def update_account(self, dt, balance):
"""
更新资金曲线
:param dt:
:param balance: 账号级别，直接使用账号得的balance；
:return:
"""
tick = TickData(gateway_name='Fund',
symbol=self.symbol,
exchange=Exchange.LOCAL,
datetime=dt)
tick.last_price = balance
tick.volume = 1
tick.ask_price_1 = balance
tick.ask_volume_1 = 1
tick.bid_price_1 = balance
tick.bid_volume_1 = 1
tick.date = tick.datetime.strftime('%Y-%m-%d')
tick.time = tick.datetime.strftime('%H:%M:%S')
tick.trading_day = get_trading_date(dt)
tick.open_interest = balance
if self.inited:
self.kline.on_tick(tick)
# 如果是从账号更新，无法更新持仓盈亏
self.closed_profit = balance
self.holding_profit = 0
def update_strategy(self, dt, closed_pnl=0, hold_pnl=0):
"""
更新资金曲线
:param dt:
:param closed_pnl: 策略提供的平仓盈亏
:param hold_pnl: 策略提供的持仓盈亏
:return:
"""
# 获取当前bar的平仓权益
open_interest = 0
if len(self.kline.line_bar) > 0:
open_interest = self.kline.line_bar[-1].open_interest
if closed_pnl != 0:
self.write_log(u'策略平仓收益:{}->{}'.format(open_interest,
open_interest + closed_pnl))
open_interest += closed_pnl
tick = TickData(gateway_name='Fund',
symbol=self.symbol,
exchange=Exchange.LOCAL,
datetime=dt)
tick.last_price = open_interest + hold_pnl
tick.volume = 1
tick.ask_price1 = open_interest + hold_pnl
tick.ask_volume1 = 1
tick.bid_price1 = open_interest + hold_pnl
tick.bid_volume1 = 1
tick.datetime = dt
tick.open_interest = open_interest
tick.date = tick.datetime.strftime('%Y-%m-%d')
tick.time = tick.datetime.strftime('%H:%M:%S')
tick.trading_day = get_trading_date(dt)
if self.inited:
self.kline.on_tick(tick)
self.closed_profit = open_interest
self.holding_profit = hold_pnl
def onRtnDepthMarketData(self, data: dict):
"""
Callback of tick data update.
"""
symbol = data["InstrumentID"]
exchange = symbol_exchange_map.get(symbol, "")
if not exchange:
return
timestamp = f"{data['ActionDay']} {data['UpdateTime']}.{int(data['UpdateMillisec']/100)}"
dt = datetime.strptime(timestamp, "%Y%m%d %H:%M:%S.%f")
# 不处理开盘前的tick数据
if dt.hour in [8, 20] and dt.minute < 59:
return
if exchange is Exchange.CFFEX and dt.hour == 9 and dt.minute < 14:
return
tick = TickData(symbol=symbol,
exchange=exchange,
datetime=dt,
date=dt.strftime('%Y-%m-%d'),
time=dt.strftime('%H:%M:%S.%f'),
trading_day=get_trading_date(dt),
name=symbol_name_map[symbol],
volume=data["Volume"],
open_interest=data["OpenInterest"],
last_price=data["LastPrice"],
limit_up=data["UpperLimitPrice"],
limit_down=data["LowerLimitPrice"],
open_price=adjust_price(data["OpenPrice"]),
high_price=adjust_price(data["HighestPrice"]),
low_price=adjust_price(data["LowestPrice"]),
pre_close=adjust_price(data["PreClosePrice"]),
bid_price_1=adjust_price(data["BidPrice1"]),
ask_price_1=adjust_price(data["AskPrice1"]),
bid_volume_1=data["BidVolume1"],
ask_volume_1=data["AskVolume1"],
gateway_name=self.gateway_name)
if data["BidVolume2"] or data["AskVolume2"]:
tick.bid_price_2 = adjust_price(data["BidPrice2"])
tick.bid_price_3 = adjust_price(data["BidPrice3"])
tick.bid_price_4 = adjust_price(data["BidPrice4"])
tick.bid_price_5 = adjust_price(data["BidPrice5"])
tick.ask_price_2 = adjust_price(data["AskPrice2"])
tick.ask_price_3 = adjust_price(data["AskPrice3"])
tick.ask_price_4 = adjust_price(data["AskPrice4"])
tick.ask_price_5 = adjust_price(data["AskPrice5"])
tick.bid_volume_2 = adjust_price(data["BidVolume2"])
tick.bid_volume_3 = adjust_price(data["BidVolume3"])
tick.bid_volume_4 = adjust_price(data["BidVolume4"])
tick.bid_volume_5 = adjust_price(data["BidVolume5"])
tick.ask_volume_2 = adjust_price(data["AskVolume2"])
tick.ask_volume_3 = adjust_price(data["AskVolume3"])
tick.ask_volume_4 = adjust_price(data["AskVolume4"])
tick.ask_volume_5 = adjust_price(data["AskVolume5"])
self.gateway.on_tick(tick)
def get_history_transaction_data(self, symbol, date, cache_folder=None):
"""获取当某一交易日的历史成交记录"""
ret_datas = []
if isinstance(date, datetime):
date = date.strftime('%Y%m%d')
if isinstance(date, str):
date = int(date)
self.connect()
cache_symbol = symbol
cache_date = str(date)
max_data_size = sys.maxsize
symbol = symbol.upper()
if '99' in symbol:
# 查询的是指数合约
symbol = symbol.replace('99', 'L9')
tdx_index_symbol = symbol
else:
# 查询的是普通合约
tdx_index_symbol = get_underlying_symbol(symbol).upper() + 'L9'
q_size = QSIZE * 5
# 每秒 2个， 10小时
max_data_size = 1000000
# 优先从缓存加载
if cache_folder:
buffer_data = self.load_cache(cache_folder, cache_symbol,
cache_date)
if buffer_data:
self.write_log(u'使用缓存文件')
return True, buffer_data
self.write_log(u'开始下载{} 历史{}分笔数据'.format(date, symbol))
cur_trading_date = get_trading_date()
if date == int(cur_trading_date.replace('-', '')):
return self.get_transaction_data(symbol)
try:
_datas = []
_pos = 0
while (True):
_res = self.api.get_history_transaction_data(
market=self.symbol_market_dict.get(tdx_index_symbol, 0),
date=date,
code=symbol,
start=_pos,
count=q_size)
if _res is not None:
for d in _res:
dt = d.pop('date')
# 星期1~星期6
if dt.hour >= 20 and 1 < dt.isoweekday() <= 6:
dt = dt - timedelta(days=1)
d.update({'datetime': dt})
elif dt.hour >= 20 and dt.isoweekday() == 1:
# 星期一取得20点后数据
dt = dt - timedelta(days=3)
d.update({'datetime': dt})
elif dt.hour < 8 and dt.isoweekday() == 1:
# 星期一取得8点前数据
dt = dt - timedelta(days=3)
d.update({'datetime': dt})
elif dt.hour >= 20 and dt.isoweekday() == 7:
# 星期天取得20点后数据，肯定是星期五夜盘
dt = dt - timedelta(days=2)
d.update({'datetime': dt})
elif dt.isoweekday() == 7:
# 星期日取得其他时间，必然是 星期六凌晨的数据
dt = dt - timedelta(days=1)
d.update({'datetime': dt})
else:
d.update({'datetime': dt})
# 接口有bug，返回价格*1000，所以要除以1000
d.update({'price': d.get('price', 0) / 1000})
_datas = sorted(_res, key=lambda s: s['datetime']) + _datas
_pos += min(q_size, len(_res))
if _res is not None and len(_res) > 0:
self.write_log(u'分段取分笔数据:{} ~{}, {}条,累计:{}条'.format(
_res[0]['datetime'], _res[-1]['datetime'], len(_res),
_pos))
else:
break
if len(_datas) >= max_data_size:
break
if len(_datas) == 0:
self.write_error(u'{}分笔成交数据获取为空'.format(date))
return False, _datas
# 缓存文件
if cache_folder:
self.save_cache(cache_folder, cache_symbol, cache_date, _datas)
return True, _datas
except Exception as ex:
self.write_error(
'exception in get_transaction_data:{},{},{}'.format(
symbol, str(ex), traceback.format_exc()))
self.write_error(u'当前异常服务器信息:{}'.format(self.best_ip))
self.write_log(u'重置连接')
self.api = None
self.connect(is_reconnect=True)
return False, ret_datas
def load(self):
"""
从本地csv文件恢复k线数据
:return:
"""
self.kline_file = str(
get_folder_path('data').joinpath('fund_{}.csv'.format(
self.kline_name)))
# 如果数据文件存在，则加载数据
if os.path.exists(self.kline_file):
self.write_log(u'加载{}数据'.format(self.kline_name))
df = pd.read_csv(self.kline_file)
dt_now = datetime.now()
df = df.set_index(pd.DatetimeIndex(df['datetime']))
for dt, bar_data in df.iterrows():
bar = BarData()
bar.symbol = self.symbol
bar.datetime = dt
bar.open_price = float(bar_data['open'])
bar.close_price = float(bar_data['close'])
bar.high_price = float(bar_data['high'])
bar.low_price = float(bar_data['low'])
bar.date = dt.strftime('%Y-%m-%d')
str_td = str(bar_data.get('trading_date', ''))
if len(str_td) == 8:
bar.trading_day = str_td[0:4] + '-' + str_td[
4:6] + '-' + str_td[6:8]
elif len(str_td) == 0:
bar.trading_day = bar.date
else:
bar.trading_day = get_trading_date(dt)
bar.time = dt.strftime('%H:%M:%S')
bar.open_interest = float(bar_data.get('open_interest', 0))
if self.use_renko:
self.kline.add_bar(bar)
else:
# bar得时间，与当前时间相隔超过一个小时，加入完整得bar
if (dt_now - dt).total_seconds() > 60 * 60:
self.kline.add_bar(bar,
bar_is_completed=True,
bar_freq=60)
# 可能是最后一根bar
else:
self.write_log(u'更新最后一根Bar:{},now:{}'.format(
dt, dt_now))
self.kline.add_bar(bar,
bar_is_completed=False,
bar_freq=dt.minute)
else:
self.write_log(u'当前没有资金历史K线文件:{}'.format(self.kline_file))
self.inited = True
# 设置 kline的输出文件
self.kline.export_filename = self.kline_file
self.kline.export_fields = [{
'name': 'datetime',
'source': 'bar',
'attr': 'datetime',
'type_': 'datetime'
}, {
'name': 'open',
'source': 'bar',
'attr': 'open_price',
'type_': 'float'
}, {
'name': 'high',
'source': 'bar',
'attr': 'high_price',
'type_': 'float'
}, {
'name': 'low',
'source': 'bar',
'attr': 'low_price',
'type_': 'float'
}, {
'name': 'close',
'source': 'bar',
'attr': 'close_price',
'type_': 'float'
}, {
'name': 'turnover',
'source': 'bar',
'attr': 'turnover',
'type_': 'float'
}, {
'name': 'volume',
'source': 'bar',
'attr': 'volume',
'type_': 'float'
}, {
'name': 'open_interest',
'source': 'bar',
'attr': 'open_interest',
'type_': 'float'
}]