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gallery-examples/notebooks/dashboard/portfolio.py at main · marimo-team/gallery-examples · GitHub

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# /// script

# requires-python = ">=3.12"

# dependencies = [

# "altair==6.0.0",

# "diskcache==5.6.3",

# "marimo",

# "pandas==3.0.0",

# "polars==1.37.1",

# "yfinance==1.1.0",

# ]

# ///

import marimo

__generated_with = "0.19.11"

app = marimo.App(

width="medium",

css_file="/usr/local/_marimo/custom.css",

auto_download=["html"],

)

with app.setup:

import marimo as mo

import altair as alt

import pandas as pd

import polars as pl

import yfinance as yf

from pathlib import Path

@app.cell(hide_code=True)

def _():

mo.md(r"""

# Portfolio tracker

Fill in your stock portfolio below and we will figure out how the value of your investment changes over time. We go back 72 months and use the `yfinance` SDK for the stock values.

""")

return

@app.cell

def _():

investments = mo.ui.data_editor(

pd.DataFrame(

[

{"Date": "2021-02-01", "Ticker": "msft", "Investment": 500},

{"Date": "2023-02-01", "Ticker": "aapl", "Investment": 800},

{"Date": "2024-02-01", "Ticker": "aapl", "Investment": 200},

]

)

investments

return (investments,)

@app.cell

def _(investments):

type(investments.value)

return

@app.cell

def _(parent_folder):

df = pl.read_csv(f"{parent_folder}/*", glob=True)

return

@app.function(hide_code=True)

def clean_data(dataf, investment_records):

# Convert investment records to polars DataFrame with uppercase ticker

investments_df = pl.DataFrame(investment_records)

return (

dataf.drop(["Dividends", "Stock Splits", "Volume"])

.join(investments_df, on=["Date", "Ticker"], how="left")

.with_columns(pl.col("Investment").fill_null(0))

.sort("Ticker", "Date")

)

@app.function(hide_code=True)

def calculate_portfolio_value(dataf):

# For each ticker, we need to track shares purchased at each investment

return (

dataf.with_columns(

# Calculate cumulative investment per ticker

CumInvestment=pl.col("Investment").cum_sum().over("Ticker")

)

# Filter to start from first investment

.filter(pl.col("CumInvestment") > 0)

.with_columns(

# When investment happens, calculate shares bought at that price

SharesBought=pl.when(pl.col("Investment") > 0)

.then(pl.col("Investment") / pl.col("Close"))

.otherwise(0)

)

.with_columns(

# Calculate total shares owned (cumulative)

TotalShares=pl.col("SharesBought").cum_sum().over("Ticker")

)

.with_columns(

# Current portfolio value = total shares × current price

PortfolioValue=pl.col("TotalShares") * pl.col("Close"),

# Profit/Loss = portfolio value – cumulative investment

PnL=(pl.col("TotalShares") * pl.col("Close")) – pl.col("CumInvestment"),

)

@app.function(hide_code=True)

def calculate_performance(dataf):

return (

dataf.group_by("Date")

.agg(

[

pl.sum("CumInvestment").alias("TotalInvested"),

pl.sum("PortfolioValue").alias("TotalValue"),

pl.sum("PnL").alias("TotalPnL"),

]

)

.with_columns(

Date=pl.col("Date").str.to_date(),

ReturnPct=((pl.col("TotalValue") / pl.col("TotalInvested")) – 1) * 100,

)

.sort("Date")

)

@app.function(hide_code=True)

def make_chart(dataf):

portfolio_chart = (

alt.Chart(dataf)

.mark_area(line=True, opacity=0.3)

.encode(

x=alt.X("Date:T", title="Date"),

y=alt.Y("TotalValue:Q", title="Value ($)"),

)

# Add invested amount line

invested_line = (

alt.Chart(dataf)

.mark_line(strokeDash=[5, 5], color="black", strokeWidth=2)

.encode(x="Date:T", y="TotalInvested:Q")

)

# Combine charts

return portfolio_chart + invested_line

@app.cell(hide_code=True)

def _(parent_folder, records):

cached = (

pl.read_csv(f"{parent_folder}/*", glob=True)

.pipe(clean_data, investment_records=records)

.pipe(calculate_portfolio_value)

.pipe(calculate_performance)

)

return (cached,)

@app.cell(hide_code=True)

def _():

mo.md(r"""

## Investment value over time

""")

return

@app.cell

def _(cached):

cached.pipe(make_chart)

return

@app.cell(hide_code=True)

def _():

mo.md(r"""

## Returns over time

""")

return

@app.cell

def _(cached):

cached.plot.line("Date", "ReturnPct")

return

@app.cell

def _(investments):

records = investments.value.assign(Ticker=lambda d: d["Ticker"].str.upper()).to_dict(

orient="records"

)

# records

return (records,)

@app.cell

def _(records):

parent_folder = Path("invest-data")

parent_folder.mkdir(exist_ok=True)

def download_tickers(tickers):

for record in tickers:

ticker = record.upper()

if not (parent_folder / f"{ticker}.csv").exists():

(

yf.Ticker("MSFT")

.history(period="72mo")

.reset_index()

.assign(Date=lambda d: d["Date"].dt.strftime("%Y-%m-%d"), Ticker=ticker)

.to_csv(f"{parent_folder}/{ticker}.csv")

)

out = download_tickers(set(_["Ticker"] for _ in records))

return (parent_folder,)

@app.cell

def _():

mo.md(r"""

## Data for download

""")

return

@app.cell

def _(cached):

cached

return

if __name__ == "__main__":

app.run()

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