{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ad4226ce",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Price filter for major indices\n",
    "\n",
    "We already looked at a price filter for the S&P500 index [here](/posts/simple-regime-filter). \n",
    "\n",
    "Let us try it for some other common indices and see how it works on them.\n",
    "\n",
    "List of indices to test\n",
    "\n",
    " * Dow Jones - United States\n",
    " * Russell 2000 - England\n",
    " * Nifty 50 - India\n",
    " * S&P500 - United States\n",
    " * Dax - Germany\n",
    " * Nikkei 225 - Japan\n",
    " * Hangseng - HongKong\n",
    " * Moex - Russia\n",
    " * SSE - China\n",
    " * Asx 200 - Australia\n",
    " * Euronext100 - Europe\n",
    " * Tsx Composite - Canada\n",
    " \n",
    "I have downloaded the data from Yahoo from the start of 2000 and applied the moving average price filter for each of the indices. The `get data` function does all the data stuff. Finally, I group by each stock exchange and the price filter and take the daily mean of the returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2d949ae7-3ebd-42a8-9c14-1f365ba744cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader as web\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set(rc={'figure.figsize':(12,6)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99dc471b-a4a8-43ce-934a-1c31b62a39c2",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# parameters\n",
    "ma = 60\n",
    "\n",
    "# tickers are specified as name versus symbol in yahoo finance\n",
    "tickers = {\n",
    "    'dow': '^DJI',\n",
    "    'russell': '^RUT',\n",
    "    'nifty': '^NSEI',\n",
    "    'snp500': '^GSPC',\n",
    "    'dax': '^GDAXI',\n",
    "    'nikkei': '^N225',\n",
    "    'hangseng': '^HSI',\n",
    "    'moex': 'IMOEX.me',\n",
    "    'sse': '000001.SS',\n",
    "    'asx': '^AXAT',\n",
    "    'euronext': '^N100',\n",
    "    'tsx comp': '^GSPTSE'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "38f72aeb-a706-4221-8fdd-d8f161e48261",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(index, name):\n",
    "    \"\"\"\n",
    "    Get the data for the \n",
    "    \"\"\"\n",
    "    df = web.DataReader(index, 'yahoo', start='2000-01-01').rename(columns = lambda x: x.lower())\n",
    "    df = df.sort_index()\n",
    "    df['year'] = df.index.year\n",
    "    df['ret'] = df.close.pct_change()\n",
    "    df['ma_price'] = df.close.rolling(ma).median().shift(1)\n",
    "    df['is_price'] = df.eval('close > ma_price')+0\n",
    "    df['is_price'] = df.is_price.shift(1) # Shifting price since we use the signal only next day\n",
    "    df['name'] = name\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cff2f4bb-9c60-4b7d-9c1a-eca398c70161",
   "metadata": {},
   "outputs": [],
   "source": [
    "collect = []\n",
    "for k,v in tickers.items():\n",
    "    try:\n",
    "        tmp = get_data(v, name=k)\n",
    "        collect.append(tmp)\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "df = pd.concat(collect)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "631d17c6-abf2-4942-af4d-bb312a7082cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='name', ylabel='ret'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_returns = df.groupby(['name', 'is_price']).ret.mean().reset_index()\n",
    "sns.barplot(data=mean_returns, x='name', y='ret', hue='is_price')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a56b3d9-2480-4e67-834c-e3ec4799df63",
   "metadata": {},
   "source": [
    "Looks like there is no common pattern among the indices.\n",
    "\n",
    "And all except hangseng and see provide positive returns for both the filters.\n",
    "\n",
    "On a second look, snp500, dow, russell,euronext does well with a filter of 0 while nifty,nikkei,sse,tsx does well with a filter of 1.\n",
    "\n",
    "May be\n",
    " * the american and european markets exhibit different behaviour than the asian counterparts (but TSX is from Canada)\n",
    " * the older exchanges may exhibit a different behaviour than the new one\n",
    " * the timezone effect\n",
    " * or just plain random\n",
    " \n",
    "I cannot come up with any conclusive explanation. This is a good topic to explore further with a whole lot of other indices\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "    There is no substantial edge to practically trade this filter. Also, we have not run any statistical test to validate significance\n",
    "    </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f300dc4d-13c7-499a-bc78-036a47d0973b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "dow        1999-12-31\n",
       "euronext   1999-12-31\n",
       "russell    1999-12-31\n",
       "snp500     1999-12-31\n",
       "tsx comp   1999-12-31\n",
       "dax        2000-01-03\n",
       "hangseng   2000-01-03\n",
       "nikkei     2000-01-04\n",
       "sse        2000-01-04\n",
       "asx        2007-06-15\n",
       "nifty      2007-09-17\n",
       "moex       2013-03-05\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Starting date from which data is computed\n",
    "df.reset_index().groupby('name').Date.min().sort_values()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.2"
  },
  "nikola": {
   "category": "",
   "date": "2021-12-05 13:26:31 UTC",
   "description": "",
   "link": "",
   "slug": "price-filter-major-indices",
   "tags": "",
   "title": "Price filter major indices",
   "type": "text"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}