diff --git a/common/bet_tools.py b/common/bet_tools.py index b3d66c8..2410f64 100644 --- a/common/bet_tools.py +++ b/common/bet_tools.py @@ -73,7 +73,9 @@ def calculate_no_vig_moneyline_multir(moneyline_odds_list: list[int]) -> list[in # 步骤3: 每个概率除以总和,得到去vig的概率(归一化) no_vig_probabilities = [prob / prob_total for prob in implied_probabilities] # 步骤4: 概率转回moneyline赔率 - no_vig_moneyline_odds = [prob_to_moneyline(p_novig) for p_novig in no_vig_probabilities] + no_vig_moneyline_odds = [ + prob_to_moneyline(p_novig) for p_novig in no_vig_probabilities + ] return no_vig_moneyline_odds diff --git a/test.ipynb b/test.ipynb index c0435be..c8c6292 100644 --- a/test.ipynb +++ b/test.ipynb @@ -12,3030 +12,156 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "94725a95", "metadata": {}, "outputs": [], "source": [ - "df = pd.read_feather(\"/Users/aszer/Documents/vscode/bet/data/api_signal_res/api_res_merged_processed.feather\")" + "df = pd.read_feather(\n", + " \"/Users/aszer/Documents/vscode/bet/data/api_signal_res/api_res_merged_processed.feather\"\n", + ")" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "9f9e8d09", - "metadata": {}, - "outputs": [], - "source": [ - "df = df[~df['id'].isna()]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b28ed7a8", + "execution_count": 2, + "id": "2c620ff4", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/km/5gc2fwqs19sbr04h1_j5_rzw0000gn/T/ipykernel_14723/1988817479.py:1: 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['first_novig_price'] = df['first_novig_price'].astype(float)\n", - "/var/folders/km/5gc2fwqs19sbr04h1_j5_rzw0000gn/T/ipykernel_14723/1988817479.py:2: 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['first_power_novig_price'] = df['first_power_novig_price'].astype(float)\n" + "/var/folders/km/5gc2fwqs19sbr04h1_j5_rzw0000gn/T/ipykernel_7502/2562396296.py:1: DtypeWarning: Columns (43,44) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df = pd.read_csv(\n" ] } ], "source": [ - "df['first_novig_price'] = df['first_novig_price'].astype(float)\n", - "df['first_power_novig_price'] = df['first_power_novig_price'].astype(float)" + "df = pd.read_csv(\n", + " \"/Users/aszer/Documents/vscode/bet/data/pinnical_1xbet_all_api.csv\",\n", + " encoding=\"utf-8-sig\",\n", + ")" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "b118efae", + "execution_count": 3, + "id": "a062a651", "metadata": {}, "outputs": [ { "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "res", - "rawType": "object", - "type": "string" - } - ], - "ref": "df5d15a5-4438-4193-9647-a2ea21d089ee", - "rows": [ - [ - "0", - "lost" - ], - [ - "11", - "won" - ], - [ - "23", - "refunded" - ] - ], - "shape": { - "columns": 1, - "rows": 3 - } - }, "text/plain": [ - "0 lost\n", - "11 won\n", - "23 refunded\n", - "Name: res, dtype: object" + "Index(['id', 'sportsbook', 'sport', 'league', 'fixture_id', 'game_id',\n", + " 'market', 'grouping_key', 'timestamp', 'first_name', 'second_name',\n", + " 'first_selection', 'second_selection', 'first_selection_line',\n", + " 'second_selection_line', 'first_selection_points',\n", + " 'second_selection_points', 'first_points', 'second_points',\n", + " 'first_deep_link', 'second_deep_link', 'first_price', 'second_price',\n", + " 'first_novig_price', 'second_novig_price', 'first_power_novig_price',\n", + " 'second_power_novig_price', 'market_width', 'sportsbook_count',\n", + " 'time_diff', 'pinnacle_novig_begin_first',\n", + " 'pinnacle_novig_begin_second', 'pinnacle_novig_realtime_first',\n", + " 'pinnacle_novig_realtime_second', 'pinnacle_power_novig_begin_first',\n", + " 'pinnacle_power_novig_begin_second',\n", + " 'pinnacle_power_novig_realtime_first',\n", + " 'pinnacle_power_novig_realtime_second', 'max_price_realtime_first',\n", + " 'max_price_realtime_second', 'gmt_created', 'gmt_modified', 'bet_id',\n", + " 'max_price_sportsbook_realtime_first',\n", + " 'max_price_sportsbook_realtime_second', 'bet_status', 'result', 'rn'],\n", + " dtype='object')" ] }, - "execution_count": 8, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df['res'].drop_duplicates()" + "df.columns" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "de5fa392", + "execution_count": 4, + "id": "49cc8dbb", "metadata": {}, "outputs": [], "source": [ - "from bet_calc import american_odds_to_probability" + "df = df[\n", + " [\n", + " \"sportsbook\",\n", + " \"sport\",\n", + " \"league\",\n", + " \"fixture_id\",\n", + " \"game_id\",\n", + " \"market\",\n", + " \"first_price\",\n", + " \"second_price\",\n", + " \"market_width\",\n", + " \"result\",\n", + " ]\n", + "]" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "94ade988", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/km/5gc2fwqs19sbr04h1_j5_rzw0000gn/T/ipykernel_14723/2328886942.py:1: 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['mult_p'] = df['first_novig_price'].apply(american_odds_to_probability)\n", - "/var/folders/km/5gc2fwqs19sbr04h1_j5_rzw0000gn/T/ipykernel_14723/2328886942.py:2: 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['power_p'] = df['first_power_novig_price'].apply(american_odds_to_probability)\n" - ] - } - ], - "source": [ - "df['mult_p'] = df['first_novig_price'].apply(american_odds_to_probability)\n", - "df['power_p'] = df['first_power_novig_price'].apply(american_odds_to_probability)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "7a626cab", - "metadata": {}, - "outputs": [], - "source": [ - "df[[\"mult_p\", \"power_p\", \"res\"]].to_feather(\"data/p_res.feather\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "5412b2a5", + "execution_count": 5, + "id": "b118efae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 lost\n", + "5 refunded\n", + "18 won\n", + "Name: result, dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"result\"].drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "881f62ec", + "metadata": {}, + "outputs": [], + "source": [ + "from common.bet_tools import calculate_no_vig_moneyline_power, moneyline_to_prob\n", + "\n", + "def get_no_vig_prob(row):\n", + " odds = [row[\"first_price\"], row[\"second_price\"]]\n", + " no_vig_odds_power = calculate_no_vig_moneyline_power(odds)\n", + " novig_probs_power = [moneyline_to_prob(o) for o in no_vig_odds_power]\n", + " # 返回两个无水概率\n", + " return pd.Series(\n", + " {\n", + " \"first_no_vig_prob\": novig_probs_power[0],\n", + " \"second_no_vig_prob\": novig_probs_power[1],\n", + " }\n", + " )\n", + "\n", + "# 防止 SettingWithCopyWarning，推荐使用 .loc 显式分配\n", + "df.loc[:, [\"first_no_vig_prob\", \"second_no_vig_prob\"]] = df[[\"first_price\", \"second_price\"]].apply(get_no_vig_prob, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5f066d95", "metadata": {}, "outputs": [ { "data": { - 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"type": "string" - }, - { - "name": "second_selection", - "rawType": "object", - "type": "string" - }, - { - "name": "first_selection_line", - "rawType": "object", - "type": "string" - }, - { - "name": "second_selection_line", - "rawType": "object", - "type": "string" - }, - { - "name": "first_selection_points", - "rawType": "object", - "type": "string" - }, - { - "name": "second_selection_points", - "rawType": "object", - "type": "string" - }, - { - "name": "first_points", - "rawType": "object", - "type": "string" - }, - { - "name": "second_points", - "rawType": "object", - "type": "string" - }, - { - "name": "first_deep_link", - "rawType": "object", - "type": "string" - }, - { - "name": "second_deep_link", - "rawType": "object", - "type": "string" - }, - { - "name": "first_price", - "rawType": "object", - "type": "string" - }, - { - "name": "second_price", - "rawType": "object", - "type": "string" - }, - { - "name": "first_novig_price", - "rawType": "float64", - "type": "float" - }, - { - "name": "second_novig_price", - "rawType": "object", - "type": "string" - }, - { - "name": "first_power_novig_price", - "rawType": "float64", - "type": "float" - }, - { - "name": "second_power_novig_price", - "rawType": "object", - "type": "string" - }, - { - "name": "market_width", - "rawType": "object", - "type": "string" - }, - { - "name": "sportsbook_count", - "rawType": "object", - "type": "string" - }, - { - "name": "time_diff", - "rawType": "object", - "type": "string" - }, - { - "name": "pinnacle_novig_begin_first", - "rawType": "object", - "type": "string" - }, - { - "name": "pinnacle_novig_begin_second", - "rawType": "object", - "type": "string" - }, - { - "name": "pinnacle_novig_realtime_first", - "rawType": "object", - "type": "string" - }, - { - "name": "pinnacle_novig_realtime_second", - "rawType": "object", - "type": "string" - }, - { - "name": "pinnacle_power_novig_begin_first", - "rawType": "object", - "type": "string" - }, - { - "name": "pinnacle_power_novig_begin_second", - 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" + ], "text/plain": [ - "sport\n", - "basketball 925450\n", - "soccer 780603\n", - "tennis 471408\n", - "baseball 257880\n", - "football 145377\n", - "hockey 106530\n", - "Name: count, dtype: int64" + " sportsbook sport count\n", + "0 1XBet soccer 170549\n", + "1 1XBet basketball 166590\n", + "2 1XBet tennis 114015\n", + "3 1XBet baseball 64485\n", + "4 Pinnacle tennis 48314\n", + "5 1XBet football 36019\n", + "6 Pinnacle baseball 13706\n", + "7 Pinnacle basketball 8588\n", + "8 1XBet hockey 3441\n", + "9 Pinnacle soccer 2435\n", + "10 Pinnacle football 1477\n", + "11 Pinnacle hockey 32" ] }, - "execution_count": 18, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df[\"sport\"].value_counts()" + "df[[\"sportsbook\", \"sport\"]].value_counts().reset_index()" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 20, + "id": "c9b48951", + "metadata": {}, + "outputs": [], + "source": [ + "df['win_prob'] = df['first_no_vig_prob']\n", + "df['res'] = df['result']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c575da9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, "id": "2dfaf8ca", "metadata": {}, "outputs": [ @@ -3535,693 +602,54 @@ "name": "stdout", "output_type": "stream", "text": [ - 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EuroBasket'}\n", - "{'logloss': 0.6376234810642204, 'brier': 0.2180371329100866, 'ece': 0.09167431133757119, 'accuracy': 0.66439443458773, 'reg_alpha': -0.3478294258464765, 'reg_beta': 0.6584022406399682, 'n_samples': 10709, 'filter_cols': 'hockey,Champions Hockey League'}\n", - "{'logloss': 0.543836628850608, 'brier': 0.18112953543645421, 'ece': 0.045054680119660885, 'accuracy': 0.7346689096192078, 'reg_alpha': -0.23513007637508887, 'reg_beta': 0.8218775009035377, 'n_samples': 13078, 'filter_cols': 'soccer,USA - Major League Soccer'}\n", - "{'logloss': 0.5270332331563206, 'brier': 0.1719470900987721, 'ece': 0.09616008154589088, 'accuracy': 0.7463364841434085, 'reg_alpha': 0.11490250282057982, 'reg_beta': 0.9362960089061145, 'n_samples': 71516, 'filter_cols': 'hockey,Russia - KHL'}\n", - "{'logloss': 0.5991151059519255, 'brier': 0.20920131398932934, 'ece': 0.028903016413601174, 'accuracy': 0.6439574745064005, 'reg_alpha': -0.0846665924239476, 'reg_beta': 1.077467899977688, 'n_samples': 23045, 'filter_cols': 'football,CFL'}\n" + "['1XBet' 'baseball'] 64485\n", + "{'logloss': 0.6387950997506274, 'brier': 0.22453871048598073, 'ece': 0.05136008172379394, 'accuracy': 0.6251531363883074, 'reg_alpha': -0.22365612018752326, 'reg_beta': 0.816534516967482, 'n_samples': 64485, 'filter_cols': '1XBet,baseball'}\n", + "['1XBet' 'basketball'] 166590\n", + "{'logloss': 0.669273027668078, 'brier': 0.23856284575034065, 'ece': 0.009400017057809669, 'accuracy': 0.5751905876703284, 'reg_alpha': 0.034476957539975685, 'reg_beta': 0.8760036858377837, 'n_samples': 166590, 'filter_cols': '1XBet,basketball'}\n", + "['1XBet' 'football'] 36019\n", + "{'logloss': 0.5156283069611564, 'brier': 0.17093288618023667, 'ece': 0.03737481116887414, 'accuracy': 0.7366389960853994, 'reg_alpha': -0.22019343021598026, 'reg_beta': 0.9096828468608887, 'n_samples': 36019, 'filter_cols': '1XBet,football'}\n", + "['1XBet' 'hockey'] 3441\n", + "['1XBet' 'soccer'] 170549\n", + "{'logloss': 0.5326802320693952, 'brier': 0.17692943714557405, 'ece': 0.03728482187098291, 'accuracy': 0.7336014869626911, 'reg_alpha': -0.18193411314913413, 'reg_beta': 0.7726877806789224, 'n_samples': 170549, 'filter_cols': '1XBet,soccer'}\n", + "['1XBet' 'tennis'] 114015\n", + "{'logloss': 0.6389011505288847, 'brier': 0.22439917328514708, 'ece': 0.01927127655619132, 'accuracy': 0.6223479366749989, 'reg_alpha': -0.06976580882770708, 'reg_beta': 0.8523393655794403, 'n_samples': 114015, 'filter_cols': '1XBet,tennis'}\n", + "['Pinnacle' 'baseball'] 13706\n", + "{'logloss': 0.6488942536621299, 'brier': 0.2288787220784783, 'ece': 0.01759828591637442, 'accuracy': 0.6151320589522836, 'reg_alpha': -0.06787819238175896, 'reg_beta': 0.9034496569376994, 'n_samples': 13706, 'filter_cols': 'Pinnacle,baseball'}\n", + "['Pinnacle' 'basketball'] 8588\n", + "['Pinnacle' 'football'] 1477\n", + "['Pinnacle' 'hockey'] 32\n", + "['Pinnacle' 'soccer'] 2435\n", + "['Pinnacle' 'tennis'] 48314\n", + "{'logloss': 0.6472225570749982, 'brier': 0.22852018039069258, 'ece': 0.018568682287188856, 'accuracy': 0.605766444508838, 'reg_alpha': -0.07073057145248554, 'reg_beta': 0.9334853391615549, 'n_samples': 48314, 'filter_cols': 'Pinnacle,tennis'}\n" ] } ], "source": [ "from pinnacle_experiments import compute_metrics\n", + "data_df = df.copy()\n", "data_list = []\n", - "cols = ['sport', 'league']\n", - "for cs in df[cols].drop_duplicates().values:\n", - " tmp_df = df[cols + [\"mult_p\", \"power_p\", \"res\"]].copy()\n", + "cols = [\"sportsbook\",\"sport\"]\n", + "for cs in data_df[cols].drop_duplicates().values:\n", + " tmp_df = data_df[cols + [\"win_prob\", \"res\"]].copy()\n", " for i, col in enumerate(cols):\n", " tmp_df = tmp_df[tmp_df[col] == cs[i]]\n", + " print(cs, len(tmp_df))\n", " if len(tmp_df) < 10000:\n", " continue\n", - " tmp_df['win_prob'] = tmp_df['power_p']\n", " res = compute_metrics(tmp_df)\n", - " res['filter_cols'] = ','.join(cs)\n", + " res[\"filter_cols\"] = \",\".join(cs)\n", " data_list.append(res)\n", - " print(res)\n" + " print(res)" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 31, "id": "ace930ea", "metadata": {}, "outputs": [ { "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "logloss", - 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