"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"df[\"alma\"] = alma_indicator(df.close, window=9,offset=0.85,sigma=6)\n",
"df[\"sma\"] = df.close.rolling(9).mean()\n",
"\n",
"\n",
"df[[\"close\",\"alma\",\"sma\"]].plot(figsize=(30,10),title=\"Comparison of SMA(9) vs ALMA(9)\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"Final Portfolio Value: 93388.75828515051\n",
"Final PnL: -6611.24\n",
"Time used (seconds): 0.068572998046875\n",
"Optimal parameters: {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'commission': 0.0075, 'stop_loss': None, 'stop_trail': None, 'execution_type': 'close', 'channel': None, 'symbol': None, 'allow_short': False, 'short_max': 1.5, 'add_cash_amount': None, 'add_cash_freq': 'M', 'alma_column': 'alma', 'macd_fast_period': 12, 'macd_slow_period': 16, 'macd_signal_period': 9}\n",
"Optimal metrics: {'rtot': -0.06839920896995008, 'ravg': -0.0006514210378090484, 'rnorm': -0.15139215492317035, 'rnorm100': -15.139215492317035, 'len': 49, 'drawdown': 12.742882982202364, 'moneydown': 13638.337586126348, 'max': AutoOrderedDict([('len', 49), ('drawdown', 12.742882982202364), ('moneydown', 13638.337586126348)]), 'maxdrawdown': 12.742882982202364, 'maxdrawdownperiod': 49, 'sharperatio': -1.3025150321622352, 'pnl': -6611.24, 'final_value': 93388.75828515051}\n"
]
},
{
"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.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 = $('');\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 = $('');\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 = $('');\n",
"\n",
" var fmt_picker = $('');\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",
" '', {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 = $('');\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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(\"\");\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('![](\"')
');\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'] = '';\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 = $('');\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 = $('');\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",
" // 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= 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"
],
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\n",
"text/plain": [
"
\n",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" open high low close volume alma \\\n",
"dt \n",
"2018-12-31 39.632500 39.840000 39.119999 39.435001 140014000 39.403000 \n",
"2019-01-02 38.722500 39.712502 38.557499 39.480000 148158800 39.272023 \n",
"2019-01-03 35.994999 36.430000 35.500000 35.547501 365248800 38.889287 \n",
"2019-01-04 36.132500 37.137501 35.950001 37.064999 234428400 38.202014 \n",
"2019-01-07 37.174999 37.207500 36.474998 36.982498 219111200 37.477802 \n",
"\n",
" sma \n",
"dt \n",
"2018-12-31 NaN \n",
"2019-01-02 NaN \n",
"2019-01-03 NaN \n",
"2019-01-04 NaN \n",
"2019-01-07 NaN "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementing our custom strategy\n",
"\n",
"In this strategy we will have the following signals:\n",
"\n",
"Buy on:\n",
"- Closing price is above ALMA\n",
"- MACD crosses above the MACD signal line\n",
"\n",
"Sell on:\n",
"- Closing price falls below ALMA"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from fastquant import CustomStrategy, BaseStrategy\n",
"from fastquant.indicators import MACD, CrossOver, CustomIndicator\n",
"\n",
"\n",
"# Create a subclass of the BaseStrategy, We call this MAMAStrategy (MACD + ALMA)\n",
"class MAMAStrategy(BaseStrategy):\n",
" \n",
" params = (\n",
" (\"alma_column\", \"alma\"), # name for the ALMA column from the dataframe\n",
" (\"macd_fast_period\", 12), # period for the MACD\n",
" (\"macd_slow_period\", 16),\n",
" (\"macd_signal_period\",9)\n",
" )\n",
"\n",
" def __init__(self):\n",
" # Initialize global variables\n",
" super().__init__()\n",
" \n",
" # Setup MACD indicator parameters\n",
" self.macd_fast_period = self.params.macd_fast_period\n",
" self.macd_slow_period = self.params.macd_slow_period\n",
" self.macd_signal_period = self.params.macd_signal_period\n",
" \n",
" \n",
" # Setup MACD indicator, macd line and macd signal line, and macd signal line crossover\n",
" self.macd_ind = MACD(\n",
" period_me1=self.macd_fast_period, \n",
" period_me2=self.macd_slow_period, \n",
" period_signal=self.macd_signal_period\n",
" )\n",
" self.macd = self.macd_ind.macd\n",
" self.macd_signal = self.macd_ind.signal\n",
" \n",
" # Add signal line cross over\n",
" self.macd_signal_crossover = CrossOver(\n",
" self.macd_ind, self.macd_signal\n",
" )\n",
" \n",
" # Assign ALMA column from the dataframe\n",
" self.alma_column = self.params.alma_column\n",
" \n",
" # Set ALMA indicator from the alma column of data\n",
" self.alma = CustomIndicator(\n",
" self.data, custom_column=self.alma_column,\n",
" )\n",
" \n",
" # Plot the ALMA indicator along with the price instead of a separate plot\n",
" self.alma.plotinfo.subplot = False\n",
" self.alma.plotinfo.plotname = \"ALMA\"\n",
"\n",
" print(\"===Strategy level arguments===\")\n",
" print(\"PARAMS: \", self.params)\n",
" \n",
"\n",
" # Buy when the custom indicator is below the lower limit, and sell when it's above the upper limit\n",
" def buy_signal(self):\n",
" alma_buy = self.dataclose[0] > self.alma[0] # Close is above ALMA\n",
" macd_buy = self.macd_signal_crossover > 0 # MACD crosses signal line upward\n",
" \n",
" \n",
" return alma_buy and macd_buy \n",
" def sell_signal(self):\n",
" return self.alma[0] > self.dataclose[0]\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"===Global level arguments===\n",
"init_cash : 100000\n",
"buy_prop : 1\n",
"sell_prop : 1\n",
"commission : 0.0075\n",
"stop_loss : None\n",
"stop_trail : None\n",
"===Strategy level arguments===\n",
"PARAMS: | \n", " | open | \n", "high | \n", "low | \n", "close | \n", "volume | \n", "alma | \n", "sma | \n", "
|---|---|---|---|---|---|---|---|
| dt | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
| 2018-12-31 | \n", "39.632500 | \n", "39.840000 | \n", "39.119999 | \n", "39.435001 | \n", "140014000 | \n", "39.403000 | \n", "NaN | \n", "
| 2019-01-02 | \n", "38.722500 | \n", "39.712502 | \n", "38.557499 | \n", "39.480000 | \n", "148158800 | \n", "39.272023 | \n", "NaN | \n", "
| 2019-01-03 | \n", "35.994999 | \n", "36.430000 | \n", "35.500000 | \n", "35.547501 | \n", "365248800 | \n", "38.889287 | \n", "NaN | \n", "
| 2019-01-04 | \n", "36.132500 | \n", "37.137501 | \n", "35.950001 | \n", "37.064999 | \n", "234428400 | \n", "38.202014 | \n", "NaN | \n", "
| 2019-01-07 | \n", "37.174999 | \n", "37.207500 | \n", "36.474998 | \n", "36.982498 | \n", "219111200 | \n", "37.477802 | \n", "NaN | \n", "
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"result, history = backtest(MAMAStrategy,df, verbose=False, return_history=True)\n",
"# result"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" strat_id init_cash buy_prop sell_prop commission stop_loss stop_trail \\\n",
"0 0 100000 1 1 0.0075 None None \n",
"\n",
" execution_type channel symbol ... rnorm100 len drawdown moneydown \\\n",
"0 close None None ... -15.139215 49 12.742883 13638.337586 \n",
"\n",
" max maxdrawdown \\\n",
"0 {'len': 49, 'drawdown': 12.742882982202364, 'm... 12.742883 \n",
"\n",
" maxdrawdownperiod sharperatio pnl final_value \n",
"0 49 -1.302515 -6611.24 93388.758285 \n",
"\n",
"[1 rows x 31 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"| \n", " | strat_id | \n", "init_cash | \n", "buy_prop | \n", "sell_prop | \n", "commission | \n", "stop_loss | \n", "stop_trail | \n", "execution_type | \n", "channel | \n", "symbol | \n", "... | \n", "rnorm100 | \n", "len | \n", "drawdown | \n", "moneydown | \n", "max | \n", "maxdrawdown | \n", "maxdrawdownperiod | \n", "sharperatio | \n", "pnl | \n", "final_value | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", "0 | \n", "100000 | \n", "1 | \n", "1 | \n", "0.0075 | \n", "None | \n", "None | \n", "close | \n", "None | \n", "None | \n", "... | \n", "-15.139215 | \n", "49 | \n", "12.742883 | \n", "13638.337586 | \n", "{'len': 49, 'drawdown': 12.742882982202364, 'm... | \n", "12.742883 | \n", "49 | \n", "-1.302515 | \n", "-6611.24 | \n", "93388.758285 | \n", "
1 rows × 31 columns
\n", "\n",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" strat_id strat_name dt type price size value \\\n",
"0 0 2019-03-13 buy 45.227501 2192 99138.682007 \n",
"1 0 2019-03-26 sell 46.697498 -2192 99138.682007 \n",
"2 0 2019-04-04 buy 48.837502 2065 100849.440651 \n",
"3 0 2019-04-12 sell 49.717499 -2065 100849.440651 \n",
"4 0 2019-04-24 buy 51.869999 1949 101094.627918 \n",
"5 0 2019-04-26 sell 51.075001 -1949 101094.627918 \n",
"6 0 2019-05-06 buy 52.937500 1853 98093.187500 \n",
"7 0 2019-05-07 sell 50.715000 -1853 98093.187500 \n",
"\n",
" commission pnl \n",
"0 743.540115 0.000000 \n",
"1 767.706872 3222.234314 \n",
"2 756.370805 0.000000 \n",
"3 769.999762 1817.194328 \n",
"4 758.209709 0.000000 \n",
"5 746.588824 -1549.451431 \n",
"6 735.698906 0.000000 \n",
"7 704.811715 -4118.292217 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"history['orders']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"| \n", " | strat_id | \n", "strat_name | \n", "dt | \n", "type | \n", "price | \n", "size | \n", "value | \n", "commission | \n", "pnl | \n", "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", "0 | \n", "\n", " | 2019-03-13 | \n", "buy | \n", "45.227501 | \n", "2192 | \n", "99138.682007 | \n", "743.540115 | \n", "0.000000 | \n", "
| 1 | \n", "0 | \n", "\n", " | 2019-03-26 | \n", "sell | \n", "46.697498 | \n", "-2192 | \n", "99138.682007 | \n", "767.706872 | \n", "3222.234314 | \n", "
| 2 | \n", "0 | \n", "\n", " | 2019-04-04 | \n", "buy | \n", "48.837502 | \n", "2065 | \n", "100849.440651 | \n", "756.370805 | \n", "0.000000 | \n", "
| 3 | \n", "0 | \n", "\n", " | 2019-04-12 | \n", "sell | \n", "49.717499 | \n", "-2065 | \n", "100849.440651 | \n", "769.999762 | \n", "1817.194328 | \n", "
| 4 | \n", "0 | \n", "\n", " | 2019-04-24 | \n", "buy | \n", "51.869999 | \n", "1949 | \n", "101094.627918 | \n", "758.209709 | \n", "0.000000 | \n", "
| 5 | \n", "0 | \n", "\n", " | 2019-04-26 | \n", "sell | \n", "51.075001 | \n", "-1949 | \n", "101094.627918 | \n", "746.588824 | \n", "-1549.451431 | \n", "
| 6 | \n", "0 | \n", "\n", " | 2019-05-06 | \n", "buy | \n", "52.937500 | \n", "1853 | \n", "98093.187500 | \n", "735.698906 | \n", "0.000000 | \n", "
| 7 | \n", "0 | \n", "\n", " | 2019-05-07 | \n", "sell | \n", "50.715000 | \n", "-1853 | \n", "98093.187500 | \n", "704.811715 | \n", "-4118.292217 | \n", "
\n",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" strat_id strat_name dt MACD (12, 16, 9) CrossOver ALMA\n",
"0 0 2018-12-31 NaN NaN 39.403000\n",
"1 0 2019-01-02 NaN NaN 39.272023\n",
"2 0 2019-01-03 NaN NaN 38.889287\n",
"3 0 2019-01-04 NaN NaN 38.202014\n",
"4 0 2019-01-07 NaN NaN 37.477802\n",
".. ... ... ... ... ... ...\n",
"100 0 2019-05-24 -0.530912 0.0 45.578881\n",
"101 0 2019-05-28 -0.553285 0.0 45.182577\n",
"102 0 2019-05-29 -0.567666 0.0 44.788032\n",
"103 0 2019-05-30 -0.559803 0.0 44.557318\n",
"104 0 2019-05-31 -0.573031 0.0 44.396019\n",
"\n",
"[105 rows x 6 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"history['indicators']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "fastquant_dev",
"language": "python",
"name": "fastquant_dev"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
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},
"nbformat": 4,
"nbformat_minor": 4
}
| \n", " | strat_id | \n", "strat_name | \n", "dt | \n", "MACD (12, 16, 9) | \n", "CrossOver | \n", "ALMA | \n", "
|---|---|---|---|---|---|---|
| 0 | \n", "0 | \n", "\n", " | 2018-12-31 | \n", "NaN | \n", "NaN | \n", "39.403000 | \n", "
| 1 | \n", "0 | \n", "\n", " | 2019-01-02 | \n", "NaN | \n", "NaN | \n", "39.272023 | \n", "
| 2 | \n", "0 | \n", "\n", " | 2019-01-03 | \n", "NaN | \n", "NaN | \n", "38.889287 | \n", "
| 3 | \n", "0 | \n", "\n", " | 2019-01-04 | \n", "NaN | \n", "NaN | \n", "38.202014 | \n", "
| 4 | \n", "0 | \n", "\n", " | 2019-01-07 | \n", "NaN | \n", "NaN | \n", "37.477802 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| 100 | \n", "0 | \n", "\n", " | 2019-05-24 | \n", "-0.530912 | \n", "0.0 | \n", "45.578881 | \n", "
| 101 | \n", "0 | \n", "\n", " | 2019-05-28 | \n", "-0.553285 | \n", "0.0 | \n", "45.182577 | \n", "
| 102 | \n", "0 | \n", "\n", " | 2019-05-29 | \n", "-0.567666 | \n", "0.0 | \n", "44.788032 | \n", "
| 103 | \n", "0 | \n", "\n", " | 2019-05-30 | \n", "-0.559803 | \n", "0.0 | \n", "44.557318 | \n", "
| 104 | \n", "0 | \n", "\n", " | 2019-05-31 | \n", "-0.573031 | \n", "0.0 | \n", "44.396019 | \n", "
105 rows × 6 columns
\n", "