
-
-# --- DATA PROCESSING ---
-try:
-    data = pd.read_csv("market_data.csv", index_col=0, parse_dates=True)
-    
-    # Filter by Year
-    data = data[data.index.year >= start_year]
-
-    # Assign Universe & Benchmark
-    if "Option B" in option:
-        universe = X_EQUITY_TICKERS
-        benchmark_ticker = "SPY"
-    else:
-        universe = FI_TICKERS
-        benchmark_ticker = "AGG"
-
-    # Run Analysis
-    results = run_trend_module(data[universe], data['SOFR_ANNUAL'], vol_target)
-    
-    # --- CALCULATE METRICS ---
-    curve = results['curve']
-    rets = results['returns']
-    
-    # Sharpe (Excess over 0)
-    sharpe = (rets.mean() * 252) / (rets.std() * np.sqrt(252))
-    
-    # Annualized Return
-    total_days = (curve.index[-1] - curve.index[0]).days
-    ann_return = (curve.iloc[-1]**(365/total_days) - 1)
-    
-    # Drawdowns
-    rolling_max = curve.cummax()
-    drawdown = (curve - rolling_max) / rolling_max
-    max_dd_peak = drawdown.min()
-    max_dd_daily = rets.min()
-
-    # NYSE Calendar for Next Day
-    nyse = mcal.get_calendar('NYSE')
-    schedule = nyse.schedule(start_date=datetime.now(), end_date='2026-12-31')
-    next_day = schedule.index[0].strftime('%Y-%m-%d')
-
-    # --- OUTPUT UI ---
-    st.title(f"📊 {option}")
-    
-    # Stats Row
-    c1, c2, c3, c4, c5 = st.columns(5)
-    c1.metric("Sharpe Ratio", f"{sharpe:.2f}")
-    c2.metric("Annual Return", f"{ann_return:.2%}")
-    c3.metric("Max DD (P-to-T)", f"{max_dd_peak:.2%}")
-    c4.metric("Max DD (Daily)", f"{max_dd_daily:.2%}")
-    c5.metric("Next Trade Date", next_day)
-
-    # Allocation Table
-    st.subheader(f"📍 Target Allocation for {next_day}")
-    alloc = results['alloc']
-    st.dataframe(alloc[alloc['Weight (%)'] > 0].sort_values("Weight (%)", ascending=False), use_container_width=True)
-
-    # Performance Chart
-    st.subheader(f"Cumulative Return vs {benchmark_ticker}")
-    bench_curve = (1 + data[benchmark_ticker].pct_change().fillna(0)).cumprod()
-    # Normalize benchmark to start at 1.0 at start_year
-    bench_curve = bench_curve / bench_curve.iloc[0]
-    
-    chart_df = pd.DataFrame({
-        "Strategy": curve,
-        f"Benchmark ({benchmark_ticker})": bench_curve
-    })
-    st.line_chart(chart_df)
-
-except Exception as e:
-    st.info("Please Click 'Sync Market Data' in the sidebar to initialize the engine.")
-    st.error(f"Waiting for data... (Technical details: {e})")
+from data.loader import load_from_hf, seed_dataset_from_scratch, X_EQUITY_TICKERS, FI_TICKERS
+# ... other imports
+
+st.sidebar.title("Data Management")
+
+# Check if data exists
+if 'master_data' not in st.session_state:
+    st.session_state.master_data = load_from_hf()
+
+if st.session_state.master_data is None:
+    st.warning("Dataset not found on Hugging Face. Please Seed the Database.")
+    if st.sidebar.button("🚀 Step 1: Seed Database (2008-Present)"):
+        with st.spinner("Downloading 18 years of data... this takes a few minutes."):
+            st.session_state.master_data = seed_dataset_from_scratch()
+        st.success("Database seeded and uploaded to HF!")
+else:
+    if st.sidebar.button("🔄 Step 2: Daily Incremental Sync"):
+        # (Existing incremental sync logic here)
+        st.sidebar.write("Last Data Point:", st.session_state.master_data.index.max())
+
+# --- REST OF THE UI ---
+# Run Option A/B logic using st.session_state.master_data

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -1,32 +1,58 @@
+import pandas as pd
 import pandas_datareader.data as web
 import yfinance as yf
-import pandas as pd
+from huggingface_hub import hf_hub_download, HfApi
+import os
 import streamlit as st
 
-# 27 "X-" EQUITY ETFS
-X_EQUITY_TICKERS = [
-    "XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF",
-    "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", 
-    "XSW", "XTN", "XTL", "XNTK", "XITK"
-]
+REPO_ID = "P2SAMAPA/etf_trend_data"
+FILENAME = "market_data.csv"
 
-# 15 FIXED INCOME / COMPARISON
+# Universes
+X_EQUITY_TICKERS = ["XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF", "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", "XSW", "XTN", "XTL", "XNTK", "XITK"]
 FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
 
-def refresh_market_data():
-    """Syncs Stooq/FRED data to local CSV and HF."""
-    all_prices = {}
-    # Download all groups + SPY Benchmark
-    for t in list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY"])):
+def seed_dataset_from_scratch():
+    """Download full history from 2008 for all 42+ tickers and upload to HF."""
+    tickers = list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY", "AGG"]))
+    master_df = pd.DataFrame()
+    
+    progress_bar = st.progress(0)
+    for i, t in enumerate(tickers):
         try:
-            all_prices[t] = web.DataReader(f"{t}.US", "stooq")['Close']
-        except:
-            all_prices[t] = yf.download(t, progress=False)['Adj Close']
-            
-    # Fetch SOFR (Cash Yield) from FRED
-    sofr = web.DataReader('SOFR', 'fred').ffill()
+            # We use yfinance for the heavy initial lift as it handles long historical ranges reliably
+            data = yf.download(t, start="2008-01-01", progress=False)['Adj Close']
+            master_df[t] = data
+        except Exception as e:
+            st.warning(f"Failed to fetch {t}: {e}")
+        progress_bar.progress((i + 1) / len(tickers))
+    
+    # Add SOFR (Cash Interest)
+    sofr = web.DataReader('SOFR', 'fred', start="2008-01-01").ffill()
+    master_df['SOFR_ANNUAL'] = sofr / 100
     
-    df = pd.DataFrame(all_prices).sort_index().ffill()
-    df['SOFR_ANNUAL'] = sofr / 100
-    df.to_csv("market_data.csv")
-    return df
+    master_df = master_df.sort_index().ffill().dropna(how='all')
+    
+    # Save and Upload
+    master_df.to_csv(FILENAME)
+    upload_to_hf(FILENAME)
+    return master_df
+
+def upload_to_hf(local_path):
+    """Pushes the local CSV to your Hugging Face Dataset repo."""
+    api = HfApi()
+    api.upload_file(
+        path_or_fileobj=local_path,
+        path_in_repo=FILENAME,
+        repo_id=REPO_ID,
+        repo_type="dataset",
+        token=st.secrets["HF_TOKEN"]
+    )
+
+def load_from_hf():
+    """Reads the dataset from Hugging Face."""
+    try:
+        path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset", token=st.secrets["HF_TOKEN"])
+        return pd.read_csv(path, index_col=0, parse_dates=True)
+    except:
+        return None

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -11,78 +11,89 @@ st.set_page_config(layout="wide", page_title="P2 Trend Suite")
 # --- SIDEBAR UI ---
 st.sidebar.title("Strategy Controls")
 
-# 1. Option Selection
-option = st.sidebar.radio("Select Strategy Module", 
+# 1. Module Toggle
+option = st.sidebar.radio("Select Module", 
                          ("Option A - FI Trend Follower", "Option B - Equity Trend Follower"))
 
 # 2. Year Slider
-start_year = st.sidebar.slider("Start Year (OOS Period)", 2008, 2025, 2015)
+start_year = st.sidebar.slider("Start Year", 2008, 2026, 2015)
 
-# 3. Vol Target & Sync
+# 3. Parameters
 vol_target = st.sidebar.slider("Annual Vol Target", 0.05, 0.25, 0.126)
+
 if st.sidebar.button("🔄 Sync Market Data"):
-    refresh_market_data()
+    with st.spinner("Fetching Data..."):
+        refresh_market_data()
     st.sidebar.success("Data Synced!")
 
-# --- CALENDAR LOGIC ---
-nyse = mcal.get_calendar('NYSE')
-today = datetime.now().strftime('%Y-%m-%d')
-schedule = nyse.schedule(start_date=today, end_date='2026-12-31')
-next_trading_day = schedule.index[0].strftime('%A, %b %d, %Y')
-
-# --- EXECUTION ---
-if st.button("▶ Run Analysis"):
+# --- DATA PROCESSING ---
+try:
     data = pd.read_csv("market_data.csv", index_col=0, parse_dates=True)
     
-    # Filter by Start Year
+    # Filter by Year
     data = data[data.index.year >= start_year]
-    
-    # Select Universe & Benchmark
+
+    # Assign Universe & Benchmark
     if "Option B" in option:
         universe = X_EQUITY_TICKERS
         benchmark_ticker = "SPY"
-        module_name = "Equity"
     else:
         universe = FI_TICKERS
         benchmark_ticker = "AGG"
-        module_name = "Fixed Income"
 
-    # Run Engine
+    # Run Analysis
     results = run_trend_module(data[universe], data['SOFR_ANNUAL'], vol_target)
     
-    # Metrics Calculation
-    returns = results['returns']
-    cum_returns = results['curve']
-    bench_returns = data[benchmark_ticker].pct_change().fillna(0)
-    bench_curve = (1 + bench_returns).cumprod()
+    # --- CALCULATE METRICS ---
+    curve = results['curve']
+    rets = results['returns']
     
-    # Stats
-    ann_return = (cum_returns.iloc[-1]**(252/len(returns)) - 1)
-    sharpe = (returns.mean() * 252) / (returns.std() * np.sqrt(252))
+    # Sharpe (Excess over 0)
+    sharpe = (rets.mean() * 252) / (rets.std() * np.sqrt(252))
     
-    rolling_max = cum_returns.cummax()
-    drawdown = (cum_returns - rolling_max) / rolling_max
-    max_dd_peak = drawdown.min()
+    # Annualized Return
+    total_days = (curve.index[-1] - curve.index[0]).days
+    ann_return = (curve.iloc[-1]**(365/total_days) - 1)
     
+    # Drawdowns
+    rolling_max = curve.cummax()
+    drawdown = (curve - rolling_max) / rolling_max
+    max_dd_peak = drawdown.min()
+    max_dd_daily = rets.min()
+
+    # NYSE Calendar for Next Day
+    nyse = mcal.get_calendar('NYSE')
+    schedule = nyse.schedule(start_date=datetime.now(), end_date='2026-12-31')
+    next_day = schedule.index[0].strftime('%Y-%m-%d')
+
     # --- OUTPUT UI ---
-    st.header(f"📊 {option} Results")
+    st.title(f"📊 {option}")
     
-    # Target Allocation Section
-    st.subheader(f"📅 Next Day Target Allocation: {next_trading_day}")
-    alloc_df = results['alloc']
-    st.table(alloc_df[alloc_df['Weight (%)'] > 0].sort_values("Weight (%)", ascending=False))
+    # Stats Row
+    c1, c2, c3, c4, c5 = st.columns(5)
+    c1.metric("Sharpe Ratio", f"{sharpe:.2f}")
+    c2.metric("Annual Return", f"{ann_return:.2%}")
+    c3.metric("Max DD (P-to-T)", f"{max_dd_peak:.2%}")
+    c4.metric("Max DD (Daily)", f"{max_dd_daily:.2%}")
+    c5.metric("Next Trade Date", next_day)
 
-    # Metrics Row
-    m1, m2, m3, m4 = st.columns(4)
-    m1.metric("Annualized Return", f"{ann_return:.2%}")
-    m2.metric("Sharpe Ratio", f"{sharpe:.2f}")
-    m3.metric("Max DD (Peak-to-Trough)", f"{max_dd_peak:.2%}")
-    m4.metric("Last Daily Return", f"{returns.iloc[-1]:.2%}")
+    # Allocation Table
+    st.subheader(f"📍 Target Allocation for {next_day}")
+    alloc = results['alloc']
+    st.dataframe(alloc[alloc['Weight (%)'] > 0].sort_values("Weight (%)", ascending=False), use_container_width=True)
 
-    # Chart
+    # Performance Chart
     st.subheader(f"Cumulative Return vs {benchmark_ticker}")
-    chart_data = pd.DataFrame({
-        "Strategy": cum_returns,
+    bench_curve = (1 + data[benchmark_ticker].pct_change().fillna(0)).cumprod()
+    # Normalize benchmark to start at 1.0 at start_year
+    bench_curve = bench_curve / bench_curve.iloc[0]
+    
+    chart_df = pd.DataFrame({
+        "Strategy": curve,
         f"Benchmark ({benchmark_ticker})": bench_curve
     })
-    st.line_chart(chart_data)
+    st.line_chart(chart_df)
+
+except Exception as e:
+    st.info("Please Click 'Sync Market Data' in the sidebar to initialize the engine.")
+    st.error(f"Waiting for data... (Technical details: {e})")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,39 +1,88 @@
 import streamlit as st
 import pandas as pd
+import numpy as np
+import pandas_market_calendars as mcal
+from datetime import datetime
 from data.loader import refresh_market_data, X_EQUITY_TICKERS, FI_TICKERS
 from engine.trend_engine import run_trend_module
 
-st.set_page_config(layout="wide", page_title="P2 ETF Trend Suite")
+st.set_page_config(layout="wide", page_title="P2 Trend Suite")
 
-st.sidebar.title("Settings")
-vol_target = st.sidebar.slider("Annual Vol Target", 0.05, 0.25, 0.126)
+# --- SIDEBAR UI ---
+st.sidebar.title("Strategy Controls")
+
+# 1. Option Selection
+option = st.sidebar.radio("Select Strategy Module", 
+                         ("Option A - FI Trend Follower", "Option B - Equity Trend Follower"))
+
+# 2. Year Slider
+start_year = st.sidebar.slider("Start Year (OOS Period)", 2008, 2025, 2015)
 
-if st.sidebar.button("🔄 Refresh Market Data"):
+# 3. Vol Target & Sync
+vol_target = st.sidebar.slider("Annual Vol Target", 0.05, 0.25, 0.126)
+if st.sidebar.button("🔄 Sync Market Data"):
     refresh_market_data()
-    st.sidebar.success("Data Updated from Stooq/SOFR!")
+    st.sidebar.success("Data Synced!")
+
+# --- CALENDAR LOGIC ---
+nyse = mcal.get_calendar('NYSE')
+today = datetime.now().strftime('%Y-%m-%d')
+schedule = nyse.schedule(start_date=today, end_date='2026-12-31')
+next_trading_day = schedule.index[0].strftime('%A, %b %d, %Y')
 
-if st.button("▶ Run All Modules"):
+# --- EXECUTION ---
+if st.button("▶ Run Analysis"):
     data = pd.read_csv("market_data.csv", index_col=0, parse_dates=True)
     
-    # Run Modules
-    eq_res = run_trend_module(data[X_EQUITY_TICKERS], data['SOFR_ANNUAL'], vol_target)
-    fi_res = run_trend_module(data[FI_TICKERS], data['SOFR_ANNUAL'], vol_target)
+    # Filter by Start Year
+    data = data[data.index.year >= start_year]
+    
+    # Select Universe & Benchmark
+    if "Option B" in option:
+        universe = X_EQUITY_TICKERS
+        benchmark_ticker = "SPY"
+        module_name = "Equity"
+    else:
+        universe = FI_TICKERS
+        benchmark_ticker = "AGG"
+        module_name = "Fixed Income"
+
+    # Run Engine
+    results = run_trend_module(data[universe], data['SOFR_ANNUAL'], vol_target)
+    
+    # Metrics Calculation
+    returns = results['returns']
+    cum_returns = results['curve']
+    bench_returns = data[benchmark_ticker].pct_change().fillna(0)
+    bench_curve = (1 + bench_returns).cumprod()
+    
+    # Stats
+    ann_return = (cum_returns.iloc[-1]**(252/len(returns)) - 1)
+    sharpe = (returns.mean() * 252) / (returns.std() * np.sqrt(252))
     
-    # Performance Comparison
-    spy_curve = (1 + data['SPY'].pct_change()).cumprod()
-    comparison = pd.DataFrame({
-        "X-ETF Strategy": eq_res['curve'],
-        "SPY Benchmark": spy_curve
-    }).dropna()
-
-    st.header("📈 Performance: Equity Strategy vs. SPY")
-    st.line_chart(comparison)
+    rolling_max = cum_returns.cummax()
+    drawdown = (cum_returns - rolling_max) / rolling_max
+    max_dd_peak = drawdown.min()
     
-    # Target Allocations
-    col1, col2 = st.columns(2)
-    with col1:
-        st.subheader("🛡️ Equity Allocation (Next Day)")
-        st.dataframe(eq_res['alloc'][eq_res['alloc']['Weight (%)'] > 0])
-    with col2:
-        st.subheader("🏦 FI Comparison Allocation")
-        st.dataframe(fi_res['alloc'][fi_res['alloc']['Weight (%)'] > 0])
+    # --- OUTPUT UI ---
+    st.header(f"📊 {option} Results")
+    
+    # Target Allocation Section
+    st.subheader(f"📅 Next Day Target Allocation: {next_trading_day}")
+    alloc_df = results['alloc']
+    st.table(alloc_df[alloc_df['Weight (%)'] > 0].sort_values("Weight (%)", ascending=False))
+
+    # Metrics Row
+    m1, m2, m3, m4 = st.columns(4)
+    m1.metric("Annualized Return", f"{ann_return:.2%}")
+    m2.metric("Sharpe Ratio", f"{sharpe:.2f}")
+    m3.metric("Max DD (Peak-to-Trough)", f"{max_dd_peak:.2%}")
+    m4.metric("Last Daily Return", f"{returns.iloc[-1]:.2%}")
+
+    # Chart
+    st.subheader(f"Cumulative Return vs {benchmark_ticker}")
+    chart_data = pd.DataFrame({
+        "Strategy": cum_returns,
+        f"Benchmark ({benchmark_ticker})": bench_curve
+    })
+    st.line_chart(chart_data)

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py

@@ -0,0 +1,40 @@
+import numpy as np
+import pandas as pd
+
+def run_trend_module(prices, daily_sofr, vol_target):
+    # 1. Dual-Trend Signal
+    d_high = prices.rolling(20).max()
+    sma = prices.rolling(20).mean()
+    atr = (prices.rolling(20).max() - prices.rolling(20).min()) / 2
+    k_upper = sma + (2 * atr)
+    
+    entry_band = np.minimum(d_high, k_upper)
+    signals = (prices > entry_band.shift(1)).astype(int)
+    
+    # 2. Risk Parity Position Sizing
+    returns = prices.pct_change()
+    realized_vol = returns.rolling(21).std() * np.sqrt(252)
+    
+    n = len(prices.columns)
+    # Target weight = (Target Vol / Total Assets) / Individual Asset Vol
+    target_weights = (vol_target / n) / realized_vol.shift(1)
+    
+    # 3. Strategy Returns (Positions + SOFR on Cash)
+    pos_rets = (signals.shift(1) * target_weights.shift(1) * returns).sum(axis=1)
+    weight_used = (signals.shift(1) * target_weights.shift(1)).sum(axis=1)
+    cash_rets = (1 - weight_used).clip(0, 1) * (daily_sofr / 252)
+    
+    strat_rets = pos_rets + cash_rets
+    equity_curve = (1 + strat_rets).fillna(0).cumprod()
+    
+    # 4. Target Allocation for Tomorrow
+    tomorrow_sig = (prices.iloc[-1] > entry_band.iloc[-1]).astype(int)
+    tomorrow_w = (vol_target / n) / realized_vol.iloc[-1]
+    
+    alloc = pd.DataFrame({
+        "Ticker": prices.columns,
+        "Signal": ["LONG" if s == 1 else "CASH" for s in tomorrow_sig],
+        "Weight (%)": (tomorrow_sig * tomorrow_w * 100).round(2)
+    })
+    
+    return {"curve": equity_curve, "alloc": alloc}

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/engine/trend_engine.py

@@ -0,0 +1,36 @@
+import numpy as np
+import pandas as pd
+
+def run_trend_module(prices, daily_sofr, vol_target):
+    # 1. Signals (20-day Keltner/Donchian)
+    d_high = prices.rolling(20).max()
+    k_sma = prices.rolling(20).mean()
+    atr = (prices.rolling(20).max() - prices.rolling(20).min()) / 2
+    k_upper = k_sma + (2 * atr)
+    
+    entry_band = np.minimum(d_high, k_upper)
+    signals = (prices > entry_band.shift(1)).astype(int)
+    
+    # 2. Risk Parity Weighting
+    rets = prices.pct_change()
+    real_vol = rets.rolling(21).std() * np.sqrt(252)
+    
+    n = len(prices.columns)
+    weights = (vol_target / n) / real_vol.shift(1)
+    
+    # 3. Strategy Returns (Positions + Cash Interest)
+    strat_rets = (signals.shift(1) * weights.shift(1) * rets).sum(axis=1)
+    unused_cap = 1 - (signals.shift(1) * weights.shift(1)).sum(axis=1)
+    strat_rets += unused_cap.clip(0, 1) * (daily_sofr / 252)
+    
+    equity_curve = (1 + strat_rets).cumprod()
+    
+    # Next Day Allocation
+    tomorrow_sig = (prices.iloc[-1] > entry_band.iloc[-1]).astype(int)
+    tomorrow_w = (vol_target / n) / real_vol.iloc[-1]
+    alloc = pd.DataFrame({
+        "Ticker": prices.columns,
+        "Weight (%)": (tomorrow_sig * tomorrow_w * 100).round(2)
+    })
+    
+    return {"curve": equity_curve, "alloc": alloc}

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,54 +1,39 @@
 import streamlit as st
+import pandas as pd
+from data.loader import refresh_market_data, X_EQUITY_TICKERS, FI_TICKERS
+from engine.trend_engine import run_trend_module
 
-st.set_page_config(page_title="P2 ETF Trend Suite", layout="wide")
+st.set_page_config(layout="wide", page_title="P2 ETF Trend Suite")
 
-st.title("📊 P2 ETF Trend Suite")
-st.markdown("Stooq-Primary Data Engine + HF Integration")
+st.sidebar.title("Settings")
+vol_target = st.sidebar.slider("Annual Vol Target", 0.05, 0.25, 0.126)
 
-# Sidebar Controls
-st.sidebar.header("Parameters")
-initial_capital = st.sidebar.number_input("Initial Capital", value=100000)
-vol_target = st.sidebar.slider("Target Volatility", 0.05, 0.30, 0.15)
-lookback = st.sidebar.slider("Lookback (Days)", 50, 300, 200)
+if st.sidebar.button("🔄 Refresh Market Data"):
+    refresh_market_data()
+    st.sidebar.success("Data Updated from Stooq/SOFR!")
 
-st.sidebar.markdown("---")
-st.sidebar.header("Hugging Face Sync")
-hf_repo = st.sidebar.text_input("Repo ID", placeholder="user/dataset-name")
-hf_token = st.sidebar.text_input("HF Token", type="password")
-
-run_button = st.sidebar.button("▶ Run Full Process")
-
-if run_button:
-    from data.loader import load_data, push_to_hf
-    from engine.backtest import run_backtest
-    from analytics.metrics import compute_metrics
-
-    # Phase 1: Data Fetching
-    with st.spinner("Fetching data from Stooq..."):
-        df = load_data()
+if st.button("▶ Run All Modules"):
+    data = pd.read_csv("market_data.csv", index_col=0, parse_dates=True)
     
-    if not df.empty:
-        st.subheader("📈 Market Data Preview")
-        st.dataframe(df.tail(5), use_container_width=True)
-
-        # Phase 2: Backtesting
-        with st.spinner("Calculating Trend Strategy..."):
-            results = run_backtest(df, initial_capital, vol_target, lookback)
-            metrics = compute_metrics(results["returns"])
-
-        # Display Results
-        st.success("Analysis Complete")
-        c1, c2, c3 = st.columns(3)
-        c1.metric("CAGR", f"{metrics['cagr']:.2%}")
-        c2.metric("Sharpe", f"{metrics['sharpe']:.2f}")
-        c3.metric("Max Drawdown", f"{metrics['max_dd']:.2%}")
-
-        st.line_chart(results["equity_curve"])
-
-        # Phase 3: HF Sync
-        if hf_repo and hf_token:
-            with st.spinner("Syncing to Hugging Face..."):
-                push_to_hf(df, hf_repo, hf_token)
-            st.sidebar.success("✅ Dataset Synced!")
-    else:
-        st.error("Data fetch failed. Verify ticker symbols.")
+    # Run Modules
+    eq_res = run_trend_module(data[X_EQUITY_TICKERS], data['SOFR_ANNUAL'], vol_target)
+    fi_res = run_trend_module(data[FI_TICKERS], data['SOFR_ANNUAL'], vol_target)
+    
+    # Performance Comparison
+    spy_curve = (1 + data['SPY'].pct_change()).cumprod()
+    comparison = pd.DataFrame({
+        "X-ETF Strategy": eq_res['curve'],
+        "SPY Benchmark": spy_curve
+    }).dropna()
+
+    st.header("📈 Performance: Equity Strategy vs. SPY")
+    st.line_chart(comparison)
+    
+    # Target Allocations
+    col1, col2 = st.columns(2)
+    with col1:
+        st.subheader("🛡️ Equity Allocation (Next Day)")
+        st.dataframe(eq_res['alloc'][eq_res['alloc']['Weight (%)'] > 0])
+    with col2:
+        st.subheader("🏦 FI Comparison Allocation")
+        st.dataframe(fi_res['alloc'][fi_res['alloc']['Weight (%)'] > 0])

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/data/loader.py

@@ -1,56 +1,32 @@
-import pandas as pd
 import pandas_datareader.data as web
 import yfinance as yf
-from datasets import Dataset
+import pandas as pd
 import streamlit as st
-from datetime import datetime
 
-# Combined Universe (All will attempt Stooq first)
-TICKERS = ["SPY", "QQQ", "IWM", "TLT", "IEF", "SHY", "GLD"]
+# 27 "X-" EQUITY ETFS
+X_EQUITY_TICKERS = [
+    "XLK", "XLY", "XLP", "XLE", "XLV", "XLI", "XLB", "XLRE", "XLU", "XLC", "XLF",
+    "XBI", "XME", "XOP", "XHB", "XSD", "XRT", "XPH", "XES", "XAR", "XHS", "XHE", 
+    "XSW", "XTN", "XTL", "XNTK", "XITK"
+]
 
-def load_data(tickers=TICKERS):
-    """Fetches data from Stooq with yfinance fallback."""
-    all_series = {}
+# 15 FIXED INCOME / COMPARISON
+FI_TICKERS = ["TLT", "IEF", "TIP", "TBT", "GLD", "SLV", "VGIT", "VCLT", "VCIT", "HYG", "PFF", "MBB", "VNQ", "LQD", "AGG"]
 
-    for ticker in tickers:
-        success = False
-        # 1. Primary: Stooq
+def refresh_market_data():
+    """Syncs Stooq/FRED data to local CSV and HF."""
+    all_prices = {}
+    # Download all groups + SPY Benchmark
+    for t in list(set(X_EQUITY_TICKERS + FI_TICKERS + ["SPY"])):
         try:
-            # Stooq format: TICKER.US (e.g., TLT.US)
-            stooq_symbol = f"{ticker}.US"
-            df_stooq = web.DataReader(stooq_symbol, 'stooq')
+            all_prices[t] = web.DataReader(f"{t}.US", "stooq")['Close']
+        except:
+            all_prices[t] = yf.download(t, progress=False)['Adj Close']
             
-            if not df_stooq.empty:
-                # Stooq returns newest data first; sort to ascending for backtests
-                all_series[ticker] = df_stooq['Close'].sort_index()
-                st.toast(f"✅ {ticker} loaded from Stooq")
-                success = True
-        except Exception as e:
-            print(f"Stooq failed for {ticker}: {e}")
-
-        # 2. Fallback: yfinance
-        if not success:
-            try:
-                yf_df = yf.download(ticker, period="max", progress=False)
-                if not yf_df.empty:
-                    # Use Adj Close to account for dividends/splits
-                    all_series[ticker] = yf_df['Adj Close']
-                    st.toast(f"⚠️ {ticker} loaded from yfinance (Fallback)")
-                    success = True
-            except Exception as e:
-                st.error(f"❌ Critical: Could not load {ticker} from any source.")
-
-    if all_series:
-        # Align all tickers on the same dates and drop missing values
-        return pd.concat(all_series, axis=1).dropna()
-    return pd.DataFrame()
-
-def push_to_hf(df, repo_id, token):
-    """Pushes the current dataframe to Hugging Face Hub."""
-    # Ensure Date is a column, not an index, for HF compatibility
-    hf_export = df.reset_index()
-    hf_export.columns = [str(col) for col in hf_export.columns] # Ensure string columns
+    # Fetch SOFR (Cash Yield) from FRED
+    sofr = web.DataReader('SOFR', 'fred').ffill()
     
-    dataset = Dataset.from_pandas(hf_export)
-    dataset.push_to_hub(repo_id, token=token)
-    return True
+    df = pd.DataFrame(all_prices).sort_index().ffill()
+    df['SOFR_ANNUAL'] = sofr / 100
+    df.to_csv("market_data.csv")
+    return df

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/README.md

@@ -1,19 +1,15 @@
 ---
-title: P2 ETF TREND SUITE
-emoji: 🚀
-colorFrom: red
-colorTo: red
+title: P2 ETF Trend Suite
+emoji: 📊
+colorFrom: blue
+colorTo: indigo
 sdk: docker
-app_port: 8501
-tags:
-- streamlit
+app_port: 7860
 pinned: false
-short_description: Streamlit template space
 ---
 
-# Welcome to Streamlit!
+# 📊 P2 ETF Trend Suite
+Institutional ETF Trend + Volatility Targeting Engine.
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+### 🚀 Setup Info
+This Space runs a Dockerized Streamlit app. It uses **Stooq** for market data with **yfinance** as a fallback.

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/Dockerfile

@@ -1,26 +1,31 @@
-FROM python:3.10
+# Use a lightweight but stable Python base
+FROM python:3.10-slim
+
+# Set environment variables for speed and logging
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1
 
 WORKDIR /app
 
-# Copy requirements first (better layer caching)
+# Install system dependencies needed for pandas/datareader
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy only requirements first to leverage Docker cache
 COPY requirements.txt .
 
-RUN pip install --upgrade pip
-RUN pip install --no-cache-dir -r requirements.txt
+# Install dependencies (use --no-cache-dir to keep image small)
+RUN pip install --upgrade pip && \
+    pip install -r requirements.txt
 
-# Copy full project
+# Copy the rest of the application
 COPY . .
 
+# Ensure the app runs on the port HF expects (7860 for Docker)
 EXPOSE 7860
 
-ENV STREAMLIT_SERVER_PORT=7860
-ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
-ENV STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
-
-# Diagnostic startup command
-CMD ["bash", "-c", "echo '===== CONTAINER BOOTING ====='; \
-echo 'Python Version:'; python -V; \
-echo 'Current Directory:'; pwd; \
-echo 'Directory Listing:'; ls -la; \
-echo 'Starting Streamlit...'; \
-python -m streamlit run app.py --server.headless=true"]
+# Correct entrypoint for Streamlit in a container
+ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]