{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import sys\n", "\n", "sys.path.insert(0, \"../../\")\n", "# 演示必要的准备代码，使用该库时不需重复此单元格命令" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import xalpha as xa\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "本文将分析大陆公募基金市场中和石油商品相关的基金，考察其相关性和可替代性" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "hb = xa.fundinfo(\"162411\") # 华宝油气\n", "gf = xa.fundinfo(\"162719\") # 广发石油\n", "ha = xa.fundinfo(\"160416\") # 华安石油\n", "nf = xa.fundinfo(\"501018\") # 南方原油\n", "yfd = xa.fundinfo(\"161129\") # 易方达原油\n", "js = xa.fundinfo(\"160723\") # 嘉实原油" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "oilcomp = xa.evaluate(hb, gf, ha, nf, yfd, js)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "oilcomp.v_correlation()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "观察上述的相关系数图，可以看到高关联的基金自动分成两个区。三个跟踪原油期货的基金关联极高。同时三个在美股为主跟踪石油产业企业股票指数的基金（对应指数不同）关联极高。另一方面，两者之间关联并没有特别高。像经常宣传和原油价格走势关联很高的华宝油气，其关联系数也只有 0.67 左右，并不是很高。这种跟踪意义不大，下面不同基金的净值图将这个问题表现的更明显。\n", "如果非想持有石油股票指数来减少原油期货换仓损失的话，华安石油相比华宝油气具有更高的关联性，相关系数在 0.73 左右，高于另两个石油股票基金。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "oilcomp.v_netvalue()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以看到三个追踪原油期货的基金，走势几乎完全一致，在两年多的时间中产生的误差很有限。\n", "奇怪的是，在这段区间石油价格有所回升，同时美股一直运行在牛市，但主力在美股的石油企业的股票走势还比不上原油价格本身，尤其是华宝油气表现极差。也就是说，美股里这些企业估值在不断走低，对应了大家的夕阳产业预期。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 华宝油气：0.15+1.28， 标普石油天然气上游股票指数\n", "* 华安石油： 0.12+1.28， 标普全球石油指数\n", "* 广发石油： 0.39 亿，规模低于清盘线，请谨慎申购， 0.12+1.3， 道琼斯美国石油开发与生产指数\n", "\n", "* 易方达原油： 0.12+1.25，标普高盛原油商品指数\n", "* 南方原油： 0.12+1.28， 60%WTI原油价格收益率 + 40%BRENT原油价格收益率\n", "* 嘉实原油： 0.12+1.28，100%WTI原油价格收益率" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "华宝标普油气上游股票 0.31330039728004055\n", "广发道琼斯石油指数人民币A 0.23449795919975377\n", "华安标普全球石油指数 0.1684688810075437\n", "南方原油A 0.23465224682648175\n", "易方达原油A类人民币 0.2244510474007535\n", "嘉实原油(QDII-LOF) 0.22860537822326174\n" ] } ], "source": [ "for fund in oilcomp.fundobjs:\n", " print(fund.name, fund.algorithm_volatility())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "大多数人投资石油基金并不是为了长期价值投资，而是为了做波段，因此波动率越高越好。由此可以看出，华宝油气是波动最大的。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们现在研究下原油基金的换仓损耗到底有多高，首先爬取过去一年的 WTI 原油期货价格日线数据" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "import requests\n", "import datetime as dt" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "r = requests.get(\n", " \"https://cn.investing.com/common/modules/js_instrument_chart/api/data.php?pair_id=8849&pair_id_for_news=8849&chart_type=area&pair_interval=86400&candle_count=120&events=yes&volume_series=yes&period=1-year\",\n", " headers={\n", " \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.100 Safari/537.36\",\n", " \"Referer\": \"https://cn.investing.com/commodities/crude-oil\",\n", " \"Host\": \"cn.investing.com\",\n", " \"X-Requested-With\": \"XMLHttpRequest\",\n", " },\n", ")" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " date netvalue\n", "0 2018-08-24 68.72\n", "1 2018-08-27 68.87\n", "2 2018-08-28 68.53\n", "3 2018-08-29 69.51\n", "4 2018-08-30 70.25\n", "5 2018-08-31 69.80\n", "6 2018-09-02 69.92\n", "7 2018-09-03 70.12\n", "8 2018-09-04 69.87\n", "9 2018-09-05 68.72\n", "10 2018-09-06 67.77\n", "11 2018-09-07 67.75\n", "12 2018-09-10 67.54\n", "13 2018-09-11 69.25\n", "14 2018-09-12 70.37\n", "15 2018-09-13 68.59\n", "16 2018-09-14 68.99\n", "17 2018-09-17 68.91\n", "18 2018-09-18 69.85\n", "19 2018-09-19 71.12\n", "20 2018-09-20 70.80\n", "21 2018-09-21 70.78\n", "22 2018-09-24 72.08\n", "23 2018-09-25 72.28\n", "24 2018-09-26 71.57\n", "25 2018-09-27 72.12\n", "26 2018-09-28 73.25\n", "27 2018-10-01 75.30\n", "28 2018-10-02 75.23\n", "29 2018-10-03 76.41\n", ".. ... ...\n", "234 2019-07-15 59.58\n", "235 2019-07-16 57.62\n", "236 2019-07-17 56.78\n", "237 2019-07-18 55.30\n", "238 2019-07-19 55.63\n", "239 2019-07-22 56.22\n", "240 2019-07-23 56.77\n", "241 2019-07-24 55.88\n", "242 2019-07-25 56.02\n", "243 2019-07-26 56.20\n", "244 2019-07-29 56.87\n", "245 2019-07-30 58.05\n", "246 2019-07-31 58.58\n", "247 2019-08-01 53.95\n", "248 2019-08-02 55.66\n", "249 2019-08-05 54.69\n", "250 2019-08-06 53.63\n", "251 2019-08-07 51.09\n", "252 2019-08-08 52.54\n", "253 2019-08-09 54.50\n", "254 2019-08-12 54.93\n", "255 2019-08-13 57.10\n", "256 2019-08-14 55.23\n", "257 2019-08-15 54.47\n", "258 2019-08-16 54.87\n", "259 2019-08-19 56.21\n", "260 2019-08-20 56.34\n", "261 2019-08-21 55.68\n", "262 2019-08-22 55.35\n", "263 2019-08-23 53.95\n", "\n", "[264 rows x 2 columns]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tz_utc = dt.timezone(dt.timedelta(hours=0))\n", "date = []\n", "netvalue = []\n", "for line in r.json()[\"candles\"]:\n", " date.append(dt.datetime.fromtimestamp(line[0] / 1000, tz_utc).replace(tzinfo=None))\n", " netvalue.append(line[1])\n", "df = pd.DataFrame(data={\"date\": date, \"netvalue\": netvalue})\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们还需要额外爬取人民币美元汇率数据用于比较" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "r = requests.get(\n", " \"https://cn.investing.com/common/modules/js_instrument_chart/api/data.php?pair_id=2111&pair_id_for_news=2111&chart_type=area&pair_interval=86400&candle_count=120&events=yes&volume_series=yes&period=1-year\",\n", " headers={\n", " \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.100 Safari/537.36\",\n", " \"Referer\": \"https://cn.investing.com/commodities/currencies/usd-cny\",\n", " \"Host\": \"cn.investing.com\",\n", " \"X-Requested-With\": \"XMLHttpRequest\",\n", " },\n", ")" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "
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261 rows × 2 columns

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" ], "text/plain": [ " date netvalue\n", "0 2018-08-24 6.8070\n", "1 2018-08-27 6.8156\n", "2 2018-08-28 6.8030\n", "3 2018-08-29 6.8213\n", "4 2018-08-30 6.8445\n", "5 2018-08-31 6.8315\n", "6 2018-09-03 6.8228\n", "7 2018-09-04 6.8442\n", "8 2018-09-05 6.8305\n", "9 2018-09-06 6.8355\n", "10 2018-09-07 6.8438\n", "11 2018-09-10 6.8556\n", "12 2018-09-11 6.8730\n", "13 2018-09-12 6.8606\n", "14 2018-09-13 6.8448\n", "15 2018-09-14 6.8690\n", "16 2018-09-17 6.8570\n", "17 2018-09-18 6.8616\n", "18 2018-09-19 6.8483\n", "19 2018-09-20 6.8468\n", "20 2018-09-21 6.8571\n", "21 2018-09-24 6.8571\n", "22 2018-09-25 6.8665\n", "23 2018-09-26 6.8786\n", "24 2018-09-27 6.8902\n", "25 2018-09-28 6.8689\n", "26 2018-10-01 6.8689\n", "27 2018-10-02 6.8689\n", "28 2018-10-03 6.8689\n", "29 2018-10-04 6.8689\n", ".. ... ...\n", "231 2019-07-15 6.8777\n", "232 2019-07-16 6.8762\n", "233 2019-07-17 6.8736\n", "234 2019-07-18 6.8800\n", "235 2019-07-19 6.8822\n", "236 2019-07-22 6.8811\n", "237 2019-07-23 6.8792\n", "238 2019-07-24 6.8725\n", "239 2019-07-25 6.8726\n", "240 2019-07-26 6.8792\n", "241 2019-07-29 6.8934\n", "242 2019-07-30 6.8845\n", "243 2019-07-31 6.8841\n", "244 2019-08-01 6.8986\n", "245 2019-08-02 6.9405\n", "246 2019-08-05 7.0508\n", "247 2019-08-06 7.0264\n", "248 2019-08-07 7.0602\n", "249 2019-08-08 7.0451\n", "250 2019-08-09 7.0624\n", "251 2019-08-12 7.0582\n", "252 2019-08-13 7.0435\n", "253 2019-08-14 7.0244\n", "254 2019-08-15 7.0340\n", "255 2019-08-16 7.0428\n", "256 2019-08-19 7.0507\n", "257 2019-08-20 7.0605\n", "258 2019-08-21 7.0632\n", "259 2019-08-22 7.0836\n", "260 2019-08-23 7.0960\n", "\n", "[261 rows x 2 columns]" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tz_utc = dt.timezone(dt.timedelta(hours=0))\n", "date = []\n", "netvalue = []\n", "for line in r.json()[\"candles\"]:\n", " date.append(dt.datetime.fromtimestamp(line[0] / 1000, tz_utc).replace(tzinfo=None))\n", " netvalue.append(line[1])\n", "usdf = pd.DataFrame(data={\"date\": date, \"netvalue\": netvalue})\n", "usdf" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8381712364505961" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[-2].netvalue * usdf.iloc[-2].netvalue / df.iloc[0].netvalue / usdf.iloc[\n", " 0\n", "].netvalue # 一年真实的油价变化（人民币计价）" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8154402005603893" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fund = js\n", "float(fund.price[fund.price[\"date\"] == \"2019-08-22\"].netvalue) / float(\n", " fund.price[fund.price[\"date\"] == \"2018-08-24\"].netvalue\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "一年大约落后跟踪基准 2.7%， 考虑到嘉实原油一年 1.28% 的管理费，大约还会跑输 1.5% 左右。特别是，这一年，美元走强，基金又有一定现金份额来抵消石油价格下降，因此真实跑输大于 1.5%。其中大部分没意外，都是期货换仓的损失。所以粗略估计，每年的持仓额外成本在 1 到 2 个百分点。\n", "\n", "由于另两个原油基准宣称的基准略有不同，以下列出仅供参考：" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8271919660100425" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fund = yfd\n", "float(fund.price[fund.price[\"date\"] == \"2019-08-22\"].netvalue) / float(\n", " fund.price[fund.price[\"date\"] == \"2018-08-24\"].netvalue\n", ")" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8347042694868781" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fund = nf\n", "float(fund.price[fund.price[\"date\"] == \"2019-08-22\"].netvalue) / float(\n", " fund.price[fund.price[\"date\"] == \"2018-08-24\"].netvalue\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }