├── README.md ├── demo.png ├── visualization ├── 000001-kline.html ├── 000001-mlpredict.html ├── KlineHistory.py ├── demo.png ├── echarts.min.js └── mlpredict_line.py ├── 传统技术面算法 ├── Moving_Average.py └── Relative_strength_index.py ├── 回测.py ├── 基本面机器学习算法 ├── NBM.py ├── data2.csv └── data3.csv ├── 机器学习算法 ├── AutoARIMA.py ├── DecisionTree.py ├── LR.py ├── LSTM.py ├── Prophet.py ├── RandomForest.py ├── SVM.py └── kNN.py └── 股票消息面分析 ├── README.txt ├── analysis_result ├── 2020年03月10日.csv ├── 2020年03月11日.csv ├── 2020年03月12日.csv ├── 2020年03月15日.csv ├── 2020年03月16日.csv ├── 2020年03月17日.csv ├── 2020年03月18日.csv ├── 2020年03月19日.csv ├── 2020年03月22日.csv ├── 2020年03月23日.csv ├── 2020年03月24日.csv ├── 2020年03月25日.csv ├── 2020年03月26日.csv ├── 2020年03月29日.csv ├── 2020年03月30日.csv ├── 2020年03月31日.csv ├── 2020年04月01日.csv ├── 2020年04月02日.csv ├── 2020年04月06日.csv ├── 2020年04月07日.csv ├── 2020年04月08日.csv ├── 2020年04月09日.csv ├── 2020年04月12日.csv ├── 2020年04月13日.csv ├── 2020年04月14日.csv ├── 2020年04月15日.csv ├── 2020年04月16日.csv ├── 2020年04月19日.csv ├── 2020年04月20日.csv ├── 2020年04月21日.csv ├── 2020年04月22日.csv ├── 2020年04月23日.csv ├── 2020年04月26日.csv ├── 2020年04月27日.csv ├── 2020年04月28日.csv ├── 2020年04月29日.csv ├── 2020年05月05日.csv ├── 2020年05月06日.csv ├── 2020年05月07日.csv ├── 2020年05月10日.csv ├── 2020年05月11日.csv ├── 2020年05月12日.csv ├── 2020年05月13日.csv ├── 2020年05月14日.csv ├── 2020年05月17日.csv ├── 2020年05月18日.csv ├── 2020年05月19日.csv ├── 2020年05月20日.csv ├── 2020年05月21日.csv ├── 2020年05月24日.csv ├── 2020年05月25日.csv ├── 2020年05月26日.csv ├── 2020年05月27日.csv ├── 2020年05月28日.csv ├── 2020年05月31日.csv ├── 2020年06月01日.csv ├── 2020年06月02日.csv ├── 2020年06月03日.csv ├── 2020年06月04日.csv ├── 2020年06月07日.csv ├── 2020年06月08日.csv ├── 2020年06月09日.csv ├── 2020年06月10日.csv ├── 2020年06月11日.csv ├── 2020年06月14日.csv ├── 2020年06月15日.csv ├── 2020年06月16日.csv ├── 2020年06月17日.csv ├── 2020年06月18日.csv ├── 2020年06月21日.csv ├── 2020年06月22日.csv ├── 2020年06月23日.csv ├── 2020年06月28日.csv ├── 2020年06月29日.csv ├── 2020年06月30日.csv ├── 2020年07月01日.csv ├── 2020年07月02日.csv ├── 2020年07月05日.csv ├── 2020年07月06日.csv ├── 2020年07月07日.csv ├── 2020年07月08日.csv ├── 2020年07月09日.csv ├── 2020年07月12日.csv ├── 2020年07月13日.csv ├── 2020年07月14日.csv ├── 2020年07月15日.csv ├── 2020年07月16日.csv ├── 2020年07月19日.csv ├── 2020年07月20日.csv ├── 2020年07月21日.csv ├── 2020年07月22日.csv ├── 2020年07月23日.csv ├── 2020年07月26日.csv ├── 2020年07月27日.csv ├── 2020年07月28日.csv ├── 2020年07月29日.csv ├── 2020年07月30日.csv ├── 2020年08月02日.csv ├── 2020年08月03日.csv ├── 2020年08月04日.csv ├── 2020年08月05日.csv ├── 2020年08月06日.csv ├── 2020年08月09日.csv ├── 2020年08月10日.csv ├── 2020年08月11日.csv ├── 2020年08月12日.csv ├── 2020年08月13日.csv ├── 2020年08月16日.csv ├── 2020年08月17日.csv ├── 2020年08月18日.csv ├── 2020年08月19日.csv ├── 2020年08月20日.csv ├── 2020年08月23日.csv ├── 2020年08月24日.csv ├── 2020年08月25日.csv ├── 2020年08月26日.csv ├── 2020年08月27日.csv ├── 2020年08月30日.csv ├── 2020年08月31日.csv ├── 2020年09月01日.csv ├── 2020年09月02日.csv ├── 2020年09月03日.csv ├── 2020年09月06日.csv ├── 2020年09月07日.csv ├── 2020年09月08日.csv ├── 2020年09月09日.csv ├── 2020年09月10日.csv ├── 2020年09月13日.csv ├── 2020年09月14日.csv ├── 2020年09月15日.csv ├── 2020年09月16日.csv ├── 2020年09月17日.csv ├── 2020年09月20日.csv ├── 2020年09月21日.csv ├── 2020年09月22日.csv ├── 2020年09月23日.csv ├── 2020年09月24日.csv ├── 2020年09月27日.csv ├── 2020年09月28日.csv ├── 2020年09月29日.csv ├── 2020年10月08日.csv ├── 2020年10月11日.csv ├── 2020年10月12日.csv ├── 2020年10月13日.csv ├── 2020年10月14日.csv ├── 2020年10月15日.csv ├── 2020年10月18日.csv ├── 2020年10月19日.csv ├── 2020年10月20日.csv ├── 2020年10月21日.csv ├── 2020年10月22日.csv ├── 2020年10月25日.csv ├── 2020年10月26日.csv ├── 2020年10月27日.csv ├── 2020年10月28日.csv ├── 2020年10月29日.csv ├── 2020年11月01日.csv ├── 2020年11月02日.csv ├── 2020年11月03日.csv ├── 2020年11月04日.csv ├── 2020年11月05日.csv ├── 2020年11月08日.csv ├── 2020年11月09日.csv ├── 2020年11月10日.csv ├── 2020年11月11日.csv ├── 2020年11月12日.csv ├── 2020年11月15日.csv ├── 2020年11月16日.csv ├── 2020年11月17日.csv ├── 2020年11月18日.csv ├── 2020年11月19日.csv ├── 2020年11月22日.csv ├── 2020年11月23日.csv ├── 2020年11月24日.csv ├── 2020年11月25日.csv ├── 2020年11月26日.csv └── 2020年11月29日.csv ├── backtest.py ├── draw_line.py ├── echarts.min.js ├── getsina_message.py ├── sentimental_analysis.py ├── sina_message ├── 2020年03月10日.csv ├── 2020年03月11日.csv ├── 2020年03月12日.csv ├── 2020年03月15日.csv ├── 2020年03月16日.csv ├── 2020年03月17日.csv ├── 2020年03月18日.csv ├── 2020年03月19日.csv ├── 2020年03月22日.csv ├── 2020年03月23日.csv ├── 2020年03月24日.csv ├── 2020年03月25日.csv ├── 2020年03月26日.csv ├── 2020年03月29日.csv ├── 2020年03月30日.csv ├── 2020年03月31日.csv ├── 2020年04月01日.csv ├── 2020年04月02日.csv ├── 2020年04月06日.csv ├── 2020年04月07日.csv ├── 2020年04月08日.csv ├── 2020年04月09日.csv ├── 2020年04月12日.csv ├── 2020年04月13日.csv ├── 2020年04月14日.csv ├── 2020年04月15日.csv ├── 2020年04月16日.csv ├── 2020年04月19日.csv ├── 2020年04月20日.csv ├── 2020年04月21日.csv ├── 2020年04月22日.csv ├── 2020年04月23日.csv ├── 2020年04月26日.csv ├── 2020年04月27日.csv ├── 2020年04月28日.csv ├── 2020年04月29日.csv ├── 2020年05月05日.csv ├── 2020年05月06日.csv ├── 2020年05月07日.csv ├── 2020年05月10日.csv ├── 2020年05月11日.csv ├── 2020年05月12日.csv ├── 2020年05月13日.csv ├── 2020年05月14日.csv ├── 2020年05月17日.csv ├── 2020年05月18日.csv ├── 2020年05月19日.csv ├── 2020年05月20日.csv ├── 2020年05月21日.csv ├── 2020年05月24日.csv ├── 2020年05月25日.csv ├── 2020年05月26日.csv ├── 2020年05月27日.csv ├── 2020年05月28日.csv ├── 2020年05月31日.csv ├── 2020年06月01日.csv ├── 2020年06月02日.csv ├── 2020年06月03日.csv ├── 2020年06月04日.csv ├── 2020年06月07日.csv ├── 2020年06月08日.csv ├── 2020年06月09日.csv ├── 2020年06月10日.csv ├── 2020年06月11日.csv ├── 2020年06月14日.csv ├── 2020年06月15日.csv ├── 2020年06月16日.csv ├── 2020年06月17日.csv ├── 2020年06月18日.csv ├── 2020年06月21日.csv ├── 2020年06月22日.csv ├── 2020年06月23日.csv ├── 2020年06月28日.csv ├── 2020年06月29日.csv ├── 2020年06月30日.csv ├── 2020年07月01日.csv ├── 2020年07月02日.csv ├── 2020年07月05日.csv ├── 2020年07月06日.csv ├── 2020年07月07日.csv ├── 2020年07月08日.csv ├── 2020年07月09日.csv ├── 2020年07月12日.csv ├── 2020年07月13日.csv ├── 2020年07月14日.csv ├── 2020年07月15日.csv ├── 2020年07月16日.csv ├── 2020年07月19日.csv ├── 2020年07月20日.csv ├── 2020年07月21日.csv ├── 2020年07月22日.csv ├── 2020年07月23日.csv ├── 2020年07月26日.csv ├── 2020年07月27日.csv ├── 2020年07月28日.csv ├── 2020年07月29日.csv ├── 2020年07月30日.csv ├── 2020年08月02日.csv ├── 2020年08月03日.csv ├── 2020年08月04日.csv ├── 2020年08月05日.csv ├── 2020年08月06日.csv ├── 2020年08月09日.csv ├── 2020年08月10日.csv ├── 2020年08月11日.csv ├── 2020年08月12日.csv ├── 2020年08月13日.csv ├── 2020年08月16日.csv ├── 2020年08月17日.csv ├── 2020年08月18日.csv ├── 2020年08月19日.csv ├── 2020年08月20日.csv ├── 2020年08月23日.csv ├── 2020年08月24日.csv ├── 2020年08月25日.csv ├── 2020年08月26日.csv ├── 2020年08月27日.csv ├── 2020年08月30日.csv ├── 2020年08月31日.csv ├── 2020年09月01日.csv ├── 2020年09月02日.csv ├── 2020年09月03日.csv ├── 2020年09月06日.csv ├── 2020年09月07日.csv ├── 2020年09月08日.csv ├── 2020年09月09日.csv ├── 2020年09月10日.csv ├── 2020年09月13日.csv ├── 2020年09月14日.csv ├── 2020年09月15日.csv ├── 2020年09月16日.csv ├── 2020年09月17日.csv ├── 2020年09月20日.csv ├── 2020年09月21日.csv ├── 2020年09月22日.csv ├── 2020年09月23日.csv ├── 2020年09月24日.csv ├── 2020年09月27日.csv ├── 2020年09月28日.csv ├── 2020年09月29日.csv ├── 2020年10月08日.csv ├── 2020年10月11日.csv ├── 2020年10月12日.csv ├── 2020年10月13日.csv ├── 2020年10月14日.csv ├── 2020年10月15日.csv ├── 2020年10月18日.csv ├── 2020年10月19日.csv ├── 2020年10月20日.csv ├── 2020年10月21日.csv ├── 2020年10月22日.csv ├── 2020年10月25日.csv ├── 2020年10月26日.csv ├── 2020年10月27日.csv ├── 2020年10月28日.csv ├── 2020年10月29日.csv ├── 2020年11月01日.csv ├── 2020年11月02日.csv ├── 2020年11月03日.csv ├── 2020年11月04日.csv ├── 2020年11月05日.csv ├── 2020年11月08日.csv ├── 2020年11月09日.csv ├── 2020年11月10日.csv ├── 2020年11月11日.csv ├── 2020年11月12日.csv ├── 2020年11月15日.csv ├── 2020年11月16日.csv ├── 2020年11月17日.csv ├── 2020年11月18日.csv ├── 2020年11月19日.csv ├── 2020年11月22日.csv ├── 2020年11月23日.csv ├── 2020年11月24日.csv ├── 2020年11月25日.csv ├── 2020年11月26日.csv └── 2020年11月29日.csv ├── 个股持有情况分析.csv ├── 各月股票队列.xlsx ├── 持有期收益率情况图.html └── 每日资金情况图.html /README.md: -------------------------------------------------------------------------------- 1 | ## Smartproxy stormproxies 海外http代理 2 | #### Smart proxy-海外HTTP代理-1亿纯净住宅IP-全球代理Smartproxy 3 | 4 | ![图片](https://github.com/moyuweiqing/bilibili-barrage-analysis/blob/main/samrt.png) 5 | 6 | **官网链接：https://www.smartproxy.cn/** 7 | **专属注册链接：https://www.smartproxy.cn/regist?invite=4DWE6S** 8 | 9 | *专业海外http代理商，千万优质纯净住宅IP资源,，全球城市覆盖,，高匿稳定提供100%原生住宅IP，支持社交账户，电商平台，网络数据收集等服务。* 10 | *真实IP住宅，可以TikTok养号，高匿名性，伪装度高，成功率高，实名注册就送500m流量，套餐价格65折！* 11 | 12 | - 超高并发备份 13 | 独享高性能服务器，以真实住宅地址进行请求访问，保持代理正常连接，不限制并发数量，降低业务成本，提高运行效率。 14 | - 优质IP资源 15 | 整合真实家庭住宅IP，汇聚IP资源池，不断更新IP，来自全球各个国家地区进行访问。自有数据节点，网络集成快捷。 16 | - 形式多样 17 | 多种代理认证模式，帮助账户灵活设置，账密模式通过region参数添加制定国家城市；API白名单模式通过API链接获取即可。 18 | - 技术服务 19 | 支持业务场景定制独享IP，千兆超高速带宽，出口IP可定制时效提供获取流量使用报告，追踪流量记录。 20 | 21 | 22 | # A-stock-prediction-algorithm-based-on-machine-learning 23 | **（陆续更新）重新整理过的基于机器学习的股票价格预测算法，里面包含了基本的回测系统以及各种不同的机器学习算法的股票价格预测，包含：LSTM算法、Prophet算法、AutoARIMA、朴素贝叶斯、SVM等** 24 | #### 强烈推荐大家去看看sklearn库的文档，地址：[https://sklearn.apachecn.org ] 25 | 26 | ### 2021-2-6 27 | 出现紧急问题，重新发布 28 | 29 | ### 12-3 30 | **股票消息面分析** 31 | 给出一个基于nlp情感分析的消息面分析算法。从新浪财经上获取新闻个股预测情况，使用jieba进行切词和使用snownlp进行情感分析，进行回测。 32 | 33 | ### 11-27 34 | 修正**机器学习算法/DecisionTree.py RandomForest.py** 上面的逻辑错误。 35 | 36 | ### 11-25 37 | **visualization/mlpredict-line.py** 38 | echarts+tushare+autoarima结合的股票走势预测，结合单次预测和逐日预测进行可视化，提高可视化的美观性和适用性，对函数进行封装处理，可以使用别的预测算法进行替换。 39 | 40 | ### 11-16 41 | 优化KineHistory的可视化情况 42 | 43 | ### 11-12 44 | **visualization/KlineHistory.py** 45 | echarts+tushare的K线可视化，可以传入参数生成echarts的html文件，具体的样式已经完成（另外的延伸可以参考echarts和pyecharts官网）。另一方面，tushare库之后可能会停止更新，之后可能会对接baostock接口或者是别的一些第三方接口 46 | ![](https://github.com/moyuweiqing/A-stock-prediction-algorithm-based-on-machine-learning/blob/master/demo.png) 47 | 48 | ### 11-10 49 | **大家好，很高兴有这么多小伙伴能给我的代码star，之前一段时间在弄爬虫的相关技术，没有怎么关心股票回测和机器学习方面的内容。今晚我重新测试后，我发现原来的一些包已经不能用了，而且tushare库也很快就会关闭，因此，我之后的时间会尝试来修改一下这部分的内容。主要的重心是数据的获取、可视化和及时性上，欢迎大家能够和我一起学习，希望我能冲到100star； 50 | ps：这个只是工作之外的爱好哈，另外股市有风险** 51 | 52 | ### 4-11 53 | **机器学习算法/LR.py** 54 | 线性回归算法，线性回归是利用数理统计中的回归分析，来确定两种或两种以上变量间相互依赖的定量关系的一种统计分析方法，运用十分广泛。在这里的调用和其他的sklearn库文件没有什么区别。 55 | 56 | ### 4-10 57 | **机器学习算法/DecisionTree.py** 58 | 决策树算法，这里也是直接调用的sklearn库。和随机森林算法的思路过程没什么不同，也是先构建x_test, y_test, x_train, y_train,然后放入模型里进行拟合。其算法效果和随机森林差别不大。 59 | 60 | ### 4-9 61 | **机器学习算法/Randomforest.py** 62 | 随机森林算法，给出一个y指标是T+1日对于T日是否看涨。在这里需要调整的参数是在new一个模型的时候给出的max_depth和n_estimators。通过给出node（分隔点）来确定训练集和测试集。在这里有一个roc_auc_score()函数，是用来测试该算法对于预测而言有没有偏袒。 63 | 64 | ### 4-7 65 | **基本面机器学习算法/NBM.py** 66 | 朴素贝叶斯算法，在这个例子中使用的是通过之前季度的财务报表中的各种指标，来预测下一季度的财务报表中的情况，朴素贝叶斯算法其实非常简单，可以自己实现，在这里使用的是sklearn.naive_bayes中的GaussianNB。主要的调用规则是： 67 | import from sklearn.naive_bayes import GaussianNB 68 | clf = GaussianNB() 69 | clf.fit() 70 | clf.predict() 71 | 72 | ### 3-15 73 | **机器学习算法/kNN.py** 74 | kNN算法，即k-近邻算法，KNN是通过测量不同特征值之间的距离进行分类。它的思路是：如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别，则该样本也属于这个类别，其中K通常是不大于20的整数。KNN算法中，所选择的邻居都是已经正确分类的对象。该方法在定类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。 75 | 76 | ### 3-13 77 | **机器学习算法/SVM.py** 78 | 支持向量机(Support Vector Machine)，通过SVM算法对该数据集第N+1日的收盘价相对于第N日的收盘价涨跌情况进行分析，若第N+1日的收盘价相对于第N日收盘价为涨，则记为1,；反之，记为0。由于SVM算法为监督学习算法，因此在训练的过程中算法会根据实际的涨跌情况对学习算法进行修正。对于测试集中的部分，预测每一个交易日的股价涨跌情况，并以真实涨跌情况进行测算，计算在测试集中算法预测的正确率。SVM算法包中提供了多种核函数，在这里提供了三种。 79 | 80 | **机器学习算法/Prophet.py** 81 | Prophet算法，也称为预言家算法。Prophet属于时间序列模型。相对于ARIMA模型，Prophet模型的优势在于可以不考虑缺失值的填充问题。另外，Prophet模型的拟合速度远比ARIMA模型更快。 82 | 83 | ### 3-12 84 | **机器学习算法/AutoARIMA.py** 85 | 差分回归积分移动平均模型(Autoregressive Integrated Moving Average Model)，通过给定d,p,q值，实现在给定的范围内自动寻找最优解，主要给定两种方法，一是一次性预测（即单一预测，使用训练集的数据直接给出所有测试集天数的预测值），另一种是按天预测，（即每日回归，每一个预测期的单位数据都用之前的数据作为训练集进行预测） 86 | 87 | **机器学习算法/LSTM.py** 88 | 长短神经网络，一种特殊的循环神经网络，用于处理和预测时间序列中间隔和延迟相对较长的重要事件。LSTM通过三个这样的本结构来实现信息的保护和控制。这三个门分别输入门、遗忘门和输出门。由于该神经网络随着时间变化具有数据的遗忘性，所以一次性预测与按天预测并没有较大的不同，但是该神经网络算法比较复杂，感兴趣的可以参考别的文献，在这里只是实现了基本的功能。 89 | 90 | ### 3-11 91 | **回测.py** 92 | 提供了一个基本的回测demo，实现股票基本的买入卖出，考虑佣金和印花税 93 | 94 | **传统技术面分析算法/Moving_Average.py** 95 | 移动平均算法，通过移动窗口的方式进行股票预测，传入窗口长度参数可以进行预测，支持tushare接口直接获取数据并即时进行预测 96 | 97 | **传统技术面分析算法/Relative_strength_index.py** 98 | 相对强度指数，相对强弱指数RSI是根据一定时期内上涨点数和涨跌点数之和的比率制作出的一种技术曲线。返回计算期内的相对强度指数计算情况 99 | 100 | ### 2020-3-10 101 | 创建项目，README文件 102 | -------------------------------------------------------------------------------- /demo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/moyuweiqing/A-stock-prediction-algorithm-based-on-machine-learning/f6a1a4f5e305b59950a5b94180067a2bb265e235/demo.png -------------------------------------------------------------------------------- /visualization/KlineHistory.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import os 3 | import tushare as ts 4 | from pyecharts.charts import Kline 5 | from pyecharts.charts import Line 6 | from pyecharts.charts import Bar 7 | from pyecharts.charts import Grid 8 | import pyecharts.options as opts 9 | 10 | tsData = pd.DataFrame() 11 | stockCode = '' 12 | 13 | date_list = [] 14 | price_list = [] 15 | ma5_list = [] 16 | ma10_list = [] 17 | ma20_list = [] 18 | volume_list = [] 19 | 20 | high_list = [] 21 | low_list = [] 22 | 23 | def draw(): 24 | min_value = int(min(low_list)) - 2 25 | max_value = int(max(high_list)) + 2 26 | 27 | kline = ( 28 | Kline() 29 | .set_global_opts( 30 | title_opts=opts.TitleOpts( 31 | title='股票价格走势', 32 | subtitle=stockCode + '股票价格走势' 33 | ), 34 | legend_opts=opts.LegendOpts( 35 | is_show=True, 36 | pos_top=10, 37 | pos_left="center", 38 | item_width=30, 39 | item_height=15, 40 | textstyle_opts=opts.TextStyleOpts( 41 | font_family='Microsoft Yahei', 42 | font_size=14, 43 | font_style='oblique' 44 | ) 45 | ), 46 | tooltip_opts=opts.TooltipOpts( 47 | trigger="axis", 48 | axis_pointer_type="cross", 49 | background_color="rgba(245, 245, 245, 0.8)", 50 | border_width=1, 51 | border_color="#ccc", 52 | textstyle_opts=opts.TextStyleOpts(color="#000"), 53 | ), 54 | xaxis_opts=opts.AxisOpts( 55 | # type_="time", 56 | name='日期', 57 | split_number=10, 58 | name_gap=35, 59 | axispointer_opts=opts.AxisPointerOpts(is_show=True), 60 | name_textstyle_opts=opts.TextStyleOpts( 61 | font_size= 16, 62 | font_family='Microsoft Yahei' 63 | ) 64 | ), 65 | yaxis_opts=opts.AxisOpts( 66 | type_="value", 67 | name='价格', 68 | min_=min_value, 69 | max_=max_value, 70 | split_number=4, 71 | axispointer_opts=opts.AxisPointerOpts(is_show=True), 72 | name_textstyle_opts=opts.TextStyleOpts( 73 | font_size=16, 74 | font_family='Microsoft Yahei' 75 | ), 76 | axistick_opts=opts.AxisTickOpts(is_show=True), 77 | splitline_opts=opts.SplitLineOpts(is_show=True), 78 | splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)) 79 | ), 80 | axispointer_opts=opts.AxisPointerOpts( 81 | is_show=True, 82 | link=[{"xAxisIndex": "all"}], 83 | label=opts.LabelOpts(background_color="#777"), 84 | ), 85 | datazoom_opts=[ 86 | opts.DataZoomOpts( 87 | is_show=False, 88 | type_="inside", 89 | xaxis_index=[0, 1], 90 | range_start=30, 91 | range_end=70, 92 | ), 93 | opts.DataZoomOpts( 94 | is_show=True, 95 | xaxis_index=[0, 1], 96 | type_="slider", 97 | pos_top="92%", 98 | range_start=38, 99 | range_end=70, 100 | ), 101 | ], 102 | visualmap_opts=opts.VisualMapOpts( 103 | is_show=False, 104 | dimension=2, 105 | series_index=5, 106 | is_piecewise=True, 107 | pieces=[ 108 | {"value": 1, "color": "#00da3c"}, 109 | {"value": -1, "color": "#ec0000"}, 110 | ], 111 | ), 112 | ) 113 | .add_xaxis( 114 | xaxis_data=date_list 115 | ) 116 | .add_yaxis( 117 | series_name="日K线图", 118 | is_selected=True, 119 | y_axis=price_list, 120 | markpoint_opts=opts.MarkPointOpts( 121 | data=[ 122 | opts.MarkPointItem(type_="max", name="最大值"), 123 | opts.MarkPointItem(type_="min", name="最小值"), 124 | opts.MarkPointItem(type_="average", name="平均值") 125 | ] 126 | ) 127 | ) 128 | ) 129 | 130 | line = ( 131 | Line(init_opts=opts.InitOpts( 132 | width='1800px', 133 | height='800px', 134 | js_host="./", 135 | )) 136 | .add_xaxis( 137 | xaxis_data=date_list 138 | ) 139 | .add_yaxis( 140 | series_name="MA5图", 141 | is_selected=True, 142 | y_axis=ma5_list, 143 | label_opts=opts.LabelOpts(is_show=False) 144 | ) 145 | .add_yaxis( 146 | series_name="MA10图", 147 | is_selected=True, 148 | y_axis=ma10_list, 149 | label_opts=opts.LabelOpts(is_show=False) 150 | ) 151 | .add_yaxis( 152 | series_name="MA20图", 153 | is_selected=True, 154 | y_axis=ma20_list, 155 | label_opts=opts.LabelOpts(is_show=False) 156 | ) 157 | .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")) 158 | ) 159 | 160 | bar = (Bar() 161 | .add_xaxis(xaxis_data=date_list) 162 | .add_yaxis( 163 | series_name="交易量", 164 | y_axis=volume_list, 165 | xaxis_index=1, 166 | yaxis_index=1, 167 | label_opts=opts.LabelOpts(is_show=False), 168 | ) 169 | .set_global_opts( 170 | xaxis_opts=opts.AxisOpts( 171 | type_="category", 172 | is_scale=True, 173 | grid_index=1, 174 | boundary_gap=False, 175 | axisline_opts=opts.AxisLineOpts(is_on_zero=False), 176 | axistick_opts=opts.AxisTickOpts(is_show=False), 177 | splitline_opts=opts.SplitLineOpts(is_show=False), 178 | axislabel_opts=opts.LabelOpts(is_show=False), 179 | split_number=20, 180 | min_="dataMin", 181 | max_="dataMax", 182 | ), 183 | yaxis_opts=opts.AxisOpts( 184 | grid_index=1, 185 | is_scale=True, 186 | split_number=2, 187 | axislabel_opts=opts.LabelOpts(is_show=False), 188 | axisline_opts=opts.AxisLineOpts(is_show=False), 189 | axistick_opts=opts.AxisTickOpts(is_show=False), 190 | splitline_opts=opts.SplitLineOpts(is_show=False), 191 | ), 192 | legend_opts=opts.LegendOpts(is_show=False), 193 | ) 194 | ) 195 | 196 | overlap_kline_line = kline.overlap(line) 197 | 198 | grid_chart = Grid( 199 | init_opts=opts.InitOpts( 200 | width="1800px", 201 | height="800px", 202 | animation_opts=opts.AnimationOpts(animation=False), 203 | page_title=stockCode + '历史K线图', 204 | js_host="./" 205 | ) 206 | ) 207 | 208 | grid_chart.add( 209 | overlap_kline_line, 210 | grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="60%") 211 | ) 212 | 213 | grid_chart.add( 214 | bar, 215 | grid_opts=opts.GridOpts( 216 | pos_left="10%", pos_right="8%", pos_top="70%", height="18%" 217 | ), 218 | ) 219 | 220 | try: 221 | grid_chart.render(path=stockCode + '-kline.html') 222 | print('K线图已生成') 223 | except: 224 | print('K线图生成失败！') 225 | 226 | 227 | def change_data(dataframe): 228 | global date_list, price_list, ma5_list, ma10_list, ma20_list, volume_list 229 | 230 | # 转成列表 231 | try: 232 | for i in range(0, len(dataframe)): 233 | alist = [] 234 | date_list.append(dataframe['date'].iloc[i]) 235 | # alist.append(tsData['date'].iloc[i]) 236 | alist.append(dataframe['open'].iloc[i]) 237 | alist.append(dataframe['close'].iloc[i]) 238 | alist.append(dataframe['low'].iloc[i]) 239 | alist.append(dataframe['high'].iloc[i]) 240 | ma5_list.append(dataframe['ma5'].iloc[i]) 241 | ma10_list.append(dataframe['ma10'].iloc[i]) 242 | ma20_list.append(dataframe['ma20'].iloc[i]) 243 | 244 | high_list.append(dataframe['high'].iloc[i]) 245 | low_list.append(dataframe['low'].iloc[i]) 246 | 247 | # 柱状图数据处理 248 | color = '' 249 | if dataframe['open'].iloc[i] > dataframe['close'].iloc[i]: 250 | color = '#ADFF2F' 251 | else: 252 | color = '#FF4500' 253 | volume_list.append(opts.BarItem(name='volume', value=dataframe['volume'].iloc[i], itemstyle_opts=opts.ItemStyleOpts(color=color))) 254 | 255 | price_list.append(alist) 256 | print('股票数据已成功转换') 257 | except: 258 | print('股票数据转换失败！') 259 | 260 | draw() 261 | 262 | def date_setting(stock_code, start_date, end_date): 263 | global tsData, stockCode 264 | 265 | stockCode = stock_code 266 | tsData = ts.get_hist_data(code=stock_code, start=start_date, end=end_date) 267 | tsData = tsData.sort_index(ascending=True).reset_index() 268 | 269 | if len(tsData) != 0: 270 | print('股票数据已成功获取') 271 | change_data(tsData) 272 | else: 273 | print('股票数据获取失败！') 274 | 275 | 276 | if __name__ == '__main__': 277 | date_setting(stock_code='300059', start_date='2020-04-01', end_date='2020-09-30') 278 | draw() 279 | -------------------------------------------------------------------------------- /visualization/demo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/moyuweiqing/A-stock-prediction-algorithm-based-on-machine-learning/f6a1a4f5e305b59950a5b94180067a2bb265e235/visualization/demo.png -------------------------------------------------------------------------------- /visualization/echarts.min.js: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/moyuweiqing/A-stock-prediction-algorithm-based-on-machine-learning/f6a1a4f5e305b59950a5b94180067a2bb265e235/visualization/echarts.min.js -------------------------------------------------------------------------------- /visualization/mlpredict_line.py: -------------------------------------------------------------------------------- 1 | import pyecharts.options as opts 2 | from pyecharts.charts import Line 3 | from pmdarima import auto_arima 4 | import tushare as ts 5 | import pandas as pd 6 | 7 | stockCode = '' 8 | date_list = [] 9 | predict_list = [] 10 | raw_list = [] 11 | day_predict_list = [] 12 | 13 | minvalue = 0 14 | maxvalue = 0 15 | 16 | def draw(): 17 | ( 18 | Line(init_opts=opts.InitOpts( 19 | width='1800px', 20 | height='800px', 21 | js_host="./", 22 | )) 23 | .set_global_opts( 24 | title_opts=opts.TitleOpts( 25 | title='股票价格走势预测', 26 | subtitle=stockCode + '股票价格走势预测' 27 | ), 28 | legend_opts=opts.LegendOpts( 29 | is_show=True, # 显示图例 30 | pos_top=10, # 离上边的像素值 31 | pos_left="center", # 居中 32 | item_width=30, # 图例宽度 33 | item_height=15, # 图例高度 34 | textstyle_opts=opts.TextStyleOpts( # 字体样式 35 | font_family='Microsoft Yahei', # 微软雅黑 36 | font_size=14, # 字体大小 37 | font_style='oblique' # 倾斜 38 | ) 39 | ), 40 | tooltip_opts=opts.TooltipOpts( # 提示框设置 41 | trigger="axis", # 坐标轴触发 42 | axis_pointer_type="cross", # 正交十字准星指示器 43 | background_color="rgba(245, 245, 245, 0.8)", # 提示框浮层背景颜色 44 | border_width=1, # 提示框边界宽度 45 | border_color="#ccc", # 提示框边界颜色 46 | textstyle_opts=opts.TextStyleOpts(color="#000"), # 提示框字体样式 47 | ), 48 | xaxis_opts=opts.AxisOpts( # x轴设置 49 | # type_="time", 50 | name='日期', # 坐标轴名称 51 | split_number=10, 52 | name_gap=35, 53 | axispointer_opts=opts.AxisPointerOpts(is_show=True), 54 | name_textstyle_opts=opts.TextStyleOpts( 55 | font_size=16, 56 | font_family='Microsoft Yahei' 57 | ) 58 | ), 59 | yaxis_opts=opts.AxisOpts( 60 | type_="value", 61 | name='价格', 62 | min_=int(minvalue) - 2, 63 | max_=int(maxvalue) + 2, 64 | split_number=4, 65 | axispointer_opts=opts.AxisPointerOpts(is_show=True), 66 | name_textstyle_opts=opts.TextStyleOpts( 67 | font_size=16, 68 | font_family='Microsoft Yahei' 69 | ), 70 | axistick_opts=opts.AxisTickOpts(is_show=True), 71 | splitline_opts=opts.SplitLineOpts(is_show=True), 72 | splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)) 73 | ), 74 | axispointer_opts=opts.AxisPointerOpts( 75 | is_show=True, 76 | link=[{"xAxisIndex": "all"}], 77 | label=opts.LabelOpts(background_color="#777"), 78 | ), 79 | datazoom_opts=opts.DataZoomOpts( 80 | is_show=True, 81 | # xaxis_index=[0, 1], 82 | type_="slider", 83 | # pos_top="92%", 84 | range_start=40, 85 | range_end=70, 86 | ) 87 | ) 88 | .add_xaxis(xaxis_data=date_list) 89 | .add_yaxis( 90 | series_name="预测值", 91 | y_axis=predict_list, 92 | symbol="emptyCircle", 93 | is_symbol_show=True, 94 | label_opts=opts.LabelOpts(is_show=False), 95 | ) 96 | .add_yaxis( 97 | series_name="实际值", 98 | y_axis=raw_list, 99 | symbol="emptyCircle", 100 | is_symbol_show=True, 101 | label_opts=opts.LabelOpts(is_show=False), 102 | ) 103 | .add_yaxis( 104 | series_name="逐日预测值", 105 | y_axis=day_predict_list, 106 | symbol="emptyCircle", 107 | is_symbol_show=True, 108 | label_opts=opts.LabelOpts(is_show=False), 109 | ) 110 | .render(stockCode + "-mlpredict.html") 111 | ) 112 | 113 | def predict(stock_code, start_date, end_date, node): 114 | global predict_list, raw_list, stockCode 115 | global minvalue, maxvalue 116 | 117 | stockCode = stock_code 118 | tsData = ts.get_hist_data(code=stock_code, start=start_date, end=end_date) 119 | tsData = tsData.sort_index(ascending=True).reset_index() 120 | 121 | train = tsData['close'].iloc[:node].values 122 | valid = tsData['close'].iloc[node:].values 123 | 124 | model = auto_arima(train, start_p=1, start_q=1, max_p=2, max_q=2, m=12, start_P=0, seasonal=True, d=1, D=1, 125 | trace=True, error_action='ignore', suppress_warnings=True) 126 | 127 | model.fit(train) 128 | # 进行预测 129 | forecast = model.predict(n_periods=len(valid)) 130 | 131 | for i in range(0, len(tsData)): 132 | date_list.append(tsData['date'].iloc[i]) 133 | raw_list.append(tsData['close'].iloc[i]) 134 | 135 | for i in range(0, node): 136 | predict_list.append(tsData['close'].iloc[i]) 137 | for i in range(0, len(valid)): 138 | predict_list.append(forecast[i]) 139 | 140 | minvalue = min(min(predict_list), min(raw_list)) 141 | maxvalue = max(max(predict_list), max(raw_list)) 142 | 143 | def day_predict(stock_code, start_date, end_date, node): 144 | global day_predict_list 145 | 146 | tsData = ts.get_hist_data(code=stock_code, start=start_date, end=end_date) 147 | tsData = tsData.sort_index(ascending=True).reset_index() 148 | 149 | train = tsData['close'].iloc[:node].values 150 | valid = tsData['close'].iloc[node:].values 151 | 152 | for i in range(0, node): 153 | day_predict_list.append(tsData['close'].iloc[i]) 154 | 155 | for day in range(0, len(tsData) - node): 156 | train = tsData['close'].iloc[:node + day].values 157 | valid = tsData['close'].iloc[node + day:].values 158 | 159 | model = auto_arima(train, start_p=1, start_q=1, max_p=2, max_q=2, m=12, start_P=0, seasonal=True, d=1, D=1, 160 | trace=True, error_action='ignore', suppress_warnings=True) 161 | 162 | model.fit(train) 163 | # 进行预测 164 | forecast = model.predict(n_periods=1) 165 | day_predict_list.append(forecast[0]) 166 | 167 | if __name__ == '__main__': 168 | predict(stock_code='000001', start_date='2020-04-01', end_date='2020-09-30', node=100) 169 | day_predict(stock_code='000001', start_date='2020-04-01', end_date='2020-09-30', node=100) 170 | draw() -------------------------------------------------------------------------------- /传统技术面算法/Moving_Average.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import tushare as ts 4 | import matplotlib.pyplot as plt 5 | 6 | from pylab import * #改变plot字体，适应中文 7 | mpl.rcParams['font.sans-serif'] = ['SimHei'] 8 | 9 | class Moving_Average_Predict: 10 | stock_code = '' 11 | tsData = pd.DataFrame() 12 | def __init__(self, stock_code): 13 | self.stock_code = stock_code 14 | def date_setting(self, start_date, end_date): 15 | self.tsData = ts.get_hist_data(code=self.stock_code, start=start_date, end=end_date) 16 | self.tsData = self.tsData.reset_index() 17 | def make_predict(self, day): # day为窗口大小 18 | new_data = pd.DataFrame(index=range(0, len(self.tsData)), columns=['date', 'close']) 19 | for i in range(0, len(self.tsData)): # 使用收盘价进行处理 20 | new_data['date'][i] = self.tsData.index[i] 21 | new_data['close'][i] = self.tsData["close"][len(self.tsData) - i - 1] 22 | new_data = new_data.sort_index(ascending=True) 23 | # 划定 24 | train = new_data[:len(self.tsData) - day] 25 | valid = new_data[len(self.tsData) - day:] 26 | # 做出预测 27 | preds = [] 28 | for i in range(0, day): 29 | a = train['close'][len(train) - day + i:].sum() + sum(preds) 30 | b = a / day 31 | preds.append(b) 32 | # 画图 33 | valid['Predictions'] = 0 34 | valid['Predictions'] = preds 35 | plt.plot(train['close'], label=u'训练集') 36 | plt.plot(valid['Predictions'], label=u'预测值') 37 | plt.plot(valid['close'], label=u'真实值') 38 | plt.show() 39 | 40 | a = Moving_Average_Predict('000001') 41 | a.date_setting(start_date='2019-05-12', end_date='2019-12-19') 42 | a.make_predict(15) -------------------------------------------------------------------------------- /传统技术面算法/Relative_strength_index.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import tushare as ts 3 | 4 | class Relative_strength_index_calculate: 5 | stock_code = '' 6 | tsData = pd.DataFrame() 7 | def __init__(self, stock_code): 8 | self.stock_code = stock_code 9 | def date_setting(self, start_date, end_date): 10 | self.tsData = ts.get_hist_data(code=self.stock_code, start=start_date, end=end_date) 11 | self.tsData = self.tsData.reset_index() 12 | def computeRSI(self, day): 13 | # 获取数据 14 | data = self.tsData.sort_index(ascending=True, axis=0) 15 | dataset = data['close'] 16 | RSI_set = [] 17 | # 计算RSI值 18 | for i in range(0, len(data) - day): 19 | RSI = 0.0 20 | bigger_set = 0 21 | smaller_set = 0 22 | for j in range(0, 13): 23 | if dataset[i + j + 1] > dataset[i + j]: 24 | bigger_set += dataset[i + j + 1] - dataset[i + j] 25 | else: 26 | smaller_set += dataset[i + j] - dataset[i + j + 1] 27 | RSI = bigger_set / (bigger_set + smaller_set) * 100 28 | if i < 5: 29 | print(bigger_set) 30 | print(smaller_set) 31 | print(RSI) 32 | RSI_set.append(RSI) 33 | 34 | # 定义RSI表格 35 | dic = {'超买市场（RSI>=80）且实际下跌': 0, 36 | '超买市场（RSI>=80）但实际上涨': 0, 37 | '强势市场（50<=RSI<80）且实际下跌': 0, 38 | '强势市场（50<=RSI<80）但实际上涨': 0, 39 | '弱式市场（50>RSI>=20）且实际上涨': 0, 40 | '弱式市场（50>RSI>=20）但实际下跌': 0, 41 | '超卖市场（RSI<20）且实际上涨': 0, 42 | '超卖市场（RSI<20）但实际下跌': 0} 43 | 44 | for i in range(0, len(data) - 15): 45 | if (RSI_set[i] >= 80) & (dataset[i + 15] >= dataset[i + 14]): 46 | dic['超买市场（RSI>=80）但实际上涨'] += 1 47 | elif (RSI_set[i] >= 80) & (dataset[i + 15] < dataset[i + 14]): 48 | dic['超买市场（RSI>=80）且实际下跌'] += 1 49 | elif (RSI_set[i] < 80) & (RSI_set[i] >= 50) & (dataset[i + 15] >= dataset[i + 14]): 50 | dic['强势市场（50<=RSI<80）但实际上涨'] += 1 51 | elif (RSI_set[i] < 80) & (RSI_set[i] >= 50) & (dataset[i + 15] < dataset[i + 14]): 52 | dic['强势市场（50<=RSI<80）且实际下跌'] += 1 53 | elif (RSI_set[i] < 50) & (RSI_set[i] >= 20) & (dataset[i + 15] >= dataset[i + 14]): 54 | dic['弱式市场（50>RSI>=20）且实际上涨'] += 1 55 | elif (RSI_set[i] < 50) & (RSI_set[i] >= 20) & (dataset[i + 15] < dataset[i + 14]): 56 | dic['弱式市场（50>RSI>=20）但实际下跌'] += 1 57 | elif (RSI_set[i] < 20) & (dataset[i + 15] >= dataset[i + 14]): 58 | dic['超卖市场（RSI<20）且实际上涨'] += 1 59 | else: 60 | dic['超卖市场（RSI<20）但实际下跌'] += 1 61 | print(dic) 62 | 63 | a = Relative_strength_index_calculate('000001') 64 | a.date_setting(start_date='2019-05-12', end_date='2019-12-19') 65 | a.computeRSI(14) -------------------------------------------------------------------------------- /回测.py: -------------------------------------------------------------------------------- 1 | import tushare as ts 2 | import pandas as pd 3 | import numpy as np 4 | import time 5 | 6 | class Huice: 7 | fund = 0; # 初始资金 8 | remaining = 0; # 寸头 9 | open_tax = 0; # 开仓印花税 10 | open_commission = 0; # 开仓佣金 11 | close_tax = 0; # 平仓印花税 12 | close_commission = 0; # 平仓佣金 13 | min_commission = 0; # 最低佣金 14 | hold_stocks = {}; # 持仓股票 15 | raw_transaction_list = []; # 原交易队列 16 | df = pd.DataFrame(columns=['stock_code', 'stock_number', 'date', 'price', 'buy']) 17 | 18 | def __init__(self, fund):# 初始化资金，也是持仓资金 19 | self.fund = fund 20 | self.remaining = fund 21 | def set_order_cost(self, open_tax, open_commission, close_tax, close_commission, min_commission): # 设置交易费用 22 | self.open_tax = open_tax 23 | self.open_commission = open_commission 24 | self.close_tax = close_tax 25 | self.close_commission = close_commission 26 | self.min_commission = min_commission 27 | def getRawData(self, list): # 传递原交易队列 28 | for item in list: 29 | self.raw_transaction_list.append(item) 30 | self.makeTransactionList() 31 | def makeTransactionList(self): 32 | for i in range(0, len(self.raw_transaction_list)): 33 | l = [] 34 | l.append(self.raw_transaction_list[i]['stock_code']) 35 | l.append(self.raw_transaction_list[i]['stock_number']) 36 | s = time.strptime(self.raw_transaction_list[i]['buy_date'], "%Y%m%d") 37 | l.append(time.strftime("%Y-%m-%d", s)) 38 | l.append(self.raw_transaction_list[i]['buy_price']) 39 | l.append(True) 40 | self.df.loc[2 * i] = l 41 | l = [] 42 | l.append(self.raw_transaction_list[i]['stock_code']) 43 | l.append(self.raw_transaction_list[i]['stock_number']) 44 | s = time.strptime(self.raw_transaction_list[i]['sell_date'], "%Y%m%d") 45 | l.append(time.strftime("%Y-%m-%d", s)) 46 | l.append(self.raw_transaction_list[i]['sell_price']) 47 | l.append(False) 48 | self.df.loc[2 * i + 1] = l 49 | self.trading() 50 | def trading(self): 51 | for i in range(0, len(self.df)): 52 | price = 0; # 交易费用 53 | allTrading = ts.get_hist_data(self.df.loc[i]['stock_code']).sort_index(ascending=True) 54 | dayTrading = allTrading.loc[allTrading.index == self.df.loc[i]['date']] 55 | if(self.df.loc[i]['price'] == 1): 56 | price = dayTrading['open'][0] 57 | elif(self.df.loc[i]['price'] == 2): 58 | price = dayTrading['close'][0] 59 | elif(self.df.loc[i]['price'] == 3): 60 | price = dayTrading['high'][0] 61 | else: 62 | price = dayTrading['low'][0] 63 | if(self.df.loc[i]['buy'] == True): 64 | self.buy(self.df.loc[i]['stock_number'], price, self.df.loc[i]['stock_code']) 65 | else: 66 | self.sell(self.df.loc[i]['stock_number'], price, self.df.loc[i]['stock_code']) 67 | def buy(self, number, price, code): # 买入股票 68 | basicCost = price * number 69 | additionalCharge1 = basicCost * self.open_tax 70 | additionalCharge2 = basicCost * self.open_commission if basicCost * self.open_commission > self.min_commission else self.min_commission 71 | allCost = basicCost + additionalCharge1 + additionalCharge2 72 | if(self.remaining > allCost): 73 | self.remaining -= allCost 74 | print("买入了代码为" + str(code) + '的股票，价格为：' + str(price) + '股数为：' + str(number) + '，可用头寸为：' + str(round(self.remaining, 2))) 75 | else: 76 | print('头寸不足，无法交易') 77 | def sell(self, number, price, code): # 卖出股票 78 | basicFund = price * number 79 | charge1 = basicFund * self.open_tax 80 | charge2 = basicFund * self.open_commission if basicFund * self.open_commission > self.min_commission else self.min_commission 81 | computedFund = basicFund - charge1 - charge2 82 | if (self.remaining > computedFund): 83 | self.remaining += computedFund 84 | print("卖出了代码为" + str(code) + '的股票，价格为：' + str(price) + '股数为：' + str(number) + '，可用头寸为：' + str(round(self.remaining, 2))) 85 | else: 86 | print('头寸不足，无法交易') 87 | def test(self): 88 | print(self.df.sort_values(by='date', ascending=True)) 89 | 90 | if __name__ == '__main__': 91 | b = [{"stock_code":"000001", "stock_number": 100, "buy_date": "20191203", "buy_price": 1, "sell_date": "20191219", 92 | "sell_price": 1}, 93 | {"stock_code": "000998", "stock_number": 100, "buy_date": "20191213", "buy_price": 2, "sell_date": "20191230", 94 | "sell_price": 1}, 95 | {"stock_code": "600004", "stock_number": 100, "buy_date": "20191206", "buy_price": 3, "sell_date": "20200108", 96 | "sell_price": 4}] 97 | a = Huice(10000) 98 | a.set_order_cost(0, 0.005, 0.001, 0.005, 5) 99 | a.getRawData(b) 100 | # a.test() -------------------------------------------------------------------------------- /基本面机器学习算法/NBM.py: -------------------------------------------------------------------------------- 1 | # 进行预测 2 | import pandas as pd 3 | import os 4 | import numpy as np 5 | # from fbprophet import Prophet 6 | # import pystan 7 | from sklearn.naive_bayes import GaussianNB 8 | 9 | df = pd.read_csv('data2.csv') 10 | print(df['code'].count()) 11 | #print(len(df)) 12 | p = pd.DataFrame(columns=('code','start', 'predict', 'rate', 'true')) 13 | for i in range(0, df['code'].count()): 14 | new_data = pd.DataFrame(columns=('ds', 'y')) 15 | new_data.loc[0] = [df['2017_2'].iloc[i], df['2017_2_roe'].iloc[i]] 16 | new_data.loc[1] = [df['2017_3'].iloc[i], df['2017_3_roe'].iloc[i]] 17 | new_data.loc[2] = [df['2018_1'].iloc[i], df['2018_1_roe'].iloc[i]] 18 | new_data.loc[3] = [df['2018_2'].iloc[i], df['2018_2_roe'].iloc[i]] 19 | new_data.loc[4] = [df['2018_3'].iloc[i], df['2018_3_roe'].iloc[i]] 20 | new_data.loc[5] = [df['2019_1'].iloc[i], df['2019_1_roe'].iloc[i]] 21 | new_data.loc[6] = [df['2019_2'].iloc[i], df['2019_2_roe'].iloc[i]] 22 | new_data['y'].astype('float') 23 | code = df['code'].iloc[i] 24 | true = df['2019_3_true_end'].iloc[i] 25 | X_list = [] 26 | y_list = [] 27 | x_pre = df['2019_3_start'].iloc[i] 28 | for j in range(0, 7): 29 | X_list.append([new_data['y'].iloc[j]]) 30 | y_list.append(new_data['ds'].iloc[j] * 10000) 31 | X = np.array(X_list) 32 | Y = np.array(y_list) 33 | print(X) 34 | print(Y) 35 | clf = GaussianNB() 36 | clf.fit(X, Y.astype('int')) 37 | rate = clf.predict([[x_pre]])[0] / 10000 38 | print(rate) 39 | pre = rate * x_pre 40 | 41 | p.loc[i] = [code, x_pre, pre, rate, true] 42 | 43 | p = p.sort_values(by='rate', ascending=False) 44 | # p.to_csv('data3.csv') -------------------------------------------------------------------------------- /机器学习算法/AutoARIMA.py: -------------------------------------------------------------------------------- 1 | # 自回归积分滑动平均模型 2 | import pandas as pd 3 | import tushare as ts 4 | import matplotlib.pyplot as plt 5 | from pmdarima import auto_arima 6 | 7 | class AutoARIMA_pridict: 8 | stock_code = '' 9 | tsData = pd.DataFrame() 10 | def __init__(self, stock_code): 11 | self.stock_code = stock_code 12 | def date_setting(self, start_date, end_date): 13 | self.tsData = ts.get_hist_data(code=self.stock_code, start=start_date, end=end_date) 14 | self.tsData = self.tsData.reset_index() 15 | def makePredictionByDay(self, node): # 按日回测 16 | new_data = pd.DataFrame(index=range(0, len(self.tsData)), columns=['Date', 'Close']) 17 | for i in range(0, len(self.tsData)): 18 | new_data['Date'][i] = self.tsData['date'][i] 19 | new_data['Close'][i] = self.tsData['close'][i] 20 | new_data['Date'] = pd.to_datetime(new_data.Date, format='%Y-%m-%d') 21 | new_data.index = new_data['Date'] 22 | # 准备数据 23 | new_data = new_data.sort_index(ascending=True) 24 | forecast = [] 25 | # 训练集和预测集 26 | prediction = new_data[node:] 27 | for i in range(0, len(new_data) - node): 28 | train = new_data[:node + i] 29 | valid = new_data[node + i:] 30 | # 对收盘价进行测试 31 | training = train['Close'] 32 | validation = valid['Close'] 33 | # 拟合模型 34 | model = auto_arima(training, start_p=1, start_q=1, max_p=2, max_q=2, m=12, start_P=0, seasonal=True, d=1, 35 | D=1, 36 | trace=True, error_action='ignore', suppress_warnings=True) # 37 | model.fit(training) 38 | # 预测 39 | forecast.append(model.predict(n_periods=1)[0]) 40 | prediction['Prediction'] = forecast 41 | plt.plot(train['Close']) 42 | plt.plot(prediction[['Close', 'Prediction']]) 43 | plt.show() 44 | def makePrediction(self, node): # node为节点天数，在这之前为训练集、之后为测试集， 45 | new_data = pd.DataFrame(index=range(0, len(self.tsData)), columns=['Date', 'Close']) 46 | for i in range(0, len(self.tsData)): 47 | new_data['Date'][i] = self.tsData['date'][i] 48 | new_data['Close'][i] = self.tsData['close'][i] 49 | new_data['Date'] = pd.to_datetime(new_data.Date, format='%Y-%m-%d') 50 | new_data.index = new_data['Date'] 51 | # 准备数据 52 | new_data = new_data.sort_index(ascending=True) 53 | # 训练集和预测集 54 | train = new_data[:node] 55 | valid = new_data[node:] 56 | # 对收盘价进行测试 57 | training = train['Close'] 58 | validation = valid['Close'] 59 | # 拟合模型 60 | model = auto_arima(training, start_p=1, start_q=1, max_p=2, max_q=2, m=12, start_P=0, seasonal=True, d=1, D=1, 61 | trace=True, error_action='ignore', suppress_warnings=True)# 62 | model.fit(training) 63 | # 进行预测 64 | forecast = model.predict(n_periods=len(valid)) 65 | forecast = pd.DataFrame(forecast, index=valid.index, columns=['Prediction']) 66 | # 画图 67 | valid['Predictions'] = forecast['Prediction'] 68 | plt.plot(train['Close'], label = '训练集') 69 | plt.plot(valid[['Close', 'Predictions']], label = ['真实值', '预测值']) 70 | plt.show() 71 | 72 | a = AutoARIMA_pridict('000001') 73 | a.date_setting(start_date='2019-05-12', end_date='2019-12-19') 74 | a.makePredictionByDay(140) -------------------------------------------------------------------------------- /机器学习算法/DecisionTree.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import tushare as ts 3 | from sklearn.metrics import accuracy_score,roc_auc_score 4 | from sklearn.tree import DecisionTreeClassifier 5 | 6 | class DT_predict: 7 | stock_code = '' 8 | tsData = pd.DataFrame() 9 | def __init__(self, stock_code): 10 | self.stock_code = stock_code 11 | def date_setting(self, start_date, end_date): 12 | self.tsData = ts.get_hist_data(code=self.stock_code, start=start_date, end=end_date) 13 | self.tsData = self.tsData.reset_index() 14 | # print(self.tsData) 15 | def make_predict(self, node): 16 | # self.tsData["(t+1)-(t)"] = self.tsData['close'] - self.tsData['close'].shift(-1) # 2020-11-27修正 17 | self.tsData["(t+1)-(t)"] = self.tsData['close'].shift(1) - self.tsData['close'] 18 | self.tsData['label'] = 0 19 | # 构建对应表 20 | for i in range(0, len(self.tsData)): 21 | if self.tsData["(t+1)-(t)"].loc[i] > 0: 22 | self.tsData['label'].loc[i] = 1 23 | else: 24 | self.tsData['label'].loc[i] = 0 25 | 26 | # 构建数据集 27 | test_data = self.tsData[: len(self.tsData) - node] 28 | train_data = self.tsData[len(self.tsData) - node : ] 29 | train_X = train_data.ix[:, 'open': 'close'].values 30 | train_y = train_data['label'].values 31 | test_X = test_data.ix[:, 'open': 'close'].values 32 | test_y = test_data['label'].values 33 | 34 | # 进行预测 35 | clf = DecisionTreeClassifier(criterion='gini', max_depth=3, min_samples_leaf=6) 36 | clf.fit(train_X, train_y) 37 | print(accuracy_score(train_y, clf.predict(train_X))) 38 | print(accuracy_score(test_y, clf.predict(test_X))) 39 | print(roc_auc_score(test_y, clf.predict(test_X))) # 召回率 40 | 41 | a = DT_predict('000001') 42 | a.date_setting(start_date='2019-05-12', end_date='2019-12-19') 43 | a.make_predict(140) 44 | -------------------------------------------------------------------------------- /机器学习算法/LR.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import tushare as ts 3 | from sklearn.linear_model import LinearRegression 4 | # from sklearn import preprocessing, cross_validation 5 | 6 | class LR_predict: 7 | stock_code = '' 8 | tsData = pd.DataFrame() 9 | def __init__(self, stock_code): 10 | self.stock_code = stock_code 11 | def date_setting(self, start_date, end_date): 12 | self.tsData = ts.get_hist_data(code=self.stock_code, start=start_date, end=end_date) 13 | self.tsData = self.tsData.reset_index() 14 | def make_predict(self, node): 15 | self.tsData["(t+1)-(t)"] = self.tsData['close'] - self.tsData['close'].shift(-1) 16 | self.tsData['label'] = 0 17 | # 构建对应表 18 | for i in range(0, len(self.tsData)): 19 | if self.tsData["(t+1)-(t)"].loc[i] > 0: 20 | self.tsData['label'].loc[i] = 1 21 | else: 22 | self.tsData['label'].loc[i] = 0 23 | 24 | # 构建数据集 25 | test_data = self.tsData[: len(self.tsData) - node] 26 | train_data = self.tsData[len(self.tsData) - node : ] 27 | train_X = train_data.ix[:, 'open': 'close'].values 28 | train_y = train_data['label'].values 29 | test_X = test_data.ix[:, 'open': 'close'].values 30 | test_y = test_data['label'].values 31 | 32 | clf = LinearRegression() 33 | clf.fit(train_X, train_y) 34 | accuracy = clf.score(test_X, test_y) 35 | 36 | print(accuracy) 37 | 38 | a = LR_predict('000001') 39 | a.date_setting(start_date='2019-05-12', end_date='2019-12-19') 40 | a.make_predict(140) -------------------------------------------------------------------------------- /机器学习算法/LSTM.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | import os 5 | import tushare as ts 6 | from sklearn.preprocessing import MinMaxScaler 7 | from keras.models import Sequential 8 | from keras.layers import Dense, LSTM 9 | 10 | class LSTM_Predict: 11 | stock_code = '' 12 | tsData = pd.DataFrame() 13 | 14 | def __init__(self, stock_code): 15 | self.stock_code = stock_code 16 | def date_setting(self, start_date, end_date): 17 | self.tsData = ts.get_hist_data(code=self.stock_code, start=start_date, end=end_date) 18 | self.tsData = self.tsData.sort_index(ascending=True).reset_index() 19 | def makePrediction(self, node): 20 | # 创建数据框 21 | new_data = pd.DataFrame(index=range(0, len(self.tsData)), columns=['Date', 'Close']) 22 | for i in range(0, len(self.tsData)): 23 | new_data['Date'][i] = self.tsData.index[i] 24 | new_data['Close'][i] = self.tsData["close"][i] 25 | # 设置索引 26 | new_data.index = new_data.Date 27 | new_data.drop('Date', axis=1, inplace=True) 28 | 29 | # 创建训练集和验证集 30 | dataset = new_data.values 31 | print(dataset) 32 | train = dataset[0:node, :] 33 | valid = dataset[node:, :] 34 | 35 | # 将数据集转换为x_train和y_train 36 | scaler = MinMaxScaler(feature_range=(0, 1)) 37 | scaled_data = scaler.fit_transform(dataset) 38 | x_train, y_train = [], [] 39 | for i in range(60, len(train)): 40 | x_train.append(scaled_data[i - 60:i, 0]) 41 | y_train.append(scaled_data[i, 0]) 42 | x_train, y_train = np.array(x_train), np.array(y_train) 43 | x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)) 44 | 45 | # 创建和拟合LSTM网络 46 | model = Sequential() 47 | model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1))) 48 | model.add(LSTM(units=50)) 49 | model.add(Dense(1)) 50 | model.compile(loss='mean_squared_error', optimizer='adam') 51 | model.fit(x_train, y_train, epochs=1, batch_size=1, verbose=2) 52 | 53 | # 使用过去值来预测 54 | inputs = new_data[len(new_data) - len(valid) - 60:].values 55 | inputs = inputs.reshape(-1, 1) 56 | inputs = scaler.transform(inputs) 57 | X_test = [] 58 | for i in range(60, inputs.shape[0]): 59 | X_test.append(inputs[i - 60:i, 0]) 60 | X_test = np.array(X_test) 61 | X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) 62 | closing_price = model.predict(X_test) 63 | closing_price = scaler.inverse_transform(closing_price) 64 | 65 | # 作图 66 | train = new_data[:node] 67 | valid = new_data[node:] 68 | print('valid长度是：' + str(len(valid))) 69 | print(len(closing_price)) 70 | valid['Predictions'] = closing_price 71 | plt.plot(train['Close'], label='训练集') 72 | plt.plot(valid['Close'], label='真实值') 73 | plt.plot(valid['Predictions'], label='预测值') 74 | plt.show() 75 | 76 | def print(self): 77 | print(self.tsData) 78 | 79 | a = LSTM_Predict('000001') 80 | a.date_setting(start_date='2019-05-12', end_date='2019-12-19') 81 | a.makePrediction(130) -------------------------------------------------------------------------------- /机器学习算法/Prophet.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import tushare as ts 4 | import matplotlib.pyplot as plt 5 | import os 6 | from fbprophet import Prophet 7 | import pystan 8 | 9 | class Prophet_Predict: 10 | stock_code = '' 11 | tsData = pd.DataFrame() 12 | def __init__(self, stock_code): 13 | self.stock_code = stock_code 14 | def date_setting(self, start_date, end_date): 15 | self.tsData = ts.get_hist_data(code=self.stock_code, start=start_date, end=end_date) 16 | self.tsData = self.tsData.reset_index() 17 | def makePredictionByDay(self, node): # 按日回测 18 | new_data = pd.DataFrame(index=range(0, len(self.tsData)), columns=['Date', 'Close']) 19 | for i in range(0, len(self.tsData)): 20 | new_data['Date'][i] = self.tsData['date'][i] 21 | new_data['Close'][i] = self.tsData['close'][i] 22 | new_data['Date'] = pd.to_datetime(new_data.Date, format='%Y-%m-%d') 23 | new_data.index = new_data['Date'] 24 | # 准备数据 25 | new_data = new_data.sort_index(ascending=True) 26 | new_data.rename(columns={'Close': 'y', 'Date': 'ds'}, inplace=True) 27 | forecast_valid = [] 28 | # 训练集和预测集 29 | prediction = new_data[node:] 30 | for i in range(0, len(new_data) - node): 31 | train = new_data[:node + i] 32 | valid = new_data[node + i:] 33 | # 拟合模型 34 | model = Prophet() 35 | model.fit(train) 36 | # 预测 37 | close_prices = model.make_future_dataframe(periods=1) 38 | forecast = model.predict(close_prices) 39 | forecast_valid.append(forecast['yhat'][len(train):len(train) + 1]) 40 | print(forecast['yhat'][len(train):len(train) + 1]) 41 | prediction['Predictions'] = forecast_valid 42 | plt.plot(train['y']) 43 | plt.plot(prediction[['y', 'Predictions']]) 44 | plt.show() 45 | def makePrediction(self, node): # node为节点天数，在这之前为训练集、之后为测试集， 46 | new_data = pd.DataFrame(index=range(0, len(self.tsData)), columns=['Date', 'Close']) 47 | for i in range(0, len(self.tsData)): 48 | new_data['Date'][i] = self.tsData['date'][i] 49 | new_data['Close'][i] = self.tsData['close'][i] 50 | new_data['Date'] = pd.to_datetime(new_data.Date, format='%Y-%m-%d') 51 | new_data.index = new_data['Date'] 52 | # 准备数据 53 | new_data = new_data.sort_index(ascending=True) 54 | new_data.rename(columns={'Close': 'y', 'Date': 'ds'}, inplace=True) 55 | forecast_valid = [] 56 | # 训练集和预测集 57 | train = new_data[:node] 58 | valid = new_data[node:] 59 | # 拟合模型 60 | model = Prophet() 61 | model.fit(train) 62 | # 预测 63 | close_prices = model.make_future_dataframe(periods=len(valid)) 64 | forecast = model.predict(close_prices) 65 | forecast_valid = forecast['yhat'][node:] 66 | # 画图 67 | valid['Predictions'] = forecast_valid.values 68 | plt.plot(train['y'], label = '训练集') 69 | plt.plot(valid[['y', 'Predictions']], label = ['真实值', '预测值']) 70 | plt.show() 71 | 72 | a = Prophet_Predict('000001') 73 | a.date_setting(start_date='2019-05-12', end_date='2019-12-19') 74 | a.makePrediction(100) -------------------------------------------------------------------------------- /机器学习算法/RandomForest.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import tushare as ts 3 | from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score,accuracy_score,roc_auc_score 4 | from sklearn.ensemble import RandomForestClassifier #随机森林分类模型 5 | 6 | 7 | class RF_predict: 8 | stock_code = '' 9 | tsData = pd.DataFrame() 10 | def __init__(self, stock_code): 11 | self.stock_code = stock_code 12 | def date_setting(self, start_date, end_date): 13 | self.tsData = ts.get_hist_data(code=self.stock_code, start=start_date, end=end_date) 14 | self.tsData = self.tsData.reset_index() 15 | # print(self.tsData) 16 | def make_predict(self, node): 17 | # self.tsData["(t+1)-(t)"] = self.tsData['close'] - self.tsData['close'].shift(-1) # 2020-11-27修正 18 | self.tsData["(t+1)-(t)"] = self.tsData['close'].shift(1) - self.tsData['close'] 19 | self.tsData['label'] = 0 20 | # 构建对应表 21 | for i in range(0, len(self.tsData)): 22 | if self.tsData["(t+1)-(t)"].loc[i] > 0: 23 | self.tsData['label'].loc[i] = 1 24 | else: 25 | self.tsData['label'].loc[i] = 0 26 | 27 | # 构建数据集 28 | test_data = self.tsData[: len(self.tsData) - node] 29 | train_data = self.tsData[len(self.tsData) - node : ] 30 | train_X = train_data.ix[:, 'open': 'close'].values 31 | train_y = train_data['label'].values 32 | test_X = test_data.ix[:, 'open': 'close'].values 33 | test_y = test_data['label'].values 34 | 35 | # 进行预测 36 | clf = RandomForestClassifier(max_depth=1, n_estimators=20) 37 | clf.fit(train_X, train_y) 38 | print(accuracy_score(train_y, clf.predict(train_X))) 39 | print(accuracy_score(test_y, clf.predict(test_X))) 40 | print(roc_auc_score(test_y,clf.predict(test_X))) # 召回率 41 | 42 | a = RF_predict('000001') 43 | a.date_setting(start_date='2019-05-12', end_date='2019-12-19') 44 | a.make_predict(140) 45 | -------------------------------------------------------------------------------- /机器学习算法/SVM.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from sklearn import svm,preprocessing 3 | import tushare as ts 4 | 5 | class SVM_Predict: 6 | stock_code = '' 7 | tsData = pd.DataFrame() 8 | def __init__(self, stock_code): 9 | self.stock_code = stock_code 10 | def date_setting(self, start_date, end_date): 11 | self.tsData = ts.get_hist_data(code=self.stock_code, start=start_date, end=end_date) 12 | self.tsData = self.tsData.reset_index() 13 | def makeSVMPrediction(self, rate): # rate表示训练集和测试集的比例 14 | df_CB = self.tsData.sort_index(ascending=True, axis=0) 15 | df_CB = df_CB.set_index('date') 16 | df_CB = df_CB.sort_index() 17 | # value表示涨跌, =1为涨，=0为跌 18 | value = pd.Series(df_CB['close'] - df_CB['close'].shift(1), \ 19 | index=df_CB.index) 20 | value = value.bfill() 21 | value[value >= 0] = 1 22 | value[value < 0] = 0 23 | df_CB['Value'] = value 24 | # 后向填充空缺值 25 | df_CB = df_CB.fillna(method='bfill') 26 | df_CB = df_CB.astype('float64') 27 | print(df_CB.head()) 28 | 29 | L = len(df_CB) 30 | train = int(L * rate) 31 | total_predict_data = L - train 32 | 33 | # 对样本特征进行归一化处理 34 | df_CB_X = df_CB.drop(['Value'], axis=1) 35 | df_CB_X = preprocessing.scale(df_CB_X) 36 | 37 | # 开始循环预测，每次向前预测一个值 38 | correct = 0 39 | train_original = train 40 | while train < L: 41 | Data_train = df_CB_X[train - train_original:train] 42 | value_train = value[train - train_original:train] 43 | Data_predict = df_CB_X[train:train + 1] 44 | value_real = value[train:train + 1] 45 | 46 | # 核函数分别选取'ploy','linear','rbf' 47 | # classifier = svm.SVC(C=1.0, kernel='poly') 48 | # classifier = svm.SVC(kernel='linear') 49 | classifier = svm.SVC(C=1.0, kernel='rbf') 50 | classifier.fit(Data_train, value_train) 51 | value_predict = classifier.predict(Data_predict) 52 | print("value_real=%d value_predict=%d" % (value_real[0], value_predict)) 53 | # 计算测试集中的正确率 54 | if (value_real[0] == int(value_predict)): 55 | correct = correct + 1 56 | train = train + 1 57 | print(correct) 58 | print(total_predict_data) 59 | correct = correct * 100 / total_predict_data 60 | print("Correct=%.2f%%" % correct) 61 | 62 | a = SVM_Predict('000001') 63 | a.date_setting(start_date='2019-05-12', end_date='2019-12-19') 64 | a.makeSVMPrediction(0.8) -------------------------------------------------------------------------------- /机器学习算法/kNN.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import tushare as ts 3 | import matplotlib.pyplot as plt 4 | from sklearn import neighbors 5 | from sklearn.model_selection import GridSearchCV 6 | from sklearn.preprocessing import MinMaxScaler 7 | scaler = MinMaxScaler(feature_range=(0, 1)) 8 | 9 | class kNN_pridict: 10 | stock_code = '' 11 | tsData = pd.DataFrame() 12 | def __init__(self, stock_code): 13 | self.stock_code = stock_code 14 | def date_setting(self, start_date, end_date): 15 | self.tsData = ts.get_hist_data(code=self.stock_code, start=start_date, end=end_date) 16 | self.tsData = self.tsData.reset_index() 17 | def makePrediction(self, node): # node为节点天数，在这之前为训练集、之后为测试集 18 | new_data = pd.DataFrame(index=range(0, len(self.tsData)), columns=['Date', 'Close']) 19 | for i in range(0, len(self.tsData)): 20 | new_data['Date'][i] = self.tsData['date'][i] 21 | new_data['Close'][i] = self.tsData['close'][i] 22 | new_data['Date'] = pd.to_datetime(new_data.Date, format='%Y-%m-%d') 23 | new_data.index = new_data['Date'] 24 | # 准备数据 25 | new_data = new_data.sort_index(ascending=True) 26 | # 训练集和预测集 27 | train = new_data[:node] 28 | valid = new_data[node:] 29 | x_train = train.drop('Close', axis=1) 30 | y_train = train['Close'] 31 | x_valid = valid.drop('Close', axis=1) 32 | y_valid = valid['Close'] 33 | # 缩放数据 34 | x_train_scaled = scaler.fit_transform(x_train) 35 | x_train = pd.DataFrame(x_train_scaled) 36 | x_valid_scaled = scaler.fit_transform(x_valid) 37 | x_valid = pd.DataFrame(x_valid_scaled) 38 | # 使用gridsearch查找最佳参数 39 | params = {'n_neighbors': [2, 3, 4, 5, 6, 7, 8, 9]} 40 | knn = neighbors.KNeighborsRegressor() 41 | model = GridSearchCV(knn, params, cv=5) 42 | # 拟合模型并进行预测 43 | model.fit(x_train, y_train) 44 | preds = model.predict(x_valid) 45 | # 画图 46 | valid['Predictions'] = 0 47 | valid['Predictions'] = preds 48 | plt.plot(valid[['Close', 'Predictions']]) 49 | plt.plot(train['Close']) 50 | plt.show() 51 | 52 | a = kNN_pridict('000001') 53 | a.date_setting(start_date='2019-05-12', end_date='2019-12-19') 54 | a.makePrediction(140) -------------------------------------------------------------------------------- /股票消息面分析/README.txt: -------------------------------------------------------------------------------- 1 | getsina_message.py: 获取新浪财经消息面 2 | sina_message: 存放每天的新浪财经消息 3 | sentimental_analysis.py: 对消息数据进行情感分析 4 | analysis_result: 情感分析结果 5 | 各月股票队列.xlsx: 股票交易队列 6 | backtest.py: 回测程序 7 | draw_line: 可视化程序 8 | echarts.mim.js: 可视化脚本 9 | 每日资金情况图 10 | 个股持有情况分析 11 | 持有期收益率情况图 -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月10日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,乐惠国际,603076,58,0.1,0.79,0.1,0.512 3 | 1,冠豪高新,600433,43,0.12,0.79,0.09,0.497 4 | 2,交建股份,603815,42,0.1,0.83,0.07,0.501 5 | 3,兴业科技,2674,62,0.16,0.77,0.06,0.521 6 | 4,东北制药,597,50,0.1,0.82,0.08,0.5 7 | 5,德方纳米,300769,60,0.18,0.8,0.02,0.55 8 | 6,以岭药业,2603,56,0.12,0.82,0.05,0.514 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月11日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,科恒股份,300340,80,0.21,0.62,0.16,0.512 3 | 1,睿创微纳,688002,67,0.1,0.72,0.18,0.472 4 | 2,开山股份,300257,59,0.12,0.69,0.19,0.469 5 | 3,露笑科技,2617,92,0.18,0.65,0.16,0.504 6 | 4,龙大肉食,2726,81,0.2,0.7,0.1,0.529 7 | 5,普洛药业,739,53,0.17,0.77,0.06,0.527 8 | 6,尖峰集团,600668,67,0.21,0.7,0.09,0.532 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月12日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,乐普医疗,300003,44,0.2,0.73,0.07,0.533 3 | 1,汇金股份,300368,59,0.12,0.73,0.15,0.503 4 | 2,中原环保,544,43,0.16,0.7,0.14,0.524 5 | 3,滨江集团,2244,34,0.18,0.74,0.09,0.527 6 | 4,内蒙一机,600967,69,0.09,0.72,0.19,0.471 7 | 5,康缘药业,600557,84,0.21,0.73,0.06,0.563 8 | 6,三一重工,600031,49,0.12,0.78,0.1,0.51 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月15日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,蓝帆医疗,2382,61,0.18,0.69,0.13,0.532 3 | 1,复星医药,600196,79,0.16,0.72,0.11,0.514 4 | 2,长盈精密,300115,36,0.08,0.89,0.03,0.512 5 | 3,数字政通,300075,54,0.17,0.74,0.09,0.516 6 | 4,三鑫医疗,300453,58,0.05,0.84,0.1,0.484 7 | 5,三特索道,2159,61,0.16,0.74,0.1,0.508 8 | 6,扬杰科技,300373,42,0.17,0.81,0.02,0.551 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月16日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,信邦制药,2390,49,0.18,0.69,0.12,0.51 3 | 1,中天科技,600522,53,0.23,0.66,0.11,0.523 4 | 2,双星新材,2585,44,0.14,0.8,0.07,0.532 5 | 3,塔牌集团,2233,52,0.1,0.79,0.12,0.486 6 | 4,上海医药,601607,57,0.14,0.72,0.14,0.5 7 | 5,捷成股份,300182,40,0.07,0.75,0.17,0.454 8 | 6,清新环境,2573,72,0.17,0.72,0.11,0.518 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月17日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,科华生物,2022,63,0.27,0.67,0.06,0.556 3 | 1,深物业A,11,49,0.18,0.78,0.04,0.533 4 | 2,四维图新,2405,75,0.17,0.8,0.03,0.548 5 | 3,云海金属,2182,83,0.25,0.72,0.02,0.564 6 | 4,威唐工业,300707,53,0.17,0.66,0.17,0.504 7 | 5,恒力石化,600346,51,0.12,0.61,0.27,0.439 8 | 6,佛慈制药,2644,37,0.08,0.81,0.11,0.492 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月18日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,安科生物,300009,35,0.17,0.77,0.06,0.529 3 | 1,冀东水泥,401,47,0.17,0.77,0.06,0.523 4 | 2,亚星客车,600213,48,0.1,0.73,0.17,0.491 5 | 3,新华制药,756,88,0.07,0.9,0.03,0.5 6 | 4,四维图新,2405,61,0.25,0.7,0.05,0.543 7 | 5,微光股份,2801,57,0.19,0.75,0.05,0.531 8 | 6,长荣股份,300195,66,0.15,0.76,0.09,0.514 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月19日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,老板电器,2508,63,0.14,0.76,0.1,0.516 3 | 1,百合花,603823,86,0.1,0.87,0.02,0.536 4 | 2,厦门港务,905,79,0.19,0.77,0.04,0.545 5 | 3,北新路桥,2307,46,0.15,0.76,0.09,0.516 6 | 4,宝钛股份,600456,47,0.19,0.72,0.09,0.533 7 | 5,通威股份,600438,67,0.16,0.67,0.16,0.508 8 | 6,雪莱特,2076,87,0.07,0.77,0.16,0.484 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月22日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,祥生医疗,688358,57,0.14,0.77,0.09,0.507 3 | 1,晶方科技,603005,51,0.16,0.69,0.16,0.499 4 | 2,润建股份,2929,53,0.13,0.83,0.04,0.519 5 | 3,宜通世纪,300310,31,0.13,0.81,0.06,0.5 6 | 4,新诺威,300765,67,0.16,0.7,0.13,0.506 7 | 5,捷昌驱动,603583,72,0.15,0.78,0.07,0.515 8 | 6,新凤鸣,603225,54,0.24,0.74,0.02,0.577 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月23日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,泽璟制药,688266,74,0.15,0.74,0.11,0.509 3 | 1,昆药集团,600422,43,0.12,0.58,0.3,0.438 4 | 2,粤水电,2060,55,0.09,0.84,0.07,0.495 5 | 3,中曼石油,603619,60,0.1,0.75,0.15,0.474 6 | 4,*ST九有,600462,81,0.14,0.74,0.12,0.508 7 | 5,联得装备,300545,61,0.13,0.72,0.15,0.492 8 | 6,恒林股份,603661,68,0.22,0.6,0.18,0.508 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月24日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,郴电国际,600969,51,0.1,0.78,0.12,0.482 3 | 1,微芯生物,688321,62,0.19,0.69,0.11,0.532 4 | 2,精工钢构,600496,65,0.14,0.85,0.02,0.535 5 | 3,双汇发展,895,68,0.13,0.82,0.04,0.516 6 | 4,透景生命,300642,45,0.22,0.73,0.04,0.543 7 | 5,健友股份,603707,95,0.22,0.68,0.09,0.534 8 | 6,博济医药,300404,66,0.17,0.8,0.03,0.539 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月25日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,寒锐钴业,300618,82,0.11,0.79,0.1,0.508 3 | 1,沪电股份,2463,53,0.19,0.77,0.04,0.531 4 | 2,口子窖,603589,41,0.15,0.59,0.27,0.461 5 | 3,合康新能,300048,103,0.16,0.77,0.08,0.533 6 | 4,ST宜化,422,82,0.24,0.72,0.04,0.561 7 | 5,健友股份,603707,65,0.22,0.68,0.11,0.524 8 | 6,三安光电,600703,66,0.32,0.56,0.12,0.549 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月26日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,通用股份,601500,51,0.04,0.76,0.2,0.445 3 | 1,瑞普生物,300119,85,0.24,0.72,0.05,0.566 4 | 2,劲嘉股份,2191,71,0.14,0.7,0.15,0.475 5 | 3,中嘉博创,889,93,0.14,0.81,0.05,0.515 6 | 4,森霸传感,300701,86,0.13,0.79,0.08,0.524 7 | 5,博世科,300422,41,0.27,0.61,0.12,0.533 8 | 6,天士力,600535,46,0.17,0.7,0.13,0.538 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月29日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,晶澳科技,2459,70,0.19,0.69,0.13,0.526 3 | 1,心脉医疗,688016,70,0.17,0.73,0.1,0.524 4 | 2,新国都,300130,59,0.1,0.78,0.12,0.486 5 | 3,红塔证券,601236,80,0.14,0.75,0.11,0.508 6 | 4,密尔克卫,603713,78,0.08,0.82,0.1,0.489 7 | 5,维尔利,300190,62,0.18,0.73,0.1,0.528 8 | 6,禾丰牧业,603609,91,0.13,0.76,0.11,0.519 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月30日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,光威复材,300699,85,0.14,0.79,0.07,0.518 3 | 1,以岭药业,2603,67,0.18,0.69,0.13,0.527 4 | 2,海格通信,2465,85,0.12,0.76,0.12,0.513 5 | 3,东华软件,2065,98,0.2,0.71,0.08,0.559 6 | 4,中恒电气,2364,58,0.12,0.81,0.07,0.513 7 | 5,得润电子,2055,43,0.23,0.72,0.05,0.536 8 | 6,阳普医疗,300030,38,0.21,0.74,0.05,0.548 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年03月31日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,歌尔股份,2241,37,0.24,0.68,0.08,0.543 3 | 1,中孚信息,300659,53,0.17,0.72,0.11,0.515 4 | 2,天士力,600535,74,0.19,0.78,0.03,0.548 5 | 3,鸿合科技,2955,88,0.16,0.73,0.11,0.497 6 | 4,三安光电,600703,87,0.3,0.59,0.11,0.549 7 | 5,览海医疗,600896,61,0.13,0.82,0.05,0.518 8 | 6,金隅集团,601992,72,0.17,0.76,0.07,0.526 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月01日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,拓斯达,300607,82,0.18,0.76,0.06,0.537 3 | 1,东方日升,300118,72,0.15,0.71,0.14,0.507 4 | 2,三七互娱,2555,75,0.13,0.79,0.08,0.516 5 | 3,华大基因,300676,61,0.21,0.7,0.08,0.534 6 | 4,迈瑞医疗,300760,44,0.14,0.77,0.09,0.514 7 | 5,奥特佳,2239,66,0.18,0.64,0.18,0.504 8 | 6,鱼跃医疗,2223,77,0.1,0.79,0.1,0.498 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月02日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,海油工程,600583,42,0.12,0.74,0.14,0.499 3 | 1,深科技,21,65,0.23,0.63,0.14,0.536 4 | 2,招商轮船,601872,77,0.14,0.77,0.09,0.524 5 | 3,江铃汽车,550,54,0.11,0.8,0.09,0.499 6 | 4,辽宁成大,600739,60,0.15,0.78,0.07,0.525 7 | 5,上海钢联,300226,83,0.12,0.77,0.11,0.497 8 | 6,天奇股份,2009,68,0.12,0.76,0.12,0.505 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月06日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,天华超净,300390,52,0.15,0.75,0.1,0.522 3 | 1,康盛股份,2418,91,0.12,0.76,0.12,0.504 4 | 2,四川路桥,600039,61,0.07,0.77,0.16,0.467 5 | 3,正邦科技,2157,68,0.18,0.78,0.04,0.519 6 | 4,景峰医药,908,66,0.09,0.76,0.15,0.493 7 | 5,博雅生物,300294,87,0.21,0.7,0.09,0.523 8 | 6,中成股份,151,94,0.27,0.61,0.13,0.55 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月07日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,三全食品,2216,33,0.18,0.79,0.03,0.534 3 | 1,海特高新,2023,94,0.14,0.76,0.11,0.515 4 | 2,天康生物,2100,56,0.25,0.64,0.11,0.533 5 | 3,星期六,2291,41,0.15,0.83,0.02,0.522 6 | 4,精工钢构,600496,63,0.11,0.75,0.14,0.473 7 | 5,宝莱特,300246,54,0.19,0.76,0.06,0.542 8 | 6,上海环境,601200,102,0.12,0.73,0.16,0.491 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月08日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,上海环境,601200,32,0.12,0.75,0.12,0.493 3 | 1,明阳智能,601615,72,0.19,0.67,0.14,0.518 4 | 2,中船科技,600072,64,0.14,0.73,0.12,0.499 5 | 3,红旗连锁,2697,50,0.1,0.78,0.12,0.499 6 | 4,香雪制药,300147,37,0.16,0.81,0.03,0.535 7 | 5,华海药业,600521,51,0.18,0.73,0.1,0.509 8 | 6,科达利,2850,75,0.13,0.77,0.09,0.509 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月09日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,中油工程,600339,90,0.12,0.74,0.13,0.506 3 | 1,东方财富,300059,61,0.18,0.7,0.11,0.518 4 | 2,迈瑞医疗,300760,76,0.16,0.79,0.05,0.552 5 | 3,东方电缆,603606,45,0.18,0.69,0.13,0.521 6 | 4,宝莱特,300246,29,0.07,0.69,0.24,0.445 7 | 5,华西能源,2630,60,0.1,0.67,0.23,0.456 8 | 6,诚志股份,990,31,0.16,0.65,0.19,0.47 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月12日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,鸿路钢构,2541,42,0.12,0.64,0.24,0.473 3 | 1,回天新材,300041,60,0.18,0.72,0.1,0.524 4 | 2,南天信息,948,63,0.22,0.62,0.16,0.534 5 | 3,龙大肉食,2726,42,0.26,0.67,0.07,0.551 6 | 4,龙宇燃油,603003,73,0.18,0.67,0.15,0.509 7 | 5,三棵树,603737,48,0.12,0.77,0.1,0.494 8 | 6,奥美医疗,2950,47,0.23,0.66,0.11,0.557 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月13日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,大北农,2385,63,0.14,0.7,0.16,0.492 3 | 1,宝莱特,300246,42,0.19,0.6,0.21,0.52 4 | 2,华阳国际,2949,31,0.19,0.71,0.1,0.529 5 | 3,盈峰环境,967,62,0.11,0.68,0.21,0.469 6 | 4,华邦健康,2004,56,0.16,0.73,0.11,0.516 7 | 5,天康生物,2100,92,0.21,0.7,0.1,0.53 8 | 6,天宜上佳,688033,83,0.11,0.78,0.11,0.51 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月14日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,达安基因,2030,85,0.21,0.69,0.09,0.532 3 | 1,梦网集团,2123,81,0.2,0.67,0.14,0.537 4 | 2,红日药业,300026,82,0.2,0.68,0.12,0.522 5 | 3,大立科技,2214,90,0.2,0.72,0.08,0.541 6 | 4,浦东建设,600284,24,0.08,0.88,0.04,0.508 7 | 5,新乡化纤,949,43,0.14,0.72,0.14,0.495 8 | 6,傲农生物,603363,54,0.24,0.69,0.07,0.565 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月15日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,哈尔斯,2615,40,0.2,0.57,0.23,0.5 3 | 1,三星医疗,601567,43,0.09,0.81,0.09,0.517 4 | 2,长阳科技,688299,60,0.22,0.73,0.05,0.557 5 | 3,禾丰牧业,603609,49,0.2,0.69,0.1,0.541 6 | 4,洲际油气,600759,82,0.13,0.78,0.09,0.517 7 | 5,延安必康,2411,53,0.11,0.75,0.13,0.488 8 | 6,*ST安凯,868,42,0.17,0.81,0.02,0.536 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月16日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,三诺生物,300298,29,0.28,0.62,0.1,0.533 3 | 1,祥生医疗,688358,29,0.24,0.76,0.0,0.589 4 | 2,勤上股份,2638,51,0.18,0.78,0.04,0.538 5 | 3,双环传动,2472,87,0.18,0.66,0.16,0.507 6 | 4,同和药业,300636,39,0.21,0.77,0.03,0.563 7 | 5,甘咨询,779,82,0.18,0.71,0.11,0.518 8 | 6,科森科技,603626,41,0.2,0.73,0.07,0.559 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月19日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,亿纬锂能,300014,42,0.21,0.67,0.12,0.527 3 | 1,铁汉生态,300197,89,0.11,0.8,0.09,0.498 4 | 2,格林美,2340,48,0.27,0.62,0.1,0.537 5 | 3,兄弟科技,2562,59,0.22,0.71,0.07,0.539 6 | 4,腾达建设,600512,40,0.17,0.78,0.05,0.525 7 | 5,新天科技,300259,26,0.15,0.73,0.12,0.501 8 | 6,硕世生物,688399,23,0.22,0.74,0.04,0.551 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月20日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,合兴包装,2228,100,0.18,0.56,0.26,0.486 3 | 1,福建水泥,600802,74,0.18,0.69,0.14,0.511 4 | 2,青岛双星,599,64,0.25,0.56,0.19,0.544 5 | 3,四维图新,2405,84,0.15,0.71,0.13,0.503 6 | 4,飞天诚信,300386,49,0.2,0.69,0.1,0.524 7 | 5,明阳智能,601615,69,0.1,0.87,0.03,0.531 8 | 6,民和股份,2234,46,0.22,0.74,0.04,0.545 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月21日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,宝信软件,600845,58,0.16,0.81,0.03,0.546 3 | 1,泰格医药,300347,61,0.21,0.67,0.11,0.553 4 | 2,沪电股份,2463,27,0.26,0.7,0.04,0.56 5 | 3,健友股份,603707,69,0.19,0.72,0.09,0.522 6 | 4,天马科技,603668,79,0.2,0.71,0.09,0.55 7 | 5,易明医药,2826,79,0.16,0.72,0.11,0.534 8 | 6,重庆百货,600729,55,0.18,0.67,0.15,0.508 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月22日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,纳尔股份,2825,25,0.16,0.72,0.12,0.503 3 | 1,华兰生物,2007,71,0.3,0.66,0.04,0.576 4 | 2,奥飞数据,300738,25,0.24,0.72,0.04,0.571 5 | 3,花王股份,603007,39,0.15,0.67,0.18,0.513 6 | 4,中科曙光,603019,45,0.36,0.6,0.04,0.588 7 | 5,拓尔思,300229,56,0.18,0.73,0.09,0.528 8 | 6,复星医药,600196,47,0.21,0.68,0.11,0.527 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月23日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,韦尔股份,603501,28,0.25,0.75,0.0,0.581 3 | 1,上海新阳,300236,29,0.17,0.76,0.07,0.528 4 | 2,普利制药,300630,34,0.18,0.65,0.18,0.511 5 | 3,京威股份,2662,46,0.2,0.59,0.22,0.501 6 | 4,广生堂,300436,45,0.27,0.67,0.07,0.566 7 | 5,三一重工,600031,38,0.18,0.71,0.11,0.53 8 | 6,通产丽星,2243,23,0.26,0.61,0.13,0.522 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月26日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,唐人神,2567,70,0.17,0.77,0.06,0.533 3 | 1,爱施德,2416,73,0.22,0.62,0.16,0.533 4 | 2,英科医疗,300677,69,0.16,0.8,0.04,0.537 5 | 3,华源控股,2787,72,0.12,0.76,0.11,0.525 6 | 4,金陵体育,300651,66,0.21,0.74,0.05,0.552 7 | 5,华策影视,300133,86,0.24,0.67,0.08,0.554 8 | 6,嘉麟杰,2486,83,0.08,0.78,0.13,0.501 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月27日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,卓胜微,300782,40,0.23,0.68,0.1,0.523 3 | 1,赛升药业,300485,20,0.25,0.65,0.1,0.547 4 | 2,圣达生物,603079,22,0.14,0.73,0.14,0.504 5 | 3,隧道股份,600820,55,0.16,0.69,0.15,0.523 6 | 4,晶方科技,603005,68,0.21,0.66,0.13,0.536 7 | 5,特一药业,2728,40,0.23,0.68,0.1,0.523 8 | 6,华邦健康,2004,34,0.24,0.68,0.09,0.536 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月28日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,万东医疗,600055,68,0.13,0.81,0.06,0.548 3 | 1,江苏国泰,2091,38,0.11,0.79,0.11,0.514 4 | 2,天康生物,2100,23,0.26,0.7,0.04,0.569 5 | 3,博敏电子,603936,50,0.16,0.7,0.14,0.517 6 | 4,华昌化工,2274,68,0.16,0.78,0.06,0.521 7 | 5,联得装备,300545,35,0.11,0.8,0.09,0.526 8 | 6,蓝帆医疗,2382,106,0.15,0.8,0.05,0.531 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年04月29日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,大立科技,2214,19,0.26,0.68,0.05,0.567 3 | 1,亚太股份,2284,59,0.14,0.73,0.14,0.508 4 | 2,华明装备,2270,19,0.26,0.68,0.05,0.56 5 | 3,塔牌集团,2233,56,0.18,0.71,0.11,0.533 6 | 4,国统股份,2205,56,0.27,0.61,0.12,0.561 7 | 5,市北高新,600604,43,0.19,0.65,0.16,0.514 8 | 6,中远海科,2401,66,0.27,0.64,0.09,0.572 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月05日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,蒙娜丽莎,2918,47,0.21,0.72,0.06,0.547 3 | 1,特锐德,300001,49,0.1,0.86,0.04,0.523 4 | 2,华润三九,999,55,0.31,0.62,0.07,0.584 5 | 3,宝钛股份,600456,63,0.29,0.68,0.03,0.589 6 | 4,卓翼科技,2369,67,0.22,0.73,0.04,0.557 7 | 5,新乳业,2946,79,0.24,0.66,0.1,0.55 8 | 6,岭南股份,2717,27,0.19,0.74,0.07,0.533 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月06日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,以岭药业,2603,32,0.22,0.62,0.16,0.517 3 | 1,四维图新,2405,70,0.26,0.66,0.09,0.557 4 | 2,德宏股份,603701,74,0.22,0.72,0.07,0.557 5 | 3,南卫股份,603880,60,0.17,0.73,0.1,0.538 6 | 4,和邦生物,603077,53,0.13,0.62,0.25,0.457 7 | 5,包钢股份,600010,31,0.19,0.61,0.19,0.488 8 | 6,国恩股份,2768,66,0.2,0.71,0.09,0.533 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月07日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,傲农生物,603363,55,0.22,0.67,0.11,0.514 3 | 1,泰达股份,652,63,0.25,0.57,0.17,0.519 4 | 2,创元科技,551,80,0.16,0.74,0.1,0.508 5 | 3,海格通信,2465,35,0.14,0.66,0.2,0.481 6 | 4,中贝通信,603220,66,0.14,0.77,0.09,0.522 7 | 5,康辰药业,603590,44,0.16,0.55,0.3,0.459 8 | 6,灵康药业,603669,55,0.27,0.58,0.15,0.559 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月10日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,荣盛发展,2146,75,0.25,0.67,0.08,0.556 3 | 1,科华生物,2022,61,0.16,0.74,0.1,0.512 4 | 2,华大基因,300676,65,0.15,0.75,0.09,0.534 5 | 3,沃施股份,300483,79,0.15,0.8,0.05,0.528 6 | 4,大北农,2385,65,0.23,0.69,0.08,0.533 7 | 5,捷昌驱动,603583,77,0.12,0.75,0.13,0.486 8 | 6,东北制药,597,94,0.15,0.78,0.07,0.532 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月11日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,第一创业,2797,23,0.13,0.74,0.13,0.493 3 | 1,天瑞仪器,300165,52,0.08,0.69,0.23,0.447 4 | 2,京能电力,600578,51,0.16,0.76,0.08,0.513 5 | 3,国信证券,2736,28,0.18,0.71,0.11,0.496 6 | 4,中材国际,600970,61,0.16,0.64,0.2,0.476 7 | 5,复星医药,600196,31,0.16,0.68,0.16,0.492 8 | 6,威海广泰,2111,90,0.13,0.76,0.11,0.519 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月12日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,沃森生物,300142,68,0.34,0.47,0.19,0.539 3 | 1,凯利泰,300326,46,0.22,0.61,0.17,0.509 4 | 2,中航光电,2179,56,0.12,0.68,0.2,0.458 5 | 3,福日电子,600203,85,0.36,0.59,0.05,0.62 6 | 4,阳煤化工,600691,33,0.27,0.58,0.15,0.557 7 | 5,黔源电力,2039,82,0.17,0.65,0.18,0.498 8 | 6,乐普医疗,300003,72,0.19,0.78,0.03,0.545 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月13日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,一品红,300723,29,0.24,0.76,0.0,0.561 3 | 1,晋亿实业,601002,94,0.22,0.71,0.06,0.557 4 | 2,红日药业,300026,70,0.24,0.64,0.11,0.536 5 | 3,ST天业,600807,84,0.1,0.83,0.07,0.5 6 | 4,高鸿股份,851,64,0.23,0.64,0.12,0.537 7 | 5,中简科技,300777,45,0.16,0.62,0.22,0.474 8 | 6,中京电子,2579,63,0.21,0.67,0.13,0.521 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月14日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,东杰智能,300486,47,0.28,0.7,0.02,0.581 3 | 1,绿色动力,601330,51,0.12,0.71,0.18,0.497 4 | 2,云赛智联,600602,62,0.23,0.76,0.02,0.574 5 | 3,神州信息,555,63,0.25,0.65,0.1,0.547 6 | 4,西水股份,600291,19,0.16,0.74,0.11,0.513 7 | 5,新疆天业,600075,92,0.13,0.74,0.13,0.484 8 | 6,腾达建设,600512,33,0.18,0.7,0.12,0.527 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月17日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,安图生物,603658,60,0.22,0.68,0.1,0.528 3 | 1,以岭药业,2603,60,0.18,0.7,0.12,0.525 4 | 2,新纶科技,2341,73,0.21,0.75,0.04,0.564 5 | 3,埃斯顿,2747,62,0.21,0.68,0.11,0.544 6 | 4,金发科技,600143,86,0.12,0.69,0.2,0.466 7 | 5,永鼎股份,600105,37,0.27,0.7,0.03,0.564 8 | 6,金新农,2548,68,0.21,0.76,0.03,0.562 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月18日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,京汉股份,615,92,0.09,0.85,0.07,0.506 3 | 1,成都路桥,2628,48,0.06,0.88,0.06,0.508 4 | 2,海鸥住工,2084,61,0.34,0.56,0.1,0.586 5 | 3,华脉科技,603042,65,0.17,0.72,0.11,0.522 6 | 4,佐力药业,300181,82,0.18,0.72,0.1,0.528 7 | 5,拓普集团,601689,77,0.13,0.73,0.14,0.505 8 | 6,迈克生物,300463,68,0.19,0.75,0.06,0.533 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月19日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,科沃斯,603486,81,0.27,0.57,0.16,0.536 3 | 1,长城影视,2071,94,0.14,0.74,0.12,0.502 4 | 2,蓝帆医疗,2382,38,0.11,0.87,0.03,0.503 5 | 3,浙商证券,601878,40,0.25,0.65,0.1,0.542 6 | 4,龙建股份,600853,32,0.22,0.75,0.03,0.558 7 | 5,新界泵业,2532,43,0.23,0.7,0.07,0.529 8 | 6,金城医药,300233,70,0.13,0.69,0.19,0.495 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月20日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,银宝山新,2786,65,0.2,0.75,0.05,0.551 3 | 1,恩华药业,2262,95,0.19,0.69,0.12,0.523 4 | 2,惠伦晶体,300460,90,0.19,0.72,0.09,0.545 5 | 3,数码科技,300079,58,0.21,0.71,0.09,0.532 6 | 4,天瑞仪器,300165,57,0.21,0.61,0.18,0.515 7 | 5,普洛药业,739,71,0.24,0.75,0.01,0.579 8 | 6,加加食品,2650,46,0.3,0.63,0.07,0.6 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月21日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,共进股份,603118,65,0.17,0.71,0.12,0.51 3 | 1,泰格医药,300347,62,0.1,0.81,0.1,0.5 4 | 2,克明面业,2661,84,0.17,0.76,0.07,0.529 5 | 3,哈高科,600095,26,0.35,0.62,0.04,0.573 6 | 4,天喻信息,300205,37,0.16,0.76,0.08,0.522 7 | 5,开立医疗,300633,82,0.24,0.7,0.06,0.563 8 | 6,诚意药业,603811,71,0.14,0.72,0.14,0.516 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月24日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,*ST德豪,2005,93,0.16,0.77,0.06,0.512 3 | 1,众应互联,2464,92,0.08,0.74,0.18,0.476 4 | 2,德邦股份,603056,94,0.2,0.72,0.07,0.531 5 | 3,富临精工,300432,54,0.09,0.72,0.19,0.479 6 | 4,依米康,300249,78,0.14,0.83,0.03,0.539 7 | 5,浙江震元,705,76,0.08,0.75,0.17,0.474 8 | 6,润建股份,2929,80,0.14,0.8,0.06,0.532 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月25日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,川环科技,300547,37,0.35,0.51,0.14,0.594 3 | 1,顺网科技,300113,23,0.26,0.7,0.04,0.562 4 | 2,国恩股份,2768,54,0.19,0.65,0.17,0.498 5 | 3,新文化,300336,73,0.27,0.66,0.07,0.569 6 | 4,易华录,300212,47,0.21,0.68,0.11,0.56 7 | 5,盈峰环境,967,62,0.16,0.76,0.08,0.534 8 | 6,巨星科技,2444,88,0.16,0.73,0.11,0.533 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月26日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,网达软件,603189,31,0.13,0.77,0.1,0.515 3 | 1,古越龙山,600059,86,0.26,0.72,0.02,0.589 4 | 2,振东制药,300158,53,0.19,0.66,0.15,0.507 5 | 3,中南传媒,601098,71,0.18,0.68,0.14,0.514 6 | 4,瀚川智能,688022,79,0.3,0.67,0.03,0.604 7 | 5,首创股份,600008,23,0.17,0.74,0.09,0.509 8 | 6,东土科技,300353,73,0.19,0.64,0.16,0.516 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月27日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,上海钢联,300226,43,0.21,0.7,0.09,0.53 3 | 1,富祥药业,300497,49,0.16,0.76,0.08,0.527 4 | 2,金莱特,2723,51,0.12,0.82,0.06,0.521 5 | 3,复星医药,600196,54,0.22,0.7,0.07,0.54 6 | 4,海印股份,861,52,0.23,0.67,0.1,0.555 7 | 5,仟源医药,300254,63,0.22,0.7,0.08,0.542 8 | 6,中核钛白,2145,43,0.19,0.7,0.12,0.529 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月28日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,山东高速,600350,64,0.12,0.78,0.09,0.527 3 | 1,鲁抗医药,600789,47,0.13,0.74,0.13,0.494 4 | 2,生益科技,600183,62,0.11,0.74,0.15,0.498 5 | 3,中电兴发,2298,91,0.24,0.71,0.04,0.557 6 | 4,龙泉股份,2671,53,0.13,0.77,0.09,0.523 7 | 5,襄阳轴承,678,85,0.27,0.58,0.15,0.543 8 | 6,泰晶科技,603738,38,0.16,0.71,0.13,0.511 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年05月31日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,同方股份,600100,76,0.21,0.71,0.08,0.543 3 | 1,神马股份,600810,57,0.21,0.7,0.09,0.532 4 | 2,网宿科技,300017,76,0.29,0.62,0.09,0.562 5 | 3,吉电股份,875,88,0.17,0.73,0.1,0.512 6 | 4,龙蟒佰利,2601,59,0.15,0.75,0.1,0.525 7 | 5,中天科技,600522,57,0.18,0.79,0.04,0.533 8 | 6,卓胜微,300782,48,0.29,0.65,0.06,0.565 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月01日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,盛屯矿业,600711,84,0.17,0.69,0.14,0.503 3 | 1,南钢股份,600282,49,0.29,0.65,0.06,0.558 4 | 2,华海药业,600521,82,0.13,0.77,0.1,0.512 5 | 3,万里扬,2434,87,0.14,0.78,0.08,0.532 6 | 4,金新农,2548,54,0.19,0.74,0.07,0.52 7 | 5,歌华有线,600037,89,0.24,0.63,0.13,0.537 8 | 6,中广核技,881,58,0.28,0.6,0.12,0.559 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月02日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,智云股份,300097,55,0.27,0.67,0.05,0.566 3 | 1,天永智能,603895,49,0.14,0.8,0.06,0.515 4 | 2,吉宏股份,2803,85,0.22,0.69,0.08,0.545 5 | 3,东方集团,600811,45,0.09,0.8,0.11,0.493 6 | 4,岭南股份,2717,42,0.1,0.83,0.07,0.501 7 | 5,东安动力,600178,33,0.18,0.7,0.12,0.511 8 | 6,中海达,300177,55,0.22,0.71,0.07,0.55 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月03日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,美凯龙,601828,71,0.25,0.63,0.11,0.56 3 | 1,福田汽车,600166,28,0.11,0.68,0.21,0.452 4 | 2,西藏矿业,762,64,0.23,0.72,0.05,0.554 5 | 3,傲农生物,603363,56,0.23,0.68,0.09,0.53 6 | 4,豫园股份,600655,78,0.28,0.65,0.06,0.573 7 | 5,欧菲光,2456,88,0.14,0.75,0.11,0.517 8 | 6,彤程新材,603650,45,0.2,0.76,0.04,0.545 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月04日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,林洋能源,601222,57,0.05,0.82,0.12,0.474 3 | 1,微芯生物,688321,41,0.22,0.68,0.1,0.535 4 | 2,海通证券,600837,29,0.17,0.76,0.07,0.501 5 | 3,东方证券,600958,29,0.14,0.79,0.07,0.5 6 | 4,龙建股份,600853,48,0.19,0.73,0.08,0.524 7 | 5,南京证券,601990,28,0.14,0.75,0.11,0.494 8 | 6,国泰君安,601211,26,0.12,0.77,0.12,0.492 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月07日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,三安光电,600703,93,0.24,0.69,0.08,0.554 3 | 1,华菱星马,600375,75,0.12,0.73,0.15,0.496 4 | 2,广生堂,300436,69,0.19,0.7,0.12,0.534 5 | 3,台华新材,603055,56,0.16,0.75,0.09,0.525 6 | 4,华孚时尚,2042,43,0.16,0.72,0.12,0.516 7 | 5,鸿博股份,2229,85,0.18,0.72,0.11,0.511 8 | 6,荣丰控股,668,94,0.21,0.69,0.1,0.532 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月08日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,科华生物,2022,87,0.09,0.84,0.07,0.514 3 | 1,东山精密,2384,82,0.18,0.71,0.11,0.524 4 | 2,新凤鸣,603225,53,0.3,0.66,0.04,0.602 5 | 3,达实智能,2421,55,0.15,0.73,0.13,0.517 6 | 4,泰达股份,652,91,0.12,0.76,0.12,0.523 7 | 5,金溢科技,2869,90,0.28,0.62,0.1,0.573 8 | 6,新希望,876,85,0.14,0.78,0.08,0.511 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月09日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,王府井,600859,39,0.03,0.87,0.1,0.483 3 | 1,环旭电子,601231,56,0.12,0.84,0.04,0.527 4 | 2,腾龙股份,603158,35,0.17,0.69,0.14,0.521 5 | 3,步步高,2251,83,0.28,0.58,0.14,0.544 6 | 4,安恒信息,688023,69,0.19,0.77,0.04,0.544 7 | 5,华熙生物,688363,30,0.33,0.63,0.03,0.585 8 | 6,金杯汽车,600609,52,0.13,0.79,0.08,0.527 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月10日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,全筑股份,603030,48,0.15,0.79,0.06,0.528 3 | 1,华邦健康,2004,51,0.2,0.69,0.12,0.529 4 | 2,凯撒文化,2425,75,0.2,0.67,0.13,0.534 5 | 3,通达动力,2576,52,0.29,0.65,0.06,0.579 6 | 4,华夏航空,2928,76,0.26,0.67,0.07,0.56 7 | 5,四维图新,2405,71,0.17,0.75,0.08,0.539 8 | 6,创元科技,551,43,0.19,0.6,0.21,0.477 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月11日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,中超控股,2471,72,0.28,0.65,0.07,0.575 3 | 1,东华软件,2065,65,0.09,0.75,0.15,0.479 4 | 2,洁美科技,2859,80,0.23,0.72,0.05,0.547 5 | 3,榕基软件,2474,68,0.21,0.68,0.12,0.544 6 | 4,达实智能,2421,28,0.11,0.82,0.07,0.513 7 | 5,三房巷,600370,61,0.15,0.74,0.11,0.512 8 | 6,阳煤化工,600691,61,0.07,0.84,0.1,0.493 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月14日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,乐歌股份,300729,57,0.32,0.58,0.11,0.564 3 | 1,亚士创能,603378,59,0.07,0.81,0.12,0.479 4 | 2,易华录,300212,74,0.24,0.65,0.11,0.552 5 | 3,凯撒文化,2425,76,0.17,0.71,0.12,0.517 6 | 4,太极实业,600667,70,0.21,0.69,0.1,0.534 7 | 5,博天环境,603603,92,0.2,0.71,0.1,0.537 8 | 6,贝达药业,300558,80,0.2,0.72,0.07,0.54 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月15日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,中新药业,600329,74,0.14,0.72,0.15,0.503 3 | 1,安图生物,603658,37,0.3,0.68,0.03,0.582 4 | 2,迪瑞医疗,300396,56,0.14,0.7,0.16,0.492 5 | 3,西藏药业,600211,77,0.25,0.64,0.12,0.539 6 | 4,沃森生物,300142,94,0.18,0.73,0.09,0.529 7 | 5,上海天洋,603330,76,0.18,0.74,0.08,0.538 8 | 6,太极实业,600667,62,0.13,0.76,0.11,0.511 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月16日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,海尔智家,600690,93,0.2,0.71,0.09,0.549 3 | 1,东方精工,2611,80,0.09,0.72,0.19,0.469 4 | 2,华统股份,2840,83,0.18,0.71,0.11,0.515 5 | 3,北京科锐,2350,97,0.06,0.87,0.07,0.491 6 | 4,巨星科技,2444,91,0.29,0.59,0.12,0.566 7 | 5,龙大肉食,2726,61,0.1,0.74,0.16,0.486 8 | 6,紫光股份,938,88,0.18,0.74,0.08,0.544 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月17日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,孚日股份,2083,67,0.12,0.81,0.07,0.506 3 | 1,吉华集团,603980,69,0.12,0.78,0.1,0.502 4 | 2,渤海汽车,600960,55,0.22,0.64,0.15,0.507 5 | 3,凯普生物,300639,68,0.13,0.78,0.09,0.512 6 | 4,达安基因,2030,75,0.2,0.75,0.05,0.545 7 | 5,宇环数控,2903,42,0.12,0.69,0.19,0.481 8 | 6,盛通股份,2599,27,0.22,0.63,0.15,0.516 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月18日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,汇嘉时代,603101,57,0.14,0.56,0.3,0.467 3 | 1,麦克奥迪,300341,50,0.38,0.6,0.02,0.603 4 | 2,赛升药业,300485,83,0.23,0.69,0.08,0.559 5 | 3,傲农生物,603363,81,0.17,0.75,0.07,0.544 6 | 4,上海建工,600170,72,0.21,0.68,0.11,0.53 7 | 5,小商品城,600415,85,0.15,0.71,0.14,0.504 8 | 6,金证股份,600446,48,0.21,0.73,0.06,0.538 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月21日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,金力泰,300225,61,0.05,0.69,0.26,0.451 3 | 1,天晟新材,300169,90,0.22,0.72,0.06,0.573 4 | 2,重庆啤酒,600132,91,0.2,0.74,0.07,0.54 5 | 3,韦尔股份,603501,36,0.22,0.61,0.17,0.505 6 | 4,吉电股份,875,71,0.27,0.58,0.15,0.54 7 | 5,TCL科技,100,65,0.17,0.74,0.09,0.527 8 | 6,硕世生物,688399,64,0.17,0.75,0.08,0.527 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月22日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,蓝光发展,600466,46,0.22,0.61,0.17,0.505 3 | 1,四维图新,2405,70,0.26,0.66,0.09,0.538 4 | 2,东港股份,2117,52,0.12,0.83,0.06,0.506 5 | 3,游族网络,2174,92,0.2,0.7,0.11,0.524 6 | 4,博瑞医药,688166,88,0.23,0.69,0.08,0.539 7 | 5,新和成,2001,47,0.11,0.81,0.09,0.505 8 | 6,森远股份,300210,67,0.22,0.7,0.07,0.526 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月23日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,隆华科技,300263,68,0.21,0.74,0.06,0.551 3 | 1,南钢股份,600282,38,0.18,0.76,0.05,0.535 4 | 2,大北农,2385,81,0.33,0.57,0.1,0.565 5 | 3,*ST雪莱,2076,90,0.36,0.58,0.07,0.601 6 | 4,特变电工,600089,64,0.2,0.75,0.05,0.529 7 | 5,中环股份,2129,61,0.18,0.67,0.15,0.525 8 | 6,三湘印象,863,27,0.26,0.59,0.15,0.555 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月28日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,星网宇达,2829,58,0.21,0.72,0.07,0.554 3 | 1,赛意信息,300687,61,0.28,0.66,0.07,0.574 4 | 2,沃森生物,300142,80,0.33,0.57,0.1,0.578 5 | 3,徐工机械,425,79,0.13,0.72,0.15,0.489 6 | 4,荣盛发展,2146,43,0.21,0.67,0.12,0.515 7 | 5,万丰奥威,2085,89,0.25,0.7,0.06,0.557 8 | 6,海格通信,2465,39,0.18,0.64,0.18,0.494 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月29日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,安泰科技,969,39,0.18,0.64,0.18,0.521 3 | 1,重庆百货,600729,38,0.11,0.76,0.13,0.504 4 | 2,华邦健康,2004,24,0.33,0.67,0.0,0.598 5 | 3,清源股份,603628,42,0.12,0.74,0.14,0.481 6 | 4,华安证券,600909,46,0.17,0.65,0.17,0.527 7 | 5,正邦科技,2157,85,0.25,0.66,0.09,0.559 8 | 6,精研科技,300709,18,0.28,0.67,0.06,0.565 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年06月30日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,精工钢构,600496,48,0.19,0.79,0.02,0.558 3 | 1,深高速,600548,79,0.23,0.7,0.08,0.559 4 | 2,网达软件,603189,47,0.13,0.77,0.11,0.509 5 | 3,通达股份,2560,43,0.16,0.77,0.07,0.517 6 | 4,中远海能,600026,88,0.09,0.74,0.17,0.47 7 | 5,三一重工,600031,29,0.17,0.72,0.1,0.545 8 | 6,福莱特,601865,28,0.14,0.68,0.18,0.503 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月01日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,柳药股份,603368,47,0.17,0.64,0.19,0.511 3 | 1,阳煤化工,600691,81,0.23,0.64,0.12,0.534 4 | 2,海欣食品,2702,46,0.22,0.7,0.09,0.547 5 | 3,皖新传媒,601801,72,0.29,0.65,0.06,0.594 6 | 4,天邦股份,2124,74,0.15,0.84,0.01,0.521 7 | 5,三全食品,2216,84,0.17,0.76,0.07,0.533 8 | 6,诚益通,300430,78,0.17,0.81,0.03,0.532 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月02日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,思源电气,2028,79,0.06,0.86,0.08,0.5 3 | 1,华贸物流,603128,31,0.16,0.71,0.13,0.498 4 | 2,三变科技,2112,60,0.15,0.78,0.07,0.51 5 | 3,汇中股份,300371,41,0.15,0.68,0.17,0.483 6 | 4,成都银行,601838,93,0.17,0.65,0.18,0.497 7 | 5,天味食品,603317,37,0.08,0.81,0.11,0.492 8 | 6,达安基因,2030,81,0.11,0.75,0.14,0.493 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月05日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,信维通信,300136,50,0.14,0.7,0.16,0.519 3 | 1,天邦股份,2124,45,0.22,0.67,0.11,0.532 4 | 2,江铃汽车,550,39,0.15,0.72,0.13,0.519 5 | 3,中联重科,157,78,0.28,0.64,0.08,0.565 6 | 4,新黄浦,600638,83,0.24,0.67,0.08,0.565 7 | 5,恒润股份,603985,62,0.13,0.84,0.03,0.537 8 | 6,赛诺医疗,688108,63,0.27,0.7,0.03,0.58 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月06日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,招商轮船,601872,78,0.31,0.59,0.1,0.573 3 | 1,天音控股,829,55,0.16,0.75,0.09,0.525 4 | 2,金利华电,300069,50,0.14,0.82,0.04,0.525 5 | 3,精准信息,300099,64,0.11,0.83,0.06,0.518 6 | 4,中鼎股份,887,61,0.07,0.79,0.15,0.499 7 | 5,天赐材料,2709,64,0.12,0.8,0.08,0.517 8 | 6,晶科科技,601778,76,0.26,0.62,0.12,0.548 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月07日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,盛弘股份,300693,36,0.28,0.61,0.11,0.557 3 | 1,振德医疗,603301,72,0.08,0.81,0.11,0.506 4 | 2,云南旅游,2059,80,0.28,0.65,0.07,0.553 5 | 3,唐人神,2567,30,0.17,0.7,0.13,0.507 6 | 4,甘李药业,603087,68,0.28,0.6,0.12,0.552 7 | 5,长春高新,661,64,0.2,0.69,0.11,0.522 8 | 6,银江股份,300020,63,0.11,0.76,0.13,0.492 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月08日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,拓斯达,300607,50,0.2,0.7,0.1,0.534 3 | 1,王府井,600859,24,0.12,0.88,0.0,0.531 4 | 2,和佳医疗,300273,73,0.12,0.84,0.04,0.529 5 | 3,嘉友国际,603871,91,0.1,0.75,0.15,0.481 6 | 4,长江证券,783,48,0.15,0.83,0.02,0.534 7 | 5,吉电股份,875,60,0.23,0.65,0.12,0.543 8 | 6,苏奥传感,300507,34,0.21,0.74,0.06,0.551 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月09日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,奥美医疗,2950,41,0.27,0.66,0.07,0.571 3 | 1,明德生物,2932,62,0.13,0.81,0.06,0.525 4 | 2,美尚生态,300495,44,0.25,0.68,0.07,0.574 5 | 3,圣农发展,2299,61,0.34,0.62,0.03,0.591 6 | 4,东华软件,2065,45,0.18,0.76,0.07,0.531 7 | 5,京蓝科技,711,60,0.27,0.57,0.17,0.542 8 | 6,招商南油,601975,68,0.18,0.69,0.13,0.518 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月12日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,木林森,2745,63,0.3,0.62,0.08,0.587 3 | 1,英科医疗,300677,52,0.21,0.67,0.12,0.53 4 | 2,健康元,600380,88,0.22,0.62,0.16,0.528 5 | 3,硕世生物,688399,55,0.18,0.76,0.05,0.544 6 | 4,金新农,2548,47,0.3,0.62,0.09,0.552 7 | 5,宁德时代,300750,74,0.15,0.76,0.09,0.511 8 | 6,金发科技,600143,57,0.11,0.81,0.09,0.513 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月13日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,欧菲光,2456,71,0.23,0.73,0.04,0.541 3 | 1,青岛双星,599,37,0.14,0.73,0.14,0.492 4 | 2,达安基因,2030,59,0.2,0.71,0.08,0.535 5 | 3,傲农生物,603363,37,0.27,0.7,0.03,0.578 6 | 4,康辰药业,603590,61,0.26,0.67,0.07,0.58 7 | 5,上机数控,603185,23,0.26,0.7,0.04,0.55 8 | 6,泰达股份,652,41,0.12,0.68,0.2,0.486 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月14日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,高德红外,2414,52,0.21,0.75,0.04,0.559 3 | 1,龙大肉食,2726,43,0.21,0.77,0.02,0.543 4 | 2,海特生物,300683,63,0.22,0.75,0.03,0.556 5 | 3,汇通能源,600605,54,0.07,0.8,0.13,0.468 6 | 4,长安汽车,625,73,0.22,0.68,0.1,0.529 7 | 5,上海贝岭,600171,31,0.13,0.77,0.1,0.509 8 | 6,高新兴,300098,38,0.34,0.55,0.11,0.583 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月15日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,TCL科技,100,24,0.29,0.62,0.08,0.567 3 | 1,厦门钨业,600549,66,0.17,0.73,0.11,0.513 4 | 2,大北农,2385,58,0.24,0.64,0.12,0.524 5 | 3,沃华医药,2107,64,0.3,0.67,0.03,0.594 6 | 4,通光线缆,300265,42,0.14,0.81,0.05,0.531 7 | 5,新宝股份,2705,34,0.21,0.76,0.03,0.568 8 | 6,国联股份,603613,31,0.32,0.61,0.06,0.583 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月16日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,青岛双星,599,32,0.31,0.53,0.16,0.574 3 | 1,中远海能,600026,79,0.19,0.66,0.15,0.505 4 | 2,银江股份,300020,59,0.27,0.58,0.15,0.54 5 | 3,奥翔药业,603229,24,0.21,0.75,0.04,0.555 6 | 4,健友股份,603707,82,0.23,0.68,0.09,0.548 7 | 5,诚志股份,990,81,0.19,0.74,0.07,0.551 8 | 6,科华恒盛,2335,54,0.13,0.74,0.13,0.505 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月19日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,山鹰纸业,600567,70,0.1,0.79,0.11,0.496 3 | 1,梦网集团,2123,49,0.12,0.67,0.2,0.491 4 | 2,睿创微纳,688002,79,0.27,0.66,0.08,0.558 5 | 3,比音勒芬,2832,85,0.11,0.85,0.05,0.517 6 | 4,闻泰科技,600745,68,0.22,0.63,0.15,0.527 7 | 5,大商股份,600694,61,0.18,0.67,0.15,0.511 8 | 6,长园集团,600525,51,0.2,0.69,0.12,0.52 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月20日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,欧亚集团,600697,59,0.15,0.68,0.17,0.497 3 | 1,国新能源,600617,24,0.29,0.5,0.21,0.51 4 | 2,宁夏建材,600449,36,0.14,0.78,0.08,0.517 5 | 3,节能风电,601016,23,0.22,0.74,0.04,0.558 6 | 4,英飞拓,2528,52,0.15,0.69,0.15,0.493 7 | 5,万丰奥威,2085,73,0.18,0.74,0.08,0.541 8 | 6,森霸传感,300701,75,0.16,0.75,0.09,0.535 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月21日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,江淮汽车,600418,54,0.26,0.67,0.07,0.553 3 | 1,金雷股份,300443,62,0.24,0.69,0.06,0.549 4 | 2,亿纬锂能,300014,32,0.22,0.72,0.06,0.558 5 | 3,艾迪精密,603638,26,0.23,0.73,0.04,0.555 6 | 4,天海防务,300008,57,0.18,0.77,0.05,0.524 7 | 5,汉缆股份,2498,31,0.16,0.81,0.03,0.539 8 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月22日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,南宁百货,600712,52,0.21,0.69,0.1,0.542 3 | 1,华菱星马,600375,54,0.04,0.85,0.11,0.48 4 | 2,深天地A,23,72,0.12,0.79,0.08,0.492 5 | 3,博天环境,603603,67,0.19,0.79,0.01,0.556 6 | 4,宏昌电子,603002,39,0.21,0.72,0.08,0.555 7 | 5,福田汽车,600166,49,0.06,0.61,0.33,0.425 8 | 6,大唐发电,601991,31,0.19,0.71,0.1,0.535 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月23日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,汉邦高科,300449,73,0.12,0.81,0.07,0.515 3 | 1,步步高,2251,78,0.12,0.78,0.1,0.517 4 | 2,创元科技,551,50,0.18,0.7,0.12,0.492 5 | 3,迈得医疗,688310,40,0.2,0.72,0.07,0.545 6 | 4,瑞玛工业,2976,77,0.17,0.68,0.16,0.525 7 | 5,南方轴承,2553,55,0.13,0.76,0.11,0.505 8 | 6,天合光能,688599,56,0.12,0.8,0.07,0.511 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月26日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,唐人神,2567,58,0.12,0.83,0.05,0.517 3 | 1,万德斯,688178,69,0.07,0.87,0.06,0.504 4 | 2,绿地控股,600606,81,0.12,0.77,0.11,0.509 5 | 3,泛微网络,603039,61,0.18,0.77,0.05,0.536 6 | 4,士兰微,600460,72,0.17,0.76,0.07,0.525 7 | 5,一拖股份,601038,52,0.19,0.67,0.13,0.506 8 | 6,东百集团,600693,58,0.09,0.72,0.19,0.48 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月27日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,世纪华通,2602,53,0.15,0.77,0.08,0.505 3 | 1,海欣食品,2702,76,0.29,0.67,0.04,0.575 4 | 2,华菱星马,600375,67,0.21,0.73,0.06,0.54 5 | 3,飞天诚信,300386,78,0.24,0.64,0.12,0.547 6 | 4,富春环保,2479,86,0.1,0.66,0.23,0.47 7 | 5,博晖创新,300318,68,0.25,0.65,0.1,0.545 8 | 6,特锐德,300001,69,0.12,0.78,0.1,0.5 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月28日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,光韵达,300227,68,0.25,0.59,0.16,0.535 3 | 1,闻泰科技,600745,70,0.27,0.66,0.07,0.546 4 | 2,生益科技,600183,66,0.2,0.74,0.06,0.543 5 | 3,东阳光,600673,87,0.17,0.72,0.1,0.515 6 | 4,海印股份,861,52,0.15,0.69,0.15,0.52 7 | 5,三房巷,600370,66,0.27,0.64,0.09,0.544 8 | 6,东华软件,2065,77,0.23,0.7,0.06,0.561 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月29日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,达安基因,2030,55,0.18,0.76,0.05,0.541 3 | 1,乐普医疗,300003,30,0.1,0.83,0.07,0.526 4 | 2,山东赫达,2810,40,0.25,0.7,0.05,0.557 5 | 3,海格通信,2465,50,0.12,0.72,0.16,0.493 6 | 4,万泰生物,603392,68,0.25,0.63,0.12,0.546 7 | 5,广联达,2410,41,0.17,0.54,0.29,0.466 8 | 6,天铁股份,300587,54,0.19,0.61,0.2,0.504 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年07月30日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,大族激光,2008,82,0.3,0.6,0.1,0.556 3 | 1,普利特,2324,70,0.21,0.7,0.09,0.549 4 | 2,艾迪药业,688488,86,0.17,0.72,0.1,0.548 5 | 3,天华超净,300390,60,0.23,0.7,0.07,0.555 6 | 4,同和药业,300636,59,0.2,0.75,0.05,0.546 7 | 5,乐普医疗,300003,67,0.22,0.72,0.06,0.555 8 | 6,科华恒盛,2335,51,0.16,0.73,0.12,0.537 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月02日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,中集集团,39,28,0.18,0.71,0.11,0.503 3 | 1,福田汽车,600166,43,0.16,0.63,0.21,0.462 4 | 2,南玻A,12,65,0.17,0.69,0.14,0.484 5 | 3,天际股份,2759,108,0.19,0.67,0.14,0.512 6 | 4,嘉友国际,603871,115,0.07,0.86,0.07,0.479 7 | 5,富祥药业,300497,50,0.2,0.76,0.04,0.549 8 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月03日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,北汽蓝谷,600733,76,0.13,0.78,0.09,0.503 3 | 1,南山铝业,600219,95,0.21,0.65,0.14,0.522 4 | 2,星网宇达,2829,33,0.09,0.67,0.24,0.455 5 | 3,三特索道,2159,43,0.16,0.7,0.14,0.515 6 | 4,旭升股份,603305,69,0.23,0.7,0.07,0.55 7 | 5,华峰测控,688200,59,0.27,0.68,0.05,0.565 8 | 6,新华医疗,600587,92,0.21,0.78,0.01,0.572 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月04日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,量子生物,300149,27,0.15,0.81,0.04,0.541 3 | 1,世联行,2285,90,0.2,0.67,0.13,0.517 4 | 2,福莱特,601865,48,0.19,0.75,0.06,0.525 5 | 3,华光环能,600475,63,0.16,0.7,0.14,0.5 6 | 4,森霸传感,300701,71,0.14,0.77,0.08,0.542 7 | 5,依米康,300249,65,0.17,0.8,0.03,0.561 8 | 6,新泉股份,603179,65,0.15,0.77,0.08,0.539 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月05日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,博通集成,603068,116,0.22,0.69,0.09,0.541 3 | 1,华北制药,600812,78,0.13,0.73,0.14,0.49 4 | 2,芯能科技,603105,52,0.27,0.63,0.1,0.547 5 | 3,拓尔思,300229,66,0.24,0.73,0.03,0.567 6 | 4,瑞玛工业,2976,95,0.08,0.78,0.14,0.498 7 | 5,正邦科技,2157,61,0.25,0.7,0.05,0.545 8 | 6,江苏吴中,600200,84,0.2,0.68,0.12,0.54 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月06日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,三鑫医疗,300453,52,0.08,0.79,0.13,0.489 3 | 1,康泰生物,300601,93,0.18,0.69,0.13,0.531 4 | 2,中核钛白,2145,36,0.25,0.72,0.03,0.538 5 | 3,三人行,605168,55,0.33,0.62,0.05,0.573 6 | 4,长江证券,783,34,0.12,0.79,0.09,0.524 7 | 5,龙大肉食,2726,42,0.33,0.64,0.02,0.557 8 | 6,恺英网络,2517,50,0.22,0.5,0.28,0.477 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月09日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,健康元,600380,44,0.14,0.7,0.16,0.501 3 | 1,晶方科技,603005,66,0.23,0.68,0.09,0.552 4 | 2,万泰生物,603392,40,0.2,0.72,0.07,0.508 5 | 3,石基信息,2153,60,0.1,0.73,0.17,0.488 6 | 4,东方生物,688298,82,0.2,0.71,0.1,0.524 7 | 5,艾迪药业,688488,57,0.18,0.7,0.12,0.531 8 | 6,江南化工,2226,103,0.15,0.71,0.15,0.49 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月10日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,锦浪科技,300763,61,0.21,0.77,0.02,0.539 3 | 1,龙建股份,600853,80,0.1,0.86,0.04,0.517 4 | 2,杭萧钢构,600477,81,0.19,0.74,0.07,0.542 5 | 3,黑猫股份,2068,66,0.21,0.68,0.11,0.528 6 | 4,江南高纤,600527,62,0.21,0.73,0.06,0.538 7 | 5,陕西煤业,601225,68,0.07,0.74,0.19,0.473 8 | 6,正平股份,603843,48,0.12,0.83,0.04,0.513 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月11日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,海利生物,603718,79,0.25,0.67,0.08,0.561 3 | 1,吉电股份,875,52,0.15,0.79,0.06,0.534 4 | 2,东北制药,597,43,0.09,0.79,0.12,0.481 5 | 3,湖北广电,665,62,0.13,0.76,0.11,0.495 6 | 4,华林证券,2945,33,0.18,0.79,0.03,0.546 7 | 5,金新农,2548,71,0.1,0.76,0.14,0.473 8 | 6,雅戈尔,600177,63,0.05,0.81,0.14,0.457 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月12日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,华新水泥,600801,85,0.16,0.67,0.16,0.506 3 | 1,东杰智能,300486,55,0.31,0.64,0.05,0.586 4 | 2,恒力石化,600346,81,0.2,0.79,0.01,0.559 5 | 3,天原集团,2386,70,0.33,0.53,0.14,0.557 6 | 4,上海凯宝,300039,38,0.13,0.74,0.13,0.497 7 | 5,嘉元科技,688388,60,0.23,0.68,0.08,0.549 8 | 6,中国电研,688128,59,0.2,0.71,0.08,0.541 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月13日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,华统股份,2840,67,0.22,0.69,0.09,0.545 3 | 1,华正新材,603186,53,0.21,0.74,0.06,0.55 4 | 2,中鼎股份,887,25,0.12,0.76,0.12,0.5 5 | 3,红相股份,300427,84,0.25,0.67,0.08,0.561 6 | 4,交控科技,688015,40,0.12,0.8,0.07,0.52 7 | 5,德赛西威,2920,48,0.25,0.73,0.02,0.59 8 | 6,秦安股份,603758,81,0.16,0.7,0.14,0.52 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月16日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,厦门港务,905,85,0.07,0.79,0.14,0.488 3 | 1,奇安信,688561,97,0.21,0.72,0.07,0.559 4 | 2,哈高科,600095,84,0.23,0.69,0.08,0.543 5 | 3,宝莱特,300246,70,0.09,0.86,0.06,0.512 6 | 4,冀中能源,937,124,0.06,0.84,0.1,0.493 7 | 5,上海新阳,300236,53,0.19,0.74,0.08,0.517 8 | 6,金新农,2548,64,0.22,0.7,0.08,0.533 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月17日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,秦安股份,603758,85,0.13,0.73,0.14,0.498 3 | 1,丝路视觉,300556,55,0.18,0.71,0.11,0.53 4 | 2,金域医学,603882,84,0.15,0.75,0.1,0.52 5 | 3,星光农机,603789,96,0.19,0.73,0.08,0.538 6 | 4,四会富仕,300852,71,0.17,0.76,0.07,0.535 7 | 5,深高速,600548,90,0.2,0.77,0.03,0.568 8 | 6,深康佳A,16,102,0.24,0.7,0.07,0.563 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月18日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,春秋电子,603890,54,0.33,0.61,0.06,0.581 3 | 1,津滨发展,897,99,0.23,0.6,0.17,0.545 4 | 2,金山办公,688111,80,0.25,0.71,0.04,0.547 5 | 3,迪安诊断,300244,58,0.16,0.79,0.05,0.532 6 | 4,天邦股份,2124,57,0.19,0.75,0.05,0.536 7 | 5,三花智控,2050,56,0.12,0.68,0.2,0.482 8 | 6,漫步者,2351,34,0.18,0.74,0.09,0.527 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月19日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,长春高新,661,29,0.14,0.76,0.1,0.524 3 | 1,温氏股份,300498,53,0.17,0.72,0.11,0.507 4 | 2,宝信软件,600845,59,0.25,0.69,0.05,0.567 5 | 3,深康佳A,16,77,0.21,0.71,0.08,0.545 6 | 4,傲农生物,603363,37,0.19,0.65,0.16,0.516 7 | 5,天味食品,603317,57,0.19,0.68,0.12,0.533 8 | 6,金洲管道,2443,65,0.22,0.68,0.11,0.539 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月20日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,理邦仪器,300206,39,0.21,0.69,0.1,0.544 3 | 1,恒润股份,603985,62,0.23,0.76,0.02,0.562 4 | 2,万泰生物,603392,88,0.24,0.69,0.07,0.548 5 | 3,海尔生物,688139,28,0.21,0.75,0.04,0.564 6 | 4,安道麦A,553,52,0.08,0.79,0.13,0.48 7 | 5,华电国际,600027,44,0.09,0.84,0.07,0.509 8 | 6,双塔食品,2481,34,0.09,0.85,0.06,0.517 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月23日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,明德生物,2932,53,0.13,0.81,0.06,0.523 3 | 1,韵达股份,2120,55,0.18,0.64,0.18,0.502 4 | 2,昊志机电,300503,81,0.2,0.77,0.04,0.552 5 | 3,中钨高新,657,72,0.22,0.71,0.07,0.538 6 | 4,宏川智慧,2930,86,0.21,0.74,0.05,0.552 7 | 5,科思股份,300856,70,0.17,0.8,0.03,0.533 8 | 6,爱乐达,300696,51,0.27,0.71,0.02,0.575 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月24日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,迈得医疗,688310,46,0.17,0.76,0.07,0.534 3 | 1,上机数控,603185,69,0.19,0.64,0.17,0.495 4 | 2,雅本化学,300261,89,0.17,0.79,0.04,0.55 5 | 3,宏大爆破,2683,47,0.32,0.62,0.06,0.567 6 | 4,大族激光,2008,61,0.23,0.67,0.1,0.542 7 | 5,柳钢股份,601003,94,0.2,0.73,0.06,0.544 8 | 6,久日新材,688199,95,0.17,0.72,0.12,0.524 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月25日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,道恩股份,2838,58,0.19,0.74,0.07,0.529 3 | 1,天宜上佳,688033,69,0.14,0.64,0.22,0.466 4 | 2,东方电缆,603606,66,0.14,0.77,0.09,0.52 5 | 3,紫金矿业,601899,90,0.22,0.7,0.08,0.53 6 | 4,东材科技,601208,64,0.25,0.7,0.05,0.559 7 | 5,孚能科技,688567,94,0.19,0.7,0.11,0.537 8 | 6,大北农,2385,50,0.24,0.68,0.08,0.536 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月26日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,传音控股,688036,78,0.22,0.69,0.09,0.54 3 | 1,大北农,2385,55,0.29,0.67,0.04,0.57 4 | 2,万华化学,600309,62,0.27,0.69,0.03,0.548 5 | 3,艾德生物,300685,89,0.25,0.69,0.07,0.567 6 | 4,东方明珠,600637,37,0.16,0.73,0.11,0.517 7 | 5,上海建工,600170,133,0.12,0.83,0.05,0.513 8 | 6,天合光能,688599,65,0.23,0.69,0.08,0.542 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月27日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,中远海能,600026,54,0.15,0.76,0.09,0.52 3 | 1,上港集团,600018,77,0.18,0.73,0.09,0.52 4 | 2,招商轮船,601872,79,0.18,0.78,0.04,0.545 5 | 3,广生堂,300436,69,0.22,0.68,0.1,0.533 6 | 4,三钢闽光,2110,49,0.14,0.71,0.14,0.491 7 | 5,山东黄金,600547,27,0.26,0.7,0.04,0.574 8 | 6,复星医药,600196,82,0.22,0.66,0.12,0.542 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月30日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,城建发展,600266,40,0.12,0.68,0.2,0.465 3 | 1,新疆交建,2941,65,0.18,0.71,0.11,0.545 4 | 2,中航沈飞,600760,37,0.19,0.81,0.0,0.555 5 | 3,闻泰科技,600745,37,0.22,0.76,0.03,0.551 6 | 4,国统股份,2205,100,0.17,0.75,0.08,0.529 7 | 5,*ST盐湖,792,48,0.08,0.83,0.08,0.509 8 | 6,新希望,876,26,0.12,0.85,0.04,0.539 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年08月31日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,雅戈尔,600177,79,0.05,0.8,0.15,0.465 3 | 1,华东医药,963,74,0.3,0.65,0.05,0.578 4 | 2,成都燃气,603053,74,0.16,0.72,0.12,0.52 5 | 3,宝胜股份,600973,37,0.14,0.81,0.05,0.51 6 | 4,泛海控股,46,82,0.15,0.79,0.06,0.525 7 | 5,GQY视讯,300076,63,0.22,0.71,0.06,0.543 8 | 6,海特高新,2023,54,0.11,0.74,0.15,0.505 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月01日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,星云股份,300648,58,0.17,0.76,0.07,0.504 3 | 1,安奈儿,2875,62,0.27,0.69,0.03,0.592 4 | 2,海南橡胶,601118,62,0.16,0.74,0.1,0.514 5 | 3,圆通速递,600233,97,0.09,0.74,0.16,0.484 6 | 4,海南海药,566,68,0.21,0.74,0.06,0.537 7 | 5,三泰控股,2312,90,0.16,0.73,0.11,0.512 8 | 6,科融环境,300152,42,0.1,0.79,0.12,0.489 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月02日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,银都股份,603277,92,0.13,0.74,0.13,0.5 3 | 1,上海电力,600021,52,0.12,0.69,0.19,0.478 4 | 2,普洛药业,739,49,0.24,0.69,0.06,0.547 5 | 3,吉电股份,875,77,0.16,0.65,0.19,0.494 6 | 4,东方中科,2819,59,0.08,0.75,0.17,0.495 7 | 5,中创物流,603967,44,0.05,0.84,0.11,0.493 8 | 6,通合科技,300491,63,0.21,0.68,0.11,0.536 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月03日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,富瀚微,300613,90,0.21,0.72,0.07,0.543 3 | 1,紫晶存储,688086,53,0.23,0.6,0.17,0.529 4 | 2,天士力,600535,54,0.09,0.78,0.13,0.495 5 | 3,香山股份,2870,91,0.26,0.68,0.05,0.58 6 | 4,鄂尔多斯,600295,48,0.1,0.75,0.15,0.496 7 | 5,海南海药,566,88,0.19,0.69,0.11,0.528 8 | 6,贵州燃气,600903,28,0.07,0.75,0.18,0.472 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月06日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,正邦科技,2157,59,0.2,0.75,0.05,0.526 3 | 1,海立股份,600619,55,0.16,0.69,0.15,0.505 4 | 2,深圳华强,62,19,0.21,0.63,0.16,0.507 5 | 3,万丰奥威,2085,83,0.22,0.66,0.12,0.514 6 | 4,吉宏股份,2803,74,0.22,0.65,0.14,0.532 7 | 5,南大光电,300346,99,0.29,0.57,0.14,0.551 8 | 6,南都电源,300068,82,0.16,0.68,0.16,0.507 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月07日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,理工环科,2322,93,0.22,0.69,0.1,0.539 3 | 1,中核钛白,2145,56,0.21,0.73,0.05,0.535 4 | 2,豫园股份,600655,63,0.21,0.62,0.17,0.514 5 | 3,友阿股份,2277,84,0.21,0.65,0.13,0.531 6 | 4,信雅达,600571,89,0.17,0.57,0.26,0.481 7 | 5,华菱星马,600375,39,0.1,0.72,0.18,0.475 8 | 6,西藏药业,600211,49,0.22,0.71,0.06,0.53 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月08日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,双鹭药业,2038,100,0.16,0.74,0.1,0.525 3 | 1,华民股份,300345,81,0.16,0.74,0.1,0.524 4 | 2,浩云科技,300448,69,0.26,0.65,0.09,0.553 5 | 3,龙马环卫,603686,59,0.07,0.76,0.17,0.466 6 | 4,上峰水泥,672,66,0.24,0.7,0.06,0.58 7 | 5,东方电缆,603606,29,0.17,0.69,0.14,0.501 8 | 6,东珠生态,603359,60,0.18,0.68,0.13,0.517 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月09日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,万泰生物,603392,58,0.29,0.5,0.21,0.533 3 | 1,三棵树,603737,103,0.12,0.74,0.15,0.506 4 | 2,南方航空,600029,82,0.16,0.76,0.09,0.529 5 | 3,金浦钛业,545,42,0.24,0.69,0.07,0.532 6 | 4,安徽建工,600502,37,0.08,0.78,0.14,0.504 7 | 5,百利科技,603959,79,0.19,0.71,0.1,0.55 8 | 6,新通联,603022,62,0.13,0.71,0.16,0.51 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月10日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,榕基软件,2474,81,0.16,0.75,0.09,0.546 3 | 1,君实生物,688180,70,0.23,0.6,0.17,0.515 4 | 2,隆基股份,601012,100,0.14,0.66,0.2,0.473 5 | 3,厦门国贸,600755,72,0.12,0.78,0.1,0.502 6 | 4,甘李药业,603087,82,0.27,0.65,0.09,0.543 7 | 5,深圳新星,603978,47,0.15,0.64,0.21,0.468 8 | 6,中曼石油,603619,94,0.15,0.72,0.13,0.504 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月13日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,江苏神通,2438,67,0.09,0.79,0.12,0.494 3 | 1,凯撒旅业,796,94,0.16,0.65,0.19,0.497 4 | 2,中鼎股份,887,62,0.11,0.69,0.19,0.493 5 | 3,华瑞股份,300626,89,0.13,0.75,0.11,0.503 6 | 4,石基信息,2153,76,0.2,0.68,0.12,0.52 7 | 5,乐心医疗,300562,89,0.21,0.72,0.07,0.549 8 | 6,会畅通讯,300578,53,0.26,0.66,0.08,0.56 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月14日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,光莆股份,300632,83,0.19,0.78,0.02,0.566 3 | 1,天海防务,300008,89,0.24,0.64,0.12,0.522 4 | 2,恒久科技,2808,99,0.1,0.73,0.17,0.502 5 | 3,雅戈尔,600177,57,0.04,0.81,0.16,0.45 6 | 4,通化东宝,600867,48,0.19,0.77,0.04,0.539 7 | 5,汉缆股份,2498,68,0.21,0.68,0.12,0.541 8 | 6,九强生物,300406,67,0.16,0.73,0.1,0.527 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月15日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,九州通,600998,39,0.1,0.69,0.21,0.452 3 | 1,海能达,2583,70,0.16,0.69,0.16,0.487 4 | 2,海目星,688559,40,0.2,0.75,0.05,0.546 5 | 3,歌力思,603808,35,0.2,0.66,0.14,0.522 6 | 4,新产业,300832,64,0.16,0.78,0.06,0.53 7 | 5,露笑科技,2617,100,0.18,0.72,0.1,0.533 8 | 6,四会富仕,300852,54,0.22,0.65,0.13,0.524 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月16日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,达华智能,2512,79,0.2,0.67,0.13,0.529 3 | 1,陕天然气,2267,74,0.11,0.65,0.24,0.45 4 | 2,起步股份,603557,55,0.27,0.64,0.09,0.564 5 | 3,北新路桥,2307,39,0.1,0.77,0.13,0.499 6 | 4,皖通科技,2331,81,0.11,0.81,0.07,0.518 7 | 5,明阳智能,601615,69,0.17,0.62,0.2,0.499 8 | 6,华峰超纤,300180,63,0.14,0.78,0.08,0.524 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月17日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,润和软件,300339,55,0.27,0.62,0.11,0.573 3 | 1,深高速,600548,80,0.12,0.74,0.14,0.5 4 | 2,高盟新材,300200,46,0.17,0.65,0.17,0.514 5 | 3,华兰生物,2007,56,0.23,0.66,0.11,0.549 6 | 4,京新药业,2020,46,0.26,0.63,0.11,0.564 7 | 5,中颖电子,300327,72,0.18,0.67,0.15,0.529 8 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月20日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,友阿股份,2277,94,0.11,0.76,0.14,0.497 3 | 1,雅戈尔,600177,54,0.11,0.67,0.22,0.459 4 | 2,深天地A,23,86,0.15,0.77,0.08,0.515 5 | 3,爱施德,2416,75,0.19,0.68,0.13,0.525 6 | 4,海南发展,2163,53,0.25,0.66,0.09,0.536 7 | 5,铜陵有色,630,70,0.21,0.66,0.13,0.535 8 | 6,宜昌交运,2627,96,0.16,0.75,0.09,0.527 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月21日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,火炬电子,603678,44,0.16,0.57,0.27,0.473 3 | 1,明阳智能,601615,74,0.14,0.72,0.15,0.489 4 | 2,隆基股份,601012,86,0.21,0.59,0.2,0.512 5 | 3,江河集团,601886,43,0.12,0.77,0.12,0.504 6 | 4,新洋丰,902,57,0.21,0.7,0.09,0.544 7 | 5,华邦健康,2004,88,0.26,0.67,0.07,0.579 8 | 6,中科创达,300496,55,0.25,0.67,0.07,0.557 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月22日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,恺英网络,2517,90,0.19,0.62,0.19,0.5 3 | 1,科创信息,300730,64,0.14,0.73,0.12,0.515 4 | 2,冠豪高新,600433,93,0.16,0.68,0.16,0.497 5 | 3,清源股份,603628,89,0.06,0.75,0.19,0.456 6 | 4,深康佳A,16,97,0.15,0.79,0.05,0.546 7 | 5,隆基股份,601012,78,0.21,0.63,0.17,0.527 8 | 6,以岭药业,2603,57,0.12,0.75,0.12,0.498 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月23日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,爱建集团,600643,48,0.12,0.56,0.31,0.446 3 | 1,交控科技,688015,50,0.12,0.7,0.18,0.48 4 | 2,浙富控股,2266,67,0.04,0.85,0.1,0.477 5 | 3,一品红,300723,82,0.28,0.7,0.02,0.601 6 | 4,达安基因,2030,36,0.14,0.75,0.11,0.501 7 | 5,益佰制药,600594,74,0.2,0.66,0.14,0.544 8 | 6,金河生物,2688,48,0.17,0.77,0.06,0.53 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月24日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,万华化学,600309,73,0.26,0.67,0.07,0.541 3 | 1,蓝焰控股,968,93,0.11,0.67,0.23,0.474 4 | 2,兄弟科技,2562,63,0.14,0.78,0.08,0.512 5 | 3,*ST华映,536,42,0.14,0.81,0.05,0.525 6 | 4,瑞普生物,300119,88,0.24,0.73,0.03,0.56 7 | 5,众兴菌业,2772,37,0.11,0.78,0.11,0.503 8 | 6,新通联,603022,88,0.17,0.73,0.1,0.541 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月27日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,寿仙谷,603896,29,0.07,0.69,0.24,0.42 3 | 1,广电网络,600831,32,0.12,0.72,0.16,0.481 4 | 2,安靠智电,300617,56,0.18,0.77,0.05,0.542 5 | 3,德恩精工,300780,67,0.1,0.81,0.09,0.504 6 | 4,启迪环境,826,53,0.13,0.72,0.15,0.503 7 | 5,弘亚数控,2833,57,0.11,0.82,0.07,0.514 8 | 6,海思科,2653,48,0.19,0.73,0.08,0.531 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月28日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,长源东谷,603950,75,0.12,0.81,0.07,0.506 3 | 1,中天科技,600522,56,0.25,0.7,0.05,0.551 4 | 2,哈高科,600095,43,0.23,0.7,0.07,0.532 5 | 3,东北制药,597,42,0.1,0.79,0.12,0.489 6 | 4,富满电子,300671,60,0.15,0.75,0.1,0.518 7 | 5,赤峰黄金,600988,73,0.14,0.66,0.21,0.479 8 | 6,天合光能,688599,74,0.11,0.8,0.09,0.512 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年09月29日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,理邦仪器,300206,37,0.11,0.59,0.3,0.453 3 | 1,亚玛顿,2623,62,0.26,0.66,0.08,0.556 4 | 2,安徽建工,600502,52,0.13,0.69,0.17,0.489 5 | 3,隧道股份,600820,62,0.15,0.76,0.1,0.526 6 | 4,ST南风,737,87,0.14,0.78,0.08,0.506 7 | 5,益盛药业,2566,66,0.17,0.77,0.06,0.545 8 | 6,豫园股份,600655,61,0.25,0.61,0.15,0.536 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月08日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,旗天科技,300061,87,0.14,0.8,0.06,0.511 3 | 1,普利制药,300630,66,0.24,0.65,0.11,0.531 4 | 2,大洋电机,2249,59,0.12,0.71,0.17,0.474 5 | 3,*ST大港,2077,59,0.1,0.76,0.14,0.494 6 | 4,复星医药,600196,69,0.2,0.7,0.1,0.528 7 | 5,振德医疗,603301,75,0.11,0.8,0.09,0.516 8 | 6,宏达新材,2211,71,0.21,0.68,0.11,0.536 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月11日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,星网宇达,2829,83,0.17,0.73,0.1,0.526 3 | 1,龙蟒佰利,2601,50,0.24,0.68,0.08,0.533 4 | 2,圣湘生物,688289,32,0.28,0.66,0.06,0.552 5 | 3,三安光电,600703,76,0.26,0.54,0.2,0.524 6 | 4,三全食品,2216,82,0.13,0.8,0.06,0.525 7 | 5,科力远,600478,93,0.12,0.8,0.09,0.511 8 | 6,格林美,2340,86,0.22,0.65,0.13,0.545 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月12日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,正邦科技,2157,38,0.24,0.68,0.08,0.541 3 | 1,梅花生物,600873,39,0.23,0.51,0.26,0.487 4 | 2,道恩股份,2838,52,0.19,0.73,0.08,0.544 5 | 3,德利股份,605198,45,0.11,0.6,0.29,0.453 6 | 4,金科环境,688466,43,0.07,0.84,0.09,0.489 7 | 5,东方航空,600115,83,0.14,0.69,0.17,0.513 8 | 6,中集集团,39,62,0.15,0.79,0.06,0.52 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月13日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,中公教育,2607,83,0.2,0.71,0.08,0.546 3 | 1,利欧股份,2131,52,0.21,0.71,0.08,0.543 4 | 2,聚龙股份,300202,74,0.08,0.77,0.15,0.467 5 | 3,京汉股份,615,60,0.1,0.8,0.1,0.503 6 | 4,纽威股份,603699,83,0.19,0.69,0.12,0.527 7 | 5,新华都,2264,55,0.15,0.75,0.11,0.502 8 | 6,长盈精密,300115,63,0.17,0.79,0.03,0.551 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月14日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,顺钠股份,533,41,0.15,0.83,0.02,0.523 3 | 1,中国化学,601117,67,0.1,0.78,0.12,0.502 4 | 2,*ST商城,600306,59,0.14,0.8,0.07,0.498 5 | 3,昊华科技,600378,95,0.22,0.67,0.11,0.528 6 | 4,招商蛇口,1979,41,0.15,0.8,0.05,0.519 7 | 5,新产业,300832,37,0.16,0.78,0.05,0.511 8 | 6,长安汽车,625,35,0.2,0.77,0.03,0.548 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月15日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,神力股份,603819,94,0.13,0.73,0.14,0.498 3 | 1,回天新材,300041,76,0.16,0.82,0.03,0.535 4 | 2,雅戈尔,600177,76,0.03,0.8,0.17,0.435 5 | 3,士兰微,600460,83,0.23,0.7,0.07,0.557 6 | 4,英搏尔,300681,30,0.13,0.7,0.17,0.502 7 | 5,天坛生物,600161,90,0.14,0.68,0.18,0.492 8 | 6,京投发展,600683,63,0.13,0.75,0.13,0.504 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月18日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,通宇通讯,2792,49,0.1,0.76,0.14,0.486 3 | 1,协鑫能科,2015,85,0.21,0.69,0.09,0.542 4 | 2,景峰医药,908,92,0.22,0.68,0.1,0.543 5 | 3,富春股份,300299,45,0.18,0.67,0.16,0.49 6 | 4,三鑫医疗,300453,49,0.2,0.71,0.08,0.554 7 | 5,洋河股份,2304,49,0.24,0.71,0.04,0.593 8 | 6,金徽酒,603919,55,0.15,0.73,0.13,0.509 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月19日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,中来股份,300393,80,0.25,0.72,0.03,0.574 3 | 1,宏达新材,2211,55,0.24,0.73,0.04,0.58 4 | 2,大北农,2385,73,0.23,0.67,0.1,0.557 5 | 3,广和通,300638,39,0.26,0.67,0.08,0.545 6 | 4,新洋丰,902,36,0.14,0.81,0.06,0.531 7 | 5,*ST辉丰,2496,72,0.1,0.82,0.08,0.513 8 | 6,太极实业,600667,53,0.19,0.7,0.11,0.522 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月20日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,神火股份,933,44,0.09,0.75,0.16,0.452 3 | 1,光峰科技,688007,65,0.32,0.51,0.17,0.554 4 | 2,顺络电子,2138,57,0.21,0.72,0.07,0.547 5 | 3,和佳医疗,300273,59,0.1,0.8,0.1,0.489 6 | 4,安利股份,300218,89,0.13,0.78,0.09,0.517 7 | 5,鹏辉能源,300438,24,0.12,0.62,0.25,0.468 8 | 6,哈工智能,584,74,0.09,0.82,0.08,0.505 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月21日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,众兴菌业,2772,47,0.15,0.72,0.13,0.505 3 | 1,明阳智能,601615,52,0.08,0.77,0.15,0.463 4 | 2,祥鑫科技,2965,60,0.15,0.68,0.17,0.501 5 | 3,巨星科技,2444,67,0.25,0.64,0.1,0.548 6 | 4,汉嘉设计,300746,57,0.11,0.75,0.14,0.48 7 | 5,汇顶科技,603160,38,0.13,0.63,0.24,0.459 8 | 6,人福医药,600079,43,0.12,0.81,0.07,0.505 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月22日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,孚能科技,688567,63,0.16,0.73,0.11,0.517 3 | 1,飞天诚信,300386,71,0.18,0.63,0.18,0.498 4 | 2,深高速,600548,73,0.1,0.77,0.14,0.484 5 | 3,神思电子,300479,83,0.19,0.66,0.14,0.52 6 | 4,浙江交科,2061,51,0.08,0.78,0.14,0.479 7 | 5,深南电A,37,80,0.23,0.74,0.04,0.565 8 | 6,金诚信,603979,62,0.1,0.77,0.13,0.495 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月25日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,青山纸业,600103,37,0.14,0.65,0.22,0.453 3 | 1,奥特维,688516,78,0.22,0.73,0.05,0.569 4 | 2,人福医药,600079,56,0.14,0.77,0.09,0.511 5 | 3,康泰医学,300869,46,0.17,0.74,0.09,0.516 6 | 4,圣达生物,603079,65,0.22,0.77,0.02,0.569 7 | 5,丰山集团,603810,43,0.19,0.77,0.05,0.545 8 | 6,名臣健康,2919,61,0.11,0.82,0.07,0.519 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月26日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,海大集团,2311,48,0.1,0.62,0.27,0.43 3 | 1,赢合科技,300457,64,0.22,0.64,0.14,0.52 4 | 2,联赢激光,688518,38,0.18,0.71,0.11,0.511 5 | 3,东安动力,600178,33,0.12,0.82,0.06,0.503 6 | 4,理邦仪器,300206,39,0.13,0.77,0.1,0.497 7 | 5,新强联,300850,61,0.16,0.77,0.07,0.534 8 | 6,杭齿前进,601177,33,0.12,0.82,0.06,0.512 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月27日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,三夫户外,2780,50,0.18,0.7,0.12,0.508 3 | 1,理邦仪器,300206,68,0.25,0.65,0.1,0.549 4 | 2,万华化学,600309,73,0.27,0.7,0.03,0.542 5 | 3,安纳达,2136,58,0.16,0.81,0.03,0.513 6 | 4,中集集团,39,85,0.22,0.72,0.06,0.542 7 | 5,正邦科技,2157,35,0.2,0.77,0.03,0.539 8 | 6,东方生物,688298,49,0.16,0.76,0.08,0.523 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月28日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,汇中股份,300371,52,0.17,0.65,0.17,0.49 3 | 1,中信博,688408,53,0.15,0.75,0.09,0.52 4 | 2,以岭药业,2603,76,0.32,0.62,0.07,0.587 5 | 3,威孚高科,581,71,0.23,0.76,0.01,0.552 6 | 4,中核钛白,2145,82,0.18,0.73,0.09,0.526 7 | 5,泉峰汽车,603982,50,0.24,0.66,0.1,0.546 8 | 6,孚能科技,688567,51,0.14,0.75,0.12,0.499 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年10月29日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,泰嘉股份,2843,49,0.16,0.59,0.24,0.47 3 | 1,圣湘生物,688289,69,0.14,0.8,0.06,0.526 4 | 2,惠云钛业,300891,47,0.26,0.68,0.06,0.548 5 | 3,彩虹股份,600707,53,0.15,0.68,0.17,0.493 6 | 4,江淮汽车,600418,51,0.1,0.76,0.14,0.48 7 | 5,中远海能,600026,75,0.19,0.75,0.07,0.536 8 | 6,方大炭素,600516,71,0.15,0.68,0.17,0.483 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月01日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,木林森,2745,37,0.19,0.7,0.11,0.503 3 | 1,亚世光电,2952,33,0.15,0.7,0.15,0.498 4 | 2,梦网集团,2123,91,0.21,0.69,0.1,0.557 5 | 3,傲农生物,603363,41,0.22,0.61,0.17,0.493 6 | 4,万胜智能,300882,75,0.07,0.83,0.11,0.488 7 | 5,东华能源,2221,93,0.23,0.67,0.11,0.547 8 | 6,国际医学,516,68,0.25,0.66,0.09,0.558 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月02日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,国联股份,603613,36,0.28,0.56,0.17,0.538 3 | 1,雪人股份,2639,60,0.1,0.73,0.17,0.486 4 | 2,上海九百,600838,49,0.08,0.76,0.16,0.47 5 | 3,上机数控,603185,96,0.08,0.77,0.15,0.48 6 | 4,金城医药,300233,65,0.18,0.69,0.12,0.528 7 | 5,东方电子,682,65,0.11,0.82,0.08,0.504 8 | 6,聚灿光电,300708,86,0.24,0.66,0.09,0.55 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月03日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,昂立教育,600661,50,0.14,0.64,0.22,0.464 3 | 1,中装建设,2822,25,0.16,0.8,0.04,0.51 4 | 2,水晶光电,2273,66,0.12,0.83,0.05,0.522 5 | 3,健友股份,603707,48,0.19,0.69,0.12,0.511 6 | 4,科林电气,603050,45,0.07,0.87,0.07,0.496 7 | 5,华翔股份,603112,55,0.07,0.8,0.13,0.483 8 | 6,利欧股份,2131,74,0.2,0.68,0.12,0.528 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月04日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,歌尔股份,2241,63,0.25,0.68,0.06,0.578 3 | 1,中国重工,601989,78,0.1,0.79,0.1,0.494 4 | 2,星徽精密,300464,53,0.19,0.74,0.08,0.522 5 | 3,东阳光,600673,52,0.13,0.75,0.12,0.492 6 | 4,歌力思,603808,82,0.16,0.76,0.09,0.513 7 | 5,京运通,601908,85,0.07,0.81,0.12,0.493 8 | 6,万泰生物,603392,62,0.18,0.77,0.05,0.533 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月05日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,阿尔特,300825,69,0.17,0.75,0.07,0.525 3 | 1,华体科技,603679,98,0.19,0.68,0.12,0.529 4 | 2,合诚股份,603909,85,0.18,0.74,0.08,0.511 5 | 3,ST浩源,2700,73,0.11,0.74,0.15,0.486 6 | 4,金力泰,300225,36,0.11,0.75,0.14,0.484 7 | 5,九洲药业,603456,59,0.25,0.68,0.07,0.557 8 | 6,安泰科技,969,87,0.21,0.69,0.1,0.541 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月08日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,东软载波,300183,89,0.17,0.73,0.1,0.523 3 | 1,锐明技术,2970,70,0.2,0.63,0.17,0.53 4 | 2,中欣氟材,2915,63,0.24,0.71,0.05,0.558 5 | 3,南新制药,688189,99,0.23,0.66,0.11,0.541 6 | 4,富森美,2818,98,0.21,0.67,0.11,0.552 7 | 5,华宇软件,300271,72,0.31,0.64,0.06,0.589 8 | 6,恺英网络,2517,86,0.19,0.58,0.23,0.487 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月09日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,科华恒盛,2335,63,0.11,0.76,0.13,0.511 3 | 1,星星科技,300256,57,0.11,0.81,0.09,0.5 4 | 2,美盛文化,2699,75,0.19,0.71,0.11,0.531 5 | 3,人福医药,600079,77,0.18,0.69,0.13,0.537 6 | 4,新疆交建,2941,51,0.12,0.69,0.2,0.484 7 | 5,唐人神,2567,53,0.13,0.74,0.13,0.494 8 | 6,乐普医疗,300003,71,0.15,0.73,0.11,0.511 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月10日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,先导智能,300450,83,0.11,0.78,0.11,0.506 3 | 1,德宏股份,603701,46,0.13,0.76,0.11,0.504 4 | 2,兴蓉环境,598,83,0.08,0.83,0.08,0.513 5 | 3,粤水电,2060,62,0.23,0.71,0.06,0.544 6 | 4,赛诺医疗,688108,74,0.19,0.72,0.09,0.546 7 | 5,久远银海,2777,75,0.29,0.63,0.08,0.551 8 | 6,杰瑞股份,2353,28,0.21,0.64,0.14,0.517 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月11日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,中芯国际,688981,35,0.2,0.71,0.09,0.545 3 | 1,*ST科陆,2121,86,0.07,0.84,0.09,0.502 4 | 2,特一药业,2728,61,0.2,0.66,0.15,0.512 5 | 3,深康佳A,16,96,0.21,0.68,0.11,0.538 6 | 4,拓维信息,2261,92,0.17,0.59,0.24,0.498 7 | 5,深天地A,23,46,0.11,0.72,0.17,0.479 8 | 6,健帆生物,300529,90,0.24,0.68,0.08,0.556 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月12日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,平治信息,300571,73,0.18,0.67,0.15,0.513 3 | 1,中科信息,300678,54,0.15,0.7,0.15,0.516 4 | 2,格林美,2340,84,0.23,0.64,0.13,0.523 5 | 3,海格通信,2465,39,0.15,0.72,0.13,0.498 6 | 4,安正时尚,603839,50,0.14,0.58,0.28,0.458 7 | 5,诚志股份,990,72,0.19,0.69,0.11,0.533 8 | 6,航民股份,600987,80,0.09,0.78,0.14,0.474 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月15日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,亚玛顿,2623,63,0.1,0.76,0.14,0.478 3 | 1,润建股份,2929,44,0.14,0.77,0.09,0.51 4 | 2,世纪华通,2602,75,0.28,0.65,0.07,0.566 5 | 3,复星医药,600196,60,0.23,0.67,0.1,0.541 6 | 4,上海天洋,603330,86,0.09,0.84,0.07,0.5 7 | 5,华铭智能,300462,53,0.15,0.77,0.08,0.522 8 | 6,长鸿高科,605008,83,0.19,0.7,0.11,0.526 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月16日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,赛诺医疗,688108,85,0.25,0.66,0.09,0.552 3 | 1,利欧股份,2131,59,0.19,0.71,0.1,0.532 4 | 2,粤水电,2060,57,0.16,0.77,0.07,0.533 5 | 3,海翔药业,2099,71,0.21,0.73,0.06,0.562 6 | 4,雅戈尔,600177,47,0.06,0.83,0.11,0.499 7 | 5,圣湘生物,688289,53,0.15,0.77,0.08,0.523 8 | 6,安利股份,300218,68,0.25,0.66,0.09,0.548 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月17日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,苏美达,600710,87,0.13,0.76,0.11,0.522 3 | 1,通威股份,600438,93,0.13,0.66,0.22,0.466 4 | 2,德展健康,813,82,0.17,0.76,0.07,0.528 5 | 3,博敏电子,603936,94,0.22,0.73,0.04,0.555 6 | 4,中核钛白,2145,87,0.23,0.67,0.1,0.546 7 | 5,惠程科技,2168,80,0.23,0.61,0.16,0.533 8 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月18日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,恒通股份,603223,50,0.14,0.74,0.12,0.519 3 | 1,爱婴室,603214,70,0.1,0.81,0.09,0.506 4 | 2,天智航,688277,25,0.12,0.8,0.08,0.538 5 | 3,万里扬,2434,59,0.22,0.64,0.14,0.538 6 | 4,双良节能,600481,32,0.12,0.78,0.09,0.506 7 | 5,新凤鸣,603225,58,0.12,0.79,0.09,0.516 8 | 6,中钢国际,928,94,0.12,0.72,0.16,0.484 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月19日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,南纺股份,600250,83,0.35,0.6,0.05,0.606 3 | 1,司太立,603520,57,0.12,0.75,0.12,0.528 4 | 2,航天动力,600343,73,0.12,0.77,0.11,0.509 5 | 3,卧龙地产,600173,66,0.03,0.77,0.2,0.445 6 | 4,天合光能,688599,39,0.1,0.64,0.26,0.456 7 | 5,上海电气,601727,35,0.0,0.83,0.17,0.467 8 | 6,广晟有色,600259,57,0.11,0.74,0.16,0.49 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月22日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,宁水集团,603700,45,0.16,0.6,0.24,0.485 3 | 1,大禹节水,300021,47,0.09,0.89,0.02,0.52 4 | 2,多喜爱,2761,43,0.23,0.67,0.09,0.545 5 | 3,芯海科技,688595,58,0.12,0.84,0.03,0.509 6 | 4,太阳能,591,47,0.21,0.72,0.06,0.539 7 | 5,超图软件,300036,42,0.24,0.64,0.12,0.523 8 | 6,浦东建设,600284,29,0.07,0.86,0.07,0.485 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月23日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,中信建投,601066,86,0.1,0.74,0.15,0.489 3 | 1,苏宁易购,2024,97,0.23,0.67,0.1,0.561 4 | 2,回天新材,300041,46,0.22,0.7,0.09,0.55 5 | 3,海格通信,2465,53,0.23,0.64,0.13,0.549 6 | 4,安纳达,2136,43,0.09,0.88,0.02,0.548 7 | 5,电子城,600658,39,0.28,0.69,0.03,0.577 8 | 6,太阳纸业,2078,72,0.21,0.69,0.1,0.532 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月24日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,家家悦,603708,49,0.14,0.65,0.2,0.468 3 | 1,靖远煤电,552,41,0.1,0.63,0.27,0.449 4 | 2,百傲化学,603360,46,0.15,0.61,0.24,0.462 5 | 3,大秦铁路,601006,44,0.16,0.64,0.2,0.474 6 | 4,江淮汽车,600418,102,0.17,0.65,0.19,0.493 7 | 5,中化国际,600500,52,0.19,0.77,0.04,0.555 8 | 6,芯海科技,688595,23,0.17,0.78,0.04,0.54 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月25日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,天音控股,829,61,0.18,0.77,0.05,0.535 3 | 1,穗恒运A,531,46,0.22,0.67,0.11,0.509 4 | 2,锦泓集团,603518,49,0.12,0.63,0.24,0.454 5 | 3,宏大爆破,2683,68,0.16,0.78,0.06,0.547 6 | 4,今飞凯达,2863,48,0.21,0.6,0.19,0.514 7 | 5,科伦药业,2422,67,0.19,0.73,0.07,0.529 8 | 6,云海金属,2182,97,0.29,0.67,0.04,0.573 9 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月26日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,上汽集团,600104,99,0.21,0.71,0.08,0.55 3 | 1,东华软件,2065,55,0.15,0.8,0.05,0.529 4 | 2,均胜电子,600699,75,0.24,0.69,0.07,0.562 5 | 3,西藏天路,600326,32,0.09,0.84,0.06,0.484 6 | 4,华东医药,963,62,0.18,0.71,0.11,0.528 7 | 5,人福医药,600079,44,0.16,0.77,0.07,0.535 8 | -------------------------------------------------------------------------------- /股票消息面分析/analysis_result/2020年11月29日.csv: -------------------------------------------------------------------------------- 1 | ,股票名称,股票代码,总分词数量,正面占比,中立占比,负面占比,平均分 2 | 0,上机数控,603185,84,0.1,0.73,0.18,0.465 3 | 1,金字火腿,2515,80,0.19,0.71,0.1,0.524 4 | 2,康尼机电,603111,83,0.16,0.66,0.18,0.484 5 | 3,太阳电缆,2300,45,0.2,0.67,0.13,0.531 6 | 4,彩虹股份,600707,83,0.17,0.72,0.11,0.521 7 | 5,宁波建工,601789,42,0.17,0.64,0.19,0.488 8 | 6,英唐智控,300131,94,0.22,0.65,0.13,0.541 9 | -------------------------------------------------------------------------------- /股票消息面分析/backtest.py: -------------------------------------------------------------------------------- 1 | import tushare as ts 2 | import pandas as pd 3 | import numpy as np 4 | import time 5 | import draw_line 6 | 7 | fund = 0; # 初始资金 8 | remaining = 0; # 头寸 9 | holding = 0; # 持有资金 10 | trading_tax = 0; # 交易费率 11 | hold_stocks = {}; # 持仓股票 12 | 13 | date_list = [] # 股票交易日 14 | fund_list = [] # 总资产情况 15 | rate_list = [] # 持有期收益率 16 | buydate_list = ['2020-04-01', '2020-05-06', '2020-06-01', '2020-07-01', '2020-08-03', '2020-09-01', '2020-10-09', '2020-11-02'] # 买入交易日 17 | selldate_list = ['2020-04-30', '2020-05-29', '2020-06-30', '2020-07-31', '2020-08-31', '2020-09-30', '2020-10-30', '2020-11-27']# 卖出交易日 18 | 19 | info_table = pd.DataFrame(columns=['股票代码','买入价格','卖出时间','卖出价格','收益率']) 20 | row = 0 21 | 22 | buyprice = {} # 股票代码——买入价 23 | 24 | # 设定初始化资金和交易费用 25 | def setting(inputfund, tax): 26 | global fund, trading_tax, remaining 27 | 28 | fund = inputfund 29 | remaining = inputfund 30 | trading_tax = tax 31 | 32 | # 持仓股票变化 33 | def holding_stock_exchange(stockcode, number, dir): 34 | global hold_stocks 35 | 36 | if stockcode not in hold_stocks: 37 | hold_stocks[stockcode] = 0 38 | 39 | if dir == 'b': 40 | hold_stocks[stockcode] += number 41 | else: 42 | hold_stocks[stockcode] -= number 43 | 44 | # 记录买入价 45 | def insert_into_buyprice(stockcode, price): 46 | global buyprice 47 | 48 | buyprice[stockcode] = price 49 | 50 | # 买入流程 51 | def buy(stockcode, date, number, price): 52 | global remaining, holding, hold_stocks 53 | basicCost = price * number 54 | additonalCharge = basicCost * trading_tax 55 | allCost = basicCost + additonalCharge 56 | if (remaining > allCost): 57 | remaining -= allCost 58 | holding += basicCost 59 | holding_stock_exchange(stockcode, number, 'b') 60 | insert_into_buyprice(stockcode, price) 61 | print("买入了代码为" + str(stockcode) + '的股票，价格为：' + str(price) + '，股数为：' + str(number) + '，可用头寸为：' + str(round(remaining, 2))) 62 | # print('持仓情况为：', hold_stocks) 63 | else: 64 | print('头寸不足，无法交易') 65 | 66 | # 卖出流程 67 | def sell(stockcode, date, number, price): 68 | global remaining, holding, hold_stocks, info_table, row, buyprice 69 | 70 | basicFund = price * number 71 | charge = basicFund * trading_tax 72 | computedFund = basicFund - charge 73 | 74 | remaining += computedFund 75 | holding -= basicFund 76 | holding_stock_exchange(stockcode, number, 's') 77 | 78 | alist = [] 79 | alist.append(str(stockcode)) 80 | alist.append(buyprice[stockcode]) 81 | alist.append(date) 82 | alist.append(price) 83 | alist.append(round((price - buyprice[stockcode]) / buyprice[stockcode], 3)) 84 | 85 | info_table.loc[row] = alist 86 | row += 1 87 | 88 | print("卖出了代码为" + str(stockcode) + '的股票，价格为：' + str(price) + '，股数为：' + str(number) + '，可用头寸为：' + str(round(remaining, 2))) 89 | # print('持仓情况为：', hold_stocks) 90 | 91 | # 交易流程 92 | def trading(stockcode, date, number, price, dir): 93 | if number == 0: 94 | return 95 | 96 | if dir == 'b': 97 | buy(stockcode, date, number, price) 98 | elif dir == 's': 99 | sell(stockcode, date, number, price) 100 | else: 101 | print('input "dir" error !') 102 | 103 | # 记录每日资金情况 104 | def compute_allfund(date): 105 | global hold_stocks, remaining, fund_list 106 | 107 | total = 0 108 | for key in hold_stocks.keys(): 109 | if hold_stocks[key] != 0: 110 | tmp = ts.get_hist_data(key, start=date, end=date) 111 | tmp = tmp.reset_index(drop=False) 112 | try: 113 | total += tmp['close'].iloc[0] * hold_stocks[key] 114 | except: 115 | total += buyprice[key] * hold_stocks[key] 116 | total += remaining 117 | fund_list.append(round(total, 2)) 118 | rate_list.append(round((total - 50000 ) / 50000, 3)) 119 | print('总资产情况为：', total) 120 | 121 | # 主函数入口 122 | def main_proc(): 123 | global date_list, hold_stocks, buyprice 124 | tmp_data = ts.get_hist_data('000001', start='2020-04-01', end='2020-11-27') 125 | tmp_data = tmp_data.reset_index(drop=False) 126 | for i in range(0, len(tmp_data)): 127 | date_list.append(tmp_data['date'].iloc[len(tmp_data) - i - 1]) 128 | print('交易日期为：', date_list) 129 | 130 | # 读入交易文件 131 | df = pd.read_excel('各月股票队列.xlsx') 132 | 133 | for date in date_list: 134 | if date in buydate_list: # 买入日 135 | print(date, '有买入交易') 136 | for i in range(0, len(df)): 137 | if date == str(df['交易时间'].iloc[i])[:10]: 138 | stockcode = str(df['股票代码'].iloc[i]) 139 | while len(stockcode) < 6: 140 | stockcode = '0' + stockcode 141 | tmp = ts.get_hist_data(stockcode, start=date, end=date) 142 | tmp = tmp.reset_index(drop=False) 143 | trading(stockcode, date, 100, tmp['close'].iloc[0], 'b') 144 | compute_allfund(date) 145 | elif date in selldate_list: # 卖出日 146 | print(date, '有卖出交易') 147 | for key in hold_stocks.keys(): 148 | if hold_stocks[key] == 0: 149 | continue; 150 | tmp = ts.get_hist_data(key, start=date, end=date) 151 | tmp = tmp.reset_index(drop=False) 152 | try: 153 | trading(key, date, hold_stocks[key], tmp['close'].iloc[0], 's') # 交易日停市 154 | except: 155 | print(key, '日期：', date, '今日停市') 156 | compute_allfund(date) 157 | else: 158 | total = 0 159 | for key in hold_stocks.keys(): 160 | if hold_stocks[key] != 0: 161 | tmp = ts.get_hist_data(key, start=date, end=date) 162 | tmp = tmp.reset_index(drop=False) 163 | try: 164 | if ( tmp['close'].iloc[0] - buyprice[key] ) / buyprice[key] <= -0.05: # 0.05止损 165 | trading(key, date, hold_stocks[key], tmp['close'].iloc[0], 's') 166 | print('止损！', date, '股票代码：', key) 167 | else: 168 | total += tmp['close'].iloc[0] * hold_stocks[key] 169 | except: 170 | print(key, '日期：', date, '今日停市') 171 | print(date, '持仓资金为：', round(total, 2)) 172 | compute_allfund(date) 173 | 174 | if __name__ == '__main__': 175 | setting(50000, 0.003) 176 | main_proc() 177 | # draw_line.draw(date_list, fund_list, '每日资金情况图') 178 | draw_line.draw(date_list, rate_list, '持有期收益率情况图') 179 | info_table.to_csv('个股持有情况分析.csv', encoding='gb18030') 180 | -------------------------------------------------------------------------------- /股票消息面分析/draw_line.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import os 3 | import tushare as ts 4 | from pyecharts.charts import Line 5 | from pyecharts.charts import Grid 6 | import pyecharts.options as opts 7 | 8 | def draw(datelist, pricelist, title): 9 | min_value = min(pricelist) 10 | max_value = max(pricelist) 11 | 12 | line = ( 13 | Line(init_opts=opts.InitOpts( 14 | width='1800px', 15 | height='800px', 16 | js_host="./", 17 | )) 18 | .set_global_opts( 19 | title_opts=opts.TitleOpts( 20 | title=title, 21 | # subtitle='股票价格走势' 22 | ), 23 | legend_opts=opts.LegendOpts( 24 | is_show=True, 25 | pos_top=10, 26 | pos_left="center", 27 | item_width=30, 28 | item_height=15, 29 | textstyle_opts=opts.TextStyleOpts( 30 | font_family='Microsoft Yahei', 31 | font_size=14, 32 | font_style='oblique' 33 | ) 34 | ), 35 | tooltip_opts=opts.TooltipOpts( 36 | trigger="axis", 37 | axis_pointer_type="cross", 38 | background_color="rgba(245, 245, 245, 0.8)", 39 | border_width=1, 40 | border_color="#ccc", 41 | textstyle_opts=opts.TextStyleOpts(color="#000"), 42 | ), 43 | xaxis_opts=opts.AxisOpts( 44 | # type_="time", 45 | name='日期', 46 | split_number=10, 47 | name_gap=35, 48 | axispointer_opts=opts.AxisPointerOpts(is_show=True), 49 | name_textstyle_opts=opts.TextStyleOpts( 50 | font_size=16, 51 | font_family='Microsoft Yahei' 52 | ) 53 | ), 54 | yaxis_opts=opts.AxisOpts( 55 | type_="value", 56 | # name='价格', 57 | min_=min_value, 58 | max_=max_value, 59 | split_number=4, 60 | axispointer_opts=opts.AxisPointerOpts(is_show=True), 61 | name_textstyle_opts=opts.TextStyleOpts( 62 | font_size=16, 63 | font_family='Microsoft Yahei' 64 | ), 65 | axistick_opts=opts.AxisTickOpts(is_show=True), 66 | splitline_opts=opts.SplitLineOpts(is_show=True), 67 | splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)) 68 | ), 69 | axispointer_opts=opts.AxisPointerOpts( 70 | is_show=True, 71 | link=[{"xAxisIndex": "all"}], 72 | label=opts.LabelOpts(background_color="#777"), 73 | ), 74 | datazoom_opts=[ 75 | opts.DataZoomOpts( 76 | is_show=False, 77 | type_="inside", 78 | # xaxis_index=[0, 1], 79 | range_start=30, 80 | range_end=70, 81 | ), 82 | opts.DataZoomOpts( 83 | is_show=True, 84 | # xaxis_index=[0, 1], 85 | type_="slider", 86 | pos_top="96%", 87 | range_start=38, 88 | range_end=70, 89 | ), 90 | ], 91 | ) 92 | .add_xaxis(xaxis_data=datelist) 93 | .add_yaxis(series_name="走势情况", 94 | is_selected=True, 95 | y_axis=pricelist, 96 | label_opts=opts.LabelOpts(is_show=False), 97 | markpoint_opts=opts.MarkPointOpts( 98 | data=[ 99 | opts.MarkPointItem(type_="max", name="最大值"), 100 | opts.MarkPointItem(type_="min", name="最小值"), 101 | opts.MarkPointItem(type_="average", name="平均值") 102 | ] 103 | ) 104 | ) 105 | .render(title + '.html') 106 | ) 107 | -------------------------------------------------------------------------------- /股票消息面分析/echarts.min.js: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/moyuweiqing/A-stock-prediction-algorithm-based-on-machine-learning/f6a1a4f5e305b59950a5b94180067a2bb265e235/股票消息面分析/echarts.min.js -------------------------------------------------------------------------------- /股票消息面分析/getsina_message.py: -------------------------------------------------------------------------------- 1 | from bs4 import BeautifulSoup as bs 2 | import pandas as pd 3 | import requests, re 4 | import lxml 5 | import time 6 | import os 7 | 8 | headers = { 9 | 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36', 10 | 'Host': 'finance.sina.com.cn' 11 | } 12 | 13 | def savemessage(df, name, code, message, row): # 信息处理 14 | alist = [] 15 | alist.append(name) 16 | alist.append(code) 17 | alist.append(message) 18 | df.loc[row] = alist 19 | 20 | return df 21 | 22 | def getnameandcode(message): 23 | code = re.findall('company(.*?)nc', str(message)) 24 | name = re.findall('target="_blank">(.*?)<', str(message)) 25 | 26 | return code[0][3:9], name[0] 27 | 28 | def getdaymessage(url): # 涨停板早知道具体内容信息 29 | info_table = pd.DataFrame(columns=['股票名称', '股票代码', '公告信息']) 30 | 31 | res = requests.get(url=url, headers=headers) 32 | res.encoding = 'utf-8' 33 | html_text = bs(res.text, 'lxml') 34 | ps = html_text.find_all('p') 35 | 36 | news_link = [] 37 | date = str(html_text.find('span', class_ = 'date').text)[:11] 38 | 39 | row = 1 40 | for i in range(0, len(ps)): 41 | if 'strong' in str(ps[i])[:20] and 'span' in str(ps[i])[:25]: # 硬核找符合条件的新闻信息 42 | code, name = getnameandcode(str(ps[i])) 43 | message = ps[i+1].text 44 | info_table = savemessage(info_table, name, code, message, row) 45 | row += 1 46 | 47 | info_table.to_csv('sina_message/' + date + '.csv', encoding='gb18030') 48 | if len(info_table) == 0: 49 | print(date, 'error!') 50 | 51 | print('完成了', date) 52 | 53 | def if_zaozhidao(html_text): 54 | link_list = [] 55 | date_list = [] 56 | 57 | html_text = bs(html_text, 'lxml') 58 | uls = html_text.find_all('ul', class_ = 'list_009') 59 | 60 | for ul in uls: 61 | messages = ul.find_all('a') 62 | for message in messages: 63 | if '涨停板早知道' in str(message): 64 | link_list.append(re.findall('href="(.*?)"', str(message))[0]) 65 | 66 | for i in range(0, len(link_list)): 67 | getdaymessage(link_list[i]) 68 | time.sleep(2) 69 | pass 70 | 71 | if __name__ == '__main__': 72 | urlhead = 'https://finance.sina.com.cn/roll/index.d.html?cid=56588&page=' 73 | for page in range(1, 22): 74 | url = urlhead + str(page) 75 | res = requests.get(url=url, headers=headers) 76 | res.encoding = 'utf-8' 77 | if_zaozhidao(res.text) 78 | print(' 完成了', page, '页') 79 | time.sleep(2) -------------------------------------------------------------------------------- /股票消息面分析/sentimental_analysis.py: -------------------------------------------------------------------------------- 1 | from snownlp import SnowNLP 2 | import pandas as pd 3 | import numpy as np 4 | import os 5 | import matplotlib.pyplot as plt 6 | import jieba 7 | 8 | def word_cut(word): # 使用jieba库进行分词处理 9 | cut_word_list = '' 10 | cut_words = jieba.cut(word) 11 | for cut_word in cut_words: 12 | cut_word_list += cut_word 13 | cut_word_list += '，' 14 | return cut_word_list 15 | 16 | def build_sentimental_analysis(file_name): 17 | info_table = pd.DataFrame(columns=['股票名称', '股票代码', '总分词数量', '正面占比', '中立占比', '负面占比', '平均分']) 18 | 19 | excel = pd.read_csv('sina_message/' + file_name, encoding='gb18030') 20 | 21 | for stock in range(0, len(excel)): 22 | alist = [] 23 | alist.append(excel['股票名称'].iloc[stock]) 24 | alist.append(str(excel['股票代码'].iloc[stock])) 25 | 26 | s1 = excel['公告信息'].iloc[stock] 27 | 28 | # 切词 29 | word_list = word_cut(s1) 30 | 31 | #建立情感分析 32 | sn1 = SnowNLP(word_list) 33 | sentimentslist = [] 34 | pos_word_cnt = 0 # 初始化情感分组数量 35 | nag_word_cnt = 0 36 | mid_word_cnt = 0 37 | 38 | for i in sn1.sentences: 39 | j = SnowNLP(i) 40 | if j.sentiments >= 0.65: # 阈值设置，大于0.65为正向情感 41 | pos_word_cnt += 1 42 | elif j.sentiments < 0.65 and j.sentiments > 0.35: # 阈值设置，大于0.35小于0.65为中立情感 43 | mid_word_cnt += 1 44 | else: # 阈值设置，小于0.3为负向情感 45 | nag_word_cnt += 1 46 | 47 | sentimentslist.append(j.sentiments) 48 | alist.append(len(sentimentslist)) 49 | alist.append(round(pos_word_cnt / len(sentimentslist), 2)) 50 | alist.append(round(mid_word_cnt / len(sentimentslist), 2)) 51 | alist.append(round(nag_word_cnt / len(sentimentslist), 2)) 52 | alist.append(round(sum(sentimentslist) / len(sentimentslist), 3)) 53 | 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