{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# RSI ETF Strategy #\n",
"\n",
"\n",
"The project’s primary objective was to create an ETF portfolio by reconstructing a popular hedge fund strategy and employing the Relative Strength Index (RSI) as the primary buy/sell trigger. Tracking each underlying stock regulated the transfers of capital between stocks and the portfolio itself. After considerations were applied the model was run through real life scenarios resulting in calculated cumulative returns which were compared to ETF returns.\n",
"\n",
"\n",
"#### Considerations ####\n",
"\n",
"- Dividends – The model’s calculations had to adhere to ex dividends, record dates, etc.\n",
"- Reinvestment capital – Cash balance was recalculated daily and applied for subsequent trades.\n",
"- Transaction fees (Adjustable parameter)\n",
"- Management fees (Adjustable parameter) – Percentage of annual portfolio.\n",
"- RSI signals – Based on adjustable parameters.\n",
"- Holdings rebalancing – Novel algorithm that rebalances the weights while shares are being bought/sold \n",
"\n",
"\n",
"#### Assumptions ####\n",
"1. Initial capital $1,000,000\n",
"2. Transaction fee 0.01\n",
"3. RSI parameters:\n",
" * RSI period – 14 days\n",
" * Sell signal – RSI 70\n",
" * Buy signal – RSI 30\n",
" * Order is placed only if RSI remains constant for 2 days.\n",
"4. Management fee – Compare returns with 0%, 0.5% and 1% mgmt fees.\n",
"5. Start date – 07/07/2015\n",
"\n",
"\n",
"#### Limitations ####\n",
"The Yahoo finance API is unable to fetch historical prices of companies that are no longer traded resulting in a stock data deficit of 2%. The start date that maximized the model’s potential was chosen and will be further explored in later sections.\n",
"\n",
"> ##### Updates #####\n",
"XIU Canadian ETF added\n",
"\n",
"\n",
"#### Case Study ####\n",
"Given the assumption that ETF AAA combines the following 3 stocks with respective weights: AAPL: 50%, AMZN: 25%, MSFT, 25%, on Day 1, all capital would be invested in ETF AAA. When the first BUY signal is placed (Assume AAPL had surpassed and remained below the bottom RSI threshold of 30 for a couple of days) the model would SELL 50% of capital invested in AAA and BUY AAPL. The resulting rebalanced portfolio weights would be AMZN: 50%, MSFT: 50%. When presented with a SELL indicator, AAPL is sold and reinvested back into AAA."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas_datareader.data as web\n",
"import bs4 as bs\n",
"import urllib.request\n",
"import datetime\n",
"import pandas as pd\n",
"import numpy as np\n",
"import time\n",
"import matplotlib.pyplot as plt\n",
"import plotly.graph_objs as go\n",
"%matplotlib inline\n",
"from plotly import __version__\n",
"from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
"init_notebook_mode(connected=True)\n",
"\n",
"\n",
"index = 'QQQ' #QQQ or XIU\n",
"index2_ticker = '^NDX' # ^NDX or HXT.TO, ^GSPTSE\n",
"initial_capital = float(1000000)\n",
"transaction_fee = 0.01\n",
"RSI_period = 14\n",
"RSI_top = 70\n",
"RSI_bottom = 30\n",
"\n",
"mgmtfeedayno = 15\n",
"mgmtfeemonthno = 12\n",
"mgmtfee = 0.005\n",
"\n",
"start_date = datetime.datetime(2015, 7, 7)\n",
"end_date = datetime.datetime(2019, 4, 18)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"A list of tickers is generated using index file data taken from\n",
"[Invesco](https://www.invesco.com/portal/site/us/financial-professional/etfs/holdings/?ticker=QQQ)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"if index == 'QQQ':\n",
" holdings = pd.read_csv('QQQ_Holdings.csv', encoding = \"ISO-8859-1\")\n",
" holdings_tickers = holdings['Issuer Ticker']\n",
" holdings.set_index('Issuer Ticker',inplace = True)\n",
" tickers = holdings_tickers[:].tolist()\n",
" all_tickers = [index] + tickers\n",
" \n",
"elif index == 'XIU':\n",
" holdings = pd.read_csv('XIU_Holdings.csv', na_filter = False)\n",
" xls = pd.ExcelFile('TSX60 DVD.xlsx')\n",
" excel_sheets = pd.read_excel(xls, None)\n",
" ticker_labels = list(excel_sheets.keys())\n",
" ticker_labels.remove('XIU')\n",
" ticker_labels = ticker_labels[:]\n",
" all_ticker_labels = ticker_labels + [index]\n",
"\n",
" def get_XIUtickers(excelfile ,sheet):\n",
" value = pd.read_excel(xls, sheet_name = sheet)['Symbol'][0]\n",
" return value.split(\" \")[0].replace('/','-').upper()\n",
"\n",
" tickers = [get_XIUtickers(xls,num) for num in ticker_labels]\n",
" all_tickers = tickers + [index]\n",
" \n",
"\n",
" holdings_tickers = holdings[holdings['Sector'] != 'Cash and/or Derivatives']['Issuer Ticker']\n",
" holdings.set_index('Issuer Ticker',inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Pull all the tickers\n",
"def get(tickers):\n",
" def data(ticker):\n",
" time.sleep(0.5)\n",
" if index == 'QQQ': \n",
" return web.DataReader(ticker, 'yahoo', start_date, end_date)\n",
" elif index == 'XIU':\n",
" return web.DataReader(ticker + '.TO', 'yahoo', start_date, end_date)\n",
" \n",
" datas = map(data,tickers) # apply the function to objects\n",
" return pd.concat(datas, keys = tickers, names = ['Ticker', 'Date'])\n",
"\n",
"#Pull data\n",
"all_data = get(all_tickers)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to obtain dividends data, a [NASDAQ](https://www.nasdaq.com/symbol/) scraping algorithm was created that per stock, cloned key tables (e.g. Declaration, Record and Payment dates), processed cloned data through pandas dataframe conversion and original dataset merging. This ensured a master dataframe consisting of all dates, prices and technical indicators."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"#Get index data\n",
"def get_QQQdivdata(tickers):\n",
" # Generate business days from AAPL stock\n",
" bday_data = web.DataReader('AAPL', 'yahoo', start_date, end_date)\n",
" business_days = bday_data.reset_index()[['Date']]\n",
" def data(ticker):\n",
" time.sleep(0.5)\n",
" try: \n",
" sauce = urllib.request.urlopen('https://www.nasdaq.com/symbol/'+ticker+'/dividend-history').read()\n",
" soup = bs.BeautifulSoup(sauce, 'lxml')\n",
" table_html = soup.find(id = 'quotes_content_left_dividendhistoryGrid')\n",
" df = pd.read_html(str(table_html))[0] \n",
" df.loc[:,['Ex/Eff Date' , 'Record Date','Declaration Date', 'Payment Date']] = df.loc[:,['Ex/Eff Date' , 'Record Date','Declaration Date', 'Payment Date']].apply(pd.to_datetime, errors='coerce')\n",
"\n",
" merged = pd.merge(business_days, df, how = 'left', left_on = 'Date', right_on = 'Payment Date')\n",
" except:\n",
" merged = business_days.copy()\n",
" merged['Ex/Eff Date'], merged['Type'], merged['Cash Amount'], merged['Declaration Date'], merged['Record Date'], merged['Payment Date'] = [np.datetime64('NaT'),np.NAN,np.NAN,np.datetime64('NaT'),np.datetime64('NaT'),np.datetime64('NaT')]\n",
"\n",
" del merged['Payment Date']\n",
" merged.set_index('Date', inplace = True)\n",
" return merged \n",
" datas = map(data,tickers) # apply the function to objects\n",
" return(pd.concat(datas, keys = tickers, names = ['Ticker', 'Date']))\n",
"\n",
"\n",
"def get_XIUdivdata(sheet_names):\n",
" bday_data = web.DataReader('TD.TO', 'yahoo', start_date, end_date)\n",
" business_days = bday_data.reset_index()[['Date']]\n",
" \n",
" def data(sheet):\n",
" data = pd.read_excel(xls, sheet_name = sheet).iloc[:,2:9]\n",
" data = data[(data['Type'] == 'Regular Cash')|(data['Type'] == 'Special Cash')|(data['Type'] == 'Stock Dividend')]\n",
" data.dropna(subset=['Ex Date', 'Payable', 'Amount'], inplace = True)\n",
" return pd.merge(business_days, data, how = 'left', left_on = 'Date', right_on = 'Payable')\n",
"\n",
" data = map(data, sheet_names) # Includes XIU\n",
" my_data = pd.concat(data, keys = all_tickers)\n",
" my_data.reset_index(inplace= True)\n",
" del my_data['Freq'], my_data['Type'], my_data['level_1'], my_data['Payable']\n",
" my_data['Type'] = 'Cash'\n",
" my_data.rename(index=str, columns={'level_0':'Ticker', 'Ex Date':'Ex/Eff Date','Amount':'Cash Amount','Declaration':'Declaration Date', 'Record':'Record Date'}, inplace = True)\n",
" return my_data.set_index(['Ticker', 'Date'])\n",
"\n",
"#Pull data\n",
"if index == 'QQQ':\n",
" div_data_withduplicates = get_QQQdivdata(all_tickers)\n",
"elif index == 'XIU':\n",
" div_data_withduplicates = get_XIUdivdata(all_ticker_labels)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAL</th>\n",
" <th>AAPL</th>\n",
" <th>ADBE</th>\n",
" <th>ADI</th>\n",
" <th>ADP</th>\n",
" <th>ADSK</th>\n",
" <th>ALGN</th>\n",
" <th>ALXN</th>\n",
" <th>AMAT</th>\n",
" <th>AMD</th>\n",
" <th>...</th>\n",
" <th>VRSK_EXDATE</th>\n",
" <th>VRSN_EXDATE</th>\n",
" <th>VRTX_EXDATE</th>\n",
" <th>WBA_EXDATE</th>\n",
" <th>WDAY_EXDATE</th>\n",
" <th>WDC_EXDATE</th>\n",
" <th>WLTW_EXDATE</th>\n",
" <th>WYNN_EXDATE</th>\n",
" <th>XEL_EXDATE</th>\n",
" <th>XLNX_EXDATE</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-04-17</th>\n",
" <td>34.380001</td>\n",
" <td>203.130005</td>\n",
" <td>269.450012</td>\n",
" <td>114.400002</td>\n",
" <td>162.850006</td>\n",
" <td>168.029999</td>\n",
" <td>273.679993</td>\n",
" <td>125.910004</td>\n",
" <td>43.889999</td>\n",
" <td>27.49</td>\n",
" <td>...</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-18</th>\n",
" <td>34.369999</td>\n",
" <td>203.860001</td>\n",
" <td>270.570007</td>\n",
" <td>114.790001</td>\n",
" <td>162.440002</td>\n",
" <td>169.199997</td>\n",
" <td>281.489990</td>\n",
" <td>126.010002</td>\n",
" <td>43.950001</td>\n",
" <td>27.68</td>\n",
" <td>...</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" <td>NaT</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 315 columns</p>\n",
"</div>"
],
"text/plain": [
" AAL AAPL ADBE ADI ADP \\\n",
"Date \n",
"2019-04-17 34.380001 203.130005 269.450012 114.400002 162.850006 \n",
"2019-04-18 34.369999 203.860001 270.570007 114.790001 162.440002 \n",
"\n",
" ADSK ALGN ALXN AMAT AMD ... \\\n",
"Date ... \n",
"2019-04-17 168.029999 273.679993 125.910004 43.889999 27.49 ... \n",
"2019-04-18 169.199997 281.489990 126.010002 43.950001 27.68 ... \n",
"\n",
" VRSK_EXDATE VRSN_EXDATE VRTX_EXDATE WBA_EXDATE WDAY_EXDATE \\\n",
"Date \n",
"2019-04-17 NaT NaT NaT NaT NaT \n",
"2019-04-18 NaT NaT NaT NaT NaT \n",
"\n",
" WDC_EXDATE WLTW_EXDATE WYNN_EXDATE XEL_EXDATE XLNX_EXDATE \n",
"Date \n",
"2019-04-17 NaT NaT NaT NaT NaT \n",
"2019-04-18 NaT NaT NaT NaT NaT \n",
"\n",
"[2 rows x 315 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Prepare stock prices\n",
"pivot_nodiv = all_data['Close'].reset_index().pivot('Date', 'Ticker', 'Close')\n",
"\n",
"#Take a sum of divs if happens in one day, take first day as ex date\n",
"div_data_noduplicates = div_data_withduplicates.reset_index().drop_duplicates(['Ticker', 'Date'])\n",
"div_data_noduplicates.set_index(['Ticker', 'Date'], inplace = True)\n",
"del div_data_noduplicates['Cash Amount']\n",
"div_data_cashonly = div_data_withduplicates.groupby(['Ticker','Date']).sum()\n",
"div_data = pd.merge(div_data_cashonly, div_data_noduplicates, how='left', left_index = True, right_index= True)\n",
"\n",
"#Prepare stock divs and ex dates\n",
"pivot_div = div_data['Cash Amount'].reset_index().pivot('Date', 'Ticker', 'Cash Amount')\n",
"pivot_div.columns = [ticker + '_DIV' for ticker in pivot_div.columns.tolist()]\n",
"pivot_exdate = div_data['Ex/Eff Date'].reset_index().pivot('Date', 'Ticker', 'Ex/Eff Date')\n",
"pivot_exdate.columns = [ticker + '_EXDATE' for ticker in pivot_exdate.columns.tolist()]\n",
"\n",
"#Concat stock prices, divs, ex dates\n",
"pivot_data = pd.concat((pivot_nodiv,pivot_div, pivot_exdate), axis=1)\n",
"\n",
"#Fill any gaps with the last filled date\n",
"pivot_data.iloc[:,0:len(all_tickers)] = pivot_data.iloc[:,0:len(all_tickers)].fillna(method='ffill')\n",
"pivot_data.tail(2)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"The following established formulas for RSI calculation were employed.\n",
"\n",
"$$ RSI_\\text{step one} = 100 - \\Bigg[ \\frac{100}{1+\\frac{\\text{Average gain}}{\\text{Average loss}}} \\Bigg] $$\n",
"\n",
"$$ RSI_\\text{step two} = 100 - \\Bigg[ \\frac{100}{1+\\frac{\\text{Previous average gain}*13+\\text{Current gain}}{\\text{Previous average loss}*13+\\text{Current loss}}} \\Bigg] $$"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def RSI(series, period):\n",
" \n",
" delta = series.diff().dropna()\n",
" ups = delta * 0\n",
" downs = ups.copy()\n",
" ups[delta > 0] = delta[delta > 0]\n",
" downs[delta < 0] = -delta[delta < 0]\n",
" ups[ups.index[period-1]] = np.mean( ups[:period] ) #first value is sum of avg gains\n",
" ups = ups.drop(ups.index[:(period-1)])\n",
" downs[downs.index[period-1]] = np.mean( downs[:period] ) #first value is sum of avg losses\n",
" downs = downs.drop(downs.index[:(period-1)])\n",
" rs = ups.ewm(com=period-1,min_periods=0,adjust=False,ignore_na=False).mean() / \\\n",
" downs.ewm(com=period-1,min_periods=0,adjust=False,ignore_na=False).mean() \n",
" return 100 - 100 / (1 + rs)\n",
"\n",
"\n",
"def RSI_signals(series, RSI_top, RSI_bottom):\n",
" \n",
" result = series*0 \n",
" result[(series.shift(2)<RSI_bottom)&(series.shift(1)<RSI_bottom)&(series>=RSI_bottom)] = 1\n",
" result[(series.shift(2)>RSI_top)&(series.shift(1)>RSI_top)&(series<=RSI_top)] = -1\n",
" \n",
" result.replace(0,np.NaN,inplace=True)\n",
" result.fillna(method='ffill',inplace=True)\n",
"\n",
" final_result = result.copy() * 0\n",
" final_result[result.shift(1) != result] = result\n",
" \n",
" #remove first -1\n",
" if final_result.abs().sum() > 0: \n",
" if final_result[final_result.first_valid_index()] == -1:\n",
" final_result[final_result.first_valid_index()] = np.NaN\n",
" \n",
" return final_result\n",
"\n",
"\n",
"for stock_name in tickers:\n",
" pivot_data[stock_name + '_RSI'] = RSI(pivot_data[stock_name], RSI_period)\n",
" pivot_data[stock_name + '_RSI_signals'] = RSI_signals(pivot_data[stock_name + '_RSI'], RSI_top = RSI_top, RSI_bottom = RSI_bottom)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAL</th>\n",
" <th>AAPL</th>\n",
" <th>ADBE</th>\n",
" <th>ADI</th>\n",
" <th>ADP</th>\n",
" <th>ADSK</th>\n",
" <th>ALGN</th>\n",
" <th>ALXN</th>\n",
" <th>AMAT</th>\n",
" <th>AMD</th>\n",
" <th>...</th>\n",
" <th>SNPS_QTY</th>\n",
" <th>MXIM_QTY</th>\n",
" <th>SWKS_QTY</th>\n",
" <th>WDC_QTY</th>\n",
" <th>SYMC_QTY</th>\n",
" <th>ASML_QTY</th>\n",
" <th>CTXS_QTY</th>\n",
" <th>XEL_QTY</th>\n",
" <th>Cash</th>\n",
" <th>Holdings</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-07-07</th>\n",
" <td>40.669998</td>\n",
" <td>125.690002</td>\n",
" <td>80.589996</td>\n",
" <td>63.380001</td>\n",
" <td>81.070000</td>\n",
" <td>53.290001</td>\n",
" <td>63.500000</td>\n",
" <td>187.330002</td>\n",
" <td>19.170000</td>\n",
" <td>2.09</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-08</th>\n",
" <td>39.310001</td>\n",
" <td>122.570000</td>\n",
" <td>79.989998</td>\n",
" <td>61.939999</td>\n",
" <td>80.029999</td>\n",
" <td>52.480000</td>\n",
" <td>62.740002</td>\n",
" <td>184.660004</td>\n",
" <td>18.549999</td>\n",
" <td>2.01</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 736 columns</p>\n",
"</div>"
],
"text/plain": [
" AAL AAPL ADBE ADI ADP ADSK \\\n",
"Date \n",
"2015-07-07 40.669998 125.690002 80.589996 63.380001 81.070000 53.290001 \n",
"2015-07-08 39.310001 122.570000 79.989998 61.939999 80.029999 52.480000 \n",
"\n",
" ALGN ALXN AMAT AMD ... SNPS_QTY MXIM_QTY \\\n",
"Date ... \n",
"2015-07-07 63.500000 187.330002 19.170000 2.09 ... 0 0 \n",
"2015-07-08 62.740002 184.660004 18.549999 2.01 ... 0 0 \n",
"\n",
" SWKS_QTY WDC_QTY SYMC_QTY ASML_QTY CTXS_QTY XEL_QTY Cash \\\n",
"Date \n",
"2015-07-07 0 0 0 0 0 0 0 \n",
"2015-07-08 0 0 0 0 0 0 0 \n",
"\n",
" Holdings \n",
"Date \n",
"2015-07-07 0 \n",
"2015-07-08 0 \n",
"\n",
"[2 rows x 736 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Add weights from csv file\n",
"piv0 = holdings.loc[tickers][['Weight (%)']]\n",
"piv0.reset_index().pivot('Weight (%)', 'Issuer Ticker')\n",
"\n",
"piv1 = piv0.transpose()\n",
"for ticker in tickers:\n",
" piv1.rename(columns = {ticker:ticker + ' weight'},inplace=True)\n",
"\n",
"piv1.reset_index(inplace = True)\n",
"piv1.drop(columns = 'index', inplace= True)\n",
"\n",
"piv1['join'] = 0 #Add any column for the cross join\n",
"result = pivot_data.reset_index()\n",
"result['join'] = 0\n",
"\n",
"result = pd.merge(result,piv1,on=['join'])\n",
"del result['join']\n",
"result.set_index('Date', inplace = True)\n",
"\n",
"## Add empty columns QTY, Cash, Holdings\n",
"result['W_COEFF'] = 0\n",
"result['Mgmt Fee'] = 0\n",
"for stock_name in all_tickers:\n",
" result[stock_name + '_QTY'] = 0\n",
"\n",
"result['Cash'] = 0\n",
"result['Holdings'] = 0\n",
"\n",
"# Add mgmt fee dates\n",
"for feeyear in list(range(start_date.year, end_date.year+1)):\n",
" try: \n",
" monthselection = result.loc[(result.index.year == feeyear)&(result.index.month == mgmtfeemonthno)].index[mgmtfeedayno]\n",
" result.loc[monthselection, 'Mgmt Fee'] = 1\n",
" except: 0\n",
"\n",
"\n",
"result.to_excel('raw_data.xlsx')\n",
"result.head(2)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"The final calculation block: takes a top down approach and calculates cash, dividends among other attributes daily. Portfolio weight rebalancing was processed using the Weight Coefficient (WCoeff).\n",
"\n",
"$$Wcoef_1 =\\frac{Wcoef_0}{ 1 \\pm weight} $$\n",
"\n",
"Assume AAPL: 50%, AMZN: 25%, MSFT, 25%. With AAPL sold first, it results in Wcoef = 1/(1-0.5) = 2. AMZN and MSFT receive 2 * 25% = 50% of the portfolio each.\n",
"\n",
"> Only dividends are considered if we had stocks available at Ex dividend day.\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>AAL</th>\n",
" <th>AAPL</th>\n",
" <th>ADBE</th>\n",
" <th>ADI</th>\n",
" <th>ADP</th>\n",
" <th>ADSK</th>\n",
" <th>ALGN</th>\n",
" <th>ALXN</th>\n",
" <th>AMAT</th>\n",
" <th>...</th>\n",
" <th>SNPS_QTY</th>\n",
" <th>MXIM_QTY</th>\n",
" <th>SWKS_QTY</th>\n",
" <th>WDC_QTY</th>\n",
" <th>SYMC_QTY</th>\n",
" <th>ASML_QTY</th>\n",
" <th>CTXS_QTY</th>\n",
" <th>XEL_QTY</th>\n",
" <th>Cash</th>\n",
" <th>Holdings</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-04-12</th>\n",
" <td>949</td>\n",
" <td>34.689999</td>\n",
" <td>198.869995</td>\n",
" <td>271.859985</td>\n",
" <td>114.209999</td>\n",
" <td>163.309998</td>\n",
" <td>171.869995</td>\n",
" <td>293.079987</td>\n",
" <td>136.199997</td>\n",
" <td>42.990002</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>127.193066</td>\n",
" <td>1.911713e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-15</th>\n",
" <td>950</td>\n",
" <td>33.750000</td>\n",
" <td>199.229996</td>\n",
" <td>272.220001</td>\n",
" <td>112.940002</td>\n",
" <td>163.679993</td>\n",
" <td>172.770004</td>\n",
" <td>292.549988</td>\n",
" <td>137.490005</td>\n",
" <td>42.709999</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>147.193066</td>\n",
" <td>1.911691e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-16</th>\n",
" <td>951</td>\n",
" <td>34.029999</td>\n",
" <td>199.250000</td>\n",
" <td>271.429993</td>\n",
" <td>114.599998</td>\n",
" <td>162.139999</td>\n",
" <td>172.669998</td>\n",
" <td>288.380005</td>\n",
" <td>136.940002</td>\n",
" <td>43.720001</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>147.193066</td>\n",
" <td>1.917512e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-17</th>\n",
" <td>952</td>\n",
" <td>34.380001</td>\n",
" <td>203.130005</td>\n",
" <td>269.450012</td>\n",
" <td>114.400002</td>\n",
" <td>162.850006</td>\n",
" <td>168.029999</td>\n",
" <td>273.679993</td>\n",
" <td>125.910004</td>\n",
" <td>43.889999</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>147.193066</td>\n",
" <td>1.922750e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-18</th>\n",
" <td>953</td>\n",
" <td>34.369999</td>\n",
" <td>203.860001</td>\n",
" <td>270.570007</td>\n",
" <td>114.790001</td>\n",
" <td>162.440002</td>\n",
" <td>169.199997</td>\n",
" <td>281.489990</td>\n",
" <td>126.010002</td>\n",
" <td>43.950001</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>147.193066</td>\n",
" <td>1.925034e+06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 737 columns</p>\n",
"</div>"
],
"text/plain": [
" index AAL AAPL ADBE ADI ADP \\\n",
"Date \n",
"2019-04-12 949 34.689999 198.869995 271.859985 114.209999 163.309998 \n",
"2019-04-15 950 33.750000 199.229996 272.220001 112.940002 163.679993 \n",
"2019-04-16 951 34.029999 199.250000 271.429993 114.599998 162.139999 \n",
"2019-04-17 952 34.380001 203.130005 269.450012 114.400002 162.850006 \n",
"2019-04-18 953 34.369999 203.860001 270.570007 114.790001 162.440002 \n",
"\n",
" ADSK ALGN ALXN AMAT ... SNPS_QTY \\\n",
"Date ... \n",
"2019-04-12 171.869995 293.079987 136.199997 42.990002 ... 0 \n",
"2019-04-15 172.770004 292.549988 137.490005 42.709999 ... 0 \n",
"2019-04-16 172.669998 288.380005 136.940002 43.720001 ... 0 \n",
"2019-04-17 168.029999 273.679993 125.910004 43.889999 ... 0 \n",
"2019-04-18 169.199997 281.489990 126.010002 43.950001 ... 0 \n",
"\n",
" MXIM_QTY SWKS_QTY WDC_QTY SYMC_QTY ASML_QTY CTXS_QTY \\\n",
"Date \n",
"2019-04-12 58 32 40 0 0 22 \n",
"2019-04-15 58 32 40 0 0 22 \n",
"2019-04-16 58 32 40 0 0 22 \n",
"2019-04-17 58 32 40 0 0 22 \n",
"2019-04-18 58 32 40 0 0 22 \n",
"\n",
" XEL_QTY Cash Holdings \n",
"Date \n",
"2019-04-12 0 127.193066 1.911713e+06 \n",
"2019-04-15 0 147.193066 1.911691e+06 \n",
"2019-04-16 0 147.193066 1.917512e+06 \n",
"2019-04-17 0 147.193066 1.922750e+06 \n",
"2019-04-18 0 147.193066 1.925034e+06 \n",
"\n",
"[5 rows x 737 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result = pd.read_excel('raw_data.xlsx')\n",
"df = result.copy().reset_index()\n",
"df.fillna(0,inplace=True)\n",
"\n",
"for i, data in df.iterrows():\n",
"\n",
" IIndQty = divmod(initial_capital,result[index][0]+transaction_fee)[0] \n",
" \n",
" stock_holdings = 0\n",
" WCoeff = 1\n",
" \n",
" if i == 0:\n",
" WCoeff = 1\n",
" df.loc[i,index+'_QTY'] = IIndQty\n",
" df.loc[i,'Cash'] = initial_capital - (df.loc[i,index]+transaction_fee) * IIndQty\n",
" df.loc[i,'Holdings'] = IIndQty * df.loc[i,index] + df.loc[i,'Cash']\n",
" for ticker in tickers:\n",
" if df[ticker+'_RSI_signals'][i] == 1:\n",
" WCoeff = WCoeff / (1-df.loc[i, ticker + ' weight']/100)\n",
" df.loc[i,'W_COEFF'] = WCoeff\n",
" \n",
" else:\n",
" \n",
" WCoeff = df.loc[i-1,'W_COEFF']\n",
" Cash = df.loc[i-1, 'Cash'] #define prev cash\n",
" IndQtyUpdtd = df.loc[i-1, index + '_QTY']\n",
" IndQtyChanges = 0\n",
" mgmtfeeIndqty = 0\n",
" \n",
" for ticker in tickers:\n",
" #Get Stock Quantity on a Exdiv day\n",
" try: ex_qtty = df.loc[df[df['Date'] == df.loc[i,ticker + '_EXDATE']].index[0],ticker + '_QTY']\n",
" except: ex_qtty = 0 \n",
" \n",
" \n",
" if abs(df[ticker+'_RSI_signals'][i]) != 1:\n",
" df.loc[i, ticker + '_QTY'] = df.loc[i-1, ticker + '_QTY'] #stock qty if 0\n",
" Cash = Cash + (ex_qtty * df.loc[i, ticker + '_DIV'])\n",
" \n",
" \n",
" elif df[ticker+'_RSI_signals'][i] == 1:\n",
"\n",
" ThisIndQtyChanges = int(WCoeff * df.loc[i, ticker + ' weight']/100 * IndQtyUpdtd)\n",
" IndQtyUpdtd = IndQtyUpdtd - ThisIndQtyChanges #updated index qty\n",
" IndQtyChanges = IndQtyChanges + ThisIndQtyChanges #all index qty changes\n",
" df.loc[i, ticker + '_QTY'] = int((Cash + ThisIndQtyChanges * (df.loc[i, index]-transaction_fee))/(df.loc[i, ticker]+transaction_fee))\n",
" Cash = (Cash + ThisIndQtyChanges * (df.loc[i, index]-transaction_fee)) - (df.loc[i, ticker + '_QTY'] * (df.loc[i, ticker]+transaction_fee)) + (ex_qtty * df.loc[i, ticker + '_DIV'])\n",
" \n",
" WCoeff = WCoeff / (1-df.loc[i, ticker + ' weight']/100)\n",
"\n",
" \n",
" else: #if signal == -1\n",
" ThisIndQtyChanges = int((Cash + df.loc[i-1, ticker + '_QTY'] * (df.loc[i, ticker]-transaction_fee))/(df.loc[i, index]+transaction_fee))\n",
" IndQtyUpdtd = IndQtyUpdtd + ThisIndQtyChanges\n",
" IndQtyChanges = IndQtyChanges - ThisIndQtyChanges\n",
" Cash = (Cash + df.loc[i-1, ticker + '_QTY'] * (df.loc[i, ticker]-transaction_fee)) - (ThisIndQtyChanges * (df.loc[i, index]+transaction_fee)) + (ex_qtty * df.loc[i, ticker + '_DIV'])\n",
" \n",
" WCoeff = WCoeff * (1-df.loc[i, ticker + ' weight']/100)\n",
" \n",
" #Add final WCoeff\n",
" df.loc[i,'W_COEFF'] = WCoeff\n",
" #Get Index Quantity on a Exdiv day\n",
" try: ex_ind_qtty = df.loc[df[df['Date'] == df.loc[i,index + '_EXDATE']].index[0],index + '_QTY']\n",
" except: ex_ind_qtty = 0 \n",
" \n",
" mgmtfeeIndqty = df.loc[i,'Mgmt Fee'] * int(df.loc[i-1, 'Holdings'] * mgmtfee / df.loc[i,index])\n",
" \n",
" df.loc[i, index + '_QTY'] = df.loc[i-1, index + '_QTY'] - IndQtyChanges - mgmtfeeIndqty #index Qtty\n",
" df.loc[i, 'Cash'] = Cash + (ex_ind_qtty * df.loc[i, index + '_DIV']) #() is index divs\n",
" \n",
" for ticker in tickers: # holdings\n",
" stock_holdings = stock_holdings + df.loc[i, ticker + '_QTY'] * df.loc[i, ticker] #holdings \n",
" df.loc[i, 'Holdings'] = stock_holdings + df.loc[i,index+'_QTY'] * df.loc[i,index] + df.loc[i,'Cash']\n",
"\n",
"\n",
"df.set_index('Date', inplace=True)\n",
"df.to_excel('Portfolio.xlsx')\n",
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df = pd.read_excel('Portfolio.xlsx')\n",
"df.set_index('Date', inplace = True)\n",
"\n",
"def get_returns(DF, column_name_for_prices, column_name_for_divs = False): \n",
" if column_name_for_divs == False:\n",
" DF['Daily returns'] = DF[column_name_for_prices] / DF[column_name_for_prices].shift(1) - 1 \n",
" else:\n",
" DF['Daily returns'] = (DF[column_name_for_prices]+DF[column_name_for_divs]) / DF[column_name_for_prices].shift(1) - 1\n",
" DF.fillna(0,inplace = True)\n",
" DF['Cumulative returns'] = (1 + DF['Daily returns']).cumprod() - 1\n",
" \n",
" \n",
"get_returns(df,'Holdings')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Portfolio Cum returns</th>\n",
" <th>Index Cum returns</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-04-12</th>\n",
" <td>0.911890</td>\n",
" <td>0.784189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-15</th>\n",
" <td>0.911868</td>\n",
" <td>0.784477</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-16</th>\n",
" <td>0.917689</td>\n",
" <td>0.790622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-17</th>\n",
" <td>0.922928</td>\n",
" <td>0.796862</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-18</th>\n",
" <td>0.925213</td>\n",
" <td>0.799167</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Portfolio Cum returns Index Cum returns\n",
"Date \n",
"2019-04-12 0.911890 0.784189\n",
"2019-04-15 0.911868 0.784477\n",
"2019-04-16 0.917689 0.790622\n",
"2019-04-17 0.922928 0.796862\n",
"2019-04-18 0.925213 0.799167"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Select returns of the portfolio and add returns of the index\n",
"\n",
"df_ind = df[[index, index + '_DIV']].reset_index()\n",
"get_returns(df_ind, index, index + '_DIV')\n",
"df_ind.set_index('Date', inplace = True)\n",
"\n",
"df1 = df_ind[['Cumulative returns']]\n",
"df1.columns = ['Index Cum returns']\n",
"\n",
"df2 = df[['Cumulative returns']]\n",
"df2.columns = ['Portfolio Cum returns']\n",
"\n",
"df_allreturns = pd.concat((df2,df1), axis=1)\n",
"df_allreturns\n",
"df_allreturns.tail()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>^NDX</th>\n",
" <th>Daily returns</th>\n",
" <th>Cumulative returns</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-04-12</th>\n",
" <td>7628.149902</td>\n",
" <td>0.004379</td>\n",
" <td>0.722295</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-15</th>\n",
" <td>7629.120117</td>\n",
" <td>0.000127</td>\n",
" <td>0.722514</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-16</th>\n",
" <td>7654.729980</td>\n",
" <td>0.003357</td>\n",
" <td>0.728297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-17</th>\n",
" <td>7680.720215</td>\n",
" <td>0.003395</td>\n",
" <td>0.734165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-18</th>\n",
" <td>7689.720215</td>\n",
" <td>0.001172</td>\n",
" <td>0.736197</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ^NDX Daily returns Cumulative returns\n",
"Date \n",
"2019-04-12 7628.149902 0.004379 0.722295\n",
"2019-04-15 7629.120117 0.000127 0.722514\n",
"2019-04-16 7654.729980 0.003357 0.728297\n",
"2019-04-17 7680.720215 0.003395 0.734165\n",
"2019-04-18 7689.720215 0.001172 0.736197"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Add second index's returns\n",
"\n",
"index2 = web.DataReader(index2_ticker, 'yahoo', start_date, end_date)[['Close']]\n",
"index2.columns = [index2_ticker]\n",
"\n",
"get_returns(index2, index2_ticker)\n",
"index2.tail()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
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},
{
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" {\"legend\": {\"x\": 1, \"y\": 1}, \"title\": {\"text\": \"RSI portfolio strategy vs ETF\"}, \"xaxis\": {\"title\": {\"text\": \"Years\"}}, \"yaxis\": {\"tickformat\": \".2%\", \"title\": {\"text\": \"Returns\"}}},\n",
" {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
" ).then(function(){\n",
" \n",
"var gd = document.getElementById('1bb9d98e-c53f-448e-a3a5-038140e701c8');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" })\n",
" };\n",
" });\n",
" </script>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trace1 = go.Scatter(\n",
" x = df_allreturns.index,\n",
" y = df_allreturns['Portfolio Cum returns'],\n",
" name = 'Portfolio Returns')\n",
"\n",
"\n",
"trace2 = go.Scatter(\n",
" x = df_allreturns.index,\n",
" y = df_allreturns['Index Cum returns'],\n",
" name = str(index) + ' Returns')\n",
"\n",
"trace3 = go.Scatter(\n",
" x = index2.index,\n",
" y = index2['Cumulative returns'],\n",
" name = str(index2_ticker) + ' Returns')\n",
"\n",
"data = [trace1,trace2,trace3]\n",
" \n",
"layout = go.Layout(title = 'RSI portfolio strategy vs ETF'\n",
" , yaxis=dict(title='Returns', tickformat=\".2%\")\n",
" , xaxis=dict(title='Years')\n",
" , legend=dict(x=1,y=1)\n",
" )\n",
"fig = go.Figure(data=data, layout=layout)\n",
" \n",
"iplot(fig)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Final results:\n",
"\n",
"> |Mgmt fee|Portfolio returns|\tQQQ returns|\tNDX Returns|\tDelta Portfolio/ QQQ|\n",
"| --- | --- | --- | --- | --- |\n",
"|0\t |0.963|0.799|0.736|0.164|\n",
"|0.005|0.925|0.799|0.736|0.121|\n",
"|0.01 |0.887|0.799|0.736|0.088|\n",
"\n",
"> The addition of differing transactional fees didn’t significantly affect the results.\n",
"\n",
"In conclusion, the strategy’s implementation successfully led to higher returns when compared to market. All the parameters were set prior to checking the results to avoid overfitting.\n",
"\n",
"By excluding limitations (no stock data deficit) thereby extending the model horizon, this algorithm could hypothetically generate returns as high as 17% with 0.5% mgmt fees. The start date remains at 07/07/2015 because it coincides with the latest significant ETF update.\n",
"\n",
"#### Anyways, Congrats on beating the market! ####"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"The following graph illustrates the amount of obtainable raw data via the Yahoo Finance api and the subsequent conversion according to portfolio weights.\n",
"\n",
"$$Utilization =\\sum_{i=0}^{len(tickers)} (ticker_i + {weight_i}) $$ where ticker is a dummy variable\n",
"\n",
"Seems like all data was fetched starting 2015-07-04 and this is exactly where we started"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x132587048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"result = pd.read_excel('QQQ_alldata.xlsx')\n",
"result['Potential'] = 0\n",
"result_c = result.copy()\n",
"result_c.replace(np.NaN, 0, inplace = True)\n",
"\n",
"for i, data in result_c.iterrows():\n",
" addition = 0\n",
" for ticker in tickers:\n",
" addition = addition + bool(result_c.loc[i,ticker]) * result_c.loc[i,ticker + ' weight']\n",
" result_c.loc[i,'Potential'] = addition\n",
"\n",
"result_c.plot(x = 'Date', y = 'Potential', figsize = (15,6), label = 'Utilization')\n",
"plt.plot(np.repeat(pd.Timestamp(2015, 7, 4),10), range(91,101), 'r', linewidth=3)\n",
"plt.text(pd.Timestamp(2015, 7, 25), 98.8, '2015-07-04', fontsize=12)\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}