#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'chengzhi'
from tqsdk.api import TqApi
api = TqApi("SIM")
# 获得 m1901 的持仓引用，当持仓有变化时 position 中的字段会对应更新
position = api.get_position("DCE.m1901")
# 获得资金账户引用，当账户有变化时 account 中的字段会对应更新
account = api.get_account()
# 下单并返回委托单的引用，当该委托单有变化时 order 中的字段会对应更新
order = api.insert_order(symbol="DCE.m1901",
direction="BUY",
offset="OPEN",
volume=5)
while True:
api.wait_update()
if api.is_changing(order, ["status", "volume_orign", "volume_left"]):
print("单状态: %s, 已成交: %d 手" %
(order["status"], order["volume_orign"] - order["volume_left"]))
if api.is_changing(position, "volume_long_today"):
print("今多头: %d 手" % (position["volume_long_today"]))
if api.is_changing(account, "available"):
print("可用资金: %.2f" % (account["available"]))
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'chengzhi'
from tqsdk.api import TqApi
api = TqApi("SIM")
# 获得 cu1812 的持仓引用，当持仓有变化时 position 中的字段会对应更新
position = api.get_position("SHFE.cu1812")
# 获得资金账户引用，当账户有变化时 account 中的字段会对应更新
account = api.get_account()
# 下单并返回委托单的引用，当该委托单有变化时 order 中的字段会对应更新
order = api.insert_order(symbol="SHFE.cu1812",
direction="BUY",
offset="OPEN",
volume=5)
while True:
api.wait_update()
if api.is_changing(order, ["status", "volume_orign", "volume_left"]):
print("单状态: %s, 已成交: %d 手" %
(order["status"], order["volume_orign"] - order["volume_left"]))
if api.is_changing(position, "volume_long_today"):
print("今多头: %d 手" % (position["volume_long_today"]))
if api.is_changing(account, "available"):
print("可用资金: %.2f" % (account["available"]))
def twap_table(api: TqApi,
symbol: str,
target_pos: int,
duration: int,
min_volume_each_step: int,
max_volume_each_step: int,
account: Optional[Union[TqAccount, TqKq, TqSim]] = None):
"""
返回基于 twap 策略的计划任务时间表。下单需要配合 TargetPosScheduler 使用。
Args:
api (TqApi): TqApi实例，该task依托于指定api下单/撤单
symbol (str): 拟下单的合约 symbol, 格式为 交易所代码.合约代码, 例如 "SHFE.cu1801"
target_pos (int): 目标持仓手数
duration (int): 算法执行的时长，以秒为单位，时长可以跨非交易时间段，但是不可以跨交易日
* 设置为 60*10, 可以是 10:10～10:15 + 10:30~10:35
min_volume_each_step (int): 调整持仓手数最小值，每步调整的持仓手数默认在最小和最大值中产生
max_volume_each_step (int): 调整持仓手数最大值，每步调整的持仓手数默认在最小和最大值中产生
account (TqAccount/TqKq/TqSim): [可选]指定发送下单指令的账户实例, 多账户模式下，该参数必须指定
Returns:
pandas.DataFrame: 本函数返回一个 pandas.DataFrame 实例. 表示一份计划任务时间表。每一行表示一项目标持仓任务，包含以下列:
+ interval: 当前这项任务的持续时间长度，单位为秒
+ target_pos: 当前这项任务的目标持仓
+ price: 当前这项任务的下单价格模式，支持 PASSIVE（排队价），ACTIVE（对价），None（不下单，表示暂停一段时间）
Example1::
from tqsdk import TqApi, TargetPosScheduler
from tqsdk.algorithm import twap_table
api = TqApi(auth="信易账户,用户密码")
quote = api.get_quote("CZCE.MA109")
# 设置twap任务参数
time_table = twap_table(api, "CZCE.MA109", -100, 600, 1, 5) # 目标持仓 -100 手，600s 内完成
print(time_table.to_string())
target_pos_sch = TargetPosScheduler(api, "CZCE.MA109", time_table)
# 启动循环
while not target_pos_sch.is_finished():
api.wait_update()
api.close()
Example2::
from tqsdk import TqApi, TargetPosScheduler
from tqsdk.algorithm import twap_table
api = TqApi(auth="信易账户,用户密码")
quote = api.get_quote("CZCE.MA109")
# 设置 twap 任务参数，
time_table = twap_table(api, "CZCE.MA109", -100, 600, 1, 5) # 目标持仓 -100 手，600s 内完成
# 定制化调整 time_table，例如希望第一项任务延迟 10s 再开始下单
# 可以在 time_table 的头部加一行
time_table = pandas.concat([
DataFrame([[10, 10, None]], columns=['interval', 'target_pos', 'price']),
time_table
], ignore_index=True)
target_pos_sch = TargetPosScheduler(api, "CZCE.MA109", time_table)
while not target_pos_sch.is_finished():
api.wait_update()
# 获取 target_pos_sch 实例所有的成交列表
print(target_pos_sch.trades_df)
# 利用成交列表，您可以计算出策略的各种表现指标，例如：
average_trade_price = sum(scheduler.trades_df['price'] * scheduler.trades_df['volume']) / sum(scheduler.trades_df['volume'])
print("成交均价:", average_trade_price)
api.close()
"""
account = api._account._check_valid(account)
if account is None:
raise Exception(f"多账户模式下, 需要指定账户实例 account")
min_volume_each_step = int(min_volume_each_step)
max_volume_each_step = int(max_volume_each_step)
if max_volume_each_step <= 0 or min_volume_each_step <= 0:
raise Exception(
"请调整参数, min_volume_each_step、max_volume_each_step 必须是大于 0 的整数。")
if min_volume_each_step > max_volume_each_step:
raise Exception(
"请调整参数, min_volume_each_step 必须小于 max_volume_each_step。")
pos = api.get_position(symbol, account)
target_pos = int(target_pos)
delta_pos = target_pos - pos.pos
volume = abs(delta_pos) # 总的下单手数
# 得到有效的手数序列和时间间隔序列
if volume < max_volume_each_step:
interval_list, volume_list = [duration], [volume]
else:
volume_list = _gen_random_list(sum_val=volume,
min_val=min_volume_each_step,
max_val=max_volume_each_step)
interval = int(duration / len(volume_list))
if interval < 3:
raise Exception("请调整参数, 每次下单时间间隔不能小于3s, 将单次下单手数阈值调大或者增长下单时间。")
min_interval = int(max(3, interval - 2))
max_interval = int(interval * 2 - max(3, interval - 2)) + 1
interval_list = _gen_random_list(sum_val=duration,
min_val=min_interval,
max_val=max_interval,
length=len(volume_list))
time_table = DataFrame(columns=['interval', 'volume', 'price'])
for index, volume in enumerate(volume_list):
assert interval_list[index] >= 3
active_interval = 2
append_time_table = pd.DataFrame([{
"interval": interval_list[index] - active_interval,
"volume": volume,
"price": "PASSIVE"
}, {
"interval": active_interval,
"volume": 0,
"price": "ACTIVE"
}])
time_table = pd.concat([time_table, append_time_table],
ignore_index=True)
time_table['volume'] = time_table['volume'].mul(-1 if delta_pos < 0 else 1)
time_table['target_pos'] = time_table['volume'].cumsum()
time_table['target_pos'] = time_table['target_pos'].add(pos.pos)
time_table.drop(columns=['volume'], inplace=True)
time_table = time_table.astype({
'target_pos': 'int64',
'interval': 'float64'
})
return time_table