一、策略的基础概念

MA概念：移动平均线，Moving Average，简称MA，MA是用统计分析的方法，将一定时期内的证券价格（指数）加以平均，并把不同时间的平均值连接起来，形成一根MA，用以观察证券价格变动趋势的一种技术指标
MA查看：招商银行查看 -> 点击指标 -> 搜索ma -> 点击“移动平均线”
5 minutes ma20：5分钟，MA20是指取最近100分钟的第5分钟、10分钟、15分钟……100分钟（20个价格）股票的收盘价的平均值

二、ma20-5min策略实现

1 – 新建类AstockTrading并添加构造方法

class AstockTrading(object):
    def __init__(self, stock_code):
        self.stock_code = stock_code
        self._Dt = []  # 交易时间
        self._Open = []  # 开盘价
        self._High = []  # 最高价
        self._Low = []  # 最低价
        self._Close = []    # 收盘价
        self._Volume = []
        self._tick = None   # 数据
        self._last_bar_start_minute = None  # 最后一次更新bar的时间
        self._isNewBar = False  # 是否有新bar

        # 当前订单，dict, 字典
        self._current_orders = {
            'order1': {
                'open_price': 1,
                'open_datetime': '2021-10-22 9:00',
                'comment': {}
            }
        }

        # 历史订单
        self._history_orders = {

        }

2 – 方法说明

get_tick：获取当前的数据
get_history_data_from_local_machine：加载历史数据
bar_generator：生成bar
_buy：买入
_sell：卖出
strategy：执行策略

3 – 完整源码

以下代码还未进行回测，仅仅参考思路，需要可运行的请参考后续的文章

import requests
from time import sleep
from datetime import datetime, time
from dateutil import parser


# __init__，构造，初始化，实例化

class AstockTrading(object):
    def __init__(self, strategy_name):
        self._strategy_name = strategy_name
        self._Dt = []  # 交易时间
        self._Open = []  # 开盘价
        self._High = []  # 最高价
        self._Low = []  # 最低价
        self._Close = []  # 最新价
        self._Volume = []
        self._tick = []  # 数据
        self._last_bar_start_minute = None  # 最后一次更新bar的时间
        self._isNewBar = False  # 是否有新bar
        self._ma20 = None

        # 当前订单，dict, 字典
        self._current_orders = {}
        # 历史订单
        self._history_orders = {}
        self._order_number = 0

    def get_tick(self):
        headers = {'Referer': "https://finance.sina.com.cn"}
        page = requests.get("https://hq.sinajs.cn/list=sh600519", headers=headers)
        stock_info = page.text
        mt_info = stock_info.replace("\"", "").split("=")[1].split(",")
        # 最新价
        last = float(mt_info[1])
        trade_datetime = mt_info[30] + ' ' + mt_info[31]
        self._tick = (trade_datetime, last)

    def get_history_data_from_local_machine(self):
        # tushare 数据来源
        # self.Open = [1, 2, 3]
        # self.High = [2, 3, 4]
        pass

    # how save and import history data?
    # 策略
    def bar_generator(self):
        # assume we have history data already
        # 1、update bars，calculate 5 minutes ma20 , not daily data
        # 2、compare last and ma20  -> buy or sell or pass
        # assume we have history data,Open,High,Low,Close,Dt
        # 这里可以是5minutes、10minutes、15minutes、20minutes、30minutes
        if self._tick[0].minute % 5 == 0 and self._tick[0].minute != self._last_bar_start_minute:
            self._last_bar_start_minute = self._tick[0].minute
            self._Open.insert(0, self._tick[1])
            self._High.insert(0, self._tick[1])
            self._Low.insert(0, self._tick[1])
            self._Close.insert(0, self._tick[1])
            self._Dt.insert(0, self._tick[0])
            self._isNewBar = True
        else:
            # update current bar
            self._High[0] = max(self._High[0], self._tick[1])
            self._Low[0] = max(self._Low[0], self._tick[1])
            self._Close[0] = self._tick[1]
            self._Dt[0] = self._tick[0]
            self._isNewBar = False

    def _buy(self, price, volume):
        # create an order
        self._order_number += 1
        key = "order" + str(self._order_number)
        self._current_orders[key] = {
            "open_datetime": self._Dt[0],
            "open_price": price,
            "volume": volume  # 股数
        }
        pass

    def _sell(self, key, price):
        self._current_orders[key]['close_price'] = price
        self._current_orders[key]['close_datetime'] = self._Dt[0]

        # move order from current orders to history orders
        self._history_orders[key] = self._current_orders.pop(key)

    def strategy(self):
        # last < 0.95 *ma20 ,long position(仓位), last > ma20 *1.05, sell
        if self._isNewBar:
            sum_ = 0
            for item in self._Close[1:21]:
                sum_ = sum_ + item
            self._ma20 = sum_ / 20

        if 0 == len(self._current_orders):
            if self._Close[0] < 0.95 * self._ma20:
                # 100000/44.28 = 2258   44.28是当前价格，10万指的你拥有的钱
                # 2258 -> 2200 shares
                volume = int(100000 / self._Close[0] / 100) * 100
                self._buy(self._Close[0] + 0.01, volume)  # 这里的0.01是为了防止挂单，我们需要即可买入
        elif 1 == len(self._current_orders):
            if self._Close[0] > self._ma20 * 1.05:
                key = self._current_orders.keys()[0]
                self._sell(key, self._Close[0] - 0.01)
        else:  # len() = 2
            raise ValueError("we have more then 1 current orders")
        # Close[0] in between 0.95*ma20 and 1.05*ma20,do nothing


ma = AstockTrading('600036')  # 类实例化
ma.get_history_data_from_local_machine()

# 交易时间是9：30-11:30,13：00-15：00
while time(9, 26) < datetime.now().time() < time(11, 32) \
        or time(13) < datetime.now().time() < time(15, 2):
    ma.get_tick()
    ma.bar_generator()
    ma.strategy()
    # trade_time = parser.parse(ma._tick[0]).time()
    # sleep(3)

3、Python量化交易-策略（bar、k线）

目录

一、策略的基础概念

二、ma20-5min策略实现

1 – 新建类AstockTrading并添加构造方法

2 – 方法说明

3 – 完整源码