├── .gitattributes ├── README.md ├── READMEmd ├── analysis ├── __pycache__ │ ├── cl_until.cpython-37.pyc │ └── until.cpython-37.pyc ├── factor_analysis_demo.ipynb └── until.py ├── data ├── quantaxis_data.ipynb ├── ricequant_data.ipynb ├── ths_data.ipynb └── wind_data.ipynb ├── factor_born ├── __pycache__ │ ├── factors_definition_1d.cpython-37.pyc │ └── mfm_operator.cpython-37.pyc ├── factor_born_and_in_library.ipynb └── mfm_operator.py └── factor_fig ├── alpha_cl_jiangtiantu_15A.png ├── alpha_cl_jiangtiantu_15B.png ├── alpha_cl_jiangtiantu_19A.png ├── alpha_cl_jiangtiantu_19B.png ├── alpha_cl_jiangtiantu_1A.png ├── alpha_cl_jiangtiantu_1B.png ├── alpha_cl_jiangtiantu_20A.png ├── alpha_cl_jiangtiantu_20B.png ├── alpha_cl_jiangtiantu_21A.png ├── alpha_cl_jiangtiantu_21B.png ├── alpha_cl_jiangtiantu_22A.png ├── alpha_cl_jiangtiantu_22B.png ├── alpha_cl_jiangtiantu_23A.png ├── alpha_cl_jiangtiantu_23B.png ├── alpha_cl_jiangtiantu_24A.png ├── alpha_cl_jiangtiantu_24B.png ├── alpha_cl_jiangtiantu_26A.png ├── alpha_cl_jiangtiantu_26B.png ├── alpha_cl_jiangtiantu_27A.png ├── alpha_cl_jiangtiantu_27B.png ├── alpha_cl_jiangtiantu_30A.png ├── alpha_cl_jiangtiantu_30B.png ├── alpha_cl_jiangtiantu_31A.png ├── alpha_cl_jiangtiantu_31B.png ├── alpha_cl_jiangtiantu_32A.png ├── alpha_cl_jiangtiantu_32B.png ├── alpha_cl_jiangtiantu_34A.png ├── alpha_cl_jiangtiantu_34B.png ├── alpha_cl_jiangtiantu_35A.png ├── alpha_cl_jiangtiantu_35B.png ├── alpha_cl_jiangtiantu_36A.png ├── alpha_cl_jiangtiantu_36B.png ├── alpha_cl_jiangtiantu_37A.png ├── alpha_cl_jiangtiantu_37B.png ├── alpha_cl_jiangtiantu_39A.png ├── alpha_cl_jiangtiantu_39B.png ├── alpha_cl_jiangtiantu_3A.png ├── alpha_cl_jiangtiantu_3B.png ├── alpha_cl_jiangtiantu_48A.png ├── alpha_cl_jiangtiantu_48B.png ├── alpha_cl_jiangtiantu_49A.png ├── alpha_cl_jiangtiantu_49B.png ├── alpha_cl_jiangtiantu_4A.png ├── alpha_cl_jiangtiantu_4B.png ├── alpha_cl_jiangtiantu_50A.png ├── alpha_cl_jiangtiantu_50B.png ├── alpha_cl_jiangtiantu_51A.png ├── alpha_cl_jiangtiantu_51B.png ├── alpha_cl_jiangtiantu_53A.png ├── alpha_cl_jiangtiantu_53B.png ├── alpha_cl_jiangtiantu_54A.png ├── alpha_cl_jiangtiantu_54B.png ├── alpha_cl_jiangtiantu_55A.png ├── alpha_cl_jiangtiantu_55B.png ├── alpha_cl_jiangtiantu_56A.png ├── alpha_cl_jiangtiantu_56B.png ├── alpha_cl_jiangtiantu_57A.png ├── alpha_cl_jiangtiantu_57B.png ├── alpha_cl_jiangtiantu_59A.png ├── alpha_cl_jiangtiantu_59B.png ├── alpha_cl_jiangtiantu_60A.png ├── alpha_cl_jiangtiantu_60B.png ├── alpha_cl_jiangtiantu_63A.png ├── alpha_cl_jiangtiantu_63B.png ├── alpha_cl_jiangtiantu_64A.png ├── alpha_cl_jiangtiantu_64B.png ├── alpha_cl_jiangtiantu_65A.png ├── alpha_cl_jiangtiantu_65B.png ├── alpha_cl_jiangtiantu_66A.png ├── alpha_cl_jiangtiantu_66B.png ├── alpha_cl_jiangtiantu_67A.png ├── alpha_cl_jiangtiantu_67B.png ├── alpha_cl_jiangtiantu_68A.png ├── alpha_cl_jiangtiantu_68B.png ├── alpha_cl_jiangtiantu_6A.png ├── alpha_cl_jiangtiantu_6B.png ├── alpha_cl_jiangtiantu_71A.png ├── alpha_cl_jiangtiantu_71B.png ├── alpha_cl_jiangtiantu_72A.png ├── alpha_cl_jiangtiantu_72B.png ├── alpha_cl_jiangtiantu_73A.png ├── alpha_cl_jiangtiantu_73B.png ├── alpha_cl_jiangtiantu_76A.png ├── alpha_cl_jiangtiantu_76B.png ├── alpha_cl_jiangtiantu_77A.png ├── alpha_cl_jiangtiantu_77B.png ├── alpha_cl_jiangtiantu_79A.png ├── alpha_cl_jiangtiantu_79B.png ├── alpha_cl_jiangtiantu_7A.png ├── alpha_cl_jiangtiantu_7B.png ├── alpha_cl_jiangtiantu_80A.png ├── alpha_cl_jiangtiantu_80B.png ├── alpha_cl_jiangtiantu_82A.png ├── alpha_cl_jiangtiantu_82B.png ├── alpha_cl_jiangtiantu_8A.png ├── alpha_cl_jiangtiantu_8B.png ├── alpha_cl_jiangtiantu_9A.png └── alpha_cl_jiangtiantu_9B.png /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Factorhub 半开源因子库+因子交换小组 2 | 3 | # factorhub 因子交换小组 4 | 5 | factorhub这个小组可能只适合做过多因子组合或因子挖掘的朋友。 6 | 7 | 我做了大半年的因子挖掘，然后总结出一个规律，没有好的数据，没有丰富的因子库是做不出好的超额的。所以我有个野一点的想法。我想建立一个小组，大家互相认可的话，可以交换下自己手上的因子，你们自己组队，沟通。或者我来介绍沟通都行，平等自愿。我微信debin16 8 | 9 | 我这边的话，开源了自己的因子框架，从数据库，到因子分析，都开源了（虽然是个小辣鸡）。但因子定义文件，我想以交换的方式互相交流。我愿意拿自己手上的3个因子换对方1个因子。每个因子的分层曲线，和多空收益我放在了factorfig 文件夹里。大家想要哪些因子可以挑。互相认可，我们就交换。 10 | 11 | 千粉大佬们愿意帮我推荐分享的话，我也愿意把因子文件直接送给您；有大佬愿意一起参与这个项也非常欢迎，互通有无。 12 | 13 | 14 | 15 | ## 因子框架 16 | 17 | 我代码很烂，水平也差。大佬们有意见随意提，我后面学习了就改进，也怕自己误人子弟。 18 | 19 | **1.data：**一个简陋的数据库，以hdf5文件保存。提供基础数据，用于因子计算，和回测计算收益。建议自己本地安装好quantaxis，即可自行下载数据。 20 | https://github.com/QUANTAXIS/QUANTAXIS 21 | 22 | **2.factor:** 计算好的因子数据，以pkl 文件保存，文件太大，我上传到了百度网盘 23 | 链接: https://pan.baidu.com/s/1HcRxXkHZ6ytyx6UThR5tcg 提取码: cust 24 | 25 | **3.analysis**：因子分析工具，目前只开源了两个功能，分层画图，和计算超额 26 | 27 | 具体流程是： 28 | 29 | ```python 30 | #读取数据 31 | datapath='E:\\Users\\Desktop\\factorhub\\data\\' 32 | factorpath='E:\\Users\\Desktop\\factorhub\\factors\\' 33 | 34 | data_hfq=pd.read_hdf(datapath+'data_hfq.h5','data_hfq') 35 | data_bfq=pd.read_hdf(datapath+'data_bfq.h5','data_bfq') 36 | ``` 37 | 38 | ```python 39 | `#对数据进行基本的处理` 40 | `Open = data_hfq["open"].unstack()` 41 | `Close = data_hfq["close"].unstack()` 42 | `High = data_hfq["high"].unstack()` 43 | `Low = data_hfq["low"].unstack()` 44 | `Vol = data_hfq["volume"].unstack()` 45 | `Amount = data_hfq["amount"].unstack()` 46 | `chg_1_d = Close.pct_change()` 47 | `stock_info=QA.QA_fetch_stock_info(code=Open.columns.to_list())` 48 | `sz = data_bfq['close'].unstack().mul(stock_info["zongguben"],axis=1)` 49 | `ltsz = data_bfq['close'].unstack().mul(stock_info["liutongguben"],axis=1)` 50 | `vwap = Amount/Vol/100` 51 | ``` 52 | 53 | ```python 54 | #去除涨跌停，去除停牌股 55 | tradeable=data_bfq['amount'].apply(lambda x :1 if x>0 else np.nan)*(data_bfq['high']-data_bfq['low']).apply(lambda x :1 if x!=0 else np.nan)*chg_1_d.stack().apply(lambda x :1 if x<0.100 else np.nan) 56 | tradeable=tradeable.unstack() 57 | ``` 58 | 59 | ```python 60 | #获取基准 61 | Benchmark=QA.QA_fetch_index_day_adv('000905',tradeable.index[0],tradeable.index[-1]).close 62 | Benchmark.index=(Benchmark.index).get_level_values(0) 63 | Benchmark=(Benchmark.pct_change(1)).shift(-1) 64 | megedata=pd.DataFrame() 65 | # megedata["period"]=Close.pct_change(1).shift(-1).stack()#以收盘价交易 66 | megedata["period"]=Open.pct_change().shift(-2).stack()#以开盘价交易 67 | 68 | 69 | ``` 70 | 71 | ```python 72 | #定义一个因子 73 | def factor_simple(): 74 | factor=-1*Close.pct_change(5) 75 | return factor 76 | test_factor=factor_simple() 77 | ``` 78 | 79 | ```python 80 | #分层画图 81 | test_factor=test_factor.replace([np.inf, -np.inf], np.nan) 82 | clean_factor_data=megedata 83 | input_factor= test_factor*tradeable 84 | input_factor=input_factor.stack() 85 | clean_factor_data["factor"]=input_factor 86 | clean_factor_data=clean_factor_data.dropna() 87 | 88 | clean_factor_data["factor_quantile"]=clean_factor_data["factor"].groupby(level=0).apply(lambda x :((pd.qcut(x.rank(), 10, labels=False,duplicates='drop') + 1))) 89 | df_factor_quantile=clean_factor_data.reset_index().groupby(['date','factor_quantile'])["period"].mean().unstack().cumsum() 90 | df_factor_quantile.plot(figsize=(16,9),title="test_factor") 91 | ``` 92 | 93 | img 94 | 95 | ```python 96 | #不算复利,计算对冲收益 97 | group_num=10 98 | commision_fee=0.0 99 | 100 | test_factor=test_factor.replace([np.inf, -np.inf], np.nan) 101 | clean_factor_data=megedata 102 | input_factor= test_factor*tradeable 103 | input_factor=input_factor.stack() 104 | 105 | 106 | clean_factor_data["factor"]=input_factor 107 | clean_factor_data=clean_factor_data.dropna() 108 | clean_factor_data["factor_quantile"]=clean_factor_data["factor"].groupby(level=0).apply(lambda x :((pd.qcut(x.rank(), 10, labels=False,duplicates='drop') + 1))) 109 | 110 | long_portfolio_data = clean_factor_data[clean_factor_data['factor_quantile'] == group_num] 111 | short_portfolio_data = clean_factor_data[clean_factor_data['factor_quantile'] == 1] 112 | 113 | long_portfolio_rate_of_return = long_portfolio_data['period'].mean(level=0) - commision_fee 114 | short_portfolio_rate_of_return = short_portfolio_data['period'].mean(level=0) - commision_fee 115 | hedged_rate_of_return = long_portfolio_rate_of_return - short_portfolio_rate_of_return - 2 * commision_fee 116 | hedged_with_Benchmark_return = long_portfolio_rate_of_return - Benchmark - commision_fee 117 | 118 | long_cumulative_return = 1+long_portfolio_rate_of_return.cumsum() 119 | short_cumulative_return = 1+short_portfolio_rate_of_return.cumsum() 120 | hedged_cumulative_return = 1+hedged_rate_of_return.cumsum() 121 | Benchmark_cumulative_return = 1+Benchmark.cumsum() 122 | hedged_with_Benchmark_cumulative_return = 1+hedged_with_Benchmark_return.cumsum() 123 | 124 | Return = pd.concat([long_cumulative_return,short_cumulative_return, hedged_cumulative_return, Benchmark_cumulative_return,hedged_with_Benchmark_cumulative_return], axis=1) 125 | Return.columns = ['long','short','long-short','benchmark','long-benchmark'] 126 | 127 | Return=Return.dropna() 128 | Return.plot(figsize=(16,9),title="test—factor") 129 | ``` 130 | 131 | **** 132 | 133 | 基本上你自己定义一个因子，之后就直接开始研究了。我这个框架是学习alphalens 写的，因为alphalens 太慢了，所以，就自己实现了，要快些。没有做任何封装，理解起来容易些。虽然代码懒，但大概的步骤是没有错的，所有曲线没有计算手续费，没有计算对冲成本。 134 | 135 | ![img](https://pic2.zhimg.com/v2-dab49293ebec08c02e7f1971504fa779_b.png) 136 | 137 | **4.factor_born:** 因子自动生成算法，基于deap，暂未开源 138 | **5.factor_fig:** 因子分层曲线和超额收益曲线（全部按照单利计算） 139 | **6.mfm_operator：**一个算子文件，定义了些常见的算子 140 | 141 | ## And More ? 142 | 143 | 欢迎加入quanthub 社区 144 | 145 | 146 | 147 | **https://zhuanlan.zhihu.com/p/148087260** -------------------------------------------------------------------------------- /READMEmd: -------------------------------------------------------------------------------- 1 | Factorhub 半开源因子库+因子交换小组 2 | 3 | # factorhub 因子交换小组 4 | 5 | factorhub这个小组可能只适合做过多因子组合或因子挖掘的朋友。 6 | 7 | 我做了大半年的因子挖掘，然后总结出一个规律，没有好的数据，没有丰富的因子库是做不出好的超额的。所以我有个野一点的想法。我想建立一个小组，大家互相认可的话，可以交换下自己手上的因子，你们自己组队，沟通。或者我来介绍沟通都行，平等自愿。我微信debin16 8 | 9 | 我这边的话，开源了自己的因子框架，从数据库，到因子分析，都开源了（虽然是个小辣鸡）。但因子定义文件，我想以交换的方式互相交流。我愿意拿自己手上的3个因子换对方1个因子。每个因子的分层曲线，和多空收益我放在了factorfig 文件夹里。大家想要哪些因子可以挑。互相认可，我们就交换。 10 | 11 | 千粉大佬们愿意帮我推荐分享的话，我也愿意把因子文件直接送给您；有大佬愿意一起参与这个项也非常欢迎，互通有无。 12 | 13 | 14 | 15 | ## 因子框架 16 | 17 | 我代码很烂，水平也差。大佬们有意见随意提，我后面学习了就改进，也怕自己误人子弟。 18 | 19 | **1.data：**一个简陋的数据库，以hdf5文件保存。提供基础数据，用于因子计算，和回测计算收益。建议自己本地安装好quantaxis，即可自行下载数据。 20 | https://github.com/QUANTAXIS/QUANTAXIS 21 | 22 | **2.factor:** 计算好的因子数据，以pkl 文件保存，文件太大，我上传到了百度网盘 23 | 链接: https://pan.baidu.com/s/1HcRxXkHZ6ytyx6UThR5tcg 提取码: cust 24 | 25 | **3.analysis**：因子分析工具，目前只开源了两个功能，分层画图，和计算超额 26 | 27 | 具体流程是： 28 | 29 | ```python 30 | #读取数据 31 | datapath='E:\\Users\\Desktop\\factorhub\\data\\' 32 | factorpath='E:\\Users\\Desktop\\factorhub\\factors\\' 33 | 34 | data_hfq=pd.read_hdf(datapath+'data_hfq.h5','data_hfq') 35 | data_bfq=pd.read_hdf(datapath+'data_bfq.h5','data_bfq') 36 | ``` 37 | 38 | ```python 39 | `#对数据进行基本的处理` 40 | `Open = data_hfq["open"].unstack()` 41 | `Close = data_hfq["close"].unstack()` 42 | `High = data_hfq["high"].unstack()` 43 | `Low = data_hfq["low"].unstack()` 44 | `Vol = data_hfq["volume"].unstack()` 45 | `Amount = data_hfq["amount"].unstack()` 46 | `chg_1_d = Close.pct_change()` 47 | `stock_info=QA.QA_fetch_stock_info(code=Open.columns.to_list())` 48 | `sz = data_bfq['close'].unstack().mul(stock_info["zongguben"],axis=1)` 49 | `ltsz = data_bfq['close'].unstack().mul(stock_info["liutongguben"],axis=1)` 50 | `vwap = Amount/Vol/100` 51 | ``` 52 | 53 | ```python 54 | #去除涨跌停，去除停牌股 55 | tradeable=data_bfq['amount'].apply(lambda x :1 if x>0 else np.nan)*(data_bfq['high']-data_bfq['low']).apply(lambda x :1 if x!=0 else np.nan)*chg_1_d.stack().apply(lambda x :1 if x<0.100 else np.nan) 56 | tradeable=tradeable.unstack() 57 | ``` 58 | 59 | ```python 60 | #获取基准 61 | Benchmark=QA.QA_fetch_index_day_adv('000905',tradeable.index[0],tradeable.index[-1]).close 62 | Benchmark.index=(Benchmark.index).get_level_values(0) 63 | Benchmark=(Benchmark.pct_change(1)).shift(-1) 64 | megedata=pd.DataFrame() 65 | # megedata["period"]=Close.pct_change(1).shift(-1).stack()#以收盘价交易 66 | megedata["period"]=Open.pct_change().shift(-2).stack()#以开盘价交易 67 | 68 | 69 | ``` 70 | 71 | ```python 72 | #定义一个因子 73 | def factor_simple(): 74 | factor=-1*Close.pct_change(5) 75 | return factor 76 | test_factor=factor_simple() 77 | ``` 78 | 79 | ```python 80 | #分层画图 81 | test_factor=test_factor.replace([np.inf, -np.inf], np.nan) 82 | clean_factor_data=megedata 83 | input_factor= test_factor*tradeable 84 | input_factor=input_factor.stack() 85 | clean_factor_data["factor"]=input_factor 86 | clean_factor_data=clean_factor_data.dropna() 87 | 88 | clean_factor_data["factor_quantile"]=clean_factor_data["factor"].groupby(level=0).apply(lambda x :((pd.qcut(x.rank(), 10, labels=False,duplicates='drop') + 1))) 89 | df_factor_quantile=clean_factor_data.reset_index().groupby(['date','factor_quantile'])["period"].mean().unstack().cumsum() 90 | df_factor_quantile.plot(figsize=(16,9),title="test_factor") 91 | ``` 92 | 93 | img 94 | 95 | ```python 96 | #不算复利,计算对冲收益 97 | group_num=10 98 | commision_fee=0.0 99 | 100 | test_factor=test_factor.replace([np.inf, -np.inf], np.nan) 101 | clean_factor_data=megedata 102 | input_factor= test_factor*tradeable 103 | input_factor=input_factor.stack() 104 | 105 | 106 | clean_factor_data["factor"]=input_factor 107 | clean_factor_data=clean_factor_data.dropna() 108 | clean_factor_data["factor_quantile"]=clean_factor_data["factor"].groupby(level=0).apply(lambda x :((pd.qcut(x.rank(), 10, labels=False,duplicates='drop') + 1))) 109 | 110 | long_portfolio_data = clean_factor_data[clean_factor_data['factor_quantile'] == group_num] 111 | short_portfolio_data = clean_factor_data[clean_factor_data['factor_quantile'] == 1] 112 | 113 | long_portfolio_rate_of_return = long_portfolio_data['period'].mean(level=0) - commision_fee 114 | short_portfolio_rate_of_return = short_portfolio_data['period'].mean(level=0) - commision_fee 115 | hedged_rate_of_return = long_portfolio_rate_of_return - short_portfolio_rate_of_return - 2 * commision_fee 116 | hedged_with_Benchmark_return = long_portfolio_rate_of_return - Benchmark - commision_fee 117 | 118 | long_cumulative_return = 1+long_portfolio_rate_of_return.cumsum() 119 | short_cumulative_return = 1+short_portfolio_rate_of_return.cumsum() 120 | hedged_cumulative_return = 1+hedged_rate_of_return.cumsum() 121 | Benchmark_cumulative_return = 1+Benchmark.cumsum() 122 | hedged_with_Benchmark_cumulative_return = 1+hedged_with_Benchmark_return.cumsum() 123 | 124 | Return = pd.concat([long_cumulative_return,short_cumulative_return, hedged_cumulative_return, Benchmark_cumulative_return,hedged_with_Benchmark_cumulative_return], axis=1) 125 | Return.columns = ['long','short','long-short','benchmark','long-benchmark'] 126 | 127 | Return=Return.dropna() 128 | Return.plot(figsize=(16,9),title="test—factor") 129 | ``` 130 | 131 | **** 132 | 133 | 基本上你自己定义一个因子，之后就直接开始研究了。我这个框架是学习alphalens 写的，因为alphalens 太慢了，所以，就自己实现了，要快些。没有做任何封装，理解起来容易些。虽然代码懒，但大概的步骤是没有错的，所有曲线没有计算手续费，没有计算对冲成本。 134 | 135 | ![img](https://pic2.zhimg.com/v2-dab49293ebec08c02e7f1971504fa779_b.png) 136 | 137 | **4.factor_born:** 因子自动生成算法，基于deap，暂未开源 138 | **5.factor_fig:** 因子分层曲线和超额收益曲线（全部按照单利计算） 139 | **6.mfm_operator：**一个算子文件，定义了些常见的算子 140 | 141 | ## And More ? 142 | 143 | 欢迎加入quanthub 社区 144 | 145 | 146 | 147 | **https://zhuanlan.zhihu.com/p/148087260** -------------------------------------------------------------------------------- /analysis/__pycache__/cl_until.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jiangtiantu/factorhub/0f30d9b301eff32cc252ab0d4b39cc9e99fbfab3/analysis/__pycache__/cl_until.cpython-37.pyc -------------------------------------------------------------------------------- /analysis/__pycache__/until.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jiangtiantu/factorhub/0f30d9b301eff32cc252ab0d4b39cc9e99fbfab3/analysis/__pycache__/until.cpython-37.pyc -------------------------------------------------------------------------------- /analysis/until.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | # import warnings; warnings.simplefilter('ignore') 4 | import os 5 | import matplotlib.pyplot as plt 6 | 7 | 8 | def backtest_1(test_factor,megedata,Benchmark,tradeable,group_num=10,commision_fee=0): 9 | #不算复利 10 | test_factor=test_factor.replace([np.inf, -np.inf], np.nan) 11 | clean_factor_data=megedata 12 | input_factor= test_factor*tradeable 13 | input_factor=input_factor.stack() 14 | 15 | clean_factor_data["factor"]=input_factor 16 | clean_factor_data=clean_factor_data.dropna() 17 | clean_factor_data["factor_quantile"]=clean_factor_data["factor"].groupby(level=0).apply(lambda x :((pd.qcut(x, 10, labels=False,duplicates='drop') + 1))) 18 | 19 | long_portfolio_data = clean_factor_data[clean_factor_data['factor_quantile'] == group_num] 20 | short_portfolio_data = clean_factor_data[clean_factor_data['factor_quantile'] == 1] 21 | 22 | long_portfolio_rate_of_return = long_portfolio_data['period'].mean(level=0) - commision_fee 23 | short_portfolio_rate_of_return = short_portfolio_data['period'].mean(level=0) - commision_fee 24 | hedged_rate_of_return = long_portfolio_rate_of_return - short_portfolio_rate_of_return - 2 * commision_fee 25 | hedged_with_Benchmark_return = long_portfolio_rate_of_return - Benchmark - commision_fee 26 | 27 | long_cumulative_return = 1+long_portfolio_rate_of_return.cumsum() 28 | short_cumulative_return = 1+short_portfolio_rate_of_return.cumsum() 29 | hedged_cumulative_return = 1+hedged_rate_of_return.cumsum() 30 | Benchmark_cumulative_return = 1+Benchmark.cumsum() 31 | hedged_with_Benchmark_cumulative_return = 1+hedged_with_Benchmark_return.cumsum() 32 | 33 | Return = pd.concat([long_cumulative_return,short_cumulative_return, hedged_cumulative_return, Benchmark_cumulative_return,hedged_with_Benchmark_cumulative_return], axis=1) 34 | Return.columns = ['long','short','long-short','benchmark','long-benchmark'] 35 | 36 | Return=Return.dropna() 37 | Return.plot(figsize=(16,9),title="test—factor") 38 | 39 | -------------------------------------------------------------------------------- /data/quantaxis_data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import QUANTAXIS as QA\n", 10 | "import numpy as np\n", 11 | "from datetime import timedelta ,datetime\n" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "stock_list=QA.QA_fetch_stock_list().index.tolist()" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 3, 26 | "metadata": { 27 | "tags": [] 28 | }, 29 | "outputs": [], 30 | "source": [ 31 | "torday=datetime.now()\n", 32 | "start_date=torday+timedelta(days=-360*2)\n", 33 | "data_bfq=QA.QA_fetch_stock_day_adv(stock_list,start=start_date,end=torday)\n", 34 | "data_hfq=data_bfq.to_hfq()" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 4, 40 | "metadata": {}, 41 | "outputs": [ 42 | { 43 | "output_type": "execute_result", 44 | "data": { 45 | "text/plain": "('E:\\\\desktop\\\\data\\\\data_hfq.h5', 'data_hfq')" 46 | }, 47 | "metadata": {}, 48 | "execution_count": 4 49 | } 50 | ], 51 | "source": [ 52 | "path='E:\\\\desktop\\\\data\\\\'\n", 53 | "data_bfq.to_hdf(path+\"data_bfq.h5\",\"data_bfq\")\n", 54 | "data_hfq.to_hdf(path+\"data_hfq.h5\",'data_hfq')" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 5, 60 | "metadata": {}, 61 | "outputs": [], 62 | "source": [ 63 | "# a=pd.read_hdf(\"C:\\\\Users\\\\clclcl\\\\Desktop\\\\data\\\\data_bfq.h5\",\"data_bfq\")\n", 64 | "# b=pd.read_hdf(\"C:\\\\Users\\\\clclcl\\\\Desktop\\\\data\\\\data_hfq.h5\",\"data_hfq\")\n", 65 | "# b[\"avg\"]=b[\"amount\"]/b[\"volume\"]/100" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 52, 71 | "metadata": {}, 72 | "outputs": [], 73 | "source": [] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 71, 78 | "metadata": {}, 79 | "outputs": [], 80 | "source": [] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": null, 85 | "metadata": {}, 86 | "outputs": [], 87 | "source": [] 88 | } 89 | ], 90 | "metadata": { 91 | "language_info": { 92 | "name": "python", 93 | "codemirror_mode": { 94 | "name": "ipython", 95 | "version": 3 96 | }, 97 | "version": "3.7.0-final" 98 | }, 99 | "orig_nbformat": 2, 100 | "file_extension": ".py", 101 | "mimetype": "text/x-python", 102 | "name": "python", 103 | "npconvert_exporter": "python", 104 | "pygments_lexer": "ipython3", 105 | "version": 3, 106 | "kernelspec": { 107 | "name": "python_defaultSpec_1595947655700", 108 | "display_name": "Python 3.7.0 64-bit ('base': conda)" 109 | } 110 | }, 111 | "nbformat": 4, 112 | "nbformat_minor": 2 113 | } -------------------------------------------------------------------------------- /data/ricequant_data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 2, 4 | "metadata": { 5 | "language_info": { 6 | "name": "python", 7 | "codemirror_mode": { 8 | "name": "ipython", 9 | "version": 3 10 | }, 11 | "version": "3.7.4-final" 12 | }, 13 | "orig_nbformat": 2, 14 | "file_extension": ".py", 15 | "mimetype": "text/x-python", 16 | "name": "python", 17 | "npconvert_exporter": "python", 18 | "pygments_lexer": "ipython3", 19 | "version": 3, 20 | "kernelspec": { 21 | "name": "python37464bitbasecondaf58f680e650245d6af9156a7f17ac0fa", 22 | "display_name": "Python 3.7.4 64-bit ('base': conda)" 23 | } 24 | }, 25 | "cells": [ 26 | { 27 | "cell_type": "code", 28 | "execution_count": 35, 29 | "metadata": {}, 30 | "outputs": [], 31 | "source": [ 32 | "import rqdatac as rq\n", 33 | "import datetime\n", 34 | "rq.init('18225006201', 'mkq6412459')" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 10, 40 | "metadata": {}, 41 | "outputs": [], 42 | "source": [ 43 | "all_instruments=rq.all_instruments(type=\"CS\")\n", 44 | "stock_list=all_instruments['order_book_id']" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 31, 50 | "metadata": {}, 51 | "outputs": [], 52 | "source": [ 53 | "today=datetime.date.today().strftime(\"%Y-%M-%d\")\n", 54 | "price_panel=rq.get_price(stock_list,start_date='2010-01-01',enddate='',adjust_type=\"post\")" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 32, 60 | "metadata": {}, 61 | "outputs": [], 62 | "source": [ 63 | "price_panel.to_pickle(\"price_panel.pkl\")" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 33, 69 | "metadata": {}, 70 | "outputs": [ 71 | { 72 | "output_type": "execute_result", 73 | "data": { 74 | "text/plain": "\nDimensions: 9 (items) x 59 (major_axis) x 3959 (minor_axis)\nItems axis: open to limit_up\nMajor_axis axis: 2010-01-04 00:00:00 to 2010-04-01 00:00:00\nMinor_axis axis: 300275.XSHE to 600386.XSHG" 75 | }, 76 | "metadata": {}, 77 | "execution_count": 33 78 | } 79 | ], 80 | "source": [ 81 | "price_panel" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 43, 87 | "metadata": {}, 88 | "outputs": [ 89 | { 90 | "output_type": "execute_result", 91 | "data": { 92 | "text/plain": "'2020-00-10'" 93 | }, 94 | "metadata": {}, 95 | "execution_count": 43 96 | } 97 | ], 98 | "source": [] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": null, 103 | "metadata": {}, 104 | "outputs": [], 105 | "source": [] 106 | } 107 | ] 108 | } -------------------------------------------------------------------------------- /data/ths_data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 23, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "output_type": "execute_result", 10 | "data": { 11 | "text/plain": "-201" 12 | }, 13 | "metadata": {}, 14 | "execution_count": 23 15 | } 16 | ], 17 | "source": [ 18 | "import pandas as pd\n", 19 | "from iFinDPy import *\n", 20 | "today=datetime.date.today().strftime(\"%Y-%M-%d\")\n", 21 | "THS_iFinDLogin('bgf003','122180')" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 31, 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "output_type": "execute_result", 31 | "data": { 32 | "text/plain": "0 000001.SZ\n1 000002.SZ\n2 000004.SZ\n3 000005.SZ\n4 000006.SZ\nName: THSCODE, dtype: object" 33 | }, 34 | "metadata": {}, 35 | "execution_count": 31 36 | } 37 | ], 38 | "source": [ 39 | "all_stock_list=THS_Trans2DataFrame(THS_DataPool('block','2020-05-22;001005010','thscode:Y,security_name:Y',True))['THSCODE']\n", 40 | "all_stock_list.head()" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 45, 46 | "metadata": {}, 47 | "outputs": [], 48 | "source": [ 49 | "factor_list=['ths_inflow_amt_stock','ths_outflow_amt_stock','ths_inflow_vol_stock','ths_outflow_vol_stock','ths_net_buy_amt_stock','ths_net_buy_vol_stock','ths_active_buy_amt_stock','ths_active_sell_amt_stock','ths_active_buy_vol_stock','ths_active_sell_vol_stock','ths_net_active_buy_amt_stock','ths_net_active_buy_vol_stock','ths_active_buy_large_vol_stock','ths_active_buy_large_amt_stock','ths_possitive_buy_large_vol_stock','ths_possitive_buy_large_amt_stock','ths_active_sell_large_vol_stock','ths_active_sell_large_amt_stock','ths_possitive_sell_large_vol_stock','ths_possitive_sell_large_amt_stock','ths_active_buy_main_vol_stock','ths_active_buy_main_amt_stock','ths_possitive_buy_main_vol_stock','ths_possitive_buy_main_amt_stock','ths_active_sell_main_vol_stock','ths_active_sell_main_amt_stock','ths_possitive_sell_main_vol_stock','ths_possitive_sell_main_amt_stock','ths_active_buy_middle_vol_stock','ths_active_buy_middle_amt_stock','ths_possitive_buy_middle_vol_stock','ths_possitive_buy_middle_amt_stock','ths_active_sell_middle_vol_stock','ths_active_sell_middle_amt_stock','ths_possitive_sell_middle_vol_stock','ths_possitive_sell_middle_amt_stock','ths_active_buy_small_vol_stock','ths_active_buy_small_amt_stock','ths_active_sellsmall_vol_stock','ths_active_sellsmall_amt_stock','ths_small_buy_vol_stock','ths_small_buy_amt_stock','ths_small_sell_vol_stock','ths_small_sell_amt_stock','ths_dde_stock','ths_dde_net_vol_stock','ths_net_infow_stock','ths_dde_5d_stock','ths_dde_10d_stock','ths_dde_20d_stock','ths_net_active_buy_amt_rate_stock','ths_net_active_buy_amt_ratio_stock','ths_net_active_buy_vol_rate_stock','ths_net_active_buy_vol_ratio_stock','ths_inflow_rate_stock','ths_main_net_inflow_amt_ratio_stock','ths_main_inflow_rate_stock','ths_main_net_inflow_vol_ratio_stock']" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 91, 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [ 58 | "# for factor in factor_list[-10:]:\n", 59 | "# df=THS_Trans2DataFrame(THS_DateSerial(all_stock_list.to_list(),factor,'','Days:Tradedays,Fill:Previous,Interval:D','2015-01-01','2020-05-22',True))\n", 60 | "# print(df.head())\n", 61 | "# df['thscode']=[i[:6]for i in df['thscode']]\n", 62 | "# df.set_index(['time','thscode'])[factor].unstack()\n", 63 | "# break\n", 64 | " \n" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 92, 70 | "metadata": {}, 71 | "outputs": [ 72 | { 73 | "output_type": "error", 74 | "ename": "JSONDecodeError", 75 | "evalue": "Expecting value: line 1 column 1 (char 0)", 76 | "traceback": [ 77 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 78 | "\u001b[1;31mJSONDecodeError\u001b[0m Traceback (most recent call last)", 79 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mstock\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mall_stock_list\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTHS_Trans2DataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mTHS_DateSerial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstock\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfactor_str\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'100'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Days:Tradedays,Fill:Previous,Interval:D'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'2020-01-01'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'2020-05-22'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 80 | "\u001b[1;32m~\\Desktop\\DataInterface_free_Windows_20200420\\DataInterface_free_Windows\\bin\\x64\\iFinDPy.py\u001b[0m in \u001b[0;36mTHS_Trans2DataFrame\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miFinD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecodeMethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 190\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 191\u001b[1;33m 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336\u001b[0m \"\"\"\n\u001b[1;32m--> 337\u001b[1;33m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 338\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 339\u001b[0m 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\u001b[0mJSONDecodeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Expecting value\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 356\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 84 | "\u001b[1;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)" 85 | ] 86 | } 87 | ], 88 | "source": [ 89 | "for factor in factor_list:\n", 90 | " data_sum_list=[]\n", 91 | " for stock in all_stock_list:\n", 92 | " df=THS_Trans2DataFrame(THS_DateSerial(stock,factor,'100','Days:Tradedays,Fill:Previous,Interval:D','2020-01-01','2020-05-22',True))\n", 93 | " data_sum_list.append(df)\n", 94 | " data_sum_df=pd.concat(data_sum_list)\n", 95 | " print(data_sum_df.head())\n", 96 | " break\n" 97 | ] 98 | }, 99 | { 100 | "cell_type": "code", 101 | "execution_count": 32, 102 | "metadata": {}, 103 | "outputs": [], 104 | "source": [ 105 | "price_panel.to_pickle(\"price_panel.pkl\")" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 33, 111 | "metadata": {}, 112 | "outputs": [ 113 | { 114 | "output_type": "execute_result", 115 | "data": { 116 | "text/plain": "\nDimensions: 9 (items) x 59 (major_axis) x 3959 (minor_axis)\nItems axis: open to limit_up\nMajor_axis axis: 2010-01-04 00:00:00 to 2010-04-01 00:00:00\nMinor_axis axis: 300275.XSHE to 600386.XSHG" 117 | }, 118 | "metadata": {}, 119 | "execution_count": 33 120 | } 121 | ], 122 | "source": [ 123 | "price_panel" 124 | ] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "execution_count": 43, 129 | "metadata": {}, 130 | "outputs": [ 131 | { 132 | "output_type": "execute_result", 133 | "data": { 134 | "text/plain": "'2020-00-10'" 135 | }, 136 | "metadata": {}, 137 | "execution_count": 43 138 | } 139 | ], 140 | "source": [] 141 | }, 142 | { 143 | "cell_type": "code", 144 | "execution_count": null, 145 | "metadata": {}, 146 | "outputs": [], 147 | "source": [] 148 | } 149 | ], 150 | "metadata": { 151 | "language_info": { 152 | "name": "python", 153 | "codemirror_mode": { 154 | "name": "ipython", 155 | "version": 3 156 | }, 157 | "version": "3.7.4-final" 158 | }, 159 | "orig_nbformat": 2, 160 | "file_extension": ".py", 161 | "mimetype": "text/x-python", 162 | "name": "python", 163 | "npconvert_exporter": "python", 164 | "pygments_lexer": "ipython3", 165 | "version": 3, 166 | "kernelspec": { 167 | "name": "python37464bitbasecondaf58f680e650245d6af9156a7f17ac0fa", 168 | "display_name": "Python 3.7.4 64-bit ('base': conda)" 169 | } 170 | }, 171 | "nbformat": 4, 172 | "nbformat_minor": 2 173 | } -------------------------------------------------------------------------------- /data/wind_data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 2, 4 | "metadata": { 5 | "language_info": { 6 | "name": "python", 7 | "codemirror_mode": { 8 | "name": "ipython", 9 | "version": 3 10 | }, 11 | "version": "3.7.1-final" 12 | }, 13 | "orig_nbformat": 2, 14 | "file_extension": ".py", 15 | "mimetype": "text/x-python", 16 | "name": "python", 17 | "npconvert_exporter": "python", 18 | "pygments_lexer": "ipython3", 19 | "version": 3, 20 | "kernelspec": { 21 | "name": "python37164bitvirtualenv939040a230e74cbaab3a77c2c7aff45a", 22 | "display_name": "Python 3.7.1 64-bit (virtualenv)" 23 | } 24 | }, 25 | "cells": [ 26 | { 27 | "cell_type": "code", 28 | "execution_count": 1, 29 | "metadata": {}, 30 | "outputs": [ 31 | { 32 | "name": "stdout", 33 | "output_type": "stream", 34 | "text": "Welcome to use Wind Quant API for Python (WindPy)!\n\nCOPYRIGHT (C) 2017 WIND INFORMATION CO., LTD. ALL RIGHTS RESERVED.\nIN NO CIRCUMSTANCE SHALL WIND BE RESPONSIBLE FOR ANY DAMAGES OR LOSSES CAUSED BY USING WIND QUANT API FOR Python.\n" 35 | } 36 | ], 37 | "source": [ 38 | "from WindPy import w\n", 39 | "w.start()\n", 40 | "import time\n", 41 | "import pandas" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": null, 47 | "metadata": {}, 48 | "outputs": [], 49 | "source": [ 50 | "print(last_trade_time)" 51 | ] 52 | } 53 | ] 54 | } -------------------------------------------------------------------------------- /factor_born/__pycache__/factors_definition_1d.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jiangtiantu/factorhub/0f30d9b301eff32cc252ab0d4b39cc9e99fbfab3/factor_born/__pycache__/factors_definition_1d.cpython-37.pyc -------------------------------------------------------------------------------- /factor_born/__pycache__/mfm_operator.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jiangtiantu/factorhub/0f30d9b301eff32cc252ab0d4b39cc9e99fbfab3/factor_born/__pycache__/mfm_operator.cpython-37.pyc -------------------------------------------------------------------------------- /factor_born/factor_born_and_in_library.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 3, 6 | "metadata": { 7 | "collapsed": false, 8 | "jupyter": { 9 | "outputs_hidden": false 10 | }, 11 | "pycharm": { 12 | "name": "#%%\n" 13 | }, 14 | "tags": [] 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "import numpy as np\n", 19 | "import pandas as pd\n", 20 | "import datetime\n", 21 | "import warnings; warnings.simplefilter('ignore')\n", 22 | "import QUANTAXIS as QA\n", 23 | "import os\n", 24 | "from mfm_operator import *\n", 25 | "import factors_definition_1d\n", 26 | "import scipy.stats as stats\n", 27 | "factor_path='E:\\\\Users\\\\Desktop\\\\factorhub\\\\factors'\n" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 4, 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [ 36 | "factor=factors_definition_1d.factors()\n", 37 | "function_str_list=[\"factor.\"+fac for fac in dir(factor) if fac[0:5]=='alpha']\n", 38 | "for function_str in function_str_list:\n", 39 | " try:\n", 40 | " eval(function_str+\"()\").to_pickle(factor_path+'\\\\'+function_str[7:]+'.pkl')\n", 41 | " except:\n", 42 | " print('error:' ,function_str)" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 15, 48 | "metadata": {}, 49 | "outputs": [], 50 | "source": [ 51 | "# sum_factors_list = [pd.read_pickle(factor_path+\"\\\\\"+i).rank(axis=1) for i in os.listdir(factor_path)]\n", 52 | "# sum_factors_df=pd.DataFrame()\n", 53 | "# for i in sum_factors_list:\n", 54 | "# sum_factors_df=sum_factors_df.add(i,fill_value=0)\n", 55 | "# sum_factors_df.to_pickle(\"sum_factors_1d.pkl\")" 56 | ] 57 | } 58 | ], 59 | "metadata": { 60 | "kernelspec": { 61 | "display_name": "Python 3", 62 | "language": "python", 63 | "name": "python3" 64 | }, 65 | "language_info": { 66 | "codemirror_mode": { 67 | "name": "ipython", 68 | "version": 3 69 | }, 70 | "file_extension": ".py", 71 | "mimetype": "text/x-python", 72 | "name": "python", 73 | "nbconvert_exporter": "python", 74 | "pygments_lexer": "ipython3", 75 | "version": "3.7.0-final" 76 | } 77 | }, 78 | "nbformat": 4, 79 | "nbformat_minor": 4 80 | } -------------------------------------------------------------------------------- /factor_born/mfm_operator.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | # import warnings; warnings.simplefilter('ignore') 4 | import os 5 | from scipy.stats import rankdata 6 | import talib as ta 7 | ##################一元函数######################## 8 | def log(x): 9 | return np.log(abs(x)) 10 | 11 | def abs(x): 12 | return np.abs(x) 13 | 14 | def sqrt(x): 15 | return np.sqrt(x) 16 | 17 | def rank(x): 18 | return x.rank(axis=1) 19 | 20 | def inv(x): 21 | return 1.0/x 22 | 23 | def percent(x): 24 | return x.div(x.sum(axis=1),axis=0) 25 | 26 | def diff(df, window=1): 27 | 28 | return df.diff(window) 29 | ##################一元函数######################## 30 | 31 | ##################二元函数######################## 32 | def delay(x,window): 33 | return x.shift(window) 34 | 35 | def ts_mean(x,window): 36 | return x.rolling(window).mean() 37 | 38 | def ts_std(x,window): 39 | return x.rolling(window).std() 40 | 41 | def ts_sum(x,window): 42 | return x.rolling(window).sum() 43 | 44 | # def ts_mul(x,window): 45 | # return x.rolling(window).max() 46 | 47 | def ts_min(x,window): 48 | return x.rolling(window).min() 49 | 50 | def ts_max(x,window): 51 | return x.rolling(window).max() 52 | 53 | def ts_min_ind(x,window): 54 | return x.rolling(window).apply(lambda w :np.argmin(w)) 55 | 56 | def ts_max_ind(x,window): 57 | return x.rolling(window).apply(lambda w :np.argmax(w)) 58 | 59 | def ts_rank(x,window): 60 | return x.rolling(window).apply(lambda w :rankdata(w)[-1]) 61 | 62 | def ts_pct_change(x,window): 63 | return x.pct_change(window).sum() 64 | ##################二元函数######################## 65 | 66 | ##################三元函数######################## 67 | def add(x,y): 68 | return np.add(x,y) 69 | def sub(x,y): 70 | return np.subtract(x,y) 71 | def mul(x,y): 72 | return np.multiply(x,y) 73 | def div(x,y): 74 | return np.divide(x,y) 75 | ##################三元函数######################## 76 | ##################元函数######################## 77 | def corr(x,y,window=5): 78 | return x.rolling(window).corr(y) 79 | def cov(x,y,window=5): 80 | return x.rolling(window).cov(y) 81 | ##################4元函数######################## 82 | def vwap(h,l,v): 83 | return div((v*(h+l)/2).cumsum() , v.cumsum()) 84 | 85 | #################TA_Lib############################# 86 | def EMA(x,window): 87 | return ta.EMA(x,window) 88 | def MACD(x, fastperiod=12, slowperiod=26, signalperiod=9): 89 | return ta.MACD(x,fastperiod=12, slowperiod=26, signalperiod=9) 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | -------------------------------------------------------------------------------- /factor_fig/alpha_cl_jiangtiantu_15A.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jiangtiantu/factorhub/0f30d9b301eff32cc252ab0d4b39cc9e99fbfab3/factor_fig/alpha_cl_jiangtiantu_15A.png -------------------------------------------------------------------------------- /factor_fig/alpha_cl_jiangtiantu_15B.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jiangtiantu/factorhub/0f30d9b301eff32cc252ab0d4b39cc9e99fbfab3/factor_fig/alpha_cl_jiangtiantu_15B.png 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