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import pandas as pd
import numpy as np
import statsmodels.api as sm
from datetime import datetime
from dateutil.relativedelta import relativedelta
from scipy.stats import zscore
###############################################
# 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 'Eq' sheet, LF98TRUU Index from 'FI' sheet,
and other macro indicators from '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
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 "CESIUSD Index", "INJCJC Index", ".HG/GC G Index", "Consumer Confidence")
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()
# Assume "Consumer Confidence" column already exists.
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-based Allocation Function
###############################################
def momentum_allocation(vix_9d, vix_mean, vix_std, max_alloc=1.0, min_alloc=0.6):
if vix_9d < vix_mean + 0.5 * vix_std:
return max_alloc
elif (vix_9d >= vix_mean + 0.5 * vix_std) and (vix_9d < vix_mean + 1 * vix_std):
return 0.8
else:
return min_alloc
###############################################
# 9. Compute FI Signal Functions (Not used here)
###############################################
def compute_fi_target_allocation(macro_data, current_date, fi_max_alloc, fi_min_alloc, slope_threshold=0.01):
available_dates = macro_data.index[macro_data.index < current_date]
if len(available_dates) == 0:
return fi_max_alloc, "risk-on"
ref_date = available_dates[-1]
fi_8 = macro_data["FI_EMA_8"].asof(ref_date)
fi_13 = macro_data["FI_EMA_13"].asof(ref_date)
if pd.isna(fi_8) or pd.isna(fi_13):
return fi_max_alloc, "risk-on"
available_ref_dates = macro_data.loc[:ref_date].index
if len(available_ref_dates) < 2:
slope = 0
else:
prev_ref_date = available_ref_dates[-2]
fi_8_prev = macro_data["FI_EMA_8"].asof(prev_ref_date)
slope = (fi_8/fi_8_prev) - 1
if fi_8 < fi_13:
return fi_max_alloc, "risk-on"
else:
if slope > 2*slope_threshold:
return fi_min_alloc, "risk-off"
elif slope > slope_threshold:
return 0.8, "risk-off"
else:
return fi_max_alloc, "no signal"
###############################################
# 10. 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, prev_date)
new_portfolio = portfolio.copy()
for ticker in portfolio:
prev_price = prices.loc[prev_date, ticker]
weight = (portfolio[ticker] * prev_price) / mom_value if mom_value > 0 else 1/len(portfolio)
invest_amount = weight * cash_available
price_today = prices.loc[current_date, ticker]
qty_to_buy = invest_amount / price_today
new_portfolio[ticker] += qty_to_buy
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):
"""
When switching from risk-on to risk-off (or when target_alloc changes),
sell a proportional amount of portfolio securities to build a cash position
in the cash_ticker. The cash position remains unchanged until the allocation percent changes.
"""
mask = prices.index < current_date
prev_date = prices.index[mask][-1]
curr_value = compute_portfolio_value(portfolio, prices, prev_date)
cash_price = prices.loc[current_date, cash_ticker]
cash_equiv = cash_qty * cash_price
total_aum = curr_value + cash_equiv
desired_value = target_alloc * total_aum
new_portfolio = portfolio.copy()
new_cash_qty = cash_qty
note = ""
if curr_value > desired_value:
excess = curr_value - desired_value
for ticker in portfolio:
price = prices.loc[current_date, ticker]
ticker_value = portfolio[ticker] * price
sell_amount = (ticker_value / curr_value) * excess
qty_to_sell = sell_amount / price
new_portfolio[ticker] -= qty_to_sell
new_cash_qty += excess / cash_price
note = f"Raised {excess:,.2f} into {cash_ticker}"
elif curr_value < desired_value and cash_qty > 0:
shortage = desired_value - curr_value
available_cash = min(shortage, cash_equiv)
for ticker in portfolio:
price = prices.loc[current_date, ticker]
ticker_value = portfolio[ticker] * price
weight = (ticker_value / curr_value) if curr_value > 0 else 1/len(portfolio)
invest_amount = weight * available_cash
qty_to_buy = invest_amount / price
new_portfolio[ticker] += qty_to_buy
new_cash_qty -= available_cash / cash_price
note = f"Deployed {available_cash:,.2f} from {cash_ticker} into portfolio"
return new_portfolio, new_cash_qty, note
###############################################
# 11. Rolling Regression for VIX Term Structure
###############################################
def calculate_rolling_regression(macro_data, prices, window=500):
"""
Calculates rolling regression estimates over a given window (default ~2 years of trading days).
Steps:
1. Compute the VIX term structure as: TS = VIX - VIX3M.
2. Compute the daily change in TS as our 'slope'.
3. Compute daily SPY returns from the prices data.
4. For each day (after accumulating enough data), run an OLS regression with:
SPY_return_next = alpha + beta_pos * max(slope,0) + beta_neg * min(slope,0) + error,
where SPY_return_next is the return from day t to t+1.
5. Use the estimated coefficients and the current day's independent variables to predict next-day return.
"""
# Compute term structure TS and its daily change (slope)
TS = macro_data['VIX'] - macro_data['VIX3M']
TS = TS.sort_index()
slope = TS.diff() # daily change in term structure
# Compute SPY daily returns
spy_prices = prices['SPY US Equity']
spy_returns = spy_prices.pct_change()
# Combine into one DataFrame
df = pd.DataFrame({'slope': slope, 'SPY_return': spy_returns})
df['slope_pos'] = df['slope'].apply(lambda x: x if x > 0 else 0)
df['slope_neg'] = df['slope'].apply(lambda x: x if x < 0 else 0)
# Dependent variable: next day's return
df['SPY_return_next'] = df['SPY_return'].shift(-1)
df = df.dropna(subset=['slope', 'SPY_return_next'])
# Prepare columns to store regression coefficients and prediction
df['alpha'] = np.nan
df['beta_pos'] = np.nan
df['beta_neg'] = np.nan
df['predicted_return'] = np.nan
# Loop over the DataFrame starting at 'window'
for i in range(window, len(df)):
window_data = df.iloc[i-window:i]
X = window_data[['slope_pos', 'slope_neg']]
X = sm.add_constant(X)
y = window_data['SPY_return_next']
model = sm.OLS(y, X).fit()
alpha = model.params['const']
beta_pos = model.params['slope_pos']
beta_neg = model.params['slope_neg']
df.iloc[i, df.columns.get_loc('alpha')] = alpha
df.iloc[i, df.columns.get_loc('beta_pos')] = beta_pos
df.iloc[i, df.columns.get_loc('beta_neg')] = beta_neg
# Use current day's independent variables to predict next-day return
current_row = df.iloc[i]
pred = alpha + beta_pos * current_row['slope_pos'] + beta_neg * current_row['slope_neg']
df.iloc[i, df.columns.get_loc('predicted_return')] = pred
return df[['alpha', 'beta_pos', 'beta_neg', 'predicted_return']]
###############################################
# 12. Simulation with Rolling Regression Signal
###############################################
def simulate_strategy_with_rolling_regression(prices, macro_data, rolling_reg_df,
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):
"""
Simulation that uses rolling regression estimates from the VIX term structure slope
to predict next-day SPY returns. If the predicted return is negative, the strategy goes risk-off.
"""
# 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 for VIX metrics
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=9, 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()
# Initialize portfolio and tracking variables
portfolio = initialize_portfolio(prices, start_date, momentum_tickers, initial_aum)
cash_qty = 0.0
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 signals using macro data ---
available_macro_dates = macro_data.index[macro_data.index < current_date]
ref_date = available_macro_dates[-1] if len(available_macro_dates) > 0 else current_date
# Get VIX metrics
vix_1m = macro_data['VIX'].asof(ref_date)
vix_3m = macro_data['VIX3M'].asof(ref_date)
vix_ema = macro_data['VIX_Spread_EMA'].asof(ref_date)
vix_mean = macro_data['Mean'].asof(ref_date)
vix_std = macro_data['Std'].asof(ref_date)
# Determine VIX-based allocation
vix_target_alloc = momentum_allocation(vix_ema, vix_mean, vix_std,
max_alloc=macro_max_alloc,
min_alloc=macro_min_alloc)
# Original VIX signal based on threshold
vix_signal = 'risk-off' if vix_ema >= (vix_mean + 0.5*vix_std) else 'risk-on'
# --- Rolling regression based signal ---
if current_date in rolling_reg_df.index:
predicted_return = rolling_reg_df.loc[current_date, 'predicted_return']
regression_signal = 'risk-off' if predicted_return < 0 else 'risk-on'
note_regression = f"Rolling reg predicted return: {predicted_return:.4f}"
else:
regression_signal = 'risk-on'
note_regression = "Rolling reg data unavailable, default risk-on"
# Combined signal: override to risk-off if regression predicts negative return
if regression_signal == 'risk-off':
final_signal = 'risk-off'
else:
final_signal = vix_signal
daily_note += " | " + note_regression
# --- 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 on final signal ---
if final_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 ---
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_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
portfolio = adjust_overweight(portfolio, prices, current_date, sorted_tickers, threshold=0.70)
# --- 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,
'Rolling Predicted Return': predicted_return if current_date in rolling_reg_df.index else np.nan,
'Adjustment Note': daily_note,
'Cash Adjustment': cash_adjustment,
'Return': ret,
'Event': 'Monthly Rebalance' if current_date in monthly_dates else 'Daily Check',
'VIX_1M': vix_1m,
'VIX_3M': vix_3m,
'VIX_Spread': vix_ema,
"VIX_Mean": vix_mean,
"VIX_std": vix_std
}
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
###############################################
# 13. 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'] # Do not include cash_ticker here
# Define the cash ticker (e.g., for short-term cash management)
cash_ticker = 'SHV US Equity'
initial_aum = 100e6 # 100 million
start_date = pd.to_datetime('2008-01-01')
end_date = pd.to_datetime('2025-02-01')
rebalance_period = 1 # monthly rebalancing
rebalance_ratio = 0.2 # portion of portfolio rebalanced each month
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)
# Calculate rolling regression estimates using a 2-year (approx 500-day) window.
rolling_reg_df = calculate_rolling_regression(macro_data, prices, window=500)
# Run simulation using the rolling regression signal.
result_df = simulate_strategy_with_rolling_regression(prices, macro_data, rolling_reg_df,
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=cash_ticker,
macro_max_alloc=1.0, macro_min_alloc=0.6)
pd.set_option('display.float_format', lambda x: f'{x:,.2f}')
# Display a few columns including cash (cash_ticker) details
print(result_df[['Total AUM', 'Momentum AUM', 'Cash Qty', 'Cash Price', 'Cash Value']].tail())
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