def operate_st(action_type):
"""
将st内容，转换成标签添加在对应code下
"""
f = h5py.File(conf.HDF5_FILE_BASIC, 'a')
console.write_head(action_type, conf.HDF5_RESOURCE_TUSHARE,
conf.HDF5_BASIC_ST)
path = '/' + conf.HDF5_BASIC_ST
# 如果文件不存在，则退出
if f.get(path) is None:
console.write_msg("st的detail文件不存在")
return
st_df = tool.df_from_dataset(f[path], conf.HDF5_BASIC_ST, None)
if st_df is not None and st_df.empty is not True:
st_df["code"] = st_df["code"].str.decode("utf-8")
# 将st内容，转换成标签添加在对应code下
tool.op_attr_by_codelist(action_type, st_df["code"].values,
conf.HDF5_BASIC_ST, True)
else:
console.write_msg("st的detail数据获取失败")
console.write_tail()
f.close()
return
def monitor():
global monitor_timer
global wallet_amount
global strategy_dict
global exec_level
# 仓位倍率
factor_multi = 0.01
factor_price_range = 5
for code in strategy_dict:
try:
obj = strategy_dict[code]
# 更新数据并检查
obj.update()
ret = obj.check_new()
console.write_msg("时间%s，bar的操作结果:%s" %
(obj.small.iloc[-1]["date"], ret))
if ret is not False:
obj.output()
# 1.结算上次交易
# 如果仓位小于0，则卖空需要赎回，如果仓位大于0，则做多需要卖出
current_position = account.position(10)
current = current_position["current"]
if current != 0:
if current > 0:
side = order.SIDE_SELL
elif current < 0:
side = order.SIDE_BUY
result = order.create_contract(code, side, abs(current),
order.TYPE_MARKET)
if result["price"]:
msg = "合约%s兑现, 操作%s, 份额%f，价格%f, 下单成功"
console.write_msg(
msg % (code, side, abs(current), result["price"]))
# 2.更新钱包数额(暂时不更新钱包)
# wallet_detail = account.wallet()
# wallet_amount = wallet_detail['amount']
# 3.判断交易类别，进行做空或做多
# 方案A，取两者的平均值，基本很难触发(已舍弃)
# 方案B，取最新价格的偏差，目前固定为5(低级别不适用)
trend_price = obj.small.iloc[-1]["close"]
amount = wallet_amount * factor_multi
if ret == conf.BUY_SIDE:
price = trend_price - factor_price_range
result = order.create_simple(code, order.SIDE_BUY, amount,
order.TYPE_LIMIT, price)
elif ret == conf.SELL_SIDE:
price = trend_price + factor_price_range
result = order.create_simple(code, order.SIDE_SELL, amount,
order.TYPE_LIMIT, price)
else:
raise Exception("交易类别异常")
if result["price"]:
msg = "合约%s, 操作%s, 份额%f，价格%f, 下单成功"
console.write_msg(msg % (code, ret, amount, price))
else:
print(result)
# 4.标记过期时间
result = order.cancel_all_after(30000)
if result["cancelTime"]:
console.write_msg("预计过期时间:%s" % (result["cancelTime"]))
else:
print(result)
except Exception as er:
print(obj.small.tail(10))
print(obj.phase.tail(10))
print(str(er))
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
print(exc_type, fname, exc_tb.tb_lineno)
sys.exit(2)
if exec_level == conf.BINSIZE_FIVE_MINUTE:
time_span = "5"
elif exec_level == conf.BINSIZE_ONE_MINUTE:
time_span = "1"
else:
raise Exception("级别异常")
remain_second = tradetime.get_remain_second(time_span)
monitor_timer = threading.Timer(remain_second + 2, monitor)
monitor_timer.start()
return
def output(self):
now = self.trade.iloc[-1]
console.write_msg(
"【%s, %s, %s】" %
(self.code, now[conf.HDF5_SHARE_DATE_INDEX], now["type"]))
msg = "个股5min，趋势%s，连续%d次，macd趋势%f, macd差值%d%%"
console.write_msg(
msg %
(now[DF_FIVE + TRADE_STATUS], now[DF_FIVE + TRADE_TREND_COUNT],
now[DF_FIVE + TRADE_MACD_PHASE], now[DF_FIVE + TRADE_MACD_DIFF]))
dtype_dict = {
DF_THIRTY: "个股30min",
DF_BIG: "个股大趋势",
DF_INDEX: "指数30min",
}
for dtype in dtype_dict:
msg = "%s，趋势%s，连续%d次，macd趋势%f, macd差值%d%%"
console.write_msg(
msg %
(dtype_dict[dtype], now[dtype + TRADE_STATUS],
now[dtype + TRADE_TREND_COUNT], now[dtype + TRADE_MACD_PHASE],
now[dtype + TRADE_MACD_DIFF]))
# TODO 背离情况要计算两次买卖点trend_count的差值
trend_count = now[DF_FIVE + TRADE_TREND_COUNT]
upper_estimate = (trend_count + 1) * 5
lower_estimate = (trend_count - 1) * 5
if self.stype == conf.STYPE_ASHARE:
remain_seconds = tradetime.get_ashare_remain_second(
now[conf.HDF5_SHARE_DATE_INDEX])
remain_minutes = round(remain_seconds / 60, 0)
msg = "剩余时间%d分钟，下个交易点预估需要%d-%d分钟，模式%s"
if (upper_estimate * 60) <= remain_seconds:
trade_opportunity = "T+0"
else:
trade_opportunity = "T+1"
console.write_msg(msg % (remain_minutes, lower_estimate,
upper_estimate, trade_opportunity))
elif self.stype == conf.STYPE_BITMEX:
msg = "下个交易点预估需要%d-%d分钟"
console.write_msg(msg % (lower_estimate, upper_estimate))
console.write_msg("建议仓位：%d/3" % (now[TRADE_POSITIONS]))
console.write_blank()
return
def all_exec(omit_list):
"""
筛选出存在背离的股票
"""
# 筛选记录内容如下:
# 1. 月线macd趋势
# 2. 周线macd趋势
# 3. 日线macd趋势，是否背离，数值差距
# 4. 30min的macd趋势，是否背离，数值差距，震荡中枢数量
# 5. 5min的macd趋势，是否背离，数值差距，震荡中枢数量
console.write_head(conf.HDF5_OPERATE_SCREEN, conf.HDF5_RESOURCE_TUSHARE,
conf.STRATEGY_TREND_AND_REVERSE)
f = h5py.File(conf.HDF5_FILE_SHARE, 'a')
filter_df = tool.init_empty_df(_ini_filter_columns())
for code_prefix in f:
if code_prefix in omit_list:
continue
for code in f[code_prefix]:
code_group_path = '/' + code_prefix + '/' + code
if f.get(code_group_path) is None:
console.write_blank()
console.write_msg(code + "的tushare数据不存在")
continue
# 忽略停牌、退市、无法获取的情况
if f[code_prefix][code].attrs.get(
conf.HDF5_BASIC_QUIT
) is not None or f[code_prefix][code].attrs.get(
conf.HDF5_BASIC_ST) is not None:
console.write_blank()
console.write_msg(code + "已退市或停牌")
continue
try:
code_dict = code_exec(f, code)
if code_dict is None:
console.write_pass()
continue
else:
console.write_exec()
filter_df = filter_df.append(code_dict, ignore_index=True)
except Exception as er:
console.write_msg("[" + code + "]" + str(er))
f.close()
f_screen = h5py.File(conf.HDF5_FILE_SCREEN, 'a')
if f_screen.get(conf.STRATEGY_TREND_AND_REVERSE) is None:
f_screen.create_group(conf.STRATEGY_TREND_AND_REVERSE)
if f_screen[conf.STRATEGY_TREND_AND_REVERSE].get(
conf.SCREEN_SHARE_FILTER) is None:
f_screen[conf.STRATEGY_TREND_AND_REVERSE].create_group(
conf.SCREEN_SHARE_FILTER)
today_str = tradetime.get_today()
tool.delete_dataset(
f_screen[conf.STRATEGY_TREND_AND_REVERSE][conf.SCREEN_SHARE_FILTER],
today_str)
tool.merge_df_dataset(
f_screen[conf.STRATEGY_TREND_AND_REVERSE][conf.SCREEN_SHARE_FILTER],
today_str, filter_df)
f_screen.close()
console.write_blank()
console.write_tail()
return
def mark_grade(today_str=None):
"""
对筛选结果进行打分
"""
console.write_head(conf.HDF5_OPERATE_SCREEN, conf.HDF5_RESOURCE_TUSHARE,
conf.SCREEN_SHARE_GRADE)
f = h5py.File(conf.HDF5_FILE_SCREEN, 'a')
f_share = h5py.File(conf.HDF5_FILE_SHARE, 'a')
if today_str is None:
today_str = tradetime.get_today()
if f[conf.STRATEGY_TREND_AND_REVERSE][conf.SCREEN_SHARE_FILTER].get(
today_str) is None:
console.write_msg(today_str + "个股筛选结果不存在")
return
grade_df = tool.init_empty_df([
"code", "status", "d_price_space", "d_price_per", "30_price_space",
"30_price_per", "d_macd", "30_macd"
])
screen_df = tool.df_from_dataset(
f[conf.STRATEGY_TREND_AND_REVERSE][conf.SCREEN_SHARE_FILTER],
today_str, None)
screen_df["d_m_status"] = screen_df["d_m_status"].str.decode("utf-8")
screen_df["w_m_status"] = screen_df["w_m_status"].str.decode("utf-8")
screen_df["m_m_status"] = screen_df["m_m_status"].str.decode("utf-8")
screen_df["code"] = screen_df["code"].str.decode("utf-8")
for index, row in screen_df.iterrows():
code = row["code"]
grade_dict = dict()
grade_dict["code"] = code
grade_dict["status"] = 0
grade_dict["status"] += _status_grade(row["d_m_status"])
grade_dict["status"] += _status_grade(row["w_m_status"])
grade_dict["status"] += _status_grade(row["m_m_status"])
code_prefix = code[0:3]
code_group_path = '/' + code_prefix + '/' + code
for ktype in ["D", "30"]:
detail_ds_name = ktype
index_ds_name = conf.HDF5_INDEX_DETAIL + "_" + ktype
if f_share[code_group_path].get(detail_ds_name) is None:
console.write_msg(code + "的detail数据不存在")
continue
if f_share[code_group_path].get(index_ds_name) is None:
console.write_msg(code + "的index数据不存在")
continue
detail_df = tool.df_from_dataset(f_share[code_group_path],
detail_ds_name, None)
index_df = tool.df_from_dataset(f_share[code_group_path],
index_ds_name, None)
latest_price = detail_df.tail(1)["close"].values[0]
latest_macd = index_df.tail(1)["macd"].values[0]
diverse_price_start = row[str.lower(ktype) +
INDEX_MACD_DIVERSE_PRICE_START]
if diverse_price_start == 0:
grade_dict[str.lower(ktype) + "_price_space"] = 0
grade_dict[str.lower(ktype) + "_price_per"] = 0
else:
grade_dict[str.lower(ktype) + "_price_space"] = round(
diverse_price_start - latest_price, 2)
grade_dict[str.lower(ktype) + "_price_per"] = round(
grade_dict[str.lower(ktype) + "_price_space"] * 100 /
diverse_price_start, 2)
grade_dict[str.lower(ktype) + "_macd"] = latest_macd
grade_df = grade_df.append(grade_dict, ignore_index=True)
if f[conf.STRATEGY_TREND_AND_REVERSE].get(conf.SCREEN_SHARE_GRADE) is None:
f[conf.STRATEGY_TREND_AND_REVERSE].create_group(
conf.SCREEN_SHARE_GRADE)
tool.delete_dataset(
f[conf.STRATEGY_TREND_AND_REVERSE][conf.SCREEN_SHARE_GRADE], today_str)
tool.merge_df_dataset(
f[conf.STRATEGY_TREND_AND_REVERSE][conf.SCREEN_SHARE_GRADE], today_str,
grade_df)
f_share.close()
f.close()
console.write_tail()
return