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import pandas as pd
import numpy as np
from datetime import datetime
from dateutil.relativedelta import relativedelta
import statsmodels.api as sm
from statsmodels.tools import add_constant
from scipy.stats import zscore

###############################################
# 0. Helper Functions for VIX Slope Signal
###############################################

def calculate_slopes(series, window=5):
    """
    Calculate a simple slope as the difference over the window, divided by the window.
    """
    return series.diff(window) / window

def split_slope(slope_series):
    """
    Split a slope series into its positive and negative components.
    Positive slopes remain; negative slopes become their absolute value.
    """
    slope_pos = slope_series.clip(lower=0)
    slope_neg = (-slope_series.clip(upper=0))
    return slope_pos, slope_neg

def logistic_regression(X, y):
    """
    Fit a logistic regression model using statsmodels.
    """
    X_const = add_constant(X, has_constant='add')
    model = sm.Logit(y, X_const).fit(disp=0)
    return model

###############################################
# 1. Data Loading (Filtering Out Weekends)
###############################################

def load_price_data(filepath):
    """
    Load historical prices from an Excel file.
    Assumes that the first column is dates and the remaining columns are tickers.
    Removes weekend data.
    """
    df = pd.read_excel(filepath, index_col=0)
    df.index = pd.to_datetime(df.index)
    df = df.sort_index()
    # Filter out weekends (Saturday=5, Sunday=6)
    df = df[df.index.dayofweek < 5]
    return df

def load_macro_data(filepath):
    """
    Load macro indicators from an Excel file.
    Loads VIX and VIX3M from the 'Eq' sheet, LF98TRUU Index from the 'FI' sheet (loaded but not used for signals),
    and other macro indicators from the 'Macro' sheet.
    Removes weekend data.
    Also computes slopes for selected macro indicators.
    """
    # VIX data
    vix_data = pd.read_excel(filepath, sheet_name='Eq', index_col=0, parse_dates=True, usecols=[0, 4, 5])
    vix_data.columns = ['VIX', 'VIX3M']
    vix_data = vix_data[vix_data.index.dayofweek < 5]
    
    # FI data (loaded but not used in the strategy)
    cdx_data = pd.read_excel(filepath, sheet_name='FI', index_col=0, parse_dates=True, usecols=[0, 2], skiprows=1)
    cdx_data.columns = ['LF98TRUU']
    cdx_data = cdx_data[cdx_data.index.dayofweek < 5]
    
    # Macro data (assumed to include columns like "CESIUSD Index", etc.)
    macro_data = pd.read_excel(filepath, sheet_name='Macro', index_col=0, parse_dates=True, usecols=range(8), skiprows=1)
    macro_data = macro_data[macro_data.index.dayofweek < 5]
    
    # Compute slopes for selected macro indicators.
    macro_data["Surprise Index Slope"] = macro_data["CESIUSD Index"].diff()
    macro_data["Jobless Claims Slope"] = macro_data["INJCJC Index"].diff()
    macro_data["Copper Gold Slope"] = macro_data['.HG/GC G Index'].diff()
    
    combined_data = pd.concat([vix_data, cdx_data, macro_data], axis=1)
    combined_data = combined_data.fillna(method='ffill').fillna(method='bfill')
    combined_data = combined_data.sort_index()  # ensure sorted index
    return combined_data

###############################################
# 2. Helper: Observation Dates (Monthly)
###############################################

def get_observation_dates(prices, start_date, end_date, rebalance_period):
    dates = []
    current_date = start_date
    while current_date < end_date:
        candidate_date = (current_date + relativedelta(months=rebalance_period)).replace(day=1)
        while candidate_date not in prices.index:
            candidate_date += pd.Timedelta(days=1)
            if candidate_date.month != (current_date + relativedelta(months=rebalance_period)).month:
                candidate_date = None
                break
        if candidate_date is None or candidate_date > end_date:
            break
        dates.append(candidate_date)
        current_date = candidate_date
    return dates

###############################################
# 3. Portfolio Initialization
###############################################

def initialize_portfolio(prices, date, tickers, initial_aum):
    portfolio = {}
    mask = prices.index < date
    prev_date = prices.index[mask][-1]
    allocation = initial_aum / len(tickers)
    for ticker in tickers:
        price = prices.loc[prev_date, ticker]
        portfolio[ticker] = allocation / price
    return portfolio

###############################################
# 4. Lookback Metric Computation
###############################################

def compute_lookback_metric(prices, current_date, ticker, lookback_period, metric_type='simple'):
    prices = prices.sort_index()
    mask = prices.index < current_date
    prev_date = prices.index[mask][-1]
    lookback_date = prev_date - relativedelta(months=lookback_period)
    
    current_price = prices[ticker].asof(prev_date)
    lookback_price = prices[ticker].asof(lookback_date)

    if pd.isna(current_price) or pd.isna(lookback_price):
        raise ValueError(f"Missing price data for {ticker} on {prev_date} or {lookback_date}.")
    if metric_type == 'simple':
        metric = (current_price / lookback_price) - 1
    elif metric_type == 'sma':
        window = prices[ticker].loc[lookback_date:current_date]
        if window.empty:
            raise ValueError(f"No price data for {ticker} between {lookback_date} and {current_date}.")
        sma = window.mean()
        metric = (current_price - sma) / sma
    else:
        raise ValueError("Invalid metric type. Choose 'simple' or 'sma'.")
    return metric

###############################################
# 5. Ranking Assets by Momentum
###############################################

def rank_assets(prices, current_date, tickers, lookback_period, metric_type):
    metrics = {}
    for ticker in tickers:
        metric = compute_lookback_metric(prices, current_date, ticker, lookback_period, metric_type)
        metrics[ticker] = metric
    sorted_tickers = sorted(metrics, key=metrics.get, reverse=True)
    ranks = {ticker: rank+1 for rank, ticker in enumerate(sorted_tickers)}
    return sorted_tickers, ranks, metrics

###############################################
# 6. Compute Current Portfolio Value
###############################################

def compute_portfolio_value(portfolio, prices, current_date):
    value = 0
    for ticker, quantity in portfolio.items():
        price = prices.loc[current_date, ticker]
        value += quantity * price
    return value

###############################################
# 7. Rebalance the Momentum Portfolio
###############################################

def rebalance_portfolio(portfolio, prices, current_date, tickers, sorted_tickers,
                        internal_rebalance_ratios, rebalance_ratio):
    mask = prices.index < current_date
    prev_date = prices.index[mask][-1]
    prev_prices = prices.loc[prev_date]
    curr_prices = prices.loc[current_date]

    portfolio_value = sum(portfolio[ticker] * prev_prices[ticker] for ticker in tickers)
    rebalance_amount = portfolio_value * rebalance_ratio

    target_trades = {ticker: rebalance_amount * internal_rebalance_ratios[i]
                     for i, ticker in enumerate(sorted_tickers)}

    total_sold = 0
    actual_trades = {}

    for ticker, target_trade in target_trades.items():
        if target_trade < 0:
            available_notional = portfolio[ticker] * curr_prices[ticker]
            sell_target = abs(target_trade)
            actual_sell = min(available_notional, sell_target)
            actual_trades[ticker] = -actual_sell
            total_sold += actual_sell
        else:
            actual_trades[ticker] = 0

    total_buy_target = sum(t for t in target_trades.values() if t > 0)
    if total_buy_target > 0:
        for ticker, target_trade in target_trades.items():
            if target_trade > 0:
                proportion = target_trade / total_buy_target
                buy_amount = total_sold * proportion
                actual_trades[ticker] = buy_amount

    new_portfolio = portfolio.copy()
    for ticker, trade_notional in actual_trades.items():
        execution_price = curr_prices[ticker]
        qty_change = trade_notional / execution_price
        new_portfolio[ticker] += qty_change

    return new_portfolio, actual_trades, portfolio_value

def adjust_overweight(portfolio, prices, current_date, sorted_tickers, threshold=0.70):
    mask = prices.index < current_date
    prev_date = prices.index[mask][-1]
    prev_prices = prices.loc[prev_date]
    curr_prices = prices.loc[current_date]

    portfolio_value = sum(portfolio[ticker] * prev_prices[ticker] for ticker in portfolio)
    weights = {ticker: (portfolio[ticker] * prev_prices[ticker]) / portfolio_value
               for ticker in portfolio}

    new_portfolio = portfolio.copy()

    for overweight in portfolio:
        if weights[overweight] > threshold:
            extra_weight = weights[overweight] - threshold
            extra_value = extra_weight * portfolio_value
            execution_price_over = curr_prices[overweight]
            qty_reduce = extra_value / execution_price_over
            new_portfolio[overweight] -= qty_reduce

            remaining_value = extra_value

            for candidate in sorted_tickers:
                if candidate == overweight:
                    continue
                candidate_value = new_portfolio[candidate] * curr_prices[candidate]
                candidate_weight = candidate_value / portfolio_value
                if candidate_weight < threshold:
                    capacity = (threshold - candidate_weight) * portfolio_value
                    allocation = min(remaining_value, capacity)
                    qty_add = allocation / curr_prices[candidate]
                    new_portfolio[candidate] += qty_add
                    remaining_value -= allocation
                    if remaining_value <= 0:
                        break
    return new_portfolio

###############################################
# 8. VIX Slope Signal Generation (Logistic Regression)
###############################################

def run_vix_slope_signal_backtest(prices, macro_data, start_date='2008-01-01', slope_window=5,
                                  lookback_window=252, forecast_horizon=5, prob_threshold=0.6):
    """
    Backtest a VIX slope–based signal.
    Calculates the VIX spread slopes, splits them into positive and negative parts,
    and uses a rolling logistic regression to predict the probability of a negative SPX return.
    
    IMPORTANT: The regression is run on data up to a given day and then its output is shifted
    forward one trading day. This means if the regression (using yesterday’s data) indicates 'risk_off',
    that signal will be applied for today.
    
    Returns a DataFrame with the predicted probability, signal, and target allocation,
    with the index shifted to represent the next trading day.
    """
    macro_data = macro_data.copy()
    macro_data['VIX_Spread'] = macro_data['VIX'] - macro_data['VIX3M']
    macro_data['Slope'] = calculate_slopes(macro_data['VIX_Spread'], window=slope_window)
    macro_data['Slope+'], macro_data['Slope−'] = split_slope(macro_data['Slope'])

    prices = prices.copy()
    prices['SPX_Return'] = prices['SPY US Equity'].pct_change(forecast_horizon).shift(-forecast_horizon)

    merged = macro_data[['Slope+', 'Slope−']].join(prices[['SPX_Return']], how='inner').dropna()
    merged = merged[merged.index >= start_date]

    results = []
    full_data = macro_data[['Slope+', 'Slope−']].join(prices[['SPX_Return']], how='inner').dropna()

    for i in range(len(merged)):
        current_date = merged.index[i]
        full_data_current_idx = full_data.index.get_loc(current_date)

        if full_data_current_idx < lookback_window:
            continue

        train = full_data.iloc[full_data_current_idx - lookback_window:full_data_current_idx]
        X_train = train[['Slope+', 'Slope−']]
        y_train = (train['SPX_Return'] < 0).astype(int)

        try:
            model = logistic_regression(X_train, y_train)
            
            X_test_data = merged[['Slope+', 'Slope−']].iloc[i:i+1]
            X_test = add_constant(X_test_data, has_constant='add')
            prob = model.predict(X_test)[0]
            
            actual_return = merged['SPX_Return'].iloc[i]
            
            # New logic for signal and target allocation based on predicted probability
            if prob <= 0.6:
                signal = 'risk_on'
                target_allocation = 1.0
            elif 0.6 < prob <= 0.8:
                signal = 'risk_off'
                target_allocation = 0.8
            else:  # prob > 0.8
                signal = 'risk_off'
                target_allocation = 0.2

            results.append({
                'Date': current_date,
                'Predicted_Prob_Negative': prob,
                'Actual_Return': actual_return,
                'Signal': signal,
                'Target_Allocation': target_allocation,
                'Slope+': merged['Slope+'].iloc[i],
                'Slope−': merged['Slope−'].iloc[i]
            })
            
        except Exception as e:
            print(f"Error at date {current_date}: {str(e)}")
            continue

    df_results = pd.DataFrame(results).set_index('Date')
    # Shift the predicted signal to the next trading day
    df_results.index = df_results.index.to_series().shift(1, freq='B')
    df_results = df_results.dropna()
    return df_results

###############################################
# 9. Helper Functions for Cash (using a cash ticker)
###############################################

def invest_cash_into_portfolio(portfolio, prices, current_date, cash_qty, cash_ticker):
    """
    When switching from risk-off to risk-on, sell the cash instrument (e.g. SHV)
    and reinvest its proceeds into the portfolio using previous-day weights.
    """
    cash_price = prices.loc[current_date, cash_ticker]
    cash_available = cash_qty * cash_price
    if cash_available <= 0:
        return portfolio, cash_qty, f"No {cash_ticker} to reinvest."

    mask = prices.index < current_date
    prev_date = prices.index[mask][-1]
    mom_value = compute_portfolio_value(portfolio, prices, current_date)
    new_portfolio = portfolio.copy()
    for ticker in portfolio:
        prev_price = prices.loc[prev_date, ticker]
        total_qty = (portfolio[ticker] * (mom_value + cash_available)) / mom_value if mom_value > 0 else 1/len(portfolio)
        new_portfolio[ticker] = total_qty
    return new_portfolio, 0.0, f"Reinvested {cash_available:,.2f} from {cash_ticker}"

def allocate_cash_from_portfolio(portfolio, prices, current_date, target_alloc, cash_qty, cash_ticker):
    """
    Adjusts portfolio positions based on target allocation for cash.
    When deploying cash, scales up existing positions proportionally to reach target allocation.
    """
    mask = prices.index < current_date
    prev_date = prices.index[mask][-1]
    curr_value = compute_portfolio_value(portfolio, prices, prev_date)
    
    cash_price = prices.loc[prev_date, cash_ticker]
    cash_equiv = cash_qty * cash_price
    total_aum = curr_value + cash_equiv
    
    desired_cash = (1 - target_alloc) * total_aum
    cash_price = prices.loc[current_date, cash_ticker]
    cash_equiv = cash_qty * cash_price
    current_cash = cash_equiv
    
    new_portfolio = portfolio.copy()
    new_cash_qty = cash_qty
    note = ""
    
    if desired_cash > current_cash:
        cash_to_raise = desired_cash - current_cash
        curr_value = compute_portfolio_value(portfolio, prices, current_date)
        for ticker in portfolio:
            price = prices.loc[current_date, ticker]
            sell_ratio = (cash_to_raise / curr_value)
            qty_to_sell = portfolio[ticker] * sell_ratio
            new_portfolio[ticker] -= qty_to_sell
        new_cash_qty += cash_to_raise / cash_price
        note = f"Raised {cash_to_raise:,.2f} into {cash_ticker}"
    
    elif desired_cash < current_cash:
        current_portfolio_value = sum(
            portfolio[ticker] * prices.loc[current_date, ticker] 
            for ticker in portfolio
        )
        desired_risk_allocation = total_aum * target_alloc
        scaling_factor = desired_risk_allocation / current_portfolio_value
        
        for ticker in portfolio:
            new_portfolio[ticker] = portfolio[ticker] * scaling_factor
            
        excess_cash = current_cash - desired_cash
        new_cash_qty -= excess_cash / cash_price
        note = f"Deployed {excess_cash:,.2f} from {cash_ticker} into portfolio"
    
    return new_portfolio, new_cash_qty, note

###############################################
# 10. Simulation: Refined Strategy with Cash & VIX Slope Signal
###############################################

def simulate_strategy(prices, macro_data, eq_tickers, fi_tickers, alts_tickers,
                      initial_aum, start_date, end_date,
                      rebalance_period, rebalance_ratio,
                      lookback_period, metric_type,
                      internal_rebalance_ratios,
                      cash_ticker='SHV US Equity',
                      macro_max_alloc=1.0, macro_min_alloc=0.6,
                      slope_window=5, lookback_window=252, forecast_horizon=5, prob_threshold=0.6):
    """
    Simulation of a momentum strategy using VIX slope signals generated via logistic regression.
    FI signals are removed.
    A designated cash_ticker is used for the cash component.
    Extra portfolio value is moved to cash when in risk-off, and reinvested when switching back to risk-on.
    """
    # Define tickers for momentum (exclude cash_ticker)
    all_tickers = eq_tickers + fi_tickers + alts_tickers
    momentum_tickers = [t for t in all_tickers if t != cash_ticker]
    
    monthly_dates = get_observation_dates(prices, start_date, end_date, rebalance_period)
    daily_dates = prices.index.sort_values()
    daily_dates = daily_dates[(daily_dates >= start_date) & (daily_dates <= end_date)]
    
    # Prepare macro data: calculate VIX spread and its EMA, mean, and std.
    macro_data = macro_data.copy()
    macro_data['VIX_Spread'] = macro_data['VIX'] - macro_data['VIX3M']
    macro_data['VIX_Spread_EMA'] = macro_data["VIX_Spread"].ewm(span=5, adjust=False).mean()  
    macro_data["Mean"] = macro_data['VIX_Spread_EMA'].rolling(window=504).mean()
    macro_data["Std"] = macro_data['VIX_Spread_EMA'].rolling(window=504).std()
    
    # Generate VIX slope signals via the new backtest function.
    vix_signal_df = run_vix_slope_signal_backtest(prices, macro_data, start_date=start_date,
                                                  slope_window=slope_window, lookback_window=lookback_window,
                                                  forecast_horizon=forecast_horizon, prob_threshold=prob_threshold)
    
    # Initialize portfolio (momentum securities only) and cash (in cash_ticker)
    portfolio = initialize_portfolio(prices, start_date, momentum_tickers, initial_aum)
    cash_qty = 0.0  # cash position held as cash_ticker units
    current_regime = 'risk-on'
    target_alloc = 1.0
    previous_regime = current_regime
    previous_target_alloc = target_alloc
    prev_total_aum = initial_aum

    results = []

    for current_date in daily_dates:
        daily_note = "No adjustment"
        cash_adjustment = 0.0

        # --- Determine VIX signal from the backtest results ---
        if current_date in vix_signal_df.index:
            vix_target_alloc = vix_signal_df.loc[current_date, 'Target_Allocation']
            vix_signal = vix_signal_df.loc[current_date, 'Signal']
        else:
            vix_target_alloc = macro_max_alloc
            vix_signal = 'risk_on'

        # --- Determine portfolio value and SPY/HYG weights ---
        mask = prices.index < current_date
        prev_date = prices.index[mask][-1]
        mom_value = compute_portfolio_value(portfolio, prices, prev_date)
        spy_weight = (portfolio.get('SPY US Equity', 0) * prices.loc[prev_date, 'SPY US Equity']) / mom_value if mom_value > 0 else 0
        hyg_weight = (portfolio.get('HYG US Equity', 0) * prices.loc[prev_date, 'HYG US Equity']) / mom_value if mom_value > 0 else 0

        # --- Determine daily regime based solely on VIX slope signal ---
        if vix_signal == 'risk_off':
            if (spy_weight + hyg_weight) < 0.40:
                current_regime = 'risk-on'
                target_alloc = 1.0
                daily_note = "Forced regime to risk-on & target alloc 100% due to SPY+HYG < 40%"
            else:
                current_regime = 'risk_off'
                target_alloc = vix_target_alloc
        else:
            current_regime = 'risk-on'
            target_alloc = 1.0

        # --- Cash rebalancing logic: Adjust cash position when regime or target allocation changes ---
        if (previous_regime != current_regime) or (current_regime == 'risk_off' and target_alloc != previous_target_alloc):
            if previous_regime == 'risk_off' and current_regime == 'risk-on' and cash_qty > 0:
                portfolio, cash_qty, note_update = invest_cash_into_portfolio(portfolio, prices, current_date, cash_qty, cash_ticker)
                daily_note += " | " + note_update
            elif (previous_regime == 'risk-on' and current_regime == 'risk_off') or (current_regime == 'risk_off' and target_alloc != previous_target_alloc):
                portfolio, cash_qty, note_update = allocate_cash_from_portfolio(portfolio, prices, current_date, target_alloc, cash_qty, cash_ticker)
                daily_note += " | " + note_update
        
        previous_regime = current_regime
        previous_target_alloc = target_alloc

        # --- Monthly Rebalancing ---
        if current_date in monthly_dates:
            sorted_tickers, ranks, metrics = rank_assets(prices, current_date, momentum_tickers, lookback_period, metric_type)

            temp_portfolio, trades, pre_rebalance_value = rebalance_portfolio(portfolio, prices, current_date,
                                                                               momentum_tickers, sorted_tickers,
                                                                               internal_rebalance_ratios, rebalance_ratio)

            temp_portfolio = adjust_overweight(temp_portfolio, prices, current_date, sorted_tickers, threshold=0.70)

            temp_value = compute_portfolio_value(temp_portfolio, prices, current_date)
            spy_temp = temp_portfolio.get('SPY US Equity', 0) * prices.loc[current_date, 'SPY US Equity']
            hyg_temp = temp_portfolio.get('HYG US Equity', 0) * prices.loc[current_date, 'HYG US Equity']
            combined_weight = (spy_temp + hyg_temp) / temp_value if temp_value > 0 else 0

            if (current_regime == 'risk_off') and (combined_weight < 0.40):
                current_regime = 'risk-on'
                target_alloc = 1.0
                daily_note += " | Monthly: Forced risk-on due to SPY+HYG weight < 40% after simulation."
                total_aum = compute_portfolio_value(portfolio, prices, current_date) + cash_qty * prices.loc[current_date, cash_ticker]
                simulated_value = temp_value
                new_portfolio = {}
                for ticker in temp_portfolio:
                    price = prices.loc[current_date, ticker]
                    simulated_weight = (temp_portfolio[ticker] * price) / simulated_value if simulated_value > 0 else 1/len(temp_portfolio)
                    new_qty = (total_aum * simulated_weight) / price
                    new_portfolio[ticker] = new_qty
                portfolio = new_portfolio
                cash_qty = 0
            else:
                portfolio = temp_portfolio
                curr_value = compute_portfolio_value(portfolio, prices, current_date)
                total_aum = curr_value + cash_qty * prices.loc[current_date, cash_ticker]
                desired_value = target_alloc * total_aum
                if curr_value > desired_value:
                    portfolio, cash_qty, note_update = allocate_cash_from_portfolio(portfolio, prices, current_date, target_alloc, cash_qty, cash_ticker)
                    daily_note += " | Monthly: " + note_update
                elif curr_value < desired_value and cash_qty > 0:
                    portfolio, cash_qty, note_update = allocate_cash_from_portfolio(portfolio, prices, current_date, target_alloc, cash_qty, cash_ticker)
                    daily_note += " | Monthly: " + note_update

        # --- Update daily AUM calculation ---
        current_mom_value = compute_portfolio_value(portfolio, prices, current_date)
        cash_price = prices.loc[current_date, cash_ticker]
        cash_value = cash_qty * cash_price
        total_aum = current_mom_value + cash_value
        ret = (total_aum - prev_total_aum) / prev_total_aum if prev_total_aum > 0 else 0
        prev_total_aum = total_aum

        # --- Log daily results ---
        row = {
            'Date': current_date,
            'Momentum AUM': current_mom_value,
            'Cash Qty': cash_qty,
            'Cash Price': cash_price,
            'Cash Value': cash_value,
            'Total AUM': total_aum,
            'Current Regime': current_regime,
            'Target Alloc': target_alloc,
            'VIX Target': vix_target_alloc,
            'VIX Signal': vix_signal,
            'Adjustment Note': daily_note,
            'Cash Adjustment': cash_adjustment,
            'Return': ret,
            'Event': 'Monthly Rebalance' if current_date in monthly_dates else 'Daily Check'
        }

        for ticker in momentum_tickers:
            price = prices.loc[current_date, ticker]
            qty = portfolio[ticker]
            notional = qty * price
            row[f'qty_{ticker}'] = qty
            row[f'notional_{ticker}'] = notional
            row[f'weight_{ticker}'] = (notional / current_mom_value) if current_mom_value > 0 else np.nan
            row[f'rank_{ticker}'] = ranks.get(ticker, np.nan) if current_date in monthly_dates else np.nan
            row[f'metric_{ticker}'] = metrics.get(ticker, np.nan) if current_date in monthly_dates else np.nan
            row[f'trade_{ticker}'] = trades.get(ticker, 0) if current_date in monthly_dates else 0

        results.append(row)

    result_df = pd.DataFrame(results)
    result_df.set_index('Date', inplace=True)
    return result_df

###############################################
# 11. Main – Example Usage
###############################################

if __name__ == '__main__':
    # Define asset tickers.
    eq_tickers   = ['SPY US Equity']
    fi_tickers   = ['TLT US Equity', 'HYG US Equity']
    alts_tickers = ['GLD US Equity', 'IGSB US Equity']
   
    initial_aum = 100e6  
    start_date  = pd.to_datetime('2008-01-01')
    end_date    = pd.to_datetime('2025-02-01')
    rebalance_period = 1   
    rebalance_ratio  = 0.2    
    lookback_period  = 6     
    metric_type      = 'simple'
    
    internal_rebalance_ratios = [0.8, 0.2, 0, -0.2, -0.8]
    
    # File paths (adjust these to your environment).
    price_filepath = r"\\asiapac.nom\data\MUM\IWM\India_IWM_IPAS\Reet\Momentum Strategy\Codes\Historic Prices.xlsx"
    macro_filepath = r"\\asiapac.nom\data\MUM\IWM\India_IWM_IPAS\Reet\Momentum Strategy\Momentum Strategy Overlay Data.xlsx"

    prices = load_price_data(price_filepath)
    macro_data = load_macro_data(macro_filepath)
    
    # Run simulation.
    result_df = simulate_strategy(prices, macro_data,
                                  eq_tickers, fi_tickers, alts_tickers,
                                  initial_aum, start_date, end_date,
                                  rebalance_period, rebalance_ratio,
                                  lookback_period, metric_type,
                                  internal_rebalance_ratios,
                                  cash_ticker='SHV US Equity',
                                  macro_max_alloc=1.0, macro_min_alloc=0.6,
                                  slope_window=5, lookback_window=252, forecast_horizon=5, prob_threshold=0.6)
    
    pd.set_option('display.float_format', lambda x: f'{x:,.2f}')
    # For example, print the last few rows:
    # print(result_df[['Total AUM', 'Momentum AUM', 'Cash Price']].tail())
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