├── 7.value_investing.ipynb ├── NIFTY50_March2022.csv ├── nifty50_tickers.csv ├── 5.kpi.ipynb ├── 2.webscraping.ipynb ├── 4.technical_indicators.ipynb └── nifty50_intraday.json /7.value_investing.ipynb: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /NIFTY50_March2022.csv: -------------------------------------------------------------------------------- 1 | Company Name,Symbol,Sector 2 | Adani Enterprises,ADANIENT,Diversified 3 | Adani Ports & SEZ,ADANIPORTS,Infrastructure 4 | Apollo Hospitals,APOLLOHOSP,Healthcare 5 | Asian Paints,ASIANPAINT,Consumer Durables 6 | Axis Bank,AXISBANK,Banking 7 | Bajaj Auto,BAJAJ-AUTO,Automobile 8 | Bajaj Finance,BAJFINANCE,Financial Services 9 | Bajaj Finserv,BAJAJFINSV,Financial Services 10 | Bharat Petroleum,BPCL,Energy - Oil & Gas 11 | Bharti Airtel,BHARTIARTL,Telecommunication 12 | Britannia Industries,BRITANNIA,Consumer Goods 13 | Cipla,CIPLA,Pharmaceuticals 14 | Coal India,COALINDIA,Energy - Coal 15 | Divi's Laboratories,DIVISLAB,Pharmaceuticals 16 | Dr. Reddy's Laboratories,DRREDDY,Pharmaceuticals 17 | Eicher Motors,EICHERMOT,Automobile 18 | Grasim Industries,GRASIM,Materials 19 | HCLTech,HCLTECH,Information Technology 20 | HDFC,HDFC,Financial Services 21 | HDFC Bank,HDFCBANK,Banking 22 | HDFC Life,HDFCLIFE,Financial Services 23 | Hero MotoCorp,HEROMOTOCO,Automobile 24 | Hindalco Industries,HINDALCO,Metals 25 | Hindustan Unilever,HINDUNILVR,Consumer Goods 26 | ICICI Bank,ICICIBANK,Banking 27 | IndusInd Bank,INDUSINDBK,Banking 28 | Infosys,INFY,Information Technology 29 | ITC,ITC,Consumer Goods 30 | JSW Steel,JSWSTEEL,Metals 31 | Kotak Mahindra Bank,KOTAKBANK,Banking 32 | Larsen & Toubro,LT,Construction 33 | Mahindra & Mahindra,M&M,Automobile 34 | Maruti Suzuki,MARUTI,Automobile 35 | Nestlé India,NESTLEIND,Consumer Goods 36 | NTPC,NTPC,Energy - Power 37 | Oil and Natural Gas Corporation,ONGC,Energy - Oil & Gas 38 | Power Grid,POWERGRID,Energy - Power 39 | Reliance Industries,RELIANCE,Diversified 40 | SBI Life Insurance Company,SBILIFE,Financial Services 41 | State Bank of India,SBIN,Banking 42 | Sun Pharma,SUNPHARMA,Pharmaceuticals 43 | Tata Motors,TATAMOTORS,Automobile 44 | Tata Steel,TATASTEEL,Metals 45 | Tata Consultancy Services,TCS,Information Technology 46 | Tata Consumer Products,TATACONSUM,Consumer Goods 47 | Tech Mahindra,TECHM,Information Technology 48 | Titan Company,TITAN,Consumer Durables 49 | UltraTech Cement,ULTRACEMCO,Materials 50 | UPL,UPL,Chemicals 51 | Wipro,WIPRO,Information Technology -------------------------------------------------------------------------------- /nifty50_tickers.csv: -------------------------------------------------------------------------------- 1 | Company Name,Symbol,Sector 2 | Adani Enterprises,ADANIENT.NS,Diversified 3 | Adani Ports & SEZ,ADANIPORTS.NS,Infrastructure 4 | Apollo Hospitals,APOLLOHOSP.NS,Healthcare 5 | Asian Paints,ASIANPAINT.NS,Consumer Durables 6 | Axis Bank,AXISBANK.NS,Banking 7 | Bajaj Auto,BAJAJ-AUTO.NS,Automobile 8 | Bajaj Finance,BAJFINANCE.NS,Financial Services 9 | Bajaj Finserv,BAJAJFINSV.NS,Financial Services 10 | Bharat Petroleum,BPCL.NS,Energy - Oil & Gas 11 | Bharti Airtel,BHARTIARTL.NS,Telecommunication 12 | Britannia Industries,BRITANNIA.NS,Consumer Goods 13 | Cipla,CIPLA.NS,Pharmaceuticals 14 | Coal India,COALINDIA.NS,Energy - Coal 15 | Divi's Laboratories,DIVISLAB.NS,Pharmaceuticals 16 | Dr. Reddy's Laboratories,DRREDDY.NS,Pharmaceuticals 17 | Eicher Motors,EICHERMOT.NS,Automobile 18 | Grasim Industries,GRASIM.NS,Materials 19 | HCLTech,HCLTECH.NS,Information Technology 20 | HDFC,HDFC.NS,Financial Services 21 | HDFC Bank,HDFCBANK.NS,Banking 22 | HDFC Life,HDFCLIFE.NS,Financial Services 23 | Hero MotoCorp,HEROMOTOCO.NS,Automobile 24 | Hindalco Industries,HINDALCO.NS,Metals 25 | Hindustan Unilever,HINDUNILVR.NS,Consumer Goods 26 | ICICI Bank,ICICIBANK.NS,Banking 27 | IndusInd Bank,INDUSINDBK.NS,Banking 28 | Infosys,INFY.NS,Information Technology 29 | ITC,ITC.NS,Consumer Goods 30 | JSW Steel,JSWSTEEL.NS,Metals 31 | Kotak Mahindra Bank,KOTAKBANK.NS,Banking 32 | Larsen & Toubro,LT.NS,Construction 33 | Mahindra & Mahindra,M&M.NS,Automobile 34 | Maruti Suzuki,MARUTI.NS,Automobile 35 | Nestlé India,NESTLEIND.NS,Consumer Goods 36 | NTPC,NTPC.NS,Energy - Power 37 | Oil and Natural Gas Corporation,ONGC.NS,Energy - Oil & Gas 38 | Power Grid,POWERGRID.NS,Energy - Power 39 | Reliance Industries,RELIANCE.NS,Diversified 40 | SBI Life Insurance Company,SBILIFE.NS,Financial Services 41 | State Bank of India,SBIN.NS,Banking 42 | Sun Pharma,SUNPHARMA.NS,Pharmaceuticals 43 | Tata Motors,TATAMOTORS.NS,Automobile 44 | Tata Steel,TATASTEEL.NS,Metals 45 | Tata Consultancy Services,TCS.NS,Information Technology 46 | Tata Consumer Products,TATACONSUM.NS,Consumer Goods 47 | Tech Mahindra,TECHM.NS,Information Technology 48 | Titan Company,TITAN.NS,Consumer Durables 49 | UltraTech Cement,ULTRACEMCO.NS,Materials 50 | UPL,UPL.NS,Chemicals 51 | Wipro,WIPRO.NS,Information Technology 52 | -------------------------------------------------------------------------------- /5.kpi.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "attachments": {}, 5 | "cell_type": "markdown", 6 | "metadata": {}, 7 | "source": [ 8 | "# CAGR\n" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": {}, 15 | "outputs": [ 16 | { 17 | "name": "stdout", 18 | "output_type": "stream", 19 | "text": [ 20 | "[*********************100%***********************] 1 of 1 completed\n", 21 | "[*********************100%***********************] 1 of 1 completed\n", 22 | "[*********************100%***********************] 1 of 1 completed\n", 23 | "CAGR of AMZN = -0.29965501007518025\n", 24 | "CAGR of GOOG = 0.08435634116713975\n", 25 | "CAGR of MSFT = 0.3329785025803442\n" 26 | ] 27 | } 28 | ], 29 | "source": [ 30 | "# Import necesary libraries\n", 31 | "import yfinance as yf\n", 32 | "\n", 33 | "# Download historical data for required stocks\n", 34 | "tickers = [\"AMZN\", \"GOOG\", \"MSFT\"]\n", 35 | "ohlcv_data = {}\n", 36 | "\n", 37 | "# looping over tickers and storing OHLCV dataframe in dictionary\n", 38 | "for ticker in tickers:\n", 39 | " temp = yf.download(ticker, period='6mo', interval='1d')\n", 40 | " temp.dropna(how=\"any\", inplace=True)\n", 41 | " ohlcv_data[ticker] = temp\n", 42 | "\n", 43 | "\n", 44 | "def CAGR(DF):\n", 45 | " \"function to calculate the Cumulative Annual Growth Rate of a trading strategy\"\n", 46 | " df = DF.copy()\n", 47 | " df[\"return\"] = DF[\"Adj Close\"].pct_change()\n", 48 | " df[\"cum_return\"] = (1 + df[\"return\"]).cumprod()\n", 49 | " n = len(df)/252\n", 50 | " CAGR = (df[\"cum_return\"][-1])**(1/n) - 1\n", 51 | " return CAGR\n", 52 | "\n", 53 | "\n", 54 | "for ticker in ohlcv_data:\n", 55 | " print(\"CAGR of {} = {}\".format(ticker, CAGR(ohlcv_data[ticker])))\n" 56 | ] 57 | }, 58 | { 59 | "attachments": {}, 60 | "cell_type": "markdown", 61 | "metadata": {}, 62 | "source": [ 63 | "# Volatility\n" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 2, 69 | "metadata": {}, 70 | "outputs": [ 71 | { 72 | "name": "stdout", 73 | "output_type": "stream", 74 | "text": [ 75 | "[*********************100%***********************] 1 of 1 completed\n", 76 | "[*********************100%***********************] 1 of 1 completed\n", 77 | "[*********************100%***********************] 1 of 1 completed\n", 78 | "vol for AMZN = 0.43964800970490847\n", 79 | "vol for GOOG = 0.3968678534888596\n", 80 | "vol for MSFT = 0.34857456252806274\n" 81 | ] 82 | } 83 | ], 84 | "source": [ 85 | "import numpy as np\n", 86 | "# Download historical data for required stocks\n", 87 | "tickers = [\"AMZN\", \"GOOG\", \"MSFT\"]\n", 88 | "ohlcv_data = {}\n", 89 | "\n", 90 | "# looping over tickers and storing OHLCV dataframe in dictionary\n", 91 | "for ticker in tickers:\n", 92 | " temp = yf.download(ticker, period='7mo', interval='1d')\n", 93 | " temp.dropna(how=\"any\", inplace=True)\n", 94 | " ohlcv_data[ticker] = temp\n", 95 | "\n", 96 | "\n", 97 | "def volatility(DF):\n", 98 | " \"function to calculate annualized volatility of a trading strategy\"\n", 99 | " df = DF.copy()\n", 100 | " df[\"daily_ret\"] = DF[\"Adj Close\"].pct_change()\n", 101 | " vol = df[\"daily_ret\"].std() * np.sqrt(252)\n", 102 | " return vol\n", 103 | "\n", 104 | "\n", 105 | "for ticker in ohlcv_data:\n", 106 | " print(\"vol for {} = {}\".format(ticker, volatility(ohlcv_data[ticker])))\n" 107 | ] 108 | }, 109 | { 110 | "attachments": {}, 111 | "cell_type": "markdown", 112 | "metadata": {}, 113 | "source": [ 114 | "# SHARPE AND SORTINO RATIO\n" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": 3, 120 | "metadata": {}, 121 | "outputs": [ 122 | { 123 | "name": "stdout", 124 | "output_type": "stream", 125 | "text": [ 126 | "[*********************100%***********************] 1 of 1 completed\n", 127 | "[*********************100%***********************] 1 of 1 completed\n", 128 | "[*********************100%***********************] 1 of 1 completed\n", 129 | "Sharpe of AMZN = -0.7928139556696726\n", 130 | "Sortino of AMZN = -1.3207677057364113\n", 131 | "Sharpe of GOOG = -0.11597406937422615\n", 132 | "Sortino of GOOG = -0.17725061183931956\n", 133 | "Sharpe of MSFT = 0.5515380308886367\n", 134 | "Sortino of MSFT = 0.8848276193510526\n" 135 | ] 136 | } 137 | ], 138 | "source": [ 139 | "import pandas as pd\n", 140 | "# Download historical data for required stocks\n", 141 | "tickers = [\"AMZN\", \"GOOG\", \"MSFT\"]\n", 142 | "ohlcv_data = {}\n", 143 | "\n", 144 | "# looping over tickers and storing OHLCV dataframe in dictionary\n", 145 | "for ticker in tickers:\n", 146 | " temp = yf.download(ticker, period='7mo', interval='1d')\n", 147 | " temp.dropna(how=\"any\", inplace=True)\n", 148 | " ohlcv_data[ticker] = temp\n", 149 | "\n", 150 | "\n", 151 | "def CAGR(DF):\n", 152 | " \"function to calculate the Cumulative Annual Growth Rate of a trading strategy\"\n", 153 | " df = DF.copy()\n", 154 | " df[\"return\"] = DF[\"Adj Close\"].pct_change()\n", 155 | " df[\"cum_return\"] = (1 + df[\"return\"]).cumprod()\n", 156 | " n = len(df)/252\n", 157 | " CAGR = (df[\"cum_return\"][-1])**(1/n) - 1\n", 158 | " return CAGR\n", 159 | "\n", 160 | "\n", 161 | "def volatility(DF):\n", 162 | " \"function to calculate annualized volatility of a trading strategy\"\n", 163 | " df = DF.copy()\n", 164 | " df[\"return\"] = df[\"Adj Close\"].pct_change()\n", 165 | " vol = df[\"return\"].std() * np.sqrt(252)\n", 166 | " return vol\n", 167 | "\n", 168 | "\n", 169 | "def sharpe(DF, rf):\n", 170 | " \"function to calculate Sharpe Ratio of a trading strategy\"\n", 171 | " df = DF.copy()\n", 172 | " return (CAGR(df) - rf)/volatility(df)\n", 173 | "\n", 174 | "\n", 175 | "def sortino(DF, rf):\n", 176 | " \"function to calculate Sortino Ratio of a trading strategy\"\n", 177 | " df = DF.copy()\n", 178 | " df[\"return\"] = df[\"Adj Close\"].pct_change()\n", 179 | " neg_return = np.where(df[\"return\"] > 0, 0, df[\"return\"])\n", 180 | " neg_vol = pd.Series(neg_return[neg_return != 0]).std() * np.sqrt(252)\n", 181 | " return (CAGR(df) - rf)/neg_vol\n", 182 | "\n", 183 | "\n", 184 | "for ticker in ohlcv_data:\n", 185 | " print(\"Sharpe of {} = {}\".format(ticker, sharpe(ohlcv_data[ticker], 0.03)))\n", 186 | " print(\"Sortino of {} = {}\".format(\n", 187 | " ticker, sortino(ohlcv_data[ticker], 0.03)))\n" 188 | ] 189 | }, 190 | { 191 | "attachments": {}, 192 | "cell_type": "markdown", 193 | "metadata": {}, 194 | "source": [ 195 | "# Maximum Drawdown & Calmar Ratio\n" 196 | ] 197 | }, 198 | { 199 | "cell_type": "code", 200 | "execution_count": 4, 201 | "metadata": {}, 202 | "outputs": [ 203 | { 204 | "name": "stdout", 205 | "output_type": "stream", 206 | "text": [ 207 | "[*********************100%***********************] 1 of 1 completed\n", 208 | "[*********************100%***********************] 1 of 1 completed\n", 209 | "[*********************100%***********************] 1 of 1 completed\n", 210 | "max drawdown of AMZN = 0.4003664234169239\n", 211 | "calmar ratio of AMZN = -0.7956688149763091\n", 212 | "max drawdown of GOOG = 0.2536873530530025\n", 213 | "calmar ratio of GOOG = -0.06317374429607005\n", 214 | "max drawdown of MSFT = 0.1965122330321846\n", 215 | "calmar ratio of MSFT = 1.1306096539471902\n" 216 | ] 217 | } 218 | ], 219 | "source": [ 220 | "import yfinance as yf\n", 221 | "\n", 222 | "# Download historical data for required stocks\n", 223 | "tickers = [\"AMZN\", \"GOOG\", \"MSFT\"]\n", 224 | "ohlcv_data = {}\n", 225 | "\n", 226 | "# looping over tickers and storing OHLCV dataframe in dictionary\n", 227 | "for ticker in tickers:\n", 228 | " temp = yf.download(ticker, period='7mo', interval='1d')\n", 229 | " temp.dropna(how=\"any\", inplace=True)\n", 230 | " ohlcv_data[ticker] = temp\n", 231 | "\n", 232 | "\n", 233 | "def CAGR(DF):\n", 234 | " \"function to calculate the Cumulative Annual Growth Rate of a trading strategy\"\n", 235 | " df = DF.copy()\n", 236 | " df[\"return\"] = DF[\"Adj Close\"].pct_change()\n", 237 | " df[\"cum_return\"] = (1 + df[\"return\"]).cumprod()\n", 238 | " n = len(df)/252\n", 239 | " CAGR = (df[\"cum_return\"][-1])**(1/n) - 1\n", 240 | " return CAGR\n", 241 | "\n", 242 | "\n", 243 | "def max_dd(DF):\n", 244 | " \"function to calculate max drawdown\"\n", 245 | " df = DF.copy()\n", 246 | " df[\"return\"] = df[\"Adj Close\"].pct_change()\n", 247 | " df[\"cum_return\"] = (1+df[\"return\"]).cumprod()\n", 248 | " df[\"cum_roll_max\"] = df[\"cum_return\"].cummax()\n", 249 | " df[\"drawdown\"] = df[\"cum_roll_max\"] - df[\"cum_return\"]\n", 250 | " return (df[\"drawdown\"]/df[\"cum_roll_max\"]).max()\n", 251 | "\n", 252 | "\n", 253 | "def calmar(DF):\n", 254 | " \"function to calculate calmar ratio\"\n", 255 | " df = DF.copy()\n", 256 | " return CAGR(df)/max_dd(df)\n", 257 | "\n", 258 | "\n", 259 | "for ticker in ohlcv_data:\n", 260 | " print(\"max drawdown of {} = {}\".format(ticker, max_dd(ohlcv_data[ticker])))\n", 261 | " print(\"calmar ratio of {} = {}\".format(ticker, calmar(ohlcv_data[ticker])))\n" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": null, 267 | "metadata": {}, 268 | "outputs": [], 269 | "source": [] 270 | } 271 | ], 272 | "metadata": { 273 | "kernelspec": { 274 | "display_name": "base", 275 | "language": "python", 276 | "name": "python3" 277 | }, 278 | "language_info": { 279 | "codemirror_mode": { 280 | "name": "ipython", 281 | "version": 3 282 | }, 283 | "file_extension": ".py", 284 | "mimetype": "text/x-python", 285 | "name": "python", 286 | "nbconvert_exporter": "python", 287 | "pygments_lexer": "ipython3", 288 | "version": "3.9.16" 289 | }, 290 | "orig_nbformat": 4, 291 | "vscode": { 292 | "interpreter": { 293 | "hash": "cd78fef2128015050713e82ca51c6520b11aee7c9ee8df750520bbbc7384cbaa" 294 | } 295 | } 296 | }, 297 | "nbformat": 4, 298 | "nbformat_minor": 2 299 | } 300 | -------------------------------------------------------------------------------- /2.webscraping.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "attachments": {}, 5 | "cell_type": "markdown", 6 | "metadata": {}, 7 | "source": [ 8 | "## Getting fundamental data from yahoo finance" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": {}, 15 | "outputs": [], 16 | "source": [ 17 | "import requests\n", 18 | "from bs4 import BeautifulSoup\n", 19 | "\n", 20 | "income_statement = {}\n", 21 | "\n", 22 | "url = \"https://finance.yahoo.com/quote/AAPL/financials?p=AAPL\"\n", 23 | "\n", 24 | "# It is important to send headers because the request won't be served as yahoo finance loathes when developers webscrape. \n", 25 | "\n", 26 | "headers = {\"User-Agent\" : \"Chrome/96.0.4664.110\"}\n", 27 | "page = requests.get(url, headers=headers)\n", 28 | "page_content = page.content\n", 29 | "soup = BeautifulSoup(page_content,\"html.parser\")\n", 30 | "\n", 31 | "# First we'll just grab the section in our html code which has all the sauce for the table and then work our way to the end goal.\n", 32 | "tabl = soup.find_all(\"div\" , {\"class\" : \"M(0) Whs(n) BdEnd Bdc($seperatorColor) D(itb)\"})\n", 33 | "\n", 34 | "# That we've got the table in our hand, we may iterate through each of it's row to get the data and store is in a dictionary using vanilla python magic. \n", 35 | "for t in tabl:\n", 36 | " rows = t.find_all(\"div\" , {\"class\": \"D(tbr) fi-row Bgc($hoverBgColor):h\"})\n", 37 | " for row in rows:\n", 38 | " income_statement[row.get_text(separator=\"|\").split(\"|\")[0]] = row.get_text(separator=\"|\").split(\"|\")[1] # 1st value in the list (list that we get after splitting it on \"|\") has the data for the current year. \n", 39 | " # Although we may fetch the data for the previous years if we want to by simply getting all that we have by using [1:] instead of [1]\n", 40 | " \n" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 2, 46 | "metadata": {}, 47 | "outputs": [ 48 | { 49 | "data": { 50 | "text/plain": [ 51 | "{'Total Revenue': '387,537,000',\n", 52 | " 'Cost of Revenue': '220,666,000',\n", 53 | " 'Gross Profit': '166,871,000',\n", 54 | " 'Operating Expense': '52,906,000',\n", 55 | " 'Operating Income': '113,965,000',\n", 56 | " 'Net Non Operating Interest Income Expense': '-197,000',\n", 57 | " 'Other Income Expense': '-283,000',\n", 58 | " 'Pretax Income': '113,485,000',\n", 59 | " 'Tax Provision': '18,314,000',\n", 60 | " 'Net Income Common Stockholders': '95,171,000',\n", 61 | " 'Diluted NI Available to Com Stockholders': '95,171,000',\n", 62 | " 'Basic EPS': '-',\n", 63 | " 'Diluted EPS': '-',\n", 64 | " 'Basic Average Shares': '-',\n", 65 | " 'Diluted Average Shares': '-',\n", 66 | " 'Total Operating Income as Reported': '113,965,000',\n", 67 | " 'Total Expenses': '273,572,000',\n", 68 | " 'Net Income from Continuing & Discontinued Operation': '95,171,000',\n", 69 | " 'Normalized Income': '95,171,000',\n", 70 | " 'Interest Income': '3,043,000',\n", 71 | " 'Interest Expense': '3,240,000',\n", 72 | " 'Net Interest Income': '-197,000',\n", 73 | " 'EBIT': '116,725,000',\n", 74 | " 'EBITDA': '128,048,000',\n", 75 | " 'Reconciled Cost of Revenue': '220,666,000',\n", 76 | " 'Reconciled Depreciation': '11,323,000',\n", 77 | " 'Net Income from Continuing Operation Net Minority Interest': '95,171,000',\n", 78 | " 'Normalized EBITDA': '128,048,000',\n", 79 | " 'Tax Rate for Calcs': '0',\n", 80 | " 'Tax Effect of Unusual Items': '0'}" 81 | ] 82 | }, 83 | "execution_count": 2, 84 | "metadata": {}, 85 | "output_type": "execute_result" 86 | } 87 | ], 88 | "source": [ 89 | "income_statement" 90 | ] 91 | }, 92 | { 93 | "attachments": {}, 94 | "cell_type": "markdown", 95 | "metadata": {}, 96 | "source": [ 97 | "## Now getting data for several tickers" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": 3, 103 | "metadata": {}, 104 | "outputs": [ 105 | { 106 | "name": "stderr", 107 | "output_type": "stream", 108 | "text": [ 109 | "/var/folders/q4/72ybtynd5sjf5ppl_5vpsw080000gn/T/ipykernel_32399/3212430218.py:96: FutureWarning: The default value of regex will change from True to False in a future version.\n", 110 | " income_statatement_dict[ticker][col] = income_statatement_dict[ticker][col].str.replace(\n", 111 | "/var/folders/q4/72ybtynd5sjf5ppl_5vpsw080000gn/T/ipykernel_32399/3212430218.py:101: FutureWarning: The default value of regex will change from True to False in a future version.\n", 112 | " cashflow_st_dict[ticker][col] = cashflow_st_dict[ticker][col].str.replace(\n", 113 | "/var/folders/q4/72ybtynd5sjf5ppl_5vpsw080000gn/T/ipykernel_32399/3212430218.py:107: FutureWarning: The default value of regex will change from True to False in a future version.\n", 114 | " balance_sheet_dict[ticker][col] = balance_sheet_dict[ticker][col].str.replace(\n" 115 | ] 116 | } 117 | ], 118 | "source": [ 119 | "import requests\n", 120 | "from bs4 import BeautifulSoup\n", 121 | "import pandas as pd\n", 122 | "\n", 123 | "tickers = [\"AAPL\", \"FB\", \"CSCO\", \"INFY.NS\", \"3988.HK\"]\n", 124 | "income_statatement_dict = {}\n", 125 | "balance_sheet_dict = {}\n", 126 | "cashflow_st_dict = {}\n", 127 | "\n", 128 | "for ticker in tickers:\n", 129 | " # scraping income statement\n", 130 | " url = f\"https://finance.yahoo.com/quote/{ticker}/financials?p={ticker}\"\n", 131 | "\n", 132 | " income_statement = {}\n", 133 | " # used to store the headings of the tables that we'll be scraping.\n", 134 | " table_title = {}\n", 135 | "\n", 136 | " headers = {\"User-Agent\": \"Chrome/96.0.4664.110\"}\n", 137 | " page = requests.get(url, headers=headers)\n", 138 | " page_content = page.content\n", 139 | "\n", 140 | " soup = BeautifulSoup(page_content, \"html.parser\")\n", 141 | " tabl = soup.find_all(\n", 142 | " \"div\", {\"class\": \"M(0) Whs(n) BdEnd Bdc($seperatorColor) D(itb)\"})\n", 143 | "\n", 144 | " for t in tabl:\n", 145 | " heading = t.find_all(\"div\", {\"class\": \"D(tbr) C($primaryColor)\"})\n", 146 | " for top_row in heading:\n", 147 | " table_title[top_row.get_text(separator=\"|\").split(\n", 148 | " \"|\")[0]] = top_row.get_text(separator=\"|\").split(\"|\")[1:]\n", 149 | " rows = t.find_all(\n", 150 | " \"div\", {\"class\": \"D(tbr) fi-row Bgc($hoverBgColor):h\"})\n", 151 | " for row in rows:\n", 152 | " income_statement[row.get_text(separator=\"|\").split(\n", 153 | " \"|\")[0]] = row.get_text(separator=\"|\").split(\"|\")[1:]\n", 154 | "\n", 155 | " temp = pd.DataFrame(income_statement).T\n", 156 | " temp.columns = table_title[\"Breakdown\"]\n", 157 | " income_statatement_dict[ticker] = temp\n", 158 | "\n", 159 | " # scraping balance sheet statement\n", 160 | " url = f\"https://finance.yahoo.com/quote/{ticker}/balance-sheet?p={ticker}\"\n", 161 | " balance_sheet = {}\n", 162 | " table_title = {}\n", 163 | "\n", 164 | " headers = {\"User-Agent\": \"Chrome/96.0.4664.110\"}\n", 165 | " page = requests.get(url, headers=headers)\n", 166 | " page_content = page.content\n", 167 | " soup = BeautifulSoup(page_content, \"html.parser\")\n", 168 | " tabl = soup.find_all(\n", 169 | " \"div\", {\"class\": \"M(0) Whs(n) BdEnd Bdc($seperatorColor) D(itb)\"})\n", 170 | " for t in tabl:\n", 171 | " heading = t.find_all(\"div\", {\"class\": \"D(tbr) C($primaryColor)\"})\n", 172 | " for top_row in heading:\n", 173 | " table_title[top_row.get_text(separator=\"|\").split(\n", 174 | " \"|\")[0]] = top_row.get_text(separator=\"|\").split(\"|\")[1:]\n", 175 | " rows = t.find_all(\n", 176 | " \"div\", {\"class\": \"D(tbr) fi-row Bgc($hoverBgColor):h\"})\n", 177 | " for row in rows:\n", 178 | " balance_sheet[row.get_text(separator=\"|\").split(\n", 179 | " \"|\")[0]] = row.get_text(separator=\"|\").split(\"|\")[1:]\n", 180 | "\n", 181 | " temp = pd.DataFrame(balance_sheet).T\n", 182 | " temp.columns = table_title[\"Breakdown\"]\n", 183 | " balance_sheet_dict[ticker] = temp\n", 184 | "\n", 185 | " # scraping cashflow statement\n", 186 | " url = f\"https://finance.yahoo.com/quote/{ticker}/cash-flow?p={ticker}\"\n", 187 | " cashflow_statement = {}\n", 188 | " table_title = {}\n", 189 | "\n", 190 | " headers = {\"User-Agent\": \"Chrome/96.0.4664.110\"}\n", 191 | " page = requests.get(url, headers=headers)\n", 192 | " page_content = page.content\n", 193 | " soup = BeautifulSoup(page_content, \"html.parser\")\n", 194 | " tabl = soup.find_all(\n", 195 | " \"div\", {\"class\": \"M(0) Whs(n) BdEnd Bdc($seperatorColor) D(itb)\"})\n", 196 | " for t in tabl:\n", 197 | " heading = t.find_all(\"div\", {\"class\": \"D(tbr) C($primaryColor)\"})\n", 198 | " for top_row in heading:\n", 199 | " table_title[top_row.get_text(separator=\"|\").split(\n", 200 | " \"|\")[0]] = top_row.get_text(separator=\"|\").split(\"|\")[1:]\n", 201 | " rows = t.find_all(\n", 202 | " \"div\", {\"class\": \"D(tbr) fi-row Bgc($hoverBgColor):h\"})\n", 203 | " for row in rows:\n", 204 | " cashflow_statement[row.get_text(separator=\"|\").split(\n", 205 | " \"|\")[0]] = row.get_text(separator=\"|\").split(\"|\")[1:]\n", 206 | "\n", 207 | " temp = pd.DataFrame(cashflow_statement).T\n", 208 | " temp.columns = table_title[\"Breakdown\"]\n", 209 | " cashflow_st_dict[ticker] = temp\n", 210 | "\n", 211 | "# converting dataframe values to numeric\n", 212 | "for ticker in tickers:\n", 213 | " for col in income_statatement_dict[ticker].columns:\n", 214 | " income_statatement_dict[ticker][col] = income_statatement_dict[ticker][col].str.replace(\n", 215 | " ',|- ', '')\n", 216 | " income_statatement_dict[ticker][col] = pd.to_numeric(\n", 217 | " income_statatement_dict[ticker][col], errors='coerce')\n", 218 | " \n", 219 | " cashflow_st_dict[ticker][col] = cashflow_st_dict[ticker][col].str.replace(\n", 220 | " ',|- ', '')\n", 221 | " cashflow_st_dict[ticker][col] = pd.to_numeric(\n", 222 | " cashflow_st_dict[ticker][col], errors='coerce')\n", 223 | " \n", 224 | " if col != \"ttm\": # yahoo has ttm column for income statement and cashflow statement only\n", 225 | " balance_sheet_dict[ticker][col] = balance_sheet_dict[ticker][col].str.replace(\n", 226 | " ',|- ', '')\n", 227 | " balance_sheet_dict[ticker][col] = pd.to_numeric(\n", 228 | " balance_sheet_dict[ticker][col], errors='coerce')\n" 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "execution_count": 4, 234 | "metadata": {}, 235 | "outputs": [ 236 | { 237 | "data": { 238 | "text/plain": [ 239 | "dict_keys(['Total Assets', 'Total Liabilities Net Minority Interest', 'Total Equity Gross Minority Interest', 'Total Capitalization', 'Preferred Stock Equity', 'Common Stock Equity', 'Net Tangible Assets', 'Invested Capital', 'Tangible Book Value', 'Total Debt', 'Share Issued', 'Ordinary Shares Number'])" 240 | ] 241 | }, 242 | "execution_count": 4, 243 | "metadata": {}, 244 | "output_type": "execute_result" 245 | } 246 | ], 247 | "source": [ 248 | "balance_sheet.keys()" 249 | ] 250 | }, 251 | { 252 | "attachments": {}, 253 | "cell_type": "markdown", 254 | "metadata": {}, 255 | "source": [ 256 | "## Getting key statistics " 257 | ] 258 | }, 259 | { 260 | "cell_type": "code", 261 | "execution_count": 5, 262 | "metadata": {}, 263 | "outputs": [], 264 | "source": [ 265 | "import requests\n", 266 | "from bs4 import BeautifulSoup\n", 267 | "\n", 268 | "tickers = [\"AAPL\",\"FB\",\"CSCO\",\"INFY.NS\",\"3988.HK\"]\n", 269 | "key_statistics = {}\n", 270 | "\n", 271 | "for ticker in tickers:\n", 272 | " #scraping key statistics\n", 273 | " url = f\"https://finance.yahoo.com/quote/{ticker}/key-statistics?p={ticker}\"\n", 274 | " headers = {\"User-Agent\" : \"Chrome/96.0.4664.110\"}\n", 275 | " page = requests.get(url, headers=headers)\n", 276 | " page_content = page.content\n", 277 | " soup = BeautifulSoup(page_content,\"html.parser\")\n", 278 | " tabl = soup.find_all(\"table\" , {\"class\" : \"W(100%) Bdcl(c)\"}) #remove/add the trailing space if getting error\n", 279 | " \n", 280 | " temp_stats = {}\n", 281 | " for t in tabl:\n", 282 | " rows = t.find_all(\"tr\")\n", 283 | " for row in rows:\n", 284 | " temp_stats[row.get_text(separator=\"|\").split(\"|\")[0]] = row.get_text(separator=\"|\").split(\"|\")[-1]\n", 285 | " \n", 286 | " key_statistics[ticker] = temp_stats" 287 | ] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "execution_count": 6, 292 | "metadata": {}, 293 | "outputs": [ 294 | { 295 | "data": { 296 | "text/plain": [ 297 | "{'Beta (5Y Monthly)': '1.30',\n", 298 | " '52-Week Change': '-4.87%',\n", 299 | " 'S&P500 52-Week Change': '-9.11%',\n", 300 | " '52 Week High': '176.15',\n", 301 | " '52 Week Low': '124.17',\n", 302 | " '50-Day Moving Average': '152.94',\n", 303 | " '200-Day Moving Average': '148.99',\n", 304 | " 'Avg Vol (3 month)': '66.49M',\n", 305 | " 'Avg Vol (10 day)': '54.94M',\n", 306 | " 'Shares Outstanding': '15.82B',\n", 307 | " 'Implied Shares Outstanding': 'N/A',\n", 308 | " 'Float': '15.81B',\n", 309 | " '% Held by Insiders': '0.07%',\n", 310 | " '% Held by Institutions': '61.29%',\n", 311 | " 'Shares Short (Mar 14, 2023)': '107.42M',\n", 312 | " 'Short Ratio (Mar 14, 2023)': '1.75',\n", 313 | " 'Short % of Float (Mar 14, 2023)': '0.68%',\n", 314 | " 'Short % of Shares Outstanding (Mar 14, 2023)': '0.68%',\n", 315 | " 'Shares Short (prior month Feb 14, 2023)': '104.34M',\n", 316 | " 'Forward Annual Dividend Rate': '0.92',\n", 317 | " 'Forward Annual Dividend Yield': '0.56%',\n", 318 | " 'Trailing Annual Dividend Rate': '0.91',\n", 319 | " 'Trailing Annual Dividend Yield': '0.56%',\n", 320 | " '5 Year Average Dividend Yield': '0.94',\n", 321 | " 'Payout Ratio': '15.45%',\n", 322 | " 'Dividend Date': 'Feb 15, 2023',\n", 323 | " 'Ex-Dividend Date': 'Feb 09, 2023',\n", 324 | " 'Last Split Factor': '4:1',\n", 325 | " 'Last Split Date': 'Aug 30, 2020',\n", 326 | " 'Fiscal Year Ends': 'Sep 23, 2022',\n", 327 | " 'Most Recent Quarter': 'Dec 30, 2022',\n", 328 | " 'Profit Margin': '24.56%',\n", 329 | " 'Operating Margin': '29.41%',\n", 330 | " 'Return on Assets': '19.57%',\n", 331 | " 'Return on Equity': '147.94%',\n", 332 | " 'Revenue': '387.54B',\n", 333 | " 'Revenue Per Share': '24.08',\n", 334 | " 'Quarterly Revenue Growth': '-5.50%',\n", 335 | " 'Gross Profit': '170.78B',\n", 336 | " 'EBITDA': '125.29B',\n", 337 | " 'Net Income Avi to Common': '95.17B',\n", 338 | " 'Diluted EPS': '5.90',\n", 339 | " 'Quarterly Earnings Growth': '-13.40%',\n", 340 | " 'Total Cash': '51.36B',\n", 341 | " 'Total Cash Per Share': '3.25',\n", 342 | " 'Total Debt': '111.11B',\n", 343 | " 'Total Debt/Equity': '195.87',\n", 344 | " 'Current Ratio': '0.94',\n", 345 | " 'Book Value Per Share': '3.58',\n", 346 | " 'Operating Cash Flow': '109.19B',\n", 347 | " 'Levered Free Cash Flow': '84.73B'}" 348 | ] 349 | }, 350 | "execution_count": 6, 351 | "metadata": {}, 352 | "output_type": "execute_result" 353 | } 354 | ], 355 | "source": [ 356 | "key_statistics[\"AAPL\"]" 357 | ] 358 | }, 359 | { 360 | "cell_type": "code", 361 | "execution_count": null, 362 | "metadata": {}, 363 | "outputs": [], 364 | "source": [] 365 | } 366 | ], 367 | "metadata": { 368 | "kernelspec": { 369 | "display_name": "base", 370 | "language": "python", 371 | "name": "python3" 372 | }, 373 | "language_info": { 374 | "codemirror_mode": { 375 | "name": "ipython", 376 | "version": 3 377 | }, 378 | "file_extension": ".py", 379 | "mimetype": "text/x-python", 380 | "name": "python", 381 | "nbconvert_exporter": "python", 382 | "pygments_lexer": "ipython3", 383 | "version": "3.9.16" 384 | }, 385 | "orig_nbformat": 4, 386 | "vscode": { 387 | "interpreter": { 388 | "hash": "cd78fef2128015050713e82ca51c6520b11aee7c9ee8df750520bbbc7384cbaa" 389 | } 390 | } 391 | }, 392 | "nbformat": 4, 393 | "nbformat_minor": 2 394 | } 395 | -------------------------------------------------------------------------------- /4.technical_indicators.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "attachments": {}, 5 | "cell_type": "markdown", 6 | "metadata": {}, 7 | "source": [ 8 | "# Coding MACD\n" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": {}, 15 | "outputs": [ 16 | { 17 | "name": "stdout", 18 | "output_type": "stream", 19 | "text": [ 20 | "[*********************100%***********************] 1 of 1 completed\n", 21 | "[*********************100%***********************] 1 of 1 completed\n", 22 | "[*********************100%***********************] 1 of 1 completed\n" 23 | ] 24 | } 25 | ], 26 | "source": [ 27 | "import yfinance as yf\n", 28 | "\n", 29 | "# Download historical data for required stocks\n", 30 | "tickers = [\"MSFT\", \"AAPL\", \"ADANIPORTS.NS\"]\n", 31 | "ohlcv_data = {}\n", 32 | "\n", 33 | "# looping over tickers and storing OHLCV dataframe in dictionary\n", 34 | "for ticker in tickers:\n", 35 | " temp = yf.download(ticker, period='1mo', interval='15m')\n", 36 | " temp.dropna(how=\"any\", inplace=True)\n", 37 | " ohlcv_data[ticker] = temp\n", 38 | "\n", 39 | "\n", 40 | "def MACD(DF, a=12, b=26, c=9):\n", 41 | " \"\"\"function to calculate MACD\n", 42 | " typical values a(fast moving average) = 12; \n", 43 | " b(slow moving average) =26; \n", 44 | " c(signal line ma window) =9\"\"\"\n", 45 | " df = DF.copy()\n", 46 | " df[\"ma_fast\"] = df[\"Adj Close\"].ewm(span=a, min_periods=a).mean()\n", 47 | " df[\"ma_slow\"] = df[\"Adj Close\"].ewm(span=b, min_periods=b).mean()\n", 48 | " df[\"macd\"] = df[\"ma_fast\"] - df[\"ma_slow\"]\n", 49 | " df[\"signal\"] = df[\"macd\"].ewm(span=c, min_periods=c).mean()\n", 50 | " return df.loc[:, [\"macd\", \"signal\"]]\n", 51 | "\n", 52 | "\n", 53 | "for ticker in ohlcv_data:\n", 54 | " ohlcv_data[ticker][[\"MACD\", \"SIGNAL\"]] = MACD(\n", 55 | " ohlcv_data[ticker], a=12, b=26, c=9)\n" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 2, 61 | "metadata": {}, 62 | "outputs": [ 63 | { 64 | "data": { 65 | "text/html": [ 66 | "
\n", 67 | "\n", 80 | "\n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | "
OpenHighLowCloseAdj CloseVolumeMACDSIGNAL
Datetime
2023-04-06 14:30:00+05:30641.950012645.599976641.849976644.400024644.4000242124612.2299253.032029
2023-04-06 14:45:00+05:30644.400024645.000000643.349976644.099976644.099976909622.0283082.831285
2023-04-06 15:00:00+05:30644.299988644.750000640.599976641.450012641.4500123160511.6358392.592196
2023-04-06 15:15:00+05:30641.500000641.900024639.799988641.250000641.2500004221971.2937522.332507
2023-04-06 15:30:00+05:30641.599976641.599976641.599976641.599976641.59997601.0389092.073788
\n", 163 | "
" 164 | ], 165 | "text/plain": [ 166 | " Open High Low Close \\\n", 167 | "Datetime \n", 168 | "2023-04-06 14:30:00+05:30 641.950012 645.599976 641.849976 644.400024 \n", 169 | "2023-04-06 14:45:00+05:30 644.400024 645.000000 643.349976 644.099976 \n", 170 | "2023-04-06 15:00:00+05:30 644.299988 644.750000 640.599976 641.450012 \n", 171 | "2023-04-06 15:15:00+05:30 641.500000 641.900024 639.799988 641.250000 \n", 172 | "2023-04-06 15:30:00+05:30 641.599976 641.599976 641.599976 641.599976 \n", 173 | "\n", 174 | " Adj Close Volume MACD SIGNAL \n", 175 | "Datetime \n", 176 | "2023-04-06 14:30:00+05:30 644.400024 212461 2.229925 3.032029 \n", 177 | "2023-04-06 14:45:00+05:30 644.099976 90962 2.028308 2.831285 \n", 178 | "2023-04-06 15:00:00+05:30 641.450012 316051 1.635839 2.592196 \n", 179 | "2023-04-06 15:15:00+05:30 641.250000 422197 1.293752 2.332507 \n", 180 | "2023-04-06 15:30:00+05:30 641.599976 0 1.038909 2.073788 " 181 | ] 182 | }, 183 | "execution_count": 2, 184 | "metadata": {}, 185 | "output_type": "execute_result" 186 | } 187 | ], 188 | "source": [ 189 | "ohlcv_data[\"ADANIPORTS.NS\"].tail()\n" 190 | ] 191 | }, 192 | { 193 | "attachments": {}, 194 | "cell_type": "markdown", 195 | "metadata": {}, 196 | "source": [ 197 | "# ATR\n" 198 | ] 199 | }, 200 | { 201 | "cell_type": "code", 202 | "execution_count": 3, 203 | "metadata": {}, 204 | "outputs": [ 205 | { 206 | "name": "stdout", 207 | "output_type": "stream", 208 | "text": [ 209 | "[*********************100%***********************] 1 of 1 completed\n", 210 | "[*********************100%***********************] 1 of 1 completed\n", 211 | "[*********************100%***********************] 1 of 1 completed\n" 212 | ] 213 | } 214 | ], 215 | "source": [ 216 | "# Import necesary libraries\n", 217 | "import yfinance as yf\n", 218 | "\n", 219 | "# Download historical data for required stocks\n", 220 | "tickers = [\"AMZN\", \"ADANIPORTS.NS\", \"MSFT\"]\n", 221 | "ohlcv_data = {}\n", 222 | "\n", 223 | "# looping over tickers and storing OHLCV dataframe in dictionary\n", 224 | "for ticker in tickers:\n", 225 | " temp = yf.download(ticker, period='1mo', interval='5m')\n", 226 | " temp.dropna(how=\"any\", inplace=True)\n", 227 | " ohlcv_data[ticker] = temp\n", 228 | "\n", 229 | "\n", 230 | "def ATR(DF, n=14):\n", 231 | " \"function to calculate True Range and Average True Range\"\n", 232 | " df = DF.copy()\n", 233 | " df[\"H-L\"] = df[\"High\"] - df[\"Low\"]\n", 234 | " df[\"H-PC\"] = abs(df[\"High\"] - df[\"Adj Close\"].shift(1))\n", 235 | " df[\"L-PC\"] = abs(df[\"Low\"] - df[\"Adj Close\"].shift(1))\n", 236 | " df[\"TR\"] = df[[\"H-L\", \"H-PC\", \"L-PC\"]].max(axis=1, skipna=False)\n", 237 | " df[\"ATR\"] = df[\"TR\"].ewm(com=n, min_periods=n).mean()\n", 238 | " return df[\"ATR\"]\n", 239 | "\n", 240 | "\n", 241 | "for ticker in ohlcv_data:\n", 242 | " ohlcv_data[ticker][\"ATR\"] = ATR(ohlcv_data[ticker])\n" 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 4, 248 | "metadata": {}, 249 | "outputs": [ 250 | { 251 | "data": { 252 | "text/html": [ 253 | "
\n", 254 | "\n", 267 | "\n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | "
OpenHighLowCloseAdj CloseVolumeATR
Datetime
2023-04-06 15:10:00+05:30643.200012643.200012640.599976641.450012641.4500121615801.771113
2023-04-06 15:15:00+05:30641.500000641.650024640.450012640.599976640.5999761335871.733040
2023-04-06 15:20:00+05:30640.549988641.900024639.799988641.500000641.5000002073201.757506
2023-04-06 15:25:00+05:30641.599976641.799988641.000000641.250000641.250000812901.693672
2023-04-06 15:30:00+05:30641.599976641.599976641.599976641.599976641.59997601.604092
\n", 343 | "
" 344 | ], 345 | "text/plain": [ 346 | " Open High Low Close \\\n", 347 | "Datetime \n", 348 | "2023-04-06 15:10:00+05:30 643.200012 643.200012 640.599976 641.450012 \n", 349 | "2023-04-06 15:15:00+05:30 641.500000 641.650024 640.450012 640.599976 \n", 350 | "2023-04-06 15:20:00+05:30 640.549988 641.900024 639.799988 641.500000 \n", 351 | "2023-04-06 15:25:00+05:30 641.599976 641.799988 641.000000 641.250000 \n", 352 | "2023-04-06 15:30:00+05:30 641.599976 641.599976 641.599976 641.599976 \n", 353 | "\n", 354 | " Adj Close Volume ATR \n", 355 | "Datetime \n", 356 | "2023-04-06 15:10:00+05:30 641.450012 161580 1.771113 \n", 357 | "2023-04-06 15:15:00+05:30 640.599976 133587 1.733040 \n", 358 | "2023-04-06 15:20:00+05:30 641.500000 207320 1.757506 \n", 359 | "2023-04-06 15:25:00+05:30 641.250000 81290 1.693672 \n", 360 | "2023-04-06 15:30:00+05:30 641.599976 0 1.604092 " 361 | ] 362 | }, 363 | "execution_count": 4, 364 | "metadata": {}, 365 | "output_type": "execute_result" 366 | } 367 | ], 368 | "source": [ 369 | "ohlcv_data[\"ADANIPORTS.NS\"].tail()\n" 370 | ] 371 | }, 372 | { 373 | "attachments": {}, 374 | "cell_type": "markdown", 375 | "metadata": {}, 376 | "source": [ 377 | "# Bollinger Band\n" 378 | ] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "execution_count": 5, 383 | "metadata": {}, 384 | "outputs": [ 385 | { 386 | "name": "stdout", 387 | "output_type": "stream", 388 | "text": [ 389 | "[*********************100%***********************] 1 of 1 completed\n", 390 | "[*********************100%***********************] 1 of 1 completed\n", 391 | "[*********************100%***********************] 1 of 1 completed\n" 392 | ] 393 | } 394 | ], 395 | "source": [ 396 | "\n", 397 | "# Download historical data for required stocks\n", 398 | "tickers = [\"AMZN\", \"ADANIPORTS.NS\", \"MSFT\"]\n", 399 | "ohlcv_data = {}\n", 400 | "\n", 401 | "# looping over tickers and storing OHLCV dataframe in dictionary\n", 402 | "for ticker in tickers:\n", 403 | " temp = yf.download(ticker, period='1mo', interval='5m')\n", 404 | " temp.dropna(how=\"any\", inplace=True)\n", 405 | " ohlcv_data[ticker] = temp\n", 406 | "\n", 407 | "\n", 408 | "def Boll_Band(DF, n=14):\n", 409 | " \"function to calculate Bollinger Band\"\n", 410 | " df = DF.copy()\n", 411 | " df[\"MB\"] = df[\"Adj Close\"].rolling(n).mean()\n", 412 | " df[\"UB\"] = df[\"MB\"] + 2*df[\"Adj Close\"].rolling(n).std(ddof=0)\n", 413 | " df[\"LB\"] = df[\"MB\"] - 2*df[\"Adj Close\"].rolling(n).std(ddof=0)\n", 414 | " df[\"BB_Width\"] = df[\"UB\"] - df[\"LB\"]\n", 415 | " return df[[\"MB\", \"UB\", \"LB\", \"BB_Width\"]]\n", 416 | "\n", 417 | "\n", 418 | "for ticker in ohlcv_data:\n", 419 | " ohlcv_data[ticker][[\"MB\", \"UB\", \"LB\", \"BB_Width\"]\n", 420 | " ] = Boll_Band(ohlcv_data[ticker])\n" 421 | ] 422 | }, 423 | { 424 | "cell_type": "code", 425 | "execution_count": 6, 426 | "metadata": {}, 427 | "outputs": [ 428 | { 429 | "data": { 430 | "text/html": [ 431 | "
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OpenHighLowCloseAdj CloseVolumeMBUBLBBB_Width
Datetime
2023-04-06 15:10:00+05:30643.200012643.200012640.599976641.450012641.450012161580643.628571645.779542641.4775994.301944
2023-04-06 15:15:00+05:30641.500000641.650024640.450012640.599976640.599976133587643.324999645.865177640.7848225.080355
2023-04-06 15:20:00+05:30640.549988641.900024639.799988641.500000641.500000207320643.178571645.881141640.4760025.405139
2023-04-06 15:25:00+05:30641.599976641.799988641.000000641.250000641.25000081290643.053571645.933667640.1734755.760192
2023-04-06 15:30:00+05:30641.599976641.599976641.599976641.599976641.5999760642.960711645.936853639.9845695.952284
\n", 542 | "
" 543 | ], 544 | "text/plain": [ 545 | " Open High Low Close \\\n", 546 | "Datetime \n", 547 | "2023-04-06 15:10:00+05:30 643.200012 643.200012 640.599976 641.450012 \n", 548 | "2023-04-06 15:15:00+05:30 641.500000 641.650024 640.450012 640.599976 \n", 549 | "2023-04-06 15:20:00+05:30 640.549988 641.900024 639.799988 641.500000 \n", 550 | "2023-04-06 15:25:00+05:30 641.599976 641.799988 641.000000 641.250000 \n", 551 | "2023-04-06 15:30:00+05:30 641.599976 641.599976 641.599976 641.599976 \n", 552 | "\n", 553 | " Adj Close Volume MB UB \\\n", 554 | "Datetime \n", 555 | "2023-04-06 15:10:00+05:30 641.450012 161580 643.628571 645.779542 \n", 556 | "2023-04-06 15:15:00+05:30 640.599976 133587 643.324999 645.865177 \n", 557 | "2023-04-06 15:20:00+05:30 641.500000 207320 643.178571 645.881141 \n", 558 | "2023-04-06 15:25:00+05:30 641.250000 81290 643.053571 645.933667 \n", 559 | "2023-04-06 15:30:00+05:30 641.599976 0 642.960711 645.936853 \n", 560 | "\n", 561 | " LB BB_Width \n", 562 | "Datetime \n", 563 | "2023-04-06 15:10:00+05:30 641.477599 4.301944 \n", 564 | "2023-04-06 15:15:00+05:30 640.784822 5.080355 \n", 565 | "2023-04-06 15:20:00+05:30 640.476002 5.405139 \n", 566 | "2023-04-06 15:25:00+05:30 640.173475 5.760192 \n", 567 | "2023-04-06 15:30:00+05:30 639.984569 5.952284 " 568 | ] 569 | }, 570 | "execution_count": 6, 571 | "metadata": {}, 572 | "output_type": "execute_result" 573 | } 574 | ], 575 | "source": [ 576 | "ohlcv_data[\"ADANIPORTS.NS\"].tail()\n" 577 | ] 578 | }, 579 | { 580 | "attachments": {}, 581 | "cell_type": "markdown", 582 | "metadata": {}, 583 | "source": [ 584 | "# RSI\n" 585 | ] 586 | }, 587 | { 588 | "cell_type": "code", 589 | "execution_count": 7, 590 | "metadata": {}, 591 | "outputs": [ 592 | { 593 | "name": "stdout", 594 | "output_type": "stream", 595 | "text": [ 596 | "[*********************100%***********************] 1 of 1 completed\n", 597 | "[*********************100%***********************] 1 of 1 completed\n", 598 | "[*********************100%***********************] 1 of 1 completed\n" 599 | ] 600 | } 601 | ], 602 | "source": [ 603 | "import numpy as np\n", 604 | "tickers = [\"AMZN\", \"ADANIPORTS.NS\", \"MSFT\"]\n", 605 | "ohlcv_data = {}\n", 606 | "\n", 607 | "# looping over tickers and storing OHLCV dataframe in dictionary\n", 608 | "for ticker in tickers:\n", 609 | " temp = yf.download(ticker, period='1mo', interval='5m')\n", 610 | " temp.dropna(how=\"any\", inplace=True)\n", 611 | " ohlcv_data[ticker] = temp\n", 612 | "\n", 613 | "\n", 614 | "def RSI(DF, n=14):\n", 615 | " \"function to calculate RSI\"\n", 616 | " df = DF.copy()\n", 617 | " df[\"change\"] = df[\"Adj Close\"] - df[\"Adj Close\"].shift(1)\n", 618 | " df[\"gain\"] = np.where(df[\"change\"] >= 0, df[\"change\"], 0)\n", 619 | " df[\"loss\"] = np.where(df[\"change\"] < 0, -1*df[\"change\"], 0)\n", 620 | " df[\"avgGain\"] = df[\"gain\"].ewm(alpha=1/n, min_periods=n).mean()\n", 621 | " df[\"avgLoss\"] = df[\"loss\"].ewm(alpha=1/n, min_periods=n).mean()\n", 622 | " df[\"rs\"] = df[\"avgGain\"]/df[\"avgLoss\"]\n", 623 | " df[\"rsi\"] = 100 - (100 / (1 + df[\"rs\"]))\n", 624 | " return df[\"rsi\"]\n", 625 | "\n", 626 | "\n", 627 | "for ticker in ohlcv_data:\n", 628 | " ohlcv_data[ticker][\"RSI\"] = RSI(ohlcv_data[ticker])\n" 629 | ] 630 | }, 631 | { 632 | "cell_type": "code", 633 | "execution_count": 8, 634 | "metadata": {}, 635 | "outputs": [ 636 | { 637 | "data": { 638 | "text/html": [ 639 | "
\n", 640 | "\n", 653 | "\n", 654 | " \n", 655 | " \n", 656 | " \n", 657 | " \n", 658 | " \n", 659 | " \n", 660 | " \n", 661 | " \n", 662 | " \n", 663 | " \n", 664 | " \n", 665 | " \n", 666 | " \n", 667 | " \n", 668 | " \n", 669 | " \n", 670 | " \n", 671 | " \n", 672 | " \n", 673 | " \n", 674 | " \n", 675 | " \n", 676 | " \n", 677 | " \n", 678 | " \n", 679 | " \n", 680 | " \n", 681 | " \n", 682 | " \n", 683 | " \n", 684 | " \n", 685 | " \n", 686 | " \n", 687 | " \n", 688 | " \n", 689 | " \n", 690 | " \n", 691 | " \n", 692 | " \n", 693 | " \n", 694 | " \n", 695 | " \n", 696 | " \n", 697 | " \n", 698 | " \n", 699 | " \n", 700 | " \n", 701 | " \n", 702 | " \n", 703 | " \n", 704 | " \n", 705 | " \n", 706 | " \n", 707 | " \n", 708 | " \n", 709 | " \n", 710 | " \n", 711 | " \n", 712 | " \n", 713 | " \n", 714 | " \n", 715 | " \n", 716 | " \n", 717 | " \n", 718 | " \n", 719 | " \n", 720 | " \n", 721 | " \n", 722 | " \n", 723 | " \n", 724 | " \n", 725 | " \n", 726 | " \n", 727 | " \n", 728 | "
OpenHighLowCloseAdj CloseVolumeRSI
Datetime
2023-04-06 15:10:00+05:30643.200012643.200012640.599976641.450012641.45001216158036.908750
2023-04-06 15:15:00+05:30641.500000641.650024640.450012640.599976640.59997613358734.223427
2023-04-06 15:20:00+05:30640.549988641.900024639.799988641.500000641.50000020732039.262230
2023-04-06 15:25:00+05:30641.599976641.799988641.000000641.250000641.2500008129038.382677
2023-04-06 15:30:00+05:30641.599976641.599976641.599976641.599976641.599976040.395692
\n", 729 | "
" 730 | ], 731 | "text/plain": [ 732 | " Open High Low Close \\\n", 733 | "Datetime \n", 734 | "2023-04-06 15:10:00+05:30 643.200012 643.200012 640.599976 641.450012 \n", 735 | "2023-04-06 15:15:00+05:30 641.500000 641.650024 640.450012 640.599976 \n", 736 | "2023-04-06 15:20:00+05:30 640.549988 641.900024 639.799988 641.500000 \n", 737 | "2023-04-06 15:25:00+05:30 641.599976 641.799988 641.000000 641.250000 \n", 738 | "2023-04-06 15:30:00+05:30 641.599976 641.599976 641.599976 641.599976 \n", 739 | "\n", 740 | " Adj Close Volume RSI \n", 741 | "Datetime \n", 742 | "2023-04-06 15:10:00+05:30 641.450012 161580 36.908750 \n", 743 | "2023-04-06 15:15:00+05:30 640.599976 133587 34.223427 \n", 744 | "2023-04-06 15:20:00+05:30 641.500000 207320 39.262230 \n", 745 | "2023-04-06 15:25:00+05:30 641.250000 81290 38.382677 \n", 746 | "2023-04-06 15:30:00+05:30 641.599976 0 40.395692 " 747 | ] 748 | }, 749 | "execution_count": 8, 750 | "metadata": {}, 751 | "output_type": "execute_result" 752 | } 753 | ], 754 | "source": [ 755 | "ohlcv_data[\"ADANIPORTS.NS\"].tail()\n" 756 | ] 757 | }, 758 | { 759 | "attachments": {}, 760 | "cell_type": "markdown", 761 | "metadata": {}, 762 | "source": [ 763 | "# ADX\n" 764 | ] 765 | }, 766 | { 767 | "cell_type": "code", 768 | "execution_count": 9, 769 | "metadata": {}, 770 | "outputs": [ 771 | { 772 | "name": "stdout", 773 | "output_type": "stream", 774 | "text": [ 775 | "[*********************100%***********************] 1 of 1 completed\n", 776 | "[*********************100%***********************] 1 of 1 completed\n", 777 | "[*********************100%***********************] 1 of 1 completed\n" 778 | ] 779 | } 780 | ], 781 | "source": [ 782 | "tickers = [\"AMZN\", \"ADANIPORTS.NS\", \"MSFT\"]\n", 783 | "ohlcv_data = {}\n", 784 | "\n", 785 | "# looping over tickers and storing OHLCV dataframe in dictionary\n", 786 | "for ticker in tickers:\n", 787 | " temp = yf.download(ticker, period='1mo', interval='5m')\n", 788 | " temp.dropna(how=\"any\", inplace=True)\n", 789 | " ohlcv_data[ticker] = temp\n", 790 | "\n", 791 | "\n", 792 | "def ATR(DF, n=14):\n", 793 | " \"function to calculate True Range and Average True Range\"\n", 794 | " df = DF.copy()\n", 795 | " df[\"H-L\"] = df[\"High\"] - df[\"Low\"]\n", 796 | " df[\"H-PC\"] = abs(df[\"High\"] - df[\"Adj Close\"].shift(1))\n", 797 | " df[\"L-PC\"] = abs(df[\"Low\"] - df[\"Adj Close\"].shift(1))\n", 798 | " df[\"TR\"] = df[[\"H-L\", \"H-PC\", \"L-PC\"]].max(axis=1, skipna=False)\n", 799 | " df[\"ATR\"] = df[\"TR\"].ewm(com=n, min_periods=n).mean()\n", 800 | " return df[\"ATR\"]\n", 801 | "\n", 802 | "\n", 803 | "def ADX(DF, n=20):\n", 804 | " \"function to calculate ADX\"\n", 805 | " df = DF.copy()\n", 806 | " df[\"ATR\"] = ATR(DF, n)\n", 807 | " df[\"upmove\"] = df[\"High\"] - df[\"High\"].shift(1)\n", 808 | " df[\"downmove\"] = df[\"Low\"].shift(1) - df[\"Low\"]\n", 809 | " df[\"+dm\"] = np.where((df[\"upmove\"] > df[\"downmove\"]) &\n", 810 | " (df[\"upmove\"] > 0), df[\"upmove\"], 0)\n", 811 | " df[\"-dm\"] = np.where((df[\"downmove\"] > df[\"upmove\"]) &\n", 812 | " (df[\"downmove\"] > 0), df[\"downmove\"], 0)\n", 813 | " df[\"+di\"] = 100 * (df[\"+dm\"]/df[\"ATR\"]).ewm(alpha=1 /\n", 814 | " n, min_periods=n).mean()\n", 815 | " df[\"-di\"] = 100 * (df[\"-dm\"]/df[\"ATR\"]).ewm(alpha=1 /\n", 816 | " n, min_periods=n).mean()\n", 817 | " df[\"ADX\"] = 100 * abs((df[\"+di\"] - df[\"-di\"])/(df[\"+di\"] +\n", 818 | " df[\"-di\"])).ewm(alpha=1/n, min_periods=n).mean()\n", 819 | " return df[\"ADX\"]\n", 820 | "\n", 821 | "\n", 822 | "for ticker in ohlcv_data:\n", 823 | " ohlcv_data[ticker][\"ADX\"] = ADX(ohlcv_data[ticker], 20)\n" 824 | ] 825 | }, 826 | { 827 | "cell_type": "code", 828 | "execution_count": 10, 829 | "metadata": {}, 830 | "outputs": [ 831 | { 832 | "data": { 833 | "text/html": [ 834 | "
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OpenHighLowCloseAdj CloseVolumeADX
Datetime
2023-04-06 15:10:00+05:30643.200012643.200012640.599976641.450012641.45001216158017.158518
2023-04-06 15:15:00+05:30641.500000641.650024640.450012640.599976640.59997613358717.602587
2023-04-06 15:20:00+05:30640.549988641.900024639.799988641.500000641.50000020732018.199039
2023-04-06 15:25:00+05:30641.599976641.799988641.000000641.250000641.2500008129018.765669
2023-04-06 15:30:00+05:30641.599976641.599976641.599976641.599976641.599976019.303967
\n", 924 | "
" 925 | ], 926 | "text/plain": [ 927 | " Open High Low Close \\\n", 928 | "Datetime \n", 929 | "2023-04-06 15:10:00+05:30 643.200012 643.200012 640.599976 641.450012 \n", 930 | "2023-04-06 15:15:00+05:30 641.500000 641.650024 640.450012 640.599976 \n", 931 | "2023-04-06 15:20:00+05:30 640.549988 641.900024 639.799988 641.500000 \n", 932 | "2023-04-06 15:25:00+05:30 641.599976 641.799988 641.000000 641.250000 \n", 933 | "2023-04-06 15:30:00+05:30 641.599976 641.599976 641.599976 641.599976 \n", 934 | "\n", 935 | " Adj Close Volume ADX \n", 936 | "Datetime \n", 937 | "2023-04-06 15:10:00+05:30 641.450012 161580 17.158518 \n", 938 | "2023-04-06 15:15:00+05:30 640.599976 133587 17.602587 \n", 939 | "2023-04-06 15:20:00+05:30 641.500000 207320 18.199039 \n", 940 | "2023-04-06 15:25:00+05:30 641.250000 81290 18.765669 \n", 941 | "2023-04-06 15:30:00+05:30 641.599976 0 19.303967 " 942 | ] 943 | }, 944 | "execution_count": 10, 945 | "metadata": {}, 946 | "output_type": "execute_result" 947 | } 948 | ], 949 | "source": [ 950 | "ohlcv_data[\"ADANIPORTS.NS\"].tail()\n" 951 | ] 952 | }, 953 | { 954 | "attachments": {}, 955 | "cell_type": "markdown", 956 | "metadata": {}, 957 | "source": [ 958 | "# RENKO\n" 959 | ] 960 | }, 961 | { 962 | "cell_type": "code", 963 | "execution_count": 11, 964 | "metadata": {}, 965 | "outputs": [ 966 | { 967 | "name": "stdout", 968 | "output_type": "stream", 969 | "text": [ 970 | "[*********************100%***********************] 1 of 1 completed\n", 971 | "[*********************100%***********************] 1 of 1 completed\n", 972 | "[*********************100%***********************] 1 of 1 completed\n", 973 | "[*********************100%***********************] 1 of 1 completed\n", 974 | "[*********************100%***********************] 1 of 1 completed\n", 975 | "[*********************100%***********************] 1 of 1 completed\n" 976 | ] 977 | } 978 | ], 979 | "source": [ 980 | "import yfinance as yf\n", 981 | "from stocktrends import Renko\n", 982 | "# Download historical data for required stocks\n", 983 | "tickers = [\"AMZN\", \"ADANIPORTS.NS\", \"MSFT\"]\n", 984 | "ohlcv_data = {}\n", 985 | "hour_data = {}\n", 986 | "renko_data = {}\n", 987 | "\n", 988 | "# looping over tickers and storing OHLCV dataframe in dictionary\n", 989 | "for ticker in tickers:\n", 990 | " temp = yf.download(ticker, period='1mo', interval='5m')\n", 991 | " temp.dropna(how=\"any\", inplace=True)\n", 992 | " ohlcv_data[ticker] = temp\n", 993 | " temp = yf.download(ticker, period='1y', interval='1h')\n", 994 | " temp.dropna(how=\"any\", inplace=True)\n", 995 | " hour_data[ticker] = temp\n", 996 | "\n", 997 | "\n", 998 | "def ATR(DF, n=14):\n", 999 | " \"function to calculate True Range and Average True Range\"\n", 1000 | " df = DF.copy()\n", 1001 | " df[\"H-L\"] = df[\"High\"] - df[\"Low\"]\n", 1002 | " df[\"H-PC\"] = abs(df[\"High\"] - df[\"Adj Close\"].shift(1))\n", 1003 | " df[\"L-PC\"] = abs(df[\"Low\"] - df[\"Adj Close\"].shift(1))\n", 1004 | " df[\"TR\"] = df[[\"H-L\", \"H-PC\", \"L-PC\"]].max(axis=1, skipna=False)\n", 1005 | " df[\"ATR\"] = df[\"TR\"].ewm(com=n, min_periods=n).mean()\n", 1006 | " return df[\"ATR\"]\n", 1007 | "\n", 1008 | "\n", 1009 | "def renko_DF(DF, hourly_df):\n", 1010 | " \"function to convert ohlc data into renko bricks\"\n", 1011 | " df = DF.copy()\n", 1012 | " df.reset_index(inplace=True)\n", 1013 | " df.drop(\"Close\", axis=1, inplace=True)\n", 1014 | " df.columns = [\"date\", \"open\", \"high\", \"low\", \"close\", \"volume\"]\n", 1015 | " df2 = Renko(df)\n", 1016 | " df2.brick_size = 3*round(ATR(hourly_df, 120).iloc[-1], 0)\n", 1017 | " # if using older version of the library please use get_bricks() instead\n", 1018 | " renko_df = df2.get_ohlc_data()\n", 1019 | " return renko_df\n", 1020 | "\n", 1021 | "\n", 1022 | "for ticker in ohlcv_data:\n", 1023 | " renko_data[ticker] = renko_DF(ohlcv_data[ticker], hour_data[ticker])\n" 1024 | ] 1025 | }, 1026 | { 1027 | "cell_type": "code", 1028 | "execution_count": 12, 1029 | "metadata": {}, 1030 | "outputs": [ 1031 | { 1032 | "data": { 1033 | "text/html": [ 1034 | "
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dateopenhighlowcloseuptrend
02023-03-08 09:15:00+05:30648.0675.0648.0675.0True
12023-03-08 12:05:00+05:30675.0702.0675.0702.0True
22023-03-14 14:00:00+05:30675.0675.0648.0648.0False
32023-03-28 09:25:00+05:30648.0648.0621.0621.0False
42023-03-28 10:50:00+05:30621.0621.0594.0594.0False
52023-03-31 09:15:00+05:30621.0648.0621.0648.0True
\n", 1117 | "
" 1118 | ], 1119 | "text/plain": [ 1120 | " date open high low close uptrend\n", 1121 | "0 2023-03-08 09:15:00+05:30 648.0 675.0 648.0 675.0 True\n", 1122 | "1 2023-03-08 12:05:00+05:30 675.0 702.0 675.0 702.0 True\n", 1123 | "2 2023-03-14 14:00:00+05:30 675.0 675.0 648.0 648.0 False\n", 1124 | "3 2023-03-28 09:25:00+05:30 648.0 648.0 621.0 621.0 False\n", 1125 | "4 2023-03-28 10:50:00+05:30 621.0 621.0 594.0 594.0 False\n", 1126 | "5 2023-03-31 09:15:00+05:30 621.0 648.0 621.0 648.0 True" 1127 | ] 1128 | }, 1129 | "execution_count": 12, 1130 | "metadata": {}, 1131 | "output_type": "execute_result" 1132 | } 1133 | ], 1134 | "source": [ 1135 | "renko_data[\"ADANIPORTS.NS\"].tail(10)\n" 1136 | ] 1137 | }, 1138 | { 1139 | "attachments": {}, 1140 | "cell_type": "markdown", 1141 | "metadata": {}, 1142 | "source": [ 1143 | "Can use talib library for patterns but I don't wish to do that\n" 1144 | ] 1145 | }, 1146 | { 1147 | "cell_type": "code", 1148 | "execution_count": null, 1149 | "metadata": {}, 1150 | "outputs": [], 1151 | "source": [] 1152 | } 1153 | ], 1154 | "metadata": { 1155 | "kernelspec": { 1156 | "display_name": "base", 1157 | "language": "python", 1158 | "name": "python3" 1159 | }, 1160 | "language_info": { 1161 | "codemirror_mode": { 1162 | "name": "ipython", 1163 | "version": 3 1164 | }, 1165 | "file_extension": ".py", 1166 | "mimetype": "text/x-python", 1167 | "name": "python", 1168 | "nbconvert_exporter": "python", 1169 | "pygments_lexer": "ipython3", 1170 | "version": "3.9.16" 1171 | }, 1172 | "orig_nbformat": 4, 1173 | "vscode": { 1174 | "interpreter": { 1175 | "hash": "cd78fef2128015050713e82ca51c6520b11aee7c9ee8df750520bbbc7384cbaa" 1176 | } 1177 | } 1178 | }, 1179 | "nbformat": 4, 1180 | "nbformat_minor": 2 1181 | } 1182 | -------------------------------------------------------------------------------- /nifty50_intraday.json: -------------------------------------------------------------------------------- 1 | {"ADANIENT.NS": {"Open": 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