From 0c0b30a4c6aa83f3c1c5579240a072b4aa254774 Mon Sep 17 00:00:00 2001 From: zengbin93 Date: Fri, 13 Oct 2023 10:32:38 +0800 Subject: [PATCH] =?UTF-8?q?0.9.31=20=E6=96=B0=E5=A2=9E=E6=8C=87=E6=95=B0?= =?UTF-8?q?=E5=90=88=E6=88=90?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- czsc/__init__.py | 1 + czsc/utils/__init__.py | 1 + czsc/utils/index_composition.py | 92 +++++++++++++++++++++++++++++++++ 3 files changed, 94 insertions(+) create mode 100644 czsc/utils/index_composition.py diff --git a/czsc/__init__.py b/czsc/__init__.py index a8ff4994e..3a70a283c 100644 --- a/czsc/__init__.py +++ b/czsc/__init__.py @@ -72,6 +72,7 @@ get_dir_size, empty_cache_path, print_df_sample, + index_composition, ) # 交易日历工具 diff --git a/czsc/utils/__init__.py b/czsc/utils/__init__.py index 8e8eb4645..f568b47e3 100644 --- a/czsc/utils/__init__.py +++ b/czsc/utils/__init__.py @@ -21,6 +21,7 @@ from .stats import daily_performance, net_value_stats, subtract_fee from .signal_analyzer import SignalAnalyzer, SignalPerformance from .cache import home_path, get_dir_size, empty_cache_path +from .index_composition import index_composition sorted_freqs = ['Tick', '1分钟', '2分钟', '3分钟', '4分钟', '5分钟', '6分钟', '10分钟', '12分钟', diff --git a/czsc/utils/index_composition.py b/czsc/utils/index_composition.py new file mode 100644 index 000000000..952e30397 --- /dev/null +++ b/czsc/utils/index_composition.py @@ -0,0 +1,92 @@ +# -*- coding: utf-8 -*- +""" +author: zengbin93 +email: zeng_bin8888@163.com +create_dt: 2023/10/13 15:01 +describe: 因子（特征）处理 +""" +import pandas as pd + + +def index_composition(klines, weights=None, base_point=1000, **kwagrs): + """设置基点，按收益率加权合成指数 + + 输入： + + 1. 成分标的K线行情 + 2. 每个时刻的成分权重 + 3. 基点 + + 过程： + + 1. 计算成分标的每根K线的收益 + 2. 在每个时刻，按成分权重对标的收益加权计算，得到每个时刻的指数涨跌幅 + 3. 用基点和每个时刻的涨跌幅计算出每个时刻的指数价 + + 输出：指数K线行情 + + :param klines: K线行情，样例如下： + + ========= =================== ======= ======= ======= ======= ========== =========== + symbol dt open close high low vol amount + ========= =================== ======= ======= ======= ======= ========== =========== + 000001.SH 2021-01-04 09:31:00 3473.82 3469.37 3474.33 3468.86 1041014208 13018854400 + 000001.SH 2021-01-04 09:32:00 3470.18 3467.58 3471.95 3467.58 570367232 7721827328 + 000001.SH 2021-01-04 09:33:00 3467.11 3466.98 3468.53 3466.94 660060480 8371049984 + 000001.SH 2021-01-04 09:34:00 3466.83 3463.5 3466.83 3463.4 563931392 7435783168 + 000001.SH 2021-01-04 09:35:00 3463.23 3460.33 3463.75 3460.33 504271904 6500695552 + ========= =================== ======= ======= ======= ======= ========== =========== + + :param weights: 权重调整记录；索引为dt，columns为成分权重，每个时间截面的权重之和可以不为1，样例如下： + + =================== =========== =========== =========== =========== + dt 000001.SH 000016.SH 000300.SH 000905.SH + =================== =========== =========== =========== =========== + 2021-01-04 09:31:00 0.244275 0.236822 0.271415 0.204674 + 2021-01-04 10:10:00 0.250273 0.140398 0.151955 0.294418 + 2021-01-04 10:54:00 0.127531 0.123941 0.199969 0.19682 + =================== =========== =========== =========== =========== + + 注意：权重调整记录的时间截面必须是K线行情的时间截面的子集，且必须包含K线行情的第一根K线的时间截面 + + :param base_point: 基点，默认为1000 + :return: 指数K线行情，样例如下： + + =================== ============ ======== ========== =========== + dt returns price vol amount + =================== ============ ======== ========== =========== + 2021-01-04 09:31:00 0 1000 2195268112 31725149952 + 2021-01-04 09:32:00 -0.000230561 999.769 1187524832 18900989440 + 2021-01-04 09:33:00 -0.000165153 999.604 1330775568 19418287360 + 2021-01-04 09:34:00 -0.00103582 998.569 1133046300 17488768512 + 2021-01-04 09:35:00 -0.00067244 997.897 1025222108 15351070080 + =================== ============ ======== ========== =========== + """ + klines["dt"] = pd.to_datetime(klines["dt"]) + + if 'returns' not in klines.columns.tolist(): + data = [] + for _, kline in klines.groupby("symbol"): + kline = kline.sort_values("dt", ascending=True).reset_index(drop=True) + kline["returns"] = kline["close"].pct_change() + kline["returns"] = kline["returns"].fillna(0) + data.append(kline) + klines = pd.concat(data, ignore_index=True) + + returns = klines.pivot_table(index="dt", columns="symbol", values="returns") + + if weights is None: + weights = returns.copy() + weights[:] = 1 / len(returns.columns.tolist()) + else: + weights['dt'] = pd.to_datetime(weights['dt']) + assert weights['dt'].min() <= klines['dt'].min(), "权重调整记录的首个时刻必须小于等于成分K线首个时刻" + weights.set_index("dt", inplace=True) + weights = weights.reindex(returns.index, method="ffill", copy=True) + + index_ = (returns * weights).sum(axis=1).to_frame("returns") + index_["price"] = base_point * (1 + index_["returns"]).cumprod() + index_['vol'] = klines.groupby("dt")["vol"].sum() + index_['amount'] = klines.groupby("dt")["amount"].sum() + index_ = index_.reset_index() + return index_