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nagishin/backtest_doten.ipynb

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backtest_doten.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "backtest_doten.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/nagishin/5804a8c07a21e7f4914e0f2389be513c/backtest_doten.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lPY-wgDfZt6J"
},
"source": [
"---\n",
"# 【ドテンくんバックテスト】\n",
"<b>タイプ</b>\n",
"* トレンドフォロー型 スイングトレード\n",
"\n",
"<b>ロジック</b><br>\n",
"　直近18時間（1h足 18期間）のHigh/Lowチャネルを\n",
"* 上抜けしたらロングを保有\n",
"* 下抜けしたらショートを保有<br>\n",
"（ポジションを保有している場合、ポジションと逆抜けでドテンする）\n",
"\n",
"<b>対象データ</b>\n",
"* tickデータ 　：　BitMEX 1m OHLCV\n",
"* 指標データ　：　BitMEX 1h OHLCV\n",
"\n",
"<b>対象期間</b>\n",
"* 2020-07-15 ～ 2020-08-15 (比較的トレンド発生のため)\n",
"\n",
"<b>[参考]</b><br>\n",
"[ビットコイン自動売買bot「ドテンくん」の特徴と評判](https://jitekineko.com/investment-trade-bitcoin-dotenkun/)<br>\n",
"※ドテンくんbotはAKAGAMI氏が公開していましたが、現在noteが非公開のため、解説サイトになります。\n",
"<br>\n",
"<br>\n",
"<b>[注意]</b><br>\n",
"本バックテストは上記noteにて公開されているロジックを参考にテストコードを作成していますが、上記botの動作を忠実に再現しているものではないことをご了承ください。\n",
"\n",
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kVzkRN8ve3eO"
},
"source": [
"# 1. 環境構築\n",
"Google Colaboratoryでテストを実行するために必要なセットアップです。\n",
"* backtest_assistant.py (バックテストツール)、chart_creator.py (チャート作成ツール) をアップロード\n",
"* 必要パッケージのインストール"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MTENMlsve7Zd"
},
"source": [
"## 1-1. バックテストに必要なファイルをアップロード\n",
"以下のツールに同梱されているpythonスクリプトファイルをアップロードします。<br>\n",
"(本notebookは以下のファイルがないと動作しません。)\n",
"* [バックテスト補助ツール「BacktestAssistant」](https://note.com/nagi7692/n/n0c874a0cf2b2) -> backtest_assistant.py\n",
"* [時系列データ可視化ツール「ChartCreator」](https://note.com/nagi7692/n/n401a95653ad0) -> chart_creator.py"
]
},
{
"cell_type": "code",
"metadata": {
"id": "qu2lD4w0e_js"
},
"source": [
"from google.colab import files\n",
"files.upload()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "sbl581lwfDnt"
},
"source": [
"## 1-2. 必要パッケージインストール\n",
"上記ツールを使用するために必要なパッケージをインストールします。<br>\n",
"また、テストデータの取得や加工に便利な以下のツールもインストールします。\n",
"* [テストデータ取得＆加工ツール【DataUtility】](https://gist.github.com/nagishin/1677ffa401476e9e98191a04012ac189)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "nZGhC2xpL1ZD"
},
"source": [
"# 共通\n",
"!pip install numpy\n",
"!pip install pandas\n",
"!pip install datetime\n",
"!pip install pytz\n",
"# ChartCreator\n",
"!pip install matplotlib\n",
"!pip install mpl_finance\n",
"!pip install plotly\n",
"!pip install webcolors\n",
"!pip install beautifulsoup4\n",
"!pip install lxml\n",
"# DataUtility\n",
"!pip install git+https://github.com/nagishin/DataUtility.git"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "vZX3RgULfP7l"
},
"source": [
"# 2. バックテスト準備\n",
"テストを実施するための前準備を行います。\n",
"* テスト期間の設定\n",
"* テストデータの取得・加工"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VE0hgjdwfUGw"
},
"source": [
"## 2-1. バックテスト期間の設定\n",
"2020年7月から現在までをテスト期間として設定します。<br>\n",
"ここで設定した期間を基にテストに必要なデータ取得を行います。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "IiUIQwPX3OgW"
},
"source": [
"# coding: utf-8\n",
"from datetime import datetime\n",
"import numpy as np\n",
"import pandas as pd\n",
"import DataUtility as du\n",
"from backtest_assistant import BacktestAssistant\n",
"\n",
"#テスト期間\n",
"TEST_START = '2020/07/15 09:00:00+0900'\n",
"TEST_END = '2020/08/15 09:00:00+0900'\n",
"\n",
"# テスト期間の日付文字列をUnix Timeに変換\n",
"start_date = datetime.strptime(TEST_START, '%Y/%m/%d %H:%M:%S%z') # テスト開始時刻 (datetime)\n",
"end_date = datetime.strptime(TEST_END, '%Y/%m/%d %H:%M:%S%z') # テスト終了時刻 (datetime)\n",
"start_ut = int(start_date.timestamp()) # テスト開始時刻 (UnixTime)\n",
"end_ut = int(end_date.timestamp()) # テスト終了時刻 (UnixTime)"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "busuByT9fksW"
},
"source": [
"## 2-2. バックテストデータの取得・加工\n",
"ロジック判定および注文は日次処理ですが、途中の含み損益の可視化、ファンディング手数料計算の観点からバックテストに設定するtickデータは1m OHLCVとします。<br>\n",
"(tickデータに沿って含み損益や証拠金維持率、ロスカット判定、ファンディング手数料etc.が執行されます。)<br>\n",
"\n",
"* 1m OHLCVを取得 (tickデータ df_ohlcv_1m)\n",
"* High/Lowチャネルを算出するため、1分→1時間足にダウンサンプリング (df_ohlcv_1h)\n",
"* 1時間足OHLCVからHigh/Lowチャネルを算出"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sQ0gSutH1NFz",
"outputId": "02af9a91-5cd2-4a9d-efa0-86f35ea9d4db",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 436
}
},
"source": [
"# チャネル期間\n",
"TERM = 18\n",
"\n",
"# High/Lowチャネルを求めるため、余分(２０時間分)に取得\n",
"sec_20hours = 60 * 60 * 20 # 20時間分の秒数 (60秒 * 60分 * 20時間)\n",
"\n",
"# DataUtilityを使用してBitMEX OHLCV(1m)を取得\n",
"df_ohlcv_1m = du.Tool.get_ohlcv_from_bitmex(\n",
" start_ut = start_ut - sec_20hours, # 取得開始(UnixTime)\n",
" end_ut = end_ut, # 取得終了(UnixTime)\n",
" period = 1, # 時間足期間(分)\n",
" csv_path = './ohlcv_1m.csv', # 取得結果保存先(CSVパス)\n",
" request_interval = 0.5) # RESTリクエスト待機時間(秒)\n",
"display(df_ohlcv_1m) # 確認表示"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"ohlcv from csv.\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>unixtime</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1594699200</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1594699260</td>\n",
" <td>9211.5</td>\n",
" <td>9211.5</td>\n",
" <td>9210.0</td>\n",
" <td>9210.5</td>\n",
" <td>329221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1594699320</td>\n",
" <td>9210.5</td>\n",
" <td>9210.5</td>\n",
" <td>9210.0</td>\n",
" <td>9210.5</td>\n",
" <td>396854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1594699380</td>\n",
" <td>9210.5</td>\n",
" <td>9210.5</td>\n",
" <td>9210.0</td>\n",
" <td>9210.5</td>\n",
" <td>59755</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1594699440</td>\n",
" <td>9210.5</td>\n",
" <td>9210.5</td>\n",
" <td>9210.0</td>\n",
" <td>9210.0</td>\n",
" <td>10851</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>45836</th>\n",
" <td>1597449360</td>\n",
" <td>11770.5</td>\n",
" <td>11771.0</td>\n",
" <td>11770.5</td>\n",
" <td>11771.0</td>\n",
" <td>189753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45837</th>\n",
" <td>1597449420</td>\n",
" <td>11771.0</td>\n",
" <td>11771.0</td>\n",
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" <td>11770.5</td>\n",
" <td>4766</td>\n",
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" <th>45838</th>\n",
" <td>1597449480</td>\n",
" <td>11770.5</td>\n",
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" <td>11770.5</td>\n",
" <td>11774.0</td>\n",
" <td>339359</td>\n",
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" <tr>\n",
" <th>45839</th>\n",
" <td>1597449540</td>\n",
" <td>11774.0</td>\n",
" <td>11777.0</td>\n",
" <td>11774.0</td>\n",
" <td>11777.0</td>\n",
" <td>391870</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45840</th>\n",
" <td>1597449600</td>\n",
" <td>11777.0</td>\n",
" <td>11791.0</td>\n",
" <td>11776.5</td>\n",
" <td>11790.5</td>\n",
" <td>2113456</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>45841 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" unixtime open high low close volume\n",
"0 1594699200 9213.5 9214.0 9211.5 9211.5 445239\n",
"1 1594699260 9211.5 9211.5 9210.0 9210.5 329221\n",
"2 1594699320 9210.5 9210.5 9210.0 9210.5 396854\n",
"3 1594699380 9210.5 9210.5 9210.0 9210.5 59755\n",
"4 1594699440 9210.5 9210.5 9210.0 9210.0 10851\n",
"... ... ... ... ... ... ...\n",
"45836 1597449360 11770.5 11771.0 11770.5 11771.0 189753\n",
"45837 1597449420 11771.0 11771.0 11770.5 11770.5 4766\n",
"45838 1597449480 11770.5 11774.5 11770.5 11774.0 339359\n",
"45839 1597449540 11774.0 11777.0 11774.0 11777.0 391870\n",
"45840 1597449600 11777.0 11791.0 11776.5 11790.5 2113456\n",
"\n",
"[45841 rows x 6 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "msRXjltEhY3p"
},
"source": [
"### OHLCV 1分→1時間足ダウンサンプリング"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ACLxAyJd1Iwp",
"outputId": "61e415ee-5711-48a3-f187-e478da613343",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 450
}
},
"source": [
"# DataUtilityを使用してOHLCVをダウンサンプリング(1m→1h)\n",
"df_ohlcv_1h = du.Tool.downsample_ohlcv(df_ohlcv_1m, period='1H')\n",
"display(df_ohlcv_1h) # 確認表示"
],
"execution_count": 11,
"outputs": [
{
"output_type": "display_data",
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>unixtime</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
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" <tr>\n",
" <th>datetime</th>\n",
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" <tr>\n",
" <th>2020-07-14 04:00:00+00:00</th>\n",
" <td>1594699200</td>\n",
" <td>9213.5</td>\n",
" <td>9214.0</td>\n",
" <td>9142.5</td>\n",
" <td>9181.5</td>\n",
" <td>132118506</td>\n",
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" <tr>\n",
" <th>2020-07-14 05:00:00+00:00</th>\n",
" <td>1594702800</td>\n",
" <td>9181.5</td>\n",
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" <td>9170.0</td>\n",
" <td>9178.5</td>\n",
" <td>25022781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-14 06:00:00+00:00</th>\n",
" <td>1594706400</td>\n",
" <td>9178.5</td>\n",
" <td>9192.0</td>\n",
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" <td>14362437</td>\n",
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" <tr>\n",
" <th>2020-07-14 07:00:00+00:00</th>\n",
" <td>1594710000</td>\n",
" <td>9184.5</td>\n",
" <td>9185.0</td>\n",
" <td>9174.0</td>\n",
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" <td>20508382</td>\n",
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" <tr>\n",
" <th>2020-07-14 08:00:00+00:00</th>\n",
" <td>1594713600</td>\n",
" <td>9175.5</td>\n",
" <td>9203.0</td>\n",
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" <td>30431198</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-14 20:00:00+00:00</th>\n",
" <td>1597435200</td>\n",
" <td>11823.0</td>\n",
" <td>11840.0</td>\n",
" <td>11762.0</td>\n",
" <td>11777.5</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2020-08-14 21:00:00+00:00</th>\n",
" <td>1597438800</td>\n",
" <td>11777.5</td>\n",
" <td>11790.5</td>\n",
" <td>11706.0</td>\n",
" <td>11779.0</td>\n",
" <td>50311048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-14 22:00:00+00:00</th>\n",
" <td>1597442400</td>\n",
" <td>11779.0</td>\n",
" <td>11804.0</td>\n",
" <td>11739.5</td>\n",
" <td>11790.0</td>\n",
" <td>28775712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-14 23:00:00+00:00</th>\n",
" <td>1597446000</td>\n",
" <td>11790.0</td>\n",
" <td>11800.5</td>\n",
" <td>11760.5</td>\n",
" <td>11777.0</td>\n",
" <td>18369402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-15 00:00:00+00:00</th>\n",
" <td>1597449600</td>\n",
" <td>11777.0</td>\n",
" <td>11791.0</td>\n",
" <td>11776.5</td>\n",
" <td>11790.5</td>\n",
" <td>2113456</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>765 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" unixtime open ... close volume\n",
"datetime ... \n",
"2020-07-14 04:00:00+00:00 1594699200 9213.5 ... 9181.5 132118506\n",
"2020-07-14 05:00:00+00:00 1594702800 9181.5 ... 9178.5 25022781\n",
"2020-07-14 06:00:00+00:00 1594706400 9178.5 ... 9184.5 14362437\n",
"2020-07-14 07:00:00+00:00 1594710000 9184.5 ... 9175.5 20508382\n",
"2020-07-14 08:00:00+00:00 1594713600 9175.5 ... 9191.5 30431198\n",
"... ... ... ... ... ...\n",
"2020-08-14 20:00:00+00:00 1597435200 11823.0 ... 11777.5 39148744\n",
"2020-08-14 21:00:00+00:00 1597438800 11777.5 ... 11779.0 50311048\n",
"2020-08-14 22:00:00+00:00 1597442400 11779.0 ... 11790.0 28775712\n",
"2020-08-14 23:00:00+00:00 1597446000 11790.0 ... 11777.0 18369402\n",
"2020-08-15 00:00:00+00:00 1597449600 11777.0 ... 11790.5 2113456\n",
"\n",
"[765 rows x 6 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "htV78lqXh7IA"
},
"source": [
"### 指標計算 (High/Lowチャネル)\n",
"1時間足OHLCVから18期間のHigh/Lowチャネルを算出します。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "m4KiTyCXjVRh",
"outputId": "b26f5499-29a8-4998-fc6b-d9728c4a94fd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 450
}
},
"source": [
"# チャネルHLを算出\n",
"high_max = df_ohlcv_1h['high'].rolling(TERM, min_periods=1).max()\n",
"low_min = df_ohlcv_1h['low'].rolling(TERM, min_periods=1).min()\n",
"\n",
"# 1期間前を判定基準にするため、shift\n",
"df_ohlcv_1h['high_max'] = np.roll(high_max, 1)\n",
"df_ohlcv_1h['low_min'] = np.roll(low_min, 1)\n",
"display(df_ohlcv_1h) # 確認表示"
],
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
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" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
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" <th>2020-07-14 06:00:00+00:00</th>\n",
" <td>1594706400</td>\n",
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" <th>2020-07-14 07:00:00+00:00</th>\n",
" <td>1594710000</td>\n",
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" <td>9185.0</td>\n",
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" <td>9175.5</td>\n",
" <td>20508382</td>\n",
" <td>9214.0</td>\n",
" <td>9142.5</td>\n",
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" <tr>\n",
" <th>2020-07-14 08:00:00+00:00</th>\n",
" <td>1594713600</td>\n",
" <td>9175.5</td>\n",
" <td>9203.0</td>\n",
" <td>9175.0</td>\n",
" <td>9191.5</td>\n",
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" <td>9142.5</td>\n",
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" <tr>\n",
" <th>2020-08-14 20:00:00+00:00</th>\n",
" <td>1597435200</td>\n",
" <td>11823.0</td>\n",
" <td>11840.0</td>\n",
" <td>11762.0</td>\n",
" <td>11777.5</td>\n",
" <td>39148744</td>\n",
" <td>11868.5</td>\n",
" <td>11658.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-14 21:00:00+00:00</th>\n",
" <td>1597438800</td>\n",
" <td>11777.5</td>\n",
" <td>11790.5</td>\n",
" <td>11706.0</td>\n",
" <td>11779.0</td>\n",
" <td>50311048</td>\n",
" <td>11868.5</td>\n",
" <td>11658.5</td>\n",
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" <tr>\n",
" <th>2020-08-14 22:00:00+00:00</th>\n",
" <td>1597442400</td>\n",
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" <td>11790.0</td>\n",
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" <td>11868.5</td>\n",
" <td>11658.5</td>\n",
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" <th>2020-08-14 23:00:00+00:00</th>\n",
" <td>1597446000</td>\n",
" <td>11790.0</td>\n",
" <td>11800.5</td>\n",
" <td>11760.5</td>\n",
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" <th>2020-08-15 00:00:00+00:00</th>\n",
" <td>1597449600</td>\n",
" <td>11777.0</td>\n",
" <td>11791.0</td>\n",
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" <td>11790.5</td>\n",
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" <td>11868.5</td>\n",
" <td>11658.5</td>\n",
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"</table>\n",
"<p>765 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" unixtime open ... high_max low_min\n",
"datetime ... \n",
"2020-07-14 04:00:00+00:00 1594699200 9213.5 ... 11868.5 11658.5\n",
"2020-07-14 05:00:00+00:00 1594702800 9181.5 ... 9214.0 9142.5\n",
"2020-07-14 06:00:00+00:00 1594706400 9178.5 ... 9214.0 9142.5\n",
"2020-07-14 07:00:00+00:00 1594710000 9184.5 ... 9214.0 9142.5\n",
"2020-07-14 08:00:00+00:00 1594713600 9175.5 ... 9214.0 9142.5\n",
"... ... ... ... ... ...\n",
"2020-08-14 20:00:00+00:00 1597435200 11823.0 ... 11868.5 11658.5\n",
"2020-08-14 21:00:00+00:00 1597438800 11777.5 ... 11868.5 11658.5\n",
"2020-08-14 22:00:00+00:00 1597442400 11779.0 ... 11868.5 11658.5\n",
"2020-08-14 23:00:00+00:00 1597446000 11790.0 ... 11868.5 11658.5\n",
"2020-08-15 00:00:00+00:00 1597449600 11777.0 ... 11868.5 11658.5\n",
"\n",
"[765 rows x 8 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "qM6EsiSwiXZM",
"outputId": "d6a91820-ffff-4ca0-ebde-6bc65fac943f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 450
}
},
"source": [
"# 1分足、1時間足それぞれのOHLCV DataFrameをテスト期間でfiltering\n",
"df_ohlcv_1m = du.Tool.filter_df(df_ohlcv_1m, 'unixtime', start_ut, end_ut)\n",
"df_ohlcv_1h = du.Tool.filter_df(df_ohlcv_1h, 'unixtime', start_ut, end_ut)\n",
"display(df_ohlcv_1h) # 確認表示\n",
"\n",
"# DataFrameからテストに使用する列をndarrayに変換\n",
"np_ut_1m = df_ohlcv_1m['unixtime'].values\n",
"np_high = df_ohlcv_1h['high_max'].values\n",
"np_low = df_ohlcv_1h['low_min'].values"
],
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
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" <th></th>\n",
" <th>unixtime</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" <th>high_max</th>\n",
" <th>low_min</th>\n",
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" <th>datetime</th>\n",
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" <th>2020-07-15 00:00:00+00:00</th>\n",
" <td>1594771200</td>\n",
" <td>9256.5</td>\n",
" <td>9273.0</td>\n",
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" <th>2020-07-15 01:00:00+00:00</th>\n",
" <td>1594774800</td>\n",
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" <tr>\n",
" <th>2020-07-15 02:00:00+00:00</th>\n",
" <td>1594778400</td>\n",
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" <td>10485902</td>\n",
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" <td>1594782000</td>\n",
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" <th>2020-07-15 04:00:00+00:00</th>\n",
" <td>1594785600</td>\n",
" <td>9236.0</td>\n",
" <td>9245.0</td>\n",
" <td>9225.0</td>\n",
" <td>9244.5</td>\n",
" <td>23853347</td>\n",
" <td>9277.0</td>\n",
" <td>9076.0</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-14 20:00:00+00:00</th>\n",
" <td>1597435200</td>\n",
" <td>11823.0</td>\n",
" <td>11840.0</td>\n",
" <td>11762.0</td>\n",
" <td>11777.5</td>\n",
" <td>39148744</td>\n",
" <td>11868.5</td>\n",
" <td>11658.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-14 21:00:00+00:00</th>\n",
" <td>1597438800</td>\n",
" <td>11777.5</td>\n",
" <td>11790.5</td>\n",
" <td>11706.0</td>\n",
" <td>11779.0</td>\n",
" <td>50311048</td>\n",
" <td>11868.5</td>\n",
" <td>11658.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-14 22:00:00+00:00</th>\n",
" <td>1597442400</td>\n",
" <td>11779.0</td>\n",
" <td>11804.0</td>\n",
" <td>11739.5</td>\n",
" <td>11790.0</td>\n",
" <td>28775712</td>\n",
" <td>11868.5</td>\n",
" <td>11658.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-14 23:00:00+00:00</th>\n",
" <td>1597446000</td>\n",
" <td>11790.0</td>\n",
" <td>11800.5</td>\n",
" <td>11760.5</td>\n",
" <td>11777.0</td>\n",
" <td>18369402</td>\n",
" <td>11868.5</td>\n",
" <td>11658.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-15 00:00:00+00:00</th>\n",
" <td>1597449600</td>\n",
" <td>11777.0</td>\n",
" <td>11791.0</td>\n",
" <td>11776.5</td>\n",
" <td>11790.5</td>\n",
" <td>2113456</td>\n",
" <td>11868.5</td>\n",
" <td>11658.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>745 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" unixtime open ... high_max low_min\n",
"datetime ... \n",
"2020-07-15 00:00:00+00:00 1594771200 9256.5 ... 9277.0 9076.0\n",
"2020-07-15 01:00:00+00:00 1594774800 9258.5 ... 9277.0 9076.0\n",
"2020-07-15 02:00:00+00:00 1594778400 9253.5 ... 9277.0 9076.0\n",
"2020-07-15 03:00:00+00:00 1594782000 9246.5 ... 9277.0 9076.0\n",
"2020-07-15 04:00:00+00:00 1594785600 9236.0 ... 9277.0 9076.0\n",
"... ... ... ... ... ...\n",
"2020-08-14 20:00:00+00:00 1597435200 11823.0 ... 11868.5 11658.5\n",
"2020-08-14 21:00:00+00:00 1597438800 11777.5 ... 11868.5 11658.5\n",
"2020-08-14 22:00:00+00:00 1597442400 11779.0 ... 11868.5 11658.5\n",
"2020-08-14 23:00:00+00:00 1597446000 11790.0 ... 11868.5 11658.5\n",
"2020-08-15 00:00:00+00:00 1597449600 11777.0 ... 11868.5 11658.5\n",
"\n",
"[745 rows x 8 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_gZXsP8Li96d"
},
"source": [
"# 3. バックテスト実行"
]
},
{
"cell_type": "code",
"metadata": {
"id": "X-9-nd4fj9nB",
"outputId": "43bfc619-e9a6-4314-edcd-b89f694dbfb1",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"start_balance = 1.0 # テスト開始時の残高(BTC)\n",
"leverage = 0 # 取引時のレバレッジ(0:クロスマージン, 1～:分離マージン)\n",
"lot = 10000 # 取引ロットサイズ($)\n",
"\n",
"# BacktestAssistant生成\n",
"test = BacktestAssistant(\n",
" balance = start_balance,\n",
" trade_leverage = leverage,\n",
" tick_mode = 'ltp', # tick_mode='ltp'のため、\n",
" take_cost = 0.5) # 成行注文時の取引コスト(スプレッド)を$0.5に設定\n",
"\n",
"test.set_funding_rate(rate = 0.01) # Funding Rate設定\n",
"test.set_output_option(disp_progress = True) # 出力オプション設定 (進捗率表示ON)\n",
"test.preset_ltp_ticks_from_ohlcv_df(df_ohlcv_1m) # tickデータ設定 (1分足OHLCVのopen, high, low, close価格をtickデータに展開)\n",
"\n",
"# バックテスト初期処理 (テスト開始時刻を設定)\n",
"test.initialize(start_time = np_ut_1m[0])\n",
"\n",
"# 現在の1時間足を示すインデックス (最初は-1を設定)\n",
"idx_1h = -1\n",
"# テストループ (1分毎)--------------------------------------------------------\n",
"for i, unixtime in enumerate(np_ut_1m):\n",
" if test.time() < unixtime:\n",
" test.sleep_to_time(unixtime) # 指定時刻(unixtime)までtickを進める\n",
"\n",
" idx = i // 60 # 現在時刻が含まれる1時間足インデックス\n",
"\n",
" # 1時間足が切り替わったら、チャネルHL更新\n",
" if idx > idx_1h:\n",
" test.cancel_order() # 現在発行している全注文キャンセル\n",
" idx_1h = int(idx) # 1時間足インデックス更新\n",
" high_break = np_high[idx_1h] # チャネルHigh\n",
" low_break = np_low[idx_1h] # チャネルLow\n",
"\n",
" price = test.get_price()['ltp'] # 現在価格取得\n",
" orders = test.get_open_orders() # アクティブ注文取得\n",
" pos = test.get_position() # 建玉取得\n",
"\n",
" high_price = max(high_break, price)\n",
" low_price = min(low_break, price)\n",
"\n",
" # アクティブ注文がない場合\n",
" if len(orders) == 0:\n",
" # ノーポジション\n",
" if pos['size'] == 0:\n",
" # チャネルHLに逆指値エントリー注文\n",
" test.stop_order('Buy', high_price, lot) # チャネル上限ブレイク\n",
" test.stop_order('Sell', low_price, lot) # チャネル下限ブレイク\n",
" # ロングポジション\n",
" elif pos['size'] > 0:\n",
" # ドテン逆指値注文\n",
" test.stop_order('Sell', low_price, lot + abs(pos['size'])) # チャネル下限ブレイク\n",
" # ショートポジション\n",
" else:\n",
" # ドテン逆指値注文\n",
" test.stop_order('Buy', high_price, lot + abs(pos['size'])) # チャネル上限ブレイク\n",
"#-------------------------------------------------------------------------------\n",
"\n",
"test.cancel_order() # 全注文キャンセル\n",
"pos = test.get_position() # テスト終了時にポジションがあればクローズ\n",
"if pos['size'] < 0:\n",
" test.market_order('Buy', abs(pos['size']))\n",
"elif pos['size'] > 0:\n",
" test.market_order('Sell', abs(pos['size']))\n",
"\n",
"# バックテスト終了処理\n",
"results = test.terminate()"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
"Now initializing...\n",
"Completed initialize process.\n",
"[Start] time:1594771200.0 ltp:9256.5 balance:1.0 [Fee] taker:0.075 maker:-0.025 fr:0.01\n",
"preset ticks period: 1594771200.0 - 1597449645.0 (102837)\n",
"0% complete. (1 / 102,837)\n",
"10% complete. (10,284 / 102,837)\n",
"20% complete. (20,568 / 102,837)\n",
"30% complete. (30,852 / 102,837)\n",
"40% complete. (41,135 / 102,837)\n",
"50% complete. (51,419 / 102,837)\n",
"60% complete. (61,703 / 102,837)\n",
"70% complete. (71,986 / 102,837)\n",
"80% complete. (82,270 / 102,837)\n",
"90% complete. (92,554 / 102,837)\n",
"Now terminating...\n",
"Save order_history 1549 records.(./result/orders.csv)\n",
"Save executions 114 records.(./result/executions.csv)\n",
"Save position_history 101675 records.(./result/positions.csv)\n",
"Save balance_history 101768 records.(./result/balances.csv)\n",
"\n",
"[Result]\n",
" [Time ] 2020/07/15 09:00:00 - 2020/08/15 09:00:00\n",
" [Balance ] 1.0 -> 1.024 (+2.40%)\n",
" [Orders ] Total:775 (Open:0 Executed:21 Canceled:754)\n",
" [Position] Max Long:10,000.0 Max Short:-10,000.0 Min Margin:9013.00%\n",
"\n",
"[Execution statistics] PF:1.38\n",
" [Total ] Count:21(Size:400,000) PnL:+0.0547 Avr:+0.0026 DD:-0.1123(-9.70%) Price Range:+280.0\n",
" [Profit ] Count:6(30.00%) Sum:+0.2005 Avr:+0.0334 Max:+0.1485 MaxLen:1(+0.0037)\n",
" [Loss ] Count:14(70.00%) Sum:-0.1458 Avr:-0.0104 Max:-0.0204 MaxLen:7(-0.0662)\n",
" [Fee ] Trade:0.028 Funding:0.0027\n",
"\n",
"[Fiat balance]\n",
" [Start ] 9,256.5 (Balance:1.0 Price:9256.5)\n",
" [End ] 12,059.6 (Balance:1.024 Price:11777.0)\n",
" [Result] 2,803.1 (+30.28%)\n",
"\n",
"Completed terminate process.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dJfECeADnh2-"
},
"source": [
"## 4. バックテスト結果\n",
"バックテスト終了処理で各履歴CSVが出力されます。\n",
"* 注文履歴 (orders.csv)<br>\n",
"注文の発行、キャンセル、約定など注文データに変更があると記録されます。\n",
"\n",
"* 約定履歴 (executions.csv)<br>\n",
"注文の約定、ファンディング手数料発生など資産変動に関わる履歴が記録されます。\n",
"\n",
"* 建玉履歴 (positions.csv)<br>\n",
"注文の約定、未実現損益の変動など建玉変動に関わる履歴が記録されます。\n",
"\n",
"* 残高履歴 (balances.csv)<br>\n",
"残高に変更があると記録されます。"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qPqAyOMjnmAB"
},
"source": [
"### 4-1. 注文履歴 (orders.csv)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iYeS9-TjnnMx",
"outputId": "5a12b893-0e51-49a7-c6e0-3bbf7d16ca85",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
}
},
"source": [
"df_orders = pd.read_csv('./result/orders.csv')\n",
"display(df_orders)"
],
"execution_count": 16,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>id</th>\n",
" <th>status</th>\n",
" <th>type</th>\n",
" <th>side</th>\n",
" <th>size</th>\n",
" <th>price</th>\n",
" <th>trigger_price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.594771e+09</td>\n",
" <td>1</td>\n",
" <td>Active</td>\n",
" <td>Stop</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>0.0</td>\n",
" <td>9277.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.594771e+09</td>\n",
" <td>2</td>\n",
" <td>Active</td>\n",
" <td>Stop</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>0.0</td>\n",
" <td>9076.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.594775e+09</td>\n",
" <td>1</td>\n",
" <td>Canceled</td>\n",
" <td>Stop</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>0.0</td>\n",
" <td>9277.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.594775e+09</td>\n",
" <td>2</td>\n",
" <td>Canceled</td>\n",
" <td>Stop</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>0.0</td>\n",
" <td>9076.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.594775e+09</td>\n",
" <td>3</td>\n",
" <td>Active</td>\n",
" <td>Stop</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>0.0</td>\n",
" <td>9277.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1544</th>\n",
" <td>1.597446e+09</td>\n",
" <td>773</td>\n",
" <td>Active</td>\n",
" <td>Stop</td>\n",
" <td>Sell</td>\n",
" <td>20000</td>\n",
" <td>0.0</td>\n",
" <td>11658.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1545</th>\n",
" <td>1.597450e+09</td>\n",
" <td>773</td>\n",
" <td>Canceled</td>\n",
" <td>Stop</td>\n",
" <td>Sell</td>\n",
" <td>20000</td>\n",
" <td>0.0</td>\n",
" <td>11658.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1546</th>\n",
" <td>1.597450e+09</td>\n",
" <td>774</td>\n",
" <td>Active</td>\n",
" <td>Stop</td>\n",
" <td>Sell</td>\n",
" <td>20000</td>\n",
" <td>0.0</td>\n",
" <td>11658.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1547</th>\n",
" <td>1.597450e+09</td>\n",
" <td>774</td>\n",
" <td>Canceled</td>\n",
" <td>Stop</td>\n",
" <td>Sell</td>\n",
" <td>20000</td>\n",
" <td>0.0</td>\n",
" <td>11658.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1548</th>\n",
" <td>1.597450e+09</td>\n",
" <td>775</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>11776.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1549 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp id status type side size price trigger_price\n",
"0 1.594771e+09 1 Active Stop Buy 10000 0.0 9277.0\n",
"1 1.594771e+09 2 Active Stop Sell 10000 0.0 9076.0\n",
"2 1.594775e+09 1 Canceled Stop Buy 10000 0.0 9277.0\n",
"3 1.594775e+09 2 Canceled Stop Sell 10000 0.0 9076.0\n",
"4 1.594775e+09 3 Active Stop Buy 10000 0.0 9277.0\n",
"... ... ... ... ... ... ... ... ...\n",
"1544 1.597446e+09 773 Active Stop Sell 20000 0.0 11658.5\n",
"1545 1.597450e+09 773 Canceled Stop Sell 20000 0.0 11658.5\n",
"1546 1.597450e+09 774 Active Stop Sell 20000 0.0 11658.5\n",
"1547 1.597450e+09 774 Canceled Stop Sell 20000 0.0 11658.5\n",
"1548 1.597450e+09 775 Filled Market Sell 10000 11776.5 0.0\n",
"\n",
"[1549 rows x 8 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nOnqIVejnrHr"
},
"source": [
"### 4-2. 約定履歴 (executions.csv)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zh3Jj-avnuDT",
"outputId": "c1cfb3b4-35b5-4e77-9ab1-cb4f0d7423f7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
}
},
"source": [
"df_executions = pd.read_csv('./result/executions.csv')\n",
"display(df_executions)"
],
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>id</th>\n",
" <th>type</th>\n",
" <th>side</th>\n",
" <th>size</th>\n",
" <th>price</th>\n",
" <th>cost</th>\n",
" <th>fee</th>\n",
" <th>pnl</th>\n",
" <th>price_range</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.594806e+09</td>\n",
" <td>20</td>\n",
" <td>Stop</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>9189.5</td>\n",
" <td>-1.088198</td>\n",
" <td>0.000816</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.594806e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>9190.0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000109</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.594806e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>9190.5</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000109</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.594832e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>9206.0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000109</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.594861e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>9205.0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000109</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>1.597352e+09</td>\n",
" <td>745</td>\n",
" <td>Stop</td>\n",
" <td>Buy</td>\n",
" <td>20000</td>\n",
" <td>11647.0</td>\n",
" <td>1.717180</td>\n",
" <td>0.001288</td>\n",
" <td>-0.012262</td>\n",
" <td>-164.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>1.597367e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>11780.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000085</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>1.597396e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>11706.5</td>\n",
" <td>0.000000</td>\n",
" <td>0.000085</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>1.597424e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>11832.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000085</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>1.597450e+09</td>\n",
" <td>775</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>11776.5</td>\n",
" <td>-0.849149</td>\n",
" <td>0.000637</td>\n",
" <td>0.009441</td>\n",
" <td>129.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>114 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp id type ... fee pnl price_range\n",
"0 1.594806e+09 20 Stop ... 0.000816 0.000000 0.0\n",
"1 1.594806e+09 0 Funding ... -0.000109 0.000000 0.0\n",
"2 1.594806e+09 0 Funding ... -0.000109 0.000000 0.0\n",
"3 1.594832e+09 0 Funding ... -0.000109 0.000000 0.0\n",
"4 1.594861e+09 0 Funding ... -0.000109 0.000000 0.0\n",
".. ... ... ... ... ... ... ...\n",
"109 1.597352e+09 745 Stop ... 0.001288 -0.012262 -164.0\n",
"110 1.597367e+09 0 Funding ... 0.000085 0.000000 0.0\n",
"111 1.597396e+09 0 Funding ... 0.000085 0.000000 0.0\n",
"112 1.597424e+09 0 Funding ... 0.000085 0.000000 0.0\n",
"113 1.597450e+09 775 Market ... 0.000637 0.009441 129.5\n",
"\n",
"[114 rows x 10 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BpjWed_Inxal"
},
"source": [
"### 4-3. 建玉履歴 (positions.csv)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7PTuSxxCn06A",
"outputId": "d90297df-7dd5-438b-bd68-c42c98223df2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
}
},
"source": [
"df_positions = pd.read_csv('./result/positions.csv')\n",
"display(df_positions)"
],
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>size</th>\n",
" <th>avr_entry</th>\n",
" <th>pos_cost</th>\n",
" <th>order_margin</th>\n",
" <th>pos_margin</th>\n",
" <th>margin_rate</th>\n",
" <th>liq_price</th>\n",
" <th>unrealised_pnl</th>\n",
" <th>leverage</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.594771e+09</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.594806e+09</td>\n",
" <td>-10000.0</td>\n",
" <td>9189.5</td>\n",
" <td>-1.088198</td>\n",
" <td>0.000000</td>\n",
" <td>0.010882</td>\n",
" <td>91.89456</td>\n",
" <td>106717.12</td>\n",
" <td>-0.000059</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.594806e+09</td>\n",
" <td>-10000.0</td>\n",
" <td>9189.5</td>\n",
" <td>-1.088198</td>\n",
" <td>0.000000</td>\n",
" <td>0.010882</td>\n",
" <td>91.82955</td>\n",
" <td>105917.01</td>\n",
" <td>-0.000059</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.594806e+09</td>\n",
" <td>-10000.0</td>\n",
" <td>9189.5</td>\n",
" <td>-1.088198</td>\n",
" <td>0.000000</td>\n",
" <td>0.010882</td>\n",
" <td>91.83911</td>\n",
" <td>106039.30</td>\n",
" <td>-0.000118</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.594806e+09</td>\n",
" <td>-10000.0</td>\n",
" <td>9189.5</td>\n",
" <td>-1.088198</td>\n",
" <td>0.000000</td>\n",
" <td>0.010882</td>\n",
" <td>91.83201</td>\n",
" <td>106039.30</td>\n",
" <td>-0.001065</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101670</th>\n",
" <td>1.597450e+09</td>\n",
" <td>10000.0</td>\n",
" <td>11647.0</td>\n",
" <td>0.858590</td>\n",
" <td>0.008577</td>\n",
" <td>0.008586</td>\n",
" <td>120.61861</td>\n",
" <td>5353.09</td>\n",
" <td>0.009261</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101671</th>\n",
" <td>1.597450e+09</td>\n",
" <td>10000.0</td>\n",
" <td>11647.0</td>\n",
" <td>0.858590</td>\n",
" <td>0.008577</td>\n",
" <td>0.008586</td>\n",
" <td>120.67482</td>\n",
" <td>5353.09</td>\n",
" <td>0.009478</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101672</th>\n",
" <td>1.597450e+09</td>\n",
" <td>10000.0</td>\n",
" <td>11647.0</td>\n",
" <td>0.858590</td>\n",
" <td>0.008577</td>\n",
" <td>0.008586</td>\n",
" <td>120.61861</td>\n",
" <td>5353.09</td>\n",
" <td>0.009261</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101673</th>\n",
" <td>1.597450e+09</td>\n",
" <td>10000.0</td>\n",
" <td>11647.0</td>\n",
" <td>0.858590</td>\n",
" <td>0.008577</td>\n",
" <td>0.008586</td>\n",
" <td>120.67482</td>\n",
" <td>5353.09</td>\n",
" <td>0.009478</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101674</th>\n",
" <td>1.597450e+09</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>101675 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp size avr_entry ... liq_price unrealised_pnl leverage\n",
"0 1.594771e+09 0.0 0.0 ... 0.00 0.000000 0\n",
"1 1.594806e+09 -10000.0 9189.5 ... 106717.12 -0.000059 0\n",
"2 1.594806e+09 -10000.0 9189.5 ... 105917.01 -0.000059 0\n",
"3 1.594806e+09 -10000.0 9189.5 ... 106039.30 -0.000118 0\n",
"4 1.594806e+09 -10000.0 9189.5 ... 106039.30 -0.001065 0\n",
"... ... ... ... ... ... ... ...\n",
"101670 1.597450e+09 10000.0 11647.0 ... 5353.09 0.009261 0\n",
"101671 1.597450e+09 10000.0 11647.0 ... 5353.09 0.009478 0\n",
"101672 1.597450e+09 10000.0 11647.0 ... 5353.09 0.009261 0\n",
"101673 1.597450e+09 10000.0 11647.0 ... 5353.09 0.009478 0\n",
"101674 1.597450e+09 0.0 0.0 ... 0.00 0.000000 0\n",
"\n",
"[101675 rows x 10 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "riS8oQFon4Qe"
},
"source": [
"### 4-4. 残高履歴 (balances.csv)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ENTFtCdEn7M6",
"outputId": "392689d4-b229-4261-d235-2d38a1f2c487",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
}
},
"source": [
"df_balances = pd.read_csv('./result/balances.csv')\n",
"display(df_balances)"
],
"execution_count": 19,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>balance</th>\n",
" <th>unrealised_pnl</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.594771e+09</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>9256.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.594806e+09</td>\n",
" <td>0.999184</td>\n",
" <td>-0.000059</td>\n",
" <td>9190.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.594806e+09</td>\n",
" <td>0.999293</td>\n",
" <td>-0.000059</td>\n",
" <td>9190.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.594806e+09</td>\n",
" <td>0.999293</td>\n",
" <td>-0.000059</td>\n",
" <td>9190.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.594806e+09</td>\n",
" <td>0.999401</td>\n",
" <td>-0.000059</td>\n",
" <td>9190.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101763</th>\n",
" <td>1.597450e+09</td>\n",
" <td>1.015188</td>\n",
" <td>0.009261</td>\n",
" <td>11774.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101764</th>\n",
" <td>1.597450e+09</td>\n",
" <td>1.015188</td>\n",
" <td>0.009478</td>\n",
" <td>11777.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101765</th>\n",
" <td>1.597450e+09</td>\n",
" <td>1.015188</td>\n",
" <td>0.009261</td>\n",
" <td>11774.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101766</th>\n",
" <td>1.597450e+09</td>\n",
" <td>1.015188</td>\n",
" <td>0.009478</td>\n",
" <td>11777.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101767</th>\n",
" <td>1.597450e+09</td>\n",
" <td>1.023992</td>\n",
" <td>0.000000</td>\n",
" <td>11777.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>101768 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp balance unrealised_pnl price\n",
"0 1.594771e+09 1.000000 0.000000 9256.5\n",
"1 1.594806e+09 0.999184 -0.000059 9190.0\n",
"2 1.594806e+09 0.999293 -0.000059 9190.0\n",
"3 1.594806e+09 0.999293 -0.000059 9190.0\n",
"4 1.594806e+09 0.999401 -0.000059 9190.5\n",
"... ... ... ... ...\n",
"101763 1.597450e+09 1.015188 0.009261 11774.0\n",
"101764 1.597450e+09 1.015188 0.009478 11777.0\n",
"101765 1.597450e+09 1.015188 0.009261 11774.0\n",
"101766 1.597450e+09 1.015188 0.009478 11777.0\n",
"101767 1.597450e+09 1.023992 0.000000 11777.0\n",
"\n",
"[101768 rows x 4 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mtarj5USoAsr"
},
"source": [
"## 5. バックテスト結果をチャート化\n",
"バックテスト終了処理のresultsにはチャート作成向けデータが出力されます。<br>\n",
"このデータからチャート作成ツール「ChartCreator」を使用してテスト結果を可視化します。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "PqXW4sddutq6"
},
"source": [
"from chart_creator import ChartCreator as cc\n",
"\n",
"dict_data = results['data']\n",
"dict_statistics = results['statistics']\n",
"dict_chart = results['chart']\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# レイアウトカスタマイズ\n",
"#-------------------------------------------------------------------------------\n",
"cc.initialize()\n",
"cc.settings['title'] = 'Result chart'\n",
"\n",
"# X軸設定\n",
"cc.set_xaxis(start=0, end=0, is_unixtime=True)\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# メインチャート(ax:0)\n",
"#-------------------------------------------------------------------------------\n",
"cc.add_subchart(ax=0, label=\"USD\", grid=True)\n",
"\n",
"# ローソクバー設定(OHLCV)\n",
"cc.set_ohlcv_df(df_ohlcv_1h)\n",
"\n",
"# 買い注文ポイント設定\n",
"cc.set_marker(dict_chart['buy']['timestamp'], dict_chart['buy']['price'], ax=0, color='blue', size=30.0, mark='^', name='Buy')\n",
"\n",
"# 売り注文ポイント設定\n",
"cc.set_marker(dict_chart['sell']['timestamp'], dict_chart['sell']['price'], ax=0, color='red', size=30.0, mark='v', name='Sell')\n",
"\n",
"# 清算注文ポイント設定\n",
"if len(dict_chart['liquid']['timestamp']) > 0:\n",
" cc.set_marker(dict_chart['liquid']['timestamp'], dict_chart['liquid']['price'], ax=0, color='magenta', size=30.0, mark='D', name='Liquidation')\n",
"\n",
"# チャネルHL設定\n",
"cc.set_band(df_ohlcv_1h['unixtime'], np_high, np_low, ax=0, up_color='orange', down_color='orange',\n",
" alpha=0.1, edge_width=1.0, edge_color='dimgray', name='High Low')\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# ポジション推移サブチャート(ax:1)\n",
"#-------------------------------------------------------------------------------\n",
"cc.add_subchart(ax=1, label=\"Position\", grid=True)\n",
"\n",
"# ポジション設定\n",
"cc.set_band(dict_chart['position']['unixtime'], dict_chart['position']['size'], dict_chart['position']['zero'], ax=1, up_color='blue', down_color='red',\n",
" alpha=0.5, edge_width=1.0, edge_color='dimgray', line_mode='hv', name='Position')\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# 損益推移サブチャート(ax:2)\n",
"#-------------------------------------------------------------------------------\n",
"cc.add_subchart(ax=2, label='PnL', grid=True)\n",
"\n",
"# 損益設定\n",
"cc.set_line(dict_chart['pnl']['unixtime'], dict_chart['pnl']['unrealised_pnl'], ax=2, color='green', width=0.8, name='Unrealised')\n",
"cc.set_band(dict_chart['pnl']['unixtime'], dict_chart['pnl']['pnl'], dict_chart['pnl']['zero'], ax=2, up_color='skyblue', down_color='red',\n",
" alpha=0.2, edge_width=1.0, edge_color='dimgray', name='PL')\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# フィアット損益推移サブチャート(ax:3)\n",
"#-------------------------------------------------------------------------------\n",
"cc.add_subchart(ax=3, label='Fiat PnL', grid=True)\n",
"\n",
"# フィアット損益設定\n",
"fiat_blc = dict_chart['pnl']['balance'] * dict_chart['pnl']['price']\n",
"fiat_start = fiat_blc[0]\n",
"fiat_blc = fiat_blc - fiat_start\n",
"fiat_unrel_blc = dict_chart['pnl']['unrealised_balance'] * dict_chart['pnl']['price']\n",
"fiat_unrel_blc = fiat_unrel_blc - fiat_start\n",
"cc.set_line(dict_chart['pnl']['unixtime'], fiat_unrel_blc, ax=3, color='green', width=0.8, name='Include unrealised')\n",
"cc.set_band(dict_chart['pnl']['unixtime'], fiat_blc, dict_chart['pnl']['zero'],\n",
" ax=3, up_color='orange', down_color='red',\n",
" alpha=0.2, edge_width=1.0, edge_color='dimgray', name='Balance')\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# チャート生成\n",
"#-------------------------------------------------------------------------------\n",
"file_path = './result_chart'\n",
"cc.create_chart(file_path + '.png', 'png')\n",
"cc.create_chart(file_path + '.html', 'html')"
],
"execution_count": 20,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "QTtu9YLjoTXU"
},
"source": [
"### チャート画像表示 (matplotlib)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ii3aeOTr3FGl",
"outputId": "b57dea5d-d6b2-42aa-acb9-8798683387ab",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 649
}
},
"source": [
"from IPython.display import Image, display_png\n",
"\n",
"# チャート画像表示\n",
"file_name = file_path + '.png'\n",
"display_png(Image(file_name))"
],
"execution_count": 21,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wtCkPS00ohS7"
},
"source": [
"### インタラクティブチャート表示 (plotly)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "yZoxBD_z4lia",
"outputId": "56dd74c6-0b25-4e91-ded4-34839cf779bc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 817
}
},
"source": [
"from IPython.display import HTML, display_html\n",
"\n",
"# HTMLチャート表示\n",
"file_name = file_path + '.html'\n",
"display_html(HTML(filename=file_name))"
],
"execution_count": 22,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<html>\n",
"<head><script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script><meta charset=\"utf-8\"/></head>\n",
"<body>\n",
"<div>\n",
"<div class=\"plotly-graph-div\" id=\"a938f3f4-2dd7-4e66-bfc6-48709a017138\" style=\"height:800px; width:1200px;\"></div>\n",
"<script type=\"text/javascript\">\n",
" \n",
" window.PLOTLYENV=window.PLOTLYENV || {};\n",
" \n",
" if (document.getElementById(\"a938f3f4-2dd7-4e66-bfc6-48709a017138\")) {\n",
" Plotly.newPlot(\n",
" 'a938f3f4-2dd7-4e66-bfc6-48709a017138',\n",
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