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Add quarto-stock-report-python content (#59)
* Add quarto-stock-report-python content * Address PR review * Update version for release
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‎.github/workflows/extensions.yml‎

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filters: |
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reaper: extensions/reaper/**
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integration-session-manager: extensions/integration-session-manager/**
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quarto-stock-report-python: extensions/quarto-stock-report-python/**
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portfolio-dashboard: extensions/portfolio-dashboard/**
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# Runs for each extension that has changed from `simple-extension-changes`

‎extensions/quarto-stock-report-python/.gitignore‎

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/.quarto/
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*.csv
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index.html
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index_files
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index.ipynb
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/env/
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.output_metadata.json
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/email-preview/
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/index.quarto_ipynb

‎extensions/quarto-stock-report-python/README.md‎

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# Quarto Stock Report using Python
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## About this example
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This stock report is generated using Quarto and Python. It is an example
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of how you might automate regular updates using a data source. The report
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also generates a custom email, sharing the results directly to
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stakeholders.
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## Learn more
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* [Quarto](https://quarto.org)
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## Requirements
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* Quarto version 1.4 or higher
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* Python version 3.9 or higher

‎extensions/quarto-stock-report-python/_quarto.yml‎

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project:
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title: Stock Report

‎extensions/quarto-stock-report-python/connect-extension.qmd‎

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---
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title: Quarto Stock Report using Python
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categories:
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- python
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- quarto
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---
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This stock report is generated using Quarto and Python. It is an example of how you might automate regular updates using a data source. The report also generates a custom email, sharing the results directly to stakeholders.

‎extensions/quarto-stock-report-python/index.qmd‎

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---
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title: Stock Report
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date: today
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date-format: long
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published-title: produced
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jupyter: python3
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format: email
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attachments:
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- data.csv
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email-preview: true
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---
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## Report for TSLA
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```{python}
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#| echo: true
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import pandas as pd
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import yfinance as yf
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import datetime
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tsla = yf.Ticker("TSLA")
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hist = tsla.history("5y")
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hist['Change'] = hist['Close'] - hist['Open']
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latest = hist.last_valid_index()
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days_90 = latest - pd.to_timedelta("90d")
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prices_1d = hist[latest:]
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prices_90d = hist[days_90:]
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prices_rolling = hist.asfreq('D').rolling(window=52*7, min_periods=1)
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data=[
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[
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prices_1d['High'].values[0],
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prices_1d['Low'].values[0],
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prices_1d['Volume'].values[0].round(),
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],
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[
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prices_rolling.max()[latest:]['High'].values[0],
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prices_rolling.min()[latest:]['Low'].values[0],
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prices_rolling.mean()[latest:]['Volume'].values[0].round(),
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]
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]
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df = pd.DataFrame(
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data,
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columns=['High', 'Low', 'Avg Volume'],
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index=['Most Recent Trading Day', '52-Week'])
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df
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```
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### Price History
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```{python}
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#| echo: false
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#| label: fig-history
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#| fig-cap: "TSLA price history"
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_ = hist['Close'].plot(grid=True)
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```
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## Raw Data
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```{python}
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#| echo: false
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hist
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```
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## Legacy Information
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This report also produces a CSV file containing recent price data, which may
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be used in other analysis.
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```{python}
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#| echo: false
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#| include: false
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prices_90d.to_csv("data.csv")
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```
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[Link to CSV](data.csv)
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## Email
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This report also produces an email that is sent to key stakeholders with summary
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information.
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::: {.email}
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::: {.subject}
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```{python}
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#| echo: false
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#| output: asis
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closes = prices_90d['Close']
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change = closes[-2] - closes[-1]
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abschange = abs(change)
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updown = "up"
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if change < 0:
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updown = "down"
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print(f"TSLA is {updown} today by ${abschange:.2f}")
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```
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:::
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Hello Team,
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```{python}
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#| echo: false
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#| output: asis
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print(f"Here are the latest stock prices for **TSLA** as of {datetime.date.today()}:")
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```
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```{python}
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#| echo: false
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prices_90d.head(5)[['Open', 'Close', 'Change', 'Volume']]
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```
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The historical trend is shown below:
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```{python}
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#| echo: false
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_ = prices_90d['Close'].plot(grid=True)
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```
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Let me know if you have any questions.
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Best,
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Team Lead
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:::

‎extensions/quarto-stock-report-python/manifest.json‎

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{
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"version": 1,
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"locale": "en_US.UTF-8",
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"metadata": {
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"appmode": "quarto-static"
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},
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"extension": {
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"name": "quarto-stock-report-python",
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"title": "Quarto Stock Report using Python",
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"description": "An example of how you might automate regular updates using a data source",
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"homepage": "https://github.com/posit-dev/connect-extensions/tree/main/extensions/quarto-stock-report-python",
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"version": "1.0.0"
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},
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"environment": {
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"python": {
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"requires": ">=3.9, <4"
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}
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},
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"quarto": {
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"version": "1.6.42",
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"engines": [
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"jupyter"
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]
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},
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"python": {
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"version": "3.11.7",
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"package_manager": {
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"name": "pip",
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"version": "24.2",
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"package_file": "requirements.txt"
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}
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},
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"files": {
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"requirements.txt": {
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"checksum": "1b9e705f62a822cffaa4dc0e988101b4"
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},
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".gitignore": {
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"checksum": "ae206a13f5875901e752811238e8fdbd"
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},
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"README.md": {
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"checksum": "837f74720c4532629d02fd4bff3df83b"
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},
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"_quarto.yml": {
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"checksum": "35b4c4c58cce875b126683c525ad40f4"
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},
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"index.qmd": {
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"checksum": "6fb0db80d26eab1544afdcbc91df228b"
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},
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"index.quarto_ipynb": {
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"checksum": "04ad8e09510236db1b397038a4c06186"
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},
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"thumbnail.jpg": {
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"checksum": "8db36358d1e9dbab78f8ffef39846eee"
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}
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}
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}

‎extensions/quarto-stock-report-python/requirements.txt‎

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# jupyter
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notebook==7.2.2
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qtconsole==5.5.1
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jupyter-console==6.6.3
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nbconvert==7.16.1
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ipykernel==6.29.3
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ipywidgets==8.1.2
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matplotlib==3.8.3
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pandas==2.2.1
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yfinance==0.2.54

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