V0.9.56 更新一批代码 (#207)

* 0.9.56 start coding

* 0.9.56 format codes

* 0.9.56 start coding

* 0.9.56 没有品种交易，直接return

* 0.9.56 logger 优化

* 0.9.56 新增ATR止盈止损信号

* 0.9.56 新增期货套利研究案例

* 0.9.56 update doc

* 0.9.56 新增策略上传接口的封装

.github/workflows/pythonpackage.yml

@@ -5,7 +5,7 @@ name: Python package
 
 on:
   push:
-    branches: [ master, V0.9.55 ]
+    branches: [ master, V0.9.56 ]
   pull_request:
     branches: [ master ]
 

czsc/__init__.py

@@ -192,10 +192,10 @@
 )
 
 
-__version__ = "0.9.55"
+__version__ = "0.9.56"
 __author__ = "zengbin93"
 __email__ = "zeng_bin8888@163.com"
-__date__ = "20240706"
+__date__ = "20240714"
 
 
 def welcome():

czsc/connectors/cooperation.py

@@ -9,9 +9,10 @@
 """
 import os
 import czsc
+import requests
+import loguru
 import pandas as pd
 from tqdm import tqdm
-from loguru import logger
 from datetime import datetime
 from czsc import RawBar, Freq
 
@@ -74,6 +75,8 @@ def get_symbols(name, **kwargs):
 
 def get_min_future_klines(code, sdt, edt, freq="1m", **kwargs):
     """分段获取期货1分钟K线后合并"""
+    logger = kwargs.pop("logger", loguru.logger)
+
     sdt = pd.to_datetime(sdt).strftime("%Y%m%d")
     edt = pd.to_datetime(edt).strftime("%Y%m%d")
     # dates = pd.date_range(start=sdt, end=edt, freq='1M')
@@ -133,6 +136,8 @@ def get_raw_bars(symbol, freq, sdt, edt, fq="前复权", **kwargs):
     >>> from czsc.connectors import cooperation as coo
     >>> df = coo.get_raw_bars(symbol="000001.SH#INDEX", freq="日线", sdt="2001-01-01", edt="2021-12-31", fq='后复权', raw_bars=False)
     """
+    logger = kwargs.pop("logger", loguru.logger)
+
     freq = czsc.Freq(freq)
     raw_bars = kwargs.get("raw_bars", True)
     ttl = kwargs.get("ttl", -1)
@@ -226,3 +231,63 @@ def stocks_daily_klines(sdt="20170101", edt="20240101", **kwargs):
     if nxb:
         dfk = czsc.update_nxb(dfk, nseq=nxb)
     return dfk
+
+
+def upload_strategy(df, meta, token=None, **kwargs):
+    """上传策略数据
+
+    :param df: pd.DataFrame, 策略持仓权重数据，至少包含 dt, symbol, weight 三列, 例如：
+
+        ===================  ========  ========
+        dt                   symbol      weight
+        ===================  ========  ========
+        2017-01-03 09:01:00  ZZSF9001  0
+        2017-01-03 09:01:00  DLj9001   0
+        2017-01-03 09:01:00  SQag9001  0
+        2017-01-03 09:06:00  ZZSF9001  0.136364
+        2017-01-03 09:06:00  SQag9001  1
+        ===================  ========  ========
+
+    :param meta: dict, 策略元数据
+
+        至少包含 name, description, base_freq, author, outsample_sdt 字段, 例如：
+
+        {'name': 'TS001_3',
+        'description': '测试策略：仅用于读写redis测试',
+        'base_freq': '1分钟',
+        'author': 'ZB',
+        'outsample_sdt': '20220101'}
+
+    :param token: str, 上传凭证码；如果不提供，将从环境变量 CZSC_TOKEN 中获取
+    :param kwargs: dict, 其他参数
+
+            - logger: loguru.logger, 日志记录器
+    :return dict
+    """
+    logger = kwargs.pop("logger", loguru.logger)
+    df = df.copy()
+    df["dt"] = pd.to_datetime(df["dt"])
+    logger.info(f"输入数据中有 {len(df)} 条权重信号")
+
+    # 去除单个品种下相邻时间权重相同的数据
+    _res = []
+    for _, dfg in df.groupby("symbol"):
+        dfg = dfg.sort_values("dt", ascending=True).reset_index(drop=True)
+        dfg = dfg[dfg["weight"].diff().fillna(1) != 0].copy()
+        _res.append(dfg)
+    df = pd.concat(_res, ignore_index=True)
+    df = df.sort_values(["dt"]).reset_index(drop=True)
+    df["dt"] = df["dt"].dt.strftime("%Y-%m-%d %H:%M:%S")
+
+    logger.info(f"去除单个品种下相邻时间权重相同的数据后，剩余 {len(df)} 条权重信号")
+
+    data = {
+        "weights": df[["dt", "symbol", "weight"]].to_json(orient="split"),
+        "token": token or os.getenv("CZSC_TOKEN"),
+        "strategy_name": meta.get("name"),
+        "meta": meta,
+    }
+    response = requests.post("http://zbczsc.com:9106/upload_strategy", json=data)
+
+    logger.info(f"上传策略接口返回: {response.json()}")
+    return response.json()

czsc/connectors/tq_connector.py

@@ -412,6 +412,12 @@ def adjust_portfolio(api: TqApi, portfolio, account=None, **kwargs):
         target_pos.set_target_volume(int(lots))
         symbol_infos[symbol] = {"quote": quote, "target_pos": target_pos, "lots": lots}
 
+    if not symbol_infos:
+        logger.warning(f"没有需要调仓的品种，跳过调仓")
+        return api
+    else:
+        logger.info(f"开始调仓：{[x for x in symbol_infos.keys()]}")
+
     while True:
         api.wait_update()
 
@@ -439,6 +445,10 @@ def adjust_portfolio(api: TqApi, portfolio, account=None, **kwargs):
 
         if (datetime.now() - start_time).seconds > timeout:
             logger.error(f"调仓超时，已运行 {timeout} 秒")
+            for symbol, info in symbol_infos.items():
+                target_pos: TargetPosTask = info["target_pos"]
+                target_pos.cancel()
+                logger.info(f"取消调仓：{symbol}")
             break
 
     return api

czsc/signals/__init__.py

@@ -234,6 +234,7 @@
     pos_stop_V240331,
     pos_stop_V240608,
     pos_stop_V240614,
+    pos_stop_V240717,
 )
 
 

czsc/signals/pos.py

@@ -20,10 +20,10 @@ def pos_ma_V230414(cat: CzscTrader, **kwargs) -> OrderedDict:
 
     **信号逻辑：**
 
-    多头止损逻辑如下，反之为空头止损逻辑：
+    多头持有状态如下，反之为空头持有状态：
 
-    1. 从多头开仓点开始，在给定对的K线周期 freq1 上向前找 N 个底分型，记为 F1
-    2. 将这 N 个底分型的最低点，记为 L1，如果 L1 的价格低于开仓点的价格，则止损
+    1. 如果持有多头，且开仓后有价格升破MA均线，则为多头升破均线；
+    2. 如果持有空头，且开仓后有价格跌破MA均线，则为空头跌破均线。
 
     **信号列表：**
 
@@ -55,15 +55,13 @@ def pos_ma_V230414(cat: CzscTrader, **kwargs) -> OrderedDict:
     c = cat.kas[freq1]
     op = pos.operates[-1]
 
-    # 多头止损逻辑
     if op["op"] == Operate.LO:
         bars = [x for x in c.bars_raw[-100:] if x.dt > op["dt"]]
         for x in bars:
             if x.close > x.cache[key]:
                 v1, v2 = "多头", "升破均线"
                 break
 
-    # 空头止损逻辑
     if op["op"] == Operate.SO:
         bars = [x for x in c.bars_raw[-100:] if x.dt > op["dt"]]
         for x in bars:
@@ -941,3 +939,69 @@ def pos_stop_V240614(cat: CzscTrader, **kwargs) -> OrderedDict:
         v1 = "空头止损"
 
     return create_single_signal(k1=k1, k2=k2, k3=k3, v1=v1)
+
+
+def pos_stop_V240717(cat: CzscTrader, **kwargs) -> OrderedDict:
+    """止损：多头开仓后，有超过N根K线的最低价在成本价-ATR*0.67下方，提示止损；空头反之。贡献者：谢磊
+
+    参数模板："{pos_name}_{freq1}N{n}T{timeperiod}_止损V240717"
+
+    **信号逻辑：**
+
+    以多头止损为例，计算过程如下：
+
+    1. 从多头开仓点开始，在给定的K线周期 freq1 上获取开仓后的所有K线，记为 bars；
+    2. 计算 bars 中的最低价小于（开仓价-ATR*0.67）的数量，记为 C；
+    3. ATR的参数为默认参数，可以调整；
+    3. 如果 C >= N，则提示多头止损信号。
+
+    空头止损逻辑同理。
+
+    **信号列表：**
+
+    - Signal('SMA5多头_15分钟N3T20_止损V240614_多头止损_任意_任意_0')
+    - Signal('SMA5空头_15分钟N3T20_止损V240614_空头止损_任意_任意_0')
+
+    :param cat: CzscTrader对象
+    :param kwargs: 参数字典
+
+        - pos_name: str，开仓信号的名称
+        - freq1: str，给定的K线周期
+        - n: int，最低价下方N个价位，默认为 3
+
+    :return: OrderedDict
+    """
+    from czsc.signals.tas import update_atr_cache
+
+    pos_name = kwargs["pos_name"]
+    freq1 = kwargs["freq1"]
+    n = int(kwargs.get("n", 10))  # N根K线
+    timeperiod = int(kwargs.get("timeperiod", 20))  # ATR参数
+
+    c = cat.kas[freq1]
+    cache_key = update_atr_cache(c, timeperiod=timeperiod)
+
+    k1, k2, k3 = f"{pos_name}_{freq1}N{n}T{timeperiod}_止损V240717".split("_")
+    v1 = "其他"
+
+    # 如果没有持仓策略，则不产生信号
+    if not cat.kas or not hasattr(cat, "positions"):
+        return create_single_signal(k1=k1, k2=k2, k3=k3, v1=v1)
+
+    pos = [x for x in cat.positions if x.name == pos_name][0]
+    if len(pos.operates) == 0 or pos.operates[-1]["op"] in [Operate.SE, Operate.LE]:
+        return create_single_signal(k1=k1, k2=k2, k3=k3, v1=v1)
+
+    op = pos.operates[-1]
+    atr = [x.cache[cache_key] if x.cache.get(cache_key) is not None else 0 for x in c.bars_raw if x.dt == op["dt"]]
+
+    # 开仓后的K线
+    a_bars = [x for x in c.bars_raw if x.dt >= op["dt"]]
+
+    if op["op"] == Operate.LO and len([x for x in a_bars if x.low < op["price"] - atr[0] * 0.67]) >= n:
+        v1 = "多头止损"
+
+    if op["op"] == Operate.SO and len([x for x in a_bars if x.high > op["price"] + atr[0] * 0.67]) >= n:
+        v1 = "空头止损"
+
+    return create_single_signal(k1=k1, k2=k2, k3=k3, v1=v1)