{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Dual Momentum Portfolio\n", "This Notebook is designed to show a simple system which loops through daily data on three instruments: a stock, a bond and cash. Momentum is measured by a moving average crossover. The system invests in stocks if the short moving avearge of the stock's price is above the long moving average. If the stock momentum indicator shows no trend but the bond short moving average is above the bond long moving average, the system invests in bonds. If the both stocks and bonds fail the momentum test, the system invests in cash.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## You will need to import the following Python packages into the Notebook" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "import datetime\n", "import logging\n", "import os\n", "import sys\n", "import ffn\n", "from collections import OrderedDict\n", "from typing import Dict, List\n", "import numpy as np\n", "import pandas as pd\n", "from numba import jit\n", "import matplotlib.pyplot as plt\n", "%matplotlib notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## You may or may not find the following options useful." ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "pd.set_option(\"max_colwidth\", 100)\n", "pd.set_option(\"display.max_rows\", 100000)\n", "pd.set_option(\"display.max_columns\", 1000)\n", "pd.set_option('precision', 6)\n", "pd.options.display.float_format = '{:20,.6f}'.format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up the built in Python logging facilty\n", "Useful for de-bugging.\n", "\n", "Currently set to: logger.setLevel(logging.INFO)\n", "\n", "Set this to DEBUG if you want some verbose output: logger.setLevel(logging.DEBUG)\n", " " ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "logger = logging.getLogger(\"a_w\")\n", "logger.setLevel(logging.INFO)\n", "\n", "# create a file handler\n", "handler = logging.StreamHandler()\n", "for handler in logger.handlers:\n", " logger.removeHandler(handler)\n", "handler.setLevel(logging.DEBUG)\n", "\n", "# create a logging format\n", "formatter = logging.Formatter(\n", " '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n", "handler.setFormatter(formatter)\n", "\n", "# add the handlers to the logger\n", "logger.addHandler(handler)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialise Variables\n", "Make sure to set the directory to where your stock data is located. \n", "\n", "Change dates, allocations and stocks as desired" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "start: datetime.date = '1900-01-01'\n", "end = datetime.date.today()\n", "\n", "starting_capital: float() =100000\n", "\n", "stock_directory: str = '..\\\\data\\\\Stocks\\\\'\n", "\n", "stock: str = 'WillshireTRFed'\n", "#bond: str = 'Fed5YR'\n", "bond: str = '30YrBond'\n", "money:str = '3mTBill'\n", "\n", "\n", "sma_length: int= 10\n", "lma_length: int=20\n", "\n", "\n", "stock_tickers_list: List[str] = [stock, bond,money]\n", "\n", "\n", "#list of files to be saved. Ensure you use your own directories and desired filenames\n", "test: str = \"C:\\\\Users\\\\agarn\\\\OneDrive\\\\Documents\\\\Articles//DMtest.csv\"\n", "drawdown: str = \"C:\\\\Users\\\\agarn\\\\OneDrive\\\\Documents\\\\Articles//DMdrawdown.csv\"\n", "rebased: str = \"C:\\\\Users\\\\agarn\\\\OneDrive\\\\Documents\\Articles//DMrebased.csv\"\n", "stats: str = \"C:\\\\Users\\\\agarn\\\\OneDrive\\\\Documents\\\\Articles//DMstats.csv\"\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "#This function will calculate the VAMI each day from the return for that day\n", "@jit()\n", "def calculator(a):\n", " res = np.empty(data.equity.shape)\n", " res[0] = 100\n", " for i in range(1, res.shape[0]):\n", " res[i] = res[i-1] +(res[i-1]* a[i])\n", " return res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The following function checks to ensure the stock directory is correct and the data files exist:" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "def check_data_files(ticker_list: List, target_directory: str):\n", " logger = logging.getLogger(\"a_w.check_data_files\")\n", "\n", " if not os.path.exists(stock_directory):\n", " logger.info('stockdirectory does not exist')\n", " for ticker in stock_tickers_list:\n", " filename = os.path.join(stock_directory, ticker + '.csv')\n", " if not os.path.exists(filename):\n", " logger.info('file does not exist in given directory:' + \" \" + ticker)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "check_data_files(stock_tickers_list,stock_directory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The next function returns a dictionary of dataframes for the stock data:\n", "Reads the desired files from the relevant directory" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "def get_dataframe_dictionary(ticker_list: List,\n", " target_directory: str) -> Dict[str, pd.DataFrame]:\n", " \"\"\"\n", " Get a list of files and put into a dictionary of dataframes\n", " :param ticker_list:\n", " :param target_directory:\n", " :return:\n", " \"\"\"\n", " ret = OrderedDict()\n", " logger = logging.getLogger(\"a_w.get_dataframe_dictionary\")\n", " logger.debug(\"get_dataframe_dictionary: %s\", ticker_list)\n", " for ticker in ticker_list:\n", " filename = os.path.join(target_directory, ticker + '.csv')\n", " if os.path.exists(filename):\n", " try:\n", " ret[ticker] = pd.read_csv(filename, parse_dates=[\"Date\"])\n", " except:\n", " continue\n", " return ret" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Merge the three separate time series into a single dataframe for back testing:" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "def merge_data(stock_tickers_list: List[str], target_directory: str,\n", " start: str, end: str) -> pd.DataFrame:\n", " \"\"\"\n", " take the dicts of stocks time series and create a dataframe to pass for backtesting\n", " :param stock_tickers_list:\n", " :param start: date like '2001-01-01' YYYY-MM-DD format\n", " :param end: date like '2001-12-31' YYYY-MM-DD format\n", " :return:\n", " \"\"\"\n", " logger = logging.getLogger('a_w.merge_data')\n", " logger.debug(\"Running: stock_tickers_list: %d\", len(stock_tickers_list))\n", " check_data_files(stock_tickers_list, target_directory)\n", " stock_data = get_dataframe_dictionary(stock_tickers_list, target_directory)\n", " logger.debug(\"Found Data: %d\", len(stock_tickers_list))\n", " data = pd.DataFrame()\n", "\n", " for ticker in stock_tickers_list:\n", " try:\n", " if data.empty:\n", " data = stock_data[ticker]\n", " else:\n", " a = stock_data[ticker]\n", " data = data.merge(\n", " a, how='left', left_on='Date',\n", " right_on='Date').fillna(method='ffill')\n", " data = data.dropna()\n", " except:\n", " continue\n", " logger.debug(\"Cols:{}\".format(data.columns.values))\n", " logger.debug(\"Now I have a datframe with shape: %s\", data.shape)\n", "\n", " if not data.empty:\n", " new_index = pd.to_datetime(pd.Series(data['Date']))\n", " data.set_index(new_index,drop=True,append=False,inplace=True,verify_integrity=False)\n", " data.drop(['Date'], inplace=True, axis=1)\n", " data = data.truncate(before=start, after=end, axis=0)\n", " if len(data.columns) == 3:\n", " data.columns = ['stock', 'bond','money']\n", " data['stock_return']=data.stock.pct_change()\n", " data['bond_return']=data.bond.pct_change()\n", " data['money_return']=data.money.pct_change()\n", " data['stock_test']=data.stock.rolling(sma_length).mean().shift(2)>data.stock.rolling(lma_length).mean().shift(2)\n", " data['bond_test']=data.bond.rolling(sma_length).mean().shift(2)>data.bond.rolling(lma_length).mean().shift(2)\n", " conditions=[data.stock_test==True,(data.stock_test==False) & (data.bond_test==False),data.stock_test==False,]\n", " outputs=[data.stock_return,data.money_return,data.bond_return]\n", " data['strategy_return']=np.select(conditions, outputs)\n", " data.dropna(inplace=True)\n", " data['equity']=0.0\n", " #data['dual_momentum'] = calculator(*data[list(data.loc[:, ['strategy_return']])].values.T)\n", " \n", "\n", " return data[lma_length:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Call the functions listed so far and inspect the dataframe." ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [], "source": [ "data=merge_data(stock_tickers_list, stock_directory,start,end)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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stockbondmoneystock_returnbond_returnmoney_returnstock_testbond_teststrategy_returnequity
Date
1980-01-021.86000029.500860221.498467-0.0210530.0057450.000333TrueFalse-0.0210530.000000
1980-01-041.88000028.871750221.6453480.010753-0.0213250.000663TrueFalse0.0107530.000000
1980-01-071.89000028.557190221.7173680.005319-0.0108950.000325TrueFalse0.0053190.000000
1980-01-081.93000028.905450221.7889250.0211640.0121950.000323TrueFalse0.0211640.000000
1980-01-091.93000029.141370221.8603830.0000000.0081620.000322FalseFalse0.0003220.000000
\n", "
" ], "text/plain": [ " stock bond money \\\n", "Date \n", "1980-01-02 1.860000 29.500860 221.498467 \n", "1980-01-04 1.880000 28.871750 221.645348 \n", "1980-01-07 1.890000 28.557190 221.717368 \n", "1980-01-08 1.930000 28.905450 221.788925 \n", "1980-01-09 1.930000 29.141370 221.860383 \n", "\n", " stock_return bond_return money_return \\\n", "Date \n", "1980-01-02 -0.021053 0.005745 0.000333 \n", "1980-01-04 0.010753 -0.021325 0.000663 \n", "1980-01-07 0.005319 -0.010895 0.000325 \n", "1980-01-08 0.021164 0.012195 0.000323 \n", "1980-01-09 0.000000 0.008162 0.000322 \n", "\n", " stock_test bond_test strategy_return equity \n", "Date \n", "1980-01-02 True False -0.021053 0.000000 \n", "1980-01-04 True False 0.010753 0.000000 \n", "1980-01-07 True False 0.005319 0.000000 \n", "1980-01-08 True False 0.021164 0.000000 \n", "1980-01-09 False False 0.000322 0.000000 " ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inspect_data = pd.DataFrame()\n", "if not data.empty:\n", " inspect_data = data\n", " #inspect_data.to_csv(\"C:\\\\Users\\\\agarn\\\\OneDrive\\\\Documents\\Articles//DM_inspect_data.csv\") \n", "else:\n", " print('data loading failed see logging')\n", "inspect_data.head()" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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stockbondmoneystock_returnbond_returnmoney_returnstock_testbond_teststrategy_returnequity
Date
1980-01-021.86000029.500860221.498467-0.0210530.0057450.000333TrueFalse-0.021053100.000000
1980-01-041.88000028.871750221.6453480.010753-0.0213250.000663TrueFalse0.010753101.075269
1980-01-071.89000028.557190221.7173680.005319-0.0108950.000325TrueFalse0.005319101.612903
1980-01-081.93000028.905450221.7889250.0211640.0121950.000323TrueFalse0.021164103.763441
1980-01-091.93000029.141370221.8603830.0000000.0081620.000322FalseFalse0.000322103.796873
\n", "
" ], "text/plain": [ " stock bond money \\\n", "Date \n", "1980-01-02 1.860000 29.500860 221.498467 \n", "1980-01-04 1.880000 28.871750 221.645348 \n", "1980-01-07 1.890000 28.557190 221.717368 \n", "1980-01-08 1.930000 28.905450 221.788925 \n", "1980-01-09 1.930000 29.141370 221.860383 \n", "\n", " stock_return bond_return money_return \\\n", "Date \n", "1980-01-02 -0.021053 0.005745 0.000333 \n", "1980-01-04 0.010753 -0.021325 0.000663 \n", "1980-01-07 0.005319 -0.010895 0.000325 \n", "1980-01-08 0.021164 0.012195 0.000323 \n", "1980-01-09 0.000000 0.008162 0.000322 \n", "\n", " stock_test bond_test strategy_return equity \n", "Date \n", "1980-01-02 True False -0.021053 100.000000 \n", "1980-01-04 True False 0.010753 101.075269 \n", "1980-01-07 True False 0.005319 101.612903 \n", "1980-01-08 True False 0.021164 103.763441 \n", "1980-01-09 False False 0.000322 103.796873 " ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['equity'] = calculator(*data[list(data.loc[:, ['strategy_return']])].values.T)\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save backtest data to file for further inspection. Use FFN package to create charts and statistics for the backtest." ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ " def show_stats(backtest: pd.DataFrame):\n", " if backtest is not None and not backtest.empty:\n", " logger = logging.getLogger('a_w.charts')\n", " res = backtest.equity.astype(np.float64)\n", " benchmarked = pd.concat(\n", " [res, backtest.stock, backtest.bond], sort=False, axis=1) \n", " log = True\n", " benchmarked.rebase().plot(figsize=(7, 6), logy=log, title='All Weather Portfolio')\n", " benchmarked.calc_stats().display()\n", " benchmarked.to_drawdown_series().plot(figsize=(8, 6))\n", "\n", " def save_1():backtest.to_csv(test)\n", " def save_2():benchmarked.rebase().to_csv(rebased)\n", " def save_3():benchmarked.calc_stats().to_csv(sep=', ', path=stats)\n", " def save_4():benchmarked.to_drawdown_series().to_csv(drawdown)\n", " save_files=[save_1,save_2,save_3,save_4]\n", " for s in save_files:\n", " try:\n", " s()\n", " except IOError:\n", " print('file open - close file ',s) \n", " continue\n", "\n", " else:\n", " print('backtest failed see logging')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Call the show-backtest function" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\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 = $('');\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 = $('');\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 = $('
')\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 = $('');\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 = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var 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", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\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= 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": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_stats(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## This is my Python version\n", "from platform import python_version\n", "\n", "python_version()\n", "\n", "'3.6.8'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Anaconda Version and System Info\n", "\n", "sys.version,sys.version_info\n", "\n", "('3.6.8 |Anaconda custom (64-bit)| (default, Feb 21 2019, 18:30:04) [MSC v.1916 64 bit (AMD64)]',\n", " sys.version_info(major=3, minor=6, micro=8, releaselevel='final', serial=0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version of the Imports\n", "\n", "import pip\n", "\n", "%pip list\n", "\n", "Package Version \n", "---------------------------------- --------\n", "ffn 0.3.4 \n", "numpy 1.16.2 \n", "pandas 0.24.2 \n", " \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }