[heavy_data] Retrieve data from yahoo finance

.github/workflows/ci.yml

@@ -38,7 +38,7 @@ jobs:
         shell: bash -l {0}
         run: pip list
       - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v8
+        uses: dawidd6/action-download-artifact@v9
         with:
           workflow: cache.yml
           branch: main

.github/workflows/collab.yml

@@ -20,7 +20,7 @@ jobs:
         shell: bash -l {0}
         run: pip list
       - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v8
+        uses: dawidd6/action-download-artifact@v9
         with:
           workflow: cache.yml
           branch: main

.github/workflows/linkcheck.yml

@@ -27,7 +27,7 @@ jobs:
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v8
+        uses: dawidd6/action-download-artifact@v9
         with:
           workflow: cache.yml
           branch: main

.github/workflows/publish.yml

@@ -39,7 +39,7 @@ jobs:
         shell: bash -l {0}
         run: pip list
       - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v8
+        uses: dawidd6/action-download-artifact@v9
         with:
           workflow: cache.yml
           branch: main

RetrieveData.ipynb

@@ -0,0 +1,60 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5a8b8795-232a-4839-aced-5dbdc7965fc4",
+   "metadata": {},
+   "source": [
+    "The Following retrieves data needed for [heavy_tails]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2822e5f8-d817-4e7b-8568-080571fcdb08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*********************100%***********************]  1 of 1 completed\n",
+      "[*********************100%***********************]  1 of 1 completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Might need to run `pip install --upgrade yfinance` first\n",
+    "import yfinance as yf\n",
+    "\n",
+    "# Download the data for AMZN\n",
+    "Amazon_data = yf.download('AMZN', start='2015-01-01', end='2022-07-01')\n",
+    "Bitcoin_data = yf.download('BTC-USD', '2015-1-1', '2022-7-1')\n",
+    "\n",
+    "Amazon_data.to_csv('lectures/datasets/Amazon_data.csv')\n",
+    "Bitcoin_data.to_csv('lectures/datasets/Bitcoin_data.csv')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

lectures/heavy_tails.md

@@ -14,20 +14,11 @@ kernelspec:
 (heavy_tail)=
 # Heavy-Tailed Distributions
 
-In addition to what's in Anaconda, this lecture will need the following libraries:
-
-```{code-cell} ipython3
-:tags: [hide-output]
-
-!pip install --upgrade yfinance pandas_datareader
-```
-
-We use the following imports.
+This lecture will use the following imports
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
 import numpy as np
-import yfinance as yf
 import pandas as pd
 import statsmodels.api as sm
 
@@ -182,12 +173,13 @@ the period from 1st January 2015 to 1st July 2022.
 
 This equates to daily returns if we set dividends aside.
 
-The code below produces the desired plot using Yahoo financial data via the `yfinance` library.
-
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-data = yf.download('AMZN', '2015-1-1', '2022-7-1')
+Amazon_data = pd.read_csv("datasets/Amazon_data.csv")
+# Convert columns to numeric
+for col in ['Price', 'Close', 'High', 'Low', 'Open', 'Volume']:
+    Amazon_data[col] = pd.to_numeric(Amazon_data[col], errors='coerce')
 ```
 
 ```{code-cell} ipython3
@@ -197,7 +189,7 @@ mystnb:
     caption: Daily Amazon returns
     name: dailyreturns-amzn
 ---
-s = data['Close']
+s = Amazon_data['Close']
 r = s.pct_change()
 
 fig, ax = plt.subplots()
@@ -219,7 +211,10 @@ We get a similar picture if we look at other assets, such as Bitcoin
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-data = yf.download('BTC-USD', '2015-1-1', '2022-7-1')
+Bitcoin_data = pd.read_csv("datasets/Bitcoin_data.csv")
+# Convert columns to numeric
+for col in ['Price', 'Close', 'High', 'Low', 'Open', 'Volume']:
+    Bitcoin_data[col] = pd.to_numeric(Amazon_data[col], errors='coerce')
 ```
 
 ```{code-cell} ipython3
@@ -229,8 +224,8 @@ mystnb:
     caption: Daily Bitcoin returns
     name: dailyreturns-btc
 ---
-s = data['Close']
-r = s.pct_change()
+s = Bitcoin_data['Close']
+r = s.pct_change(fill_method=None)
 
 fig, ax = plt.subplots()