3146 lines
79 KiB
Plaintext
3146 lines
79 KiB
Plaintext
{
|
||
"cells": [
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||
{
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||
"cell_type": "code",
|
||
"execution_count": 2,
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||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"import pandas as pd\n",
|
||
"from datetime import datetime\n",
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||
"from dao.Database import Database\n",
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||
"from data_model import MysqlConfig, OddsJamOrder, OddsjamBet"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 3,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"\n",
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||
"def get_oddsjam_order_data_from_db()->List:\n",
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||
" config_file_path = 'config\\mysql_config.json'\n",
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||
" mysql_config = MysqlConfig.parse_file(config_file_path)\n",
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" dao = Database(mysql_config)\n",
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"\n",
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" select_query = \"SELECT * FROM bet.oddsjam_order where bet_status in ('won', 'lost');\"\n",
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" raw_data_list = dao.fetchall(query=select_query)\n",
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||
" order_data_list = [OddsJamOrder(**data).model_dump() for data in raw_data_list]\n",
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"\n"
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]
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||
},
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{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"simulation_data = get_oddsjam_order_data_from_db()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"db_df = pd.DataFrame(order_data_list)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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||
"outputs": [],
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"source": [
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"def get_benefit(row):\n",
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" home_or_away = row['home_or_away']\n",
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" price = row[f'{home_or_away}_price'] / 100\n",
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" if row['outcome'] == -1:\n",
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" return -1\n",
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" if price >= 0:\n",
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" return price \n",
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" else:\n",
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" return 1 / abs(price) \n",
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" \n",
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"db_df['outcome'] = db_df['bet_status'].apply(lambda x: 1 if x == 'won' else -1)\n",
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"db_df['benefit'] = db_df.apply(get_benefit, axis=1)\n",
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"db_df['date'] = db_df['start_timestamp'].apply(lambda x: datetime.fromtimestamp(x // 1000).strftime(\"%Y-%m-%d\"))"
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]
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},
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{
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||
"cell_type": "code",
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"execution_count": 7,
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||
"metadata": {},
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||
"outputs": [],
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"source": [
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"def get_odds(row):\n",
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" home_or_away = row['home_or_away']\n",
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" price = row[f'{home_or_away}_price'] / 100\n",
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" if price >= 0:\n",
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" return price\n",
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" else:\n",
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" return 1 / abs(price) "
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]
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},
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{
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||
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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||
"outputs": [],
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"source": [
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"db_df['odds2'] = db_df.apply(get_odds, axis=1)"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 9,
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||
"metadata": {},
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||
"outputs": [
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||
{
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||
"data": {
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"text/plain": [
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"3.5232254542910955"
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]
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},
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"execution_count": 9,
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||
"metadata": {},
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"output_type": "execute_result"
|
||
}
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||
],
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||
"source": [
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"db_df['odds2'].mean()"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 6,
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||
"metadata": {},
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||
"outputs": [
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||
{
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||
"data": {
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||
"text/plain": [
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||
"bet_status\n",
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||
"lost 19316\n",
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||
"won 8249\n",
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||
"dtype: int64"
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||
]
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||
},
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||
"execution_count": 6,
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||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
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||
],
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||
"source": [
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||
"db_df.groupby('bet_status').size()"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": 130,
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||
"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"3.4551825216755248"
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]
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},
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"execution_count": 130,
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||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
|
||
],
|
||
"source": [
|
||
"db_df[db_df['bet_status'].isin(['won', 'lost'])]['benefit'].mean()"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 149,
|
||
"metadata": {},
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||
"outputs": [
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{
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||
"data": {
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"text/plain": [
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"-1477.1127282023094"
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]
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},
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"execution_count": 149,
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||
"metadata": {},
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"output_type": "execute_result"
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||
}
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||
],
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||
"source": [
|
||
"db_df[['home_price', 'away_price', 'home_or_away', 'bet_status', 'benefit']]['benefit']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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||
"execution_count": null,
|
||
"metadata": {},
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||
"outputs": [],
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||
"source": []
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 166,
|
||
"metadata": {},
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||
"outputs": [
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||
{
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||
"data": {
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||
"text/plain": [
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||
"count 1780.000000\n",
|
||
"mean 1.165759\n",
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||
"std 0.338662\n",
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||
"min 0.359712\n",
|
||
"25% 0.909091\n",
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||
"50% 1.080000\n",
|
||
"75% 1.360000\n",
|
||
"max 4.000000\n",
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||
"Name: odds2, dtype: float64"
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||
]
|
||
},
|
||
"execution_count": 166,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"db_df[(db_df['market_width'] <= 25) & (db_df['market_width'] >= 20)]['odds2'].describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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||
"execution_count": 10,
|
||
"metadata": {},
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||
"outputs": [
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||
{
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||
"data": {
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||
"text/plain": [
|
||
"bet_status\n",
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||
"lost 1231\n",
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||
"won 1080\n",
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||
"dtype: int64"
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||
]
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||
},
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||
"execution_count": 10,
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||
"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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||
"db_df[(db_df['market_width'] <= 25) & (db_df['market_width'] >= 20)].groupby(['bet_status']).size()"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 171,
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||
"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.4764044943820225"
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]
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},
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"execution_count": 171,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"848 / (848+932)"
|
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]
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||
},
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{
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"cell_type": "code",
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"execution_count": 170,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1.0318921348314607"
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]
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},
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"execution_count": 170,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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||
"848 / (848+932) * (1.166 + 1)"
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||
]
|
||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 156,
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||
"metadata": {},
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||
"outputs": [
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{
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"data": {
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"text/plain": [
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"count 5902.000000\n",
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"mean 2.026751\n",
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"std 1.825651\n",
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||
"min 0.166667\n",
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||
"25% 1.020000\n",
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"50% 1.430000\n",
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"75% 2.200000\n",
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"max 25.000000\n",
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"Name: odds2, dtype: float64"
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]
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},
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"execution_count": 156,
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||
"metadata": {},
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"output_type": "execute_result"
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||
}
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||
],
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||
"source": [
|
||
"db_df[db_df['bet_status'] == 'won']['odds2'].describe()"
|
||
]
|
||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 157,
|
||
"metadata": {},
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||
"outputs": [
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||
{
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"data": {
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"text/plain": [
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"count 13439.000000\n",
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||
"mean 4.082506\n",
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"std 3.955954\n",
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"min 0.235294\n",
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"25% 1.500000\n",
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"50% 2.700000\n",
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"75% 5.400000\n",
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||
"max 35.000000\n",
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||
"Name: odds2, dtype: float64"
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]
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||
},
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||
"execution_count": 157,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
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}
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],
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||
"source": [
|
||
"db_df[db_df['bet_status'] == 'lost']['odds2'].describe()"
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||
]
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||
},
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{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"raw_df = pd.read_excel('PEV 3.11-10.26.xlsx', sheet_name='原始数据')"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": 20,
|
||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"\n",
|
||
"data_df = db_df.copy()\n",
|
||
"data_df = data_df[data_df['market_width'] <= 25]\n",
|
||
"data_df = data_df[data_df['market_width'] >= 20]\n",
|
||
"# data_df = data_df[data_df['selected_sportsbook'] == '1XBet']\n",
|
||
"data_df['investment'] = 1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 57,
|
||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"odds_df = data_df.groupby('date').agg({'odds2': 'mean'}).reset_index()"
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||
]
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||
},
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||
{
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"cell_type": "code",
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||
"ticks": ""
|
||
},
|
||
"baxis": {
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"ticks": ""
|
||
},
|
||
"bgcolor": "#E5ECF6",
|
||
"caxis": {
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"ticks": ""
|
||
}
|
||
},
|
||
"title": {
|
||
"x": 0.05
|
||
},
|
||
"xaxis": {
|
||
"automargin": true,
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"ticks": "",
|
||
"title": {
|
||
"standoff": 15
|
||
},
|
||
"zerolinecolor": "white",
|
||
"zerolinewidth": 2
|
||
},
|
||
"yaxis": {
|
||
"automargin": true,
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"ticks": "",
|
||
"title": {
|
||
"standoff": 15
|
||
},
|
||
"zerolinecolor": "white",
|
||
"zerolinewidth": 2
|
||
}
|
||
}
|
||
},
|
||
"title": {
|
||
"text": "Comparison of Two Variables Across Categories"
|
||
},
|
||
"xaxis": {
|
||
"title": {
|
||
"text": "日期"
|
||
}
|
||
},
|
||
"yaxis": {
|
||
"title": {
|
||
"text": "金额"
|
||
}
|
||
},
|
||
"yaxis2": {
|
||
"overlaying": "y",
|
||
"side": "right",
|
||
"title": {
|
||
"text": "收益率"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import plotly.graph_objects as go\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"df = pd.pivot_table(data_df, index=['date'], columns=['bet_status'], aggfunc='size', fill_value=0).reset_index()\n",
|
||
"df = df.sort_values(by='date')\n",
|
||
"date_x = df['date'].tolist()\n",
|
||
"# 创建Plotly图形对象\n",
|
||
"fig = go.Figure()\n",
|
||
"\n",
|
||
"cols = df.columns[1:]\n",
|
||
"for col in cols:\n",
|
||
" y_data = df[col].tolist()\n",
|
||
" # 添加第一个变量的数据\n",
|
||
" fig.add_trace(go.Bar(\n",
|
||
" x=date_x,\n",
|
||
" y=y_data,\n",
|
||
" name=col,\n",
|
||
" # marker_color='indianred' # 设置颜色\n",
|
||
" yaxis='y1'\n",
|
||
" ))\n",
|
||
"\n",
|
||
"fig.add_trace(go.Scatter(\n",
|
||
" x=odds_df['date'],\n",
|
||
" y=odds_df['odds2'],\n",
|
||
" mode='markers+lines',\n",
|
||
" name='平均赔率',\n",
|
||
" yaxis='y2'\n",
|
||
"))\n",
|
||
"\n",
|
||
"# 更新布局\n",
|
||
"fig.update_layout(\n",
|
||
" barmode='group', # 将柱状图设置为并排显示\n",
|
||
" title='Comparison of Two Variables Across Categories',\n",
|
||
" xaxis=dict(title='日期'),\n",
|
||
" yaxis=dict(title='金额'),\n",
|
||
" yaxis2=dict(title='收益率', overlaying='y', side='right'),\n",
|
||
" legend_title='Variables'\n",
|
||
")\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>id</th>\n",
|
||
" <th>bet_id</th>\n",
|
||
" <th>game_id</th>\n",
|
||
" <th>away_bet_name</th>\n",
|
||
" <th>home_bet_name</th>\n",
|
||
" <th>away_no_vig_price</th>\n",
|
||
" <th>home_no_vig_price</th>\n",
|
||
" <th>away_price</th>\n",
|
||
" <th>home_price</th>\n",
|
||
" <th>away_sportsbooks</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>sportsbook_orderid</th>\n",
|
||
" <th>home_or_away</th>\n",
|
||
" <th>start_timestamp</th>\n",
|
||
" <th>selected_sportsbook</th>\n",
|
||
" <th>bet_status</th>\n",
|
||
" <th>outcome</th>\n",
|
||
" <th>benefit</th>\n",
|
||
" <th>date</th>\n",
|
||
" <th>odds2</th>\n",
|
||
" <th>investment</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>edge:10012-40335-2024-08-31:1st_half_team_tota...</td>\n",
|
||
" <td>edge:10012-40335-2024-08-31:1st_half_team_tota...</td>\n",
|
||
" <td>10012-40335-2024-08-31</td>\n",
|
||
" <td>Oud-Heverlee Leuven Under 0.5</td>\n",
|
||
" <td>Oud-Heverlee Leuven Over 0.5</td>\n",
|
||
" <td>102.85</td>\n",
|
||
" <td>-102.85</td>\n",
|
||
" <td>-110.0</td>\n",
|
||
" <td>-102.0</td>\n",
|
||
" <td>[FanDuel, DraftKings]</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>home</td>\n",
|
||
" <td>1725129900000</td>\n",
|
||
" <td>Bovada</td>\n",
|
||
" <td>won</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.980392</td>\n",
|
||
" <td>2024-09-01</td>\n",
|
||
" <td>0.980392</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>edge:10012-40335-2024-08-31:total_goals:over_2...</td>\n",
|
||
" <td>edge:10012-40335-2024-08-31:total_goals:over_2...</td>\n",
|
||
" <td>10012-40335-2024-08-31</td>\n",
|
||
" <td>Under 2.5</td>\n",
|
||
" <td>Over 2.5</td>\n",
|
||
" <td>-101.35</td>\n",
|
||
" <td>101.35</td>\n",
|
||
" <td>-102.0</td>\n",
|
||
" <td>105.0</td>\n",
|
||
" <td>[Bodog, Bovada]</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>home</td>\n",
|
||
" <td>1725129900000</td>\n",
|
||
" <td>BetOnline</td>\n",
|
||
" <td>lost</td>\n",
|
||
" <td>-1</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>2024-09-01</td>\n",
|
||
" <td>1.050000</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>edge:10035-41288-2024-08-31:total_goals:over_2...</td>\n",
|
||
" <td>edge:10035-41288-2024-08-31:total_goals:over_2...</td>\n",
|
||
" <td>10035-41288-2024-08-31</td>\n",
|
||
" <td>Under 2.5</td>\n",
|
||
" <td>Over 2.5</td>\n",
|
||
" <td>-121.90</td>\n",
|
||
" <td>121.90</td>\n",
|
||
" <td>-134.0</td>\n",
|
||
" <td>124.0</td>\n",
|
||
" <td>[FanDuel]</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>home</td>\n",
|
||
" <td>1725129000000</td>\n",
|
||
" <td>BetOnline</td>\n",
|
||
" <td>won</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1.240000</td>\n",
|
||
" <td>2024-09-01</td>\n",
|
||
" <td>1.240000</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>edge:10068-27381-24-35:point_spread:northweste...</td>\n",
|
||
" <td>edge:10068-27381-24-35:point_spread:northweste...</td>\n",
|
||
" <td>10068-27381-24-35</td>\n",
|
||
" <td>Tulsa -37.5</td>\n",
|
||
" <td>Northwestern State +37.5</td>\n",
|
||
" <td>100.40</td>\n",
|
||
" <td>-100.40</td>\n",
|
||
" <td>105.0</td>\n",
|
||
" <td>-110.0</td>\n",
|
||
" <td>[betPARX, BetRivers]</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>away</td>\n",
|
||
" <td>1724976000000</td>\n",
|
||
" <td>betPARX</td>\n",
|
||
" <td>lost</td>\n",
|
||
" <td>-1</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>2024-08-30</td>\n",
|
||
" <td>1.050000</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25</th>\n",
|
||
" <td>edge:10068-27381-24-35:total_points:over_53_5:...</td>\n",
|
||
" <td>edge:10068-27381-24-35:total_points:over_53_5:...</td>\n",
|
||
" <td>10068-27381-24-35</td>\n",
|
||
" <td>Under 53.5</td>\n",
|
||
" <td>Over 53.5</td>\n",
|
||
" <td>132.39</td>\n",
|
||
" <td>-132.39</td>\n",
|
||
" <td>122.0</td>\n",
|
||
" <td>-130.0</td>\n",
|
||
" <td>[DraftKings]</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>home</td>\n",
|
||
" <td>1724976000000</td>\n",
|
||
" <td>betPARX</td>\n",
|
||
" <td>won</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.769231</td>\n",
|
||
" <td>2024-08-30</td>\n",
|
||
" <td>0.769231</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>26</th>\n",
|
||
" <td>edge:10068-27381-24-35:total_points:over_53:un...</td>\n",
|
||
" <td>edge:10068-27381-24-35:total_points:over_53:un...</td>\n",
|
||
" <td>10068-27381-24-35</td>\n",
|
||
" <td>Under 53</td>\n",
|
||
" <td>Over 53</td>\n",
|
||
" <td>109.70</td>\n",
|
||
" <td>-109.70</td>\n",
|
||
" <td>110.0</td>\n",
|
||
" <td>-110.0</td>\n",
|
||
" <td>[betPARX, BetRivers]</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>away</td>\n",
|
||
" <td>1724976000000</td>\n",
|
||
" <td>betPARX</td>\n",
|
||
" <td>lost</td>\n",
|
||
" <td>-1</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>2024-08-30</td>\n",
|
||
" <td>1.100000</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>27</th>\n",
|
||
" <td>edge:10068-27381-24-35:total_points:over_54_5:...</td>\n",
|
||
" <td>edge:10068-27381-24-35:total_points:over_54_5:...</td>\n",
|
||
" <td>10068-27381-24-35</td>\n",
|
||
" <td>Under 54.5</td>\n",
|
||
" <td>Over 54.5</td>\n",
|
||
" <td>114.29</td>\n",
|
||
" <td>-114.29</td>\n",
|
||
" <td>105.0</td>\n",
|
||
" <td>-114.0</td>\n",
|
||
" <td>[Fanatics]</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>home</td>\n",
|
||
" <td>1724976000000</td>\n",
|
||
" <td>betPARX</td>\n",
|
||
" <td>won</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.877193</td>\n",
|
||
" <td>2024-08-30</td>\n",
|
||
" <td>0.877193</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>28</th>\n",
|
||
" <td>edge:10068-27381-24-35:total_points:over_54:un...</td>\n",
|
||
" <td>edge:10068-27381-24-35:total_points:over_54:un...</td>\n",
|
||
" <td>10068-27381-24-35</td>\n",
|
||
" <td>Under 54</td>\n",
|
||
" <td>Over 54</td>\n",
|
||
" <td>122.41</td>\n",
|
||
" <td>-122.41</td>\n",
|
||
" <td>112.0</td>\n",
|
||
" <td>-122.0</td>\n",
|
||
" <td>[DraftKings]</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>home</td>\n",
|
||
" <td>1724976000000</td>\n",
|
||
" <td>BetRivers</td>\n",
|
||
" <td>won</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.819672</td>\n",
|
||
" <td>2024-08-30</td>\n",
|
||
" <td>0.819672</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>30</th>\n",
|
||
" <td>edge:10068-27381-24-35:total_points:over_55:un...</td>\n",
|
||
" <td>edge:10068-27381-24-35:total_points:over_55:un...</td>\n",
|
||
" <td>10068-27381-24-35</td>\n",
|
||
" <td>Under 55</td>\n",
|
||
" <td>Over 55</td>\n",
|
||
" <td>-112.29</td>\n",
|
||
" <td>112.29</td>\n",
|
||
" <td>-110.0</td>\n",
|
||
" <td>110.0</td>\n",
|
||
" <td>[BookMaker]</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>away</td>\n",
|
||
" <td>1724976000000</td>\n",
|
||
" <td>BookMaker</td>\n",
|
||
" <td>lost</td>\n",
|
||
" <td>-1</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>2024-08-30</td>\n",
|
||
" <td>0.909091</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>32</th>\n",
|
||
" <td>edge:10068-27381-24-35:total_points:over_56:un...</td>\n",
|
||
" <td>edge:10068-27381-24-35:total_points:over_56:un...</td>\n",
|
||
" <td>10068-27381-24-35</td>\n",
|
||
" <td>Under 56</td>\n",
|
||
" <td>Over 56</td>\n",
|
||
" <td>-104.24</td>\n",
|
||
" <td>104.24</td>\n",
|
||
" <td>-112.0</td>\n",
|
||
" <td>105.0</td>\n",
|
||
" <td>[DraftKings]</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>home</td>\n",
|
||
" <td>1724976000000</td>\n",
|
||
" <td>betPARX</td>\n",
|
||
" <td>won</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1.050000</td>\n",
|
||
" <td>2024-08-30</td>\n",
|
||
" <td>1.050000</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>10 rows × 50 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" id \\\n",
|
||
"1 edge:10012-40335-2024-08-31:1st_half_team_tota... \n",
|
||
"9 edge:10012-40335-2024-08-31:total_goals:over_2... \n",
|
||
"15 edge:10035-41288-2024-08-31:total_goals:over_2... \n",
|
||
"19 edge:10068-27381-24-35:point_spread:northweste... \n",
|
||
"25 edge:10068-27381-24-35:total_points:over_53_5:... \n",
|
||
"26 edge:10068-27381-24-35:total_points:over_53:un... \n",
|
||
"27 edge:10068-27381-24-35:total_points:over_54_5:... \n",
|
||
"28 edge:10068-27381-24-35:total_points:over_54:un... \n",
|
||
"30 edge:10068-27381-24-35:total_points:over_55:un... \n",
|
||
"32 edge:10068-27381-24-35:total_points:over_56:un... \n",
|
||
"\n",
|
||
" bet_id game_id \\\n",
|
||
"1 edge:10012-40335-2024-08-31:1st_half_team_tota... 10012-40335-2024-08-31 \n",
|
||
"9 edge:10012-40335-2024-08-31:total_goals:over_2... 10012-40335-2024-08-31 \n",
|
||
"15 edge:10035-41288-2024-08-31:total_goals:over_2... 10035-41288-2024-08-31 \n",
|
||
"19 edge:10068-27381-24-35:point_spread:northweste... 10068-27381-24-35 \n",
|
||
"25 edge:10068-27381-24-35:total_points:over_53_5:... 10068-27381-24-35 \n",
|
||
"26 edge:10068-27381-24-35:total_points:over_53:un... 10068-27381-24-35 \n",
|
||
"27 edge:10068-27381-24-35:total_points:over_54_5:... 10068-27381-24-35 \n",
|
||
"28 edge:10068-27381-24-35:total_points:over_54:un... 10068-27381-24-35 \n",
|
||
"30 edge:10068-27381-24-35:total_points:over_55:un... 10068-27381-24-35 \n",
|
||
"32 edge:10068-27381-24-35:total_points:over_56:un... 10068-27381-24-35 \n",
|
||
"\n",
|
||
" away_bet_name home_bet_name \\\n",
|
||
"1 Oud-Heverlee Leuven Under 0.5 Oud-Heverlee Leuven Over 0.5 \n",
|
||
"9 Under 2.5 Over 2.5 \n",
|
||
"15 Under 2.5 Over 2.5 \n",
|
||
"19 Tulsa -37.5 Northwestern State +37.5 \n",
|
||
"25 Under 53.5 Over 53.5 \n",
|
||
"26 Under 53 Over 53 \n",
|
||
"27 Under 54.5 Over 54.5 \n",
|
||
"28 Under 54 Over 54 \n",
|
||
"30 Under 55 Over 55 \n",
|
||
"32 Under 56 Over 56 \n",
|
||
"\n",
|
||
" away_no_vig_price home_no_vig_price away_price home_price \\\n",
|
||
"1 102.85 -102.85 -110.0 -102.0 \n",
|
||
"9 -101.35 101.35 -102.0 105.0 \n",
|
||
"15 -121.90 121.90 -134.0 124.0 \n",
|
||
"19 100.40 -100.40 105.0 -110.0 \n",
|
||
"25 132.39 -132.39 122.0 -130.0 \n",
|
||
"26 109.70 -109.70 110.0 -110.0 \n",
|
||
"27 114.29 -114.29 105.0 -114.0 \n",
|
||
"28 122.41 -122.41 112.0 -122.0 \n",
|
||
"30 -112.29 112.29 -110.0 110.0 \n",
|
||
"32 -104.24 104.24 -112.0 105.0 \n",
|
||
"\n",
|
||
" away_sportsbooks ... sportsbook_orderid home_or_away \\\n",
|
||
"1 [FanDuel, DraftKings] ... None home \n",
|
||
"9 [Bodog, Bovada] ... None home \n",
|
||
"15 [FanDuel] ... None home \n",
|
||
"19 [betPARX, BetRivers] ... None away \n",
|
||
"25 [DraftKings] ... None home \n",
|
||
"26 [betPARX, BetRivers] ... None away \n",
|
||
"27 [Fanatics] ... None home \n",
|
||
"28 [DraftKings] ... None home \n",
|
||
"30 [BookMaker] ... None away \n",
|
||
"32 [DraftKings] ... None home \n",
|
||
"\n",
|
||
" start_timestamp selected_sportsbook bet_status outcome benefit \\\n",
|
||
"1 1725129900000 Bovada won 1 0.980392 \n",
|
||
"9 1725129900000 BetOnline lost -1 -1.000000 \n",
|
||
"15 1725129000000 BetOnline won 1 1.240000 \n",
|
||
"19 1724976000000 betPARX lost -1 -1.000000 \n",
|
||
"25 1724976000000 betPARX won 1 0.769231 \n",
|
||
"26 1724976000000 betPARX lost -1 -1.000000 \n",
|
||
"27 1724976000000 betPARX won 1 0.877193 \n",
|
||
"28 1724976000000 BetRivers won 1 0.819672 \n",
|
||
"30 1724976000000 BookMaker lost -1 -1.000000 \n",
|
||
"32 1724976000000 betPARX won 1 1.050000 \n",
|
||
"\n",
|
||
" date odds2 investment \n",
|
||
"1 2024-09-01 0.980392 1 \n",
|
||
"9 2024-09-01 1.050000 1 \n",
|
||
"15 2024-09-01 1.240000 1 \n",
|
||
"19 2024-08-30 1.050000 1 \n",
|
||
"25 2024-08-30 0.769231 1 \n",
|
||
"26 2024-08-30 1.100000 1 \n",
|
||
"27 2024-08-30 0.877193 1 \n",
|
||
"28 2024-08-30 0.819672 1 \n",
|
||
"30 2024-08-30 0.909091 1 \n",
|
||
"32 2024-08-30 1.050000 1 \n",
|
||
"\n",
|
||
"[10 rows x 50 columns]"
|
||
]
|
||
},
|
||
"execution_count": 40,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data_df.head(10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"status date active inactive\n",
|
||
"0 2024-01-01 2 1\n",
|
||
"1 2024-01-02 1 1\n",
|
||
"2 2024-01-03 0 1\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"# 示例数据\n",
|
||
"data = {\n",
|
||
" 'date': ['2024-01-01', '2024-01-01', '2024-01-01', '2024-01-02', '2024-01-02', '2024-01-03'],\n",
|
||
" 'status': ['active', 'inactive', 'active', 'active', 'inactive', 'inactive']\n",
|
||
"}\n",
|
||
"\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"# 将日期列转换为日期类型\n",
|
||
"df['date'] = pd.to_datetime(df['date'])\n",
|
||
"\n",
|
||
"# 使用 pivot_table 进行聚合\n",
|
||
"result = pd.pivot_table(df, values='status', index=['date'], columns=['status'], aggfunc='size', fill_value=0).reset_index()\n",
|
||
"\n",
|
||
"print(result)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 50,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"bet_status date lost won\n",
|
||
"0 2024-08-26 11 12\n",
|
||
"1 2024-08-27 96 113\n",
|
||
"2 2024-08-28 138 94\n",
|
||
"3 2024-08-29 109 79\n",
|
||
"4 2024-08-30 190 178\n",
|
||
"5 2024-08-31 99 88\n",
|
||
"6 2024-09-01 289 284\n",
|
||
"7 2024-09-02 62 59\n",
|
||
"8 2024-09-03 89 56\n",
|
||
"9 2024-09-04 98 80\n",
|
||
"10 2024-09-05 50 37\n",
|
||
"Index(['date', 'lost', 'won'], dtype='object', name='bet_status')\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"# 使用 pivot_table 进行聚合\n",
|
||
"result = pd.pivot_table(data_df, index=['date'], columns=['bet_status'], aggfunc='size', fill_value=0).reset_index()\n",
|
||
"\n",
|
||
"print(result)\n",
|
||
"print(result.columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>date</th>\n",
|
||
" <th>status</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2024-01-01</td>\n",
|
||
" <td>active</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2024-01-01</td>\n",
|
||
" <td>inactive</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2024-01-01</td>\n",
|
||
" <td>active</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>2024-01-02</td>\n",
|
||
" <td>active</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>2024-01-02</td>\n",
|
||
" <td>inactive</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>2024-01-03</td>\n",
|
||
" <td>inactive</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" date status\n",
|
||
"0 2024-01-01 active\n",
|
||
"1 2024-01-01 inactive\n",
|
||
"2 2024-01-01 active\n",
|
||
"3 2024-01-02 active\n",
|
||
"4 2024-01-02 inactive\n",
|
||
"5 2024-01-03 inactive"
|
||
]
|
||
},
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"年化夏普率: -1.4002737338136018\n",
|
||
"ROI: -0.011875712320100193\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def clac_closing_balance(day_benefit_list, pre_balance=1000, pre_benefit=0):\n",
|
||
" closing_balance_list = []\n",
|
||
" for i, benefit in enumerate(day_benefit_list):\n",
|
||
" closing_balance = pre_balance + pre_benefit / 3 + benefit * 2 / 3\n",
|
||
" closing_balance_list.append(closing_balance)\n",
|
||
" pre_balance = closing_balance\n",
|
||
" pre_benefit = benefit\n",
|
||
" return closing_balance_list\n",
|
||
"\n",
|
||
"def calc_in_transit_funds_ratio(daily_investment_list, closing_balance_list, start_closing_balance=1000):\n",
|
||
" assert len(daily_investment_list) == len(closing_balance_list)\n",
|
||
" ratio_list = []\n",
|
||
" for i, daily_investment in enumerate(daily_investment_list):\n",
|
||
" if i == 0:\n",
|
||
" ratio = daily_investment / start_closing_balance\n",
|
||
" else:\n",
|
||
" ratio = daily_investment / closing_balance_list[i-1]\n",
|
||
" ratio_list.append(ratio)\n",
|
||
" return ratio_list\n",
|
||
"\n",
|
||
"\n",
|
||
"init_balance = 1000\n",
|
||
"res_df = data_df.groupby('date').agg({'investment':'sum', 'benefit':'sum'}).rename(columns={'investment':'当日投入', 'benefit':'日收益'}).reset_index()\n",
|
||
"res_df['日收益率'] = res_df['日收益'] / res_df['当日投入']\n",
|
||
"res_df['累计收益'] = res_df['日收益'].cumsum()\n",
|
||
"res_df['累计投入'] = res_df['当日投入'].cumsum()\n",
|
||
"res_df['累计收益率'] = res_df['累计收益'] / res_df['累计投入']\n",
|
||
"\n",
|
||
"day_benefit_list = res_df['日收益'].tolist()\n",
|
||
"closing_balance_list = clac_closing_balance(day_benefit_list=day_benefit_list, pre_balance=init_balance)\n",
|
||
"res_df['日末余额(1.6天结算)'] = closing_balance_list\n",
|
||
"\n",
|
||
"daily_investment_list = res_df['当日投入'].tolist()\n",
|
||
"res_df['在途资金比例'] = calc_in_transit_funds_ratio(daily_investment_list=daily_investment_list, closing_balance_list=closing_balance_list, start_closing_balance=init_balance)\n",
|
||
"annualized_sharpe_ratio = res_df['日收益'].sum() / init_balance / res_df['日收益率'].std() * ((365 / len(res_df))**0.5)\n",
|
||
"print(f'年化夏普率: {annualized_sharpe_ratio}')\n",
|
||
"roi = res_df['日收益'].sum() / res_df['当日投入'].sum()\n",
|
||
"print(f'ROI: {roi}')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 70,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
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"application/vnd.plotly.v1+json": {
|
||
"config": {
|
||
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|
||
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|
||
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|
||
{
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"2024-08-29T00:00:00",
|
||
"2024-08-30T00:00:00",
|
||
"2024-08-31T00:00:00",
|
||
"2024-09-01T00:00:00",
|
||
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|
||
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|
||
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|
||
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|
||
],
|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
||
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|
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|
||
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|
||
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|
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|
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|
||
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|
||
{
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"2024-09-01T00:00:00",
|
||
"2024-09-02T00:00:00",
|
||
"2024-09-03T00:00:00",
|
||
"2024-09-04T00:00:00",
|
||
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|
||
],
|
||
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|
||
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|
||
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||
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"import plotly.graph_objects as go\n",
|
||
"from plotly.subplots import make_subplots\n",
|
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"\n",
|
||
"\n",
|
||
"\n",
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"res_df['日期'] = pd.to_datetime(res_df['date'])\n",
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"\n",
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"fig = go.Figure()\n",
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"fig.add_trace(go.Bar(\n",
|
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" x=res_df['日期'],\n",
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" y=res_df['日末余额(1.6天结算)'],\n",
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"\n",
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"\n",
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"\n",
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" title='日末余额(1.6天结算) 和 累计收益率 折线图',\n",
|
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" xaxis=dict(title='日期'),\n",
|
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" yaxis=dict(title='金额'),\n",
|
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" yaxis2=dict(title='收益率', overlaying='y', side='right', tickformat='.1%'),\n",
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")\n",
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"\n",
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]
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"metadata": {},
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"outputs": [],
|
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"source": [
|
||
"# 基于market width 进行分层， 然后计算年华夏普率值， 进行资金配比"
|
||
]
|
||
},
|
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{
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"cell_type": "code",
|
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|
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|
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|
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|
||
" <th></th>\n",
|
||
" <th>outcome</th>\n",
|
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" <th>count</th>\n",
|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
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