defi-cefi arbitrage-strategy-only.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "213878e8",
   "metadata": {},
   "source": [
    "# DeFi-CeFi arbitrage-strategy-walk-through\n",
    "\n",
    "### Created by Albert Yao \n",
    "\n",
    "\n",
    "## Structure\n",
    "\n",
    "\n",
    "* **Goal of the project**\n",
    ">            1. Leverage Amberdata DeFi API to build backtest framework for Defi/Cefi arbitrage \n",
    "             2. Understand the potential arbitrage profit by using uniswap v2 equation\n",
    "             \n",
    "\n",
    "* **Overview of the dex/cex price**   \n",
    ">            1. Visualize the dex/cex price difference and create entry signal for arbitrage\n",
    "* **Arbitrage Time window analysis** \n",
    ">            1. Table for time window\n",
    "             2. Multi-plot of price difference and time window\n",
    "* **Introduction to Uniswap v2 equation** \n",
    ">            1. Uniswap v2 math equation walk-through\n",
    "             2. Swap function with fee in python \n",
    "             2. Stress test on v2 swap equation with Amberdata liquidity API \n",
    "* **Strategy codebase** \n",
    ">            1.Strategy framework design \n",
    "             2.Customized strategy on buy/sell condition \n",
    "             \n",
    "* **Strategy Dashboard** \n",
    ">            1.Cumulative PnL, PnL %, entry/exit time \n",
    "* **Strategy Trade analysis** \n",
    ">            1.Detail PnL analysis based on day/hour  \n",
    "             2.Gas fee per trade tracker\n",
    "             3.Sharp, Sortino Ratio, SQN \n",
    "\n",
    "\n",
    "\n",
    "                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e9faed1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f27e0cd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "from io import StringIO ## for Python 3\n",
    "import quantstats as qs\n",
    "\n",
    "from dotenv import load_dotenv # use for api key \n",
    "import os #provides ways to access the Operating System and allows us to read the environment variables\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f120682d",
   "metadata": {},
   "source": [
    "## run code for existing merged cex/dex data start from here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e2c1da54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>timestamp</th>\n",
       "      <th>cex_price</th>\n",
       "      <th>dex_price</th>\n",
       "      <th>token0Amount</th>\n",
       "      <th>token1Amount</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>390135</th>\n",
       "      <td>1643691658000</td>\n",
       "      <td>2746.175304</td>\n",
       "      <td>2747.714408</td>\n",
       "      <td>9.909518e+07</td>\n",
       "      <td>36064.585536</td>\n",
       "      <td>2022-02-01 00:00:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45772</th>\n",
       "      <td>1643691660000</td>\n",
       "      <td>2747.518422</td>\n",
       "      <td>2747.714408</td>\n",
       "      <td>9.909518e+07</td>\n",
       "      <td>36064.585536</td>\n",
       "      <td>2022-02-01 00:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390136</th>\n",
       "      <td>1643691679000</td>\n",
       "      <td>2747.518422</td>\n",
       "      <td>2749.652800</td>\n",
       "      <td>9.913018e+07</td>\n",
       "      <td>36051.890360</td>\n",
       "      <td>2022-02-01 00:01:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45773</th>\n",
       "      <td>1643691720000</td>\n",
       "      <td>2747.143086</td>\n",
       "      <td>2749.652800</td>\n",
       "      <td>9.913018e+07</td>\n",
       "      <td>36051.890360</td>\n",
       "      <td>2022-02-01 00:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390138</th>\n",
       "      <td>1643691757000</td>\n",
       "      <td>2747.143086</td>\n",
       "      <td>2749.081728</td>\n",
       "      <td>9.911990e+07</td>\n",
       "      <td>36055.640360</td>\n",
       "      <td>2022-02-01 00:02:37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp    cex_price    dex_price  token0Amount  token1Amount  \\\n",
       "390135  1643691658000  2746.175304  2747.714408  9.909518e+07  36064.585536   \n",
       "45772   1643691660000  2747.518422  2747.714408  9.909518e+07  36064.585536   \n",
       "390136  1643691679000  2747.518422  2749.652800  9.913018e+07  36051.890360   \n",
       "45773   1643691720000  2747.143086  2749.652800  9.913018e+07  36051.890360   \n",
       "390138  1643691757000  2747.143086  2749.081728  9.911990e+07  36055.640360   \n",
       "\n",
       "                   datetime  \n",
       "390135  2022-02-01 00:00:58  \n",
       "45772   2022-02-01 00:01:00  \n",
       "390136  2022-02-01 00:01:19  \n",
       "45773   2022-02-01 00:02:00  \n",
       "390138  2022-02-01 00:02:37  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cex_dex = pd.read_csv('amber_merge_data_new.csv', index_col=0)\n",
    "\n",
    "df_cex_dex.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "095faa94",
   "metadata": {},
   "source": [
    "## Overview of dex/cex price "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fb1ca5f1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "def plot_specific(df, d, start, end, step, file):\n",
    "    \n",
    "    \"\"\" A function to plot the plot the price difference between cex and dex\n",
    "            \n",
    "\n",
    "    Keyword arguments:\n",
    "    d -- input parameter to check the price difference %, if above the threshold, will be treated as entry signal\n",
    "    start,end -- input start/end time in date to check transactions\n",
    "    step -- input parameter to contorl how many rows being ignored (if plot too many rows in chart will be hard to analyze)\n",
    "    file -- input file for analysis \n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    df = df.loc[(df['timestamp'] > start.timestamp()*1000) & (df['timestamp'] < end.timestamp()*1000)]\n",
    "    df['datetime'] = df['timestamp'].apply(lambda x: datetime.fromtimestamp(x/1000))\n",
    "    df['entry'] = (df['price_diff(%)'] > d) & (df['price_diff(%)'].shift() < d)\n",
    "    df['exit'] = (df['price_diff(%)'] < d) & (df['price_diff(%)'].shift() > d)\n",
    "    df['signal'] = df.apply(lambda x: 'entry' if x['entry'] else ('exit' if x['exit'] else np.nan), axis=1)\n",
    "    df = df[['datetime', 'cex_price', 'dex_price', 'price_diff(%)', 'signal']]\n",
    "\n",
    "    df_resample = df.iloc[::step]\n",
    "    fig = px.line(df_resample, x='datetime', y=['cex_price', 'dex_price']\n",
    "            )\n",
    "\n",
    "    entry_list = df.loc[df['signal']=='entry'].datetime.tolist()\n",
    "    exit_list = df.loc[df['signal']=='exit'].datetime.tolist()\n",
    "    \n",
    "    if len(entry_list) == len(exit_list) + 1:\n",
    "        exit_list.append(df.datetime.values[-1])\n",
    "        \n",
    "    for i in range(len(entry_list)):\n",
    "        #print(i)\n",
    "        fig.add_vrect(x0=entry_list[i], x1=exit_list[i], fillcolor=\"#283747\", opacity=0.5, line_width=1)\n",
    "        \n",
    "    \n",
    "    fig.update_layout(height= 800, width=1000,    xaxis_title=\"Time in Date\",\n",
    "    yaxis_title=\"ETH price\", title_text=\" Overview of defi-cefi price\")\n",
    "    \n",
    "    #fig.show(config={'scrollZoom': True})\n",
    "    fig.show(\"png\",config={'scrollZoom': True})\n",
    "    \n",
    "    # if want to save the chart, comment out this line of code \n",
    "    #fig.write_html(f'{file}.html')\n",
    "    \n",
    "    return 0\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "df = pd.read_csv('./amber_merge_data_new.csv', index_col=0)\n",
    "\n",
    "df['price_diff(%)'] = df.apply(lambda x: 100 * max(x['cex_price'],x['dex_price'])/min(x['cex_price'], x['dex_price']) - 100, axis=1)\n",
    "\n",
    "# overview\n",
    "# price_diff = 1 # (%)\n",
    "# step = 100\n",
    "# start = datetime(2022, 5, 1, 0, 0, 0)\n",
    "# end = datetime(2022, 8, 1, 0, 0, 0)\n",
    "# plot_overview(df, price_diff, start, end, step, file='0501_0801_diff_1_triangle')\n",
    "\n",
    "# specific\n",
    "\n",
    "price_diff = 1 # (%)\n",
    "step = 100\n",
    "start = datetime(2022, 2, 1, 0, 0, 0)\n",
    "end = datetime(2022, 5, 1, 0, 0, 0)\n",
    "\n",
    "\n",
    "df = df.loc[(df['timestamp'] > start.timestamp()*1000) & (df['timestamp'] < end.timestamp()*1000)]\n",
    "plot_specific(df, price_diff, start, end, step, file='output/Feb_1_to_May_1_price_data.csv')\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4c9e72",
   "metadata": {},
   "source": [
    "## analyze arbitrage opportunity with time window "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0b2620ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "input_file_for_time_window_analysis = 'amber_merge_data_new.csv'\n",
    "start = datetime(2022, 2, 1, 0, 0, 0)\n",
    "end = datetime(2022, 5, 1, 0, 0, 0)\n",
    "\n",
    "\n",
    "\n",
    "def timestamp_4( d, input_file = input_file_for_time_window_analysis):\n",
    "    \n",
    "    \n",
    "    \"\"\" A function to classify transaction into multiple price group (if price of dex > cex or cex > dex)\n",
    "            \n",
    "\n",
    "    Keyword arguments:\n",
    "    d -- input parameter to check the price difference %, if above the threshold, will be counted \n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    \n",
    "    df_merge = pd.read_csv( input_file, index_col=0)\n",
    "\n",
    "    df_merge = df_merge.loc[(df_merge['timestamp'] > start.timestamp()*1000) & (df_merge['timestamp'] < end.timestamp()*1000)]\n",
    "    df_merge = df_merge.drop_duplicates(subset=['timestamp'], keep='last')\n",
    "\n",
    "    \n",
    "    \n",
    "    df_merge['upper'] = df_merge['cex_price'] * (1 + d * 0.01)\n",
    "    df_merge['lower'] = df_merge['cex_price'] * (1 - d * 0.01)\n",
    "\n",
    "    df_merge['bigger'] = df_merge['dex_price'] > df_merge['upper']\n",
    "    df_merge['smaller'] = df_merge['dex_price'] < df_merge['lower']\n",
    "\n",
    "    df_merge['OverUpper'] = (df_merge['bigger'] == True) & (df_merge.shift(1)['bigger'] == False)\n",
    "    df_merge['DownUpper'] = (df_merge['bigger'] == False) & (df_merge.shift(1)['bigger'] == True)\n",
    "    df_merge['DownLower'] = (df_merge['smaller'] == True) & (df_merge.shift(1)['smaller'] == False)\n",
    "    df_merge['OverLower'] = (df_merge['smaller'] == False) & (df_merge.shift(1)['smaller'] == True)\n",
    "\n",
    "    # df_merge = df_merge.set_index(\"timestamp\")\n",
    "\n",
    "    df_over_up = df_merge.loc[df_merge['OverUpper']]\n",
    "    timestamp_over_up = df_over_up.timestamp.tolist()\n",
    "\n",
    "    df_down_up = df_merge.loc[df_merge['DownUpper']]\n",
    "    timestamp_down_up = df_down_up.timestamp.tolist()\n",
    "\n",
    "    df_down_lower = df_merge.loc[df_merge['DownLower']]\n",
    "    timestamp_down_lower = df_down_lower.timestamp.tolist()\n",
    "\n",
    "    df_over_lower = df_merge.loc[df_merge['OverLower']]\n",
    "    timestamp_over_lower = df_over_lower.timestamp.tolist()\n",
    "\n",
    "    return timestamp_over_up, timestamp_down_up, timestamp_down_lower, timestamp_over_lower\n",
    "\n",
    "\n",
    "def count(timestamp_over_up, timestamp_down_up, timestamp_down_lower, timestamp_over_lower, d):\n",
    "    \n",
    "    \n",
    "    \"\"\" A helper function to understand price difference and total duration of time the price divergence merges\n",
    "            \n",
    "    \"\"\"\n",
    "    \n",
    "    def duration_df(timestamp_diverge, timestamp_converge, direction, difference):\n",
    "        stats_dict = {\n",
    "            'last_time(secs)': [],\n",
    "            'start_time': [],\n",
    "            'direction': [],\n",
    "            'difference': []\n",
    "        }\n",
    "\n",
    "        for i in range(len(timestamp_diverge)):\n",
    "            stats_dict['last_time(secs)'].append(timestamp_converge[i] - timestamp_diverge[i])\n",
    "            stats_dict['start_time'].append(timestamp_diverge[i])\n",
    "            stats_dict['direction'].append(direction)\n",
    "            stats_dict['difference'].append(difference)\n",
    "        df = pd.DataFrame(data=stats_dict)\n",
    "        return df\n",
    "\n",
    "    def count_df(duration_df):\n",
    "        duration_levels = [30, 60, 180, 300]\n",
    "        count_dict = {\n",
    "            'duration(s)': [],\n",
    "            'count': []\n",
    "        }\n",
    "        duration_df['last_time(secs)'] = duration_df['last_time(secs)'] / 1000\n",
    "        # <30s\n",
    "        count_dict['duration(s)'].append(f'<{duration_levels[0]}')\n",
    "        df = duration_df.loc[duration_df['last_time(secs)'] < duration_levels[0]]\n",
    "        count_dict['count'].append(len(df))\n",
    "\n",
    "        # 30~60s\n",
    "        count_dict['duration(s)'].append(f'{duration_levels[0]}~{duration_levels[1]}')\n",
    "        df = duration_df.loc[\n",
    "            (duration_levels[0] <= duration_df['last_time(secs)']) & (\n",
    "                    duration_df['last_time(secs)'] < duration_levels[1])]\n",
    "        count_dict['count'].append(len(df))\n",
    "\n",
    "        # 60~180\n",
    "        count_dict['duration(s)'].append(f'{duration_levels[1]}~{duration_levels[2]}')\n",
    "        df = duration_df.loc[\n",
    "            (duration_levels[1] <= duration_df['last_time(secs)']) & (\n",
    "                    duration_df['last_time(secs)'] < duration_levels[2])]\n",
    "        count_dict['count'].append(len(df))\n",
    "\n",
    "        # 60~180\n",
    "        count_dict['duration(s)'].append(f'{duration_levels[2]}~{duration_levels[3]}')\n",
    "        df = duration_df.loc[\n",
    "            (duration_levels[2] <= duration_df['last_time(secs)']) & (\n",
    "                    duration_df['last_time(secs)'] < duration_levels[3])]\n",
    "        count_dict['count'].append(len(df))\n",
    "\n",
    "        # >180\n",
    "        count_dict['duration(s)'].append(f'>{duration_levels[3]}')\n",
    "        df = duration_df.loc[duration_df['last_time(secs)'] > duration_levels[3]]\n",
    "        count_dict['count'].append(len(df))\n",
    "\n",
    "        df = pd.DataFrame(data=count_dict)\n",
    "        return df\n",
    "\n",
    "    if len(timestamp_over_up) == len(timestamp_down_up) + 1:\n",
    "        timestamp_over_up = timestamp_over_up[:-1]\n",
    "\n",
    "    if len(timestamp_down_lower) == len(timestamp_over_lower) + 1:\n",
    "        timestamp_down_lower = timestamp_down_lower[:-1]\n",
    "\n",
    "    if len(timestamp_over_up) > 1:\n",
    "        df_up = duration_df(timestamp_diverge=timestamp_over_up, timestamp_converge=timestamp_down_up,\n",
    "                            direction='up', difference=d)\n",
    "        df_up_count = count_df(df_up)\n",
    "    else:\n",
    "        df_up_count = None\n",
    "\n",
    "    if len(timestamp_down_lower) > 1:\n",
    "        df_down = duration_df(timestamp_diverge=timestamp_down_lower, timestamp_converge=timestamp_over_lower,\n",
    "                              direction='down', difference=d)\n",
    "        df_down_count = count_df(df_down)\n",
    "    else:\n",
    "        df_down_count = None\n",
    "\n",
    "    return df_up_count, df_down_count\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c7e40fb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2, 1.3, 1.4, 1.5,\n",
       "       1.6, 1.7, 1.8, 1.9, 2. ])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_list = np.arange(3, 21, 1) / 10\n",
    "d_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "4b2f362c",
   "metadata": {},
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>difference(%)</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0.3</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0.4</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0.5</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0.6</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0.7</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1.6</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1.7</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1.8</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1.9</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direction</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "      <th>...</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;30</th>\n",
       "      <td>1419</td>\n",
       "      <td>1622</td>\n",
       "      <td>646</td>\n",
       "      <td>1052</td>\n",
       "      <td>159</td>\n",
       "      <td>308</td>\n",
       "      <td>71</td>\n",
       "      <td>108</td>\n",
       "      <td>38</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30~60</th>\n",
       "      <td>1127</td>\n",
       "      <td>1421</td>\n",
       "      <td>237</td>\n",
       "      <td>493</td>\n",
       "      <td>43</td>\n",
       "      <td>95</td>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60~180</th>\n",
       "      <td>1304</td>\n",
       "      <td>2430</td>\n",
       "      <td>88</td>\n",
       "      <td>322</td>\n",
       "      <td>16</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180~300</th>\n",
       "      <td>133</td>\n",
       "      <td>472</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;300</th>\n",
       "      <td>29</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "difference(%)   0.3        0.4        0.5      0.6      0.7       ... 1.6  \\\n",
       "direction        up  down   up  down   up down  up down  up down  ...  up   \n",
       "<30            1419  1622  646  1052  159  308  71  108  38   42  ...   2   \n",
       "30~60          1127  1421  237   493   43   95   9   34   6   17  ...   2   \n",
       "60~180         1304  2430   88   322   16   43   5   11   3    5  ...   0   \n",
       "180~300         133   472    2    22    1    3   0    3   0    0  ...   0   \n",
       ">300             29   158    0     5    0    2   0    1   0    1  ...   0   \n",
       "\n",
       "difference(%)      1.7      1.8      1.9      2.0       \n",
       "direction     down  up down  up down  up down  up down  \n",
       "<30              1   2    1   2    1   2    1   2    1  \n",
       "30~60            2   2    2   2    2   2    1   2    1  \n",
       "60~180           0   0    0   0    0   0    0   0    0  \n",
       "180~300          0   0    0   0    0   0    0   0    0  \n",
       ">300             0   0    0   0    0   0    0   0    0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# set the threshold to calculate price difference \n",
    "d_list = np.arange(3, 21, 1) / 10\n",
    "iterables = [d_list, [\"up\", \"down\"]]\n",
    "index = pd.MultiIndex.from_product(iterables, names=[\"difference(%)\", \"direction\"])\n",
    "\n",
    "# classify transaction into different time buckets \n",
    "df = pd.DataFrame(0, index=[\"<30\", \"30~60\", \"60~180\", \"180~300\", \">300\"], columns=index)\n",
    "\n",
    "\n",
    "# based on the price difference threshold list you set, build the count number table \n",
    "for i in d_list:\n",
    "    d = i\n",
    "    t1, t2, t3, t4 = timestamp_4(d=d)\n",
    "    df_up_count, df_down_count = count(t1, t2, t3, t4, d=d)\n",
    "\n",
    "    if isinstance(df_up_count, pd.DataFrame):\n",
    "        df.loc[:, (i, 'up')] = df_up_count['count'].tolist()\n",
    "    else:\n",
    "        df.loc[:, (i, 'up')] = 0\n",
    "\n",
    "    if isinstance(df_down_count, pd.DataFrame):\n",
    "        df.loc[:, (i, 'down')] = df_down_count['count'].tolist()\n",
    "    else:\n",
    "        df.loc[:, (i, 'down')] = 0\n",
    "\n",
    "df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6c8305fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_n = df[1:]\n",
    "\n",
    "#df_np.to_numeric(df_n)\n",
    "df_n = df_n.loc[:, df_n.columns != 'difference(%)']\n",
    "# convert all columns of DataFrame\n",
    "df_n\n",
    "\n",
    "df_n = df_n.apply(pd.to_numeric) # convert all columns of DataFrame\n",
    "df_n['0.3 - 0.5'] = df_n[ (0.3 ,'up')]+ df_n[(0.3 ,'down') ] + df_n[(0.4 ,'up')] + df_n[(0.4 ,'down')]\n",
    "df_n['0.5 - 0.7'] = df_n[ (0.5 ,'up')]+ df_n[(0.5 ,'down') ] + df_n[(0.6 ,'up')] + df_n[(0.6 ,'down')]\n",
    "df_n['0.7 - 0.9'] = df_n[ (0.7 ,'up')]+ df_n[(0.7 ,'down') ] + df_n[(0.8 ,'up')] + df_n[(0.8 ,'down')]\n",
    "df_n['0.9 - 1.1'] = df_n[ (0.9 ,'up')]+ df_n[(0.9 ,'down') ] + df_n[(1.0 ,'up')] + df_n[(1.0 ,'down')]\n",
    "df_n['1.1 - 1.3'] = df_n[ (1.1 ,'up')]+ df_n[(1.1 ,'down') ] + df_n[(1.2 ,'up')] + df_n[(1.2 ,'down')]\n",
    "df_n['1.3 - 1.5'] = df_n[ (1.3 ,'up')]+ df_n[(1.3 ,'down') ] + df_n[(1.4 ,'up')] + df_n[(1.4 ,'down')]\n",
    "df_n['>1.5'] = df_n[ (1.5 ,'up')]+ df_n[( 1.5 ,'down') ] +  df_n[ (1.6 ,'up')]+ df_n[( 1.6 ,'down') ] +  df_n[ (1.7 ,'up')]+ df_n[( 1.7 ,'down') ] +  df_n[ (1.8 ,'up')]+ df_n[( 1.8 ,'down') ]+  df_n[ (1.9 ,'up')]+ df_n[( 1.9 ,'down') ]+  df_n[ (2.0 ,'up')]+ df_n[( 2.0 ,'down') ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d4a4231c",
   "metadata": {},
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>difference(%)</th>\n",
       "      <th>0.3 - 0.5</th>\n",
       "      <th>0.5 - 0.7</th>\n",
       "      <th>0.7 - 0.9</th>\n",
       "      <th>0.9 - 1.1</th>\n",
       "      <th>1.1 - 1.3</th>\n",
       "      <th>1.3 - 1.5</th>\n",
       "      <th>&gt;1.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direction</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>30~60</th>\n",
       "      <td>3278</td>\n",
       "      <td>181</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60~180</th>\n",
       "      <td>4144</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180~300</th>\n",
       "      <td>629</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;300</th>\n",
       "      <td>192</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "difference(%) 0.3 - 0.5 0.5 - 0.7 0.7 - 0.9 0.9 - 1.1 1.1 - 1.3 1.3 - 1.5 >1.5\n",
       "direction                                                                     \n",
       "30~60              3278       181        39        14        12        10   22\n",
       "60~180             4144        75        12         4         1         0    0\n",
       "180~300             629         7         0         0         0         0    0\n",
       ">300                192         3         1         0         0         0    0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_n[['0.3 - 0.5','0.5 - 0.7','0.7 - 0.9' ,'0.9 - 1.1','1.1 - 1.3','1.3 - 1.5', '>1.5']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "390cfb0f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = make_subplots(rows=7, cols=1,\n",
    "    subplot_titles=(\"0.3-0.5%\",\"0.5-0.7%\", \"0.7-0.9%\", \"0.9-1.1%\",\"1.1-1.3%\",\"1.3-1.5%\", \">1.5%\")\n",
    "                   )\n",
    "\n",
    "df_n_column_names = ['<30s','30-60s','60-180s','180-300s','>300s']\n",
    "fig.append_trace(go.Scatter(\n",
    "    y = df_n['0.3 - 0.5'],\n",
    "    x = df_n_column_names,\n",
    "\n",
    "    showlegend=False\n",
    "), row=1, col=1)\n",
    "fig.append_trace(go.Scatter(\n",
    "    y = df_n['0.5 - 0.7'],\n",
    "    x = df_n_column_names,\n",
    "\n",
    "    showlegend=False\n",
    "), row=2, col=1)\n",
    "\n",
    "fig.append_trace(go.Scatter(\n",
    "    y = df_n['0.7 - 0.9'],\n",
    "    x = df_n_column_names,\n",
    "    showlegend=False\n",
    "), row=3, col=1)\n",
    "\n",
    "fig.append_trace(go.Scatter(\n",
    "    y = df_n['0.9 - 1.1'],\n",
    "    x = df_n_column_names,\n",
    "   showlegend=False\n",
    "), row=4, col=1)\n",
    "\n",
    "\n",
    "fig.append_trace(go.Scatter(\n",
    "    y = df_n['1.1 - 1.3'],\n",
    "    x = df_n_column_names,\n",
    "    showlegend=False \n",
    "), row=5, col=1)\n",
    "\n",
    "fig.append_trace(go.Scatter(\n",
    "    y = df_n['1.3 - 1.5'],\n",
    "    x = df_n_column_names,\n",
    "    showlegend=False \n",
    "), row=6, col=1)\n",
    "\n",
    "\n",
    "fig.append_trace(go.Scatter(\n",
    "    y = df_n['>1.5'],\n",
    "    x = df_n_column_names,\n",
    "    showlegend=False \n",
    "), row=7, col=1)\n",
    "\n",
    "fig.update_layout(height= 1200, width=1000, title_text=\"Cefi-Defi price divergence counts chart\",\n",
    "                yaxis_title=\"total number of counts\")\n",
    "#fig.show()\n",
    "fig.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dc02214",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aa31dc63",
   "metadata": {},
   "source": [
    "# introduction to uniswap v2 equation:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75495904",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "#### given X * Y = K, Where x and y are pair reserves ( eth and usdc here)\n",
    "\n",
    "#### When doing a swap, x and y are changed but k remains the same ( or grow slowly due to swap fee). \n",
    "\n",
    "#### We can write this formula\n",
    "\n",
    "\n",
    "$\n",
    "   (x + r \\Delta X) (y - r \\Delta X) = xy \n",
    "$\n",
    "\n",
    "#### where r is 1 - swap fee ( 1 - 0.3%) = 0.997,  $ \\Delta X$ is the amount we give in exchange for  $ \\Delta y$, the amount we get.\n",
    "\n",
    "#### the product of reserves after a swap must be equal to the product of reserves before the swap, which is the definition of constant product formula. We can use this formula to calculate the amount we get during swap.\n",
    "\n",
    "\n",
    "#### after some basic algebraic operations: \n",
    "\n",
    "$\n",
    "   \\Delta Y = \\frac{ ( y ＊  r \\Delta  X)}{( x  +  r \\Delta X)}\n",
    "$\n",
    "\n",
    "\n",
    "#### by using this formula, we can calculate amount out after swap of token x to token y\n",
    "\n",
    "#### check this [reference]( https://betterprogramming.pub/uniswap-v2-in-depth-98075c826254) for detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ff4ce08",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "864d23de",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "###############################################\n",
    "# uniswap v2 swap amount equation function \n",
    "###############################################\n",
    "\n",
    "#swap_amount_eth_to_usdc( eth_amountIn = 2,  usdc_reserve = 102819284 , eth_reserve = 27978, fee_tier = 0.003)\n",
    "\n",
    "def swap_amount_eth_to_usdc( eth_amountIn,  usdc_reserve , eth_reserve , fee_tier = 0.003,):\n",
    "    \n",
    "    \n",
    "    \"\"\" A function that uses uniswap equation above to calculate swap amount from eth to usdc \"\"\"\n",
    "\n",
    "    \n",
    "\n",
    "    eth_amountInWithFee = eth_amountIn * ( 1 - fee_tier)\n",
    "\n",
    "    numerator = eth_amountInWithFee * usdc_reserve\n",
    "    denominator = eth_reserve + eth_amountInWithFee\n",
    "\n",
    "    usdc_amount_out = numerator * 1.00 / denominator\n",
    "\n",
    "    return usdc_amount_out\n",
    "\n",
    "\n",
    "def swap_amount_usdc_to_eth( usdc_amountIn, usdc_reserve , eth_reserve, fee_tier = 0.003):\n",
    "    \n",
    "    \"\"\" A function that uses uniswap equation above to calculate swap amount from usdc to eth \"\"\"\n",
    "\n",
    "\n",
    "    usdc_amountInWithFee = usdc_amountIn * ( 1 - fee_tier)\n",
    "\n",
    "    numerator = usdc_amountInWithFee * eth_reserve\n",
    "    denominator = usdc_reserve + usdc_amountInWithFee\n",
    "\n",
    "    eth_amount_out = numerator * 1.00 / denominator\n",
    "\n",
    "    return eth_amount_out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8028ced",
   "metadata": {},
   "source": [
    "## Simulate uniswap v2 swap amount impact on slippage:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6d001cd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merge = pd.read_csv('amber_merge_data_new.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ab4e8f63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime: 2022-06-10 23:59:00 \n",
      "eth pool price on v2 usdc/weth pair: 1670.4182280461116 \n",
      "liquidity (K) : 3505245174705.0513 \n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "eth_to_usdc = swap_amount_eth_to_usdc( eth_amountIn = 2,  usdc_reserve = 102819284 , eth_reserve = 27978, fee_tier = 0.003)\n",
    "\n",
    "\n",
    "# based on differenc  eth swap amount to calculate price impact \n",
    "eth_amount = [1, 10, 50 , 100, 250 , 500, 1000, 1500, 2000, 2500, 3000,4000]\n",
    "result = []\n",
    "eth_initial_price = df_merge['dex_price'].iloc[-1]\n",
    "liquidity = df_merge['token0Amount'].iloc[-1] * df_merge['token1Amount'].iloc[-1]\n",
    "date_time = df_merge['datetime'].iloc[-1]\n",
    "\n",
    "for eth in eth_amount:\n",
    "    eth_to_usdc = swap_amount_eth_to_usdc( eth_amountIn = eth,  usdc_reserve = df_merge['token0Amount'].iloc[-1]\n",
    "    ,eth_reserve = df_merge['token1Amount'].iloc[-1], fee_tier = 0.003)\n",
    "    avg_filled_price_per_eth = eth_to_usdc / eth\n",
    "    \n",
    "    \n",
    "    percent = ( eth_initial_price - avg_filled_price_per_eth)  / eth_initial_price\n",
    "    \n",
    "    result.append([eth_initial_price, liquidity, eth, percent ])\n",
    "    \n",
    "\n",
    "\n",
    "result_df = pd.DataFrame( result ,columns=['eth_initial_price','liquidity','eth_amount','price_diff'])  \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print(f\"datetime: { date_time} \")\n",
    "print(f\"eth pool price on v2 usdc/weth pair: { eth_initial_price} \")\n",
    "print(f\"liquidity (K) : { liquidity} \")\n",
    "\n",
    "\n",
    "\n",
    "fig = px.line( result_df, x=\"eth_amount\", y=\"price_diff\", \n",
    "                labels ={\n",
    "                     \"price_diff\": \"slippage(%) due to price impact\",\n",
    "                     \"eth_amount\": \"swap eth amount\"\n",
    "                 },\n",
    "              title= f'uniswap v2 pool slippage (weth/usdc pair) avg filled price vs initial')\n",
    "fig.show(\"png\")\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5fd5ae4",
   "metadata": {},
   "source": [
    "# Design defi/cefi arbitrage strategy framework"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4285b618",
   "metadata": {},
   "source": [
    "### get historical gas fee by leveraging amberdata API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "cb66f2a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import importlib\n",
    "import dotenv\n",
    "importlib.reload(dotenv)\n",
    "from dotenv import load_dotenv # use for api key \n",
    "import os #provides ways to access the Operating System and allows us to read the environment variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8789573d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5000000000000002e-08\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "################################\n",
    "# load api key from .env file \n",
    "################################\n",
    "\n",
    "load_dotenv()\n",
    "\n",
    "my_secret_key = os.getenv(\"AMBER_DATA_API_SECRET_KEY_n\")\n",
    "\n",
    "\n",
    "################################\n",
    "# get historical gas fee \n",
    "################################\n",
    "\n",
    "\n",
    "headers = {\n",
    "    \"Accept\": \"application/json\",\n",
    "    \"x-api-key\": my_secret_key\n",
    "}\n",
    "\n",
    "\n",
    "# gas_fee_api(1578531600)\n",
    "\n",
    "def gas_fee_api(time_stamp):\n",
    "\n",
    "    url = f\"https://web3api.io/api/v2/transactions/gas/percentiles?timestamp={time_stamp}\"\n",
    "\n",
    "    response = requests.get(url, headers=headers)\n",
    "    response = json.loads(response.text)\n",
    "\n",
    "    try:\n",
    "        gas_fee_in_wei_per_TX = response['payload']['percentile_080']\n",
    "        gas_fee_in_eth_per_TX = gas_fee_in_wei_per_TX * 10**(-18)\n",
    "\n",
    "    except:\n",
    "        gas_fee_in_eth_per_TX = -1\n",
    "\n",
    "    return gas_fee_in_eth_per_TX\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "\n",
    "\n",
    "    my_secret_key = os.getenv(\"AMBER_DATA_API_SECRET_KEY_n\")\n",
    "\n",
    "    #print(f'My secret key is: {my_secret_key}.')\n",
    "        \n",
    "    \n",
    "    print(gas_fee_api(1578531600))\n",
    "      \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "129ba54e",
   "metadata": {},
   "source": [
    "## Strategy codebase "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5ad3761d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from enum import Enum\n",
    "import time\n",
    "import uniswap_v2_equation_update as uni\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "class Mode(Enum):\n",
    "    BACKTEST = \"BACKTEST\"\n",
    "    LIVE = \"LIVE\"\n",
    "\n",
    "\n",
    "class Position(Enum):\n",
    "    LONG = 0\n",
    "    SHORT = 1\n",
    "\n",
    "\n",
    "class StrategyBase_notebook:\n",
    "    def __init__(self, params):\n",
    "        self.df = None\n",
    "        self.mode = None\n",
    "        self.do_print = None\n",
    "\n",
    "        self.params = params\n",
    "        self.order_amount = None\n",
    "        self.signal = 0\n",
    "        self.buy_symbol = None\n",
    "        self.sell_symbol = None\n",
    "        self.buy_quote_amount = None\n",
    "        self.sell_quote_amount = None\n",
    "        self.row = None\n",
    "        self.price_diff = None\n",
    "        self.df_record = pd.DataFrame()\n",
    "        self.pnl = None\n",
    "        self.trade_num = 0\n",
    "        self.status = 0\n",
    "\n",
    "        # gas parameters add here\n",
    "        self.gas_fee = None\n",
    "        self.gas_fee_uniswap_usd = None\n",
    "        self.wei_transfer_to_uniswap_fee_usd = 600000\n",
    "\n",
    "\n",
    "        # slippage parameters add here\n",
    "        self.cex_buy_slippage = 1.001\n",
    "        self.cex_sell_slippage = 0.999\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        # avg filled price between dex and cex\n",
    "        self.avg_filled_cex_price = None\n",
    "        self.avg_filled_dex_price = None\n",
    "        \n",
    "        # 30% slippage for prfoit\n",
    "        self.overall_slippage_factor = 1\n",
    "\n",
    "\n",
    "    def log(self, txt):\n",
    "        if self.do_print:\n",
    "            print(txt)\n",
    "\n",
    "    def historical_data_feed(self, df):\n",
    "        self.df = df\n",
    "\n",
    "    def live_data_feed(self):\n",
    "        pass\n",
    "\n",
    "    def broker(self):\n",
    "        if self.mode == Mode.BACKTEST:\n",
    "            if self.signal == 1:\n",
    "\n",
    "                #########################################\n",
    "                # place your trade logic here\n",
    "                #########################################\n",
    "\n",
    "\n",
    "\n",
    "                per_trade_usdc_on_hand = self.params['order_quote_amount'] * 0.8\n",
    "\n",
    "                if self.buy_symbol == 'cex':\n",
    "                    \n",
    "                # if buy_symbol == cex\n",
    "                # usdc buy eth on cex, sell on dex (borrow eth and sell)\n",
    "\n",
    "                    #######################################\n",
    "                    # buy eth on cex\n",
    "                    #######################################\n",
    "                    self.avg_filled_cex_price = self.row['cex_price'] * self.cex_buy_slippage\n",
    "                    self.order_amount = per_trade_usdc_on_hand / self.avg_filled_cex_price\n",
    "                    self.buy_quote_amount = self.order_amount * self.avg_filled_cex_price\n",
    "\n",
    "                    #######################################\n",
    "                    # sell eth on dex\n",
    "                    #######################################\n",
    "                    # we know the amount of eth to sell from cex\n",
    "                    eth_to_usdc_from_dex = uni.swap_amount_eth_to_usdc(eth_amountIn= self.order_amount,\n",
    "                                                                   usdc_reserve= self.row['token0Amount'],\n",
    "                                                                   eth_reserve= self.row['token1Amount'],\n",
    "                                                                    fee_tier = 0.003)\n",
    "\n",
    "                    self.avg_filled_dex_price = eth_to_usdc_from_dex / self.order_amount\n",
    "                    self.sell_quote_amount = self.order_amount * self.avg_filled_dex_price\n",
    "\n",
    "                elif self.buy_symbol == 'dex':\n",
    "\n",
    "                # if buy_symbol = dex \n",
    "                # usdc buy eth on dex, sell eth on cex (borrow eth and sell)\n",
    "                    \n",
    "\n",
    "                    #########################################\n",
    "                    # usdc buy eth on dex\n",
    "                    #########################################\n",
    "                    \n",
    "                    # swap usdc for eth on uniswap \n",
    "                    usdc_to_eth_from_dex = uni.swap_amount_usdc_to_eth( usdc_amountIn = per_trade_usdc_on_hand ,\n",
    "                                                                        usdc_reserve = self.row['token0Amount'],\n",
    "                                                                        eth_reserve = self.row['token1Amount'],\n",
    "                                                                        fee_tier=0.003)\n",
    "                    \n",
    "                    \n",
    "                    self.order_amount = usdc_to_eth_from_dex\n",
    "                    self.avg_filled_dex_price =  per_trade_usdc_on_hand /  usdc_to_eth_from_dex\n",
    "                    self.buy_quote_amount = self.order_amount * self.avg_filled_dex_price\n",
    "\n",
    "                    #########################################\n",
    "                    # sell eth on cex\n",
    "                    #########################################\n",
    "\n",
    "                    self.avg_filled_cex_price = self.row['cex_price'] * self.cex_sell_slippage\n",
    "                    self.sell_quote_amount = self.order_amount * self.avg_filled_cex_price\n",
    "\n",
    "\n",
    "\n",
    "                #print(f'{self.buy_symbol}: + base  {self.order_amount}, - quote  {self.buy_quote_amount}')\n",
    "                #print(f'{self.sell_symbol}: - base  {self.order_amount}, + quote  {self.sell_quote_amount}')\n",
    "\n",
    "\n",
    "# calculate PnL per trade\n",
    "    def account(self):\n",
    "        if self.signal == 1:\n",
    "            \n",
    "            \n",
    "            #########################################################\n",
    "            # calculate PnL based on trade logic (in function broker)\n",
    "            #########################################################\n",
    "\n",
    "\n",
    "\n",
    "            if self.buy_symbol == 'cex':\n",
    "                pnl = self.sell_quote_amount - self.buy_quote_amount - self.gas_fee_uniswap_usd * 2\n",
    "\n",
    "            elif self.buy_symbol == 'dex':\n",
    "                pnl = self.sell_quote_amount - self.buy_quote_amount - self.gas_fee_uniswap_usd * 1 \n",
    "\n",
    "            self.pnl = pnl * self.overall_slippage_factor\n",
    "\n",
    "\n",
    "\n",
    "    def record(self):\n",
    "        row = self.row\n",
    "        if self.signal == 1:\n",
    "            if self.buy_symbol == 'cex':\n",
    "                cex_base_amount = self.order_amount\n",
    "                cex_quote_amount = -self.buy_quote_amount\n",
    "                dex_base_amount = -self.order_amount\n",
    "                dex_quote_amount = self.sell_quote_amount\n",
    "            elif self.buy_symbol == 'dex':\n",
    "                dex_base_amount = self.order_amount\n",
    "                dex_quote_amount = -self.buy_quote_amount\n",
    "                cex_base_amount = -self.order_amount\n",
    "                cex_quote_amount = self.sell_quote_amount\n",
    "        elif self.signal == 0:\n",
    "            dex_base_amount = dex_quote_amount = cex_base_amount = cex_quote_amount = None\n",
    "\n",
    "        new_df = pd.DataFrame(data={\n",
    "            'timestamp': [row['timestamp']],\n",
    "            'datetime': [row['datetime']],\n",
    "            'dex_token_0_amount': [row['token0Amount']],\n",
    "            'dex_token_1_amount': [row['token1Amount']],\n",
    "            'cex_price': [row['cex_price']],\n",
    "            'dex_price': [row['dex_price']],\n",
    "            'price_diff(%)': [self.price_diff],\n",
    "            'cex_base_amount': [cex_base_amount],\n",
    "            'cex_quote_amount': [cex_quote_amount],\n",
    "            'dex_base_amount': [dex_base_amount],\n",
    "            'dex_quote_amount': [dex_quote_amount],\n",
    "            'avg_filled_cex_price':[self.avg_filled_cex_price],\n",
    "            'avg_filled_dex_price': [self.avg_filled_dex_price],\n",
    "            'gas_fee_per_TX_eth_amount': [self.gas_fee],\n",
    "            'gas_fee_uniswap_usd_amount': [self.gas_fee_uniswap_usd],\n",
    "            'pnl': self.pnl\n",
    "        })\n",
    "\n",
    "        self.df_record = pd.concat([self.df_record, new_df], ignore_index=False)\n",
    "\n",
    "        if self.signal == 1:\n",
    "            print(new_df)\n",
    "            print(self.gas_fee)\n",
    "\n",
    "    def output_record(self):\n",
    "        df = self.df_record\n",
    "        #print(df.columns)\n",
    "        df['price_diff(%)'] = df['price_diff(%)'] * 100\n",
    "        df['entry'] = (df['price_diff(%)'] > self.params['price_diff']) & (df['price_diff(%)'].shift() < self.params['price_diff'])\n",
    "        df['exit'] = (df['price_diff(%)'] < self.params['price_diff']) & (df['price_diff(%)'].shift() > self.params['price_diff'])\n",
    "\n",
    "        df = df.reset_index(drop=True)\n",
    "\n",
    "        df = df[['timestamp', 'datetime', 'entry','exit','dex_token_0_amount','dex_token_1_amount',\n",
    "            'price_diff(%)','cex_price', 'dex_price','avg_filled_cex_price', 'avg_filled_dex_price',\n",
    "            'cex_base_amount', 'cex_quote_amount', 'dex_base_amount',\n",
    "            'dex_quote_amount', 'gas_fee_per_TX_eth_amount',\n",
    "            'gas_fee_uniswap_usd_amount', 'pnl']]\n",
    "\n",
    "\n",
    "        df.to_csv('output/trade_record.csv')\n",
    "\n",
    "    def reset_params(self):\n",
    "        if self.signal == 1:\n",
    "            self.trade_num += 1\n",
    "\n",
    "        self.signal = 0\n",
    "        self.buy_symbol = None\n",
    "        self.sell_symbol = None\n",
    "        self.buy_quote_amount = None\n",
    "        self.sell_quote_amount = None\n",
    "\n",
    "        self.price_diff = None\n",
    "        self.pnl = None\n",
    "\n",
    "        self.order_amount = self.params['order_quote_amount'] * 0.8 / self.row['cex_price']\n",
    "\n",
    "        # defi parameters added here\n",
    "        self.gas_fee = None\n",
    "        self.gas_fee_uniswap_usd = None\n",
    "\n",
    "        # fill_Ed\n",
    "        self.avg_filled_cex_price = None\n",
    "        self.avg_filled_dex_price = None\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    def condition(self):\n",
    "        pass\n",
    "\n",
    "    def loop(self):\n",
    "        self.reset_params()\n",
    "        self.condition()\n",
    "        self.broker()\n",
    "        self.account()\n",
    "        self.record()\n",
    "\n",
    "    def run(self, mode=Mode, do_print=True):\n",
    "        self.do_print = do_print\n",
    "        self.mode = mode\n",
    "\n",
    "        if mode == Mode.BACKTEST:\n",
    "            for index, row in self.df.iterrows():\n",
    "                self.row = row\n",
    "                self.loop()\n",
    "            self.output_record()\n",
    "\n",
    "        elif mode == Mode.LIVE:\n",
    "            while True:\n",
    "                self.row = self.live_data_feed()\n",
    "                self.loop()\n",
    "                self.output_record()\n",
    "                time.sleep(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "231301c4",
   "metadata": {},
   "source": [
    "## Customize buy/sell signal and bet amount here "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b3f5d00d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1652644500000  2022-05-15 15:55:00        1.027293e+08        47334.700824   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  2091.290749  2170.274339       0.037768        38.215671          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -38.215671      82623.169669            2093.38204   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           2162.023243               2.364970e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount          pnl  \n",
      "0                   29.675043  2563.819582  \n",
      "2.3649703191400006e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1652716610000  2022-05-16 11:56:50        9.838119e+07        49427.732513   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  2015.946326  1990.404665       0.012832       -40.039792      80637.353779   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        40.039792          -80000.0            2013.93038   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1998.012371               3.615373e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   43.730391  593.623389  \n",
      "3.6153732506800003e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1652716620000  2022-05-16 11:57:00        9.838119e+07        49427.732513   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  2014.539332  1990.404665       0.012126       -40.039792      80581.074372   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        40.039792          -80000.0           2012.524793   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1998.012371               3.615373e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                    43.69987  537.374503  \n",
      "3.6153732506800003e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1652717131000  2022-05-16 12:05:31        9.832246e+07        49459.577078   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  2012.504512  1987.935721       0.012359       -40.089501      80599.620742   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        40.089501          -80000.0           2010.492008   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1995.534956               4.368419e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   52.748774  546.871967  \n",
      "4.3684187152400013e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653219511000  2022-05-22 07:38:31        8.258963e+07        40195.419868   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  2015.093153  2054.702412       0.019656        39.660737          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -39.660737      81166.692529           2017.108247   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           2046.525061               1.584456e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount          pnl  \n",
      "0                   19.156956  1128.378618  \n",
      "1.5844557438e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653219540000  2022-05-22 07:39:00        8.258963e+07        40195.419868   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  2015.270375  2054.702412       0.019567        39.657249          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -39.657249       81159.56181           2017.285645   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           2046.525237               1.584456e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                    19.15864  1121.24453  \n",
      "1.5844557438e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653553260000  2022-05-26 04:21:00        7.972651e+07        41851.981277   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1881.954246  1904.963784       0.012226        42.466537          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -42.466537      80573.012804             1883.8362   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1897.329479               2.754730e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   31.105651  510.801501  \n",
      "2.7547296932000004e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653553295000  2022-05-26 04:21:35        7.871714e+07        42391.260742   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1881.954246  1856.919113       0.013482       -42.909389       80672.75389   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        42.909389          -80000.0           1880.072292   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1864.393814               2.790903e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   31.514107  641.239782  \n",
      "2.7909026987e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653574846000  2022-05-26 10:20:46        7.903841e+07        42197.299986   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1837.452001  1873.067859       0.019383        43.495057          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -43.495057       81141.40017           1839.289453   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1865.531519               7.900000e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   87.095225  967.209721  \n",
      "7.9e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653575030000  2022-05-26 10:23:50        7.922235e+07        42105.233008   \n",
      "\n",
      "    cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1848.50958  1881.532141       0.017864        43.234875          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -43.234875      81020.817857            1850.35809   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1873.969072               8.211021e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   91.068906  838.680046  \n",
      "8.2110209666e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653575040000  2022-05-26 10:24:00        7.922235e+07        42105.233008   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1846.791996  1881.532141       0.018811        43.275085          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -43.275085      81096.093036           1848.638788   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1873.967289               8.211021e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   90.984287  914.124463  \n",
      "8.2110209666e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653756965000  2022-05-28 12:56:05        7.849795e+07        44675.213083   \n",
      "\n",
      "     cex_price   dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1781.836804  1757.08058       0.014089       -45.347402      80720.868705   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        45.347402          -80000.0           1780.054968   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1764.158385               2.015126e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   21.543751  699.324954  \n",
      "2.0151257335e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653757013000  2022-05-28 12:56:53        7.853060e+07        44658.009172   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1781.836804  1758.488513       0.013277       -45.311114       80656.27362   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        45.311114          -80000.0           1780.054968   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1765.571245               2.059451e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   22.017638  634.255982  \n",
      "2.0594514168000003e-08\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653757020000  2022-05-28 12:57:00        7.853060e+07        44658.009172   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1781.084866  1758.488513        0.01285       -45.311114       80622.23652   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        45.311114          -80000.0           1779.303781   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1765.571245               2.059451e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   22.008347  600.228173  \n",
      "2.0594514168000003e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653758231000  2022-05-28 13:17:11        7.846550e+07        44699.786646   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1780.238975  1755.388783       0.014157       -45.391088      80726.176727   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        45.391088          -80000.0           1778.458736   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1762.460513               2.731702e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   29.178489  696.998238  \n",
      "2.7317015568000003e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653892145000  2022-05-30 02:29:05        8.370539e+07          41918.2987   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1899.467846  1996.869972       0.051279        42.074984          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -42.074984      83682.474754           1901.367314   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1988.889028               3.000000e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount          pnl  \n",
      "0                   34.190421  3614.093912  \n",
      "3.0000000000000004e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1653892161000  2022-05-30 02:29:21        8.251343e+07        42528.676699   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1899.467846  1940.183245       0.021435        42.074984          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -42.074984      81308.080774           1901.367314   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1932.456589               3.008238e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount          pnl  \n",
      "0                    34.28431  1239.512154  \n",
      "3.0082381688e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1654561380000  2022-06-06 20:23:00        7.987717e+07         43333.60825   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1813.486952  1843.307573       0.016444         44.06984          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        -44.06984      80908.530792           1815.300439   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1835.916143               6.686038e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   72.750258  763.030277  \n",
      "6.686038136700001e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1654561393000  2022-06-06 20:23:13        7.981860e+07        43365.498807   \n",
      "\n",
      "     cex_price   dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1813.486952  1840.60153       0.014952         44.06984          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        -44.06984      80789.814302           1815.300439   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1833.222317               6.725430e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   73.178877  643.456547  \n",
      "6.7254299985e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1654561404000  2022-06-06 20:23:24        7.979505e+07        43378.337646   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1813.486952  1839.513786       0.014352         44.06984          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        -44.06984      80742.093969           1815.300439   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1832.139483               6.780761e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   73.780928  594.532113  \n",
      "6.780760850300001e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1654562280000  2022-06-06 20:38:00        7.858668e+07        44047.839574   \n",
      "\n",
      "    cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1759.45073  1784.121039       0.014022        45.423312          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -45.423312      80714.579673           1761.210181   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1776.941743               1.017834e-07   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                  107.449705  499.680262  \n",
      "1.0178337995900001e-07\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1654562313000  2022-06-06 20:38:33        7.853723e+07        44075.654574   \n",
      "\n",
      "    cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1759.45073  1781.873298       0.012744        45.423312          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -45.423312      80612.942946           1761.210181   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1774.704198               1.029007e-07   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                  108.629177  395.684592  \n",
      "1.02900652238e-07\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1654562340000  2022-06-06 20:39:00        7.849569e+07        44099.050153   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1755.155135  1779.985982       0.014147        45.534482          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -45.534482      80724.485427            1756.91029   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1772.820989               1.031804e-07   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                  108.658514  507.168398  \n",
      "1.0318035147300001e-07\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1654618140000  2022-06-07 12:09:00        7.785832e+07        44331.222776   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1778.282032  1756.286289       0.012524       -45.367533      80595.591805   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        45.367533          -80000.0            1776.50375   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0             1763.3756               6.294280e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   67.158033  528.433771  \n",
      "6.2942802791e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1654618190000  2022-06-07 12:09:50        7.787351e+07        44322.601828   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1778.282032  1756.970595        0.01213       -45.349872      80564.217469   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        45.349872          -80000.0            1776.50375   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1764.062316               6.423453e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   68.536263  495.681206  \n",
      "6.423452708800001e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1654642020000  2022-06-07 18:47:00        7.948030e+07         43526.30432   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1803.640677  1826.029127       0.012413        44.310422          -80000.0   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0       -44.310422      80587.591957           1805.444318   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1818.705125               8.749411e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   94.684766  398.222424  \n",
      "8.7494114562e-08\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1654878524000  2022-06-10 12:28:44        7.646931e+07        45820.990821   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1691.922241  1668.870691       0.013813       -47.743001      80696.667096   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        47.743001          -80000.0           1690.230319   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1675.638293               6.620641e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount         pnl  \n",
      "0                   67.209654  629.457442  \n",
      "6.620640548000001e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1654882221000  2022-06-10 13:30:21        7.616538e+07        46011.847484   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1688.732479  1655.342893       0.020171       -48.132966      81202.419212   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        48.132966          -80000.0           1687.043746   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1662.062547               6.986826e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount          pnl  \n",
      "0                   70.793279  1131.625933  \n",
      "6.9868258876e-08\n",
      "       timestamp             datetime  dex_token_0_amount  dex_token_1_amount  \\\n",
      "0  1654882236000  2022-06-10 13:30:36        7.618462e+07        46000.267864   \n",
      "\n",
      "     cex_price    dex_price  price_diff(%)  cex_base_amount  cex_quote_amount  \\\n",
      "0  1688.732479  1656.177649       0.019657       -48.108718      81161.512541   \n",
      "\n",
      "   dex_base_amount  dex_quote_amount  avg_filled_cex_price  \\\n",
      "0        48.108718          -80000.0           1687.043746   \n",
      "\n",
      "   avg_filled_dex_price  gas_fee_per_TX_eth_amount  \\\n",
      "0           1662.900253               6.986826e-08   \n",
      "\n",
      "   gas_fee_uniswap_usd_amount          pnl  \n",
      "0                   70.793279  1090.719262  \n",
      "6.9868258876e-08\n",
      "time spent on backtesting 328.36875009536743\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from importlib import reload\n",
    "import uniswap_v2_equation_update as uni\n",
    "reload(uni)\n",
    "import time\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "class Example_Strategy(StrategyBase_notebook):\n",
    "    def condition(self):\n",
    "        #print(self.row.to_dict())\n",
    "        row = self.row\n",
    "        cex_price = row['cex_price']\n",
    "        dex_price = row['dex_price']\n",
    "\n",
    "        price_diff = abs(cex_price-dex_price) / min(cex_price,dex_price)\n",
    "\n",
    "        timestamp = int(row['timestamp'])\n",
    "\n",
    "\n",
    "        self.price_diff = price_diff\n",
    "\n",
    "        if price_diff > self.params['price_diff'] * 0.01:\n",
    "            \n",
    "            # buy cex, sell dex\n",
    "            # if buy_symbol = cex => usdc buy eth on cex, sell on dex (borrow eth and sell)\n",
    "            # if buy_symbol = dex => usdc buy on dex, sell on cex (borrow eth and sell)\n",
    "\n",
    "            if min(cex_price,dex_price) == cex_price:\n",
    "                buy_symbol = 'cex'\n",
    "                sell_symbol = 'dex'\n",
    "\n",
    "            # buy dex, sell cex\n",
    "            elif min(cex_price,dex_price) == dex_price:\n",
    "\n",
    "                buy_symbol = 'dex'\n",
    "                sell_symbol = 'cex'\n",
    "\n",
    "            ###################################\n",
    "            # get historical gas fee \n",
    "            ###################################\n",
    "            \n",
    "            trade_gas_fee_in_eth_per_TX_in_eth = gas_fee_api(timestamp)\n",
    "            trade_gas_fee_in_usd = trade_gas_fee_in_eth_per_TX_in_eth * cex_price * self.wei_transfer_to_uniswap_fee_usd\n",
    "\n",
    "            self.signal = 1\n",
    "            self.buy_symbol = buy_symbol\n",
    "            self.sell_symbol = sell_symbol\n",
    "            self.gas_fee = trade_gas_fee_in_eth_per_TX_in_eth\n",
    "            self.gas_fee_uniswap_usd = trade_gas_fee_in_usd\n",
    "\n",
    "\n",
    "\n",
    "    def loop(self):\n",
    "        self.reset_params()\n",
    "        self.condition()\n",
    "        self.broker()\n",
    "        self.account()\n",
    "        self.record()\n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "if __name__ == '__main__':\n",
    "     \n",
    "    \n",
    "    #######################################\n",
    "    # input your trade parameters here \n",
    "    #######################################\n",
    "    \n",
    "    \n",
    "    start_time = time.time()\n",
    "\n",
    "    params = {\n",
    "        'price_diff': 1.2, # the entry signal in percentage for aribtrage\n",
    "        'order_base_amount': 1, \n",
    "        'order_quote_amount': 100000 # inital capital on each side(dex,cex), the total capital will be order_quote_amount*2 \n",
    "    }\n",
    "\n",
    "\n",
    "    historical_data = pd.read_csv('amber_merge_data_new.csv')\n",
    "    \n",
    "    \n",
    "    start = datetime(2022, 5, 14, 0, 0, 0)\n",
    "    end = datetime(2022, 6, 11, 0, 0, 0)\n",
    "    \n",
    "    historical_data_part = historical_data.loc[(historical_data['timestamp'] > start.timestamp()*1000) & (historical_data['timestamp'] < end.timestamp()*1000)]\n",
    "    historical_data_part = historical_data_part.drop_duplicates(subset=['timestamp'], keep='last')\n",
    "\n",
    "\n",
    "    # start the strategy object \n",
    "    arb_strat = Example_Strategy(params=params)\n",
    "    # feed historical data\n",
    "    arb_strat.historical_data_feed(historical_data_part)  \n",
    "    # run the strategy \n",
    "    arb_strat.run(mode=Mode.BACKTEST)\n",
    "\n",
    "    print(f'time spent on backtesting {time.time()- start_time}')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e446f0db",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>45808.813283</td>\n",
       "      <td>2022-06-10 23:57:53</td>\n",
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       "    <tr>\n",
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       "      <td>2022-06-10 23:57:57</td>\n",
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       "    <tr>\n",
       "      <th>424224</th>\n",
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       "      <td>2022-06-10 23:58:00</td>\n",
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       "      <td>2022-06-10 23:58:48</td>\n",
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       "        Unnamed: 0      timestamp    cex_price    dex_price  token0Amount  \\\n",
       "329370      193665  1652500860000  2049.285027  2044.730631  9.966593e+07   \n",
       "329371      193666  1652500920000  2046.718294  2044.730631  9.966593e+07   \n",
       "329372      193667  1652500980000  2046.405989  2044.730631  9.966593e+07   \n",
       "329373      638328  1652501010000  2046.405989  2044.779097  9.966711e+07   \n",
       "329374      193668  1652501040000  2046.417025  2044.779097  9.966711e+07   \n",
       "...            ...            ...          ...          ...           ...   \n",
       "424222      714729  1654919873000  1669.260519  1670.399138  7.651900e+07   \n",
       "424223      714730  1654919877000  1669.260519  1670.348007  7.651783e+07   \n",
       "424224      234377  1654919880000  1669.768650  1670.348007  7.651783e+07   \n",
       "424225      714732  1654919928000  1669.768650  1670.418228  7.651944e+07   \n",
       "424226      234378  1654919940000  1669.522267  1670.418228  7.651944e+07   \n",
       "\n",
       "        token1Amount             datetime  \n",
       "329370  48742.816530  2022-05-14 00:01:00  \n",
       "329371  48742.816530  2022-05-14 00:02:00  \n",
       "329372  48742.816530  2022-05-14 00:03:00  \n",
       "329373  48742.239882  2022-05-14 00:03:30  \n",
       "329374  48742.239882  2022-05-14 00:04:00  \n",
       "...              ...                  ...  \n",
       "424222  45808.813283  2022-06-10 23:57:53  \n",
       "424223  45809.515454  2022-06-10 23:57:57  \n",
       "424224  45809.515454  2022-06-10 23:58:00  \n",
       "424225  45808.554721  2022-06-10 23:58:48  \n",
       "424226  45808.554721  2022-06-10 23:59:00  \n",
       "\n",
       "[94857 rows x 7 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data_part"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d6064f",
   "metadata": {},
   "source": [
    "## Show all transactions in table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc825690",
   "metadata": {},
   "outputs": [
    {
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       "   Unnamed: 0      timestamp             datetime  entry   exit  \\\n",
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       "1           1  1652500920000  2022-05-14 00:02:00  False  False   \n",
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       "\n",
       "   dex_token_0_amount  dex_token_1_amount  price_diff(%)    cex_price  \\\n",
       "0        9.966593e+07        48742.816530       0.222738  2049.285027   \n",
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       "\n",
       "     dex_price  avg_filled_cex_price  avg_filled_dex_price  cex_base_amount  \\\n",
       "0  2044.730631                   NaN                   NaN              NaN   \n",
       "1  2044.730631                   NaN                   NaN              NaN   \n",
       "2  2044.730631                   NaN                   NaN              NaN   \n",
       "3  2044.779097                   NaN                   NaN              NaN   \n",
       "4  2044.779097                   NaN                   NaN              NaN   \n",
       "\n",
       "   cex_quote_amount  dex_base_amount  dex_quote_amount  \\\n",
       "0               NaN              NaN               NaN   \n",
       "1               NaN              NaN               NaN   \n",
       "2               NaN              NaN               NaN   \n",
       "3               NaN              NaN               NaN   \n",
       "4               NaN              NaN               NaN   \n",
       "\n",
       "   gas_fee_per_TX_eth_amount  gas_fee_uniswap_usd_amount  pnl  \n",
       "0                        NaN                         NaN  NaN  \n",
       "1                        NaN                         NaN  NaN  \n",
       "2                        NaN                         NaN  NaN  \n",
       "3                        NaN                         NaN  NaN  \n",
       "4                        NaN                         NaN  NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data_strategy = pd.read_csv('output/trade_record.csv')\n",
    "\n",
    "historical_data_strategy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c55eeb",
   "metadata": {},
   "source": [
    "## Show only the part that the strategy trades "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "87b85d40",
   "metadata": {},
   "outputs": [
    {
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       "      <th>43255</th>\n",
       "      <td>2022-05-26 10:23:50</td>\n",
       "      <td>1.786443</td>\n",
       "      <td>1848.509580</td>\n",
       "      <td>1881.532141</td>\n",
       "      <td>1850.358090</td>\n",
       "      <td>1873.969072</td>\n",
       "      <td>43.234875</td>\n",
       "      <td>-80000.000000</td>\n",
       "      <td>-43.234875</td>\n",
       "      <td>81020.817857</td>\n",
       "      <td>91.068906</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>838.680046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50547</th>\n",
       "      <td>2022-05-28 12:56:05</td>\n",
       "      <td>1.408941</td>\n",
       "      <td>1781.836804</td>\n",
       "      <td>1757.080580</td>\n",
       "      <td>1780.054968</td>\n",
       "      <td>1764.158385</td>\n",
       "      <td>-45.347402</td>\n",
       "      <td>80720.868705</td>\n",
       "      <td>45.347402</td>\n",
       "      <td>-80000.000000</td>\n",
       "      <td>21.543751</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>699.324954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50550</th>\n",
       "      <td>2022-05-28 12:56:53</td>\n",
       "      <td>1.327748</td>\n",
       "      <td>1781.836804</td>\n",
       "      <td>1758.488513</td>\n",
       "      <td>1780.054968</td>\n",
       "      <td>1765.571245</td>\n",
       "      <td>-45.311114</td>\n",
       "      <td>80656.273620</td>\n",
       "      <td>45.311114</td>\n",
       "      <td>-80000.000000</td>\n",
       "      <td>22.017638</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>634.255982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50611</th>\n",
       "      <td>2022-05-28 13:17:11</td>\n",
       "      <td>1.415652</td>\n",
       "      <td>1780.238975</td>\n",
       "      <td>1755.388783</td>\n",
       "      <td>1778.458736</td>\n",
       "      <td>1762.460513</td>\n",
       "      <td>-45.391088</td>\n",
       "      <td>80726.176727</td>\n",
       "      <td>45.391088</td>\n",
       "      <td>-80000.000000</td>\n",
       "      <td>29.178489</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>696.998238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55472</th>\n",
       "      <td>2022-05-30 02:29:05</td>\n",
       "      <td>5.127864</td>\n",
       "      <td>1899.467846</td>\n",
       "      <td>1996.869972</td>\n",
       "      <td>1901.367314</td>\n",
       "      <td>1988.889028</td>\n",
       "      <td>42.074984</td>\n",
       "      <td>-80000.000000</td>\n",
       "      <td>-42.074984</td>\n",
       "      <td>83682.474754</td>\n",
       "      <td>34.190421</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3614.093912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82065</th>\n",
       "      <td>2022-06-06 20:23:00</td>\n",
       "      <td>1.644380</td>\n",
       "      <td>1813.486952</td>\n",
       "      <td>1843.307573</td>\n",
       "      <td>1815.300439</td>\n",
       "      <td>1835.916143</td>\n",
       "      <td>44.069840</td>\n",
       "      <td>-80000.000000</td>\n",
       "      <td>-44.069840</td>\n",
       "      <td>80908.530792</td>\n",
       "      <td>72.750258</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>763.030277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82107</th>\n",
       "      <td>2022-06-06 20:38:00</td>\n",
       "      <td>1.402160</td>\n",
       "      <td>1759.450730</td>\n",
       "      <td>1784.121039</td>\n",
       "      <td>1761.210181</td>\n",
       "      <td>1776.941743</td>\n",
       "      <td>45.423312</td>\n",
       "      <td>-80000.000000</td>\n",
       "      <td>-45.423312</td>\n",
       "      <td>80714.579673</td>\n",
       "      <td>107.449705</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>499.680262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82110</th>\n",
       "      <td>2022-06-06 20:39:00</td>\n",
       "      <td>1.414738</td>\n",
       "      <td>1755.155135</td>\n",
       "      <td>1779.985982</td>\n",
       "      <td>1756.910290</td>\n",
       "      <td>1772.820989</td>\n",
       "      <td>45.534482</td>\n",
       "      <td>-80000.000000</td>\n",
       "      <td>-45.534482</td>\n",
       "      <td>80724.485427</td>\n",
       "      <td>108.658514</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>507.168398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84278</th>\n",
       "      <td>2022-06-07 12:09:00</td>\n",
       "      <td>1.252401</td>\n",
       "      <td>1778.282032</td>\n",
       "      <td>1756.286289</td>\n",
       "      <td>1776.503750</td>\n",
       "      <td>1763.375600</td>\n",
       "      <td>-45.367533</td>\n",
       "      <td>80595.591805</td>\n",
       "      <td>45.367533</td>\n",
       "      <td>-80000.000000</td>\n",
       "      <td>67.158033</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>528.433771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85166</th>\n",
       "      <td>2022-06-07 18:47:00</td>\n",
       "      <td>1.241292</td>\n",
       "      <td>1803.640677</td>\n",
       "      <td>1826.029127</td>\n",
       "      <td>1805.444318</td>\n",
       "      <td>1818.705125</td>\n",
       "      <td>44.310422</td>\n",
       "      <td>-80000.000000</td>\n",
       "      <td>-44.310422</td>\n",
       "      <td>80587.591957</td>\n",
       "      <td>94.684766</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>398.222424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93347</th>\n",
       "      <td>2022-06-10 12:28:44</td>\n",
       "      <td>1.381266</td>\n",
       "      <td>1691.922241</td>\n",
       "      <td>1668.870691</td>\n",
       "      <td>1690.230319</td>\n",
       "      <td>1675.638293</td>\n",
       "      <td>-47.743001</td>\n",
       "      <td>80696.667096</td>\n",
       "      <td>47.743001</td>\n",
       "      <td>-80000.000000</td>\n",
       "      <td>67.209654</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>629.457442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93517</th>\n",
       "      <td>2022-06-10 13:30:21</td>\n",
       "      <td>2.017080</td>\n",
       "      <td>1688.732479</td>\n",
       "      <td>1655.342893</td>\n",
       "      <td>1687.043746</td>\n",
       "      <td>1662.062547</td>\n",
       "      <td>-48.132966</td>\n",
       "      <td>81202.419212</td>\n",
       "      <td>48.132966</td>\n",
       "      <td>-80000.000000</td>\n",
       "      <td>70.793279</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1131.625933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime  price_diff(%)    cex_price    dex_price  \\\n",
       "5948   2022-05-15 15:55:00       3.776787  2091.290749  2170.274339   \n",
       "8763   2022-05-16 11:56:50       1.283240  2015.946326  1990.404665   \n",
       "8787   2022-05-16 12:05:31       1.235895  2012.504512  1987.935721   \n",
       "27520  2022-05-22 07:38:31       1.965629  2015.093153  2054.702412   \n",
       "42200  2022-05-26 04:21:00       1.222641  1881.954246  1904.963784   \n",
       "42202  2022-05-26 04:21:35       1.348208  1881.954246  1856.919113   \n",
       "43245  2022-05-26 10:20:46       1.938329  1837.452001  1873.067859   \n",
       "43255  2022-05-26 10:23:50       1.786443  1848.509580  1881.532141   \n",
       "50547  2022-05-28 12:56:05       1.408941  1781.836804  1757.080580   \n",
       "50550  2022-05-28 12:56:53       1.327748  1781.836804  1758.488513   \n",
       "50611  2022-05-28 13:17:11       1.415652  1780.238975  1755.388783   \n",
       "55472  2022-05-30 02:29:05       5.127864  1899.467846  1996.869972   \n",
       "82065  2022-06-06 20:23:00       1.644380  1813.486952  1843.307573   \n",
       "82107  2022-06-06 20:38:00       1.402160  1759.450730  1784.121039   \n",
       "82110  2022-06-06 20:39:00       1.414738  1755.155135  1779.985982   \n",
       "84278  2022-06-07 12:09:00       1.252401  1778.282032  1756.286289   \n",
       "85166  2022-06-07 18:47:00       1.241292  1803.640677  1826.029127   \n",
       "93347  2022-06-10 12:28:44       1.381266  1691.922241  1668.870691   \n",
       "93517  2022-06-10 13:30:21       2.017080  1688.732479  1655.342893   \n",
       "\n",
       "       avg_filled_cex_price  avg_filled_dex_price  cex_base_amount  \\\n",
       "5948            2093.382040           2162.023243        38.215671   \n",
       "8763            2013.930380           1998.012371       -40.039792   \n",
       "8787            2010.492008           1995.534956       -40.089501   \n",
       "27520           2017.108247           2046.525061        39.660737   \n",
       "42200           1883.836200           1897.329479        42.466537   \n",
       "42202           1880.072292           1864.393814       -42.909389   \n",
       "43245           1839.289453           1865.531519        43.495057   \n",
       "43255           1850.358090           1873.969072        43.234875   \n",
       "50547           1780.054968           1764.158385       -45.347402   \n",
       "50550           1780.054968           1765.571245       -45.311114   \n",
       "50611           1778.458736           1762.460513       -45.391088   \n",
       "55472           1901.367314           1988.889028        42.074984   \n",
       "82065           1815.300439           1835.916143        44.069840   \n",
       "82107           1761.210181           1776.941743        45.423312   \n",
       "82110           1756.910290           1772.820989        45.534482   \n",
       "84278           1776.503750           1763.375600       -45.367533   \n",
       "85166           1805.444318           1818.705125        44.310422   \n",
       "93347           1690.230319           1675.638293       -47.743001   \n",
       "93517           1687.043746           1662.062547       -48.132966   \n",
       "\n",
       "       cex_quote_amount  dex_base_amount  dex_quote_amount  \\\n",
       "5948      -80000.000000       -38.215671      82623.169669   \n",
       "8763       80637.353779        40.039792     -80000.000000   \n",
       "8787       80599.620742        40.089501     -80000.000000   \n",
       "27520     -80000.000000       -39.660737      81166.692529   \n",
       "42200     -80000.000000       -42.466537      80573.012804   \n",
       "42202      80672.753890        42.909389     -80000.000000   \n",
       "43245     -80000.000000       -43.495057      81141.400170   \n",
       "43255     -80000.000000       -43.234875      81020.817857   \n",
       "50547      80720.868705        45.347402     -80000.000000   \n",
       "50550      80656.273620        45.311114     -80000.000000   \n",
       "50611      80726.176727        45.391088     -80000.000000   \n",
       "55472     -80000.000000       -42.074984      83682.474754   \n",
       "82065     -80000.000000       -44.069840      80908.530792   \n",
       "82107     -80000.000000       -45.423312      80714.579673   \n",
       "82110     -80000.000000       -45.534482      80724.485427   \n",
       "84278      80595.591805        45.367533     -80000.000000   \n",
       "85166     -80000.000000       -44.310422      80587.591957   \n",
       "93347      80696.667096        47.743001     -80000.000000   \n",
       "93517      81202.419212        48.132966     -80000.000000   \n",
       "\n",
       "       gas_fee_uniswap_usd_amount  entry   exit          pnl  \n",
       "5948                    29.675043   True  False  2563.819582  \n",
       "8763                    43.730391   True  False   593.623389  \n",
       "8787                    52.748774   True  False   546.871967  \n",
       "27520                   19.156956   True  False  1128.378618  \n",
       "42200                   31.105651   True  False   510.801501  \n",
       "42202                   31.514107   True  False   641.239782  \n",
       "43245                   87.095225   True  False   967.209721  \n",
       "43255                   91.068906   True  False   838.680046  \n",
       "50547                   21.543751   True  False   699.324954  \n",
       "50550                   22.017638   True  False   634.255982  \n",
       "50611                   29.178489   True  False   696.998238  \n",
       "55472                   34.190421   True  False  3614.093912  \n",
       "82065                   72.750258   True  False   763.030277  \n",
       "82107                  107.449705   True  False   499.680262  \n",
       "82110                  108.658514   True  False   507.168398  \n",
       "84278                   67.158033   True  False   528.433771  \n",
       "85166                   94.684766   True  False   398.222424  \n",
       "93347                   67.209654   True  False   629.457442  \n",
       "93517                   70.793279   True  False  1131.625933  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data_strategy = pd.read_csv('output/trade_record.csv')\n",
    "\n",
    "historical_data_strategy.head()\n",
    "\n",
    "\n",
    "###################################\n",
    "# show all trades being placed\n",
    "###################################\n",
    "\n",
    "historical_data_strategy= historical_data_strategy[ historical_data_strategy['entry']==True]\n",
    "\n",
    "#historical_data_strategy= historical_data_strategy[ ~ historical_data_strategy['pnl'].isna()]\n",
    "historical_data_strategy_concise = historical_data_strategy[['datetime','price_diff(%)',\n",
    "                                    'cex_price', 'dex_price', 'avg_filled_cex_price', 'avg_filled_dex_price',\n",
    "                                     'cex_base_amount', 'cex_quote_amount','dex_base_amount', 'dex_quote_amount',\n",
    "                                    'gas_fee_uniswap_usd_amount', 'entry','exit','pnl']]\n",
    "\n",
    "                                                                    \n",
    "historical_data_strategy_concise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c31268e",
   "metadata": {},
   "source": [
    "# Strategy Dashboard with PnL "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d83f33be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# edit bet amount here \n",
    "total_bet_amount = 200000\n",
    "\n",
    "# if entry => calculate profit, else not consider\n",
    "\n",
    "def resample_dataframe(df, step):\n",
    "    \n",
    "    \"\"\" A helper function that build metrics for dashboard visualization \"\"\"\n",
    "\n",
    "    df['datetime'] = df['timestamp'].apply(lambda x: datetime.fromtimestamp(x/1000))\n",
    "    df['cum_pnl'] = df.apply(lambda x: x['pnl'] if x['entry'] else 0, axis=1)\n",
    "    df['cum_pnl'] = df['cum_pnl'].cumsum()\n",
    "    df['cum_pnl(%)'] = 100 * df['cum_pnl'] / total_bet_amount \n",
    "    df_resample = df.iloc[::step]\n",
    "    return  df_resample\n",
    "\n",
    "\n",
    "def subplot(df):\n",
    "    fig = make_subplots(rows=3, cols=1)\n",
    "    \n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=df['datetime'],\n",
    "        y=df['cum_pnl(%)'],\n",
    "        name=\"pnl cumulative(%)\"\n",
    "\n",
    "    ), row=1, col=1)\n",
    "        \n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=df['datetime'],\n",
    "        y=df['cum_pnl'],\n",
    "        name=\"pnl cumulative($)\"\n",
    "    ), row=2, col=1)\n",
    "\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=df_resample['datetime'],\n",
    "        y=df_resample['dex_price'],\n",
    "        name=\"dex price\"\n",
    "\n",
    "    ), row=3, col=1)\n",
    "\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=df_resample['datetime'],\n",
    "        y=df_resample['cex_price'],\n",
    "        name=\"cex price\"\n",
    "    ), row=3, col=1)\n",
    "    return fig\n",
    "\n",
    "\n",
    "def background_color(df, fig):\n",
    "    df['signal'] = df.apply(lambda x: 'entry' if x['entry'] else ('exit' if x['exit'] else np.nan), axis=1)\n",
    "    df = df[['datetime', 'cex_price', 'dex_price', 'price_diff(%)', 'signal']]\n",
    "\n",
    "    entry_list = df.loc[df['signal'] == 'entry'].datetime.tolist()\n",
    "    exit_list = df.loc[df['signal'] == 'exit'].datetime.tolist()\n",
    "    if len(exit_list) == len(entry_list) + 1:\n",
    "        exit_list.append(df.datetime.values[-1])\n",
    "    #print(range(len(entry_list)))\n",
    "    for i in range(len(entry_list)):\n",
    "        #print(i)\n",
    "        fig.add_vrect(x0=entry_list[i], x1=exit_list[i], fillcolor=\"#2E4053\", opacity=0.6, line_width= 2, row=3, col=1)\n",
    "    \n",
    "    return fig\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    df = pd.read_csv('output/trade_record.csv', index_col=0)\n",
    "    df_resample = resample_dataframe(df, step=10)\n",
    "\n",
    "    fig = subplot(df_resample)\n",
    "    fig = background_color(df, fig)\n",
    "    \n",
    "    fig.update_layout(height= 900, width=1000, title_text=\" Trade Strategy Dashboard\")\n",
    "\n",
    "    #fig.show(config={'scrollZoom': True})\n",
    "\n",
    "\n",
    "    fig.show(\"png\",config={'scrollZoom': True})\n",
    "    fig.write_html('output/strategy performance chart.html')\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071b145a",
   "metadata": {},
   "source": [
    "# Trade statistics summary ( with gas fee)\n",
    "\n",
    "- show every trade pnl with gas fee and entry time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a7f9fe39",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# change freq for the bar chart : 2h or 1d \n",
    "# edit df.groupby function \n",
    "#freq_selector = \"1d\" \n",
    "freq_selector = \"1d\" \n",
    "\n",
    "# groupby date data\n",
    "def groupby_date_data(df):\n",
    "    df['datetime'] = pd.to_datetime(df['datetime'])\n",
    "\n",
    "    group_df = df.groupby(pd.Grouper(key='datetime', axis=0, freq= freq_selector)).agg(\n",
    "        gas_sum=('gas_fee_uniswap_usd_amount', 'sum'),\n",
    "        gas_avg=('gas_fee_uniswap_usd_amount', 'mean'),\n",
    "        pnl_sum=('pnl', 'sum'),\n",
    "        pnl_avg=('pnl', 'mean')\n",
    "    )\n",
    "    group_df = group_df.fillna(0)\n",
    "    #group_df = group_df.dropna()\n",
    "    return group_df\n",
    "\n",
    "# plot sum\n",
    "def plot_sum(group_df, file_name):\n",
    "    date = group_df.index.tolist()\n",
    "\n",
    "    fig = go.Figure()\n",
    "    fig.add_trace(go.Bar(\n",
    "        x=date,\n",
    "        y=group_df['pnl_sum'],\n",
    "        name='pnl sum'\n",
    "    ))\n",
    "    fig.add_trace(go.Bar(\n",
    "        x=date,\n",
    "        y=group_df['gas_sum'],\n",
    "        name='uniswap gas fee sum'\n",
    "    ))\n",
    "    fig.update_layout(barmode='group', xaxis_tickangle=-45)\n",
    "    #fig.show(config={'scrollZoom': True})\n",
    "\n",
    "    fig.show(\"png\",config={'scrollZoom': True})\n",
    "    #fig.write_html(f'{file_name}.html')\n",
    "\n",
    "\n",
    "# plot avg\n",
    "def plot_avg(group_df, file_name):\n",
    "    date = group_df.index.tolist()\n",
    "\n",
    "    fig = go.Figure()\n",
    "    fig.add_trace(go.Bar(\n",
    "        x=date,\n",
    "        y=group_df['pnl_avg'],\n",
    "        name='pnl avg'\n",
    "    ))\n",
    "    fig.add_trace(go.Bar(\n",
    "        x=date,\n",
    "        y=group_df['gas_avg'],\n",
    "        name='uniswap gas fee average'\n",
    "    ))\n",
    "\n",
    "    fig.update_layout(barmode='group', xaxis_tickangle=-45)\n",
    "    #fig.show(config={'scrollZoom': True})\n",
    "\n",
    "    fig.show(\"png\",config={'scrollZoom': True})\n",
    "    #fig.write_html(f'{file_name}.html')\n",
    "\n",
    "\n",
    "if __name__==\"__main__\":\n",
    "    df = pd.read_csv('output/trade_record.csv')\n",
    "    groupby_df = groupby_date_data(df)\n",
    "    plot_sum(groupby_df, 'output/all_trades_sum')\n",
    "    plot_avg(groupby_df, 'output/all_trades_avg')\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7efceef6",
   "metadata": {},
   "source": [
    "## Calculate Sharp/Sortino/SQN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a056f818",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the strategy sharp ratio is: 10.149192823246096 \n"
     ]
    }
   ],
   "source": [
    "pnl_list = np.array(groupby_df['pnl_avg'])\n",
    "equity = 200000 + np.cumsum(pnl_list)\n",
    "return_ = np.diff(equity) / equity[:-1]\n",
    "return_ = pd.Series(return_)\n",
    "\n",
    "def system_quality_number(pnl):\n",
    "    trade_num = len(pnl[pnl != 0])\n",
    "    pnl_loss = pnl[pnl < 0]\n",
    "    avg_loss = np.sum(pnl_loss) / len(pnl_loss)\n",
    "    r_multiples = pnl / -avg_loss\n",
    "    r_expectancy = np.mean(r_multiples)\n",
    "    r_std = np.std(r_multiples)\n",
    "    sqn = (r_expectancy / r_std) * trade_num ** 0.5\n",
    "    return sqn\n",
    "\n",
    "sharpe_ratio = qs.stats.sharpe(returns=return_, rf=0, periods=365)\n",
    "# Sortino_Ratio = qs.stats.sortino(returns=return_, rf=0, periods=365)\n",
    "# SQN = system_quality_number(pnl_list)\n",
    "\n",
    "print(f\"the strategy sharp ratio is: {sharpe_ratio} \" )\n",
    "# print(sortino_ratio)\n",
    "# print(SQN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64fab54f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}