tcapy_notebooks/additional_benchmarks_metrics_for_tcapy.ipynb

{
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![cuemacro_logo.png](attachment:cuemacro_logo.png)\n",
    "\n",
    "# Additional benchmarks and metrics for tcapy\n",
    "\n",
    "**Saeed Amen / Founder of Cuemacro**\n",
    "\n",
    "https://www.cuemacro.com / saeed@cuemacro.com / @saeedamenfx / All material is copyright Cuemacro / 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we discuss some of the additional benchmarks and metrics which are available in tcapy. You can also create your own benchmarks and metrics with tcapy. Before going through this notebook, we recommend first reading through other notebooks available for tcapy [here](https://github.com/cuemacro/tcapy/blob/master/tcapy_notebooks/jupyter_notebooks_description.md) in particular the introduction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmarks\n",
    "\n",
    "Here we'll go through some of the benchmarks available in tcapy, which can be used for trades/orders (although some are more appropriate for orders). The abstract class `Benchmark` in tcapy can be extended to create your own customised benchmarks. Some which extend `BenchmarkMarket` are calculated purely on market data to provide more granularity over the market. However, there are many which extend `BenchmarkTrade` which can be used to calculate specific benchmarks for trades/orders.\n",
    "\n",
    "* Arrival price (`BenchmarkArrival`) - \n",
    "    * arrival price at start of order/trade fill\n",
    "* TWAP (`BenchmarkTWAP`) \n",
    "    * time weighted average price over the order window\n",
    "* VWAP (`BenchmarkVWAP`) - \n",
    "    * volume weighted average price over the order window\n",
    "* Best price (`BenchmarkBest`) \n",
    "    * best price over the order over the order window, ie. the minimum price for buys and maximum price for sells\n",
    "* Worst price (`BenchmarkWorst`) \n",
    "    * worst price over the order over the order window, ie. the maximum price for buys and minimum price for sells, if we repeatedly get filled at the worst price in our window, it is worth investigating why and talk to our liquidity providers\n",
    "* Median price (`BenchmarkMedian`)\n",
    "    * median price over the order window\n",
    "\n",
    "We can also select parameters to start the window earlier before our trade/order and finish later. This enables us to create a window for trades, which are points-in-time (unlike orders which already have a start and finish times). We can also assign a specific time of day as a benchmark. Hence for example, we could benchmark our trades/orders against:\n",
    "\n",
    "* A TWAP calculated on the window between 15:59:30 - 16:00:30 (London time) on the same day as a trade/order window as a proxy for the WMR fix (note, for Sunday evening FX trades the benchmark would be last tick of Friday)\n",
    "* Arrival/mid price at 16:00:00\n",
    "\n",
    "We later show how to do these benchmarks when calculating the slippage metric. Slippage is simply the difference between the execution price and the benchmark. If it is negative, it implies you executed worse than the benchmark, whilst positive numbers indicate you've executed better than the benchmark."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing and setting up source for tcapy\n",
    "\n",
    "Our first step is to add the tcapy library to our `PYTHONPATH`. You may need to change these lines. Note, that in our setup, we are running the Jupyter notebook on Windows, and the backend Celery workers on Linux. If you are running everything in the same environment, the paths are likely to be same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:30:50.810108Z",
     "start_time": "2020-07-27T11:30:50.805109Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "windows_tcapy_path = 'e:/cuemacro/tcapy' # Windows platform\n",
    "linux_tcapy_path = '/home/tcapyuser/cuemacro/tcapy' # Linux platform\n",
    "local_test_data_path = '../test/resources/' # Windows platform\n",
    "remote_test_data_path = '../test/resources/' # WSL drive\n",
    "\n",
    "# Assuming the front end is on Windows\n",
    "sys.path.insert(0, windows_tcapy_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the Python path now includes tcapy, we can do all the imports from it, that we'll need later. We'll also import all the other Python libraries we'll use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:30:56.260829Z",
     "start_time": "2020-07-27T11:30:50.812110Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-07-27 12:30:56,255; INFO:tcapy.analysis.tcaengine: Init TCAEngine version: pro - Env: desktop_laptop_linux (tcaengine.py:53)\n"
     ]
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "# This is the entry point for our TCA analysis\n",
    "from tcapy.analysis.tcaengine import TCAEngineImpl\n",
    "\n",
    "# To construct the parameters for our TCA calculation\n",
    "from tcapy.analysis.tcarequest import TCARequest\n",
    "\n",
    "# To access trade and market data\n",
    "from tcapy.data.databasesource import DatabaseSourceCSVBinary, \\\n",
    "    DatabaseSourceDataFrame, DatabaseSourceDukascopy\n",
    "\n",
    "# Import all the metrics and benchmarks we'll use\n",
    "from tcapy.analysis.algos.benchmark import *\n",
    "from tcapy.analysis.algos.metric import *\n",
    "\n",
    "# To aggregate the TCA results\n",
    "from tcapy.analysis.algos.resultsform import TimelineResultsForm, DistResultsForm, BarResultsForm\n",
    "\n",
    "# To help display the output of a TCA calculation\n",
    "from tcapy.vis.tcaresults import TCAResults\n",
    "from tcapy.vis.report.tcareport import TCAReport\n",
    "from tcapy.vis.displaylisteners import PlotRender\n",
    "\n",
    "# General classes\n",
    "from tcapy.conf.constants import Constants\n",
    "from tcapy.util.mediator import Mediator\n",
    "\n",
    "# Prevent requests from displaying debug messages for certain libraries\n",
    "import logging\n",
    "\n",
    "logging.getLogger(\"findatapy\").setLevel(logging.WARNING)\n",
    "logging.getLogger(\"requests\").setLevel(logging.WARNING)\n",
    "logging.getLogger(\"urllib3\").setLevel(logging.WARNING)\n",
    "\n",
    "# For plotting later\n",
    "import plotly.io as pio\n",
    "\n",
    "# For interactive plots (but these disappear when we reopen Jupyter)\n",
    "pio.renderers.default = \"notebook\" \n",
    "\n",
    "# Using https://github.com/plotly/Kaleido to generate static plot images\n",
    "# pio.renderers.default = \"svg\"\n",
    "pio.renderers.default = \"png\" \n",
    "\n",
    "import plotly\n",
    "\n",
    "from chartpy import Chart, Style\n",
    "\n",
    "# Default size for Plotly charts we use\n",
    "chart_width = 800\n",
    "chart_height = 500\n",
    "\n",
    "chart = Chart()\n",
    "constants = Constants()\n",
    "style = Style(width=chart_width, height=chart_height, scale_factor=-1, silent_display=True)\n",
    "\n",
    "# Entry point for TCA calculations\n",
    "tca_engine = TCAEngineImpl(version=constants.tcapy_version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now create a mapping so tcapy knows where the CSV files for our trade/orders. These test files come with tcapy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:30:56.273830Z",
     "start_time": "2020-07-27T11:30:56.262834Z"
    }
   },
   "outputs": [],
   "source": [
    "# The test trade/order data is populated between 25 Apr 2017-05 Jun 2017\n",
    "# with trades/orders for 'EURUSD', 'USDJPY' and 'EURJPY'\n",
    "local_csv_trade_order_mapping = OrderedDict([('trade_df', os.path.join(local_test_data_path, 'small_test_trade_df.csv')),\n",
    "                                             ('order_df', os.path.join(local_test_data_path, 'small_test_order_df.csv'))])\n",
    "\n",
    "remote_csv_trade_order_mapping = OrderedDict([('trade_df', os.path.join(remote_test_data_path, 'small_test_trade_df.csv')),\n",
    "                                              ('order_df', os.path.join(remote_test_data_path, 'small_test_order_df.csv'))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating slippage with 1600 LDN vs mid (arrival) price benchmark\n",
    "\n",
    "In this case, we create a benchmark price at 1600 LDN to proxy WMR, calculating a TWAP between 15:58:00 - 16:00:30, which can be used to benchmark all trades that day. We then use this value to calculate the slippage. We'll also use a mid price (and arrival price, although this is essentially the same thing) as other benchmarks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:30:56.280829Z",
     "start_time": "2020-07-27T11:30:56.274829Z"
    }
   },
   "outputs": [],
   "source": [
    "benchmark_calcs = [\n",
    "    # Calculate mid as a benchmark\n",
    "    BenchmarkMarketMid(),\n",
    "    \n",
    "    # Calculate TWAP around 1600 LDN as a benchmark for every trade/order\n",
    "    BenchmarkTWAP(start_time_before_offset={'m' : 2}, \n",
    "        finish_time_after_offset={'s' : 30},\n",
    "        overwrite_time_of_day='16:00', \n",
    "        overwrite_timezone='Europe/London',\n",
    "        benchmark_post_fix=\"1600LDN\"), \n",
    "    \n",
    "    # Calculate arrival of mid price for every trade/order\n",
    "    BenchmarkArrival()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll calculate slippage using two benchmarks 1600 LDN TWAP and the mid price at time of trade/order (the same as the arrival price)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:30:56.291831Z",
     "start_time": "2020-07-27T11:30:56.281830Z"
    }
   },
   "outputs": [],
   "source": [
    "metric_calcs = [MetricSlippage(bid_benchmark='twap1600LDN', ask_benchmark='twap1600LDN', metric_post_fix='twap1600LDN'),\n",
    "               MetricSlippage(bid_benchmark='mid', ask_benchmark='mid', metric_post_fix='mid')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to calculate a timeline of these two slippage metrics, calculated with these two respective benchmarks. Note, that we multiply the `scalar` by `10000.0` given that we want to convert the results into basis points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:30:56.299829Z",
     "start_time": "2020-07-27T11:30:56.293833Z"
    }
   },
   "outputs": [],
   "source": [
    "results_form=[TimelineResultsForm(metric_name='slippagetwap1600LDN', scalar=10000.0),\n",
    "              TimelineResultsForm(metric_name='slippagemid', scalar=10000.0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-31T22:36:42.307195Z",
     "start_time": "2020-05-31T22:36:42.250839Z"
    }
   },
   "source": [
    "Let's wrap this calculation up in a `TCARequest`. Here we specify the dates, ticker and other parameters, alongside all the `metric_calcs`, `results_form` and `benchmark_calcs`. For simplicity we are not using multithreading here and we are downloading the data directly from Dukascopy over the web, so not using any database for market data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:30:56.313829Z",
     "start_time": "2020-07-27T11:30:56.301834Z"
    }
   },
   "outputs": [],
   "source": [
    "# We are conducting a TCA computation for trade/order data between 07 May-15 May 2017 for EURUSD\n",
    "# The trade dataset is from CSV and the market data is downloaded from Dukascopy on the fly\n",
    "tca_request = TCARequest(start_date='07 May 2017', finish_date='15 May 2017', ticker=['EURUSD'],\n",
    "                         tca_type='detailed',\n",
    "                         trade_data_store='csv', market_data_store='dukascopy',\n",
    "                         trade_order_mapping=local_csv_trade_order_mapping,\n",
    "                         metric_calcs=metric_calcs,\n",
    "                         results_form=results_form,\n",
    "                         benchmark_calcs=benchmark_calcs,\n",
    "                         extra_lines_to_plot='twap1600LDN',\n",
    "                         use_multithreading=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's kick off the TCA calculation, on our trade/order data, with market data from Dukascopy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:31:10.927135Z",
     "start_time": "2020-07-27T11:30:56.315829Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-07-27 12:30:56,319; DEBUG:tcapy.analysis.tcamarkettradeloader: Start loading trade/data/computation (tcamarkettradeloader.py:241)\n",
      "2020-07-27 12:30:56,322; DEBUG:tcapy.analysis.tcatickerloaderimpl: Get market and trade/order data for EURUSD from 2017-05-07 00:00:00+00:00 - 2017-05-15 00:00:00+00:00 (tcatickerloaderimpl.py:80)\n",
      "2020-07-27 12:30:56,324; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-07 00:00:00+00:00_2017-05-15 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-07-27 12:30:56,334; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-07 00:00:00+00:00_2017-05-15 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-07-27 12:30:56,509; DEBUG:tcapy.data.databasesource: Downloading 2017-05-07 00:00:00 - 2017-05-15 00:00:00 for EURUSD (databasesource.py:3819)\n",
      "2020-07-27 12:31:09,655; WARNING:tcapy.analysis.algos.benchmark: mid not in market data (benchmark.py:90)\n",
      "2020-07-27 12:31:09,657; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-07 00:00:00+00:00_2017-05-15 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-07-27 12:31:09,813; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 26.054 MB ----------- in 1 chunk(s) (deltaizeserialize.py:219)\n",
      "2020-07-27 12:31:09,885; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-07 00:00:00+00:00_2017-05-15 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:443)\n",
      "2020-07-27 12:31:09,958; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-07 00:00:00+00:00_2017-05-15 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:461)\n",
      "2020-07-27 12:31:09,960; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-07 00:00:00+00:00_2017-05-15 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-07-27 12:31:10,113; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 26.054 MB ----------- in 1 chunk(s) (deltaizeserialize.py:219)\n",
      "2020-07-27 12:31:10,181; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-07 00:00:00+00:00_2017-05-15 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:443)\n",
      "2020-07-27 12:31:10,222; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-07 00:00:00+00:00_2017-05-15 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:461)\n",
      "2020-07-27 12:31:10,223; DEBUG:tcapy.analysis.tcatickerloader: Get trade order holder for EURUSD from 2017-05-07 00:00:00+00:00 - 2017-05-15 00:00:00+00:00 (tcatickerloader.py:386)\n",
      "2020-07-27 12:31:10,267; WARNING:tcapy.data.datafactory: Dataframe empty for ticker USDEUR (datafactory.py:97)\n",
      "2020-07-27 12:31:10,269; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-07 00:00:00+00:00_2017-05-15 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-07-27 12:31:10,403; WARNING:tcapy.data.datafactory: Dataframe empty for ticker USDEUR (datafactory.py:97)\n",
      "2020-07-27 12:31:10,405; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-07 00:00:00+00:00_2017-05-15 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-07-27 12:31:10,513; DEBUG:tcapy.analysis.tcatickerloader: Filter the market date by start/finish date (tcatickerloader.py:761)\n",
      "2020-07-27 12:31:10,523; DEBUG:tcapy.analysis.tcatickerloader: Combine trade/order data (tcatickerloader.py:782)\n",
      "2020-07-27 12:31:10,536; DEBUG:tcapy.analysis.tcatickerloader: Calculating BenchmarkMarketMid for market data (tcatickerloader.py:600)\n",
      "2020-07-27 12:31:10,537; WARNING:tcapy.analysis.algos.benchmark: mid not in market data (benchmark.py:90)\n",
      "2020-07-27 12:31:10,538; DEBUG:tcapy.analysis.tcatickerloader: Filter trades by venue (tcatickerloader.py:458)\n",
      "2020-07-27 12:31:10,540; DEBUG:tcapy.analysis.tcatickerloader: Calculating derived fields and benchmarks (tcatickerloader.py:508)\n",
      "2020-07-27 12:31:10,541; DEBUG:tcapy.analysis.tcatickerloader: Calculating execution fields (tcatickerloader.py:510)\n",
      "2020-07-27 12:31:10,551; DEBUG:tcapy.analysis.tcatickerloader: Calculating benchmarks (tcatickerloader.py:536)\n",
      "2020-07-27 12:31:10,552; DEBUG:tcapy.analysis.tcatickerloader: Calculating BenchmarkTWAP for trade_df (tcatickerloader.py:545)\n",
      "2020-07-27 12:31:10,569; DEBUG:tcapy.analysis.tcatickerloader: Calculating BenchmarkArrival for trade_df (tcatickerloader.py:545)\n",
      "2020-07-27 12:31:10,575; DEBUG:tcapy.analysis.tcatickerloader: Calculating BenchmarkTWAP for order_df (tcatickerloader.py:545)\n",
      "2020-07-27 12:31:10,591; DEBUG:tcapy.analysis.tcatickerloader: Calculating BenchmarkArrival for order_df (tcatickerloader.py:545)\n",
      "2020-07-27 12:31:10,598; DEBUG:tcapy.analysis.tcatickerloader: Calculating metrics (tcatickerloader.py:554)\n",
      "2020-07-27 12:31:10,599; DEBUG:tcapy.analysis.tcatickerloader: Calculating MetricSlippage for trade_df (tcatickerloader.py:560)\n",
      "2020-07-27 12:31:10,614; DEBUG:tcapy.analysis.tcatickerloader: Calculating MetricSlippage for trade_df (tcatickerloader.py:560)\n",
      "2020-07-27 12:31:10,634; DEBUG:tcapy.analysis.tcatickerloader: Calculating MetricSlippage for order_df (tcatickerloader.py:560)\n",
      "2020-07-27 12:31:10,647; DEBUG:tcapy.analysis.tcatickerloader: Calculating MetricSlippage for order_df (tcatickerloader.py:560)\n",
      "2020-07-27 12:31:10,664; DEBUG:tcapy.analysis.tcatickerloader: Completed derived field calculations for EURUSD (tcatickerloader.py:568)\n",
      "2020-07-27 12:31:10,665; DEBUG:tcapy.analysis.tcatickerloaderimpl: Generating downsampled market data for potentional display (tcatickerloaderimpl.py:275)\n",
      "2020-07-27 12:31:10,715; DEBUG:tcapy.analysis.tcatickerloaderimpl: About to join (tcatickerloaderimpl.py:340)\n",
      "2020-07-27 12:31:10,720; DEBUG:tcapy.analysis.tcatickerloaderimpl: Finished joining (tcatickerloaderimpl.py:356)\n",
      "2020-07-27 12:31:10,723; DEBUG:tcapy.analysis.tcatickerloaderimpl: About to join (tcatickerloaderimpl.py:340)\n",
      "2020-07-27 12:31:10,727; DEBUG:tcapy.analysis.tcatickerloaderimpl: Finished joining (tcatickerloaderimpl.py:356)\n",
      "2020-07-27 12:31:10,862; DEBUG:tcapy.analysis.tcamarkettradeloader: Finished loading data and calculating metrics on individual tickers (tcamarkettradeloader.py:248)\n",
      "2020-07-27 12:31:10,864; DEBUG:tcapy.analysis.tcamarkettradeloaderimpl: Constructing results form to summarize analysis... (tcamarkettradeloaderimpl.py:74)\n",
      "2020-07-27 12:31:10,923; DEBUG:tcapy.analysis.tcamarkettradeloaderimpl: Now join table results... (tcamarkettradeloaderimpl.py:122)\n",
      "2020-07-27 12:31:10,924; DEBUG:tcapy.analysis.tcamarkettradeloaderimpl: Finished calculating results form and join table results! (tcamarkettradeloaderimpl.py:135)\n"
     ]
    }
   ],
   "source": [
    "# Dictionary of (mostly) dataframes as output from TCA calculation\n",
    "dict_of_df = tca_engine.calculate_tca(tca_request)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get back a bunch of DataFrames (and candlestick Plotly Figure) etc. from our calculations, which we display below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:31:10.936135Z",
     "start_time": "2020-07-27T11:31:10.928135Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['trade_df', 'order_df', 'sparse_market_trade_df', 'sparse_market_order_df', 'market_df_downsampled', 'candlestick_fig', 'timeline_trade_df_slippagetwap1600LDN_by/mean_date/all', 'timeline_trade_df_slippagemid_by/mean_date/all', 'timeline_order_df_slippagetwap1600LDN_by/mean_date/all', 'timeline_order_df_slippagemid_by/mean_date/all', 'market_df'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_of_df.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we display the first few trades, we note all the additional calculated fields to the right of the table, such as `twap1600LDN`, `arrival`, `slippagetwap1600LDN` etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:31:10.962135Z",
     "start_time": "2020-07-27T11:31:10.938136Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>algo_id</th>\n",
       "      <th>algo_settings</th>\n",
       "      <th>ancestor_pointer_id</th>\n",
       "      <th>broker_id</th>\n",
       "      <th>broker_sub_id</th>\n",
       "      <th>event_type</th>\n",
       "      <th>executed_notional</th>\n",
       "      <th>executed_price</th>\n",
       "      <th>id</th>\n",
       "      <th>...</th>\n",
       "      <th>reporting_currency</th>\n",
       "      <th>executed_notional_in_reporting_currency</th>\n",
       "      <th>order_notional_in_reporting_currency</th>\n",
       "      <th>notional</th>\n",
       "      <th>twap1600LDN</th>\n",
       "      <th>arrival</th>\n",
       "      <th>slippagetwap1600LDN_benchmark</th>\n",
       "      <th>slippagetwap1600LDN</th>\n",
       "      <th>slippagemid_benchmark</th>\n",
       "      <th>slippagemid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08 10:31:33.011000+00:00</th>\n",
       "      <td>account1</td>\n",
       "      <td>algo3</td>\n",
       "      <td>default</td>\n",
       "      <td>order_EURUSD2017-05-08 10:30:57.526000+00:00_2...</td>\n",
       "      <td>broker4</td>\n",
       "      <td>subbroker2</td>\n",
       "      <td>trade</td>\n",
       "      <td>3276653</td>\n",
       "      <td>1.094703</td>\n",
       "      <td>execution_EURUSD2017-05-08 10:31:33.011000+00:...</td>\n",
       "      <td>...</td>\n",
       "      <td>USD</td>\n",
       "      <td>3.586887e+06</td>\n",
       "      <td>3.586887e+06</td>\n",
       "      <td>3276653</td>\n",
       "      <td>1.093353</td>\n",
       "      <td>1.09468</td>\n",
       "      <td>1.093353</td>\n",
       "      <td>-0.001235</td>\n",
       "      <td>1.09468</td>\n",
       "      <td>-0.000021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08 14:37:25.382000+00:00</th>\n",
       "      <td>account5</td>\n",
       "      <td>algo1</td>\n",
       "      <td>default</td>\n",
       "      <td>order_EURUSD2017-05-08 14:36:43.422000+00:00_2...</td>\n",
       "      <td>broker3</td>\n",
       "      <td>subbroker5</td>\n",
       "      <td>trade</td>\n",
       "      <td>14184843</td>\n",
       "      <td>1.093766</td>\n",
       "      <td>execution_EURUSD2017-05-08 14:37:25.382000+00:...</td>\n",
       "      <td>...</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.551467e+07</td>\n",
       "      <td>1.551467e+07</td>\n",
       "      <td>14184843</td>\n",
       "      <td>1.093353</td>\n",
       "      <td>1.09375</td>\n",
       "      <td>1.093353</td>\n",
       "      <td>-0.000378</td>\n",
       "      <td>1.09375</td>\n",
       "      <td>-0.000015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09 06:56:00.013000+00:00</th>\n",
       "      <td>account4</td>\n",
       "      <td>algo5</td>\n",
       "      <td>default</td>\n",
       "      <td>order_EURUSD2017-05-09 06:55:28.484000+00:00_2...</td>\n",
       "      <td>broker6</td>\n",
       "      <td>subbroker3</td>\n",
       "      <td>trade</td>\n",
       "      <td>6852731</td>\n",
       "      <td>1.090660</td>\n",
       "      <td>execution_EURUSD2017-05-09 06:56:00.013000+00:...</td>\n",
       "      <td>...</td>\n",
       "      <td>USD</td>\n",
       "      <td>7.473932e+06</td>\n",
       "      <td>7.473932e+06</td>\n",
       "      <td>6852731</td>\n",
       "      <td>1.089211</td>\n",
       "      <td>1.09065</td>\n",
       "      <td>1.089211</td>\n",
       "      <td>-0.001330</td>\n",
       "      <td>1.09065</td>\n",
       "      <td>-0.000009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 account_id algo_id algo_settings  \\\n",
       "Date                                                                \n",
       "2017-05-08 10:31:33.011000+00:00   account1   algo3       default   \n",
       "2017-05-08 14:37:25.382000+00:00   account5   algo1       default   \n",
       "2017-05-09 06:56:00.013000+00:00   account4   algo5       default   \n",
       "\n",
       "                                                                ancestor_pointer_id  \\\n",
       "Date                                                                                  \n",
       "2017-05-08 10:31:33.011000+00:00  order_EURUSD2017-05-08 10:30:57.526000+00:00_2...   \n",
       "2017-05-08 14:37:25.382000+00:00  order_EURUSD2017-05-08 14:36:43.422000+00:00_2...   \n",
       "2017-05-09 06:56:00.013000+00:00  order_EURUSD2017-05-09 06:55:28.484000+00:00_2...   \n",
       "\n",
       "                                 broker_id broker_sub_id event_type  \\\n",
       "Date                                                                  \n",
       "2017-05-08 10:31:33.011000+00:00   broker4    subbroker2      trade   \n",
       "2017-05-08 14:37:25.382000+00:00   broker3    subbroker5      trade   \n",
       "2017-05-09 06:56:00.013000+00:00   broker6    subbroker3      trade   \n",
       "\n",
       "                                  executed_notional  executed_price  \\\n",
       "Date                                                                  \n",
       "2017-05-08 10:31:33.011000+00:00            3276653        1.094703   \n",
       "2017-05-08 14:37:25.382000+00:00           14184843        1.093766   \n",
       "2017-05-09 06:56:00.013000+00:00            6852731        1.090660   \n",
       "\n",
       "                                                                                 id  \\\n",
       "Date                                                                                  \n",
       "2017-05-08 10:31:33.011000+00:00  execution_EURUSD2017-05-08 10:31:33.011000+00:...   \n",
       "2017-05-08 14:37:25.382000+00:00  execution_EURUSD2017-05-08 14:37:25.382000+00:...   \n",
       "2017-05-09 06:56:00.013000+00:00  execution_EURUSD2017-05-09 06:56:00.013000+00:...   \n",
       "\n",
       "                                  ...  reporting_currency  \\\n",
       "Date                              ...                       \n",
       "2017-05-08 10:31:33.011000+00:00  ...                 USD   \n",
       "2017-05-08 14:37:25.382000+00:00  ...                 USD   \n",
       "2017-05-09 06:56:00.013000+00:00  ...                 USD   \n",
       "\n",
       "                                  executed_notional_in_reporting_currency  \\\n",
       "Date                                                                        \n",
       "2017-05-08 10:31:33.011000+00:00                             3.586887e+06   \n",
       "2017-05-08 14:37:25.382000+00:00                             1.551467e+07   \n",
       "2017-05-09 06:56:00.013000+00:00                             7.473932e+06   \n",
       "\n",
       "                                  order_notional_in_reporting_currency  \\\n",
       "Date                                                                     \n",
       "2017-05-08 10:31:33.011000+00:00                          3.586887e+06   \n",
       "2017-05-08 14:37:25.382000+00:00                          1.551467e+07   \n",
       "2017-05-09 06:56:00.013000+00:00                          7.473932e+06   \n",
       "\n",
       "                                  notional  twap1600LDN  arrival  \\\n",
       "Date                                                               \n",
       "2017-05-08 10:31:33.011000+00:00   3276653     1.093353  1.09468   \n",
       "2017-05-08 14:37:25.382000+00:00  14184843     1.093353  1.09375   \n",
       "2017-05-09 06:56:00.013000+00:00   6852731     1.089211  1.09065   \n",
       "\n",
       "                                 slippagetwap1600LDN_benchmark  \\\n",
       "Date                                                             \n",
       "2017-05-08 10:31:33.011000+00:00                      1.093353   \n",
       "2017-05-08 14:37:25.382000+00:00                      1.093353   \n",
       "2017-05-09 06:56:00.013000+00:00                      1.089211   \n",
       "\n",
       "                                  slippagetwap1600LDN  slippagemid_benchmark  \\\n",
       "Date                                                                           \n",
       "2017-05-08 10:31:33.011000+00:00            -0.001235                1.09468   \n",
       "2017-05-08 14:37:25.382000+00:00            -0.000378                1.09375   \n",
       "2017-05-09 06:56:00.013000+00:00            -0.001330                1.09065   \n",
       "\n",
       "                                 slippagemid  \n",
       "Date                                          \n",
       "2017-05-08 10:31:33.011000+00:00   -0.000021  \n",
       "2017-05-08 14:37:25.382000+00:00   -0.000015  \n",
       "2017-05-09 06:56:00.013000+00:00   -0.000009  \n",
       "\n",
       "[3 rows x 33 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_of_df['trade_df'].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we create a `TCAResults` object, we can render lots of nice charts from our TCA output. It also organises the various DataFrames in an easier to access way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:31:11.301138Z",
     "start_time": "2020-07-27T11:31:10.963136Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-07-27 12:31:11,165; DEBUG:tcapy.vis.displaylisteners: Plotting main timeline 2840 (displaylisteners.py:988)\n",
      "2020-07-27 12:31:11,230; DEBUG:tcapy.vis.displaylisteners: Rendered plot 2840 and  (displaylisteners.py:998)\n",
      "2020-07-27 12:31:11,233; DEBUG:tcapy.vis.displaylisteners: Plotting main timeline 2840 (displaylisteners.py:988)\n",
      "2020-07-27 12:31:11,296; DEBUG:tcapy.vis.displaylisteners: Rendered plot 2840 and  (displaylisteners.py:998)\n"
     ]
    }
   ],
   "source": [
    "tca_results = TCAResults(dict_of_df, tca_request, chart_width=chart_width, chart_height=chart_height)\n",
    "tca_results.render_computation_charts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the timeline DataFrame objects which have been output. In this instance, these are time series of average slippage both for our trades and also orders, which have been calculated with the mid price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:31:11.309135Z",
     "start_time": "2020-07-27T11:31:11.303135Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['trade_df_slippagetwap1600LDN_by/mean_date/all', 'trade_df_slippagemid_by/mean_date/all', 'order_df_slippagetwap1600LDN_by/mean_date/all', 'order_df_slippagemid_by/mean_date/all'])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tca_results.timeline.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the slippage (in basis points on the y-axis) based upon the 1600 LDN TWAP as benchmark and also later using the mid price. We see that we've generally got quite large amounts of slippage versus 1600 LDN TWAP, hence, it would have generally been better to trade at 1600 LDN. Looking at the mid price as a benchmark, our slippage hasn't been too bad.\n",
    "\n",
    "We should note that these are all randomized trades, so it's difficult to read too much into these results (they would of course be more meaningful with real trade data). The time series is also relatively short to make any specific inferences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:31:15.269515Z",
     "start_time": "2020-07-27T11:31:11.310137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tca_results.timeline_charts['trade_df_slippagetwap1600LDN_by/mean_date/all']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:31:15.321515Z",
     "start_time": "2020-07-27T11:31:15.271518Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tca_results.timeline_charts['trade_df_slippagemid_by/mean_date/all']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also display the market data charts alongside our orders executed (and the benchmarks, which in our case is the arrival price, as well as the TWAP around 1600 LDN the same day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:31:15.328517Z",
     "start_time": "2020-07-27T11:31:15.322517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['EURUSD_trade_df', 'EURUSD_order_df'])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tca_results.sparse_market_charts.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-27T11:31:15.471516Z",
     "start_time": "2020-07-27T11:31:15.329517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tca_results.sparse_market_charts['EURUSD_order_df']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "We have seen how to use tcapy to calculate metrics using benchmarks, which can be heavily parameterized. We hope to add more examples later."
   ]
  }
 ],
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