import time
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import datetime
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import decimal
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import logging
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import threading
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from gm.api import *
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import basic_methods
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# 赋值账户ID
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account_id = 'aaee2221-839c-11ee-a7cd-00163e022aa6'
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# 设置token
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set_token("6c1dbe95191fb77cced9d805cb9c853805551ddb")
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# 为当日新增加持仓代码创建一个初始集合
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global_addition_position_symbols = set()
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# 初始化数据的全局化函数
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def init_data():
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logging.info("初始化数据开始")
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global today_date, Pre_trading_day, Double_Pre_trading_day, Next_trading_day
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# 设置token
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set_token("6c1dbe95191fb77cced9d805cb9c853805551ddb")
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# 今日时间初始化
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now = datetime.datetime.now() # 获取本机时间
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print(f"本机时间::{now}")
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today_date = now.strftime('%Y-%m-%d') # 获取今日日期参数
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print(f"今日日期{today_date}")
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Pre_trading_day = get_previous_trading_date(exchange='SHSE', date=today_date) # 获取前一个交易日
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Double_Pre_trading_day = get_previous_trading_date(exchange='SHSE', date=Pre_trading_day) # 获取上上一个交易日
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Next_trading_day = get_next_trading_date(exchange='SHSE', date=today_date) # 获取下一个交易日
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print(f"前一个交易日{Pre_trading_day}")
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print(f"上上一个交易日{Double_Pre_trading_day}")
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print(f"下一个交易日{Next_trading_day}")
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# 给全局context 赋初始值 下文有用
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global_context = None
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# 第一步：初始化context函数，数据订阅
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def init(context):
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global global_context # 在函数内部的一些初始化函数就把他直接全局化了
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global_context = context
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init_data()
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# print(f"global_context==={global_context.account(account_id = account_id).positions()}")
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# 初始化仓位管理，主线逻辑才会运行
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# position_management()
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# 初始化get_subscribe_data方法函数，下单买逻辑才会运行中。。。【核心主线程，随时考虑其启动顺序】>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
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# threading.Thread(target=lambda: get_subscribe_data(), daemon=True).start()
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subscribe(symbols='SZSE.000333', frequency='tick', count=5, unsubscribe_previous=True)
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# time.sleep(5)
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# print(f"test===={test}")
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# account_finance = context.account(account_id = 'aaee2221-839c-11ee-a7cd-00163e022aa6').cash
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# print(f"账户对象列表==={account_finance}")
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# print(f"资金初始时间==={account_finance.created_at}")
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# print(f"资金变更时间==={account_finance.updated_at}")
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# print(f"总资金==={account_finance.nav}")
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# print(f"可用资金==={account_finance.available}")
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# print(f"持仓占用资金==={account_finance.frozen}")
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# print(f"冻结资金==={account_finance.order_frozen}")
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# print(f"盈亏===={account_finance.pnl}")
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# print(f"持仓浮动盈亏==={account_finance.fpnl}")
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# print(f"持仓市值==={account_finance.market_value}")
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# print(f"资金余额==={account_finance.balance}")
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# print(f"累计出入金额==={account_finance.cum_inout}")
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# print(f"累计交易金额==={account_finance.cum_trade}")
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# print(f"累计盈亏==={account_finance.cum_pnl}")
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# print(f"累计委托==={account_finance.cum_commission}")
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# print(f"最后一笔交易==={account_finance.last_trade}")
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# print(f"最后一笔委托==={account_finance.last_commission}")
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# print(f"最后一笔出入金额==={account_finance.last_inout}")
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# print(f"允许保释??==={account_finance.enable_bail}")
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# print(f"纯浮动盈亏？？==={account_finance.fpnl_diluted}")
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# 保持开启 ， 为了实现永不完成这堆初始化线程 ，因为一旦完成，整体获取current数据变慢为15S+
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while True: # 不得擅动！！！
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time.sleep(10)
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# 获取tick数据
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def on_tick(context, tick):
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print(f"tick==={tick}")
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pass
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print(f"tick==={tick}")
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__process(tick)
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# 初始化 subscribe_datas 数据
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# subscribe_datas = None
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# 获取subscribe_data数据 。
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def get_subscribe_data():
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set_token("6c1dbe95191fb77cced9d805cb9c853805551ddb")
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logging.info(f"get_subscribe_data进入")
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while True:
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# logging.info(f"运行中...")
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# print(f"运行中...")
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# 获取当前时间
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# now = time.time()
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try:
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now_start = time.time()
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# print(f"now_start=={now_start}")
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subscribe_datas = subscribe('SHSE.603839,SHSE.6003839,SHSE.600805', frequency='tick', count=1, unsubscribe_previous=False)
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now_end = time.time()
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# print(f"now_end=={now_end}")
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now_start_to_end = now_end - now_start
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for subscribe_data in subscribe_datas:
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# print(f"subscribe_data==={subscribe_data}")
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try:
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__process(subscribe_data)
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except Exception as e:
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logging.exception(e)
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# basic_info = basic_info_symbols_dict.get(subscribe_data['symbol'])
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print("异常：", subscribe_data)
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print(f"now_start_to_end==={round(now_start_to_end, 2)} 秒")
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except Exception as e:
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logging.exception(e)
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def __process(subscribe_data):
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symbol = subscribe_data['symbol']
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print(f"symbol===={symbol}")
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# 为持仓代码创建一个初始集合
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global_position_symbols = set()
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# 仓位管理策略（核心逻辑流程：先卖后买，止盈止损）
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# 如果有持仓，那么就看开盘价是否为涨停价（是则不卖，炸板则卖），开盘价是否为当日涨幅>5%（是则不卖，走跌则卖），否则开盘走跌振幅大于1.5%则卖。
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def position_management():
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# 仓位管理及查询函数
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def position_query():
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Account_positions = global_context.account(account_id = account_id).positions() # 查询当前账户全部持仓
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# print(f"Account_positions==={Account_positions}")
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for d in Account_positions:
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global_position_symbols.add(d['symbol'])
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print(
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f"仓位管理前持仓===============================================================【{global_position_symbols}】")
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print(
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f"仓位管理前持仓数量===============================================================【{len(global_position_symbols)}】")
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for d in Account_positions:
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symbol = d['symbol']
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symbol_code = symbol.split('.')[1] # 将symbol转为纯数字编号
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available_now = d['available_now']
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# price = d['price'] #当前行情价，，，非最新价，没有实际意义不要用！！！
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vwap = d['vwap']
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volume = d['volume']
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volume_today = d['volume_today']
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volume_yesterday = volume - volume_today
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new_price = current(symbol, fields='price')[0]['price']
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# print(symbol,available_now, price, vwap)
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instruments = get_instruments(symbols=symbol, exchanges=None, sec_types=None, names=None,
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fields="symbol,sec_name,pre_close,upper_limit",
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df=False)
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if available_now != 0: # 如果可用资金不等于0 昨日收盘价获取到非0值时（不知名原因导致有些票会获取到0值导致后续公式报错，阻止intraday_growth函数正常运行）
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# print(f"持仓可用股{symbol}")
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history_data = history(symbol=symbol, frequency='1d', start_time=Double_Pre_trading_day,
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end_time=Pre_trading_day, fields='open, close, low, high, eob',
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adjust=ADJUST_PREV, df=False)
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# print(f"len(history_data)==={len(history_data)}history_data========={history_data} ")
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print(
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f"【{instruments[0]['sec_name']}】上个交易日 最高{history_data[1]['high']}，收盘{history_data[1]['close']}")
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# 获取昨日涨停价 目前函数中的pre_close应该传入前天的收盘价，而非昨天的
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yesterday_limit_up_price = basic_methods.limit_up_price(history_data[0]['close'])
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today_limit_up_price = basic_methods.limit_up_price(history_data[1]['close'])
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# print(f'涨停价算对了吗？{yesterday_top_price}')
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if history_data[0]['close'] == 0:
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print(f"{symbol}的修改后的history_data的昨日收盘价也为000000000000000000")
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today_growth = basic_methods.intraday_growth(new_price, history_data[1]['close'])
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# 如果持仓股 昨日未涨停 并且 今日最新价不等于今日涨停价 并且 今日涨幅小于3%
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if abs(yesterday_limit_up_price - float(decimal.Decimal(history_data[1]['close']))) < 0.001:
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print(
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f"昨日涨停 不卖！【{instruments[0]['sec_name']}】它的今日涨幅为{today_growth}。它的最新价：{new_price}，它的昨日收盘价：{history_data[1]['close']}，它的昨日涨停价{yesterday_limit_up_price}")
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elif today_limit_up_price == new_price:
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print(
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f"今日涨停 不卖！【{instruments[0]['sec_name']}】它的今日涨幅为{today_growth}。它的最新价：{new_price}，它的昨日收盘价：{history_data[1]['close']}，它的昨日涨停价{yesterday_limit_up_price}")
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elif today_growth > 2:
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print(
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f"开盘涨幅大于2% 不卖！【{instruments[0]['sec_name']}】它的今日涨幅为{today_growth}。它的最新价：{new_price}，它的昨日收盘价：{history_data[1]['close']}，它的昨日涨停价{yesterday_limit_up_price}")
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else:
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print(f"卖前持仓数量{len(global_position_symbols)}")
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sell_order(symbol, volume_yesterday, instruments[0]['sec_name'])
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if history_data[1]['close'] == history_data[1]['high']:
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print(f"昨日涨停可能判断出错！！!{instruments[0]['sec_name']}")
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print(
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f"持仓可用今日开盘涨幅【{instruments[0]['sec_name']}】,它的今日涨幅为{today_growth}。它的最新价：{new_price}，它的昨日收盘价：{history_data[1]['close']}，它的昨日涨停价{yesterday_limit_up_price},它的前日收盘价：{history_data[0]['close']}")
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# print(f"持仓股 历史数据{history_data}")
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print(f"--------------------------------------------------------------------------------------[分割线]")
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if available_now == 0: # 如果可用资金等于0,又在持仓中，就是今天新增持仓
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global_addition_position_symbols.add(symbol)
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print(f"今日新增持仓数量==【{len(global_addition_position_symbols)}】")
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# 仓位管理时间定时执行函数 （默认设定为9:31）
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def check_time():
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# now = global_context.now # 获取当前时间
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now = datetime.datetime.now()
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hour = now.hour
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minute = now.minute
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if hour > 9 or (hour == 9 and minute >= 31):
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print(f"开盘时间到了:{now}")
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position_query()
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return False # 返回False，表示结束循环
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else:
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return True # 返回True，表示继续循环
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# 使用while循环和check_time函数检查时间，并设置固定的时间间隔
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while check_time():
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time.sleep(1)
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# 下单卖出函数（以手数卖）
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def sell_order(symbol, volume_yesterday, sec_name):
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set_account_id('aaee2221-839c-11ee-a7cd-00163e022aa6')
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print(f"命令卖掉【{sec_name}】")
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order_volume(symbol=symbol, volume=volume_yesterday, side=OrderSide_Sell, order_type=OrderType_Market,
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position_effect=PositionEffect_Close)
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print(f"卖票结果提示：下单成功：【{sec_name}】")
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# 卖票后移除持仓代码集合
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# global_position_symbols.remove(symbol)
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# print(f"卖后持仓数量{len(global_position_symbols)}")
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# 下单买入函数（以价钱买）
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def buy_order(current_data,ratio_value):
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now = datetime.datetime.now()
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hour = now.hour
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minute = now.minute
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if hour > 9 or (hour == 9 and minute >= 30):
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set_account_id('aaee2221-839c-11ee-a7cd-00163e022aa6')
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result = order_value(symbol=current_data['symbol'], value=ratio_value, side=OrderSide_Buy, order_type=OrderType_Market,
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position_effect=PositionEffect_Open)
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print(f"result===={result}")
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print('下单成功', current_data['symbol'])
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# global_position_symbols.add(current_data['symbol'])
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# global_addition_position_symbols.add(current_data['symbol'])
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# print(f"当前持仓数量:::{len(global_position_symbols)}")
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# print(f"今日新增持仓数量:::{len(global_addition_position_symbols)}")
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else:
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print('未到开盘时间不允许下单', current_data['symbol'])
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# 模式选择
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if __name__ == '__main__':
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'''
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strategy_id策略ID,由系统生成
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filename文件名,请与本文件名保持一致
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mode实时模式:MODE_LIVE回测模式:MODE_BACKTEST
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token绑定计算机的ID,可在系统设置-密钥管理中生成
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backtest_start_time回测开始时间
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backtest_end_time回测结束时间
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backtest_adjust股票复权方式不复权:ADJUST_NONE前复权:ADJUST_PREV后复权:ADJUST_POST
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backtest_initial_cash回测初始资金
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backtest_commission_ratio回测佣金比例
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backtest_slippage_ratio回测滑点比例
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'''
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run(
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strategy_id='507bb60a-a919-11ee-b6ad-0ae0afd621cd',
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filename='position_management.py',
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mode=MODE_LIVE, # 实时模式
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# mode=MODE_BACKTEST, #回测模式
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token='6c1dbe95191fb77cced9d805cb9c853805551ddb',
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backtest_start_time='2024-06-11 09:30:00',
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backtest_end_time='2024-06-11 10:00:00',
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backtest_adjust=ADJUST_PREV,
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backtest_initial_cash=500000, # 实时模式测试状态下帐户金额由掘金客户端所连接的账户绑定，所以在客户端去充值就可以了（虚拟充值）
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backtest_commission_ratio=0.0001,
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backtest_slippage_ratio=0.0001)
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