<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
import json

%matplotlib inline
# Load .env enviroment variables
load_dotenv()
True<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
my_btc = 1.2
my_eth = 5.3
# 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"
# Fetch current BTC price
btc_price = requests.get(btc_url)
btc_price = btc_price.json()["data"]["1"]["quotes"]["USD"]["price"]

# Fetch current ETH price
eth_price = requests.get(eth_url)
eth_price = eth_price.json()["data"]["1027"]["quotes"]["CAD"]["price"]

# Compute current value of my crpto
my_btc_value =btc_price * my_btc
my_eth_value = eth_price * my_eth

# 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}")<jupyter_output>The current value of your 1.2 BTC is $49876.80
The current value of your 5.3 ETH is $20464.07<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
api = tradeapi.REST(
    alpaca_api_key, 
    alpaca_secret_key, 
    api_version="v2"
)
# Format current date as ISO format
current_date = pd.Timestamp("2020-05-01", 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)
current_date = api.get_barset(
    tickers,
    timeframe,
    start = current_date,
    limit = 1000,
).df


# Preview DataFrame
current_date.head()
# Pick AGG and SPY close prices
agg_close_price = current_date["AGG"]["close"][0]
spy_close_price = current_date["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
agg_share_value = agg_close_price * my_agg
spy_share_value = spy_close_price * my_spy

# Print current value of shares
print(f"The current value of your {my_spy} SPY shares is ${spy_share_value:0.2f}")
print(f"The current value of your {my_agg} AGG shares is ${agg_share_value:0.2f}")<jupyter_output>The current value of your 50 SPY shares is $14136.50
The current value of your 200 AGG shares is $23380.00<jupyter_text>Savings Health Analysis<jupyter_code># Set monthly household income
monthly_income = 12000

# Consolidate financial assets data
crypto_price =  btc_price + eth_price
stock_price = agg_share_value + spy_share_value
data = [crypto_price, stock_price]

# Create savings DataFrame
savings_df = pd.DataFrame(data, columns= ["Amount"], index= ["Shares", "Crypto"])
savings_df

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

# Calculate total amount of savings
total_savings = savings_df.sum()
total_savings = total_savings["Amount"]
savings_goal = total_savings - emergency_fund

# Validate saving health
if total_savings > emergency_fund:
    print("Congratulations! You have enough money in your emergency fund")
elif total_savings == emergency_fund:
    print("Congratulations on reaching your financial goal!")
else: 
    print(f"You are ${total_savings-emergency_fund} from reaching your goal")<jupyter_output>Congratulations! You have enough money in your emergency fund<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 = api.get_barset(
    tickers,
    timeframe,
    start = start_date,
    end = end_date,
    limit = 1000
).df.dropna()

# Display sample data
df_stock_data.head()
# Configuring a Monte Carlo simulation to forecast 30 years cumulative returns
MC_forecast = MCSimulation(
    portfolio_data = df_stock_data,
    num_simulation = 500,
    num_trading_days = 252*30
)
# Printing the simulation input data
MC_forecast.portfolio_data.head()
# Running a Monte Carlo simulation to forecast 30 years cumulative returns
MC_forecast.calc_cumulative_return()
# Plot simulation outcomes
plot = MC_forecast.plot_simulation()
# Plot probability distribution and confidence intervals
plot = MC_forecast.plot_distribution()<jupyter_output><empty_output><jupyter_text>Retirement Analysis<jupyter_code># Fetch summary statistics from the Monte Carlo simulation results
summary_stats = MC_forecast.summarize_cumulative_return()

# Print summary statistics
print(summary_stats)<jupyter_output>count           500.000000
mean             14.123997
std               8.856775
min               1.527974
25%               8.013639
50%              12.075416
75%              17.800647
max              74.039828
95% CI Lower      3.487164
95% CI Upper     36.153561
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_stats[8]*initial_investment,2)
ci_upper = round(summary_stats[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 $69743.29 and $723071.23<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_stats[8]*initial_investment*1.5,2)
ci_upper = round(summary_stats[9]*initial_investment*1.5,2)

# 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 $156922.39 and $1626910.26<jupyter_text>Optional Challenge - Early Retirement Five Years Retirement Option<jupyter_code># Configuring a Monte Carlo simulation to forecast 5 years cumulative returns
MC_forecast5 = MCSimulation(
    portfolio_data = df_stock_data,
    num_simulation = 500,
    num_trading_days = 252*5
)
# Running a Monte Carlo simulation to forecast 5 years cumulative returns
MC_forecast5.calc_cumulative_return()
# Plot simulation outcomes
plot5 = MC_forecast5.plot_simulation()
# Plot probability distribution and confidence intervals
plot5 = MC_forecast5.plot_distribution()
# Fetch summary statistics from the Monte Carlo simulation results
summary_stats5 = MC_forecast5.summarize_cumulative_return()

# Print summary statistics
print(summary_stats5)
# Set initial investment
initial_investment = 10000

# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $60,000
ci_lower_five = round(summary_stats5[8]*initial_investment,2)
ci_upper_five = round(summary_stats5[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 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 $10000 in the portfolio over the next 5 years will end within in the range of $9258.87 and $25307.87<jupyter_text>Ten Years Retirement Option<jupyter_code># Configuring a Monte Carlo simulation to forecast 10 years cumulative returns
MC_forecast10 = MCSimulation(
    portfolio_data = df_stock_data,
    num_simulation = 500,
    num_trading_days = 252*10
)
# Running a Monte Carlo simulation to forecast 10 years cumulative returns
MC_forecast10.calc_cumulative_return()
# Plot simulation outcomes
plot10 = MC_forecast10.plot_simulation()
# Plot probability distribution and confidence intervals
plot10 = MC_forecast10.plot_distribution()
# Fetch summary statistics from the Monte Carlo simulation results
summary_stats10 = MC_forecast10.summarize_cumulative_return()

# Print summary statistics
print(summary_stats10)
# Set initial investment
initial_investment = 30000

# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $60,000
ci_lower_ten = round(summary_stats10[8]*initial_investment,2)
ci_upper_ten = round(summary_stats10[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 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 $30000 in the portfolio over the next 10 years will end within in the range of $35759.84 and $134688.43
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