daily update
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David Brazda
272
to_explore/pyquantnews/6_RollingZScore.ipynb
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272
to_explore/pyquantnews/6_RollingZScore.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "9bcf2ee3",
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"metadata": {},
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"source": [
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"<div style=\"background-color:#000;\"><img src=\"pqn.png\"></img></div>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a5b4c5df",
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"metadata": {},
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"source": [
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"This code retrieves historical stock price data for Netflix from Yahoo Finance and performs statistical analysis. It calculates the rolling z-score of the closing prices over a 30-day window, allowing for the detection of significant deviations from the mean. The z-score is then plotted and its distribution visualized using a histogram. Additionally, it computes the minimum percentage change in closing prices over a 30-day rolling window and visualizes it. This is useful for identifying extreme price movements and understanding the stock's volatility."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "50d5e330",
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"metadata": {},
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"outputs": [],
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"source": [
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"import yfinance as yf"
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]
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},
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{
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"cell_type": "markdown",
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"id": "393360c8",
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"metadata": {},
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"source": [
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"Download historical stock price data for Netflix from Yahoo Finance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b89ccf32",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"data = yf.download(\"NFLX\", start=\"2020-01-01\", end=\"2022-06-30\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "87c13794",
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"metadata": {},
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"source": [
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"Define a function to calculate the z-score for a given chunk of data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2b6152e4",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"def z_score(chunk):\n",
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" \"\"\"Calculate z-score for a given chunk.\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" chunk : pd.Series\n",
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" A series of stock prices or values.\n",
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" \n",
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" Returns\n",
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" -------\n",
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" float\n",
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" The z-score of the last value in the chunk.\n",
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" \n",
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" Notes\n",
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" -----\n",
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" This method computes the z-score, which is the number \n",
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" of standard deviations a value is from the mean.\n",
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" \"\"\"\n",
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" return (chunk[-1] - chunk.mean()) / chunk.std()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d92d5b53",
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"metadata": {},
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"source": [
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"Calculate the rolling z-score of the closing prices over a 30-day window"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9c3b8292",
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"metadata": {},
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"outputs": [],
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"source": [
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"rolled = data.Close.rolling(window=30).apply(z_score)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f0c0ce32",
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"metadata": {},
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"source": [
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"Plot the rolling z-score to visualize deviations from the mean"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5c0f3295",
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"metadata": {},
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"outputs": [],
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"source": [
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"rolled.plot()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5fc5a361",
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"metadata": {},
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"source": [
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"Plot a histogram of the rolling z-score to understand its distribution"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "979cf4f9",
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"metadata": {},
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"outputs": [],
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"source": [
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"rolled.hist(bins=20)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1acedb50",
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"metadata": {},
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"source": [
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"Find the minimum z-score value to identify significant deviations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "127b0f43",
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"metadata": {},
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"outputs": [],
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"source": [
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"rolled.min()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f13fc8f4",
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"metadata": {},
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"source": [
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"Calculate the percentage change from the closing price on 20 April 2022"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "502e5d91",
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"metadata": {},
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"outputs": [],
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"source": [
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"(226.19 - 348.61) / 348.61"
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]
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},
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{
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"cell_type": "markdown",
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"id": "da9e48db",
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"metadata": {},
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"source": [
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"Calculate the minimum percentage change in closing prices over a 30-day rolling window"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5c98be53",
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"metadata": {},
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"outputs": [],
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"source": [
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"min_pct_change = (\n",
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" data\n",
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" .Close\n",
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" .pct_change()\n",
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" .rolling(window=30)\n",
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" .min()\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "47be23a9",
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"metadata": {},
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"source": [
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"Plot the minimum percentage change to visualize extreme price movements"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e1fabbf2",
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"metadata": {},
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"outputs": [],
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"source": [
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"min_pct_change.plot()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3c0c5951",
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"metadata": {},
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"source": [
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"Plot a histogram of the minimum percentage change to understand its distribution"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a1697d6b",
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"metadata": {},
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"outputs": [],
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"source": [
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"min_pct_change.hist(bins=20)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d97d6472",
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"metadata": {},
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"source": [
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"<a href=\"https://pyquantnews.com/\">PyQuant News</a> is where finance practitioners level up with Python for quant finance, algorithmic trading, and market data analysis. Looking to get started? Check out the fastest growing, top-selling course to <a href=\"https://gettingstartedwithpythonforquantfinance.com/\">get started with Python for quant finance</a>. For educational purposes. Not investment advise. Use at your own risk."
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]
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}
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],
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"metadata": {
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"jupytext": {
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"cell_metadata_filter": "-all",
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"main_language": "python",
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"notebook_metadata_filter": "-all"
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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