CCXT/CCXT_Calculate_Support_and_Resistance.ipynb

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    "<img width=\"8%\" alt=\"CCXT.png\" src=\"https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/.github/assets/logos/CCXT.png\" style=\"border-radius: 15%\">"
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   "source": [
    "# CCXT - Calculate Support and Resistance\n",
    "<a href=\"https://bit.ly/3JyWIk6\">Give Feedback</a> | <a href=\"https://github.com/jupyter-naas/awesome-notebooks/issues/new?assignees=&labels=bug&template=bug_report.md&title=CCXT+-+Calculate+Support+and+Resistance:+Error+short+description\">Bug report</a>"
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   "source": [
    "**Tags:** #ccxt #bitcoin #trading #investors #analytics #plotly"
   ]
  },
  {
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   "id": "3b76e00f-ebd8-4d6d-bb9a-239754ba9992",
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   "source": [
    "**Author:** [Jeremy Ravenel](https://www.linkedin.com/in/ACoAAAJHE7sB5OxuKHuzguZ9L6lfDHqw--cdnJg/)"
   ]
  },
  {
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   "source": [
    "**Last update:** 2023-04-12 (Created: 2021-02-28)"
   ]
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   "id": "naas-description",
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   "source": [
    "**Description:** This notebook provides a guide to using the CCXT library to calculate support and resistance levels for cryptocurrency trading."
   ]
  },
  {
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   "source": [
    "## Input"
   ]
  },
  {
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   "execution_count": null,
   "id": "sustainable-xerox",
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   "source": [
    "!pip install trendln matplotlib==3.1.3 --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fifty-tackle",
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   "source": [
    "import naas\n",
    "import ccxt\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import naas_drivers\n",
    "import trendln\n",
    "import plotly.tools as tls\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da5f1c81-fe85-49e4-9a82-79726ce065bb",
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   "source": [
    "### Setup Binance\n",
    "👉 <a href='https://www.binance.com/en/support/faq/360002502072'>How to create API ?</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f50e4667-00b8-47fa-906c-b875bc778556",
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   "outputs": [],
   "source": [
    "binance_api = \"\"\n",
    "binance_secret = \"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f19b5fa1-7e85-4f52-9bcc-918b2524a11d",
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   "source": [
    "### Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8d797ae-c958-4170-bec1-950c2bf66f22",
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   "outputs": [],
   "source": [
    "symbol = \"BTC/USDT\"\n",
    "limit = 180\n",
    "timeframe = \"4h\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5400ab8-80e2-483c-bc67-7e6c61e1831a",
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   },
   "source": [
    "## Model"
   ]
  },
  {
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   "source": [
    "### Get data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "sublime-policy",
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   "source": [
    "binance = ccxt.binance({\"apiKey\": binance_api, \"secret\": binance_secret})\n",
    "\n",
    "data = binance.fetch_ohlcv(symbol=symbol, limit=limit, timeframe=timeframe)"
   ]
  },
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   "cell_type": "markdown",
   "id": "f168d842-5011-4916-806e-122f2a3a5e9b",
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   "source": [
    "### Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "beautiful-banner",
   "metadata": {
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   "outputs": [],
   "source": [
    "df = pd.DataFrame(data, columns=[\"Date\", \"Open\", \"High\", \"Low\", \"Close\", \"Volume\"])\n",
    "df[\"Date\"] = [datetime.fromtimestamp(float(time) / 1000) for time in df[\"Date\"]]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ce3c07-aeac-44c2-aea8-f97659a94ebe",
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   "source": [
    "## Output"
   ]
  },
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   "source": [
    "### Plotting figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "brown-consent",
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   "source": [
    "fig = trendln.plot_support_resistance(\n",
    "    df[-1000:].Close,  # as per h for calc_support_resistance\n",
    "    xformatter=None,  # x-axis data formatter turning numeric indexes to display output\n",
    "    # e.g. ticker.FuncFormatter(func) otherwise just display numeric indexes\n",
    "    numbest=1,  # number of best support and best resistance lines to display\n",
    "    fromwindows=True,  # draw numbest best from each window, otherwise draw numbest across whole range\n",
    "    pctbound=0.1,  # bound trend line based on this maximum percentage of the data range above the high or below the low\n",
    "    extmethod=trendln.METHOD_NUMDIFF,\n",
    "    method=trendln.METHOD_PROBHOUGH,\n",
    "    window=125,\n",
    "    errpct=0.005,\n",
    "    hough_prob_iter=50,\n",
    "    sortError=False,\n",
    "    accuracy=1,\n",
    ")"
   ]
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    "plotly_fig = tls.mpl_to_plotly(fig)"
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    "layout = dict(\n",
    "    dragmode=\"pan\",\n",
    "    xaxis_rangeslider_visible=False,\n",
    "    showlegend=True,\n",
    ")\n",
    "new_data = list(plotly_fig.data)\n",
    "new_data.pop(2)\n",
    "new_data.pop(2)\n",
    "new_data.pop(1)\n",
    "new_data.pop(1)\n",
    "fig = go.Figure(data=new_data, layout=layout)\n",
    "fig"
   ]
  }
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