diff --git "a/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/README.md" "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/README.md" new file mode 100644 index 0000000..c0f9af3 --- /dev/null +++ "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/README.md" @@ -0,0 +1,5 @@ +# 说明 + +数据位置 + +https://drive.google.com/drive/folders/1AKnaQVwp_DdZ6wl27miQTWwuJAcAPDPv?usp=share_link \ No newline at end of file diff --git "a/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__init__.py" "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__init__.py" new file mode 100644 index 0000000..a8ce586 --- /dev/null +++ "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__init__.py" @@ -0,0 +1 @@ +from .core import * \ No newline at end of file diff --git "a/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__pycache__/__init__.cpython-38.pyc" "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__pycache__/__init__.cpython-38.pyc" new file mode 100644 index 0000000..0e8f9d5 Binary files /dev/null and "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__pycache__/__init__.cpython-38.pyc" differ diff --git "a/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__pycache__/core.cpython-38.pyc" "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__pycache__/core.cpython-38.pyc" new file mode 100644 index 0000000..62d5abf Binary files /dev/null and "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__pycache__/core.cpython-38.pyc" differ diff --git "a/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/core.py" "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/core.py" new file mode 100644 index 0000000..f4d1de5 --- /dev/null +++ "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/core.py" @@ -0,0 +1,178 @@ +""" +Author: hugo2046 shen.lan123@gmail.com +Date: 2023-03-07 15:42:11 +LastEditors: hugo2046 shen.lan123@gmail.com +LastEditTime: 2023-03-07 15:47:21 +Description: +""" + +from collections import Counter +from typing import Dict + +import numpy as np +import pandas as pd +import plotly.graph_objects as go + + +def find_stage_stock( + pivot_price: pd.DataFrame, + window: int, + method: str = "high", + offset: int = None, +) -> pd.DataFrame: + """获取近期创新高/新低股票数量 + + Args: + pivot_price (pd.DataFrame): index-date columns-code values-price + window (int): 窗口期 + method (str, optional): high-创新高/low-创新低. Defaults to "high". + offset (int, optional): 是否offset. Defaults to None. + + Returns: + pd.DataFrame: index-date columns-code values-True/False + """ + oper: str = {"high": "ge", "low": "le"}[method] + method: str = {"high": "max", "low": "min"}[method] + + roll_: pd.DataFrame = pivot_price.rolling(window) + + roll_df: pd.DataFrame = getattr(roll_, method)() + + if offset is not None: + + roll_df: pd.DataFrame = roll_df.shift(offset) + + return getattr(pivot_price, oper)(roll_df) + + +def get_ind_stage_num(pivot_num: pd.DataFrame, sw_cons_dict: Dict) -> pd.DataFrame: + """通过个股数量统计行业创新高情况 + + Args: + pivot_num (pd.DataFrame): 个股创新高数量标记 index-date columns-code values-True/False + sw_cons_dict (Dict): 个股所属行业字典 k-code v-industry_code + + Returns: + pd.DataFrame: index-date columns-indutsry_code values-num + """ + industry_num: pd.DataFrame = pivot_num.copy() + industry_num.columns = industry_num.columns.map(sw_cons_dict) + + return industry_num.groupby(level=0, axis=1).sum() + + +def calc_industry_nhnl( + pivot_price: pd.DataFrame, + sw_cons_dict: Dict, + window: int, + classify_num: pd.DataFrame = None, + tradition: bool = True, +) -> pd.DataFrame: + """获取行业净新高占比(NHNL) + + Args: + pivot_price (pd.DataFrame): 个股价格数据 index-date columns MultiIndex level0 fields have low|high;level1 codes + sw_cons_dict (Dict): k-code v-indutsry_name/industry_code + window (int): 窗口期 + tradition:True-传统构建方法;False-研报方式 使用close判断创新高/新低;默认为true + + Returns: + pd.DataFrame: index-date columns-industry_code values-per + """ + if classify_num is None: + classify_num: pd.Series = pd.Series(Counter(tuple(sw_cons_dict.values()))) + + h_field: str = "high" + l_field: str = "low" + if tradition: + h_field, l_field = "close", "close" + + high_num: pd.DataFrame = find_stage_stock(pivot_price[h_field], window, "high", 5) + low_num: pd.DataFrame = find_stage_stock(pivot_price[l_field], window, "low", 5) + + ind_high: pd.DataFrame = get_ind_stage_num(high_num, sw_cons_dict) + ind_low: pd.DataFrame = get_ind_stage_num(low_num, sw_cons_dict) + + return (ind_high - ind_low).div(classify_num) + + +def plot_nhnl_signal( + price: pd.Series, + siganl: pd.Series, + cons_num: int = None, + title: str = "", + align: bool = False, +) -> go.Figure: + """画NH-NL图 + plotly >= 5.13 + Args: + price (pd.Series): 价格数据 index-date values-price + siganl (pd.Series): NH-NL信号 index-date values-sigbal + cons_num (int): 行业个股上市天数超过一年的个数.Defaults is None + title (str, optional): 标题. Defaults to "". + align (bool, optional): 是否按照信号对齐价格数据. Defaults to False. + + Returns: + go.Figure: 图表 + """ + fig = go.Figure() + + THRESHOLD: Dict = { + "normal": {"贪婪": 0.3, "乐观": 0.2, "悲观": -0.2, "恐惧": -0.3}, + "other": {"贪婪": 0.4, "乐观": 0.3, "悲观": -0.3, "恐惧": -0.4}, + } + + COLOR: Dict = { + "贪婪": {"color": "LightSeaGreen"}, + "乐观": {"color": "LightSeaGreen", "dash": "dashdot"}, + "悲观": {"color": "Crimson", "dash": "dashdot"}, + "恐惧": {"color": "Crimson"}, + } + + if align: + siganl, price = siganl.align(price, join="inner") + + price_ax = go.Scatter( + x=price.index, + y=price.values, + line=dict(color="darkgray"), + name="close", + ) + nhnl_ax = go.Scatter( + x=siganl.index, + y=siganl.values, + line=dict(color="DarkSalmon"), + name="NH-NL", + yaxis="y2", + ) + + fig.add_trace(price_ax) + fig.add_trace(nhnl_ax) + + method: str = "normal" if (cons_num > 40 or cons_num is None) else "other" + threshold_range: Dict = THRESHOLD[method] + + for name, value in threshold_range.items(): + + fig.add_trace( + go.Scatter( + x=price.index, + y=np.ones(len(price)) * value, + line=COLOR[name], + name=name, + yaxis="y2", + ) + ) + + fig.update_layout( + hovermode="x unified", + yaxis2=dict( + title="NHNL", + overlaying="y", + side="right", + ), + title={"text": title}, + legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), + ) + + return fig diff --git "a/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267.ipynb" new file mode 100644 index 0000000..b1a5e03 --- /dev/null +++ "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267.ipynb" @@ -0,0 +1,1570 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 行业指数顶部和底部信号：净新高占比（(NH-NL)%）\n", + "\n", + "## 背景\n", + "\n", + "**行业涨跌分化增加了行业指数顶部和底部信号的必要性：**\n", + "\n", + "一方面，近年来A股行业指数同涨同跌的现象减弱,尤其是2020年以来，**行业指数间的分化越来越严重，因此寻找行业指数的顶部和底部信号显得尤为重要**。28个中信一级行业估值分位数的波动率验证了行情的分化。另一方面，各行业指数ETF、主题基金的兴起和壮大，增加了对行业指数顶部和底部判断的需求。\n", + "\n", + "**行业指数顶部和底部信号的构建及简单反转策略：**\n", + "我们通过构建净新高占比（(NH-NL)%）指标来刻画行业指数的情绪，这延 续了我们对价格新高和价格新低的重视，与我们衡量宽基指数的情绪指标 （NHNL）一脉相承，**其核心逻辑仍基于行为金融学的锚定效应**。在净新高占比（(NH-NL)%）信号的基础上，我们构建了一个简单的反转策略，包括买入、卖出、仓位、止损及移动等要素。\n", + "\n", + "**净新高占比能较好地提示科技板块行业指数的顶部和底部：**\n", + "\n", + "通过对科技板块的电子、通信、计算机和传媒行业的逐一复盘，从准确率 和简单反转策略盈利性等角度进行分析，我们发现净新高占比（(NH-NL)%） 指标能较好地提示行业指数的顶部和顶部；简单反转策略有效，且2020年 以来策略在科技板块的表现较好。考虑到反转策略资金占用时间较少，因此比较适合作为指数增强型策略。\n", + "\n", + "\n", + "近年来，A 股行业指数同涨同跌的现象减弱，尤其是 2020 年行业指数间分化更 加明显；同时，伴随各行业指数 ETF、主题基金的兴起和壮大，增加了对行业指数顶部和底部判断的需求。因此，我们尝试寻找提示行业指数顶部和底部的信号。我们通过构建净新高占比指标（简称“(NH-NL)%”），来寻找行业指数（中信一 级行业指数）的顶部和底部。通过回溯检验发现，净新高占比（(NH-NL)%）在科技 板块和周期板块表现较好。本篇报告并以科技板块为例，复盘了净新高占比（(NH-NL)%）提示科技板块行业指数的准确率及简单反转策略的有效性。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from typing import Dict, List\n", + "from tqdm.notebook import tqdm\n", + "from scr import calc_industry_nhnl,plot_nhnl_signal" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 构建逻辑\n", + "\n", + "净新高占比指标（(NH-NL)%）即行业指数中创年度新高与年度新低之差的个股 数占全行业个股数的百分比，其逻辑自洽性来源包括以下四个方面：\n", + "\n", + "- 锚定效应与新高新低。行为金融学告诉我们，绝大多数投资者都有很强的锚 定效应，他们都清楚地记得所持有个股/基金的成本价（即“锚定成本”）， 甚至他们的很多决定都是视他们处于浮盈还是浮亏状态而定。创年度新高的 个股，其股票持有者均为浮盈状态，因此即使其计划卖出，也愿意等行情再 上涨一段时间，因此其抛压相对较小；而创年度新低的个股，因其股票持有者均为浮亏状态，因此只要行情反弹便有投资者卖出，抛压反而较大。\n", + " \n", + "- 新高新低是个股走强走弱最直接的刻画。创新高/新低是个股走强/走弱最直 接的刻画，10 倍股都是从 1 倍股开始涨的，优秀的个股能不断创出新高；另一方面，个股跌到 2 折前都是先腰斩，而最开始的表现则是创年度新低。\n", + " \n", + "- 新高新低差代表行业内个股整体强弱，亦即代表行业指数的强弱。如果一 个行业指数中创年度新高多于创年度新低的个股数，即表示其中更多个股正在走强，反之亦然。\n", + " \n", + "- 净新高占比（(NH-NL)%）能较好地刻画行业指数强弱及市场情绪。净新高占比通过归一化（指标值在【-1,1】之间），能有效去除行业规模的影响（机 械行业目前有接近 500 个公司上市超过 1 年，煤炭行业上市超过 1 年的上\n", + "市公司仅 36 个），而且能进行横向比较，去除行业的特殊性。\n", + "\n", + "## 信号构建方法\n", + "\n", + "我们通过构建指标净新高占比（Net Percent of New High Minus New Low，简 称“(NH-NL)%”，亦简称“NPNMN”）来衡量中信一级行业指数的强弱：\n", + "\n", + "$$净新高占比(NH-NL)\\%=\\frac{创年度新高的个股数-创年度新低的个股数}{行业内部上市超过1年的个股数}$$\n", + "\n", + "其中,\"创年度新高/新低\"指*收盘价大于/小于过去52周至一周前的区间最高/最低收盘价*\n", + "\n", + "这里有两个注意:\n", + "1. 传统使用high>Max(high)表示最高(最低同),而这里用的close>Max(close);\n", + "2. 识别前期高点的时间是$T_{1-52}$而非$T_{0-51}$\n", + "\n", + "## (NH-NL)%指标的阈值选择\n", + "\n", + "我们用20%和30%作为中信一级行业指数**乐观**和**贪婪**的阈值，用-20%和-30% 作为**悲观**和**恐惧**的阈值。\n", + "\n", + "$$NHNL = \\begin{cases} \n", + "x \\geq 30\\% \\ 贪婪\\\\\\\n", + "20\\% \\leq x \\lt 30\\% \\ 乐观\\\\\\\n", + "-20\\% \\lt x \\lt 20\\% \\ 正常区间\\\\\\\n", + "-30\\% \\lt x \\leq -20\\% \\ 悲观\\\\\\\n", + "x \\leq -30\\% \\ 恐惧\n", + "\\end{cases}$$\n", + "\n", + "为了防止一级行业指数个股数太少引发的跳跃，当一级行业指数上市超过 1 年的个股数小于 40 时，将阈值放宽为±30%/40%：\n", + "\n", + "$$NHNL = \\begin{cases} \n", + "x \\geq 40\\% \\ 贪婪\\\\\\\n", + "30\\% \\leq x \\lt 40\\% \\ 乐观\\\\\\\n", + "-30\\% \\lt x \\lt 30\\% \\ 正常区间\\\\\\\n", + "-40\\% \\lt x \\leq -30\\% \\ 悲观\\\\\\\n", + "x \\leq -40\\ \\ 恐惧%\n", + "\\end{cases}$$\n", + "\n", + "## 跟踪方法\n", + "\n", + "我们构建一个基于净新高占比指标（(NH-NL)%）简单的反转策略：当净新高占比指标提示贪婪后开始关注，第一天回到阈值内视为做空信号，下一个交易日开盘做空，止损点为前一周（5 个交易日）区间最高点，否则持有30天后以收盘价平仓，入场一周后若未止损则将止损位移至成本价。\n", + "\n", + "当净新高占比指标提示恐慌后开始关注，第一天回到阈值内视为做多信号，下一个交易日开盘做多，止损点为前一周区间最低点，否则持有 30 天后以收盘价平仓，入场一周后若未止损则将止损位移至成本价。" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 数据获取\n", + "\n", + "**此部分可以跳过直接读取data文件中的数据构建信号**" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import sys\n", + "from pathlib import Path\n", + "\n", + "sys.path.append(str(Path().resolve().parents[0]))\n", + "\n", + "from knight_scr.scr.sql_service import (\n", + " get_ts_price,\n", + " get_ind_classify,\n", + " get_ind_concept_cons,\n", + " get_ind_price,\n", + " get_ts_stock_basic,\n", + " get_trade_days,\n", + ")\n", + "\n", + "def get_ind_overthreshold_num(\n", + " sw_cons_name: Dict, start_dt: str, end_dt: str, limit: int\n", + ") -> pd.DataFrame:\n", + " \"\"\"交易日至ipodate的天数(自然日)\n", + "\n", + " Args:\n", + " sw_cons_name (Dict): k-codes v-industry_name\n", + " start_dt (str): 起始日期\n", + " end_dt (str): 结束日期\n", + " limit (int): 阈值\n", + "\n", + " Returns:\n", + " pd.DataFrame: index-date columns-code values-days\n", + " \"\"\"\n", + " # 获取股票基础数据\n", + " stock_basic: pd.DataFrame = get_ts_stock_basic()\n", + " codes: List = list(sw_cons_name.keys())\n", + "\n", + " cond: pd.DataFrame = stock_basic[\"code\"].isin(codes)\n", + " stock_basic: pd.DataFrame = stock_basic[cond]\n", + " # 获取交易日期\n", + " periods: np.ndarray = get_trade_days(start_dt, end_dt)\n", + "\n", + " stock_ipo_list: pd.Series = stock_basic.set_index(\"code\")[\"list_date\"]\n", + " stock_ipo_list: pd.Series = stock_ipo_list.astype(np.datetime64)\n", + "\n", + " data: np.ndarray = np.broadcast_to(periods, (len(codes), len(periods))).T\n", + " ipo_date: pd.DataFrame = (\n", + " pd.DataFrame(data=data, index=periods, columns=codes)\n", + " .sub(stock_ipo_list)\n", + " .applymap(lambda x: x.days)\n", + " )\n", + "\n", + " ipo_date.columns = ipo_date.columns.map(sw_cons_name)\n", + "\n", + " return ipo_date.apply(lambda x: x > limit).groupby(level=0, axis=1).sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "watch_date = \"2023-03-03\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "sw_classify: pd.DataFrame = get_ind_classify(watch_date, \"sw_level1\")\n", + "sw_cons: pd.DataFrame = get_ind_concept_cons(watch_date, \"sw_level1\")\n", + "\n", + "# k-code v-industry_code\n", + "sw_cons_dict: Dict = sw_cons.set_index(\"code\")[\"industry_code\"].to_dict()\n", + "# k-industry_code v-industry_name\n", + "classify_dict: Dict = sw_classify.set_index(\"code\")[\"sec_name\"].to_dict()\n", + "# k-code v-industry_code\n", + "sw_cons_name: Dict = {k: classify_dict[v] for k, v in sw_cons_dict.items()}\n", + "# name-cons_num\n", + "# classify_num: pd.Series = pd.Series(Counter(sw_cons_name.values()))" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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codetrade_datesec_name
0801010.SI2023-03-03农林牧渔
1801030.SI2023-03-03基础化工
2801040.SI2023-03-03钢铁
3801050.SI2023-03-03有色金属
4801080.SI2023-03-03电子
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codetrade_datesec_nameindustry_code
0000019.SZ2023-03-03深粮控股801010.SI
1000505.SZ2023-03-03京粮控股801010.SI
2000592.SZ2023-03-03平潭发展801010.SI
3000639.SZ2023-03-03西王食品801010.SI
4000663.SZ2023-03-03永安林业801010.SI
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" + ], + "text/plain": [ + " code trade_date sec_name industry_code\n", + "0 000019.SZ 2023-03-03 深粮控股 801010.SI\n", + "1 000505.SZ 2023-03-03 京粮控股 801010.SI\n", + "2 000592.SZ 2023-03-03 平潭发展 801010.SI\n", + "3 000639.SZ 2023-03-03 西王食品 801010.SI\n", + "4 000663.SZ 2023-03-03 永安林业 801010.SI" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sw_cons.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "ind_price: pd.DataFrame = get_ind_price(\n", + " list(classify_dict.keys()),\n", + " start_date=\"2014-01-01\",\n", + " end_date=\"2023-03-03\",\n", + " fields=[\"close\"],\n", + " level=\"sw_level1\",\n", + ")\n", + "ind_price[\"name\"] = ind_price[\"code\"].map(classify_dict)\n", + "pivot_ind_price: pd.DataFrame = pd.pivot_table(\n", + " ind_price, index=\"trade_date\", columns=\"name\", values=\"close\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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codeclosetrade_datename
0801010.SI3265.372022-11-16农林牧渔
1801030.SI4242.142022-11-16基础化工
2801040.SI2350.872022-11-16钢铁
3801050.SI5028.192022-11-16有色金属
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trade_datecodehighlow
02013-01-04600839.SH56.3589455.28030
15322013-01-04300271.SZ24.4524623.45694
15332013-01-04002678.SZ11.8000011.50000
15342013-01-04002394.SZ24.8600524.03910
15352013-01-04300323.SZ11.8300011.28000
\n", + "" + ], + "text/plain": [ + " trade_date code high low\n", + "0 2013-01-04 600839.SH 56.35894 55.28030\n", + "1532 2013-01-04 300271.SZ 24.45246 23.45694\n", + "1533 2013-01-04 002678.SZ 11.80000 11.50000\n", + "1534 2013-01-04 002394.SZ 24.86005 24.03910\n", + "1535 2013-01-04 300323.SZ 11.83000 11.28000" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "price.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "pivot_table: pd.DataFrame = pd.pivot_table(price,\n", + " index=\"trade_date\",\n", + " columns=\"code\",\n", + " values=[\"low\", \"high\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "start_dt = pivot_table.index.min()\n", + "end_dt = pivot_table.index.max()\n", + "\n", + "classify_num: pd.DataFrame = get_ind_overthreshold_num(\n", + " sw_cons_name, start_dt, end_dt, 252\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 信号构建\n", + "\n", + "这里直接构建已有数据构建" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "with open('data/sw_cons_name.json','r') as f:\n", + " \n", + " sw_cons_name:Dict = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "price: pd.DataFrame = pd.read_csv(\"data/data.csv\", index_col=[0], parse_dates=[0])\n", + "\n", + "pivot_table: pd.DataFrame = pd.pivot_table(\n", + " price, index=\"trade_date\", columns=\"code\", values=[\"low\", \"high\"]\n", + ")\n", + "\n", + "ind_price: pd.DataFrame = pd.read_csv(\n", + " \"data/industry_price.csv\", index_col=[0], parse_dates=[0]\n", + ")\n", + "\n", + "pivot_ind_price:pd.DataFrame = pd.pivot_table(\n", + " ind_price, index=\"trade_date\", columns=\"name\", values='close'\n", + ")\n", + "classify_num: pd.DataFrame = pd.read_csv(\n", + " \"data/industry_classify_num.csv\", index_col=[0], parse_dates=[0]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "nhnl_df: pd.DataFrame = calc_industry_nhnl(\n", + " pivot_table, sw_cons_name, 52 * 5, classify_num,tradition=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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name交通运输传媒公用事业农林牧渔医药生物商贸零售国防军工基础化工家用电器建筑材料...纺织服饰综合美容护理计算机轻工制造通信钢铁银行非银金融食品饮料
trade_date
2014-01-021615.66869.631830.181910.495051.733068.11967.031820.032949.753110.23...1654.621633.074451.862442.391641.971446.491561.162085.201061.484658.96
2014-01-031600.54883.521814.651896.495028.633047.10952.461803.592881.533041.16...1633.761608.184436.662468.341638.411445.331562.542051.371034.814615.06
2014-01-061547.14864.401771.891818.074919.262953.29914.791752.002807.652945.86...1581.041562.434320.382402.111605.001403.291517.682028.911028.314500.21
2014-01-071549.88877.561777.651818.744970.162966.80920.691759.832813.322943.22...1587.111569.974364.412418.801611.901410.631508.782022.181022.424521.77
2014-01-081538.35914.001783.921818.815016.002961.86934.771755.682830.832939.19...1576.371564.094294.822466.131624.171427.981493.702032.831030.184552.21
..................................................................
2023-02-272386.26583.412250.093404.769416.412744.151588.844473.797041.166537.20...1695.062997.437324.374516.412586.312132.332554.973127.161547.2924368.50
2023-02-282400.09590.742271.143422.689577.382761.721593.424491.057045.126576.73...1725.233020.477378.864617.622605.862180.572563.843139.451562.7924394.10
2023-03-012424.65611.742283.593427.029572.932762.031601.774499.707169.866705.74...1724.573027.177356.284800.492625.232264.472574.993178.121592.5624651.70
2023-03-022436.05623.222282.973404.219469.682746.051604.844492.887141.956760.43...1728.873014.667351.534825.392627.052287.312586.223187.211591.1524573.10
2023-03-032451.72620.292302.853404.769473.962763.931629.944476.907165.706755.17...1717.343040.547315.954774.172599.052272.002600.953212.641600.3624429.90
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" + ], + "text/plain": [ + "name 交通运输 传媒 公用事业 农林牧渔 医药生物 商贸零售 国防军工 \\\n", + "trade_date \n", + "2014-01-02 1615.66 869.63 1830.18 1910.49 5051.73 3068.11 967.03 \n", + "2014-01-03 1600.54 883.52 1814.65 1896.49 5028.63 3047.10 952.46 \n", + "2014-01-06 1547.14 864.40 1771.89 1818.07 4919.26 2953.29 914.79 \n", + "2014-01-07 1549.88 877.56 1777.65 1818.74 4970.16 2966.80 920.69 \n", + "2014-01-08 1538.35 914.00 1783.92 1818.81 5016.00 2961.86 934.77 \n", + "... ... ... ... ... ... ... ... \n", + "2023-02-27 2386.26 583.41 2250.09 3404.76 9416.41 2744.15 1588.84 \n", + "2023-02-28 2400.09 590.74 2271.14 3422.68 9577.38 2761.72 1593.42 \n", + "2023-03-01 2424.65 611.74 2283.59 3427.02 9572.93 2762.03 1601.77 \n", + "2023-03-02 2436.05 623.22 2282.97 3404.21 9469.68 2746.05 1604.84 \n", + "2023-03-03 2451.72 620.29 2302.85 3404.76 9473.96 2763.93 1629.94 \n", + "\n", + "name 基础化工 家用电器 建筑材料 ... 纺织服饰 综合 美容护理 \\\n", + "trade_date ... \n", + "2014-01-02 1820.03 2949.75 3110.23 ... 1654.62 1633.07 4451.86 \n", + "2014-01-03 1803.59 2881.53 3041.16 ... 1633.76 1608.18 4436.66 \n", + "2014-01-06 1752.00 2807.65 2945.86 ... 1581.04 1562.43 4320.38 \n", + "2014-01-07 1759.83 2813.32 2943.22 ... 1587.11 1569.97 4364.41 \n", + "2014-01-08 1755.68 2830.83 2939.19 ... 1576.37 1564.09 4294.82 \n", + "... ... ... ... ... ... ... ... \n", + "2023-02-27 4473.79 7041.16 6537.20 ... 1695.06 2997.43 7324.37 \n", + "2023-02-28 4491.05 7045.12 6576.73 ... 1725.23 3020.47 7378.86 \n", + "2023-03-01 4499.70 7169.86 6705.74 ... 1724.57 3027.17 7356.28 \n", + "2023-03-02 4492.88 7141.95 6760.43 ... 1728.87 3014.66 7351.53 \n", + "2023-03-03 4476.90 7165.70 6755.17 ... 1717.34 3040.54 7315.95 \n", + "\n", + "name 计算机 轻工制造 通信 钢铁 银行 非银金融 食品饮料 \n", + "trade_date \n", + "2014-01-02 2442.39 1641.97 1446.49 1561.16 2085.20 1061.48 4658.96 \n", + "2014-01-03 2468.34 1638.41 1445.33 1562.54 2051.37 1034.81 4615.06 \n", + "2014-01-06 2402.11 1605.00 1403.29 1517.68 2028.91 1028.31 4500.21 \n", + 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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32md:\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3802\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3803\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[0;32m 3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", + "File \u001b[1;32md:\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32md:\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: '电子'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[18], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m plot_nhnl_signal(pivot_ind_price[\u001b[39m\"\u001b[39;49m\u001b[39m电子\u001b[39;49m\u001b[39m\"\u001b[39;49m], nhnl_df[\u001b[39m\"\u001b[39m\u001b[39m电子\u001b[39m\u001b[39m\"\u001b[39m], \u001b[39m40\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m电子行业净新高占比\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mTrue\u001b[39;00m)\n", + "File \u001b[1;32md:\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3805\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3803\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m 3804\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3805\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[0;32m 3806\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 3807\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n", + "File \u001b[1;32md:\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3803\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m 3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m-> 3805\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m 3806\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m 3807\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3808\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3809\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3810\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[1;31mKeyError\u001b[0m: '电子'" + ] + } + ], + "source": [ + "plot_nhnl_signal(pivot_ind_price[\"电子\"], nhnl_df[\"电子\"], 40, \"电子行业净新高占比\", True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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code801010.SI801030.SI801040.SI801050.SI801080.SI801110.SI801120.SI801130.SI801140.SI801150.SI...801760.SI801770.SI801780.SI801790.SI801880.SI801890.SI801950.SI801960.SI801970.SI801980.SI
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