├── README.md ├── experiment1-proposeModel ├── Figure_1-2.png ├── Final-Result-Comparison.png ├── ProposeModel │ ├── .ipynb_checkpoints │ │ ├── Input-Pingan-from-20080101-to-20181118.csv │ │ ├── PlotTrendGraph-checkpoint.ipynb │ │ ├── Prediction-DynamicTraining-checkpoint.ipynb │ │ ├── Prediction-checkpoint.ipynb │ │ ├── Result-Pingan-601318-from-20080101-to-20181118.csv │ │ ├── TrendCount.png │ │ ├── change实际曲线.png │ │ ├── stockprediction-3-classification-checkpoint.ipynb │ │ ├── stockprediction-3-classification.ipynb │ │ ├── 学习曲线.png │ │ └── 学习曲线2.png │ ├── 601318SH-from-20110101-to-20181118.csv │ ├── Figure_1-1.png │ ├── Figure_1-2.png │ ├── Figure_1.png │ ├── Figure_2-1.png │ ├── Figure_2.png │ ├── PlotTrendGraph.ipynb │ ├── Prediction-DynamicTraining.ipynb │ ├── Prediction.ipynb │ ├── Result-20181204.csv │ ├── Result-Pingan-from-20110101-to-20181118-Copy1.csv │ ├── Result-Pingan-from-20110101-to-20181118.csv │ └── Result-SVR&Random-Pingan-from-20080101-to-20181118.csv └── SVR&RandomSelect │ ├── .ipynb_checkpoints │ └── StockPrediction-SVR-checkpoint.ipynb │ ├── Input-Pingan-from-20080101-to-20181118.csv │ ├── Pingan-from-20080101-to-20181118.csv │ ├── Pingan-from-20110101-to-20181118.csv │ ├── RandomSelect-trendCount.png │ ├── Result-SVR&Random-Pingan-from-20080101-to-20181118.csv │ ├── SVR-change实际曲线.png │ ├── SVR-trendCount.png │ └── StockPrediction-SVR.ipynb ├── experiment2-feature-selection ├── 1 │ ├── 1-Result-Vanke-from-20110701-to-20181118.csv │ ├── Vanke-from-20110701-to-20181118.csv │ ├── stockprediction-1-individualStock.ipynb │ ├── 实验一trend.png │ └── 实验一回归图.png ├── 2 │ ├── 2-Result-Vanke-from-20110701-to-20181118.csv │ ├── stockprediction-2-individualStock+stockIndex.ipynb │ ├── 实验二trend.png │ └── 实验二回归图.png ├── 3 │ ├── 3-Result-Vanke-from-20110701-to-20181118.csv │ ├── stockprediction-3-individualStock+macroIndex.ipynb │ ├── 实验三trend.png │ └── 实验三回归图.png ├── 4 │ ├── 4-Result-Vanke-from-20110701-to-20181118.csv │ ├── stockprediction-4-individualStock+stockIndex+macroIndex.ipynb │ ├── 实验四trend.png │ └── 实验四回归图.png ├── 5 │ ├── 5-Result-Vanke-from-20110701-to-20181118.csv │ ├── Total-data-after-Autoencoder-dimension-reduction.csv │ ├── stockprediction-5-individualStock+stockIndex+macroIndex+autoEncoder.ipynb │ ├── 实验五trend.png │ └── 实验五回归图.png ├── .ipynb_checkpoints │ ├── stockprediction-1-individualStock-checkpoint.ipynb │ ├── stockprediction-2-individualStock+stockIndex-checkpoint.ipynb │ ├── stockprediction-3-individualStock+macroIndex-checkpoint.ipynb │ ├── stockprediction-4-individualStock+stockIndex+macroIndex-checkpoint.ipynb │ └── stockprediction-5-individualStock+stockIndex+macroIndex+autoEncoder-checkpoint.ipynb ├── Ashare-from-20110701-to-20181118.csv ├── ExperiementResultComparison.png ├── Figure_1-1.png ├── PlotTrendGraph │ ├── .ipynb_checkpoints │ │ └── PlotTrendGraph-checkpoint.ipynb │ ├── 1-Result-Vanke-from-20110701-to-20181118.csv │ ├── 2-Result-Vanke-from-20110701-to-20181118.csv │ ├── 3-Result-Vanke-from-20110701-to-20181118.csv │ ├── 4-Result-Vanke-from-20110701-to-20181118.csv │ ├── 5-Result-Vanke-from-20110701-to-20181118.csv │ ├── PlotTrendGraph.ipynb │ └── Total-data-after-Autoencoder-dimension-reduction.csv ├── Total-data-from-20110701-to-20181118.csv ├── Vanke-from-20110701-to-20181118.csv └── modelStructure.png └── experiment3-sentimentAnalysis ├── .ipynb_checkpoints ├── 1GubaData-checkpoint.ipynb ├── 3stockprediction-A-share+sentimentResult-checkpoint.ipynb ├── PlotTrendGraph-checkpoint.ipynb └── stockprediction-A-share-withoutSentiment-checkpoint.ipynb ├── 1GubaData.ipynb ├── 2Sentiment_test.ipynb ├── 3stockprediction-A-share+sentimentResult.ipynb ├── Ashare-from-20160401-to-20170930.csv ├── PlotTrendGraph.ipynb ├── combine_df.csv └── stockprediction-A-share-withoutSentiment.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # StockTrendPrediction 2 | 股票趋势预测 3 | -------------------------------------------------------------------------------- /experiment1-proposeModel/Figure_1-2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/Figure_1-2.png -------------------------------------------------------------------------------- /experiment1-proposeModel/Final-Result-Comparison.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/Final-Result-Comparison.png -------------------------------------------------------------------------------- /experiment1-proposeModel/ProposeModel/.ipynb_checkpoints/TrendCount.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/ProposeModel/.ipynb_checkpoints/TrendCount.png -------------------------------------------------------------------------------- /experiment1-proposeModel/ProposeModel/.ipynb_checkpoints/change实际曲线.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/ProposeModel/.ipynb_checkpoints/change实际曲线.png -------------------------------------------------------------------------------- /experiment1-proposeModel/ProposeModel/.ipynb_checkpoints/学习曲线.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/ProposeModel/.ipynb_checkpoints/学习曲线.png -------------------------------------------------------------------------------- /experiment1-proposeModel/ProposeModel/.ipynb_checkpoints/学习曲线2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/ProposeModel/.ipynb_checkpoints/学习曲线2.png -------------------------------------------------------------------------------- /experiment1-proposeModel/ProposeModel/Figure_1-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/ProposeModel/Figure_1-1.png -------------------------------------------------------------------------------- /experiment1-proposeModel/ProposeModel/Figure_1-2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/ProposeModel/Figure_1-2.png -------------------------------------------------------------------------------- /experiment1-proposeModel/ProposeModel/Figure_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/ProposeModel/Figure_1.png -------------------------------------------------------------------------------- /experiment1-proposeModel/ProposeModel/Figure_2-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/ProposeModel/Figure_2-1.png -------------------------------------------------------------------------------- /experiment1-proposeModel/ProposeModel/Figure_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/ProposeModel/Figure_2.png -------------------------------------------------------------------------------- /experiment1-proposeModel/SVR&RandomSelect/RandomSelect-trendCount.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/SVR&RandomSelect/RandomSelect-trendCount.png -------------------------------------------------------------------------------- /experiment1-proposeModel/SVR&RandomSelect/SVR-change实际曲线.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/SVR&RandomSelect/SVR-change实际曲线.png -------------------------------------------------------------------------------- /experiment1-proposeModel/SVR&RandomSelect/SVR-trendCount.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment1-proposeModel/SVR&RandomSelect/SVR-trendCount.png -------------------------------------------------------------------------------- /experiment2-feature-selection/1/1-Result-Vanke-from-20110701-to-20181118.csv: -------------------------------------------------------------------------------- 1 | ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount,ave_change,pred_ave_change,real_trend,pred_trend 2 | 000002.SZ,20170714,24.45,24.83,24.36,24.59,24.43,0.16,0.65,201797.31,495832.29299999995,0.2533333333333333,0.2408801398674646,1,1 3 | 000002.SZ,20170718,25.58,25.8,24.88,25.3,24.59,0.71,2.89,791833.77,2004962.4419999998,-0.17333333333333334,0.15818304191033034,-1,1 4 | 000002.SZ,20170719,24.85,26.06,24.43,25.14,25.3,-0.16,-0.63,1008070.7,2526934.175,-0.4466666666666666,-0.2581329633792243,-1,-1 5 | 000002.SZ,20170720,24.85,25.64,24.73,25.35,25.14,0.21,0.84,633677.05,1598967.701,-0.6966666666666667,-0.3352842005093894,-1,-1 6 | 000002.SZ,20170721,25.19,25.64,24.65,24.78,25.35,-0.57,-2.25,477662.55,1195588.636,-0.45333333333333337,-0.31019924481709815,-1,-1 7 | 000002.SZ,20170724,24.7,24.72,23.38,23.8,24.78,-0.98,-3.95,835260.09,2000738.2,-0.26666666666666666,-0.10826048900683735,-1,-1 8 | 000002.SZ,20170725,23.6,23.74,23.22,23.26,23.8,-0.54,-2.27,362654.8,849637.9190000001,0.12333333333333334,-0.04929621001084659,1,0 9 | 000002.SZ,20170726,23.28,23.68,23.23,23.42,23.26,0.16,0.69,307159.42,719797.707,-0.016666666666666663,0.17385120878616958,0,1 10 | 000002.SZ,20170727,23.45,23.63,22.94,23.0,23.42,-0.42,-1.79,426351.28,991318.7790000001,0.13999999999999999,-0.036000389059384796,1,0 11 | 000002.SZ,20170728,23.0,23.84,23.0,23.63,23.0,0.63,2.74,412507.68,971981.311,-0.016666666666666673,0.021656383077303443,0,0 12 | 000002.SZ,20170731,23.52,23.58,23.1,23.37,23.63,-0.26,-1.1,309424.82,721127.9190000001,-0.08666666666666666,-0.019581846793492765,0,0 13 | 000002.SZ,20170801,23.35,23.55,23.2,23.42,23.37,0.05,0.21,209522.62,490486.919,-0.19333333333333333,-0.07243468691905353,-1,0 14 | 000002.SZ,20170802,23.45,24.12,23.43,23.58,23.42,0.16,0.68,353910.17,841309.695,-0.29,-0.11072586139043186,-1,-1 15 | 000002.SZ,20170803,23.58,23.58,22.79,23.11,23.58,-0.47,-1.99,455189.39,1047661.125,-0.11333333333333334,-0.13088075717290257,-1,-1 16 | 000002.SZ,20170804,23.0,23.06,22.71,22.84,23.11,-0.27,-1.17,296123.06,676533.26,0.03666666666666666,0.009594675103823218,0,0 17 | 000002.SZ,20170807,22.82,23.05,22.68,22.71,22.84,-0.13,-0.57,234091.49,533908.529,0.03,0.1025810701648393,0,1 18 | 000002.SZ,20170808,22.81,22.81,22.58,22.77,22.71,0.06,0.26,191585.01,434919.029,-0.25,0.014422919551531348,-1,0 19 | 000002.SZ,20170809,22.8,23.08,22.71,22.95,22.77,0.18,0.79,244983.95,561145.593,-0.3333333333333333,-0.23547137548526142,-1,-1 20 | 000002.SZ,20170810,22.99,22.99,22.53,22.8,22.95,-0.15,-0.65,239302.29,543681.994,-0.26,-0.20015442242225026,-1,-1 21 | 000002.SZ,20170811,22.6,22.69,22.0,22.02,22.8,-0.78,-3.42,344130.96,768450.896,-0.05333333333333334,-0.052825622459252805,0,0 22 | 000002.SZ,20170814,22.12,22.25,21.73,21.95,22.02,-0.07,-0.32,400870.39,879296.825,-0.01,0.06080134640137343,0,0 23 | 000002.SZ,20170815,21.99,22.18,21.86,22.02,21.95,0.07,0.32,247722.45,544574.594,0.26666666666666666,0.033554752568403755,1,0 24 | 000002.SZ,20170816,22.1,22.1,21.75,21.86,22.02,-0.16,-0.73,229564.85,501790.367,0.25333333333333335,0.2260626799861589,1,1 25 | 000002.SZ,20170817,21.9,21.97,21.72,21.92,21.86,0.06,0.27,197748.76,433006.766,0.43,0.12228692779938369,1,1 26 | 000002.SZ,20170818,21.88,22.95,21.81,22.82,21.92,0.9,4.11,552724.8,1242948.88,0.13999999999999999,0.2288818953434625,1,1 27 | 000002.SZ,20170821,22.7,22.81,22.39,22.62,22.82,-0.2,-0.88,287832.45,649571.377,0.15666666666666665,0.00642720977465299,1,0 28 | 000002.SZ,20170822,22.78,23.4,22.65,23.21,22.62,0.59,2.61,504123.49,1167246.978,-0.003333333333333332,0.0355964265267053,0,0 29 | 000002.SZ,20170823,23.25,23.3,22.92,23.24,23.21,0.03,0.13,222221.18,514484.895,0.25666666666666665,0.014031637708345923,1,0 30 | 000002.SZ,20170824,23.18,23.32,22.85,23.09,23.24,-0.15,-0.65,180488.96,417300.22799999994,0.35333333333333333,0.2632393632332483,1,1 31 | 000002.SZ,20170825,23.5,23.68,23.14,23.2,23.09,0.11,0.48,483898.58,1131198.66,0.27666666666666667,0.25693502287069947,1,1 32 | 000002.SZ,20170828,23.27,24.42,23.27,24.01,23.2,0.81,3.49,898477.81,2154997.9080000003,0.003333333333333339,0.06117379466692594,0,0 33 | 000002.SZ,20170829,23.38,23.57,22.85,23.36,23.22,0.14,0.6,436613.54,1015608.551,-0.16,-0.10894237866004322,-1,-1 34 | 000002.SZ,20170830,23.52,23.58,23.23,23.24,23.36,-0.12,-0.51,337202.17,789291.579,-0.09333333333333332,-0.11013094862302159,0,-1 35 | 000002.SZ,20170831,23.19,23.43,23.01,23.23,23.24,-0.01,-0.04,298893.58,694243.105,0.17666666666666667,0.03136389384667066,1,0 36 | 000002.SZ,20170901,23.26,23.37,22.74,22.88,23.23,-0.35,-1.51,407080.29,933710.1290000001,0.3066666666666667,0.2584806318084398,1,1 37 | 000002.SZ,20170904,22.9,23.04,22.69,22.96,22.88,0.08,0.35,268283.34,614727.601,1.0033333333333332,0.2193969632188478,1,1 38 | 000002.SZ,20170905,23.05,23.89,23.05,23.76,22.96,0.8,3.48,539792.91,1279252.9209999999,0.6766666666666666,0.7935490785042445,1,1 39 | 000002.SZ,20170906,23.79,24.27,23.5,23.8,23.76,0.04,0.17,361719.63,865955.696,0.49333333333333335,0.2108740274111429,1,1 40 | 000002.SZ,20170907,23.96,26.18,23.96,25.97,23.8,2.17,9.12,1274569.91,3234474.327,-0.23666666666666666,-0.09128732641537998,-1,0 41 | 000002.SZ,20170908,25.9,26.28,25.16,25.79,25.97,-0.18,-0.69,795767.75,2043297.94,-0.010000000000000009,-0.2876643555363021,0,-1 42 | 000002.SZ,20170911,25.61,26.39,25.03,25.28,25.79,-0.51,-1.98,582773.4,1493511.044,0.7600000000000001,0.1951490173737207,1,1 43 | 000002.SZ,20170912,25.03,25.47,24.82,25.26,25.28,-0.02,-0.08,442261.63,1112832.817,1.3466666666666667,0.8964486698309581,1,1 44 | 000002.SZ,20170913,25.3,26.05,24.99,25.76,25.26,0.5,1.98,467463.77,1201265.103,0.7600000000000001,1.0565144471327463,1,1 45 | 000002.SZ,20170914,25.78,28.06,25.55,27.56,25.76,1.8,6.99,1150696.14,3120331.89,0.4033333333333333,-0.16691452761491155,1,-1 46 | 000002.SZ,20170915,27.5,30.22,27.47,29.3,27.56,1.74,6.31,1550495.61,4549589.506,-0.19000000000000003,-0.22419816662867878,-1,-1 47 | 000002.SZ,20170918,29.01,29.74,27.99,28.04,29.3,-1.26,-4.3,1063043.07,3051116.143,0.11999999999999998,-0.1148871689041457,1,-1 48 | 000002.SZ,20170919,28.27,30.1,28.1,28.77,28.04,0.73,2.6,1196308.5,3484566.602,-0.32,0.3174382905165353,-1,1 49 | 000002.SZ,20170920,28.5,29.55,28.0,28.73,28.77,-0.04,-0.14,613095.75,1763105.246,-0.87,-0.14484225948651647,-1,-1 50 | 000002.SZ,20170921,28.5,29.06,27.75,28.4,28.73,-0.33,-1.15,536324.02,1515969.57,-0.5466666666666665,-0.5764201970895133,-1,-1 51 | 000002.SZ,20170922,28.39,28.67,27.52,27.81,28.4,-0.59,-2.08,423093.1,1184003.287,-0.3233333333333333,-0.23154006590445853,-1,-1 52 | 000002.SZ,20170925,27.2,27.2,26.1,26.12,27.81,-1.69,-6.08,722702.58,1913322.9309999999,0.09666666666666666,-0.002030322154363124,1,0 53 | 000002.SZ,20170926,26.12,27.22,26.1,26.76,26.12,0.64,2.45,593044.56,1585098.003,-0.17,0.15267621795336395,-1,1 54 | 000002.SZ,20170927,27.0,27.28,26.52,26.84,26.76,0.08,0.3,367534.16,990737.9670000001,-0.12333333333333334,-0.152856232126554,-1,-1 55 | 000002.SZ,20170928,27.0,27.15,26.4,26.41,26.84,-0.43,-1.6,262347.0,699235.16,0.06,-0.11691126485665654,0,-1 56 | 000002.SZ,20170929,26.56,26.8,26.0,26.25,26.41,-0.16,-0.61,345752.68,908847.292,0.20666666666666667,0.0916645578543344,1,1 57 | 000002.SZ,20171009,27.45,27.65,26.24,26.47,26.25,0.22,0.84,711737.42,1923169.657,0.21,0.1903701967994371,1,1 58 | 000002.SZ,20171010,26.4,26.69,26.19,26.59,26.47,0.12,0.45,367101.28,970161.609,0.11333333333333333,0.10003180275360732,1,1 59 | 000002.SZ,20171011,26.69,27.27,26.47,26.87,26.59,0.28,1.05,425997.39,1141669.506,-0.18666666666666668,-0.025447421868642302,-1,0 60 | 000002.SZ,20171012,26.99,27.2,26.72,27.1,26.87,0.23,0.86,348979.7,941856.3940000001,-0.2966666666666667,-0.20466717590888356,-1,-1 61 | 000002.SZ,20171013,27.2,27.58,26.81,26.93,27.1,-0.17,-0.63,329928.03,894966.6109999999,-0.16666666666666666,-0.15401775022347783,-1,-1 62 | 000002.SZ,20171016,26.96,26.97,26.13,26.31,26.93,-0.62,-2.3,315008.07,833062.3309999999,-0.08666666666666667,0.023223108450571572,0,0 63 | 000002.SZ,20171017,26.32,26.52,26.14,26.21,26.31,-0.1,-0.38,184811.06,485403.63700000005,-0.016666666666666666,0.011176353196302924,0,0 64 | 000002.SZ,20171018,26.21,26.56,25.61,26.43,26.21,0.22,0.84,334052.39,870086.806,-0.16666666666666666,0.0009079442421593982,-1,0 65 | 000002.SZ,20171019,26.44,26.57,26.01,26.05,26.43,-0.38,-1.44,202525.56,532422.122,0.3166666666666667,-0.13176924546559665,1,-1 66 | 000002.SZ,20171020,26.0,26.23,25.73,26.16,26.05,0.11,0.42,145915.37,379610.561,0.42333333333333334,0.3195010809103647,1,1 67 | 000002.SZ,20171023,26.17,26.2,25.77,25.93,26.16,-0.23,-0.88,197527.2,512609.825,0.5333333333333333,0.33583013514677673,1,1 68 | 000002.SZ,20171024,25.81,27.2,25.81,27.0,25.93,1.07,4.13,509187.97,1367035.3969999999,0.16666666666666666,0.2153008312980333,1,1 69 | 000002.SZ,20171025,27.01,28.0,26.86,27.43,27.0,0.43,1.59,445727.42,1230082.31,0.5166666666666667,-0.10610969444115971,1,-1 70 | 000002.SZ,20171026,27.3,27.88,26.91,27.53,27.43,0.1,0.36,422766.85,1159749.1409999998,0.4766666666666666,0.42468969384829197,1,1 71 | 000002.SZ,20171027,27.7,28.28,27.26,27.5,27.53,-0.03,-0.11,447443.84,1241370.688,0.5499999999999999,0.3187213707963625,1,1 72 | 000002.SZ,20171030,27.5,29.0,27.41,28.98,27.5,1.48,5.38,676711.06,1913212.3830000001,0.15666666666666665,0.2165218178431192,1,1 73 | 000002.SZ,20171031,28.8,29.28,28.28,28.96,28.98,-0.02,-0.07,463277.75,1334576.822,-0.25666666666666665,-0.08082841296990727,-1,0 74 | 000002.SZ,20171101,28.96,30.54,28.73,29.15,28.96,0.19,0.66,622931.18,1849006.197,-0.5633333333333334,-0.2909011056025825,-1,-1 75 | 000002.SZ,20171102,29.3,29.48,28.68,29.45,29.15,0.3,1.03,361906.48,1053595.804,-0.5333333333333333,-0.2931353386243186,-1,-1 76 | 000002.SZ,20171103,29.23,29.52,28.05,28.19,29.45,-1.26,-4.28,455428.48,1305014.433,-0.18333333333333332,-0.1377762435873351,-1,-1 77 | 000002.SZ,20171106,28.2,28.27,26.95,27.46,28.19,-0.73,-2.59,509485.09,1395554.475,0.07,0.04367066661516813,0,0 78 | 000002.SZ,20171107,27.36,27.95,27.3,27.85,27.46,0.39,1.42,379818.14,1052691.827,-0.11333333333333333,0.12096207122007994,-1,1 79 | 000002.SZ,20171108,27.75,28.14,27.41,27.64,27.85,-0.21,-0.75,398661.3,1105269.5159999998,-0.19333333333333336,-0.13323746552069995,-1,-1 80 | 000002.SZ,20171109,27.52,27.79,27.3,27.67,27.64,0.03,0.11,234126.98,645092.373,0.27666666666666667,-0.1998363061745963,1,-1 81 | 000002.SZ,20171110,27.6,27.83,27.36,27.51,27.67,-0.16,-0.58,305912.74,841979.145,0.31,0.352778300444285,1,1 82 | 000002.SZ,20171113,27.45,27.49,26.8,27.06,27.51,-0.45,-1.64,347146.47,941038.615,0.6266666666666666,0.2421951801578203,1,1 83 | 000002.SZ,20171114,27.05,28.5,26.7,28.5,27.06,1.44,5.32,735825.54,2042078.5880000002,0.13333333333333333,0.3357511482636133,1,1 84 | 000002.SZ,20171115,28.28,28.66,27.91,28.44,28.5,-0.06,-0.21,541077.77,1532168.876,0.15333333333333335,-0.13593546112378452,1,-1 85 | 000002.SZ,20171116,28.35,29.38,28.32,28.94,28.44,0.5,1.76,574432.13,1663430.8080000002,0.9500000000000001,0.004913903276125463,1,0 86 | 000002.SZ,20171117,28.85,29.6,28.6,28.9,28.94,-0.04,-0.14,563943.99,1639436.3180000002,1.2,1.052335904041926,1,1 87 | 000002.SZ,20171120,28.8,29.01,27.48,28.9,28.9,0.0,0.0,395088.49,1118755.733,0.8000000000000002,0.8153079102436701,1,1 88 | 000002.SZ,20171121,28.54,31.79,28.54,31.79,28.9,2.89,10.0,1081788.62,3289379.108,0.003333333333333336,-0.15012798865636204,0,-1 89 | 000002.SZ,20171122,31.9,33.08,31.54,32.5,31.79,0.71,2.23,1052969.77,3398210.521,-0.32666666666666666,-0.435756657322248,-1,-1 90 | 000002.SZ,20171123,32.42,32.74,31.02,31.3,32.5,-1.2,-3.69,580325.38,1851670.2880000002,-0.18333333333333335,-0.09388521869977329,-1,0 91 | 000002.SZ,20171124,31.18,32.29,30.47,31.8,31.3,0.5,1.6,524343.06,1653488.217,0.6733333333333332,0.11533037493626265,1,1 92 | 000002.SZ,20171127,31.45,32.6,30.59,31.52,31.8,-0.28,-0.88,521581.28,1640697.007,-0.10000000000000009,0.9036873839298883,-1,1 93 | 000002.SZ,20171128,31.09,31.35,30.1,30.75,31.52,-0.77,-2.44,405786.49,1243495.707,-0.006666666666666747,-0.3609202380975089,0,-1 94 | 000002.SZ,20171129,30.78,33.83,30.28,33.82,30.75,3.07,9.98,897712.59,2894400.95,-0.9899999999999999,-0.32270633836587287,-1,-1 95 | 000002.SZ,20171130,33.1,33.4,30.68,31.22,33.82,-2.6,-7.69,1000665.49,3213854.4039999996,-0.06333333333333334,-0.5232880493005119,0,-1 96 | 000002.SZ,20171201,30.5,32.03,30.5,30.73,31.22,-0.49,-1.57,557438.55,1733100.368,0.013333333333333327,0.2517757640282312,0,1 97 | 000002.SZ,20171204,30.6,31.3,29.9,30.85,30.73,0.12,0.39,379890.01,1160069.2759999998,-0.3,0.19390508810679108,-1,1 98 | 000002.SZ,20171205,30.6,31.48,30.2,31.03,30.85,0.18,0.58,564302.01,1747240.9219999998,-0.4033333333333333,-0.3272306202848754,-1,-1 99 | 000002.SZ,20171206,30.73,31.2,30.26,30.77,31.03,-0.26,-0.84,389794.45,1197517.7240000002,-0.27999999999999997,-0.3267719941337905,-1,-1 100 | 000002.SZ,20171207,30.57,31.9,29.9,29.95,30.77,-0.82,-2.66,590007.16,1807843.249,-0.12666666666666668,-0.0665624915560088,-1,0 101 | 000002.SZ,20171208,29.38,29.88,29.05,29.82,29.95,-0.13,-0.43,588187.18,1731418.639,0.04,0.02952208409706739,0,0 102 | 000002.SZ,20171211,29.82,29.95,29.3,29.93,29.82,0.11,0.37,502135.46,1487020.649,0.060000000000000005,0.08973668436209349,0,0 103 | 000002.SZ,20171212,29.73,30.36,29.51,29.57,29.93,-0.36,-1.2,467638.92,1401462.6940000001,-0.15333333333333332,0.016015212933222327,-1,0 104 | 000002.SZ,20171213,29.57,30.1,29.35,29.94,29.57,0.37,1.25,334953.94,998153.483,-0.49333333333333335,-0.22137895127137516,-1,-1 105 | 000002.SZ,20171214,29.94,30.37,29.85,30.11,29.94,0.17,0.57,382067.77,1150698.146,-0.31666666666666665,-0.3540290891130767,-1,-1 106 | 000002.SZ,20171215,30.12,30.12,29.1,29.11,30.11,-1.0,-3.32,396229.18,1165712.213,-0.17000000000000004,-0.046145751972993344,-1,0 107 | 000002.SZ,20171218,29.11,29.26,28.04,28.46,29.11,-0.65,-2.23,421286.94,1207131.428,0.42,0.012377250591913733,1,0 108 | 000002.SZ,20171219,28.66,29.25,28.4,29.16,28.46,0.7,2.46,340084.1,981540.6479999999,0.23,0.43336432556311283,1,1 109 | 000002.SZ,20171220,29.09,29.1,28.52,28.6,29.16,-0.56,-1.92,329026.69,943187.281,0.59,0.06034157882134108,1,0 110 | 000002.SZ,20171221,28.72,30.19,28.63,29.72,28.6,1.12,3.92,595163.86,1756898.8669999999,0.26,0.2748518047730127,1,1 111 | 000002.SZ,20171222,29.6,30.2,29.57,29.85,29.72,0.13,0.44,342206.42,1023085.433,0.3133333333333333,0.017903364698092016,1,0 112 | 000002.SZ,20171225,30.3,31.33,30.27,30.37,29.85,0.52,1.74,506382.57,1561915.0690000001,0.10999999999999999,0.12905457417170196,1,1 113 | 000002.SZ,20171226,30.49,30.8,30.12,30.5,30.37,0.13,0.43,278845.61,850307.97,0.18666666666666665,0.02263093511263517,1,0 114 | 000002.SZ,20171227,30.55,31.09,30.4,30.79,30.5,0.29,0.95,395488.84,1218292.9679999999,0.59,0.13462531814972548,1,1 115 | 000002.SZ,20171228,30.4,30.84,29.87,30.7,30.79,-0.09,-0.29,367316.18,1117505.168,0.5433333333333333,0.5559567622343699,1,1 116 | 000002.SZ,20171229,30.75,31.71,30.74,31.06,30.7,0.36,1.17,385307.4,1204645.832,0.6866666666666666,0.22106219391028076,1,1 117 | 000002.SZ,20180102,31.45,32.99,31.45,32.56,31.06,1.5,4.83,683433.5,2218502.766,0.7333333333333334,0.2830621031920114,1,1 118 | 000002.SZ,20180103,32.5,33.78,32.23,32.33,32.56,-0.23,-0.71,646870.2,2130249.691,1.22,0.4392415432135263,1,1 119 | 000002.SZ,20180104,32.76,33.53,32.1,33.12,32.33,0.79,2.44,529085.8,1740602.533,0.9066666666666667,0.8600421086947123,1,1 120 | 000002.SZ,20180105,32.98,35.88,32.8,34.76,33.12,1.64,4.95,843101.96,2916787.8710000003,0.30333333333333334,0.20413103590408951,1,1 121 | 000002.SZ,20180108,35.11,36.96,35.11,35.99,34.76,1.23,3.54,830783.59,2994515.8710000003,-0.28,-0.296165832479795,-1,-1 122 | 000002.SZ,20180109,35.63,36.11,34.95,35.84,35.99,-0.15,-0.42,478459.09,1700947.8930000002,-0.09666666666666668,-0.27754775663216924,0,-1 123 | 000002.SZ,20180110,35.6,36.7,35.5,35.67,35.84,-0.17,-0.47,453695.91,1633034.914,0.47333333333333333,0.2163890696565309,1,1 124 | 000002.SZ,20180111,35.55,35.78,34.54,35.15,35.67,-0.52,-1.46,402483.81,1411859.648,1.6833333333333333,0.6672335288921991,1,1 125 | 000002.SZ,20180112,35.1,36.62,35.09,35.55,35.15,0.4,1.14,537013.33,1927916.4719999998,1.406666666666667,1.536520475546519,1,1 126 | 000002.SZ,20180115,35.84,38.0,35.61,37.09,35.55,1.54,4.33,757186.09,2784027.858,0.89,0.2957071955005327,1,1 127 | 000002.SZ,20180116,36.5,40.7,36.4,40.2,37.09,3.11,8.39,978464.78,3763499.498,-0.26,-0.35142891397078846,-1,-1 128 | 000002.SZ,20180117,39.48,40.6,38.3,39.77,40.2,-0.43,-1.07,812191.93,3199234.88,-0.08000000000000002,-0.5380272071560226,0,-1 129 | 000002.SZ,20180118,39.6,40.64,38.9,39.76,39.77,-0.01,-0.03,558848.4,2226747.475,0.4066666666666667,0.3644109452764192,1,1 130 | 000002.SZ,20180119,39.85,41.09,39.25,39.42,39.76,-0.34,-0.86,548758.23,2207882.77,0.57,0.7103555730978648,1,1 131 | 000002.SZ,20180122,39.0,39.84,38.51,39.53,39.42,0.11,0.28,406526.41,1597213.093,0.22333333333333327,0.22597330609957367,1,1 132 | 000002.SZ,20180123,40.0,41.51,39.99,40.98,39.53,1.45,3.67,648657.5,2653177.559,-0.31333333333333335,-0.3337372218569121,-1,-1 133 | 000002.SZ,20180124,40.7,42.24,40.46,41.13,40.98,0.15,0.37,542629.59,2236496.975,-0.8533333333333334,-0.39894100805123667,-1,-1 134 | 000002.SZ,20180125,41.12,41.12,38.8,40.2,41.13,-0.93,-2.26,648064.57,2577574.871,-1.28,-0.4500592186053596,-1,-1 135 | 000002.SZ,20180126,40.0,40.66,39.12,40.04,40.2,-0.16,-0.4,410062.37,1640757.7,-0.8266666666666665,-0.6001402320464454,-1,-1 136 | 000002.SZ,20180129,40.38,40.56,38.09,38.57,40.04,-1.47,-3.67,597862.74,2338982.473,-0.3566666666666667,-0.24417488535245274,-1,-1 137 | 000002.SZ,20180130,38.0,38.0,36.27,36.36,38.57,-2.21,-5.73,908234.42,3355539.693,0.34,-0.022538604338963958,1,0 138 | 000002.SZ,20180131,36.61,37.76,35.62,37.56,36.36,1.2,3.3,898147.86,3297773.29,-0.19000000000000003,0.3575485603014627,-1,1 139 | 000002.SZ,20180201,37.5,38.04,36.9,37.5,37.56,-0.06,-0.16,635425.38,2380069.195,-0.5599999999999999,-0.35230587383111334,-1,-1 140 | 000002.SZ,20180202,36.5,37.9,35.8,37.38,37.5,-0.12,-0.32,596793.68,2208198.824,-1.4666666666666668,-0.5524570377667747,-1,-1 141 | 000002.SZ,20180205,36.8,38.06,36.31,36.99,37.38,-0.39,-1.04,552634.72,2046513.9309999999,-1.4133333333333333,-0.7899936523040137,-1,-1 142 | 000002.SZ,20180206,35.93,36.1,34.88,35.82,36.99,-1.17,-3.16,699836.71,2489127.586,-1.5166666666666666,-0.6253231223424278,-1,-1 143 | 000002.SZ,20180207,36.43,36.58,32.5,32.98,35.82,-2.84,-7.93,952129.68,3283016.479,-0.36000000000000004,-0.6773337020476661,-1,-1 144 | 000002.SZ,20180208,33.0,33.88,32.49,32.75,32.98,-0.23,-0.7,580805.14,1920999.544,-0.006666666666666672,-0.10690926541884754,0,-1 145 | 000002.SZ,20180209,31.5,31.96,30.2,31.27,32.75,-1.48,-4.52,724944.9,2259035.363,0.6333333333333333,0.2049132571617761,1,1 146 | 000002.SZ,20180212,31.43,32.32,31.31,31.9,31.27,0.63,2.01,407654.31,1297185.205,0.6,0.5075204531351725,1,1 147 | 000002.SZ,20180213,32.28,33.79,32.28,32.73,31.9,0.83,2.6,454668.11,1500999.45,0.6633333333333333,0.17122398177782683,1,1 148 | 000002.SZ,20180214,32.87,33.32,32.17,33.17,32.73,0.44,1.34,270175.5,884454.4240000001,0.33666666666666667,0.16781190822521835,1,1 149 | 000002.SZ,20180222,34.0,34.05,33.33,33.7,33.17,0.53,1.6,302373.26,1019234.56,-0.26666666666666666,-0.06732359637816761,-1,0 150 | 000002.SZ,20180223,33.9,35.1,33.33,34.72,33.7,1.02,3.03,384538.9,1323487.16,-0.6733333333333333,-0.37271759112676,-1,-1 151 | 000002.SZ,20180226,35.07,35.1,33.11,34.18,34.72,-0.54,-1.56,540655.01,1832518.3190000001,-0.59,-0.3288694839676223,-1,-1 152 | 000002.SZ,20180227,34.2,34.28,32.65,32.9,34.18,-1.28,-3.74,506598.14,1683177.664,-0.20666666666666667,-0.1559351225694022,-1,-1 153 | 000002.SZ,20180228,32.51,33.23,31.84,32.7,32.9,-0.2,-0.61,500004.47,1621987.601,-0.17333333333333334,0.04448108116785673,-1,0 154 | 000002.SZ,20180301,32.17,33.09,32.02,32.41,32.7,-0.29,-0.89,336064.32,1092337.514,0.42,-0.06712692817052218,1,0 155 | 000002.SZ,20180302,32.0,32.73,31.51,32.28,32.41,-0.13,-0.4,322838.49,1038873.9240000001,0.20666666666666664,0.42606854001681,1,1 156 | 000002.SZ,20180305,32.49,33.15,32.07,32.18,32.28,-0.1,-0.31,373354.38,1213151.363,0.48666666666666664,-0.024796122511228057,1,0 157 | 000002.SZ,20180306,32.35,33.71,32.01,33.67,32.18,1.49,4.63,581008.06,1912870.91,-0.07,0.22385230282942445,0,1 158 | 000002.SZ,20180307,33.44,33.92,32.8,32.9,33.67,-0.77,-2.29,387992.55,1292495.378,-0.09333333333333331,-0.21511273463567113,0,-1 159 | 000002.SZ,20180308,33.4,33.74,32.85,33.64,32.9,0.74,2.25,351022.54,1175142.105,-0.3866666666666667,-0.12415511538585042,-1,-1 160 | 000002.SZ,20180309,33.87,33.91,33.34,33.46,33.64,-0.18,-0.54,325102.39,1092069.971,-0.25333333333333335,-0.18911463578542087,-1,-1 161 | 000002.SZ,20180312,33.54,33.68,32.35,32.62,33.46,-0.84,-2.51,606208.29,1979204.52,0.013333333333333329,-0.03805028130610797,0,0 162 | 000002.SZ,20180313,32.59,32.84,32.37,32.48,32.62,-0.14,-0.43,220920.41,719093.277,0.049999999999999996,0.11507058570782332,0,1 163 | 000002.SZ,20180314,32.44,32.95,32.2,32.7,32.48,0.22,0.68,315833.0,1031091.8759999999,-0.37333333333333335,0.021616547207037484,-1,0 164 | 000002.SZ,20180315,32.5,33.1,32.41,32.66,32.7,-0.04,-0.12,252215.34,825259.6059999999,-0.44,-0.38294719149669026,-1,-1 165 | 000002.SZ,20180316,32.58,33.35,32.5,32.63,32.66,-0.03,-0.09,337572.15,1111490.314,-0.3233333333333333,-0.2755470599730811,-1,-1 166 | 000002.SZ,20180319,32.57,32.58,31.52,31.58,32.63,-1.05,-3.22,366967.49,1166386.364,-0.13333333333333333,-0.041312028666337465,-1,0 167 | 000002.SZ,20180320,31.4,31.59,30.75,31.34,31.58,-0.24,-0.76,290117.65,903296.819,-0.08999999999999998,0.014531697730223211,0,0 168 | 000002.SZ,20180321,31.47,32.15,31.31,31.66,31.34,0.32,1.02,331725.74,1055783.63,-0.44333333333333336,-0.05249905963738773,-1,0 169 | 000002.SZ,20180322,31.8,31.88,31.04,31.18,31.66,-0.48,-1.52,235787.08,739606.988,-0.053333333333333344,-0.35580172826846457,0,-1 170 | 000002.SZ,20180323,30.02,31.07,29.21,31.07,31.18,-0.11,-0.35,505125.39,1527314.429,0.08666666666666666,0.06586232821146636,0,0 171 | 000002.SZ,20180326,30.76,30.79,29.9,30.33,31.07,-0.74,-2.38,338288.96,1021310.9709999999,1.2766666666666666,0.19426988879839568,1,1 172 | 000002.SZ,20180327,30.89,31.87,30.71,31.02,30.33,0.69,2.28,578853.91,1803144.937,0.7566666666666667,1.2453211770455042,1,1 173 | 000002.SZ,20180328,30.51,32.15,30.3,31.33,31.02,0.31,1.0,426728.97,1341512.9170000001,0.7200000000000001,0.0869045708576837,1,0 174 | 000002.SZ,20180329,31.54,34.46,31.03,34.16,31.33,2.83,9.03,999700.72,3311802.728,-0.1733333333333333,-0.08127790768941257,-1,0 175 | 000002.SZ,20180330,33.7,33.99,33.17,33.29,34.16,-0.87,-2.55,572692.05,1918089.36,-0.16333333333333333,-0.35953454335530616,-1,-1 176 | 000002.SZ,20180402,33.65,34.47,33.32,33.49,33.29,0.2,0.6,531635.65,1801173.138,-0.6233333333333333,0.019451598922411474,-1,0 177 | 000002.SZ,20180403,33.2,34.1,32.52,33.64,33.49,0.15,0.45,431945.71,1440337.6369999999,-0.48666666666666664,-0.28317112048467014,-1,-1 178 | 000002.SZ,20180404,32.3,33.95,32.1,32.8,33.64,-0.84,-2.5,584509.65,1927433.7040000001,-0.0633333333333333,-0.1662459926803908,0,-1 179 | 000002.SZ,20180409,32.7,32.76,30.96,31.62,32.8,-1.18,-3.6,478119.4,1510647.265,0.07666666666666666,0.1507571334640184,0,1 180 | 000002.SZ,20180410,31.52,32.18,31.06,32.18,31.62,0.56,1.77,477145.43,1517032.39,-0.31,0.05677872250477461,-1,0 181 | 000002.SZ,20180411,32.15,33.24,31.73,32.61,32.18,0.43,1.34,485930.43,1578103.542,-0.86,-0.344532057841619,-1,-1 182 | 000002.SZ,20180412,32.57,32.86,31.75,31.85,32.61,-0.76,-2.33,335219.99,1079000.357,-0.6433333333333333,-0.5762638213237129,-1,-1 183 | 000002.SZ,20180413,32.16,32.24,31.03,31.25,31.85,-0.6,-1.88,351572.61,1102968.777,-0.4033333333333333,-0.26275597890218116,-1,-1 184 | 000002.SZ,20180416,31.2,31.23,29.56,30.03,31.25,-1.22,-3.9,528068.65,1588112.26,0.06666666666666667,-0.05308358540137622,0,0 185 | 000002.SZ,20180417,30.16,30.55,29.6,29.92,30.03,-0.11,-0.37,258197.72,775976.183,0.07666666666666666,0.18893188218275694,0,1 186 | 000002.SZ,20180418,30.29,30.69,29.74,30.04,29.92,0.12,0.4,328835.33,989193.213,-0.043333333333333335,0.016787617901960882,0,0 187 | 000002.SZ,20180419,30.33,30.53,30.03,30.23,30.04,0.19,0.63,256275.25,776421.7740000001,0.2333333333333333,-0.17220470835765217,1,-1 188 | 000002.SZ,20180420,30.18,30.5,29.92,30.15,30.23,-0.08,-0.26,254795.46,769678.4559999999,-0.010000000000000009,0.20629370133082062,0,1 189 | 000002.SZ,20180423,29.88,30.29,29.79,29.91,30.15,-0.24,-0.8,201714.25,605310.89,-0.47000000000000003,-0.06780927439530704,-1,0 190 | 000002.SZ,20180424,29.85,31.1,29.8,30.93,29.91,1.02,3.41,469832.12,1447945.429,-0.8433333333333334,-0.43794443418582296,-1,-1 191 | 000002.SZ,20180425,30.65,30.65,29.96,30.12,30.93,-0.81,-2.62,397684.18,1199524.613,-0.8133333333333335,-0.4432439746459327,-1,-1 192 | 000002.SZ,20180426,30.3,30.3,28.26,28.5,30.12,-1.62,-5.38,812262.55,2348260.6459999997,-0.38999999999999996,-0.32698532720406864,-1,-1 193 | 000002.SZ,20180427,28.66,28.75,27.42,28.4,28.5,-0.1,-0.35,381958.29,1071392.412,-0.4966666666666666,-0.09673947592576358,-1,0 194 | 000002.SZ,20180502,28.4,28.66,27.63,27.68,28.4,-0.72,-2.54,397170.81,1111292.763,-0.10000000000000002,-0.2249551623066268,-1,-1 195 | 000002.SZ,20180503,27.65,27.66,26.56,27.33,27.68,-0.35,-1.26,542419.86,1466857.435,0.45333333333333337,0.00989247391621259,1,0 196 | 000002.SZ,20180504,27.26,27.56,26.81,26.91,27.33,-0.42,-1.54,425383.6,1154814.762,0.43,0.4777487901846567,1,1 197 | 000002.SZ,20180507,27.01,27.51,26.72,27.38,26.91,0.47,1.75,408453.67,1110387.754,0.23,0.17086865494648604,1,1 198 | 000002.SZ,20180508,27.4,28.86,27.33,28.69,27.38,1.31,4.78,655817.53,1857899.321,-0.25,-0.12404862006505345,-1,-1 199 | 000002.SZ,20180509,28.5,28.5,28.0,28.2,28.69,-0.49,-1.71,409030.79,1152922.298,-0.08666666666666667,-0.2878492670257888,0,-1 200 | 000002.SZ,20180510,28.32,28.53,27.7,28.07,28.2,-0.13,-0.46,429639.57,1204133.432,-0.10666666666666667,0.05476820955673841,-1,0 201 | 000002.SZ,20180511,28.1,28.19,27.73,27.94,28.07,-0.13,-0.46,318303.73,889939.605,-0.23666666666666666,0.049431915084520855,-1,0 202 | 000002.SZ,20180514,28.08,28.29,27.87,27.94,27.94,0.0,0.0,350838.54,984200.058,-0.3833333333333333,-0.16398174007733676,-1,-1 203 | 000002.SZ,20180515,28.11,28.15,27.34,27.75,27.94,-0.19,-0.68,405069.26,1120666.813,-0.19999999999999998,-0.26335477252801276,-1,-1 204 | 000002.SZ,20180516,27.55,27.75,27.21,27.23,27.75,-0.52,-1.87,373238.06,1020836.429,-0.053333333333333344,-0.03131756265958163,0,0 205 | 000002.SZ,20180517,27.19,27.22,26.77,26.79,27.23,-0.44,-1.62,445623.87,1199275.7,-0.023333333333333334,0.06742996672789243,0,0 206 | 000002.SZ,20180518,26.7,27.15,26.53,27.15,26.79,0.36,1.34,407222.27,1092945.215,-0.11666666666666665,-0.019999609788259,-1,0 207 | 000002.SZ,20180521,27.23,27.31,26.81,27.07,27.15,-0.08,-0.29,432446.76,1167666.221,-0.13999999999999999,-0.10393572886784885,-1,-1 208 | 000002.SZ,20180522,27.05,27.05,26.58,26.72,27.07,-0.35,-1.29,415469.03,1111277.324,-0.11,-0.087335014541944,-1,0 209 | 000002.SZ,20180523,26.72,27.17,26.56,26.8,26.72,0.08,0.3,433170.62,1164157.877,-0.10000000000000002,-0.03904503663380954,-1,0 210 | 000002.SZ,20180524,26.8,26.85,26.52,26.65,26.8,-0.15,-0.56,241610.93,643575.797,-0.35000000000000003,-0.015173305769761532,-1,0 211 | 000002.SZ,20180525,26.68,26.91,26.32,26.39,26.65,-0.26,-0.98,270005.46,717574.35,-0.5833333333333334,-0.2411159699161849,-1,-1 212 | 000002.SZ,20180528,26.39,26.82,26.3,26.5,26.39,0.11,0.42,254594.99,676789.912,-0.3033333333333333,-0.3705678238471351,-1,-1 213 | 000002.SZ,20180529,26.3,26.37,25.34,25.6,26.5,-0.9,-3.4,556052.35,1428341.707,0.13333333333333333,-0.036852351625760525,1,0 214 | 000002.SZ,20180530,25.21,25.21,24.58,24.64,25.6,-0.96,-3.75,540168.82,1342440.709,0.8866666666666667,0.2826989005009332,1,1 215 | 000002.SZ,20180531,24.77,25.68,24.56,25.59,24.64,0.95,3.86,705893.25,1768260.9019999998,0.6333333333333333,0.7742157832781473,1,1 216 | 000002.SZ,20180601,25.39,26.46,25.3,26.0,25.59,0.41,1.6,520770.82,1355014.4540000001,0.33,0.19895534048477798,1,1 217 | 000002.SZ,20180604,26.3,27.38,26.28,27.3,26.0,1.3,5.0,789190.77,2138820.837,0.03000000000000001,-0.22482309897740696,0,-1 218 | 000002.SZ,20180605,27.29,27.77,26.84,27.49,27.3,0.19,0.7,606841.37,1658473.532,-0.26,-0.08721310993035648,-1,0 219 | 000002.SZ,20180606,27.3,27.54,26.85,26.99,27.49,-0.5,-1.82,508162.53,1378427.3390000002,0.10666666666666665,-0.08930774619181965,1,0 220 | 000002.SZ,20180607,27.07,27.64,26.95,27.39,26.99,0.4,1.48,597535.75,1637918.2959999999,0.10666666666666665,0.24517088542381912,1,1 221 | 000002.SZ,20180608,27.1,27.18,26.55,26.71,27.39,-0.68,-2.48,388545.08,1040501.593,0.31,0.149235266248385,1,1 222 | 000002.SZ,20180611,26.74,27.73,26.68,27.31,26.71,0.6,2.25,526436.79,1437705.8909999998,0.02,0.13803020069996505,0,1 223 | 000002.SZ,20180612,27.57,27.83,27.23,27.71,27.31,0.4,1.46,498372.48,1376048.9840000002,0.09333333333333332,-0.11769782354434345,1,-1 224 | 000002.SZ,20180613,27.41,28.25,27.31,27.64,27.71,-0.07,-0.25,602843.57,1680583.497,-0.18000000000000002,0.062063196102777995,-1,0 225 | 000002.SZ,20180614,27.48,27.76,27.12,27.37,27.64,-0.27,-0.98,357604.45,980418.255,-0.1366666666666667,-0.12413411309321735,-1,-1 226 | 000002.SZ,20180615,27.37,28.3,27.33,27.99,27.37,0.62,2.27,698195.18,1951841.87,-0.08666666666666667,-0.0659398420651755,0,0 227 | 000002.SZ,20180619,27.85,27.85,26.91,27.1,27.99,-0.89,-3.18,600442.11,1639680.595,0.3333333333333333,0.06498456935087828,1,0 228 | 000002.SZ,20180620,26.99,27.29,26.41,26.96,27.1,-0.14,-0.52,393400.42,1059357.118,-0.15666666666666665,0.3350733677546182,-1,1 229 | 000002.SZ,20180621,26.97,28.5,26.82,27.73,26.96,0.77,2.86,777932.09,2167319.528,-0.7300000000000001,-0.25225334872802113,-1,-1 230 | 000002.SZ,20180622,27.5,28.2,27.44,28.1,27.73,0.37,1.33,444366.6,1235687.451,-1.2933333333333332,-0.5577595459421477,-1,-1 231 | 000002.SZ,20180625,28.25,28.38,26.41,26.49,28.1,-1.61,-5.73,615811.76,1686677.9019999998,-0.87,-0.6185463445385299,-1,-1 232 | 000002.SZ,20180626,25.82,26.06,24.83,25.54,26.49,-0.95,-3.59,591809.32,1503936.8669999999,-0.3133333333333334,-0.37049100567897175,-1,-1 233 | 000002.SZ,20180627,25.5,25.51,23.81,24.22,25.54,-1.32,-5.17,672347.28,1652394.698,-0.4733333333333334,0.019678514997164283,-1,0 234 | 000002.SZ,20180628,23.86,24.57,23.5,23.88,24.22,-0.34,-1.4,462512.82,1114653.844,-0.15333333333333335,-0.24341480781634664,-1,-1 235 | 000002.SZ,20180629,24.1,24.6,23.89,24.6,23.88,0.72,3.02,352884.91,859583.948,-0.5333333333333333,-0.09070531179507588,-1,0 236 | 000002.SZ,20180702,24.5,24.55,22.52,22.8,24.6,-1.8,-7.32,846203.86,1981131.638,0.08333333333333333,-0.303472689886888,0,-1 237 | 000002.SZ,20180703,23.1,23.48,22.8,23.42,22.8,0.62,2.72,549964.88,1274023.575,-0.06999999999999999,0.09227385262648252,0,1 238 | 000002.SZ,20180704,23.46,23.75,23.0,23.0,23.42,-0.42,-1.79,249881.03,582470.214,0.33666666666666667,0.037666636208693066,1,0 239 | 000002.SZ,20180705,23.02,23.41,22.85,23.05,23.0,0.05,0.22,267278.61,619393.007,0.3666666666666667,0.27026497254769,1,1 240 | 000002.SZ,20180706,23.34,23.6,22.65,23.21,23.05,0.16,0.69,346930.01,805107.0959999999,0.08333333333333337,0.20550908307234436,0,1 241 | 000002.SZ,20180709,23.37,24.05,23.37,24.01,23.21,0.8,3.45,404626.6,964798.9959999999,-0.05333333333333331,-0.13347819288571688,0,-1 242 | 000002.SZ,20180710,24.2,24.49,23.77,24.15,24.01,0.14,0.58,389259.48,936534.189,-0.1433333333333333,-0.08781041969855641,-1,0 243 | 000002.SZ,20180711,23.48,23.63,23.17,23.46,24.15,-0.69,-2.86,286052.97,668957.022,-0.10333333333333332,-0.02140333910783145,-1,0 244 | 000002.SZ,20180712,23.58,24.22,23.47,23.85,23.46,0.39,1.66,384228.4,916845.446,-0.29,-0.014364831646283596,-1,0 245 | 000002.SZ,20180713,24.24,24.35,23.64,23.72,23.85,-0.13,-0.55,407555.34,976014.5859999999,-0.4166666666666667,-0.1511656617124877,-1,-1 246 | 000002.SZ,20180716,23.59,23.66,22.81,23.15,23.72,-0.57,-2.4,430694.06,994746.276,-0.1566666666666667,-0.23562718053658818,-1,-1 247 | 000002.SZ,20180717,23.05,23.28,22.65,22.98,23.15,-0.17,-0.73,313869.3,718627.835,0.05333333333333332,-0.012384338279565305,0,0 248 | 000002.SZ,20180718,23.02,23.05,22.42,22.47,22.98,-0.51,-2.22,410641.88,930105.2729999999,0.4266666666666667,0.16410785714785248,1,1 249 | 000002.SZ,20180719,22.7,22.96,22.5,22.68,22.47,0.21,0.93,506949.38,1154160.7959999999,0.5666666666666668,0.35343987266222626,1,1 250 | 000002.SZ,20180720,22.64,23.49,22.02,23.14,22.68,0.46,2.0282,610195.34,1390937.5990000002,0.3466666666666667,0.33512553105751663,1,1 251 | 000002.SZ,20180723,22.96,23.9,22.9,23.75,23.14,0.61,2.6361,555706.13,1306045.471,-0.013333333333333327,0.01138569126526502,0,0 252 | 000002.SZ,20180724,24.04,25.09,24.04,24.38,23.75,0.63,2.6526,832814.22,2055205.705,-0.35333333333333333,-0.1911470641692481,-1,-1 253 | 000002.SZ,20180725,24.38,24.58,24.0,24.18,24.38,-0.2,-0.8203,355182.97,860631.782,-0.3333333333333333,-0.2112327893575034,-1,-1 254 | 000002.SZ,20180726,24.34,24.51,23.57,23.71,24.18,-0.47,-1.9438,385134.8,924376.0,-0.13,-0.07421520193417881,-1,0 255 | 000002.SZ,20180727,23.55,23.81,23.24,23.32,23.71,-0.39,-1.6449,322827.63,757327.314,-0.3233333333333333,0.05939213822285322,-1,0 256 | 000002.SZ,20180730,23.54,23.7,22.82,23.18,23.32,-0.14,-0.6003,357066.58,828078.838,-0.6,-0.18974333484967565,-1,-1 257 | 000002.SZ,20180731,23.29,23.49,23.06,23.32,23.18,0.14,0.604,354578.15,825861.7640000001,-0.7466666666666666,-0.3988928397496543,-1,-1 258 | 000002.SZ,20180801,23.24,23.24,22.21,22.35,23.32,-0.97,-4.1595,673866.01,1523419.154,-0.49666666666666665,-0.37984821190436696,-1,-1 259 | 000002.SZ,20180802,22.34,22.34,21.07,21.38,22.35,-0.97,-4.34,677777.75,1457805.725,0.16,-0.18253680904706335,1,-1 260 | 000002.SZ,20180803,21.39,21.54,21.05,21.08,21.38,-0.3,-1.4032,346072.3,735143.747,0.14,0.29591185371081025,1,1 261 | 000002.SZ,20180806,21.18,21.32,20.52,20.86,21.08,-0.22,-1.0436,315702.05,662879.8879999999,0.54,0.13931750426689773,1,1 262 | 000002.SZ,20180807,21.15,21.86,20.93,21.86,20.86,1.0,4.7939,451653.09,969166.202,0.43999999999999995,0.31116362730662017,1,1 263 | 000002.SZ,20180808,21.89,22.29,21.5,21.5,21.86,-0.36,-1.6468,410720.18,903023.745,0.5133333333333333,0.21052132586638123,1,1 264 | 000002.SZ,20180809,21.5,22.55,21.4,22.48,21.5,0.98,4.5581,896200.17,1997096.145,-0.053333333333333344,0.17935255388418822,0,1 265 | 000002.SZ,20180810,23.0,24.07,22.93,23.18,22.48,0.7,3.1139,1201163.03,2820091.738,-0.29333333333333333,-0.20056773384412144,-1,-1 266 | 000002.SZ,20180813,22.6,23.21,22.46,23.04,23.18,-0.14,-0.604,489494.76,1113409.912,-0.10666666666666667,-0.18532041182120657,-1,-1 267 | 000002.SZ,20180814,23.0,23.05,22.05,22.32,23.04,-0.72,-3.125,486784.81,1090898.794,0.12333333333333334,0.11082435588041929,1,1 268 | 000002.SZ,20180815,22.39,22.58,22.22,22.3,22.32,-0.02,-0.0896,472553.5,1058911.645,0.35000000000000003,0.20879776815573364,1,1 269 | 000002.SZ,20180816,21.63,23.08,21.61,22.72,22.3,0.42,1.8834,490384.44,1107212.515,0.39666666666666667,0.2637748075524965,1,1 270 | 000002.SZ,20180817,22.9,23.2,22.64,22.69,22.72,-0.03,-0.132,424160.44,969847.2159999999,0.4100000000000001,0.21032248894373565,1,1 271 | 000002.SZ,20180820,22.93,23.44,22.59,23.35,22.69,0.66,2.9088,476133.14,1101391.513,0.12333333333333335,0.14057033667961746,1,1 272 | 000002.SZ,20180821,23.39,23.98,23.22,23.91,23.35,0.56,2.3983,495846.24,1173410.505,-0.07666666666666667,-0.054190543194612,0,0 273 | 000002.SZ,20180822,23.91,24.08,23.67,23.92,23.91,0.01,0.0418,421833.83,1007170.63,0.29666666666666663,-0.0807796739538512,1,0 274 | 000002.SZ,20180823,23.0,23.15,22.24,22.82,23.02,-0.2,-0.8688,543058.67,1235013.686,0.3533333333333333,0.3621067426602045,1,1 275 | 000002.SZ,20180824,22.66,23.01,22.39,22.78,22.82,-0.04,-0.1753,273417.65,621888.066,0.37666666666666665,0.2870249162117639,1,1 276 | 000002.SZ,20180827,22.98,24.06,22.85,23.91,22.78,1.13,4.9605,594341.1,1403513.889,-0.13666666666666666,0.12292190472284942,-1,1 277 | 000002.SZ,20180828,23.8,24.14,23.73,23.88,23.91,-0.03,-0.1255,348101.15,832284.975,0.05666666666666668,-0.25101889332135535,0,-1 278 | 000002.SZ,20180829,23.74,24.4,23.72,23.91,23.88,0.03,0.1256,291143.33,700596.432,-0.11999999999999998,0.07887553135553983,-1,0 279 | 000002.SZ,20180830,23.8,24.18,23.48,23.5,23.91,-0.41,-1.7148,285923.67,681645.3840000001,0.31,0.00601823578278211,1,0 280 | 000002.SZ,20180831,23.2,24.36,23.18,24.05,23.5,0.55,2.3404,428145.12,1024351.411,-0.21333333333333335,0.3016783527533212,-1,1 281 | 000002.SZ,20180903,23.85,24.57,23.38,23.55,24.05,-0.5,-2.079,497612.43,1191130.969,-0.24666666666666667,-0.19868003865083073,-1,-1 282 | 000002.SZ,20180904,23.66,24.44,23.41,24.43,23.55,0.88,3.7367,437654.47,1052057.112,-0.40666666666666673,-0.2462801369031272,-1,-1 283 | 000002.SZ,20180905,24.11,24.2,23.41,23.41,24.43,-1.02,-4.1752,546644.37,1296346.119,0.01666666666666668,-0.14621870865424488,0,-1 284 | 000002.SZ,20180906,23.32,23.56,22.69,22.81,23.41,-0.6,-2.563,375368.9,864994.565,0.17666666666666667,0.17407161871592192,1,1 285 | 000002.SZ,20180907,22.85,23.65,22.85,23.21,22.81,0.4,1.7536,334943.33,780678.316,-0.11,0.18851252118746428,-1,1 286 | 000002.SZ,20180910,23.42,23.85,23.11,23.46,23.21,0.25,1.0771,474917.79,1115789.487,-0.12000000000000004,-0.16596702744563435,-1,-1 287 | 000002.SZ,20180911,23.39,23.64,23.09,23.34,23.46,-0.12,-0.5115,358358.75,832895.939,-0.006666666666666672,-0.12088537802298878,0,-1 288 | 000002.SZ,20180912,23.24,23.3,22.61,22.88,23.34,-0.46,-1.9709,313821.54,717459.921,-0.016666666666666663,0.07817960212628035,0,0 289 | 000002.SZ,20180913,23.31,23.47,22.78,23.1,22.88,0.22,0.9615,468837.96,1080871.662,-0.03333333333333333,0.024832409123579535,0,0 290 | 000002.SZ,20180914,23.13,23.39,22.81,23.32,23.1,0.22,0.9524,334870.79,775981.815,0.2833333333333333,-0.02009514739116046,1,0 291 | 000002.SZ,20180917,23.04,23.23,22.72,22.83,23.32,-0.49,-2.1012,255061.69,584416.325,0.3933333333333333,0.2861423633495966,1,1 292 | 000002.SZ,20180918,22.65,23.1,22.47,23.0,22.83,0.17,0.7446,404634.05,921475.475,0.9333333333333332,0.25873870889345796,1,1 293 | 000002.SZ,20180919,23.03,24.67,22.91,24.17,23.0,1.17,5.087,1317837.01,3170366.001,0.15666666666666673,0.6851350156466166,1,1 294 | 000002.SZ,20180920,24.01,24.43,23.8,24.01,24.17,-0.16,-0.662,521852.72,1257974.0320000001,0.22000000000000006,-0.2108265547951064,1,-1 295 | 000002.SZ,20180921,24.2,26.0,23.93,25.8,24.01,1.79,7.4552,1064216.38,2663500.909,-0.44999999999999996,-0.07285393377145145,-1,0 296 | 000002.SZ,20180925,24.8,25.12,24.4,24.64,25.8,-1.16,-4.4961,908838.54,2247254.08,-0.11333333333333333,-0.2280798240502677,-1,-1 297 | 000002.SZ,20180926,24.66,25.33,23.89,24.67,24.64,0.03,0.1218,845884.37,2088651.9519999998,-0.8733333333333334,0.0764184914032617,-1,0 298 | 000002.SZ,20180927,24.63,24.78,24.3,24.45,24.67,-0.22,-0.8918,445294.33,1090622.0420000001,-0.8766666666666666,-0.44486343403657297,-1,-1 299 | 000002.SZ,20180928,24.5,24.9,23.95,24.3,24.45,-0.15,-0.6135,683236.25,1671576.7140000002,-0.85,-0.48199560155471183,-1,-1 300 | 000002.SZ,20181008,22.95,23.2,21.98,22.05,24.3,-2.25,-9.2593,1037914.88,2339001.429,-0.37333333333333335,-0.3613072966535888,-1,-1 301 | 000002.SZ,20181009,22.05,22.12,21.26,21.82,22.05,-0.23,-1.0431,628633.52,1360687.509,-0.006666666666666636,-0.1389562528332076,0,-1 302 | 000002.SZ,20181010,21.77,22.03,21.4,21.75,21.82,-0.07,-0.3208,437797.76,951971.015,-0.09333333333333332,0.1514037529627481,0,1 303 | 000002.SZ,20181011,21.0,21.5,20.81,20.93,21.75,-0.82,-3.7701,709553.01,1498597.0990000002,0.026666666666666672,-0.0337772278984389,0,0 304 | 000002.SZ,20181012,21.14,21.95,21.05,21.8,20.93,0.87,4.1567,531382.96,1141837.523,-0.19333333333333336,-0.07392779012521121,-1,0 305 | 000002.SZ,20181015,21.78,21.78,21.15,21.47,21.8,-0.33,-1.5138,445515.74,953879.735,-0.29333333333333333,-0.12967015763123843,-1,-1 306 | 000002.SZ,20181016,21.6,22.06,20.86,21.01,21.47,-0.46,-2.1425,397553.12,853848.661,0.18333333333333332,-0.16404851367076254,1,-1 307 | 000002.SZ,20181017,21.35,21.46,20.88,21.22,21.01,0.21,0.9995,307507.86,651794.911,0.5333333333333333,0.24828995416561753,1,1 308 | 000002.SZ,20181018,21.1,21.22,20.51,20.59,21.22,-0.63,-2.9689,577959.29,1199550.666,0.5166666666666666,0.5347239009539285,1,1 309 | 000002.SZ,20181019,20.61,21.99,20.4,21.56,20.59,0.97,4.711,759809.75,1618603.263,0.34,0.1880170268813768,1,1 310 | 000002.SZ,20181022,21.88,23.26,21.73,22.82,21.56,1.26,5.8442,1008289.94,2292054.56,0.2233333333333333,0.0007730273405709582,1,0 311 | 000002.SZ,20181023,22.88,22.92,22.05,22.14,22.82,-0.68,-2.9798,509732.7,1144781.713,0.5566666666666668,0.11343834231297163,1,1 312 | 000002.SZ,20181024,22.23,23.12,22.1,22.58,22.14,0.44,1.9874,513460.91,1161439.9679999999,0.07333333333333332,0.4525024364391962,0,1 313 | 000002.SZ,20181025,22.18,23.5,22.0,23.49,22.58,0.91,4.0301,932699.5,2150598.9869999997,0.0666666666666667,-0.07032886465390537,0,0 314 | 000002.SZ,20181026,23.71,24.75,23.46,23.81,23.49,0.32,1.3623,729756.6,1754616.8380000002,0.14,-0.012357172270616024,1,0 315 | 000002.SZ,20181029,24.28,24.33,22.53,22.8,23.81,-1.01,-4.2419,618876.62,1435756.0,0.54,0.19448858658472687,1,1 316 | 000002.SZ,20181030,23.31,23.88,22.65,23.69,22.8,0.89,3.9035,602920.35,1415832.355,0.31,0.4379994402329126,1,1 317 | 000002.SZ,20181031,23.68,24.6,23.3,24.23,23.69,0.54,2.2794,565648.85,1358918.219,-0.06333333333333331,0.11164881000916152,0,1 318 | 000002.SZ,20181101,24.9,25.27,24.28,24.42,24.23,0.19,0.7842,617847.25,1531833.907,-0.09333333333333331,-0.2421143778165183,0,-1 319 | 000002.SZ,20181102,25.2,25.25,24.0,24.62,24.42,0.2,0.8190000000000001,652339.22,1601122.935,-0.25666666666666665,-0.06381815393765781,-1,0 320 | 000002.SZ,20181105,24.48,24.48,23.7,24.04,24.62,-0.58,-2.3558,583695.56,1399713.072,-0.016666666666666653,-0.06055241157611225,0,0 321 | 000002.SZ,20181106,24.03,24.4,23.71,24.14,24.04,0.1,0.41600000000000004,420823.93,1014339.811,-0.19666666666666666,0.09166090528170256,-1,1 322 | 000002.SZ,20181107,24.18,24.5,23.71,23.85,24.14,-0.29,-1.2013,441458.18,1062603.97,0.010000000000000009,-0.10225212117036198,0,-1 323 | 000002.SZ,20181108,24.15,24.59,23.92,23.99,23.85,0.14,0.5870000000000001,548200.71,1330024.287,-0.016666666666666663,0.027199276189009223,0,0 324 | 000002.SZ,20181109,23.89,24.33,23.53,23.55,23.99,-0.44,-1.8341,369283.45,877009.944,0.19999999999999998,0.02560926566521314,1,0 325 | 000002.SZ,20181112,23.41,23.96,23.4,23.88,23.55,0.33,1.4013,386742.23,920396.95,0.16666666666666666,0.15598282347122816,1,1 326 | 000002.SZ,20181113,23.48,24.03,23.44,23.94,23.88,0.06,0.2513,453424.04,1079145.537,0.28,0.10450437794129042,1,1 327 | 000002.SZ,20181114,24.18,24.49,23.96,24.15,23.94,0.21,0.8772,618882.72,1500411.129,0.21,0.16250848690668732,1,1 328 | 000002.SZ,20181115,24.05,24.4,23.96,24.38,24.15,0.23,0.9524,258623.58,627715.21,0.23,0.0823294953505197,1,0 329 | -------------------------------------------------------------------------------- /experiment2-feature-selection/1/实验一trend.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/1/实验一trend.png -------------------------------------------------------------------------------- /experiment2-feature-selection/1/实验一回归图.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/1/实验一回归图.png -------------------------------------------------------------------------------- /experiment2-feature-selection/2/实验二trend.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/2/实验二trend.png -------------------------------------------------------------------------------- /experiment2-feature-selection/2/实验二回归图.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/2/实验二回归图.png -------------------------------------------------------------------------------- /experiment2-feature-selection/3/实验三trend.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/3/实验三trend.png -------------------------------------------------------------------------------- /experiment2-feature-selection/3/实验三回归图.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/3/实验三回归图.png -------------------------------------------------------------------------------- /experiment2-feature-selection/4/实验四trend.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/4/实验四trend.png -------------------------------------------------------------------------------- /experiment2-feature-selection/4/实验四回归图.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/4/实验四回归图.png -------------------------------------------------------------------------------- /experiment2-feature-selection/5/实验五trend.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/5/实验五trend.png -------------------------------------------------------------------------------- /experiment2-feature-selection/5/实验五回归图.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/5/实验五回归图.png -------------------------------------------------------------------------------- /experiment2-feature-selection/ExperiementResultComparison.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/ExperiementResultComparison.png -------------------------------------------------------------------------------- /experiment2-feature-selection/Figure_1-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/Figure_1-1.png -------------------------------------------------------------------------------- /experiment2-feature-selection/PlotTrendGraph/.ipynb_checkpoints/PlotTrendGraph-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "from pandas import DataFrame\n", 11 | "import numpy as np\n", 12 | "from matplotlib.patches import Rectangle\n", 13 | "import matplotlib.pyplot as plt" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": {}, 20 | "outputs": [ 21 | { 22 | "data": { 23 | "text/html": [ 24 | "
\n", 25 | "\n", 38 | "\n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | "
ts_codetrade_dateopenhighlowclosepre_closechangepct_chgvolamountave_changepred_ave_changereal_trendpred_trend
0601318.SH2017042837.9038.0737.6137.9637.820.140.37678663.872568647.819-0.046667-0.00761511
1601318.SH2017050237.8038.3837.8037.9137.96-0.05-0.13619325.392359210.184-0.216667-0.07022600
2601318.SH2017050337.9538.2837.8238.0037.910.090.24505383.861920918.772-0.070000-0.21626600
3601318.SH2017050437.9538.1037.7337.8238.00-0.18-0.47548573.552080732.8930.056667-0.00226700
4601318.SH2017050537.7037.8637.2037.2637.82-0.56-1.48623979.602337681.7120.8600000.21084010
\n", 152 | "
" 153 | ], 154 | "text/plain": [ 155 | " ts_code trade_date open high low close pre_close change \\\n", 156 | "0 601318.SH 20170428 37.90 38.07 37.61 37.96 37.82 0.14 \n", 157 | "1 601318.SH 20170502 37.80 38.38 37.80 37.91 37.96 -0.05 \n", 158 | "2 601318.SH 20170503 37.95 38.28 37.82 38.00 37.91 0.09 \n", 159 | "3 601318.SH 20170504 37.95 38.10 37.73 37.82 38.00 -0.18 \n", 160 | "4 601318.SH 20170505 37.70 37.86 37.20 37.26 37.82 -0.56 \n", 161 | "\n", 162 | " pct_chg vol amount ave_change pred_ave_change real_trend \\\n", 163 | "0 0.37 678663.87 2568647.819 -0.046667 -0.007615 1 \n", 164 | "1 -0.13 619325.39 2359210.184 -0.216667 -0.070226 0 \n", 165 | "2 0.24 505383.86 1920918.772 -0.070000 -0.216266 0 \n", 166 | "3 -0.47 548573.55 2080732.893 0.056667 -0.002267 0 \n", 167 | "4 -1.48 623979.60 2337681.712 0.860000 0.210840 1 \n", 168 | "\n", 169 | " pred_trend \n", 170 | "0 1 \n", 171 | "1 0 \n", 172 | "2 0 \n", 173 | "3 0 \n", 174 | "4 0 " 175 | ] 176 | }, 177 | "execution_count": 2, 178 | "metadata": {}, 179 | "output_type": "execute_result" 180 | } 181 | ], 182 | "source": [ 183 | "df1 = pd.read_csv('1-Result-Vanke-from-20110701-to-20181118.csv')\n", 184 | "df2 = pd.read_csv('1-Result-Vanke-from-20110701-to-20181118.csv')\n", 185 | "df3 = pd.read_csv('1-Result-Vanke-from-20110701-to-20181118.csv')\n", 186 | "df4 = pd.read_csv('1-Result-Vanke-from-20110701-to-20181118.csv')\n", 187 | "df5 = pd.read_csv('1-Result-Vanke-from-20110701-to-20181118.csv')\n", 188 | "df1.head()" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": 3, 194 | "metadata": {}, 195 | "outputs": [], 196 | "source": [ 197 | "real_trend = df['real_trend']\n", 198 | "pred_trend = df['pred_trend']\n" 199 | ] 200 | }, 201 | { 202 | "cell_type": "code", 203 | "execution_count": 4, 204 | "metadata": {}, 205 | "outputs": [], 206 | "source": [ 207 | "xlims = (0,len(real_trend))\n", 208 | "ylims = (0, 300)" 209 | ] 210 | }, 211 | { 212 | "cell_type": "code", 213 | "execution_count": 5, 214 | "metadata": {}, 215 | "outputs": [], 216 | "source": [ 217 | "def plot_renko(data, brick_size):\n", 218 | " fig = plt.figure(figsize=(20,10))\n", 219 | " fig.clf()\n", 220 | " axes = fig.gca() \n", 221 | " prev_num = 0\n", 222 | " for index, number in enumerate(data):\n", 223 | "# print(index, number)\n", 224 | " if number == 1:\n", 225 | " facecolor='red'\n", 226 | " elif number == 0:\n", 227 | " facecolor='blue'\n", 228 | " else:\n", 229 | " facecolor='green'\n", 230 | "\n", 231 | " prev_num += number\n", 232 | "\n", 233 | " renko = Rectangle(\n", 234 | " (index, prev_num * brick_size+100), 1, brick_size,\n", 235 | " facecolor=facecolor, alpha=0.5\n", 236 | " )\n", 237 | " axes.add_patch(renko)\n", 238 | " plt.xlim(xlims)\n", 239 | " plt.ylim(ylims)\n", 240 | "# plt.show()\n" 241 | ] 242 | }, 243 | { 244 | "cell_type": "code", 245 | "execution_count": 16, 246 | "metadata": {}, 247 | "outputs": [ 248 | { 249 | "data": { 250 | "text/plain": [ 251 | "(0, 300)" 252 | ] 253 | }, 254 | "execution_count": 16, 255 | "metadata": {}, 256 | "output_type": "execute_result" 257 | } 258 | ], 259 | "source": [ 260 | "%matplotlib qt\n", 261 | "fig = plt.figure(figsize=(20,10))\n", 262 | "fig.clf()\n", 263 | "axes = fig.gca() \n", 264 | "prev_num = 0\n", 265 | "data = real_trend\n", 266 | "brick_size = 2\n", 267 | "for index, number in enumerate(data):\n", 268 | "# print(index, number)\n", 269 | " if number == 1:\n", 270 | " facecolor='red'\n", 271 | " elif number == 0:\n", 272 | " facecolor='blue'\n", 273 | " else:\n", 274 | " facecolor='green'\n", 275 | "\n", 276 | " prev_num += number\n", 277 | "\n", 278 | " renko = Rectangle(\n", 279 | " (index, prev_num * brick_size+200), 1, brick_size,\n", 280 | " facecolor=facecolor, alpha=0.5\n", 281 | " )\n", 282 | " axes.add_patch(renko)\n", 283 | "\n", 284 | "prev_num = 0\n", 285 | "data = pred_trend\n", 286 | "brick_size = 2\n", 287 | "for index, number in enumerate(data):\n", 288 | "# print(index, number)\n", 289 | " if number == 1:\n", 290 | " facecolor='red'\n", 291 | " elif number == 0:\n", 292 | " facecolor='blue'\n", 293 | " else:\n", 294 | " facecolor='green'\n", 295 | "\n", 296 | " prev_num += number\n", 297 | "\n", 298 | " renko = Rectangle(\n", 299 | " (index, prev_num * brick_size+130), 1, brick_size,\n", 300 | " facecolor=facecolor, alpha=0.5\n", 301 | " )\n", 302 | " axes.add_patch(renko) \n", 303 | "\n", 304 | " \n", 305 | "prev_num = 0\n", 306 | "data = svr_pred_trend\n", 307 | "brick_size = 2\n", 308 | "for index, number in enumerate(data):\n", 309 | "# print(index, number)\n", 310 | " if number == 1:\n", 311 | " facecolor='red'\n", 312 | " elif number == 0:\n", 313 | " facecolor='blue'\n", 314 | " else:\n", 315 | " facecolor='green'\n", 316 | "\n", 317 | " prev_num += number\n", 318 | "\n", 319 | " renko = Rectangle(\n", 320 | " (index, prev_num * brick_size+20), 1, brick_size,\n", 321 | " facecolor=facecolor, alpha=0.5\n", 322 | " )\n", 323 | " axes.add_patch(renko) \n", 324 | " \n", 325 | "# prev_num = 0\n", 326 | "# data = random_trend\n", 327 | "# brick_size = 2\n", 328 | "# for index, number in enumerate(data):\n", 329 | "# # print(index, number)\n", 330 | "# if number == 1:\n", 331 | "# facecolor='red'\n", 332 | "# elif number == 0:\n", 333 | "# facecolor='blue'\n", 334 | "# else:\n", 335 | "# facecolor='green'\n", 336 | "\n", 337 | "# prev_num += number\n", 338 | "\n", 339 | "# renko = Rectangle(\n", 340 | "# (index, prev_num * brick_size+100), 1, brick_size,\n", 341 | "# facecolor=facecolor, alpha=0.5\n", 342 | "# )\n", 343 | "# axes.add_patch(renko) \n", 344 | "plt.tick_params(axis='both', which='major', labelsize=30)\n", 345 | "# plt.tick_params(axis='both', which='minor', labelsize=8)\n", 346 | "plt.xlabel('Days', fontsize=40)\n", 347 | "plt.ylabel('Trend Prediction', fontsize=40)\n", 348 | "plt.xlim(xlims)\n", 349 | "plt.ylim(ylims)" 350 | ] 351 | }, 352 | { 353 | "cell_type": "code", 354 | "execution_count": 10, 355 | "metadata": {}, 356 | "outputs": [ 357 | { 358 | "data": { 359 | "text/html": [ 360 | "
\n", 361 | "\n", 374 | "\n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | "
ts_codetrade_dateopenhighlowclosepre_closechangepct_chgvolamountave_changepred_ave_changereal_trendpred_trend
0601318.SH2017042837.9038.0737.6137.9637.820.140.37678663.872568647.819-0.046667-0.00761511
1601318.SH2017050237.8038.3837.8037.9137.96-0.05-0.13619325.392359210.184-0.216667-0.07022600
2601318.SH2017050337.9538.2837.8238.0037.910.090.24505383.861920918.772-0.070000-0.21626600
3601318.SH2017050437.9538.1037.7337.8238.00-0.18-0.47548573.552080732.8930.056667-0.00226700
4601318.SH2017050537.7037.8637.2037.2637.82-0.56-1.48623979.602337681.7120.8600000.21084010
\n", 488 | "
" 489 | ], 490 | "text/plain": [ 491 | " ts_code trade_date open high low close pre_close change \\\n", 492 | "0 601318.SH 20170428 37.90 38.07 37.61 37.96 37.82 0.14 \n", 493 | "1 601318.SH 20170502 37.80 38.38 37.80 37.91 37.96 -0.05 \n", 494 | "2 601318.SH 20170503 37.95 38.28 37.82 38.00 37.91 0.09 \n", 495 | "3 601318.SH 20170504 37.95 38.10 37.73 37.82 38.00 -0.18 \n", 496 | "4 601318.SH 20170505 37.70 37.86 37.20 37.26 37.82 -0.56 \n", 497 | "\n", 498 | " pct_chg vol amount ave_change pred_ave_change real_trend \\\n", 499 | "0 0.37 678663.87 2568647.819 -0.046667 -0.007615 1 \n", 500 | "1 -0.13 619325.39 2359210.184 -0.216667 -0.070226 0 \n", 501 | "2 0.24 505383.86 1920918.772 -0.070000 -0.216266 0 \n", 502 | "3 -0.47 548573.55 2080732.893 0.056667 -0.002267 0 \n", 503 | "4 -1.48 623979.60 2337681.712 0.860000 0.210840 1 \n", 504 | "\n", 505 | " pred_trend \n", 506 | "0 1 \n", 507 | "1 0 \n", 508 | "2 0 \n", 509 | "3 0 \n", 510 | "4 0 " 511 | ] 512 | }, 513 | "execution_count": 10, 514 | "metadata": {}, 515 | "output_type": "execute_result" 516 | } 517 | ], 518 | "source": [ 519 | "df = pd.read_csv('Result-Pingan-from-20110101-to-20181118.csv')\n", 520 | "df.head()" 521 | ] 522 | }, 523 | { 524 | "cell_type": "code", 525 | "execution_count": 11, 526 | "metadata": {}, 527 | "outputs": [], 528 | "source": [ 529 | "real_trend = df['real_trend']\n", 530 | "pred_trend = df['pred_trend']" 531 | ] 532 | }, 533 | { 534 | "cell_type": "code", 535 | "execution_count": 12, 536 | "metadata": {}, 537 | "outputs": [], 538 | "source": [ 539 | "temp_list = []\n", 540 | "temp_df = real_trend - pred_trend\n", 541 | "for i in range(0, len(temp_df)):\n", 542 | " temp_list.append([i, temp_df[i]])" 543 | ] 544 | }, 545 | { 546 | "cell_type": "code", 547 | "execution_count": 13, 548 | "metadata": {}, 549 | "outputs": [], 550 | "source": [ 551 | "%matplotlib qt\n", 552 | "plt.clf()\n", 553 | "# plt.figure()\n", 554 | "plt.scatter( *zip(*temp_list), )\n", 555 | "# plt.scatter(real_df.index.values, pred_df[0],color='blue', marker='^')\n", 556 | "# plt.scatter(real_df.index.values, real_df[0],color='red', marker='.')\n", 557 | "plt.title('Trend Prediction Accuracy of 601318SH' )\n", 558 | "plt.xlabel('Days')\n", 559 | "plt.ylabel('Trend Prediction')\n", 560 | "plt.show()" 561 | ] 562 | }, 563 | { 564 | "cell_type": "code", 565 | "execution_count": 14, 566 | "metadata": {}, 567 | "outputs": [ 568 | { 569 | "ename": "NameError", 570 | "evalue": "name 'pred_df' is not defined", 571 | "output_type": "error", 572 | "traceback": [ 573 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 574 | "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", 575 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mtemp_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtemp_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreal_trend\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mpred_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtemp_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mtemp_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtemp_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 576 | "\u001b[1;31mNameError\u001b[0m: name 'pred_df' is not defined" 577 | ] 578 | } 579 | ], 580 | "source": [ 581 | "temp_list = []\n", 582 | "temp_df = real_trend[0] - pred_df[0]\n", 583 | "for i in range(0, len(temp_df)):\n", 584 | " temp_list.append([i, temp_df[i]])" 585 | ] 586 | } 587 | ], 588 | "metadata": { 589 | "kernelspec": { 590 | "display_name": "Python 3", 591 | "language": "python", 592 | "name": "python3" 593 | }, 594 | "language_info": { 595 | "codemirror_mode": { 596 | "name": "ipython", 597 | "version": 3 598 | }, 599 | "file_extension": ".py", 600 | "mimetype": "text/x-python", 601 | "name": "python", 602 | "nbconvert_exporter": "python", 603 | "pygments_lexer": "ipython3", 604 | "version": "3.6.5" 605 | } 606 | }, 607 | "nbformat": 4, 608 | "nbformat_minor": 2 609 | } 610 | -------------------------------------------------------------------------------- /experiment2-feature-selection/modelStructure.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tutan123/StockTrendPrediction/e5cf9f8469398611c7361727d3003f6dd4ffcc20/experiment2-feature-selection/modelStructure.png -------------------------------------------------------------------------------- /experiment3-sentimentAnalysis/.ipynb_checkpoints/1GubaData-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "from pandas import DataFrame\n", 11 | "import numpy as np\n", 12 | "from numpy import row_stack,column_stack\n", 13 | "import matplotlib.pyplot as plt\n", 14 | "from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY,YEARLY\n", 15 | "# from mpl_finance import quotes_historical_yahoo_ohlc, candlestick_ohlc\n", 16 | "from matplotlib.pylab import date2num\n", 17 | "\n", 18 | "import tushare as ts\n", 19 | "import datetime\n", 20 | "import time" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 2, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "df = pd.read_csv('./cjpl/20160701_20160801.csv', sep=',',header = 0, usecols=range(8))" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 3, 35 | "metadata": {}, 36 | "outputs": [ 37 | { 38 | "data": { 39 | "text/html": [ 40 | "
\n", 41 | "\n", 54 | "\n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | "
页面信息评论标题发表作者发表时间阅读量评论量内容链接评论内容
0['page_2360', 12]中国今年调减玉米种植面积3000万亩 13年来首次财经评论2016-07-31 23:52:03113828http://guba.eastmoney.com/news,cjpl,501393101....来源：中国新闻网 编辑：东方财富网记者31日从中国农业部获悉，今年以来，中国农业结构调整进展...
1['page_2360', 13]科研项目资金改革解读：打酱油的钱可以买醋了财经评论2016-07-31 23:37:0197752http://guba.eastmoney.com/news,cjpl,501370861....来源：新华社 编辑：东方财富网意见指出，科研项目实施期间，年度剩余资金可以结转下一年度继续使...
2['page_2360', 14]A股交易策略：战略性配置绩优价值股财经评论2016-07-31 23:20:58235635http://guba.eastmoney.com/news,cjpl,501350935....来源：兴业证券 编辑：东方财富网投资要点★展望：周中调整后，市场短期再度大幅波动的可能性下降...
3['page_2360', 15]中国将发SDR计价债券 IMF发言人回应市场疑惑财经评论2016-07-31 22:48:53212319http://guba.eastmoney.com/news,cjpl,501315579....来源：一财网 编辑：东方财富网第一财经记者日前从知情人士处获悉，中国工商银行(亚洲)有限公司...
4['page_2360', 16]安徽放大招控楼市：买地首付款最少50% 闲置两年财经评论2016-07-31 22:34:52698812http://guba.eastmoney.com/news,cjpl,501299784....来源：澎湃 编辑：东方财富网安徽政府对开发商拿地盯得更加紧了。7月22日，安徽省国土资源厅印...
5['page_2360', 17]牛散出没请注意 赵建平坚守这家上市公司三年赚财经评论2016-07-31 22:19:5118983360http://guba.eastmoney.com/news,cjpl,501282167....来源：证券时报 编辑：东方财富网随着上市公司半年报的陆续公布，A股多位知名牛散在第二季度的动...
6['page_2360', 18]八月首周新股发行猛增至9只 抽奖靠运气不妨关注财经评论2016-07-31 22:04:514134846http://guba.eastmoney.com/news,cjpl,501266649....来源：微信公众号莲花财经 编辑：东方财富网正当7月A股难得走出不错行情的时候，新股发行却悄然...
7['page_2360', 19]中报行情要不要跟一波？41股业绩预增超10倍财经评论2016-07-31 21:51:5010965928http://guba.eastmoney.com/news,cjpl,501256128....来源：券商中国 编辑：东方财富网本周A股正式进入中报业绩披露密集期，全市场共184只个股已经...
8['page_2360', 20]市值配售尽情嗨：8月首周可打9只新股 周三有三财经评论2016-07-31 21:47:49112641299http://guba.eastmoney.com/news,cjpl,501252589....来源：券商中国 编辑：东方财富网新股发行骤然增多了！就在7月24日-7月29日这一周，每天都...
9['page_2360', 21]最多一日发9封问询函 近一周29家上市公司被交易财经评论2016-07-31 21:40:4710415540http://guba.eastmoney.com/news,cjpl,501245776....来源：微信公众号莲花财经 编辑：东方财富网7月24日到29日，沪深两市交易所共发出29封各类...
10['page_2360', 22]从爱理不理到高攀不起 绩优股弱市成功集体逆袭财经评论2016-07-31 21:36:48484074http://guba.eastmoney.com/news,cjpl,501242056....来源：每日经济新闻 编辑：东方财富网炒股，你还看业绩？相信很多人在刚刚踏入股市之时，会被一部...
11['page_2360', 23]奥运会要来了！一文让你搞懂“奥运魔咒”是否那财经评论2016-07-31 21:23:46224138http://guba.eastmoney.com/news,cjpl,501229373....来源：每日经济新闻 编辑：东方财富网时间过得真快，8月份将来临！在本周三，一条理财新规的传闻...
12['page_2360', 24]国泰君安：监管趋严下短期暂离旋涡 拥抱业绩周财经评论2016-07-31 21:09:41117456http://guba.eastmoney.com/news,cjpl,501214802....来源： 编辑：东方财富网【国泰君安：监管趋严下短期暂离旋涡 拥抱业绩周期】国泰君安乔永远认为...
13['page_2360', 25]芝麻信用：11%的P2P用户存在10家以上的多头借贷财经评论2016-07-31 20:28:44110611http://guba.eastmoney.com/news,cjpl,501169778....来源： 编辑：东方财富网【芝麻信用：11%的P2P用户存在10家以上的多头借贷】芝麻信用总经...
14['page_2360', 26]永远观市：暂离旋涡 拥抱业绩财经评论2016-07-31 20:26:428313423http://guba.eastmoney.com/news,cjpl,501167675....来源：国泰君安 编辑：东方财富网金融去杠杆短期难言结束，监管趋严下，短期暂离旋涡。配置上拥抱...
15['page_2360', 27]监管风向趋严 大金融稳步前行财经评论2016-07-31 20:24:43234308http://guba.eastmoney.com/news,cjpl,501166473....来源：华泰证券 编辑：东方财富网大金融：沧海横流，方显大金融之稳健银行理财监管新规征求意见稿...
16['page_2360', 28]达志科技网上发行中签率为0.0182%财经评论2016-07-31 19:48:383936918http://guba.eastmoney.com/news,cjpl,501137699....来源：全景网 编辑：东方财富网达志科技(300530)周日发布公告称，其首次公开发行股票网上...
17['page_2360', 29]任泽平：经济通胀回落 抑制资产泡沫去产能财经评论2016-07-31 19:40:36120145http://guba.eastmoney.com/news,cjpl,501130754....来源： 编辑：东方财富网【任泽平：经济通胀回落，抑制资产泡沫去产能】货币政策落入流动性陷阱情...
18['page_2360', 30]晚间上市公司利好消息一览：世纪游轮拟进军移动财经评论2016-07-31 19:08:471861253http://guba.eastmoney.com/news,cjpl,501101150....来源：东方财富网美克家居8月1日起停牌 正筹划非公开发行美克家居(600337)周日发布公告...
19['page_2360', 31]小心！股市正发生一件45年来都没出现的事财经评论2016-07-31 18:58:29139846http://guba.eastmoney.com/news,cjpl,501090726....来源：凤凰网 编辑：东方财富网现在，股市已变得让人烦躁不安，正在发生一件45年来都没发生的事...
\n", 291 | "
" 292 | ], 293 | "text/plain": [ 294 | " 页面信息 评论标题 发表作者 发表时间 \\\n", 295 | "0 ['page_2360', 12] 中国今年调减玉米种植面积3000万亩 13年来首次 财经评论 2016-07-31 23:52:03 \n", 296 | "1 ['page_2360', 13] 科研项目资金改革解读：打酱油的钱可以买醋了 财经评论 2016-07-31 23:37:01 \n", 297 | "2 ['page_2360', 14] A股交易策略：战略性配置绩优价值股 财经评论 2016-07-31 23:20:58 \n", 298 | "3 ['page_2360', 15] 中国将发SDR计价债券 IMF发言人回应市场疑惑 财经评论 2016-07-31 22:48:53 \n", 299 | "4 ['page_2360', 16] 安徽放大招控楼市：买地首付款最少50% 闲置两年 财经评论 2016-07-31 22:34:52 \n", 300 | "5 ['page_2360', 17] 牛散出没请注意 赵建平坚守这家上市公司三年赚 财经评论 2016-07-31 22:19:51 \n", 301 | "6 ['page_2360', 18] 八月首周新股发行猛增至9只 抽奖靠运气不妨关注 财经评论 2016-07-31 22:04:51 \n", 302 | "7 ['page_2360', 19] 中报行情要不要跟一波？41股业绩预增超10倍 财经评论 2016-07-31 21:51:50 \n", 303 | "8 ['page_2360', 20] 市值配售尽情嗨：8月首周可打9只新股 周三有三 财经评论 2016-07-31 21:47:49 \n", 304 | "9 ['page_2360', 21] 最多一日发9封问询函 近一周29家上市公司被交易 财经评论 2016-07-31 21:40:47 \n", 305 | "10 ['page_2360', 22] 从爱理不理到高攀不起 绩优股弱市成功集体逆袭 财经评论 2016-07-31 21:36:48 \n", 306 | "11 ['page_2360', 23] 奥运会要来了！一文让你搞懂“奥运魔咒”是否那 财经评论 2016-07-31 21:23:46 \n", 307 | "12 ['page_2360', 24] 国泰君安：监管趋严下短期暂离旋涡 拥抱业绩周 财经评论 2016-07-31 21:09:41 \n", 308 | "13 ['page_2360', 25] 芝麻信用：11%的P2P用户存在10家以上的多头借贷 财经评论 2016-07-31 20:28:44 \n", 309 | "14 ['page_2360', 26] 永远观市：暂离旋涡 拥抱业绩 财经评论 2016-07-31 20:26:42 \n", 310 | "15 ['page_2360', 27] 监管风向趋严 大金融稳步前行 财经评论 2016-07-31 20:24:43 \n", 311 | "16 ['page_2360', 28] 达志科技网上发行中签率为0.0182% 财经评论 2016-07-31 19:48:38 \n", 312 | "17 ['page_2360', 29] 任泽平：经济通胀回落 抑制资产泡沫去产能 财经评论 2016-07-31 19:40:36 \n", 313 | "18 ['page_2360', 30] 晚间上市公司利好消息一览：世纪游轮拟进军移动 财经评论 2016-07-31 19:08:47 \n", 314 | "19 ['page_2360', 31] 小心！股市正发生一件45年来都没出现的事 财经评论 2016-07-31 18:58:29 \n", 315 | "\n", 316 | " 阅读量 评论量 内容链接 \\\n", 317 | "0 11382 8 http://guba.eastmoney.com/news,cjpl,501393101.... \n", 318 | "1 9775 2 http://guba.eastmoney.com/news,cjpl,501370861.... \n", 319 | "2 23563 5 http://guba.eastmoney.com/news,cjpl,501350935.... \n", 320 | "3 21231 9 http://guba.eastmoney.com/news,cjpl,501315579.... \n", 321 | "4 6988 12 http://guba.eastmoney.com/news,cjpl,501299784.... \n", 322 | "5 189833 60 http://guba.eastmoney.com/news,cjpl,501282167.... \n", 323 | "6 41348 46 http://guba.eastmoney.com/news,cjpl,501266649.... \n", 324 | "7 109659 28 http://guba.eastmoney.com/news,cjpl,501256128.... \n", 325 | "8 112641 299 http://guba.eastmoney.com/news,cjpl,501252589.... \n", 326 | "9 104155 40 http://guba.eastmoney.com/news,cjpl,501245776.... \n", 327 | "10 48407 4 http://guba.eastmoney.com/news,cjpl,501242056.... \n", 328 | "11 22413 8 http://guba.eastmoney.com/news,cjpl,501229373.... \n", 329 | "12 11745 6 http://guba.eastmoney.com/news,cjpl,501214802.... \n", 330 | "13 11061 1 http://guba.eastmoney.com/news,cjpl,501169778.... \n", 331 | "14 83134 23 http://guba.eastmoney.com/news,cjpl,501167675.... \n", 332 | "15 23430 8 http://guba.eastmoney.com/news,cjpl,501166473.... \n", 333 | "16 39369 18 http://guba.eastmoney.com/news,cjpl,501137699.... \n", 334 | "17 12014 5 http://guba.eastmoney.com/news,cjpl,501130754.... \n", 335 | "18 18612 53 http://guba.eastmoney.com/news,cjpl,501101150.... \n", 336 | "19 13984 6 http://guba.eastmoney.com/news,cjpl,501090726.... \n", 337 | "\n", 338 | " 评论内容 \n", 339 | "0 来源：中国新闻网 编辑：东方财富网记者31日从中国农业部获悉，今年以来，中国农业结构调整进展... \n", 340 | "1 来源：新华社 编辑：东方财富网意见指出，科研项目实施期间，年度剩余资金可以结转下一年度继续使... \n", 341 | "2 来源：兴业证券 编辑：东方财富网投资要点★展望：周中调整后，市场短期再度大幅波动的可能性下降... \n", 342 | "3 来源：一财网 编辑：东方财富网第一财经记者日前从知情人士处获悉，中国工商银行(亚洲)有限公司... \n", 343 | "4 来源：澎湃 编辑：东方财富网安徽政府对开发商拿地盯得更加紧了。7月22日，安徽省国土资源厅印... \n", 344 | "5 来源：证券时报 编辑：东方财富网随着上市公司半年报的陆续公布，A股多位知名牛散在第二季度的动... \n", 345 | "6 来源：微信公众号莲花财经 编辑：东方财富网正当7月A股难得走出不错行情的时候，新股发行却悄然... \n", 346 | "7 来源：券商中国 编辑：东方财富网本周A股正式进入中报业绩披露密集期，全市场共184只个股已经... \n", 347 | "8 来源：券商中国 编辑：东方财富网新股发行骤然增多了！就在7月24日-7月29日这一周，每天都... \n", 348 | "9 来源：微信公众号莲花财经 编辑：东方财富网7月24日到29日，沪深两市交易所共发出29封各类... \n", 349 | "10 来源：每日经济新闻 编辑：东方财富网炒股，你还看业绩？相信很多人在刚刚踏入股市之时，会被一部... \n", 350 | "11 来源：每日经济新闻 编辑：东方财富网时间过得真快，8月份将来临！在本周三，一条理财新规的传闻... \n", 351 | "12 来源： 编辑：东方财富网【国泰君安：监管趋严下短期暂离旋涡 拥抱业绩周期】国泰君安乔永远认为... \n", 352 | "13 来源： 编辑：东方财富网【芝麻信用：11%的P2P用户存在10家以上的多头借贷】芝麻信用总经... \n", 353 | "14 来源：国泰君安 编辑：东方财富网金融去杠杆短期难言结束，监管趋严下，短期暂离旋涡。配置上拥抱... \n", 354 | "15 来源：华泰证券 编辑：东方财富网大金融：沧海横流，方显大金融之稳健银行理财监管新规征求意见稿... \n", 355 | "16 来源：全景网 编辑：东方财富网达志科技(300530)周日发布公告称，其首次公开发行股票网上... \n", 356 | "17 来源： 编辑：东方财富网【任泽平：经济通胀回落，抑制资产泡沫去产能】货币政策落入流动性陷阱情... \n", 357 | "18 来源：东方财富网美克家居8月1日起停牌 正筹划非公开发行美克家居(600337)周日发布公告... \n", 358 | "19 来源：凤凰网 编辑：东方财富网现在，股市已变得让人烦躁不安，正在发生一件45年来都没发生的事... " 359 | ] 360 | }, 361 | "execution_count": 3, 362 | "metadata": {}, 363 | "output_type": "execute_result" 364 | } 365 | ], 366 | "source": [ 367 | "df.head(20)" 368 | ] 369 | }, 370 | { 371 | "cell_type": "code", 372 | "execution_count": 4, 373 | "metadata": {}, 374 | "outputs": [ 375 | { 376 | "data": { 377 | "text/html": [ 378 | "
\n", 379 | "\n", 392 | "\n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | "
发表时间评论标题
02016-07-31 23:52:03中国今年调减玉米种植面积3000万亩 13年来首次
12016-07-31 23:37:01科研项目资金改革解读：打酱油的钱可以买醋了
22016-07-31 23:20:58A股交易策略：战略性配置绩优价值股
32016-07-31 22:48:53中国将发SDR计价债券 IMF发言人回应市场疑惑
42016-07-31 22:34:52安徽放大招控楼市：买地首付款最少50% 闲置两年
\n", 428 | "
" 429 | ], 430 | "text/plain": [ 431 | " 发表时间 评论标题\n", 432 | "0 2016-07-31 23:52:03 中国今年调减玉米种植面积3000万亩 13年来首次\n", 433 | "1 2016-07-31 23:37:01 科研项目资金改革解读：打酱油的钱可以买醋了\n", 434 | "2 2016-07-31 23:20:58 A股交易策略：战略性配置绩优价值股\n", 435 | "3 2016-07-31 22:48:53 中国将发SDR计价债券 IMF发言人回应市场疑惑\n", 436 | "4 2016-07-31 22:34:52 安徽放大招控楼市：买地首付款最少50% 闲置两年" 437 | ] 438 | }, 439 | "execution_count": 4, 440 | "metadata": {}, 441 | "output_type": "execute_result" 442 | } 443 | ], 444 | "source": [ 445 | "df[['发表时间','评论标题']].head()" 446 | ] 447 | }, 448 | { 449 | "cell_type": "code", 450 | "execution_count": 6, 451 | "metadata": {}, 452 | "outputs": [], 453 | "source": [ 454 | "new_df = df[['发表时间','评论标题']].copy()\n", 455 | "\n", 456 | "new_df.to_csv('title20160701_20160801.csv',index = False)" 457 | ] 458 | }, 459 | { 460 | "cell_type": "code", 461 | "execution_count": 2, 462 | "metadata": {}, 463 | "outputs": [], 464 | "source": [ 465 | "df20160401 = pd.read_csv('./cjpl/20160401_20160501.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 466 | "df20160501 = pd.read_csv('./cjpl/20160501_20160601.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 467 | "df20160601 = pd.read_csv('./cjpl/20160601_20160701.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 468 | "df20160701 = pd.read_csv('./cjpl/20160701_20160801.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 469 | "df20160801 = pd.read_csv('./cjpl/20160801_20160901.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 470 | "df20160901 = pd.read_csv('./cjpl/20160901_20161001.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 471 | "df20161001 = pd.read_csv('./cjpl/20161001_20161101.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 472 | "df20161101 = pd.read_csv('./cjpl/20161101_20161201.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 473 | "df20161201 = pd.read_csv('./cjpl/20161201_20170101.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n" 474 | ] 475 | }, 476 | { 477 | "cell_type": "code", 478 | "execution_count": 4, 479 | "metadata": {}, 480 | "outputs": [], 481 | "source": [ 482 | "df20160401[['发表时间','评论标题']].to_csv('df20160401.csv',index = False)\n", 483 | "df20160501[['发表时间','评论标题']].to_csv('df20160501.csv',index = False)\n", 484 | "df20160601[['发表时间','评论标题']].to_csv('df20160601.csv',index = False)\n", 485 | "df20160701[['发表时间','评论标题']].to_csv('df20160701.csv',index = False)\n", 486 | "df20160801[['发表时间','评论标题']].to_csv('df20160801.csv',index = False)\n", 487 | "df20160901[['发表时间','评论标题']].to_csv('df20160901.csv',index = False)\n", 488 | "df20161001[['发表时间','评论标题']].to_csv('df20161001.csv',index = False)\n", 489 | "df20161101[['发表时间','评论标题']].to_csv('df20161101.csv',index = False)\n", 490 | "df20161201[['发表时间','评论标题']].to_csv('df20161201.csv',index = False)" 491 | ] 492 | }, 493 | { 494 | "cell_type": "code", 495 | "execution_count": 5, 496 | "metadata": {}, 497 | "outputs": [], 498 | "source": [ 499 | "df20170101 = pd.read_csv('./cjpl/20170101_20170201.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 500 | "df20170201 = pd.read_csv('./cjpl/20170201_20170301.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 501 | "df20170301 = pd.read_csv('./cjpl/20170301_20170401.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 502 | "df20170401 = pd.read_csv('./cjpl/20170401_20170501.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 503 | "df20170501 = pd.read_csv('./cjpl/20170501_20170601.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 504 | "df20170601 = pd.read_csv('./cjpl/20170601_20170701.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 505 | "df20170701 = pd.read_csv('./cjpl/20170701_20170801.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 506 | "df20170801 = pd.read_csv('./cjpl/20170801_20170901.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 507 | "df20170901 = pd.read_csv('./cjpl/20170901_20171001.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]" 508 | ] 509 | }, 510 | { 511 | "cell_type": "code", 512 | "execution_count": 6, 513 | "metadata": {}, 514 | "outputs": [], 515 | "source": [ 516 | "df20170101[['发表时间','评论标题']].to_csv('df20170101.csv',index = False)\n", 517 | "df20170201[['发表时间','评论标题']].to_csv('df20170201.csv',index = False)\n", 518 | "df20170301[['发表时间','评论标题']].to_csv('df20170301.csv',index = False)\n", 519 | "df20170401[['发表时间','评论标题']].to_csv('df20170401.csv',index = False)\n", 520 | "df20170501[['发表时间','评论标题']].to_csv('df20170501.csv',index = False)\n", 521 | "df20170601[['发表时间','评论标题']].to_csv('df20170601.csv',index = False)\n", 522 | "df20170701[['发表时间','评论标题']].to_csv('df20170701.csv',index = False)\n", 523 | "df20170801[['发表时间','评论标题']].to_csv('df20170801.csv',index = False)\n", 524 | "df20170901[['发表时间','评论标题']].to_csv('df20170901.csv',index = False)" 525 | ] 526 | }, 527 | { 528 | "cell_type": "code", 529 | "execution_count": 7, 530 | "metadata": {}, 531 | "outputs": [], 532 | "source": [ 533 | "combine_df = pd.concat([df20160401,df20160501,df20160601,df20160701,df20160801,df20160901,df20161001,\n", 534 | " df20161101,df20161201,df20170101,df20170201,df20170301,df20170401,df20170501,df20170601,\n", 535 | " df20170701,df20170801,df20170901],axis=0,ignore_index=True)" 536 | ] 537 | }, 538 | { 539 | "cell_type": "code", 540 | "execution_count": 8, 541 | "metadata": {}, 542 | "outputs": [], 543 | "source": [ 544 | "combine_df[['发表时间','评论标题']].to_csv('combine_df.csv',index = False)" 545 | ] 546 | }, 547 | { 548 | "cell_type": "code", 549 | "execution_count": null, 550 | "metadata": {}, 551 | "outputs": [], 552 | "source": [] 553 | } 554 | ], 555 | "metadata": { 556 | "kernelspec": { 557 | "display_name": "Python 3", 558 | "language": "python", 559 | "name": "python3" 560 | }, 561 | "language_info": { 562 | "codemirror_mode": { 563 | "name": "ipython", 564 | "version": 3 565 | }, 566 | "file_extension": ".py", 567 | "mimetype": "text/x-python", 568 | "name": "python", 569 | "nbconvert_exporter": "python", 570 | "pygments_lexer": "ipython3", 571 | "version": "3.6.5" 572 | } 573 | }, 574 | "nbformat": 4, 575 | "nbformat_minor": 2 576 | } 577 | -------------------------------------------------------------------------------- /experiment3-sentimentAnalysis/.ipynb_checkpoints/PlotTrendGraph-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "from pandas import DataFrame\n", 11 | "import numpy as np\n", 12 | "from matplotlib.patches import Rectangle\n", 13 | "import matplotlib.pyplot as plt" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": {}, 20 | "outputs": [ 21 | { 22 | "data": { 23 | "text/html": [ 24 | "
\n", 25 | "\n", 38 | "\n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | "
00.1
011
110
201
300
400
\n", 74 | "
" 75 | ], 76 | "text/plain": [ 77 | " 0 0.1\n", 78 | "0 1 1\n", 79 | "1 1 0\n", 80 | "2 0 1\n", 81 | "3 0 0\n", 82 | "4 0 0" 83 | ] 84 | }, 85 | "execution_count": 2, 86 | "metadata": {}, 87 | "output_type": "execute_result" 88 | } 89 | ], 90 | "source": [ 91 | "df = pd.read_csv('outputtrendwithsentiment.csv')\n", 92 | "df.head()" 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": null, 98 | "metadata": {}, 99 | "outputs": [], 100 | "source": [] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": 4, 105 | "metadata": {}, 106 | "outputs": [], 107 | "source": [ 108 | "real_trend = df['0']\n", 109 | "pred_trend1 = df['0.1']\n", 110 | "\n" 111 | ] 112 | }, 113 | { 114 | "cell_type": "code", 115 | "execution_count": 17, 116 | "metadata": {}, 117 | "outputs": [], 118 | "source": [ 119 | "xlims = (0,len(real_trend))\n", 120 | "ylims = (0, 100)" 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": 5, 126 | "metadata": {}, 127 | "outputs": [ 128 | { 129 | "ename": "NameError", 130 | "evalue": "name 'pred_trend' is not defined", 131 | "output_type": "error", 132 | "traceback": [ 133 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 134 | "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", 135 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[0mprev_num\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpred_trend\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 27\u001b[0m \u001b[0mbrick_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumber\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 136 | "\u001b[1;31mNameError\u001b[0m: name 'pred_trend' is not defined" 137 | ] 138 | } 139 | ], 140 | "source": [ 141 | "%matplotlib qt\n", 142 | "fig = plt.figure(figsize=(20,10))\n", 143 | "fig.clf()\n", 144 | "axes = fig.gca() \n", 145 | "prev_num = 0\n", 146 | "data = real_trend\n", 147 | "brick_size = 2\n", 148 | "for index, number in enumerate(data):\n", 149 | "# print(index, number)\n", 150 | " if number == 1:\n", 151 | " facecolor='red'\n", 152 | " elif number == 0:\n", 153 | " facecolor='blue'\n", 154 | " else:\n", 155 | " facecolor='green'\n", 156 | "\n", 157 | " prev_num += number\n", 158 | "\n", 159 | " renko = Rectangle(\n", 160 | " (index, prev_num * brick_size+570), 1, brick_size,\n", 161 | " facecolor=facecolor, alpha=0.5\n", 162 | " )\n", 163 | " axes.add_patch(renko)\n", 164 | "\n", 165 | "prev_num = 0\n", 166 | "data = pred_trend\n", 167 | "brick_size = 2\n", 168 | "for index, number in enumerate(data):\n", 169 | "# print(index, number)\n", 170 | " if number == 1:\n", 171 | " facecolor='red'\n", 172 | " elif number == 0:\n", 173 | " facecolor='blue'\n", 174 | " else:\n", 175 | " facecolor='green'\n", 176 | "\n", 177 | " prev_num += number\n", 178 | "\n", 179 | " renko = Rectangle(\n", 180 | " (index, prev_num * brick_size+470), 1, brick_size,\n", 181 | " facecolor=facecolor, alpha=0.5\n", 182 | " )\n", 183 | " axes.add_patch(renko) \n", 184 | "\n", 185 | "\n", 186 | "\n", 187 | "plt.tick_params(axis='both', which='major', labelsize=30)\n", 188 | "# plt.tick_params(axis='both', which='minor', labelsize=8)\n", 189 | "plt.xlabel('Days', fontsize=30)\n", 190 | "plt.ylabel('Trend Prediction', fontsize=30)\n", 191 | "plt.xlim(xlims)\n", 192 | "plt.ylim(ylims)" 193 | ] 194 | }, 195 | { 196 | "cell_type": "code", 197 | "execution_count": 24, 198 | "metadata": {}, 199 | "outputs": [], 200 | "source": [ 201 | "pd.set_option('display.expand_frame_repr', False)" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": 23, 207 | "metadata": {}, 208 | "outputs": [ 209 | { 210 | "data": { 211 | "text/html": [ 212 | "
\n", 213 | "\n", 226 | "\n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | "
01234567891011121314151617ave_change
0-1.246198e+08-1.624767e+07605322.994191-534771.378880-1192.570274-99.424949-27.775339-1.784126-10.406661-2.6284748.653167-7.20426830.10881360.94038312.6179031.981505-1.0715630.4777710.046667
1-7.603515e+07-1.414783e+07-223391.883688-295583.717229-651.246783-99.021349-43.027941-75.475923-1.720760-78.1854587.904326-3.92589313.8261288.918976-16.078816-1.7404700.8178250.0191400.020000
2-9.524888e+07-1.499555e+07700388.585755-591209.025691-1261.343344-53.327956-30.164170-12.401522-22.223717-77.7250140.6783928.80453211.8214808.867001-13.4332070.4663930.2900210.030882-0.006667
3-1.046750e+08-1.691048e+07-330458.275464-215077.517631-668.117141-50.723226-45.4004741.948180-4.795344-56.385197-2.24601813.57416017.238350-5.969432-9.628242-0.9515680.5119390.000355-0.023333
4-8.225343e+07-1.641113e+07-165579.837059-312551.020340-722.054412-60.112287-35.44994714.736202-11.081554-23.5334938.493759-8.03632810.7307040.536932-0.851757-0.9860550.1023430.136171-0.090000
\n", 364 | "
" 365 | ], 366 | "text/plain": [ 367 | " 0 1 2 3 4 \\\n", 368 | "0 -1.246198e+08 -1.624767e+07 605322.994191 -534771.378880 -1192.570274 \n", 369 | "1 -7.603515e+07 -1.414783e+07 -223391.883688 -295583.717229 -651.246783 \n", 370 | "2 -9.524888e+07 -1.499555e+07 700388.585755 -591209.025691 -1261.343344 \n", 371 | "3 -1.046750e+08 -1.691048e+07 -330458.275464 -215077.517631 -668.117141 \n", 372 | "4 -8.225343e+07 -1.641113e+07 -165579.837059 -312551.020340 -722.054412 \n", 373 | "\n", 374 | " 5 6 7 8 9 10 11 \\\n", 375 | "0 -99.424949 -27.775339 -1.784126 -10.406661 -2.628474 8.653167 -7.204268 \n", 376 | "1 -99.021349 -43.027941 -75.475923 -1.720760 -78.185458 7.904326 -3.925893 \n", 377 | "2 -53.327956 -30.164170 -12.401522 -22.223717 -77.725014 0.678392 8.804532 \n", 378 | "3 -50.723226 -45.400474 1.948180 -4.795344 -56.385197 -2.246018 13.574160 \n", 379 | "4 -60.112287 -35.449947 14.736202 -11.081554 -23.533493 8.493759 -8.036328 \n", 380 | "\n", 381 | " 12 13 14 15 16 17 ave_change \n", 382 | "0 30.108813 60.940383 12.617903 1.981505 -1.071563 0.477771 0.046667 \n", 383 | "1 13.826128 8.918976 -16.078816 -1.740470 0.817825 0.019140 0.020000 \n", 384 | "2 11.821480 8.867001 -13.433207 0.466393 0.290021 0.030882 -0.006667 \n", 385 | "3 17.238350 -5.969432 -9.628242 -0.951568 0.511939 0.000355 -0.023333 \n", 386 | "4 10.730704 0.536932 -0.851757 -0.986055 0.102343 0.136171 -0.090000 " 387 | ] 388 | }, 389 | "execution_count": 23, 390 | "metadata": {}, 391 | "output_type": "execute_result" 392 | } 393 | ], 394 | "source": [ 395 | "df = pd.read_csv('Total-data-after-Autoencoder-dimension-reduction.csv')\n" 396 | ] 397 | }, 398 | { 399 | "cell_type": "code", 400 | "execution_count": 26, 401 | "metadata": {}, 402 | "outputs": [ 403 | { 404 | "data": { 405 | "text/html": [ 406 | "
\n", 407 | "\n", 420 | "\n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | "
012345...1314151617ave_change
0-1.246198e+08-1.624767e+07605322.994191-534771.378880-1192.570274-99.424949...60.94038312.6179031.981505-1.0715630.4777710.046667
1-7.603515e+07-1.414783e+07-223391.883688-295583.717229-651.246783-99.021349...8.918976-16.078816-1.7404700.8178250.0191400.020000
2-9.524888e+07-1.499555e+07700388.585755-591209.025691-1261.343344-53.327956...8.867001-13.4332070.4663930.2900210.030882-0.006667
3-1.046750e+08-1.691048e+07-330458.275464-215077.517631-668.117141-50.723226...-5.969432-9.628242-0.9515680.5119390.000355-0.023333
4-8.225343e+07-1.641113e+07-165579.837059-312551.020340-722.054412-60.112287...0.536932-0.851757-0.9860550.1023430.136171-0.090000
\n", 522 | "

5 rows × 19 columns

\n", 523 | "
" 524 | ], 525 | "text/plain": [ 526 | " 0 1 2 3 4 5 ... 13 14 15 16 17 ave_change\n", 527 | "0 -1.246198e+08 -1.624767e+07 605322.994191 -534771.378880 -1192.570274 -99.424949 ... 60.940383 12.617903 1.981505 -1.071563 0.477771 0.046667\n", 528 | "1 -7.603515e+07 -1.414783e+07 -223391.883688 -295583.717229 -651.246783 -99.021349 ... 8.918976 -16.078816 -1.740470 0.817825 0.019140 0.020000\n", 529 | "2 -9.524888e+07 -1.499555e+07 700388.585755 -591209.025691 -1261.343344 -53.327956 ... 8.867001 -13.433207 0.466393 0.290021 0.030882 -0.006667\n", 530 | "3 -1.046750e+08 -1.691048e+07 -330458.275464 -215077.517631 -668.117141 -50.723226 ... -5.969432 -9.628242 -0.951568 0.511939 0.000355 -0.023333\n", 531 | "4 -8.225343e+07 -1.641113e+07 -165579.837059 -312551.020340 -722.054412 -60.112287 ... 0.536932 -0.851757 -0.986055 0.102343 0.136171 -0.090000\n", 532 | "\n", 533 | "[5 rows x 19 columns]" 534 | ] 535 | }, 536 | "execution_count": 26, 537 | "metadata": {}, 538 | "output_type": "execute_result" 539 | } 540 | ], 541 | "source": [ 542 | "pd.set_option('display.max_columns', 12)\n", 543 | "df.head()" 544 | ] 545 | }, 546 | { 547 | "cell_type": "code", 548 | "execution_count": null, 549 | "metadata": {}, 550 | "outputs": [], 551 | "source": [] 552 | }, 553 | { 554 | "cell_type": "code", 555 | "execution_count": 11, 556 | "metadata": {}, 557 | "outputs": [], 558 | "source": [ 559 | "real_trend = df['real_trend']\n", 560 | "pred_trend = df['pred_trend']" 561 | ] 562 | }, 563 | { 564 | "cell_type": "code", 565 | "execution_count": 12, 566 | "metadata": {}, 567 | "outputs": [], 568 | "source": [ 569 | "temp_list = []\n", 570 | "temp_df = real_trend - pred_trend\n", 571 | "for i in range(0, len(temp_df)):\n", 572 | " temp_list.append([i, temp_df[i]])" 573 | ] 574 | }, 575 | { 576 | "cell_type": "code", 577 | "execution_count": 13, 578 | "metadata": {}, 579 | "outputs": [], 580 | "source": [ 581 | "%matplotlib qt\n", 582 | "plt.clf()\n", 583 | "# plt.figure()\n", 584 | "plt.scatter( *zip(*temp_list), )\n", 585 | "# plt.scatter(real_df.index.values, pred_df[0],color='blue', marker='^')\n", 586 | "# plt.scatter(real_df.index.values, real_df[0],color='red', marker='.')\n", 587 | "plt.title('Trend Prediction Accuracy of 601318SH' )\n", 588 | "plt.xlabel('Days')\n", 589 | "plt.ylabel('Trend Prediction')\n", 590 | "plt.show()" 591 | ] 592 | }, 593 | { 594 | "cell_type": "code", 595 | "execution_count": 14, 596 | "metadata": {}, 597 | "outputs": [ 598 | { 599 | "ename": "NameError", 600 | "evalue": "name 'pred_df' is not defined", 601 | "output_type": "error", 602 | "traceback": [ 603 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 604 | "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", 605 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mtemp_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtemp_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreal_trend\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mpred_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtemp_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mtemp_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtemp_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 606 | "\u001b[1;31mNameError\u001b[0m: name 'pred_df' is not defined" 607 | ] 608 | } 609 | ], 610 | "source": [ 611 | "def plot_renko(data, brick_size):\n", 612 | " fig = plt.figure(figsize=(20,10))\n", 613 | " fig.clf()\n", 614 | " axes = fig.gca() \n", 615 | " prev_num = 0\n", 616 | " for index, number in enumerate(data):\n", 617 | "# print(index, number)\n", 618 | " if number == 1:\n", 619 | " facecolor='red'\n", 620 | " elif number == 0:\n", 621 | " facecolor='blue'\n", 622 | " else:\n", 623 | " facecolor='green'\n", 624 | "\n", 625 | " prev_num += number\n", 626 | "\n", 627 | " renko = Rectangle(\n", 628 | " (index, prev_num * brick_size+100), 1, brick_size,\n", 629 | " facecolor=facecolor, alpha=0.5\n", 630 | " )\n", 631 | " axes.add_patch(renko)\n", 632 | " plt.xlim(xlims)\n", 633 | " plt.ylim(ylims)\n", 634 | "# plt.show()" 635 | ] 636 | } 637 | ], 638 | "metadata": { 639 | "kernelspec": { 640 | "display_name": "Python 3", 641 | "language": "python", 642 | "name": "python3" 643 | }, 644 | "language_info": { 645 | "codemirror_mode": { 646 | "name": "ipython", 647 | "version": 3 648 | }, 649 | "file_extension": ".py", 650 | "mimetype": "text/x-python", 651 | "name": "python", 652 | "nbconvert_exporter": "python", 653 | "pygments_lexer": "ipython3", 654 | "version": "3.6.5" 655 | } 656 | }, 657 | "nbformat": 4, 658 | "nbformat_minor": 2 659 | } 660 | -------------------------------------------------------------------------------- /experiment3-sentimentAnalysis/1GubaData.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "from pandas import DataFrame\n", 11 | "import numpy as np\n", 12 | "from numpy import row_stack,column_stack\n", 13 | "import matplotlib.pyplot as plt\n", 14 | "from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY,YEARLY\n", 15 | "# from mpl_finance import quotes_historical_yahoo_ohlc, candlestick_ohlc\n", 16 | "from matplotlib.pylab import date2num\n", 17 | "\n", 18 | "import tushare as ts\n", 19 | "import datetime\n", 20 | "import time" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 2, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "df = pd.read_csv('./cjpl/20160701_20160801.csv', sep=',',header = 0, usecols=range(8))" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 3, 35 | "metadata": {}, 36 | "outputs": [ 37 | { 38 | "data": { 39 | "text/html": [ 40 | "
\n", 41 | "\n", 54 | "\n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | "
页面信息评论标题发表作者发表时间阅读量评论量内容链接评论内容
0['page_2360', 12]中国今年调减玉米种植面积3000万亩 13年来首次财经评论2016-07-31 23:52:03113828http://guba.eastmoney.com/news,cjpl,501393101....来源：中国新闻网 编辑：东方财富网记者31日从中国农业部获悉，今年以来，中国农业结构调整进展...
1['page_2360', 13]科研项目资金改革解读：打酱油的钱可以买醋了财经评论2016-07-31 23:37:0197752http://guba.eastmoney.com/news,cjpl,501370861....来源：新华社 编辑：东方财富网意见指出，科研项目实施期间，年度剩余资金可以结转下一年度继续使...
2['page_2360', 14]A股交易策略：战略性配置绩优价值股财经评论2016-07-31 23:20:58235635http://guba.eastmoney.com/news,cjpl,501350935....来源：兴业证券 编辑：东方财富网投资要点★展望：周中调整后，市场短期再度大幅波动的可能性下降...
3['page_2360', 15]中国将发SDR计价债券 IMF发言人回应市场疑惑财经评论2016-07-31 22:48:53212319http://guba.eastmoney.com/news,cjpl,501315579....来源：一财网 编辑：东方财富网第一财经记者日前从知情人士处获悉，中国工商银行(亚洲)有限公司...
4['page_2360', 16]安徽放大招控楼市：买地首付款最少50% 闲置两年财经评论2016-07-31 22:34:52698812http://guba.eastmoney.com/news,cjpl,501299784....来源：澎湃 编辑：东方财富网安徽政府对开发商拿地盯得更加紧了。7月22日，安徽省国土资源厅印...
5['page_2360', 17]牛散出没请注意 赵建平坚守这家上市公司三年赚财经评论2016-07-31 22:19:5118983360http://guba.eastmoney.com/news,cjpl,501282167....来源：证券时报 编辑：东方财富网随着上市公司半年报的陆续公布，A股多位知名牛散在第二季度的动...
6['page_2360', 18]八月首周新股发行猛增至9只 抽奖靠运气不妨关注财经评论2016-07-31 22:04:514134846http://guba.eastmoney.com/news,cjpl,501266649....来源：微信公众号莲花财经 编辑：东方财富网正当7月A股难得走出不错行情的时候，新股发行却悄然...
7['page_2360', 19]中报行情要不要跟一波？41股业绩预增超10倍财经评论2016-07-31 21:51:5010965928http://guba.eastmoney.com/news,cjpl,501256128....来源：券商中国 编辑：东方财富网本周A股正式进入中报业绩披露密集期，全市场共184只个股已经...
8['page_2360', 20]市值配售尽情嗨：8月首周可打9只新股 周三有三财经评论2016-07-31 21:47:49112641299http://guba.eastmoney.com/news,cjpl,501252589....来源：券商中国 编辑：东方财富网新股发行骤然增多了！就在7月24日-7月29日这一周，每天都...
9['page_2360', 21]最多一日发9封问询函 近一周29家上市公司被交易财经评论2016-07-31 21:40:4710415540http://guba.eastmoney.com/news,cjpl,501245776....来源：微信公众号莲花财经 编辑：东方财富网7月24日到29日，沪深两市交易所共发出29封各类...
10['page_2360', 22]从爱理不理到高攀不起 绩优股弱市成功集体逆袭财经评论2016-07-31 21:36:48484074http://guba.eastmoney.com/news,cjpl,501242056....来源：每日经济新闻 编辑：东方财富网炒股，你还看业绩？相信很多人在刚刚踏入股市之时，会被一部...
11['page_2360', 23]奥运会要来了！一文让你搞懂“奥运魔咒”是否那财经评论2016-07-31 21:23:46224138http://guba.eastmoney.com/news,cjpl,501229373....来源：每日经济新闻 编辑：东方财富网时间过得真快，8月份将来临！在本周三，一条理财新规的传闻...
12['page_2360', 24]国泰君安：监管趋严下短期暂离旋涡 拥抱业绩周财经评论2016-07-31 21:09:41117456http://guba.eastmoney.com/news,cjpl,501214802....来源： 编辑：东方财富网【国泰君安：监管趋严下短期暂离旋涡 拥抱业绩周期】国泰君安乔永远认为...
13['page_2360', 25]芝麻信用：11%的P2P用户存在10家以上的多头借贷财经评论2016-07-31 20:28:44110611http://guba.eastmoney.com/news,cjpl,501169778....来源： 编辑：东方财富网【芝麻信用：11%的P2P用户存在10家以上的多头借贷】芝麻信用总经...
14['page_2360', 26]永远观市：暂离旋涡 拥抱业绩财经评论2016-07-31 20:26:428313423http://guba.eastmoney.com/news,cjpl,501167675....来源：国泰君安 编辑：东方财富网金融去杠杆短期难言结束，监管趋严下，短期暂离旋涡。配置上拥抱...
15['page_2360', 27]监管风向趋严 大金融稳步前行财经评论2016-07-31 20:24:43234308http://guba.eastmoney.com/news,cjpl,501166473....来源：华泰证券 编辑：东方财富网大金融：沧海横流，方显大金融之稳健银行理财监管新规征求意见稿...
16['page_2360', 28]达志科技网上发行中签率为0.0182%财经评论2016-07-31 19:48:383936918http://guba.eastmoney.com/news,cjpl,501137699....来源：全景网 编辑：东方财富网达志科技(300530)周日发布公告称，其首次公开发行股票网上...
17['page_2360', 29]任泽平：经济通胀回落 抑制资产泡沫去产能财经评论2016-07-31 19:40:36120145http://guba.eastmoney.com/news,cjpl,501130754....来源： 编辑：东方财富网【任泽平：经济通胀回落，抑制资产泡沫去产能】货币政策落入流动性陷阱情...
18['page_2360', 30]晚间上市公司利好消息一览：世纪游轮拟进军移动财经评论2016-07-31 19:08:471861253http://guba.eastmoney.com/news,cjpl,501101150....来源：东方财富网美克家居8月1日起停牌 正筹划非公开发行美克家居(600337)周日发布公告...
19['page_2360', 31]小心！股市正发生一件45年来都没出现的事财经评论2016-07-31 18:58:29139846http://guba.eastmoney.com/news,cjpl,501090726....来源：凤凰网 编辑：东方财富网现在，股市已变得让人烦躁不安，正在发生一件45年来都没发生的事...
\n", 291 | "
" 292 | ], 293 | "text/plain": [ 294 | " 页面信息 评论标题 发表作者 发表时间 \\\n", 295 | "0 ['page_2360', 12] 中国今年调减玉米种植面积3000万亩 13年来首次 财经评论 2016-07-31 23:52:03 \n", 296 | "1 ['page_2360', 13] 科研项目资金改革解读：打酱油的钱可以买醋了 财经评论 2016-07-31 23:37:01 \n", 297 | "2 ['page_2360', 14] A股交易策略：战略性配置绩优价值股 财经评论 2016-07-31 23:20:58 \n", 298 | "3 ['page_2360', 15] 中国将发SDR计价债券 IMF发言人回应市场疑惑 财经评论 2016-07-31 22:48:53 \n", 299 | "4 ['page_2360', 16] 安徽放大招控楼市：买地首付款最少50% 闲置两年 财经评论 2016-07-31 22:34:52 \n", 300 | "5 ['page_2360', 17] 牛散出没请注意 赵建平坚守这家上市公司三年赚 财经评论 2016-07-31 22:19:51 \n", 301 | "6 ['page_2360', 18] 八月首周新股发行猛增至9只 抽奖靠运气不妨关注 财经评论 2016-07-31 22:04:51 \n", 302 | "7 ['page_2360', 19] 中报行情要不要跟一波？41股业绩预增超10倍 财经评论 2016-07-31 21:51:50 \n", 303 | "8 ['page_2360', 20] 市值配售尽情嗨：8月首周可打9只新股 周三有三 财经评论 2016-07-31 21:47:49 \n", 304 | "9 ['page_2360', 21] 最多一日发9封问询函 近一周29家上市公司被交易 财经评论 2016-07-31 21:40:47 \n", 305 | "10 ['page_2360', 22] 从爱理不理到高攀不起 绩优股弱市成功集体逆袭 财经评论 2016-07-31 21:36:48 \n", 306 | "11 ['page_2360', 23] 奥运会要来了！一文让你搞懂“奥运魔咒”是否那 财经评论 2016-07-31 21:23:46 \n", 307 | "12 ['page_2360', 24] 国泰君安：监管趋严下短期暂离旋涡 拥抱业绩周 财经评论 2016-07-31 21:09:41 \n", 308 | "13 ['page_2360', 25] 芝麻信用：11%的P2P用户存在10家以上的多头借贷 财经评论 2016-07-31 20:28:44 \n", 309 | "14 ['page_2360', 26] 永远观市：暂离旋涡 拥抱业绩 财经评论 2016-07-31 20:26:42 \n", 310 | "15 ['page_2360', 27] 监管风向趋严 大金融稳步前行 财经评论 2016-07-31 20:24:43 \n", 311 | "16 ['page_2360', 28] 达志科技网上发行中签率为0.0182% 财经评论 2016-07-31 19:48:38 \n", 312 | "17 ['page_2360', 29] 任泽平：经济通胀回落 抑制资产泡沫去产能 财经评论 2016-07-31 19:40:36 \n", 313 | "18 ['page_2360', 30] 晚间上市公司利好消息一览：世纪游轮拟进军移动 财经评论 2016-07-31 19:08:47 \n", 314 | "19 ['page_2360', 31] 小心！股市正发生一件45年来都没出现的事 财经评论 2016-07-31 18:58:29 \n", 315 | "\n", 316 | " 阅读量 评论量 内容链接 \\\n", 317 | "0 11382 8 http://guba.eastmoney.com/news,cjpl,501393101.... \n", 318 | "1 9775 2 http://guba.eastmoney.com/news,cjpl,501370861.... \n", 319 | "2 23563 5 http://guba.eastmoney.com/news,cjpl,501350935.... \n", 320 | "3 21231 9 http://guba.eastmoney.com/news,cjpl,501315579.... \n", 321 | "4 6988 12 http://guba.eastmoney.com/news,cjpl,501299784.... \n", 322 | "5 189833 60 http://guba.eastmoney.com/news,cjpl,501282167.... \n", 323 | "6 41348 46 http://guba.eastmoney.com/news,cjpl,501266649.... \n", 324 | "7 109659 28 http://guba.eastmoney.com/news,cjpl,501256128.... \n", 325 | "8 112641 299 http://guba.eastmoney.com/news,cjpl,501252589.... \n", 326 | "9 104155 40 http://guba.eastmoney.com/news,cjpl,501245776.... \n", 327 | "10 48407 4 http://guba.eastmoney.com/news,cjpl,501242056.... \n", 328 | "11 22413 8 http://guba.eastmoney.com/news,cjpl,501229373.... \n", 329 | "12 11745 6 http://guba.eastmoney.com/news,cjpl,501214802.... \n", 330 | "13 11061 1 http://guba.eastmoney.com/news,cjpl,501169778.... \n", 331 | "14 83134 23 http://guba.eastmoney.com/news,cjpl,501167675.... \n", 332 | "15 23430 8 http://guba.eastmoney.com/news,cjpl,501166473.... \n", 333 | "16 39369 18 http://guba.eastmoney.com/news,cjpl,501137699.... \n", 334 | "17 12014 5 http://guba.eastmoney.com/news,cjpl,501130754.... \n", 335 | "18 18612 53 http://guba.eastmoney.com/news,cjpl,501101150.... \n", 336 | "19 13984 6 http://guba.eastmoney.com/news,cjpl,501090726.... \n", 337 | "\n", 338 | " 评论内容 \n", 339 | "0 来源：中国新闻网 编辑：东方财富网记者31日从中国农业部获悉，今年以来，中国农业结构调整进展... \n", 340 | "1 来源：新华社 编辑：东方财富网意见指出，科研项目实施期间，年度剩余资金可以结转下一年度继续使... \n", 341 | "2 来源：兴业证券 编辑：东方财富网投资要点★展望：周中调整后，市场短期再度大幅波动的可能性下降... \n", 342 | "3 来源：一财网 编辑：东方财富网第一财经记者日前从知情人士处获悉，中国工商银行(亚洲)有限公司... \n", 343 | "4 来源：澎湃 编辑：东方财富网安徽政府对开发商拿地盯得更加紧了。7月22日，安徽省国土资源厅印... \n", 344 | "5 来源：证券时报 编辑：东方财富网随着上市公司半年报的陆续公布，A股多位知名牛散在第二季度的动... \n", 345 | "6 来源：微信公众号莲花财经 编辑：东方财富网正当7月A股难得走出不错行情的时候，新股发行却悄然... \n", 346 | "7 来源：券商中国 编辑：东方财富网本周A股正式进入中报业绩披露密集期，全市场共184只个股已经... \n", 347 | "8 来源：券商中国 编辑：东方财富网新股发行骤然增多了！就在7月24日-7月29日这一周，每天都... \n", 348 | "9 来源：微信公众号莲花财经 编辑：东方财富网7月24日到29日，沪深两市交易所共发出29封各类... \n", 349 | "10 来源：每日经济新闻 编辑：东方财富网炒股，你还看业绩？相信很多人在刚刚踏入股市之时，会被一部... \n", 350 | "11 来源：每日经济新闻 编辑：东方财富网时间过得真快，8月份将来临！在本周三，一条理财新规的传闻... \n", 351 | "12 来源： 编辑：东方财富网【国泰君安：监管趋严下短期暂离旋涡 拥抱业绩周期】国泰君安乔永远认为... \n", 352 | "13 来源： 编辑：东方财富网【芝麻信用：11%的P2P用户存在10家以上的多头借贷】芝麻信用总经... \n", 353 | "14 来源：国泰君安 编辑：东方财富网金融去杠杆短期难言结束，监管趋严下，短期暂离旋涡。配置上拥抱... \n", 354 | "15 来源：华泰证券 编辑：东方财富网大金融：沧海横流，方显大金融之稳健银行理财监管新规征求意见稿... \n", 355 | "16 来源：全景网 编辑：东方财富网达志科技(300530)周日发布公告称，其首次公开发行股票网上... \n", 356 | "17 来源： 编辑：东方财富网【任泽平：经济通胀回落，抑制资产泡沫去产能】货币政策落入流动性陷阱情... \n", 357 | "18 来源：东方财富网美克家居8月1日起停牌 正筹划非公开发行美克家居(600337)周日发布公告... \n", 358 | "19 来源：凤凰网 编辑：东方财富网现在，股市已变得让人烦躁不安，正在发生一件45年来都没发生的事... " 359 | ] 360 | }, 361 | "execution_count": 3, 362 | "metadata": {}, 363 | "output_type": "execute_result" 364 | } 365 | ], 366 | "source": [ 367 | "df.head(20)" 368 | ] 369 | }, 370 | { 371 | "cell_type": "code", 372 | "execution_count": 4, 373 | "metadata": {}, 374 | "outputs": [ 375 | { 376 | "data": { 377 | "text/html": [ 378 | "
\n", 379 | "\n", 392 | "\n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | "
发表时间评论标题
02016-07-31 23:52:03中国今年调减玉米种植面积3000万亩 13年来首次
12016-07-31 23:37:01科研项目资金改革解读：打酱油的钱可以买醋了
22016-07-31 23:20:58A股交易策略：战略性配置绩优价值股
32016-07-31 22:48:53中国将发SDR计价债券 IMF发言人回应市场疑惑
42016-07-31 22:34:52安徽放大招控楼市：买地首付款最少50% 闲置两年
\n", 428 | "
" 429 | ], 430 | "text/plain": [ 431 | " 发表时间 评论标题\n", 432 | "0 2016-07-31 23:52:03 中国今年调减玉米种植面积3000万亩 13年来首次\n", 433 | "1 2016-07-31 23:37:01 科研项目资金改革解读：打酱油的钱可以买醋了\n", 434 | "2 2016-07-31 23:20:58 A股交易策略：战略性配置绩优价值股\n", 435 | "3 2016-07-31 22:48:53 中国将发SDR计价债券 IMF发言人回应市场疑惑\n", 436 | "4 2016-07-31 22:34:52 安徽放大招控楼市：买地首付款最少50% 闲置两年" 437 | ] 438 | }, 439 | "execution_count": 4, 440 | "metadata": {}, 441 | "output_type": "execute_result" 442 | } 443 | ], 444 | "source": [ 445 | "df[['发表时间','评论标题']].head()" 446 | ] 447 | }, 448 | { 449 | "cell_type": "code", 450 | "execution_count": 6, 451 | "metadata": {}, 452 | "outputs": [], 453 | "source": [ 454 | "new_df = df[['发表时间','评论标题']].copy()\n", 455 | "\n", 456 | "new_df.to_csv('title20160701_20160801.csv',index = False)" 457 | ] 458 | }, 459 | { 460 | "cell_type": "code", 461 | "execution_count": 2, 462 | "metadata": {}, 463 | "outputs": [], 464 | "source": [ 465 | "df20160401 = pd.read_csv('./cjpl/20160401_20160501.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 466 | "df20160501 = pd.read_csv('./cjpl/20160501_20160601.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 467 | "df20160601 = pd.read_csv('./cjpl/20160601_20160701.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 468 | "df20160701 = pd.read_csv('./cjpl/20160701_20160801.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 469 | "df20160801 = pd.read_csv('./cjpl/20160801_20160901.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 470 | "df20160901 = pd.read_csv('./cjpl/20160901_20161001.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 471 | "df20161001 = pd.read_csv('./cjpl/20161001_20161101.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 472 | "df20161101 = pd.read_csv('./cjpl/20161101_20161201.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 473 | "df20161201 = pd.read_csv('./cjpl/20161201_20170101.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n" 474 | ] 475 | }, 476 | { 477 | "cell_type": "code", 478 | "execution_count": 4, 479 | "metadata": {}, 480 | "outputs": [], 481 | "source": [ 482 | "df20160401[['发表时间','评论标题']].to_csv('df20160401.csv',index = False)\n", 483 | "df20160501[['发表时间','评论标题']].to_csv('df20160501.csv',index = False)\n", 484 | "df20160601[['发表时间','评论标题']].to_csv('df20160601.csv',index = False)\n", 485 | "df20160701[['发表时间','评论标题']].to_csv('df20160701.csv',index = False)\n", 486 | "df20160801[['发表时间','评论标题']].to_csv('df20160801.csv',index = False)\n", 487 | "df20160901[['发表时间','评论标题']].to_csv('df20160901.csv',index = False)\n", 488 | "df20161001[['发表时间','评论标题']].to_csv('df20161001.csv',index = False)\n", 489 | "df20161101[['发表时间','评论标题']].to_csv('df20161101.csv',index = False)\n", 490 | "df20161201[['发表时间','评论标题']].to_csv('df20161201.csv',index = False)" 491 | ] 492 | }, 493 | { 494 | "cell_type": "code", 495 | "execution_count": 5, 496 | "metadata": {}, 497 | "outputs": [], 498 | "source": [ 499 | "df20170101 = pd.read_csv('./cjpl/20170101_20170201.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 500 | "df20170201 = pd.read_csv('./cjpl/20170201_20170301.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 501 | "df20170301 = pd.read_csv('./cjpl/20170301_20170401.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 502 | "df20170401 = pd.read_csv('./cjpl/20170401_20170501.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 503 | "df20170501 = pd.read_csv('./cjpl/20170501_20170601.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 504 | "df20170601 = pd.read_csv('./cjpl/20170601_20170701.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 505 | "df20170701 = pd.read_csv('./cjpl/20170701_20170801.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 506 | "df20170801 = pd.read_csv('./cjpl/20170801_20170901.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]\n", 507 | "df20170901 = pd.read_csv('./cjpl/20170901_20171001.csv', sep=',',header = 0, usecols=range(8)).iloc[::-1]" 508 | ] 509 | }, 510 | { 511 | "cell_type": "code", 512 | "execution_count": 6, 513 | "metadata": {}, 514 | "outputs": [], 515 | "source": [ 516 | "df20170101[['发表时间','评论标题']].to_csv('df20170101.csv',index = False)\n", 517 | "df20170201[['发表时间','评论标题']].to_csv('df20170201.csv',index = False)\n", 518 | "df20170301[['发表时间','评论标题']].to_csv('df20170301.csv',index = False)\n", 519 | "df20170401[['发表时间','评论标题']].to_csv('df20170401.csv',index = False)\n", 520 | "df20170501[['发表时间','评论标题']].to_csv('df20170501.csv',index = False)\n", 521 | "df20170601[['发表时间','评论标题']].to_csv('df20170601.csv',index = False)\n", 522 | "df20170701[['发表时间','评论标题']].to_csv('df20170701.csv',index = False)\n", 523 | "df20170801[['发表时间','评论标题']].to_csv('df20170801.csv',index = False)\n", 524 | "df20170901[['发表时间','评论标题']].to_csv('df20170901.csv',index = False)" 525 | ] 526 | }, 527 | { 528 | "cell_type": "code", 529 | "execution_count": 7, 530 | "metadata": {}, 531 | "outputs": [], 532 | "source": [ 533 | "combine_df = pd.concat([df20160401,df20160501,df20160601,df20160701,df20160801,df20160901,df20161001,\n", 534 | " df20161101,df20161201,df20170101,df20170201,df20170301,df20170401,df20170501,df20170601,\n", 535 | " df20170701,df20170801,df20170901],axis=0,ignore_index=True)" 536 | ] 537 | }, 538 | { 539 | "cell_type": "code", 540 | "execution_count": 8, 541 | "metadata": {}, 542 | "outputs": [], 543 | "source": [ 544 | "combine_df[['发表时间','评论标题']].to_csv('combine_df.csv',index = False)" 545 | ] 546 | }, 547 | { 548 | "cell_type": "code", 549 | "execution_count": null, 550 | "metadata": {}, 551 | "outputs": [], 552 | "source": [] 553 | } 554 | ], 555 | "metadata": { 556 | "kernelspec": { 557 | "display_name": "Python 3", 558 | "language": "python", 559 | "name": "python3" 560 | }, 561 | "language_info": { 562 | "codemirror_mode": { 563 | "name": "ipython", 564 | "version": 3 565 | }, 566 | "file_extension": ".py", 567 | "mimetype": "text/x-python", 568 | "name": "python", 569 | "nbconvert_exporter": "python", 570 | "pygments_lexer": "ipython3", 571 | "version": "3.6.5" 572 | } 573 | }, 574 | "nbformat": 4, 575 | "nbformat_minor": 2 576 | } 577 | -------------------------------------------------------------------------------- /experiment3-sentimentAnalysis/Ashare-from-20160401-to-20170930.csv: -------------------------------------------------------------------------------- 1 | ts_code,trade_date,close,open,high,low,pre_close,change,pct_chg,vol,amount 2 | 000001.SH,20160401,3009.53,2997.088,3009.673,2956.247,3003.915,5.615,0.1869,206545866.0,217520534.02 3 | 000001.SH,20160405,3053.065,3000.938,3057.33,2993.148,3009.53,43.535,1.4466,256730344.0,276654687.085 4 | 000001.SH,20160406,3050.592,3039.745,3059.794,3029.005,3053.065,-2.473,-0.081,232262051.0,254914554.28 5 | 000001.SH,20160407,3008.42,3058.339,3062.358,3007.06,3050.592,-42.172,-1.3824,220919506.0,243961813.691 6 | 000001.SH,20160408,2984.958,2988.2,2996.171,2960.46,3008.42,-23.462,-0.7799,187904633.0,207337095.111 7 | 000001.SH,20160411,3033.957,3006.909,3048.977,3006.909,2984.958,48.999,1.6415,220018324.0,252328393.482 8 | 000001.SH,20160412,3023.646,3031.301,3036.825,3001.315,3033.957,-10.311,-0.3399,182718668.0,207079807.862 9 | 000001.SH,20160413,3066.638,3041.358,3097.165,3041.358,3023.646,42.992,1.4219,310003246.0,331420683.224 10 | 000001.SH,20160414,3082.362,3080.09,3086.698,3056.989,3066.638,15.724,0.5127,208237830.0,235056056.274 11 | 000001.SH,20160415,3078.117,3085.026,3089.951,3066.872,3082.362,-4.245,-0.1377,189613051.0,224141313.373 12 | 000001.SH,20160418,3033.66,3058.463,3058.463,3022.97,3078.117,-44.457,-1.4443,183304512.0,208613086.291 13 | 000001.SH,20160419,3042.823,3047.133,3054.503,3024.895,3033.66,9.163,0.302,156533200.0,180948014.276 14 | 000001.SH,20160420,2972.584,3050.381,3055.689,2905.049,3042.823,-70.239,-2.3083,283144219.0,311154870.354 15 | 000001.SH,20160421,2952.891,2954.37,2990.673,2943.464,2972.584,-19.693,-0.6625,189406849.0,206477126.88 16 | 000001.SH,20160422,2959.24,2933.035,2960.209,2926.776,2952.891,6.349,0.215,137211668.0,148800430.845 17 | 000001.SH,20160425,2946.67,2949.973,2954.092,2917.02,2959.24,-12.57,-0.4248,125856243.0,140638756.29 18 | 000001.SH,20160426,2964.7,2944.716,2965.429,2933.971,2946.67,18.03,0.6119,117711604.0,134292458.713 19 | 000001.SH,20160427,2953.671,2967.19,2976.022,2949.432,2964.7,-11.029,-0.372,130867640.0,150286301.521 20 | 000001.SH,20160428,2945.589,2955.742,2959.512,2916.374,2953.671,-8.082,-0.2736,138663274.0,154614698.978 21 | 000001.SH,20160429,2938.324,2935.376,2950.576,2930.357,2945.589,-7.265,-0.2466,109310628.0,128502732.169 22 | 000001.SH,20160503,2992.643,2940.392,2993.535,2929.815,2938.324,54.319,1.8486,168703643.0,195816604.813 23 | 000001.SH,20160504,2991.272,2983.027,3004.418,2978.33,2992.643,-1.371,-0.0458,165407106.0,189046374.911 24 | 000001.SH,20160505,2997.842,2987.022,2999.118,2977.2,2991.272,6.57,0.2196,144831383.0,163113776.353 25 | 000001.SH,20160506,2913.248,2998.402,3003.59,2913.036,2997.842,-84.594,-2.8218,206796498.0,234077433.966 26 | 000001.SH,20160509,2832.113,2896.162,2896.162,2821.826,2913.248,-81.135,-2.785,180378330.0,188705685.662 27 | 000001.SH,20160510,2832.591,2822.327,2845.238,2820.164,2832.113,0.478,0.0169,120829310.0,128535266.1 28 | 000001.SH,20160511,2837.037,2843.545,2857.254,2818.699,2832.591,4.446,0.157,135795466.0,146022521.906 29 | 000001.SH,20160512,2835.862,2812.108,2839.276,2781.236,2837.037,-1.175,-0.0414,136337655.0,140838630.936 30 | 000001.SH,20160513,2827.109,2828.462,2850.093,2814.115,2835.862,-8.753,-0.3087,114319710.0,123692646.942 31 | 000001.SH,20160516,2850.862,2816.775,2851.229,2804.991,2827.109,23.753,0.8402,114653592.0,126844299.054 32 | 000001.SH,20160517,2843.684,2850.926,2860.321,2832.459,2850.862,-7.178,-0.2518,123369064.0,140000997.865 33 | 000001.SH,20160518,2807.514,2828.178,2828.255,2781.417,2843.684,-36.17,-1.2719,140487826.0,149396820.878 34 | 000001.SH,20160519,2806.906,2802.315,2829.402,2801.548,2807.514,-0.608,-0.0217,111595071.0,124151052.18 35 | 000001.SH,20160520,2825.483,2792.888,2825.952,2785.08,2806.906,18.577,0.6618,108700899.0,123794592.281 36 | 000001.SH,20160523,2843.645,2826.312,2848.071,2826.256,2825.483,18.162,0.6428,120470455.0,139950365.055 37 | 000001.SH,20160524,2821.666,2839.682,2839.695,2807.188,2843.645,-21.979,-0.7729,111452445.0,121096058.813 38 | 000001.SH,20160525,2815.0863,2835.0293,2843.165,2807.7488,2821.666,-6.5797,-0.2332,103527265.0,117767628.7 39 | 000001.SH,20160526,2822.443,2813.543,2827.092,2780.763,2815.086,7.357,0.2613,114766797.0,127105183.33 40 | 000001.SH,20160527,2821.046,2817.968,2832.799,2809.799,2822.443,-1.397,-0.0495,109845823.0,123858864.609 41 | 000001.SH,20160530,2822.451,2814.651,2830.97,2794.661,2821.046,1.405,0.0498,106319589.0,115587479.346 42 | 000001.SH,20160531,2916.616,2822.593,2917.135,2822.593,2822.451,94.165,3.3363,215260341.0,236528504.779 43 | 000001.SH,20160601,2913.508,2917.154,2929.076,2909.512,2916.616,-3.108,-0.1066,188386421.0,219868045.396 44 | 000001.SH,20160602,2925.229,2911.219,2925.67,2906.524,2913.508,11.721,0.4023,159716916.0,185084899.734 45 | 000001.SH,20160603,2938.682,2929.788,2945.519,2915.185,2925.229,13.453,0.4599,172021023.0,211302476.506 46 | 000001.SH,20160606,2934.098,2940.994,2945.942,2922.283,2938.682,-4.584,-0.156,141993742.0,172426791.396 47 | 000001.SH,20160607,2936.045,2936.282,2938.444,2923.502,2934.098,1.947,0.0664,132987867.0,163605603.318 48 | 000001.SH,20160608,2927.159,2932.376,2937.986,2908.367,2936.045,-8.886,-0.3027,143046071.0,177377309.482 49 | 000001.SH,20160613,2833.071,2897.273,2911.158,2832.507,2927.159,-94.088,-3.2143,169255434.0,198547765.495 50 | 000001.SH,20160614,2842.189,2824.229,2843.456,2822.061,2833.071,9.118,0.3218,119755274.0,138531751.701 51 | 000001.SH,20160615,2887.21,2814.693,2894.264,2811.781,2842.189,45.021,1.584,166671069.0,197199330.848 52 | 000001.SH,20160616,2872.817,2878.402,2887.743,2865.388,2887.21,-14.393,-0.4985,173882767.0,201957154.966 53 | 000001.SH,20160617,2885.105,2873.009,2900.299,2872.185,2872.817,12.288,0.4277,171472517.0,195432492.227 54 | 000001.SH,20160620,2888.809,2887.636,2891.658,2864.02,2885.105,3.704,0.1284,136571051.0,163734439.57 55 | 000001.SH,20160621,2878.558,2898.384,2919.302,2869.176,2888.809,-10.251,-0.3549,164359089.0,198201921.486 56 | 000001.SH,20160622,2905.55,2872.734,2905.978,2869.513,2878.558,26.992,0.9377,130590424.0,156339384.142 57 | 000001.SH,20160623,2891.96,2902.398,2904.112,2878.661,2905.55,-13.59,-0.4677,136137375.0,163680266.691 58 | 000001.SH,20160624,2854.286,2883.759,2899.569,2807.598,2891.96,-37.674,-1.3027,190842609.0,214913449.682 59 | 000001.SH,20160627,2895.703,2840.559,2895.731,2840.277,2854.286,41.417,1.451,156697472.0,189854731.207 60 | 000001.SH,20160628,2912.557,2885.011,2913.582,2878.824,2895.703,16.854,0.582,173871047.0,211079306.343 61 | 000001.SH,20160629,2931.592,2918.534,2933.997,2915.062,2912.557,19.035,0.6535,193215586.0,223406351.275 62 | 000001.SH,20160630,2929.606,2931.481,2938.137,2922.312,2931.592,-1.986,-0.0677,154464930.0,187459798.1 63 | 000001.SH,20160701,2932.476,2931.802,2944.989,2925.81,2929.606,2.87,0.098,141246227.0,173006184.794 64 | 000001.SH,20160704,2988.604,2924.293,2992.498,2922.52,2932.476,56.128,1.914,222017132.0,253150365.202 65 | 000001.SH,20160705,3006.392,2991.752,3010.275,2990.642,2988.604,17.788,0.5952,235204450.0,270418011.629 66 | 000001.SH,20160706,3017.292,2998.521,3017.652,2984.828,3006.392,10.9,0.3626,212370920.0,246497840.951 67 | 000001.SH,20160707,3016.847,3009.353,3024.109,2994.643,3017.292,-0.445,-0.0147,222681667.0,270197134.238 68 | 000001.SH,20160708,2988.094,3000.326,3001.551,2983.881,3016.847,-28.753,-0.9531,168986859.0,209672668.129 69 | 000001.SH,20160711,2994.917,2993.749,3022.944,2990.907,2988.094,6.823,0.2283,224338224.0,271916499.904 70 | 000001.SH,20160712,3049.381,2992.52,3049.681,2984.421,2994.917,54.464,1.8185,259486295.0,288656744.057 71 | 000001.SH,20160713,3060.689,3049.513,3069.047,3048.199,3049.381,11.308,0.3708,253659384.0,279350871.891 72 | 000001.SH,20160714,3054.018,3054.975,3057.046,3036.523,3060.689,-6.671,-0.218,180194804.0,208939031.31 73 | 000001.SH,20160715,3054.296,3056.682,3062.683,3044.538,3054.018,0.278,0.0091,172866398.0,202840549.58 74 | 000001.SH,20160718,3043.564,3047.637,3058.321,3031.639,3054.296,-10.732,-0.3514,177496562.0,206667709.265 75 | 000001.SH,20160719,3036.598,3040.226,3043.6,3014.289,3043.564,-6.966,-0.2289,155361466.0,187206723.214 76 | 000001.SH,20160720,3027.9,3034.714,3042.997,3022.631,3036.598,-8.698,-0.2864,138242815.0,171472917.221 77 | 000001.SH,20160721,3039.009,3027.604,3053.334,3027.368,3027.9,11.109,0.3669,163767370.0,200278455.546 78 | 000001.SH,20160722,3012.816,3038.118,3039.27,3007.457,3039.009,-26.193,-0.8619,162010181.0,197517350.36 79 | 000001.SH,20160725,3015.828,3008.094,3027.123,3003.292,3012.816,3.012,0.1,144206582.0,175422000.819 80 | 000001.SH,20160726,3050.166,3014.04,3050.615,3013.802,3015.828,34.338,1.1386,156694506.0,185474467.705 81 | 000001.SH,20160727,2991.999,3050.368,3057.424,2939.227,3050.166,-58.167,-1.907,280160915.0,316811388.907 82 | 000001.SH,20160728,2994.323,2980.501,3003.363,2968.182,2991.999,2.324,0.0777,190564950.0,217626998.763 83 | 000001.SH,20160729,2979.339,2992.536,3000.055,2972.917,2994.323,-14.984,-0.5004,150101534.0,170198566.552 84 | 000001.SH,20160801,2953.385,2971.949,2972.875,2931.963,2979.339,-25.954,-0.8711,147407763.0,162372674.323 85 | 000001.SH,20160802,2971.279,2950.08,2971.279,2946.637,2953.385,17.894,0.6059,115468900.0,129266872.711 86 | 000001.SH,20160803,2978.461,2963.215,2981.156,2956.786,2971.279,7.182,0.2417,141141332.0,159088262.525 87 | 000001.SH,20160804,2982.426,2976.41,2982.86,2958.933,2978.461,3.965,0.1331,133933301.0,153307963.507 88 | 000001.SH,20160805,2976.696,2978.778,2991.677,2971.564,2982.426,-5.73,-0.1921,141857101.0,161795980.405 89 | 000001.SH,20160808,3004.277,2972.625,3004.718,2959.048,2976.696,27.581,0.9266,155729833.0,172089654.259 90 | 000001.SH,20160809,3025.681,3001.306,3025.911,2998.677,3004.277,21.404,0.7125,169995446.0,187716865.344 91 | 000001.SH,20160810,3018.746,3023.472,3033.196,3017.09,3025.681,-6.935,-0.2292,164675165.0,184525184.723 92 | 000001.SH,20160811,3002.638,3013.678,3038.048,3001.168,3018.746,-16.108,-0.5336,161879481.0,179861525.628 93 | 000001.SH,20160812,3050.667,3000.273,3051.054,2999.039,3002.638,48.029,1.5996,168173657.0,179304885.162 94 | 000001.SH,20160815,3125.195,3056.484,3137.476,3053.871,3050.667,74.528,2.443,297616506.0,327991129.268 95 | 000001.SH,20160816,3110.037,3130.533,3140.441,3102.065,3125.195,-15.158,-0.485,278328934.0,308544861.178 96 | 000001.SH,20160817,3109.555,3106.991,3114.255,3090.281,3110.037,-0.482,-0.0155,213839637.0,243405250.612 97 | 000001.SH,20160818,3104.114,3107.753,3125.581,3093.317,3109.555,-5.441,-0.175,229359081.0,256969677.017 98 | 000001.SH,20160819,3108.102,3100.391,3113.344,3082.771,3104.114,3.988,0.1285,194347453.0,212050971.111 99 | 000001.SH,20160822,3084.805,3107.381,3112.74,3083.593,3108.102,-23.297,-0.7496,185387927.0,207638874.518 100 | 000001.SH,20160823,3089.706,3081.572,3101.11,3073.529,3084.805,4.901,0.1589,161368965.0,179183487.675 101 | 000001.SH,20160824,3085.88,3092.02,3097.148,3079.548,3089.706,-3.826,-0.1238,145707440.0,168362528.488 102 | 000001.SH,20160825,3068.329,3073.444,3073.444,3041.505,3085.88,-17.551,-0.5688,174032980.0,200859751.351 103 | 000001.SH,20160826,3070.309,3069.85,3087.652,3063.894,3068.329,1.98,0.0645,149663901.0,176309498.242 104 | 000001.SH,20160829,3070.027,3068.46,3074.943,3058.785,3070.309,-0.282,-0.0092,144500007.0,166397346.222 105 | 000001.SH,20160830,3074.677,3071.44,3082.988,3065.971,3070.027,4.65,0.1515,140434811.0,163878823.044 106 | 000001.SH,20160831,3085.491,3072.916,3087.698,3063.396,3074.677,10.814,0.3517,141368609.0,168986117.678 107 | 000001.SH,20160901,3063.305,3083.961,3088.704,3062.876,3085.491,-22.186,-0.719,155191093.0,177093295.28 108 | 000001.SH,20160902,3067.352,3057.495,3072.526,3050.492,3063.305,4.047,0.1321,150446920.0,170592139.865 109 | 000001.SH,20160905,3072.095,3070.707,3085.485,3065.329,3067.352,4.743,0.1546,144963317.0,161243675.297 110 | 000001.SH,20160906,3090.713,3071.055,3095.509,3053.187,3072.095,18.618,0.606,172908524.0,193057729.108 111 | 000001.SH,20160907,3091.928,3091.329,3105.678,3087.879,3090.713,1.215,0.0393,186956763.0,207162121.474 112 | 000001.SH,20160908,3095.954,3089.953,3096.783,3083.902,3091.928,4.026,0.1302,145883771.0,165944472.734 113 | 000001.SH,20160909,3078.855,3095.427,3101.792,3078.218,3095.954,-17.099,-0.5523,160692924.0,182056232.52 114 | 000001.SH,20160912,3021.977,3037.507,3040.952,2999.929,3078.855,-56.878,-1.8474,206012086.0,221846569.008 115 | 000001.SH,20160913,3023.51,3025.035,3029.721,3008.741,3021.977,1.533,0.0507,135387580.0,152791306.018 116 | 000001.SH,20160914,3002.849,3008.904,3017.945,2995.416,3023.51,-20.661,-0.6833,133413401.0,148148969.909 117 | 000001.SH,20160919,3026.051,3005.323,3026.651,3005.323,3002.849,23.202,0.7727,119225027.0,133168828.076 118 | 000001.SH,20160920,3023.0,3027.172,3027.817,3015.88,3026.051,-3.051,-0.1008,118924117.0,139662303.158 119 | 000001.SH,20160921,3025.874,3021.576,3032.45,3017.541,3023.0,2.874,0.0951,115721213.0,134133726.403 120 | 000001.SH,20160922,3042.313,3038.422,3054.435,3035.07,3025.874,16.439,0.5433,139309554.0,159031064.132 121 | 000001.SH,20160923,3033.896,3044.786,3046.805,3032.802,3042.313,-8.417,-0.2767,125590976.0,147377736.332 122 | 000001.SH,20160926,2980.43,3028.243,3028.243,2980.117,3033.896,-53.466,-1.7623,144330286.0,162257246.419 123 | 000001.SH,20160927,2998.172,2974.593,2998.233,2969.132,2980.43,17.742,0.5953,121701450.0,133372708.394 124 | 000001.SH,20160928,2987.858,3000.695,3000.695,2984.321,2998.172,-10.314,-0.344,104625697.0,116437377.488 125 | 000001.SH,20160929,2998.483,2992.168,3009.198,2991.909,2987.858,10.625,0.3556,113213919.0,122728570.07 126 | 000001.SH,20160930,3004.703,2994.251,3009.195,2993.062,2998.483,6.22,0.2074,101333562.0,112794538.478 127 | 000001.SH,20161010,3048.143,3020.457,3048.236,3014.62,3004.703,43.44,1.4457,160051104.0,175521792.58 128 | 000001.SH,20161011,3065.25,3051.622,3066.104,3048.018,3048.143,17.107,0.5612,167273496.0,183787648.35 129 | 000001.SH,20161012,3058.498,3057.316,3060.514,3048.888,3065.25,-6.752,-0.2203,142493577.0,159982961.361 130 | 000001.SH,20161013,3061.346,3057.972,3064.999,3052.64,3058.498,2.848,0.0931,154756725.0,166335869.075 131 | 000001.SH,20161014,3063.809,3056.993,3064.79,3043.184,3061.346,2.463,0.0805,155373705.0,163792994.643 132 | 000001.SH,20161017,3041.166,3064.691,3068.812,3033.753,3063.809,-22.643,-0.739,163733265.0,180594077.051 133 | 000001.SH,20161018,3083.875,3037.404,3084.187,3037.404,3041.166,42.709,1.4044,182042460.0,196036127.966 134 | 000001.SH,20161019,3084.719,3085.749,3096.223,3076.771,3083.875,0.844,0.0274,182470143.0,197481136.362 135 | 000001.SH,20161020,3084.458,3084.908,3089.682,3076.295,3084.719,-0.261,-0.0085,160691364.0,177674542.71 136 | 000001.SH,20161021,3090.941,3081.389,3101.849,3069.267,3084.458,6.483,0.2102,187388072.0,201953662.395 137 | 000001.SH,20161024,3128.247,3092.047,3137.034,3090.786,3090.941,37.306,1.2069,240522349.0,250354393.568 138 | 000001.SH,20161025,3131.939,3127.968,3132.505,3121.05,3128.247,3.692,0.118,203380898.0,220861464.075 139 | 000001.SH,20161026,3116.312,3129.838,3129.838,3110.387,3131.939,-15.627,-0.499,191663263.0,215767706.369 140 | 000001.SH,20161027,3112.35,3112.603,3114.759,3100.392,3116.312,-3.962,-0.1271,158008789.0,182237125.269 141 | 000001.SH,20161028,3104.27,3111.704,3128.643,3101.237,3112.35,-8.08,-0.2596,181774123.0,212593009.855 142 | 000001.SH,20161031,3100.492,3097.188,3102.302,3081.072,3104.27,-3.778,-0.1217,151506764.0,178427745.444 143 | 000001.SH,20161101,3122.436,3101.664,3122.615,3097.042,3100.492,21.944,0.7078,160232032.0,195831592.265 144 | 000001.SH,20161102,3102.733,3115.729,3118.977,3099.82,3122.436,-19.703,-0.631,182722067.0,221676542.065 145 | 000001.SH,20161103,3128.936,3096.765,3140.93,3094.103,3102.733,26.203,0.8445,222986260.0,265820360.415 146 | 000001.SH,20161104,3125.317,3126.349,3141.334,3119.535,3128.936,-3.619,-0.1157,197683197.0,239043257.281 147 | 000001.SH,20161107,3133.333,3124.892,3139.202,3117.096,3125.317,8.016,0.2565,179418490.0,210602324.385 148 | 000001.SH,20161108,3147.888,3140.944,3156.881,3134.955,3133.333,14.555,0.4645,190846979.0,221585117.19 149 | 000001.SH,20161109,3128.37,3146.078,3146.95,3096.947,3147.888,-19.518,-0.62,241114958.0,277229392.735 150 | 000001.SH,20161110,3171.282,3148.544,3172.309,3148.544,3128.37,42.912,1.3717,244192602.0,285992810.523 151 | 000001.SH,20161111,3196.044,3169.401,3202.74,3166.07,3171.282,24.762,0.7808,310854579.0,343773951.274 152 | 000001.SH,20161114,3210.371,3187.709,3221.458,3186.796,3196.044,14.327,0.4483,329608313.0,346974628.517 153 | 000001.SH,20161115,3206.986,3209.955,3214.292,3195.035,3210.371,-3.385,-0.1054,240886350.0,267003870.859 154 | 000001.SH,20161116,3205.057,3208.497,3210.893,3195.411,3206.986,-1.929,-0.0601,221766993.0,244854511.188 155 | 000001.SH,20161117,3208.453,3198.499,3211.053,3187.206,3205.057,3.396,0.106,214206240.0,226775851.18 156 | 000001.SH,20161118,3192.856,3207.193,3212.394,3187.496,3208.453,-15.597,-0.4861,209826639.0,235352062.308 157 | 000001.SH,20161121,3218.148,3188.496,3229.757,3188.28,3192.856,25.292,0.7921,230652844.0,263177808.131 158 | 000001.SH,20161122,3248.352,3220.983,3249.677,3220.983,3218.148,30.204,0.9386,261631547.0,292484830.64 159 | 000001.SH,20161123,3241.137,3247.945,3262.879,3231.586,3248.352,-7.215,-0.2221,249959592.0,277942467.717 160 | 000001.SH,20161124,3241.736,3237.428,3257.857,3232.869,3241.137,0.599,0.0185,232324527.0,267224712.392 161 | 000001.SH,20161125,3261.938,3241.243,3262.438,3209.598,3241.736,20.202,0.6232,233921601.0,259117040.4 162 | 000001.SH,20161128,3277.0,3270.047,3288.337,3267.674,3261.938,15.062,0.4618,281551253.0,300460150.894 163 | 000001.SH,20161129,3282.924,3269.234,3301.213,3263.397,3277.0,5.924,0.1808,320836287.0,348149349.67 164 | 000001.SH,20161130,3250.034,3272.142,3277.269,3239.521,3282.924,-32.89,-1.0019,243576053.0,262808738.181 165 | 000001.SH,20161201,3273.309,3257.027,3279.671,3256.256,3250.034,23.275,0.7161,237759864.0,261553934.531 166 | 000001.SH,20161202,3243.843,3270.121,3279.714,3235.277,3273.309,-29.466,-0.9002,259567205.0,281559095.557 167 | 000001.SH,20161205,3204.709,3203.784,3219.518,3194.879,3243.843,-39.134,-1.2064,223010394.0,238598033.019 168 | 000001.SH,20161206,3199.647,3202.029,3215.311,3196.525,3204.709,-5.062,-0.158,157572693.0,177170154.406 169 | 000001.SH,20161207,3222.242,3198.475,3222.428,3189.485,3199.647,22.595,0.7062,169072847.0,189443846.603 170 | 000001.SH,20161208,3215.366,3225.55,3228.122,3211.471,3222.242,-6.876,-0.2134,170710173.0,197802065.189 171 | 000001.SH,20161209,3232.883,3209.34,3244.801,3207.043,3215.366,17.517,0.5448,204023956.0,226387156.318 172 | 000001.SH,20161212,3152.97,3233.666,3245.095,3149.896,3232.883,-79.913,-2.4719,274708461.0,294260138.539 173 | 000001.SH,20161213,3155.037,3138.988,3162.496,3118.712,3152.97,2.067,0.0656,185164495.0,203175456.917 174 | 000001.SH,20161214,3140.531,3149.377,3170.021,3136.348,3155.037,-14.506,-0.4598,201358887.0,219206160.238 175 | 000001.SH,20161215,3117.677,3125.757,3138.779,3100.913,3140.531,-22.854,-0.7277,189990626.0,212863239.254 176 | 000001.SH,20161216,3122.982,3111.515,3128.874,3106.346,3117.677,5.305,0.1702,164988699.0,189844752.856 177 | 000001.SH,20161219,3118.0846,3120.6963,3125.284,3110.0817,3122.9815,-4.8969,-0.1568,154169716.0,174500240.6 178 | 000001.SH,20161220,3102.8759,3115.8984,3117.0145,3084.7997,3118.0846,-15.2087,-0.4878,157426759.0,171611881.1 179 | 000001.SH,20161221,3137.4297,3107.2396,3140.2512,3107.2396,3102.8759,34.5538,1.1136,186706607.0,204247805.1 180 | 000001.SH,20161222,3139.558,3132.1558,3143.1685,3126.8872,3137.4297,2.1283,0.0678,167804990.0,185981556.9 181 | 000001.SH,20161223,3110.1544,3134.9292,3138.404,3103.7476,3139.558,-29.4036,-0.9366,166176769.0,189281177.4 182 | 000001.SH,20161226,3122.569,3095.5787,3122.8812,3068.415,3110.1544,12.4146,0.3992,152571881.0,170555610.8 183 | 000001.SH,20161227,3114.664,3117.3868,3127.8828,3113.7451,3122.569,-7.905,-0.2532,141528939.0,162193908.5 184 | 000001.SH,20161228,3102.2357,3113.7671,3118.7818,3094.5488,3114.664,-12.4283,-0.399,135727087.0,154329653.0 185 | 000001.SH,20161229,3096.0968,3095.8447,3111.7994,3087.344,3102.2357,-6.1389,-0.1979,132623292.0,149919182.8 186 | 000001.SH,20161230,3103.6373,3097.3454,3108.8393,3089.9894,3096.0968,7.5405,0.2435,133267130.0,151732781.7 187 | 000001.SH,20170103,3135.9208,3105.3085,3136.4558,3105.3085,3103.6373,32.2835,1.0402,141567187.0,159887139.4 188 | 000001.SH,20170104,3158.794,3133.7873,3160.1029,3130.1145,3135.9208,22.8732,0.7294,167860850.0,195914292.6 189 | 000001.SH,20170105,3165.4109,3157.9063,3168.5021,3154.281,3158.794,6.6169,0.2095,174727645.0,199692026.7 190 | 000001.SH,20170106,3154.321,3163.7761,3172.0347,3153.0253,3165.4109,-11.0899,-0.3503,183708966.0,207296038.4 191 | 000001.SH,20170109,3171.2362,3148.5317,3173.136,3147.7351,3154.321,16.9152,0.5363,171714075.0,192110579.3 192 | 000001.SH,20170110,3161.6713,3167.5701,3174.5781,3157.3322,3171.2362,-9.5649,-0.3016,179759216.0,194963216.5 193 | 000001.SH,20170111,3136.7535,3156.6856,3167.0288,3136.2667,3161.6713,-24.9178,-0.7881,178362221.0,189186459.3 194 | 000001.SH,20170112,3119.2886,3133.6015,3144.9699,3115.9787,3136.7535,-17.4649,-0.5568,148889240.0,161922296.4 195 | 000001.SH,20170113,3112.7644,3116.0827,3130.5146,3102.1628,3119.2886,-6.5242,-0.2092,156274214.0,174438553.9 196 | 000001.SH,20170116,3103.428,3104.4924,3105.1423,3044.2912,3112.7644,-9.3364,-0.2999,257885996.0,262826041.8 197 | 000001.SH,20170117,3108.7746,3087.0295,3108.9072,3072.3384,3103.428,5.3466,0.1723,136157861.0,154757554.7 198 | 000001.SH,20170118,3113.0123,3104.7664,3123.7203,3098.5864,3108.7746,4.2377,0.1363,131780128.0,144546719.2 199 | 000001.SH,20170119,3101.2992,3104.9715,3115.7778,3094.0055,3113.0123,-11.7131,-0.3763,123851420.0,139280461.4 200 | 000001.SH,20170120,3123.1389,3095.8185,3125.6599,3095.2147,3101.2992,21.8397,0.7042,122366777.0,141471811.3 201 | 000001.SH,20170123,3136.7748,3125.4209,3145.8411,3125.4209,3123.1389,13.6359,0.4366,132660392.0,148145735.8 202 | 000001.SH,20170124,3142.5533,3134.5921,3149.5312,3131.2181,3136.7748,5.7785,0.1842,125973881.0,134553852.1 203 | 000001.SH,20170125,3149.5547,3137.6462,3151.4723,3133.1911,3142.5533,7.0014,0.2228,112073827.0,126765829.9 204 | 000001.SH,20170126,3159.166,3149.2167,3163.1041,3148.9083,3149.5547,9.6113,0.3052,113876772.0,125013193.9 205 | 000001.SH,20170203,3140.17,3160.0816,3162.6771,3136.013,3159.166,-18.996,-0.6013,92224736.0,108023659.8 206 | 000001.SH,20170206,3156.9838,3143.0931,3158.8433,3135.3869,3140.17,16.8138,0.5354,127198283.0,150067694.7 207 | 000001.SH,20170207,3153.0878,3154.4046,3159.544,3140.0356,3156.9838,-3.896,-0.1234,128337442.0,152559086.1 208 | 000001.SH,20170208,3166.9818,3148.086,3167.4465,3132.0333,3153.0878,13.894,0.4406,144920187.0,170854126.2 209 | 000001.SH,20170209,3183.1794,3164.6875,3186.838,3162.5744,3166.9818,16.1976,0.5115,191640327.0,209723158.6 210 | 000001.SH,20170210,3196.699,3183.0069,3205.049,3182.8021,3183.1794,13.5196,0.4247,239348913.0,245772163.9 211 | 000001.SH,20170213,3216.8394,3198.9946,3219.4072,3198.9946,3196.699,20.1404,0.63,220191508.0,236158766.8 212 | 000001.SH,20170214,3217.928,3216.137,3219.4047,3205.2853,3216.8394,1.0886,0.0338,188798088.0,204253821.8 213 | 000001.SH,20170215,3212.9857,3215.4639,3235.9977,3206.5606,3217.928,-4.9423,-0.1536,241507706.0,257965244.4 214 | 000001.SH,20170216,3229.6184,3210.3567,3230.2754,3207.7852,3212.9857,16.6327,0.5177,216955942.0,226587611.9 215 | 000001.SH,20170217,3202.0756,3227.7073,3238.3959,3199.4248,3229.6184,-27.5428,-0.8528,226228177.0,248572751.5 216 | 000001.SH,20170220,3239.9613,3198.9633,3241.458,3198.9633,3202.0756,37.8857,1.1832,232152096.0,250276131.8 217 | 000001.SH,20170221,3253.3257,3242.2226,3254.3355,3239.8775,3239.9613,13.3644,0.4125,211673066.0,233551176.0 218 | 000001.SH,20170222,3261.2184,3252.6898,3261.3808,3243.8398,3253.3257,7.8927,0.2426,207453776.0,234815325.8 219 | 000001.SH,20170223,3251.375,3258.8319,3264.0819,3236.3549,3261.2184,-9.8434,-0.3018,209179800.0,236822582.2 220 | 000001.SH,20170224,3253.4327,3246.8603,3253.9556,3233.5348,3251.375,2.0577,0.0633,186406362.0,213839036.8 221 | 000001.SH,20170227,3228.6602,3249.1945,3251.6543,3224.0884,3253.4327,-24.7725,-0.7614,182581071.0,211395715.8 222 | 000001.SH,20170228,3241.7331,3225.9689,3242.679,3225.9689,3228.6602,13.0729,0.4049,151244318.0,186019288.8 223 | 000001.SH,20170301,3246.9335,3240.0726,3259.9781,3237.8714,3241.7331,5.2004,0.1604,190677550.0,225940967.9 224 | 000001.SH,20170302,3230.0281,3250.5183,3256.8074,3228.6647,3246.9335,-16.9054,-0.5207,181215076.0,223043958.7 225 | 000001.SH,20170303,3218.3118,3219.2019,3221.1553,3206.6127,3230.0281,-11.7163,-0.3627,157082368.0,192707923.0 226 | 000001.SH,20170306,3233.8657,3217.3345,3234.6626,3215.067,3218.3118,15.5539,0.4833,156092158.0,193769172.3 227 | 000001.SH,20170307,3242.4063,3233.0939,3242.6591,3226.8219,3233.8657,8.5406,0.2641,164064235.0,209931208.3 228 | 000001.SH,20170308,3240.6646,3240.5323,3245.3043,3230.6083,3242.4063,-1.7417,-0.0537,160731388.0,198225782.5 229 | 000001.SH,20170309,3216.7457,3233.7007,3233.8749,3205.2794,3240.6646,-23.9189,-0.7381,167371108.0,199217646.8 230 | 000001.SH,20170310,3212.7601,3213.7294,3222.3189,3208.4472,3216.7457,-3.9856,-0.1239,136672743.0,173663170.2 231 | 000001.SH,20170313,3237.0244,3209.4492,3237.1212,3193.1565,3212.7601,24.2643,0.7552,163673760.0,206919741.9 232 | 000001.SH,20170314,3239.3278,3235.2515,3246.3285,3231.5209,3237.0244,2.3034,0.0712,146946022.0,191535126.6 233 | 000001.SH,20170315,3241.7597,3235.403,3243.713,3227.7377,3239.3278,2.4319,0.0751,144055682.0,185727250.3 234 | 000001.SH,20170316,3268.9354,3247.1628,3269.77,3247.1628,3241.7597,27.1757,0.8383,189416002.0,244508344.4 235 | 000001.SH,20170317,3237.4471,3271.8665,3274.1903,3232.2806,3268.9354,-31.4883,-0.9633,200583223.0,262184046.4 236 | 000001.SH,20170320,3250.8082,3241.1101,3251.1271,3228.1177,3237.4471,13.3611,0.4127,170548430.0,213951759.5 237 | 000001.SH,20170321,3261.6108,3250.2473,3262.2209,3246.6959,3250.8082,10.8026,0.3323,162719306.0,219121269.7 238 | 000001.SH,20170322,3245.2198,3246.2233,3255.7779,3229.1283,3261.6108,-16.391,-0.5025,189731649.0,244545459.0 239 | 000001.SH,20170323,3248.5495,3245.8078,3262.0914,3221.9344,3245.2198,3.3297,0.1026,193029144.0,258246691.0 240 | 000001.SH,20170324,3269.4451,3247.3495,3275.2066,3241.1233,3248.5495,20.8956,0.6432,219777914.0,267094164.3 241 | 000001.SH,20170327,3266.9552,3268.924,3283.2393,3262.1184,3269.4451,-2.4899,-0.0762,201852675.0,249185540.3 242 | 000001.SH,20170328,3252.9479,3265.6344,3265.6344,3246.0861,3266.9552,-14.0073,-0.4288,161710013.0,203452481.7 243 | 000001.SH,20170329,3241.3144,3252.8653,3262.0976,3233.2795,3252.9479,-11.6335,-0.3576,216105575.0,245371852.6 244 | 000001.SH,20170330,3210.2369,3235.1369,3240.0174,3195.8524,3241.3144,-31.0775,-0.9588,247135479.0,265694871.6 245 | 000001.SH,20170331,3222.5142,3206.2529,3226.2483,3205.5365,3210.2369,12.2773,0.3824,196442922.0,214036159.9 246 | 000001.SH,20170405,3270.3054,3235.6603,3270.6453,3233.2367,3222.5142,47.7912,1.483,248320202.0,273201155.6 247 | 000001.SH,20170406,3281.0047,3272.1926,3286.6739,3265.7645,3270.3054,10.6993,0.3272,245287999.0,262335587.5 248 | 000001.SH,20170407,3286.616,3280.6239,3295.187,3275.0507,3281.0047,5.6113,0.171,236108942.0,266738322.8 249 | 000001.SH,20170410,3269.3926,3285.4594,3285.4594,3265.0121,3286.616,-17.2234,-0.524,232694616.0,279415195.3 250 | 000001.SH,20170411,3288.9657,3266.2219,3290.3887,3244.4047,3269.3926,19.5731,0.5987,281281248.0,326622406.9 251 | 000001.SH,20170412,3273.8301,3283.8369,3284.9338,3262.2773,3288.9657,-15.1356,-0.4602,269381790.0,311505523.7 252 | 000001.SH,20170413,3275.9603,3265.2181,3281.1369,3261.4901,3273.8301,2.1302,0.0651,207346875.0,223287214.7 253 | 000001.SH,20170414,3246.0668,3276.1381,3276.7112,3238.8959,3275.9603,-29.8935,-0.9125,214508558.0,224106679.4 254 | 000001.SH,20170417,3222.1673,3229.9491,3229.9491,3199.9121,3246.0668,-23.8995,-0.7363,212737189.0,227214681.3 255 | 000001.SH,20170418,3196.7133,3215.3963,3225.0546,3196.4884,3222.1673,-25.454,-0.79,188661353.0,211866767.6 256 | 000001.SH,20170419,3170.6867,3184.666,3189.4368,3147.0655,3196.7133,-26.0266,-0.8142,213238075.0,225806056.5 257 | 000001.SH,20170420,3172.1003,3165.665,3178.1826,3148.1844,3170.6867,1.4136,0.0446,190873985.0,220098232.7 258 | 000001.SH,20170421,3173.1512,3170.2899,3180.7944,3158.6286,3172.1003,1.0509,0.0331,164761740.0,184131440.7 259 | 000001.SH,20170424,3129.5312,3164.2479,3164.2479,3111.2145,3173.1512,-43.62,-1.3747,186277434.0,197845253.5 260 | 000001.SH,20170425,3134.5674,3123.8943,3145.2672,3117.4488,3129.5312,5.0362,0.1609,153418307.0,174129249.6 261 | 000001.SH,20170426,3140.8471,3132.9181,3152.9534,3131.418,3134.5674,6.2797,0.2003,169878107.0,197112873.0 262 | 000001.SH,20170427,3152.1869,3131.3496,3155.0033,3097.3334,3140.8471,11.3398,0.361,211793073.0,235748319.3 263 | 000001.SH,20170428,3154.6584,3144.0219,3154.7266,3136.5781,3152.1869,2.4715,0.0784,162889899.0,183195769.8 264 | 000001.SH,20170502,3143.7121,3147.2275,3154.7812,3136.5391,3154.6584,-10.9463,-0.347,154222962.0,176389916.6 265 | 000001.SH,20170503,3135.346,3138.3068,3148.2857,3123.7509,3143.7121,-8.3661,-0.2661,163763924.0,190236600.6 266 | 000001.SH,20170504,3127.3687,3127.1058,3143.8179,3111.3912,3135.346,-7.9773,-0.2544,177967485.0,200170851.5 267 | 000001.SH,20170505,3103.0378,3114.7735,3117.6141,3092.0918,3127.3687,-24.3309,-0.778,176213641.0,200273908.1 268 | 000001.SH,20170508,3078.6129,3090.0652,3093.4516,3067.6943,3103.0378,-24.4249,-0.7871,180526904.0,198171689.4 269 | 000001.SH,20170509,3080.5269,3064.8484,3084.2077,3056.558,3078.6129,1.914,0.0622,135066674.0,152795901.6 270 | 000001.SH,20170510,3052.785,3078.1728,3090.8191,3051.5896,3080.5269,-27.7419,-0.9006,160794452.0,181624151.6 271 | 000001.SH,20170511,3061.5003,3036.7891,3063.5637,3016.5305,3052.785,8.7153,0.2855,191341901.0,197604070.1 272 | 000001.SH,20170512,3083.5132,3054.1123,3090.4914,3051.8713,3061.5003,22.0129,0.719,159684099.0,175107245.7 273 | 000001.SH,20170515,3090.2289,3085.9319,3098.9125,3085.9319,3083.5132,6.7157,0.2178,135660728.0,158269607.7 274 | 000001.SH,20170516,3112.9642,3082.8717,3113.5116,3060.5344,3090.2289,22.7353,0.7357,173775564.0,193292104.5 275 | 000001.SH,20170517,3104.4415,3107.802,3119.5816,3101.2953,3112.9642,-8.5227,-0.2738,168674158.0,194560594.1 276 | 000001.SH,20170518,3090.139,3082.3288,3103.4413,3077.9631,3104.4415,-14.3025,-0.4607,148620035.0,168149514.1 277 | 000001.SH,20170519,3090.6309,3086.7054,3095.4826,3081.278,3090.139,0.4919,0.0159,129635230.0,150164226.2 278 | 000001.SH,20170522,3075.6756,3087.1705,3103.9375,3063.1529,3090.6309,-14.9553,-0.4839,153683730.0,170340982.6 279 | 000001.SH,20170523,3061.947,3069.3935,3084.2352,3050.8423,3075.6756,-13.7286,-0.4464,178310599.0,195073653.6 280 | 000001.SH,20170524,3064.0759,3047.5681,3064.8127,3022.3026,3061.947,2.1289,0.0695,139813353.0,152669201.5 281 | 000001.SH,20170525,3107.8311,3055.3425,3114.6578,3052.8309,3064.0759,43.7552,1.428,191391841.0,201910125.0 282 | 000001.SH,20170526,3110.0587,3101.2864,3120.6627,3100.3877,3107.8311,2.2276,0.0717,155923486.0,171327384.6 283 | 000001.SH,20170531,3117.1778,3125.3252,3143.2787,3111.5563,3110.0587,7.1191,0.2289,152950430.0,177913309.9 284 | 000001.SH,20170601,3102.6232,3108.4214,3113.5212,3097.6794,3117.1778,-14.5546,-0.4669,163015719.0,180782624.7 285 | 000001.SH,20170602,3105.54,3094.2253,3110.3891,3081.8485,3102.6232,2.9168,0.094,142504925.0,154454347.9 286 | 000001.SH,20170605,3091.6561,3102.1095,3105.5056,3084.8301,3105.54,-13.8839,-0.4471,132570300.0,143429802.0 287 | 000001.SH,20170606,3102.126,3084.5401,3102.8633,3078.7872,3091.6561,10.4699,0.3387,113439963.0,127804624.2 288 | 000001.SH,20170607,3140.3249,3101.7608,3140.7743,3098.9508,3102.126,38.1989,1.2314,173229014.0,194287945.5 289 | 000001.SH,20170608,3150.3336,3136.4705,3153.2635,3132.8278,3140.3249,10.0087,0.3187,152277659.0,178894298.5 290 | 000001.SH,20170609,3158.4004,3147.4533,3165.9197,3146.1075,3150.3336,8.0668,0.2561,160136334.0,181584884.7 291 | 000001.SH,20170612,3139.8766,3149.5266,3164.9502,3135.314,3158.4004,-18.5238,-0.5865,146729425.0,169392918.451 292 | 000001.SH,20170613,3153.7429,3134.0089,3155.9895,3131.0431,3139.8766,13.8663,0.4416,128318279.0,148457870.0 293 | 000001.SH,20170614,3130.674,3146.748,3149.1731,3125.3546,3153.7429,-23.0689,-0.7315,138347042.0,160641229.6 294 | 000001.SH,20170615,3132.4863,3125.5865,3137.5854,3117.084,3130.674,1.8123,0.0579,146954245.0,171124529.3 295 | 000001.SH,20170616,3123.1662,3126.3728,3134.2511,3117.8567,3132.4863,-9.3201,-0.2975,129652758.0,149215477.6 296 | 000001.SH,20170619,3144.3739,3122.1578,3146.7703,3121.7787,3123.1662,21.2077,0.679,134891758.0,151741797.8 297 | 000001.SH,20170620,3140.0136,3148.0187,3150.4646,3134.6106,3144.3739,-4.3603,-0.1387,141191802.0,162498014.5 298 | 000001.SH,20170621,3156.2118,3148.9861,3157.0275,3132.6173,3140.0136,16.1982,0.5159,136688268.0,167048215.9 299 | 000001.SH,20170622,3147.4532,3152.2424,3186.9823,3146.6432,3156.2118,-8.7586,-0.2775,191344006.0,220438439.5 300 | 000001.SH,20170623,3157.873,3138.4441,3158.0474,3118.0946,3147.4532,10.4198,0.3311,154843648.0,175568524.1 301 | 000001.SH,20170626,3185.4439,3157.0022,3187.8891,3156.9767,3157.873,27.5709,0.8731,173579211.0,200419302.2 302 | 000001.SH,20170627,3191.1969,3183.4198,3193.4613,3172.463,3185.4439,5.753,0.1806,148201198.0,167523800.3 303 | 000001.SH,20170628,3173.2014,3183.6334,3193.4397,3170.7859,3191.1969,-17.9955,-0.5639,146516720.0,162081099.7 304 | 000001.SH,20170629,3188.0625,3174.9811,3188.7741,3174.2835,3173.2014,14.8611,0.4683,128755470.0,147249798.1 305 | 000001.SH,20170630,3192.4269,3176.9481,3193.2411,3171.5703,3188.0625,4.3644,0.1369,121464543.0,143195603.4 306 | 000001.SH,20170703,3195.9116,3191.9982,3196.2861,3177.0244,3192.4269,3.4847,0.1092,140305717.0,156575784.3 307 | 000001.SH,20170704,3182.8039,3192.8885,3193.0644,3174.314,3195.9116,-13.1077,-0.4101,141114967.0,161454562.3 308 | 000001.SH,20170705,3207.1342,3179.2169,3207.3095,3174.7081,3182.8039,24.3303,0.7644,148296483.0,173554012.0 309 | 000001.SH,20170706,3212.444,3203.8631,3215.95,3188.7733,3207.1342,5.3098,0.1656,175809287.0,202209616.9 310 | 000001.SH,20170707,3217.9567,3203.8225,3219.5232,3195.2905,3212.444,5.5127,0.1716,176715416.0,201938313.2 311 | 000001.SH,20170710,3212.6319,3208.4627,3223.3402,3203.2089,3217.9567,-5.3248,-0.1655,198928010.0,222828311.7 312 | 000001.SH,20170711,3203.0375,3201.5169,3226.908,3199.2235,3212.6319,-9.5944,-0.2986,187837262.0,205493366.2 313 | 000001.SH,20170712,3197.5439,3201.9292,3215.1962,3177.9346,3203.0375,-5.4936,-0.1715,186900880.0,201799622.1 314 | 000001.SH,20170713,3218.1632,3192.3615,3219.2683,3190.3393,3197.5439,20.6193,0.6448,195303611.0,210820896.3 315 | 000001.SH,20170714,3222.4168,3212.0316,3222.9784,3204.8529,3218.1632,4.2536,0.1322,160126773.0,174312138.1 316 | 000001.SH,20170717,3176.4648,3219.7914,3230.354,3139.5035,3222.4168,-45.952,-1.426,266205274.0,274653250.0 317 | 000001.SH,20170718,3187.5672,3159.7318,3187.671,3150.1284,3176.4648,11.1024,0.3495,190623060.0,196452361.5 318 | 000001.SH,20170719,3230.9762,3181.4015,3232.9408,3179.73,3187.5672,43.409,1.3618,272420726.0,269771019.5 319 | 000001.SH,20170720,3244.8647,3227.5056,3246.236,3225.4328,3230.9762,13.8885,0.4299,232108392.0,245825421.9 320 | 000001.SH,20170721,3237.9817,3236.5881,3247.7122,3231.9556,3244.8647,-6.883,-0.2121,206003251.0,221956909.9 321 | 000001.SH,20170724,3250.5989,3230.898,3261.1046,3230.0705,3237.9817,12.6172,0.3897,233056399.0,250631194.2 322 | 000001.SH,20170725,3243.6894,3249.1376,3261.6454,3233.1376,3250.5989,-6.9095,-0.2126,205573873.0,213405498.9 323 | 000001.SH,20170726,3247.6748,3244.4608,3264.8483,3228.0389,3243.6894,3.9854,0.1229,213542005.0,228460518.5 324 | 000001.SH,20170727,3249.7814,3243.765,3251.9261,3220.6366,3247.6748,2.1066,0.0649,228485945.0,242572081.4 325 | 000001.SH,20170728,3253.2404,3240.1728,3256.3706,3232.9634,3249.7814,3.459,0.1064,182226880.0,198428584.0 326 | 000001.SH,20170731,3273.0283,3252.7519,3276.9461,3251.1941,3253.2404,19.7879,0.6083,246039440.0,253525920.8 327 | 000001.SH,20170801,3292.6383,3274.3685,3292.6383,3273.5038,3273.0283,19.61,0.5991,237194594.0,256419942.2 328 | 000001.SH,20170802,3285.0568,3288.5183,3305.4313,3282.0377,3292.6383,-7.5815,-0.2303,266730628.0,279352341.7 329 | 000001.SH,20170803,3272.9286,3279.9864,3293.3686,3262.1554,3285.0568,-12.1282,-0.3692,233277004.0,243549542.7 330 | 000001.SH,20170804,3262.0809,3269.3182,3287.1929,3261.3066,3272.9286,-10.8477,-0.3314,275906340.0,285634760.3 331 | 000001.SH,20170807,3279.4566,3257.67,3280.1035,3243.7153,3262.0809,17.3757,0.5327,231173379.0,231874371.8 332 | 000001.SH,20170808,3281.8728,3277.1887,3285.4833,3269.6582,3279.4566,2.4162,0.0737,252043537.0,255770184.5 333 | 000001.SH,20170809,3275.573,3277.8083,3277.9433,3263.8468,3281.8728,-6.2998,-0.192,235597907.0,237815125.5 334 | 000001.SH,20170810,3261.7494,3269.7347,3282.519,3236.1829,3275.573,-13.8236,-0.422,240684622.0,251854039.7 335 | 000001.SH,20170811,3208.5413,3237.9222,3245.1163,3200.7481,3261.7494,-53.2081,-1.6313,262962995.0,266998262.4 336 | 000001.SH,20170814,3237.3602,3206.0436,3240.0517,3206.0436,3208.5413,28.8189,0.8982,190346796.0,209096078.7 337 | 000001.SH,20170815,3251.2617,3235.2298,3263.5892,3235.1013,3237.3602,13.9015,0.4294,182297997.0,204595858.6 338 | 000001.SH,20170816,3246.4512,3247.8525,3248.785,3228.8705,3251.2617,-4.8105,-0.148,176852051.0,200832533.7 339 | 000001.SH,20170817,3268.4298,3253.8455,3269.1389,3251.4593,3246.4512,21.9786,0.677,203622551.0,228376944.2 340 | 000001.SH,20170818,3268.7243,3253.2434,3275.0763,3248.0833,3268.4298,0.2945,0.009,191122483.0,218437740.7 341 | 000001.SH,20170821,3286.9055,3274.5805,3287.5186,3270.4753,3268.7243,18.1812,0.5562,186122483.0,209718592.9 342 | 000001.SH,20170822,3290.2257,3287.6147,3293.476,3274.941,3286.9055,3.3202,0.101,186537991.0,222927491.5 343 | 000001.SH,20170823,3287.7049,3283.7966,3299.4572,3274.4404,3290.2257,-2.5208,-0.0766,179832208.0,204330421.4 344 | 000001.SH,20170824,3271.5117,3287.9594,3297.9886,3266.3589,3287.7049,-16.1932,-0.4925,163468937.0,186245056.6 345 | 000001.SH,20170825,3331.5221,3271.4608,3331.9146,3271.4608,3271.5117,60.0104,1.8343,205839482.0,227141029.4 346 | 000001.SH,20170828,3362.6514,3336.1264,3375.0339,3336.1264,3331.5221,31.1293,0.9344,257461438.0,305319883.1 347 | 000001.SH,20170829,3365.2261,3362.0604,3374.5947,3354.4627,3362.6514,2.5747,0.0766,219504535.0,268916094.8 348 | 000001.SH,20170830,3363.6266,3361.8207,3376.6481,3357.0803,3365.2261,-1.5995,-0.0475,246863312.0,279640796.6 349 | 000001.SH,20170831,3360.8103,3361.4621,3367.3581,3340.6865,3363.6266,-2.8163,-0.0837,234419781.0,269133107.8 350 | 000001.SH,20170901,3367.1194,3365.9913,3381.9252,3358.4724,3360.8103,6.3091,0.1877,282497584.0,313722092.7 351 | 000001.SH,20170904,3379.583,3369.7185,3381.4027,3359.1309,3367.1194,12.4636,0.3702,267427849.0,296348432.3 352 | 000001.SH,20170905,3384.317,3377.1968,3390.8233,3371.5706,3379.583,4.734,0.1401,216552946.0,245596608.3 353 | 000001.SH,20170906,3385.3888,3372.4277,3391.0105,3364.7645,3384.317,1.0718,0.0317,229090785.0,262034653.5 354 | 000001.SH,20170907,3365.4974,3383.6281,3387.7956,3363.1765,3385.3888,-19.8914,-0.5876,221118685.0,264037023.5 355 | 000001.SH,20170908,3365.2426,3364.4275,3380.8898,3353.6876,3365.4974,-0.2548,-0.0076,198405184.0,235160227.4 356 | 000001.SH,20170911,3376.4188,3365.3506,3384.81,3360.0462,3365.2426,11.1762,0.3321,219011019.0,256888810.5 357 | 000001.SH,20170912,3379.488,3381.487,3391.0694,3370.8519,3376.4188,3.0692,0.0909,272910319.0,325024952.0 358 | 000001.SH,20170913,3384.147,3374.7185,3387.1397,3366.5412,3379.488,4.659,0.1379,194550715.0,230733408.9 359 | 000001.SH,20170914,3371.4256,3383.47,3391.6435,3361.3335,3384.147,-12.7214,-0.3759,221306487.0,256744421.3 360 | 000001.SH,20170915,3353.6192,3365.1454,3365.5277,3345.3283,3371.4256,-17.8064,-0.5282,219765816.0,252910687.8 361 | 000001.SH,20170918,3362.8587,3352.5134,3371.7486,3352.5134,3353.6192,9.2395,0.2755,190319676.0,227938635.4 362 | 000001.SH,20170919,3356.8446,3365.5315,3370.4009,3344.7054,3362.8587,-6.0141,-0.1788,191129691.0,220728730.0 363 | 000001.SH,20170920,3365.9959,3352.1848,3370.0978,3346.5356,3356.8446,9.1513,0.2726,192196661.0,225839069.3 364 | 000001.SH,20170921,3357.8123,3364.6977,3377.8844,3356.8754,3365.9959,-8.1836,-0.2431,197448695.0,231946124.4 365 | 000001.SH,20170922,3352.5294,3347.1569,3356.4514,3334.9846,3357.8123,-5.2829,-0.1573,179234007.0,206583469.0 366 | 000001.SH,20170925,3341.5487,3344.5886,3350.9612,3334.9438,3352.5294,-10.9807,-0.3275,169621293.0,202401690.5 367 | 000001.SH,20170926,3343.5826,3336.3497,3347.1629,3332.5985,3341.5487,2.0339,0.0609,132628595.0,159039634.0 368 | 000001.SH,20170927,3345.2717,3340.8219,3349.6949,3340.2989,3343.5826,1.6891,0.0505,143086945.0,168981337.7 369 | 000001.SH,20170928,3339.6421,3343.8446,3344.6005,3336.1784,3345.2717,-5.6296,-0.1683,149444300.0,181955627.4 370 | 000001.SH,20170929,3348.9431,3340.3109,3357.0154,3340.3109,3339.6421,9.301,0.2785,144862443.0,175892823.0 371 | -------------------------------------------------------------------------------- /experiment3-sentimentAnalysis/PlotTrendGraph.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 12, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "from pandas import DataFrame\n", 11 | "import numpy as np\n", 12 | "from matplotlib.patches import Rectangle\n", 13 | "import matplotlib.pyplot as plt" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 13, 19 | "metadata": {}, 20 | "outputs": [ 21 | { 22 | "data": { 23 | "text/html": [ 24 | "
\n", 25 | "\n", 38 | "\n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | "
00.1
011
110
201
300
400
\n", 74 | "
" 75 | ], 76 | "text/plain": [ 77 | " 0 0.1\n", 78 | "0 1 1\n", 79 | "1 1 0\n", 80 | "2 0 1\n", 81 | "3 0 0\n", 82 | "4 0 0" 83 | ] 84 | }, 85 | "execution_count": 13, 86 | "metadata": {}, 87 | "output_type": "execute_result" 88 | } 89 | ], 90 | "source": [ 91 | "df1 = pd.read_csv('outputtrendwithsentiment.csv')\n", 92 | "df2 = pd.read_csv('outputtrendwithoutsentiment.csv')\n", 93 | "df.head()" 94 | ] 95 | }, 96 | { 97 | "cell_type": "code", 98 | "execution_count": null, 99 | "metadata": {}, 100 | "outputs": [], 101 | "source": [] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 14, 106 | "metadata": {}, 107 | "outputs": [], 108 | "source": [ 109 | "real_trend = df1['0']\n", 110 | "pred_trend1 = df1['0.1']\n", 111 | "pred_trend2 = df2['0.1']\n" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 19, 117 | "metadata": {}, 118 | "outputs": [], 119 | "source": [ 120 | "xlims = (0,len(real_trend))\n", 121 | "ylims = (0, 200)" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": 20, 127 | "metadata": {}, 128 | "outputs": [ 129 | { 130 | "data": { 131 | "text/plain": [ 132 | "(0, 200)" 133 | ] 134 | }, 135 | "execution_count": 20, 136 | "metadata": {}, 137 | "output_type": "execute_result" 138 | } 139 | ], 140 | "source": [ 141 | "%matplotlib qt\n", 142 | "fig = plt.figure(figsize=(20,10))\n", 143 | "fig.clf()\n", 144 | "axes = fig.gca() \n", 145 | "prev_num = 0\n", 146 | "data = real_trend\n", 147 | "brick_size = 2\n", 148 | "for index, number in enumerate(data):\n", 149 | "# print(index, number)\n", 150 | " if number == 1:\n", 151 | " facecolor='red'\n", 152 | " elif number == 0:\n", 153 | " facecolor='blue'\n", 154 | " else:\n", 155 | " facecolor='green'\n", 156 | "\n", 157 | " prev_num += number\n", 158 | "\n", 159 | " renko = Rectangle(\n", 160 | " (index, prev_num * brick_size+150), 1, brick_size,\n", 161 | " facecolor=facecolor, alpha=0.5\n", 162 | " )\n", 163 | " axes.add_patch(renko)\n", 164 | "\n", 165 | "prev_num = 0\n", 166 | "data = pred_trend1\n", 167 | "brick_size = 2\n", 168 | "for index, number in enumerate(data):\n", 169 | "# print(index, number)\n", 170 | " if number == 1:\n", 171 | " facecolor='red'\n", 172 | " elif number == 0:\n", 173 | " facecolor='blue'\n", 174 | " else:\n", 175 | " facecolor='green'\n", 176 | "\n", 177 | " prev_num += number\n", 178 | "\n", 179 | " renko = Rectangle(\n", 180 | " (index, prev_num * brick_size+100), 1, brick_size,\n", 181 | " facecolor=facecolor, alpha=0.5\n", 182 | " )\n", 183 | " axes.add_patch(renko) \n", 184 | "\n", 185 | "prev_num = 0\n", 186 | "data = pred_trend2\n", 187 | "brick_size = 2\n", 188 | "for index, number in enumerate(data):\n", 189 | "# print(index, number)\n", 190 | " if number == 1:\n", 191 | " facecolor='red'\n", 192 | " elif number == 0:\n", 193 | " facecolor='blue'\n", 194 | " else:\n", 195 | " facecolor='green'\n", 196 | "\n", 197 | " prev_num += number\n", 198 | "\n", 199 | " renko = Rectangle(\n", 200 | " (index, prev_num * brick_size+50), 1, brick_size,\n", 201 | " facecolor=facecolor, alpha=0.5\n", 202 | " )\n", 203 | " axes.add_patch(renko) \n", 204 | "\n", 205 | "plt.tick_params(axis='both', which='major', labelsize=30)\n", 206 | "# plt.tick_params(axis='both', which='minor', labelsize=8)\n", 207 | "plt.xlabel('Days', fontsize=30)\n", 208 | "plt.ylabel('Trend Prediction', fontsize=30)\n", 209 | "plt.xlim(xlims)\n", 210 | "plt.ylim(ylims)" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 24, 216 | "metadata": {}, 217 | "outputs": [], 218 | "source": [ 219 | "pd.set_option('display.expand_frame_repr', False)" 220 | ] 221 | }, 222 | { 223 | "cell_type": "code", 224 | "execution_count": 23, 225 | "metadata": {}, 226 | "outputs": [ 227 | { 228 | "data": { 229 | "text/html": [ 230 | "
\n", 231 | "\n", 244 | "\n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | "
01234567891011121314151617ave_change
0-1.246198e+08-1.624767e+07605322.994191-534771.378880-1192.570274-99.424949-27.775339-1.784126-10.406661-2.6284748.653167-7.20426830.10881360.94038312.6179031.981505-1.0715630.4777710.046667
1-7.603515e+07-1.414783e+07-223391.883688-295583.717229-651.246783-99.021349-43.027941-75.475923-1.720760-78.1854587.904326-3.92589313.8261288.918976-16.078816-1.7404700.8178250.0191400.020000
2-9.524888e+07-1.499555e+07700388.585755-591209.025691-1261.343344-53.327956-30.164170-12.401522-22.223717-77.7250140.6783928.80453211.8214808.867001-13.4332070.4663930.2900210.030882-0.006667
3-1.046750e+08-1.691048e+07-330458.275464-215077.517631-668.117141-50.723226-45.4004741.948180-4.795344-56.385197-2.24601813.57416017.238350-5.969432-9.628242-0.9515680.5119390.000355-0.023333
4-8.225343e+07-1.641113e+07-165579.837059-312551.020340-722.054412-60.112287-35.44994714.736202-11.081554-23.5334938.493759-8.03632810.7307040.536932-0.851757-0.9860550.1023430.136171-0.090000
\n", 382 | "
" 383 | ], 384 | "text/plain": [ 385 | " 0 1 2 3 4 \\\n", 386 | "0 -1.246198e+08 -1.624767e+07 605322.994191 -534771.378880 -1192.570274 \n", 387 | "1 -7.603515e+07 -1.414783e+07 -223391.883688 -295583.717229 -651.246783 \n", 388 | "2 -9.524888e+07 -1.499555e+07 700388.585755 -591209.025691 -1261.343344 \n", 389 | "3 -1.046750e+08 -1.691048e+07 -330458.275464 -215077.517631 -668.117141 \n", 390 | "4 -8.225343e+07 -1.641113e+07 -165579.837059 -312551.020340 -722.054412 \n", 391 | "\n", 392 | " 5 6 7 8 9 10 11 \\\n", 393 | "0 -99.424949 -27.775339 -1.784126 -10.406661 -2.628474 8.653167 -7.204268 \n", 394 | "1 -99.021349 -43.027941 -75.475923 -1.720760 -78.185458 7.904326 -3.925893 \n", 395 | "2 -53.327956 -30.164170 -12.401522 -22.223717 -77.725014 0.678392 8.804532 \n", 396 | "3 -50.723226 -45.400474 1.948180 -4.795344 -56.385197 -2.246018 13.574160 \n", 397 | "4 -60.112287 -35.449947 14.736202 -11.081554 -23.533493 8.493759 -8.036328 \n", 398 | "\n", 399 | " 12 13 14 15 16 17 ave_change \n", 400 | "0 30.108813 60.940383 12.617903 1.981505 -1.071563 0.477771 0.046667 \n", 401 | "1 13.826128 8.918976 -16.078816 -1.740470 0.817825 0.019140 0.020000 \n", 402 | "2 11.821480 8.867001 -13.433207 0.466393 0.290021 0.030882 -0.006667 \n", 403 | "3 17.238350 -5.969432 -9.628242 -0.951568 0.511939 0.000355 -0.023333 \n", 404 | "4 10.730704 0.536932 -0.851757 -0.986055 0.102343 0.136171 -0.090000 " 405 | ] 406 | }, 407 | "execution_count": 23, 408 | "metadata": {}, 409 | "output_type": "execute_result" 410 | } 411 | ], 412 | "source": [ 413 | "df = pd.read_csv('Total-data-after-Autoencoder-dimension-reduction.csv')\n" 414 | ] 415 | }, 416 | { 417 | "cell_type": "code", 418 | "execution_count": 26, 419 | "metadata": {}, 420 | "outputs": [ 421 | { 422 | "data": { 423 | "text/html": [ 424 | "
\n", 425 | "\n", 438 | "\n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | " \n", 523 | " \n", 524 | " \n", 525 | " \n", 526 | " \n", 527 | " \n", 528 | " \n", 529 | " \n", 530 | " \n", 531 | " \n", 532 | " \n", 533 | " \n", 534 | " \n", 535 | " \n", 536 | " \n", 537 | " \n", 538 | " \n", 539 | "
012345...1314151617ave_change
0-1.246198e+08-1.624767e+07605322.994191-534771.378880-1192.570274-99.424949...60.94038312.6179031.981505-1.0715630.4777710.046667
1-7.603515e+07-1.414783e+07-223391.883688-295583.717229-651.246783-99.021349...8.918976-16.078816-1.7404700.8178250.0191400.020000
2-9.524888e+07-1.499555e+07700388.585755-591209.025691-1261.343344-53.327956...8.867001-13.4332070.4663930.2900210.030882-0.006667
3-1.046750e+08-1.691048e+07-330458.275464-215077.517631-668.117141-50.723226...-5.969432-9.628242-0.9515680.5119390.000355-0.023333
4-8.225343e+07-1.641113e+07-165579.837059-312551.020340-722.054412-60.112287...0.536932-0.851757-0.9860550.1023430.136171-0.090000
\n", 540 | "

5 rows × 19 columns

\n", 541 | "
" 542 | ], 543 | "text/plain": [ 544 | " 0 1 2 3 4 5 ... 13 14 15 16 17 ave_change\n", 545 | "0 -1.246198e+08 -1.624767e+07 605322.994191 -534771.378880 -1192.570274 -99.424949 ... 60.940383 12.617903 1.981505 -1.071563 0.477771 0.046667\n", 546 | "1 -7.603515e+07 -1.414783e+07 -223391.883688 -295583.717229 -651.246783 -99.021349 ... 8.918976 -16.078816 -1.740470 0.817825 0.019140 0.020000\n", 547 | "2 -9.524888e+07 -1.499555e+07 700388.585755 -591209.025691 -1261.343344 -53.327956 ... 8.867001 -13.433207 0.466393 0.290021 0.030882 -0.006667\n", 548 | "3 -1.046750e+08 -1.691048e+07 -330458.275464 -215077.517631 -668.117141 -50.723226 ... -5.969432 -9.628242 -0.951568 0.511939 0.000355 -0.023333\n", 549 | "4 -8.225343e+07 -1.641113e+07 -165579.837059 -312551.020340 -722.054412 -60.112287 ... 0.536932 -0.851757 -0.986055 0.102343 0.136171 -0.090000\n", 550 | "\n", 551 | "[5 rows x 19 columns]" 552 | ] 553 | }, 554 | "execution_count": 26, 555 | "metadata": {}, 556 | "output_type": "execute_result" 557 | } 558 | ], 559 | "source": [ 560 | "pd.set_option('display.max_columns', 12)\n", 561 | "df.head()" 562 | ] 563 | }, 564 | { 565 | "cell_type": "code", 566 | "execution_count": null, 567 | "metadata": {}, 568 | "outputs": [], 569 | "source": [] 570 | }, 571 | { 572 | "cell_type": "code", 573 | "execution_count": 11, 574 | "metadata": {}, 575 | "outputs": [], 576 | "source": [ 577 | "real_trend = df['real_trend']\n", 578 | "pred_trend = df['pred_trend']" 579 | ] 580 | }, 581 | { 582 | "cell_type": "code", 583 | "execution_count": 12, 584 | "metadata": {}, 585 | "outputs": [], 586 | "source": [ 587 | "temp_list = []\n", 588 | "temp_df = real_trend - pred_trend\n", 589 | "for i in range(0, len(temp_df)):\n", 590 | " temp_list.append([i, temp_df[i]])" 591 | ] 592 | }, 593 | { 594 | "cell_type": "code", 595 | "execution_count": 13, 596 | "metadata": {}, 597 | "outputs": [], 598 | "source": [ 599 | "%matplotlib qt\n", 600 | "plt.clf()\n", 601 | "# plt.figure()\n", 602 | "plt.scatter( *zip(*temp_list), )\n", 603 | "# plt.scatter(real_df.index.values, pred_df[0],color='blue', marker='^')\n", 604 | "# plt.scatter(real_df.index.values, real_df[0],color='red', marker='.')\n", 605 | "plt.title('Trend Prediction Accuracy of 601318SH' )\n", 606 | "plt.xlabel('Days')\n", 607 | "plt.ylabel('Trend Prediction')\n", 608 | "plt.show()" 609 | ] 610 | }, 611 | { 612 | "cell_type": "code", 613 | "execution_count": 14, 614 | "metadata": {}, 615 | "outputs": [ 616 | { 617 | "ename": "NameError", 618 | "evalue": "name 'pred_df' is not defined", 619 | "output_type": "error", 620 | "traceback": [ 621 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 622 | "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", 623 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mtemp_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtemp_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreal_trend\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mpred_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtemp_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mtemp_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtemp_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 624 | "\u001b[1;31mNameError\u001b[0m: name 'pred_df' is not defined" 625 | ] 626 | } 627 | ], 628 | "source": [ 629 | "def plot_renko(data, brick_size):\n", 630 | " fig = plt.figure(figsize=(20,10))\n", 631 | " fig.clf()\n", 632 | " axes = fig.gca() \n", 633 | " prev_num = 0\n", 634 | " for index, number in enumerate(data):\n", 635 | "# print(index, number)\n", 636 | " if number == 1:\n", 637 | " facecolor='red'\n", 638 | " elif number == 0:\n", 639 | " facecolor='blue'\n", 640 | " else:\n", 641 | " facecolor='green'\n", 642 | "\n", 643 | " prev_num += number\n", 644 | "\n", 645 | " renko = Rectangle(\n", 646 | " (index, prev_num * brick_size+100), 1, brick_size,\n", 647 | " facecolor=facecolor, alpha=0.5\n", 648 | " )\n", 649 | " axes.add_patch(renko)\n", 650 | " plt.xlim(xlims)\n", 651 | " plt.ylim(ylims)\n", 652 | "# plt.show()" 653 | ] 654 | } 655 | ], 656 | "metadata": { 657 | "kernelspec": { 658 | "display_name": "Python 3", 659 | "language": "python", 660 | "name": "python3" 661 | }, 662 | "language_info": { 663 | "codemirror_mode": { 664 | "name": "ipython", 665 | "version": 3 666 | }, 667 | "file_extension": ".py", 668 | "mimetype": "text/x-python", 669 | "name": "python", 670 | "nbconvert_exporter": "python", 671 | "pygments_lexer": "ipython3", 672 | "version": "3.6.5" 673 | } 674 | }, 675 | "nbformat": 4, 676 | "nbformat_minor": 2 677 | } 678 | --------------------------------------------------------------------------------