diff --git a/data/loader.py b/data/loader.py
index b0f0580004b489f9ac41f20977177f6f192bc44f..7f5795411e327196e36563def95a0792d9a0e5cd 100644
--- a/data/loader.py
+++ b/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)
diff --git a/hf_space/engine/trend_engine.py b/hf_space/engine/trend_engine.py
index a0d0a193b9fcf88520a81ad2e5c720c73ababdbe..569e6c9584654bd785a7b0cf7c7b982917f3ce49 100644
--- a/hf_space/engine/trend_engine.py
+++ b/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
     }
diff --git a/hf_space/hf_space/data/loader.py b/hf_space/hf_space/data/loader.py
index 1a7c1eee50b61e7ab279bcbd5f09d1363b3218a2..b0f0580004b489f9ac41f20977177f6f192bc44f 100644
--- a/hf_space/hf_space/data/loader.py
+++ b/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}")
diff --git a/hf_space/hf_space/hf_space/data/loader.py b/hf_space/hf_space/hf_space/data/loader.py
index ad6222dfb7ec282b9a27a39c863a9234502141ce..1a7c1eee50b61e7ab279bcbd5f09d1363b3218a2 100644
--- a/hf_space/hf_space/hf_space/data/loader.py
+++ b/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}")
diff --git a/hf_space/hf_space/hf_space/hf_space/data/loader.py b/hf_space/hf_space/hf_space/hf_space/data/loader.py
index 02059f1978476193b6dbfec9e6c8492e5a322828..ad6222dfb7ec282b9a27a39c863a9234502141ce 100644
--- a/hf_space/hf_space/hf_space/hf_space/data/loader.py
+++ b/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
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/app.py b/hf_space/hf_space/hf_space/hf_space/hf_space/app.py
index 610eeb78af93610d6ce5b478f1b3fb1f20e131b0..5fc44b3841a08115e73d8ef738ef41f35fcd0576 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/app.py
+++ b/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'.")
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py
index d16607f5a5b49e28edd41696f75abfc6cad24212..a0d0a193b9fcf88520a81ad2e5c720c73ababdbe 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py
+++ b/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],
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py
index a3bfe754990b0f15ef813b872879b22bc4bf5275..d16607f5a5b49e28edd41696f75abfc6cad24212 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py
+++ b/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,
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py
index 0e5b7a6519a9e812cd0d2b79b40f1d6975a4eb84..610eeb78af93610d6ce5b478f1b3fb1f20e131b0 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py
+++ b/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'.")
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py
index bae2a2ae15f3bdfcb384b84f9673873cf5b0616d..a3bfe754990b0f15ef813b872879b22bc4bf5275 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/engine/trend_engine.py
+++ b/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],
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py
index e200fc8366c0d4f3ce8e97b3ed3e26c28ec226fa..0e5b7a6519a9e812cd0d2b79b40f1d6975a4eb84 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py
+++ b/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?**
diff --git a/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 b/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
index 3a52e4e6af718ab97a3984e292bbd4f353884670..bae2a2ae15f3bdfcb384b84f9673873cf5b0616d 100644
--- a/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
+++ b/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],
diff --git a/hf_space/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 b/hf_space/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
index 0d4e13c6b83f702ffe2c2fc6463cf5037b36ede8..3a52e4e6af718ab97a3984e292bbd4f353884670 100644
--- a/hf_space/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
+++ b/hf_space/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]
     }
diff --git a/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/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
index ccb0cfb6b9cd162cea9497db63905176eb4e30db..e200fc8366c0d4f3ce8e97b3ed3e26c28ec226fa 100644
--- a/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/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.")
diff --git a/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/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
index 49f65b68d854511f5490106f41db4eb253c9b6cf..0d4e13c6b83f702ffe2c2fc6463cf5037b36ede8 100644
--- a/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/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))
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/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
index a2d9aa1dcc7eeeed886de4a229f73cd58f347943..02059f1978476193b6dbfec9e6c8492e5a322828 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/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)
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index d0649323f62d86c22f1dd7db8330c7d9afe59886..49f65b68d854511f5490106f41db4eb253c9b6cf 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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]
     }
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 6395d603107a5909740f065940e42e935ae3ed41..a2d9aa1dcc7eeeed886de4a229f73cd58f347943 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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)
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index b90e73874ec136083460d9ad203ac9edbd59b4fe..ccb0cfb6b9cd162cea9497db63905176eb4e30db 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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.")
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index aceee11eade2c4eab9ab6fc13fa316d87640e197..d0649323f62d86c22f1dd7db8330c7d9afe59886 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
     }
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 3dccd952718ee84e244b96c41ea568c2f78e73da..aceee11eade2c4eab9ab6fc13fa316d87640e197 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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]
+    }
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 33e492bf4ee58669b1dfc3023817f256203c92dd..6395d603107a5909740f065940e42e935ae3ed41 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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()
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 7df0d082ac77c3087335fa601e43a9ae21d4376b..b90e73874ec136083460d9ad203ac9edbd59b4fe 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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.")
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index aa01eac308bac7f4e8e4fa1250f3495bfd4829d0..33e492bf4ee58669b1dfc3023817f256203c92dd 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+    )
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index ee4abe7cfb2071274d7276b5b2b0e81159f4d683..aa01eac308bac7f4e8e4fa1250f3495bfd4829d0 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 645d71ed1f473b7c4266a646f01864bd9ca3eb16..ee4abe7cfb2071274d7276b5b2b0e81159f4d683 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index a20a1cb412cbec493abd5517e7ae2ee1b3606ef2..645d71ed1f473b7c4266a646f01864bd9ca3eb16 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 4925585a822ea2ff474da864a26cf47c57f3207a..7df0d082ac77c3087335fa601e43a9ae21d4376b 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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.")
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index ebaf87099ff871f8101b342b823cce879a0404a4..a20a1cb412cbec493abd5517e7ae2ee1b3606ef2 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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"])
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 73d05e92a63d16a80d3ec2d83b293e3d909fca7d..4925585a822ea2ff474da864a26cf47c57f3207a 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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)
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index dea08874c93f19d4b81c631e3195859a17d068e7..ebaf87099ff871f8101b342b823cce879a0404a4 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 8db20e432d7396da7548ca0c1189a71995ca7e81..73d05e92a63d16a80d3ec2d83b293e3d909fca7d 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index fdbf82ba4101d3e404ed0d1733aa4022f49ebf01..dea08874c93f19d4b81c631e3195859a17d068e7 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 576bbca619cc3d82d8c06feacd010ccf7ab85fda..8db20e432d7396da7548ca0c1189a71995ca7e81 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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})")
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 442585e5c8b7f93d5ed33ec2f0ba1e0da4ee70d2..576bbca619cc3d82d8c06feacd010ccf7ab85fda 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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)
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
new file mode 100644
index 0000000000000000000000000000000000000000..3dccd952718ee84e244b96c41ea568c2f78e73da
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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}
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
new file mode 100644
index 0000000000000000000000000000000000000000..4f91f0d59d3b04af8afbd0e377304edc931041f3
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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}
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index bece99ec6fd21a872c830a3bb47c0bf22f7ce199..442585e5c8b7f93d5ed33ec2f0ba1e0da4ee70d2 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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])
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 8d61a9376d9b5add40b9fa6566ba0a29dfbade3e..fdbf82ba4101d3e404ed0d1733aa4022f49ebf01 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index c571620450ee0aefd6e05bfa3d1141e69f691ae4..1817b1ac4751066c5cee3fc1b3a3c5a7a50c7a77 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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.
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 82c92244713b3df072dd26346b589f1bccbcf1fa..9724d10731e201046939ae744ab6b9db13db0ba2 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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"]
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/__init__.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/__init__.py
@@ -0,0 +1 @@
+
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/data/__init__.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/data/__init__.py
@@ -0,0 +1 @@
+
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/analytics/analytics/__init__.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/analytics/analytics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/analytics/analytics/__init__.py
@@ -0,0 +1 @@
+
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 7db2ad2d93446fe2a9d1d9df77c9e11e5b103daa..bece99ec6fd21a872c830a3bb47c0bf22f7ce199 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
@@ -1,80 +1,54 @@
 import streamlit as st
 
-# =====================================================
-# PAGE CONFIG (ONLY LIGHT CODE HERE)
-# =====================================================
-
-st.set_page_config(
-    page_title="P2 ETF Trend Suite",
-    layout="wide",
-)
+st.set_page_config(page_title="P2 ETF Trend Suite", layout="wide")
 
 st.title("📊 P2 ETF Trend Suite")
-st.markdown("Institutional ETF Trend + Volatility Targeting Engine")
-
-# =====================================================
-# SIDEBAR
-# =====================================================
-
-st.sidebar.header("Strategy Controls")
-
-initial_capital = st.sidebar.number_input(
-    "Initial Capital",
-    value=100000,
-    step=10000,
-)
+st.markdown("Stooq-Primary Data Engine + HF Integration")
 
-vol_target = st.sidebar.slider(
-    "Target Annual Volatility",
-    0.05, 0.30, 0.15
-)
-
-lookback = st.sidebar.slider(
-    "Momentum Lookback (days)",
-    50, 300, 200
-)
-
-run_button = st.sidebar.button("▶ Run Backtest")
+# 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)
 
 st.sidebar.markdown("---")
-st.sidebar.info("Backtest runs only when button is pressed.")
+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")
 
-# =====================================================
-# EXECUTION BLOCK
-# =====================================================
+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
 
-    with st.spinner("Loading engine..."):
-
-        # Lazy imports happen HERE
-        from engine.backtest import run_backtest
-        from data.loader import load_data
-        from analytics.metrics import compute_metrics
-
-    with st.spinner("Loading market data..."):
+    # Phase 1: Data Fetching
+    with st.spinner("Fetching data from Stooq..."):
         df = load_data()
-
-    with st.spinner("Running strategy..."):
-        results = run_backtest(
-            df=df,
-            initial_capital=initial_capital,
-            vol_target=vol_target,
-            lookback=lookback,
-        )
-
-    metrics = compute_metrics(results["returns"])
-
-    st.success("Backtest Complete")
-
-    col1, col2, col3, col4 = st.columns(4)
-    col1.metric("CAGR", f"{metrics['cagr']:.2%}")
-    col2.metric("Sharpe", f"{metrics['sharpe']:.2f}")
-    col3.metric("Max Drawdown", f"{metrics['max_dd']:.2%}")
-    col4.metric("Volatility", f"{metrics['vol']:.2%}")
-
-    st.subheader("Equity Curve")
-    st.line_chart(results["equity_curve"])
-
-else:
-    st.info("Configure parameters and click Run Backtest.")
+    
+    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.")
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index a76b4304287b6a19c2be7d3cd453573e4870d0d8..8d61a9376d9b5add40b9fa6566ba0a29dfbade3e 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
@@ -1,15 +1,56 @@
-def load_data():
-    import pandas as pd
-    import yfinance as yf
+import pandas as pd
+import pandas_datareader.data as web
+import yfinance as yf
+from datasets import Dataset
+import streamlit as st
+from datetime import datetime
 
-    tickers = ["SPY", "QQQ", "TLT"]
+# Combined Universe (All will attempt Stooq first)
+TICKERS = ["SPY", "QQQ", "IWM", "TLT", "IEF", "SHY", "GLD"]
 
-    data = yf.download(
-        tickers,
-        start="2015-01-01",
-        progress=False,
-    )["Adj Close"]
+def load_data(tickers=TICKERS):
+    """Fetches data from Stooq with yfinance fallback."""
+    all_series = {}
 
-    data = data.dropna()
+    for ticker in tickers:
+        success = False
+        # 1. Primary: Stooq
+        try:
+            # Stooq format: TICKER.US (e.g., TLT.US)
+            stooq_symbol = f"{ticker}.US"
+            df_stooq = web.DataReader(stooq_symbol, 'stooq')
+            
+            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}")
 
-    return data
+        # 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
+    
+    dataset = Dataset.from_pandas(hf_export)
+    dataset.push_to_hub(repo_id, token=token)
+    return True
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 8f51b9f478c983ec0d96e445d44e7917612d2b79..82c92244713b3df072dd26346b589f1bccbcf1fa 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
@@ -2,11 +2,13 @@ FROM python:3.10
 
 WORKDIR /app
 
+# Copy requirements first (better layer caching)
 COPY requirements.txt .
 
 RUN pip install --upgrade pip
 RUN pip install --no-cache-dir -r requirements.txt
 
+# Copy full project
 COPY . .
 
 EXPOSE 7860
@@ -15,4 +17,10 @@ ENV STREAMLIT_SERVER_PORT=7860
 ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
 ENV STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
 
-CMD streamlit run app.py
+# 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"]
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
new file mode 100644
index 0000000000000000000000000000000000000000..a76b4304287b6a19c2be7d3cd453573e4870d0d8
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
@@ -0,0 +1,15 @@
+def load_data():
+    import pandas as pd
+    import yfinance as yf
+
+    tickers = ["SPY", "QQQ", "TLT"]
+
+    data = yf.download(
+        tickers,
+        start="2015-01-01",
+        progress=False,
+    )["Adj Close"]
+
+    data = data.dropna()
+
+    return data
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/analytics/metrics.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/analytics/metrics.py
index e8e5888d2346d958ef68df6c9d21dedb6e433d33..f4f4494f19a1f479a3f1f368c04296ea247c420d 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/analytics/metrics.py
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/analytics/metrics.py
@@ -1,26 +1,21 @@
-import numpy as np
-import pandas as pd
+def compute_metrics(returns):
 
-def compute_metrics(returns, sofr):
+    import numpy as np
 
-    sofr_daily = sofr.reindex(returns.index).fillna(method="ffill")["sofr"] / 252
-    excess = returns - sofr_daily
+    ann_factor = 252
 
-    sharpe = np.sqrt(252) * excess.mean() / excess.std()
+    cagr = (1 + returns.mean()) ** ann_factor - 1
+    vol = returns.std() * (ann_factor ** 0.5)
+    sharpe = cagr / vol if vol != 0 else 0
 
-    equity = (1 + returns).cumprod()
-    cagr = equity.iloc[-1] ** (252 / len(equity)) - 1
-
-    vol = returns.std() * np.sqrt(252)
-
-    rolling_max = equity.cummax()
-    drawdown = equity / rolling_max - 1
+    cumulative = (1 + returns).cumprod()
+    peak = cumulative.cummax()
+    drawdown = (cumulative - peak) / peak
     max_dd = drawdown.min()
 
     return {
-        "sharpe": sharpe,
         "cagr": cagr,
         "vol": vol,
-        "max_dd": max_dd
+        "sharpe": sharpe,
+        "max_dd": max_dd,
     }
-
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/backtest.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/backtest.py
index 3cf746a2e564a42893ab1ee7101f7b944eb322a5..a9c0aed248148b72044c880828766352daf18659 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/backtest.py
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/backtest.py
@@ -1,46 +1,20 @@
-import pandas as pd
-import numpy as np
-
 def run_backtest(df, initial_capital, vol_target, lookback):
 
-    df = df.sort_values(["ticker", "date"])
-    prices = df.pivot(index="date", columns="ticker", values="adjusted_close")
-    returns = prices.pct_change().dropna()
-
-    momentum = prices.pct_change(lookback)
-    signal = momentum.rank(axis=1, ascending=False)
-    top = signal <= 3
-
-    weights = top.div(top.sum(axis=1), axis=0)
-
-    rolling_cov = returns.rolling(60).cov()
-    vol = []
+    import numpy as np
+    import pandas as pd
 
-    for date in weights.index:
-        if date not in rolling_cov.index:
-            vol.append(0)
-            continue
+    returns = df.pct_change().dropna()
 
-        w = weights.loc[date].values
-        cov = rolling_cov.loc[date].values.reshape(len(w), len(w))
-        portfolio_vol = np.sqrt(w @ cov @ w) * np.sqrt(252)
+    momentum = df.pct_change(lookback)
 
-        scale = vol_target / portfolio_vol if portfolio_vol > 0 else 0
-        weights.loc[date] = w * scale
-        vol.append(portfolio_vol)
+    weights = (momentum > 0).astype(int)
+    weights = weights.div(weights.sum(axis=1), axis=0).fillna(0)
 
     strategy_returns = (weights.shift(1) * returns).sum(axis=1)
-    equity_curve = (1 + strategy_returns).cumprod() * initial_capital
 
-    latest_weights = weights.iloc[-1]
-    allocation = pd.DataFrame({
-        "Ticker": latest_weights.index,
-        "Weight": latest_weights.values
-    }).sort_values("Weight", ascending=False)
+    equity_curve = (1 + strategy_returns).cumprod() * initial_capital
 
     return {
         "returns": strategy_returns,
         "equity_curve": equity_curve,
-        "latest_allocation": allocation
     }
-
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 980decdea60b6cc75e90d9c75f9e5b66d3b08949..7db2ad2d93446fe2a9d1d9df77c9e11e5b103daa 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
@@ -1,7 +1,7 @@
 import streamlit as st
 
 # =====================================================
-# PAGE CONFIG (must be first Streamlit command)
+# PAGE CONFIG (ONLY LIGHT CODE HERE)
 # =====================================================
 
 st.set_page_config(
@@ -13,7 +13,7 @@ st.title("📊 P2 ETF Trend Suite")
 st.markdown("Institutional ETF Trend + Volatility Targeting Engine")
 
 # =====================================================
-# SIDEBAR CONTROLS
+# SIDEBAR
 # =====================================================
 
 st.sidebar.header("Strategy Controls")
@@ -26,16 +26,12 @@ initial_capital = st.sidebar.number_input(
 
 vol_target = st.sidebar.slider(
     "Target Annual Volatility",
-    min_value=0.05,
-    max_value=0.30,
-    value=0.15,
+    0.05, 0.30, 0.15
 )
 
 lookback = st.sidebar.slider(
     "Momentum Lookback (days)",
-    min_value=50,
-    max_value=300,
-    value=200,
+    50, 300, 200
 )
 
 run_button = st.sidebar.button("▶ Run Backtest")
@@ -44,34 +40,22 @@ st.sidebar.markdown("---")
 st.sidebar.info("Backtest runs only when button is pressed.")
 
 # =====================================================
-# MAIN EXECUTION (runs ONLY when button clicked)
+# EXECUTION BLOCK
 # =====================================================
 
 if run_button:
 
-    # Import heavy modules only when needed
-    from data.hf_store import load_dataset
-    from data.updater import update_market_data
-    from data.fred import get_sofr_series
-    from engine.backtest import run_backtest
-    from analytics.metrics import compute_metrics
-
-    # ---------------------------
-    # Load Dataset
-    # ---------------------------
-    with st.spinner("Loading ETF dataset from Hugging Face..."):
-        df = load_dataset()
-
-    # ---------------------------
-    # Pull SOFR
-    # ---------------------------
-    with st.spinner("Pulling SOFR from FRED..."):
-        sofr = get_sofr_series()
-
-    # ---------------------------
-    # Run Backtest
-    # ---------------------------
-    with st.spinner("Running backtest engine..."):
+    with st.spinner("Loading engine..."):
+
+        # Lazy imports happen HERE
+        from engine.backtest import run_backtest
+        from data.loader import load_data
+        from analytics.metrics import compute_metrics
+
+    with st.spinner("Loading market data..."):
+        df = load_data()
+
+    with st.spinner("Running strategy..."):
         results = run_backtest(
             df=df,
             initial_capital=initial_capital,
@@ -79,38 +63,18 @@ if run_button:
             lookback=lookback,
         )
 
-    metrics = compute_metrics(results["returns"], sofr)
+    metrics = compute_metrics(results["returns"])
 
     st.success("Backtest Complete")
 
-    # =====================================================
-    # METRICS PANEL
-    # =====================================================
-
     col1, col2, col3, col4 = st.columns(4)
-
     col1.metric("CAGR", f"{metrics['cagr']:.2%}")
-    col2.metric("Sharpe (SOFR)", f"{metrics['sharpe']:.2f}")
+    col2.metric("Sharpe", f"{metrics['sharpe']:.2f}")
     col3.metric("Max Drawdown", f"{metrics['max_dd']:.2%}")
     col4.metric("Volatility", f"{metrics['vol']:.2%}")
 
-    st.markdown("---")
-
-    # =====================================================
-    # EQUITY CURVE
-    # =====================================================
-
     st.subheader("Equity Curve")
     st.line_chart(results["equity_curve"])
 
-    st.markdown("---")
-
-    # =====================================================
-    # ALLOCATION TABLE
-    # =====================================================
-
-    st.subheader("Latest Portfolio Allocation")
-    st.dataframe(results["latest_allocation"], use_container_width=True)
-
 else:
-    st.info("Configure parameters in the sidebar and click **Run Backtest**.")
+    st.info("Configure parameters and click Run Backtest.")
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index bb3931c783bea026eefd60555fce13c6cc877681..8f51b9f478c983ec0d96e445d44e7917612d2b79 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
@@ -1,13 +1,18 @@
-FROM python:3.10-slim
+FROM python:3.10
 
 WORKDIR /app
 
 COPY requirements.txt .
 
+RUN pip install --upgrade pip
 RUN pip install --no-cache-dir -r requirements.txt
 
 COPY . .
 
 EXPOSE 7860
 
-CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]
+ENV STREAMLIT_SERVER_PORT=7860
+ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
+ENV STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+
+CMD streamlit run app.py
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 2f8ce9d33ba2c163b56cfd50e515d779360b2791..980decdea60b6cc75e90d9c75f9e5b66d3b08949 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
@@ -1,70 +1,116 @@
 import streamlit as st
-import os
 
-from data.hf_store import load_dataset
-from data.updater import update_market_data
-from data.fred import get_sofr_series
-from engine.backtest import run_backtest
-from analytics.metrics import compute_metrics
+# =====================================================
+# PAGE CONFIG (must be first Streamlit command)
+# =====================================================
 
-st.set_page_config(layout="wide")
+st.set_page_config(
+    page_title="P2 ETF Trend Suite",
+    layout="wide",
+)
 
 st.title("📊 P2 ETF Trend Suite")
 st.markdown("Institutional ETF Trend + Volatility Targeting Engine")
 
-# ========================
-# Sidebar Controls
-# ========================
+# =====================================================
+# SIDEBAR CONTROLS
+# =====================================================
 
-st.sidebar.header("Controls")
+st.sidebar.header("Strategy Controls")
 
-initial_capital = st.sidebar.number_input("Initial Capital", value=100000, step=10000)
-vol_target = st.sidebar.slider("Target Annual Volatility", 0.05, 0.30, 0.15)
-lookback = st.sidebar.slider("Momentum Lookback (days)", 50, 300, 200)
+initial_capital = st.sidebar.number_input(
+    "Initial Capital",
+    value=100000,
+    step=10000,
+)
 
-refresh = st.sidebar.button("🔄 Refresh Market Data")
+vol_target = st.sidebar.slider(
+    "Target Annual Volatility",
+    min_value=0.05,
+    max_value=0.30,
+    value=0.15,
+)
 
-# ========================
-# Data Load
-# ========================
+lookback = st.sidebar.slider(
+    "Momentum Lookback (days)",
+    min_value=50,
+    max_value=300,
+    value=200,
+)
 
-with st.spinner("Loading ETF dataset..."):
-    df = load_dataset()
+run_button = st.sidebar.button("▶ Run Backtest")
 
-if refresh:
-    with st.spinner("Updating market data from yfinance..."):
-        df = update_market_data(df)
-    st.success("Dataset updated successfully.")
+st.sidebar.markdown("---")
+st.sidebar.info("Backtest runs only when button is pressed.")
 
-# ========================
-# Backtest
-# ========================
+# =====================================================
+# MAIN EXECUTION (runs ONLY when button clicked)
+# =====================================================
 
-with st.spinner("Pulling SOFR from FRED..."):
-    sofr = get_sofr_series()
+if run_button:
 
-with st.spinner("Running backtest..."):
-    results = run_backtest(
-        df=df,
-        initial_capital=initial_capital,
-        vol_target=vol_target,
-        lookback=lookback,
-    )
+    # Import heavy modules only when needed
+    from data.hf_store import load_dataset
+    from data.updater import update_market_data
+    from data.fred import get_sofr_series
+    from engine.backtest import run_backtest
+    from analytics.metrics import compute_metrics
 
-metrics = compute_metrics(results["returns"], sofr)
+    # ---------------------------
+    # Load Dataset
+    # ---------------------------
+    with st.spinner("Loading ETF dataset from Hugging Face..."):
+        df = load_dataset()
 
-# ========================
-# Layout
-# ========================
+    # ---------------------------
+    # Pull SOFR
+    # ---------------------------
+    with st.spinner("Pulling SOFR from FRED..."):
+        sofr = get_sofr_series()
 
-col1, col2, col3, col4 = st.columns(4)
+    # ---------------------------
+    # Run Backtest
+    # ---------------------------
+    with st.spinner("Running backtest engine..."):
+        results = run_backtest(
+            df=df,
+            initial_capital=initial_capital,
+            vol_target=vol_target,
+            lookback=lookback,
+        )
 
-col1.metric("CAGR", f"{metrics['cagr']:.2%}")
-col2.metric("Sharpe (SOFR)", f"{metrics['sharpe']:.2f}")
-col3.metric("Max Drawdown", f"{metrics['max_dd']:.2%}")
-col4.metric("Volatility", f"{metrics['vol']:.2%}")
+    metrics = compute_metrics(results["returns"], sofr)
 
-st.line_chart(results["equity_curve"])
+    st.success("Backtest Complete")
 
-st.subheader("Current Allocation")
-st.dataframe(results["latest_allocation"])
+    # =====================================================
+    # METRICS PANEL
+    # =====================================================
+
+    col1, col2, col3, col4 = st.columns(4)
+
+    col1.metric("CAGR", f"{metrics['cagr']:.2%}")
+    col2.metric("Sharpe (SOFR)", f"{metrics['sharpe']:.2f}")
+    col3.metric("Max Drawdown", f"{metrics['max_dd']:.2%}")
+    col4.metric("Volatility", f"{metrics['vol']:.2%}")
+
+    st.markdown("---")
+
+    # =====================================================
+    # EQUITY CURVE
+    # =====================================================
+
+    st.subheader("Equity Curve")
+    st.line_chart(results["equity_curve"])
+
+    st.markdown("---")
+
+    # =====================================================
+    # ALLOCATION TABLE
+    # =====================================================
+
+    st.subheader("Latest Portfolio Allocation")
+    st.dataframe(results["latest_allocation"], use_container_width=True)
+
+else:
+    st.info("Configure parameters in the sidebar and click **Run Backtest**.")
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 5f51ead59f36f13043e036290df9440e25fe8cbe..bb3931c783bea026eefd60555fce13c6cc877681 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
@@ -1,20 +1,13 @@
-FROM python:3.13.5-slim
+FROM python:3.10-slim
 
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+COPY requirements.txt .
 
-COPY requirements.txt ./
-COPY src/ ./src/
+RUN pip install --no-cache-dir -r requirements.txt
 
-RUN pip3 install -r requirements.txt
+COPY . .
 
-EXPOSE 8501
+EXPOSE 7860
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
\ No newline at end of file
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
index 91487ec3342b010a22a9da2fba99b9249e9d42c7..2f8ce9d33ba2c163b56cfd50e515d779360b2791 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 +1,70 @@
 import streamlit as st
+import os
+
+from data.hf_store import load_dataset
+from data.updater import update_market_data
+from data.fred import get_sofr_series
+from engine.backtest import run_backtest
+from analytics.metrics import compute_metrics
 
 st.set_page_config(layout="wide")
 
-st.title("P2 ETF Trend Suite - Debug Mode")
-
-try:
-    from data.hf_store import load_dataset
-    st.success("hf_store imported successfully")
-except Exception as e:
-    st.error(f"hf_store failed: {e}")
-
-try:
-    from data.updater import update_market_data
-    st.success("updater imported successfully")
-except Exception as e:
-    st.error(f"updater failed: {e}")
-
-try:
-    from data.fred import get_sofr_series
-    st.success("fred imported successfully")
-except Exception as e:
-    st.error(f"fred failed: {e}")
-
-try:
-    from engine.backtest import run_backtest
-    st.success("backtest imported successfully")
-except Exception as e:
-    st.error(f"backtest failed: {e}")
-
-try:
-    from analytics.metrics import compute_metrics
-    st.success("metrics imported successfully")
-except Exception as e:
-    st.error(f"metrics failed: {e}")
+st.title("📊 P2 ETF Trend Suite")
+st.markdown("Institutional ETF Trend + Volatility Targeting Engine")
+
+# ========================
+# Sidebar Controls
+# ========================
+
+st.sidebar.header("Controls")
+
+initial_capital = st.sidebar.number_input("Initial Capital", value=100000, step=10000)
+vol_target = st.sidebar.slider("Target Annual Volatility", 0.05, 0.30, 0.15)
+lookback = st.sidebar.slider("Momentum Lookback (days)", 50, 300, 200)
+
+refresh = st.sidebar.button("🔄 Refresh Market Data")
+
+# ========================
+# Data Load
+# ========================
+
+with st.spinner("Loading ETF dataset..."):
+    df = load_dataset()
+
+if refresh:
+    with st.spinner("Updating market data from yfinance..."):
+        df = update_market_data(df)
+    st.success("Dataset updated successfully.")
+
+# ========================
+# Backtest
+# ========================
+
+with st.spinner("Pulling SOFR from FRED..."):
+    sofr = get_sofr_series()
+
+with st.spinner("Running backtest..."):
+    results = run_backtest(
+        df=df,
+        initial_capital=initial_capital,
+        vol_target=vol_target,
+        lookback=lookback,
+    )
+
+metrics = compute_metrics(results["returns"], sofr)
+
+# ========================
+# Layout
+# ========================
+
+col1, col2, col3, col4 = st.columns(4)
+
+col1.metric("CAGR", f"{metrics['cagr']:.2%}")
+col2.metric("Sharpe (SOFR)", f"{metrics['sharpe']:.2f}")
+col3.metric("Max Drawdown", f"{metrics['max_dd']:.2%}")
+col4.metric("Volatility", f"{metrics['vol']:.2%}")
+
+st.line_chart(results["equity_curve"])
+
+st.subheader("Current Allocation")
+st.dataframe(results["latest_allocation"])
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/analytics/metrics.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/analytics/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8e5888d2346d958ef68df6c9d21dedb6e433d33
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/analytics/metrics.py
@@ -0,0 +1,26 @@
+import numpy as np
+import pandas as pd
+
+def compute_metrics(returns, sofr):
+
+    sofr_daily = sofr.reindex(returns.index).fillna(method="ffill")["sofr"] / 252
+    excess = returns - sofr_daily
+
+    sharpe = np.sqrt(252) * excess.mean() / excess.std()
+
+    equity = (1 + returns).cumprod()
+    cagr = equity.iloc[-1] ** (252 / len(equity)) - 1
+
+    vol = returns.std() * np.sqrt(252)
+
+    rolling_max = equity.cummax()
+    drawdown = equity / rolling_max - 1
+    max_dd = drawdown.min()
+
+    return {
+        "sharpe": sharpe,
+        "cagr": cagr,
+        "vol": vol,
+        "max_dd": max_dd
+    }
+
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
new file mode 100644
index 0000000000000000000000000000000000000000..91487ec3342b010a22a9da2fba99b9249e9d42c7
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
@@ -0,0 +1,35 @@
+import streamlit as st
+
+st.set_page_config(layout="wide")
+
+st.title("P2 ETF Trend Suite - Debug Mode")
+
+try:
+    from data.hf_store import load_dataset
+    st.success("hf_store imported successfully")
+except Exception as e:
+    st.error(f"hf_store failed: {e}")
+
+try:
+    from data.updater import update_market_data
+    st.success("updater imported successfully")
+except Exception as e:
+    st.error(f"updater failed: {e}")
+
+try:
+    from data.fred import get_sofr_series
+    st.success("fred imported successfully")
+except Exception as e:
+    st.error(f"fred failed: {e}")
+
+try:
+    from engine.backtest import run_backtest
+    st.success("backtest imported successfully")
+except Exception as e:
+    st.error(f"backtest failed: {e}")
+
+try:
+    from analytics.metrics import compute_metrics
+    st.success("metrics imported successfully")
+except Exception as e:
+    st.error(f"metrics failed: {e}")
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/fred.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/fred.py
new file mode 100644
index 0000000000000000000000000000000000000000..060d85abd48d015dcdb9f7d1ff09d804f0739e35
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/fred.py
@@ -0,0 +1,11 @@
+import os
+import pandas as pd
+from fredapi import Fred
+
+def get_sofr_series():
+    fred = Fred(api_key=os.getenv("FRED_API_KEY"))
+    sofr = fred.get_series("SOFR")
+    sofr = sofr.to_frame("sofr")
+    sofr.index = pd.to_datetime(sofr.index)
+    sofr["sofr"] = sofr["sofr"] / 100
+    return sofr
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/hf_store.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/hf_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9e4f70959dd855e9ba41ebf4b0a50955528e08c
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/hf_store.py
@@ -0,0 +1,17 @@
+import pandas as pd
+from datasets import load_dataset as hf_load_dataset
+
+DATASET_PATH = "P2SAMAPA/etf_trend_data"
+
+def load_dataset():
+    dataset = hf_load_dataset(DATASET_PATH)
+
+    # Handle split safely
+    if isinstance(dataset, dict) and "train" in dataset:
+        dataset = dataset["train"]
+
+    df = dataset.to_pandas()
+    df["date"] = pd.to_datetime(df["date"])
+    df = df.sort_values(["ticker", "date"])
+
+    return df
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/updater.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/updater.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a8e05f0bbd4aa1571b26bbec0d19800fb197cef
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/updater.py
@@ -0,0 +1,55 @@
+import os
+import pandas as pd
+import yfinance as yf
+from huggingface_hub import HfApi
+
+DATASET_PATH = "P2SAMAPA/etf_trend_data"
+
+def update_market_data(df):
+
+    tickers = df["ticker"].unique()
+    all_new = []
+
+    for ticker in tickers:
+        last_date = df[df["ticker"] == ticker]["date"].max()
+        start_date = (last_date + pd.Timedelta(days=1)).strftime("%Y-%m-%d")
+
+        new_data = yf.download(ticker, start=start_date, progress=False)
+
+        if new_data.empty:
+            continue
+
+        new_data.reset_index(inplace=True)
+        new_data["ticker"] = ticker
+        new_data.rename(columns={
+            "Date": "date",
+            "Open": "open",
+            "High": "high",
+            "Low": "low",
+            "Close": "close",
+            "Adj Close": "adjusted_close",
+            "Volume": "volume"
+        }, inplace=True)
+
+        all_new.append(new_data)
+
+    if not all_new:
+        return df
+
+    new_df = pd.concat(all_new)
+    df = pd.concat([df, new_df])
+    df.drop_duplicates(subset=["date", "ticker"], inplace=True)
+    df.sort_values(["ticker", "date"], inplace=True)
+
+    df.to_parquet("updated.parquet")
+
+    api = HfApi()
+    api.upload_file(
+        path_or_fileobj="updated.parquet",
+        path_in_repo="data/train-00000-of-00001.parquet",
+        repo_id=DATASET_PATH,
+        repo_type="dataset",
+        token=os.getenv("HF_TOKEN")
+    )
+
+    return df
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/backtest.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/backtest.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cf746a2e564a42893ab1ee7101f7b944eb322a5
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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/backtest.py
@@ -0,0 +1,46 @@
+import pandas as pd
+import numpy as np
+
+def run_backtest(df, initial_capital, vol_target, lookback):
+
+    df = df.sort_values(["ticker", "date"])
+    prices = df.pivot(index="date", columns="ticker", values="adjusted_close")
+    returns = prices.pct_change().dropna()
+
+    momentum = prices.pct_change(lookback)
+    signal = momentum.rank(axis=1, ascending=False)
+    top = signal <= 3
+
+    weights = top.div(top.sum(axis=1), axis=0)
+
+    rolling_cov = returns.rolling(60).cov()
+    vol = []
+
+    for date in weights.index:
+        if date not in rolling_cov.index:
+            vol.append(0)
+            continue
+
+        w = weights.loc[date].values
+        cov = rolling_cov.loc[date].values.reshape(len(w), len(w))
+        portfolio_vol = np.sqrt(w @ cov @ w) * np.sqrt(252)
+
+        scale = vol_target / portfolio_vol if portfolio_vol > 0 else 0
+        weights.loc[date] = w * scale
+        vol.append(portfolio_vol)
+
+    strategy_returns = (weights.shift(1) * returns).sum(axis=1)
+    equity_curve = (1 + strategy_returns).cumprod() * initial_capital
+
+    latest_weights = weights.iloc[-1]
+    allocation = pd.DataFrame({
+        "Ticker": latest_weights.index,
+        "Weight": latest_weights.values
+    }).sort_values("Weight", ascending=False)
+
+    return {
+        "returns": strategy_returns,
+        "equity_curve": equity_curve,
+        "latest_allocation": allocation
+    }
+
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.gitattributes b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.gitattributes
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
new file mode 100644
index 0000000000000000000000000000000000000000..5f51ead59f36f13043e036290df9440e25fe8cbe
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
@@ -0,0 +1,20 @@
+FROM python:3.13.5-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt ./
+COPY src/ ./src/
+
+RUN pip3 install -r requirements.txt
+
+EXPOSE 8501
+
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+
+ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
\ No newline at end of file
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
new file mode 100644
index 0000000000000000000000000000000000000000..c571620450ee0aefd6e05bfa3d1141e69f691ae4
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
@@ -0,0 +1,19 @@
+---
+title: P2 ETF TREND SUITE
+emoji: 🚀
+colorFrom: red
+colorTo: red
+sdk: docker
+app_port: 8501
+tags:
+- streamlit
+pinned: false
+short_description: Streamlit template space
+---
+
+# Welcome to Streamlit!
+
+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).
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f192dfc876989523e2d5bb36a77336030d50527c
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
@@ -0,0 +1,8 @@
+streamlit
+pandas
+numpy
+yfinance
+datasets
+huggingface_hub
+fredapi
+scipy
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/src/streamlit_app.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/src/streamlit_app.py
new file mode 100644
index 0000000000000000000000000000000000000000..99d0b84662681e7d21a08fcce44908344fa86f80
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/src/streamlit_app.py
@@ -0,0 +1,40 @@
+import altair as alt
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+"""
+# Welcome to Streamlit!
+
+Edit `/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).
+
+In the meantime, below is an example of what you can do with just a few lines of code:
+"""
+
+num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
+num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+
+indices = np.linspace(0, 1, num_points)
+theta = 2 * np.pi * num_turns * indices
+radius = indices
+
+x = radius * np.cos(theta)
+y = radius * np.sin(theta)
+
+df = pd.DataFrame({
+    "x": x,
+    "y": y,
+    "idx": indices,
+    "rand": np.random.randn(num_points),
+})
+
+st.altair_chart(alt.Chart(df, height=700, width=700)
+    .mark_point(filled=True)
+    .encode(
+        x=alt.X("x", axis=None),
+        y=alt.Y("y", axis=None),
+        color=alt.Color("idx", legend=None, scale=alt.Scale()),
+        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
+    ))
\ No newline at end of file
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/streamlit_app.py b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/streamlit_app.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f8ce9d33ba2c163b56cfd50e515d779360b2791
--- /dev/null
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/streamlit_app.py
@@ -0,0 +1,70 @@
+import streamlit as st
+import os
+
+from data.hf_store import load_dataset
+from data.updater import update_market_data
+from data.fred import get_sofr_series
+from engine.backtest import run_backtest
+from analytics.metrics import compute_metrics
+
+st.set_page_config(layout="wide")
+
+st.title("📊 P2 ETF Trend Suite")
+st.markdown("Institutional ETF Trend + Volatility Targeting Engine")
+
+# ========================
+# Sidebar Controls
+# ========================
+
+st.sidebar.header("Controls")
+
+initial_capital = st.sidebar.number_input("Initial Capital", value=100000, step=10000)
+vol_target = st.sidebar.slider("Target Annual Volatility", 0.05, 0.30, 0.15)
+lookback = st.sidebar.slider("Momentum Lookback (days)", 50, 300, 200)
+
+refresh = st.sidebar.button("🔄 Refresh Market Data")
+
+# ========================
+# Data Load
+# ========================
+
+with st.spinner("Loading ETF dataset..."):
+    df = load_dataset()
+
+if refresh:
+    with st.spinner("Updating market data from yfinance..."):
+        df = update_market_data(df)
+    st.success("Dataset updated successfully.")
+
+# ========================
+# Backtest
+# ========================
+
+with st.spinner("Pulling SOFR from FRED..."):
+    sofr = get_sofr_series()
+
+with st.spinner("Running backtest..."):
+    results = run_backtest(
+        df=df,
+        initial_capital=initial_capital,
+        vol_target=vol_target,
+        lookback=lookback,
+    )
+
+metrics = compute_metrics(results["returns"], sofr)
+
+# ========================
+# Layout
+# ========================
+
+col1, col2, col3, col4 = st.columns(4)
+
+col1.metric("CAGR", f"{metrics['cagr']:.2%}")
+col2.metric("Sharpe (SOFR)", f"{metrics['sharpe']:.2f}")
+col3.metric("Max Drawdown", f"{metrics['max_dd']:.2%}")
+col4.metric("Volatility", f"{metrics['vol']:.2%}")
+
+st.line_chart(results["equity_curve"])
+
+st.subheader("Current Allocation")
+st.dataframe(results["latest_allocation"])
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
index f192dfc876989523e2d5bb36a77336030d50527c..2ee50822b0d35d13e955b7b90f1b6006f603d6cb 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
@@ -1,8 +1,6 @@
 streamlit
 pandas
-numpy
 yfinance
+pandas-datareader
 datasets
 huggingface_hub
-fredapi
-scipy
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
index 2ee50822b0d35d13e955b7b90f1b6006f603d6cb..1e7c22b61165889689d808bacd2a9fa4d4680a07 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
@@ -1,6 +1,6 @@
 streamlit
 pandas
-yfinance
 pandas-datareader
-datasets
-huggingface_hub
+yfinance
+numpy
+pandas_market_calendars
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
index 1e7c22b61165889689d808bacd2a9fa4d4680a07..002997e6a6781dc622c55a07b6a1c85c34a30149 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
@@ -4,3 +4,4 @@ pandas-datareader
 yfinance
 numpy
 pandas_market_calendars
+huggingface_hub
diff --git a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
index 002997e6a6781dc622c55a07b6a1c85c34a30149..7553b8d97272a2c826fd1ddd39e38d16ea29e297 100644
--- a/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
+++ b/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt
@@ -1,7 +1,9 @@
 streamlit
 pandas
+numpy
 pandas-datareader
 yfinance
-numpy
-pandas_market_calendars
-huggingface_hub
+huggingface-hub
+pandas-market-calendars
+plotly
+scipy