V0.9.6 重要更新 (#116)

* 0.9.6 start coding * 0.9.6 Event / Factor 对象优化 * 0.9.6 Event / Factor 对象优化 * 0.9.6 Event / Factor 对象优化 * 0.9.6 update word writer * 0.9.6 优化 dummy backtest 过程 * 0.9.6 update * 0.9.6 update * 0.9.6 update * 0.9.6 update * 0.9.6 删除 ts_backtest.py * 0.9.6 优化 BI 对象的缓存机制 * 0.9.6 新增大小级别中枢共振信号函数 * 0.9.6 新增 CzscSignals 作为信号计算的基类 * 0.9.6 update * 0.9.6 新增简单持仓对象 * 0.9.6 update * 0.9.6 update * 0.9.6 update * 0.9.6 update * 0.9.6 优化缓存机制 * 0.9.6 新增 qmt 工具集 * 0.9.6 update * 0.9.6 update * 0.9.6 update * 0.9.6 update * 0.9.6 update * 0.9.6 update * 0.9.6 更新单元测试 * 0.9.6 fix ta * 0.9.6 fix ta
waditu · Jan 1, 2023 · ecd5c81 · ecd5c81
1 parent b49a48e
commit ecd5c81
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diff --git a/czsc/__init__.py b/czsc/__init__.py
@@ -17,10 +17,10 @@
 from czsc.utils.cache import home_path, get_dir_size, empty_cache_path
 
 
-__version__ = "0.9.5"
+__version__ = "0.9.6"
 __author__ = "zengbin93"
 __email__ = "[email protected]"
-__date__ = "20221212"
+__date__ = "20221221"
 
 
 if envs.get_welcome():

diff --git a/czsc/cmd.py b/czsc/cmd.py
@@ -7,10 +7,7 @@
 
 https://click.palletsprojects.com/en/8.0.x/quickstart/
 """
-import os
 import click
-import glob
-import pandas as pd
 from loguru import logger
 
 
@@ -28,153 +25,46 @@ def aphorism():
     print_one()
 
 
-@czsc.command()
-@click.option('-f', '--file_strategy', type=str, required=True, help="Python择时策略文件路径")
-def backtest(file_strategy):
-    """使用 TradeSimulator 进行择时策略回测/仿真研究"""
-    from collections import Counter
-    from czsc.traders.ts_simulator import TradeSimulator
-    from czsc.utils import get_py_namespace
-
-    py = get_py_namespace(file_strategy)
-    results_path = os.path.join(py['results_path'], "backtest")
-    os.makedirs(results_path, exist_ok=True)
-
-    strategy = py['trader_strategy']
-    dc = py['dc']
-    symbols = py['symbols']
-
-    ts = TradeSimulator(dc, strategy, res_path=results_path)
-    counter = Counter([x.split("#")[1] for x in symbols])
-    for asset, c in counter.items():
-        ts_codes = [x.split("#")[0] for x in symbols if x.endswith(asset)]
-        ts.update_traders(ts_codes, asset)
-
-
 @czsc.command()
 @click.option('-f', '--file_strategy', type=str, required=True, help="Python择时策略文件路径")
 def dummy(file_strategy):
     """使用 CzscDummyTrader 进行快速的择时策略研究"""
-    import shutil
-    from datetime import datetime
-    from czsc.traders.advanced import CzscDummyTrader
-    from czsc.sensors.utils import generate_symbol_signals
-    from czsc.utils import get_py_namespace, dill_dump, dill_load
-
-    py = get_py_namespace(file_strategy)
-    signals_path = os.path.join(py['results_path'], "signals")
-    results_path = os.path.join(py['results_path'], f"DEXP{datetime.now().strftime('%Y%m%d%H%M')}")
-    os.makedirs(signals_path, exist_ok=True)
-    os.makedirs(results_path, exist_ok=True)
-    shutil.copy(file_strategy, os.path.join(results_path, 'strategy.py'))
-
-    strategy = py['trader_strategy']
-    dc = py['dc']
-    symbols = py['symbols']
-
-    for symbol in symbols:
-        file_dfs = os.path.join(signals_path, f"{symbol}_signals.pkl")
-
-        try:
-            # 可以直接生成信号，也可以直接读取信号
-            if os.path.exists(file_dfs):
-                dfs = pd.read_pickle(file_dfs)
-            else:
-                ts_code, asset = symbol.split('#')
-                dfs = generate_symbol_signals(dc, ts_code, asset, "20170101", "20221001", strategy, 'hfq')
-                dfs.to_pickle(file_dfs)
-
-            cdt = CzscDummyTrader(dfs, strategy)
-            dill_dump(cdt, os.path.join(results_path, f"{symbol}.cdt"))
-
-            res = cdt.results
-            if "long_performance" in res.keys():
-                logger.info(f"{res['long_performance']}")
-
-            if "short_performance" in res.keys():
-                logger.info(f"{res['short_performance']}")
-        except:
-            logger.exception(f"fail on {symbol}")
-
-    # 汇总结果
-    tactic = strategy('symbol')
-    files = glob.glob(f"{results_path}/*.cdt")
-    if tactic.get("long_pos", None):
-        lpf = pd.DataFrame([dill_load(file).results['long_performance'] for file in files])
-        lpf.to_excel(os.path.join(results_path, f'{strategy.__doc__}多头回测结果.xlsx'), index=False)
-
-    if tactic.get("short_pos", None):
-        spf = pd.DataFrame([dill_load(file).results['short_performance'] for file in files])
-        spf.to_excel(os.path.join(results_path, f'{strategy.__doc__}空头回测结果.xlsx'), index=False)
+    from czsc.traders.dummy import DummyBacktest
+    dbt = DummyBacktest(file_strategy)
+    dbt.replay()
+    dbt.execute()
+    dbt.report()
 
 
 @czsc.command()
 @click.option('-f', '--file_strategy', type=str, required=True, help="Python择时策略文件路径")
 def replay(file_strategy):
     """执行择时策略在某个品种上的交易回放"""
-    from czsc.data import freq_cn2ts
-    from czsc.utils import BarGenerator
-    from czsc.traders.utils import trade_replay
-    from czsc.utils import get_py_namespace
-
-    py = get_py_namespace(file_strategy)
-    strategy = py['trader_strategy']
-    dc = py['dc']
-    replay_params = py.get('replay_params', None)
-
-    if not replay_params:
-        logger.warning(f"{file_strategy} 中没有设置策略回放参数，将使用默认参数执行")
-
-    # 获取单个品种的基础周期K线
-    tactic = strategy("000001.SZ")
-    base_freq = tactic['base_freq']
-    symbol = replay_params.get('symbol', "000001.SZ#E")
-    ts_code, asset = symbol.split("#")
-    sdt = replay_params.get('sdt', '20150101')
-    edt = replay_params.get('edt', '20220101')
-    bars = dc.pro_bar_minutes(ts_code, sdt, edt, freq_cn2ts[base_freq], asset, adj="hfq")
-    logger.info(f"交易回放参数 | {symbol} - sdt:{sdt} - edt: {edt}")
-
-    # 设置回放快照文件保存目录
-    res_path = os.path.join(py['results_path'], f"replay_{symbol}")
-    os.makedirs(res_path, exist_ok=True)
-
-    # 拆分基础周期K线，一部分用来初始化BarGenerator，随后的K线是回放区间
-    start_date = pd.to_datetime(replay_params.get('mdt', '20200101'))
-    bg = BarGenerator(base_freq, freqs=tactic['freqs'])
-    bars1 = [x for x in bars if x.dt <= start_date]
-    bars2 = [x for x in bars if x.dt > start_date]
-    for bar in bars1:
-        bg.update(bar)
-
-    trade_replay(bg, bars2, strategy, res_path)
+    from czsc.traders.dummy import DummyBacktest
+    dbt = DummyBacktest(file_strategy)
+    dbt.replay()
 
 
 @czsc.command()
-@click.option('-f', '--file_strategy', type=str, required=True, help="Python择时策略文件路径")
-@click.option('-d', '--delta_days', type=int, required=False, default=5, help="两次相同信号之间的间隔天数")
+@click.option('-f', '--file_strategy', type=str, required=True, help="Python信号检查文件路径")
+@click.option('-d', '--delta_days', type=int, required=False, default=1, help="两次相同信号之间的间隔天数")
 def check(file_strategy, delta_days):
     """执行择时策略中使用的信号在某个品种上的校验"""
-    from czsc.data import freq_cn2ts
     from czsc.sensors.utils import check_signals_acc
     from czsc.utils import get_py_namespace
 
     py = get_py_namespace(file_strategy)
     strategy = py['trader_strategy']
-    dc = py['dc']
     check_params = py.get('check_params', None)
 
     if not check_params:
         logger.warning(f"{file_strategy} 中没有设置策略回放参数，将使用默认参数执行")
 
     # 获取单个品种的基础周期K线
-    tactic = strategy("000001.SZ")
-    base_freq = tactic['base_freq']
     symbol = check_params.get('symbol', "000001.SZ#E")
-    ts_code, asset = symbol.split("#")
     sdt = check_params.get('sdt', '20200101')
     edt = check_params.get('edt', '20220101')
-    bars = dc.pro_bar_minutes(ts_code, sdt, edt, freq_cn2ts[base_freq], asset, adj="hfq")
+    bars = py['read_bars'](symbol, sdt, edt)
     logger.info(f"信号检查参数 | {symbol} - sdt: {sdt} - edt: {edt}")
 
     check_signals_acc(bars, strategy=strategy, delta_days=delta_days)
diff --git a/czsc/connectors/__init__.py b/czsc/connectors/__init__.py
@@ -0,0 +1,7 @@
+# -*- coding: utf-8 -*-
+"""
+author: zengbin93
+email: [email protected]
+create_dt: 2022/12/31 16:02
+describe: 常用第三方交易框架的连接器
+"""
diff --git a/czsc/connectors/qmt_connector.py b/czsc/connectors/qmt_connector.py
@@ -0,0 +1,124 @@
+# -*- coding: utf-8 -*-
+"""
+author: zengbin93
+email: [email protected]
+create_dt: 2022/12/31 16:03
+describe: 
+"""
+import pandas as pd
+from typing import List
+from czsc.objects import Freq, RawBar
+from xtquant import xtdata
+
+
+def format_stock_kline(kline: pd.DataFrame, freq: Freq) -> List[RawBar]:
+    """QMT A股市场K线数据转换
+
+    :param kline: QMT 数据接口返回的K线数据
+                         time   open   high    low  close  volume      amount  \
+        0 2022-12-01 10:15:00  13.22  13.22  13.16  13.18   20053  26432861.0
+        1 2022-12-01 10:20:00  13.18  13.19  13.15  13.15   32667  43002512.0
+        2 2022-12-01 10:25:00  13.16  13.18  13.13  13.16   32466  42708049.0
+        3 2022-12-01 10:30:00  13.16  13.19  13.13  13.18   15606  20540461.0
+        4 2022-12-01 10:35:00  13.20  13.25  13.19  13.22   29959  39626170.0
+              symbol
+        0  000001.SZ
+        1  000001.SZ
+        2  000001.SZ
+        3  000001.SZ
+        4  000001.SZ
+    :param freq: K线周期
+    :return: 转换好的K线数据
+    """
+    bars = []
+    dt_key = 'time'
+    kline = kline.sort_values(dt_key, ascending=True, ignore_index=True)
+    records = kline.to_dict('records')
+
+    for i, record in enumerate(records):
+        # 将每一根K线转换成 RawBar 对象
+        bar = RawBar(symbol=record['symbol'], dt=pd.to_datetime(record[dt_key]), id=i, freq=freq,
+                     open=record['open'], close=record['close'], high=record['high'], low=record['low'],
+                     vol=record['volume'] * 100 if record['volume'] else 0,  # 成交量，单位：股
+                     amount=record['amount'] if record['amount'] > 0 else 0,  # 成交额，单位：元
+                     )
+        bars.append(bar)
+    return bars
+
+
+def get_local_kline(symbol, period, start_time, end_time, count=-1, dividend_type='none', data_dir=None, update=True):
+    """获取 QMT 本地K线数据
+
+    :param symbol: 股票代码 例如：'300001.SZ'
+    :param period: 周期 分笔"tick" 分钟线"1m"/"5m" 日线"1d"
+    :param start_time: 开始时间，格式YYYYMMDD/YYYYMMDDhhmmss/YYYYMMDDhhmmss.milli，
+        例如："20200427" "20200427093000" "20200427093000.000"
+    :param end_time: 结束时间 格式同上
+    :param count: 数量 -1全部，n: 从结束时间向前数n个
+    :param dividend_type: 除权类型"none" "front" "back" "front_ratio" "back_ratio"
+    :param data_dir: 下载QMT本地数据路径，如 D:/迅投极速策略交易系统交易终端/datadir
+    :param update: 更新QMT本地数据路径中的数据
+    :return: df Dataframe格式的数据，样例如下
+                         time   open   high    low  close  volume      amount  \
+        0 2022-12-01 10:15:00  13.22  13.22  13.16  13.18   20053  26432861.0
+        1 2022-12-01 10:20:00  13.18  13.19  13.15  13.15   32667  43002512.0
+        2 2022-12-01 10:25:00  13.16  13.18  13.13  13.16   32466  42708049.0
+        3 2022-12-01 10:30:00  13.16  13.19  13.13  13.18   15606  20540461.0
+        4 2022-12-01 10:35:00  13.20  13.25  13.19  13.22   29959  39626170.0
+              symbol
+        0  000001.SZ
+        1  000001.SZ
+        2  000001.SZ
+        3  000001.SZ
+        4  000001.SZ
+    """
+    field_list = ['time', 'open', 'high', 'low', 'close', 'volume', 'amount']
+    if update:
+        xtdata.download_history_data(symbol, period, start_time='20100101', end_time='21000101')
+    local_data = xtdata.get_local_data(field_list, [symbol], period, count=count, dividend_type=dividend_type,
+                                       start_time=start_time, end_time=end_time, data_dir=data_dir)
+
+    df = pd.DataFrame({key: value.values[0] for key, value in local_data.items()})
+    df['time'] = pd.to_datetime(df['time'], unit='ms') + pd.to_timedelta('8H')
+    df.reset_index(inplace=True, drop=True)
+    df['symbol'] = symbol
+    return df
+
+
+def get_symbols(step):
+    """获取择时策略投研不同阶段对应的标的列表
+
+    :param step: 投研阶段
+    :return: 标的列表
+    """
+    stocks = xtdata.get_stock_list_in_sector('沪深A股')
+    stocks_map = {
+        "index": ['000905.SH', '000016.SH', '000300.SH', '000001.SH', '000852.SH',
+                  '399001.SZ', '399006.SZ', '399376.SZ', '399377.SZ', '399317.SZ', '399303.SZ'],
+        "stock": stocks.ts_code.to_list(),
+        "check": ['000001.SZ'],
+        "train": stocks[:200],
+        "valid": stocks[200:600],
+        "etfs": ['512880.SH', '518880.SH', '515880.SH', '513050.SH', '512690.SH',
+                 '512660.SH', '512400.SH', '512010.SH', '512000.SH', '510900.SH',
+                 '510300.SH', '510500.SH', '510050.SH', '159992.SZ', '159985.SZ',
+                 '159981.SZ', '159949.SZ', '159915.SZ'],
+    }
+    return stocks_map[step]
+
+
+def test_local_kline():
+    # 获取所有板块
+    slt = xtdata.get_sector_list()
+    stocks = xtdata.get_stock_list_in_sector('沪深A股')
+
+    df = get_local_kline(symbol='000001.SZ', period='1m', count=1000, dividend_type='front',
+                         data_dir=r"D:\迅投极速策略交易系统交易终端 华鑫证券QMT实盘\datadir",
+                         start_time='20200427', end_time='20221231', update=True)
+    assert not df.empty
+    # df = get_local_kline(symbol='000001.SZ', period='5m', count=1000, dividend_type='front',
+    #                      data_dir=r"D:\迅投极速策略交易系统交易终端 华鑫证券QMT实盘\datadir",
+    #                      start_time='20200427', end_time='20221231', update=False)
+    # df = get_local_kline(symbol='000001.SZ', period='1d', count=1000, dividend_type='front',
+    #                      data_dir=r"D:\迅投极速策略交易系统交易终端 华鑫证券QMT实盘\datadir",
+    #                      start_time='20200427', end_time='20221231', update=False)
diff --git a/czsc/data/ts.py b/czsc/data/ts.py
@@ -15,8 +15,7 @@
 from functools import partial
 from loguru import logger
 
-from ..analyze import RawBar
-from ..enum import Freq
+from czsc.objects import RawBar, Freq
 
 
 # 数据频度 ：支持分钟(min)/日(D)/周(W)/月(M)K线，其中1min表示1分钟（类推1/5/15/30/60分钟）。
@@ -25,18 +24,6 @@
             Freq.F60: "60min", Freq.D: 'D', Freq.W: "W", Freq.M: "M"}
 freq_cn_map = {"1分钟": Freq.F1, "5分钟": Freq.F5, "15分钟": Freq.F15, "30分钟": Freq.F30,
                "60分钟": Freq.F60, "日线": Freq.D}
-exchanges = {
-    "SSE": "上交所",
-    "SZSE": "深交所",
-    "CFFEX": "中金所",
-    "SHFE": "上期所",
-    "CZCE": "郑商所",
-    "DCE": "大商所",
-    "INE": "能源",
-    "IB": "银行间",
-    "XHKG": "港交所"
-}
-
 dt_fmt = "%Y-%m-%d %H:%M:%S"
 date_fmt = "%Y%m%d"