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Remove the CWD from sys.path while we load stuff.\n", "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " \n" ] } ] }, { "cell_type": "code", "source": [ "def time(df):\n", " df['TIME'] = pd.to_datetime(df['TIME'])\n", " df=df.set_index(df.TIME)\n", " return df" ], "metadata": { "id": "pSAxsJEefAzw" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df_1=time(df_1)" ], "metadata": { "id": "1s5Ph2jdTwWU", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "28b6b024-84a1-4a3f-c68f-f69f912a233d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " \n" ] } ] }, { "cell_type": "code", "source": [ "#相乘 累積報酬，畫圖畫出曲線變化\n", "df_1" ], "metadata": { "id": "_u26jmgOU-H2", "colab": { "base_uri": "https://localhost:8080/", "height": 455 }, "outputId": "94cd7bf3-88b8-43bc-c751-460b2c44bb86" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " TIME OPEN MAX MIN CLOSE 日報酬 1+日報酬 \\\n", "TIME \n", "2021-02-01 2021-02-01 595.0 612.0 587.0 611.0 \n", "2021-02-02 2021-02-02 629.0 638.0 622.0 632.0 0.03437 1.03437 \n", "2021-02-03 2021-02-03 638.0 642.0 630.0 630.0 -0.003165 0.996835 \n", "2021-02-04 2021-02-04 626.0 632.0 620.0 627.0 -0.004762 0.995238 \n", "2021-02-05 2021-02-05 638.0 641.0 631.0 632.0 0.007974 1.007974 \n", "... ... ... ... ... ... ... ... \n", "2021-12-24 2021-12-24 606.0 609.0 604.0 604.0 -0.0033 0.9967 \n", "2021-12-27 2021-12-27 604.0 610.0 604.0 606.0 0.003311 1.003311 \n", "2021-12-28 2021-12-28 610.0 615.0 610.0 615.0 0.014851 1.014851 \n", "2021-12-29 2021-12-29 615.0 619.0 614.0 616.0 0.001626 1.001626 \n", "2021-12-30 2021-12-30 619.0 620.0 615.0 615.0 -0.001623 0.998377 \n", "\n", " 相乘 \n", "TIME \n", "2021-02-01 \n", "2021-02-02 1.03437 \n", "2021-02-03 1.031097 \n", "2021-02-04 1.026187 \n", "2021-02-05 1.03437 \n", "... ... \n", "2021-12-24 0.988543 \n", "2021-12-27 0.991817 \n", "2021-12-28 1.006547 \n", "2021-12-29 1.008183 \n", "2021-12-30 1.006547 \n", "\n", "[224 rows x 8 columns]" ], "text/html": [ "\n", "
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TIMEOPENMAXMINCLOSE日報酬1+日報酬相乘
TIME
2021-02-012021-02-01595.0612.0587.0611.0
2021-02-022021-02-02629.0638.0622.0632.00.034371.034371.03437
2021-02-032021-02-03638.0642.0630.0630.0-0.0031650.9968351.031097
2021-02-042021-02-04626.0632.0620.0627.0-0.0047620.9952381.026187
2021-02-052021-02-05638.0641.0631.0632.00.0079741.0079741.03437
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2021-12-242021-12-24606.0609.0604.0604.0-0.00330.99670.988543
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2021-12-282021-12-28610.0615.0610.0615.00.0148511.0148511.006547
2021-12-292021-12-29615.0619.0614.0616.00.0016261.0016261.008183
2021-12-302021-12-30619.0620.0615.0615.0-0.0016230.9983771.006547
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224 rows × 8 columns

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TIMEOPENMAXMINCLOSE日報酬1+日報酬相乘rl_reward
TIME
2021-02-012021-02-01595.0612.0587.0611.00.0
2021-02-022021-02-02629.0638.0622.0632.00.034371.034371.034370.0
2021-02-032021-02-03638.0642.0630.0630.0-0.0031650.9968351.0310970.0
2021-02-042021-02-04626.0632.0620.0627.0-0.0047620.9952381.0261870.0
2021-02-052021-02-05638.0641.0631.0632.00.0079741.0079741.034370.0
..............................
2021-12-242021-12-24606.0609.0604.0604.0-0.00330.99670.988543-0.0033
2021-12-272021-12-27604.0610.0604.0606.00.0033111.0033110.9918170.003311
2021-12-282021-12-28610.0615.0610.0615.00.0148511.0148511.0065470.014851
2021-12-292021-12-29615.0619.0614.0616.00.0016261.0016261.0081830.001626
2021-12-302021-12-30619.0620.0615.0615.0-0.0016230.9983771.006547NaN
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224 rows × 9 columns

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\n", " " ] }, "metadata": {}, "execution_count": 24 } ] }, { "cell_type": "code", "source": [ "rl_2['1+rl日報酬'] = ''\n", "for i in range(0,len(rl_2)):\n", " rl_2['1+rl日報酬'][i] = rl_2['rl_reward'][i]+1\n", "\n", "rl_2['rl相乘'] = ''\n", "a = 1\n", "for i in range(0,len(rl_2)): \n", " a *= rl_2['1+rl日報酬'][i]\n", " rl_2['rl相乘'][i] = a" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Un7HmnDz-VWc", "outputId": "584e51b7-05cb-4873-c288-cff9fb7356b5" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " This is separate from the ipykernel package so we can avoid doing imports until\n", "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " if __name__ == '__main__':\n" ] } ] }, { "cell_type": "code", "source": [ "rl_2" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 455 }, "id": "9ls9IHrLPsap", "outputId": "6b19dfc3-edc0-4a64-c05d-b44ba5d86e24" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " TIME OPEN MAX MIN CLOSE 日報酬 1+日報酬 \\\n", "TIME \n", "2021-02-01 2021-02-01 595.0 612.0 587.0 611.0 \n", "2021-02-02 2021-02-02 629.0 638.0 622.0 632.0 0.03437 1.03437 \n", "2021-02-03 2021-02-03 638.0 642.0 630.0 630.0 -0.003165 0.996835 \n", "2021-02-04 2021-02-04 626.0 632.0 620.0 627.0 -0.004762 0.995238 \n", "2021-02-05 2021-02-05 638.0 641.0 631.0 632.0 0.007974 1.007974 \n", "... ... ... ... ... ... ... ... \n", "2021-12-24 2021-12-24 606.0 609.0 604.0 604.0 -0.0033 0.9967 \n", "2021-12-27 2021-12-27 604.0 610.0 604.0 606.0 0.003311 1.003311 \n", "2021-12-28 2021-12-28 610.0 615.0 610.0 615.0 0.014851 1.014851 \n", "2021-12-29 2021-12-29 615.0 619.0 614.0 616.0 0.001626 1.001626 \n", "2021-12-30 2021-12-30 619.0 620.0 615.0 615.0 -0.001623 0.998377 \n", "\n", " 相乘 rl_reward 1+rl日報酬 rl相乘 \n", "TIME \n", "2021-02-01 0.0 1.0 1.0 \n", "2021-02-02 1.03437 0.0 1.0 1.0 \n", "2021-02-03 1.031097 0.0 1.0 1.0 \n", "2021-02-04 1.026187 0.0 1.0 1.0 \n", "2021-02-05 1.03437 0.0 1.0 1.0 \n", "... ... ... ... ... \n", "2021-12-24 0.988543 -0.0033 0.9967 1.015126 \n", "2021-12-27 0.991817 0.003311 1.003311 1.018487 \n", "2021-12-28 1.006547 0.014851 1.014851 1.033613 \n", "2021-12-29 1.008183 0.001626 1.001626 1.035294 \n", "2021-12-30 1.006547 NaN NaN NaN \n", "\n", "[224 rows x 11 columns]" ], "text/html": [ "\n", "
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TIMEOPENMAXMINCLOSE日報酬1+日報酬相乘rl_reward1+rl日報酬rl相乘
TIME
2021-02-012021-02-01595.0612.0587.0611.00.01.01.0
2021-02-022021-02-02629.0638.0622.0632.00.034371.034371.034370.01.01.0
2021-02-032021-02-03638.0642.0630.0630.0-0.0031650.9968351.0310970.01.01.0
2021-02-042021-02-04626.0632.0620.0627.0-0.0047620.9952381.0261870.01.01.0
2021-02-052021-02-05638.0641.0631.0632.00.0079741.0079741.034370.01.01.0
....................................
2021-12-242021-12-24606.0609.0604.0604.0-0.00330.99670.988543-0.00330.99671.015126
2021-12-272021-12-27604.0610.0604.0606.00.0033111.0033110.9918170.0033111.0033111.018487
2021-12-282021-12-28610.0615.0610.0615.00.0148511.0148511.0065470.0148511.0148511.033613
2021-12-292021-12-29615.0619.0614.0616.00.0016261.0016261.0081830.0016261.0016261.035294
2021-12-302021-12-30619.0620.0615.0615.0-0.0016230.9983771.006547NaNNaNNaN
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224 rows × 11 columns

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\n", " \n", " \n", " \n", "\n", " \n", "
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\n", " " ] }, "metadata": {}, "execution_count": 26 } ] }, { "cell_type": "code", "metadata": { "id": "aZEERlPbTSDL" }, "source": [ "import matplotlib.gridspec as gridspec" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#某列更換成數值類型_使用pd.to_numeric\n", "df_1['相乘']=pd.to_numeric(df_1['相乘'])\n", "rl_2['rl相乘']=pd.to_numeric(rl_2['rl相乘'])" ], "metadata": { "id": "pB-IH98AdyBp" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "w8-u4OmbPYZP", "colab": { "base_uri": "https://localhost:8080/", "height": 378 }, "outputId": "406fa174-d527-4dfe-ee42-ebdc34efe6c0" }, "source": [ "plt.figure(figsize = (10,6))\n", "plt.title('', fontsize = 16)\n", "df_1.相乘.plot(color = 'blue', linewidth = 1)\n", "rl_2.rl相乘.plot(color = 'green', linewidth = 1)\n", "\n", "plt.grid()\n", "plt.xlabel('time', fontsize = 13) # X座標名稱\n", "plt.ylabel('return(%)', fontsize = 14) # Y座標名稱\n", "plt.legend(['buy&hold','RL_model'], bbox_to_anchor=(1, 1));\n", "plt.savefig('績效圖.png') # 儲存圖片;" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "DEGn_QOPcQOf" }, "execution_count": null, "outputs": [] } ] }