Does Harry Browne's permanent portfolio withstand the test of time?¶
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
# 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-venv-p313/lib/python3.13/site-packages 5: /home/jared/Cloud_Storage/Dropbox/Websites/jaredszajkowski.github.io_congo/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 bb_clean_data import bb_clean_data
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 strategy_harry_brown_perm_port import strategy_harry_brown_perm_port
from summary_stats import summary_stats
Data Overview¶
Load Data¶
In [5]:
# Set decimal places
pandas_set_decimal_places(2)
# Bonds dataframe
bb_clean_data(
base_directory=DATA_DIR,
fund_ticker_name="SPBDU10T_S&P US Treasury Bond 7-10 Year Total Return Index",
source="Bloomberg",
asset_class="Indices",
excel_export=True,
pickle_export=True,
output_confirmation=True,
)
bonds_data = load_data(
base_directory=DATA_DIR,
ticker="SPBDU10T_S&P US Treasury Bond 7-10 Year Total Return Index_Clean",
source="Bloomberg",
asset_class="Indices",
timeframe="Daily",
file_format="excel",
)
bonds_data['Date'] = pd.to_datetime(bonds_data['Date'])
bonds_data.set_index('Date', inplace = True)
bonds_data = bonds_data[(bonds_data.index >= '1990-01-01') & (bonds_data.index <= '2023-12-31')]
bonds_data.rename(columns={'Close':'Bonds_Close'}, inplace=True)
bonds_data['Bonds_Daily_Return'] = bonds_data['Bonds_Close'].pct_change()
bonds_data['Bonds_Total_Return'] = (1 + bonds_data['Bonds_Daily_Return']).cumprod()
display(bonds_data.head())
The first and last date of data for SPBDU10T_S&P US Treasury Bond 7-10 Year Total Return Index is:
| Close | |
|---|---|
| Date | |
| 1989-12-29 | 100 |
| Close | |
|---|---|
| Date | |
| 2024-04-30 | 579.02 |
Bloomberg data cleaning complete for SPBDU10T_S&P US Treasury Bond 7-10 Year Total Return Index --------------------
| Bonds_Close | Bonds_Daily_Return | Bonds_Total_Return | |
|---|---|---|---|
| Date | |||
| 1990-01-02 | 99.97 | NaN | NaN |
| 1990-01-03 | 99.73 | -0.00 | 1.00 |
| 1990-01-04 | 99.81 | 0.00 | 1.00 |
| 1990-01-05 | 99.77 | -0.00 | 1.00 |
| 1990-01-08 | 99.68 | -0.00 | 1.00 |
In [6]:
# Copy this <!-- INSERT_01_Bonds_Data_Head_HERE --> to index_temp.md
export_track_md_deps(
dep_file=dep_file,
md_filename="01_Bonds_Data_Head.md",
content=bonds_data.head().to_markdown(floatfmt=".3f"),
output_type="markdown",
)
✅ Exported and tracked: 01_Bonds_Data_Head.md
In [7]:
# Stocks dataframe
bb_clean_data(
base_directory=DATA_DIR,
fund_ticker_name="SPXT_S&P 500 Total Return Index",
source="Bloomberg",
asset_class="Indices",
excel_export=True,
pickle_export=True,
output_confirmation=True,
)
stocks_data = load_data(
base_directory=DATA_DIR,
ticker="SPXT_S&P 500 Total Return Index_Clean",
source="Bloomberg",
asset_class="Indices",
timeframe="Daily",
file_format="excel",
)
stocks_data['Date'] = pd.to_datetime(stocks_data['Date'])
stocks_data.set_index('Date', inplace = True)
stocks_data = stocks_data[(stocks_data.index >= '1990-01-01') & (stocks_data.index <= '2023-12-31')]
stocks_data.rename(columns={'Close':'Stocks_Close'}, inplace=True)
stocks_data['Stocks_Daily_Return'] = stocks_data['Stocks_Close'].pct_change()
stocks_data['Stocks_Total_Return'] = (1 + stocks_data['Stocks_Daily_Return']).cumprod()
display(stocks_data.head())
The first and last date of data for SPXT_S&P 500 Total Return Index is:
| Close | |
|---|---|
| Date | |
| 1988-01-04 | 256.02 |
| Close | |
|---|---|
| Date | |
| 2024-04-30 | 10951.66 |
Bloomberg data cleaning complete for SPXT_S&P 500 Total Return Index --------------------
| Stocks_Close | Stocks_Daily_Return | Stocks_Total_Return | |
|---|---|---|---|
| Date | |||
| 1990-01-01 | NaN | NaN | NaN |
| 1990-01-02 | 386.16 | NaN | NaN |
| 1990-01-03 | 385.17 | -0.00 | 1.00 |
| 1990-01-04 | 382.02 | -0.01 | 0.99 |
| 1990-01-05 | 378.30 | -0.01 | 0.98 |
In [8]:
# Copy this <!-- INSERT_01_Stocks_Data_Head_HERE --> to index_temp.md
export_track_md_deps(
dep_file=dep_file,
md_filename="01_Stocks_Data_Head.md",
content=stocks_data.head().to_markdown(floatfmt=".3f"),
output_type="markdown",
)
✅ Exported and tracked: 01_Stocks_Data_Head.md
In [9]:
# Gold dataframe
bb_clean_data(
base_directory=DATA_DIR,
fund_ticker_name="XAU_Gold USD Spot",
source="Bloomberg",
asset_class="Commodities",
excel_export=True,
pickle_export=True,
output_confirmation=True,
)
gold_data = load_data(
base_directory=DATA_DIR,
ticker="XAU_Gold USD Spot_Clean",
source="Bloomberg",
asset_class="Commodities",
timeframe="Daily",
file_format="excel",
)
gold_data['Date'] = pd.to_datetime(gold_data['Date'])
gold_data.set_index('Date', inplace = True)
gold_data = gold_data[(gold_data.index >= '1990-01-01') & (gold_data.index <= '2023-12-31')]
gold_data.rename(columns={'Close':'Gold_Close'}, inplace=True)
gold_data['Gold_Daily_Return'] = gold_data['Gold_Close'].pct_change()
gold_data['Gold_Total_Return'] = (1 + gold_data['Gold_Daily_Return']).cumprod()
display(gold_data.head())
The first and last date of data for XAU_Gold USD Spot is:
| Close | |
|---|---|
| Date | |
| 1949-12-30 | 34.69 |
| Close | |
|---|---|
| Date | |
| 2024-05-01 | 2299.31 |
Bloomberg data cleaning complete for XAU_Gold USD Spot --------------------
| Gold_Close | Gold_Daily_Return | Gold_Total_Return | |
|---|---|---|---|
| Date | |||
| 1990-01-02 | 399.00 | NaN | NaN |
| 1990-01-03 | 395.00 | -0.01 | 0.99 |
| 1990-01-04 | 396.50 | 0.00 | 0.99 |
| 1990-01-05 | 405.00 | 0.02 | 1.02 |
| 1990-01-08 | 404.60 | -0.00 | 1.01 |
In [10]:
# Copy this <!-- INSERT_01_Gold_Data_Head_HERE --> to index_temp.md
export_track_md_deps(
dep_file=dep_file,
md_filename="01_Gold_Data_Head.md",
content=gold_data.head().to_markdown(floatfmt=".3f"),
output_type="markdown",
)
✅ Exported and tracked: 01_Gold_Data_Head.md
Combine Data¶
In [11]:
# Merge the stock data and bond data into a single DataFrame using their indices (dates)
perm_port = pd.merge(stocks_data['Stocks_Close'], bonds_data['Bonds_Close'], left_index=True, right_index=True)
# Add gold data to the portfolio DataFrame by merging it with the existing data on indices (dates)
perm_port = pd.merge(perm_port, gold_data['Gold_Close'], left_index=True, right_index=True)
# Add a column for cash with a constant value of 1 (assumes the value of cash remains constant at $1 over time)
perm_port['Cash_Close'] = 1
# Remove any rows with missing values (NaN) to ensure clean data for further analysis
perm_port.dropna(inplace=True)
# Display the finalized portfolio DataFrame
display(perm_port)
| Stocks_Close | Bonds_Close | Gold_Close | Cash_Close | |
|---|---|---|---|---|
| Date | ||||
| 1990-01-02 | 386.16 | 99.97 | 399.00 | 1 |
| 1990-01-03 | 385.17 | 99.73 | 395.00 | 1 |
| 1990-01-04 | 382.02 | 99.81 | 396.50 | 1 |
| 1990-01-05 | 378.30 | 99.77 | 405.00 | 1 |
| 1990-01-08 | 380.04 | 99.68 | 404.60 | 1 |
| ... | ... | ... | ... | ... |
| 2023-12-22 | 10292.37 | 604.17 | 2053.08 | 1 |
| 2023-12-26 | 10335.98 | 604.55 | 2067.81 | 1 |
| 2023-12-27 | 10351.60 | 609.36 | 2077.49 | 1 |
| 2023-12-28 | 10356.59 | 606.83 | 2065.61 | 1 |
| 2023-12-29 | 10327.83 | 606.18 | 2062.98 | 1 |
8479 rows × 4 columns
Check For Missing Values¶
In [12]:
# Check for any missing values in each column
perm_port.isnull().any()
Out[12]:
Stocks_Close False Bonds_Close False Gold_Close False Cash_Close False dtype: bool
Permanent Portfolio DataFrame Info¶
In [13]:
df_info(perm_port)
The columns, shape, and data types are: <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 8479 entries, 1990-01-02 to 2023-12-29 Data columns (total 4 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Stocks_Close 8479 non-null float64 1 Bonds_Close 8479 non-null float64 2 Gold_Close 8479 non-null float64 3 Cash_Close 8479 non-null int64 dtypes: float64(3), int64(1) memory usage: 331.2 KB None The first 5 rows are:
| Stocks_Close | Bonds_Close | Gold_Close | Cash_Close | |
|---|---|---|---|---|
| Date | ||||
| 1990-01-02 | 386.16 | 99.97 | 399.00 | 1 |
| 1990-01-03 | 385.17 | 99.73 | 395.00 | 1 |
| 1990-01-04 | 382.02 | 99.81 | 396.50 | 1 |
| 1990-01-05 | 378.30 | 99.77 | 405.00 | 1 |
| 1990-01-08 | 380.04 | 99.68 | 404.60 | 1 |
The last 5 rows are:
| Stocks_Close | Bonds_Close | Gold_Close | Cash_Close | |
|---|---|---|---|---|
| Date | ||||
| 2023-12-22 | 10292.37 | 604.17 | 2053.08 | 1 |
| 2023-12-26 | 10335.98 | 604.55 | 2067.81 | 1 |
| 2023-12-27 | 10351.60 | 609.36 | 2077.49 | 1 |
| 2023-12-28 | 10356.59 | 606.83 | 2065.61 | 1 |
| 2023-12-29 | 10327.83 | 606.18 | 2062.98 | 1 |
In [14]:
# Copy this <!-- INSERT_02_Perm_Port_DF_Info_HERE --> to index_temp.md
export_track_md_deps(
dep_file=dep_file,
md_filename="02_Perm_Port_DF_Info.md",
content=df_info_markdown(perm_port),
output_type="markdown",
)
✅ Exported and tracked: 02_Perm_Port_DF_Info.md
Execute Strategy¶
In [15]:
# List of funds to be used
fund_list = ['Stocks', 'Bonds', 'Gold', 'Cash']
# Starting cash contribution
starting_cash = 10000
# Monthly cash contribution
cash_contrib = 0
strat = strategy_harry_brown_perm_port(
fund_list=fund_list,
starting_cash=starting_cash,
cash_contrib=cash_contrib,
close_prices_df=perm_port,
rebal_month=1,
rebal_day=1,
rebal_per_high=0.35,
rebal_per_low=0.15,
excel_export=True,
pickle_export=True,
output_confirmation=True,
)
strat = strat.set_index('Date')
Strategy complete for Stocks_Bonds_Gold_Cash
In [16]:
df_info(strat)
The columns, shape, and data types are: <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 8479 entries, 1990-01-02 to 2023-12-29 Data columns (total 34 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Stocks_Close 8479 non-null float64 1 Bonds_Close 8479 non-null float64 2 Gold_Close 8479 non-null float64 3 Cash_Close 8479 non-null int64 4 Stocks_BA_Shares 8479 non-null float64 5 Stocks_BA_$_Invested 8479 non-null float64 6 Stocks_BA_Port_% 8479 non-null float64 7 Bonds_BA_Shares 8479 non-null float64 8 Bonds_BA_$_Invested 8479 non-null float64 9 Bonds_BA_Port_% 8479 non-null float64 10 Gold_BA_Shares 8479 non-null float64 11 Gold_BA_$_Invested 8479 non-null float64 12 Gold_BA_Port_% 8479 non-null float64 13 Cash_BA_Shares 8479 non-null float64 14 Cash_BA_$_Invested 8479 non-null float64 15 Cash_BA_Port_% 8479 non-null float64 16 Total_BA_$_Invested 8479 non-null float64 17 Contribution 8479 non-null int64 18 Rebalance 8479 non-null object 19 Stocks_AA_Shares 8479 non-null float64 20 Stocks_AA_$_Invested 8479 non-null float64 21 Stocks_AA_Port_% 8479 non-null float64 22 Bonds_AA_Shares 8479 non-null float64 23 Bonds_AA_$_Invested 8479 non-null float64 24 Bonds_AA_Port_% 8479 non-null float64 25 Gold_AA_Shares 8479 non-null float64 26 Gold_AA_$_Invested 8479 non-null float64 27 Gold_AA_Port_% 8479 non-null float64 28 Cash_AA_Shares 8479 non-null float64 29 Cash_AA_$_Invested 8479 non-null float64 30 Cash_AA_Port_% 8479 non-null float64 31 Total_AA_$_Invested 8479 non-null float64 32 Return 8478 non-null float64 33 Cumulative_Return 8478 non-null float64 dtypes: float64(31), int64(2), object(1) memory usage: 2.3+ MB None The first 5 rows are:
| Stocks_Close | Bonds_Close | Gold_Close | Cash_Close | Stocks_BA_Shares | Stocks_BA_$_Invested | Stocks_BA_Port_% | Bonds_BA_Shares | Bonds_BA_$_Invested | Bonds_BA_Port_% | ... | Bonds_AA_Port_% | Gold_AA_Shares | Gold_AA_$_Invested | Gold_AA_Port_% | Cash_AA_Shares | Cash_AA_$_Invested | Cash_AA_Port_% | Total_AA_$_Invested | Return | Cumulative_Return | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||||||||||||||
| 1990-01-02 | 386.16 | 99.97 | 399.00 | 1 | 6.47 | 2500.00 | 0.25 | 25.01 | 2500.00 | 0.25 | ... | 0.25 | 6.27 | 2500.00 | 0.25 | 2500.00 | 2500.00 | 0.25 | 10000.00 | NaN | NaN |
| 1990-01-03 | 385.17 | 99.73 | 395.00 | 1 | 6.47 | 2493.59 | 0.25 | 25.01 | 2494.02 | 0.25 | ... | 0.25 | 6.27 | 2474.94 | 0.25 | 2500.00 | 2500.00 | 0.25 | 9962.55 | -0.00 | 1.00 |
| 1990-01-04 | 382.02 | 99.81 | 396.50 | 1 | 6.47 | 2473.20 | 0.25 | 25.01 | 2496.02 | 0.25 | ... | 0.25 | 6.27 | 2484.34 | 0.25 | 2500.00 | 2500.00 | 0.25 | 9953.56 | -0.00 | 1.00 |
| 1990-01-05 | 378.30 | 99.77 | 405.00 | 1 | 6.47 | 2449.11 | 0.25 | 25.01 | 2494.92 | 0.25 | ... | 0.25 | 6.27 | 2537.59 | 0.25 | 2500.00 | 2500.00 | 0.25 | 9981.63 | 0.00 | 1.00 |
| 1990-01-08 | 380.04 | 99.68 | 404.60 | 1 | 6.47 | 2460.38 | 0.25 | 25.01 | 2492.72 | 0.25 | ... | 0.25 | 6.27 | 2535.09 | 0.25 | 2500.00 | 2500.00 | 0.25 | 9988.19 | 0.00 | 1.00 |
5 rows × 34 columns
The last 5 rows are:
| Stocks_Close | Bonds_Close | Gold_Close | Cash_Close | Stocks_BA_Shares | Stocks_BA_$_Invested | Stocks_BA_Port_% | Bonds_BA_Shares | Bonds_BA_$_Invested | Bonds_BA_Port_% | ... | Bonds_AA_Port_% | Gold_AA_Shares | Gold_AA_$_Invested | Gold_AA_Port_% | Cash_AA_Shares | Cash_AA_$_Invested | Cash_AA_Port_% | Total_AA_$_Invested | Return | Cumulative_Return | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||||||||||||||
| 2023-12-22 | 10292.37 | 604.17 | 2053.08 | 1 | 1.81 | 18595.87 | 0.29 | 25.03 | 15124.46 | 0.23 | ... | 0.23 | 8.00 | 16426.12 | 0.25 | 14717.17 | 14717.17 | 0.23 | 64863.62 | 0.00 | 6.49 |
| 2023-12-26 | 10335.98 | 604.55 | 2067.81 | 1 | 1.81 | 18674.66 | 0.29 | 25.03 | 15134.20 | 0.23 | ... | 0.23 | 8.00 | 16543.97 | 0.25 | 14717.17 | 14717.17 | 0.23 | 65070.01 | 0.00 | 6.51 |
| 2023-12-27 | 10351.60 | 609.36 | 2077.49 | 1 | 1.81 | 18702.89 | 0.29 | 25.03 | 15254.36 | 0.23 | ... | 0.23 | 8.00 | 16621.42 | 0.25 | 14717.17 | 14717.17 | 0.23 | 65295.84 | 0.00 | 6.53 |
| 2023-12-28 | 10356.59 | 606.83 | 2065.61 | 1 | 1.81 | 18711.90 | 0.29 | 25.03 | 15191.10 | 0.23 | ... | 0.23 | 8.00 | 16526.37 | 0.25 | 14717.17 | 14717.17 | 0.23 | 65146.54 | -0.00 | 6.51 |
| 2023-12-29 | 10327.83 | 606.18 | 2062.98 | 1 | 1.81 | 18659.94 | 0.29 | 25.03 | 15175.01 | 0.23 | ... | 0.23 | 8.00 | 16505.33 | 0.25 | 14717.17 | 14717.17 | 0.23 | 65057.44 | -0.00 | 6.51 |
5 rows × 34 columns
In [17]:
# Copy this <!-- INSERT_03_Strategy_HERE --> to index_temp.md
export_track_md_deps(
dep_file=dep_file,
md_filename="03_Strategy.md",
content=df_info_markdown(strat),
output_type="markdown",
)
✅ Exported and tracked: 03_Strategy.md
Summary Statistics¶
In [18]:
sum_stats = summary_stats(
fund_list=fund_list,
df=strat[['Return']],
period="Daily",
use_calendar_days=False,
excel_export=True,
pickle_export=True,
output_confirmation=True,
)
strat_pre_1999 = strat[strat.index < '2000-01-01']
sum_stats_pre_1999 = summary_stats(
fund_list=fund_list,
df=strat_pre_1999[['Return']],
period="Daily",
use_calendar_days=False,
excel_export=False,
pickle_export=False,
output_confirmation=True,
)
strat_post_1999 = strat[strat.index >= '2000-01-01']
sum_stats_post_1999 = summary_stats(
fund_list=fund_list,
df=strat_post_1999[['Return']],
period="Daily",
use_calendar_days=False,
excel_export=False,
pickle_export=False,
output_confirmation=True,
)
strat_post_2009 = strat[strat.index >= '2010-01-01']
sum_stats_post_2009 = summary_stats(
fund_list=fund_list,
df=strat_post_2009[['Return']],
period="Daily",
use_calendar_days=False,
excel_export=False,
pickle_export=False,
output_confirmation=True,
)
Summary stats complete for Stocks_Bonds_Gold_Cash Summary stats complete for Stocks_Bonds_Gold_Cash Summary stats complete for Stocks_Bonds_Gold_Cash Summary stats complete for Stocks_Bonds_Gold_Cash
In [19]:
all_sum_stats = pd.concat([sum_stats])
all_sum_stats = all_sum_stats.rename(index={'Return': '1990 - 2023'})
all_sum_stats = pd.concat([all_sum_stats, sum_stats_pre_1999])
all_sum_stats = all_sum_stats.rename(index={'Return': 'Pre 1999'})
all_sum_stats = pd.concat([all_sum_stats, sum_stats_post_1999])
all_sum_stats = all_sum_stats.rename(index={'Return': 'Post 1999'})
all_sum_stats = pd.concat([all_sum_stats, sum_stats_post_2009])
all_sum_stats = all_sum_stats.rename(index={'Return': 'Post 2009'})
display(all_sum_stats)
| Annualized Mean | Annualized Volatility | Annualized Sharpe Ratio | CAGR | Daily Max Return | Daily Max Return (Date) | Daily Min Return | Daily Min Return (Date) | Max Drawdown | Peak | Trough | Recovery Date | Days to Recover | MAR Ratio | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1990 - 2023 | 0.06 | 0.06 | 0.96 | 0.06 | 0.03 | 2020-03-24 | -0.03 | 2020-03-12 | -0.15 | 2008-03-18 | 2008-11-12 | 2009-10-06 | 328 | 0.37 |
| Pre 1999 | 0.06 | 0.05 | 1.21 | 0.06 | 0.02 | 1999-09-28 | -0.02 | 1993-08-05 | -0.06 | 1998-07-20 | 1998-08-31 | 1998-11-05 | 66 | 0.98 |
| Post 1999 | 0.06 | 0.06 | 0.88 | 0.06 | 0.03 | 2020-03-24 | -0.03 | 2020-03-12 | -0.15 | 2008-03-18 | 2008-11-12 | 2009-10-06 | 328 | 0.36 |
| Post 2009 | 0.06 | 0.06 | 0.93 | 0.06 | 0.03 | 2020-03-24 | -0.03 | 2020-03-12 | -0.13 | 2021-12-27 | 2022-10-20 | 2023-12-01 | 407 | 0.44 |
In [20]:
# Copy this <!-- INSERT_04_Summary_Stats_HERE --> to index_temp.md
export_track_md_deps(
dep_file=dep_file,
md_filename="04_Summary_Stats.md",
content=all_sum_stats.to_markdown(floatfmt=".3f"),
output_type="markdown",
)
✅ Exported and tracked: 04_Summary_Stats.md
Annual Returns¶
In [21]:
# Create dataframe for the annual returns
strat_annual_returns = strat['Cumulative_Return'].resample('Y').last().pct_change().dropna()
strat_annual_returns_df = strat_annual_returns.to_frame()
strat_annual_returns_df['Year'] = strat_annual_returns_df.index.year # Add a 'Year' column with just the year
strat_annual_returns_df.reset_index(drop=True, inplace=True) # Reset the index to remove the datetime index
# Now the DataFrame will have 'Year' and 'Cumulative_Return' columns
strat_annual_returns_df = strat_annual_returns_df[['Year', 'Cumulative_Return']] # Keep only 'Year' and 'Cumulative_Return' columns
strat_annual_returns_df.rename(columns = {'Cumulative_Return':'Return'}, inplace=True)
strat_annual_returns_df.set_index('Year', inplace=True)
display(strat_annual_returns_df)
| Return | |
|---|---|
| Year | |
| 1991 | 0.10 |
| 1992 | 0.03 |
| 1993 | 0.10 |
| 1994 | -0.02 |
| 1995 | 0.15 |
| 1996 | 0.05 |
| 1997 | 0.06 |
| 1998 | 0.10 |
| 1999 | 0.04 |
| 2000 | 0.00 |
| 2001 | -0.01 |
| 2002 | 0.04 |
| 2003 | 0.12 |
| 2004 | 0.05 |
| 2005 | 0.06 |
| 2006 | 0.10 |
| 2007 | 0.12 |
| 2008 | -0.03 |
| 2009 | 0.11 |
| 2010 | 0.14 |
| 2011 | 0.07 |
| 2012 | 0.07 |
| 2013 | -0.01 |
| 2014 | 0.05 |
| 2015 | -0.02 |
| 2016 | 0.05 |
| 2017 | 0.09 |
| 2018 | -0.01 |
| 2019 | 0.15 |
| 2020 | 0.13 |
| 2021 | 0.06 |
| 2022 | -0.08 |
| 2023 | 0.11 |
In [22]:
# Copy this <!-- INSERT_05_Annual_Returns_HERE --> to index_temp.md
export_track_md_deps(
dep_file=dep_file,
md_filename="05_Annual_Returns.md",
content=strat_annual_returns_df.to_markdown(floatfmt=".3f"),
output_type="markdown",
)
✅ Exported and tracked: 05_Annual_Returns.md
In [23]:
# Export the annual returns DataFrame to Excel and pickle files
plan_name = '_'.join(fund_list)
strat_annual_returns_df.to_excel(f"{plan_name}_Annual_Returns.xlsx", sheet_name="data")
strat_annual_returns_df.to_pickle(f"{plan_name}_Annual_Returns.pkl")
Plots¶
Plot Cumulative Return¶
In [24]:
def plot_cumulative_return(strat_df):
# Generate plot
plt.figure(figsize=(10, 5), facecolor = '#F5F5F5')
# Plotting data
plt.plot(strat_df.index, strat_df['Cumulative_Return'], label = 'Strategy Cumulative Return', linestyle='-', color='green', linewidth=1)
# Set X axis
# x_tick_spacing = 5 # Specify the interval for x-axis ticks
# plt.gca().xaxis.set_major_locator(MultipleLocator(x_tick_spacing))
plt.gca().xaxis.set_major_locator(mdates.YearLocator())
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
plt.xlabel('Year', fontsize = 9)
plt.xticks(rotation = 45, fontsize = 7)
# plt.xlim(, )
# Set Y axis
y_tick_spacing = 0.5 # Specify the interval for y-axis ticks
plt.gca().yaxis.set_major_locator(MultipleLocator(y_tick_spacing))
plt.ylabel('Cumulative Return', fontsize = 9)
plt.yticks(fontsize = 7)
# plt.ylim(0, 7.5)
# Set title, etc.
plt.title('Cumulative Return', fontsize = 12)
# Set the grid & legend
plt.tight_layout()
plt.grid(True)
plt.legend(fontsize=8)
# Save the figure
plt.savefig('06_Cumulative_Return.png', dpi=300, bbox_inches='tight')
# Display the plot
return plt.show()
In [25]:
plot_cumulative_return(strat)
Plot Portfolio & Component Values¶
In [26]:
def plot_values(strat_df):
# Generate plot
plt.figure(figsize=(10, 5), facecolor = '#F5F5F5')
# Plotting data
plt.plot(strat_df.index, strat_df['Total_AA_$_Invested'], label='Total Portfolio Value', linestyle='-', color='black', linewidth=1)
plt.plot(strat_df.index, strat_df['Stocks_AA_$_Invested'], label='Stocks Position Value', linestyle='-', color='orange', linewidth=1)
plt.plot(strat_df.index, strat_df['Bonds_AA_$_Invested'], label='Bond Position Value', linestyle='-', color='yellow', linewidth=1)
plt.plot(strat_df.index, strat_df['Gold_AA_$_Invested'], label='Gold Position Value', linestyle='-', color='blue', linewidth=1)
plt.plot(strat_df.index, strat_df['Cash_AA_$_Invested'], label='Cash Position Value', linestyle='-', color='brown', linewidth=1)
# Set X axis
# x_tick_spacing = 5 # Specify the interval for x-axis ticks
# plt.gca().xaxis.set_major_locator(MultipleLocator(x_tick_spacing))
plt.gca().xaxis.set_major_locator(mdates.YearLocator())
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
plt.xlabel('Year', fontsize = 9)
plt.xticks(rotation = 45, fontsize = 7)
# plt.xlim(, )
# Set Y axis
y_tick_spacing = 5000 # Specify the interval for y-axis ticks
plt.gca().yaxis.set_major_locator(MultipleLocator(y_tick_spacing))
plt.gca().yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, pos: '{:,.0f}'.format(x))) # Adding commas to y-axis labels
plt.ylabel('Total Value ($)', fontsize = 9)
plt.yticks(fontsize = 7)
# plt.ylim(0, 75000)
# Set title, etc.
plt.title('Total Values For Stocks, Bonds, Gold, and Cash Positions and Portfolio', fontsize = 12)
# Set the grid & legend
plt.tight_layout()
plt.grid(True)
plt.legend(fontsize=8)
# Save the figure
plt.savefig('07_Portfolio_Values.png', dpi=300, bbox_inches='tight')
# Display the plot
return plt.show()
In [27]:
plot_values(strat)
Plot Portfolio Drawdown¶
In [28]:
def plot_drawdown(strat_df):
rolling_max = strat_df['Total_AA_$_Invested'].cummax()
drawdown = (strat_df['Total_AA_$_Invested'] - rolling_max) / rolling_max * 100
# Generate plot
plt.figure(figsize=(10, 5), facecolor = '#F5F5F5')
# Plotting data
plt.plot(strat_df.index, drawdown, label='Drawdown', linestyle='-', color='red', linewidth=1)
# Set X axis
# x_tick_spacing = 5 # Specify the interval for x-axis ticks
# plt.gca().xaxis.set_major_locator(MultipleLocator(x_tick_spacing))
plt.gca().xaxis.set_major_locator(mdates.YearLocator())
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
plt.xlabel('Year', fontsize = 9)
plt.xticks(rotation = 45, fontsize = 7)
# plt.xlim(, )
# Set Y axis
y_tick_spacing = 1 # Specify the interval for y-axis ticks
plt.gca().yaxis.set_major_locator(MultipleLocator(y_tick_spacing))
# plt.gca().yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, pos: '{:,.0f}'.format(x))) # Adding commas to y-axis labels
plt.gca().yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, pos: '{:.0f}'.format(x))) # Adding 0 decimal places to y-axis labels
plt.ylabel('Drawdown (%)', fontsize = 9)
plt.yticks(fontsize = 7)
# plt.ylim(-20, 0)
# Set title, etc.
plt.title('Portfolio Drawdown', fontsize = 12)
# Set the grid & legend
plt.tight_layout()
plt.grid(True)
plt.legend(fontsize=8)
# Save the figure
plt.savefig('08_Portfolio_Drawdown.png', dpi=300, bbox_inches='tight')
# Display the plot
return plt.show()
In [29]:
plot_drawdown(strat)
Plot Asset Weights¶
In [30]:
def plot_asset_weights(strat_df):
# Generate plot
plt.figure(figsize=(10, 5), facecolor = '#F5F5F5')
# Plotting data
plt.plot(strat_df.index, strat_df['Stocks_AA_Port_%'] * 100, label='Stocks Portfolio Weight', linestyle='-', color='orange', linewidth=1)
plt.plot(strat_df.index, strat_df['Bonds_AA_Port_%'] * 100, label='Bonds Portfolio Weight', linestyle='-', color='yellow', linewidth=1)
plt.plot(strat_df.index, strat_df['Gold_AA_Port_%'] * 100, label='Gold Portfolio Weight', linestyle='-', color='blue', linewidth=1)
plt.plot(strat_df.index, strat_df['Cash_AA_Port_%'] * 100, label='Cash Portfolio Weight', linestyle='-', color='brown', linewidth=1)
# Set X axis
# x_tick_spacing = 5 # Specify the interval for x-axis ticks
# plt.gca().xaxis.set_major_locator(MultipleLocator(x_tick_spacing))
plt.gca().xaxis.set_major_locator(mdates.YearLocator())
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
plt.xlabel('Year', fontsize = 9)
plt.xticks(rotation = 45, fontsize = 7)
# plt.xlim(, )
# Set Y axis
y_tick_spacing = 1 # Specify the interval for y-axis ticks
plt.gca().yaxis.set_major_locator(MultipleLocator(y_tick_spacing))
# plt.gca().yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, pos: '{:,.0f}'.format(x))) # Adding commas to y-axis labels
plt.ylabel('Asset Weight (%)', fontsize = 9)
plt.yticks(fontsize = 7)
# plt.ylim(14, 36)
# Set title, etc.
plt.title('Portfolio Asset Weights For Stocks, Bonds, Gold, and Cash Positions', fontsize = 12)
# Set the grid & legend
plt.tight_layout()
plt.grid(True)
plt.legend(fontsize=8)
# Save the figure
plt.savefig('09_Portfolio_Weights.png', dpi=300, bbox_inches='tight')
# Display the plot
return plt.show()
In [31]:
plot_asset_weights(strat)
Plot Annual Returns¶
In [32]:
def plot_annual_returns(return_df):
# Generate plot
plt.figure(figsize=(10, 5), facecolor = '#F5F5F5')
# Plotting data
plt.bar(return_df.index, return_df['Return'] * 100, label='Annual Returns', width=0.5) # width adjusted for better spacing
# Set X axis
x_tick_spacing = 1 # Specify the interval for x-axis ticks
plt.gca().xaxis.set_major_locator(MultipleLocator(x_tick_spacing))
# plt.gca().xaxis.set_major_locator(mdates.YearLocator())
# plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
plt.xlabel('Year', fontsize = 9)
plt.xticks(rotation = 45, fontsize = 7)
# plt.xlim(, )
# Set Y axis
y_tick_spacing = 1 # Specify the interval for y-axis ticks
plt.gca().yaxis.set_major_locator(MultipleLocator(y_tick_spacing))
# plt.gca().yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, pos: '{:,.0f}'.format(x))) # Adding commas to y-axis labels
plt.ylabel('Annual Return (%)', fontsize = 9)
plt.yticks(fontsize = 7)
# plt.ylim(-20, 20)
# Set title, etc.
plt.title('Portfolio Annual Returns', fontsize = 12)
# Set the grid & legend
plt.tight_layout()
plt.grid(True)
plt.legend(fontsize=8)
# Save the figure
plt.savefig('10_Portfolio_Annual_Returns.png', dpi=300, bbox_inches='tight')
# Display the plot
return plt.show()
In [33]:
plot_annual_returns(strat_annual_returns_df)
Portfolio Summary Statistics For Various Rebalance Dates¶
In [34]:
# # Set ranges for months and days
# months = list(range(1, 13))
# days = list(range(1, 32))
# # Create an empty DataFrame to store the results
# stats = pd.DataFrame(columns = ['Rebal_Month', 'Rebal_Day', 'Annualized Mean', 'Annualized Volatility', 'Annualized Sharpe Ratio', 'CAGR',
# 'Daily Max Return', 'Daily Max Return (Date)', 'Daily Min Return', 'Daily Min Return (Date)', 'Max Drawdown',
# 'Peak', 'Bottom', 'Recovery Date',])
# # Loop through each combination of month and day
# for month in months:
# for day in days:
# try:
# strat = strategy_harry_brown_perm_port(
# fund_list=fund_list,
# starting_cash=starting_cash,
# cash_contrib=cash_contrib,
# close_prices_df=perm_port,
# rebal_month=month,
# rebal_day=day,
# rebal_per_high=0.35,
# rebal_per_low=0.15,
# excel_export=False,
# pickle_export=False,
# output_confirmation=False,
# ).set_index('Date')
# sum_stats = summary_stats(
# fund_list=fund_list,
# df=strat[['Return']],
# period="Daily",
# excel_export=False,
# pickle_export=False,
# output_confirmation=False,
# )
# stats = pd.concat([stats, sum_stats], ignore_index=True)
# stats.loc[stats.index[-1], 'Rebal_Month'] = month
# stats.loc[stats.index[-1], 'Rebal_Day'] = day
# print(f"Month: {month}, Day: {day} - Stats added successfully.")
# except Exception as e:
# print(f"Error for month {month} and day {day}: {e}")
# continue
In [35]:
# # Export the stats DataFrame to Excel and pickle files
# plan_name = '_'.join(fund_list)
# stats.to_excel(f"{plan_name}_Various_Rebalance_Summary_Stats.xlsx", sheet_name="data")
# stats.to_pickle(f"{plan_name}_Various_Rebalance_Summary_Stats.pkl")
In [36]:
# Load the stats DataFrame from the pickle file
# stats = load_data(f"{plan_name}_Various_Rebalance_Summary_Stats.pkl")
In [37]:
# stats