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import pandas as pd
import numpy as np
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, sheet_name = "Sheet2", 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]
# df = df.drop(df[df == 0].dropna(how='all').index)
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='Eq2', index_col=0, parse_dates=True, usecols=[0, 4, 5, 6, 7, 8])
vix_data.columns = ['VIX', 'VIX3M', 'UX1', 'UX2', 'UX3']
vix_data = vix_data[vix_data.index.dayofweek < 5]
# vix_data = vix_data.drop(vix_data[vix_data == 0].dropna(how='all').index)
vix_data = vix_data.sort_index() # ensure sorted index
return vix_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 = {}
allocation = initial_aum / len(tickers)
for ticker in tickers:
price = prices.loc[date, ticker] #Dhrumil Said
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. Modularized Risk Signal Function
###############################################
def generate_risk_signals(current_date, macro_data, prices, portfolio):
"""
Generates risk signals based solely on VIX indicators and current portfolio weights.
Returns:
- regime: 'risk-on' or 'risk-off'
- target_alloc: target allocation for the momentum portfolio
- vix_signal: derived VIX signal ('risk-on' or 'risk-off')
- note: any message regarding forced regime changes
- vix_params: dictionary of key VIX parameters for logging purposes
"""
# Use the most recent macro data available prior to the current date
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
# Retrieve VIX-related metrics
vix_1m = macro_data['UX1'].asof(ref_date)
vix_3m = macro_data['UX2'].asof(ref_date)
ux1_ux2_spread = macro_data['UX1_UX2_Spread'].asof(ref_date)
slope_5d = macro_data['Slope5D'].asof(ref_date)
slope_10d = macro_data['Slope10D'].asof(ref_date)
slope_15d = macro_data['Slope15D'].asof(ref_date)
vix_mom_signal = macro_data['Signal_Momentum'].asof(ref_date)
# Determine VIX signal based on momentum signal
vix_signal = 'risk-off' if vix_mom_signal > 0 else 'risk-on'
# Set target allocation based on momentum signal
if vix_mom_signal == 3:
vix_target_alloc = 0.7
elif vix_mom_signal == 1:
vix_target_alloc = 0.85
else:
vix_target_alloc = 1.0
# Get previous trading day from prices
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
note = ""
# Logic to force risk-on if SPY+HYG weights are too low
if vix_signal == 'risk-off':
if (spy_weight + hyg_weight) < 0.4:
regime = 'risk-on'
target_alloc = 1
note = "Forced regime to risk-on & target alloc 100% due to SPY+HYG < 40%"
else:
regime = 'risk-off'
target_alloc = vix_target_alloc
else:
regime = 'risk-on'
target_alloc = 1.0
vix_params = {
'vix_1m': vix_1m,
'vix_3m': vix_3m,
'ux1_ux2_spread': ux1_ux2_spread,
'slope_5d': slope_5d,
'slope_10d': slope_10d,
'slope_15d': slope_15d,
'vix_mom_signal': vix_mom_signal,
'vix_target_alloc': vix_target_alloc,
'spy_weight': spy_weight,
'hyg_weight': hyg_weight
}
return regime, target_alloc, vix_signal, note, vix_params
###############################################
# 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, 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.
"""
# Get previous date and calculate total portfolio value
mask = prices.index < current_date
prev_date = prices.index[mask][-1]
curr_value = compute_portfolio_value(portfolio, prices, prev_date)
# Calculate cash position value
cash_price = prices.loc[prev_date, cash_ticker]
cash_equiv = cash_qty * cash_price
total_aum = curr_value + cash_equiv
# Determine desired cash position
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 we need more cash, sell proportionally from other positions
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]
ticker_value = portfolio[ticker] * price
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}"
# If we have excess cash, scale up positions to reach target allocation
elif desired_cash < current_cash:
# Calculate current portfolio value at today's prices
current_portfolio_value = sum(
portfolio[ticker] * prices.loc[current_date, ticker]
for ticker in portfolio
)
# Calculate desired total risk allocation
desired_risk_allocation = total_aum * target_alloc
# Calculate scaling factor to reach target allocation
scaling_factor = desired_risk_allocation / current_portfolio_value
# Scale up all positions proportionally
for ticker in portfolio:
new_portfolio[ticker] = portfolio[ticker] * scaling_factor
# Adjust cash position
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
###############################################
# 11. Simulation: Strategy
###############################################
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'):
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)]
macro_data = macro_data.copy()
macro_data['UX1_UX2_Spread'] = macro_data['UX1'] - macro_data['UX2']
macro_data['UX1_UX2_SpreadEMA_12'] = macro_data["UX1_UX2_Spread"].ewm(span=12, adjust=False).mean()
macro_data['Slope5D'] = macro_data["UX1_UX2_SpreadEMA_12"].diff(5)
macro_data['Slope10D'] = macro_data["UX1_UX2_SpreadEMA_12"].diff(10)
macro_data['Slope15D'] = macro_data["UX1_UX2_SpreadEMA_12"].diff(15)
# Create signal based on sum of slope signs
macro_data["Signal_Momentum"] = (
(macro_data['Slope5D'] > 0).astype(int) * 2 - 1 +
(macro_data['Slope10D'] > 0).astype(int) * 2 - 1 +
(macro_data['Slope15D'] > 0).astype(int) * 2 - 1
)
available_dates = prices.index[prices.index >= start_date]
if len(available_dates) == 0:
raise ValueError("No trading dates found after the specified start_date")
start_date = available_dates[0]
portfolio = initialize_portfolio(prices, start_date, momentum_tickers, initial_aum)
cash_qty = 0.0
current_regime, target_alloc = 'risk-on', 1.0
previous_regime, previous_target_alloc = current_regime, target_alloc
prev_total_aum = initial_aum
results = []
for current_date in daily_dates:
daily_note = "No adjustment"
vix_params = {}
ranks, metrics, trades = {}, {}, {}
# --- Generate regime & allocation ---
regime, target_alloc, vix_signal, signal_note, vix_params = generate_risk_signals(
current_date, macro_data, prices, portfolio)
current_regime = regime
daily_note = signal_note
# --- Monthly Rebalancing Logic ---
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)
# Simulate post-rebalance regime override
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
daily_note += " | Monthly: Forced risk-on due to SPY+HYG weight < 40% after simulation."
# Apply simulated portfolio
portfolio = temp_portfolio
# Finalize cash adjustment based on post-rebalance target_alloc
total_aum = compute_portfolio_value(portfolio, prices, current_date) + cash_qty * prices.loc[current_date, cash_ticker]
desired_value = target_alloc * total_aum
actual_value = compute_portfolio_value(portfolio, prices, current_date)
if abs(actual_value - desired_value) > 1e-6:
portfolio, cash_qty, note_update = allocate_cash_from_portfolio(
portfolio, prices, current_date, target_alloc, cash_qty, cash_ticker)
daily_note += " | Monthly: " + note_update
# --- Daily Regime Handling: Only if NOT monthly date ---
elif (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
# --- Log AUM and daily stats ---
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
row = {
'Total AUM': total_aum,
'Momentum AUM': current_mom_value,
'Cash Qty': cash_qty,
'Cash Price': cash_price,
'Cash Value': cash_value,
'Current Regime': current_regime,
'Target Alloc': target_alloc,
'VIX Target': vix_params.get('vix_target_alloc', np.nan),
'VIX Signal': vix_signal,
'Adjustment Note': daily_note,
'Cash Adjustment': 0.0,
'Return': ret,
'Event': 'Monthly Rebalance' if current_date in monthly_dates else 'Daily Check',
'Slope_5D': vix_params.get('slope_5d', np.nan),
'Slope_10D': vix_params.get('slope_10d', np.nan),
'Slope_15D': vix_params.get('slope_15d', np.nan),
'VIX_Mom_Signal': vix_params.get('vix_mom_signal', np.nan),
'Date': current_date
}
# Quantities, Prices, Notionals, Weights, Ranks, Metrics, Trades
for ticker in momentum_tickers:
qty = portfolio.get(ticker, 0)
price = prices.loc[current_date, ticker]
notional = qty * price
weight = notional / current_mom_value if current_mom_value > 0 else np.nan
row[f'qty_{ticker}'] = qty
row[f'price_{ticker}'] = price
row[f'notional_{ticker}'] = notional
row[f'weight_{ticker}'] = weight
row[f'rank_{ticker}'] = ranks.get(ticker, np.nan)
row[f'metric_{ticker}'] = metrics.get(ticker, np.nan)
row[f'trade_{ticker}'] = trades.get(ticker, 0)
results.append(row)
result_df = pd.DataFrame(results)
result_df.set_index('Date', inplace=True)
return result_df
def restructure_results(result_df, momentum_tickers):
"""
Restructures the result DataFrame to group similar types of data together.
"""
# Define column groups
base_columns = ['Total AUM', 'Momentum AUM', 'Cash Value', 'Cash Qty', 'Cash Price', 'Return',
'Current Regime', 'Target Alloc', 'VIX Target', 'VIX Signal',
'Adjustment Note', 'Event']
vix_columns = ['Slope_5D', 'Slope_10D', 'Slope_15D', 'VIX_Mom_Signal']
# Create lists for each type of metric
price_columns = [f'price_{ticker}' for ticker in momentum_tickers]
qty_columns = [f'qty_{ticker}' for ticker in momentum_tickers]
notional_columns = [f'notional_{ticker}' for ticker in momentum_tickers]
weight_columns = [f'weight_{ticker}' for ticker in momentum_tickers]
rank_columns = [f'rank_{ticker}' for ticker in momentum_tickers]
metric_columns = [f'metric_{ticker}' for ticker in momentum_tickers]
trade_columns = [f'trade_{ticker}' for ticker in momentum_tickers]
# Create new column names without the prefix
new_price_columns = {col: f"Price - {col.replace('price_', '')}" for col in price_columns}
new_qty_columns = {col: f"Quantity - {col.replace('qty_', '')}" for col in qty_columns}
new_notional_columns = {col: f"Notional - {col.replace('notional_', '')}" for col in notional_columns}
new_weight_columns = {col: f"Weight - {col.replace('weight_', '')}" for col in weight_columns}
new_rank_columns = {col: f"Rank - {col.replace('rank_', '')}" for col in rank_columns}
new_metric_columns = {col: f"Metric - {col.replace('metric_', '')}" for col in metric_columns}
new_trade_columns = {col: f"Trade - {col.replace('trade_', '')}" for col in trade_columns}
# Rename columns
result_df = result_df.rename(columns={
**new_price_columns,
**new_qty_columns,
**new_notional_columns,
**new_weight_columns,
**new_rank_columns,
**new_metric_columns,
**new_trade_columns
})
# Reorder columns
new_column_order = (
base_columns +
[new_price_columns[col] for col in price_columns] +
[new_qty_columns[col] for col in qty_columns] +
[new_notional_columns[col] for col in notional_columns] +
[new_weight_columns[col] for col in weight_columns] +
[new_rank_columns[col] for col in rank_columns] +
[new_metric_columns[col] for col in metric_columns] +
[new_trade_columns[col] for col in trade_columns] +
vix_columns
)
# Return reordered DataFrame with only columns that exist
existing_columns = [col for col in new_column_order if col in result_df.columns]
return result_df[existing_columns]
###############################################
# 12. Main – Example Usage
###############################################
if __name__ == '__main__':
# Define asset tickers.
eq_tickers = ['SPY US Equity']
fi_tickers = ['TLT US Equity', 'HYG US Equity'] # FI tickers are still included if desired
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')
pd.set_option('display.float_format', lambda x: f'{x:,.2f}')
# For example, print a summary of the simulation.
all_tickers = eq_tickers + fi_tickers + alts_tickers
momentum_tickers = [t for t in all_tickers]
result_df = restructure_results(result_df, momentum_tickers)
# print(result_df[['Total AUM', 'Momentum AUM', 'Cash Price']].tail())Editor is loading...
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