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开源软件名称:fooltrader开源软件地址:https://gitee.com/foolcage/fooltrader开源软件介绍:该项目已停止更新,请移步新项目https://github.com/zvtvz/zvt如果有人想继续该项目,只需要知道其核心点即可: 构建标准的数据schema,然后实现各种connector导入 你熟悉的系统 进行分析Read this in other languages: English. fooltrader:trade as a fool
fooltrader是一个利用大数据技术设计的量化分析交易系统,包括数据的抓取,清洗,结构化,计算,展示,回测和交易. 1. 能做什么1.1 自定义分析逻辑和视图
1.2 免费数据源和精心分类的统一apiapi输出结果具体字段含义请参考数据协议. A股数据In [1]:import fooltrader as ftIn [2]:ft.get_kdata('000778')#试一试#ft.get_kdata('300027',start_date='20170630',end_date='20170715')#ft.get_kdata('300027',start_date='20170630',end_date='20170715')timestamp code name low open close high volume turnover securityId ... mCap factor hfqClose hfqOpen hfqHigh hfqLow qfqClose qfqOpen qfqHigh qfqLowtimestamp ... 1997-06-06 1997-06-06 000778 新兴铸管 18.00 18.10 19.68 20.70 45335789 8.904533e+08 stock_sz_000778 ... 1.416960e+09 1.000 19.68000 18.10000 20.70000 18.00000 1.497375 1.377159 1.574983 1.3695501997-06-09 1997-06-09 000778 新兴铸管 18.00 20.00 18.51 20.44 11333248 2.148290e+08 stock_sz_000778 ... 1.332720e+09 1.000 18.51000 20.00000 20.44000 18.00000 1.408354 1.521723 1.555200 1.3695501997-06-10 1997-06-10 000778 新兴铸管 16.66 18.50 16.75 18.60 6641283 1.155679e+08 stock_sz_000778 ... 1.206000e+09 1.000 16.75000 18.50000 18.60000 16.66000 1.274443 1.407593 1.415202 1.2675951997-06-11 1997-06-11 000778 新兴铸管 15.90 16.60 17.35 17.40 5560642 9.365633e+07 stock_sz_000778 ... 1.249200e+09 1.000 17.35000 16.60000 17.40000 15.90000 1.320094 1.263030 1.323899 1.2097691997-06-12 1997-06-12 000778 新兴铸管 16.80 17.68 16.80 17.70 3022235 5.142033e+07 stock_sz_000778 ... 1.209600e+09 1.000 16.80000 17.68000 17.70000 16.80000 1.278247 1.345203 1.346724 1.278247 期货数据In [3]:ft.get_kdata('rb1601') timestamp code name low open close high volume turnover securityId preClose change changePct openInterest settlement preSettlement change1 changePct1timestamp 2015-01-16 20150116 rb1601 螺纹钢rb 2533.0 2545.0 2550.0 2568.0 96.0 244.468 future_shfe_rb1601 2518.0 32.0 0.012708 66.0 2546.0 2518.0 28.0 0.0111202015-01-19 20150119 rb1601 螺纹钢rb 2515.0 2534.0 2541.0 2558.0 486.0 1231.174 future_shfe_rb1601 2550.0 -5.0 -0.001961 212.0 2533.0 2546.0 -13.0 -0.0051062015-01-20 20150120 rb1601 螺纹钢rb 2521.0 2554.0 2529.0 2554.0 134.0 339.290 future_shfe_rb1601 2541.0 -4.0 -0.001574 286.0 2532.0 2533.0 -1.0 -0.0003952015-01-21 20150121 rb1601 螺纹钢rb 2516.0 2520.0 2516.0 2540.0 294.0 743.266 future_shfe_rb1601 2529.0 -16.0 -0.006327 410.0 2528.0 2532.0 -4.0 -0.0015802015-01-22 20150122 rb1601 螺纹钢rb 2515.0 2519.0 2521.0 2530.0 310.0 782.114 future_shfe_rb1601 2516.0 -7.0 -0.002782 576.0 2522.0 2528.0 -6.0 -0.002373 数字货币In [4]: ft.get_kdata('BTC-USD',exchange='kraken') timestamp code name low open close high volume securityId preClose change changePcttimestamp 2016-07-08 2016-07-08 BTC-USD BTC/USD 634.0 640.4 671.4 671.4 1651.592635 cryptocurrency_kraken_BTC-USD NaN NaN NaN2016-07-09 2016-07-09 BTC-USD BTC/USD 622.0 671.9 652.0 671.9 1908.295953 cryptocurrency_kraken_BTC-USD 671.4 -19.4 -0.0297552016-07-10 2016-07-10 BTC-USD BTC/USD 642.4 652.0 650.0 655.6 429.290787 cryptocurrency_kraken_BTC-USD 652.0 -2.0 -0.0030772016-07-11 2016-07-11 BTC-USD BTC/USD 645.3 652.5 650.7 663.3 814.157258 cryptocurrency_kraken_BTC-USD 650.0 0.7 0.0010762016-07-12 2016-07-12 BTC-USD BTC/USD 647.1 650.7 666.0 675.9 923.800268 cryptocurrency_kraken_BTC-USD 650.7 15.3 0.022973 tickIn [5]: for item in ft.get_ticks('000338'): ...: print(item) 基本面数据In [5]: ft.get_income_statement_items('300027',report_period='2017-06-30')#试一试#ft.get_balance_sheet_items('300027',,report_event_date='2017-01-01')#ft.get_cash_flow_statement_items('300027')Out[2]:{'EPS': 0.15, 'ManagingCosts': 257005115.85, 'accumulatedOtherComprehensiveIncome': 471486112.3, 'assetsDevaluation': -21647912.31, 'attributableToMinorityShareholders': 90255906.93, 'attributableToOwnersOfParentCompany': 381230205.37, 'businessTaxesAndSurcharges': 80033207.21, 'code': '300027', 'dilutedEPS': 0.15, 'disposalLossOnNonCurrentLiability': 281050.25, 'exchangeGains': 0.0, 'financingExpenses': 132202866.43, 'id': 'stock_sz_300027_20170630', 'incomeFromChangesInFairValue': 0.0, 'incomeTaxExpense': 111864455.56, 'investmentIncome': 541478955.17, 'investmentIncomeFromRelatedEnterpriseAndJointlyOperating': '45035770.67', 'minorityInterestIncome': 91203287.92, 'netProfit': 521516997.38, 'netProfitAttributedToParentCompanyOwner': 430313709.46, 'nonOperatingExpenditure': 13775609.35, 'nonOperatingIncome': 27864700.17, 'operatingCosts': 679308123.4, 'operatingProfit': 619292362.12, 'operatingRevenue': 1465863805.45, 'operatingTotalCosts': 1388050398.5, 'otherComprehensiveIncome': -50030885.08, 'reportDate': '2017-06-30', 'reportEventDate': '2017-08-29', 'securityId': 'stock_sz_300027', 'sellingExpenses': 261148997.92, 'totalProfits': 633381452.94} 财务报表的勾稽关系验证# 营业利润=营业收入-营业成本-营业税金及附加-销售费用-管理费用-财务费用-资产减值损失+公允价值变动收益(损失的话用减)+投资收益def check_operating_profit(security_item): income_statement_list = get_income_statement_items(security_item=security_item) for income_statement in income_statement_list: operatingProfit = income_statement["operatingRevenue"] \ - income_statement["operatingCosts"] \ - income_statement["businessTaxesAndSurcharges"] \ - income_statement["sellingExpenses"] \ - income_statement["ManagingCosts"] \ - income_statement["financingExpenses"] \ - income_statement["assetsDevaluation"] \ + income_statement["incomeFromChangesInFairValue"] \ + income_statement["investmentIncome"] diff = operatingProfit - income_statement["operatingProfit"] if abs(diff) >= 1: print("{} operating profit calculating not pass,calculating result:{},report result:{}".format( income_statement['id'], operatingProfit, income_statement["operatingProfit"])) else: print("{} operating profit calculating pass".format(income_statement['id'])) 可以用该工具迅速检查财务报表的质量,同时也可以让你对财务报表有更深入的认识.更多例子 In [3]: from fooltrader.datamanager import finance_checkIn [4]: finance_check.check_operating_profit('300027')stock_sz_300027_20061231 operating profit calculating pass...stock_sz_300027_20170630 operating profit calculating passstock_sz_300027_20170930 operating profit calculating pass 我的博客介绍fooltrader投资之财务指标 事件(消息)数据In [12]: ft.get_finance_forecast_event('000002') timestamp reportPeriod securityId type description preEPS changeStart change idtimestamp 2004-04-02 2004-04-02 2004-03-31 stock_sz_000002 预增 预计公司2004年第1季度净利润较去年同期增长幅度超过150%。 NaN NaN 1.50 stock_sz_000002_2004-04-022004-07-05 2004-07-05 2004-06-30 stock_sz_000002 预增 预计公司2004年上半年度净利润较去年同期增长幅度将超过50%。 NaN NaN 0.50 stock_sz_000002_2004-07-052005-01-12 2005-01-12 2004-12-31 stock_sz_000002 预增 预计本公司2004年全年净利润较去年增长50%-65%之间。 NaN NaN 0.65 stock_sz_000002_2005-01-122005-04-06 2005-04-06 2005-03-31 stock_sz_000002 预增 预计本公司2005年1季度净利润较上年同期增长100%-150%。 NaN NaN 1.50 stock_sz_000002_2005-04-062005-04-25 2005-04-25 2005-06-30 stock_sz_000002 预增 预计本公司2005年上半年净利润较去年同期增长150—200%。 NaN NaN 2.00 stock_sz_000002_2005-04-252005-08-01 2005-08-01 2005-09-30 stock_sz_000002 预增 预计2005年1~9月份可实现净利润将较去年同期增长110~130%。 NaN NaN 1.30 stock_sz_000002_2005-08-012006-01-06 2006-01-06 2005-12-31 stock_sz_000002 预增 预计本公司2005年全年净利润较去年增幅将超过50%。 NaN NaN 0.50 stock_sz_000002_2006-01-062006-03-21 2006-03-21 2006-03-31 stock_sz_000002 预增 经初步测算,预计本公司2006年第1季度净利润较上年同期增长超过50%。 NaN NaN 0.50 stock_sz_000002_2006-03-212006-06-23 2006-06-23 2006-06-30 stock_sz_000002 预增 预计本公司2006年半年度净利润较上年同期增长50%~60% NaN NaN 0.60 stock_sz_000002_2006-06-232006-09-28 2006-09-28 2006-09-30 stock_sz_000002 预增 预计2006年一至三季度净利润较上年同期增长50%-60%。 NaN NaN 0.60 stock_sz_000002_2006-09-282007-01-12 2007-01-12 2006-12-31 stock_sz_000002 预增 预计2006年全年净利润较去年增长50%-65%。 0.26 NaN 0.65 stock_sz_000002_2007-01-122007-04-04 2007-04-04 2007-03-31 stock_sz_000002 预增 预计2007年第一季度净利润较上年同期增长幅度为50-60%。 NaN NaN 0.60 stock_sz_000002_2007-04-042007-10-30 2007-10-30 2007-12-31 stock_sz_000002 预增 公司预计2007年全年净利润较去年增长100%-150%。 NaN NaN 1.50 stock_sz_000002_2007-10-302015-04-03 2015-04-03 2015-03-31 stock_sz_000002 预减 预计2015年1月1日-2015年3月31日归属于上市公司股东的净利润为盈利:60,000万... 0.14 -0.61 -0.54 stock_sz_000002_2015-04-03In [13]: ft.get_finance_report_event('600338') reportPeriod securityId timestamp title url idtimestamp 2010-02-12 2009-12-31 stock_sh_600338 2010-02-12 西藏珠峰工业股份有限公司2009年年度报告 http://vip.stock.finance.sina.com.cn/corp/view... stock_sh_600338_2010-02-122011-04-27 2010-12-31 stock_sh_600338 2011-04-27 ST珠峰:年报 http://vip.stock.finance.sina.com.cn/corp/view... stock_sh_600338_2011-04-272012-04-26 2011-12-31 stock_sh_600338 2012-04-26 西藏珠峰工业股份有限公司2011年年度报告 http://vip.stock.finance.sina.com.cn/corp/view... stock_sh_600338_2012-04-262013-03-06 2012-12-31 stock_sh_600338 2013-03-06 西藏珠峰工业股份有限公司年报 http://vip.stock.finance.sina.com.cn/corp/view... stock_sh_600338_2013-03-062014-04-30 2013-12-31 stock_sh_600338 2014-04-30 西藏珠峰工业股份有限公司2013年年度报告 http://vip.stock.finance.sina.com.cn/corp/view... stock_sh_600338_2014-04-302015-04-30 2014-12-31 stock_sh_600338 2015-04-30 西藏珠峰工业股份有限公司年报 http://vip.stock.finance.sina.com.cn/corp/view... stock_sh_600338_2015-04-302016-04-15 2015-12-31 stock_sh_600338 2016-04-15 西藏珠峰年报 http://vip.stock.finance.sina.com.cn/corp/view... stock_sh_600338_2016-04-152017-01-05 2015-12-31 stock_sh_600338 2017-01-05 西藏珠峰2015年年度报告(更正稿) http://vip.stock.finance.sina.com.cn/corp/view... stock_sh_600338_2017-01-052017-02-28 2016-12-31 stock_sh_600338 2017-02-28 西藏珠峰2016年年度报告 http://vip.stock.finance.sina.com.cn/corp/view... stock_sh_600338_2017-02-282018-03-10 2017-12-31 stock_sh_600338 2018-03-10 西藏珠峰2017年年度报告 http://vip.stock.finance.sina.com.cn/corp/view... stock_sh_600338_2018-03-10 各指数数据In [14]: ft.get_kdata('index_sh_000001') timestamp code name low open close high volume turnover securityId preClose change changePct turnoverRate tCap mCap petimestamp 2018-06-11 2018-06-11 000001 上证指数 3037.9138 3057.3393 3052.7831 3063.6102 108563786 1.430373e+11 index_sh_000001 3067.1478 -14.3647 -0.4683 0.3412 3.199517e+13 2.689125e+13 15.062018-06-12 2018-06-12 000001 上证指数 3034.1012 3053.0279 3079.8018 3081.4473 113275096 1.544691e+11 index_sh_000001 3052.7831 27.0187 0.8851 0.3545 3.229036e+13 2.719762e+13 15.222018-06-13 2018-06-13 000001 上证指数 3044.1198 3071.4636 3049.7965 3071.4636 119607886 1.559353e+11 index_sh_000001 3079.8018 -30.0053 -0.9743 0.3733 3.205392e+13 2.699151e+13 15.092018-06-14 2018-06-14 000001 上证指数 3032.4062 3038.0704 3044.1597 3066.0469 115469487 1.475888e+11 index_sh_000001 3049.7965 -5.6368 -0.1848 0.3618 3.193968e+13 2.694981e+13 15.042018-06-15 2018-06-15 000001 上证指数 3008.7324 3037.4522 3021.9008 3048.7967 144532571 1.621960e+11 index_sh_000001 3044.1597 -22.2589 -0.7312 0.4490 3.171228e+13 2.680721e+13 14.93 技术指标In [15]: ft.macd('000778',start_date='20170101',end_date='20170301') close close_ema12 close_ema26 diff dea macdtimestamp 2017-01-03 5.21 NaN NaN NaN NaN NaN2017-01-04 5.24 NaN NaN NaN NaN NaN2017-01-05 5.31 NaN NaN NaN NaN NaN2017-01-06 5.28 NaN NaN NaN NaN NaN2017-01-09 5.33 NaN NaN NaN NaN NaN2017-01-10 5.30 NaN NaN NaN NaN NaN2017-01-11 5.34 NaN NaN NaN NaN NaN2017-01-12 5.21 NaN NaN NaN NaN NaN2017-01-13 5.11 NaN NaN NaN NaN NaN2017-01-16 4.95 NaN NaN NaN NaN NaN2017-01-17 5.00 NaN NaN NaN NaN NaN2017-01-18 5.05 5.146697 NaN NaN NaN NaN2017-01-19 4.96 5.117975 NaN NaN NaN NaN2017-01-20 5.00 5.099825 NaN NaN NaN NaN2017-01-23 5.05 5.092159 NaN NaN NaN NaN2017-01-24 5.06 5.087212 NaN NaN NaN NaN2017-01-25 5.06 5.083025 NaN NaN NaN NaN2017-01-26 5.07 5.081022 NaN NaN NaN NaN2017-02-03 5.03 5.073172 NaN NaN NaN NaN2017-02-06 5.03 5.066530 NaN NaN NaN NaN2017-02-07 5.01 5.057833 NaN NaN NaN NaN2017-02-08 5.05 5.056628 NaN NaN NaN NaN2017-02-09 5.12 5.066378 NaN NaN NaN NaN2017-02-10 5.27 5.097704 NaN NaN NaN NaN2017-02-13 5.31 5.130365 NaN NaN NaN NaN2017-02-14 5.84 5.239540 5.184121 0.055419 0.055419 0.0000002017-02-15 6.09 5.370380 5.251223 0.119157 0.068166 0.1019812017-02-16 5.98 5.464167 5.305206 0.158961 0.086325 0.1452712017-02-17 5.70 5.500449 5.334450 0.165999 0.102260 0.1274782017-02-20 5.78 5.543457 5.367454 0.176003 0.117009 0.1179892017-02-21 5.81 5.584464 5.400235 0.184229 0.130453 0.1075522017-02-22 5.95 5.640700 5.440959 0.199742 0.144310 0.1108622017-02-23 5.81 5.666746 5.468295 0.198451 0.155139 0.0866252017-02-24 5.69 5.670324 5.484718 0.185606 0.161232 0.0487482017-02-27 5.59 5.657966 5.492516 0.165450 0.162076 0.0067492017-02-28 5.66 5.658279 5.504922 0.153357 0.160332 -0.0139502017-03-01 5.63 5.653928 5.514187 0.139741 0.156214 -0.032945
更多用法请查看api文档. 1.3 回测策略的编写,可以采用事件驱动或者时间漫步的方式,查看设计文档 class EventTrader(Trader): def on_init(self): self.trader_id = 'aa' self.only_event_mode = True self.universe = ['stock_sz_000338'] self.df_map = {} def on_day_bar(self, bar_item): current_security = bar_item['securityId'] current_df = self.df_map.get(current_security, pd.DataFrame()) if current_df.empty: self.df_map[current_security] = current_df current_df = current_df.append(bar_item, ignore_index=True) self.df_map[current_security] = current_df if len(current_df.index) == 10: ma5 = np.mean(current_df.loc[5:, 'close']) ma10 = np.mean(current_df.loc[:, 'close']) # 5日线在10日线上,并且没有持仓,就买入 if ma5 > ma10 and not self.account_service.get_position(current_security): self.buy(security_id=current_security, current_price=bar_item['close']) # 5日线在10日线下,并且有持仓,就卖出 elif ma5 < ma10 and self.account_service.get_position(current_security): self.sell(security_id=current_security, current_price=bar_item['close']) current_df = current_df.loc[1:, ] self.df_map[current_security] = current_df 运行策略可以实时查看效果,并做进一步的评估 2. 架构图fooltrader是一个层次清晰的系统,你可以在不同的层次对其进行使用,也可以扩展,改造或替换里面的模块. 3. 使用step by step使用的层次跟架构图里面的模块是一一对应的, 你可以在任何step停下来,进行扩展或者对接你自己熟悉的系统. 3.1 环境准备操作系统:Ubuntu 16.04.3 LTS clone或者fork代码 $ git clone https://github.com/foolcage/fooltrader.git $ cd fooltrader$ ./init_env.sh 如果你最后看到: Requirements installed. env ok 那么恭喜你,你可以以各种姿势去玩耍了. 两种方式去下载历史数据(目前包含到2018-07-19的数据)
看一下数据协议,设置好FOOLTRADER_STORE_PATH,解压下载的文件到该目录. $ source ve/bin/activate抓股票列表$ python fooltrader/sched/sched_stock_meta.py抓行情$ python fooltrader/sched/sched_china_stock_quote.py抓财报$ python fooltrader/sched/sched_finance.py 该项目的目的之一是方便大家共享数据,不需要每个人都去抓历史数据而导致被屏蔽. 这些脚本会定时去抓取"缺少"的数据,在历史数据完整性检查通过后,其实就是只是抓取当天的数据,这样我们就有了一个自动化自我维护的完整数据源. 这里把抓取数据作为一个单独的模块,而不是像某些开源项目那样api和爬虫耦合在一起,主要是为了:
最后强调一下,数据抓下来了,怎么使用?请参考数据协议 3.4 elastic-search和kibana安装(6.1.1)
可以参考官方文档进行安装:https://www.elastic.co/guide/en/elastic-stack/current/installing-elastic-stack.html $ #下载xpack$ wget https://artifacts.elastic.co/downloads/packs/x-pack/x-pack-6.1.1.zip$ #下载es$ wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-6.1.1.zip$ unzip elasticsearch-6.1.1.zip$ cd elasticsearch-6.1.1/$ #为es安装xpcck插件,就是刚刚下载的那个x-pack-6.1.1.zip,格式为file://+其路径$ bin/elasticsearch-plugin install file:///path/to/file/x-pack-6.1.1.zip$ #用fooltrader中的elasticsearch.yml覆盖es默认配置$ cp ../fooltrader/config/elasticsearch.yml config/$ #启动es,可根据自己的情况更改heap大小,<=32g$ ES_JAVA_OPTS="-Xms8g -Xmx8g" ./bin/elasticsearch$$ #下载kibana$ wget https://artifacts.elastic.co/downloads/kibana/kibana-6.1.1-linux-x86_64.tar.gz$ tar -xzf kibana-6.1.1-linux-x86_64.tar.gz$ cd kibana-6.1.1-linux-x86_64/$ #为kibana安装xpcck插件,就是刚刚下载的那个x-pack-6.1.1.zip,格式为file://+其路径$ bin/kibana-plugin install file:///path/to/file/x-pack-6.1.1.zip$ #用fooltrader中的kibana.yml覆盖kibana默认配置$ cp ../fooltrader/config/kibana.yml config/$ ./bin/kibana 3.5 数据存储到elastic-search到这里,我还是默认你在fooltrader的ipython环境下. In [1]: from fooltrader.connector import es_connector#股票元信息->esIn [2]: es_connector.security_meta_to_es()#指数数据->esIn [3]: es_connector.kdata_to_es(security_type='index')#个股k线->esIn [4]: es_connector.kdata_to_es(security_type='stock')#你也可以多开几个窗口,指定范围,提高索引速度In [4]: es_connector.kdata_to_es(start='002000',end='002999')#财务数据->esIn [5]: es_connector.finance_sheet_to_es('balance_sheet')In [5]: es_connector.finance_sheet_to_es('cash_flow_statement')In [5]: es_connector.finance_sheet_to_es('income_statement') 更多功能可以直接查看es_connector的源码,也可以加到定时任务里面,所有索引函数都做了时间判断,只会添加没有添加的数据. 然后,我们简单的来领略一下它的威力 curl -XPOST 'localhost:9200/income_statement/doc/_search?pretty&filter_path=hits.hits._source' -H 'Content-Type: application/json' -d'{ "query": { "range": { "reportDate": { "gte": "20170630", "lte": "20170630" } } }, "size": 5, "sort": [ { "netProfit": { "order": "desc" } } ]}'{ "hits": { "hits": [ { "_source": { "exchangeGains": 1.3242E10, "netProfit": 1.827E9, "securityId": "stock_sh_601318", "investmentIncome": 2.0523E10, "operatingProfit": 7.8107E10, "accumulatedOtherComprehensiveIncome": 2.0E8, "attributableToMinorityShareholders": 6.5548E10, "sellingExpenses": 1.0777E10, "investmentIncomeFromRelatedEnterpriseAndJointlyOperating": "398259000000.00", "id": "stock_sh_601318_20170630", "minorityInterestIncome": 6.238E10, "code": "601318", "otherComprehensiveIncome": 6.5506E10, "nonOperatingIncome": 4.006E9, "financingExpenses": 0.0, "reportEventDate": "2017-08-18", "netProfitAttributedToParentCompanyOwner": 5.778E10, "disposalLossOnNonCurrentLiability": 9.01E8, "incomeFromChangesInFairValue": -2.56E8, "incomeTaxExpense": 2.2E7, "operatingTotalCosts": 3.4139E11, "assetsDevaluation": 8.75E8, "EPS": 1.9449E10, "operatingCosts": 9.4E7, "attributableToOwnersOfParentCompany": 1.58E8, "ManagingCosts": 6.402E10, "totalProfits": 8.403E9, "dilutedEPS": 2.4575E10, "reportDate": "20170630", "businessTaxesAndSurcharges": 9.442E9, "operatingRevenue": 4.63765E11, "nonOperatingExpenditure": 1.35892E11 } ] } }} 实际上REST接口天然就有了,做跨平台接口非常方便,根据数据协议 和ES DSL可以非常方便的进行查询和聚合计算. 3.6 使用kibana进行分析(文档待完善) 3.7 回测(文档待完善) 3.8 交易(文档待完善) 支持的功能
没错:回测框架必须要考虑这些问题TODO
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