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May 21, 2019 10:28
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<B> pandas DataFrameのapply関数を用いて時系列データの汎用的な簡易バックテストを行うサンプル </B>\n", | |
| "1. テスト対象期間のデータを取得\n", | |
| "2. テストパラメータ組み合わせ作成\n", | |
| "3. テストするストラテジー定義\n", | |
| "4. テスト実行\n", | |
| "5. 各パラメータ別のテスト結果表示\n", | |
| "6. 最大パフォーマンスの損益推移を表示\n", | |
| "<BR><BR>\n", | |
| "[2.各パラメータ組み合わせ(df_params)]にapply関数を用いて<BR>\n", | |
| "[1.テストデータ(df_ohlcv)]を与えて[3.ストラテジー(def strategy)]を実行します。\n", | |
| "<BR><BR>\n", | |
| "[1.テストデータ], [2.ストラテジー]をカスタマイズすることで様々なテストを作成できます。" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "本サンプルは以下の条件とサンプルロジックを例に説明しています。<BR><BR>\n", | |
| "- [テストデータ]<BR>\n", | |
| " - BitMEX 1分足 直近1週間分のOHLCV<BR>\n", | |
| "- [テストストラテジー]<BR>\n", | |
| " - OHLCV終値の短期SMAと長期SMAのゴールデンクロス/デッドクロスでドテン注文を繰り返す<BR>\n", | |
| "- [パラメータ]<BR>\n", | |
| " - 短期/長期SMAそれぞれの移動平均期間<BR>\n", | |
| "\n", | |
| "【注意】ロジックも評価方法も解説のためのサンプルであり、有効なテスト結果ではありません。" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### テスト対象期間のデータを取得\n", | |
| "<BR>\n", | |
| "BitMEX REST APIにて時間足と期間数を取得してOHLCVデータを取得する\n", | |
| "* PERIOD : 時間足(分) \n", | |
| "* BARS : 期間数 " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 102, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
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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>datetime</th>\n", | |
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| " <th>2019-05-14 19:23:00+09:00</th>\n", | |
| " <td>1557829380</td>\n", | |
| " <td>8024.0</td>\n", | |
| " <td>8032.0</td>\n", | |
| " <td>8015.5</td>\n", | |
| " <td>8016.0</td>\n", | |
| " <td>4146640</td>\n", | |
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| " <tr>\n", | |
| " <th>2019-05-14 19:24:00+09:00</th>\n", | |
| " <td>1557829440</td>\n", | |
| " <td>8016.0</td>\n", | |
| " <td>8022.5</td>\n", | |
| " <td>8005.5</td>\n", | |
| " <td>8021.0</td>\n", | |
| " <td>4138129</td>\n", | |
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| " <tr>\n", | |
| " <th>2019-05-14 19:25:00+09:00</th>\n", | |
| " <td>1557829500</td>\n", | |
| " <td>8021.0</td>\n", | |
| " <td>8020.5</td>\n", | |
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| " <td>7988.5</td>\n", | |
| " <td>3155079</td>\n", | |
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| " <th>2019-05-14 19:26:00+09:00</th>\n", | |
| " <td>1557829560</td>\n", | |
| " <td>7988.5</td>\n", | |
| " <td>8019.0</td>\n", | |
| " <td>7988.5</td>\n", | |
| " <td>8012.5</td>\n", | |
| " <td>3760529</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-05-14 19:27:00+09:00</th>\n", | |
| " <td>1557829620</td>\n", | |
| " <td>8012.5</td>\n", | |
| " <td>8013.0</td>\n", | |
| " <td>7993.0</td>\n", | |
| " <td>7993.0</td>\n", | |
| " <td>2983371</td>\n", | |
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| " <tr>\n", | |
| " <th>2019-05-21 19:18:00+09:00</th>\n", | |
| " <td>1558433880</td>\n", | |
| " <td>7940.0</td>\n", | |
| " <td>7944.5</td>\n", | |
| " <td>7939.5</td>\n", | |
| " <td>7944.5</td>\n", | |
| " <td>2240989</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-05-21 19:19:00+09:00</th>\n", | |
| " <td>1558433940</td>\n", | |
| " <td>7944.5</td>\n", | |
| " <td>7950.0</td>\n", | |
| " <td>7941.0</td>\n", | |
| " <td>7941.5</td>\n", | |
| " <td>4931035</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-05-21 19:20:00+09:00</th>\n", | |
| " <td>1558434000</td>\n", | |
| " <td>7941.5</td>\n", | |
| " <td>7952.5</td>\n", | |
| " <td>7941.0</td>\n", | |
| " <td>7952.5</td>\n", | |
| " <td>1381971</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-05-21 19:21:00+09:00</th>\n", | |
| " <td>1558434060</td>\n", | |
| " <td>7952.5</td>\n", | |
| " <td>7952.5</td>\n", | |
| " <td>7948.5</td>\n", | |
| " <td>7951.5</td>\n", | |
| " <td>854896</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-05-21 19:22:00+09:00</th>\n", | |
| " <td>1558434120</td>\n", | |
| " <td>7951.5</td>\n", | |
| " <td>7951.5</td>\n", | |
| " <td>7941.5</td>\n", | |
| " <td>7943.5</td>\n", | |
| " <td>2762757</td>\n", | |
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| "<p>10080 rows × 6 columns</p>\n", | |
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| "text/plain": [ | |
| " timestamp open high low close volume\n", | |
| "datetime \n", | |
| "2019-05-14 19:23:00+09:00 1557829380 8024.0 8032.0 8015.5 8016.0 4146640\n", | |
| "2019-05-14 19:24:00+09:00 1557829440 8016.0 8022.5 8005.5 8021.0 4138129\n", | |
| "2019-05-14 19:25:00+09:00 1557829500 8021.0 8020.5 7988.5 7988.5 3155079\n", | |
| "2019-05-14 19:26:00+09:00 1557829560 7988.5 8019.0 7988.5 8012.5 3760529\n", | |
| "2019-05-14 19:27:00+09:00 1557829620 8012.5 8013.0 7993.0 7993.0 2983371\n", | |
| "... ... ... ... ... ... ...\n", | |
| "2019-05-21 19:18:00+09:00 1558433880 7940.0 7944.5 7939.5 7944.5 2240989\n", | |
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| "2019-05-21 19:21:00+09:00 1558434060 7952.5 7952.5 7948.5 7951.5 854896\n", | |
| "2019-05-21 19:22:00+09:00 1558434120 7951.5 7951.5 7941.5 7943.5 2762757\n", | |
| "\n", | |
| "[10080 rows x 6 columns]" | |
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| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# coding: utf-8\n", | |
| "import time\n", | |
| "import requests\n", | |
| "from collections import OrderedDict\n", | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "pd.set_option(\"display.max_rows\", 10)\n", | |
| "\n", | |
| "#------------------------------------\n", | |
| "# [テスト対象期間設定]\n", | |
| "PERIOD = 1 # 時間足(分)\n", | |
| "BARS = 10080 # 期間数\n", | |
| "#------------------------------------\n", | |
| "\n", | |
| "# BitMEX OHLCVデータ取得 (1m足 直近1週間)\n", | |
| "to_time = int(time.time())\n", | |
| "from_time = to_time - BARS * PERIOD * 60\n", | |
| "param = {\"period\": PERIOD, \"from\": from_time, \"to\": to_time}\n", | |
| "url = \"https://www.bitmex.com/api/udf/history?symbol=XBTUSD&resolution={period}&from={from}&to={to}\".format(**param)\n", | |
| "data = requests.get(url).json()\n", | |
| "df_ohlcv = pd.DataFrame( \\\n", | |
| " OrderedDict(timestamp=data[\"t\"], open=data[\"o\"], high=data[\"h\"], \\\n", | |
| " low=data[\"l\"], close=data[\"c\"], volume=data[\"v\"]))\n", | |
| "df_ohlcv = df_ohlcv.iloc[:BARS]\n", | |
| "\n", | |
| "# UnixTimeから日付変換してindexに設定\n", | |
| "df_ohlcv[\"datetime\"] = pd.to_datetime(df_ohlcv[\"timestamp\"], unit=\"s\")\n", | |
| "df_ohlcv = df_ohlcv.set_index(\"datetime\")\n", | |
| "df_ohlcv.index = df_ohlcv.index.tz_localize(\"UTC\")\n", | |
| "df_ohlcv.index = df_ohlcv.index.tz_convert(\"Asia/Tokyo\")\n", | |
| "\n", | |
| "# 取得結果表示\n", | |
| "display(df_ohlcv)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### テストパラメータ組み合わせ作成\n", | |
| "短期SMAと長期SMAに設定する期間数のリストを作成し、その全パラメータ組み合わせを作成する\n", | |
| "* SHORT_TERM : 短期SMA期間リスト\n", | |
| "* LONG_TERM : 長期SMA期間リスト" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 103, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
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| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>short</th>\n", | |
| " <th>long</th>\n", | |
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| ], | |
| "text/plain": [ | |
| " short long\n", | |
| "0 1 3\n", | |
| "1 1 5\n", | |
| "2 1 10\n", | |
| "3 1 15\n", | |
| "4 1 30\n", | |
| ".. ... ...\n", | |
| "85 720 1440\n", | |
| "86 720 2880\n", | |
| "87 720 4320\n", | |
| "88 1440 2880\n", | |
| "89 1440 4320\n", | |
| "\n", | |
| "[90 rows x 2 columns]" | |
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| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import itertools\n", | |
| "\n", | |
| "#------------------------------------\n", | |
| "# [パラメータリスト]\n", | |
| "SHORT_TERM = [1, 3, 5, 10, 15, 30, 60, 120, 240, 480, 720, 1440] # 短期SMA期間\n", | |
| "LONG_TERM = [3, 5, 10, 15, 30, 60, 120, 240, 480, 720, 1440, 2880, 4320] # 長期SMA期間\n", | |
| "#------------------------------------\n", | |
| "\n", | |
| "# パラメータ全組み合わせ(itertools.product : 直積)\n", | |
| "df_params = pd.DataFrame(list(itertools.product(SHORT_TERM, LONG_TERM)), \\\n", | |
| " columns=[\"short\", \"long\"])\n", | |
| "# SHORT < LONGの組み合わせのみに絞り込み\n", | |
| "df_params = df_params[df_params[\"short\"] < df_params[\"long\"]]\n", | |
| "# indexリセット\n", | |
| "df_params.reset_index(drop=True, inplace=True)\n", | |
| "\n", | |
| "# パラメータ組み合わせ表示\n", | |
| "display(df_params)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### テストするストラテジー定義\n", | |
| "\n", | |
| "df_params.apply()に適用するストラテジー関数を定義します。<BR>\n", | |
| "apply関数のargsにdf_ohlcvを設定するのでストラテジー内で使用することができます。<BR>\n", | |
| "\n", | |
| "[params]\n", | |
| "* params : テストパラメータ(df_paramsの各行データ) <BR>\n", | |
| "* ohlcv : テストデータ(df_ohlcv)<BR>\n", | |
| "\n", | |
| "[returns]<BR>\n", | |
| "* total_pl : トータル損益値幅<BR>\n", | |
| "* total_trades : トータルトレード回数<BR>\n", | |
| "* lst_pl : 期間毎の損益値幅リスト<BR>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 104, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# ストラテジー定義\n", | |
| "def strategy(params, ohlcv):\n", | |
| " # 現在ポジションの約定価格とポジション方向\n", | |
| " position = {\"price\":0, \"side\":0} # side: 0-> no, -1->short, 1->long\n", | |
| " # トレード回数\n", | |
| " total_trades = 0\n", | |
| " # 期間毎の損益値幅\n", | |
| " lst_pl = []\n", | |
| "\n", | |
| " # 終値(close)の短期/長期SMAをそれぞれ算出\n", | |
| " close = ohlcv[\"close\"].values # 終値ndarray\n", | |
| " # 短期SMA\n", | |
| " sma_s = ohlcv[\"close\"].rolling(window=params[\"short\"], min_periods=1).mean().values\n", | |
| " # 長期SMA\n", | |
| " sma_l = ohlcv[\"close\"].rolling(window=params[\"long\"], min_periods=1).mean().values\n", | |
| "\n", | |
| " # 全期間分を順次判定していく\n", | |
| " # GC/DCでドテンするロジック\n", | |
| " for i in range(1,len(close)):\n", | |
| " pl = 0 # 損益値幅\n", | |
| "\n", | |
| " # ゴールデンクロス判定\n", | |
| " if (sma_s[i-1] <= sma_l[i-1] and sma_s[i] > sma_l[i]):\n", | |
| " if position[\"side\"] == 0:\n", | |
| " # ポジションがない場合、Longポジション設定\n", | |
| " position = {\"price\":close[i], \"side\":1}\n", | |
| " total_trades += 1\n", | |
| " elif position[\"side\"] == -1:\n", | |
| " # ショートポジションの場合、ドテンロング\n", | |
| " pl = position[\"price\"] - close[i] # ポジション約定価格と終値から損益値幅計算\n", | |
| " position = {\"price\":close[i], \"side\":1} # Longポジション設定\n", | |
| " total_trades += 1\n", | |
| "\n", | |
| " # デッドクロス判定\n", | |
| " elif (sma_s[i-1] >= sma_l[i-1] and sma_s[i] < sma_l[i]):\n", | |
| " if position[\"side\"] == 0:\n", | |
| " # ポジションがない場合、Shortポジション設定\n", | |
| " position = {\"price\":close[i], \"side\":-1}\n", | |
| " total_trades += 1\n", | |
| " elif position[\"side\"] == 1:\n", | |
| " # ロングポジションの場合、ドテンショート\n", | |
| " pl = close[i] - position[\"price\"] # ポジション約定価格と終値から損益値幅計算\n", | |
| " position = {\"price\":close[i], \"side\":-1} # Shortポジション設定\n", | |
| " total_trades += 1\n", | |
| "\n", | |
| " # 損益値幅リスト追加\n", | |
| " lst_pl.append(pl)\n", | |
| " \n", | |
| " # 最後にポジションクローズ\n", | |
| " if position[\"side\"] == -1:\n", | |
| " lst_pl.append(position[\"price\"] - close[-1])\n", | |
| " total_trades += 1\n", | |
| " elif position[\"side\"] == 1:\n", | |
| " lst_pl.append(close[-1] - position[\"price\"])\n", | |
| " total_trades += 1\n", | |
| "\n", | |
| " # トータルPL(獲得値幅)とPL推移リストをreturn\n", | |
| " return sum(lst_pl), total_trades, lst_pl" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### テスト実行\n", | |
| "\n", | |
| "各パラメータ組み合わせに対して定義したテストストラテジーを適用します。<BR>\n", | |
| "また、ストラテジー引数にテスト期間のohlcvデータを渡します。" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 105, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "パラメータNo. (トータル損益値幅, トータルトレード回数, [期間毎の損益値幅リスト])\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0 (1101.5, 3561, [0, -32.5, -24.0, -19.5, 0, -0....\n", | |
| "1 (6163.5, 2473, [0, -32.5, -24.0, -19.5, 0, 0, ...\n", | |
| "2 (4983.5, 1639, [0, -32.5, -24.0, -19.5, 0, 0, ...\n", | |
| "3 (2756.5, 1347, [0, -32.5, -24.0, -19.5, 0, 0, ...\n", | |
| "4 (501.5, 1033, [0, -32.5, -24.0, -19.5, 0, 0, 0...\n", | |
| " ... \n", | |
| "85 (-103.5, 9, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n", | |
| "86 (698.5, 7, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...\n", | |
| "87 (-298.5, 5, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n", | |
| "88 (963.0, 8, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...\n", | |
| "89 (-420.0, 7, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n", | |
| "Length: 90, dtype: object" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# パラメータ組み合わせにストラテジーを適用\n", | |
| "result = df_params.apply(strategy, axis=1, args=(df_ohlcv,))\n", | |
| "\n", | |
| "# テスト結果表示\n", | |
| "print(\"パラメータNo. (トータル損益値幅, トータルトレード回数, [期間毎の損益値幅リスト])\")\n", | |
| "display(result)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### 各パラメータ別のテスト結果表示\n", | |
| "\n", | |
| "パラメータ毎のトータル損益値幅を散布図にて表示する" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 106, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead 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>pl</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1101.5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>6163.5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>4983.5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>2756.5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>501.5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>85</th>\n", | |
| " <td>-103.5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>86</th>\n", | |
| " <td>698.5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>87</th>\n", | |
| " <td>-298.5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>88</th>\n", | |
| " <td>963.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>89</th>\n", | |
| " <td>-420.0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>90 rows × 1 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " pl\n", | |
| "0 1101.5\n", | |
| "1 6163.5\n", | |
| "2 4983.5\n", | |
| "3 2756.5\n", | |
| "4 501.5\n", | |
| ".. ...\n", | |
| "85 -103.5\n", | |
| "86 698.5\n", | |
| "87 -298.5\n", | |
| "88 963.0\n", | |
| "89 -420.0\n", | |
| "\n", | |
| "[90 rows x 1 columns]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import matplotlib.dates as mdates\n", | |
| "%matplotlib inline\n", | |
| "\n", | |
| "# パラメータ別トータル損益値幅をDataFrame化\n", | |
| "df_total_pl = pd.DataFrame([r[0] for r in result], columns=[\"pl\"])\n", | |
| "\n", | |
| "# トータル損益値幅表示\n", | |
| "display(df_total_pl)\n", | |
| "\n", | |
| "# 散布図\n", | |
| "plt.grid(color=\"gray\")\n", | |
| "plt.scatter(x=df_total_pl.index, y=df_total_pl[\"pl\"]) # x:パラメータNo, y:トータル損益値幅\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### 最大パフォーマンスの損益推移を表示" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 107, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "best params no:1 short:1 long:5 total pl:6163.5\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# 最大利益値幅のパラメータNoを取得\n", | |
| "max_idx = df_total_pl[\"pl\"].values.argmax()\n", | |
| "print(\"best params no:{} short:{} long:{} total pl:{}\".format(\n", | |
| " max_idx, df_params[\"short\"][max_idx], df_params[\"long\"][max_idx], df_total_pl[\"pl\"][max_idx]))\n", | |
| "\n", | |
| "# 最大利益値幅の損益推移を表示\n", | |
| "fig, ax = plt.subplots()\n", | |
| "ax.set_title(\"Profit and loss graph\") # タイトル\n", | |
| "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%m/%d\\n%H:%M\")) # X軸ラベル\n", | |
| "plt.grid(color=\"gray\")\n", | |
| "\n", | |
| "np_date = df_ohlcv.index.values # X:OHLCV期間時刻\n", | |
| "np_sum_pl = np.cumsum(result[max_idx][2]) # Y:累積損益値幅\n", | |
| "plt.plot(np_date, np_sum_pl)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.6.2" | |
| }, | |
| "toc": { | |
| "nav_menu": {}, | |
| "number_sections": true, | |
| "sideBar": true, | |
| "skip_h1_title": false, | |
| "title_cell": "Table of Contents", | |
| "title_sidebar": "Contents", | |
| "toc_cell": false, | |
| "toc_position": {}, | |
| "toc_section_display": true, | |
| "toc_window_display": false | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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