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Hedgefundie_Value_at_risk.ipynb
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{ | |
"nbformat": 4, | |
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"metadata": { | |
"colab": { | |
"name": "Hedgefundie_Value_at_risk.ipynb", | |
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"include_colab_link": true | |
}, | |
"kernelspec": { | |
"name": "python3", | |
"display_name": "Python 3" | |
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{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "view-in-github", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"<a href=\"https://colab.research.google.com/gist/spiyer99/e5c62362e564ce95437060ebfe15c06c/hedgefundie_value_at_risk.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "BSfacWOFGUS_" | |
}, | |
"source": [ | |
"# Attempt 2: Get daily returns from backtrader" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "9DBffc0GGqsN", | |
"outputId": "952f7f1a-fbca-4e12-cd49-2d71a07eeaf8" | |
}, | |
"source": [ | |
"!pip install backtrader yfinance arch" | |
], | |
"execution_count": 25, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"Requirement already satisfied: backtrader in /usr/local/lib/python3.7/dist-packages (1.9.76.123)\n", | |
"Requirement already satisfied: yfinance in /usr/local/lib/python3.7/dist-packages (0.1.63)\n", | |
"Requirement already satisfied: arch in /usr/local/lib/python3.7/dist-packages (5.0.1)\n", | |
"Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n", | |
"Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.6.3)\n", | |
"Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.9)\n", | |
"Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.23.0)\n", | |
"Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n", | |
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n", | |
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n", | |
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n", | |
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2.10)\n", | |
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n", | |
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2021.5.30)\n", | |
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n", | |
"Requirement already satisfied: statsmodels>=0.11 in /usr/local/lib/python3.7/dist-packages (from arch) (0.12.2)\n", | |
"Requirement already satisfied: scipy>=1.3 in /usr/local/lib/python3.7/dist-packages (from arch) (1.4.1)\n", | |
"Requirement already satisfied: property-cached>=1.6.4 in /usr/local/lib/python3.7/dist-packages (from arch) (1.6.4)\n", | |
"Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.11->arch) (0.5.1)\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "hiXXnfV8GmFd" | |
}, | |
"source": [ | |
"import backtrader as bt\n", | |
"from pandas_datareader import data as pdr\n", | |
"from pathlib import Path\n", | |
"import os\n", | |
"from arch import arch_model\n", | |
"import numpy as np\n", | |
"from random import gauss, seed\n", | |
"from scipy.stats import binom\n", | |
"import pandas as pd\n", | |
"import datetime\n", | |
"import yfinance as yf\n", | |
"\n", | |
"yf.pdr_override()" | |
], | |
"execution_count": 26, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "e5HKTM_sGZah" | |
}, | |
"source": [ | |
"def sim_leverage(proxy, leverage=1, expense_ratio = 0.0, initial_value=1.0):\n", | |
" pct_change = proxy.pct_change(1)\n", | |
" pct_change = (pct_change - expense_ratio / 252) * leverage\n", | |
" sim = (1 + pct_change).cumprod() * initial_value\n", | |
" sim[0] = initial_value\n", | |
" return sim" | |
], | |
"execution_count": 27, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "eeACbOfeGbtK", | |
"outputId": "6e0fb02f-ff72-4439-a861-d6693e480f58" | |
}, | |
"source": [ | |
"start = datetime.datetime(1986, 5, 19)\n", | |
"end = datetime.datetime(2021, 6, 12)\n", | |
"\n", | |
"vfinx = pdr.get_data_yahoo(\"VFINX\", start, end)[\"Adj Close\"]\n", | |
"qqq = pdr.get_data_yahoo(\"QQQ\", datetime.datetime(2000, 3, 1), end)[\"Adj Close\"]\n", | |
"vustx = pdr.get_data_yahoo(\"VUSTX\", start, end)[\"Adj Close\"]\n", | |
"psq = pdr.get_data_yahoo(\"PSQ\", start, end)[\"Adj Close\"]\n", | |
"\n", | |
"upro_sim = sim_leverage(vfinx, leverage=3.0, expense_ratio=0.0092).to_frame(\"close\")\n", | |
"tqqq_sim = sim_leverage(qqq, leverage=3.0, expense_ratio=0.0092).to_frame(\"close\")\n", | |
"tmf_sim = sim_leverage(vustx, leverage=3.0, expense_ratio=0.0109).to_frame(\"close\")" | |
], | |
"execution_count": 28, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"[*********************100%***********************] 1 of 1 completed\n", | |
"[*********************100%***********************] 1 of 1 completed\n", | |
"[*********************100%***********************] 1 of 1 completed\n", | |
"[*********************100%***********************] 1 of 1 completed\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "5lK-GYF1GfaL" | |
}, | |
"source": [ | |
"for column in [\"open\", \"high\", \"low\"]:\n", | |
" upro_sim[column] = upro_sim[\"close\"]\n", | |
" tmf_sim[column] = tmf_sim[\"close\"]\n", | |
" tqqq_sim[column] = tqqq_sim[\"close\"]\n", | |
" \n", | |
"upro_sim[\"volume\"] = 0\n", | |
"tqqq_sim[\"volume\"] = 0\n", | |
"tmf_sim[\"volume\"] = 0\n", | |
"\n", | |
"upro_sim = bt.feeds.PandasData(dataname=upro_sim)\n", | |
"tmf_sim = bt.feeds.PandasData(dataname=tmf_sim)\n", | |
"tqqq_sim = bt.feeds.PandasData(dataname=tqqq_sim) " | |
], | |
"execution_count": 29, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "Jz8J-1YBGhaz" | |
}, | |
"source": [ | |
"def backtest(datas, strategy, plot=False, **kwargs):\n", | |
" cerebro = bt.Cerebro()\n", | |
" for data in datas:\n", | |
" cerebro.adddata(data)\n", | |
" cerebro.addanalyzer(bt.analyzers.SharpeRatio, riskfreerate=0.0)\n", | |
" cerebro.addanalyzer(bt.analyzers.Returns)\n", | |
" cerebro.addanalyzer(bt.analyzers.DrawDown)\n", | |
" if Path('hedgefundie_strategy_daily_returns.csv').exists(): os.remove('hedgefundie_strategy_daily_returns.csv')\n", | |
" cerebro.addwriter(bt.WriterFile, csv = True, out='hedgefundie_strategy_daily_returns.csv')\n", | |
" cerebro.addstrategy(strategy, **kwargs)\n", | |
" results = cerebro.run()\n", | |
" if plot:\n", | |
" cerebro.plot()\n", | |
" return (results[0].analyzers.drawdown.get_analysis()['max']['drawdown'],\n", | |
" results[0].analyzers.returns.get_analysis()['rnorm100'],\n", | |
" results[0].analyzers.sharperatio.get_analysis()['sharperatio'])" | |
], | |
"execution_count": 30, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "b_6h1ydhGovR" | |
}, | |
"source": [ | |
"class AssetAllocation(bt.Strategy):\n", | |
" params = (\n", | |
" ('equity',0.6),\n", | |
" )\n", | |
" def __init__(self):\n", | |
" self.UPRO = self.datas[0]\n", | |
" self.TMF = self.datas[1]\n", | |
" self.counter = 0\n", | |
" \n", | |
" def next(self):\n", | |
" if self.counter % 252 == 0: # annual rebalance\n", | |
" self.order_target_percent(self.UPRO, target=self.params.equity)\n", | |
" self.order_target_percent(self.TMF, target=(1 - self.params.equity))\n", | |
" self.counter += 1" | |
], | |
"execution_count": 31, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "YB7Ge_xrGzVb", | |
"outputId": "634358bc-2723-4c2f-ad6b-7fc30edc7b32" | |
}, | |
"source": [ | |
"dd, cagr, sharpe = backtest([upro_sim, tmf_sim], AssetAllocation, plot=True, equity=0.55)\n", | |
"print(f\"Max Drawdown: {dd:.2f}%\\nCAGR: {cagr:.2f}%\\nSharpe: {sharpe:.3f}\")" | |
], | |
"execution_count": 32, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"Max Drawdown: 54.66%\n", | |
"CAGR: 16.27%\n", | |
"Sharpe: 0.592\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "04u598UWslkR" | |
}, | |
"source": [ | |
"def fix_csv(name = 'hedgefundie_strategy_daily_returns.csv'):\n", | |
" # fix the fix exported by cerebro\n", | |
" with open(name,'r') as f:\n", | |
" new_name = Path(name).stem + '_fixed.csv'\n", | |
" if Path(new_name).exists(): os.remove(new_name)\n", | |
" with open(Path(name).stem + '_fixed.csv','w') as f1:\n", | |
" next(f) # skip header line\n", | |
" for line in f:\n", | |
" f1.write(line)\n", | |
"fix_csv()" | |
], | |
"execution_count": 33, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 0 | |
}, | |
"id": "hF935hNrqkN1", | |
"outputId": "5b97ab15-26bf-4104-b1f9-22eabcc0b298" | |
}, | |
"source": [ | |
"backtest = pd.read_csv('hedgefundie_strategy_daily_returns_fixed.csv', parse_dates = True)\n", | |
"cols = ['datetime', 'open', 'high', 'low', 'close', 'volume', 'value']\n", | |
"backtest = backtest[backtest['datetime'].notnull()][cols].reset_index(drop=True)\n", | |
"backtest.head()" | |
], | |
"execution_count": 34, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/html": [ | |
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"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>datetime</th>\n", | |
" <th>open</th>\n", | |
" <th>high</th>\n", | |
" <th>low</th>\n", | |
" <th>close</th>\n", | |
" <th>volume</th>\n", | |
" <th>value</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>1986-05-19 00:00:00</td>\n", | |
" <td>1.000000</td>\n", | |
" <td>1.000000</td>\n", | |
" <td>1.000000</td>\n", | |
" <td>1.000000</td>\n", | |
" <td>0.0</td>\n", | |
" <td>10000.000000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>1986-05-20 00:00:00</td>\n", | |
" <td>1.037538</td>\n", | |
" <td>1.037538</td>\n", | |
" <td>1.037538</td>\n", | |
" <td>1.037538</td>\n", | |
" <td>0.0</td>\n", | |
" <td>10000.000000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>1986-05-21 00:00:00</td>\n", | |
" <td>1.030191</td>\n", | |
" <td>1.030191</td>\n", | |
" <td>1.030191</td>\n", | |
" <td>1.030191</td>\n", | |
" <td>0.0</td>\n", | |
" <td>9959.593644</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>1986-05-22 00:00:00</td>\n", | |
" <td>1.091267</td>\n", | |
" <td>1.091267</td>\n", | |
" <td>1.091267</td>\n", | |
" <td>1.091267</td>\n", | |
" <td>0.0</td>\n", | |
" <td>10295.507652</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>1986-05-23 00:00:00</td>\n", | |
" <td>1.107348</td>\n", | |
" <td>1.107348</td>\n", | |
" <td>1.107348</td>\n", | |
" <td>1.107348</td>\n", | |
" <td>0.0</td>\n", | |
" <td>10383.958368</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" datetime open high ... close volume value\n", | |
"0 1986-05-19 00:00:00 1.000000 1.000000 ... 1.000000 0.0 10000.000000\n", | |
"1 1986-05-20 00:00:00 1.037538 1.037538 ... 1.037538 0.0 10000.000000\n", | |
"2 1986-05-21 00:00:00 1.030191 1.030191 ... 1.030191 0.0 9959.593644\n", | |
"3 1986-05-22 00:00:00 1.091267 1.091267 ... 1.091267 0.0 10295.507652\n", | |
"4 1986-05-23 00:00:00 1.107348 1.107348 ... 1.107348 0.0 10383.958368\n", | |
"\n", | |
"[5 rows x 7 columns]" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
}, | |
"execution_count": 34 | |
} | |
] | |
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"backtest.plot(figsize=(10,10))" | |
], | |
"execution_count": 35, | |
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"\n", | |
" function canvas_keyboard_event(event) {\n", | |
" return fig.key_event(event, event['data']);\n", | |
" }\n", | |
"\n", | |
" canvas_div.keydown('key_press', canvas_keyboard_event);\n", | |
" canvas_div.keyup('key_release', canvas_keyboard_event);\n", | |
" this.canvas_div = canvas_div\n", | |
" this._canvas_extra_style(canvas_div)\n", | |
" this.root.append(canvas_div);\n", | |
"\n", | |
" var canvas = $('<canvas/>');\n", | |
" canvas.addClass('mpl-canvas');\n", | |
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", | |
"\n", | |
" this.canvas = canvas[0];\n", | |
" this.context = canvas[0].getContext(\"2d\");\n", | |
"\n", | |
" var backingStore = this.context.backingStorePixelRatio ||\n", | |
"\tthis.context.webkitBackingStorePixelRatio ||\n", | |
"\tthis.context.mozBackingStorePixelRatio ||\n", | |
"\tthis.context.msBackingStorePixelRatio ||\n", | |
"\tthis.context.oBackingStorePixelRatio ||\n", | |
"\tthis.context.backingStorePixelRatio || 1;\n", | |
"\n", | |
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", | |
"\n", | |
" var rubberband = $('<canvas/>');\n", | |
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", | |
"\n", | |
" var pass_mouse_events = true;\n", | |
"\n", | |
" canvas_div.resizable({\n", | |
" start: function(event, ui) {\n", | |
" pass_mouse_events = false;\n", | |
" },\n", | |
" resize: function(event, ui) {\n", | |
" fig.request_resize(ui.size.width, ui.size.height);\n", | |
" },\n", | |
" stop: function(event, ui) {\n", | |
" pass_mouse_events = true;\n", | |
" fig.request_resize(ui.size.width, ui.size.height);\n", | |
" },\n", | |
" });\n", | |
"\n", | |
" function mouse_event_fn(event) {\n", | |
" if (pass_mouse_events)\n", | |
" return fig.mouse_event(event, event['data']);\n", | |
" }\n", | |
"\n", | |
" rubberband.mousedown('button_press', mouse_event_fn);\n", | |
" rubberband.mouseup('button_release', mouse_event_fn);\n", | |
" // Throttle sequential mouse events to 1 every 20ms.\n", | |
" rubberband.mousemove('motion_notify', mouse_event_fn);\n", | |
"\n", | |
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n", | |
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n", | |
"\n", | |
" canvas_div.on(\"wheel\", function (event) {\n", | |
" event = event.originalEvent;\n", | |
" event['data'] = 'scroll'\n", | |
" if (event.deltaY < 0) {\n", | |
" event.step = 1;\n", | |
" } else {\n", | |
" event.step = -1;\n", | |
" }\n", | |
" mouse_event_fn(event);\n", | |
" });\n", | |
"\n", | |
" canvas_div.append(canvas);\n", | |
" canvas_div.append(rubberband);\n", | |
"\n", | |
" this.rubberband = rubberband;\n", | |
" this.rubberband_canvas = rubberband[0];\n", | |
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n", | |
" this.rubberband_context.strokeStyle = \"#000000\";\n", | |
"\n", | |
" this._resize_canvas = function(width, height) {\n", | |
" // Keep the size of the canvas, canvas container, and rubber band\n", | |
" // canvas in synch.\n", | |
" canvas_div.css('width', width)\n", | |
" canvas_div.css('height', height)\n", | |
"\n", | |
" canvas.attr('width', width * mpl.ratio);\n", | |
" canvas.attr('height', height * mpl.ratio);\n", | |
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", | |
"\n", | |
" rubberband.attr('width', width);\n", | |
" rubberband.attr('height', height);\n", | |
" }\n", | |
"\n", | |
" // Set the figure to an initial 600x600px, this will subsequently be updated\n", | |
" // upon first draw.\n", | |
" this._resize_canvas(600, 600);\n", | |
"\n", | |
" // Disable right mouse context menu.\n", | |
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", | |
" return false;\n", | |
" });\n", | |
"\n", | |
" function set_focus () {\n", | |
" canvas.focus();\n", | |
" canvas_div.focus();\n", | |
" }\n", | |
"\n", | |
" window.setTimeout(set_focus, 100);\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype._init_toolbar = function() {\n", | |
" var fig = this;\n", | |
"\n", | |
" var nav_element = $('<div/>');\n", | |
" nav_element.attr('style', 'width: 100%');\n", | |
" this.root.append(nav_element);\n", | |
"\n", | |
" // Define a callback function for later on.\n", | |
" function toolbar_event(event) {\n", | |
" return fig.toolbar_button_onclick(event['data']);\n", | |
" }\n", | |
" function toolbar_mouse_event(event) {\n", | |
" return fig.toolbar_button_onmouseover(event['data']);\n", | |
" }\n", | |
"\n", | |
" for(var toolbar_ind in mpl.toolbar_items) {\n", | |
" var name = mpl.toolbar_items[toolbar_ind][0];\n", | |
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", | |
" var image = mpl.toolbar_items[toolbar_ind][2];\n", | |
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n", | |
"\n", | |
" if (!name) {\n", | |
" // put a spacer in here.\n", | |
" continue;\n", | |
" }\n", | |
" var button = $('<button/>');\n", | |
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", | |
" 'ui-button-icon-only');\n", | |
" button.attr('role', 'button');\n", | |
" button.attr('aria-disabled', 'false');\n", | |
" button.click(method_name, toolbar_event);\n", | |
" button.mouseover(tooltip, toolbar_mouse_event);\n", | |
"\n", | |
" var icon_img = $('<span/>');\n", | |
" icon_img.addClass('ui-button-icon-primary ui-icon');\n", | |
" icon_img.addClass(image);\n", | |
" icon_img.addClass('ui-corner-all');\n", | |
"\n", | |
" var tooltip_span = $('<span/>');\n", | |
" tooltip_span.addClass('ui-button-text');\n", | |
" tooltip_span.html(tooltip);\n", | |
"\n", | |
" button.append(icon_img);\n", | |
" button.append(tooltip_span);\n", | |
"\n", | |
" nav_element.append(button);\n", | |
" }\n", | |
"\n", | |
" var fmt_picker_span = $('<span/>');\n", | |
"\n", | |
" var fmt_picker = $('<select/>');\n", | |
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", | |
" fmt_picker_span.append(fmt_picker);\n", | |
" nav_element.append(fmt_picker_span);\n", | |
" this.format_dropdown = fmt_picker[0];\n", | |
"\n", | |
" for (var ind in mpl.extensions) {\n", | |
" var fmt = mpl.extensions[ind];\n", | |
" var option = $(\n", | |
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", | |
" fmt_picker.append(option);\n", | |
" }\n", | |
"\n", | |
" // Add hover states to the ui-buttons\n", | |
" $( \".ui-button\" ).hover(\n", | |
" function() { $(this).addClass(\"ui-state-hover\");},\n", | |
" function() { $(this).removeClass(\"ui-state-hover\");}\n", | |
" );\n", | |
"\n", | |
" var status_bar = $('<span class=\"mpl-message\"/>');\n", | |
" nav_element.append(status_bar);\n", | |
" this.message = status_bar[0];\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", | |
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", | |
" // which will in turn request a refresh of the image.\n", | |
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.send_message = function(type, properties) {\n", | |
" properties['type'] = type;\n", | |
" properties['figure_id'] = this.id;\n", | |
" this.ws.send(JSON.stringify(properties));\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.send_draw_message = function() {\n", | |
" if (!this.waiting) {\n", | |
" this.waiting = true;\n", | |
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", | |
" }\n", | |
"}\n", | |
"\n", | |
"\n", | |
"mpl.figure.prototype.handle_save = function(fig, msg) {\n", | |
" var format_dropdown = fig.format_dropdown;\n", | |
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", | |
" fig.ondownload(fig, format);\n", | |
"}\n", | |
"\n", | |
"\n", | |
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n", | |
" var size = msg['size'];\n", | |
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", | |
" fig._resize_canvas(size[0], size[1]);\n", | |
" fig.send_message(\"refresh\", {});\n", | |
" };\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", | |
" var x0 = msg['x0'] / mpl.ratio;\n", | |
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", | |
" var x1 = msg['x1'] / mpl.ratio;\n", | |
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", | |
" x0 = Math.floor(x0) + 0.5;\n", | |
" y0 = Math.floor(y0) + 0.5;\n", | |
" x1 = Math.floor(x1) + 0.5;\n", | |
" y1 = Math.floor(y1) + 0.5;\n", | |
" var min_x = Math.min(x0, x1);\n", | |
" var min_y = Math.min(y0, y1);\n", | |
" var width = Math.abs(x1 - x0);\n", | |
" var height = Math.abs(y1 - y0);\n", | |
"\n", | |
" fig.rubberband_context.clearRect(\n", | |
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n", | |
"\n", | |
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", | |
" // Updates the figure title.\n", | |
" fig.header.textContent = msg['label'];\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", | |
" var cursor = msg['cursor'];\n", | |
" switch(cursor)\n", | |
" {\n", | |
" case 0:\n", | |
" cursor = 'pointer';\n", | |
" break;\n", | |
" case 1:\n", | |
" cursor = 'default';\n", | |
" break;\n", | |
" case 2:\n", | |
" cursor = 'crosshair';\n", | |
" break;\n", | |
" case 3:\n", | |
" cursor = 'move';\n", | |
" break;\n", | |
" }\n", | |
" fig.rubberband_canvas.style.cursor = cursor;\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_message = function(fig, msg) {\n", | |
" fig.message.textContent = msg['message'];\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n", | |
" // Request the server to send over a new figure.\n", | |
" fig.send_draw_message();\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", | |
" fig.image_mode = msg['mode'];\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.updated_canvas_event = function() {\n", | |
" // Called whenever the canvas gets updated.\n", | |
" this.send_message(\"ack\", {});\n", | |
"}\n", | |
"\n", | |
"// A function to construct a web socket function for onmessage handling.\n", | |
"// Called in the figure constructor.\n", | |
"mpl.figure.prototype._make_on_message_function = function(fig) {\n", | |
" return function socket_on_message(evt) {\n", | |
" if (evt.data instanceof Blob) {\n", | |
" /* FIXME: We get \"Resource interpreted as Image but\n", | |
" * transferred with MIME type text/plain:\" errors on\n", | |
" * Chrome. But how to set the MIME type? It doesn't seem\n", | |
" * to be part of the websocket stream */\n", | |
" evt.data.type = \"image/png\";\n", | |
"\n", | |
" /* Free the memory for the previous frames */\n", | |
" if (fig.imageObj.src) {\n", | |
" (window.URL || window.webkitURL).revokeObjectURL(\n", | |
" fig.imageObj.src);\n", | |
" }\n", | |
"\n", | |
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", | |
" evt.data);\n", | |
" fig.updated_canvas_event();\n", | |
" fig.waiting = false;\n", | |
" return;\n", | |
" }\n", | |
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", | |
" fig.imageObj.src = evt.data;\n", | |
" fig.updated_canvas_event();\n", | |
" fig.waiting = false;\n", | |
" return;\n", | |
" }\n", | |
"\n", | |
" var msg = JSON.parse(evt.data);\n", | |
" var msg_type = msg['type'];\n", | |
"\n", | |
" // Call the \"handle_{type}\" callback, which takes\n", | |
" // the figure and JSON message as its only arguments.\n", | |
" try {\n", | |
" var callback = fig[\"handle_\" + msg_type];\n", | |
" } catch (e) {\n", | |
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", | |
" return;\n", | |
" }\n", | |
"\n", | |
" if (callback) {\n", | |
" try {\n", | |
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", | |
" callback(fig, msg);\n", | |
" } catch (e) {\n", | |
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", | |
" }\n", | |
" }\n", | |
" };\n", | |
"}\n", | |
"\n", | |
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", | |
"mpl.findpos = function(e) {\n", | |
" //this section is from http://www.quirksmode.org/js/events_properties.html\n", | |
" var targ;\n", | |
" if (!e)\n", | |
" e = window.event;\n", | |
" if (e.target)\n", | |
" targ = e.target;\n", | |
" else if (e.srcElement)\n", | |
" targ = e.srcElement;\n", | |
" if (targ.nodeType == 3) // defeat Safari bug\n", | |
" targ = targ.parentNode;\n", | |
"\n", | |
" // jQuery normalizes the pageX and pageY\n", | |
" // pageX,Y are the mouse positions relative to the document\n", | |
" // offset() returns the position of the element relative to the document\n", | |
" var x = e.pageX - $(targ).offset().left;\n", | |
" var y = e.pageY - $(targ).offset().top;\n", | |
"\n", | |
" return {\"x\": x, \"y\": y};\n", | |
"};\n", | |
"\n", | |
"/*\n", | |
" * return a copy of an object with only non-object keys\n", | |
" * we need this to avoid circular references\n", | |
" * http://stackoverflow.com/a/24161582/3208463\n", | |
" */\n", | |
"function simpleKeys (original) {\n", | |
" return Object.keys(original).reduce(function (obj, key) {\n", | |
" if (typeof original[key] !== 'object')\n", | |
" obj[key] = original[key]\n", | |
" return obj;\n", | |
" }, {});\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.mouse_event = function(event, name) {\n", | |
" var canvas_pos = mpl.findpos(event)\n", | |
"\n", | |
" if (name === 'button_press')\n", | |
" {\n", | |
" this.canvas.focus();\n", | |
" this.canvas_div.focus();\n", | |
" }\n", | |
"\n", | |
" var x = canvas_pos.x * mpl.ratio;\n", | |
" var y = canvas_pos.y * mpl.ratio;\n", | |
"\n", | |
" this.send_message(name, {x: x, y: y, button: event.button,\n", | |
" step: event.step,\n", | |
" guiEvent: simpleKeys(event)});\n", | |
"\n", | |
" /* This prevents the web browser from automatically changing to\n", | |
" * the text insertion cursor when the button is pressed. We want\n", | |
" * to control all of the cursor setting manually through the\n", | |
" * 'cursor' event from matplotlib */\n", | |
" event.preventDefault();\n", | |
" return false;\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype._key_event_extra = function(event, name) {\n", | |
" // Handle any extra behaviour associated with a key event\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.key_event = function(event, name) {\n", | |
"\n", | |
" // Prevent repeat events\n", | |
" if (name == 'key_press')\n", | |
" {\n", | |
" if (event.which === this._key)\n", | |
" return;\n", | |
" else\n", | |
" this._key = event.which;\n", | |
" }\n", | |
" if (name == 'key_release')\n", | |
" this._key = null;\n", | |
"\n", | |
" var value = '';\n", | |
" if (event.ctrlKey && event.which != 17)\n", | |
" value += \"ctrl+\";\n", | |
" if (event.altKey && event.which != 18)\n", | |
" value += \"alt+\";\n", | |
" if (event.shiftKey && event.which != 16)\n", | |
" value += \"shift+\";\n", | |
"\n", | |
" value += 'k';\n", | |
" value += event.which.toString();\n", | |
"\n", | |
" this._key_event_extra(event, name);\n", | |
"\n", | |
" this.send_message(name, {key: value,\n", | |
" guiEvent: simpleKeys(event)});\n", | |
" return false;\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", | |
" if (name == 'download') {\n", | |
" this.handle_save(this, null);\n", | |
" } else {\n", | |
" this.send_message(\"toolbar_button\", {name: name});\n", | |
" }\n", | |
"};\n", | |
"\n", | |
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", | |
" this.message.textContent = tooltip;\n", | |
"};\n", | |
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", | |
"\n", | |
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", | |
"\n", | |
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", | |
" // Create a \"websocket\"-like object which calls the given IPython comm\n", | |
" // object with the appropriate methods. Currently this is a non binary\n", | |
" // socket, so there is still some room for performance tuning.\n", | |
" var ws = {};\n", | |
"\n", | |
" ws.close = function() {\n", | |
" comm.close()\n", | |
" };\n", | |
" ws.send = function(m) {\n", | |
" //console.log('sending', m);\n", | |
" comm.send(m);\n", | |
" };\n", | |
" // Register the callback with on_msg.\n", | |
" comm.on_msg(function(msg) {\n", | |
" //console.log('receiving', msg['content']['data'], msg);\n", | |
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n", | |
" ws.onmessage(msg['content']['data'])\n", | |
" });\n", | |
" return ws;\n", | |
"}\n", | |
"\n", | |
"mpl.mpl_figure_comm = function(comm, msg) {\n", | |
" // This is the function which gets called when the mpl process\n", | |
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n", | |
"\n", | |
" var id = msg.content.data.id;\n", | |
" // Get hold of the div created by the display call when the Comm\n", | |
" // socket was opened in Python.\n", | |
" var element = $(\"#\" + id);\n", | |
" var ws_proxy = comm_websocket_adapter(comm)\n", | |
"\n", | |
" function ondownload(figure, format) {\n", | |
" window.open(figure.imageObj.src);\n", | |
" }\n", | |
"\n", | |
" var fig = new mpl.figure(id, ws_proxy,\n", | |
" ondownload,\n", | |
" element.get(0));\n", | |
"\n", | |
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", | |
" // web socket which is closed, not our websocket->open comm proxy.\n", | |
" ws_proxy.onopen();\n", | |
"\n", | |
" fig.parent_element = element.get(0);\n", | |
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", | |
" if (!fig.cell_info) {\n", | |
" console.error(\"Failed to find cell for figure\", id, fig);\n", | |
" return;\n", | |
" }\n", | |
"\n", | |
" var output_index = fig.cell_info[2]\n", | |
" var cell = fig.cell_info[0];\n", | |
"\n", | |
"};\n", | |
"\n", | |
"mpl.figure.prototype.handle_close = function(fig, msg) {\n", | |
" var width = fig.canvas.width/mpl.ratio\n", | |
" fig.root.unbind('remove')\n", | |
"\n", | |
" // Update the output cell to use the data from the current canvas.\n", | |
" fig.push_to_output();\n", | |
" var dataURL = fig.canvas.toDataURL();\n", | |
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n", | |
" // the notebook keyboard shortcuts fail.\n", | |
" IPython.keyboard_manager.enable()\n", | |
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", | |
" fig.close_ws(fig, msg);\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.close_ws = function(fig, msg){\n", | |
" fig.send_message('closing', msg);\n", | |
" // fig.ws.close()\n", | |
"}\n", | |
"\n", | |
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" var width = this.canvas.width/mpl.ratio\n", | |
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" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", | |
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" // Define a callback function for later on.\n", | |
" function toolbar_event(event) {\n", | |
" return fig.toolbar_button_onclick(event['data']);\n", | |
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" button.mouseover(tooltip, toolbar_mouse_event);\n", | |
" nav_element.append(button);\n", | |
" }\n", | |
"\n", | |
" // Add the status bar.\n", | |
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", | |
" nav_element.append(status_bar);\n", | |
" this.message = status_bar[0];\n", | |
"\n", | |
" // Add the close button to the window.\n", | |
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", | |
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", | |
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n", | |
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n", | |
" buttongrp.append(button);\n", | |
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", | |
" titlebar.prepend(buttongrp);\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype._root_extra_style = function(el){\n", | |
" var fig = this\n", | |
" el.on(\"remove\", function(){\n", | |
"\tfig.close_ws(fig, {});\n", | |
" });\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype._canvas_extra_style = function(el){\n", | |
" // this is important to make the div 'focusable\n", | |
" el.attr('tabindex', 0)\n", | |
" // reach out to IPython and tell the keyboard manager to turn it's self\n", | |
" // off when our div gets focus\n", | |
"\n", | |
" // location in version 3\n", | |
" if (IPython.notebook.keyboard_manager) {\n", | |
" IPython.notebook.keyboard_manager.register_events(el);\n", | |
" }\n", | |
" else {\n", | |
" // location in version 2\n", | |
" IPython.keyboard_manager.register_events(el);\n", | |
" }\n", | |
"\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype._key_event_extra = function(event, name) {\n", | |
" var manager = IPython.notebook.keyboard_manager;\n", | |
" if (!manager)\n", | |
" manager = IPython.keyboard_manager;\n", | |
"\n", | |
" // Check for shift+enter\n", | |
" if (event.shiftKey && event.which == 13) {\n", | |
" this.canvas_div.blur();\n", | |
" // select the cell after this one\n", | |
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", | |
" IPython.notebook.select(index + 1);\n", | |
" }\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_save = function(fig, msg) {\n", | |
" fig.ondownload(fig, null);\n", | |
"}\n", | |
"\n", | |
"\n", | |
"mpl.find_output_cell = function(html_output) {\n", | |
" // Return the cell and output element which can be found *uniquely* in the notebook.\n", | |
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", | |
" // IPython event is triggered only after the cells have been serialised, which for\n", | |
" // our purposes (turning an active figure into a static one), is too late.\n", | |
" var cells = IPython.notebook.get_cells();\n", | |
" var ncells = cells.length;\n", | |
" for (var i=0; i<ncells; i++) {\n", | |
" var cell = cells[i];\n", | |
" if (cell.cell_type === 'code'){\n", | |
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n", | |
" var data = cell.output_area.outputs[j];\n", | |
" if (data.data) {\n", | |
" // IPython >= 3 moved mimebundle to data attribute of output\n", | |
" data = data.data;\n", | |
" }\n", | |
" if (data['text/html'] == html_output) {\n", | |
" return [cell, data, j];\n", | |
" }\n", | |
" }\n", | |
" }\n", | |
" }\n", | |
"}\n", | |
"\n", | |
"// Register the function which deals with the matplotlib target/channel.\n", | |
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"if (IPython.notebook.kernel != null) {\n", | |
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", | |
"}\n" | |
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"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe4e8012b90>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
}, | |
"execution_count": 35 | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "Y-H7z0GCvwWh" | |
}, | |
"source": [ | |
"backtest['return'] = backtest['value'].pct_change()" | |
], | |
"execution_count": 36, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "2LPrjSOwvtQR" | |
}, | |
"source": [ | |
"return_list=backtest['return'].tolist()[1:]" | |
], | |
"execution_count": 37, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "d2NjCsJLvtQS" | |
}, | |
"source": [ | |
"GARCH(1,1) model is used to determined the volatility." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "mOtX5ZA4vtQS", | |
"outputId": "c43a2680-8969-41b5-91b2-0b4f406e60d0" | |
}, | |
"source": [ | |
"# Using GARCH(1,1) to determine the volatility\n", | |
"model = arch_model(return_list, mean='Zero', vol='GARCH', p=1, q=1)\n", | |
"model_fit = model.fit()\n", | |
"volatility = model_fit.conditional_volatility\n", | |
"print(model_fit.summary())" | |
], | |
"execution_count": 38, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"/usr/local/lib/python3.7/dist-packages/arch/univariate/base.py:310: DataScaleWarning: y is poorly scaled, which may affect convergence of the optimizer when\n", | |
"estimating the model parameters. The scale of y is 0.0003138. Parameter\n", | |
"estimation work better when this value is between 1 and 1000. The recommended\n", | |
"rescaling is 100 * y.\n", | |
"\n", | |
"This warning can be disabled by either rescaling y before initializing the\n", | |
"model or by setting rescale=False.\n", | |
"\n", | |
" data_scale_warning.format(orig_scale, rescale), DataScaleWarning\n" | |
], | |
"name": "stderr" | |
}, | |
{ | |
"output_type": "stream", | |
"text": [ | |
"Iteration: 1, Func. Count: 5, Neg. LLF: -23438.325824725303\n", | |
"Optimization terminated successfully. (Exit mode 0)\n", | |
" Current function value: -23438.325854447183\n", | |
" Iterations: 5\n", | |
" Function evaluations: 5\n", | |
" Gradient evaluations: 1\n", | |
" Zero Mean - GARCH Model Results \n", | |
"==============================================================================\n", | |
"Dep. Variable: y R-squared: 0.000\n", | |
"Mean Model: Zero Mean Adj. R-squared: 0.000\n", | |
"Vol Model: GARCH Log-Likelihood: 23438.3\n", | |
"Distribution: Normal AIC: -46870.7\n", | |
"Method: Maximum Likelihood BIC: -46849.4\n", | |
" No. Observations: 8693\n", | |
"Date: Fri, Jul 30 2021 Df Residuals: 8693\n", | |
"Time: 01:05:55 Df Model: 0\n", | |
" Volatility Model \n", | |
"============================================================================\n", | |
" coef std err t P>|t| 95.0% Conf. Int.\n", | |
"----------------------------------------------------------------------------\n", | |
"omega 6.2879e-06 6.998e-13 8.985e+06 0.000 [6.288e-06,6.288e-06]\n", | |
"alpha[1] 0.0500 1.334e-04 374.781 0.000 [4.974e-02,5.026e-02]\n", | |
"beta[1] 0.9300 1.715e-03 542.304 0.000 [ 0.927, 0.933]\n", | |
"============================================================================\n", | |
"\n", | |
"Covariance estimator: robust\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "V6yQunngvtQX" | |
}, | |
"source": [ | |
"Estimate the Volatility in the next date with the GARCH(1,1) model" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "eHMM0akDvtQX", | |
"outputId": "3fab0049-0add-4214-81bc-93a6a55438df" | |
}, | |
"source": [ | |
"# Estimate the volatility in the next date\n", | |
"omega = model_fit.params['omega'] \n", | |
"alpha = model_fit.params['alpha[1]']\n", | |
"beta = model_fit.params['beta[1]']\n", | |
"vola_estimate = omega + alpha * np.power(return_list[-1] , 2) + beta * np.power( volatility[-1] , 2 )\n", | |
"vola_estimate = np.sqrt(vola_estimate)\n", | |
"print('The volatility in the next date=', vola_estimate )\n", | |
"#return_scienarios = []\n", | |
"#for i in range(0,return_list)" | |
], | |
"execution_count": 39, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"The volatility in the next date= 0.016502061111247215\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "mamGxP2zvtQY" | |
}, | |
"source": [ | |
"Value at Risk Calculation: historical simulation with volatility updating" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "FdBXb1-VvtQY" | |
}, | |
"source": [ | |
"# calculate the predicted value in the next date under different scenario and inporporate volatility updating\n", | |
"Market_scienarios = []\n", | |
"N_history = len(return_list)\n", | |
"Value_current = backtest.loc[backtest.index[-1], 'value']\n", | |
"backtest.loc[backtest.index[-1], 'value']\n", | |
"for i in range(0, N_history ):\n", | |
" Market_value = Value_current * ( 1 + return_list[i] * vola_estimate/volatility[i] )\n", | |
" Market_scienarios.append(Market_value)" | |
], | |
"execution_count": 40, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "NqvRVtjNvtQZ", | |
"outputId": "c3190e4d-b281-4a93-ad69-ad06d43754cf" | |
}, | |
"source": [ | |
"# Value at Risk (VaR) determination\n", | |
"# CL_Var: condifence level for VaR\n", | |
"CL_VaR = 95\n", | |
"Value_current = backtest.loc[backtest.index[-1], 'value']\n", | |
"Value_at_Risk = np.percentile(Market_scienarios,100-CL_VaR) - Value_current\n", | |
"print(\"The 1 day\", CL_VaR,\"% VaR:\", -Value_at_Risk)" | |
], | |
"execution_count": 41, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"The 1 day 95 % VaR: 47264.910781804705\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "icgRS2X2vtQZ" | |
}, | |
"source": [ | |
"# Make the histogram of loss for the scenaros considered between current date and the next date\n", | |
"import matplotlib.pyplot as plt\n", | |
"mss = np.asarray(Value_current - Market_scienarios )\n", | |
"\n", | |
"plt.xlim([min(mss), max(mss)])\n", | |
"\n", | |
"plt.hist(mss, bins='auto', alpha=0.5)\n", | |
"plt.title('Plot of loss for the scenaros')\n", | |
"plt.xlabel('Loss')\n", | |
"plt.ylabel('Frequency')\n", | |
"plt.axvline( -Value_at_Risk , color='k', linestyle='dashed', linewidth=1)\n", | |
"min_ylim, max_ylim = plt.ylim()\n", | |
"plt.text(-Value_at_Risk*1.1, max_ylim*0.9, '{}% VaR: {:.2f}'.format(CL_VaR ,-Value_at_Risk))\n", | |
"plt.show()" | |
], | |
"execution_count": 42, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "1PyJ8mx7vtQa" | |
}, | |
"source": [ | |
"Value-at-Risk backtesting:\n", | |
"here the binomial one-tailed test is used" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "O9rBa7H4vtQa", | |
"outputId": "b17d47b8-3b62-4059-b2f5-7c00b6b16e5e" | |
}, | |
"source": [ | |
"# Confidence level for backtesting\n", | |
"CL_backtesting = 95\n", | |
"# The probability of the VaR beeing exceeded on given day\n", | |
"p = 1 - CL_VaR/100\n", | |
"N_confidence = binom.ppf( CL_backtesting*0.01, N_history, p)\n", | |
"N_exceeded = sum( v < Value_at_Risk + Value_current for v in Market_scienarios)\n", | |
"\n", | |
"print(\"We look at total\", N_history,\"days. VaR level is reached at\", N_exceeded , \"days.\")\n", | |
"if N_exceeded <= N_confidence:\n", | |
" print (\"This VaR model is accepted at\", CL_backtesting,\"% confidence level.\")\n", | |
"else:\n", | |
" print (\"This VaR model is rejected at\", CL_backtesting,\"% confidence level.\")" | |
], | |
"execution_count": 43, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"We look at total 8693 days. VaR level is reached at 435 days.\n", | |
"This VaR model is accepted at 95 % confidence level.\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "J6dtFBbawKaa" | |
}, | |
"source": [ | |
"" | |
], | |
"execution_count": 43, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "BzkzWtjk-3vv" | |
}, | |
"source": [ | |
"# Attempt 1: Single long VAR" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "av5Mm9F_0egA", | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"outputId": "42b7b99a-9c7f-4a32-ecca-31b0df871dee" | |
}, | |
"source": [ | |
"!pip install arch" | |
], | |
"execution_count": 61, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"Requirement already satisfied: arch in /usr/local/lib/python3.7/dist-packages (5.0.1)\n", | |
"Requirement already satisfied: scipy>=1.3 in /usr/local/lib/python3.7/dist-packages (from arch) (1.4.1)\n", | |
"Requirement already satisfied: pandas>=1.0 in /usr/local/lib/python3.7/dist-packages (from arch) (1.1.5)\n", | |
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from arch) (1.19.5)\n", | |
"Requirement already satisfied: statsmodels>=0.11 in /usr/local/lib/python3.7/dist-packages (from arch) (0.12.2)\n", | |
"Requirement already satisfied: property-cached>=1.6.4 in /usr/local/lib/python3.7/dist-packages (from arch) (1.6.4)\n", | |
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0->arch) (2.8.1)\n", | |
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0->arch) (2018.9)\n", | |
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0->arch) (1.15.0)\n", | |
"Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.11->arch) (0.5.1)\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "XC7Ce1A_zoF_" | |
}, | |
"source": [ | |
"# example of ARCH model\n", | |
"import numpy as np\n", | |
"from random import gauss\n", | |
"from random import seed\n", | |
"from matplotlib import pyplot\n", | |
"from arch import arch_model\n", | |
"import datetime\n", | |
"import pandas as pd\n", | |
"# from pandas_datareader import data as pdr\n", | |
"# import yfinance as yf\n", | |
"from scipy.stats import binom" | |
], | |
"execution_count": 62, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "TlKwBVLjQqUY" | |
}, | |
"source": [ | |
"Read and preprocess the data such as calculating the continously compounded return." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "e_-w3EY12eHo", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 204 | |
}, | |
"outputId": "08eee641-4f8d-429f-c938-150281d18b69" | |
}, | |
"source": [ | |
"# hedgefundie monthly returns taken from: \n", | |
"# https://www.portfoliovisualizer.com/backtest-portfolio?s=y&timePeriod=2&startYear=1985&firstMonth=8&endYear=2021&lastMonth=12&calendarAligned=true&includeYTD=false&initialAmount=10000&annualOperation=0&annualAdjustment=0&inflationAdjusted=true&annualPercentage=0.0&frequency=4&rebalanceType=3&absoluteDeviation=5.0&relativeDeviation=25.0&reinvestDividends=true&showYield=false&showFactors=false&factorModel=3&benchmark=VFINX&portfolioNames=true&portfolioName1=3x+AWG&portfolioName2=Hedgefundie+55%2F45&portfolioName3=Portfolio+3&symbol1=VFINX&allocation1_1=90&allocation1_2=165&symbol2=VUSTX&allocation2_1=120&allocation2_2=135&symbol3=VFITX&allocation3_1=45&symbol4=%5EGOLD&allocation4_1=30&symbol5=GSG&symbol6=CASHX&allocation6_1=-185&allocation6_2=-200&symbol7=IJH&symbol8=IJR\n", | |
"data = pd.read_csv(\"hedgefundie_monthly_returns.csv\", dtype = {'Year': int, 'Month': int, 'Return': 'string', 'Balance': 'string'}) \n", | |
"data['Return'] = data['Return'].str.replace('%', '').astype(float)\n", | |
"data['Return'] = (data['Return']/100).astype(float)\n", | |
"data['Balance'] = data['Balance'].str.replace('$', '')\n", | |
"data['Balance'] = data['Balance'].str.replace(',', '').astype(float)\n", | |
"data.head()" | |
], | |
"execution_count": 118, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
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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>Year</th>\n", | |
" <th>Month</th>\n", | |
" <th>Return</th>\n", | |
" <th>Balance</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>1991</td>\n", | |
" <td>11</td>\n", | |
" <td>-0.0740</td>\n", | |
" <td>9260.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>1991</td>\n", | |
" <td>12</td>\n", | |
" <td>0.2778</td>\n", | |
" <td>11833.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>1992</td>\n", | |
" <td>1</td>\n", | |
" <td>-0.0809</td>\n", | |
" <td>10875.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>1992</td>\n", | |
" <td>2</td>\n", | |
" <td>0.0239</td>\n", | |
" <td>11136.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>1992</td>\n", | |
" <td>3</td>\n", | |
" <td>-0.0557</td>\n", | |
" <td>10515.0</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" Year Month Return Balance\n", | |
"0 1991 11 -0.0740 9260.0\n", | |
"1 1991 12 0.2778 11833.0\n", | |
"2 1992 1 -0.0809 10875.0\n", | |
"3 1992 2 0.0239 11136.0\n", | |
"4 1992 3 -0.0557 10515.0" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
}, | |
"execution_count": 118 | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "EjaJZtf7Kt7A" | |
}, | |
"source": [ | |
"return_list=data['Return'].tolist()" | |
], | |
"execution_count": 119, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "41E2WxK-P9RO" | |
}, | |
"source": [ | |
"GARCH(1,1) model is used to determined the volatility." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "E-RHDDEcN7bf", | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"outputId": "85e07ac5-888d-4d7d-9f68-f09eabf87477" | |
}, | |
"source": [ | |
"# Using GARCH(1,1) to determine the volatility\n", | |
"model = arch_model(return_list, mean='Zero', vol='GARCH', p=1, q=1)\n", | |
"model_fit = model.fit()\n", | |
"volatility = model_fit.conditional_volatility\n", | |
"print(model_fit.summary())" | |
], | |
"execution_count": 120, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"Iteration: 1, Func. Count: 5, Neg. LLF: -428.1139665133687\n", | |
"Iteration: 2, Func. Count: 13, Neg. LLF: -428.1925706963546\n", | |
"Iteration: 3, Func. Count: 20, Neg. LLF: -428.336400163769\n", | |
"Iteration: 4, Func. Count: 26, Neg. LLF: -428.4391125510042\n", | |
"Iteration: 5, Func. Count: 32, Neg. LLF: -428.4751234916761\n", | |
"Iteration: 6, Func. Count: 37, Neg. LLF: -428.4817581531861\n", | |
"Iteration: 7, Func. Count: 42, Neg. LLF: -428.48270478855306\n", | |
"Iteration: 8, Func. Count: 47, Neg. LLF: -428.48271083311005\n", | |
"Optimization terminated successfully. (Exit mode 0)\n", | |
" Current function value: -428.4827108330161\n", | |
" Iterations: 8\n", | |
" Function evaluations: 47\n", | |
" Gradient evaluations: 8\n", | |
" Zero Mean - GARCH Model Results \n", | |
"==============================================================================\n", | |
"Dep. Variable: y R-squared: 0.000\n", | |
"Mean Model: Zero Mean Adj. R-squared: 0.003\n", | |
"Vol Model: GARCH Log-Likelihood: 428.483\n", | |
"Distribution: Normal AIC: -850.965\n", | |
"Method: Maximum Likelihood BIC: -839.341\n", | |
" No. Observations: 356\n", | |
"Date: Thu, Jul 29 2021 Df Residuals: 356\n", | |
"Time: 23:42:20 Df Model: 0\n", | |
" Volatility Model \n", | |
"=============================================================================\n", | |
" coef std err t P>|t| 95.0% Conf. Int.\n", | |
"-----------------------------------------------------------------------------\n", | |
"omega 6.8528e-04 3.967e-04 1.728 8.405e-02 [-9.216e-05,1.463e-03]\n", | |
"alpha[1] 0.1651 6.942e-02 2.378 1.741e-02 [2.902e-02, 0.301]\n", | |
"beta[1] 0.7145 0.109 6.549 5.780e-11 [ 0.501, 0.928]\n", | |
"=============================================================================\n", | |
"\n", | |
"Covariance estimator: robust\n" | |
], | |
"name": "stdout" | |
}, | |
{ | |
"output_type": "stream", | |
"text": [ | |
"/usr/local/lib/python3.7/dist-packages/arch/univariate/base.py:310: DataScaleWarning: y is poorly scaled, which may affect convergence of the optimizer when\n", | |
"estimating the model parameters. The scale of y is 0.005512. Parameter\n", | |
"estimation work better when this value is between 1 and 1000. The recommended\n", | |
"rescaling is 10 * y.\n", | |
"\n", | |
"This warning can be disabled by either rescaling y before initializing the\n", | |
"model or by setting rescale=False.\n", | |
"\n", | |
" data_scale_warning.format(orig_scale, rescale), DataScaleWarning\n" | |
], | |
"name": "stderr" | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "gXxF-vwnUFfU" | |
}, | |
"source": [ | |
"Estimate the Volatility in the next date with the GARCH(1,1) model" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "e3EuHIKqUFGf", | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"outputId": "6b60c36d-bbe0-4979-9887-c2d4cb306219" | |
}, | |
"source": [ | |
"# Estimate the volatility in the next date\n", | |
"omega = model_fit.params['omega'] \n", | |
"alpha = model_fit.params['alpha[1]']\n", | |
"beta = model_fit.params['beta[1]']\n", | |
"vola_estimate = omega + alpha * np.power(return_list[-1] , 2) + beta * np.power( volatility[-1] , 2 )\n", | |
"vola_estimate = np.sqrt(vola_estimate)\n", | |
"print('The volatility in the next date=', vola_estimate )\n", | |
"#return_scienarios = []\n", | |
"#for i in range(0,return_list)" | |
], | |
"execution_count": 121, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"The volatility in the next date= 0.07786391557051343\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "4KJXGsPbLYov" | |
}, | |
"source": [ | |
"Value at Risk Calculation: historical simulation with volatility updating" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "V_5xkz4Wl1Pz" | |
}, | |
"source": [ | |
"# calculate the predicted value in the next date under different scenario and inporporate volatility updating\n", | |
"Market_scienarios = []\n", | |
"N_history = len(return_list)\n", | |
"Value_current = df.loc[df.index[-1], 'value']\n", | |
"df.loc[df.index[-1], 'value']\n", | |
"for i in range(0, N_history ):\n", | |
" Market_value = Value_current * ( 1 + return_list[i] * vola_estimate/volatility[i] )\n", | |
" Market_scienarios.append(Market_value)" | |
], | |
"execution_count": 122, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "jg2kWLUWl1CM", | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"outputId": "5a751bb2-4611-43be-e597-10fb4b2ea2b4" | |
}, | |
"source": [ | |
"# Value at Risk (VaR) determination\n", | |
"# CL_Var: condifence level for VaR\n", | |
"CL_VaR = 95\n", | |
"Value_current = df.loc[df.index[-1], 'value']\n", | |
"Value_at_Risk = np.percentile(Market_scienarios,100-CL_VaR) - Value_current\n", | |
"print(\"The 1 Month\", CL_VaR,\"% VaR:\", -Value_at_Risk)" | |
], | |
"execution_count": 123, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"The 1 Month 95 % VaR: 1.3969630967506657\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "ZLxs5LYPl04T", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 295 | |
}, | |
"outputId": "ba5822a3-eed9-4ef9-8735-3fec5d43f8cd" | |
}, | |
"source": [ | |
"# Make the histogram of loss for the scenaros considered between current date and the next date\n", | |
"import matplotlib.pyplot as plt\n", | |
"mss = np.asarray(Value_current - Market_scienarios )\n", | |
"\n", | |
"plt.xlim([min(mss), max(mss)])\n", | |
"\n", | |
"plt.hist(mss, bins='auto', alpha=0.5)\n", | |
"plt.title('Plot of loss for the scenaros')\n", | |
"plt.xlabel('Loss')\n", | |
"plt.ylabel('Frequency')\n", | |
"plt.axvline( -Value_at_Risk , color='k', linestyle='dashed', linewidth=1)\n", | |
"min_ylim, max_ylim = plt.ylim()\n", | |
"plt.text(-Value_at_Risk*1.1, max_ylim*0.9, '{}% VaR: {:.2f}'.format(CL_VaR ,-Value_at_Risk))\n", | |
"plt.show()" | |
], | |
"execution_count": 124, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
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" }\n", | |
"\n", | |
" var fmt_picker_span = $('<span/>');\n", | |
"\n", | |
" var fmt_picker = $('<select/>');\n", | |
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", | |
" fmt_picker_span.append(fmt_picker);\n", | |
" nav_element.append(fmt_picker_span);\n", | |
" this.format_dropdown = fmt_picker[0];\n", | |
"\n", | |
" for (var ind in mpl.extensions) {\n", | |
" var fmt = mpl.extensions[ind];\n", | |
" var option = $(\n", | |
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", | |
" fmt_picker.append(option);\n", | |
" }\n", | |
"\n", | |
" // Add hover states to the ui-buttons\n", | |
" $( \".ui-button\" ).hover(\n", | |
" function() { $(this).addClass(\"ui-state-hover\");},\n", | |
" function() { $(this).removeClass(\"ui-state-hover\");}\n", | |
" );\n", | |
"\n", | |
" var status_bar = $('<span class=\"mpl-message\"/>');\n", | |
" nav_element.append(status_bar);\n", | |
" this.message = status_bar[0];\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", | |
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", | |
" // which will in turn request a refresh of the image.\n", | |
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.send_message = function(type, properties) {\n", | |
" properties['type'] = type;\n", | |
" properties['figure_id'] = this.id;\n", | |
" this.ws.send(JSON.stringify(properties));\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.send_draw_message = function() {\n", | |
" if (!this.waiting) {\n", | |
" this.waiting = true;\n", | |
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", | |
" }\n", | |
"}\n", | |
"\n", | |
"\n", | |
"mpl.figure.prototype.handle_save = function(fig, msg) {\n", | |
" var format_dropdown = fig.format_dropdown;\n", | |
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", | |
" fig.ondownload(fig, format);\n", | |
"}\n", | |
"\n", | |
"\n", | |
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n", | |
" var size = msg['size'];\n", | |
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", | |
" fig._resize_canvas(size[0], size[1]);\n", | |
" fig.send_message(\"refresh\", {});\n", | |
" };\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", | |
" var x0 = msg['x0'] / mpl.ratio;\n", | |
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", | |
" var x1 = msg['x1'] / mpl.ratio;\n", | |
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", | |
" x0 = Math.floor(x0) + 0.5;\n", | |
" y0 = Math.floor(y0) + 0.5;\n", | |
" x1 = Math.floor(x1) + 0.5;\n", | |
" y1 = Math.floor(y1) + 0.5;\n", | |
" var min_x = Math.min(x0, x1);\n", | |
" var min_y = Math.min(y0, y1);\n", | |
" var width = Math.abs(x1 - x0);\n", | |
" var height = Math.abs(y1 - y0);\n", | |
"\n", | |
" fig.rubberband_context.clearRect(\n", | |
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n", | |
"\n", | |
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", | |
" // Updates the figure title.\n", | |
" fig.header.textContent = msg['label'];\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", | |
" var cursor = msg['cursor'];\n", | |
" switch(cursor)\n", | |
" {\n", | |
" case 0:\n", | |
" cursor = 'pointer';\n", | |
" break;\n", | |
" case 1:\n", | |
" cursor = 'default';\n", | |
" break;\n", | |
" case 2:\n", | |
" cursor = 'crosshair';\n", | |
" break;\n", | |
" case 3:\n", | |
" cursor = 'move';\n", | |
" break;\n", | |
" }\n", | |
" fig.rubberband_canvas.style.cursor = cursor;\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_message = function(fig, msg) {\n", | |
" fig.message.textContent = msg['message'];\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n", | |
" // Request the server to send over a new figure.\n", | |
" fig.send_draw_message();\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", | |
" fig.image_mode = msg['mode'];\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.updated_canvas_event = function() {\n", | |
" // Called whenever the canvas gets updated.\n", | |
" this.send_message(\"ack\", {});\n", | |
"}\n", | |
"\n", | |
"// A function to construct a web socket function for onmessage handling.\n", | |
"// Called in the figure constructor.\n", | |
"mpl.figure.prototype._make_on_message_function = function(fig) {\n", | |
" return function socket_on_message(evt) {\n", | |
" if (evt.data instanceof Blob) {\n", | |
" /* FIXME: We get \"Resource interpreted as Image but\n", | |
" * transferred with MIME type text/plain:\" errors on\n", | |
" * Chrome. But how to set the MIME type? It doesn't seem\n", | |
" * to be part of the websocket stream */\n", | |
" evt.data.type = \"image/png\";\n", | |
"\n", | |
" /* Free the memory for the previous frames */\n", | |
" if (fig.imageObj.src) {\n", | |
" (window.URL || window.webkitURL).revokeObjectURL(\n", | |
" fig.imageObj.src);\n", | |
" }\n", | |
"\n", | |
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", | |
" evt.data);\n", | |
" fig.updated_canvas_event();\n", | |
" fig.waiting = false;\n", | |
" return;\n", | |
" }\n", | |
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", | |
" fig.imageObj.src = evt.data;\n", | |
" fig.updated_canvas_event();\n", | |
" fig.waiting = false;\n", | |
" return;\n", | |
" }\n", | |
"\n", | |
" var msg = JSON.parse(evt.data);\n", | |
" var msg_type = msg['type'];\n", | |
"\n", | |
" // Call the \"handle_{type}\" callback, which takes\n", | |
" // the figure and JSON message as its only arguments.\n", | |
" try {\n", | |
" var callback = fig[\"handle_\" + msg_type];\n", | |
" } catch (e) {\n", | |
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", | |
" return;\n", | |
" }\n", | |
"\n", | |
" if (callback) {\n", | |
" try {\n", | |
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", | |
" callback(fig, msg);\n", | |
" } catch (e) {\n", | |
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", | |
" }\n", | |
" }\n", | |
" };\n", | |
"}\n", | |
"\n", | |
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", | |
"mpl.findpos = function(e) {\n", | |
" //this section is from http://www.quirksmode.org/js/events_properties.html\n", | |
" var targ;\n", | |
" if (!e)\n", | |
" e = window.event;\n", | |
" if (e.target)\n", | |
" targ = e.target;\n", | |
" else if (e.srcElement)\n", | |
" targ = e.srcElement;\n", | |
" if (targ.nodeType == 3) // defeat Safari bug\n", | |
" targ = targ.parentNode;\n", | |
"\n", | |
" // jQuery normalizes the pageX and pageY\n", | |
" // pageX,Y are the mouse positions relative to the document\n", | |
" // offset() returns the position of the element relative to the document\n", | |
" var x = e.pageX - $(targ).offset().left;\n", | |
" var y = e.pageY - $(targ).offset().top;\n", | |
"\n", | |
" return {\"x\": x, \"y\": y};\n", | |
"};\n", | |
"\n", | |
"/*\n", | |
" * return a copy of an object with only non-object keys\n", | |
" * we need this to avoid circular references\n", | |
" * http://stackoverflow.com/a/24161582/3208463\n", | |
" */\n", | |
"function simpleKeys (original) {\n", | |
" return Object.keys(original).reduce(function (obj, key) {\n", | |
" if (typeof original[key] !== 'object')\n", | |
" obj[key] = original[key]\n", | |
" return obj;\n", | |
" }, {});\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.mouse_event = function(event, name) {\n", | |
" var canvas_pos = mpl.findpos(event)\n", | |
"\n", | |
" if (name === 'button_press')\n", | |
" {\n", | |
" this.canvas.focus();\n", | |
" this.canvas_div.focus();\n", | |
" }\n", | |
"\n", | |
" var x = canvas_pos.x * mpl.ratio;\n", | |
" var y = canvas_pos.y * mpl.ratio;\n", | |
"\n", | |
" this.send_message(name, {x: x, y: y, button: event.button,\n", | |
" step: event.step,\n", | |
" guiEvent: simpleKeys(event)});\n", | |
"\n", | |
" /* This prevents the web browser from automatically changing to\n", | |
" * the text insertion cursor when the button is pressed. We want\n", | |
" * to control all of the cursor setting manually through the\n", | |
" * 'cursor' event from matplotlib */\n", | |
" event.preventDefault();\n", | |
" return false;\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype._key_event_extra = function(event, name) {\n", | |
" // Handle any extra behaviour associated with a key event\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.key_event = function(event, name) {\n", | |
"\n", | |
" // Prevent repeat events\n", | |
" if (name == 'key_press')\n", | |
" {\n", | |
" if (event.which === this._key)\n", | |
" return;\n", | |
" else\n", | |
" this._key = event.which;\n", | |
" }\n", | |
" if (name == 'key_release')\n", | |
" this._key = null;\n", | |
"\n", | |
" var value = '';\n", | |
" if (event.ctrlKey && event.which != 17)\n", | |
" value += \"ctrl+\";\n", | |
" if (event.altKey && event.which != 18)\n", | |
" value += \"alt+\";\n", | |
" if (event.shiftKey && event.which != 16)\n", | |
" value += \"shift+\";\n", | |
"\n", | |
" value += 'k';\n", | |
" value += event.which.toString();\n", | |
"\n", | |
" this._key_event_extra(event, name);\n", | |
"\n", | |
" this.send_message(name, {key: value,\n", | |
" guiEvent: simpleKeys(event)});\n", | |
" return false;\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", | |
" if (name == 'download') {\n", | |
" this.handle_save(this, null);\n", | |
" } else {\n", | |
" this.send_message(\"toolbar_button\", {name: name});\n", | |
" }\n", | |
"};\n", | |
"\n", | |
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", | |
" this.message.textContent = tooltip;\n", | |
"};\n", | |
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", | |
"\n", | |
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", | |
"\n", | |
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", | |
" // Create a \"websocket\"-like object which calls the given IPython comm\n", | |
" // object with the appropriate methods. Currently this is a non binary\n", | |
" // socket, so there is still some room for performance tuning.\n", | |
" var ws = {};\n", | |
"\n", | |
" ws.close = function() {\n", | |
" comm.close()\n", | |
" };\n", | |
" ws.send = function(m) {\n", | |
" //console.log('sending', m);\n", | |
" comm.send(m);\n", | |
" };\n", | |
" // Register the callback with on_msg.\n", | |
" comm.on_msg(function(msg) {\n", | |
" //console.log('receiving', msg['content']['data'], msg);\n", | |
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n", | |
" ws.onmessage(msg['content']['data'])\n", | |
" });\n", | |
" return ws;\n", | |
"}\n", | |
"\n", | |
"mpl.mpl_figure_comm = function(comm, msg) {\n", | |
" // This is the function which gets called when the mpl process\n", | |
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n", | |
"\n", | |
" var id = msg.content.data.id;\n", | |
" // Get hold of the div created by the display call when the Comm\n", | |
" // socket was opened in Python.\n", | |
" var element = $(\"#\" + id);\n", | |
" var ws_proxy = comm_websocket_adapter(comm)\n", | |
"\n", | |
" function ondownload(figure, format) {\n", | |
" window.open(figure.imageObj.src);\n", | |
" }\n", | |
"\n", | |
" var fig = new mpl.figure(id, ws_proxy,\n", | |
" ondownload,\n", | |
" element.get(0));\n", | |
"\n", | |
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", | |
" // web socket which is closed, not our websocket->open comm proxy.\n", | |
" ws_proxy.onopen();\n", | |
"\n", | |
" fig.parent_element = element.get(0);\n", | |
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", | |
" if (!fig.cell_info) {\n", | |
" console.error(\"Failed to find cell for figure\", id, fig);\n", | |
" return;\n", | |
" }\n", | |
"\n", | |
" var output_index = fig.cell_info[2]\n", | |
" var cell = fig.cell_info[0];\n", | |
"\n", | |
"};\n", | |
"\n", | |
"mpl.figure.prototype.handle_close = function(fig, msg) {\n", | |
" var width = fig.canvas.width/mpl.ratio\n", | |
" fig.root.unbind('remove')\n", | |
"\n", | |
" // Update the output cell to use the data from the current canvas.\n", | |
" fig.push_to_output();\n", | |
" var dataURL = fig.canvas.toDataURL();\n", | |
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n", | |
" // the notebook keyboard shortcuts fail.\n", | |
" IPython.keyboard_manager.enable()\n", | |
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", | |
" fig.close_ws(fig, msg);\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.close_ws = function(fig, msg){\n", | |
" fig.send_message('closing', msg);\n", | |
" // fig.ws.close()\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", | |
" // Turn the data on the canvas into data in the output cell.\n", | |
" var width = this.canvas.width/mpl.ratio\n", | |
" var dataURL = this.canvas.toDataURL();\n", | |
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.updated_canvas_event = function() {\n", | |
" // Tell IPython that the notebook contents must change.\n", | |
" IPython.notebook.set_dirty(true);\n", | |
" this.send_message(\"ack\", {});\n", | |
" var fig = this;\n", | |
" // Wait a second, then push the new image to the DOM so\n", | |
" // that it is saved nicely (might be nice to debounce this).\n", | |
" setTimeout(function () { fig.push_to_output() }, 1000);\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype._init_toolbar = function() {\n", | |
" var fig = this;\n", | |
"\n", | |
" var nav_element = $('<div/>');\n", | |
" nav_element.attr('style', 'width: 100%');\n", | |
" this.root.append(nav_element);\n", | |
"\n", | |
" // Define a callback function for later on.\n", | |
" function toolbar_event(event) {\n", | |
" return fig.toolbar_button_onclick(event['data']);\n", | |
" }\n", | |
" function toolbar_mouse_event(event) {\n", | |
" return fig.toolbar_button_onmouseover(event['data']);\n", | |
" }\n", | |
"\n", | |
" for(var toolbar_ind in mpl.toolbar_items){\n", | |
" var name = mpl.toolbar_items[toolbar_ind][0];\n", | |
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", | |
" var image = mpl.toolbar_items[toolbar_ind][2];\n", | |
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n", | |
"\n", | |
" if (!name) { continue; };\n", | |
"\n", | |
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", | |
" button.click(method_name, toolbar_event);\n", | |
" button.mouseover(tooltip, toolbar_mouse_event);\n", | |
" nav_element.append(button);\n", | |
" }\n", | |
"\n", | |
" // Add the status bar.\n", | |
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", | |
" nav_element.append(status_bar);\n", | |
" this.message = status_bar[0];\n", | |
"\n", | |
" // Add the close button to the window.\n", | |
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", | |
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", | |
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n", | |
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n", | |
" buttongrp.append(button);\n", | |
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", | |
" titlebar.prepend(buttongrp);\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype._root_extra_style = function(el){\n", | |
" var fig = this\n", | |
" el.on(\"remove\", function(){\n", | |
"\tfig.close_ws(fig, {});\n", | |
" });\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype._canvas_extra_style = function(el){\n", | |
" // this is important to make the div 'focusable\n", | |
" el.attr('tabindex', 0)\n", | |
" // reach out to IPython and tell the keyboard manager to turn it's self\n", | |
" // off when our div gets focus\n", | |
"\n", | |
" // location in version 3\n", | |
" if (IPython.notebook.keyboard_manager) {\n", | |
" IPython.notebook.keyboard_manager.register_events(el);\n", | |
" }\n", | |
" else {\n", | |
" // location in version 2\n", | |
" IPython.keyboard_manager.register_events(el);\n", | |
" }\n", | |
"\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype._key_event_extra = function(event, name) {\n", | |
" var manager = IPython.notebook.keyboard_manager;\n", | |
" if (!manager)\n", | |
" manager = IPython.keyboard_manager;\n", | |
"\n", | |
" // Check for shift+enter\n", | |
" if (event.shiftKey && event.which == 13) {\n", | |
" this.canvas_div.blur();\n", | |
" // select the cell after this one\n", | |
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", | |
" IPython.notebook.select(index + 1);\n", | |
" }\n", | |
"}\n", | |
"\n", | |
"mpl.figure.prototype.handle_save = function(fig, msg) {\n", | |
" fig.ondownload(fig, null);\n", | |
"}\n", | |
"\n", | |
"\n", | |
"mpl.find_output_cell = function(html_output) {\n", | |
" // Return the cell and output element which can be found *uniquely* in the notebook.\n", | |
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", | |
" // IPython event is triggered only after the cells have been serialised, which for\n", | |
" // our purposes (turning an active figure into a static one), is too late.\n", | |
" var cells = IPython.notebook.get_cells();\n", | |
" var ncells = cells.length;\n", | |
" for (var i=0; i<ncells; i++) {\n", | |
" var cell = cells[i];\n", | |
" if (cell.cell_type === 'code'){\n", | |
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n", | |
" var data = cell.output_area.outputs[j];\n", | |
" if (data.data) {\n", | |
" // IPython >= 3 moved mimebundle to data attribute of output\n", | |
" data = data.data;\n", | |
" }\n", | |
" if (data['text/html'] == html_output) {\n", | |
" return [cell, data, j];\n", | |
" }\n", | |
" }\n", | |
" }\n", | |
" }\n", | |
"}\n", | |
"\n", | |
"// Register the function which deals with the matplotlib target/channel.\n", | |
"// The kernel may be null if the page has been refreshed.\n", | |
"if (IPython.notebook.kernel != null) {\n", | |
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", | |
"}\n" | |
], | |
"text/plain": [ | |
"<IPython.core.display.Javascript object>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
}, | |
{ | |
"output_type": "display_data", | |
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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [], | |
"needs_background": "light" | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "GdR_yJpCELCA" | |
}, | |
"source": [ | |
"Value-at-Risk backtesting:\n", | |
"here the binomial one-tailed test is used" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "XCj_nmDdEKAJ", | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"outputId": "a6e1951f-34d7-4f44-c14d-c099408bf032" | |
}, | |
"source": [ | |
"# Confidence level for backtesting\n", | |
"CL_backtesting = 95\n", | |
"# The probability of the VaR beeing exceeded on given day\n", | |
"p = 1 - CL_VaR/100\n", | |
"N_confidence = binom.ppf( CL_backtesting*0.01, N_history, p)\n", | |
"N_exceeded = sum( v < Value_at_Risk + Value_current for v in Market_scienarios)\n", | |
"\n", | |
"print(\"We look at total\", N_history,\"months. VaR level is reached at\", N_exceeded , \"months.\")\n", | |
"if N_exceeded <= N_confidence:\n", | |
" print (\"This VaR model is accepted at\", CL_backtesting,\"% confidence level.\")\n", | |
"else:\n", | |
" print (\"This VaR model is rejected at\", CL_backtesting,\"% confidence level.\")" | |
], | |
"execution_count": 127, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"We look at total 356 months. VaR level is reached at 18 months.\n", | |
"This VaR model is accepted at 95 % confidence level.\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "ImAjg4ceBJgf" | |
}, | |
"source": [ | |
"" | |
], | |
"execution_count": null, | |
"outputs": [] | |
} | |
] | |
} |
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