288 lines
7.2 KiB
Plaintext
288 lines
7.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b7d86319",
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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": "59c0ce89",
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"metadata": {},
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"source": [
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"This code acquires stock price data, calculates financial indicators, and analyzes their relationships with future returns. It uses OpenBB SDK to fetch stock data, filters it, and computes the Average True Range (ATR) as a volatility measure. The code then calculates historical returns over multiple time lags and sets up target variables for future returns. Finally, it visualizes the relationship between ATR and future returns and computes their correlation. This workflow is useful for quantitative analysis in financial markets."
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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": "ddc92576",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from openbb_terminal.sdk import openbb\n",
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"from talib import ATR\n",
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"from scipy.stats import spearmanr\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "markdown",
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"id": "47f42114",
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"metadata": {},
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"source": [
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"Acquire stock data using OpenBB SDK and filter based on country and price criteria"
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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": "73be12b2",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = openbb.stocks.screener.screener_data(preset_loaded=\"most_volatile\")"
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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": "1407bbe9",
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"metadata": {},
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"outputs": [],
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"source": [
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"universe = data[(data.Country == \"USA\") & (data.Price > 5)]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "36d938a6",
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"metadata": {},
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"source": [
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"Fetch historical price data for each ticker and store in a list of DataFrames"
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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": "eb90090d",
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"metadata": {},
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"outputs": [],
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"source": [
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"stocks = []\n",
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"for ticker in universe.Ticker.tolist():\n",
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" df = openbb.stocks.load(ticker, start_date=\"2010-01-01\", verbose=False).drop(\"Close\", axis=1)\n",
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" df[\"ticker\"] = ticker\n",
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" stocks.append(df)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "97e1c7aa",
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"metadata": {},
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"source": [
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"Concatenate all DataFrames into a single DataFrame and rename columns"
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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": "95b04c04",
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"metadata": {},
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"outputs": [],
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"source": [
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"prices = pd.concat(stocks)\n",
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"prices.columns = [\"open\", \"high\", \"low\", \"close\", \"volume\", \"ticker\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "badcbca5",
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"metadata": {},
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"source": [
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"Filter out stocks with insufficient data and remove duplicate entries"
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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": "bcb8ae26",
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"metadata": {},
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"outputs": [],
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"source": [
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"nobs = prices.groupby(\"ticker\").size()\n",
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"mask = nobs[nobs > 2 * 12 * 21].index\n",
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"prices = prices[prices.ticker.isin(mask)]"
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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": "469ec0d5",
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"metadata": {},
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"outputs": [],
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"source": [
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"prices = prices.set_index(\"ticker\", append=True).reorder_levels([\"ticker\", \"date\"]).drop_duplicates()"
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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": "e31308f2",
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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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"prices.drop_duplicates()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2db6025a",
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"metadata": {},
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"source": [
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"Calculate Average True Range (ATR) for each stock and standardize it"
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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": "55211855",
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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 atr(data):\n",
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" \"\"\"Calculate and standardize ATR.\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" data : DataFrame\n",
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" Data containing high, low, and close prices.\n",
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" \n",
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" Returns\n",
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" -------\n",
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" DataFrame\n",
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" Standardized ATR values.\n",
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" \"\"\"\n",
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" df = ATR(data.high, data.low, data.close, timeperiod=14)\n",
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" return df.sub(df.mean()).div(df.std())"
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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": "3f03ebd4",
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"metadata": {},
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"outputs": [],
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"source": [
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"prices[\"atr\"] = prices.groupby('ticker', group_keys=False).apply(atr)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "55ed7582",
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"metadata": {},
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"source": [
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"Calculate historical returns over different time lags and add them to the DataFrame"
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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": "3d96a26d",
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"metadata": {},
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"outputs": [],
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"source": [
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"lags = [1, 5, 10, 21, 42, 63]\n",
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"for lag in lags:\n",
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" prices[f\"return_{lag}d\"] = prices.groupby(level=\"ticker\").close.pct_change(lag)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7d161381",
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"metadata": {},
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"source": [
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"Set up target variables for future returns by shifting historical returns"
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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": "112b42a8",
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"metadata": {},
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"outputs": [],
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"source": [
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"for t in [1, 5, 10, 21]:\n",
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" prices[f\"target_{t}d\"] = prices.groupby(level=\"ticker\")[f\"return_{t}d\"].shift(-t)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e97c0708",
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"metadata": {},
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"source": [
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"Visualize the relationship between ATR and future 1-day returns using Seaborn"
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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": "e7740b40",
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"metadata": {},
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"outputs": [],
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"source": [
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"target = \"target_1d\"\n",
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"metric = \"atr\"\n",
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"j = sns.jointplot(x=metric, y=target, data=prices)\n",
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"plt.tight_layout()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3578ee83",
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"metadata": {},
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"source": [
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"Calculate and print the Spearman correlation between ATR and future 1-day returns"
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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": "18751214",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = prices[[metric, target]].dropna()\n",
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"r, p = spearmanr(df[metric], df[target])\n",
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"print(f\"{r:,.2%} ({p:.2%})\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "69e275a0",
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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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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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