Windows平台下单指令

从HLFX_Pool过滤MA5均线后
板块热点（当下热点概念）成交3只
并通过MA5规则抛出

real_time_order_MA_HLFX_1025.py

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+from future.types import newint
+from jqdatasdk import *
+from datetime import datetime as dt
+import pandas as pd
+import pymysql
+from sqlalchemy import create_engine
+import time
+from xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback
+from xtquant.xttype import StockAccount
+from xtquant import xtconstant
+from xtquant import xtdata
+
+
+
+auth('18616891214', 'Ea?*7f68nD.dafcW34d!')
+
+#启动交易系统
+path = 'D:\\安信证券QMT实盘_交易终端\\userdata_mini'
+# session_id为会话编号，策略使用方对于不同的Python策略需要使用不同的会话编号
+session_id = 20221123
+#后续的所有示例将使用该实例对象
+xt_trader = XtQuantTrader(path, session_id)
+xt_trader.start()
+connect_result = xt_trader.connect()
+if connect_result == 0:
+    print('QMTmini 已连接')
+else:
+    print('连接失败')
+
+account = StockAccount('920000207040', 'SECURITY')  # xt_trader为XtQuant API实例对象
+positions = xt_trader.query_stock_positions(account)
+
+
+
+
+fre = '1d'
+
+engine_hlfx_pool = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx_pool?charset=utf8')
+# engine_stock = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/stocks?charset=utf8')
+
+db_pool = pymysql.connect(host='localhost',
+                              user='root',
+                              port=3307,
+                              password='r6kEwqWU9!v3',
+                              database='hlfx_pool')
+cursor_pool = db_pool.cursor()
+
+
+
+fut = locals()
+print(dt.now(), '开始寻找MA5趋势！')
+
+
+def XtTrader(new_keep_stock):
+    # 获取账号信息
+    # account = StockAccount('888824600221', 'CREDIT') #xt_trader为XtQuant API实例对象
+    account = StockAccount('920000207040', 'SECURITY')  # xt_trader为XtQuant API实例对象
+    # print('acc:', account.account_type, account.account_id)
+    # print("query asset:")
+    asset = xt_trader.query_stock_asset(account)
+    positions = xt_trader.query_stock_positions(account)
+    if asset:
+        print("asset:")
+        print(asset.account_type, asset.account_id, asset.cash, asset.frozen_cash, asset.market_value,
+              asset.total_asset)
+    # 开始交易
+    for i in new_keep_stock:
+        print(i)
+        price = get_bars(i.replace('SH', 'XSHG').replace('SZ', 'XSHE'), count=1, unit=fre, fields=['close'],
+                         include_now=True).iloc[-1].at['close']
+        print('price:', price)
+        print(asset.cash / price)
+        if asset.cash > 2000:
+            volume = int((asset.cash / 2 / price) // 100 * 100)
+            print('volume：', volume)
+            order_id = xt_trader.order_stock(account, i, xtconstant.STOCK_BUY, volume, xtconstant.LATEST_PRICE, price, 'strategy1', 'order_test')
+            print(order_id)
+    # for i in positions:
+    #     price = get_bars(i.replace('SH', 'XSHG').replace('SZ', 'XSHE'), count=1, unit=fre, fields=['close'],
+    #                      include_now=True).iloc[-1].at['close']
+    #     if Sell_Trader(i):
+    #         print('yao maihu de gupiao !!!!!!!!!!!', i.stock_code)
+    #         order_id = xt_trader.order_stock(account, i, xtconstant.STOCK_SELL,
+    #                                              1000, xtconstant.FIX_PRICE, 10.90, 'strategy1', 'order_test')
+    # print(positions[1].stock_code, positions[1].volume)
+
+    print('今日成交：')
+    for trades in xt_trader.query_stock_trades(account):
+        print(trades.stock_code, trades.traded_volume, trades.traded_price)
+
+    positions = xt_trader.query_stock_positions(account)
+    print("positions:", len(positions))
+    if len(positions) != 0:
+        print("last position:")
+        print("{0} {1} {2}".format(positions[-1].account_id, positions[-1].stock_code, positions[-1].volume))
+
+    print(positions)
+    xt_trader.stop()
+
+def Sell_Trader(stock, account, positions, volume):
+    price = get_bars(stock, count=1, unit=fre, fields=['close'],
+                     include_now=True).iloc[-1].at['close']
+    print(type(stock.replace('XSHG', 'SH').replace('XSHE', 'SZ')),stock.replace('XSHG', 'SH').replace('XSHE', 'SZ') )
+    order_id = xt_trader.order_stock(account, stock.replace('XSHG', 'SH').replace('XSHE', 'SZ'), xtconstant.STOCK_SELL,
+                                     volume, xtconstant.LATEST_PRICE, 0,  'strategy1', 'order_test')
+    print(order_id, i)
+
+
+while True:
+
+    now_date = dt.now()
+    date_morning_begin = now_date.replace(hour=9, minute=25, second=0)
+    date_morning_end = now_date.replace(hour=11, minute=31, second=0)
+    date_afternooe_begin = now_date.replace(hour=13, minute=0, second=0)
+    date_afternooe_end = now_date.replace(hour=15, minute=0, second=0)
+    # if True:
+    if date_morning_begin < now_date < date_morning_end or date_afternooe_begin < now_date < date_afternooe_end:
+        # time.sleep(1800)
+        try:
+            account = StockAccount('920000207040', 'SECURITY')  # xt_trader为XtQuant API实例对象
+            positions = xt_trader.query_stock_positions(account)
+            print(positions)
+        except BaseException:
+            continue
+        for i in positions:
+            # print(i.stock_code, i.volume)
+            volume = i.volume
+            stock = i.stock_code.replace('SH', 'XSHG').replace('SZ', 'XSHE')
+            df_stock = get_bars(stock, count=60, unit=fre,
+                                     fields=['date', 'open', 'close', 'high', 'low', 'volume'],
+                                     include_now=True, df=True)
+
+            price = df_stock.iloc[-1].at['close']
+            MA5_1 = df_stock['close'][-7:-2].mean()
+            MA5 = df_stock['close'][-6:-1].mean()
+            MA10 = df_stock['close'][-11:-1].mean()
+            MA20 = df_stock['close'][-21:-1].mean()
+            if price < MA5 or MA5 < MA5_1 or price > MA5 * 1.12:
+                print(MA5, MA5_1)
+                Sell_Trader(stock, account, positions, volume)
+
+
+        for fre in ['1d']:
+            print('开始：', fre)
+            results = []
+            try:
+                stock_pool = pd.read_sql_query(
+                    'select value from `%s`' % fre, engine_hlfx_pool)
+                stock_pool = stock_pool.iloc[-1, 0].split(",")
+                print(stock_pool)
+            except BaseException:
+                continue
+            for stock in stock_pool:
+                # print(stock)
+                try:
+                    df_stock = get_bars(stock, count=60, unit=fre, fields=['date', 'open', 'close', 'high', 'low','volume'],
+                                           include_now=True, df=True)
+                    price = df_stock.iloc[-1].at['close']
+                    price_open = df_stock.iloc[-1].at['open']
+                    MA5_1 = df_stock['close'][-7:-2].mean()
+                    MA5 = df_stock['close'][-6:-1].mean()
+                    MA10 = df_stock['close'][-11:-1].mean()
+                    MA20 = df_stock['close'][-21:-1].mean()
+                    # print(price,price_open, 'ma5_1:',MA5_1, 'ma5:', MA5,MA10)
+                    if (price > price_open) & (price > MA5) & (MA5 > MA5_1) & (price < MA5 * 1.03) & (MA20 < MA10)  \
+                            & (df_stock.iloc[-1].at['volume'] > df_stock.iloc[-2].at['volume']):
+                        print(stock)
+                        results.append(stock)
+                    elif price < MA5 or MA5<MA5_1 or price > MA5*1.09:
+                        stock_pool.remove(stock)
+                        print(stock, '已失败！')
+                except BaseException:
+                    continue
+            results = list(set(results))
+            print(results)
+            now_time = dt.now().strftime('%Y-%m-%d %H:%M:%S')
+            # results_list =','.join(results)
+            # print(fre, '\n', results_list)
+
+
+
+            if len(results) == 0:
+                continue
+            else:
+                num_industry = get_industry(results)
+                print(num_industry)
+                industry_list = []
+                for key in num_industry.values():
+                    for key2 in key.values():
+                        industry_list.append(key2['industry_name'])
+                industry_list = pd.value_counts(industry_list)
+                # 最热集中的n个板块
+                max_industry_list = list(industry_list[0:3].index)
+                results_industry = []
+                for key, value in num_industry.items():
+                    for key2 in value.values():
+                        if key2['industry_name'] in max_industry_list:
+                            results_industry.append(key)
+                print('suoyou:', set(results_industry))
+                results_industry = ','.join(set(results_industry))
+                print(fre, '\n', results_industry)
+
+                sql = "INSERT INTO MA5_%s (date,value) VALUES('%s','%s')" % (fre, dt.now().strftime('%Y-%m-%d %H:%M:%S'),
+                                                                             results_industry)
+                cursor_pool.execute(sql)
+                db_pool.commit()
+
+                print(len(results_industry), results_industry)
+                print(dt.now(), '数据库数据已赋值！')
+
+                # 取值交易
+                engine_hlfx_pool = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx_pool?charset=utf8')
+
+                # stocks = xtdata.get_stock_list_in_sector('沪深A股')
+                keep_stocks = pd.read_sql_query(
+                    'select value from `MA5_%s`' % fre, engine_hlfx_pool)
+                keep_stocks = keep_stocks.iloc[-1, 0].split(",")
+                new_keep_stock = [stock.replace('XSHG', 'SH').replace('XSHE', 'SZ') for stock in keep_stocks]
+                print(new_keep_stock)
+                price = get_bars(keep_stocks, count=1, unit=fre, fields=['close'])
+
+                XtTrader(new_keep_stock)
+        time.sleep(1800)
+    elif now_date > date_afternooe_end:
+        pass
+        # print("MA5_收盘了", now_date)
+        # break
+
+
+
+
+