Performance Of Various Asset Classes During Fed Policy Cycles¶
Python Imports¶
In [1]:
# Standard Library
import datetime
import io
import os
import random
import sys
import warnings
from datetime import datetime, timedelta
from pathlib import Path
# Data Handling
import numpy as np
import pandas as pd
# Data Visualization
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import seaborn as sns
from matplotlib.ticker import FormatStrFormatter, FuncFormatter, MultipleLocator
# Data Sources
import yfinance as yf
import pandas_datareader.data as web
# Statistical Analysis
import statsmodels.api as sm
# Machine Learning
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
# Suppress warnings
warnings.filterwarnings("ignore")
Add Directories To Path¶
In [2]:
# Add the source subdirectory to the system path to allow import config from settings.py
current_directory = Path(os.getcwd())
website_base_directory = current_directory.parent.parent.parent
src_directory = website_base_directory / "src"
sys.path.append(str(src_directory)) if str(src_directory) not in sys.path else None
# Import settings.py
from settings import config
# Add configured directories from config to path
SOURCE_DIR = config("SOURCE_DIR")
sys.path.append(str(Path(SOURCE_DIR))) if str(Path(SOURCE_DIR)) not in sys.path else None
# Add other configured directories
BASE_DIR = config("BASE_DIR")
CONTENT_DIR = config("CONTENT_DIR")
POSTS_DIR = config("POSTS_DIR")
PAGES_DIR = config("PAGES_DIR")
PUBLIC_DIR = config("PUBLIC_DIR")
SOURCE_DIR = config("SOURCE_DIR")
DATA_DIR = config("DATA_DIR")
DATA_MANUAL_DIR = config("DATA_MANUAL_DIR")
# Print system path
for i, path in enumerate(sys.path):
print(f"{i}: {path}")
0: /usr/lib/python313.zip 1: /usr/lib/python3.13 2: /usr/lib/python3.13/lib-dynload 3: 4: /home/jared/python-virtual-envs/general_313/lib/python3.13/site-packages 5: /home/jared/python-virtual-envs/general_313/lib/python3.13/site-packages/setuptools/_vendor 6: /home/jared/Cloud_Storage/Dropbox/Websites/jaredszajkowski.github.io/src
Track Index Dependencies¶
In [3]:
# Create file to track markdown dependencies
dep_file = Path("index_dep.txt")
dep_file.write_text("")
Out[3]:
0
Python Functions¶
In [4]:
from calc_fed_cycle_asset_performance import calc_fed_cycle_asset_performance
from df_info import df_info
from df_info_markdown import df_info_markdown
from export_track_md_deps import export_track_md_deps
from load_data import load_data
from pandas_set_decimal_places import pandas_set_decimal_places
from plot_bar_returns_ffr_change import plot_bar_returns_ffr_change
from plot_timeseries import plot_timeseries
from plot_scatter_regression_ffr_vs_returns import plot_scatter_regression_ffr_vs_returns
from sm_ols_summary_markdown import sm_ols_summary_markdown
from summary_stats import summary_stats
from yf_pull_data import yf_pull_data
Data Overview¶
In [5]:
# Set timeframe
start_date = "2004-11-30" # GLD inception (month end)
end_date = "2025-10-31"
Acquire & Plot Fed Funds Data¶
In [6]:
# Set decimal places
pandas_set_decimal_places(4)
# Pull Effective Fed Funds Rate from FRED
fedfunds = web.DataReader("FEDFUNDS", "fred", start="1900-01-01", end=datetime.today())
fedfunds["FEDFUNDS"] = fedfunds["FEDFUNDS"] / 100 # Convert to decimal
# Resample to monthly frequency and compute change in rate
fedfunds_monthly = fedfunds.resample("M").last()
fedfunds_monthly = fedfunds_monthly[(fedfunds_monthly.index >= pd.to_datetime(start_date)) & (fedfunds_monthly.index <= pd.to_datetime(end_date))]
fedfunds_monthly["FedFunds_Change"] = fedfunds_monthly["FEDFUNDS"].diff()
In [7]:
df_info(fedfunds_monthly)
The columns, shape, and data types are: <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 252 entries, 2004-11-30 to 2025-10-31 Freq: ME Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 FEDFUNDS 252 non-null float64 1 FedFunds_Change 251 non-null float64 dtypes: float64(2) memory usage: 5.9 KB None The first 5 rows are:
| FEDFUNDS | FedFunds_Change | |
|---|---|---|
| DATE | ||
| 2004-11-30 | 0.0193 | NaN |
| 2004-12-31 | 0.0216 | 0.0023 |
| 2005-01-31 | 0.0228 | 0.0012 |
| 2005-02-28 | 0.0250 | 0.0022 |
| 2005-03-31 | 0.0263 | 0.0013 |
The last 5 rows are:
| FEDFUNDS | FedFunds_Change | |
|---|---|---|
| DATE | ||
| 2025-06-30 | 0.0433 | 0.0000 |
| 2025-07-31 | 0.0433 | 0.0000 |
| 2025-08-31 | 0.0433 | 0.0000 |
| 2025-09-30 | 0.0422 | -0.0011 |
| 2025-10-31 | 0.0409 | -0.0013 |
In [8]:
# Copy this <!-- INSERT_01_Fed_Funds_Monthly_Rate_Change_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_Fed_Funds_Monthly_Rate_Change.md", content=df_info_markdown(df=fedfunds_monthly, decimal_places=4))
✅ Exported and tracked: 01_Fed_Funds_Monthly_Rate_Change.md
In [9]:
plot_timeseries(
price_df=fedfunds_monthly,
plot_start_date=start_date,
plot_end_date=end_date,
plot_columns=["FEDFUNDS"],
title="Fed Funds Rate",
x_label="Date",
x_format="Year",
y_label="Rate (%)",
y_format="Percentage",
y_format_decimal_places=1,
y_tick_spacing=0.005,
grid=True,
legend=False,
export_plot=True,
plot_file_name="01_Fed_Funds_Rate",
)
In [10]:
plot_timeseries(
price_df=fedfunds_monthly,
plot_start_date=start_date,
plot_end_date=end_date,
plot_columns=["FedFunds_Change"],
title="Fed Funds Change In Rate",
x_label="Date",
x_format="Year",
y_label="Rate (%)",
y_format="Percentage",
y_format_decimal_places=2,
y_tick_spacing=0.0025,
grid=True,
legend=False,
export_plot=True,
plot_file_name="01_Fed_Funds_Change_In_Rate",
)
Define Fed Policy Cycles¶
In [11]:
# # Define manually specified Fed policy cycles
# fed_cycles = [
# # ("2002-01-01", "2003-07-01"),
# # ("2003-07-01", "2004-06-01"),
# # ("2004-06-01", "2006-07-01"),
# ("2004-11-01", "2006-07-01"),
# ("2006-07-01", "2007-07-01"),
# ("2007-07-01", "2008-12-01"),
# ("2008-12-01", "2015-11-01"),
# ("2015-11-01", "2019-01-01"),
# ("2019-01-01", "2019-07-01"),
# ("2019-07-01", "2020-04-01"),
# ("2020-04-01", "2022-02-01"),
# ("2022-02-01", "2023-08-01"),
# ("2023-08-01", "2024-08-01"),
# ("2024-08-01", datetime.today().strftime('%Y-%m-%d')),
# ]
# # Optional: assign a name to each cycle
# cycle_labels = [f"Cycle {i+1}" for i in range(len(fed_cycles))]
In [12]:
# Define manually specified Fed policy cycles
fed_cycles = [
("2004-11-01", "2006-07-01"),
("2006-07-01", "2007-07-01"),
("2007-07-01", "2008-12-01"),
("2008-12-01", "2015-11-01"),
("2015-11-01", "2019-01-01"),
("2019-01-01", "2019-07-01"),
("2019-07-01", "2020-04-01"),
("2020-04-01", "2022-02-01"),
("2022-02-01", "2023-08-01"),
("2023-08-01", "2024-08-01"),
("2024-08-01", datetime.today().strftime('%Y-%m-%d')),
]
# Optional: assign a name to each cycle
cycle_labels = [f"Cycle {i+1}" for i in range(len(fed_cycles))]
In [13]:
# Set decimal places
pandas_set_decimal_places(4)
# Calc changes by fed cycle defined above
fed_changes = []
for (start, end) in fed_cycles:
start = pd.to_datetime(start)
end = pd.to_datetime(end)
try:
rate_start = fedfunds.loc[start, "FEDFUNDS"]
except KeyError:
rate_start = fedfunds.loc[:start].iloc[-1]["FEDFUNDS"]
try:
rate_end = fedfunds.loc[end, "FEDFUNDS"]
except KeyError:
rate_end = fedfunds.loc[:end].iloc[-1]["FEDFUNDS"]
change = rate_end - rate_start
fed_changes.append(change)
fed_changes_df = pd.DataFrame({
"Cycle": cycle_labels,
"FedFunds_Change": fed_changes
})
fed_changes_df
Out[13]:
| Cycle | FedFunds_Change | |
|---|---|---|
| 0 | Cycle 1 | 0.0331 |
| 1 | Cycle 2 | 0.0002 |
| 2 | Cycle 3 | -0.0510 |
| 3 | Cycle 4 | -0.0004 |
| 4 | Cycle 5 | 0.0228 |
| 5 | Cycle 6 | 0.0000 |
| 6 | Cycle 7 | -0.0235 |
| 7 | Cycle 8 | 0.0003 |
| 8 | Cycle 9 | 0.0525 |
| 9 | Cycle 10 | 0.0000 |
| 10 | Cycle 11 | -0.0145 |
In [14]:
# Copy this <!-- INSERT_01_Fed_Funds_Cycle_Change_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_Fed_Funds_Cycle_Change.md", content=fed_changes_df.to_markdown(floatfmt=".4f"))
✅ Exported and tracked: 01_Fed_Funds_Cycle_Change.md
Return Performance By Fed Policy Cycle¶
Stocks (SPY)¶
In [15]:
# Set decimal places
pandas_set_decimal_places(2)
yf_pull_data(
base_directory=DATA_DIR,
ticker="SPY",
source="Yahoo_Finance",
asset_class="Exchange_Traded_Funds",
excel_export=True,
pickle_export=True,
output_confirmation=True,
)
[*********************100%***********************] 1 of 1 completed
The first and last date of data for SPY is:
| Close | High | Low | Open | Volume | |
|---|---|---|---|---|---|
| Date | |||||
| 1993-01-29 | 24.31 | 24.33 | 24.21 | 24.33 | 1003200 |
| Close | High | Low | Open | Volume | |
|---|---|---|---|---|---|
| Date | |||||
| 2025-12-05 | 685.69 | 688.39 | 684.58 | 685.47 | 79241000 |
Yahoo Finance data complete for SPY --------------------
Out[15]:
| Close | High | Low | Open | Volume | |
|---|---|---|---|---|---|
| Date | |||||
| 1993-01-29 | 24.31 | 24.33 | 24.21 | 24.33 | 1003200 |
| 1993-02-01 | 24.49 | 24.49 | 24.33 | 24.33 | 480500 |
| 1993-02-02 | 24.54 | 24.56 | 24.42 | 24.47 | 201300 |
| 1993-02-03 | 24.80 | 24.81 | 24.56 | 24.57 | 529400 |
| 1993-02-04 | 24.90 | 24.95 | 24.61 | 24.88 | 531500 |
| ... | ... | ... | ... | ... | ... |
| 2025-12-01 | 680.27 | 682.99 | 678.74 | 678.81 | 61201200 |
| 2025-12-02 | 681.53 | 683.82 | 679.33 | 681.92 | 62953800 |
| 2025-12-03 | 683.89 | 684.91 | 679.69 | 680.57 | 57238500 |
| 2025-12-04 | 684.39 | 685.37 | 681.34 | 685.30 | 61970300 |
| 2025-12-05 | 685.69 | 688.39 | 684.58 | 685.47 | 79241000 |
8271 rows × 5 columns
In [16]:
spy = load_data(
base_directory=DATA_DIR,
ticker="SPY",
source="Yahoo_Finance",
asset_class="Exchange_Traded_Funds",
timeframe="Daily",
file_format="pickle",
)
# Filter SPY to date range
spy = spy[(spy.index >= pd.to_datetime(start_date)) & (spy.index <= pd.to_datetime(end_date))]
# Resample to monthly frequency
spy_monthly = spy.resample("M").last()
spy_monthly["Monthly_Return"] = spy_monthly["Close"].pct_change()
In [17]:
df_info(spy_monthly)
The columns, shape, and data types are: <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 252 entries, 2004-11-30 to 2025-10-31 Freq: ME Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Close 252 non-null float64 1 High 252 non-null float64 2 Low 252 non-null float64 3 Open 252 non-null float64 4 Volume 252 non-null int64 5 Monthly_Return 251 non-null float64 dtypes: float64(5), int64(1) memory usage: 13.8 KB None The first 5 rows are:
| Close | High | Low | Open | Volume | Monthly_Return | |
|---|---|---|---|---|---|---|
| Date | ||||||
| 2004-11-30 | 79.83 | 80.07 | 79.66 | 79.90 | 53685200 | NaN |
| 2004-12-31 | 82.23 | 82.77 | 82.19 | 82.53 | 28648800 | 0.03 |
| 2005-01-31 | 80.39 | 80.45 | 80.09 | 80.25 | 52532700 | -0.02 |
| 2005-02-28 | 82.07 | 82.53 | 81.67 | 82.43 | 69381300 | 0.02 |
| 2005-03-31 | 80.57 | 80.91 | 80.51 | 80.73 | 64575400 | -0.02 |
The last 5 rows are:
| Close | High | Low | Open | Volume | Monthly_Return | |
|---|---|---|---|---|---|---|
| Date | ||||||
| 2025-06-30 | 616.14 | 617.51 | 613.34 | 615.67 | 92502500 | 0.05 |
| 2025-07-31 | 630.33 | 638.08 | 629.03 | 637.69 | 103385200 | 0.02 |
| 2025-08-31 | 643.27 | 646.05 | 641.36 | 645.68 | 74522200 | 0.02 |
| 2025-09-30 | 666.18 | 666.65 | 661.61 | 662.93 | 86288000 | 0.04 |
| 2025-10-31 | 682.06 | 685.08 | 679.24 | 685.04 | 87164100 | 0.02 |
In [18]:
# Copy this <!-- INSERT_02_SPY_Monthly_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="02_SPY_Monthly.md", content=df_info_markdown(df=spy_monthly, decimal_places=2))
✅ Exported and tracked: 02_SPY_Monthly.md
In [19]:
plot_timeseries(
price_df=spy,
plot_start_date=start_date,
plot_end_date=end_date,
plot_columns=["Close"],
title="SPY Close Price",
x_label="Date",
x_format="Year",
y_label="Price ($)",
y_format="Decimal",
y_format_decimal_places=0,
y_tick_spacing=50,
grid=True,
legend=False,
export_plot=True,
plot_file_name="02_SPY_Price",
)
In [20]:
spy_cycle_df = calc_fed_cycle_asset_performance(
fed_cycles=fed_cycles,
cycle_labels=cycle_labels,
fed_changes=fed_changes,
monthly_df=spy_monthly,
)
spy_cycle_df
Out[20]:
| Cycle | Start | End | Months | CumulativeReturn | CumulativeReturnPct | AverageMonthlyReturn | AverageMonthlyReturnPct | AnnualizedReturn | AnnualizedReturnPct | Volatility | FedFundsChange | FedFundsChange_bps | FFR_AnnualizedChange | FFR_AnnualizedChange_bps | Label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Cycle 1 | 2004-11-01 | 2006-07-01 | 20 | 0.11 | 11.32 | 0.01 | 0.59 | 0.07 | 6.64 | 0.08 | 0.03 | 331.00 | 0.02 | 198.60 | Cycle 1, 2004-11-01 to 2006-07-01 |
| 1 | Cycle 2 | 2006-07-01 | 2007-07-01 | 12 | 0.20 | 20.36 | 0.02 | 1.57 | 0.20 | 20.36 | 0.07 | 0.00 | 2.00 | 0.00 | 2.00 | Cycle 2, 2006-07-01 to 2007-07-01 |
| 2 | Cycle 3 | 2007-07-01 | 2008-12-01 | 17 | -0.39 | -38.55 | -0.03 | -2.67 | -0.29 | -29.09 | 0.19 | -0.05 | -510.00 | -0.04 | -360.00 | Cycle 3, 2007-07-01 to 2008-12-01 |
| 3 | Cycle 4 | 2008-12-01 | 2015-11-01 | 83 | 1.67 | 167.34 | 0.01 | 1.28 | 0.15 | 15.28 | 0.15 | -0.00 | -4.00 | -0.00 | -0.58 | Cycle 4, 2008-12-01 to 2015-11-01 |
| 4 | Cycle 5 | 2015-11-01 | 2019-01-01 | 38 | 0.28 | 28.30 | 0.01 | 0.70 | 0.08 | 8.19 | 0.11 | 0.02 | 228.00 | 0.01 | 72.00 | Cycle 5, 2015-11-01 to 2019-01-01 |
| 5 | Cycle 6 | 2019-01-01 | 2019-07-01 | 6 | 0.18 | 18.33 | 0.03 | 2.95 | 0.40 | 40.01 | 0.18 | 0.00 | 0.00 | 0.00 | 0.00 | Cycle 6, 2019-01-01 to 2019-07-01 |
| 6 | Cycle 7 | 2019-07-01 | 2020-04-01 | 9 | -0.11 | -10.67 | -0.01 | -1.10 | -0.14 | -13.96 | 0.19 | -0.02 | -235.00 | -0.03 | -313.33 | Cycle 7, 2019-07-01 to 2020-04-01 |
| 7 | Cycle 8 | 2020-04-01 | 2022-02-01 | 22 | 0.79 | 79.13 | 0.03 | 2.78 | 0.37 | 37.43 | 0.16 | 0.00 | 3.00 | 0.00 | 1.64 | Cycle 8, 2020-04-01 to 2022-02-01 |
| 8 | Cycle 9 | 2022-02-01 | 2023-08-01 | 18 | 0.04 | 4.18 | 0.00 | 0.40 | 0.03 | 2.77 | 0.21 | 0.05 | 525.00 | 0.03 | 350.00 | Cycle 9, 2022-02-01 to 2023-08-01 |
| 9 | Cycle 10 | 2023-08-01 | 2024-08-01 | 12 | 0.22 | 22.00 | 0.02 | 1.75 | 0.22 | 22.00 | 0.15 | 0.00 | 0.00 | 0.00 | 0.00 | Cycle 10, 2023-08-01 to 2024-08-01 |
| 10 | Cycle 11 | 2024-08-01 | 2025-12-09 | 15 | 0.26 | 25.72 | 0.02 | 1.59 | 0.20 | 20.09 | 0.11 | -0.01 | -145.00 | -0.01 | -116.00 | Cycle 11, 2024-08-01 to 2025-12-09 |
In [21]:
# Copy this <!-- INSERT_02_SPY_Cycle_DF_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="02_SPY_Cycle_DF.md", content=spy_cycle_df.to_markdown(floatfmt=".2f"))
✅ Exported and tracked: 02_SPY_Cycle_DF.md
In [22]:
plot_bar_returns_ffr_change(
cycle_df=spy_cycle_df,
asset_label="SPY",
annualized_or_cumulative="Cumulative",
index_num="02",
)
In [23]:
plot_bar_returns_ffr_change(
cycle_df=spy_cycle_df,
asset_label="SPY",
annualized_or_cumulative="Annualized",
index_num="02",
)
In [24]:
df = spy_cycle_df
####################################
### Don't modify below this line ###
####################################
# Run OLS regression with statsmodels
X = df["FFR_AnnualizedChange_bps"]
y = df["AnnualizedReturnPct"]
X = sm.add_constant(X)
model = sm.OLS(y, X).fit()
print(model.summary())
print(f"Intercept: {model.params[0]}, Slope: {model.params[1]}") # Intercept and slope
# Calc X and Y values for regression line
X_vals = np.linspace(X.min(), X.max(), 100)
Y_vals = model.params[0] + model.params[1] * X_vals
OLS Regression Results
===============================================================================
Dep. Variable: AnnualizedReturnPct R-squared: 0.180
Model: OLS Adj. R-squared: 0.089
Method: Least Squares F-statistic: 1.973
Date: Tue, 09 Dec 2025 Prob (F-statistic): 0.194
Time: 01:22:30 Log-Likelihood: -47.173
No. Observations: 11 AIC: 98.35
Df Residuals: 9 BIC: 99.14
Df Model: 1
Covariance Type: nonrobust
============================================================================================
coef std err t P>|t| [0.025 0.975]
--------------------------------------------------------------------------------------------
const 12.4404 5.894 2.111 0.064 -0.893 25.774
FFR_AnnualizedChange_bps 0.0430 0.031 1.405 0.194 -0.026 0.112
==============================================================================
Omnibus: 1.065 Durbin-Watson: 3.078
Prob(Omnibus): 0.587 Jarque-Bera (JB): 0.665
Skew: 0.026 Prob(JB): 0.717
Kurtosis: 1.796 Cond. No. 193.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Intercept: 12.440384433286768, Slope: 0.04297363417265454
In [25]:
# Copy this <!-- INSERT_02_SPY_Annualized_Regression_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="02_SPY_Annualized_Regression.md", content=sm_ols_summary_markdown(result=model, file_path="02_SPY_Annualized_Regression.md"))
✅ Exported and tracked: 02_SPY_Annualized_Regression.md
In [26]:
plot_scatter_regression_ffr_vs_returns(
cycle_df=spy_cycle_df,
asset_label="SPY",
index_num="02",
x_vals=X_vals,
y_vals=Y_vals,
intercept=model.params[0],
slope=model.params[1],
)
Bonds (TLT)¶
In [27]:
# Set decimal places
pandas_set_decimal_places(2)
yf_pull_data(
base_directory=DATA_DIR,
ticker="TLT",
source="Yahoo_Finance",
asset_class="Exchange_Traded_Funds",
excel_export=True,
pickle_export=True,
output_confirmation=True,
)
[*********************100%***********************] 1 of 1 completed
The first and last date of data for TLT is:
| Close | High | Low | Open | Volume | |
|---|---|---|---|---|---|
| Date | |||||
| 2002-07-30 | 36.79 | 36.96 | 36.79 | 36.89 | 6100 |
| Close | High | Low | Open | Volume | |
|---|---|---|---|---|---|
| Date | |||||
| 2025-12-05 | 88.17 | 88.55 | 88.05 | 88.55 | 32808100 |
Yahoo Finance data complete for TLT --------------------
Out[27]:
| Close | High | Low | Open | Volume | |
|---|---|---|---|---|---|
| Date | |||||
| 2002-07-30 | 36.79 | 36.96 | 36.79 | 36.89 | 6100 |
| 2002-07-31 | 37.25 | 37.37 | 36.96 | 36.98 | 29400 |
| 2002-08-01 | 37.46 | 37.47 | 37.25 | 37.25 | 25000 |
| 2002-08-02 | 37.84 | 37.96 | 37.40 | 37.53 | 52800 |
| 2002-08-05 | 38.01 | 38.11 | 37.84 | 37.93 | 61100 |
| ... | ... | ... | ... | ... | ... |
| 2025-12-01 | 88.77 | 88.99 | 88.71 | 88.85 | 47086600 |
| 2025-12-02 | 88.81 | 88.96 | 88.46 | 88.58 | 27605700 |
| 2025-12-03 | 89.06 | 89.16 | 88.74 | 88.92 | 23278600 |
| 2025-12-04 | 88.58 | 88.93 | 88.48 | 88.91 | 25541500 |
| 2025-12-05 | 88.17 | 88.55 | 88.05 | 88.55 | 32808100 |
5878 rows × 5 columns
In [28]:
tlt = load_data(
base_directory=DATA_DIR,
ticker="TLT",
source="Yahoo_Finance",
asset_class="Exchange_Traded_Funds",
timeframe="Daily",
file_format="pickle",
)
# Filter TLT to date range
tlt = tlt[(tlt.index >= pd.to_datetime(start_date)) & (tlt.index <= pd.to_datetime(end_date))]
# Resample to monthly frequency
tlt_monthly = tlt.resample("M").last()
tlt_monthly["Monthly_Return"] = tlt_monthly["Close"].pct_change()
In [29]:
df_info(tlt_monthly)
The columns, shape, and data types are: <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 252 entries, 2004-11-30 to 2025-10-31 Freq: ME Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Close 252 non-null float64 1 High 252 non-null float64 2 Low 252 non-null float64 3 Open 252 non-null float64 4 Volume 252 non-null int64 5 Monthly_Return 251 non-null float64 dtypes: float64(5), int64(1) memory usage: 13.8 KB None The first 5 rows are:
| Close | High | Low | Open | Volume | Monthly_Return | |
|---|---|---|---|---|---|---|
| Date | ||||||
| 2004-11-30 | 43.97 | 44.08 | 43.81 | 43.97 | 1754500 | NaN |
| 2004-12-31 | 45.14 | 45.19 | 45.01 | 45.05 | 1056400 | 0.03 |
| 2005-01-31 | 46.75 | 46.77 | 46.53 | 46.55 | 1313900 | 0.04 |
| 2005-02-28 | 46.06 | 46.61 | 45.99 | 46.61 | 2797300 | -0.01 |
| 2005-03-31 | 45.85 | 45.88 | 45.61 | 45.78 | 2410900 | -0.00 |
The last 5 rows are:
| Close | High | Low | Open | Volume | Monthly_Return | |
|---|---|---|---|---|---|---|
| Date | ||||||
| 2025-06-30 | 86.33 | 86.52 | 85.71 | 85.95 | 53695200 | 0.03 |
| 2025-07-31 | 85.35 | 85.83 | 85.27 | 85.55 | 49814100 | -0.01 |
| 2025-08-31 | 85.36 | 85.61 | 85.20 | 85.52 | 41686400 | 0.00 |
| 2025-09-30 | 88.42 | 89.09 | 88.27 | 88.71 | 38584000 | 0.04 |
| 2025-10-31 | 89.64 | 90.01 | 89.56 | 89.91 | 38247300 | 0.01 |
In [30]:
# Copy this <!-- INSERT_03_TLT_Monthly_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="03_TLT_Monthly.md", content=df_info_markdown(df=tlt_monthly, decimal_places=2))
✅ Exported and tracked: 03_TLT_Monthly.md
In [31]:
plot_timeseries(
price_df=tlt,
plot_start_date=start_date,
plot_end_date=end_date,
plot_columns=["Close"],
title="TLT Close Price",
x_label="Date",
x_format="Year",
y_label="Price ($)",
y_format="Decimal",
y_format_decimal_places=0,
y_tick_spacing=10,
grid=True,
legend=False,
export_plot=True,
plot_file_name="03_TLT_Price",
)
In [32]:
tlt_cycle_df = calc_fed_cycle_asset_performance(
fed_cycles=fed_cycles,
cycle_labels=cycle_labels,
fed_changes=fed_changes,
monthly_df=tlt_monthly,
)
tlt_cycle_df
Out[32]:
| Cycle | Start | End | Months | CumulativeReturn | CumulativeReturnPct | AverageMonthlyReturn | AverageMonthlyReturnPct | AnnualizedReturn | AnnualizedReturnPct | Volatility | FedFundsChange | FedFundsChange_bps | FFR_AnnualizedChange | FFR_AnnualizedChange_bps | Label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Cycle 1 | 2004-11-01 | 2006-07-01 | 20 | 0.04 | 4.23 | 0.00 | 0.25 | 0.03 | 2.51 | 0.09 | 0.03 | 331.00 | 0.02 | 198.60 | Cycle 1, 2004-11-01 to 2006-07-01 |
| 1 | Cycle 2 | 2006-07-01 | 2007-07-01 | 12 | 0.06 | 5.76 | 0.00 | 0.49 | 0.06 | 5.76 | 0.07 | 0.00 | 2.00 | 0.00 | 2.00 | Cycle 2, 2006-07-01 to 2007-07-01 |
| 2 | Cycle 3 | 2007-07-01 | 2008-12-01 | 17 | 0.32 | 32.42 | 0.02 | 1.73 | 0.22 | 21.92 | 0.14 | -0.05 | -510.00 | -0.04 | -360.00 | Cycle 3, 2007-07-01 to 2008-12-01 |
| 3 | Cycle 4 | 2008-12-01 | 2015-11-01 | 83 | 0.46 | 45.67 | 0.01 | 0.55 | 0.06 | 5.59 | 0.15 | -0.00 | -4.00 | -0.00 | -0.58 | Cycle 4, 2008-12-01 to 2015-11-01 |
| 4 | Cycle 5 | 2015-11-01 | 2019-01-01 | 38 | 0.07 | 7.42 | 0.00 | 0.23 | 0.02 | 2.29 | 0.10 | 0.02 | 228.00 | 0.01 | 72.00 | Cycle 5, 2015-11-01 to 2019-01-01 |
| 5 | Cycle 6 | 2019-01-01 | 2019-07-01 | 6 | 0.10 | 10.48 | 0.02 | 1.73 | 0.22 | 22.05 | 0.13 | 0.00 | 0.00 | 0.00 | 0.00 | Cycle 6, 2019-01-01 to 2019-07-01 |
| 6 | Cycle 7 | 2019-07-01 | 2020-04-01 | 9 | 0.26 | 26.18 | 0.03 | 2.73 | 0.36 | 36.34 | 0.18 | -0.02 | -235.00 | -0.03 | -313.33 | Cycle 7, 2019-07-01 to 2020-04-01 |
| 7 | Cycle 8 | 2020-04-01 | 2022-02-01 | 22 | -0.11 | -11.33 | -0.00 | -0.50 | -0.06 | -6.35 | 0.11 | 0.00 | 3.00 | 0.00 | 1.64 | Cycle 8, 2020-04-01 to 2022-02-01 |
| 8 | Cycle 9 | 2022-02-01 | 2023-08-01 | 18 | -0.27 | -26.96 | -0.02 | -1.62 | -0.19 | -18.90 | 0.17 | 0.05 | 525.00 | 0.03 | 350.00 | Cycle 9, 2022-02-01 to 2023-08-01 |
| 9 | Cycle 10 | 2023-08-01 | 2024-08-01 | 12 | -0.02 | -1.52 | 0.00 | 0.02 | -0.02 | -1.52 | 0.20 | 0.00 | 0.00 | 0.00 | 0.00 | Cycle 10, 2023-08-01 to 2024-08-01 |
| 10 | Cycle 11 | 2024-08-01 | 2025-12-09 | 15 | 0.00 | 0.42 | 0.00 | 0.08 | 0.00 | 0.33 | 0.11 | -0.01 | -145.00 | -0.01 | -116.00 | Cycle 11, 2024-08-01 to 2025-12-09 |
In [33]:
# Copy this <!-- INSERT_03_TLT_Cycle_DF_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="03_TLT_Cycle_DF.md", content=tlt_cycle_df.to_markdown(floatfmt=".2f"))
✅ Exported and tracked: 03_TLT_Cycle_DF.md
In [34]:
plot_bar_returns_ffr_change(
cycle_df=tlt_cycle_df,
asset_label="TLT",
annualized_or_cumulative="Cumulative",
index_num="03",
)
In [35]:
plot_bar_returns_ffr_change(
cycle_df=tlt_cycle_df,
asset_label="TLT",
annualized_or_cumulative="Annualized",
index_num="03",
)
In [36]:
df = tlt_cycle_df
####################################
### Don't modify below this line ###
####################################
# Run OLS regression with statsmodels
X = df["FFR_AnnualizedChange_bps"]
y = df["AnnualizedReturnPct"]
X = sm.add_constant(X)
model = sm.OLS(y, X).fit()
print(model.summary())
print(f"Intercept: {model.params[0]}, Slope: {model.params[1]}") # Intercept and slope
# Calc X and Y values for regression line
X_vals = np.linspace(X.min(), X.max(), 100)
Y_vals = model.params[0] + model.params[1] * X_vals
OLS Regression Results
===============================================================================
Dep. Variable: AnnualizedReturnPct R-squared: 0.623
Model: OLS Adj. R-squared: 0.582
Method: Least Squares F-statistic: 14.90
Date: Tue, 09 Dec 2025 Prob (F-statistic): 0.00385
Time: 01:22:34 Log-Likelihood: -39.665
No. Observations: 11 AIC: 83.33
Df Residuals: 9 BIC: 84.13
Df Model: 1
Covariance Type: nonrobust
============================================================================================
coef std err t P>|t| [0.025 0.975]
--------------------------------------------------------------------------------------------
const 5.4676 2.978 1.836 0.100 -1.270 12.205
FFR_AnnualizedChange_bps -0.0597 0.015 -3.860 0.004 -0.095 -0.025
==============================================================================
Omnibus: 0.710 Durbin-Watson: 1.219
Prob(Omnibus): 0.701 Jarque-Bera (JB): 0.663
Skew: 0.412 Prob(JB): 0.718
Kurtosis: 2.123 Cond. No. 193.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Intercept: 5.467598307234372, Slope: -0.05966486727015462
In [37]:
# Copy this <!-- INSERT_03_TLT_Annualized_Regression_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="03_TLT_Annualized_Regression.md", content=sm_ols_summary_markdown(result=model, file_path="03_TLT_Annualized_Regression.md"))
✅ Exported and tracked: 03_TLT_Annualized_Regression.md
In [38]:
plot_scatter_regression_ffr_vs_returns(
cycle_df=tlt_cycle_df,
asset_label="TLT",
index_num="03",
x_vals=X_vals,
y_vals=Y_vals,
intercept=model.params[0],
slope=model.params[1],
)
Gold (GLD)¶
In [39]:
# Set decimal places
pandas_set_decimal_places(2)
yf_pull_data(
base_directory=DATA_DIR,
ticker="GLD",
source="Yahoo_Finance",
asset_class="Exchange_Traded_Funds",
excel_export=True,
pickle_export=True,
output_confirmation=True,
)
[*********************100%***********************] 1 of 1 completed
The first and last date of data for GLD is:
| Close | High | Low | Open | Volume | |
|---|---|---|---|---|---|
| Date | |||||
| 2004-11-18 | 44.38 | 44.49 | 44.07 | 44.43 | 5992000 |
| Close | High | Low | Open | Volume | |
|---|---|---|---|---|---|
| Date | |||||
| 2025-12-05 | 386.44 | 391.74 | 386.18 | 389.12 | 9449800 |
Yahoo Finance data complete for GLD --------------------
Out[39]:
| Close | High | Low | Open | Volume | |
|---|---|---|---|---|---|
| Date | |||||
| 2004-11-18 | 44.38 | 44.49 | 44.07 | 44.43 | 5992000 |
| 2004-11-19 | 44.78 | 44.92 | 44.47 | 44.49 | 11655300 |
| 2004-11-22 | 44.95 | 44.97 | 44.74 | 44.75 | 11996000 |
| 2004-11-23 | 44.75 | 44.92 | 44.72 | 44.88 | 3169200 |
| 2004-11-24 | 45.05 | 45.05 | 44.79 | 44.93 | 6105100 |
| ... | ... | ... | ... | ... | ... |
| 2025-12-01 | 389.75 | 390.70 | 388.06 | 390.61 | 11177100 |
| 2025-12-02 | 387.24 | 388.98 | 382.91 | 388.87 | 8103900 |
| 2025-12-03 | 386.88 | 390.13 | 385.83 | 388.46 | 7886800 |
| 2025-12-04 | 387.13 | 388.00 | 385.40 | 386.92 | 6754400 |
| 2025-12-05 | 386.44 | 391.74 | 386.18 | 389.12 | 9449800 |
5296 rows × 5 columns
In [40]:
gld = load_data(
base_directory=DATA_DIR,
ticker="GLD",
source="Yahoo_Finance",
asset_class="Exchange_Traded_Funds",
timeframe="Daily",
file_format="pickle",
)
# Filter GLD to date range
gld = gld[(gld.index >= pd.to_datetime(start_date)) & (gld.index <= pd.to_datetime(end_date))]
# Resample to monthly frequency
gld_monthly = gld.resample("M").last()
gld_monthly["Monthly_Return"] = gld_monthly["Close"].pct_change()
In [41]:
df_info(gld_monthly)
The columns, shape, and data types are: <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 252 entries, 2004-11-30 to 2025-10-31 Freq: ME Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Close 252 non-null float64 1 High 252 non-null float64 2 Low 252 non-null float64 3 Open 252 non-null float64 4 Volume 252 non-null int64 5 Monthly_Return 251 non-null float64 dtypes: float64(5), int64(1) memory usage: 13.8 KB None The first 5 rows are:
| Close | High | Low | Open | Volume | Monthly_Return | |
|---|---|---|---|---|---|---|
| Date | ||||||
| 2004-11-30 | 45.12 | 45.41 | 44.82 | 45.37 | 3857200 | NaN |
| 2004-12-31 | 43.80 | 43.94 | 43.73 | 43.85 | 531600 | -0.03 |
| 2005-01-31 | 42.22 | 42.30 | 41.96 | 42.21 | 1692400 | -0.04 |
| 2005-02-28 | 43.53 | 43.74 | 43.52 | 43.68 | 755300 | 0.03 |
| 2005-03-31 | 42.82 | 42.87 | 42.70 | 42.87 | 1363200 | -0.02 |
The last 5 rows are:
| Close | High | Low | Open | Volume | Monthly_Return | |
|---|---|---|---|---|---|---|
| Date | ||||||
| 2025-06-30 | 304.83 | 304.92 | 301.95 | 302.39 | 8192100 | 0.00 |
| 2025-07-31 | 302.96 | 304.61 | 302.86 | 304.59 | 8981000 | -0.01 |
| 2025-08-31 | 318.07 | 318.09 | 314.64 | 314.72 | 15642600 | 0.05 |
| 2025-09-30 | 355.47 | 355.57 | 350.87 | 351.13 | 13312400 | 0.12 |
| 2025-10-31 | 368.12 | 370.66 | 365.50 | 370.47 | 11077900 | 0.04 |
In [42]:
# Copy this <!-- INSERT_04_GLD_Monthly_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="04_GLD_Monthly.md", content=df_info_markdown(df=gld_monthly, decimal_places=2))
✅ Exported and tracked: 04_GLD_Monthly.md
In [43]:
plot_timeseries(
price_df=gld,
plot_start_date=start_date,
plot_end_date=end_date,
plot_columns=["Close"],
title="GLD Close Price",
x_label="Date",
x_format="Year",
y_label="Price ($)",
y_format="Decimal",
y_format_decimal_places=0,
y_tick_spacing=25,
grid=True,
legend=False,
export_plot=True,
plot_file_name="04_GLD_Price",
)
In [44]:
gld_cycle_df = calc_fed_cycle_asset_performance(
fed_cycles=fed_cycles,
cycle_labels=cycle_labels,
fed_changes=fed_changes,
monthly_df=gld_monthly,
)
gld_cycle_df
Out[44]:
| Cycle | Start | End | Months | CumulativeReturn | CumulativeReturnPct | AverageMonthlyReturn | AverageMonthlyReturnPct | AnnualizedReturn | AnnualizedReturnPct | Volatility | FedFundsChange | FedFundsChange_bps | FFR_AnnualizedChange | FFR_AnnualizedChange_bps | Label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Cycle 1 | 2004-11-01 | 2006-07-01 | 20 | 0.36 | 35.70 | 0.02 | 1.73 | 0.20 | 20.10 | 0.17 | 0.03 | 331.00 | 0.02 | 198.60 | Cycle 1, 2004-11-01 to 2006-07-01 |
| 1 | Cycle 2 | 2006-07-01 | 2007-07-01 | 12 | 0.05 | 4.96 | 0.00 | 0.45 | 0.05 | 4.96 | 0.11 | 0.00 | 2.00 | 0.00 | 2.00 | Cycle 2, 2006-07-01 to 2007-07-01 |
| 2 | Cycle 3 | 2007-07-01 | 2008-12-01 | 17 | 0.25 | 24.96 | 0.02 | 1.59 | 0.17 | 17.03 | 0.26 | -0.05 | -510.00 | -0.04 | -360.00 | Cycle 3, 2007-07-01 to 2008-12-01 |
| 3 | Cycle 4 | 2008-12-01 | 2015-11-01 | 83 | 0.36 | 36.10 | 0.01 | 0.51 | 0.05 | 4.56 | 0.18 | -0.00 | -4.00 | -0.00 | -0.58 | Cycle 4, 2008-12-01 to 2015-11-01 |
| 4 | Cycle 5 | 2015-11-01 | 2019-01-01 | 38 | 0.11 | 10.93 | 0.00 | 0.35 | 0.03 | 3.33 | 0.14 | 0.02 | 228.00 | 0.01 | 72.00 | Cycle 5, 2015-11-01 to 2019-01-01 |
| 5 | Cycle 6 | 2019-01-01 | 2019-07-01 | 6 | 0.10 | 9.86 | 0.02 | 1.63 | 0.21 | 20.68 | 0.12 | 0.00 | 0.00 | 0.00 | 0.00 | Cycle 6, 2019-01-01 to 2019-07-01 |
| 6 | Cycle 7 | 2019-07-01 | 2020-04-01 | 9 | 0.11 | 11.15 | 0.01 | 1.24 | 0.15 | 15.13 | 0.13 | -0.02 | -235.00 | -0.03 | -313.33 | Cycle 7, 2019-07-01 to 2020-04-01 |
| 7 | Cycle 8 | 2020-04-01 | 2022-02-01 | 22 | 0.14 | 13.54 | 0.01 | 0.69 | 0.07 | 7.17 | 0.16 | 0.00 | 3.00 | 0.00 | 1.64 | Cycle 8, 2020-04-01 to 2022-02-01 |
| 8 | Cycle 9 | 2022-02-01 | 2023-08-01 | 18 | 0.08 | 8.48 | 0.01 | 0.53 | 0.06 | 5.58 | 0.14 | 0.05 | 525.00 | 0.03 | 350.00 | Cycle 9, 2022-02-01 to 2023-08-01 |
| 9 | Cycle 10 | 2023-08-01 | 2024-08-01 | 12 | 0.24 | 24.24 | 0.02 | 1.89 | 0.24 | 24.24 | 0.13 | 0.00 | 0.00 | 0.00 | 0.00 | Cycle 10, 2023-08-01 to 2024-08-01 |
| 10 | Cycle 11 | 2024-08-01 | 2025-12-09 | 15 | 0.62 | 62.49 | 0.03 | 3.36 | 0.47 | 47.46 | 0.14 | -0.01 | -145.00 | -0.01 | -116.00 | Cycle 11, 2024-08-01 to 2025-12-09 |
In [45]:
# Copy this <!-- INSERT_04_GLD_Cycle_DF_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="04_GLD_Cycle_DF.md", content=gld_cycle_df.to_markdown(floatfmt=".2f"))
✅ Exported and tracked: 04_GLD_Cycle_DF.md
In [46]:
plot_bar_returns_ffr_change(
cycle_df=gld_cycle_df,
asset_label="GLD",
annualized_or_cumulative="Cumulative",
index_num="04",
)
In [47]:
plot_bar_returns_ffr_change(
cycle_df=gld_cycle_df,
asset_label="GLD",
annualized_or_cumulative="Annualized",
index_num="04",
)
In [48]:
df = gld_cycle_df
####################################
### Don't modify below this line ###
####################################
# Run OLS regression with statsmodels
X = df["FFR_AnnualizedChange_bps"]
y = df["AnnualizedReturnPct"]
X = sm.add_constant(X)
model = sm.OLS(y, X).fit()
print(model.summary())
print(f"Intercept: {model.params[0]}, Slope: {model.params[1]}") # Intercept and slope
# Calc X and Y values for regression line
X_vals = np.linspace(X.min(), X.max(), 100)
Y_vals = model.params[0] + model.params[1] * X_vals
OLS Regression Results
===============================================================================
Dep. Variable: AnnualizedReturnPct R-squared: 0.084
Model: OLS Adj. R-squared: -0.018
Method: Least Squares F-statistic: 0.8274
Date: Tue, 09 Dec 2025 Prob (F-statistic): 0.387
Time: 01:22:37 Log-Likelihood: -42.830
No. Observations: 11 AIC: 89.66
Df Residuals: 9 BIC: 90.46
Df Model: 1
Covariance Type: nonrobust
============================================================================================
coef std err t P>|t| [0.025 0.975]
--------------------------------------------------------------------------------------------
const 15.1947 3.972 3.826 0.004 6.210 24.179
FFR_AnnualizedChange_bps -0.0187 0.021 -0.910 0.387 -0.065 0.028
==============================================================================
Omnibus: 8.035 Durbin-Watson: 0.915
Prob(Omnibus): 0.018 Jarque-Bera (JB): 3.686
Skew: 1.328 Prob(JB): 0.158
Kurtosis: 3.993 Cond. No. 193.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Intercept: 15.19466510751222, Slope: -0.018749970694326247
In [49]:
# Copy this <!-- INSERT_04_GLD_Annualized_Regression_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="04_GLD_Annualized_Regression.md", content=sm_ols_summary_markdown(result=model, file_path="04_GLD_Annualized_Regression.md"))
✅ Exported and tracked: 04_GLD_Annualized_Regression.md
In [50]:
plot_scatter_regression_ffr_vs_returns(
cycle_df=gld_cycle_df,
asset_label="GLD",
index_num="04",
x_vals=X_vals,
y_vals=Y_vals,
intercept=model.params[0],
slope=model.params[1],
)
Hybrid Portfolio¶
Asset Allocation¶
In [51]:
fed_cycles
Out[51]:
[('2004-11-01', '2006-07-01'),
('2006-07-01', '2007-07-01'),
('2007-07-01', '2008-12-01'),
('2008-12-01', '2015-11-01'),
('2015-11-01', '2019-01-01'),
('2019-01-01', '2019-07-01'),
('2019-07-01', '2020-04-01'),
('2020-04-01', '2022-02-01'),
('2022-02-01', '2023-08-01'),
('2023-08-01', '2024-08-01'),
('2024-08-01', '2025-12-09')]
In [52]:
cycle_labels
Out[52]:
['Cycle 1', 'Cycle 2', 'Cycle 3', 'Cycle 4', 'Cycle 5', 'Cycle 6', 'Cycle 7', 'Cycle 8', 'Cycle 9', 'Cycle 10', 'Cycle 11']
In [53]:
# Calculate cumulative returns and drawdown for SPY
spy_monthly['Cumulative_Return'] = (1 + spy_monthly['Monthly_Return']).cumprod() - 1
spy_monthly['Cumulative_Return_Plus_One'] = 1 + spy_monthly['Cumulative_Return']
spy_monthly['Rolling_Max'] = spy_monthly['Cumulative_Return_Plus_One'].cummax()
spy_monthly['Drawdown'] = spy_monthly['Cumulative_Return_Plus_One'] / spy_monthly['Rolling_Max'] - 1
spy_monthly.drop(columns=['Cumulative_Return_Plus_One', 'Rolling_Max'], inplace=True)
# Calculate cumulative returns and drawdown for TLT
tlt_monthly['Cumulative_Return'] = (1 + tlt_monthly['Monthly_Return']).cumprod() - 1
tlt_monthly['Cumulative_Return_Plus_One'] = 1 + tlt_monthly['Cumulative_Return']
tlt_monthly['Rolling_Max'] = tlt_monthly['Cumulative_Return_Plus_One'].cummax()
tlt_monthly['Drawdown'] = tlt_monthly['Cumulative_Return_Plus_One'] / tlt_monthly['Rolling_Max'] - 1
tlt_monthly.drop(columns=['Cumulative_Return_Plus_One', 'Rolling_Max'], inplace=True)
# Isolate the returns for SPY and TLT
spy_ret = spy_monthly['Monthly_Return']
tlt_ret = tlt_monthly['Monthly_Return']
# Create a blended portfolio based on Fed policy cycles
portfolio = (
spy_ret[spy_ret.index <= "2007-07-01"]
.combine_first(tlt_ret[(tlt_ret.index >= "2007-07-01") & (tlt_ret.index <= "2008-12-01")])
.combine_first(spy_ret[(spy_ret.index > "2008-12-01") & (spy_ret.index <= "2019-07-01")])
.combine_first(tlt_ret[(tlt_ret.index >= "2019-07-01") & (tlt_ret.index <= "2020-04-01")])
.combine_first(spy_ret[(spy_ret.index > "2020-04-01") & (spy_ret.index <= "2024-08-01")])
.combine_first(tlt_ret[tlt_ret.index > "2024-08-01"])
)
# Convert to DataFrame
portfolio_monthly = portfolio.to_frame(name="Portfolio_Monthly_Return")
# Calculate cumulative returns and drawdown for the portfolio
portfolio_monthly['Portfolio_Cumulative_Return'] = (1 + portfolio_monthly['Portfolio_Monthly_Return']).cumprod() - 1
portfolio_monthly['Portfolio_Cumulative_Return_Plus_One'] = 1 + portfolio_monthly['Portfolio_Cumulative_Return']
portfolio_monthly['Portfolio_Rolling_Max'] = portfolio_monthly['Portfolio_Cumulative_Return_Plus_One'].cummax()
portfolio_monthly['Portfolio_Drawdown'] = portfolio_monthly['Portfolio_Cumulative_Return_Plus_One'] / portfolio_monthly['Portfolio_Rolling_Max'] - 1
portfolio_monthly.drop(columns=['Portfolio_Cumulative_Return_Plus_One', 'Portfolio_Rolling_Max'], inplace=True)
# Merge "spy_monthly" and "tlt_monthly" into "portfolio_monthly" to compare cumulative returns
portfolio_monthly = portfolio_monthly.join(
spy_monthly['Monthly_Return'].rename('SPY_Monthly_Return'),
how='left'
).join(
spy_monthly['Cumulative_Return'].rename('SPY_Cumulative_Return'),
how='left'
).join(
spy_monthly['Drawdown'].rename('SPY_Drawdown'),
how='left'
).join(
tlt_monthly['Monthly_Return'].rename('TLT_Monthly_Return'),
how='left'
).join(
tlt_monthly['Cumulative_Return'].rename('TLT_Cumulative_Return'),
how='left'
).join(
tlt_monthly['Drawdown'].rename('TLT_Drawdown'),
how='left'
)
In [54]:
df_info(portfolio_monthly)
The columns, shape, and data types are: <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 252 entries, 2004-11-30 to 2025-10-31 Freq: ME Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Portfolio_Monthly_Return 251 non-null float64 1 Portfolio_Cumulative_Return 251 non-null float64 2 Portfolio_Drawdown 251 non-null float64 3 SPY_Monthly_Return 251 non-null float64 4 SPY_Cumulative_Return 251 non-null float64 5 SPY_Drawdown 251 non-null float64 6 TLT_Monthly_Return 251 non-null float64 7 TLT_Cumulative_Return 251 non-null float64 8 TLT_Drawdown 251 non-null float64 dtypes: float64(9) memory usage: 19.7 KB None The first 5 rows are:
| Portfolio_Monthly_Return | Portfolio_Cumulative_Return | Portfolio_Drawdown | SPY_Monthly_Return | SPY_Cumulative_Return | SPY_Drawdown | TLT_Monthly_Return | TLT_Cumulative_Return | TLT_Drawdown | |
|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||
| 2004-11-30 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2004-12-31 | 0.03 | 0.03 | 0.00 | 0.03 | 0.03 | 0.00 | 0.03 | 0.03 | 0.00 |
| 2005-01-31 | -0.02 | 0.01 | -0.02 | -0.02 | 0.01 | -0.02 | 0.04 | 0.06 | 0.00 |
| 2005-02-28 | 0.02 | 0.03 | -0.00 | 0.02 | 0.03 | -0.00 | -0.01 | 0.05 | -0.01 |
| 2005-03-31 | -0.02 | 0.01 | -0.02 | -0.02 | 0.01 | -0.02 | -0.00 | 0.04 | -0.02 |
The last 5 rows are:
| Portfolio_Monthly_Return | Portfolio_Cumulative_Return | Portfolio_Drawdown | SPY_Monthly_Return | SPY_Cumulative_Return | SPY_Drawdown | TLT_Monthly_Return | TLT_Cumulative_Return | TLT_Drawdown | |
|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||
| 2025-06-30 | 0.03 | 19.00 | -0.07 | 0.05 | 6.72 | 0.00 | 0.03 | 0.96 | -0.41 |
| 2025-07-31 | -0.01 | 18.78 | -0.08 | 0.02 | 6.90 | 0.00 | -0.01 | 0.94 | -0.41 |
| 2025-08-31 | 0.00 | 18.78 | -0.08 | 0.02 | 7.06 | 0.00 | 0.00 | 0.94 | -0.41 |
| 2025-09-30 | 0.04 | 19.49 | -0.05 | 0.04 | 7.34 | 0.00 | 0.04 | 1.01 | -0.39 |
| 2025-10-31 | 0.01 | 19.77 | -0.04 | 0.02 | 7.54 | 0.00 | 0.01 | 1.04 | -0.39 |
In [55]:
# Copy this <!-- INSERT_05_Portfolio_DF_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="05_Portfolio_DF.md", content=df_info_markdown(df=portfolio_monthly, decimal_places=3))
✅ Exported and tracked: 05_Portfolio_DF.md
Performance Statistics¶
In [56]:
plot_timeseries(
price_df=portfolio_monthly,
plot_start_date=start_date,
plot_end_date=end_date,
plot_columns=["Portfolio_Monthly_Return", "SPY_Monthly_Return", "TLT_Monthly_Return"],
title="Portfolio, SPY, and TLT Monthly Returns",
x_label="Date",
x_format="Year",
y_label="Return",
y_format="Decimal",
y_format_decimal_places=2,
y_tick_spacing=0.02,
grid=True,
legend=True,
export_plot=True,
plot_file_name="05_Monthly_Returns",
)
In [57]:
plot_timeseries(
price_df=portfolio_monthly,
plot_start_date=start_date,
plot_end_date=end_date,
plot_columns=["Portfolio_Cumulative_Return", "SPY_Cumulative_Return", "TLT_Cumulative_Return"],
title="Portfolio, SPY, and TLT Cumulative Returns",
x_label="Date",
x_format="Year",
y_label="Cumulative Return",
y_format="Decimal",
y_format_decimal_places=0,
y_tick_spacing=2,
grid=True,
legend=True,
export_plot=True,
plot_file_name="05_Cumulative_Returns",
)
In [58]:
plot_timeseries(
price_df=portfolio_monthly,
plot_start_date=start_date,
plot_end_date=end_date,
plot_columns=["Portfolio_Drawdown", "SPY_Drawdown", "TLT_Drawdown"],
title="Portfolio, SPY, and TLT Drawdowns",
x_label="Date",
x_format="Year",
y_label="Drawdown",
y_format="Decimal",
y_format_decimal_places=2,
y_tick_spacing=0.05,
grid=True,
legend=True,
export_plot=True,
plot_file_name="05_Drawdowns",
)
In [59]:
port_sum_stats = summary_stats(
fund_list=["Portfolio", "SPY", "TLT"],
df=portfolio_monthly[["Portfolio_Monthly_Return"]],
period="Monthly",
use_calendar_days=False,
excel_export=False,
pickle_export=False,
output_confirmation=False,
)
spy_sum_stats = summary_stats(
fund_list=["Portfolio", "SPY", "TLT"],
df=portfolio_monthly[["SPY_Monthly_Return"]],
period="Monthly",
use_calendar_days=False,
excel_export=False,
pickle_export=False,
output_confirmation=False,
)
tlt_sum_stats = summary_stats(
fund_list=["Portfolio", "SPY", "TLT"],
df=portfolio_monthly[["TLT_Monthly_Return"]],
period="Monthly",
use_calendar_days=False,
excel_export=False,
pickle_export=False,
output_confirmation=False,
)
sum_stats = port_sum_stats.combine_first(spy_sum_stats).combine_first(tlt_sum_stats)
In [60]:
# Copy this <!-- INSERT_05_Portfolio_Stats_DF_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="05_Portfolio_Stats_DF.md", content=sum_stats.to_markdown(floatfmt=".3f"))
✅ Exported and tracked: 05_Portfolio_Stats_DF.md