quantconnect--lean/Research/KitchenSinkQuantBookTemplate.ipynb

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   "source": [
    "![QuantConnect Logo](https://cdn.quantconnect.com/web/i/qc_notebook_logo_rev0.png)\n",
    "## Welcome to The QuantConnect Research Page\n",
    "#### Refer to this page for documentation https://www.quantconnect.com/docs/research/overview#\n",
    "#### Contribute to this template file https://github.com/QuantConnect/Lean/blob/master/Research/KitchenSinkQuantBookTemplate.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QuantBook Basics\n",
    "The following example is ready to be used in our Docker container, reference the readme for more details on setting this up.\n",
    "\n",
    "\n",
    "\n",
    "In order to use this notebook locally you will need to make a few small changes:\n",
    "\n",
    "1. Either create the notebook in your build folder (`bin/debug`) **or** set working directory of the notebook to it like so in the first cell:\n",
    "\n",
    "    ```%cd \"PathToLean/Lean/Launcher/bin/Debug/```\n",
    "\n",
    "2. Run the following command in another cell to load in QuantConnect libraries:\n",
    "\n",
    "    ```%run start.py```\n",
    "\n",
    "### Start QuantBook\n",
    "- Add the references and imports\n",
    "- Create a QuantBook instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in our startup script, required to set runtime for PythonNet\n",
    "%run ../start.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an instance of our QuantBook\n",
    "qb = QuantBook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the Web API\n",
    "Our script `start.py` automatically loads an instance of the web API for you to use.**\n",
    "\n",
    "Look at Lean's [Api](https://github.com/QuantConnect/Lean/tree/master/Api) class for more functions to interact with the cloud\n",
    "\n",
    "\n",
    "##### **Note: This will only connect if you have your User ID and Api token in `config.json` \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show that our api object is connected to the Web Api\n",
    "print(api.Connected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get our list of projects from the cloud and print their names\n",
    "projectResponse = api.ListProjects()\n",
    "for project in projectResponse.Projects:\n",
    "    print(project.Name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting Asset Data\n",
    "Checkout the QuantConnect [docs](https://www.quantconnect.com/docs#Initializing-Algorithms-Selecting-Asset-Data) to learn how to select asset data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spy = qb.AddEquity(\"SPY\")\n",
    "eur = qb.AddForex(\"EURUSD\")\n",
    "btc = qb.AddCrypto(\"BTCUSD\")\n",
    "fxv = qb.AddData[FxcmVolume](\"EURUSD_Vol\", Resolution.Hour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Historical Data Requests\n",
    "\n",
    "We can use the QuantConnect API to make Historical Data Requests. The data will be presented as multi-index pandas.DataFrame where the first index is the Symbol.\n",
    "\n",
    "For more information, please follow the [link](https://www.quantconnect.com/docs#Historical-Data-Historical-Data-Requests)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Gets historical data from the subscribed assets, the last 360 datapoints with daily resolution\n",
    "h1 = qb.History(qb.Securities.Keys, 360, Resolution.Daily)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot closing prices from \"SPY\" \n",
    "h1.loc[\"SPY\"][\"close\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gets historical data from the subscribed assets, from the last 30 days with daily resolution\n",
    "h2 = qb.History(qb.Securities.Keys, datetime(2014,1,1), datetime.now(), Resolution.Daily)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot high prices from \"EURUSD\" \n",
    "h2.loc[\"EURUSD\"][\"high\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gets historical data from the subscribed assets, between two dates with daily resolution\n",
    "h3 = qb.History([btc.Symbol], datetime(2014,1,1), datetime.now(), Resolution.Daily)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot closing prices from \"BTCUSD\" \n",
    "h3.loc[\"BTCUSD\"][\"close\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only fetchs historical data from a desired symbol\n",
    "# NOTE: This will return empty when ran locally because this data is not included\n",
    "h4 = qb.History([spy.Symbol], timedelta(360), Resolution.Daily)\n",
    "# or qb.History([\"SPY\"], 360, Resolution.Daily)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only fetchs historical data from a desired symbol\n",
    "# NOTE: This will return empty when ran locally because this data is not included\n",
    "h5 = qb.History([eur.Symbol], timedelta(30), Resolution.Daily)\n",
    "# or qb.History([\"EURUSD\"], 30, Resolution.Daily)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Historical Options Data Requests\n",
    "- Select the option data\n",
    "- Sets the filter, otherwise the default will be used SetFilter(-1, 1, timedelta(0), timedelta(35))\n",
    "- Get the OptionHistory, an object that has information about the historical options data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "goog = qb.AddOption(\"GOOG\")\n",
    "goog.SetFilter(-2, 2, timedelta(0), timedelta(180))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "option_history = qb.GetOptionHistory(goog.Symbol, datetime(2015, 12, 24))\n",
    "print (option_history.GetStrikes())\n",
    "print (option_history.GetExpiryDates())\n",
    "h7 = option_history.GetAllData()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Historical Future Data Requests\n",
    "- Select the future data\n",
    "- Sets the filter, otherwise the default will be used SetFilter(timedelta(0), timedelta(35))\n",
    "- Get the FutureHistory, an object that has information about the historical future data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "es = qb.AddFuture(\"ES\")\n",
    "es.SetFilter(timedelta(0), timedelta(180))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_history = qb.GetFutureHistory(es.Symbol, datetime(2013, 10, 7))\n",
    "print (future_history.GetExpiryDates())\n",
    "h7 = future_history.GetAllData()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Fundamental Data\n",
    "\n",
    "- *GetFundamental([symbol], selector, start_date = datetime(1998,1,1), end_date = datetime.now())*\n",
    "\n",
    "We will get a pandas.DataFrame with fundamental data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = qb.GetFundamental([\"AAPL\",\"AIG\",\"BAC\",\"GOOG\",\"IBM\"], \"ValuationRatios.PERatio\")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indicators\n",
    "\n",
    "We can easily get the indicator of a given symbol with QuantBook. \n",
    "\n",
    "For all indicators, please checkout QuantConnect Indicators [Reference Table](https://www.quantconnect.com/docs#Indicators-Reference-Table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example with BB, it is a datapoint indicator\n",
    "# Define the indicator\n",
    "bb = BollingerBands(30, 2)\n",
    "\n",
    "# Gets historical data of indicator\n",
    "bbdf = qb.Indicator(bb, \"SPY\", 360, Resolution.Daily)\n",
    "\n",
    "# drop undesired fields\n",
    "bbdf = bbdf.drop('standarddeviation', 1)\n",
    "\n",
    "# Plot\n",
    "bbdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For EURUSD\n",
    "bbdf = qb.Indicator(bb, \"EURUSD\", 360, Resolution.Daily)\n",
    "bbdf = bbdf.drop('standarddeviation', 1)\n",
    "bbdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example with ADX, it is a bar indicator\n",
    "adx = AverageDirectionalIndex(\"adx\", 14)\n",
    "adxdf = qb.Indicator(adx, \"SPY\", 360, Resolution.Daily)\n",
    "adxdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For EURUSD\n",
    "adxdf = qb.Indicator(adx, \"EURUSD\", 360, Resolution.Daily)\n",
    "adxdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example with ADO, it is a tradebar indicator (requires volume in its calculation)\n",
    "ado = AccumulationDistributionOscillator(\"ado\", 5, 30)\n",
    "adodf = qb.Indicator(ado, \"SPY\", 360, Resolution.Daily)\n",
    "adodf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For EURUSD. \n",
    "# Uncomment to check that this SHOULD fail, since Forex is data type is not TradeBar.\n",
    "# adodf = qb.Indicator(ado, \"EURUSD\", 360, Resolution.Daily)\n",
    "# adodf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SMA cross:\n",
    "symbol = \"EURUSD\"\n",
    "# Get History \n",
    "hist = qb.History([symbol], 500, Resolution.Daily)\n",
    "# Get the fast moving average\n",
    "fast = qb.Indicator(SimpleMovingAverage(50), symbol, 500, Resolution.Daily)\n",
    "# Get the fast moving average\n",
    "slow = qb.Indicator(SimpleMovingAverage(200), symbol, 500, Resolution.Daily)\n",
    "\n",
    "# Remove undesired columns and rename others \n",
    "fast = fast.drop('rollingsum', 1).rename(columns={'simplemovingaverage': 'fast'})\n",
    "slow = slow.drop('rollingsum', 1).rename(columns={'simplemovingaverage': 'slow'})\n",
    "\n",
    "# Concatenate the information and plot \n",
    "df = pd.concat([hist.loc[symbol][\"close\"], fast, slow], axis=1).dropna(axis=0)\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get indicator defining a lookback period in terms of timedelta\n",
    "ema1 = qb.Indicator(ExponentialMovingAverage(50), \"SPY\", timedelta(100), Resolution.Daily)\n",
    "# Get indicator defining a start and end date\n",
    "ema2 = qb.Indicator(ExponentialMovingAverage(50), \"SPY\", datetime(2016,1,1), datetime(2016,10,1), Resolution.Daily)\n",
    "\n",
    "ema = pd.concat([ema1, ema2], axis=1)\n",
    "ema.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsi = RelativeStrengthIndex(14)\n",
    "\n",
    "# Selects which field we want to use in our indicator (default is Field.Close)\n",
    "rsihi = qb.Indicator(rsi, \"SPY\", 360, Resolution.Daily, Field.High)\n",
    "rsilo = qb.Indicator(rsi, \"SPY\", 360, Resolution.Daily, Field.Low)\n",
    "rsihi = rsihi.rename(columns={'relativestrengthindex': 'high'})\n",
    "rsilo = rsilo.rename(columns={'relativestrengthindex': 'low'})\n",
    "rsi = pd.concat([rsihi['high'], rsilo['low']], axis=1)\n",
    "rsi.plot()"
   ]
  }
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