class TqsdkClient(LocalClient):
def __init__(self, **kwargs):
super().__init__()
self.p = Thread(target=self.init_api, args=())
self.p.start()
self.api = None
def init_api(self):
self.api = TqApi()
while True:
self.api.wait_update()
def get(self, *argsm, **params):
""" 调用他们的历史数据 """
# tick数据
if params.get("level") == "tick":
del params['level']
temp = self.api.get_tick_serial(*argsm, **params)
return temp
# 分钟线数据
if params.get("level") != "tick":
return self.api.get_kline_serial(*argsm, **params)
def set(self):
pass
class TqSdkClient(LocalClient):
def __init__(self, **kwargs):
self.api = TqApi()
def get(self, **params):
""" 调用他们的历史数据 """
if params.get("level") == "tick":
return self.api.get_tick_serial(*self._parse_params(params))
if params.get("level") != "tick":
return self.api.get_kline_serial(*self._parse_params(params))
def _parse_params(self, params):
"""
将参数解析为tq可以理解的方式
都需要返回一个*args
"""
level = params.get("level")
local_symbol = params.get("local_symbol")
length = params.get("length")
if level != "tick":
return [local_symbol, self.get_seconds(level), length]
return [local_symbol, length]
@staticmethod
def get_seconds(level) -> int:
"""
将level转换到秒
* level: 数据等级
"""
min_r = r"(\d{1,2})min"
hour_r = r"(\d{1,2})h"
day_r = r"(\d{1,2})day"
if "min" in level:
try:
return int(re.match(min_r, level).group(1)) * 60
except Exception:
raise ValueError("你输入的level数据等级存在问题, 请检查是否符合1min这样的格式")
if "h" in level:
try:
return int(re.match(hour_r, level).group(1)) * 3600
except Exception:
raise ValueError("你输入的level数据等级存在问题, 请检查是否符合1min这样的格式")
if "day" in level:
try:
return int(re.match(day_r, level).group(1)) * 3600 * 24
except Exception:
raise ValueError("你输入的level数据等级存在问题, 请检查是否符合1day这样的格式")
def test_get_tick_serial(self):
"""
获取tick数据
"""
# 预设服务器端响应
dir_path = os.path.dirname(os.path.realpath(__file__))
self.mock.run(
os.path.join(dir_path, "log_file",
"test_md_basic_get_tick_serial.script.lzma"))
# 测试: 获取tick数据
utils.RD = random.Random(2)
api = TqApi(_ins_url=self.ins_url_2019_07_03,
_td_url=self.td_url,
_md_url=self.md_url)
ticks = api.get_tick_serial("SHFE.cu1909")
self.assertEqual(ticks.iloc[-1].id, 2822951.0)
self.assertEqual(ticks.iloc[-1].datetime, 1.5686171999995e+18)
self.assertEqual(ticks.iloc[-1].last_price, 47580)
self.assertEqual(ticks.iloc[-1].average, 47732.3516)
self.assertEqual(ticks.iloc[-1].highest, 47860)
self.assertEqual(ticks.iloc[-1].lowest, 47580)
self.assertEqual(ticks.iloc[-1].ask_price1, 47650)
self.assertEqual(ticks.iloc[-1].ask_volume1, 10)
self.assertEqual(ticks.iloc[-1].bid_price1, 47570)
self.assertEqual(ticks.iloc[-1].bid_volume1, 5)
self.assertEqual(ticks.iloc[-1].volume, 9020)
self.assertEqual(ticks.iloc[-1].amount, 2152729000.0)
self.assertEqual(ticks.iloc[-1].open_interest, 6940)
self.assertEqual(ticks.iloc[-1].duration, 0)
# 其他调用方式
self.assertEqual(ticks.open_interest.iloc[-1], 6940)
self.assertEqual(ticks["open_interest"].iloc[-2], 6940)
self.assertEqual(ticks.iloc[-1]["ask_price1"], 47650)
# 报错测试
self.assertRaises(Exception, api.get_tick_serial, "SHFE.au1999")
self.assertRaises(AttributeError, ticks.iloc[-1].__getattribute__,
"dur")
self.assertRaises(KeyError, ticks.iloc[-1].__getitem__, "dur")
api.close()
class CuatroStrategy(Process):
''''''
author = 'XIAO LI'
boll_window = 20
boll_dev = 1.8
rsi_window = 14
rsi_signal = 20
fast_window = 4
slow_window = 26
trailing_long = 0.5
trailing_short = 0.3
vol = 1
boll_up = float('nan')
boll_down = float('nan')
rsi_value = float('nan')
rsi_long = float('nan')
rsi_short = float('nan')
fast_ma = float('nan')
slow_ma = float('nan')
ma_trend = float('nan')
intra_trade_high = float('nan')
intra_trade_low = float('nan')
long_stop = float('nan')
short_stop = float('nan')
parameters = [
'boll_window'
'boll_dev'
'rsi_window'
'rsi_signal'
'fast_window'
'slow_window'
'trailing_long'
'trailing_short'
'vol'
]
variables = [
'boll_up'
'boll_down'
'rsi_value'
'rsi_long'
'rsi_short'
'fast_ma'
'slow_ma'
'ma_trend'
'intra_trade_high'
'intra_trade_low '
'long_stop'
'short_stop'
]
def __init__(self, symbol):
Process.__init__(self)
self.symbol = symbol
self.rsi_long = 50 + self.rsi_signal
self.rsi_short = 50 - self.rsi_signal
self.api = TqApi(TqSim(init_balance=50000))
self.now = datetime.now()
self.target_pos = TargetPosTask(self.api, self.symbol)
self.ticks = self.api.get_tick_serial(self.symbol)
self.klines5 = self.api.get_kline_serial(self.symbol, 60 * 5)
self.klines15 = self.api.get_kline_serial(self.symbol, 60 * 15)
self.position = self.api.get_position(self.symbol)
def on_init(self):
print(self.now, '策略初始化')
def on_start(self):
print(self.now, '策略启动')
def on_stop(self):
print(self.now, '策略停止')
def on_tick(self, ticks):
if self.api.is_changing(ticks, 'datetime'):
if self.position.pos_long == 0 and self.position.pos_short == 0:
if self.ma_trend > 0 and self.rsi_value >= self.rsi_long and ticks.iloc[-1].last_price > self.boll_up:
self.target_pos.set_target_volume(self.vol)
self.intra_trade_high = ticks.iloc[-1].last_price
if self.ma_trend < 0 and self.rsi_value <= self.rsi_short and ticks.iloc[
-1].last_price < self.boll_down:
self.target_pos.set_target_volume(-self.vol)
self.intra_trade_low = ticks.iloc[-1].last_price
elif self.position.pos_long > 0:
self.intra_trade_high = max(self.intra_trade_high, ticks.iloc[-1].last_price)
self.long_stop = (self.intra_trade_high - self.trailing_long * (self.boll_up - self.boll_down))
if ticks.iloc[-1].last_price < self.long_stop:
self.target_pos.set_target_volume(0)
self.intra_trade_high = float('nan')
else:
self.intra_trade_low = min(self.intra_trade_low, ticks.iloc[-1].last_price)
self.short_stop = (self.intra_trade_low + self.trailing_short * (self.boll_up - self.boll_down))
if ticks.iloc[-1].last_price > self.short_stop:
self.target_pos.set_target_volume(0)
self.intra_trade_low = float('nan')
def on_5minbar(self, klines5):
if self.api.is_changing(klines5, 'datetime'):
boll = ta.BOLL(klines5.iloc[:-1], self.boll_window, self.boll_dev).iloc[-1]
self.boll_up = boll['top']
self.boll_down = boll['bottom']
self.rsi_value = ta.RSI(klines5.iloc[:-1], self.rsi_window).iloc[-1]['rsi']
def on_15minbar(self, klines15):
if self.api.is_changing(klines15, 'datetime'):
self.fast_ma = ta.SMA(klines15.iloc[:-1], self.fast_window, 2)
self.slow_ma = ta.SMA(klines15.iloc[:-1], self.slow_window, 2)
if self.fast_ma > self.slow_ma:
self.ma_trend = 1
elif self.fast_ma < self.slow_ma:
self.ma_trend = -1
else:
self.ma_trend = 0
def on_order(self):
if self.api.is_changing(self.api.get_order()):
pass
def on_trade(self):
if self.api.is_changing(self.api.get_trade()):
pass
def run(self):
self.on_init()
self.on_start()
while True:
self.api.wait_update()
self.on_tick(self.ticks)
self.on_5minbar(self.klines5)
self.on_15minbar(self.klines15)
self.on_order()
self.on_trade()
self.on_stop()
################################################################################# from tqsdk import TqApi from tqsdk.tafunc import time_to_datetime from datetime import datetime import pandas as pd import numpy as np from functools import reduce ################################################################################# api = TqApi(web_gui=True) symbol = "SHFE.rb2010" close = "CLOSETODAY" # 平今方式 quote = api.get_quote(symbol) ticks = api.get_tick_serial(symbol) position = api.get_position(symbol) GRID_AMOUNT = 10 grid_region_long = [1 * quote.price_tick] * GRID_AMOUNT # 多头每格价格跌幅(网格密度) grid_region_short = [1 * quote.price_tick] * GRID_AMOUNT # 空头每格价格涨幅(网格密度) grid_volume_long = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55] # 多头每格持仓手数 grid_volume_short = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55] # 空头每格持仓手数 profit_long = 1 * quote.price_tick # 多头利润 profit_short = 1 * quote.price_tick # 空头利润 is_clear_all1 = False is_clear_all2 = False ################################################################################# def reset_df_long(quote, GRID_AMOUNT, grid_region_long, grid_volume_long):
#自然灾害（5），趋势影响（4），政策影响（3）
# 止损区间，突破HH2和LL2，如果持仓方向不对，坚决出厂
# LL1 和 HH 2 安全区间
BAR_LENGTH = 380
#************************ var ****************************
#/************************init***********************************/
mylog.info("walkswing", "game begin")
SYMBOL = "SHFE.rb2010"
api = TqApi(TqSim())
klines = api.get_kline_serial(SYMBOL, 60, BAR_LENGTH)
day_klines = api.get_kline_serial(SYMBOL, 24 * 60 * 60)
serial = api.get_tick_serial(SYMBOL)
# 账户情况
account = api.get_account()
print(account)
# 运行时记录
avg_recorder = AvgRecorder("AvgRecord", 60)
pos_manage = PositionManger(api, SYMBOL)
strage_manager = StrategyManager(pos_manage, SYMBOL)
mc_indictor = McIndictors(SYMBOL, strage_manager)
mc_indictor.set_avg_recorder(avg_recorder)
strage_manager.set_indictor(mc_indictor)
strage_manager.set_avg_record(avg_recorder)
pos_manage.set_logger(mylog)
strage_manager.set_logger(mylog)
log.logger.info('初始账户权益：{0}'.format(account.balance))
log.logger.info('初始浮动盈亏：{0}'.format(account.float_profit))
SEC_LIST = []
for g_sec in g_security:
ls = api.get_quote('KQ.m@%s' % g_sec)
SEC_LIST.append(ls.underlying_symbol)
log.logger.info('品种集合：{0}'.format(SEC_LIST))
# 获取主力合约
# domain = api.get_quote("[email protected]")
# 获取主力合约的K线引用
for sec in SEC_LIST:
positions[sec] = api.get_position(sec)
klines_dict[sec] = api.get_kline_serial(sec, para['BAR_UNIT'],
para['BAR_NUM'])
quotes[sec] = api.get_quote(sec)
ticks[sec] = api.get_tick_serial(sec)
un_buy_open[sec] = []
un_sell_open[sec] = []
un_buy_close[sec] = []
un_sell_close[sec] = []
g_HS[sec] = []
g_LS[sec] = []
g_ZS[sec] = []
OPEN_DICT[sec] = open_num_list[SEC_LIST.index(sec)]
orders = api.get_order()
for order_id, val in orders.items():
secid = val['exchange_id'] + '.' + val['instrument_id']
if val['direction'] == 'BUY' and (
val['offset'] == 'OPEN') and val['status'] != 'FINISHED':
un_buy_open[secid].append(order_id)
elif val['direction'] == 'SELL' and (
from tqsdk import TqApi
import datetime
api = TqApi()
# 获得 cu2001 tick序列的引用
ticks = api.get_tick_serial("SHFE.cu2001", 200)
print(ticks)
# 获得 cu2001 10秒K线的引用
# klines = api.get_kline_serial("SHFE.cu2001", 10)
# print(datetime.datetime.fromtimestamp(klines.iloc[-1]["datetime"] / 1e9))
#
# while True:
# api.wait_update()
# # 判断整个tick序列是否有变化
# if api.is_changing(ticks):
# # ticks.iloc[-1]返回序列中最后一个tick
# print("tick变化", ticks.iloc[-1])
# # 判断最后一根K线的时间是否有变化，如果发生变化则表示新产生了一根K线
# if api.is_changing(klines.iloc[-1], "datetime"):
# # datetime: 自unix epoch(1970-01-01 00:00:00 GMT)以来的纳秒数
# print("新K线", datetime.datetime.fromtimestamp(klines.iloc[-1]["datetime"] / 1e9))
# # 判断最后一根K线的收盘价是否有变化
# if api.is_changing(klines.iloc[-1], "close"):
# # klines.close返回收盘价序列
# print("K线变化", datetime.datetime.fromtimestamp(klines.iloc[-1]["datetime"] / 1e9), klines.close.iloc[-1])
'''
klines
'''
from tqsdk import TqApi, TqSim, tafunc
from tqsdk.tafunc import time_to_datetime
api = TqApi()
klines = api.get_kline_serial("SHFE.rb2005", 60)
serial = api.get_tick_serial("SHFE.rb2005")
#print(time_to_datetime(serial.iloc[-1].datetime))
#llv = tafunc.llv(klines.low, 5) # 求5根k线最低点(包含当前k线)
#print(list(llv))
while True:
api.wait_update()
print(serial.iloc[-1].bid_price1)
print('波动太大平仓')
当前状态 = '寻求开仓机会'
def 平仓检测(行情, tick序列):
global 当前状态
if 当前状态 == '等待平仓':
持仓检测模块(行情, tick序列)
固定止损止盈(行情, tick序列)
一分钟检测(行情, tick序列)
try:
api = TqApi(acc,
backtest=TqBacktest(start_dt=date(2019, 11, 1),
end_dt=date(2019, 11, 8)))
行情 = api.get_kline_serial(品种, 60, 100)
tick序列 = api.get_tick_serial(品种, 检测最近多少tick序列)
while True:
api.wait_update()
开仓判断(行情, tick序列)
检测委托(行情, tick序列)
平仓检测(行情, tick序列)
except BacktestFinished as e:
# 回测结束时会执行这里的代码
print(acc.trade_log)
pass
api.close()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'chengzhi'
from tqsdk import TqApi, TqSim
import datetime
api = TqApi()
# 获得cu1906 tick序列的引用
ticks = api.get_tick_serial("SHFE.cu1906")
# 获得cu1906 10秒K线的引用
klines = api.get_kline_serial("SHFE.cu1906", 10)
print(datetime.datetime.fromtimestamp(klines.iloc[-1]["datetime"] / 1e9))
while True:
api.wait_update()
# 判断整个tick序列是否有变化
if api.is_changing(ticks):
# ticks.iloc[-1]返回序列中最后一个tick
print("tick变化", ticks.iloc[-1])
# 判断最后一根K线的时间是否有变化，如果发生变化则表示新产生了一根K线
if api.is_changing(klines.iloc[-1], "datetime"):
# datetime: 自unix epoch(1970-01-01 00:00:00 GMT)以来的纳秒数
print(
"新K线",
datetime.datetime.fromtimestamp(klines.iloc[-1]["datetime"] / 1e9))
# 判断最后一根K线的收盘价是否有变化
if api.is_changing(klines.iloc[-1], "close"):
# klines.close返回收盘价序列
print(
"K线变化",
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'chengzhi'
from tqsdk import TqApi
import datetime
api = TqApi("SIM")
# 获得cu1812 tick序列的引用
ticks = api.get_tick_serial("SHFE.cu1812")
# 获得cu1812 10秒K线的引用
klines = api.get_kline_serial("SHFE.cu1812", 10)
while True:
api.wait_update()
# 判断整个tick序列是否有变化
if api.is_changing(ticks):
# ticks[-1]返回序列中最后一个tick
print("tick变化", ticks[-1])
# 判断最后一根K线的时间是否有变化，如果发生变化则表示新产生了一根K线
if api.is_changing(klines[-1], "datetime"):
# datetime: 自unix epoch(1970-01-01 00:00:00 GMT)以来的纳秒数
print("新K线", datetime.datetime.fromtimestamp(klines[-1]["datetime"]/1e9))
# 判断最后一根K线的收盘价是否有变化
if api.is_changing(klines[-1], "close"):
# klines.close返回收盘价序列
print("K线变化", datetime.datetime.fromtimestamp(klines[-1]["datetime"]/1e9), klines.close[-1])
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'chengzhi'
from tqsdk import TqApi
import datetime
api = TqApi()
# 获得 cu2003 tick序列的引用
ticks = api.get_tick_serial("SHFE.cu2003")
# 获得 cu2003 10秒K线的引用
klines = api.get_kline_serial("SHFE.cu2003", 10)
print(datetime.datetime.fromtimestamp(klines.iloc[-1]["datetime"] / 1e9))
while True:
api.wait_update()
# 判断整个tick序列是否有变化
if api.is_changing(ticks):
# ticks.iloc[-1]返回序列中最后一个tick
print("tick变化", ticks.iloc[-1])
# 判断最后一根K线的时间是否有变化，如果发生变化则表示新产生了一根K线
if api.is_changing(klines.iloc[-1], "datetime"):
# datetime: 自unix epoch(1970-01-01 00:00:00 GMT)以来的纳秒数
print(
"新K线",
datetime.datetime.fromtimestamp(klines.iloc[-1]["datetime"] / 1e9))
# 判断最后一根K线的收盘价是否有变化
if api.is_changing(klines.iloc[-1], "close"):
# klines.close返回收盘价序列
print(
"K线变化",
from tqsdk import TqApi
api = TqApi(
web_gui=True,
) #web_gui="http://172.20.155.135:62964"要将你订阅的K线或策略图形化显示, 只需在 TqApi() 中传入参数 web_gui = True即可:
quote = api.get_quote("SHFE.rb2005")
print(quote.last_price, quote.volume)
klines = api.get_kline_serial("SHFE.rb2005", 1 * 60) # 获取K线 pandas结构 60是秒数
#klines = api.get_kline_serial(["SHFE.rb2005","DCE.i2005", "CZCE.AP005"], 60) # 入一个合约列表作为参数，来获取包含多个合约数据的K线:
ticks = api.get_tick_serial(
"SHFE.rb2005",
data_length=1,
)
# for i in klines.iterrows():
# print (i)
#
while True:
api.wait_update()
print(ticks.to_dict())
# print("最后一根K线收盘价", klines.close.iloc[-1])
# print (quote.datetime, quote.last_price)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'chengzhi'
from tqsdk import TqApi, TqAuth
import datetime
api = TqApi(auth=TqAuth("信易账户", "账户密码"))
# 获得 i2209 tick序列的引用
ticks = api.get_tick_serial("DCE.i2209")
# 获得 i2209 10秒K线的引用
klines = api.get_kline_serial("DCE.i2209", 10)
print(datetime.datetime.fromtimestamp(klines.iloc[-1]["datetime"] / 1e9))
while True:
api.wait_update()
# 判断整个tick序列是否有变化
if api.is_changing(ticks):
# ticks.iloc[-1]返回序列中最后一个tick
print("tick变化", ticks.iloc[-1])
# 判断最后一根K线的时间是否有变化，如果发生变化则表示新产生了一根K线
if api.is_changing(klines.iloc[-1], "datetime"):
# datetime: 自unix epoch(1970-01-01 00:00:00 GMT)以来的纳秒数
print(
"新K线",
datetime.datetime.fromtimestamp(klines.iloc[-1]["datetime"] / 1e9))
# 判断最后一根K线的收盘价是否有变化
if api.is_changing(klines.iloc[-1], "close"):
# klines.close返回收盘价序列
print(
"K线变化",
class TqsdkClient(LocalClient):
def __init__(self, **kwargs):
super().__init__()
self.p = Thread(target=self.init_api, args=())
self.p.start()
self.api = None
def init_api(self):
self.api = TqApi()
while True:
sleep(60)
def get(self, *argsm, **params):
""" 调用他们的历史数据 """
# tick数据
if params.get("level") == "tick":
return self.api.get_tick_serial(*self._parse_params(params))
# 分钟线数据
if params.get("level") != "tick":
return self.api.get_kline_serial(*self._parse_params(params))
def set(self):
pass
def _parse_params(self, params):
"""
解析参数
"""
level = params.get("level")
local_symbol = params.get("local_symbol")
length = params.get("length")
if level != "tick":
return [local_symbol, self.get_seconds(level), length]
return [local_symbol, length]
@staticmethod
def get_seconds(level) -> int:
"""
将level转换到秒
* level: 数据等级
"""
min_r = r"(\d{1,2})min"
hour_r = r"(\d{1,2})h"
day_r = r"(\d{1,2})day"
if "min" in level:
try:
return int(re.match(min_r, level).group(1)) * 60
except Exception:
raise ValueError("你输入的level数据等级存在问题, 请检查是否符合1min这样的格式")
if "h" in level:
try:
return int(re.match(hour_r, level).group(1)) * 3600
except Exception:
raise ValueError("你输入的level数据等级存在问题, 请检查是否符合1min这样的格式")
if "day" in level:
try:
return int(re.match(day_r, level).group(1)) * 3600 * 24
except Exception:
raise ValueError("你输入的level数据等级存在问题, 请检查是否符合1day这样的格式")