Financial Planning¶

In [1]:

# Import libraries and dependencies
import requests
import pandas as pd
from MCForecastTools import MCSimulation
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline

/opt/anaconda3/envs/dev/lib/python3.12/site-packages/requests/__init__.py:86: RequestsDependencyWarning: Unable to find acceptable character detection dependency (chardet or charset_normalizer).
  warnings.warn(

In [2]:

import os
from dotenv import load_dotenv
import alpaca_trade_api as tradeapi

# Load .env file
load_dotenv('api.env')

# Fetch keys
alpaca_api_key = os.getenv("ALPACA_API_KEY")
alpaca_secret_key = os.getenv("ALPACA_SECRET_KEY")

# Initialize API
api = tradeapi.REST(
    alpaca_api_key,
    alpaca_secret_key,
    base_url='https://paper-api.alpaca.markets',
    api_version='v2'
)

Part 1 - Personal Finance Planner¶

Collect Crypto Prices Using the requests Library¶

In [3]:

# Crypto API URLs
btc_url = "https://api.alternative.me/v2/ticker/Bitcoin/?convert=USD"
eth_url = "https://api.alternative.me/v2/ticker/Ethereum/?convert=USD"

# Fetch current BTC price
btc_data = requests.get(btc_url).json()
btc_price = btc_data['data']['1']['quotes']['USD']['price']
print(f"The current price of BTC is ${btc_price:0.2f}")

# Fetch current ETH price
eth_data = requests.get(eth_url).json()
eth_price = eth_data['data']['1027']['quotes']['USD']['price']
print(f"The current price of ETH is ${eth_price:0.2f}")

The current price of BTC is $118841.00
The current price of ETH is $3154.76

In [4]:

# Compute current value of btc
my_btc = 1.2 
my_btc_value = my_btc * btc_price

# Compute current value of eth
my_eth = 5.3
my_eth_value = my_eth * eth_price

# Compute current value of my crypto portfolio
my_crypto_port = my_btc + my_eth
my_crypto_value = my_btc_value + my_eth_value

# Print current crypto wallet balance
print(f"The current value of your {my_btc} BTC is ${my_btc_value:0.2f}")
print(f"The current value of your {my_eth} ETH is ${my_eth_value:0.2f}")
print(f"The current value of your {my_crypto_port} Crypto Portfolio is ${my_crypto_value:0.2f}")

The current value of your 1.2 BTC is $142609.20
The current value of your 5.3 ETH is $16720.23
The current value of your 6.5 Crypto Portfolio is $159329.43

Collect Investments Data Using Alpaca: SPY (stocks) and AGG (bonds)¶

In [5]:

# Format current date as ISO format
today = pd.Timestamp("2024-02-5", tz="America/New_York").isoformat()

# Set the tickers
tickers = ["AGG", "SPY"]

# Set timeframe to "1Day" for Alpaca API
timeframe = "1Day"

# Get current closing prices for SPY and AGG
df_portfolio = api.get_bars(tickers, timeframe, start = today, end = today).df

# Display sample data
df_portfolio

Out[5]:

	close	high	low	trade_count	open	volume	vwap	symbol
timestamp
2024-02-05 05:00:00+00:00	97.65	97.9100	97.56	33471	97.890	7656888	97.681006	AGG
2024-02-05 05:00:00+00:00	492.55	494.3778	490.23	537117	493.695	75685102	492.614541	SPY

In [6]:

# Reorganize the DataFrame. Separate ticker data
AGG = df_portfolio[df_portfolio['symbol']=='AGG'].drop('symbol', axis=1)
SPY = df_portfolio[df_portfolio['symbol']=='SPY'].drop('symbol', axis=1)

# Concatenate the ticker DataFrames
df_portfolio = pd.concat([AGG, SPY],axis=1, keys=['AGG','SPY'])

# Drop the time component of the date
df_portfolio.index = df_portfolio.index.date

In [7]:

# Pick AGG and SPY close prices
agg_close_price = df_portfolio[('AGG', 'close')]
spy_close_price = df_portfolio[('SPY', 'close')]

# Print AGG and SPY close prices
print(f"Current AGG closing price: ${agg_close_price}")
print(f"Current SPY closing price: ${spy_close_price}")

Current AGG closing price: $2024-02-05    97.65
Name: (AGG, close), dtype: float64
Current SPY closing price: $2024-02-05    492.55
Name: (SPY, close), dtype: float64

In [8]:

# Set current amount of shares
my_spy = 50
my_agg = 200

# Compute the current value of shares
my_spy_value = my_spy * spy_close_price[0]
my_agg_value = my_agg * agg_close_price[0]

# Print current value of shares
print(f"The current value of your {my_spy} SPY shares is ${my_spy_value:0.2f}")
print(f"The current value of your {my_agg} AGG shares is ${my_agg_value:0.2f}")

The current value of your 50 SPY shares is $24627.50
The current value of your 200 AGG shares is $19530.00

Savings Health Analysis¶

In [9]:

# Consolidate financial assets data
savings_data = [my_crypto_value, my_spy_value + my_agg_value]

# Create savings DataFrame
savings_df = pd.DataFrame(savings_data, columns=['amount'], index = ["crypto", "shares"])

# Display savings DataFrame
display(savings_df)

	amount
crypto	159329.428
shares	44157.500

In [10]:

# Plot savings pie chart
savings_df.plot.pie(y="amount", title='Composition of Personal Savings')
plt.tight_layout
plt.show()

No description has been provided for this image

In [11]:

# Set monthly household income
monthly_income = 12000
print(f"Monthly Income equals {monthly_income}")

# Set ideal emergency fund
emergency_fund = monthly_income * 3
print(f"Emergency Fund equals {emergency_fund}")

# Calculate total amount of savings
total_portfolio_value = int(savings_df.sum()[0]) 
print(f"Total Portfolio Value equals {total_portfolio_value}")

Monthly Income equals 12000
Emergency Fund equals 36000
Total Portfolio Value equals 203486

In [12]:

# Validate saving health
if total_portfolio_value > emergency_fund:
    print("Congratulations you have enough money in this fund")
    
elif total_portfolio_value == emergency_fund:
    print("Congratulations you have reached an important financial goal of having just enough savings")
        
else:
    print(f"Sorry you are {emergency_fund} from reaching your goal")

Congratulations you have enough money in this fund

Part 2 - Retirement Planning¶

Monte Carlo Simulation¶

Get Past 3 Year's Worth of Price Data via Alpaca API Call for AGG & SPY¶

In [13]:

# Set timeframe to "1Day"
timeframe = "1Day"

# Set start and end datetimes between now and 3 years ago.
start_date = pd.Timestamp('2021-02-06', tz="America/New_York").isoformat()
end_date = pd.Timestamp('2024-02-06', tz="America/New_York").isoformat()

# Set the ticker information
tickers = ["AGG","SPY"]

# Get 3 year's worth of historical price data
df_ticker = api.get_bars(tickers,timeframe,start=start_date,end=end_date).df

# Display sample data
df_ticker.head()

Out[13]:

	close	high	low	trade_count	open	volume	vwap	symbol
timestamp
2021-02-08 05:00:00+00:00	116.83	116.9285	116.7200	17012	116.75	5022996	116.827144	AGG
2021-02-09 05:00:00+00:00	116.88	116.9700	116.8215	15321	116.94	4766282	116.909346	AGG
2021-02-10 05:00:00+00:00	116.99	117.0100	116.9200	13889	116.97	5139131	116.978509	AGG
2021-02-11 05:00:00+00:00	116.85	117.0300	116.8000	12628	117.03	3597042	116.930479	AGG
2021-02-12 05:00:00+00:00	116.58	116.7400	116.5418	14201	116.67	3422139	116.628811	AGG

In [14]:

# Reorganize the DataFrame. Separate ticker data
AGG = df_ticker[df_ticker["symbol"]=="AGG"].drop("symbol", axis=1)
SPY = df_ticker[df_ticker["symbol"]=="SPY"].drop("symbol", axis=1)

# Concatenate the ticker DataFrames
df_ticker = pd.concat([AGG,SPY], axis=1, keys=["AGG","SPY"])

# Display sample data
df_ticker.head()

Out[14]:

	AGG							SPY
	close	high	low	trade_count	open	volume	vwap	close	high	low	trade_count	open	volume	vwap
timestamp
2021-02-08 05:00:00+00:00	116.83	116.9285	116.7200	17012	116.75	5022996	116.827144	390.52	390.56	388.35	240716	389.27	39380252	389.448700
2021-02-09 05:00:00+00:00	116.88	116.9700	116.8215	15321	116.94	4766282	116.909346	390.31	390.89	389.17	235682	389.61	36248491	390.254656
2021-02-10 05:00:00+00:00	116.99	117.0100	116.9200	13889	116.97	5139131	116.978509	390.17	392.28	387.50	352180	392.12	59678427	389.851166
2021-02-11 05:00:00+00:00	116.85	117.0300	116.8000	12628	117.03	3597042	116.930479	390.71	391.69	388.10	272447	391.24	43822781	390.154207
2021-02-12 05:00:00+00:00	116.58	116.7400	116.5418	14201	116.67	3422139	116.628811	392.73	392.90	389.77	251164	389.85	51799016	391.585723

In [15]:

# Configure a Monte Carlo simulation to forecast five years cumulative returns
MC_even_dist = MCSimulation(portfolio_data = df_ticker, weights = [.40,.60], num_simulation = 500, num_trading_days = 252*30)

# Print the simulation input data
MC_even_dist.portfolio_data.head()

Out[15]:

	AGG								SPY
	close	high	low	trade_count	open	volume	vwap	daily_return	close	high	low	trade_count	open	volume	vwap	daily_return
timestamp
2021-02-08 05:00:00+00:00	116.83	116.9285	116.7200	17012	116.75	5022996	116.827144	NaN	390.52	390.56	388.35	240716	389.27	39380252	389.448700	NaN
2021-02-09 05:00:00+00:00	116.88	116.9700	116.8215	15321	116.94	4766282	116.909346	0.000428	390.31	390.89	389.17	235682	389.61	36248491	390.254656	-0.000538
2021-02-10 05:00:00+00:00	116.99	117.0100	116.9200	13889	116.97	5139131	116.978509	0.000941	390.17	392.28	387.50	352180	392.12	59678427	389.851166	-0.000359
2021-02-11 05:00:00+00:00	116.85	117.0300	116.8000	12628	117.03	3597042	116.930479	-0.001197	390.71	391.69	388.10	272447	391.24	43822781	390.154207	0.001384
2021-02-12 05:00:00+00:00	116.58	116.7400	116.5418	14201	116.67	3422139	116.628811	-0.002311	392.73	392.90	389.77	251164	389.85	51799016	391.585723	0.005170

In [16]:

# Run a Monte Carlo simulation to forecast thirty years cumulative returns
MC_even_dist.calc_cumulative_return()

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Out[16]:

	0	1	2	3	4	5	6	7	8	9	...	490	491	492	493	494	495	496	497	498	499
0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	1.003204	1.000119	1.012604	1.005102	0.989331	1.004718	0.996018	1.005640	0.987078	1.003725	...	1.001325	0.993700	0.987104	1.001868	1.001903	0.991314	0.998468	1.004519	1.007071	1.005935
2	0.985801	1.006227	1.003960	1.009120	0.997721	1.009395	0.991927	1.004882	0.981104	1.012074	...	0.993165	0.993061	0.991126	1.008984	0.993293	0.989430	0.995351	1.000966	1.005849	0.999203
3	0.978981	1.013963	1.007317	0.995534	1.004474	1.005296	0.984037	0.995395	0.973532	1.002707	...	0.988731	0.991871	0.993276	1.007796	0.991724	0.976800	1.001212	1.011073	0.998124	1.001077
4	0.993136	1.015046	0.999851	1.002406	1.006490	0.998183	0.995389	0.996047	0.977041	1.004628	...	0.991400	0.994769	0.997443	1.021671	0.989517	0.974263	1.009125	1.018308	1.006633	0.998498
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7556	1.867061	1.548299	1.193088	9.562023	2.152656	4.680691	2.610067	2.546608	1.430731	1.971441	...	1.805447	4.479677	1.780729	3.015321	1.654884	2.354878	5.031860	1.669546	1.224447	2.947676
7557	1.889999	1.554944	1.194366	9.454720	2.139823	4.697877	2.608140	2.523638	1.424121	1.965644	...	1.805798	4.487902	1.765170	3.001138	1.658373	2.372542	5.046396	1.658362	1.232009	2.941151
7558	1.879903	1.547495	1.188868	9.447247	2.130714	4.720892	2.599267	2.506908	1.413706	1.983415	...	1.824500	4.504066	1.752838	2.994184	1.648464	2.410131	5.071703	1.656778	1.217567	2.923600
7559	1.892328	1.560564	1.179985	9.517726	2.140060	4.710551	2.607579	2.495121	1.412069	1.975245	...	1.824206	4.550974	1.762618	2.994415	1.654502	2.442290	5.143401	1.663081	1.223265	2.921678
7560	1.906886	1.571335	1.178256	9.416646	2.121916	4.749879	2.591621	2.514609	1.425328	1.975258	...	1.837070	4.586123	1.752053	2.990244	1.655598	2.434351	5.128849	1.646921	1.237461	2.913373

7561 rows × 500 columns

In [17]:

# Plot simulation outcomes
line_plot = MC_even_dist.plot_simulation()
plt.tight_layout
plt.show()

In [18]:

# Plot probability distribution and confidence intervals
dist_plot = MC_even_dist.plot_distribution()

In [19]:

# Fetch summary statistics from the Monte Carlo simulation results
even_tbl = MC_even_dist.summarize_cumulative_return()

# Print summary statistics
print(even_tbl)

count           500.000000
mean              2.802675
std               1.762083
min               0.419950
25%               1.583829
50%               2.306270
75%               3.483734
max              13.293602
95% CI Lower      0.830669
95% CI Upper      7.386548
Name: 7560, dtype: float64

In [20]:

# Set Initial Investment
initial_investment = 20000

# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $15,000 investments in stocks
even_ci_lower = round(even_tbl[8]*initial_investment,2)
even_ci_upper = round(even_tbl[9]*initial_investment,2)

# Print results
print(f"There is a 95% chance that an initial investment of $20,000 in the portfolio"
      f" over the next 5 years will end within in the range of"
      f" ${even_ci_lower} and ${even_ci_upper}.")

There is a 95% chance that an initial investment of $20,000 in the portfolio over the next 5 years will end within in the range of $16613.38 and $147730.95.

Early Retirement¶

Five Years Retirement Option¶

In [21]:

# Configuring a Monte Carlo simulation to forecast 10 years cumulative returns
MC_even_dist = MCSimulation(portfolio_data = df_ticker, weights = [.20,.80], num_simulation = 500, num_trading_days = 252*10)

# Print the simulation input data
MC_even_dist.portfolio_data.head()

Out[21]:

	AGG								SPY
	close	high	low	trade_count	open	volume	vwap	daily_return	close	high	low	trade_count	open	volume	vwap	daily_return
timestamp
2021-02-08 05:00:00+00:00	116.83	116.9285	116.7200	17012	116.75	5022996	116.827144	NaN	390.52	390.56	388.35	240716	389.27	39380252	389.448700	NaN
2021-02-09 05:00:00+00:00	116.88	116.9700	116.8215	15321	116.94	4766282	116.909346	0.000428	390.31	390.89	389.17	235682	389.61	36248491	390.254656	-0.000538
2021-02-10 05:00:00+00:00	116.99	117.0100	116.9200	13889	116.97	5139131	116.978509	0.000941	390.17	392.28	387.50	352180	392.12	59678427	389.851166	-0.000359
2021-02-11 05:00:00+00:00	116.85	117.0300	116.8000	12628	117.03	3597042	116.930479	-0.001197	390.71	391.69	388.10	272447	391.24	43822781	390.154207	0.001384
2021-02-12 05:00:00+00:00	116.58	116.7400	116.5418	14201	116.67	3422139	116.628811	-0.002311	392.73	392.90	389.77	251164	389.85	51799016	391.585723	0.005170

In [22]:

# Running a Monte Carlo simulation to forecast 10 years cumulative returns
MC_even_dist.calc_cumulative_return()

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Out[22]:

	0	1	2	3	4	5	6	7	8	9	...	490	491	492	493	494	495	496	497	498	499
0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	0.996167	1.000851	1.000649	0.997784	0.995560	0.993029	0.989345	0.990485	0.996272	0.997725	...	1.007965	1.000846	1.003590	0.987262	0.996550	1.006967	1.007883	0.996019	1.003603	1.012476
2	0.997598	1.007988	0.995040	0.994521	1.003876	0.997889	0.979497	0.978171	1.006638	0.976856	...	1.015826	1.003888	1.001433	0.994949	0.997667	0.993882	1.003826	0.986372	1.001047	1.017333
3	1.003755	1.002872	0.989829	0.994760	0.992807	0.983775	0.972871	0.989205	1.006660	0.983436	...	1.013864	1.018187	1.015175	1.006813	1.008873	0.995359	0.997939	0.975482	1.017562	1.004009
4	1.012336	0.999020	0.989207	0.988877	0.998457	0.966823	0.969741	0.991850	0.998811	0.983347	...	1.015791	1.044457	1.020494	1.006324	1.012624	0.983829	0.991577	0.984492	1.011486	1.000753
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2516	1.002390	2.818109	2.853148	1.360125	1.022064	2.946971	1.863717	1.314694	2.172356	1.754403	...	1.873494	2.988420	2.899911	3.232998	1.486186	1.378355	1.070163	2.249612	0.990972	2.406265
2517	1.024335	2.832969	2.856496	1.372663	1.023998	2.964430	1.877884	1.341329	2.161234	1.736271	...	1.873974	3.020369	2.833336	3.206866	1.502259	1.366764	1.079219	2.236625	0.990749	2.439796
2518	1.026539	2.850640	2.852947	1.391632	1.031350	2.953021	1.871521	1.344611	2.162653	1.734532	...	1.874377	3.026167	2.861372	3.198498	1.496510	1.363438	1.074463	2.248704	0.993303	2.481243
2519	1.032611	2.865814	2.903328	1.384588	1.014867	2.944334	1.865918	1.345609	2.138341	1.734360	...	1.891539	2.993534	2.860177	3.213181	1.535001	1.356934	1.079276	2.253157	0.991859	2.453504
2520	1.036207	2.896447	2.936293	1.369887	1.014015	2.931878	1.864548	1.324220	2.104036	1.763696	...	1.888993	3.001408	2.852821	3.191897	1.522324	1.361770	1.076610	2.246741	1.009353	2.418645

2521 rows × 500 columns

In [23]:

# Plot simulation outcomes
line_plot = MC_even_dist.plot_simulation()
plt.tight_layout
plt.show()

In [24]:

# Plot probability distribution and confidence intervals
dist_plot = MC_even_dist.plot_distribution()
plt.tight_layout
plt.show()

In [25]:

# Fetch summary statistics from the Monte Carlo simulation results
even_tbl = MC_even_dist.summarize_cumulative_return()

# Print summary statistics
print(even_tbl)

count           500.000000
mean              1.830267
std               0.820703
min               0.187619
25%               1.232357
50%               1.667490
75%               2.246873
max               4.975646
95% CI Lower      0.664741
95% CI Upper      3.968226
Name: 2520, dtype: float64

In [26]:

# Set Initial Investment
initial_investment = 20000

# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $15,000 investments in stocks
even_ci_lower = round(even_tbl[8]*initial_investment,2)
even_ci_upper = round(even_tbl[9]*initial_investment,2)

# Print results
print(f"There is a 95% chance that an initial investment of $20,000 in the portfolio"
      f" over the next 5 years will end within in the range of"
      f" ${even_ci_lower} and ${even_ci_upper}.")

There is a 95% chance that an initial investment of $20,000 in the portfolio over the next 5 years will end within in the range of $13294.83 and $79364.53.

In [27]:

# Yes, it is much more likely that credit union members can retire comfortably due to
# a much higher upper bound ($80K) and not that much less lower bound ($14K)

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