Backtrader and TA-lib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd39af20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 100000.00\n",
      "Ending Portfolio Value: 100000.00\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "import backtrader as bt\n",
    "import talib\n",
    "import numpy as np\n",
    "\n",
    "# Fetch historical data for Tata Motors\n",
    "tata = yf.Ticker(\"TATAMOTORS.BO\")\n",
    "data = tata.history(period=\"max\")\n",
    "\n",
    "# Define the custom Backtrader Strategy\n",
    "class MyStrategy(bt.Strategy):\n",
    "    params = (\n",
    "        (\"pattern_list\", []),\n",
    "    )\n",
    "    \n",
    "    def __init__(self):\n",
    "        # Convert the data to NumPy arrays\n",
    "        open_prices = np.array(self.data.open)\n",
    "        high_prices = np.array(self.data.high)\n",
    "        low_prices = np.array(self.data.low)\n",
    "        close_prices = np.array(self.data.close)\n",
    "        \n",
    "        for pattern in self.params.pattern_list:\n",
    "            pattern_func = getattr(talib, pattern)\n",
    "            setattr(self, pattern, pattern_func(open_prices, high_prices, low_prices, close_prices))\n",
    "            \n",
    "    def next(self):\n",
    "        for pattern in self.params.pattern_list:\n",
    "            pattern_values = getattr(self, pattern)\n",
    "            if pattern_values[-1] != 0:\n",
    "                print(f\"{pattern} pattern detected on {self.data.datetime.date(-1)}\")\n",
    "\n",
    "cerebro = bt.Cerebro()\n",
    "\n",
    "# Add data feed to cerebro\n",
    "data_feed = bt.feeds.PandasData(dataname=data)\n",
    "cerebro.adddata(data_feed)\n",
    "\n",
    "# Add strategy to cerebro\n",
    "pattern_list = [\n",
    "    \"CDL2CROWS\",\n",
    "    \"CDL3BLACKCROWS\",\n",
    "    # ... (Add other pattern names here)\n",
    "    \"CDLTASUKIGAP\",\n",
    "]\n",
    "cerebro.addstrategy(MyStrategy, pattern_list=pattern_list)\n",
    "\n",
    "# Set initial cash and commission\n",
    "cerebro.broker.set_cash(100000)  # Set an initial cash amount for backtesting\n",
    "cerebro.broker.setcommission(commission=0.001)  # Set a simple commission rate\n",
    "\n",
    "# Print starting cash amount\n",
    "print(\"Starting Portfolio Value: %.2f\" % cerebro.broker.getvalue())\n",
    "\n",
    "# Run the backtest\n",
    "cerebro.run()\n",
    "\n",
    "# Print ending cash amount\n",
    "print(\"Ending Portfolio Value: %.2f\" % cerebro.broker.getvalue())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4f3f0f69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 100000.00\n",
      "Pattern CDLUPSIDEGAP2CROWS: [0 0 0 ... 0 0 0]\n",
      "Ending Portfolio Value: 100000.00\n"
     ]
    },
    {
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       "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",
       "    fig.canvas_div.style.cursor = msg['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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "function getModifiers(event) {\n",
       "    var mods = [];\n",
       "    if (event.ctrlKey) {\n",
       "        mods.push('ctrl');\n",
       "    }\n",
       "    if (event.altKey) {\n",
       "        mods.push('alt');\n",
       "    }\n",
       "    if (event.shiftKey) {\n",
       "        mods.push('shift');\n",
       "    }\n",
       "    if (event.metaKey) {\n",
       "        mods.push('meta');\n",
       "    }\n",
       "    return mods;\n",
       "}\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://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",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        modifiers: getModifiers(event),\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[<Figure size 640x480 with 4 Axes>]]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import backtrader as bt\n",
    "import talib\n",
    "import numpy as np\n",
    "\n",
    "class MyStrategy(bt.Strategy):\n",
    "    params = (\n",
    "        (\"pattern_list\", []),\n",
    "        (\"stop_loss_pct\", 0.02),   # 2% stop loss\n",
    "        (\"take_profit_pct\", 0.03), # 3% take profit\n",
    "        (\"min_pattern_confidence\", 0),  # Minimum confidence level for pattern recognition\n",
    "    )\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.trades = []\n",
    "        \n",
    "        # Convert the data to NumPy arrays\n",
    "        open_prices = np.array(self.data.open)\n",
    "        high_prices = np.array(self.data.high)\n",
    "        low_prices = np.array(self.data.low)\n",
    "        close_prices = np.array(self.data.close)\n",
    "        \n",
    "        for pattern in self.params.pattern_list:\n",
    "            pattern_func = getattr(talib, pattern)\n",
    "            try:\n",
    "                pattern_values = pattern_func(open_prices, high_prices, low_prices, close_prices)\n",
    "                print(f\"Pattern {pattern}: {pattern_values}\")\n",
    "                setattr(self, pattern, pattern_values)\n",
    "            except Exception as e:\n",
    "                print(f\"Error setting pattern {pattern}: {e}\")\n",
    "    \n",
    "    def next(self):\n",
    "        for pattern in self.params.pattern_list:\n",
    "            pattern_values = getattr(self, pattern, None)\n",
    "            if pattern_values is not None and pattern_values[-1] != 0 and pattern_values[-1] >= self.params.min_pattern_confidence:\n",
    "                self.execute_trade(pattern)\n",
    "    \n",
    "    def execute_trade(self, pattern):\n",
    "        if self.position:  # If there's an open position, close it first\n",
    "            self.close()\n",
    "        \n",
    "        entry_price = self.data.close[0]\n",
    "        stop_loss_price = entry_price * (1 - self.params.stop_loss_pct)\n",
    "        take_profit_price = entry_price * (1 + self.params.take_profit_pct)\n",
    "        \n",
    "        trade = {\n",
    "            \"pattern\": pattern,\n",
    "            \"entry_price\": entry_price,\n",
    "            \"stop_loss_price\": stop_loss_price,\n",
    "            \"take_profit_price\": take_profit_price,\n",
    "        }\n",
    "        self.trades.append(trade)\n",
    "        \n",
    "        self.buy(price=entry_price, exectype=bt.Order.Market)\n",
    "        self.sell(exectype=bt.Order.Stop, price=stop_loss_price)\n",
    "        self.sell(exectype=bt.Order.Limit, price=take_profit_price)\n",
    "    \n",
    "    def stop(self):\n",
    "        self.plot_trades()\n",
    "    \n",
    "    def plot_trades(self):\n",
    "        for trade in self.trades:\n",
    "            plot_color = 'g' if trade['entry_price'] < trade['take_profit_price'] else 'r'\n",
    "            self.plotarrow(\n",
    "                buy=trade['entry_price'],\n",
    "                marker='^' if plot_color == 'g' else 'v',\n",
    "                color=plot_color,\n",
    "                size=12\n",
    "            )\n",
    "\n",
    "cerebro = bt.Cerebro()\n",
    "\n",
    "# Add data feed to cerebro\n",
    "data_feed = bt.feeds.PandasData(dataname=data)\n",
    "cerebro.adddata(data_feed)\n",
    "\n",
    "# Add strategy to cerebro\n",
    "pattern_list = [\n",
    "    \"CDLUPSIDEGAP2CROWS\",  # Upside Gap Two Crows pattern\n",
    "    # ... (Add other pattern names here)\n",
    "]\n",
    "cerebro.addstrategy(MyStrategy, pattern_list=pattern_list, min_pattern_confidence=50)  # Adjust min_pattern_confidence as needed\n",
    "\n",
    "# Set initial cash and commission\n",
    "cerebro.broker.set_cash(100000)  # Set an initial cash amount for backtesting\n",
    "cerebro.broker.setcommission(commission=0.001)  # Set a simple commission rate\n",
    "\n",
    "# Print starting cash amount\n",
    "print(\"Starting Portfolio Value: %.2f\" % cerebro.broker.getvalue())\n",
    "\n",
    "try:\n",
    "    # Run the backtest\n",
    "    cerebro.run()\n",
    "except Exception as e:\n",
    "    print(f\"Error during backtest: {e}\")\n",
    "\n",
    "# Print ending cash amount\n",
    "print(\"Ending Portfolio Value: %.2f\" % cerebro.broker.getvalue())\n",
    "\n",
    "# Plot the graph\n",
    "cerebro.plot(style='candle')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4117bdb5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e719d7fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import backtrader as bt\n",
    "import talib\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "710bffed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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