我的投资理念和开源项目fooltrader
且说投资理念吧:
- 全市场(股票,期货,数字货币,债券等)
- 只做”明显的”底部(多),顶部(空)
- 基本面,技术面,政策面,资金面结合的”乘法原理”
- 分散投资
所以,大部分时间我是在写程序,看书,然后等待”那个点”的出现.
我不知道,为什么那么多人要么搞股票,要么搞期货,要么炒币,甚至只关心某某行业的几只票,研究得很”深入”,所谓”只搞自己懂的”.
然而,市场在干什么呢?
“成交量”会有一个基本的答案,如果很明显某某金属品种,技术上有底部特征,且成交量足够大,而股市已经明显走熊,成交萎靡,为啥还要赖在股市里面不走?去”陪伟大的企业成长”?
而即使你不懂”区块链”的意义,然而芝加哥商品上线了”比特币”期货,且流动性足够,你总不能继续骗自己——“那只是一个传销”.
我说这些,只是想说,不要被自己的”喜好”蒙蔽了双眼,拒绝充满活力的”少女”,而在”老媪”虔诚的灵魂中不能自拔.
为了践行这样的理念,人力为之是不可能的,这行情那行情这新闻那指标的,都去看不累死?人是不是应该把时间花在有意思一点的事情上?嗯,重复性的东西让程序去做,然而市面上的软件都太偏门了,不太可能完成这种复杂的任务,且各有一套,学习成本太高.
好吧,自己撸.爬虫,数据分类,存储,计算,回测,通知……只做我认为最有用的,尽量用现成的成熟组件,然后,就跑起来了……
直接传送项目地址
“要在市场上生存，就必须远离聪明，因为，你的聪明在市场面前一钱不值”——缠中说禅
且说开源的目的:
- 由于是全品种,爬虫会比较多,希望对此有兴趣的人能够加入进来,一起完善
- 有人免费帮找BUG,系统更稳定
- 有人点赞,我的虚荣心还是能够得到一些满足的——这是最重要的
下面展示几个使用场景:
自定义分析逻辑和视图
股价利润关系
市场平均PE对比
大势dashboard
API使用
获取利润表
In [1]: from fooltrader.api import finance
In [2]: finance.get_income_statement_items('300027',report_period='2017-06-30')
#试一试
#finance.get_balance_sheet_items('300027',,report_event_date='2017-01-01')
#finance.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.
In [3]: from fooltrader.datamanager import finance_check
In [4]: finance_check.check_operating_profit('300027')
stock_sz_300027_20061231 operating profit calculating pass
...
stock_sz_300027_20170630 operating profit calculating pass
stock_sz_300027_20170930 operating profit calculating pass
我的博客介绍fooltrader投资之财务指标
行情信息
K线数据
In [5]: from fooltrader.api import quote
In [6]: quote.get_kdata('300027',start_date='20170630',end_date='20170715')
#试一试
#quote.get_kdata('300027',start_date='20170630',end_date='20170715',fuquan='qfq')
#quote.get_kdata('300027',start_date='20170630',end_date='20170715',fuquan='hfq')
Out[6]:
timestamp code name low open close high volume turnover securityId preClose change changePct turnoverRate tCap mCap factor
timestamp
2017-06-30 2017-06-30 300027 华谊兄弟 8.03 8.11 8.09 8.11 9515735 7.684533e+07 stock_sz_300027 8.11 -0.02 -0.2466 0.3832 2.244575e+10 2.008766e+10 15.055
2017-07-03 2017-07-03 300027 华谊兄弟 8.07 8.09 8.20 8.22 15577742 1.270549e+08 stock_sz_300027 8.09 0.11 1.3597 0.6274 2.275095e+10 2.036079e+10 15.055
2017-07-04 2017-07-04 300027 华谊兄弟 8.12 8.20 8.15 8.22 8672705 7.068477e+07 stock_sz_300027 8.20 -0.05 -0.6098 0.3493 2.261222e+10 2.023664e+10 15.055
2017-07-05 2017-07-05 300027 华谊兄弟 8.11 8.15 8.19 8.22 12458305 1.017991e+08 stock_sz_300027 8.15 0.04 0.4908 0.5017 2.272320e+10 2.033596e+10 15.055
2017-07-06 2017-07-06 300027 华谊兄弟 8.17 8.20 8.20 8.29 18642574 1.533405e+08 stock_sz_300027 8.19 0.01 0.1221 0.7508 2.275095e+10 2.036079e+10 15.055
2017-07-07 2017-07-07 300027 华谊兄弟 8.13 8.18 8.17 8.19 9414275 7.679682e+07 stock_sz_300027 8.20 -0.03 -0.3659 0.3791 2.266771e+10 2.028630e+10 15.055
2017-07-10 2017-07-10 300027 华谊兄弟 8.08 8.18 8.09 8.19 12679949 1.029643e+08 stock_sz_300027 8.17 -0.08 -0.9792 0.5107 2.244575e+10 2.008766e+10 15.055
2017-07-11 2017-07-11 300027 华谊兄弟 8.06 8.10 8.08 8.13 11412820 9.235337e+07 stock_sz_300027 8.09 -0.01 -0.1236 0.4596 2.241801e+10 2.006283e+10 15.055
2017-07-12 2017-07-12 300027 华谊兄弟 7.93 8.07 8.03 8.10 13776145 1.104951e+08 stock_sz_300027 8.08 -0.05 -0.6188 0.5548 2.227928e+10 1.993868e+10 15.055
2017-07-13 2017-07-13 300027 华谊兄弟 8.09 8.10 8.28 8.37 43554957 3.590739e+08 stock_sz_300027 8.03 0.25 3.1133 1.7541 2.297291e+10 2.055944e+10 15.055
2017-07-14 2017-07-14 300027 华谊兄弟 8.23 8.25 8.41 8.55 37967053 3.193673e+08 stock_sz_300027 8.28 0.13 1.5700 1.5291 2.333359e+10 2.088223e+10 15.055
tick数据
In [7]: for tick in quote.get_ticks('300027',the_date='2017-07-03'):
...: print(tick)
...:
...:
#试一试
#quote.get_ticks('300027',start='2017-06-30',end='2017-07-10')
timestamp price volume turnover direction code securityId
timestamp
2017-07-03 09:25:03 2017-07-03 09:25:03 8.09 41 33169 1 300027 stock_sz_300027
2017-07-03 09:30:03 2017-07-03 09:30:03 8.09 10 8090 1 300027 stock_sz_300027
... ... ... ... ... ... ... ...
2017-07-03 14:57:00 2017-07-03 14:57:00 8.19 4 3276 -1 300027 stock_sz_300027
2017-07-03 14:57:03 2017-07-03 14:57:03 8.19 0 0 -1 300027 stock_sz_300027
2017-07-03 15:00:03 2017-07-03 15:00:03 8.20 4394 3603079 1 300027 stock_sz_300027
[2313 rows x 7 columns]
事件(消息)数据
In [8]: from fooltrader.api import event
In [9]: for item in event.get_forecast_items('000338'):
...: print(item)
...:
{'changeStart': None, 'reportDate': '2008-01-28', 'id': 'stock_sz_000338_2008-01-28', 'preEPS': None, 'securityId': 'stock_sz_000338', 'reportPeriod': '2007-12-31', 'description': '潍柴动力预计2007年1-12月净利润较2006年度备考合并净利润增长约140%左右。', 'change': 1.4, 'type': '预增'}
{'changeStart': None, 'reportDate': '2008-07-24', 'id': 'stock_sz_000338_2008-07-24', 'preEPS': 1.87, 'securityId': 'stock_sz_000338', 'reportPeriod': '2008-06-30', 'description': '预计本公司2008年1-6月归属于母公司所有者净利润与2007年同期调整前及调整后的归属于母公司所有者净利润相比增长50%-100%之间。', 'change': 1.0, 'type': '预增'}
{'changeStart': None, 'reportDate': '2009-08-19', 'id': 'stock_sz_000338_2009-08-19', 'preEPS': 3.19, 'securityId': 'stock_sz_000338', 'reportPeriod': '2009-06-30', 'description': '预计本公司2009年半年度营业收入约为人民币158亿元，营业利润约在人民币15-18亿元之间，利润总额约在人民币15-18亿元之间，归属于上市公司股东的净利润约在人民币10.0-12.5亿元之间。', 'change': 0.0, 'type': '预降'}
...
{'changeStart': 1.4, 'reportDate': '2017-04-14', 'id': 'stock_sz_000338_2017-04-14', 'preEPS': 0.11, 'securityId': 'stock_sz_000338', 'reportPeriod': '2017-03-31', 'description': '预计2017年1-3月归属于上市公司股东的净利润为：109,600.00万元至123,300.00万元，较上年同期相比变动幅度：140.00%至170.00%。', 'change': 1.7, 'type': '预增'}
{'changeStart': 0.7, 'reportDate': '2017-04-28', 'id': 'stock_sz_000338_2017-04-28', 'preEPS': 0.26, 'securityId': 'stock_sz_000338', 'reportPeriod': '2017-06-30', 'description': '预计2017年1-6月归属于上市公司股东的净利润为：183,000.00万元至215,000.00万元，较上年同期相比变动幅度：70.00%至100.00%。', 'change': 1.0, 'type': '预增'}
{'changeStart': 1.5, 'reportDate': '2017-08-31', 'id': 'stock_sz_000338_2017-08-31', 'preEPS': 0.38, 'securityId': 'stock_sz_000338', 'reportPeriod': '2017-09-30', 'description': '预计2017年1-9月归属于上市公司股东的净利润为：385,000.00万元至431,400.00万元，较上年同期相比变动幅度：150.00%至180.00%。', 'change': 1.8, 'type': '预增'}
技术指标
In [10]: from fooltrader.api import technical
In [11]: technical.macd('000778',start_date='20170101',end_date='20170301')
Out[11]:
close close_ema12 close_ema26 diff dea macd
timestamp
2017-01-03 5.21 NaN NaN NaN NaN NaN
2017-01-04 5.24 NaN NaN NaN NaN NaN
2017-01-05 5.31 NaN NaN NaN NaN NaN
2017-01-06 5.28 NaN NaN NaN NaN NaN
2017-01-09 5.33 NaN NaN NaN NaN NaN
2017-01-10 5.30 NaN NaN NaN NaN NaN
2017-01-11 5.34 NaN NaN NaN NaN NaN
2017-01-12 5.21 NaN NaN NaN NaN NaN
2017-01-13 5.11 NaN NaN NaN NaN NaN
2017-01-16 4.95 NaN NaN NaN NaN NaN
2017-01-17 5.00 NaN NaN NaN NaN NaN
2017-01-18 5.05 5.146697 NaN NaN NaN NaN
2017-01-19 4.96 5.117975 NaN NaN NaN NaN
2017-01-20 5.00 5.099825 NaN NaN NaN NaN
2017-01-23 5.05 5.092159 NaN NaN NaN NaN
2017-01-24 5.06 5.087212 NaN NaN NaN NaN
2017-01-25 5.06 5.083025 NaN NaN NaN NaN
2017-01-26 5.07 5.081022 NaN NaN NaN NaN
2017-02-03 5.03 5.073172 NaN NaN NaN NaN
2017-02-06 5.03 5.066530 NaN NaN NaN NaN
2017-02-07 5.01 5.057833 NaN NaN NaN NaN
2017-02-08 5.05 5.056628 NaN NaN NaN NaN
2017-02-09 5.12 5.066378 NaN NaN NaN NaN
2017-02-10 5.27 5.097704 NaN NaN NaN NaN
2017-02-13 5.31 5.130365 NaN NaN NaN NaN
2017-02-14 5.84 5.239540 5.184121 0.055419 0.055419 0.000000
2017-02-15 6.09 5.370380 5.251223 0.119157 0.068166 0.101981
2017-02-16 5.98 5.464167 5.305206 0.158961 0.086325 0.145271
2017-02-17 5.70 5.500449 5.334450 0.165999 0.102260 0.127478
2017-02-20 5.78 5.543457 5.367454 0.176003 0.117009 0.117989
2017-02-21 5.81 5.584464 5.400235 0.184229 0.130453 0.107552
2017-02-22 5.95 5.640700 5.440959 0.199742 0.144310 0.110862
2017-02-23 5.81 5.666746 5.468295 0.198451 0.155139 0.086625
2017-02-24 5.69 5.670324 5.484718 0.185606 0.161232 0.048748
2017-02-27 5.59 5.657966 5.492516 0.165450 0.162076 0.006749
2017-02-28 5.66 5.658279 5.504922 0.153357 0.160332 -0.013950
2017-03-01 5.63 5.653928 5.514187 0.139741 0.156214 -0.032945
我们不需要那么多技术指标,但一定要知道所使用指标的内涵,所以,我们选择自己计算;没错,由于数据的统一性,理所当然地,计算的统一性也有了. 不管是A股,港股,还是数字货币,不管是1分钟级别,还是日线,使用的都是统一的api.
更多用法请查看api文档.
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
注意:回测框架目前还处于非常初期的阶段
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
最后,说真的:
项目会持续做下去,因为我要用……
欢迎大家提意见
如果能给你的投资节省一些时间,那就真的阿弥陀佛了
当然,它只是一个工具,人的思想才是最重要的