<jupyter_start><jupyter_text>Unit 5 - Financial Planning<jupyter_code># Initial imports
import os
import requests
import pandas as pd
from dotenv import load_dotenv
import alpaca_trade_api as tradeapi
from MCForecastTools import MCSimulation

%matplotlib inline
# Load .env enviroment variables
load_dotenv()<jupyter_output><empty_output><jupyter_text>Part 1 - Personal Finance Planner  Collect Crypto Prices Using the `requests` Library<jupyter_code># Set current amount of crypto assets

btc_quant = 2
eth_quant = 32
# Crypto API URLs
btc_url = "https://api.alternative.me/v2/ticker/Bitcoin/?convert=CAD"
eth_url = "https://api.alternative.me/v2/ticker/Ethereum/?convert=CAD"
btc_url = btc_url + "?format=json"
response_data_btc = requests.get(btc_url).json()

eth_url = eth_url + "?format=json"
response_data_eth = requests.get(eth_url).json()

# Fetch current BTC price
btc_price = response_data_btc['data']['1']['quotes']['USD']['price']

# Fetch current ETH price
eth_price = response_data_eth['data']['1027']['quotes']['USD']['price']

# Compute current value of my crpto
my_btc_value = btc_quant * btc_price
my_eth_value = eth_quant * eth_price

# Print current crypto wallet balance
print(f"The current value of your {btc_quant} BTC is ${my_btc_value:0.2f}")
print(f"The current value of your {eth_quant} ETH is ${my_eth_value:0.2f}")<jupyter_output>The current value of your 2 BTC is $83442.00
The current value of your 32 ETH is $99497.92<jupyter_text>Collect Investments Data Using Alpaca: `SPY` (stocks) and `AGG` (bonds)<jupyter_code># Set current amount of shares
my_agg = 200
my_spy = 50
# Set Alpaca API key and secret
alpaca_api_key = os.getenv("ALPACA_API_KEY")
alpaca_secret_key = os.getenv("ALPACA_SECRET_KEY")

# Create the Alpaca API object
alpaca = tradeapi.REST(
    alpaca_api_key,
    alpaca_secret_key,
    api_version="v2")
# Format current date as ISO format
start = pd.Timestamp("2016-02-01", tz="America/New_York").isoformat()
end = pd.Timestamp("2022-01-18", tz="America/New_York").isoformat()

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

# Set timeframe to '1D' for Alpaca API
timeframe = "1D"

# Get current closing prices for SPY and AGG
# (use a limit=1000 parameter to call the most recent 1000 days of data)
stock_data = alpaca.get_barset(
    tickers,
    timeframe,
    start = start,
    end = end,
    limit = 1000
).df

# Preview DataFrame
stock_data.sort_index(ascending = False, inplace = True)
stock_data
# Pick AGG and SPY close prices
agg_close_price = float(stock_data["AGG"]["close"][0]) 
spy_close_price = float(stock_data["SPY"]["close"][0])

# Print AGG and SPY close prices
print(f"Current AGG closing price: ${agg_close_price}")
print(f"Current SPY closing price: ${spy_close_price}")
# Compute the current value of shares
my_agg_value = agg_close_price * my_agg
my_spy_value = spy_close_price * my_spy

# 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}")<jupyter_output>The current value of your 50 SPY shares is $22826.50
The current value of your 200 AGG shares is $22298.00<jupyter_text>Savings Health Analysis<jupyter_code># Set monthly household income
monthly_income = 200000

# Consolidate financial assets data
crypto = my_eth_value + my_btc_value
shares = my_agg_value + my_spy_value

# Create savings DataFrame
savings = {
    "amount":[crypto,shares]
}

index = ["crypto", "shares"]

port = pd.DataFrame(savings, index=index)



# Display savings DataFrame
display(port)
# Plot savings pie chart
port.plot.pie(y="amount", title="Composition of Personal Savings")
# Set ideal emergency fund
emergency_fund = monthly_income * 3

# Calculate total amount of savings
total_savings = float(port.sum())

# Validate saving health
goal_value = emergency_fund - total_savings

if total_savings > emergency_fund:
    print("Congratulations! You have enough money in your emergency fund.")

elif total_savings == emergency_fund:
    print("Congratulations! You have reached your emergency fund goal.")

else:
    print(f"You are {goal_value} dollars away from reaching your emergency fund goal.")<jupyter_output>You are 368657.98 dollars away from reaching your emergency fund goal.<jupyter_text>Part 2 - Retirement Planning Monte Carlo Simulation<jupyter_code># Set start and end dates of five years back from today.
# Sample results may vary from the solution based on the time frame chosen
start_date = pd.Timestamp('2016-05-01', tz='America/New_York').isoformat()
end_date = pd.Timestamp('2021-05-01', tz='America/New_York').isoformat()
# Get 5 years' worth of historical data for SPY and AGG
# (use a limit=1000 parameter to call the most recent 1000 days of data)
df_stock_data = alpaca.get_barset(
    tickers,
    timeframe,
    start = start,
    end = end,
    limit = 1000
).df

# Display sample data
df_stock_data.head()
# Configuring a Monte Carlo simulation to forecast 30 years cumulative returns
simulations = 1000

monte_carlo = MCSimulation(
    portfolio_data = df_stock_data,
    weights = [.40,.60],
    num_simulation = simulations,
    num_trading_days = 252*30
)
# Printing the simulation input data
monte_carlo.portfolio_data.head()
# Running a Monte Carlo simulation to forecast 30 years cumulative returns
monte_carlo.calc_cumulative_return()
# Plot simulation outcomes
cumulative_return_chart = monte_carlo.plot_simulation()
# Plot probability distribution and confidence intervals
dist_plot = monte_carlo.plot_distribution()<jupyter_output><empty_output><jupyter_text>Retirement Analysis<jupyter_code># Fetch summary statistics from the Monte Carlo simulation results
summary = monte_carlo.summarize_cumulative_return()

# Print summary statistics
print(summary)<jupyter_output>count           1000.000000
mean              14.881277
std               11.645738
min                0.567160
25%                7.206544
50%               11.546620
75%               18.031129
max               95.849404
95% CI Lower       3.092556
95% CI Upper      44.102148
Name: 7560, dtype: float64<jupyter_text>Calculate the expected portfolio return at the `95%` lower and upper confidence intervals based on a `$20,000` initial investment.<jupyter_code># Set initial investment
initial_investment = 20000

# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $20,000
ci_lower = round(summary[8]*initial_investment,2)
ci_upper = round(summary[9]*initial_investment,2)

# Print results
print(f"There is a 95% chance that an initial investment of ${initial_investment} in the portfolio"
      f" over the next 30 years will end within in the range of"
      f" ${ci_lower} and ${ci_upper}")<jupyter_output>There is a 95% chance that an initial investment of $20000 in the portfolio over the next 30 years will end within in the range of $61851.13 and $882042.97<jupyter_text>Calculate the expected portfolio return at the `95%` lower and upper confidence intervals based on a `50%` increase in the initial investment.<jupyter_code># Set initial investment
initial_investment = 20000 * 1.5

# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $30,000
ci_lower = round(summary[8]*initial_investment,2)
ci_upper = round(summary[9]*initial_investment,2)

# Print results
print(f"There is a 95% chance that an initial investment of ${initial_investment} in the portfolio"
      f" over the next 30 years will end within in the range of"
      f" ${ci_lower} and ${ci_upper}")<jupyter_output>There is a 95% chance that an initial investment of $30000.0 in the portfolio over the next 30 years will end within in the range of $92776.69 and $1323064.45<jupyter_text>Optional Challenge - Early Retirement Five Years Retirement Option<jupyter_code># Configuring a Monte Carlo simulation to forecast 5 years cumulative returns
# YOUR CODE HERE!
# Running a Monte Carlo simulation to forecast 5 years cumulative returns
# YOUR CODE HERE!
# Plot simulation outcomes
# YOUR CODE HERE!
# Plot probability distribution and confidence intervals
# YOUR CODE HERE!
# Fetch summary statistics from the Monte Carlo simulation results
# YOUR CODE HERE!

# Print summary statistics
# YOUR CODE HERE!
# Set initial investment
# YOUR CODE HERE!

# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $60,000
# YOUR CODE HERE!

# Print results
print(f"There is a 95% chance that an initial investment of ${initial_investment} in the portfolio"
      f" over the next 5 years will end within in the range of"
      f" ${ci_lower_five} and ${ci_upper_five}")<jupyter_output>There is a 95% chance that an initial investment of $60000 in the portfolio over the next 5 years will end within in the range of $57091.37 and $95195.55<jupyter_text>Ten Years Retirement Option<jupyter_code># Configuring a Monte Carlo simulation to forecast 10 years cumulative returns
# YOUR CODE HERE!
# Running a Monte Carlo simulation to forecast 10 years cumulative returns
# YOUR CODE HERE!
# Plot simulation outcomes
# YOUR CODE HERE!
# Plot probability distribution and confidence intervals
# YOUR CODE HERE!
# Fetch summary statistics from the Monte Carlo simulation results
# YOUR CODE HERE!

# Print summary statistics
# YOUR CODE HERE!
# Set initial investment
# YOUR CODE HERE!

# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $60,000
# YOUR CODE HERE!

# Print results
print(f"There is a 95% chance that an initial investment of ${initial_investment} in the portfolio"
      f" over the next 10 years will end within in the range of"
      f" ${ci_lower_ten} and ${ci_upper_ten}")<jupyter_output>There is a 95% chance that an initial investment of $60000 in the portfolio over the next 10 years will end within in the range of $62986.85 and $129596.12
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