Metadata-Version: 1.1
Name: pyalgotrade_mootdx
Version: 0.1.1
Summary: pyalgotrade mootdx module
Home-page: https://github.com/bopo/pyalgotrade_mootdx
Author: bopo.wang
Author-email: ibopo@126.com
License: MIT license
Description: PyAlgoTrade mootdx module
        =========================
        此项目是 PyAlgoTrade [mootdx](https://github.com/bopo/mootdx) (基于 pytdx 的二次封装版本) 的一个数据源
        
        一个简单的用法:
        
        ```
        from pyalgotrade import plotter, strategy
        from pyalgotrade.bar import Frequency
        from pyalgotrade.barfeed.csvfeed import GenericBarFeed
        from pyalgotrade.stratanalyzer import sharpe
        from pyalgotrade.technical import ma
        
        from pyalgotrade_mootdx import tools
        
        
        class Strategy(strategy.BacktestingStrategy):
            def __init__(self, feed, instrument):
                super(Strategy, self).__init__(feed)
        
                self.__position = None
                self.__sma = ma.SMA(feed[instrument].getCloseDataSeries(), 150)
                self.__instrument = instrument
                self.getBroker()
        
            def onEnterOk(self, position):
                execInfo = position.getEntryOrder().getExecutionInfo()
                self.info("买入 %.2f" % (execInfo.getPrice()))
        
            def onEnterCanceled(self, position):
                self.__position = None
        
            def onExitOk(self, position):
                execInfo = position.getExitOrder().getExecutionInfo()
                self.info("卖出 %.2f" % (execInfo.getPrice()))
                self.__position = None
        
            def onExitCanceled(self, position):
                # If the exit was canceled, re-submit it.
                self.__position.exitMarket()
        
            def getSMA(self):
                return self.__sma
        
            def onBars(self, bars):
                # 每一个数据都会抵达这里，就像becktest中的next
                # Wait for enough bars to be available to calculate a SMA.
                if self.__sma[-1] is None:
                    return
        
                # bar.getTyoicalPrice = (bar.getHigh() + bar.getLow() + bar.getClose())/ 3.0
                bar = bars[self.__instrument]
        
                # If a position was not opened, check if we should enter a long position.
                if self.__position is None:
                    if bar.getPrice() > self.__sma[-1]:
                        # 开多头.
                        self.__position = self.enterLong(self.__instrument, 100, True)
        
                # 平掉多头头寸.
                elif bar.getPrice() < self.__sma[-1] and not self.__position.exitActive():
                    self.__position.exitMarket()
        
        
        def main():
            instruments = ["600036"]
            feeds = tools.build_feed(instruments, 2017, 2018, "histdata")
        
            # 3.实例化策略
            strat = Strategy(feeds, instruments[0])
        
            # 4.设置指标和绘图
            ratio = sharpe.SharpeRatio()
            strat.attachAnalyzer(ratio)
            plter = plotter.StrategyPlotter(strat)
        
            # 5.运行策略
            strat.run()
            strat.info("最终收益: %.2f" % strat.getResult())
        
            # 6.输出夏普率、绘图
            strat.info("夏普比率: " + str(ratio.getSharpeRatio(0)))
            # plter.plot()
        
        
        if __name__ == '__main__':
            main()
        ```
        
        ```
        2018-04-03 00:00:00 strategy [INFO] 卖出 28.86
        2018-04-11 00:00:00 strategy [INFO] 买入 30.34
        2018-04-17 00:00:00 strategy [INFO] 卖出 28.25
        2018-05-02 00:00:00 strategy [INFO] 买入 29.98
        2018-05-04 00:00:00 strategy [INFO] 卖出 29.28
        2018-05-09 00:00:00 strategy [INFO] 买入 29.70
        2018-05-24 00:00:00 strategy [INFO] 卖出 29.60
        2018-09-25 00:00:00 strategy [INFO] 买入 29.83
        2018-10-12 00:00:00 strategy [INFO] 卖出 28.70
        2018-10-15 00:00:00 strategy [INFO] 买入 29.16
        2018-10-19 00:00:00 strategy [INFO] 卖出 27.75
        2018-10-22 00:00:00 strategy [INFO] 买入 29.53
        2018-10-30 00:00:00 strategy [INFO] 卖出 28.12
        2018-11-01 00:00:00 strategy [INFO] 买入 29.45
        2018-11-15 00:00:00 strategy [INFO] 卖出 28.24
        2018-11-19 00:00:00 strategy [INFO] 买入 28.70
        2018-11-23 00:00:00 strategy [INFO] 卖出 28.29
        2018-12-03 00:00:00 strategy [INFO] 买入 29.49
        2018-12-11 00:00:00 strategy [INFO] 卖出 28.35
        2018-12-12 00:00:00 strategy [INFO] 买入 29.00
        2018-12-18 00:00:00 strategy [INFO] 卖出 28.05
        2018-12-21 13:48:01,740 strategy [INFO] 最终收益: 999254.00
        2018-12-21 13:48:01,740 strategy [INFO] 夏普比率: -0.6957113853538597
        ```
        
        
        
Keywords: pyalgotrade_mootdx
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
