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Preparing Distribution of Returns SpreadSheet analysis w Excel and Python
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Data_COPPER.csv
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Data_COPPER.csv
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Yahoo_Finance_DL_COPPER_EXCEL.ipynb
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Yahoo_Finance_DL_COPPER_EXCEL.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "initial_id",
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"%pip install \"yfinance[optional]\"==\"0.2.37\""
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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": 1,
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"id": "904104edfdda9e89",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-03-15T12:53:50.140736Z",
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"start_time": "2024-03-15T12:53:49.262218Z"
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},
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import yfinance as yf\n",
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"\n",
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"hg_f = yf.Ticker(\"HG=F\")"
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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": 5,
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"id": "f8531df814e529fb",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-03-15T12:59:52.590852Z",
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"start_time": "2024-03-15T12:59:52.243074Z"
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},
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"# get historical market data\n",
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"hist = hg_f.history(period=\"100y\")\n",
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"\n",
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"# Replace 0 values with NaN in specific columns\n",
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"hist.replace({'Volume': {0: pd.NA}, 'Open': {0: pd.NA}}, inplace=True)\n",
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"hist.replace(0, np.nan, inplace=True) # Replace 0 with NaN\n",
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"\n",
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"columns = ['Open', 'High', 'Low', 'Close', 'Volume']\n",
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"\n",
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"hist.to_csv(f'./Data_COPPER.csv')"
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]
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},
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{
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"cell_type": "code",
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"id": "de2634931db1a5d6",
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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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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1208
Yahoo_Finance_QUANT_COPPER.ipynb
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1208
Yahoo_Finance_QUANT_COPPER.ipynb
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