twap_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"float:right; width:100px; text-align: center; margin: 10px;\">\n",
    "<img src=\"https://crypto-lake.com/assets/img/lake.png\" alt=\"Lake\"/>\n",
    "</div>\n",
    "\n",
    "# Twap detection\n",
    "\n",
    "Detect simple TWAP executions in tick trades data.\n",
    "\n",
    "We use [crypto-lake.com](https://crypto-lake.com/#data) sample/free market data, FTRB-USDT market on Ascendex.\n",
    "\n",
    "Quick links:\n",
    "- [edit this notebook online](https://mybinder.org/v2/gh/crypto-lake/analysis-sharing/main?filepath=twap_detection.ipynb) using Binder\n",
    "- [follow our activity on twitter](https://twitter.com/intent/user?screen_name=crypto_lake_com)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.16.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "import math\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cufflinks as cf\n",
    "from scipy import signal\n",
    "import plotly.express as px\n",
    "\n",
    "import lakeapi\n",
    "\n",
    "cf.go_offline()\n",
    "lakeapi.use_sample_data(anonymous_access=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "symbol = 'FTRB-USDT'\n",
    "exchange = 'ASCENDEX'\n",
    "\n",
    "# Free sample data contain subset of the below time period\n",
    "start = datetime.datetime(2022, 9, 1)\n",
    "end = datetime.datetime(2022, 12, 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading trades\n"
     ]
    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/81 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Loading trades')\n",
    "trades = lakeapi.load_data(\n",
    "    table = 'trades',\n",
    "    start = start,\n",
    "    end = end,\n",
    "    symbols = [symbol],\n",
    "    exchanges = [exchange],\n",
    "    drop_partition_cols = True,\n",
    ").sort_values('origin_time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(99539, 10)\n"
     ]
    },
    {
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       "    }\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>side</th>\n",
       "      <th>quantity</th>\n",
       "      <th>price</th>\n",
       "      <th>trade_id</th>\n",
       "      <th>origin_time</th>\n",
       "      <th>received_time</th>\n",
       "      <th>signed_quantity</th>\n",
       "      <th>price_ewm</th>\n",
       "      <th>aggressivity</th>\n",
       "      <th>aggr_quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>sell</td>\n",
       "      <td>2586.0</td>\n",
       "      <td>0.010007</td>\n",
       "      <td>108086470187312032</td>\n",
       "      <td>2022-09-01 15:57:58.421999872</td>\n",
       "      <td>2022-09-01 15:57:58.613365504</td>\n",
       "      <td>-2586.0</td>\n",
       "      <td>0.010023</td>\n",
       "      <td>-0.001642</td>\n",
       "      <td>-4.246117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>buy</td>\n",
       "      <td>2550.0</td>\n",
       "      <td>0.010013</td>\n",
       "      <td>108086470187322384</td>\n",
       "      <td>2022-09-01 15:58:23.803000064</td>\n",
       "      <td>2022-09-01 15:58:24.033291776</td>\n",
       "      <td>2550.0</td>\n",
       "      <td>0.010022</td>\n",
       "      <td>-0.000908</td>\n",
       "      <td>-2.316488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     side  quantity     price            trade_id  \\\n",
       "100  sell    2586.0  0.010007  108086470187312032   \n",
       "101   buy    2550.0  0.010013  108086470187322384   \n",
       "\n",
       "                      origin_time                 received_time  \\\n",
       "100 2022-09-01 15:57:58.421999872 2022-09-01 15:57:58.613365504   \n",
       "101 2022-09-01 15:58:23.803000064 2022-09-01 15:58:24.033291776   \n",
       "\n",
       "     signed_quantity  price_ewm  aggressivity  aggr_quantity  \n",
       "100          -2586.0   0.010023     -0.001642      -4.246117  \n",
       "101           2550.0   0.010022     -0.000908      -2.316488  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades['signed_quantity'] = trades.quantity * trades.side.map({'buy': +1, 'sell': -1}).astype(int)\n",
    "\n",
    "# A trick here: instead of using trade side as 'aggressor', I use something else as aggressivity: \n",
    "# direction and strength of current short-term trend.\n",
    "trades['price_ewm'] = trades.price.ewm(halflife = 5).mean()\n",
    "trades['aggressivity'] = (trades.price - trades.price_ewm) / trades.price_ewm\n",
    "trades['aggr_quantity'] = trades.quantity * trades.aggressivity\n",
    "\n",
    "print(trades.shape)\n",
    "trades[100:].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def volume_share_plot(selector, title = None, resample = '60min'):\n",
    "\treturn (\n",
    "\t\ttrades[selector].set_index('origin_time').resample(resample).quantity.sum() \n",
    "\t\t/ \n",
    "\t\ttrades.set_index('origin_time').resample(resample).quantity.sum() \n",
    "\t).iplot(title = title, yTitle = 'volume share')\n",
    "\n",
    "def volume_plot(selector, title = None, resample = '60min'):\n",
    "\tdf = trades\n",
    "\treturn df[selector].set_index('origin_time').resample(resample).agg({'aggr_quantity': 'sum'}).iplot(title = title, yTitle = 'adjusted imbalance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Estimate power spectral density using a periodogram\n",
    "f, Pxx = signal.periodogram(trades.quantity, fs = 1e6, nfft = 1000)\n",
    "plot = px.line(x = f, y = Pxx, title = 'Peak frequencies (in ms)')\n",
    "for i in [6, 60, 5*60]:\n",
    "\tplot.add_vline(i * 1_000, line_width=1, line_color=\"orange\")\n",
    "plot"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a lot of quantity is submited in frequencies of 6 seconds, 60 seconds and 5 minutes. Lets focus on 60-second twap orders aligned to second boundary = eg. around 12:35:45.000 +- a few millis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Minute twap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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   "source": [
    "around_whole_second = (trades.origin_time.dt.microsecond < 80_000) | (trades.origin_time.dt.microsecond > 950_000)\n",
    "\n",
    "volume_share_plot(selector = around_whole_second, resample = '60min')\n",
    "volume_plot(selector = around_whole_second, resample = '60min')\n",
    "trades.set_index('origin_time').price[::100].iplot(yTitle = 'price')"
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    "We can see a few strong twaps in the charts. Eg. Nov 7 -- Nov 11 had a significant sell twap, it's effect is clearly visible in price chart as well. Other sells are around Dec 4 and perhaps Nov 2. One buy twap with smaller volume is around Oct 25. Some of the twap chunks don't seem to be imbalanced and therefore affecting price, eg. in September."
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   "source": [
    "## Conclusion\n",
    "\n",
    "We detected a few twap executions, some of which were affecting price significantly. This analysis can be extended to run in real-time to serve as a trading signal or MM bot adjustment. The advantage of filtering out twap data compared to following price trend is that the signal is more clearly readable and can be easily used for trend-following."
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