def get_tq_auth() -> TqAuth:
print(get_application_path())
if 'TQ_ACCOUNT' not in os.environ or 'TQ_PASSWORD' not in os.environ:
load_dotenv(find_dotenv())
tq_account: str = os.environ['TQ_ACCOUNT']
tq_password: str = os.environ['TQ_PASSWORD']
del os.environ['TQ_ACCOUNT']
del os.environ['TQ_PASSWORD']
pprint.pprint(dict(os.environ), width=1)
return TqAuth(tq_account, tq_password)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'mayanqiong'
from datetime import date
from tqsdk import TqApi, TqAuth, TqReplay
'''
复盘模式示例: 指定日期行情完全复盘
复盘 2020-05-26 行情
'''
# 在创建 api 实例时传入 TqReplay 就会进入复盘模式
api = TqApi(backtest=TqReplay(date(2020, 5, 26)), auth=TqAuth("信易账户", "账户密码"))
quote = api.get_quote("SHFE.cu2009")
while True:
api.wait_update()
if api.is_changing(quote):
print("最新价", quote.datetime, quote.last_price)
data = pd.read_excel(para['io_in'],
sheet_name=0,
converters={'stk_code': str}) # excel读取的品种集合
log.logger.info('品种数：{0}'.format(len(data['stk_code'])))
g_security = data['stk_code'].tolist() # "'a' 品种前缀 g_security
open_num_list = data['open_num'].tolist()
stg = pd.read_excel('stg_cfg.xlsx', sheet_name=0) # excel读stg对应关系
para['STG_NUM1'] = stg['k' + str(para['BAR_UNIT'])].iloc[0]
para['STG_NUM2'] = stg['k' + str(para['BAR_UNIT'])].iloc[1]
# wt_data = pd.DataFrame(columns=['date', 'stk_code', 'win_rate', 'short_val', 'long_val']) #输出落地的excel字段
# wt_data.to_excel(io_out, index=False)
# 登录连接信易快期账户
api = TqApi(TqAccount(para['FUT_BROKER'], para['FUT_USER'],
para["FUT_PWD"]),
auth=TqAuth(para['ACCT_ID'], para['ACCT_PWD']))
account = api.get_account()
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'])
ma_list = [np.mean(data_list[-n:])]
for i in range(1, len(data_list) - n + 1):
ma = np.mean(data_list[-n - i:-i])
ma_list.append(ma)
return np.array(ma_list)
if __name__ == '__main__':
data = pd.read_excel(io_in, sheet_name=0, converters={'stk_code':
str}) # excel读取的品种集合
log.logger.info('品种数：{0}'.format(len(data['stk_code'])))
g_security = data['stk_code'].tolist() # "'a' 品种前缀 g_security
# wt_data = pd.DataFrame(columns=['date', 'stk_code', 'win_rate', 'short_val', 'long_val']) #输出落地的excel字段
# wt_data.to_excel(io_out, index=False)
# 登录连接信易快期账户
api = TqApi(TqKq(), web_gui=True, auth=TqAuth(ACCT_ID, ACCT_PWD))
account = api.get_account()
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.query_cont_quotes(product_id=g_sec)
SEC_LIST.append(ls[0])
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, BAR_UNIT, BAR_NUM + 1)
quotes[sec] = api.get_quote(sec)
wma_data = ema2(close_prices, 30)[-n:] # WMA指标
mom_data = trma(close_prices, 30)[-n:] # MOM指标
x_all = list(zip(sma_data, wma_data, mom_data)) # 样本特征组
y_all = list(klines.close.iloc[i] >= klines.close.iloc[i - 1] for i in list(reversed(range(-1, -n - 1, -1)))) # 样本标签组
# x_all: 大前天指标 前天指标 昨天指标 (今天指标)
# y_all: (大前天) 前天 昨天 今天 -明天-
# 准备算法需要用到的数据
x_train = x_all[: -1] # 训练数据: 特征
x_predict = x_all[-1] # 预测数据(用本交易日的指标预测下一交易日的涨跌)
y_train = y_all[1:] # 训练数据: 标签 (去掉第一个数据后让其与指标隔一位对齐(例如: 昨天的特征 -> 对应预测今天的涨跌标签))
return x_train, y_train, x_predict
predictions = [] # 用于记录每次的预测结果(在每个交易日收盘时用收盘数据预测下一交易日的涨跌,并记录在此列表里)
api = TqApi(backtest=TqBacktest(start_dt=datetime.date(2018, 7, 2), end_dt=datetime.date(2018, 9, 26)), auth=TqAuth("信易账户", "账户密码"))
quote = api.get_quote(symbol)
klines = api.get_kline_serial(symbol, duration_seconds=24 * 60 * 60) # 日线
target_pos = TargetPosTask(api, symbol)
with closing(api):
try:
while True:
while not api.is_changing(klines.iloc[-1], "datetime"): # 等到达下一个交易日
api.wait_update()
while True:
api.wait_update()
# 在收盘后预测下一交易日的涨跌情况
if api.is_changing(quote, "datetime"):
now = datetime.datetime.strptime(quote.datetime, "%Y-%m-%d %H:%M:%S.%f") # 当前quote的时间
# 判断是否到达预定收盘时间: 如果到达 则认为本交易日收盘, 此时预测下一交易日的涨跌情况, 并调整为对应仓位
if now.hour == close_hour and now.minute >= close_minute:
from matplotlib.pyplot import rcParams, xticks, autoscale, figure, show
from matplotlib.animation import FuncAnimation
from matplotlib import style
system("mode con cols=71 lines=30")
stdout.write(f"+{'-' * 68}+\n"
f"|{'期货跨月偏离度监测':^59}|\n"
f"+{'-' * 68}+\n"
f"|{' ':68}|\n"
f"|{'数据行情由天勤量化提供支持，如不同意天勤量化使用条款，请终止使用':^36}|\n"
f"|{' ':68}|\n"
f"+{'-' * 68}+\n\n")
tq_account = input('天勤量化账号: ')
tq_password = input('天勤量化密码: ')
try:
api = TqApi(auth=TqAuth(tq_account, tq_password))
except Exception as err:
print(err, '\n')
input(f"{'连接失败, 按任意键程序将退出, 请检查后重试'}")
raise SystemExit()
quotes = []
labels = []
product = input("\n请输入品种代码(例rb/hc): ").lower()
try:
available_contracts = sorted(
api.query_quotes(ins_class="FUTURE", product_id=product,
expired=False))
m_contract = api.query_cont_quotes(product_id=product)[0]
except IndexError:
available_contracts = sorted(
api.query_quotes(ins_class="FUTURE",
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'chengzhi'
import datetime
from tqsdk import TqApi, TqAuth
from tqsdk.ta import *
api = TqApi(auth=TqAuth("信易账户", "账户密码"))
# 获得 cu1909 10秒K线的引用
klines = api.get_kline_serial("SHFE.cu1910", 10, data_length=3000)
print("K线时间",
datetime.datetime.fromtimestamp(klines.iloc[-1]["datetime"] / 1e9))
print(klines)
print("ATR", ATR(klines, 26))
print("BIAS", BIAS(klines, 6))
print("BOLL", BOLL(klines, 3, 5))
print("DMI", DMI(klines, 14, 6))
print("KDJ", KDJ(klines, 9, 3, 3))
print("MA", MA(klines, 3))
print("MACD", MACD(klines, 20, 35, 10))
print("SAR", SAR(klines, 4, 0.02, 0.2))
api.close()
# -*- coding: utf-8 -*-
__author__ = 'limin'
'''
菲阿里四价 策略(日内突破策略, 在每日收盘前对所持合约进行平仓)
参考: https://www.shinnytech.com/blog/fairy-four-price/
注: 该示例策略仅用于功能示范, 实盘时请根据自己的策略/经验进行修改
'''
from tqsdk import TqApi, TqAuth, TargetPosTask
from datetime import datetime
import time
symbol = "SHFE.ni2012" # 合约代码
close_hour, close_minute = 14, 50 # 平仓时间
api = TqApi(auth=TqAuth("信易账户", "账户密码")) # 使用模拟帐号直连行情和交易服务器
quote = api.get_quote(symbol) # 获取指定合约的盘口行情
klines = api.get_kline_serial(symbol, 24 * 60 * 60) # 获取日线
position = api.get_position(symbol) # 持仓信息
target_pos = TargetPosTask(api, symbol) # 目标持仓
top_rail = klines.high.iloc[-2] # 上轨: 昨日高点
bottom_rail = klines.low.iloc[-2] # 下轨: 昨日低点
print("上轨:", top_rail, ",下轨:", bottom_rail, ",昨日收盘价:", klines.close.iloc[-2],
",今日开盘价:", klines.open.iloc[-1])
while True:
api.wait_update()
if api.is_changing(klines.iloc[-1],
"datetime"): # 如果产生一根新日线 (即到达下一个交易日): 重新获取上下轨
top_rail = klines.high.iloc[-2]
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'hongyan'
from tqsdk import TqApi, TqAuth, TqAccount, TqKq, TqSim, TqMultiAccount
# 多账户模式下, 同时操作实盘、模拟交易和快期模拟账户交易
tqact = TqAccount("H海通期货", "123456", "123456")
sim = TqSim()
kq = TqKq()
with TqApi(TqMultiAccount([tqact, sim, kq]), auth=TqAuth("信易账户",
"账户密码")) as api:
order1 = api.insert_order(symbol="DCE.m2101",
direction="BUY",
offset="OPEN",
volume=5,
account=tqact)
order2 = api.insert_order(symbol="SHFE.au2012C308",
direction="BUY",
offset="OPEN",
volume=5,
limit_price=78.1,
account=sim)
order3 = api.insert_order(symbol="SHFE.cu2101",
direction="Sell",
offset="OPEN",
volume=10,
limit_price=51610,
account=kq)
api.cancel_order(order3, kq)
while order1.status != "FINISHED" or order2.status != "FINISHED":
elif n == 60 or n % 60 == 0:
return 'minute' if n == 60 else f'{n // 60}minute'
else:
return 'second' if n == 1 else f'{n}second'
if __name__ == '__main__':
# 加载 .env 变量
load_dotenv(find_dotenv())
# 天勤账号
TQ_ACCOUNT: str = os.environ.get('TQ_ACCOUNT')
TQ_PASSWORD: str = os.environ.get('TQ_PASSWORD')
# 天勤API
tq_api: TqApi = TqApi(auth=TqAuth(TQ_ACCOUNT, TQ_PASSWORD))
# Application path
application_path: str = get_application_path()
# 下载需求
download_request_list: List[dict] = []
csv_path: str = os.path.join(application_path, 'download.csv')
with open(csv_path, newline='', encoding='utf-8') as csv_file:
reader = csv.DictReader(csv_file)
for row in reader:
download_request: dict = {
'symbol': row['symbol'],
'start': date(int(row['start'][:4]), int(row['start'][4:6]), int(row['start'][6:8])),
'end': date(int(row['end'][:4]), int(row['end'][4:6]), int(row['end'][6:8])),
'period': int(row['period']),
画图示例: 在附图中画 macd 指标示例
注意: 画图示例中用到的数据不含有实际意义，请根据自己的实际策略情况进行修改
'''
def calc_macd_klines(klines):
# 计算 macd 指标
macd = MACD(klines, 12, 26, 9)
# 用 K 线图模拟 MACD 指标柱状图
klines["MACD.open"] = 0.0
klines["MACD.close"] = macd["bar"]
klines["MACD.high"] = klines["MACD.close"].where(klines["MACD.close"] > 0,
0)
klines["MACD.low"] = klines["MACD.close"].where(klines["MACD.close"] < 0,
0)
klines["MACD.board"] = "MACD"
# 在 board=MACD 上添加 diff、dea 线
klines["diff"] = macd["diff"]
klines["diff.board"] = "MACD"
klines["diff.color"] = "gray"
klines["dea"] = macd["dea"]
klines["dea.board"] = "MACD"
klines["dea.color"] = "rgb(255,128,0)"
api = TqApi(auth=TqAuth("信易账户", "账户密码"), web_gui=True)
klines = api.get_kline_serial("SHFE.rb2105", 5 * 60, 200)
while True:
calc_macd_klines(klines)
api.wait_update()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'limin'
from tqsdk import TqApi, TqAuth
'''
画图示例: 在附图中画K线
注意: 画图示例中用到的数据不含有实际意义，请根据自己的实际策略情况进行修改
'''
api = TqApi(web_gui=True, auth=TqAuth("信易账户", "账户密码"))
klines = api.get_kline_serial("SHFE.rb2104", 86400)
klines2 = api.get_kline_serial("SHFE.rb2105", 86400)
while True:
# 将画图代码放在循环中即可使图像随着行情推进而更新
# 在附图画出 rb2105 的K线: 需要将open、high、log、close的数据都设置正确
klines["rb2105.open"] = klines2["open"]
klines["rb2105.high"] = klines2["high"]
klines["rb2105.low"] = klines2["low"]
klines["rb2105.close"] = klines2["close"]
klines["rb2105.board"] = "B2"
api.wait_update()
# api = TqApi(account=TqKq(), auth=TqAuth(LG_ACC, LG_PWD))
rule = api.get_risk_management_rule(exchange_id="SSE")
logger.info(rule)
logger.info("自成交限制:", rule.self_trade)
logger.info("频繁报撤单限制:", rule.frequent_cancellation)
logger.info("成交持仓比限制:", rule.trade_position_ratio)
except Exception as e:
logger.info(e)
if __name__ == "__main__":
logger.info(
"*************************** strategy start *******************************"
)
api = TqApi(account=TqKq(), auth=TqAuth(LG_ACC, LG_PWD))
# api.set_risk_management_rule(exchange_id="SSE", enable=True)
# # 等待发送数据
# api.wait_update()
# tr = Timer(10,risk_controller,(api,))
# 资金管理
cm = CapitalManager.CapitalManager(api, 10000, 0.80)
# 根据资金情况 调整初始化建仓单位？
quoteList = cm.query_quotes(
[Tao.EXCHANGE_SHFE, Tao.EXCHANGE_DCE, Tao.EXCHANGE_CZCE])
pos_pd = cm.calc_pos_list_by_turle(quoteList, 20)
pos_pd.apply(lambda x: init_turtle(api, x), axis=1)
with closing(api):
while True:
config_params = get_yaml_data(yaml_path)
if config_params['run_level'] == 'replay':
acc = str(config_params['sim_account'])
pwd = str(config_params['sim_pwd'])
start_text = config_params['start_from']
end_text = config_params['to_end']
start_date = datetime.strptime(start_text, "%Y-%m-%d").date()
end_date = datetime.strptime(end_text, "%Y-%m-%d").date()
api = TqApi(web_gui=config_params['web_url'],
backtest=TqBacktest(start_dt=start_date, end_dt=end_date),
auth=TqAuth(acc, pwd))
else:
if config_params['future_company'] == '' or config_params[
'future_account'] == '' or config_params['future_pwd'] == '':
print('没有配置期货账号')
exit(0)
future_account = TqAccount(config_params['future_company'],
config_params['future_account'],
config_params['sim_pwd'])
api = TqApi(future_account,
auth=TqAuth(config_params['sim_account'],
config_params['sim_pwd']))
# replay.set_replay_speed(10.0)
account = api.get_account()
from tqsdk import TqApi, TqAuth, TqKq, TqAccount
import json
import pandas as pd
import time
import sys
sys.path.append("E:/coderepo/skwquant/")
from skwutils import RawList
pd.set_option('display.max_columns', None)
api = TqApi(TqKq(), auth=TqAuth("keviniiii", "qkyhpys,1"))
# api = TqApi(TqAccount("G国海良时", "88500007", "702211"), auth=TqAuth("keviniiii", "qkyhpys,1"))
def open_order_json(unit, holding_set, alive_open_set):
symbol_raw = unit['symbol']
vc = unit['ans']
v_prob = unit['prob']
v_prone = unit['v_prone']
buynum = int(float(unit['buynum']))
price_limit = 0
open_order(symbol_raw,
vc,
v_prob,
v_prone,
buynum,
price_limit=price_limit,
holding_set=holding_set,
alive_open_set=alive_open_set)
return unit
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'chengzhi'
from datetime import date
from tqsdk import TqApi, TqAuth, TqBacktest, TargetPosTask
'''
如果当前价格大于5分钟K线的MA15则开多仓
如果小于则平仓
回测从 2018-05-01 到 2018-10-01
'''
# 在创建 api 实例时传入 TqBacktest 就会进入回测模式
api = TqApi(backtest=TqBacktest(start_dt=date(2018, 5, 1),
end_dt=date(2018, 10, 1)),
auth=TqAuth("信易账户", "账户密码"))
# 获得 m1901 5分钟K线的引用
klines = api.get_kline_serial("DCE.m1901", 5 * 60, data_length=15)
# 创建 m1901 的目标持仓 task，该 task 负责调整 m1901 的仓位到指定的目标仓位
target_pos = TargetPosTask(api, "DCE.m1901")
while True:
api.wait_update()
if api.is_changing(klines):
ma = sum(klines.close.iloc[-15:]) / 15
print("最新价", klines.close.iloc[-1], "MA", ma)
if klines.close.iloc[-1] > ma:
print("最新价大于MA: 目标多头5手")
# 设置目标持仓为多头5手
target_pos.set_target_volume(5)
elif klines.close.iloc[-1] < ma:
print("最新价小于MA: 目标空仓")
"instrument_id", "direction", "offset", "price", "volume",
"trade_date_time"
]
def write_csv(file_name, cols, datas):
file_exists = os.path.exists(file_name) and os.path.getsize(file_name) > 0
with open(file_name, 'a', newline='') as csvfile:
csv_writer = csv.writer(csvfile, dialect='excel')
if not file_exists:
csv_writer.writerow(['datetime'] + cols)
for item in datas.values():
if 'insert_date_time' in cols:
dt = datetime.fromtimestamp(
item['insert_date_time'] /
1e9).strftime('%Y-%m-%d %H:%M:%S.%f')
elif 'trade_date_time' in cols:
dt = datetime.fromtimestamp(
item['trade_date_time'] /
1e9).strftime('%Y-%m-%d %H:%M:%S.%f')
else:
dt = None
row = [dt] + [item[k] for k in cols]
csv_writer.writerow(row)
with TqApi(TqKq(), auth=TqAuth("信易账户", "账户密码")) as api:
# 将当前账户下全部委托单、成交信息写入 csv 文件中
write_csv("orders.csv", order_cols, api.get_order())
write_csv("trades.csv", trade_cols, api.get_trade())
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'chengzhi'
from tqsdk import TqApi, TqAuth
# 可以指定debug选项将调试信息写入指定的文件中
api = TqApi(debug="debug.log", auth=TqAuth("信易账户", "账户密码"))
quote = api.get_quote("CZCE.FG209")
print(quote.datetime, quote.last_price, quote.ask_price1, quote.ask_price2)
while True:
# 调用 wait_update 等待业务信息发生变化，例如: 行情发生变化, 委托单状态变化, 发生成交等等
# 注意：其他合约的行情的更新也会触发业务信息变化，因此下面使用 is_changing 判断 FG209 的行情是否有变化
api.wait_update()
# 如果 FG209 的任何字段有变化，is_changing就会返回 True
if api.is_changing(quote):
print("行情变化", quote)
# 只有当 FG209 的最新价有变化，is_changing才会返回 True
if api.is_changing(quote, "last_price"):
print("最新价变化", quote.last_price)
# 当 FG209 的买1价/买1量/卖1价/卖1量中任何一个有变化，is_changing都会返回 True
if api.is_changing(quote, ["ask_price1", "ask_volume1", "bid_price1", "bid_volume1"]):
print("盘口变化", quote.ask_price1, quote.ask_volume1, quote.bid_price1, quote.bid_volume1)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'limin'
from tqsdk import TqApi, TqAuth
'''
画图示例: 在主图中画线和方框
注意: 画图示例中用到的数据不含有实际意义，请根据自己的实际策略情况进行修改
'''
api = TqApi(web_gui=True,
auth=TqAuth("信易账户", "账户密码")) # web_gui=True, 开启使用 web 界面查看绘图结果的功能
klines = api.get_kline_serial("SHFE.rb2105", 60)
# 由于需要在浏览器中查看绘图结果，因此程序不能退出
while True:
api.wait_update() # 当有业务信息发生变化时执行
# 当最后 1 根柱子最大最小值价差大于 0.05 时，在主图绘制信号
high = klines.iloc[-1].high
low = klines.iloc[-1].low
if high - low > 0.05:
# 绘制直线, 每一个 id 对应同一条直线
api.draw_line(klines,
-1,
high,
-1,
low,
id="box%.0f" % (klines.iloc[-1].id),
color=0xaa662244,
width=4)
# 绘制字符串
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'limin'
from tqsdk import TqApi, TqAuth
from tqsdk.ta import MA
'''
画图示例: 在附图中画指标线
(将画图代码放在循环中即可使图像随着行情推进而更新)
注意: 画图示例中用到的数据不含有实际意义，请根据自己的实际策略情况进行修改
'''
api = TqApi(web_gui=":9878",
auth=TqAuth("信易账户",
"账户密码")) # web_gui="[ip]:port", 指定 web 界面地址的 ip 和 port
klines = api.get_kline_serial("SHFE.rb2105", 24 * 60 * 60)
while True:
# 将画图代码放在循环中即可使图像随着行情推进而更新
ma = MA(klines, 30) # 使用tqsdk自带指标函数计算均线
# 示例1: 在附图中画一根绿色的ma指标线
klines["ma_B2"] = ma.ma
klines["ma_B2.board"] = "B2" # 设置附图: 可以设置任意字符串,同一字符串表示同一副图
klines[
"ma_B2.color"] = "green" # 设置为绿色. 以下设置颜色方式都可行: "green", "#00FF00", "rgb(0,255,0)", "rgba(0,125,0,0.5)"
# 示例2: 在另一个附图画一根比ma小4的宽度为4的紫色指标线
klines["ma_4"] = ma.ma - 4
klines["ma_4.board"] = "MA4" # 设置为另一个附图
klines["ma_4.color"] = 0xFF9933CC # 设置为紫色, 或者 "#9933FF"