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"*[data-root-id] > * {\n",
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"source": [
"import pandas as pd\n",
"from datetime import datetime, timedelta\n",
"from script import processing\n",
"from script import api\n",
"from sqlalchemy import create_engine\n",
"import pytz\n",
"import numpy as np\n",
"import hvplot.pandas\n",
"db_url = 'sqlite:///instance/local.db'\n",
"engine = create_engine(db_url)\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: './data/p_profile.pkl'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m## initialize by batchprocess to have initial result \u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m p_profile \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_pickle(\u001b[39m'\u001b[39;49m\u001b[39m./data/p_profile.pkl\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m 3\u001b[0m start_date \u001b[39m=\u001b[39m p_profile\u001b[39m.\u001b[39mdate\u001b[39m.\u001b[39mmin()\n\u001b[1;32m 4\u001b[0m end_date \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mto_datetime(datetime\u001b[39m.\u001b[39mnow()\u001b[39m-\u001b[39mtimedelta(days\u001b[39m=\u001b[39m\u001b[39m7\u001b[39m))\n",
"File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/portfolio_risk_assesment/lib/python3.11/site-packages/pandas/io/pickle.py:179\u001b[0m, in \u001b[0;36mread_pickle\u001b[0;34m(filepath_or_buffer, compression, storage_options)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 116\u001b[0m \u001b[39mLoad pickled pandas object (or any object) from file.\u001b[39;00m\n\u001b[1;32m 117\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[39m4 4 9\u001b[39;00m\n\u001b[1;32m 177\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 178\u001b[0m excs_to_catch \u001b[39m=\u001b[39m (\u001b[39mAttributeError\u001b[39;00m, \u001b[39mImportError\u001b[39;00m, \u001b[39mModuleNotFoundError\u001b[39;00m, \u001b[39mTypeError\u001b[39;00m)\n\u001b[0;32m--> 179\u001b[0m \u001b[39mwith\u001b[39;00m get_handle(\n\u001b[1;32m 180\u001b[0m filepath_or_buffer,\n\u001b[1;32m 181\u001b[0m \u001b[39m\"\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 182\u001b[0m compression\u001b[39m=\u001b[39;49mcompression,\n\u001b[1;32m 183\u001b[0m is_text\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m 184\u001b[0m storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m 185\u001b[0m ) \u001b[39mas\u001b[39;00m handles:\n\u001b[1;32m 186\u001b[0m \u001b[39m# 1) try standard library Pickle\u001b[39;00m\n\u001b[1;32m 187\u001b[0m \u001b[39m# 2) try pickle_compat (older pandas version) to handle subclass changes\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[39m# 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError\u001b[39;00m\n\u001b[1;32m 190\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 191\u001b[0m \u001b[39m# TypeError for Cython complaints about object.__new__ vs Tick.__new__\u001b[39;00m\n\u001b[1;32m 192\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
"File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/portfolio_risk_assesment/lib/python3.11/site-packages/pandas/io/common.py:868\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 859\u001b[0m handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(\n\u001b[1;32m 860\u001b[0m handle,\n\u001b[1;32m 861\u001b[0m ioargs\u001b[39m.\u001b[39mmode,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 864\u001b[0m newline\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 865\u001b[0m )\n\u001b[1;32m 866\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 867\u001b[0m \u001b[39m# Binary mode\u001b[39;00m\n\u001b[0;32m--> 868\u001b[0m handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(handle, ioargs\u001b[39m.\u001b[39;49mmode)\n\u001b[1;32m 869\u001b[0m handles\u001b[39m.\u001b[39mappend(handle)\n\u001b[1;32m 871\u001b[0m \u001b[39m# Convert BytesIO or file objects passed with an encoding\u001b[39;00m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './data/p_profile.pkl'"
]
}
],
"source": [
"## initialize by batchprocess to have initial result \n",
"p_profile = pd.read_pickle('./data/p_profile.pkl')\n",
"start_date = p_profile.date.min()\n",
"end_date = pd.to_datetime(datetime.now()-timedelta(days=7))\n",
"# collect data upto 7 days ago \n",
"b_profile, error = api.update_benchmark_profile(start_date, end_date)\n",
"p_stocks, error = api.get_stocks_price(p_profile, start_date, end_date)\n",
"b_stocks, error = api.get_stocks_price(b_profile, start_date, end_date)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# save result \n",
"# p_profile.to_pickle('./data/p_profile.pkl')\n",
"# b_profile.to_pickle('./data/b_profile.pkl')\n",
"p_stocks.to_pickle('./data/p_stocks.pkl')\n",
"b_stocks.to_pickle('./data/b_stocks.pkl')\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/lamonkey/Desktop/risk monitor/script/processing.py:262: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df.fillna(0, inplace=True)\n",
"/Users/lamonkey/Desktop/risk monitor/script/processing.py:263: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['active_return'] = df.pct_p * \\\n",
"/Users/lamonkey/Desktop/risk monitor/script/processing.py:266: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['allocation'] = (df.prev_w_in_p_p - df.prev_w_in_p_b) * df.pct_b\n",
"/Users/lamonkey/Desktop/risk monitor/script/processing.py:267: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['selection'] = (df.pct_p - df.pct_b) * df.prev_w_in_p_b\n",
"/Users/lamonkey/Desktop/risk monitor/script/processing.py:268: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['interaction'] = (df.pct_p - df.pct_b) * \\\n",
"/Users/lamonkey/Desktop/risk monitor/script/processing.py:270: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['notinal_return'] = df.allocation + df.selection + df.interaction\n"
]
}
],
"source": [
"## batch processing \n",
"calculated_p_stock = processing.get_processing_result_of_stocks_df(p_stocks, p_profile)\n",
"calculated_b_stock = processing.get_processing_result_of_stocks_df(b_stocks, b_profile)\n",
"p_eval_df = processing.get_portfolio_evaluation(calculated_p_stock, calculated_b_stock, p_profile)\n",
"sector_eval_df = processing.get_portfolio_sector_evaluation(calculated_p_stock, calculated_b_stock)\n",
"attribution_result_df = processing.calculate_total_attribution(calculated_p_stock, calculated_b_stock)\n",
"s_attribution_result_df = processing.calculate_total_attribution_by_sector(calculated_p_stock, calculated_b_stock)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"## save result to db\n",
"with engine.connect() as connection:\n",
" # all_stock_info.to_sql('all_stock_info', con=connection, if_exists='replace', index=False)\n",
" calculated_b_stock.to_sql('calculated_b_stock', con=connection, if_exists='replace', index=False)\n",
" calculated_p_stock.to_sql('calculated_p_stock', con=connection, if_exists='replace', index=False)\n",
" p_eval_df.to_sql('p_eval_result', con=connection, if_exists='replace', index=False)\n",
" sector_eval_df.to_sql('sector_eval_result', con=connection, if_exists='replace', index=False)\n",
" attribution_result_df.to_sql('attribution_result', con=connection, if_exists='replace', index=False)\n",
" s_attribution_result_df.to_sql('s_attribution_result', con=connection, if_exists='replace', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"# load from sql\n",
"name_df_map = dict()\n",
"with engine.connect() as connection:\n",
" for table in ['calculated_b_stock','calculated_p_stock','p_eval_result','sector_eval_result']:\n",
" try:\n",
" df = pd.read_sql_table(table, con=connection)\n",
" name_df_map[table] = df\n",
" except:\n",
" pass\n",
" # TODO load data from api and calculate result \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"# load data upto now\n",
"# Get the current time in UTC\n",
"current_time = datetime.datetime.utcnow()\n",
"# Set the timezone to Beijing\n",
"beijing_timezone = pytz.timezone('Asia/Shanghai')\n",
"# Convert the current time to Beijing time\n",
"end_time = pd.to_datetime(current_time.astimezone(beijing_timezone).date())\n",
"start_time = name_df_map['p_eval_result'].date.max() + timedelta(days=1)\n",
"\n",
"# get data up to today\n",
"b_profile, error = api.update_benchmark_profile(start_time, end_time)\n",
"p_stocks, error = api.get_stocks_price(p_profile, start_time, end_time)\n",
"b_stocks, error = api.get_stocks_price(b_profile, start_time, end_time)"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/var/folders/v5/2108rh5964q9j741wg_s8r1w0000gn/T/ipykernel_35506/2587190678.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmost_recent_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname_df_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'calculated_p_stock'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ticker'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mconcat_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmost_recent_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_stocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'outer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_processing_result_of_stocks_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconcat_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_profile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/Desktop/risk monitor/script/processing.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(stock_df, profile_df)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0mcur_w\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'nan'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;31m# if has initial weight, the following row all use this initial weight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'initial_weight'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 43\u001b[0m \u001b[0mini_w\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'initial_weight'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mcur_w\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mini_w\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/portfolio_risk_assesment/lib/python3.11/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1464\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1465\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mNoReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1466\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 1467\u001b[0m \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1468\u001b[0m \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1469\u001b[0m )\n",
"\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
]
}
],
"source": [
"most_recent_df = name_df_map['calculated_p_stock'].groupby('ticker').last().reset_index()\n",
"concat_df = pd.concat([most_recent_df, p_stocks], axis=0, join='outer')\n",
"processing.get_processing_result_of_stocks_df(concat_df, p_profile)"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>ticker</th>\n",
" <th>open</th>\n",
" <th>close</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>volume</th>\n",
" <th>money</th>\n",
" <th>display_name</th>\n",
" <th>name</th>\n",
" <th>...</th>\n",
" <th>portfolio_pct</th>\n",
" <th>prev_w_in_sectore</th>\n",
" <th>ini_w_in_sector</th>\n",
" <th>sector_pct</th>\n",
" <th>portfolio_return</th>\n",
" <th>cum_pct</th>\n",
" <th>return</th>\n",
" <th>sector_return</th>\n",
" <th>cur_w_in_p</th>\n",
" <th>pre_w_in_sector</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-01-05</td>\n",
" <td>600409.XSHG</td>\n",
" <td>9.23</td>\n",
" <td>9.57</td>\n",
" <td>9.66</td>\n",
" <td>9.08</td>\n",
" <td>82669289.0</td>\n",
" <td>7.803391e+08</td>\n",
" <td>δΈεεε·₯</td>\n",
" <td>SYHG</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2021-01-05</td>\n",
" <td>300274.XSHE</td>\n",
" <td>76.03</td>\n",
" <td>76.45</td>\n",
" <td>80.20</td>\n",
" <td>75.27</td>\n",
" <td>51384827.0</td>\n",
" <td>3.961995e+09</td>\n",
" <td>ι³ε
η΅ζΊ</td>\n",
" <td>YGDY</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.5</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2021-01-05</td>\n",
" <td>002920.XSHE</td>\n",
" <td>85.44</td>\n",
" <td>87.25</td>\n",
" <td>87.95</td>\n",
" <td>84.07</td>\n",
" <td>3852674.0</td>\n",
" <td>3.322598e+08</td>\n",
" <td>εΎ·θ΅θ₯Ώε¨</td>\n",
" <td>DSXW</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2021-01-05</td>\n",
" <td>002709.XSHE</td>\n",
" <td>32.54</td>\n",
" <td>33.89</td>\n",
" <td>34.22</td>\n",
" <td>31.39</td>\n",
" <td>59152352.0</td>\n",
" <td>1.942406e+09</td>\n",
" <td>倩θ΅ζζ</td>\n",
" <td>TCCL</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.5</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2021-01-05</td>\n",
" <td>603882.XSHG</td>\n",
" <td>125.25</td>\n",
" <td>124.64</td>\n",
" <td>128.31</td>\n",
" <td>121.68</td>\n",
" <td>6803710.0</td>\n",
" <td>8.458543e+08</td>\n",
" <td>ιεε»ε¦</td>\n",
" <td>JYYX</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3609</th>\n",
" <td>2023-06-27</td>\n",
" <td>600415.XSHG</td>\n",
" <td>8.52</td>\n",
" <td>8.69</td>\n",
" <td>8.78</td>\n",
" <td>8.40</td>\n",
" <td>151396630.0</td>\n",
" <td>1.305075e+09</td>\n",
" <td>ε°εεε</td>\n",
" <td>XSPC</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.027187</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.301143</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3610</th>\n",
" <td>2023-06-28</td>\n",
" <td>600415.XSHG</td>\n",
" <td>8.60</td>\n",
" <td>8.63</td>\n",
" <td>8.68</td>\n",
" <td>8.37</td>\n",
" <td>103167271.0</td>\n",
" <td>8.798186e+08</td>\n",
" <td>ε°εεε</td>\n",
" <td>XSPC</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.006904</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.299958</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3611</th>\n",
" <td>2023-06-29</td>\n",
" <td>600415.XSHG</td>\n",
" <td>8.60</td>\n",
" <td>8.74</td>\n",
" <td>8.88</td>\n",
" <td>8.58</td>\n",
" <td>128969467.0</td>\n",
" <td>1.125704e+09</td>\n",
" <td>ε°εεε</td>\n",
" <td>XSPC</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.012746</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.301804</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3612</th>\n",
" <td>2023-06-30</td>\n",
" <td>600415.XSHG</td>\n",
" <td>8.74</td>\n",
" <td>8.53</td>\n",
" <td>8.77</td>\n",
" <td>8.48</td>\n",
" <td>103029932.0</td>\n",
" <td>8.844883e+08</td>\n",
" <td>ε°εεε</td>\n",
" <td>XSPC</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.024027</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.293612</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3613</th>\n",
" <td>2023-07-03</td>\n",
" <td>600415.XSHG</td>\n",
" <td>8.45</td>\n",
" <td>8.37</td>\n",
" <td>8.46</td>\n",
" <td>8.05</td>\n",
" <td>133732493.0</td>\n",
" <td>1.108033e+09</td>\n",
" <td>ε°εεε</td>\n",
" <td>XSPC</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.018757</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.286010</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3614 rows Γ 27 columns</p>\n",
"</div>"
],
"text/plain": [
" date ticker open close high low volume \\\n",
"0 2021-01-05 600409.XSHG 9.23 9.57 9.66 9.08 82669289.0 \n",
"1 2021-01-05 300274.XSHE 76.03 76.45 80.20 75.27 51384827.0 \n",
"2 2021-01-05 002920.XSHE 85.44 87.25 87.95 84.07 3852674.0 \n",
"3 2021-01-05 002709.XSHE 32.54 33.89 34.22 31.39 59152352.0 \n",
"4 2021-01-05 603882.XSHG 125.25 124.64 128.31 121.68 6803710.0 \n",
"... ... ... ... ... ... ... ... \n",
"3609 2023-06-27 600415.XSHG 8.52 8.69 8.78 8.40 151396630.0 \n",
"3610 2023-06-28 600415.XSHG 8.60 8.63 8.68 8.37 103167271.0 \n",
"3611 2023-06-29 600415.XSHG 8.60 8.74 8.88 8.58 128969467.0 \n",
"3612 2023-06-30 600415.XSHG 8.74 8.53 8.77 8.48 103029932.0 \n",
"3613 2023-07-03 600415.XSHG 8.45 8.37 8.46 8.05 133732493.0 \n",
"\n",
" money display_name name ... portfolio_pct prev_w_in_sectore \\\n",
"0 7.803391e+08 δΈεεε·₯ SYHG ... NaN NaN \n",
"1 3.961995e+09 ι³ε
η΅ζΊ YGDY ... NaN NaN \n",
"2 3.322598e+08 εΎ·θ΅θ₯Ώε¨ DSXW ... NaN NaN \n",
"3 1.942406e+09 倩θ΅ζζ TCCL ... NaN NaN \n",
"4 8.458543e+08 ιεε»ε¦ JYYX ... NaN NaN \n",
"... ... ... ... ... ... ... \n",
"3609 1.305075e+09 ε°εεε XSPC ... NaN NaN \n",
"3610 8.798186e+08 ε°εεε XSPC ... NaN NaN \n",
"3611 1.125704e+09 ε°εεε XSPC ... NaN NaN \n",
"3612 8.844883e+08 ε°εεε XSPC ... NaN NaN \n",
"3613 1.108033e+09 ε°εεε XSPC ... NaN NaN \n",
"\n",
" ini_w_in_sector sector_pct portfolio_return cum_pct return \\\n",
"0 1.0 NaN NaN NaN NaN \n",
"1 0.5 NaN NaN NaN NaN \n",
"2 1.0 NaN NaN NaN NaN \n",
"3 0.5 NaN NaN NaN NaN \n",
"4 1.0 NaN NaN NaN NaN \n",
"... ... ... ... ... ... \n",
"3609 NaN 0.027187 NaN NaN NaN \n",
"3610 NaN -0.006904 NaN NaN NaN \n",
"3611 NaN 0.012746 NaN NaN NaN \n",
"3612 NaN -0.024027 NaN NaN NaN \n",
"3613 NaN -0.018757 NaN NaN NaN \n",
"\n",
" sector_return cur_w_in_p pre_w_in_sector \n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"... ... ... ... \n",
"3609 NaN 0.301143 1.0 \n",
"3610 NaN 0.299958 1.0 \n",
"3611 NaN 0.301804 1.0 \n",
"3612 NaN 0.293612 1.0 \n",
"3613 NaN 0.286010 1.0 \n",
"\n",
"[3614 rows x 27 columns]"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"most_recent_df = name_df_map['calculated_p_stock'].groupby('ticker').last().reset_index()\n",
"\n",
"def get_last_values(row):\n",
" ticker = row['ticker']\n",
" if ticker in p_profile['ticker'].values:\n",
" return p_profile.loc[p_profile['ticker'] == ticker, ['display_name', 'name', 'aggregate_sector']].iloc[-1]\n",
" else:\n",
" return pd.Series([np.nan, np.nan, np.nan], index=['display_name', 'name', 'aggregate_sector'])\n",
"# dispaly_name, name and aggregate_sector\n",
"p_stocks[['display_name', 'name', 'aggregate_sector']] = p_stocks.apply(get_last_values, axis=1)\n",
"\n",
"# use the most recent result to resume calculation\n",
"concat_df = pd.concat([most_recent_df, p_stocks], axis=0, join='outer')\n",
"\n",
"# pct\n",
"concat_df['pct'] = concat_df.groupby('ticker')['close'].pct_change()\n",
"\n",
"# calculate not normalized previous weight and current weight\n",
"groups = concat_df.groupby('ticker')\n",
"for _, group in groups:\n",
" cur_weight = np.nan\n",
" for index, row in group.iterrows():\n",
" if pd.notna(row['current_weight']):\n",
" cur_weight = row['current_weight']\n",
" else:\n",
" concat_df.loc[index, 'previous_weight'] = cur_weight\n",
" cur_weight = cur_weight * (1 + row['pct'])\n",
" concat_df.loc[index, 'current_weight'] = cur_weight\n",
"\n",
"# calculate normalized previous and current weight\n",
"concat_df['prev_w_in_p'] = concat_df['previous_weight'] / \\\n",
" concat_df.groupby('date')['previous_weight'].transform('sum')\n",
"concat_df['cur_w_in_p'] = concat_df['current_weight'] / \\\n",
" concat_df.groupby('date')['current_weight'].transform('sum')\n",
"\n",
"# calculate previous weight in sector\n",
"concat_df['pre_w_in_sector'] = concat_df['prev_w_in_p'] / \\\n",
" concat_df.groupby(['date', 'aggregate_sector'])['prev_w_in_p'].transform('sum')\n",
"\n",
"# calculate pct in sector\n",
"concat_df['sector_pct'] = concat_df['pct'] * concat_df['pre_w_in_sector']\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"# remove group with first date\n",
"min_date_group = concat_df.groupby('date')['date'].idxmin()\n",
"concat_df = concat_df.drop(min_date_group)\n",
"\n",
"# merge back to calculated_stock\n",
"pd.concat([name_df_map['calculated_p_stock'],concat_df]).reset_index(drop=True)\n",
"\n",
"# concat_df[concat_df.ticker == '002709.XSHE'][['date','pct','current_weight','previous_weight','prev_w_in_p','cur_w_in_p']]"
]
}
],
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|