stock_plots.ipynb

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   "source": [
    "## Sample of plots using util.ipynb\n",
    "These are usage examples. The stock data is in `Data` folder and assumes Yahoo Finance EOD is in use. \n",
    "The data retrieved uses `Adjusted Close` prices.\n",
    "`SPY` is automatically included but any number of stock symbols can be used."
   ]
  },
  {
   "cell_type": "code",
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      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run util.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dates = pd.date_range('2018-01-01','2018-09-27')\n",
    "df_data = get_data(['TSLA','MSFT'],dates)"
   ]
  },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "plot_bollinger_bands(df_data,'TSLA',window=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prices = get_data(['TSLA'],dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "returns = daily_returns(prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(returns['TSLA'], 'TSLA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "returns['TSLA'].hist(bins=20)\n",
    "returns['SPY'].hist(bins=20)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd04c67edd8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.plot(kind='scatter',x='SPY', y='TSLA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "beta and alpha can be calculated with numpy. Y = mx + b where x is the base (SPY) and the Symbol compared (TSLA) is y\n",
    " m = beta and b = alpha, therefore TSLA plot = beta * returns of SPY + alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd04c6216a0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta,alpha = np.polyfit(returns['SPY'],returns['TSLA'],1)\n",
    "plt.plot(returns['TSLA'], beta*returns['SPY'] + alpha, '-', color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd056401d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vv3RDXffsccGmbVs3Kil0vfQ4yUvrTLc9Lnnh0i59WN+hKyUJSRxckM8he/I5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd04c65a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.00069794370429325102, 1.4543944439826908)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha, beta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lazy trader\n",
    "Lets make a dumb assumption and say we should go with bollinger bands and we need signal there to long/short and surf around"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rm = get_rolling_mean(prices['TSLA'],window=20)\n",
    "rstd = get_rolling_std(prices['TSLA'],window=20)\n",
    "upper, lower = get_bollinger_bands(rm, rstd)\n",
    "# This is not telling us if we actually crossed over, just that we are outside the bands\n",
    "signal_long = prices['TSLA'] < lower \n",
    "signal_short = prices['TSLA'] > upper\n",
    "long = signal_long.astype(np.float64)\n",
    "#False = 0 so -1 from it to get negative\n",
    "short = signal_short.astype(np.float64) - 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#should I short today?\n",
    "signal_short[-1] * prices['TSLA'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#should I go long today?\n",
    "signal_long[-1] * prices['TSLA'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "265.47355155525605"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#whats the current lower band so I can set a limit buy\n",
    "lower[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-02-08    315.230011\n",
       "2018-02-09    310.420013\n",
       "2018-03-27    279.179993\n",
       "2018-03-28    257.779999\n",
       "2018-03-29    266.130005\n",
       "2018-04-02    252.479996\n",
       "2018-07-30    290.170013\n",
       "2018-09-07    263.239990\n",
       "Name: TSLA, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets backtest\n",
    "long_positions = prices['TSLA'] * signal_long\n",
    "long_positions[long_positions != 0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-06-06    319.500000\n",
       "2018-06-07    316.089996\n",
       "2018-06-08    317.660004\n",
       "2018-06-11    332.100006\n",
       "2018-06-12    342.769989\n",
       "2018-06-13    344.779999\n",
       "2018-06-14    357.720001\n",
       "2018-08-02    349.540009\n",
       "2018-08-03    348.170013\n",
       "2018-08-07    379.570007\n",
       "Name: TSLA, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#all short positions same as long\n",
    "short_positions = prices['TSLA'] * signal_short\n",
    "short_positions[short_positions != 0]\n"
   ]
  }
 ],
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