Sync from GitHub

engine/trend_engine.py

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

hf_space/data/loader.py

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

hf_space/hf_space/hf_space/app.py

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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"]

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 @@
+

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 @@
+

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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 @@
+

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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"]

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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,
     }
-

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
     }
-

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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**.")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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"]
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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"])

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+    }
+

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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}")

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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
+    }
+

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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"]

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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).

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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])),
+    ))

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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"])

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/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

hf_space/hf_space/hf_space/hf_space/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