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backtest_heikin.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "backtest_heikin.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/06edb1244ca10d44041ad2c2c8d9a8ab/backtest_heikin.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IT8KDp1hQmYv"
},
"source": [
"---\n",
"# 【平均様バックテスト】\n",
"<b>タイプ</b>\n",
"* トレンドフォロー型 日足スイングトレード\n",
"\n",
"<b>ロジック</b>\n",
"* 日本時間のAM09:00の時点で前日の平均足(日足)が陰線ならショート、陽線ならロングを保有する\n",
"* すでにポジションを保有している場合、ポジションと逆シグナルでドテンする<br>\n",
"(前日からトレンドが継続されている場合は反転するまでポジションを持ち続ける)\n",
"\n",
"<b>対象データ</b>\n",
"* tickデータ 　：　BitMEX 1h OHLCV\n",
"* 指標データ　：　BitMEX 1d 平均足\n",
"\n",
"<b>対象期間</b>\n",
"* 2020-01-01 ～ 2021-05-19\n",
"\n",
"<b>[参考]</b><br>\n",
"[れたすさん著 【BTC自動取引bot】HEIKIN_ORACLE_ver2.0](https://note.com/letasun/n/n9366d3055824)\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": "y_9-OuvGNvWW"
},
"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": "9qvSZ0v3zeks"
},
"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": "mMMxBUEPzbEr"
},
"source": [
"from google.colab import files\n",
"files.upload()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "2avmQKiAw5Ps"
},
"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": "RzalryYnwxue"
},
"source": [
"# 共通\n",
"!pip install numpy\n",
"!pip install pandas\n",
"!pip install datetime\n",
"!pip install pytz\n",
"!pip install numba\n",
"!pip install sortedcontainers\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": "2NS_ZXf9N244"
},
"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": "STbU1X9uYkFy"
},
"source": [
"## 2-1. バックテスト期間の設定\n",
"2020年の年初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": "LT5IYePqzY_7",
"outputId": "07d0e74e-fd9c-4cac-9026-5cf84a403473",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"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 = '2020/01/01 09:00:00'\n",
"TEST_END = '2021/05/19 09:00:00'\n",
"\n",
"# テスト期間の日付文字列をUnix Timeに変換\n",
"start_ut = int(du.Time(TEST_START, tz='JST').unixtime())\n",
"end_ut = int(du.Time(TEST_END, tz='JST').unixtime())\n",
"print(f'start:{TEST_START}({start_ut}) - end:{TEST_END}({end_ut})')"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"start:2020/01/01 09:00:00(1577836800) - end:2021/05/19 09:00:00(1621382400)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ClYxRtnRZsX_"
},
"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1h 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",
"* 1h OHLCVを取得 (tickデータ df_ohlcv_1h)\n",
"* 平均足を算出するため、1時間→日足にダウンサンプリング (df_ohlcv_1d)\n",
"* 日足OHLCVから平均足を算出"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oMtkc78OMjnI"
},
"source": [
"### BitMEX OHLCV 1時間足取得"
]
},
{
"cell_type": "code",
"metadata": {
"id": "rWmpYDCj10IJ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 453
},
"outputId": "597d6a49-16af-4797-d688-7d70d22bac91"
},
"source": [
"# 平均足は過去足の値に依存するため、テスト期間の平均足を正しく計算できるように余分(180日分)に取得\n",
"sec_180days = 60 * 60 * 24 * 180 # 180日分の秒数 (60秒 * 60分 * 24時間 * 180日)\n",
"\n",
"# DataUtilityを使用してBitMEX OHLCV(1h)を取得\n",
"df_ohlcv_1h = du.Tool.get_ohlcv_from_bitmex(\n",
" start_ut = start_ut - sec_180days, # 取得開始(UnixTime)\n",
" end_ut = end_ut, # 取得終了(UnixTime)\n",
" period = 60, # 時間足期間(分)\n",
" csv_path = './ohlcv_1h.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_1h) # 確認表示"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
"read csv: 1499212800 - 1621378800 (len=33936)\n",
"get df : 1562284800 - 1621378800 (len=16416)\n"
],
"name": "stdout"
},
{
"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>unixtime</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1562284800</td>\n",
" <td>11143.0</td>\n",
" <td>11209.0</td>\n",
" <td>10960.0</td>\n",
" <td>11158.5</td>\n",
" <td>297185761</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1562288400</td>\n",
" <td>11158.5</td>\n",
" <td>11244.0</td>\n",
" <td>11145.0</td>\n",
" <td>11195.5</td>\n",
" <td>145973058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1562292000</td>\n",
" <td>11195.5</td>\n",
" <td>11249.5</td>\n",
" <td>11086.5</td>\n",
" <td>11103.5</td>\n",
" <td>149431960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1562295600</td>\n",
" <td>11103.5</td>\n",
" <td>11169.5</td>\n",
" <td>11055.5</td>\n",
" <td>11131.5</td>\n",
" <td>132631845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1562299200</td>\n",
" <td>11131.5</td>\n",
" <td>11163.0</td>\n",
" <td>11009.0</td>\n",
" <td>11157.5</td>\n",
" <td>162736890</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",
" </tr>\n",
" <tr>\n",
" <th>16411</th>\n",
" <td>1621364400</td>\n",
" <td>42835.5</td>\n",
" <td>43672.0</td>\n",
" <td>42518.5</td>\n",
" <td>43280.0</td>\n",
" <td>122080046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16412</th>\n",
" <td>1621368000</td>\n",
" <td>43280.0</td>\n",
" <td>43563.0</td>\n",
" <td>42750.5</td>\n",
" <td>43261.5</td>\n",
" <td>99665855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16413</th>\n",
" <td>1621371600</td>\n",
" <td>43261.5</td>\n",
" <td>43518.5</td>\n",
" <td>42798.0</td>\n",
" <td>42988.0</td>\n",
" <td>42508117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16414</th>\n",
" <td>1621375200</td>\n",
" <td>42988.0</td>\n",
" <td>43268.0</td>\n",
" <td>42725.5</td>\n",
" <td>42835.5</td>\n",
" <td>53696624</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16415</th>\n",
" <td>1621378800</td>\n",
" <td>42835.5</td>\n",
" <td>43225.0</td>\n",
" <td>42250.0</td>\n",
" <td>42854.5</td>\n",
" <td>159697745</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>16416 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" unixtime open high low close volume\n",
"0 1562284800 11143.0 11209.0 10960.0 11158.5 297185761\n",
"1 1562288400 11158.5 11244.0 11145.0 11195.5 145973058\n",
"2 1562292000 11195.5 11249.5 11086.5 11103.5 149431960\n",
"3 1562295600 11103.5 11169.5 11055.5 11131.5 132631845\n",
"4 1562299200 11131.5 11163.0 11009.0 11157.5 162736890\n",
"... ... ... ... ... ... ...\n",
"16411 1621364400 42835.5 43672.0 42518.5 43280.0 122080046\n",
"16412 1621368000 43280.0 43563.0 42750.5 43261.5 99665855\n",
"16413 1621371600 43261.5 43518.5 42798.0 42988.0 42508117\n",
"16414 1621375200 42988.0 43268.0 42725.5 42835.5 53696624\n",
"16415 1621378800 42835.5 43225.0 42250.0 42854.5 159697745\n",
"\n",
"[16416 rows x 6 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wc-MZnQ1MspP"
},
"source": [
"### OHLCV 1時間→日足ダウンサンプリング"
]
},
{
"cell_type": "code",
"metadata": {
"id": "lFhANxJt2UPk",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 450
},
"outputId": "4f4b1301-7c71-4442-e5c6-bdea6e584339"
},
"source": [
"# DataUtilityを使用してOHLCVをダウンサンプリング(1h→1d)\n",
"df_ohlcv_1d = du.Tool.downsample_ohlcv(df_ohlcv_1h, period='1D')\n",
"display(df_ohlcv_1d) # 確認表示"
],
"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>unixtime</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</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>2019-07-05 00:00:00+00:00</th>\n",
" <td>1562284800</td>\n",
" <td>11143.0</td>\n",
" <td>11439.0</td>\n",
" <td>10755.0</td>\n",
" <td>10991.0</td>\n",
" <td>4884327016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-06 00:00:00+00:00</th>\n",
" <td>1562371200</td>\n",
" <td>10991.0</td>\n",
" <td>11747.0</td>\n",
" <td>10952.0</td>\n",
" <td>11234.0</td>\n",
" <td>4233836613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-07 00:00:00+00:00</th>\n",
" <td>1562457600</td>\n",
" <td>11234.0</td>\n",
" <td>11650.0</td>\n",
" <td>11070.0</td>\n",
" <td>11475.5</td>\n",
" <td>3337511399</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-08 00:00:00+00:00</th>\n",
" <td>1562544000</td>\n",
" <td>11475.5</td>\n",
" <td>12387.0</td>\n",
" <td>11321.5</td>\n",
" <td>12290.0</td>\n",
" <td>4103506231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-09 00:00:00+00:00</th>\n",
" <td>1562630400</td>\n",
" <td>12290.0</td>\n",
" <td>12840.0</td>\n",
" <td>12105.0</td>\n",
" <td>12563.5</td>\n",
" <td>5990811853</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",
" </tr>\n",
" <tr>\n",
" <th>2021-05-14 00:00:00+00:00</th>\n",
" <td>1620950400</td>\n",
" <td>49700.0</td>\n",
" <td>51533.5</td>\n",
" <td>48887.5</td>\n",
" <td>49888.0</td>\n",
" <td>1541974906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-15 00:00:00+00:00</th>\n",
" <td>1621036800</td>\n",
" <td>49888.0</td>\n",
" <td>50671.5</td>\n",
" <td>46563.5</td>\n",
" <td>46757.0</td>\n",
" <td>2319470758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-16 00:00:00+00:00</th>\n",
" <td>1621123200</td>\n",
" <td>46757.0</td>\n",
" <td>49788.5</td>\n",
" <td>43868.0</td>\n",
" <td>46436.0</td>\n",
" <td>3209427626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-17 00:00:00+00:00</th>\n",
" <td>1621209600</td>\n",
" <td>46436.0</td>\n",
" <td>46653.0</td>\n",
" <td>42134.0</td>\n",
" <td>43582.0</td>\n",
" <td>4181106482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-18 00:00:00+00:00</th>\n",
" <td>1621296000</td>\n",
" <td>43582.0</td>\n",
" <td>45798.5</td>\n",
" <td>42250.0</td>\n",
" <td>42854.5</td>\n",
" <td>2717396004</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>684 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" unixtime open ... close volume\n",
"datetime ... \n",
"2019-07-05 00:00:00+00:00 1562284800 11143.0 ... 10991.0 4884327016\n",
"2019-07-06 00:00:00+00:00 1562371200 10991.0 ... 11234.0 4233836613\n",
"2019-07-07 00:00:00+00:00 1562457600 11234.0 ... 11475.5 3337511399\n",
"2019-07-08 00:00:00+00:00 1562544000 11475.5 ... 12290.0 4103506231\n",
"2019-07-09 00:00:00+00:00 1562630400 12290.0 ... 12563.5 5990811853\n",
"... ... ... ... ... ...\n",
"2021-05-14 00:00:00+00:00 1620950400 49700.0 ... 49888.0 1541974906\n",
"2021-05-15 00:00:00+00:00 1621036800 49888.0 ... 46757.0 2319470758\n",
"2021-05-16 00:00:00+00:00 1621123200 46757.0 ... 46436.0 3209427626\n",
"2021-05-17 00:00:00+00:00 1621209600 46436.0 ... 43582.0 4181106482\n",
"2021-05-18 00:00:00+00:00 1621296000 43582.0 ... 42854.5 2717396004\n",
"\n",
"[684 rows x 6 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rufikfJvniF4"
},
"source": [
"### 指標計算 (平均足)\n",
"日足OHLCVから平均足を算出します。<br>\n",
"(詳しい平均足の解説は[こちら](https://orange2.net/a_method/standard_indicators2/heiken.php)をご確認ください。)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "NvlGQ8wK2yAV",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 450
},
"outputId": "f905de88-fc28-4900-8f4d-95258087b9f0"
},
"source": [
"# 平均足計算\n",
"np_o = df_ohlcv_1d.open.values\n",
"np_h = df_ohlcv_1d.high.values\n",
"np_l = df_ohlcv_1d.low.values\n",
"np_c = df_ohlcv_1d.close.values\n",
"np_ho = np.zeros(df_ohlcv_1d.shape[0], dtype=float)\n",
"np_hc = np.zeros(df_ohlcv_1d.shape[0], dtype=float)\n",
"\n",
"np_ho[0] = df_ohlcv_1d.iloc[0, 1]\n",
"np_hc[0] = df_ohlcv_1d.iloc[0, 4]\n",
"np_ho[1] = (np_o[0] + np_h[0] + np_l[0] + np_c[0]) / 4.0\n",
"np_hc[1] = (np_o[1] + np_h[1] + np_l[1] + np_c[1]) / 4.0\n",
"\n",
"for i in range(2, df_ohlcv_1d.shape[0]):\n",
" np_ho[i] = (np_ho[i-1] + np_hc[i-1]) / 2.0\n",
" np_hc[i] = (np_o[i] + np_h[i] + np_l[i] + np_c[i]) / 4.0\n",
"np_ho = np.round(np_ho, 2)\n",
"np_hc = np.round(np_hc, 2)\n",
"\n",
"# 日足DataFrameに平均足OHLCV列を追加\n",
"df_ohlcv_1d['hopen'] = np_ho # 平均足 open\n",
"df_ohlcv_1d['hhigh'] = [max(ohlc) for ohlc in zip(np_ho, np_h, np_hc)] # 平均足 high\n",
"df_ohlcv_1d['hlow'] = [min(ohlc) for ohlc in zip(np_ho, np_l, np_hc)] # 平均足 low\n",
"df_ohlcv_1d['hclose'] = np_hc # 平均足 close\n",
"display(df_ohlcv_1d) # 確認表示"
],
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
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" unixtime open ... hlow hclose\n",
"datetime ... \n",
"2019-07-05 00:00:00+00:00 1562284800 11143.0 ... 10755.00 10991.00\n",
"2019-07-06 00:00:00+00:00 1562371200 10991.0 ... 10952.00 11231.00\n",
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"... ... ... ... ... ...\n",
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"2021-05-18 00:00:00+00:00 1621296000 43582.0 ... 42250.00 43621.25\n",
"\n",
"[684 rows x 10 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1kna4sNd3AcU",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 450
},
"outputId": "03c09577-b396-41f5-ac44-9ff1c054f880"
},
"source": [
"# 1時間足、日足それぞれのOHLCV DataFrameをテスト期間でfiltering\n",
"df_ohlcv_1h = du.Tool.filter_df(df_ohlcv_1h, 'unixtime', start_ut, end_ut)\n",
"df_ohlcv_1d = du.Tool.filter_df(df_ohlcv_1d, 'unixtime', start_ut, end_ut)\n",
"display(df_ohlcv_1d) # 確認表示\n",
"\n",
"# 日足DataFrameからテストに使用する列をndarrayに変換\n",
"np_ut = df_ohlcv_1d['unixtime'].values\n",
"np_hopen = df_ohlcv_1d['hopen'].values\n",
"np_hclose = df_ohlcv_1d['hclose'].values"
],
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
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" <tr>\n",
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"text/plain": [
" unixtime open ... hlow hclose\n",
"datetime ... \n",
"2020-01-01 00:00:00+00:00 1577836800 7163.0 ... 7153.00 7182.38\n",
"2020-01-02 00:00:00+00:00 1577923200 7179.0 ... 6905.50 7052.38\n",
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"... ... ... ... ... ...\n",
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"\n",
"[504 rows x 10 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "20KNI_OxfZSG"
},
"source": [
"# 3. バックテスト実行"
]
},
{
"cell_type": "code",
"metadata": {
"id": "kQqcfZ3R3dAm",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3a00afe9-fcec-49e5-f878-956e4e8a9d5a"
},
"source": [
"start_balance = 1.0 # テスト開始時の残高(BTC)\n",
"leverage = 1.0 # 残高に対する取引ロットのレバレッジ\n",
"\n",
"# BacktestAssistant生成\n",
"test = BacktestAssistant(\n",
" balance = start_balance,\n",
" trade_leverage = 0, # レバレッジ(0:クロスマージン, 1～:分離マージン)\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$0.5に設定\n",
"\n",
"test.set_funding_rate(rate = 0.01) # Funding Rate設定\n",
"test.set_output_option(disp_progress = True) # 出力オプション設定 (進捗率表示ON)\n",
"test.preset_ltp_ticks_from_ohlcv_df(df_ohlcv_1h) # tickデータ設定 (1時間足OHLCVのopen, high, low, close価格をtickデータに展開)\n",
"\n",
"# バックテスト初期処理 (テスト開始時刻を設定)\n",
"test.initialize(start_time = np_ut[0])\n",
"\n",
"# テストループ (1時間毎)--------------------------------------------------------\n",
"for i, unixtime in enumerate(np_ut):\n",
" if test.time() < unixtime:\n",
" test.sleep_to_time(unixtime) # 指定時刻(unixtime)までtickを進める\n",
" if i < 1:\n",
" continue\n",
"\n",
" heikin = 1 if np_hopen[i-1] < np_hclose[i-1] else -1 # 前日の平均足の陰陽判定 (1:陽線, -1:隠線)\n",
" price = test.get_price()['ltp'] # 現在の価格\n",
" pos = test.get_position() # 現在のポジション取得\n",
" bal = test.get_balance() # 現在の残高\n",
"\n",
" # 取引ロットサイズ($)\n",
" lot = int(price * (bal + pos['unrealised_pnl']) * leverage) # 現在価格 * (残高 + 含み損益) * レバレッジ\n",
"\n",
" if pos['size'] == 0: # ノーポジションで\n",
" if heikin > 0: # 前日の平均足 陽線\n",
" test.market_order('Buy', lot) # 買いエントリー\n",
" elif heikin < 0: # 前日の平均足 隠線\n",
" test.market_order('Sell', lot) # 売りエントリー\n",
"\n",
" elif pos['size'] > 0: # ロングポジションで\n",
" if heikin < 0: # 前日の平均足 隠線\n",
" test.market_order('Sell', abs(pos['size']) + lot) # ドテンショート\n",
"\n",
" elif pos['size'] < 0: # ショートポジションで\n",
" if heikin > 0: # 前日の平均足 陽線\n",
" test.market_order('Buy', abs(pos['size']) + lot) # ドテンロング\n",
"#-------------------------------------------------------------------------------\n",
"\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": 19,
"outputs": [
{
"output_type": "stream",
"text": [
"Now initializing...\n",
"Completed initialize process.\n",
"[Start] time:1577836800.0 ltp:7163.0 balance:1.0 [Fee] taker:0.075 maker:-0.025 fr:0.01\n",
"preset ticks period: 1577836800.0 - 1621381500.0 (35565)\n",
"0% complete. (1 / 35,565)\n",
"10% complete. (3,557 / 35,565)\n",
"20% complete. (7,114 / 35,565)\n",
"30% complete. (10,670 / 35,565)\n",
"40% complete. (14,227 / 35,565)\n",
"50% complete. (17,783 / 35,565)\n",
"60% complete. (21,340 / 35,565)\n",
"70% complete. (24,896 / 35,565)\n",
"80% complete. (28,452 / 35,565)\n",
"90% complete. (32,009 / 35,565)\n",
"Now terminating...\n",
"Save order_history 108 records.(./result/orders.csv)\n",
"Save executions 1617 records.(./result/executions.csv)\n",
"Save position_history 34242 records.(./result/positions.csv)\n",
"Save balance_history 35751 records.(./result/balances.csv)\n",
"\n",
"[Result]\n",
" [Time ] 2020/01/01 09:00:00 - 2021/05/18 09:00:00\n",
" [Balance ] 1.0 -> 2.3541 (+135.41%)\n",
" [Orders ] Total:108 (Open:0 Executed:108 Canceled:0)\n",
" [Position] Max Long:112,289.0 Max Short:-112,385.0 Min Margin:6734.18%\n",
"\n",
"[Execution statistics] PF:1.55\n",
" [Total ] Count:108(Size:6,529,970) PnL:+1.5959 Avr:+0.0148 Price Range:+46,770.0\n",
" [Profit ] Count:38(35.51%) Sum:+4.5093 Avr:+0.1187 Max:+0.5192 MaxLen:4(+0.1552)\n",
" [Loss ] Count:69(64.49%) Sum:-2.9135 Avr:-0.0422 Max:-0.1344 MaxLen:8(-0.2914)\n",
" [Fee ] Trade:0.2059 Funding:0.0359\n",
" [Max risk]\n",
" Drawdown :-36.07%(-0.5552) 2020/09/03 09:00:00\n",
" Unrealised loss:-13.99%(-0.2154) 2021/01/16 01:30:00\n",
"\n",
"[Fiat balance]\n",
" [Start ] 7,163.0 (Balance:1.0 Price:7163.0)\n",
" [End ] 102,596.4 (Balance:2.3541 Price:43582.0)\n",
" [Result] 95,433.4 (+1,332.31%)\n",
"\n",
"[Fiat execution statistics] PF:2.66\n",
" [Total ] Count:108(Size:0) PnL:+95,424 Avr:+884.0 Price Range:+0\n",
" [Profit ] Count:34(32.08%) Sum:+152,827 Avr:+4,495.0 Max:+50,532 MaxLen:3(+2,436)\n",
" [Loss ] Count:72(67.92%) Sum:-57,403 Avr:-797.0 Max:-12,819 MaxLen:16(-3,861)\n",
" [Fee ] Trade:0 Funding:0\n",
" [Max risk]\n",
" Drawdown :-38.98%(-4,181) 2020/03/09 09:00:00\n",
" Unrealised loss:-13.99%(-7,398) 2021/01/16 01:30:00\n",
"\n",
"Completed terminate process.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bSfpX3JyBWv4"
},
"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。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JG1XVyWfE9CX"
},
"source": [
"### 4-1. 注文履歴 (orders.csv)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "1NraYu2v_8eV",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
},
"outputId": "c5ed69b6-c452-459d-e6b5-19bfcc39e389"
},
"source": [
"df_orders = pd.read_csv('./result/orders.csv')\n",
"display(df_orders)"
],
"execution_count": 20,
"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>1577923200</td>\n",
" <td>1</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>7179</td>\n",
" <td>7178.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1578096000</td>\n",
" <td>2</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>14358</td>\n",
" <td>7335.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1578614400</td>\n",
" <td>3</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>15263</td>\n",
" <td>7810.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1578700800</td>\n",
" <td>4</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>16158</td>\n",
" <td>8204.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1579478400</td>\n",
" <td>5</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>17089</td>\n",
" <td>8700.0</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",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>1619481600</td>\n",
" <td>104</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>216496</td>\n",
" <td>54025.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>1620172800</td>\n",
" <td>105</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>213214</td>\n",
" <td>53262.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>1620345600</td>\n",
" <td>106</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>209637</td>\n",
" <td>56417.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>1620691200</td>\n",
" <td>107</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>207376</td>\n",
" <td>55912.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>1621296000</td>\n",
" <td>108</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>102612</td>\n",
" <td>43582.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>108 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp id status type side size price trigger_price\n",
"0 1577923200 1 Filled Market Sell 7179 7178.5 0.0\n",
"1 1578096000 2 Filled Market Buy 14358 7335.5 0.0\n",
"2 1578614400 3 Filled Market Sell 15263 7810.0 0.0\n",
"3 1578700800 4 Filled Market Buy 16158 8204.5 0.0\n",
"4 1579478400 5 Filled Market Sell 17089 8700.0 0.0\n",
".. ... ... ... ... ... ... ... ...\n",
"103 1619481600 104 Filled Market Buy 216496 54025.5 0.0\n",
"104 1620172800 105 Filled Market Sell 213214 53262.0 0.0\n",
"105 1620345600 106 Filled Market Buy 209637 56417.5 0.0\n",
"106 1620691200 107 Filled Market Sell 207376 55912.0 0.0\n",
"107 1621296000 108 Filled Market Buy 102612 43582.5 0.0\n",
"\n",
"[108 rows x 8 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_m9FwRKRQlSQ"
},
"source": [
"### 4-2. 約定履歴 (executions.csv)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uZ2L11cqANvr",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
},
"outputId": "d50e21a8-5f5a-499f-96bd-56627a4c4f71"
},
"source": [
"df_executions = pd.read_csv('./result/executions.csv')\n",
"display(df_executions)"
],
"execution_count": 21,
"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>1.577923e+09</td>\n",
" <td>1</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>7179</td>\n",
" <td>7178.5</td>\n",
" <td>-1.000070</td>\n",
" <td>0.000750</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.577924e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Sell</td>\n",
" <td>7179</td>\n",
" <td>7183.0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000100</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.577925e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Sell</td>\n",
" <td>7179</td>\n",
" <td>7164.0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000100</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.577926e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Sell</td>\n",
" <td>7179</td>\n",
" <td>7183.0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000100</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.577929e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Sell</td>\n",
" <td>7179</td>\n",
" <td>7161.5</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000100</td>\n",
" <td>0.00000</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>1612</th>\n",
" <td>1.621185e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Sell</td>\n",
" <td>102612</td>\n",
" <td>47450.0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000216</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1613</th>\n",
" <td>1.621214e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Sell</td>\n",
" <td>102612</td>\n",
" <td>45610.5</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000225</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1614</th>\n",
" <td>1.621243e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Sell</td>\n",
" <td>102612</td>\n",
" <td>45527.0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000225</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1615</th>\n",
" <td>1.621272e+09</td>\n",
" <td>0</td>\n",
" <td>Funding</td>\n",
" <td>Sell</td>\n",
" <td>102612</td>\n",
" <td>43119.5</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000238</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1616</th>\n",
" <td>1.621296e+09</td>\n",
" <td>108</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>102612</td>\n",
" <td>43582.5</td>\n",
" <td>2.354431</td>\n",
" <td>0.001766</td>\n",
" <td>0.51919</td>\n",
" <td>12329.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1617 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp id type ... fee pnl price_range\n",
"0 1.577923e+09 1 Market ... 0.000750 0.00000 0.0\n",
"1 1.577924e+09 0 Funding ... -0.000100 0.00000 0.0\n",
"2 1.577925e+09 0 Funding ... -0.000100 0.00000 0.0\n",
"3 1.577926e+09 0 Funding ... -0.000100 0.00000 0.0\n",
"4 1.577929e+09 0 Funding ... -0.000100 0.00000 0.0\n",
"... ... ... ... ... ... ... ...\n",
"1612 1.621185e+09 0 Funding ... -0.000216 0.00000 0.0\n",
"1613 1.621214e+09 0 Funding ... -0.000225 0.00000 0.0\n",
"1614 1.621243e+09 0 Funding ... -0.000225 0.00000 0.0\n",
"1615 1.621272e+09 0 Funding ... -0.000238 0.00000 0.0\n",
"1616 1.621296e+09 108 Market ... 0.001766 0.51919 12329.5\n",
"\n",
"[1617 rows x 10 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mf66IwavQtT3"
},
"source": [
"### 4-3. 建玉履歴 (positions.csv)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "DD6Ecjf3Apwr",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
},
"outputId": "06ad3517-e261-48cf-bf74-ac3c67873607"
},
"source": [
"df_positions = pd.read_csv('./result/positions.csv')\n",
"display(df_positions)"
],
"execution_count": 22,
"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>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>1.577837e+09</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</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>1.577923e+09</td>\n",
" <td>-7179.0</td>\n",
" <td>7178.5</td>\n",
" <td>-1.000070</td>\n",
" <td>0.0</td>\n",
" <td>0.010001</td>\n",
" <td>99.99303</td>\n",
" <td>1415961.54</td>\n",
" <td>-0.000070</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.577924e+09</td>\n",
" <td>-7179.0</td>\n",
" <td>7178.5</td>\n",
" <td>-1.000070</td>\n",
" <td>0.0</td>\n",
" <td>0.010001</td>\n",
" <td>99.92798</td>\n",
" <td>1254927.58</td>\n",
" <td>-0.000627</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.577925e+09</td>\n",
" <td>-7179.0</td>\n",
" <td>7178.5</td>\n",
" <td>-1.000070</td>\n",
" <td>0.0</td>\n",
" <td>0.010001</td>\n",
" <td>99.93817</td>\n",
" <td>1277319.35</td>\n",
" <td>0.002024</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.577926e+09</td>\n",
" <td>-7179.0</td>\n",
" <td>7178.5</td>\n",
" <td>-1.000070</td>\n",
" <td>0.0</td>\n",
" <td>0.010001</td>\n",
" <td>99.94801</td>\n",
" <td>1300462.22</td>\n",
" <td>-0.000627</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>34237</th>\n",
" <td>1.621292e+09</td>\n",
" <td>-102612.0</td>\n",
" <td>55912.0</td>\n",
" <td>-1.835241</td>\n",
" <td>0.0</td>\n",
" <td>0.018352</td>\n",
" <td>100.06099</td>\n",
" <td>13294428.14</td>\n",
" <td>0.553223</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34238</th>\n",
" <td>1.621293e+09</td>\n",
" <td>-102612.0</td>\n",
" <td>55912.0</td>\n",
" <td>-1.835241</td>\n",
" <td>0.0</td>\n",
" <td>0.018352</td>\n",
" <td>100.06229</td>\n",
" <td>13294428.14</td>\n",
" <td>0.503254</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34239</th>\n",
" <td>1.621294e+09</td>\n",
" <td>-102612.0</td>\n",
" <td>55912.0</td>\n",
" <td>-1.835241</td>\n",
" <td>0.0</td>\n",
" <td>0.018352</td>\n",
" <td>100.06040</td>\n",
" <td>13294428.14</td>\n",
" <td>0.576690</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34240</th>\n",
" <td>1.621295e+09</td>\n",
" <td>-102612.0</td>\n",
" <td>55912.0</td>\n",
" <td>-1.835241</td>\n",
" <td>0.0</td>\n",
" <td>0.018352</td>\n",
" <td>100.06187</td>\n",
" <td>13294428.14</td>\n",
" <td>0.519217</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34241</th>\n",
" <td>1.621296e+09</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</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>34242 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp size avr_entry ... liq_price unrealised_pnl leverage\n",
"0 1.577837e+09 0.0 0.0 ... 0.00 0.000000 0\n",
"1 1.577923e+09 -7179.0 7178.5 ... 1415961.54 -0.000070 0\n",
"2 1.577924e+09 -7179.0 7178.5 ... 1254927.58 -0.000627 0\n",
"3 1.577925e+09 -7179.0 7178.5 ... 1277319.35 0.002024 0\n",
"4 1.577926e+09 -7179.0 7178.5 ... 1300462.22 -0.000627 0\n",
"... ... ... ... ... ... ... ...\n",
"34237 1.621292e+09 -102612.0 55912.0 ... 13294428.14 0.553223 0\n",
"34238 1.621293e+09 -102612.0 55912.0 ... 13294428.14 0.503254 0\n",
"34239 1.621294e+09 -102612.0 55912.0 ... 13294428.14 0.576690 0\n",
"34240 1.621295e+09 -102612.0 55912.0 ... 13294428.14 0.519217 0\n",
"34241 1.621296e+09 0.0 0.0 ... 0.00 0.000000 0\n",
"\n",
"[34242 rows x 10 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BuE6IZSsQxeW"
},
"source": [
"### 4-4. 残高履歴 (balances.csv)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Olp3YKuyBCsN",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
},
"outputId": "055ffdab-2e43-4acd-a915-c3405f2da3ab"
},
"source": [
"df_balances = pd.read_csv('./result/balances.csv')\n",
"display(df_balances)"
],
"execution_count": 23,
"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",
" <th>type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.577837e+09</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7163.0</td>\n",
" <td>Stamp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.577923e+09</td>\n",
" <td>0.999250</td>\n",
" <td>-0.000070</td>\n",
" <td>7179.0</td>\n",
" <td>Trade</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.577924e+09</td>\n",
" <td>0.999350</td>\n",
" <td>-0.000070</td>\n",
" <td>7183.0</td>\n",
" <td>Funding</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.577924e+09</td>\n",
" <td>0.999350</td>\n",
" <td>-0.000627</td>\n",
" <td>7183.0</td>\n",
" <td>Stamp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.577925e+09</td>\n",
" <td>0.999450</td>\n",
" <td>-0.000627</td>\n",
" <td>7164.0</td>\n",
" <td>Funding</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35746</th>\n",
" <td>1.621292e+09</td>\n",
" <td>1.836698</td>\n",
" <td>0.553223</td>\n",
" <td>42961.5</td>\n",
" <td>Stamp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35747</th>\n",
" <td>1.621293e+09</td>\n",
" <td>1.836698</td>\n",
" <td>0.503254</td>\n",
" <td>43879.5</td>\n",
" <td>Stamp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35748</th>\n",
" <td>1.621294e+09</td>\n",
" <td>1.836698</td>\n",
" <td>0.576690</td>\n",
" <td>42543.5</td>\n",
" <td>Stamp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35749</th>\n",
" <td>1.621295e+09</td>\n",
" <td>1.836698</td>\n",
" <td>0.519217</td>\n",
" <td>43582.0</td>\n",
" <td>Stamp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35750</th>\n",
" <td>1.621296e+09</td>\n",
" <td>2.354122</td>\n",
" <td>0.000000</td>\n",
" <td>43582.0</td>\n",
" <td>Trade</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>35751 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp balance unrealised_pnl price type\n",
"0 1.577837e+09 1.000000 0.000000 7163.0 Stamp\n",
"1 1.577923e+09 0.999250 -0.000070 7179.0 Trade\n",
"2 1.577924e+09 0.999350 -0.000070 7183.0 Funding\n",
"3 1.577924e+09 0.999350 -0.000627 7183.0 Stamp\n",
"4 1.577925e+09 0.999450 -0.000627 7164.0 Funding\n",
"... ... ... ... ... ...\n",
"35746 1.621292e+09 1.836698 0.553223 42961.5 Stamp\n",
"35747 1.621293e+09 1.836698 0.503254 43879.5 Stamp\n",
"35748 1.621294e+09 1.836698 0.576690 42543.5 Stamp\n",
"35749 1.621295e+09 1.836698 0.519217 43582.0 Stamp\n",
"35750 1.621296e+09 2.354122 0.000000 43582.0 Trade\n",
"\n",
"[35751 rows x 5 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9QfrSGDB-ykd"
},
"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": "MCDYfTdM4BTd"
},
"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_1d)\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=20.0, mark='^', name='Buy')\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=20.0, mark='v', name='Sell')\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=20.0, mark='D', name='Liquidation')\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# 平均足サブチャート(ax:1)\n",
"#-------------------------------------------------------------------------------\n",
"cc.add_subchart(ax=1, label=\"USD\", grid=True)\n",
"\n",
"# 平均足設定\n",
"df_heikin = df_ohlcv_1d[['unixtime', 'hopen', 'hhigh', 'hlow', 'hclose', 'volume']].copy()\n",
"df_heikin.rename(columns={'hopen': 'open','hhigh': 'high','hlow': 'low','hclose': 'close'}, inplace=True)\n",
"cc.set_ohlcv_df(df_heikin, ax=1, vol_bar=False)\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# ポジション推移サブチャート(ax:2)\n",
"#-------------------------------------------------------------------------------\n",
"cc.add_subchart(ax=2, 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=2, 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:3)\n",
"#-------------------------------------------------------------------------------\n",
"cc.add_subchart(ax=3, label='PnL', grid=True)\n",
"\n",
"# 損益設定\n",
"cc.set_line(dict_chart['pnl']['unixtime'], dict_chart['pnl']['unrealised_pnl'], ax=3, color='green', width=0.8, name='Unrealised')\n",
"cc.set_band(dict_chart['pnl']['unixtime'], dict_chart['pnl']['pnl'], dict_chart['pnl']['zero'], ax=3, 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:4)\n",
"#-------------------------------------------------------------------------------\n",
"cc.add_subchart(ax=4, 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=4, color='green', width=0.8, name='Include unrealised')\n",
"cc.set_band(dict_chart['pnl']['unixtime'], fiat_blc, dict_chart['pnl']['zero'],\n",
" ax=4, 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": 24,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ka8uIr9vR6jr"
},
"source": [
"### チャート画像表示 (matplotlib)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "m2DZLkdB4KZc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 649
},
"outputId": "f7447500-80e0-45b9-b92f-a7dadc61dbf6"
},
"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": 25,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tLbin79-SBOK"
},
"source": [
"### インタラクティブチャート表示 (plotly)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "yqalYwNT4b5s",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 817
},
"outputId": "7c735375-7035-49af-d2f9-f14752de35fa"
},
"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": 26,
"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=\"e242fe84-2e4e-40d6-9305-610457dc4247\" style=\"height:800px; width:1200px;\"></div>\n",
"<script type=\"text/javascript\">\n",
" \n",
" window.PLOTLYENV=window.PLOTLYENV || {};\n",
" \n",
" if (document.getElementById(\"e242fe84-2e4e-40d6-9305-610457dc4247\")) {\n",
" Plotly.newPlot(\n",
" 'e242fe84-2e4e-40d6-9305-610457dc4247',\n",
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