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polars_backtest.ipynb
{
"nbformat": 4,
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"metadata": {
"colab": {
"provenance": [],
"toc_visible": true,
"authorship_tag": "ABX9TyNw5VBD4foz8oG3Jl8jpIs6",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/nagishin/3cc9348347bc3cae5bb86354dea3bba9/polars_backtest.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"---\n",
"# 【DataFrameで高速バックテスト(pandas vs polars)】\n",
"* このnotebookは下記noteで解説されているバックテストをベースにpandasとpolarsのパフォーマンス比較を行っています。\n",
"* ロジックやpandasでの詳細な解説はnoteに記載されているため、本コードを参照する前に一読することをおすすめします。\n",
"\n",
"<b>[参考]</b><br>\n",
"くもすけさん著<br>\n",
"[pandasを用いた超高速バックテスト](https://note.com/kunmosky1/n/nc15678b3dcc2)\n",
"<br><br>\n",
"<b>[注意]</b><br>\n",
"このnotebookはpandasとpolarsのコーディングやパフォーマンス比較を目的としています。<br>\n",
"バックテストのロジック内容や評価方法は一例としてお考えください。\n",
"\n",
"---"
],
"metadata": {
"id": "1BEKAMEhOzCW"
}
},
{
"cell_type": "markdown",
"source": [
"## 【polarsインストール】"
],
"metadata": {
"id": "GzpivxK2Finy"
}
},
{
"cell_type": "code",
"source": [
"!pip install polars"
],
"metadata": {
"id": "nWC3SbV2XPMu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### 各種バージョン"
],
"metadata": {
"id": "6qw9YYrM3n5i"
}
},
{
"cell_type": "code",
"source": [
"import os\n",
"import time\n",
"from datetime import datetime\n",
"import pandas as pd\n",
"import polars as pl\n",
"\n",
"!python --version\n",
"print('pandas', pd.__version__)\n",
"print('polars', pl.__version__)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TKDre2BA1DCc",
"outputId": "6c1f53fc-53a5-4802-dd57-5b11accce477"
},
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Python 3.8.10\n",
"pandas 1.3.5\n",
"polars 0.16.6\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## 【初期設定】\n",
"\n",
"<b>ロジック</b><br>\n",
"* 長期SMAが上がっていて、短期SMAが長期SMAを超えた時にロング\n",
"* 長期SMAが下がっていて、短期SMAが長期SMAを下抜けした時にショート\n",
"* 同方向のエントリーが連続した場合はポジションを積み増し(ピラミッディング)\n",
"* 逆方向のエントリーでドテン"
],
"metadata": {
"id": "a74yTXcRFojC"
}
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"id": "ott94-XvRyj8"
},
"outputs": [],
"source": [
"SYMBOL = 'BTCUSDT' # 通貨ペア\n",
"PERIOD = '5m' # 時間足\n",
"YEAR = 2022 # 対象年\n",
"\n",
"SHORT_SMA = 20 # 短期 移動平均期間\n",
"LONG_SMA = 47 # 長期 移動平均期間\n",
"PYRAMIDING = 4 # ピラミッディング回数\n",
"COMMISION = 0.075 # taker手数料%\n",
"\n",
"CSV_PATH = f'./ohlcv_{SYMBOL}_{YEAR}_{PERIOD}.csv' # CSVパス(取得したOHLCV保存)\n",
"\n",
"NANOSECONDS = 1_000_000_000 # nano秒単位"
]
},
{
"cell_type": "markdown",
"source": [
"## 【OHLCV取得 & CSV保存】\n",
"<b>対象データ</b>\n",
"* OHLCVデータ ： Binance 5m OHLCV\n",
"\n",
"<b>対象期間</b>\n",
"* 2022-01-01 ～ 2022-12-31"
],
"metadata": {
"id": "0gqut-tpF7LB"
}
},
{
"cell_type": "code",
"source": [
"# ファイル存在チェック\n",
"df_ohlcv_base = None\n",
"if os.path.isfile(CSV_PATH):\n",
" # ローカルCSV読み込み\n",
" df_ohlcv_base = pd.read_csv(CSV_PATH)\n",
"else:\n",
" # csvをDataFrameに読み込み\n",
" for month in range(1,13):\n",
" df = pd.read_csv(f'https://data.binance.vision/data/spot/monthly/klines/{SYMBOL}/{PERIOD}/{SYMBOL}-{PERIOD}-{YEAR}-{month:02}.zip',\n",
" usecols=[0,1,2,3,4,5],\n",
" names=['timestamp', 'open', 'high', 'low', 'close', 'volume'],\n",
" dtype={'timestamp':'int', 'open':'float', 'high':'float', 'low':'float', 'close':'float', 'volume':'float'})\n",
" if df is not None and len(df.index) > 0:\n",
" if df_ohlcv_base is None:\n",
" df_ohlcv_base = df\n",
" else:\n",
" df_ohlcv_base = pd.concat([df_ohlcv_base, df])\n",
" # UnixTime(timestamp)順ソート\n",
" df_ohlcv_base.sort_values(by='timestamp', ascending=True, inplace=True)\n",
" df_ohlcv_base.reset_index(drop=True, inplace=True)\n",
" df_ohlcv_base['timestamp'] = (df_ohlcv_base['timestamp'] / 1000).astype(int) # ms->s\n",
" # ローカルに保存\n",
" df_ohlcv_base.to_csv(CSV_PATH, header=True, index=False)\n",
"\n",
"display(df_ohlcv_base)"
],
"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "8i83tLdXFvva",
"outputId": "2d523e8d-7146-4d32-c1e0-dbedfef6f8c4"
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"execution_count": 26,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" timestamp open high low close volume\n",
"0 1640995200 46216.93 46391.49 46208.37 46321.34 185.67558\n",
"1 1640995500 46321.34 46527.26 46280.00 46371.11 123.43577\n",
"2 1640995800 46369.79 46394.00 46276.22 46332.51 77.54574\n",
"3 1640996100 46332.52 46332.52 46236.27 46293.90 101.14315\n",
"4 1640996400 46295.42 46421.27 46286.25 46395.53 135.32479\n",
"... ... ... ... ... ... ...\n",
"105115 1672529700 16538.17 16551.24 16536.84 16545.68 501.06148\n",
"105116 1672530000 16545.68 16550.26 16543.45 16546.13 277.70571\n",
"105117 1672530300 16546.14 16550.55 16536.62 16539.05 292.41232\n",
"105118 1672530600 16538.39 16541.26 16534.92 16540.42 180.18579\n",
"105119 1672530900 16540.42 16544.47 16535.05 16542.40 227.06684\n",
"\n",
"[105120 rows x 6 columns]"
],
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" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <thead>\n",
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" <th></th>\n",
" <th>timestamp</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>0</th>\n",
" <td>1640995200</td>\n",
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" <td>46321.34</td>\n",
" <td>46527.26</td>\n",
" <td>46280.00</td>\n",
" <td>46371.11</td>\n",
" <td>123.43577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1640995800</td>\n",
" <td>46369.79</td>\n",
" <td>46394.00</td>\n",
" <td>46276.22</td>\n",
" <td>46332.51</td>\n",
" <td>77.54574</td>\n",
" </tr>\n",
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" <th>3</th>\n",
" <td>1640996100</td>\n",
" <td>46332.52</td>\n",
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" <td>46293.90</td>\n",
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" <td>46295.42</td>\n",
" <td>46421.27</td>\n",
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" <th>105115</th>\n",
" <td>1672529700</td>\n",
" <td>16538.17</td>\n",
" <td>16551.24</td>\n",
" <td>16536.84</td>\n",
" <td>16545.68</td>\n",
" <td>501.06148</td>\n",
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" <tr>\n",
" <th>105116</th>\n",
" <td>1672530000</td>\n",
" <td>16545.68</td>\n",
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" <td>16543.45</td>\n",
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" <td>277.70571</td>\n",
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" <tr>\n",
" <th>105117</th>\n",
" <td>1672530300</td>\n",
" <td>16546.14</td>\n",
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" <td>292.41232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105118</th>\n",
" <td>1672530600</td>\n",
" <td>16538.39</td>\n",
" <td>16541.26</td>\n",
" <td>16534.92</td>\n",
" <td>16540.42</td>\n",
" <td>180.18579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105119</th>\n",
" <td>1672530900</td>\n",
" <td>16540.42</td>\n",
" <td>16544.47</td>\n",
" <td>16535.05</td>\n",
" <td>16542.40</td>\n",
" <td>227.06684</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>105120 rows × 6 columns</p>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-efc288c4-612a-4708-934a-789eec15a15c')\"\n",
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" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
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"\n",
" .colab-df-convert {\n",
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"\n",
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"\n",
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" document.querySelector('#df-efc288c4-612a-4708-934a-789eec15a15c button.colab-df-convert');\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
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" const element = document.querySelector('#df-efc288c4-612a-4708-934a-789eec15a15c');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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},
"metadata": {}
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]
},
{
"cell_type": "markdown",
"source": [
"## 【バックテスト】"
],
"metadata": {
"id": "pBaWX2fGGS2h"
}
},
{
"cell_type": "markdown",
"source": [
"### pandas"
],
"metadata": {
"id": "N-JKYYucGXUh"
}
},
{
"cell_type": "markdown",
"source": [
"#### CSV読み込み"
],
"metadata": {
"id": "4ag9sF2sJcZN"
}
},
{
"cell_type": "code",
"source": [
"s = time.time()\n",
"\n",
"# csvをDataFrameに読み込み\n",
"df_ohlcv_pd = pd.read_csv(CSV_PATH)\n",
"\n",
"ret = round((time.time() - s) * 1000, 3)\n",
"print(f'[処理時間]: {ret} ms\\n')"
],
"metadata": {
"id": "Z87Xv3pMRJRD",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "58032fe7-bf19-4e67-faa1-43061e9aaba0"
},
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[処理時間]: 103.784 ms\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### バックテスト関数\n",
"このコードの詳細な解説は下記noteを参照してください。<br>\n",
"\n",
"くもすけさん著<br>\n",
"[pandasを用いた超高速バックテスト](https://note.com/kunmosky1/n/nc15678b3dcc2)"
],
"metadata": {
"id": "jLKJFD7vJqlp"
}
},
{
"cell_type": "code",
"source": [
"def backtest_pandas(short_sma:int, long_sma:int, pyramid:int, df_ohlcv:pd.DataFrame=None) -> pd.DataFrame:\n",
" \"\"\"**【バックテスト(pandas)】**\n",
"\n",
" 短期/長期移動平均のゴールデンクロス/デッドクロスでエントリー \n",
" 同方向に注文が重なった場合は指定回数まで積み増し\n",
"\n",
" :param short_sma: 短期 移動平均期間\n",
" :param long_sma: 長期 移動平均期間\n",
" :param pyramid: 積み増し回数\n",
" :param df_ohlcv: OHLCV DataFrame(pandas)\n",
"\n",
" :return: 処理結果 DataFrame\n",
" \"\"\"\n",
" global df_ohlcv_pd\n",
"\n",
" if df_ohlcv is None:\n",
" df = df_ohlcv_pd.copy()\n",
" else:\n",
" df = df_ohlcv.copy()\n",
"\n",
" # UnixTime(timestamp)からdatetime列生成\n",
" df['datetime'] = pd.to_datetime(df['timestamp'], unit='s', utc=True)\n",
" # datetimeをindexに設定\n",
" df.set_index('datetime', inplace=True)\n",
"\n",
" # SMA計算\n",
" df['short_sma'] = df['close'].rolling(short_sma, min_periods=1).mean()\n",
" df['long_sma'] = df['close'].rolling(long_sma, min_periods=1).mean()\n",
"\n",
" # エントリー判定\n",
" def crossover(x, y):\n",
" return ((x - y) > 0) & ((x.shift(1) - y) < 0)\n",
" def crossunder(x, y):\n",
" return ((x - y) < 0) & ((x.shift(1) - y) > 0)\n",
" df['long'] = (df['long_sma'].diff(1) > 0) & crossover(df['long_sma'], df['short_sma'])\n",
" df['short'] = (df['long_sma'].diff(1) < 0) & crossunder(df['long_sma'], df['short_sma'])\n",
"\n",
" # ポジション計算（ドテン＆ピラミッディング）\n",
" df['order'] = 0\n",
" df['order'] = df['order'].where(df['long'] != True, 1)\n",
" df['order'] = df['order'].where(df['short'] != True, -1)\n",
" df['pos'] = df['order'].where(df['order'] != 0,).fillna(method='ffill').fillna(0)\n",
" df['pos'] = df.groupby((df['pos'] * df['pos'].shift(1) < 0).cumsum().fillna(0))['order'].cumsum()\n",
" df['pos'] = df['pos'].where(df['pos'] <= pyramid, pyramid) \n",
" df['pos'] = df['pos'].where(df['pos'] >= -pyramid, -pyramid)\n",
" df['pos'] /= pyramid\n",
"\n",
" # 損益計算\n",
" df['return']= -df['open'].diff(-1)\n",
" df['commision'] = df['open'].shift(-1) * COMMISION / 100 * abs(df['pos'] - df['pos'].shift(1))\n",
" df['profit'] = df['return'] * df['pos'].shift(1) - df['commision']\n",
" df['pnl'] = df['profit'].cumsum()\n",
" df['pnl'] = df['pnl'].fillna(method='ffill')\n",
"\n",
" return df"
],
"metadata": {
"id": "1Fks-_RfHmDK"
},
"execution_count": 28,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"#### バックテスト実行"
],
"metadata": {
"id": "hCeRoPc2J1qz"
}
},
{
"cell_type": "code",
"source": [
"s = time.time()\n",
"\n",
"# バックテスト\n",
"df_result_pd = backtest_pandas(SHORT_SMA, LONG_SMA, PYRAMIDING, df_ohlcv_pd)\n",
"\n",
"ret = round((time.time() - s) * 1000, 3)\n",
"print(f'[処理時間]: {ret} ms\\n')\n",
"\n",
"# 結果表示\n",
"display(df_result_pd)\n",
"# 結果表示(ポジション変化が有ったところだけ)\n",
"#display(df_result_pd[df_result_pd['pos'].diff(1) != 0][['open','long','short','pos','pnl']])"
],
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 707
},
"id": "USKdfuYnS9B-",
"outputId": "2ec28e43-1a89-40e2-be3b-42d1d8c1cd03"
},
"execution_count": 44,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[処理時間]: 76.425 ms\n",
"\n"
]
},
{
"output_type": "display_data",
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" timestamp open high low close \\\n",
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"... ... ... ... ... ... \n",
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"\n",
" short order pos return commision profit \\\n",
"datetime \n",
"2022-01-01 00:00:00+00:00 False 0 0.0 104.41 NaN NaN \n",
"2022-01-01 00:05:00+00:00 False 0 0.0 48.45 0.0 0.000 \n",
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"2022-01-01 00:20:00+00:00 False 0 0.0 100.11 0.0 0.000 \n",
"... ... ... ... ... ... ... \n",
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"2022-12-31 23:50:00+00:00 False 0 0.5 2.03 0.0 1.015 \n",
"2022-12-31 23:55:00+00:00 False 0 0.5 NaN NaN NaN \n",
"\n",
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"2022-01-01 00:10:00+00:00 0.000000 \n",
"2022-01-01 00:15:00+00:00 0.000000 \n",
"2022-01-01 00:20:00+00:00 0.000000 \n",
"... ... \n",
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},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"### polars"
],
"metadata": {
"id": "8adUlBikI7Ia"
}
},
{
"cell_type": "markdown",
"source": [
"#### CSV読み込み"
],
"metadata": {
"id": "kFxI4R6xJ97h"
}
},
{
"cell_type": "code",
"source": [
"s = time.time()\n",
"\n",
"# csvをDataFrameに読み込み\n",
"df_ohlcv_pl = pl.read_csv(CSV_PATH)\n",
"\n",
"ret = round((time.time() - s) * 1000, 3)\n",
"print(f'[処理時間]: {ret} ms\\n')"
],
"metadata": {
"id": "EIhmesRnR5Qj",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "55409498-70c5-4f64-ee49-40ee16215afa"
},
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[処理時間]: 41.912 ms\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### バックテスト関数\n",
"上記のbacktest_pandas関数をpolarsに置き換えたコードになります。"
],
"metadata": {
"id": "A9YmBDxQKEEm"
}
},
{
"cell_type": "code",
"source": [
"def backtest_polars(short_sma:int, long_sma:int, pyramid:int, df_ohlcv:pl.DataFrame=None) -> pl.DataFrame:\n",
" \"\"\"**【バックテスト(polars)】**\n",
"\n",
" 短期/長期移動平均のゴールデンクロス/デッドクロスでエントリー \n",
" 同方向に注文が重なった場合は指定回数まで積み増し\n",
"\n",
" :param short_sma: 短期 移動平均期間\n",
" :param long_sma: 長期 移動平均期間\n",
" :param pyramid: 積み増し回数\n",
" :param df_ohlcv: OHLCV DataFrame(polars)\n",
"\n",
" :return: 処理結果 DataFrame\n",
" \"\"\"\n",
" global df_ohlcv_pl\n",
"\n",
" if df_ohlcv is None:\n",
" df = df_ohlcv_pl.clone()\n",
" else:\n",
" df = df_ohlcv.clone()\n",
"\n",
" # DataFrame生成\n",
" df = (\n",
" df.lazy()\n",
" .with_columns([ # SMA計算\n",
" # datetime\n",
" (pl.col('timestamp') * NANOSECONDS) # timestamp(秒)をnano秒単位に\n",
" .cast(pl.Datetime(time_unit='ns')) # datetime(nano秒)変換\n",
" .dt.replace_time_zone('UTC') # タイムゾーン設定\n",
" .alias('datetime'),\n",
" # short_sma\n",
" pl.col('close').rolling_mean(short_sma, min_periods=1).alias('short_sma'),\n",
" # long_sma\n",
" pl.col('close').rolling_mean(long_sma, min_periods=1).alias('long_sma'),\n",
" ])\n",
" .with_columns([ # エントリー判定\n",
" # long\n",
" ((pl.col('long_sma').diff(n=1, null_behavior='ignore') > 0) &\n",
" (pl.col('long_sma') - pl.col('short_sma') > 0) &\n",
" (pl.col('long_sma').shift(periods=1) - pl.col('short_sma') < 0)).alias('long'),\n",
" # short\n",
" ((pl.col('long_sma').diff(n=1, null_behavior='ignore') < 0) &\n",
" (pl.col('long_sma') - pl.col('short_sma') < 0) &\n",
" (pl.col('long_sma').shift(periods=1) - pl.col('short_sma') > 0)).alias('short'),\n",
" ])\n",
" .with_columns([ # 注文\n",
" # order\n",
" pl.when(pl.col('long') == True).then(1)\n",
" .when(pl.col('short') == True).then(-1)\n",
" .otherwise(0).alias('order'),\n",
" ])\n",
" .with_columns([ # ポジション計算（ドテン＆ピラミッディング）\n",
" # pos\n",
" pl.when(pl.col('order') != 0).then(pl.col('order'))\n",
" .otherwise(None).fill_null(strategy='forward').fill_null(0).alias('pos'),\n",
" ])\n",
" .with_columns([ # ポジション方向がドテンする単位でグループNo.を設定\n",
" # doten_group\n",
" (pl.col('pos') * pl.col('pos').shift(periods=1) < 0).cumsum().fill_null(0).alias('doten_group'),\n",
" ])\n",
" .collect()\n",
" )\n",
"\n",
" # ドテンするグループ内でポジションを積み増し\n",
" df = (\n",
" df.lazy()\n",
" .with_columns(\n",
" # pos\n",
" df.groupby('doten_group')\n",
" .agg(pl.col('order').cumsum())\n",
" .explode('order')\n",
" .sort(by='doten_group')\n",
" .select(pl.col('order').alias('pos'))\n",
" )\n",
" .with_columns([ # ピラミッディング回数でポジションサイズ調整\n",
" # pos\n",
" pl.when(pl.col('pos') > pyramid).then(1)\n",
" .when(pl.col('pos') < -pyramid).then(-1)\n",
" .otherwise(pl.col('pos') / pyramid).alias('pos'),\n",
" ])\n",
" .with_columns([ # 損益計算\n",
" # return\n",
" (pl.col('open').shift(periods=-1) - pl.col('open')).alias('return'),\n",
" # commision\n",
" (pl.col('open').shift(periods=-1) * COMMISION / 100 * abs(pl.col('pos') - pl.col('pos').shift(periods=1))).alias('commision'),\n",
" ])\n",
" .with_columns([\n",
" # profit\n",
" (pl.col('return') * pl.col('pos').shift(periods=1) - pl.col('commision')).alias('profit'),\n",
" ])\n",
" .with_columns([\n",
" # pnl\n",
" (pl.col('profit').cumsum().fill_null(strategy='forward').fill_null(0)).alias('pnl'),\n",
" ])\n",
" .collect()\n",
" )\n",
"\n",
" return df"
],
"metadata": {
"id": "jTYX40cqH-kh"
},
"execution_count": 31,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"#### バックテスト実行"
],
"metadata": {
"id": "ApdiP5uAKMmv"
}
},
{
"cell_type": "code",
"source": [
"s = time.time()\n",
"\n",
"# バックテスト\n",
"df_result_pl = backtest_polars(SHORT_SMA, LONG_SMA, PYRAMIDING, df_ohlcv_pl)\n",
"\n",
"ret = round((time.time() - s) * 1000, 3)\n",
"print(f'[処理時間]: {ret} ms\\n')\n",
"\n",
"# 結果表示\n",
"display(df_result_pl)\n",
"# 結果表示(ポジション変化が有ったところだけ)\n",
"#display(df_result_pl.filter(pl.col('pos').diff(n=1, null_behavior='ignore') != 0).select(pl.col(['open','long','short','pos','pnl'])))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "Lo0sTQSdblY2",
"outputId": "0f336c5b-87cb-4f23-e61b-691a9fd3bc7b"
},
"execution_count": 42,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[処理時間]: 50.098 ms\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
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"│ 1672530000 ┆ 16545.68 ┆ 16550.26 ┆ 16543.45 ┆ ... ┆ 0.46 ┆ 0.0 ┆ 0.23 ┆ 7149.158371 │\n",
"│ 1672530300 ┆ 16546.14 ┆ 16550.55 ┆ 16536.62 ┆ ... ┆ -7.75 ┆ 0.0 ┆ -3.875 ┆ 7145.283371 │\n",
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"│ 1672530900 ┆ 16540.42 ┆ 16544.47 ┆ 16535.05 ┆ ... ┆ null ┆ null ┆ null ┆ 7146.298371 │\n",
"└────────────┴──────────┴──────────┴──────────┴─────┴────────┴───────────┴────────┴─────────────┘"
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},
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]
},
{
"cell_type": "markdown",
"source": [
"#### (調整版)バックテスト関数\n",
"上記のbacktest_polars関数をベースにピラミッディングのポジション計算にgroupbyを使用せず、損益計算までワンライナーで処理するように調整したコードになります。"
],
"metadata": {
"id": "_hrrqn9YKi2S"
}
},
{
"cell_type": "code",
"source": [
"def backtest_polars2(short_sma:int, long_sma:int, pyramid:int, df_ohlcv:pl.DataFrame=None) -> pl.DataFrame:\n",
" \"\"\"**【バックテスト(polars)】**\n",
"\n",
" 短期/長期移動平均のゴールデンクロス/デッドクロスでエントリー \n",
" 同方向に注文が重なった場合は指定回数まで積み増し\n",
"\n",
" :param short_sma: 短期 移動平均期間\n",
" :param long_sma: 長期 移動平均期間\n",
" :param pyramid: 積み増し回数\n",
" :param df_ohlcv: OHLCV DataFrame(polars)\n",
"\n",
" :return: 処理結果 DataFrame\n",
" \"\"\"\n",
" global df_ohlcv_pl\n",
"\n",
" if df_ohlcv is None:\n",
" df = df_ohlcv_pl.clone()\n",
" else:\n",
" df = df_ohlcv.clone()\n",
"\n",
" # DataFrame生成\n",
" df = (\n",
" df.lazy()\n",
" .with_columns([ # SMA計算\n",
" # datetime\n",
" (pl.col('timestamp') * NANOSECONDS) # timestamp(秒)をnano秒単位に\n",
" .cast(pl.Datetime(time_unit='ns')) # datetime(nano秒)変換\n",
" .dt.replace_time_zone('UTC') # タイムゾーン設定\n",
" .alias('datetime'),\n",
" # short_sma\n",
" pl.col('close').rolling_mean(short_sma, min_periods=1).alias('short_sma'),\n",
" # long_sma\n",
" pl.col('close').rolling_mean(long_sma, min_periods=1).alias('long_sma'),\n",
" ])\n",
" .with_columns([ # エントリー判定\n",
" # long(1)\n",
" pl.when((pl.col('long_sma').diff(n=1, null_behavior='ignore') > 0) &\n",
" (pl.col('long_sma') - pl.col('short_sma') > 0) &\n",
" (pl.col('long_sma').shift(periods=1) - pl.col('short_sma') < 0)).then(1)\n",
" # short(-1)\n",
" .when((pl.col('long_sma').diff(n=1, null_behavior='ignore') < 0) &\n",
" (pl.col('long_sma') - pl.col('short_sma') < 0) &\n",
" (pl.col('long_sma').shift(periods=1) - pl.col('short_sma') > 0)).then(-1)\n",
" # none(0)\n",
" .otherwise(0).alias('order'),\n",
" ])\n",
" .with_columns([ # ポジション方向(ドテン判定のため)\n",
" # side\n",
" pl.when(pl.col('order') != 0).then(pl.col('order'))\n",
" .otherwise(None).fill_null(strategy='forward').fill_null(0).alias('side'),\n",
" ])\n",
" .with_columns([ # ポジション方向がドテンする単位でグループNo.を設定\n",
" # doten_group\n",
" (pl.col('side') * pl.col('side').shift(periods=1) < 0).cumsum().fill_null(0).alias('doten_group'),\n",
" # order_cumsum\n",
" pl.col('order').cumsum().alias('order_cumsum'), # ポジションサイズ計算のため、order累積和\n",
" ])\n",
" .with_columns([ # ドテン時にorder累積和をリセットする調整サイズ\n",
" # doten_reset\n",
" pl.when(pl.col('doten_group').diff(n=1, null_behavior='ignore') != 0).then(pl.col('order_cumsum').shift(periods=1) * -1)\n",
" .otherwise(None).fill_null(strategy='forward').fill_null(0).alias('doten_reset'),\n",
" ])\n",
" .with_columns([ # ポジション計算（order累積和＆調整サイズ）\n",
" # pos\n",
" (pl.col('order_cumsum') + pl.col('doten_reset')).alias('pos'),\n",
" ])\n",
" .with_columns([ # ピラミッディング回数でポジションサイズ調整\n",
" # pos\n",
" pl.when(pl.col('pos') > pyramid).then(1)\n",
" .when(pl.col('pos') < -pyramid).then(-1)\n",
" .otherwise(pl.col('pos') / pyramid).alias('pos'),\n",
" ])\n",
" .with_columns([ # 損益計算\n",
" # return\n",
" (pl.col('open').shift(periods=-1) - pl.col('open')).alias('return'),\n",
" # commision\n",
" (pl.col('open').shift(periods=-1) * COMMISION / 100 * abs(pl.col('pos') - pl.col('pos').shift(periods=1))).fill_null(0).alias('commision'),\n",
" ])\n",
" .with_columns([\n",
" # profit\n",
" (pl.col('return') * pl.col('pos').shift(periods=1) - pl.col('commision')).alias('profit'),\n",
" ])\n",
" .with_columns([\n",
" # pnl\n",
" (pl.col('profit').cumsum().fill_null(strategy='forward').fill_null(0)).alias('pnl'),\n",
" ])\n",
" .collect()\n",
" )\n",
"\n",
" return df"
],
"metadata": {
"id": "JJRzi7YGPM8a"
},
"execution_count": 33,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"#### (調整版)バックテスト実行"
],
"metadata": {
"id": "jmbjOdA4LxNE"
}
},
{
"cell_type": "code",
"source": [
"s = time.time()\n",
"\n",
"# バックテスト\n",
"df_result_pl2 = backtest_polars2(SHORT_SMA, LONG_SMA, PYRAMIDING, df_ohlcv_pl)\n",
"\n",
"ret = round((time.time() - s) * 1000, 3)\n",
"print(f'[処理時間]: {ret} ms\\n')\n",
"\n",
"# 結果表示\n",
"display(df_result_pl2.select(pl.col(['datetime','open','high','low','close','volume','short_sma','long_sma','order','pos','return','commision','profit','pnl'])))\n",
"# 結果表示(ポジション変化が有ったところだけ)\n",
"#display(df_result_pl2.filter(pl.col('pos').diff(n=1, null_behavior='ignore') != 0).select(pl.col(['open','long','short','pos','pnl'])))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 917
},
"id": "Dd4LMw3sUbrL",
"outputId": "4a0826ff-5268-4a26-cf29-b7854195b82a"
},
"execution_count": 39,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[処理時間]: 38.621 ms\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
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"│ 00:10:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
"│ 2022-01-01 ┆ 46332.52 ┆ 46332.52 ┆ 46236.27 ┆ ... ┆ -37.1 ┆ 0.0 ┆ -0.0 ┆ 0.0 │\n",
"│ 00:15:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
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"│ 2022-12-31 ┆ 16545.68 ┆ 16550.26 ┆ 16543.45 ┆ ... ┆ 0.46 ┆ 0.0 ┆ 0.23 ┆ 7149.158371 │\n",
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"│ 23:45:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
"│ 2022-12-31 ┆ 16538.39 ┆ 16541.26 ┆ 16534.92 ┆ ... ┆ 2.03 ┆ 0.0 ┆ 1.015 ┆ 7146.298371 │\n",
"│ 23:50:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
"│ 2022-12-31 ┆ 16540.42 ┆ 16544.47 ┆ 16535.05 ┆ ... ┆ null ┆ 0.0 ┆ null ┆ 7146.298371 │\n",
"│ 23:55:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
"└───────────────┴──────────┴──────────┴──────────┴─────┴────────┴───────────┴────────┴─────────────┘"
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]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## 【一括バックテスト】"
],
"metadata": {
"id": "MkVGGcb3LP5b"
}
},
{
"cell_type": "markdown",
"source": [
"### パラメータ組み合わせ設定\n",
"短期/長期移動平均とピラミッディング回数を定義してパラメータの全組み合わせを作成"
],
"metadata": {
"id": "a91uES3OLZYE"
}
},
{
"cell_type": "code",
"source": [
"import itertools\n",
"\n",
"#-----------------------------------------------------------\n",
"# [パラメータリスト]\n",
"SMA_LIST = [3,5,6,10,12,15,20,24,30,36,48,60,72,96,100,120,144,200,240,288] # 移動平均期間(short/long)\n",
"PYRAMID_LIST = [1,2,3,4,5] # ピラミッディング(積み増し)回数\n",
"#-----------------------------------------------------------\n",
"\n",
"# パラメータ全組み合わせ(itertools.product : 直積)\n",
"df_params_pd = pd.DataFrame(list(itertools.product(SMA_LIST, SMA_LIST, PYRAMID_LIST,)),\n",
" columns=['short_sma', 'long_sma', 'pyramid'])\n",
"# short_sma < long_smaの組み合わせのみに絞り込み\n",
"df_params_pd = df_params_pd[(df_params_pd['short_sma'] < df_params_pd['long_sma'])]\n",
"# indexリセット\n",
"df_params_pd.reset_index(drop=True, inplace=True)\n",
"\n",
"# polars DataFrameコピー\n",
"df_params_pl = pl.from_pandas(df_params_pd)\n",
"df_params_pl2 = df_params_pl.clone()\n",
"\n",
"# パラメータ組み合わせ表示\n",
"display(df_params_pd)"
],
"metadata": {
"id": "hrmj3h-gd-Uu",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"outputId": "181c73dd-5b80-48d2-b1ce-0891d1f42444"
},
"execution_count": 35,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" short_sma long_sma pyramid\n",
"0 3 5 1\n",
"1 3 5 2\n",
"2 3 5 3\n",
"3 3 5 4\n",
"4 3 5 5\n",
".. ... ... ...\n",
"945 240 288 1\n",
"946 240 288 2\n",
"947 240 288 3\n",
"948 240 288 4\n",
"949 240 288 5\n",
"\n",
"[950 rows x 3 columns]"
],
"text/html": [
"\n",
" <div id=\"df-e36a27a8-637d-4b5b-bb6a-8f4fab0e44f4\">\n",
" <div class=\"colab-df-container\">\n",
" <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>short_sma</th>\n",
" <th>long_sma</th>\n",
" <th>pyramid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>5</td>\n",
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" <tr>\n",
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" <th>945</th>\n",
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" <td>1</td>\n",
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" <tr>\n",
" <th>946</th>\n",
" <td>240</td>\n",
" <td>288</td>\n",
" <td>2</td>\n",
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" <td>240</td>\n",
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" <th>948</th>\n",
" <td>240</td>\n",
" <td>288</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>949</th>\n",
" <td>240</td>\n",
" <td>288</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>950 rows × 3 columns</p>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e36a27a8-637d-4b5b-bb6a-8f4fab0e44f4')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
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" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-e36a27a8-637d-4b5b-bb6a-8f4fab0e44f4 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-e36a27a8-637d-4b5b-bb6a-8f4fab0e44f4');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## 【apply一括処理】\n",
"パラメータ組み合わせDataFrameにバックテスト関数を適用して総当りバックテストを行う"
],
"metadata": {
"id": "w50NIBOaMBLT"
}
},
{
"cell_type": "markdown",
"source": [
"### pandas"
],
"metadata": {
"id": "UCDNmQ2oMKgj"
}
},
{
"cell_type": "markdown",
"source": [
"#### 一括バックテスト実行"
],
"metadata": {
"id": "K-wHU70nORuh"
}
},
{
"cell_type": "code",
"source": [
"s = time.time()\n",
"\n",
"# パラメータ組み合わせにストラテジーを適用\n",
"result = df_params_pd.apply(lambda c: backtest_pandas(c['short_sma'], c['long_sma'], c['pyramid'],)['pnl'].values[-1], axis=1)\n",
"df_params_pd['result'] = result\n",
"df_params_pd = df_params_pd.sort_values('result') # result昇順ソート\n",
"\n",
"# テスト結果表示\n",
"display(df_params_pd)\n",
"\n",
"ret = round((time.time() - s) * 1000, 3)\n",
"print(f'[処理時間]: {ret} ms\\n')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 458
},
"id": "CvzrpVlzvPd7",
"outputId": "5a5ac0e4-2564-4dc4-ec0b-9d2b7901f12a"
},
"execution_count": 36,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" short_sma long_sma pyramid result\n",
"95 5 6 1 -302595.554868\n",
"96 5 6 2 -231074.405474\n",
"0 3 5 1 -207403.641505\n",
"270 10 12 1 -187248.424130\n",
"97 5 6 3 -179552.447503\n",
".. ... ... ... ...\n",
"672 30 288 3 30100.642723\n",
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"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"[処理時間]: 57940.527 ms\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"### polars"
],
"metadata": {
"id": "4LMe81_SMNJ7"
}
},
{
"cell_type": "markdown",
"source": [
"#### 一括バックテスト実行"
],
"metadata": {
"id": "9xRsOmLqOcIg"
}
},
{
"cell_type": "code",
"source": [
"s = time.time()\n",
"\n",
"# パラメータ組み合わせにストラテジーを適用\n",
"df_params_pl = (\n",
" df_params_pl.lazy()\n",
" .with_columns(\n",
" pl.struct(['short_sma', 'long_sma', 'pyramid',])\n",
" .apply(lambda c: backtest_polars(c['short_sma'], c['long_sma'], c['pyramid'],).get_column('pnl')[-1])\n",
" .alias('result')\n",
" )\n",
" .sort(pl.col('result')) # result昇順ソート\n",
" .collect()\n",
")\n",
"\n",
"# テスト結果表示\n",
"display(df_params_pl)\n",
"\n",
"ret = round((time.time() - s) * 1000, 3)\n",
"print(f'[処理時間]: {ret} ms\\n')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 917
},
"id": "oJVh_TcsylGE",
"outputId": "43be4a28-5396-4f0e-afeb-b5c2e4a06707"
},
"execution_count": 37,
"outputs": [
{
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"shape: (950, 4)\n",
"┌───────────┬──────────┬─────────┬────────────────┐\n",
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},
{
"output_type": "stream",
"name": "stdout",
"text": [
"[処理時間]: 36584.938 ms\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### (調整版)一括バックテスト実行"
],
"metadata": {
"id": "io5YfkKwOeEo"
}
},
{
"cell_type": "code",
"source": [
"s = time.time()\n",
"\n",
"# パラメータ組み合わせにストラテジーを適用\n",
"df_params_pl2 = (\n",
" df_params_pl2.lazy()\n",
" .with_columns(\n",
" pl.struct(['short_sma', 'long_sma', 'pyramid',])\n",
" .apply(lambda c: backtest_polars2(c['short_sma'], c['long_sma'], c['pyramid'],).get_column('pnl')[-1])\n",
" .alias('result')\n",
" )\n",
" .sort(pl.col('result')) # result昇順ソート\n",
" .collect()\n",
")\n",
"\n",
"# テスト結果表示\n",
"display(df_params_pl2)\n",
"\n",
"ret = round((time.time() - s) * 1000, 3)\n",
"print(f'[処理時間]: {ret} ms\\n')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 917
},
"id": "r8yEU7EuGtTD",
"outputId": "f63fcaa9-cf8a-4efd-868f-cee92de4c01d"
},
"execution_count": 38,
"outputs": [
{
"output_type": "display_data",
"data": {
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"shape: (950, 4)\n",
"┌───────────┬──────────┬─────────┬────────────────┐\n",
"│ short_sma ┆ long_sma ┆ pyramid ┆ result │\n",
"│ --- ┆ --- ┆ --- ┆ --- │\n",
"│ i64 ┆ i64 ┆ i64 ┆ f64 │\n",
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]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"[処理時間]: 35160.366 ms\n",
"\n"
]
}
]
}
]
}
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