2551 lines
250 KiB
Plaintext
2551 lines
250 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "79a7758178bafdd3",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:46:06.987506Z",
|
||
"start_time": "2025-04-03T12:46:06.259551Z"
|
||
},
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"e:\\PyProject\\NewStock\\main\\train\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%load_ext autoreload\n",
|
||
"%autoreload 2\n",
|
||
"\n",
|
||
"import gc\n",
|
||
"import os\n",
|
||
"import sys\n",
|
||
"sys.path.append('../../')\n",
|
||
"print(os.getcwd())\n",
|
||
"import pandas as pd\n",
|
||
"from main.factor.factor import get_rolling_factor, get_simple_factor\n",
|
||
"from main.utils.factor import read_industry_data\n",
|
||
"from main.utils.factor_processor import calculate_score\n",
|
||
"from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data\n",
|
||
"\n",
|
||
"import warnings\n",
|
||
"\n",
|
||
"warnings.filterwarnings(\"ignore\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "a79cafb06a7e0e43",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:00.212859Z",
|
||
"start_time": "2025-04-03T12:46:06.998047Z"
|
||
},
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"daily data\n",
|
||
"daily basic\n",
|
||
"inner merge on ['ts_code', 'trade_date']\n",
|
||
"stk limit\n",
|
||
"left merge on ['ts_code', 'trade_date']\n",
|
||
"money flow\n",
|
||
"left merge on ['ts_code', 'trade_date']\n",
|
||
"cyq perf\n",
|
||
"left merge on ['ts_code', 'trade_date']\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 8638639 entries, 0 to 8638638\n",
|
||
"Data columns (total 32 columns):\n",
|
||
" # Column Dtype \n",
|
||
"--- ------ ----- \n",
|
||
" 0 ts_code object \n",
|
||
" 1 trade_date datetime64[ns]\n",
|
||
" 2 open float64 \n",
|
||
" 3 close float64 \n",
|
||
" 4 high float64 \n",
|
||
" 5 low float64 \n",
|
||
" 6 vol float64 \n",
|
||
" 7 pct_chg float64 \n",
|
||
" 8 turnover_rate float64 \n",
|
||
" 9 pe_ttm float64 \n",
|
||
" 10 circ_mv float64 \n",
|
||
" 11 total_mv float64 \n",
|
||
" 12 volume_ratio float64 \n",
|
||
" 13 is_st bool \n",
|
||
" 14 up_limit float64 \n",
|
||
" 15 down_limit float64 \n",
|
||
" 16 buy_sm_vol float64 \n",
|
||
" 17 sell_sm_vol float64 \n",
|
||
" 18 buy_lg_vol float64 \n",
|
||
" 19 sell_lg_vol float64 \n",
|
||
" 20 buy_elg_vol float64 \n",
|
||
" 21 sell_elg_vol float64 \n",
|
||
" 22 net_mf_vol float64 \n",
|
||
" 23 his_low float64 \n",
|
||
" 24 his_high float64 \n",
|
||
" 25 cost_5pct float64 \n",
|
||
" 26 cost_15pct float64 \n",
|
||
" 27 cost_50pct float64 \n",
|
||
" 28 cost_85pct float64 \n",
|
||
" 29 cost_95pct float64 \n",
|
||
" 30 weight_avg float64 \n",
|
||
" 31 winner_rate float64 \n",
|
||
"dtypes: bool(1), datetime64[ns](1), float64(29), object(1)\n",
|
||
"memory usage: 2.0+ GB\n",
|
||
"None\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from main.utils.utils import read_and_merge_h5_data\n",
|
||
"\n",
|
||
"print('daily data')\n",
|
||
"df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n",
|
||
" columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg'],\n",
|
||
" df=None)\n",
|
||
"\n",
|
||
"print('daily basic')\n",
|
||
"df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n",
|
||
" columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio',\n",
|
||
" 'is_st'], df=df, join='inner')\n",
|
||
"\n",
|
||
"print('stk limit')\n",
|
||
"df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n",
|
||
" columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n",
|
||
" df=df)\n",
|
||
"print('money flow')\n",
|
||
"df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n",
|
||
" columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n",
|
||
" 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n",
|
||
" df=df)\n",
|
||
"print('cyq perf')\n",
|
||
"df = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n",
|
||
" columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n",
|
||
" 'cost_50pct',\n",
|
||
" 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n",
|
||
" df=df)\n",
|
||
"print(df.info())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "cac01788dac10678",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:10.527104Z",
|
||
"start_time": "2025-04-03T12:47:00.488715Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"industry\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print('industry')\n",
|
||
"industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n",
|
||
" columns=['ts_code', 'l2_code', 'in_date'],\n",
|
||
" df=None, on=['ts_code'], join='left')\n",
|
||
"\n",
|
||
"\n",
|
||
"def merge_with_industry_data(df, industry_df):\n",
|
||
" # 确保日期字段是 datetime 类型\n",
|
||
" df['trade_date'] = pd.to_datetime(df['trade_date'])\n",
|
||
" industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n",
|
||
"\n",
|
||
" # 对 industry_df 按 ts_code 和 in_date 排序\n",
|
||
" industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n",
|
||
"\n",
|
||
" # 对原始 df 按 ts_code 和 trade_date 排序\n",
|
||
" df_sorted = df.sort_values(['trade_date', 'ts_code'])\n",
|
||
"\n",
|
||
" # 使用 merge_asof 进行向后合并\n",
|
||
" merged = pd.merge_asof(\n",
|
||
" df_sorted,\n",
|
||
" industry_df_sorted,\n",
|
||
" by='ts_code', # 按 ts_code 分组\n",
|
||
" left_on='trade_date',\n",
|
||
" right_on='in_date',\n",
|
||
" direction='backward'\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 获取每个 ts_code 的最早 in_date 记录\n",
|
||
" min_in_date_per_ts = (industry_df_sorted\n",
|
||
" .groupby('ts_code')\n",
|
||
" .first()\n",
|
||
" .reset_index()[['ts_code', 'l2_code']])\n",
|
||
"\n",
|
||
" # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n",
|
||
" merged['l2_code'] = merged['l2_code'].fillna(\n",
|
||
" merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 保留需要的列并重置索引\n",
|
||
" result = merged.reset_index(drop=True)\n",
|
||
" return result\n",
|
||
"\n",
|
||
"\n",
|
||
"# 使用示例\n",
|
||
"df = merge_with_industry_data(df, industry_df)\n",
|
||
"# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "c4e9e1d31da6dba6",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:10.719252Z",
|
||
"start_time": "2025-04-03T12:47:10.541247Z"
|
||
},
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from main.factor.factor import *\n",
|
||
"\n",
|
||
"def calculate_indicators(df):\n",
|
||
" \"\"\"\n",
|
||
" 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n",
|
||
" \"\"\"\n",
|
||
" df = df.sort_values('trade_date')\n",
|
||
" df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n",
|
||
" # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n",
|
||
" delta = df['close'].diff()\n",
|
||
" gain = delta.where(delta > 0, 0)\n",
|
||
" loss = -delta.where(delta < 0, 0)\n",
|
||
" avg_gain = gain.rolling(window=14).mean()\n",
|
||
" avg_loss = loss.rolling(window=14).mean()\n",
|
||
" rs = avg_gain / avg_loss\n",
|
||
" df['RSI'] = 100 - (100 / (1 + rs))\n",
|
||
"\n",
|
||
" # 计算MACD\n",
|
||
" ema12 = df['close'].ewm(span=12, adjust=False).mean()\n",
|
||
" ema26 = df['close'].ewm(span=26, adjust=False).mean()\n",
|
||
" df['MACD'] = ema12 - ema26\n",
|
||
" df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n",
|
||
" df['MACD_hist'] = df['MACD'] - df['Signal_line']\n",
|
||
"\n",
|
||
" # 4. 情绪因子1：市场上涨比例（Up Ratio）\n",
|
||
" df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n",
|
||
" df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n",
|
||
"\n",
|
||
" # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n",
|
||
" df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n",
|
||
" df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n",
|
||
"\n",
|
||
" # 6. 情绪因子3：波动率（Volatility）\n",
|
||
" df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n",
|
||
"\n",
|
||
" # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n",
|
||
" df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n",
|
||
" df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n",
|
||
"\n",
|
||
" # df = sentiment_panic_greed_index(df)\n",
|
||
" # df = sentiment_market_breadth_proxy(df)\n",
|
||
" # df = sentiment_reversal_indicator(df)\n",
|
||
"\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"def generate_index_indicators(h5_filename):\n",
|
||
" df = pd.read_hdf(h5_filename, key='index_data')\n",
|
||
" df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n",
|
||
" df = df.sort_values('trade_date')\n",
|
||
"\n",
|
||
" # 计算每个ts_code的相关指标\n",
|
||
" df_indicators = []\n",
|
||
" for ts_code in df['ts_code'].unique():\n",
|
||
" df_index = df[df['ts_code'] == ts_code].copy()\n",
|
||
" df_index = calculate_indicators(df_index)\n",
|
||
" df_indicators.append(df_index)\n",
|
||
"\n",
|
||
" # 合并所有指数的结果\n",
|
||
" df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n",
|
||
"\n",
|
||
" # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n",
|
||
" df_final = df_all_indicators.pivot_table(\n",
|
||
" index='trade_date',\n",
|
||
" columns='ts_code',\n",
|
||
" values=['daily_return', \n",
|
||
" 'RSI', 'MACD', 'Signal_line', 'MACD_hist', \n",
|
||
" # 'sentiment_panic_greed_index',\n",
|
||
" 'up_ratio_20d', 'volume_change_rate', 'volatility',\n",
|
||
" 'amount_change_rate', 'amount_mean'],\n",
|
||
" aggfunc='last'\n",
|
||
" )\n",
|
||
"\n",
|
||
" df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n",
|
||
" df_final = df_final.reset_index()\n",
|
||
"\n",
|
||
" return df_final\n",
|
||
"\n",
|
||
"\n",
|
||
"# 使用函数\n",
|
||
"h5_filename = '../../data/index_data.h5'\n",
|
||
"index_data = generate_index_indicators(h5_filename)\n",
|
||
"index_data = index_data.dropna()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "a735bc02ceb4d872",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:10.821169Z",
|
||
"start_time": "2025-04-03T12:47:10.751831Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import talib\n",
|
||
"import numpy as np"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "53f86ddc0677a6d7",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:15.944254Z",
|
||
"start_time": "2025-04-03T12:47:10.826179Z"
|
||
},
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
},
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from main.utils.factor import get_act_factor\n",
|
||
"\n",
|
||
"\n",
|
||
"def read_industry_data(h5_filename):\n",
|
||
" # 读取 H5 文件中所有的行业数据\n",
|
||
" industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n",
|
||
" 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n",
|
||
" ]) # 假设 H5 文件的键是 'industry_data'\n",
|
||
" industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
" industry_data = industry_data.reindex()\n",
|
||
" industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n",
|
||
"\n",
|
||
" grouped = industry_data.groupby('ts_code', group_keys=False)\n",
|
||
" industry_data['obv'] = grouped.apply(\n",
|
||
" lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n",
|
||
" )\n",
|
||
" industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n",
|
||
" industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n",
|
||
"\n",
|
||
" industry_data = get_act_factor(industry_data, cat=False)\n",
|
||
" industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n",
|
||
"\n",
|
||
" # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n",
|
||
" # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n",
|
||
" # \n",
|
||
" # for factor in factor_columns:\n",
|
||
" # if factor in industry_data.columns:\n",
|
||
" # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n",
|
||
" # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n",
|
||
" # lambda x: x - x.mean())\n",
|
||
"\n",
|
||
" industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n",
|
||
" lambda x: x.rank(pct=True))\n",
|
||
" industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n",
|
||
" lambda x: x.rank(pct=True))\n",
|
||
"\n",
|
||
" # cs_rank_intraday_range(industry_data)\n",
|
||
" # cs_rank_close_pos_in_range(industry_data)\n",
|
||
"\n",
|
||
" industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n",
|
||
"\n",
|
||
" industry_data = industry_data.rename(\n",
|
||
" columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n",
|
||
"\n",
|
||
" industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n",
|
||
" return industry_data\n",
|
||
"\n",
|
||
"\n",
|
||
"industry_df = read_industry_data('../../data/sw_daily.h5')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "dbe2fd8021b9417f",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:15.969344Z",
|
||
"start_time": "2025-04-03T12:47:15.963327Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"['ts_code', 'open', 'close', 'high', 'low', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"origin_columns = df.columns.tolist()\n",
|
||
"origin_columns = [col for col in origin_columns if\n",
|
||
" col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'vol', 'pct_chg', 'l2_code', 'winner_rate']]\n",
|
||
"origin_columns = [col for col in origin_columns if col not in index_data.columns]\n",
|
||
"origin_columns = [col for col in origin_columns if 'cyq' not in col]\n",
|
||
"print(origin_columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "85c3e3d0235ffffa",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:16.089879Z",
|
||
"start_time": "2025-04-03T12:47:15.990101Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"fina_indicator_df = read_and_merge_h5_data('../../data/fina_indicator.h5', key='fina_indicator',\n",
|
||
" columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps'],\n",
|
||
" df=None)\n",
|
||
"cashflow_df = read_and_merge_h5_data('../../data/cashflow.h5', key='cashflow',\n",
|
||
" columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n",
|
||
" df=None)\n",
|
||
"balancesheet_df = read_and_merge_h5_data('../../data/balancesheet.h5', key='balancesheet',\n",
|
||
" columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n",
|
||
" df=None)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "92d84ce15a562ec6",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T13:08:01.612695Z",
|
||
"start_time": "2025-04-03T12:47:16.121802Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
|
||
"开始计算因子: AR, BR (原地修改)...\n",
|
||
"因子 AR, BR 计算成功。\n",
|
||
"因子 AR, BR 计算流程结束。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
|
||
"计算 BBI...\n",
|
||
"--- 计算日级别偏离度 (使用 pct_chg) ---\n",
|
||
"--- 计算日级别动量基准 (使用 pct_chg) ---\n",
|
||
"日级别动量基准计算完成 (使用 pct_chg)。\n",
|
||
"日级别偏离度计算完成 (使用 pct_chg)。\n",
|
||
"--- 计算日级别行业偏离度 (使用 pct_chg 和行业基准) ---\n",
|
||
"--- 计算日级别行业动量基准 (使用 pct_chg 和 cat_l2_code) ---\n",
|
||
"错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n",
|
||
"错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n",
|
||
"Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n",
|
||
" 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n",
|
||
" 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n",
|
||
" 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n",
|
||
" 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n",
|
||
" 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n",
|
||
" 'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n",
|
||
" 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n",
|
||
" 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n",
|
||
" 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n",
|
||
" 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n",
|
||
" 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n",
|
||
" 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n",
|
||
" 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n",
|
||
" 'cost_support_15pct_change', 'cat_winner_price_zone',\n",
|
||
" 'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n",
|
||
" '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n",
|
||
" '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n",
|
||
" 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate',\n",
|
||
" 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n",
|
||
" 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n",
|
||
" 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n",
|
||
" dtype='object')\n",
|
||
"Calculating lg_flow_mom_corr_20_60...\n",
|
||
"Finished lg_flow_mom_corr_20_60.\n",
|
||
"Calculating lg_flow_accel...\n",
|
||
"Finished lg_flow_accel.\n",
|
||
"Calculating profit_pressure...\n",
|
||
"Finished profit_pressure.\n",
|
||
"Calculating underwater_resistance...\n",
|
||
"Finished underwater_resistance.\n",
|
||
"Calculating cost_conc_std_20...\n",
|
||
"Finished cost_conc_std_20.\n",
|
||
"Calculating profit_decay_20...\n",
|
||
"Finished profit_decay_20.\n",
|
||
"Calculating vol_amp_loss_20...\n",
|
||
"Finished vol_amp_loss_20.\n",
|
||
"Calculating vol_drop_profit_cnt_5...\n",
|
||
"Finished vol_drop_profit_cnt_5.\n",
|
||
"Calculating lg_flow_vol_interact_20...\n",
|
||
"Finished lg_flow_vol_interact_20.\n",
|
||
"Calculating cost_break_confirm_cnt_5...\n",
|
||
"Finished cost_break_confirm_cnt_5.\n",
|
||
"Calculating atr_norm_channel_pos_14...\n",
|
||
"Finished atr_norm_channel_pos_14.\n",
|
||
"Calculating turnover_diff_skew_20...\n",
|
||
"Finished turnover_diff_skew_20.\n",
|
||
"Calculating lg_sm_flow_diverge_20...\n",
|
||
"Finished lg_sm_flow_diverge_20.\n",
|
||
"Calculating pullback_strong_20_20...\n",
|
||
"Finished pullback_strong_20_20.\n",
|
||
"Calculating vol_wgt_hist_pos_20...\n",
|
||
"Finished vol_wgt_hist_pos_20.\n",
|
||
"Calculating vol_adj_roc_20...\n",
|
||
"Finished vol_adj_roc_20.\n",
|
||
"Calculating cs_rank_net_lg_flow_val...\n",
|
||
"Finished cs_rank_net_lg_flow_val.\n",
|
||
"Calculating cs_rank_flow_divergence...\n",
|
||
"Finished cs_rank_flow_divergence.\n",
|
||
"Calculating cs_rank_ind_adj_lg_flow...\n",
|
||
"Finished cs_rank_ind_adj_lg_flow.\n",
|
||
"Calculating cs_rank_elg_buy_ratio...\n",
|
||
"Finished cs_rank_elg_buy_ratio.\n",
|
||
"Calculating cs_rank_rel_profit_margin...\n",
|
||
"Finished cs_rank_rel_profit_margin.\n",
|
||
"Calculating cs_rank_cost_breadth...\n",
|
||
"Finished cs_rank_cost_breadth.\n",
|
||
"Calculating cs_rank_dist_to_upper_cost...\n",
|
||
"Finished cs_rank_dist_to_upper_cost.\n",
|
||
"Calculating cs_rank_winner_rate...\n",
|
||
"Finished cs_rank_winner_rate.\n",
|
||
"Calculating cs_rank_intraday_range...\n",
|
||
"Finished cs_rank_intraday_range.\n",
|
||
"Calculating cs_rank_close_pos_in_range...\n",
|
||
"Finished cs_rank_close_pos_in_range.\n",
|
||
"Calculating cs_rank_opening_gap...\n",
|
||
"Error calculating cs_rank_opening_gap: Missing 'pre_close' column. Assigning NaN.\n",
|
||
"Calculating cs_rank_pos_in_hist_range...\n",
|
||
"Finished cs_rank_pos_in_hist_range.\n",
|
||
"Calculating cs_rank_vol_x_profit_margin...\n",
|
||
"Finished cs_rank_vol_x_profit_margin.\n",
|
||
"Calculating cs_rank_lg_flow_price_concordance...\n",
|
||
"Finished cs_rank_lg_flow_price_concordance.\n",
|
||
"Calculating cs_rank_turnover_per_winner...\n",
|
||
"Finished cs_rank_turnover_per_winner.\n",
|
||
"Calculating cs_rank_ind_cap_neutral_pe (Placeholder - requires statsmodels)...\n",
|
||
"Finished cs_rank_ind_cap_neutral_pe (Placeholder).\n",
|
||
"Calculating cs_rank_volume_ratio...\n",
|
||
"Finished cs_rank_volume_ratio.\n",
|
||
"Calculating cs_rank_elg_buy_sell_sm_ratio...\n",
|
||
"Finished cs_rank_elg_buy_sell_sm_ratio.\n",
|
||
"Calculating cs_rank_cost_dist_vol_ratio...\n",
|
||
"Finished cs_rank_cost_dist_vol_ratio.\n",
|
||
"Calculating cs_rank_size...\n",
|
||
"Finished cs_rank_size.\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 4524625 entries, 0 to 4524624\n",
|
||
"Columns: 178 entries, ts_code to cs_rank_size\n",
|
||
"dtypes: bool(10), datetime64[ns](1), float64(162), int32(3), object(2)\n",
|
||
"memory usage: 5.7+ GB\n",
|
||
"None\n",
|
||
"['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"from main.factor.factor import *\n",
|
||
"\n",
|
||
"def filter_data(df):\n",
|
||
" # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n",
|
||
" df = df[~df['is_st']]\n",
|
||
" df = df[~df['ts_code'].str.endswith('BJ')]\n",
|
||
" df = df[~df['ts_code'].str.startswith('30')]\n",
|
||
" df = df[~df['ts_code'].str.startswith('68')]\n",
|
||
" df = df[~df['ts_code'].str.startswith('8')]\n",
|
||
" df = df[df['trade_date'] >= '2019-01-01']\n",
|
||
" if 'in_date' in df.columns:\n",
|
||
" df = df.drop(columns=['in_date'])\n",
|
||
" df = df.reset_index(drop=True)\n",
|
||
" return df\n",
|
||
"\n",
|
||
"gc.collect()\n",
|
||
"\n",
|
||
"df = filter_data(df)\n",
|
||
"df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
"df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n",
|
||
"df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n",
|
||
"calculate_arbr(df, N=26)\n",
|
||
"df['log_circ_mv'] = np.log(df['circ_mv'])\n",
|
||
"df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n",
|
||
"df = caculate_book_to_price_ratio(df, fina_indicator_df)\n",
|
||
"df = turnover_rate_n(df, n=5)\n",
|
||
"df = variance_n(df, n=20)\n",
|
||
"df = bbi_ratio_factor(df)\n",
|
||
"df = daily_deviation(df)\n",
|
||
"df = daily_industry_deviation(df)\n",
|
||
"df, _ = get_rolling_factor(df)\n",
|
||
"df, _ = get_simple_factor(df)\n",
|
||
"\n",
|
||
"df = df.rename(columns={'l1_code': 'cat_l1_code'})\n",
|
||
"df = df.rename(columns={'l2_code': 'cat_l2_code'})\n",
|
||
"\n",
|
||
"lg_flow_mom_corr(df, N=20, M=60)\n",
|
||
"lg_flow_accel(df)\n",
|
||
"profit_pressure(df)\n",
|
||
"underwater_resistance(df)\n",
|
||
"cost_conc_std(df, N=20)\n",
|
||
"profit_decay(df, N=20)\n",
|
||
"vol_amp_loss(df, N=20)\n",
|
||
"vol_drop_profit_cnt(df, N=20, M=5)\n",
|
||
"lg_flow_vol_interact(df, N=20)\n",
|
||
"cost_break_confirm_cnt(df, M=5)\n",
|
||
"atr_norm_channel_pos(df, N=14)\n",
|
||
"turnover_diff_skew(df, N=20)\n",
|
||
"lg_sm_flow_diverge(df, N=20)\n",
|
||
"pullback_strong(df, N=20, M=20)\n",
|
||
"vol_wgt_hist_pos(df, N=20)\n",
|
||
"vol_adj_roc(df, N=20)\n",
|
||
"\n",
|
||
"cs_rank_net_lg_flow_val(df)\n",
|
||
"cs_rank_flow_divergence(df)\n",
|
||
"cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n",
|
||
"cs_rank_elg_buy_ratio(df)\n",
|
||
"cs_rank_rel_profit_margin(df)\n",
|
||
"cs_rank_cost_breadth(df)\n",
|
||
"cs_rank_dist_to_upper_cost(df)\n",
|
||
"cs_rank_winner_rate(df)\n",
|
||
"cs_rank_intraday_range(df)\n",
|
||
"cs_rank_close_pos_in_range(df)\n",
|
||
"cs_rank_opening_gap(df) # Needs pre_close\n",
|
||
"cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n",
|
||
"cs_rank_vol_x_profit_margin(df)\n",
|
||
"cs_rank_lg_flow_price_concordance(df)\n",
|
||
"cs_rank_turnover_per_winner(df)\n",
|
||
"cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n",
|
||
"cs_rank_volume_ratio(df) # Needs volume_ratio\n",
|
||
"cs_rank_elg_buy_sell_sm_ratio(df)\n",
|
||
"cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n",
|
||
"cs_rank_size(df) # Needs circ_mv\n",
|
||
"\n",
|
||
"\n",
|
||
"# df = df.merge(index_data, on='trade_date', how='left')\n",
|
||
"\n",
|
||
"print(df.info())\n",
|
||
"print(df.columns.tolist())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "b87b938028afa206",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T13:08:03.658725Z",
|
||
"start_time": "2025-04-03T13:08:02.469611Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from scipy.stats import ks_2samp, wasserstein_distance\n",
|
||
"\n",
|
||
"\n",
|
||
"def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.05, wasserstein_threshold=0.1,\n",
|
||
" importance_threshold=0.05):\n",
|
||
" dropped_features = []\n",
|
||
"\n",
|
||
" # **统计数据漂移**\n",
|
||
" numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns\n",
|
||
" numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
|
||
" for feature in numeric_columns:\n",
|
||
" ks_stat, p_value = ks_2samp(train_data[feature], test_data[feature])\n",
|
||
" wasserstein_dist = wasserstein_distance(train_data[feature], test_data[feature])\n",
|
||
"\n",
|
||
" if p_value < ks_threshold or wasserstein_dist > wasserstein_threshold:\n",
|
||
" dropped_features.append(feature)\n",
|
||
"\n",
|
||
" print(f\"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}\")\n",
|
||
"\n",
|
||
" # **应用阈值进行最终筛选**\n",
|
||
" filtered_features = [f for f in feature_columns if f not in dropped_features]\n",
|
||
"\n",
|
||
" return filtered_features, dropped_features\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "f4f16d63ad18d1bc",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T13:08:03.670700Z",
|
||
"start_time": "2025-04-03T13:08:03.665739Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import statsmodels.api as sm # 用于中性化回归\n",
|
||
"from tqdm import tqdm # 可选，用于显示进度条\n",
|
||
"\n",
|
||
"# --- 常量 ---\n",
|
||
"epsilon = 1e-10 # 防止除零\n",
|
||
"\n",
|
||
"# --- 1. 中位数去极值 (MAD) ---\n",
|
||
"\n",
|
||
"def cs_mad_filter(df: pd.DataFrame,\n",
|
||
" features: list,\n",
|
||
" k: float = 3.0,\n",
|
||
" scale_factor: float = 1.4826):\n",
|
||
" \"\"\"\n",
|
||
" 对指定特征列进行截面 MAD 去极值处理 (原地修改)。\n",
|
||
"\n",
|
||
" 方法: 对每日截面数据，计算 median 和 MAD，\n",
|
||
" 将超出 [median - k * scale * MAD, median + k * scale * MAD] 范围的值\n",
|
||
" 替换为边界值 (Winsorization)。\n",
|
||
" scale_factor=1.4826 使得 MAD 约等于正态分布的标准差。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n",
|
||
" features (list): 需要处理的特征列名列表。\n",
|
||
" k (float): MAD 的倍数，用于确定边界。默认为 3.0。\n",
|
||
" scale_factor (float): MAD 的缩放因子。默认为 1.4826。\n",
|
||
"\n",
|
||
" WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n",
|
||
" \"\"\"\n",
|
||
" print(f\"开始截面 MAD 去极值处理 (k={k})...\")\n",
|
||
" if not all(col in df.columns for col in features):\n",
|
||
" missing = [col for col in features if col not in df.columns]\n",
|
||
" print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过去极值处理。\")\n",
|
||
" return\n",
|
||
"\n",
|
||
" grouped = df.groupby('trade_date')\n",
|
||
"\n",
|
||
" for col in tqdm(features, desc=\"MAD Filtering\"):\n",
|
||
" try:\n",
|
||
" # 计算截面中位数\n",
|
||
" median = grouped[col].transform('median')\n",
|
||
" # 计算截面 MAD (Median Absolute Deviation from Median)\n",
|
||
" mad = (df[col] - median).abs().groupby(df['trade_date']).transform('median')\n",
|
||
"\n",
|
||
" # 计算上下边界\n",
|
||
" lower_bound = median - k * scale_factor * mad\n",
|
||
" upper_bound = median + k * scale_factor * mad\n",
|
||
"\n",
|
||
" # 原地应用 clip\n",
|
||
" df[col] = np.clip(df[col], lower_bound, upper_bound)\n",
|
||
"\n",
|
||
" except KeyError:\n",
|
||
" print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的 MAD 处理。\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的 MAD 处理。\")\n",
|
||
"\n",
|
||
" print(\"截面 MAD 去极值处理完成。\")\n",
|
||
"\n",
|
||
"\n",
|
||
"# --- 2. 行业市值中性化 ---\n",
|
||
"\n",
|
||
"def cs_neutralize_industry_cap(df: pd.DataFrame,\n",
|
||
" features: list,\n",
|
||
" industry_col: str = 'cat_l2_code',\n",
|
||
" market_cap_col: str = 'circ_mv'):\n",
|
||
" \"\"\"\n",
|
||
" 对指定特征列进行截面行业和对数市值中性化 (原地修改)。\n",
|
||
" 使用 OLS 回归: feature ~ 1 + log(market_cap) + C(industry)\n",
|
||
" 将回归残差写回原特征列。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date', features 列,\n",
|
||
" industry_col, market_cap_col。\n",
|
||
" features (list): 需要处理的特征列名列表。\n",
|
||
" industry_col (str): 行业分类列名。\n",
|
||
" market_cap_col (str): 流通市值列名。\n",
|
||
"\n",
|
||
" WARNING: 此函数会原地修改输入的 DataFrame 'df' 的 features 列。\n",
|
||
" 计算量较大，可能耗时较长。\n",
|
||
" 需要安装 statsmodels 库 (pip install statsmodels)。\n",
|
||
" \"\"\"\n",
|
||
" print(\"开始截面行业市值中性化...\")\n",
|
||
" required_cols = features + ['trade_date', industry_col, market_cap_col]\n",
|
||
" if not all(col in df.columns for col in required_cols):\n",
|
||
" missing = [col for col in required_cols if col not in df.columns]\n",
|
||
" print(f\"错误: DataFrame 中缺少必需列: {missing}。无法进行中性化。\")\n",
|
||
" return\n",
|
||
"\n",
|
||
" # 预处理：计算 log 市值，处理 industry code 可能的 NaN\n",
|
||
" log_cap_col = '_log_market_cap'\n",
|
||
" df[log_cap_col] = np.log1p(df[market_cap_col]) # log1p 处理 0 值\n",
|
||
" # df[industry_col] = df[industry_col].cat.add_categories('UnknownIndustry')\n",
|
||
" # df[industry_col] = df[industry_col].fillna('UnknownIndustry') # 填充行业 NaN\n",
|
||
" # df[industry_col] = df[industry_col].astype('category') # 转为类别，ols 会自动处理\n",
|
||
"\n",
|
||
" dates = df['trade_date'].unique()\n",
|
||
" all_residuals = [] # 用于收集所有日期的残差\n",
|
||
"\n",
|
||
" for date in tqdm(dates, desc=\"Neutralizing\"):\n",
|
||
" daily_data = df.loc[df['trade_date'] == date, features + [log_cap_col, industry_col]].copy() # 使用 .loc 获取副本\n",
|
||
"\n",
|
||
" # 准备自变量 X (常数项 + log市值 + 行业哑变量)\n",
|
||
" X = daily_data[[log_cap_col]]\n",
|
||
" X = sm.add_constant(X, prepend=True) # 添加常数项\n",
|
||
" # 创建行业哑变量 (drop_first=True 避免共线性)\n",
|
||
" industry_dummies = pd.get_dummies(daily_data[industry_col], prefix=industry_col, drop_first=True)\n",
|
||
" industry_dummies = industry_dummies.astype(int)\n",
|
||
" X = pd.concat([X, industry_dummies], axis=1)\n",
|
||
"\n",
|
||
" daily_residuals = daily_data[[col for col in features]].copy() # 创建用于存储残差的df\n",
|
||
"\n",
|
||
" for col in features:\n",
|
||
" Y = daily_data[col]\n",
|
||
"\n",
|
||
" # 处理 NaN 值，确保 X 和 Y 在相同位置有有效值\n",
|
||
" valid_mask = Y.notna() & X.notna().all(axis=1)\n",
|
||
" if valid_mask.sum() < (X.shape[1] + 1): # 数据点不足以估计模型\n",
|
||
" print(f\"警告: 日期 {date}, 特征 {col} 有效数据不足 ({valid_mask.sum()}个)，无法中性化，填充 NaN。\")\n",
|
||
" daily_residuals[col] = np.nan\n",
|
||
" continue\n",
|
||
"\n",
|
||
" Y_valid = Y[valid_mask]\n",
|
||
" X_valid = X[valid_mask]\n",
|
||
"\n",
|
||
" # 执行 OLS 回归\n",
|
||
" try:\n",
|
||
" model = sm.OLS(Y_valid.to_numpy(), X_valid.to_numpy())\n",
|
||
" results = model.fit()\n",
|
||
" # 将残差填回对应位置\n",
|
||
" daily_residuals.loc[valid_mask, col] = results.resid\n",
|
||
" daily_residuals.loc[~valid_mask, col] = np.nan # 原本无效的位置填充 NaN\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"警告: 日期 {date}, 特征 {col} 回归失败: {e}，填充 NaN。\")\n",
|
||
" daily_residuals[col] = np.nan\n",
|
||
" break\n",
|
||
"\n",
|
||
" all_residuals.append(daily_residuals)\n",
|
||
"\n",
|
||
" # 合并所有日期的残差结果\n",
|
||
" if all_residuals:\n",
|
||
" residuals_df = pd.concat(all_residuals)\n",
|
||
" # 将残差结果更新回原始 df (原地修改)\n",
|
||
" # 使用 update 比 merge 更适合基于索引的原地更新\n",
|
||
" # 确保 residuals_df 的索引与 df 中对应部分一致\n",
|
||
" df.update(residuals_df)\n",
|
||
" else:\n",
|
||
" print(\"没有有效的残差结果可以合并。\")\n",
|
||
"\n",
|
||
"\n",
|
||
" # 清理临时列\n",
|
||
" df.drop(columns=[log_cap_col], inplace=True)\n",
|
||
" print(\"截面行业市值中性化完成。\")\n",
|
||
"\n",
|
||
"\n",
|
||
"# --- 3. Z-Score 标准化 ---\n",
|
||
"\n",
|
||
"def cs_zscore_standardize(df: pd.DataFrame, features: list, epsilon: float = 1e-10):\n",
|
||
" \"\"\"\n",
|
||
" 对指定特征列进行截面 Z-Score 标准化 (原地修改)。\n",
|
||
" 方法: Z = (value - cross_sectional_mean) / (cross_sectional_std + epsilon)\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n",
|
||
" features (list): 需要处理的特征列名列表。\n",
|
||
" epsilon (float): 防止除以零的小常数。\n",
|
||
"\n",
|
||
" WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n",
|
||
" \"\"\"\n",
|
||
" print(\"开始截面 Z-Score 标准化...\")\n",
|
||
" if not all(col in df.columns for col in features):\n",
|
||
" missing = [col for col in features if col not in df.columns]\n",
|
||
" print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过标准化处理。\")\n",
|
||
" return\n",
|
||
"\n",
|
||
" grouped = df.groupby('trade_date')\n",
|
||
"\n",
|
||
" for col in tqdm(features, desc=\"Standardizing\"):\n",
|
||
" try:\n",
|
||
" # 使用 transform 计算截面均值和标准差\n",
|
||
" mean = grouped[col].transform('mean')\n",
|
||
" std = grouped[col].transform('std')\n",
|
||
"\n",
|
||
" # 计算 Z-Score 并原地赋值\n",
|
||
" df[col] = (df[col] - mean) / (std + epsilon)\n",
|
||
"\n",
|
||
" except KeyError:\n",
|
||
" print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的标准化处理。\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的标准化处理。\")\n",
|
||
"\n",
|
||
" print(\"截面 Z-Score 标准化完成。\")\n",
|
||
"\n",
|
||
"def fill_nan_with_daily_median(df: pd.DataFrame, feature_columns: list[str]) -> pd.DataFrame:\n",
|
||
" \"\"\"\n",
|
||
" 对指定特征列进行每日截面中位数填充缺失值 (NaN)。\n",
|
||
"\n",
|
||
" 参数:\n",
|
||
" df (pd.DataFrame): 包含多日数据的DataFrame，需要包含 'trade_date' 和 feature_columns 中的列。\n",
|
||
" feature_columns (list[str]): 需要进行缺失值填充的特征列名称列表。\n",
|
||
"\n",
|
||
" 返回:\n",
|
||
" pd.DataFrame: 包含缺失值填充后特征列的DataFrame。在输入DataFrame的副本上操作。\n",
|
||
" \"\"\"\n",
|
||
" processed_df = df.copy() # 在副本上操作，保留原始数据\n",
|
||
"\n",
|
||
" # 确保 trade_date 是 datetime 类型以便正确分组\n",
|
||
" processed_df['trade_date'] = pd.to_datetime(processed_df['trade_date'])\n",
|
||
"\n",
|
||
" def _fill_daily_nan(group):\n",
|
||
" # group 是某一个交易日的 DataFrame\n",
|
||
"\n",
|
||
" # 遍历指定的特征列\n",
|
||
" for feature_col in feature_columns:\n",
|
||
" # 检查列是否存在于当前分组中\n",
|
||
" if feature_col in group.columns:\n",
|
||
" # 计算当日该特征的中位数\n",
|
||
" median_val = group[feature_col].median()\n",
|
||
"\n",
|
||
" # 使用当日中位数填充该特征列的 NaN 值\n",
|
||
" # inplace=True 会直接修改 group DataFrame\n",
|
||
" group[feature_col].fillna(median_val, inplace=True)\n",
|
||
" # else:\n",
|
||
" # print(f\"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.\")\n",
|
||
"\n",
|
||
" return group\n",
|
||
"\n",
|
||
" # 按交易日期分组，并应用每日填充函数\n",
|
||
" # group_keys=False 避免将分组键添加到结果索引中\n",
|
||
" filled_df = processed_df.groupby('trade_date', group_keys=False).apply(_fill_daily_nan)\n",
|
||
"\n",
|
||
" return filled_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "40e6b68a91b30c79",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T13:08:04.694262Z",
|
||
"start_time": "2025-04-03T13:08:03.694904Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"\n",
|
||
"def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n",
|
||
" log=True):\n",
|
||
" if not (0 <= lower_percentile < upper_percentile <= 1):\n",
|
||
" raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n",
|
||
"\n",
|
||
" # Calculate lower and upper bounds based on percentiles\n",
|
||
" lower_bound = label.quantile(lower_percentile)\n",
|
||
" upper_bound = label.quantile(upper_percentile)\n",
|
||
"\n",
|
||
" # Filter out values outside the bounds\n",
|
||
" filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n",
|
||
"\n",
|
||
" # Print the number of removed outliers\n",
|
||
" if log:\n",
|
||
" print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n",
|
||
" return filtered_label\n",
|
||
"\n",
|
||
"\n",
|
||
"def calculate_risk_adjusted_target(df, days=5):\n",
|
||
" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
"\n",
|
||
" df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n",
|
||
" df['future_open'] = df.groupby('ts_code')['open'].shift(-1)\n",
|
||
" df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n",
|
||
"\n",
|
||
" df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n",
|
||
" level=0, drop=True)\n",
|
||
" sharpe_ratio = df['future_return'] * df['future_volatility']\n",
|
||
" sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
|
||
"\n",
|
||
" return sharpe_ratio\n",
|
||
"\n",
|
||
"\n",
|
||
"def calculate_score(df, days=5, lambda_param=1.0):\n",
|
||
" def calculate_max_drawdown(prices):\n",
|
||
" peak = prices.iloc[0] # 初始化峰值\n",
|
||
" max_drawdown = 0 # 初始化最大回撤\n",
|
||
"\n",
|
||
" for price in prices:\n",
|
||
" if price > peak:\n",
|
||
" peak = price # 更新峰值\n",
|
||
" else:\n",
|
||
" drawdown = (peak - price) / peak # 计算当前回撤\n",
|
||
" max_drawdown = max(max_drawdown, drawdown) # 更新最大回撤\n",
|
||
"\n",
|
||
" return max_drawdown\n",
|
||
"\n",
|
||
" def compute_stock_score(stock_df):\n",
|
||
" stock_df = stock_df.sort_values(by=['trade_date'])\n",
|
||
" future_return = stock_df['future_return']\n",
|
||
" # 使用已有的 pct_chg 字段计算波动率\n",
|
||
" volatility = stock_df['pct_chg'].rolling(days).std().shift(-days)\n",
|
||
" max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n",
|
||
" score = future_return - lambda_param * max_drawdown\n",
|
||
" return score\n",
|
||
"\n",
|
||
" # # 确保 DataFrame 按照股票代码和交易日期排序\n",
|
||
" # df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
"\n",
|
||
" # 对每个股票分别计算 score\n",
|
||
" df['score'] = df.groupby('ts_code').apply(compute_stock_score).reset_index(level=0, drop=True)\n",
|
||
"\n",
|
||
" return df['score']\n",
|
||
"\n",
|
||
"\n",
|
||
"def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n",
|
||
" numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n",
|
||
" if not numeric_features:\n",
|
||
" raise ValueError(\"No numeric features found in the provided data.\")\n",
|
||
"\n",
|
||
" corr_matrix = df[numeric_features].corr().abs()\n",
|
||
" upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
|
||
" to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n",
|
||
" remaining_features = [col for col in feature_columns if col not in to_drop\n",
|
||
" or 'act' in col or 'af' in col]\n",
|
||
" return remaining_features\n",
|
||
"\n",
|
||
"\n",
|
||
"def cross_sectional_standardization(df, features):\n",
|
||
" df_sorted = df.sort_values(by='trade_date') # 按时间排序\n",
|
||
" df_standardized = df_sorted.copy()\n",
|
||
"\n",
|
||
" for date in df_sorted['trade_date'].unique():\n",
|
||
" # 获取当前时间点的数据\n",
|
||
" current_data = df_standardized[df_standardized['trade_date'] == date]\n",
|
||
"\n",
|
||
" # 只对指定特征进行标准化\n",
|
||
" scaler = StandardScaler()\n",
|
||
" standardized_values = scaler.fit_transform(current_data[features])\n",
|
||
"\n",
|
||
" # 将标准化结果重新赋值回去\n",
|
||
" df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values\n",
|
||
"\n",
|
||
" return df_standardized\n",
|
||
"\n",
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"\n",
|
||
"def neutralize_manual_revised(df: pd.DataFrame, features: list, industry_col: str, mkt_cap_col: str) -> pd.DataFrame:\n",
|
||
" \"\"\"\n",
|
||
" 手动实现简单回归以提升速度，通过构建 Series 确保索引对齐。\n",
|
||
" 对特征在行业内部进行市值中性化。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df: 输入的 DataFrame，包含特征、行业分类和市值列。\n",
|
||
" features: 需要进行中性化的特征列名列表。\n",
|
||
" industry_col: 行业分类列的列名。\n",
|
||
" mkt_cap_col: 市值列的列名。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" 中性化后的 DataFrame。\n",
|
||
" \"\"\"\n",
|
||
"\n",
|
||
" df[mkt_cap_col] = pd.to_numeric(df[mkt_cap_col], errors='coerce')\n",
|
||
" df_cleaned = df.dropna(subset=[mkt_cap_col]).copy()\n",
|
||
" df_cleaned = df_cleaned[df_cleaned[mkt_cap_col] > 0].copy()\n",
|
||
"\n",
|
||
" if df_cleaned.empty:\n",
|
||
" print(\"警告: 清理市值异常值后 DataFrame 为空。\")\n",
|
||
" return df # 返回原始或空df，取决于清理前的状态\n",
|
||
"\n",
|
||
" processed_df = df\n",
|
||
"\n",
|
||
" for col in features:\n",
|
||
" if col not in df_cleaned.columns:\n",
|
||
" print(f\"警告: 特征列 '{col}' 不存在于清理后的 DataFrame 中，已跳过。\")\n",
|
||
" # 对于原始 df 中该列不存在的，在结果 df 中也保持原样（可能全是NaN）\n",
|
||
" processed_df[col] = df[col] if col in df.columns else np.nan\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # 跳过对控制变量本身进行中性化\n",
|
||
" if col == mkt_cap_col or col == industry_col:\n",
|
||
" print(f\"警告: 特征列 '{col}' 是控制变量或内部使用的列，跳过中性化。\")\n",
|
||
" # 在结果 df 中也保持原样\n",
|
||
" processed_df[col] = df[col] if col in df.columns else np.nan\n",
|
||
" continue\n",
|
||
"\n",
|
||
" residual_series = pd.Series(index=df_cleaned.index, dtype=float)\n",
|
||
"\n",
|
||
" # 在分组前处理特征列的 NaN，只对有因子值的行进行回归计算\n",
|
||
" df_subset_factor = df_cleaned.dropna(subset=[col]).copy()\n",
|
||
"\n",
|
||
" if not df_subset_factor.empty:\n",
|
||
" for industry, group in df_subset_factor.groupby(industry_col):\n",
|
||
" x = group[mkt_cap_col] # 市值对数\n",
|
||
" y = group[col] # 因子值\n",
|
||
"\n",
|
||
" # 确保有足够的数据点 (>1) 且市值对数有方差 (>0) 进行回归计算\n",
|
||
" # 检查 np.var > 一个很小的正数，避免浮点数误差导致的零方差判断问题\n",
|
||
" if len(group) > 1 and np.var(x) > 1e-9:\n",
|
||
" try:\n",
|
||
" beta = np.cov(y, x)[0, 1] / np.var(x)\n",
|
||
" alpha = np.mean(y) - beta * np.mean(x)\n",
|
||
"\n",
|
||
" # 计算残差\n",
|
||
" resid = y - (alpha + beta * x)\n",
|
||
"\n",
|
||
" # 将计算出的残差存储到 residual_series 中，通过索引自动对齐\n",
|
||
" residual_series.loc[resid.index] = resid\n",
|
||
"\n",
|
||
" except Exception as e:\n",
|
||
" # 捕获可能的计算异常，例如np.cov或np.var因为极端数据报错\n",
|
||
" print(f\"警告: 在行业 {industry} 计算回归时发生错误: {e}。该组残差将设为原始值或 NaN。\")\n",
|
||
" # 此时该组的残差会保持 residual_series 初始化时的 NaN 或后续处理\n",
|
||
" # 也可以选择保留原始值：residual_series.loc[group.index] = group[col]\n",
|
||
"\n",
|
||
" else:\n",
|
||
" residual_series.loc[group.index] = group[col] # 保留原始因子值\n",
|
||
" processed_df.loc[residual_series.index, col] = residual_series\n",
|
||
"\n",
|
||
"\n",
|
||
" else:\n",
|
||
" processed_df[col] = np.nan # 或 df[col] if col in df.columns else np.nan\n",
|
||
"\n",
|
||
" return processed_df\n",
|
||
"\n",
|
||
"\n",
|
||
"import gc\n",
|
||
"\n",
|
||
"gc.collect()\n",
|
||
"\n",
|
||
"\n",
|
||
"def mad_filter(df, features, n=3):\n",
|
||
" for col in features:\n",
|
||
" median = df[col].median()\n",
|
||
" mad = np.median(np.abs(df[col] - median))\n",
|
||
" upper = median + n * mad\n",
|
||
" lower = median - n * mad\n",
|
||
" df[col] = np.clip(df[col], lower, upper) # 截断极值\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"def percentile_filter(df, features, lower_percentile=0.01, upper_percentile=0.99):\n",
|
||
" for col in features:\n",
|
||
" # 按日期分组计算上下百分位数\n",
|
||
" lower_bound = df.groupby('trade_date')[col].transform(\n",
|
||
" lambda x: x.quantile(lower_percentile)\n",
|
||
" )\n",
|
||
" upper_bound = df.groupby('trade_date')[col].transform(\n",
|
||
" lambda x: x.quantile(upper_percentile)\n",
|
||
" )\n",
|
||
" # 截断超出范围的值\n",
|
||
" df[col] = np.clip(df[col], lower_bound, upper_bound)\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"from scipy.stats import iqr\n",
|
||
"\n",
|
||
"\n",
|
||
"def iqr_filter(df, features):\n",
|
||
" for col in features:\n",
|
||
" df[col] = df.groupby('trade_date')[col].transform(\n",
|
||
" lambda x: (x - x.median()) / iqr(x) if iqr(x) != 0 else x\n",
|
||
" )\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"def quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n",
|
||
" df = df.copy()\n",
|
||
" for col in features:\n",
|
||
" # 计算 rolling 统计量，需要按日期进行 groupby\n",
|
||
" rolling_lower = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(lower_quantile))\n",
|
||
" rolling_upper = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(upper_quantile))\n",
|
||
"\n",
|
||
" # 对数据进行裁剪\n",
|
||
" df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n",
|
||
" \n",
|
||
" return df\n",
|
||
"\n",
|
||
"def select_top_features_by_rankic(df: pd.DataFrame, feature_columns: list, n: int, target_column: str = 'future_return') -> list:\n",
|
||
" \"\"\"\n",
|
||
" 计算给定特征与目标列的 RankIC，并返回 RankIC 绝对值最高的 n 个特征。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df: 包含特征列和目标列的 Pandas DataFrame。\n",
|
||
" feature_columns: 包含所有待评估特征列名的列表。\n",
|
||
" n: 希望选取的 RankIC 绝对值最高的特征数量。\n",
|
||
" target_column: 目标列的名称，用于计算 RankIC。默认为 'future_return'。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" 包含 RankIC 绝对值最高的 n 个特征列名的列表。\n",
|
||
" \"\"\"\n",
|
||
" numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
|
||
" numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
|
||
" if target_column not in df.columns:\n",
|
||
" raise ValueError(f\"目标列 '{target_column}' 不存在于 DataFrame 中。\")\n",
|
||
"\n",
|
||
" rankic_scores = {}\n",
|
||
" for feature in numeric_columns:\n",
|
||
" if feature not in df.columns:\n",
|
||
" print(f\"警告: 特征列 '{feature}' 不存在于 DataFrame 中，已跳过。\")\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # 计算特征与目标列的 RankIC (斯皮尔曼相关系数)\n",
|
||
" # dropna() 是为了处理缺失值，确保相关性计算不失败\n",
|
||
" valid_data = df[[feature, target_column]].dropna()\n",
|
||
" if len(valid_data) > 1: # 确保有足够的数据点进行相关性计算\n",
|
||
" # 计算斯皮尔曼相关性\n",
|
||
" correlation = valid_data[feature].corr(valid_data[target_column], method='spearman')\n",
|
||
" rankic_scores[feature] = abs(correlation) # 使用绝对值来衡量相关性强度\n",
|
||
" else:\n",
|
||
" rankic_scores[feature] = 0 # 数据不足，RankIC设为0或跳过\n",
|
||
"\n",
|
||
" # 将 RankIC 分数转换为 Series 便于排序\n",
|
||
" rankic_series = pd.Series(rankic_scores)\n",
|
||
"\n",
|
||
" # 按 RankIC 绝对值降序排序，选取前 n 个特征\n",
|
||
" # handle case where n might be larger than available features\n",
|
||
" n_actual = min(n, len(rankic_series))\n",
|
||
" top_features = rankic_series.sort_values(ascending=False).head(n_actual).index.tolist()\n",
|
||
" top_features = [col for col in feature_columns if col in top_features or col not in numeric_columns]\n",
|
||
" return top_features\n",
|
||
"\n",
|
||
"def create_deviation_within_dates(df, feature_columns):\n",
|
||
" groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n",
|
||
" new_columns = {}\n",
|
||
" ret_feature_columns = feature_columns[:]\n",
|
||
"\n",
|
||
" # 自动选择所有数值型特征\n",
|
||
" num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n",
|
||
"\n",
|
||
" # num_features = ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'cat_vol_spike', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n",
|
||
" num_features = [col for col in num_features if 'cat' not in col and 'industry' not in col]\n",
|
||
" num_features = [col for col in num_features if 'limit' not in col]\n",
|
||
" num_features = [col for col in num_features if 'cyq' not in col]\n",
|
||
"\n",
|
||
" # 遍历所有数值型特征\n",
|
||
" for feature in num_features:\n",
|
||
" if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # grouped_mean = df.groupby(['trade_date'])[feature].transform('mean')\n",
|
||
" # deviation_col_name = f'deviation_mean_{feature}'\n",
|
||
" # new_columns[deviation_col_name] = df[feature] - grouped_mean\n",
|
||
" # ret_feature_columns.append(deviation_col_name)\n",
|
||
"\n",
|
||
" grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n",
|
||
" deviation_col_name = f'deviation_mean_{feature}'\n",
|
||
" new_columns[deviation_col_name] = df[feature] - grouped_mean\n",
|
||
" ret_feature_columns.append(deviation_col_name)\n",
|
||
"\n",
|
||
" # 将新计算的偏差特征与原始 DataFrame 合并\n",
|
||
" df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n",
|
||
"\n",
|
||
" # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n",
|
||
" # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n",
|
||
"\n",
|
||
" return df, ret_feature_columns\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "47c12bb34062ae7a",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T14:57:50.841165Z",
|
||
"start_time": "2025-04-03T14:49:25.889057Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"days = 5\n",
|
||
"validation_days = 120\n",
|
||
"\n",
|
||
"import gc\n",
|
||
"\n",
|
||
"gc.collect()\n",
|
||
"\n",
|
||
"df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
"# df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n",
|
||
"df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n",
|
||
" df.groupby('ts_code')['open'].shift(-1)\n",
|
||
"\n",
|
||
"df['cat_up_limit'] = df['pct_chg'] > 5\n",
|
||
"# df['label'] = df.groupby('ts_code')['cat_up_limit'].rolling(window=5, min_periods=1).max().shift(-5).fillna(0).astype(int).reset_index(level=0, drop=True)\n",
|
||
"df['label'] = df['future_return']\n",
|
||
"\n",
|
||
"filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n",
|
||
"\n",
|
||
"# for col in [col for col in df.columns]:\n",
|
||
"# train_data[col] = train_data[col].astype('str')\n",
|
||
"# test_data[col] = test_data[col].astype('str')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "29221dde",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"feature_columns = [col for col in df.head(10).merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left').merge(index_data, on='trade_date', how='left').columns]\n",
|
||
"feature_columns = [col for col in feature_columns if col not in ['trade_date',\n",
|
||
" 'ts_code',\n",
|
||
" 'label']]\n",
|
||
"feature_columns = [col for col in feature_columns if 'future' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'label' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'score' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'gen' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'is_st' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'pe_ttm' not in col]\n",
|
||
"# feature_columns = [col for col in feature_columns if 'volatility' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'circ_mv' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'code' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if col not in origin_columns]\n",
|
||
"feature_columns = [col for col in feature_columns if not col.startswith('_')]\n",
|
||
"# feature_columns = [col for col in feature_columns if col not in ['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']]\n",
|
||
"feature_columns = [col for col in feature_columns if col not in ['intraday_lg_flow_corr_20', \n",
|
||
" 'cap_neutral_cost_metric', \n",
|
||
" 'hurst_net_mf_vol_60', \n",
|
||
" 'complex_factor_deap_1', \n",
|
||
" 'lg_buy_consolidation_20',\n",
|
||
" 'cs_rank_ind_cap_neutral_pe',\n",
|
||
" 'cs_rank_opening_gap',\n",
|
||
" 'cs_rank_ind_adj_lg_flow']]\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "03ee5daf",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# df = fill_nan_with_daily_median(df, feature_columns)\n",
|
||
"for feature_col in [col for col in feature_columns if col in df.columns]:\n",
|
||
" # median_val = df[feature_col].median()\n",
|
||
" df[feature_col].fillna(0, inplace=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "b76ea08a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" ts_code trade_date log_circ_mv\n",
|
||
"0 000001.SZ 2019-01-02 16.574219\n",
|
||
"1 000001.SZ 2019-01-03 16.583965\n",
|
||
"2 000001.SZ 2019-01-04 16.633371\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
|
||
"去除极值\n",
|
||
"开始截面 MAD 去极值处理 (k=3.0)...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MAD Filtering: 100%|██████████| 131/131 [00:28<00:00, 4.63it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"截面 MAD 去极值处理完成。\n",
|
||
"开始截面 MAD 去极值处理 (k=3.0)...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MAD Filtering: 100%|██████████| 131/131 [00:23<00:00, 5.60it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"截面 MAD 去极值处理完成。\n",
|
||
"开始截面 MAD 去极值处理 (k=3.0)...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MAD Filtering: 0it [00:00, ?it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"截面 MAD 去极值处理完成。\n",
|
||
"开始截面 MAD 去极值处理 (k=3.0)...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MAD Filtering: 0it [00:00, ?it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"截面 MAD 去极值处理完成。\n",
|
||
"feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
|
||
"df最小日期: 2019-01-02\n",
|
||
"df最大日期: 2025-05-16\n",
|
||
"2057777\n",
|
||
"train_data最小日期: 2020-01-02\n",
|
||
"train_data最大日期: 2022-12-30\n",
|
||
"1751669\n",
|
||
"test_data最小日期: 2023-01-03\n",
|
||
"test_data最大日期: 2025-05-16\n",
|
||
" ts_code trade_date log_circ_mv\n",
|
||
"0 000001.SZ 2019-01-02 16.574219\n",
|
||
"1 000001.SZ 2019-01-03 16.583965\n",
|
||
"2 000001.SZ 2019-01-04 16.633371\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"split_date = '2023-01-01'\n",
|
||
"train_data = df[filter_index & (df['trade_date'] <= split_date) & (df['trade_date'] >= '2020-01-01')]\n",
|
||
"test_data = df[(df['trade_date'] >= split_date)]\n",
|
||
"\n",
|
||
"print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n",
|
||
"\n",
|
||
"industry_df = industry_df.sort_values(by=['trade_date'])\n",
|
||
"index_data = index_data.sort_values(by=['trade_date'])\n",
|
||
"\n",
|
||
"# train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
"# train_data = train_data.merge(index_data, on='trade_date', how='left')\n",
|
||
"# test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
"# test_data = test_data.merge(index_data, on='trade_date', how='left')\n",
|
||
"\n",
|
||
"train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n",
|
||
"\n",
|
||
"# feature_columns_new = feature_columns[:]\n",
|
||
"# train_data, _ = create_deviation_within_dates(train_data, [col for col in feature_columns if col in train_data.columns])\n",
|
||
"# test_data, _ = create_deviation_within_dates(test_data, [col for col in feature_columns if col in train_data.columns])\n",
|
||
"\n",
|
||
"# feature_columns = [\n",
|
||
"# 'undist_profit_ps', \n",
|
||
"# 'AR_BR',\n",
|
||
"# 'pe_ttm',\n",
|
||
"# 'alpha_22_improved', \n",
|
||
"# 'alpha_003', \n",
|
||
"# 'alpha_007', \n",
|
||
"# 'alpha_013', \n",
|
||
"# 'cat_up_limit', \n",
|
||
"# 'cat_down_limit', \n",
|
||
"# 'up_limit_count_10d', \n",
|
||
"# 'down_limit_count_10d', \n",
|
||
"# 'consecutive_up_limit', \n",
|
||
"# 'vol_break', \n",
|
||
"# 'weight_roc5', \n",
|
||
"# 'price_cost_divergence', \n",
|
||
"# 'smallcap_concentration', \n",
|
||
"# 'cost_stability', \n",
|
||
"# 'high_cost_break_days', \n",
|
||
"# 'liquidity_risk', \n",
|
||
"# 'turnover_std', \n",
|
||
"# 'mv_volatility', \n",
|
||
"# 'volume_growth', \n",
|
||
"# 'mv_growth', \n",
|
||
"# 'lg_flow_mom_corr_20_60', \n",
|
||
"# 'lg_flow_accel', \n",
|
||
"# 'profit_pressure', \n",
|
||
"# 'underwater_resistance', \n",
|
||
"# 'cost_conc_std_20', \n",
|
||
"# 'profit_decay_20', \n",
|
||
"# 'vol_amp_loss_20', \n",
|
||
"# 'vol_drop_profit_cnt_5', \n",
|
||
"# 'lg_flow_vol_interact_20', \n",
|
||
"# 'cost_break_confirm_cnt_5', \n",
|
||
"# 'atr_norm_channel_pos_14', \n",
|
||
"# 'turnover_diff_skew_20', \n",
|
||
"# 'lg_sm_flow_diverge_20', \n",
|
||
"# 'pullback_strong_20_20', \n",
|
||
"# 'vol_wgt_hist_pos_20', \n",
|
||
"# 'vol_adj_roc_20',\n",
|
||
"# 'cashflow_to_ev_factor',\n",
|
||
"# 'ocfps',\n",
|
||
"# 'book_to_price_ratio',\n",
|
||
"# 'turnover_rate_mean_5',\n",
|
||
"# 'variance_20',\n",
|
||
"# 'bbi_ratio_factor'\n",
|
||
"# ]\n",
|
||
"# feature_columns = [col for col in feature_columns if col in train_data.columns]\n",
|
||
"# feature_columns = [col for col in feature_columns if not col.startswith('_')]\n",
|
||
"\n",
|
||
"numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
|
||
"numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
|
||
"# feature_columns = select_top_features_by_rankic(df, numeric_columns, n=10)\n",
|
||
"print(feature_columns)\n",
|
||
"\n",
|
||
"# train_data = fill_nan_with_daily_median(train_data, feature_columns)\n",
|
||
"# test_data = fill_nan_with_daily_median(test_data, feature_columns)\n",
|
||
"\n",
|
||
"train_data = train_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n",
|
||
"train_data = train_data.dropna(subset=['label'])\n",
|
||
"train_data = train_data.reset_index(drop=True)\n",
|
||
"# print(test_data.tail())\n",
|
||
"test_data = test_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n",
|
||
"# test_data = test_data.dropna(subset=['label'])\n",
|
||
"test_data = test_data.reset_index(drop=True)\n",
|
||
"\n",
|
||
"transform_feature_columns = feature_columns\n",
|
||
"transform_feature_columns = [col for col in transform_feature_columns if col in feature_columns and not col.startswith('cat') and col in train_data.columns]\n",
|
||
"# transform_feature_columns.remove('undist_profit_ps')\n",
|
||
"print('去除极值')\n",
|
||
"cs_mad_filter(train_data, transform_feature_columns)\n",
|
||
"# print('中性化')\n",
|
||
"# cs_neutralize_industry_cap(train_data, transform_feature_columns)\n",
|
||
"# print('标准化')\n",
|
||
"# cs_zscore_standardize(train_data, transform_feature_columns)\n",
|
||
"\n",
|
||
"cs_mad_filter(test_data, transform_feature_columns)\n",
|
||
"# cs_neutralize_industry_cap(test_data, transform_feature_columns)\n",
|
||
"# cs_zscore_standardize(test_data, transform_feature_columns)\n",
|
||
"\n",
|
||
"mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat') and col in train_data.columns]\n",
|
||
"cs_mad_filter(train_data, mad_filter_feature_columns)\n",
|
||
"cs_mad_filter(test_data, mad_filter_feature_columns)\n",
|
||
"\n",
|
||
"\n",
|
||
"print(f'feature_columns: {feature_columns}')\n",
|
||
"\n",
|
||
"\n",
|
||
"print(f\"df最小日期: {df['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
||
"print(f\"df最大日期: {df['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
||
"print(len(train_data))\n",
|
||
"print(f\"train_data最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
||
"print(f\"train_data最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
||
"print(len(test_data))\n",
|
||
"print(f\"test_data最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
||
"print(f\"test_data最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
||
"\n",
|
||
"cat_columns = [col for col in feature_columns if col.startswith('cat')]\n",
|
||
"for col in cat_columns:\n",
|
||
" train_data[col] = train_data[col].astype('category')\n",
|
||
" test_data[col] = test_data[col].astype('category')\n",
|
||
"\n",
|
||
"print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "3ff2d1c5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"from sklearn.linear_model import LogisticRegression\n",
|
||
"import matplotlib.pyplot as plt # 保持 matplotlib 导入，尽管LightGBM的绘图功能已移除\n",
|
||
"from sklearn.decomposition import PCA\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import datetime # 用于日期计算\n",
|
||
"from catboost import CatBoostClassifier, CatBoostRegressor\n",
|
||
"from catboost import Pool\n",
|
||
"import lightgbm as lgb\n",
|
||
"from hypergbm import make_experiment\n",
|
||
"# from supervised.automl import AutoML\n",
|
||
"from flaml import AutoML, tune\n",
|
||
"from flaml.automl.model import LGBMEstimator, CatBoostEstimator\n",
|
||
"from lightgbm import LGBMClassifier, LGBMRegressor\n",
|
||
"from tabpfn import TabPFNClassifier, TabPFNRegressor\n",
|
||
"\n",
|
||
"class MyLGBM(LGBMEstimator):\n",
|
||
" def __init__(self, **config):\n",
|
||
" super().__init__(max_depth=-1, **config)\n",
|
||
"\n",
|
||
"class MyCat(CatBoostEstimator):\n",
|
||
" def __init__(self, **config):\n",
|
||
" config = {\n",
|
||
" 'depth': 10, # 控制模型复杂度\n",
|
||
" 'l2_leaf_reg': 50, # L2 正则化\n",
|
||
" # 'task_type': 'GPU',\n",
|
||
" }\n",
|
||
" super().__init__(**config)\n",
|
||
"\n",
|
||
"def train_model(train_data_df, feature_columns,\n",
|
||
" print_info=True, # 调整参数名，更通用\n",
|
||
" validation_days=180, use_pca=False, split_date=None,\n",
|
||
" target_column='label', type='light'): # 增加目标列参数\n",
|
||
"\n",
|
||
" print('train data size: ', len(train_data_df))\n",
|
||
" print(train_data_df[['ts_code', 'trade_date', 'log_circ_mv']])\n",
|
||
" # 确保数据按时间排序\n",
|
||
" train_data_df = train_data_df.sort_values(by='trade_date')\n",
|
||
"\n",
|
||
" # 去除标签为空的样本\n",
|
||
" initial_len = len(train_data_df)\n",
|
||
" train_data_df = train_data_df.dropna(subset=[target_column])\n",
|
||
"\n",
|
||
" if print_info:\n",
|
||
" print(f'原始样本数: {initial_len}, 去除标签为空后样本数: {len(train_data_df)}')\n",
|
||
"\n",
|
||
" # 提取特征和标签，只取数值型特征用于线性回归\n",
|
||
" \n",
|
||
" if split_date is None:\n",
|
||
" all_dates = train_data_df['trade_date'].unique() # 获取所有唯一的 trade_date\n",
|
||
" split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天\n",
|
||
" train_data_split = train_data_df[train_data_df['trade_date'] < split_date] # 训练集\n",
|
||
" val_data_split = train_data_df[train_data_df['trade_date'] >= split_date] # 验证集\n",
|
||
" \n",
|
||
" X_train = train_data_split[feature_columns]\n",
|
||
" y_train = train_data_split[target_column]\n",
|
||
" train_data_split = pd.concat([X_train, y_train], axis=1)\n",
|
||
" \n",
|
||
" X_val = val_data_split[feature_columns]\n",
|
||
" y_val = val_data_split['label']\n",
|
||
" val_data_split = pd.concat([X_val, y_val], axis=1)\n",
|
||
"\n",
|
||
"\n",
|
||
" automl = AutoML()\n",
|
||
" automl.add_learner(learner_name=\"my_lgbm\", learner_class=MyLGBM)\n",
|
||
" automl.add_learner(learner_name=\"my_cat\", learner_class=MyCat)\n",
|
||
"\n",
|
||
" automl_settings = {\n",
|
||
" \"time_budget\": 600, # in seconds\n",
|
||
" \"metric\": \"r2\",\n",
|
||
" \"task\": \"regression\",\n",
|
||
" \"estimator_list\": [\n",
|
||
" \"catboost\",\n",
|
||
" \"lgbm\",\n",
|
||
" \"xgboost\"\n",
|
||
" ], \n",
|
||
" \"ensemble\": {\n",
|
||
" \"final_estimator\": LGBMRegressor(),\n",
|
||
" \"passthrough\": True,\n",
|
||
" },\n",
|
||
" }\n",
|
||
"\n",
|
||
" custom_hp = {\n",
|
||
" # \"lgbm\": {\n",
|
||
" # \"subsample\": {\n",
|
||
" # \"num_leaves\": tune.uniform(lower=10, upper=64),\n",
|
||
" # \"init_value\": 10,\n",
|
||
" # },\n",
|
||
" # },\n",
|
||
" }\n",
|
||
" # Train with labeled input data\n",
|
||
" automl.fit(X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, custom_hp=custom_hp, **automl_settings)\n",
|
||
" # model = TabPFNRegressor(model_path=\"../../model/tabpfn-v2-regressor.ckpt\", ignore_pretraining_limits=True)\n",
|
||
"\n",
|
||
" return automl"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T15:03:18.426481Z",
|
||
"start_time": "2025-04-03T15:02:19.926352Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"train data size: 364000\n",
|
||
" ts_code trade_date log_circ_mv\n",
|
||
"0 600306.SH 2020-01-02 11.552040\n",
|
||
"1 603269.SH 2020-01-02 11.324801\n",
|
||
"2 002633.SZ 2020-01-02 11.759023\n",
|
||
"3 603991.SH 2020-01-02 11.181150\n",
|
||
"4 000691.SZ 2020-01-02 11.677910\n",
|
||
"... ... ... ...\n",
|
||
"363995 603182.SH 2022-12-30 11.207510\n",
|
||
"363996 600749.SH 2022-12-30 12.594148\n",
|
||
"363997 605259.SH 2022-12-30 11.763909\n",
|
||
"363998 603600.SH 2022-12-30 12.594561\n",
|
||
"363999 603116.SH 2022-12-30 12.594781\n",
|
||
"\n",
|
||
"[364000 rows x 3 columns]\n",
|
||
"原始样本数: 364000, 去除标签为空后样本数: 364000\n",
|
||
"[flaml.automl.logger: 05-22 16:22:47] {1728} INFO - task = regression\n",
|
||
"[flaml.automl.logger: 05-22 16:22:47] {1736} INFO - Data split method: uniform\n",
|
||
"[flaml.automl.logger: 05-22 16:22:47] {1739} INFO - Evaluation method: holdout\n",
|
||
"[flaml.automl.logger: 05-22 16:22:47] {1838} INFO - Minimizing error metric: 1-r2\n",
|
||
"[flaml.automl.logger: 05-22 16:22:47] {1955} INFO - List of ML learners in AutoML Run: ['catboost', 'lgbm', 'xgboost']\n",
|
||
"[flaml.automl.logger: 05-22 16:22:47] {2258} INFO - iteration 0, current learner catboost\n",
|
||
"[flaml.automl.logger: 05-22 16:22:57] {2393} INFO - Estimated sufficient time budget=2563037s. Estimated necessary time budget=2563s.\n",
|
||
"[flaml.automl.logger: 05-22 16:22:57] {2442} INFO - at 21.3s,\testimator catboost's best error=1.1178,\tbest estimator catboost's best error=1.1178\n",
|
||
"[flaml.automl.logger: 05-22 16:22:57] {2258} INFO - iteration 1, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:22:57] {2442} INFO - at 21.5s,\testimator lgbm's best error=1.0006,\tbest estimator lgbm's best error=1.0006\n",
|
||
"[flaml.automl.logger: 05-22 16:22:57] {2258} INFO - iteration 2, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:22:57] {2442} INFO - at 21.8s,\testimator lgbm's best error=0.9969,\tbest estimator lgbm's best error=0.9969\n",
|
||
"[flaml.automl.logger: 05-22 16:22:57] {2258} INFO - iteration 3, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:22:57] {2442} INFO - at 22.1s,\testimator lgbm's best error=0.9969,\tbest estimator lgbm's best error=0.9969\n",
|
||
"[flaml.automl.logger: 05-22 16:22:57] {2258} INFO - iteration 4, current learner catboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:21] {2442} INFO - at 105.3s,\testimator catboost's best error=1.0733,\tbest estimator lgbm's best error=0.9969\n",
|
||
"[flaml.automl.logger: 05-22 16:24:21] {2258} INFO - iteration 5, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:21] {2442} INFO - at 105.6s,\testimator lgbm's best error=0.9969,\tbest estimator lgbm's best error=0.9969\n",
|
||
"[flaml.automl.logger: 05-22 16:24:21] {2258} INFO - iteration 6, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:21] {2442} INFO - at 105.8s,\testimator lgbm's best error=0.9969,\tbest estimator lgbm's best error=0.9969\n",
|
||
"[flaml.automl.logger: 05-22 16:24:21] {2258} INFO - iteration 7, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:21] {2442} INFO - at 106.0s,\testimator lgbm's best error=0.9917,\tbest estimator lgbm's best error=0.9917\n",
|
||
"[flaml.automl.logger: 05-22 16:24:21] {2258} INFO - iteration 8, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:22] {2442} INFO - at 106.3s,\testimator lgbm's best error=0.9917,\tbest estimator lgbm's best error=0.9917\n",
|
||
"[flaml.automl.logger: 05-22 16:24:22] {2258} INFO - iteration 9, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:22] {2442} INFO - at 106.5s,\testimator lgbm's best error=0.9917,\tbest estimator lgbm's best error=0.9917\n",
|
||
"[flaml.automl.logger: 05-22 16:24:22] {2258} INFO - iteration 10, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:22] {2442} INFO - at 106.8s,\testimator lgbm's best error=0.9917,\tbest estimator lgbm's best error=0.9917\n",
|
||
"[flaml.automl.logger: 05-22 16:24:22] {2258} INFO - iteration 11, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:22] {2442} INFO - at 107.0s,\testimator lgbm's best error=0.9917,\tbest estimator lgbm's best error=0.9917\n",
|
||
"[flaml.automl.logger: 05-22 16:24:22] {2258} INFO - iteration 12, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:23] {2442} INFO - at 107.6s,\testimator lgbm's best error=0.9828,\tbest estimator lgbm's best error=0.9828\n",
|
||
"[flaml.automl.logger: 05-22 16:24:23] {2258} INFO - iteration 13, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:23] {2442} INFO - at 108.0s,\testimator xgboost's best error=1.0014,\tbest estimator lgbm's best error=0.9828\n",
|
||
"[flaml.automl.logger: 05-22 16:24:23] {2258} INFO - iteration 14, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:24] {2442} INFO - at 108.4s,\testimator xgboost's best error=0.9876,\tbest estimator lgbm's best error=0.9828\n",
|
||
"[flaml.automl.logger: 05-22 16:24:24] {2258} INFO - iteration 15, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:24] {2442} INFO - at 108.7s,\testimator xgboost's best error=0.9876,\tbest estimator lgbm's best error=0.9828\n",
|
||
"[flaml.automl.logger: 05-22 16:24:24] {2258} INFO - iteration 16, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:24] {2442} INFO - at 109.1s,\testimator xgboost's best error=0.9876,\tbest estimator lgbm's best error=0.9828\n",
|
||
"[flaml.automl.logger: 05-22 16:24:24] {2258} INFO - iteration 17, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:25] {2442} INFO - at 109.7s,\testimator lgbm's best error=0.9828,\tbest estimator lgbm's best error=0.9828\n",
|
||
"[flaml.automl.logger: 05-22 16:24:25] {2258} INFO - iteration 18, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:25] {2442} INFO - at 110.1s,\testimator xgboost's best error=0.9876,\tbest estimator lgbm's best error=0.9828\n",
|
||
"[flaml.automl.logger: 05-22 16:24:25] {2258} INFO - iteration 19, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:26] {2442} INFO - at 110.5s,\testimator xgboost's best error=0.9876,\tbest estimator lgbm's best error=0.9828\n",
|
||
"[flaml.automl.logger: 05-22 16:24:26] {2258} INFO - iteration 20, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:26] {2442} INFO - at 111.2s,\testimator xgboost's best error=0.9802,\tbest estimator xgboost's best error=0.9802\n",
|
||
"[flaml.automl.logger: 05-22 16:24:26] {2258} INFO - iteration 21, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:27] {2442} INFO - at 111.8s,\testimator lgbm's best error=0.9828,\tbest estimator xgboost's best error=0.9802\n",
|
||
"[flaml.automl.logger: 05-22 16:24:27] {2258} INFO - iteration 22, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:28] {2442} INFO - at 112.4s,\testimator lgbm's best error=0.9828,\tbest estimator xgboost's best error=0.9802\n",
|
||
"[flaml.automl.logger: 05-22 16:24:28] {2258} INFO - iteration 23, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:28] {2442} INFO - at 113.1s,\testimator xgboost's best error=0.9802,\tbest estimator xgboost's best error=0.9802\n",
|
||
"[flaml.automl.logger: 05-22 16:24:28] {2258} INFO - iteration 24, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:29] {2442} INFO - at 113.9s,\testimator xgboost's best error=0.9802,\tbest estimator xgboost's best error=0.9802\n",
|
||
"[flaml.automl.logger: 05-22 16:24:29] {2258} INFO - iteration 25, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:30] {2442} INFO - at 114.4s,\testimator lgbm's best error=0.9828,\tbest estimator xgboost's best error=0.9802\n",
|
||
"[flaml.automl.logger: 05-22 16:24:30] {2258} INFO - iteration 26, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:30] {2442} INFO - at 115.1s,\testimator xgboost's best error=0.9750,\tbest estimator xgboost's best error=0.9750\n",
|
||
"[flaml.automl.logger: 05-22 16:24:30] {2258} INFO - iteration 27, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:31] {2442} INFO - at 115.8s,\testimator xgboost's best error=0.9750,\tbest estimator xgboost's best error=0.9750\n",
|
||
"[flaml.automl.logger: 05-22 16:24:31] {2258} INFO - iteration 28, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:32] {2442} INFO - at 116.6s,\testimator xgboost's best error=0.9750,\tbest estimator xgboost's best error=0.9750\n",
|
||
"[flaml.automl.logger: 05-22 16:24:32] {2258} INFO - iteration 29, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:33] {2442} INFO - at 117.3s,\testimator lgbm's best error=0.9806,\tbest estimator xgboost's best error=0.9750\n",
|
||
"[flaml.automl.logger: 05-22 16:24:33] {2258} INFO - iteration 30, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:33] {2442} INFO - at 117.8s,\testimator lgbm's best error=0.9806,\tbest estimator xgboost's best error=0.9750\n",
|
||
"[flaml.automl.logger: 05-22 16:24:33] {2258} INFO - iteration 31, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:34] {2442} INFO - at 118.9s,\testimator lgbm's best error=0.9806,\tbest estimator xgboost's best error=0.9750\n",
|
||
"[flaml.automl.logger: 05-22 16:24:34] {2258} INFO - iteration 32, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:35] {2442} INFO - at 119.9s,\testimator xgboost's best error=0.9750,\tbest estimator xgboost's best error=0.9750\n",
|
||
"[flaml.automl.logger: 05-22 16:24:35] {2258} INFO - iteration 33, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:36] {2442} INFO - at 120.5s,\testimator lgbm's best error=0.9806,\tbest estimator xgboost's best error=0.9750\n",
|
||
"[flaml.automl.logger: 05-22 16:24:36] {2258} INFO - iteration 34, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:36] {2442} INFO - at 121.1s,\testimator lgbm's best error=0.9806,\tbest estimator xgboost's best error=0.9750\n",
|
||
"[flaml.automl.logger: 05-22 16:24:36] {2258} INFO - iteration 35, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:37] {2442} INFO - at 122.0s,\testimator xgboost's best error=0.9750,\tbest estimator xgboost's best error=0.9750\n",
|
||
"[flaml.automl.logger: 05-22 16:24:37] {2258} INFO - iteration 36, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:40] {2442} INFO - at 124.6s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:24:40] {2258} INFO - iteration 37, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:42] {2442} INFO - at 127.2s,\testimator lgbm's best error=0.9766,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:24:42] {2258} INFO - iteration 38, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:24:46] {2442} INFO - at 130.6s,\testimator lgbm's best error=0.9766,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:24:46] {2258} INFO - iteration 39, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:48] {2442} INFO - at 133.1s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:24:48] {2258} INFO - iteration 40, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:52] {2442} INFO - at 136.4s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:24:52] {2258} INFO - iteration 41, current learner catboost\n",
|
||
"[flaml.automl.logger: 05-22 16:24:57] {2442} INFO - at 142.1s,\testimator catboost's best error=1.0733,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:24:57] {2258} INFO - iteration 42, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:25:00] {2442} INFO - at 144.7s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:25:00] {2258} INFO - iteration 43, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:25:05] {2442} INFO - at 149.7s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:25:05] {2258} INFO - iteration 44, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:25:07] {2442} INFO - at 152.0s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:25:07] {2258} INFO - iteration 45, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:25:10] {2442} INFO - at 154.7s,\testimator lgbm's best error=0.9766,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:25:10] {2258} INFO - iteration 46, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:25:16] {2442} INFO - at 161.0s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:25:16] {2258} INFO - iteration 47, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:25:19] {2442} INFO - at 163.5s,\testimator lgbm's best error=0.9766,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:25:19] {2258} INFO - iteration 48, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:25:22] {2442} INFO - at 166.5s,\testimator lgbm's best error=0.9766,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:25:22] {2258} INFO - iteration 49, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:25:24] {2442} INFO - at 168.9s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:25:24] {2258} INFO - iteration 50, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:25:28] {2442} INFO - at 172.4s,\testimator lgbm's best error=0.9766,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:25:28] {2258} INFO - iteration 51, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:25:30] {2442} INFO - at 175.0s,\testimator lgbm's best error=0.9766,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:25:30] {2258} INFO - iteration 52, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:25:33] {2442} INFO - at 178.1s,\testimator lgbm's best error=0.9766,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:25:33] {2258} INFO - iteration 53, current learner catboost\n",
|
||
"[flaml.automl.logger: 05-22 16:26:56] {2442} INFO - at 261.2s,\testimator catboost's best error=1.0733,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:26:56] {2258} INFO - iteration 54, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:26:59] {2442} INFO - at 263.9s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:26:59] {2258} INFO - iteration 55, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:27:02] {2442} INFO - at 266.7s,\testimator lgbm's best error=0.9765,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:27:02] {2258} INFO - iteration 56, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:27:05] {2442} INFO - at 269.5s,\testimator lgbm's best error=0.9765,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:27:05] {2258} INFO - iteration 57, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:27:08] {2442} INFO - at 273.1s,\testimator lgbm's best error=0.9765,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:27:08] {2258} INFO - iteration 58, current learner catboost\n",
|
||
"[flaml.automl.logger: 05-22 16:27:14] {2442} INFO - at 278.7s,\testimator catboost's best error=1.0733,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:27:14] {2258} INFO - iteration 59, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:27:17] {2442} INFO - at 281.8s,\testimator lgbm's best error=0.9765,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:27:17] {2258} INFO - iteration 60, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:27:20] {2442} INFO - at 285.1s,\testimator lgbm's best error=0.9765,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:27:20] {2258} INFO - iteration 61, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:27:24] {2442} INFO - at 288.5s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:27:24] {2258} INFO - iteration 62, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:27:27] {2442} INFO - at 291.5s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:27:27] {2258} INFO - iteration 63, current learner catboost\n",
|
||
"[flaml.automl.logger: 05-22 16:29:45] {2442} INFO - at 429.8s,\testimator catboost's best error=1.0733,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:29:45] {2258} INFO - iteration 64, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:29:48] {2442} INFO - at 432.5s,\testimator lgbm's best error=0.9765,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:29:48] {2258} INFO - iteration 65, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:29:50] {2442} INFO - at 434.7s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:29:50] {2258} INFO - iteration 66, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:29:53] {2442} INFO - at 437.6s,\testimator lgbm's best error=0.9765,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:29:53] {2258} INFO - iteration 67, current learner catboost\n",
|
||
"[flaml.automl.logger: 05-22 16:29:59] {2442} INFO - at 443.3s,\testimator catboost's best error=1.0733,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:29:59] {2258} INFO - iteration 68, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:30:04] {2442} INFO - at 449.0s,\testimator lgbm's best error=0.9765,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:30:04] {2258} INFO - iteration 69, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:30:10] {2442} INFO - at 454.8s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:30:10] {2258} INFO - iteration 70, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:30:13] {2442} INFO - at 457.7s,\testimator lgbm's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:30:13] {2258} INFO - iteration 71, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:30:15] {2442} INFO - at 460.0s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:30:15] {2258} INFO - iteration 72, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:30:18] {2442} INFO - at 463.1s,\testimator lgbm's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:30:18] {2258} INFO - iteration 73, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:30:21] {2442} INFO - at 466.0s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:30:21] {2258} INFO - iteration 74, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:30:24] {2442} INFO - at 468.6s,\testimator lgbm's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:30:24] {2258} INFO - iteration 75, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:30:28] {2442} INFO - at 473.0s,\testimator lgbm's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:30:28] {2258} INFO - iteration 76, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:30:31] {2442} INFO - at 475.7s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:30:31] {2258} INFO - iteration 77, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:30:34] {2442} INFO - at 478.3s,\testimator lgbm's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:30:34] {2258} INFO - iteration 78, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:30:36] {2442} INFO - at 480.8s,\testimator lgbm's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:30:36] {2258} INFO - iteration 79, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:30:41] {2442} INFO - at 485.5s,\testimator xgboost's best error=0.9734,\tbest estimator xgboost's best error=0.9734\n",
|
||
"[flaml.automl.logger: 05-22 16:30:41] {2258} INFO - iteration 80, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:30:45] {2442} INFO - at 489.4s,\testimator lgbm's best error=0.9731,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:30:45] {2258} INFO - iteration 81, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:30:51] {2442} INFO - at 495.4s,\testimator lgbm's best error=0.9731,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:30:51] {2258} INFO - iteration 82, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:30:54] {2442} INFO - at 498.4s,\testimator lgbm's best error=0.9731,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:30:54] {2258} INFO - iteration 83, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:30:58] {2442} INFO - at 502.7s,\testimator xgboost's best error=0.9734,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:30:58] {2258} INFO - iteration 84, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:31:01] {2442} INFO - at 505.3s,\testimator lgbm's best error=0.9731,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:31:01] {2258} INFO - iteration 85, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:31:12] {2442} INFO - at 517.1s,\testimator lgbm's best error=0.9731,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:31:12] {2258} INFO - iteration 86, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:31:15] {2442} INFO - at 519.9s,\testimator lgbm's best error=0.9731,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:31:15] {2258} INFO - iteration 87, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:31:27] {2442} INFO - at 531.4s,\testimator lgbm's best error=0.9731,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:31:27] {2258} INFO - iteration 88, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:31:29] {2442} INFO - at 533.8s,\testimator xgboost's best error=0.9734,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:31:29] {2258} INFO - iteration 89, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:31:32] {2442} INFO - at 536.6s,\testimator lgbm's best error=0.9731,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:31:32] {2258} INFO - iteration 90, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:31:35] {2442} INFO - at 539.6s,\testimator xgboost's best error=0.9734,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:31:35] {2258} INFO - iteration 91, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:31:37] {2442} INFO - at 542.2s,\testimator xgboost's best error=0.9734,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:31:37] {2258} INFO - iteration 92, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:31:43] {2442} INFO - at 547.9s,\testimator xgboost's best error=0.9734,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:31:43] {2258} INFO - iteration 93, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:32:01] {2442} INFO - at 565.6s,\testimator lgbm's best error=0.9731,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:32:01] {2258} INFO - iteration 94, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:32:03] {2442} INFO - at 568.0s,\testimator xgboost's best error=0.9734,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:32:03] {2258} INFO - iteration 95, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:32:12] {2442} INFO - at 577.0s,\testimator lgbm's best error=0.9731,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:32:12] {2258} INFO - iteration 96, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:32:15] {2442} INFO - at 580.2s,\testimator xgboost's best error=0.9734,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:32:15] {2258} INFO - iteration 97, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:32:18] {2442} INFO - at 583.0s,\testimator lgbm's best error=0.9731,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:32:18] {2258} INFO - iteration 98, current learner lgbm\n",
|
||
"[flaml.automl.logger: 05-22 16:32:28] {2442} INFO - at 592.6s,\testimator lgbm's best error=0.9731,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:32:28] {2258} INFO - iteration 99, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:32:30] {2442} INFO - at 595.1s,\testimator xgboost's best error=0.9734,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:32:30] {2258} INFO - iteration 100, current learner xgboost\n",
|
||
"[flaml.automl.logger: 05-22 16:32:33] {2442} INFO - at 598.0s,\testimator xgboost's best error=0.9734,\tbest estimator lgbm's best error=0.9731\n",
|
||
"[flaml.automl.logger: 05-22 16:32:33] {2550} INFO - selected model: LGBMRegressor(colsample_bytree=0.912038573841834,\n",
|
||
" learning_rate=0.02078849055957606, max_bin=1023,\n",
|
||
" min_child_samples=38, n_estimators=18, n_jobs=-1, num_leaves=11,\n",
|
||
" reg_alpha=0.061774595197632225, reg_lambda=0.003793636570300973,\n",
|
||
" verbose=-1)\n",
|
||
"[flaml.automl.logger: 05-22 16:32:33] {2582} INFO - [('lgbm', {'n_jobs': -1, 'n_estimators': 18, 'num_leaves': 11, 'min_child_samples': 38, 'learning_rate': 0.02078849055957606, 'colsample_bytree': 0.912038573841834, 'reg_alpha': 0.061774595197632225, 'reg_lambda': 0.003793636570300973, 'max_bin': 1023, 'verbose': -1}), ('xgboost', {'n_jobs': -1, 'n_estimators': 4, 'max_leaves': 12, 'min_child_weight': 5.4998639205908075, 'learning_rate': 0.04700136686803946, 'subsample': 1.0, 'colsample_bylevel': 0.8425853155117716, 'colsample_bytree': 0.9245352674213118, 'reg_alpha': 0.025118956715098555, 'reg_lambda': 27.89255832621344, 'max_depth': 0, 'grow_policy': 'lossguide', 'tree_method': 'hist', 'verbosity': 0}), ('catboost', {'early_stopping_rounds': 10, 'learning_rate': 0.005, 'n_estimators': 8192, 'thread_count': -1, 'verbose': False, 'random_seed': 10242048})]\n",
|
||
"[flaml.automl.logger: 05-22 16:32:33] {2625} INFO - Building ensemble with tuned estimators\n",
|
||
"[flaml.automl.logger: 05-22 16:33:39] {2631} INFO - ensemble: StackingRegressor(estimators=[('lgbm',\n",
|
||
" <flaml.automl.model.LGBMEstimator object at 0x0000028A9571BF50>),\n",
|
||
" ('xgboost',\n",
|
||
" <flaml.automl.model.XGBoostSklearnEstimator object at 0x0000028A95719BD0>),\n",
|
||
" ('catboost',\n",
|
||
" <flaml.automl.model.CatBoostEstimator object at 0x0000028A95719D50>)],\n",
|
||
" final_estimator=LGBMRegressor(), n_jobs=1, passthrough=True)\n",
|
||
"[flaml.automl.logger: 05-22 16:33:39] {1985} INFO - fit succeeded\n",
|
||
"[flaml.automl.logger: 05-22 16:33:39] {1986} INFO - Time taken to find the best model: 489.3946213722229\n",
|
||
"[flaml.automl.logger: 05-22 16:33:39] {1996} WARNING - Time taken to find the best model is 82% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"gc.collect()\n",
|
||
"\n",
|
||
"use_pca = False\n",
|
||
"type = 'cat'\n",
|
||
"# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n",
|
||
"# light_params['feature_contri'] = feature_contri\n",
|
||
"# print(f'feature_contri: {feature_contri}')\n",
|
||
"model = train_model(train_data\n",
|
||
" .dropna(subset=['label']).groupby('trade_date', group_keys=False)\n",
|
||
" .apply(lambda x: x.nsmallest(500, 'total_mv'))\n",
|
||
" .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
" .merge(index_data, on='trade_date', how='left'), feature_columns, type=type)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "59132b85",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# for e in model.model.estimators:\n",
|
||
"# print(e[1].estimator)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"id": "5d1522a7538db91b",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T15:04:39.656944Z",
|
||
"start_time": "2025-04-03T15:04:39.298483Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|██████████| 6/6 [00:01<00:00, 3.22it/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(500, 'total_mv'))\n",
|
||
"# score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n",
|
||
"# score_df = score_df[score_df['pe_ttm'] > 0]\n",
|
||
"score_df = score_df.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
"score_df = score_df.merge(index_data, on='trade_date', how='left')\n",
|
||
"# score_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')).reset_index()\n",
|
||
"numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n",
|
||
"numeric_columns = [col for col in feature_columns if col in numeric_columns]\n",
|
||
"# score_df.loc[:, numeric_columns] = scaler.transform(score_df[numeric_columns])\n",
|
||
"# score_df = cross_sectional_standardization(score_df, numeric_columns)\n",
|
||
"\n",
|
||
"# score_df['score'] = model.predict_proba(score_df[feature_columns])[:, -1]\n",
|
||
"# score_df['score'] = model.predict(score_df[feature_columns])\n",
|
||
"chunk_size = 5000 # 设置您的切块大小\n",
|
||
"predictions_list = []\n",
|
||
"num_rows = len(score_df)\n",
|
||
"\n",
|
||
"from tqdm import tqdm\n",
|
||
"for i in tqdm(range(0, num_rows, chunk_size)):\n",
|
||
" chunk_df = score_df.iloc[i : i + chunk_size].copy()\n",
|
||
" chunk_features = chunk_df[feature_columns]\n",
|
||
" chunk_predictions_np = model.predict(chunk_features)\n",
|
||
" chunk_predictions_series = pd.Series(chunk_predictions_np, index=chunk_df.index)\n",
|
||
" predictions_list.append(chunk_predictions_series)\n",
|
||
" del chunk_df, chunk_features, chunk_predictions_np, chunk_predictions_series\n",
|
||
" gc.collect()\n",
|
||
"\n",
|
||
"combined_predictions = pd.concat(predictions_list)\n",
|
||
"score_df['score'] = combined_predictions\n",
|
||
"\n",
|
||
"score_df['score_ranks'] = score_df.groupby('trade_date')['score'].rank(ascending=True)\n",
|
||
"\n",
|
||
"score_df = score_df.groupby('trade_date', group_keys=False).apply(\n",
|
||
" lambda x: x[x['score'] >= x['score'].quantile(0.90)] # 计算90%分位数作为阈值，筛选分数>=阈值的行\n",
|
||
").reset_index(drop=True) # drop=True 避免添加旧索引列\n",
|
||
"save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(2, 'score')).reset_index()\n",
|
||
"# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(2, 'total_mv')).reset_index(drop=True)\n",
|
||
"save_df = save_df.sort_values(['trade_date', 'score'])\n",
|
||
"save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "09b1799e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"191\n",
|
||
"['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(len(feature_columns))\n",
|
||
"print(feature_columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "7e9023cc",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def analyze_factors(\n",
|
||
" df: pd.DataFrame,\n",
|
||
" feature_columns: list[str],\n",
|
||
" target_column: str = 'target', # 假设目标列默认为 'target'\n",
|
||
" trade_date_col: str = 'trade_date', # 假设日期列默认为 'trade_date'\n",
|
||
" mcap_col: str = 'total_mv', # 新增: 市值列名称\n",
|
||
" mcap_bins: int = 5 # 新增: 市值分位数的数量 (例如 5 表示五分位数)\n",
|
||
") -> pd.DataFrame:\n",
|
||
" \"\"\"\n",
|
||
" 分析DataFrame中指定特征列的各种指标，包括基本统计、相关性、日间IC、ICIR以及在不同市值分位数上的IC。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df (pd.DataFrame): 包含日期、目标列、特征列和市值列的DataFrame。\n",
|
||
" 需要包含 trade_date_col, target_column, feature_columns 和 mcap_col 中的所有列。\n",
|
||
" feature_columns (list[str]): 需要分析的特征列名称列表。\n",
|
||
" target_column (str): 目标变量列的名称。\n",
|
||
" trade_date_col (str): 交易日期列的名称。\n",
|
||
" mcap_col (str): 市值列的名称。\n",
|
||
" mcap_bins (int): 市值分位数的数量 (例如 5 表示五分位数)。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" pd.DataFrame: 包含各个因子分析指标的汇总DataFrame。\n",
|
||
" 同时打印因子在不同市值分位数上的平均IC表格。\n",
|
||
" 如果输入数据或列有问题，可能返回空或包含NaN的DataFrame。\n",
|
||
" \"\"\"\n",
|
||
"\n",
|
||
" # --- 数据校验 ---\n",
|
||
" required_cols = [trade_date_col, target_column, mcap_col] + feature_columns\n",
|
||
" if not all(col in df.columns for col in required_cols):\n",
|
||
" missing = [col for col in required_cols if col not in df.columns]\n",
|
||
" print(f\"错误: 输入DataFrame缺少必需的列: {missing}\")\n",
|
||
" return pd.DataFrame() # 返回空DataFrame\n",
|
||
"\n",
|
||
" # 确保日期列是 datetime 类型\n",
|
||
" df = df.copy() # 在副本上操作\n",
|
||
" df[trade_date_col] = pd.to_datetime(df[trade_date_col], errors='coerce')\n",
|
||
" df.dropna(subset=[trade_date_col], inplace=True) # 移除日期转换失败的行\n",
|
||
"\n",
|
||
" # 过滤掉那些在 feature_columns, target_column, mcap_col 上有 NaN 的行，以确保后续计算是在完整数据上\n",
|
||
" # 直接在 df 副本上进行清洗\n",
|
||
" initial_rows_before_clean = len(df)\n",
|
||
" df.dropna(subset=feature_columns + [target_column, mcap_col], inplace=True)\n",
|
||
" rows_dropped_clean = initial_rows_before_clean - len(df)\n",
|
||
" if rows_dropped_clean > 0:\n",
|
||
" print(f\"警告: 移除了 {rows_dropped_clean} 行，因为其特征、目标或市值列存在空值。\")\n",
|
||
"\n",
|
||
" if df.empty:\n",
|
||
" print(\"错误: 清理缺失值后数据为空，无法进行因子分析。\")\n",
|
||
" return pd.DataFrame() # 返回空DataFrame\n",
|
||
"\n",
|
||
"\n",
|
||
" print(f\"开始分析 {len(feature_columns)} 个因子指标...\")\n",
|
||
"\n",
|
||
" # --- 1. 基本因子统计量 ---\n",
|
||
" basic_stats = df[feature_columns].describe().T\n",
|
||
"\n",
|
||
" print(\"\\n--- 基本因子统计量 ---\")\n",
|
||
" print(basic_stats)\n",
|
||
"\n",
|
||
" # --- 2. 因子与目标变量的整体相关性 ---\n",
|
||
" overall_correlation = {}\n",
|
||
" for feature in feature_columns:\n",
|
||
" # 在清理后的 df 上计算相关性\n",
|
||
" if df[[feature, target_column]].dropna().shape[0] > 1: # 确保至少有两个有效数据点\n",
|
||
" overall_correlation[feature] = {\n",
|
||
" 'Pearson_Correlation_with_Target': df[feature].corr(df[target_column], method='pearson'),\n",
|
||
" 'Spearman_Correlation_with_Target': df[feature].corr(df[target_column], method='spearman')\n",
|
||
" }\n",
|
||
" else:\n",
|
||
" overall_correlation[feature] = {\n",
|
||
" 'Pearson_Correlation_with_Target': np.nan,\n",
|
||
" 'Spearman_Correlation_with_Target': np.nan\n",
|
||
" }\n",
|
||
" overall_corr_df = pd.DataFrame.from_dict(overall_correlation, orient='index')\n",
|
||
"\n",
|
||
" print(\"\\n--- 因子与目标变量的整体相关性 ---\")\n",
|
||
" print(overall_corr_df)\n",
|
||
"\n",
|
||
" # --- 3. 因子之间的相关性矩阵 ---\n",
|
||
" # 在清理后的 df 上计算相关性\n",
|
||
" factor_correlation_matrix = df[feature_columns].corr(method='spearman') # 改回 Spearman\n",
|
||
"\n",
|
||
" print(\"\\n--- 因子之间的相关性矩阵 (Spearman) ---\") # 修正打印信息\n",
|
||
" print(factor_correlation_matrix)\n",
|
||
"\n",
|
||
" # --- 4. 日间 IC 和 ICIR ---\n",
|
||
" print(\"\\n--- 计算日间 IC (Spearman 相关性) 和 ICIR ---\")\n",
|
||
"\n",
|
||
" # 直接在清理后的 df 上计算每日 IC\n",
|
||
" if df.empty: # 理论上上面已经检查过，这里再检查一次更安全\n",
|
||
" daily_ic_series = pd.Series(dtype=float) # 空 Series\n",
|
||
" ic_stats = pd.DataFrame({\n",
|
||
" 'Mean_IC (Spearman)': np.nan, 'Std_Dev_IC': np.nan, 'ICIR': np.nan\n",
|
||
" }, index=feature_columns)\n",
|
||
" else:\n",
|
||
" daily_ic_series = df.groupby(trade_date_col).apply(\n",
|
||
" lambda day_group: {\n",
|
||
" feature: day_group[feature].corr(day_group[target_column], method='spearman')\n",
|
||
" for feature in feature_columns if day_group.shape[0] > 1 # 确保每日数据点多于1才能计算相关性\n",
|
||
" }\n",
|
||
" ).apply(pd.Series) # 将字典结果转换为 DataFrame\n",
|
||
"\n",
|
||
" # 计算 IC 的统计量\n",
|
||
" if not daily_ic_series.empty:\n",
|
||
" ic_mean = daily_ic_series.mean()\n",
|
||
" ic_std = daily_ic_series.std()\n",
|
||
" # 避免除以零\n",
|
||
" ic_ir = ic_mean / ic_std.replace(0, np.nan) # 使用 replace 0 为 NaN\n",
|
||
"\n",
|
||
" ic_stats = pd.DataFrame({\n",
|
||
" 'Mean_IC (Spearman)': ic_mean,\n",
|
||
" 'Std_Dev_IC': ic_std,\n",
|
||
" 'ICIR': ic_ir\n",
|
||
" })\n",
|
||
" print(\"\\n--- 日间 IC 和 ICIR (Spearman) ---\")\n",
|
||
" print(ic_stats)\n",
|
||
" else:\n",
|
||
" ic_stats = pd.DataFrame({\n",
|
||
" 'Mean_IC (Spearman)': np.nan, 'Std_Dev_IC': np.nan, 'ICIR': np.nan\n",
|
||
" }, index=feature_columns)\n",
|
||
"\n",
|
||
"\n",
|
||
" # --- 5. 因子在不同市值分位数上的平均 IC ---\n",
|
||
" print(f\"\\n--- 计算因子在 {mcap_bins} 个市值分位数上的平均 IC (Spearman) ---\")\n",
|
||
"\n",
|
||
" # 在清理后的 df 上计算每日市值分位数，直接添加到 df 中\n",
|
||
" # 使用 transform() 和 qcut() 在每个日期分组内计算分位数\n",
|
||
" # labels=False 返回整数 0 to mcap_bins-1\n",
|
||
" # duplicates='drop' 处理在某些日期股票数量少于 bins 导致分位数边缘重复的情况，会返回 NaN\n",
|
||
" # 添加一个临时列来存储分位数\n",
|
||
" mcap_bin_col_name = f'_mcap_bin_{mcap_bins}'\n",
|
||
" df[mcap_bin_col_name] = df.groupby(trade_date_col)[mcap_col].transform(\n",
|
||
" lambda x: pd.qcut(x, q=mcap_bins, labels=False, duplicates='drop') if len(x) >= mcap_bins else np.nan # 确保股票数量足够进行分位数划分\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 过滤掉无法划分分位数 (NaN) 的行，进行分位数 IC 计算\n",
|
||
" # 创建一个临时 DataFrame df_binned_analysis\n",
|
||
" df_binned_analysis = df.dropna(subset=[mcap_bin_col_name]).copy()\n",
|
||
"\n",
|
||
" if df_binned_analysis.empty:\n",
|
||
" print(\"错误: 划分市值分位数后数据为空，无法计算分位数上的 IC。\")\n",
|
||
" avg_ic_by_bin = pd.DataFrame(index=range(mcap_bins), columns=feature_columns) # Placeholder\n",
|
||
" else:\n",
|
||
" # 按日期和市值分位数分组，计算每个分组内的因子与目标变量的截面相关性 (分位数IC)\n",
|
||
" binned_ic_by_day = df_binned_analysis.groupby([trade_date_col, mcap_bin_col_name]).apply(\n",
|
||
" lambda group: {\n",
|
||
" feature: group[feature].corr(group[target_column], method='spearman')\n",
|
||
" for feature in feature_columns if group.shape[0] > 1 # 确保分位数组内数据点多于1\n",
|
||
" }\n",
|
||
" ).apply(pd.Series) # 将嵌套结果转为 DataFrame\n",
|
||
"\n",
|
||
" # 对每个分位数组的每日 IC 求平均\n",
|
||
" # unstack(level=mcap_bin_col_name) 将 mcap_bin 作为列\n",
|
||
" # mean(axis=0) 对日期索引求平均\n",
|
||
" avg_ic_by_bin = binned_ic_by_day.unstack(level=mcap_bin_col_name).mean(axis=0).unstack()\n",
|
||
"\n",
|
||
" # 重命名索引和列，使表格更清晰\n",
|
||
" if not avg_ic_by_bin.empty:\n",
|
||
" # Index name will be the original column name used for grouping ('_mcap_bin_X')\n",
|
||
" # Rename the index name explicitly\n",
|
||
" avg_ic_by_bin.index.name = 'MarketCap_Bin'\n",
|
||
" avg_ic_by_bin.columns.name = 'Feature'\n",
|
||
" # 可以根据需要对分位数 bin 索引进行排序 (虽然 pd.qcut labels=False usually sorts)\n",
|
||
" avg_ic_by_bin = avg_ic_by_bin.sort_index()\n",
|
||
"\n",
|
||
" print(avg_ic_by_bin)\n",
|
||
"\n",
|
||
"\n",
|
||
" # --- 6. 汇总所有指标 ---\n",
|
||
" # 将基本统计、整体相关性、IC/ICIR 合并到一个 DataFrame\n",
|
||
" # 注意：合并时需要根据索引进行对齐 (因子名称)\n",
|
||
" summary_df = basic_stats\n",
|
||
" summary_df = summary_df.merge(overall_corr_df, left_index=True, right_index=True, how='left')\n",
|
||
" summary_df = summary_df.merge(ic_stats, left_index=True, right_index=True, how='left')\n",
|
||
"\n",
|
||
" # print(\"\\n--- 因子分析汇总报告 ---\")\n",
|
||
" # print(summary_df)\n",
|
||
"\n",
|
||
" # --- 清理临时列 'mcap_bin' ---\n",
|
||
" # 修正：在函数结束时从我们一直在操作的 df 副本中删除临时列\n",
|
||
" if mcap_bin_col_name in df.columns:\n",
|
||
" df.drop(columns=[mcap_bin_col_name], inplace=True)\n",
|
||
"\n",
|
||
"\n",
|
||
" return summary_df # 主要返回汇总报告，分位数IC单独打印\n",
|
||
"\n",
|
||
"# # 运行分析函数\n",
|
||
"# factor_analysis_report = analyze_factors(test_data.copy(), feature_columns, 'future_return')\n",
|
||
"\n",
|
||
"# print(\"\\n--- 最终汇总报告 DataFrame ---\")\n",
|
||
"# print(factor_analysis_report)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "a0000d75",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"开始分析 'score' 在 'circ_mv' 和 'future_return' 下的表现...\n",
|
||
"准备数据，处理 NaN 值...\n",
|
||
"原始数据 28550 行，移除 NaN 后剩余 28175 行用于分析。\n",
|
||
"对 'circ_mv' 和 'future_return' 进行 100 分位数分箱...\n",
|
||
"按二维分箱分组计算 Spearman Rank IC...\n",
|
||
"整理结果用于绘图...\n",
|
||
"circ_mv_bin 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 \\\n",
|
||
"future_return_bin ... \n",
|
||
"0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"... .. .. .. .. .. .. .. .. .. .. ... .. .. .. \n",
|
||
"94 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"95 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"96 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"97 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"98 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"\n",
|
||
"circ_mv_bin 93 94 95 96 97 98 99 \n",
|
||
"future_return_bin \n",
|
||
"0 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"1 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"2 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"3 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"4 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"... .. .. .. .. .. .. .. \n",
|
||
"94 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"95 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"96 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"97 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"98 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"\n",
|
||
"[99 rows x 100 columns]\n",
|
||
"生成热力图...\n",
|
||
"分析完成。\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAABdEAAASgCAYAAAAXXAHaAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Xd0VFX79vErvdCRokgvQiQiSBUFpRcFFAVUDIKidCk2uoIFUUAERBFEFORHk6YiIEVUQLqQgVAEVJoUQwmTkITJef/gzTyEZEIyTObMZL6ftbKeH2euPfs+ueO8693Z2cfPMAxDAAAAAAAAAAAgHX+zCwAAAAAAAAAAwFOxiA4AAAAAAAAAgAMsogMAAAAAAAAA4ACL6AAAAAAAAAAAOMAiOgAAAAAAAAAADrCIDgAAAAAAAACAAyyiAwAAAAAAAADgAIvoAAAAAAAAAAA4wCI6AADwKu3bt1eLFi2UkpJidikOXbx4UStWrJBhGPZre/bsUffu3fXzzz9n+/3Wrl2rwYMHa8eOHVkeYxiG9u3bl+25AAAAAABpBZpdAAAAQHb8999/Sk5Olr9/1vcCJCYmKjk5WeHh4WnGXb16VVeuXFHevHk1cOBAWSyWm77XokWLVKBAgUwzCxYs0Lhx47Rv3z69+uqrkqQCBQpo06ZNunz5sh5++OEs1y5JR48e1ZIlS9S6desMX09OTtbZs2f1999/68iRI9q9e7e2bNmif//9Vx9//LFatmyZrfmy4t9//9X8+fP18ssvy8/Pz+XvD5hh5syZeuCBB1S5cmWzSwEAAIAHYREdAAB4lGPHjun7779XgQIFFBQUpICAgDSvJyUlKSUlRYsXL043NiUlRYmJiSpYsKAeeeQR+/Xly5dr+PDhGc5XpEgRbdy4UZcuXdL58+fVuXPnDHObNm3Snj17FBQUlGn98fHx+uqrrxQaGqqoqCj79TJlyujxxx/XokWLtGTJEj3++OOZvs/1wsLC0vxvqtjYWLVq1UoXL16073r39/dX8eLFVaZMGTVo0EDHjh2TzWZL9328FXFxcerWrZsCAgL0wgsvKG/evC57b8AsV69e1fr16zVz5kwtXLhQd9xxh9klAQAAwEOwiA4AADzKyZMnNXHixJvmhgwZ4vC1e++9N80ieuXKldWnTx8FBQXpp59+0p9//qmePXsqKSlJwcHBkqTAwEDlz59fAwcOzPA94+PjtWfPHoWEhGRa18yZM3X27Fm9+OKLKl68eJrXBgwYoJUrV+q9995TrVq1VKpUqZvepyT77vkbd3wHBATowoULqlOnjnr37q0777xTt99+u/2ecsprr72mpKQkzZ07176AfuPO3cDAQBUpUkT169dXjx49VLZs2RytSZK2bNmiLl26qG/fvurXr1+OzdO4cWOdOHFCa9euVcmSJR3mli1bpq+++kqHDx9W3rx51aRJEw0YMECFCxfOsdrcLSoqSlu3btWBAwdu+b0qV66se++9VwsWLHBBZRk7fvy4mjRposcff1zvv/9+mtcCAwP16aef2n+G5s2bd9NfmgEAAMA3sIgOAAA8SrVq1fTLL7+oQIECCg4OTndsS7NmzWSz2bRu3bp0Y1NSUhQfHy+r1ZruPatVqyZJ+vvvv/XPP/+od+/eaTI37tReuHChgoKC1KhRozTHt2S2o/vAgQP67LPPVLhwYfXo0SPd60WLFtXrr7+ukSNHqk+fPvrmm2+UL18+h++VlJSkoKAgnTt3TtK1XzAcPHhQiYmJKlOmjH2x/M4779T999/vsC5XWrp0qX755RctWLAg3S8JJKlnz56SpPPnz2vnzp1avHixVq1apblz56pKlSpuqdETTJgwQdOmTVOJEiXUsWNHnThxQgsWLNDWrVu1cOFCh32HufLmzatPPvlEjzzyiGbMmKFevXqZXRIAAAA8AIvoAADAo4SFhSkuLk4LFy5UaGhoukVrq9Uqm82W7jgXm82mpKQkhYWFqX379rdcx5w5c7R//35t3rw5S/nk5GS98cYbSk5O1oABAxwuknbq1EkbN27UqlWr1L17d3366acZ7kweMmSI9u7dm+baa6+9Zv+/p0+frnr16mXjjm5dUlKSxo8fr65duyoyMjLDzPU7+VNSUjR8+HB9++23mjhxoj777DN3lWqqbdu2adq0aSpXrpwWLFig/PnzS7rWs3HjxumTTz7R4MGDTa4Sjtxxxx165ZVXNG7cOD311FMqVKiQ2SUBAADAZCyiAwAAj3Py5Em98847mWYcHedSqVIllyyi22w2FS5cOMtHb0ycOFExMTF68MEH1bFjx0yzY8eO1fnz57V161Z16NBBEyZM0L333psmM3ToUCUkJCgkJETjx4/XH3/8obfeekuVKlVSQkKC7r77brcfNbFmzRqdP39eL774Ypby/v7+6tWrl7799lvt2rUrh6vzHNOnT5ck9e/f376ALknPPPOMJk+erB9++IFFdA/XqVMnTZkyRYsXL9YLL7xgdjkAAAAwGYvoAADA40RGRuqPP/5QcHBwup3ojo5zMQxDycnJSk5OdkkNNptNRYsWzVJ2wYIFmjFjhooUKaKxY8emO7v8RmFhYZo+fboGDBig9evX6+mnn9Zzzz2nXr162Rdda9WqJUm6fPmyYmJiJF37BUHq9VT+/v46ePCgPv/88wznunr1qq5cuaJBgwZl6V4ys27dOj344IPZ2pl72223SZKuXLlyy/N7g8TERG3atEn+/v5q0KBBmtfy5Mmj4sWL659//tHFixfTHBMEzxIYGKjWrVtr7dq1LKIDAACARXQAADzBv//+q8mTJ2vz5s06d+6cihQpooYNG6pfv372RchUNptNs2fP1qJFi/T333/rtttuU7Vq1dS/f39VqFAh3XsvX75cX331lQ4dOqSwsDDdf//9evnll1W+fPk0uRsfyrhixQrNmzdPBw4c0FdffZXuPOt9+/Zp6tSp2rZtm6xWq8qVK6cuXbqoQ4cOTn8fDh8+rODgYAUGBjpciLbZbLLZbPr3338dvn769GklJSXZa963b5/mzZun4OBgWSwWJSYm6t1331VSUpLq1q2r1q1bp3uflJSULC2iL1iwQG+++aaCgoLUoUMHjRo1SiEhIQoICLjpYnqVKlVUsWJFTZ8+XTNnzlRISIgGDBiQJrN06VIlJiba//3PP/+oVKlS9vc2DEOHDx/WV199Zc9cvHhRNptN+fPnV2JiopKSklyyiG6xWG66y/5G+/btk6R0D1G1Wq2aOXOmfvjhB506dUoFCxZUjRo1NHDgQJUpU8aeW7x4sYYMGaIxY8aoWrVq+vDDD7Vjxw4FBgbqgQce0LBhwzL9a4GUlBQNGTJES5cuVa9evdJ9f13t8OHDSk5OVokSJewPXb3e8OHDdeHChWw//NVms+mhhx6Sn5+fNmzYkO5ZAY0bN1Z8fLx+/fVX+18oXLlyRTNnztT333+vkydPKk+ePLrnnnvUv39/RUREOH+T2bR//35NnTpVu3btUlxcnEqUKKF27dqpW7duGX4fNm7cqHHjxunPP//Ubbfdpscee0y9e/dOl01OTtaXX36p5cuX6++//1Z4eLgefPBBDRo0SHfeeect1127dm0tXrxYhmHc9L9lAAAA5G4sogMAYLK4uDh17txZJ06cUOPGjVW2bFkdP35c8+fP1549e7Ro0SL7gpnNZlOfPn20fv16lS1bVp06dVJsbKxWr16tn3/+WbNnz05zLMh7772nr776SsWKFdPjjz+u2NhYrVq1Shs2bNCMGTNUs2bNDGsaPny4Fi5cqJIlS6pMmTIKDQ1N8/qGDRvUt29fhYeHq1mzZgoNDdXPP/+s4cOH699//1W/fv2c+l506dLF/hDNm3nooYdumjlw4ICka8fDLF68WEFBQUpOTtbVq1e1ePFiJSUlKU+ePBkuosfGxqp69eqZvn98fLzmzZsnPz8/ffDBB7p48aI+/fTTLNUvSQ8//LCmTZum++67TwsWLNDLL7+c5nXDMDR79mz7v3/88Uf93//9n0aNGqUOHTrIZrPJMAy1bdtWb7/9tj3XsWNHnTt3LsOHr96K06dPZ/gw0YwkJSVp9+7dGj58uL2mVFevXlXPnj21detW1a5dW40aNdK5c+f0448/ateuXfruu+/SHIMiSYcOHdK7776rChUq6IknntBvv/2m77//Xlar1eFZ64ZhaOTIkW5bQJekU6dOSVK6X36lysrPbUYCAgLUpk0bzZw5U9u3b1edOnXsr/3xxx86ceKEnn322TRH/Lz66qv66aefdO+99+rpp59WXFycVq5cqS5dumjp0qUuWWi+mX379qlz585KSUlRy5YtVahQIe3evVsTJkzQmTNnNGLEiDT5Y8eOqUePHrrvvvvUoUMHbdq0SZ9++qn279+vTz/91L6YnZycrBdffFGbN29W7dq11aBBAx0/flw//vijtmzZoiVLlmT5L0kcKV68uOLj43XhwgXORQcAAPBxLKIDAGCyLVu26Pjx43ryySf17rvv2q9/8MEHWrlypY4fP67SpUtLuvawy/Xr1+uhhx7SlClT7Dszv//+e73yyiuaMmWK/TzmDRs26KuvvlLlypU1e/Zs+9ERv/zyi1566SW9+uqrWrVqVbrdncuWLVNcXJxmzJiR7jgKSUpISNDgwYOVJ08eLVmyRHfccYckacCAAXr00Uf1+eefKyoqSgULFsz29+KVV16Rv7+/8ubNq/DwcPv1+Ph4jRgxQrGxsXr11VdVtmxZJSUlZbiwZbPZlJiYqPPnz9uvNW3aVBaLRZI0ePBgrVmzRtu3b083LtWlS5d06dKldLunpWs7m1N/qREeHq65c+dq+/btevDBB5WYmKg2bdooNDRU27ZtU9euXdWtW7d0519HR0frySeftC9IN27cWI0bN04313fffae//vpLhQoV0vnz51WvXj0tWbJE48ePV4sWLXT16lVJ146HcYfExMSbHkFSuXLlNP/28/PTs88+q86dO9uv/f7779q6dav9lwipqlevrtGjR2vDhg1q06ZNmvf58ssv9eyzz2rYsGHy8/NTfHy8mjdvrg0bNujKlSvpftEjSaNHj9bChQvdtoAuXftZlaSQkBCXv3e7du3su/evX0T/4Ycf7K+nunTpkn766SeVLVtW8+bNs//MNmzYUO+//7527NjhlkX0L774QvHx8Zo4caJatWplv/7EE09o4cKFGjZsWJpd9bGxsYqKirL/8iU+Pl7PPvus1q9fr7Vr16pp06aSpK+//lqbN2/Wiy++qFdffdU+ftasWRozZoxmzpypN95445ZqT/1Zv/4vQQAAAOCbWEQHAMBkqbsljxw5ori4OOXLl0+S9Prrr+v1119Pk126dKmkawvB1y9+N2vWTBMmTEhzfMS3334rSRo0aFCahc+GDRuqWbNmWr16tTZt2qSHH344zRzHjh3TrFmzdP/992dY78aNGxUbG6uIiAjNmzcvzWv58uXTqVOntHPnzgwXhW8moweCJiQkqE+fPoqNjVW/fv3UuXNnNWrUSK1bt9abb75pz12+fFnvvvuuXnvttSw/DPR615+lnnoG+Y1H3kjXdlhfv2AbGhqqBx98UNK1hdPUxdPjx49LksqWLZvuPc6ePStJme7qTkpK0sSJExUUFKSOHTtq2rRpKlSokJ577jlNnTpVU6dO1ZNPPilJKlKkSHZu1WmhoaH677//Ms307NlT0rXz0w8ePKixY8emWdyVpAcffND+VwKp/v77b+3Zs0fStSNrblSiRAm9/vrr9p3I4eHhqlmzplauXKn//vsv3YLwe++9p7lz56pKlSpuW0CXZK/v+l/KuEqVKlVUuXJlrV69WiNHjlRAQIBSUlK0cuVKlS9fXtWqVbNn8+TJo/DwcJ0/f15///23ypUrJ0lq3ry5mjdv7vLaHBk/frzGjx9v/3dSUpJ27NihixcvKjExUadPn7b/Ik6SgoOD0/xFRnh4uLp166ZXX31V69atsy+iL1++XNK1I2s++ugjez4uLk7StV9O3qrY2FhJ7vslFQAAADwXi+gAAJjs3nvvVdu2bbV8+XI98MADqly5siIiIlS3bl01a9YszWL5kSNHFBISYl8QSxUSEqJHHnkkzbU///xTkjI8+7hq1apavXq1/vzzz3SL6I0bN3a4gC5JR48elXRtoTl1sflGp0+fdnzD2XDs2DH16dNHBw4cUP369dW3b19J184qXrJkiXr27KnixYvLZrPplVde0c8//6z9+/dr7ty52V74uueee+w7/n/++WdJ13a1fvXVV5o/f75q1aolm82mlJSULL1fdHS0pPQ7s6WsLaJPnjxZJ06cUOfOndMsMnbp0kV//fWXOnToYO/F7bffnqWabtWdd955094OHDhQklSzZk29+OKLWrJkSbpFdOna92D+/Pnatm2b9u/frwsXLth3JGf0PW7RokW6v5pI7bFhGGmuL1y4UKdPn1aFChW0f/9+/fzzz+l+znNK6i/BUhdzb9SpUyft27dP69atc+q4kccee0xjx47V5s2b9eCDD2r79u06c+ZMmp3+0rXjX9544w2NHj1arVu3Vrly5RQREaEaNWqodevWTv2iyVlbtmzRihUrtGvXLh05ckTJyclpjqi6XokSJdId5VOxYkVJ//vFlCT99ddfkpTmuKPrueIz6PTp08qXLx8PgAUAAID8bx4BAAA57cMPP9SiRYvUt29flSlTRr/99psGDRqktm3b6uLFi1l6jytXrshqtaZbUMzuA/HuvvvuTF9Pff+hQ4fqwIEDGX49/fTT2ZozI99++62eeOIJHTp0SJLSHA/Tp08fXblyRSNHjlRiYqL69++vn3/+Wc2aNdPXX3/t1M7RgQMH6q233pLNZtOqVatUrVo1BQQE6MSJE5KuLeKOHDkyzTEzjqSkpOi3335TWFiYIiMj072euoherFixDMdv2LBB06dPV8GCBdOdL1+oUCF99NFHqlChgv2hnamLjDntnnvu0aZNm7KUbdiwoapWrarNmzdr165daV6Ljo5WixYt9Pnnn6tQoUJ68cUXNX36dIdnm0sZ7+h35PTp03rppZe0YMECFS1aVO+8847bjuRI/euFEydOZLgb/ezZs0pOTk63UJxVbdq0UUBAgP0Ilx9++EF+fn5q27ZtuuxTTz2ln376ScOHD1etWrV09OhRvf3222ratKl2797t1PzZNX78eHXp0kUbN25U7dq1NWLECC1dujTDX6xI1xb/b5S64H79Z5thGMqfP7/Dz6CNGzfecu0bN27UPffcc8vvAwAAAO/HIjoAACY7duyY/vjjD91111166aWXNG7cOK1bt07dunXT0aNH9c0339iz5cuXV2Jion0H8vUeeeQR3Xfffbp06ZKk/y2sZrRb/FYWX1MXCQ8fPpzh+86ZM8e+C9sZu3btUlRUlIYOHaqQkBDNmjUrXSYiIkLPPvusfv75Zz3yyCP66aef1LVrV02ePNm+E/hmDh8+rFmzZqlXr15prn/33Xc6ceKE/WiZ689rzqqffvpJJ06cUJMmTdI86DHVmTNnJDneiV6oUCHlz59fr732WqYPNNyyZYuCgoLctojepEkTbdmyJcu7fF966SVJ0pQpU9Jc//jjj2W1WvXFF19o4sSJ6t69uxo2bJjuF0DXy+j76Ei7du30yiuvKG/evBowYICOHTuW5uz1nFSqVCmVLFlSCQkJ2rlzZ5rXYmNjderUKZUpU8bpM9OLFi2q+++/X2vWrFFCQoJWr16t2rVrq0SJEunm+uOPPxQaGqrOnTtr9OjRWrx4saZOnSqr1ZrmCJSccvbsWU2fPl3lypXTihUrNGLECHXq1EkRERH2s+NvdPz4cVmt1jTXjhw5Iklp/iKjXLlyunTpkv2/pevNmzdPc+fOvaXak5KStHr1avvxMQAAAPBtLKIDAGCyBQsWqFOnTlq3bl2a6xUqVJAknTt3zn4tdffm+++/r6SkJPv1rVu36sSJE6pSpYr96IEnnnhCkvTRRx/ZF9ala7srV69erRIlSqh+/frZrveBBx5Q4cKF9f333+vgwYP26zabTaNHj9bbb7+tCxcuZPt9JWnatGl66qmntHXrVrVs2VLLli1T3bp10+VWrFihdu3aqWrVqjp27Jjq1KmjwYMHO9x1b7VaFR0drfnz52vv3r2Ki4tT69atNWbMGPuucEm6cOGCJk2apPLly9u/f9kVFxenMWPGyM/PT88991yGmdSeOlpEr1atmr7++mv7mecZST17vnr16umOOckpDz/8sEqWLKmPP/44S/nmzZurfPny+u233+znnUv/+yXC9Yuix44d09ixY11S5/UPhG3fvr2qVq2q6dOn6++//3bJ+99Mly5dJF3bhX39DvgpU6YoJSUlzQM2nfHYY4/p0qVLGjNmjGJjY/XYY4+ly+zdu1edOnVK9wuMSpUqSUr7uZJTzp49K8MwVLRo0TQ/o8uWLdNPP/2U4ZjExER9+umnaf6d+ou064/kSX3w7KRJk9Ls+N+wYYPefPNNLVu27JZq//zzzxUQEOBwxzwAAAB8C2eiAwBgsvbt22vOnDkaPHiwVq1apRIlSujcuXNatWqV/P391aJFC3v22Wef1caNG7Vhwwa1adNGDRo00Pnz57Vq1SoFBwdr5MiR9uxDDz2k5557Tl999ZUeffRRNW7cWLGxsVqzZo3CwsI0btw4pxZfw8LC9N5776lfv3564okn1LRpUxUrVky///679u/frxYtWqhBgwZOfS+6deumgwcP6oknnshwgf/YsWN69913tX79evXu3VvTpk1T9+7dtXXrVj311FN65pln1KhRo3RHZURFRWnv3r2Srj2osHnz5nrooYf0wAMP2Bdyk5OT9dprr+nkyZP6+uuvM/zeLF++XDt27NBTTz2V4VnziYmJ6tOnj06dOqVnnnkmzYMer3fy5EmFhISkOaLmRlWqVLH/3xnt0J4+fbpSUlLUqFEjh+/hagEBARo8eLB69+6t1q1b2x+o6oi/v79efPFFDRkyRFOnTrUf19KgQQMdOHBA3bt318MPP6yTJ0/q559/tj+w9fpf+twqf39/DR06VJ07d9bbb7+tGTNmuOy9HYmKitL27du1evVqtW3bVg8//LAsFou2b9+uihUrqnv37rf0/k2bNlWePHk0f/58hYaGpvmMSFW3bl3dddddmjt3rv766y9VqVJFV65csS9e3+pCflZUrFhRd9xxh7Zu3ao+ffqoZMmS2rFjh6Kjo1WgQAFdvHgx3dnxBQoU0MyZM7V3716VL19emzdv1uHDh1WvXr0099mlSxdt2LBBCxcu1O7du1W3bl1dunRJK1euVHh4uIYOHep03QcPHtRnn32mIUOGpHlYMwAAAHwXO9EBADBZuXLl9O233+rRRx/V3r17NWfOHP3666+qXbu2Zs6cmWYndmBgoD799FO98cYbCgwM1Lx58/Tbb7+pUaNGWrhwoWrWrJnmvYcOHaoPPvhARYsW1eLFi7VlyxY1b95c3377bbpsdjRq1Ejz5s1TgwYNtHHjRs2fP1/+/v4aPXr0LR0TERwcrPHjx6dZQE/dybtt2za1bt1a69evV7NmzdSyZUsVLVpUc+fO1TPPPKPo6Gi9/vrrqlOnjlq3bq2+ffvaF+hatmypevXq6cMPP9SmTZs0efJkPfnkk/YF9H///Vfdu3fXL7/8otdee0116tSxz3/9Ltd9+/Zp3rx59oe2Xu/vv//W008/rS1btqh+/foZLuIZhqGtW7fqwIEDaXZL30xycrKk/z1wc8eOHZo3b57Cw8Ptx85cz2azZXo0yq1o0qSJOnbsqEGDBmn//v03zbdt21Z33nmn1q9fbz9GaMCAAerVq5eSkpI0Z84c7d27V88++6xmz54tf39/rVu3zqVnmNeqVUutWrXSr7/+qtWrV7vsfR3x9/fXxIkTNXz4cAUHB+ubb77R33//rWeffVZz58695YXZsLAw+4Jy06ZNM3y/4OBgzZkzR3379rU/xHX58uUqXry43n77bfXu3fuWasiK4OBgzZw5U02bNtX27ds1b948BQQE6KOPPrL/lcaKFSvSjClbtqw+/vhje82XL19W9+7d7TvDr3/vL774QoMGDZLNZtP8+fP1+++/q1mzZlqyZInuvfdep2o+efKkevbsqXr16umZZ55x/uYBAACQq/gZOfX/wwIAAHCBNWvWqE+fPpKku+66SyNHjlTt2rXT5Q4fPqxvvvlG3333nS5duqTHH39c77//vqRri88ZnW0eGxur2bNna9asWUpOTtbIkSPVsWNH++vdunXT5s2bNWLECIWEhOjDDz9UUlKSfvnlF/vZ66dOndJXX32lb775RklJSWrRokWGu/yjoqK0Y8cO+6L866+/rhdeeCFL34MZM2boww8/1MyZM3XnnXfq6aefVmxsrHr37q3+/funy7dp00bnz5/Xb7/9lqX3z66kpCR1795df/75p1auXOn0QzIBT3L16lW1bdtWAQEBmjt3bpafrwAAAIDcj+NcAACAR2vYsKHuvfde1a5dW/3793d4BE2FChU0cuRIDR06VH/88Ueao1IcPRz0559/1owZM1S5cmW98847aY5QkaTOnTvr0KFDGj16tKRrfzUwcODANItrKSkp+uWXXxQYGKg33nhDzz77bIZzde7cWUePHlVkZKTatm2r1q1bZ/l7kHr+fWJiosqUKaPRo0fr888/tz+4M6N8QkJClt8/u4KDg/XZZ59p06ZNLKAj1wgMDNSwYcNUpUoVFtABAACQBjvRAQCAx0tKSsqxh2cePXpUZcuWdfhQ0qw4fvy4goODVaxYMRdW9j+xsbE6d+6cSpQoYT+6wzCMW6oZAAAAAJA1LKIDAAAAAAAAAOAADxYFAAAAAAAAAMABFtEBAAAAAAAAAHCARXQAAAAA8DKJiYk3zXByJwAAgGuwiA4AyFV+/PFHffvttw5fX7p0qb7//vssvZfValVSUpJSUlKyPP/Vq1dltVp14cKFLI+B50pOTtaVK1fMLgNALvD999/r4sWL9n9fuXJFo0eP1rBhw7L9XhcuXNADDzygbt266dixYxlmbDabHnnkEfXs2VP//fef03UDAABACjS7AAAAXOnTTz/VsWPH9MQTT2T4+pgxY1SgQAE9+uijN32v4cOHa8WKFU7VUaRIEW3cuNGpsTBHSkqKzp49qyNHjujAgQPavXu3fvnlF/Xv319dunQxuzwAXuz06dMaPHiwbrvtNq1Zs0ZBQUEKDQ3VwYMHtXPnTj3//POqUKFClt9vypQpiouLk81mU1JSkg4fPizp2i/+goODVb58ef3www86fPiw7r33Xt122232sYZhKCEhQaGhofL3Z08VAABAVrCIDgDwaufPn9dff/2lkJAQhYSEKCAgQEFBQfYFhRsFBgbaXzcMQ4mJiSpYsKDuvPPOdNk6deqoUKFCCgwMVEBAQJrXfvrpJx07dkwdO3ZU3rx57devXr2qpKQkhYaGuvZGkSPOnj2rXr16KTY2VmfOnFFycrL9tdDQUN155506dOiQiRUCyA2mT5+u5ORkdevWTUFBQfbrAwYMUOfOnTV69Gh99dVXWXqv7du3a+7cuZKkLVu2qHXr1mleb9GihcaNG6eJEydKuvYXWMuWLbO/brPZJElr165VyZIlb+W2AAAAfIafwUF5AAAvtmbNGg0YMMC+gG61WmWz2VSgQIEM83FxcfLz81PevHllGIaSkpLUrVs39e/fP1vz9ujRQz///LNHLkJERUVp69atOnDggNmleIWXX35ZNptNd9xxh0JCQjRjxgy9/vrrev755+Xn52fP7dy5U3v27MnwPQICAhQVFXXLtSQnJ+vzzz/X0qVLderUKeXJk0f16tXTxx9/fMvvDdzo8OHDeuqppzRkyBC1b98+w0xcXJymTp2qVatW6dy5c7rnnns0ZMgQRUZGpssmJSXpiy++sP/8VqhQQa+++qoeeOABl9U8cuRI/fbbb1q3bp3DzKZNmzRlyhTt379f4eHheuKJJ9SvXz8FBqbfP2SxWDRx4kTt2bNHfn5+atWqlV577TXlyZPHZTX/9ddfevTRR1W0aFGtWrVKwcHBaV7v37+/Vq5cqSFDhqhr166Zvtfx48fVoUMHXbp0SdOnT9fOnTs1efJkjR8/XhEREUpKSlJ4eLgWLlyo6dOnq0ePHqpRo4ZmzpypvXv36oMPPlBycrISExPVrFkzl94nAABAbsZOdACAV2vatKksFov931FRUTpy5IjDo1QeffRRhYeHa8GCBe4qER5u0qRJ9v/78OHDmjFjhsLDw9MsoEvXFuYmT56c4XuEh4e7ZBF94sSJmjFjhu6//341b95c58+f1x9//HHL7wvcKDY2Vj179tSlS5ccZqxWq7p166bo6GhVq1ZNLVq00C+//KKoqCgtWrQozfEjNptNL7/8stavX68KFSqoc+fO2rFjh1588UXNnDlT9erVu+WaZ82apfnz52f4l0Opvv/+e7322msKDQ3Vo48+qqSkJM2YMUNnz57Ve++9lya7detWde/eXTabTa1bt1ZYWJiWLl2qv/76S19++WW6zwBnpKSkaOjQoUpOTtagQYPSLaBL144O27x5s8aNG6eKFSvqwQcfzPC9oqOj1adPH8XGxqp///6qX7++QkJCNHnyZB0+fNh+TNnGjRs1c+ZMVa1aVQMGDJC/v7/++ecfbd26VQUKFFDt2rVv+b4AAAB8DYvoAIBc59y5c6pcubLD1++99143VuN+Y8eOVUJCgtll5DqpR/T89NNPKlGihP16165d9eeff7pkju+++06lS5dOs4CXnQfbZuT48eNq0qSJHn/8cb3//vuuKNMnbNmyRV26dFHfvn3Vr18/s8txqUOHDql37976559/Ms1NnTpV0dHReuSRRzRu3Dj5+/urX79+euyxxzRs2DDNmzfPnp0/f77Wr1+vWrVq6csvv1RwcLBsNpuioqI0dOhQrVq1Ks0xJtmRkpKiiRMnatq0aZnmzp07p5EjRyooKEhz5sxR1apVJUk1atTQm2++qdatW9sXqBMTEzV48GAlJSXps88+08MPPyxJatasmbp3764FCxaoU6dOTtV7vZkzZ2rHjh2qUaOGw2dxFC1aVO+//7569+6tfv36adKkSWrQoEGajGEY+vTTT3X69Gk99thj6tWrlyTp7rvvVkBAgLZv3y7p2vcq9Zd9o0aNsp95Xq9ePfn5+clisbCIDgAA4ASeJAMAyHXy5s2rMWPGZPhVvHhxs8vLcSVKlMjWA+p82eXLl5WcnCxHp9slJSXp8uXLio+Ptx8F4e/vr8DAQPuXn59fujPznXX69GndfvvtaXbA8uA/uNKRI0fUsWNHSdeOpXLk6tWrmj9/voKCgjR8+HD7z2F4eLheeOEF7dq1S0eOHLHnv/nmG0nSiBEj7LutAwIC1Lt3b504cUKbN292uuY333xT06ZNU69evTLdhb506VJZrVY9/fTT9gV0SXryySd1xx136Ntvv7VfW7dunU6cOKGmTZvaF9AlqUGDBqpRo0aarLP27dunjz/+WOHh4RozZkymO9sbN26sESNGKD4+Xj169ND06dPT/ALNz89PkydP1qhRo+zv9dhjj2nw4MGaMWOGvvjiC0lSfHy8Ro8erVGjRik8PFyHDx/W4cOHFRgYqE8++UQNGzZM0zcAAABkDTvRAQC5TlBQkMPd5mFhYTcdb7VaFRQUpKCgIKf/nN8wDNlsNiUmJiokJCTDs3hhvscffzzD3bhvvfWW3nrrLfu/X3zxRRUpUsSNlQE549y5c/YF28zOFY+JiVFcXJzq16+vwoULp3kt9YzzjRs3qnz58oqNjdWff/6pMmXKqEqVKmmyderUUVBQkDZt2qSGDRs6VbPVatUnn3yipk2bavny5Q5z27ZtkyQ1b948zfXAwEDVq1dP69evv2k29f4++eQTXb58Oc2Do7Pj5MmT6tGjh5KSkjR69GiVK1fupmM6d+6ssLAwjRgxQuPGjdPatWs1fPhw+/nzAQEBeuqpp+z548ePK0+ePKpfv7792oYNGzRo0KBM5wkPD9euXbucui8AAABfxf+PHgCQ65w/f16tW7d2+PrNjnPp1KmTDh06lKW5mjRpctPM119/rbp162bp/W60c+dOTZ06Vbt27ZK/v7/KlSunqKgoPfroow4X+LPyYNHGjRtLurYb88iRI/riiy+0adMmtWzZUm+88Uaa7IULF/TJJ5/op59+UmxsrEqUKKGGDRuqd+/eKliwYLbvafHixRoyZIiGDh2q6OhorVmzRuXKldOkSZM0a9YsLV68WEWKFNGYMWNUq1YtDRgwQD/++KN+/PFHlS9fPs17DR48WEuWLNGSJUt09913Z7uWPn36KDk52b6TfMmSJdq6dasee+wx1a1bVzabTcnJybrrrrvSnL3vSjcePbR169Y0167/+Um934weaHt9T1OPcLle6vcp1fXvkdqTMWPGpHvAZEZz3njUyYoVKzRv3jwdOHBAX331VbqF1H379mnq1Knatm2brFarypUrpy5duqhDhw7Z/n45uueb/RxfvnxZn3/+uVauXKmTJ0+qYMGCaty4sQYOHKhChQqlua/rTZkyRVOmTLH/+/r/riZPnqwpU6Zk+N94Rv8dXv99fuyxx7RgwQItWbJEhw8f1vr165UvX740dfTt21cdO3bU2LFjtXHjRqWkpKhGjRoaMWKESpUq5dT3rGbNmqpTp85Nc2fOnJGkDP+7KlmypIKCguy/gMosGxwcrBIlSqT5ZdWUKVM0efJkdevWTYMHD7Zf/+ijj/TZZ5+pZ8+eGjhwoP36hx9+mKW/9jhz5oz8/PwyrKNMmTK6cOGC4uLilC9fPnvN1+9Yvz5rGIaOHTumiIiIm857o2PHjum5557TmTNn1KlTJ61evVq//vqrAgMDs/SXJe+9957ef/997dq1S6+88oqWLVtmP07qeoGBgem+L6m/KJ4+fXqGv7SIiorK8v/7BgAAgP9hER0AkOsUKVLE4YNFUxfdMtO0aVPVqVMnwwWKVD/99JOOHTumjh07ZrhTMSUlRTabTQkJCU7vYF6+fLkGDx6soKAgtWjRQvnz59fatWv16quvymKxaMiQIU697/V+/fVXvfzyy5KkSpUqpTvu5syZM3rmmWd07Ngx1apVSy1atLAvlK5bt06LFy9W/vz5nZp74sSJuu+++1S9enVt3rxZTz75pIoWLaqWLVtq8eLF+uijj/TNN9+oXbt2+vHHH7VixQr17dvXPj4pKUlr1qzRXXfd5dQCuiQ99thj9v/76tWr+vjjjyVJ1atXV4sWLRQUFGQ/miKnFtF79uxp/78/++wzlShRQm3btrVfu/789azKnz+//X0vX76sOXPmqHLlymrUqFGajCsMHz5cCxcuVMmSJVWmTJl0i30bNmxQ3759FR4ermbNmik0NFQ///yzhg8frn///dcl543f7Oc4Li5OzzzzjA4dOqQGDRqoSZMmOnjwoObPn68dO3Zo0aJFCgsLU4kSJezft5MnT2r58uWqVauWatWqdcs1Xu/q1at66aWX9Ouvv6p8+fIqX758hourZ8+etX/GPPbYY9q5c6c2bNig06dPa8mSJU4d9ZPVo4cuX74sSQ4/v/Lly6fTp09nKZs/f357Vrr2M79u3TrNnj1b7du311133aWjR4/qiy++0F133aU+ffo4XXN4eHiGf3GU+vP+77//Kl++fPaab7vttnTZAgUKSLp2vJIzi+g//vij/aiYN954Q/fdd1+2xr/++uuqV6+ehgwZotdff93+39SyZcv04YcfKjg4WP7+/rp06ZJ2796tRo0aKSkpSVFRUfbPwkuXLuns2bPp3vv6XxoCAAAg61hEBwB4rX/++UcXL160H70iSQkJCbLZbDp8+HCGY5KTk5WYmJjm9eTkZNlsNvuOxAEDBtx07iNHjujYsWPq0aNHuh3BrnDmzBmNGDFCISEhWrRokf2M85dfflmtWrXS119/re7du6to0aJOzxEXF6dXXnlFnTt3Vq9evZQnT550mVGjRunYsWPq37+/evfubb8+cuRIzZ8/X/Pnz9eLL77o1PzVqlXTF198of3796tdu3aSpLlz59p/WXDq1ClJ184oLly4cLpF9F9++UVxcXFpFqFvxaJFi/Tvv/9KunbkxVNPPaWqVavm+MM4r99x+9lnn6lkyZJprjkjf/789vc4fvy45syZo7vvvvuW3/dGy5YtU1xcnGbMmJHuQYjStf8eBw8erDx58mjJkiW64447JF37b+zRRx/V559/rqioKKf+oiFVVn6OP/roIx08eFBvvfWWnn76afv1d999V19//bUWL16szp07q1SpUvbv0ZYtW7R8+XLVq1fP5Q8W/fzzz2UYhhYuXKhq1ao5zC1YsEDNmjXThAkTFBQUJJvNpieeeEIxMTH666+/0v1lhiulLrRmtANaurbD/MqVK1nOXrx40f7vwMBAvf/++2rfvr1Gjx6t2bNn6+2335ZhGBo7dqz9F1fZ5e/v7/DIrtT3TH3ocmrNGeVvzGbXSy+9pCpVqqhevXoKDg7W1q1bFRYWpsDAQDVp0kTx8fHasmVLunGPPvqo/v77b/vxOTNnzkzzeu3atfXuu+8qODhYP/zwgxYuXKiIiAgNHTpUV69eVfHixe07/l955RWH9XE0FQAAQPaxiA4A8FrTpk3TokWLMnwts+Nczpw5k+71zHavm2HlypW6cuWKXnrppTQPCc2fP78++ugjnT171v6LA2ddunRJ7dq106uvvprh6xcvXtS6detUvHjxdA8g7Nmzp+rWrasyZco4PX/qcSOpO/lr165t3y16/e7+wMBAPfLII5o9e7b2799vPypkxYoV8vf3V5s2bZyuIVVcXJwmTZqksLAwJSQkqHDhwipSpIiWLFmiSpUq6YUXXrjlOXKjY8eOadasWbr//vszfH3jxo2KjY1VRESE5s2bl+a1fPny6dSpU9q5c2eW/kLEkZv9HKekpOj7779XYGCgTp06pY8++sj+2vnz5yVJv//+uzp37ux0Ddl1+vRpLV269KYPAM6TJ49GjRpl/289ICBA9evXV0xMjM6dO5eji+jh4eGSrv3FR0aSk5OVnJyc7Wyqu+66S3379tVHH32kgQMHauPGjerTp4/Tf1UiXft+xcbGOqzh+v+9vuaQkJA02dT7uLHm7Lj+KJXUne1JSUn6999/Hf7i5OzZs5k+/LpEiRL2v0wZM2aMpGv/HcXExKhKlSoqW7asfRF90qRJGf532aNHjwyfAwEAAIDMsYgOAPBagwcP1muvvabQ0FCFhIQoOTlZ/fv314YNGzR9+nQ98MADWr58ueLi4vT000/LMAz17NlTf/zxh32BIfXMa2d3HOaUI0eOSEp/XrakLJ1nnBXBwcEOFx4l6e+//1ZKSooqVaqU7s//r1/McdaNx+Bk9gC/xx57TLNnz9aKFStUpUoVJSQkaP369br//vszXXTKqlGjRum///7TgAEDNHHiRPn7+2vChAlq27atPvroI/uDFD2ZzWbLkWMabDabw9caN27scAFdko4ePSrp2kMqY2JiMsxcf8yHM272c3z+/Hn7Luhp06blSA0Zyez71qlTp5suoEvXHnB540M9U3dOG4ZxawXexO233y7p2i9KbnT16lVduHDB/kuvzLKS9N9//6W7D+naA3t/+ukn/fjjj6pcubJ69ep1SzUXL15ce/fu1cWLF+0L19fXIP1vQTv1c+PYsWPpFu5vzLrK3r17lZKSkuHnelJSki5cuKCKFSve9H02btyoAwcOyM/PT2fPntXUqVOVnJysb775xp4JCwvL8MgmHnINAADgnOwfpAgAgIfIly+fChYsqNDQUEVHR+upp57Shg0bNGrUKPui5xdffGFfFA0ICNBHH32kChUqqHv37powYYKSkpIUGhpqf7CgN7h69aqsVust7ZKUpKJFi6pYsWJOj7darW775UNkZKQqVqyoFStWSJLWr1+v+Pj4NGeHO2vFihX67rvv1KxZMzVv3tx+vVChQhoyZIiSk5O1Zs2aW54nJyUlJWV4/rErnDhxwuFrN9s1nLrQO3ToUB04cCDDr+uPV3HGzX6OU2uIiIhwWMOCBQtuqYaM3Mr3LVXZsmVdVE32VahQQSEhIYqOjk73WnR0tGw2m/04qQIFCujOO++UxWJJt7h/5swZnThxIsOjp6xWq33BOjY21n5OubNSv6979uxJ99off/wh6X9HmWQleyvHZWVkw4YNkq791c2NUv/7vdlnss1m0wcffKC7775bt912m4oWLarp06fr6tWr6tWrl+Li4iRd+wVF5cqV031t3brVpfcEAADgK1hEBwB4rStXrmjNmjV64YUX1LFjRx0/flxPPfWUChcurI0bN2rTpk1KTExUQECANm3apE2bNmnr1q2KiopSkyZNNG3aND300EN65513tGHDBodHEZgh9ZiGAwcOpHvts88+03333Zdm12FOKFOmjPz9/XXo0KF0u2pPnjyp++67T506dcrRGq7Xrl07HTt2THv27NGKFSsUHh6eZtHbGXv27NGwYcNUtGhRjR49Ot3rrVu31pdffpnmLPY1a9Zo2bJl9q9z587dUg3Z4efnJyn9Ludt27Y53PmclYdPOnrfCxcuaP/+/c6UKul/P8cZPaNg3759mjNnToaLtK5UuHBhFShQQH///Xe6/8avXLmiOXPmaNmyZenG3cr37ejRoy7Z3X6rRzbdipCQEDVo0EB//PFHuofqLlmyRJJUv359+7WmTZvqzJkz+umnn26aTTVq1CidOnVK3bt319mzZ/Xmm2/eUs3NmjWTpHSfjSdOnNCWLVtUtWpV+/n7Dz30kIKCgjRv3jylpKTYs5cvX9bq1atVuHDhDHeMOyshIUELFy5UWFiYHn744XSvpy6i3+wva6ZOnar9+/eneRZF1apVNXToUEVFRdn/ouedd97RihUr0n3dc889LrsnAAAAX8IiOgDAaz3//PPq06ePNm7cqLZt2+rHH3/Ur7/+qt69e+v5559Xt27ddPToUZ0/f17dunVTt27d1KtXL7333nuaNGmSPvjgA/n7+2v27Nk5/vDI7GrZsqVCQkI0Z86cNAuQV65c0cqVKyVJdevWzdEaChQooEaNGun06dPpjsFYvHixW2q4Xtu2beXv76+5c+fql19+UfPmze3nGjvj6NGjeumll5SYmKjx48dneNyElH7xb8yYMXr99dftX6lH77hDao2HDh2yX0tKStK4ceMcjilWrJiCgoLSHbVx9erVdO978ODBNJmJEyfKarU6XW/qcSTff/99mve22WwaPXq03n77bV24cMHp988Kf39/PfLII4qPj9fnn3+e5rXFixfr7bfftu8Qvt6dd94pKf0RJTf7vhmGoffffz/Hj1txhxdeeEH+/v4aNGiQ/vzzT/vDUL/99lvdcccdevDBB+3ZqKgohYWF6c0339SOHTskSevWrdO0adOUN29etWzZMs17//DDD/r+++/Vrl07vfbaa+rQoYNWrVplX3R3RpUqVdSgQQOtX79ekydPVnJysk6fPq1XXnlFycnJeuKJJ+zZ2267zf6Q1jfffFPx8fG6dOmSXn/9dZ0/f16PP/64S49HGj9+vM6dO6f27dsrX7586V5P/WVcZovoGzZs0Keffqrq1aurVatWaV7r0KGDunbtav/FTvHixVWhQoV0X44evAoAAIDMcSgeAMBrDRo0SMuXL1fXrl3tO16nTZumoKAgBQYGys/PT7169VJsbKzmz58vm82mixcvKj4+XtK1nc1NmzbVN998ozvuuEPBwcFm3k4axYoV0+jRozV06FA98cQTat68ufLmzauff/5ZJ06cUFRUlCIiInK8jjfffFP79+/Xxx9/rE2bNqlq1ao6ePCgNm3apFKlSt3yGcbZcfvtt6tOnTr2RbZ27drd0vsVK1ZMtWrVUq1atbL0y4DU3cY//fRTmvPgu3btqj///POWasmq5s2b64svvtD777+voKAgBQcH65NPPlFAQID9mIobBQYGqk2bNlq8eLH69OmjcuXK6fTp0zp16pTmzJkjSapZs6aKFCmiBQsWqFKlSrrrrru0ePFirVq1SjVq1NCuXbucqjcsLEzvvfee+vXrpyeeeEJNmzZVsWLF9Pvvv2v//v1q0aKFGjRo4PT3I6sGDhyorVu3avLkyfr1119177336t9//9WaNWt02223aeDAgenGlChRQvXq1dN3330nwzBUrFgxHTt2TIGBgZowYYKka7uZQ0ND9dlnn6lYsWIqVqyYZs2apcOHD6tixYpu+7nIKffdd58GDx6ssWPH6pFHHrE/eDc8PFzjxo1L85lZqlQpjRkzRoMHD9YzzzxjzwYFBemDDz5IczTKv//+q1GjRqlIkSIaOnSoJOmNN97QL7/8onfeeUe1a9dWyZIlnar5/fffV9euXTVlyhR9/vnnstlsstlsatKkiZ555pk02cGDB+vIkSNasGCBlixZIsMwdPXqVVWrVk39+/d3av6MzJ8/X7Nnz1bx4sUdvm/q8T+OFtENw9CECRMUFBSkd955R35+fhn+oiYrv7y5fuc9AAAAsoZFdACA10pdAL1e6mJ6qqCgIAUEBNgffJe6uzRVnjx59NJLL2VpvoMHD9rPIk/deZqVIx+c9dhjj6lkyZL69NNPtWbNGl29elUVK1ZU79699eSTT+bYvNcrXry4Fi1apKlTp2r16tXatWuXihQpomeffVZ9+/Z1+1nyjz32mH7//XcVL15c9erVu6X3ypMnjyZOnJjmQXuZLUClnv/u7++fZoyfn58SExNvqZasuvfeezVu3DhNnTpV/fr1U+HChdWqVSv169dPjz76qMNxI0aMUMGCBbVq1SqtX79e+fPnT5PPmzevPv/8c7377rsaM2aMgoODdf/992vRokX65JNPnF5El6RGjRpp3rx5mjp1qjZu3KikpCSVK1dOo0ePdtvPcf78+TV//nx99tlnWrlypebOnatixYqpQ4cO6tOnj8NzqD/66CNNmDBBGzZs0H///afbbrtNUVFR9tfvvPNOTZ06VePGjdOQIUOUN29eNWrUSAsWLHDpIqyZnnvuOdWsWVNff/21jh07pooVK6pnz57pPkslqVWrVrr77rv1xRdf6M8//1SJEiXsZ3OnMgxDgwcP1sWLFzVp0iT78Sr58uXTqFGj1LNnT73++uuaM2eOU5+vRYoU0eLFizV79mz98ssvCgwMVOvWrdW+fXv7Lu1UYWFh+uqrr7Ro0SKtWrVKV69eVaNGjfTss8+65AGcSUlJ+vjjjzVjxgzly5dP06ZNy/BhpefPn9d3330nSQ5/eeDn56eJEydqz549qlSpkiTZf0FwvdR/X3/cy41Sv+cAAADIOj8jN/ytKQDAZyUlJWnKlCkKCQmx7z6/3ty5c2W1WtMtKKSkpMhmsykpKUlPPvmkSpUqddO5Vq9erX79+tn/HRwcrG3btik0NNQ1NwPTWSwWPfHEExoxYoSeffbZNK/9888/On78uGrWrKmQkBD79QsXLujq1asOd4ID8C1JSUn67rvv9Nlnn+mff/5R8eLF9fnnn6tKlSppcgsXLtTo0aPtZ/VXrlxZS5cuzfIvD+677z7dddddmjdvnv3aypUr1b9/f73zzju677770o154403dOTIEe3cufMW7hAAAMD3sBMdAODVkpOTNWPGDAUHBys4ODjDB/EFBwfrq6++SnPNZrMpJSVFCQkJeuCBB7K0iF6/fn2FhYWpcOHCKleunDp06MAC+v83c+ZMXbx4MUvZF154Qfnz58/hipxz5coVSf/bdX690qVLq3Tp0umus6vz1h07dkyLFi3KUrZkyZLq0KFDDlcEOC8gIEAxMTH6559/1KJFC40aNSrDv9p5/PHHNXnyZBUtWtS+Az47u+8TExPT/RVM6r9Tz0S/UVhYmJKTk7N5RwAAAGAnOgAAuGWNGze2n+l7M2vXrnX6vGPkTlu2bFGXLl2ylK1Tp45mz56dwxUBt8YwDO3Zs0f33ntvjs1x6NAhBQcHq0yZMjk2BwAAAK5hER0AAAAAAAAAAAdy7mloAAAAAAAAAACPde7cOfXu3Vs1atRQ+/bttX///myNnzVrlqKiotJd/+uvvxQVFaUaNWqoS5cuOnnyZJrXlyxZosaNG6tu3boaP368UlJSbuk+chqL6AAAAAAAAADgYwzDUN++fRUbG6tFixYpKipKvXv3ltVqzdL4OXPmaOzYsemuJyYm2p+FtWzZMj3wwAPq27evfaH8l19+0bBhw9SrVy/Nnz9f27Zt05w5c1x6b67GIjoAAAAAAAAA+JidO3dq165deuedd1ShQgU9/vjjKleunNasWXPTsUuXLtXy5cvVoUOHdK+tWrVK586d0zvvvKPSpUurR48eSkhI0M6dOyVJX375pZo3b64OHTqobNmyGjx4sMcvogeaXQAAAAAAAAAAwDlNmjTJ9PW1a9dmeH3fvn0qUaKEKlasaL9Wo0YN7d69W+3atcv0PWvVqqU2bdpo6tSpOnr0aLr3veeee1SoUCH7terVq2v37t2qVauW9u3bp9dff93+2j333KOTJ08qNjZWhQsXznRes3jFIvq5c+c0cuRIbd68WeXKldN7772nKlWqOPVeFovFxdUBAAAAAAAA5oiMjDS7BK+S8u9dZpeQA0o5NSouLk6lS5dOc61AgQKKiYm56diSJUtm+31Pnz5tf71MmTL21wICApQnTx6dOXPGYxfRZXi4lJQUo1OnTkanTp2MP//801i8eLHRqFEj4/Lly069X3R0dLprVqvV2L59u2G1WrM8JrtzkHdf3tGYzPrsafdA3rkx9Nhz866agx67L++OOeixa/PumIMeuzbvjjnosbl5d8xBj12bd8cc3pD3ph67Y47cmDezx+6Yw9fyyB7bqUq57stZ06ZNM1588cU01xYsWGB07do1y+8xadIk49lnn01zbcSIEcZbb72V5tpHH31kDBs2zDAMw4iMjDR2796d5vWGDRsa27dvz075buXxZ6Lfytk8AAAAAAAAAID0ChUqpHPnzqW5dvnyZQUHB+fo++bUvDnJ4xfRMzubBwAAAAAAAACQfdWrV9ehQ4d06dIl+7Xo6Gjdcccdt/S+NWrU0K5du2Sz2TJ83+rVq2vHjh32144cOaLLly/f8rw5yeMX0W92hg4AAAAAAAAAIHsqVaqk8uXLa8KECUpJSdHevXu1evVqNW7cWCkpKbp06VKahfCseuCBB3T16lV98cUXkqT169drz549aty4sSSpbdu2WrBggQ4fPiybzabJkyfr3nvvVZEiRVx6f67k8Q8WDQwMVEhISJproaGhio+Pd/o9bxybkJCQ5n+zMia7c5B3bz6jMTfrs6fdA/nsj6HHnp13xRz02L15d8xBj12bd8cc9Ni1eXfMQY/NzbtjDnrs2rw75vD0vLf12B1z5La82T12xxy+lA8PD8/We/u6FKWYXYLL3cou6TFjxqhHjx5auXKl4uLi1K5dOzVs2FDHjx9XkyZNtHTpUkVERGTrPYOCgjRu3DgNHDhQs2bN0oULF9SnTx9VqFBBktS0aVP99ttvateunfLmzStJmjlz5i3cRc7zMwzDMLuIzCxcuFD/93//p8WLF9uvffnll/r99981bdq0bL+fxWJRYmJitsaEhIRkawx5c/OeWBN51+Y9sSby5s9B3rV5T6yJvPlzkHdt3hNrIu/avCfWRN78Oci7Nu+JNZE3fw5fy9esWTPLWUhX/61485CXCbz9z1saHx8fr23btqlQoUKqVq2ai6qSLly4oB07dqh06dKqVKlSutcPHTqkf/75RzVr1lTBggVdNm9O8PhF9EOHDql9+/bauHGj8ufPL0kaNGiQ8ufPr7feeivb72exWFS+fPk01xISEvTXX3+pbNmyCgsLSzfmyJEj6cZkhry5eUdjMuuzp90DeefG0GPPzbtqDnrsvrxZNdFj5/OeWBM9Nn8Oemxu3hNrosfmz+ENeW/qsSfW5A15M3vsjjl8Lc9O9OxhER3O8PjjXK4/m2fkyJGKiYnR6tWrNXXqVKff09GHS1hYmMPXsvuBRN7cfGZjHPXZ0+6BvPNj6LFn5l05Bz12T94dc9Bj1+bdMQc9dm3eHXPQY3Pz7piDHrs27445vCXvLT12xxy5NW9Wj90xh6/lAeQsj19ElxyfzQMAAAAAAAAAWWUzct+Z6F6xwOvlvOJ7fPfdd2vVqlU5cjYPAAAAAAAAAACOePyZ6K5msVjMLgEAAAAAAABwicjISLNL8CqJp7J3hr83CLnjiNkl5H6Gj4mOjk53zWq1Gtu3bzesVmuWx2R3DvLuyzsak1mfPe0eyDs3hh57bt5Vc9Bj9+XdMQc9dm3eHXPQY9fm3TEHPTY374456LFr8+6Ywxvy3tRjd8yRG/Nm9tgdc/haHtlz5WS5XPeFnOcVx7kAAAAAAAAAwK1KkU8dygEX8Te7AAAAAAAAAAAAPBWL6AAAAAAAAAAAOMAiOgAAAAAAAAAADnAmOgAAAAAAAACfkKIUs0uAF2InOgAAAAAAAAAADrCIDgAAAAAAAACAAyyiAwAAAAAAAADgAGeiAwAAAAAAAPAJNsMwuwR4IT/D8K2fHIvFYnYJAAAAAAAAgEtERkaaXYJXuXSytNkluFz+Ev+YXULuZ/iY6OjodNesVquxfft2w2q1ZnlMducg7768ozGZ9dnT7oG8c2PosefmXTUHPXZf3h1z0GPX5t0xBz12bd4dc9Bjc/PumIMeuzbvjjm8Ie9NPXbHHLkxb2aP3TGHr+WRPRdPlMp1X8h5nIkOAAAAAAAAAIADnIkOAAAAAAAAwCekyKdOtoaLsBMdAAAAAAAAAAAHWEQHAAAAAAAAAMABFtEBAAAAAAAAAHCAM9EBAAAAAAAA+AQbZ6LDCexEBwAAAAAAAADAARbRAQAAAAAAAABwgEV0AAAAAAAAAAAc8DMMw6cOArJYLGaXAAAAAAAAALhEZGSk2SV4lf9OljS7BJe7rcRxs0vI/QwfEx0dne6a1Wo1tm/fblit1iyPye4c5N2XdzQmsz572j2Qd24MPfbcvKvmoMfuy7tjDnrs2rw75qDHrs27Yw56bG7eHXPQY9fm3TGHN+S9qcfumCM35s3ssTvm8LU8sufMiRK57gs5j+NcAAAAAAAAAABwgEV0AAAAAAAAAAAcYBEdAAAAAAAAAAAHAs0uAAAAAAAAAADcwWYYZpcAL8ROdAAAAAAAAAAAHGARHQAAAAAAAAAAB1hEBwAAAAAAAADAAc5EBwAAAAAAAOATUswuAF7JzzB86zR9i8VidgkAAAAAAACAS0RGRppdglc5eaKE2SW4XIk7T5pdQu5n+Jjo6Oh016xWq7F9+3bDarVmeUx25yDvvryjMZn12dPugbxzY+ix5+ZdNQc9dl/eHXPQY9fm3TEHPXZt3h1z0GNz8+6Ygx67Nu+OObwh7009dsccuTFvZo/dMYev5ZE9J47fkeu+kPM4Ex0AAAAAAAAAAAc4Ex0AAAAAAACAT7DJp062houwEx0AAAAAAAAAAAdYRAcAAAAAAAAAwAEW0QEAAAAAAAAAcIAz0QEAAAAAAAD4BBtHosMJ7EQHAAAAAAAAAMABFtEBAAAAAAAAAHCARXQAAAAAAAAAABzwMwzDp04CslgsZpcAAAAAAAAAuERkZKTZJXiVI8fvMLsElytf8pTZJeR+ho+Jjo5Od81qtRrbt283rFZrlsdkdw7y7ss7GpNZnz3tHsg7N4Yee27eVXPQY/fl3TEHPXZt3h1z0GPX5t0xBz02N++OOeixa/PumMMb8t7UY3fMkRvzZvbYHXP4Wh7Zc/jY7bnuCzmP41wAAAAAAAAAAHCARXQAAAAAAAAAABwINLsAAAAAAAAAAHAHm/zMLgFeiJ3oAAAAAAAAAAA4wCI6AAAAAAAAAAAOsIgOAAAAAAAAAIADLKIDAAAAAAAAAOAADxYFAAAAAAAA4BNSDLMrgDdiJzoAAAAAAAAAAA74GYbhU79/sVgsZpcAAAAAAAAAuERkZKTZJXiVA8dKmF2Cy1UuddLsEnI/w8dER0enu2a1Wo3t27cbVqs1y2OyOwd59+Udjcmsz552D+SdG0OPPTfvqjnosfvy7piDHrs274456LFr8+6Ygx6bm3fHHPTYtXl3zOENeW/qsTvmyI15M3vsjjl8LY/s2f/PHbnuCzmPM9EBAAAAAAAA+ASb/MwuAV6IM9EBAAAAAAAAAHCARXQAAAAAAAAAABxgER0AAAAAAAAAAAc4Ex0AAAAAAACAT+BMdDiDnegAAAAAAAAAADjAIjoAAAAAAAAAAA6wiA4AAAAAAAAAgAN+hmEYZhfhThaLxewSAAAAAAAAAJeIjIw0uwSvsvufUmaX4HL3lj5mdgm5n+FjoqOj012zWq3G9u3bDavVmuUx2Z2DvPvyjsZk1mdPuwfyzo2hx56bd9Uc9Nh9eXfMQY9dm3fHHPTYtXl3zEGPzc27Yw567Nq8O+bwhrw39dgdc+TGvJk9dsccvpZH9vzxd8lc94Wcx3EuAAAAAAAAAAA4wCI6AAAAAAAAAAAOBJpdAAAAAAAAAAC4g01+ZpcAL8ROdAAAAAAAAAAAHGARHQAAAAAAAAAAB1hEBwAAAAAAAADAAc5EBwAAAAAAAOATbOwphhP4qQEAAAAAAAAAwAEW0QEAAAAAAAAAcMDPMAzD7CLcyWKxmF0CAAAAAAAA4BKRkZFml+BVtv1T1uwSXK526b/MLiH3M3xMdHR0umtWq9XYvn27YbVaszwmu3OQd1/e0ZjM+uxp90DeuTH02HPzrpqDHrsv74456LFr8+6Ygx67Nu+OOeixuXl3zEGPXZt3xxzekPemHrtjjtyYN7PH7pjD1/LInt//KpvrvpDzOM4FAAAAAAAAAAAHWEQHAAAAAAAAAMABFtEBAAAAAAAAAHCARXQAAAAAAAAAABwINLsAAAAAAAAAAHAHm/zMLgFeiJ3oAAAAAAAAAAA4wCI6AAAAAAAAAAAOsIgOAAAAAAAAAIADnIkOAAAAAAAAwCfYDPYUI/v8DMMwzC7CnSwWi9klAAAAAAAAAC4RGRlpdgle5de/Kppdgss1KPun2SXkfoaPiY6OTnfNarUa27dvN6xWa5bHZHcO8u7LOxqTWZ897R7IOzeGHntu3lVz0GP35d0xBz12bd4dc9Bj1+bdMQc9NjfvjjnosWvz7pjDG/Le1GN3zJEb82b22B1z+Foe2fPL0Qq57gs5j79fAAAAAAAAAADAAc5EBwAAAAAAAOATUsSeYmQfPzUAAAAAAAAAADjAIjoAAAAAAAAAAA6wiA4AAAAAAAAAgAOciQ4AAAAAAADAJ9jkZ3YJ8ELsRAcAAAAAAAAAwAEW0QEAAAAAAAAAcMDPMAzD7CIkKSUlRf3799ddd92lfv362a9v2LBBY8eO1enTp9WqVSuNGDFCISEhTs9jsVhcUS4AAAAAAABgusjISLNL8Crr/qpsdgku17jsAbNLyP0MD3DlyhXjtddeM+666y5j0qRJ9uv79+83qlatanzyySfGP//8Y/Tt29d47733bmmu6OjodNesVquxfft2w2q1ZnlMducg7768ozGZ9dnT7oG8c2PosefmXTUHPXZf3h1z0GPX5t0xBz12bd4dc9Bjc/PumIMeuzbvjjm8Ie9NPXbHHLkxb2aP3TGHr+WRPauPVMl1X8h5HnGcy1tvvaWgoCDVqFEjzfXZs2crIiJCvXv3VqlSpTR8+HAtXLhQiYmJJlUKAAAAAAAAAPAlHrGI3rNnT7377rsKCgpKc33fvn1q0KCB/d/FixdXoUKFdPDgQXeXCAAAAAAAAADwQR6xiF6mTJkMr8fFxal06dJprhUoUECnT592R1kAAAAAAAAAAB8XaHYBmQkICEj3ENHQ0FDFx8ff0vveOD4hISHN/2ZlTHbnIO/efEZjbtZnT7sH8tkfQ489O++KOeixe/PumIMeuzbvjjnosWvz7piDHpubd8cc9Ni1eXfM4el5b+uxO+bIbXmze+yOOXwpHx4enq339nUp8jO7BHghP8MwDLOLSBUVFaU6deqoX79+kqSnn35arVu3VlRUlD3Tpk0b9enTRy1btnRqDovFku0z1UNCQrI1hry5eU+sibxr855YE3nz5yDv2rwn1kTe/DnIuzbviTWRd23eE2sib/4c5F2b98SayJs/h6/la9asmeUspFVH7za7BJdrUW6f2SXkeh69iD527FidOnVKEydOlCRZrVbVrVtXc+fOVbVq1Zyaw2KxqHz58mmuJSQk6K+//lLZsmUVFhaWbsyRI0fSjckMeXPzjsZk1mdPuwfyzo2hx56bd9Uc9Nh9ebNqosfO5z2xJnps/hz02Ny8J9ZEj82fwxvy3tRjT6zJG/Jm9tgdc/hanp3o2cMiOpzh0ce5tGnTRh07dtS2bdtUu3ZtTZkyRYUKFVJkZOQtva+jD5ewsDCHr2X3A4m8ufnMxjjqs6fdA3nnx9Bjz8y7cg567J68O+agx67Nu2MOeuzavDvmoMfm5t0xBz12bd4dc3hL3lt67I45cmverB67Yw5fywPIWR69iH733XerX79+6tq1qwoWLKj4+Hh9/PHH8vf3iOehAgAAAAAAAPAiNrGuiOzzqEX02bNnp7vWo0cPtW7dWgcOHNA999yj4sWLm1AZAAAAAAAAAMAXedSZ6O5gsVjMLgEAAAAAAABwiVs99tjXrDia+75frcux3pnjDB8THR2d7prVajW2b99uWK3WLI/J7hzk3Zd3NCazPnvaPZB3bgw99ty8q+agx+7Lu2MOeuzavDvmoMeuzbtjDnpsbt4dc9Bj1+bdMYc35L2px+6YIzfmzeyxO+bwtTyy54cjVXPdF3KeRx3nAgAAAAAAAAA5xWZwJjqyj58aAAAAAAAAAAAcYBEdAAAAAAAAAAAHWEQHAAAAAAAAAMABFtEBAAAAAAAAAHCAB4sCAAAAAAAA8Akp7CmGE/ipAQAAAAAAAADAARbRAQAAAAAAAABwgEV0AAAAAAAAAPBB586dU+/evVWjRg21b99e+/fvz9I4m82msWPHqm7dumrUqJFWrFhhf23y5MmqXLlyuq/GjRtLkgzDUN26ddO8VqtWrRy5P1fxMwzDMLsId7JYLGaXAAAAAAAAALhEZGSk2SV4lcWHa5hdgsu1r7DLqXGGYejpp5+WJL377rvas2ePJk+erO+++0558uTJdOz48eP17bffauLEiQoKClK/fv00bdo0Va1aVYmJiUpMTEyTHzlypAoUKKBRo0bpyJEjat++vdavX6+AgABJkp+fn/Lly+fUfbiF4WOio6PTXbNarcb27dsNq9Wa5THZnYO8+/KOxmTWZ0+7B/LOjaHHnpt31Rz02H15d8xBj12bd8cc9Ni1eXfMQY/NzbtjDnrs2rw75vCGvDf12B1z5Ma8mT12xxy+lkf2fPtn9Vz35azt27cbd911l3Ho0CH7teeff95YunRppuMSExON6tWrG99884392rRp04yhQ4dmmN+3b59Rp04d47///jMMwzAWLVpkdO3a1em6zcBxLgAAAAAAAADgY/bt26cSJUqoYsWK9ms1atTQ7t27Mx139OhRxcfHq0GDBlkaN378eHXt2lWFCxeWJO3cuVN///237r//flWvXl09e/bUsWPHXHBHOSfQ7AIAAAAAAAAAAM5p0qRJpq+vXbs2w+txcXEqXbp0mmsFChRQTExMpu8XFxengIAAlSpVyn4tf/78On36dLpsTEyMdu7cqQkTJtivHT16VA0bNtTzzz+v5ORkvf322xo4cKAWLVqU6bxmYhEdAAAAAAAAgE+wiYM5UgUGBiokJCTNtdDQUMXHx990XHBwcJprYWFhGY6bOXOm2rdvr/z589uvzZ07N03m7bffVtOmTXXkyBGVL18+u7fhFiyiAwAAAAAAAICXcrTT/GYKFSqkc+fOpbl2+fLldAvkGY1LSEjQ5cuXlTdvXknXdqffOC4uLk6rVq3S//3f/2X6fsWKFZMkHT9+3GMX0fnVCwAAAAAAAAD4mOrVq+vQoUO6dOmS/Vp0dLTuuOOOTMeVKlVKRYoU0Y4dOzId9+OPP+rOO+9U1apV7df+++8/tWrVKs2u9dT3KVGixC3dT05iER0AAAAAAAAAfEylSpVUvnx5TZgwQSkpKdq7d69Wr16txo0bKyUlRZcuXZLNZks3zt/fX61bt9akSZN0+fJlnT9/XrNmzVLjxo3T5NauXasHH3wwzbXbbrtNefPm1bBhw2SxWLR+/XoNHz5c9evXT/OAU0/DIjoAAAAAAAAAn5Bi+Oe6r1sxZswYrV27VvXr11fHjh3Vtm1bNWzYUCdPnlTt2rV18ODBDMe9/PLLCgoKUsOGDdW4cWOFhoaqZ8+e9teTkpK0detW1a1bN93YSZMm6dKlS3rmmWc0YsQItWjRQlOmTLml+8hpnIkOAAAAAAAAAD7o7rvv1qpVq7Rt2zYVKlRI1apVkySVLFlSBw4ccDguX758mjt3rnbs2KGkpCTVqVNHQUFB9teDg4O1a9euDMfecccd+uKLL1x7IzmMRXQAAAAAAAAA8FHh4eF66KGHsj3O399ftWvXzoGKPI+fYRiG2UW4k8ViMbsEAAAAAAAAwCUiIyPNLsGrzP8z9y36dqq4zewScj2f3Il+44dLfHy8YmJiFBERofDw8HR5i8WSrQ8k8ubmHY3JrM+edg/knRtDjz0376o56LH78mbVRI+dz3tiTfTY/Dnosbl5T6yJHps/hzfkvanHnliTN+TN7LE75vC1PLLHxiMi4QR+agAAAAAAAAAAcIBFdAAAAAAAAAAAHGARHQAAAAAAAAAAB3zyTHQAAAAAAAAAvsdm+JldArwQO9EBAAAAAAAAAHCARXQAAAAAAAAAABxgER0AAAAAAAAAAAc4Ex0AAAAAAACAT0hhTzGc4GcYhmF2Ee5ksVjMLgEAAAAAAABwicjISLNL8CpfHapvdgku91ylTWaXkOv55E70Gz9c4uPjFRMTo4iICIWHh6fLWyyWbH0gkTc372hMZn32tHsg79wYeuy5eVfNQY/dlzerJnrsfN4Ta6LH5s9Bj83Ne2JN9Nj8Obwh70099sSavCFvZo/dMYev5QHkPP5+AQAAAAAAAAAAB1hEBwAAAAAAAADAAZ88zgUAAAAAAACA77EZ7ClG9vFTAwAAAAAAAACAAyyiAwAAAAAAAADgAIvoAAAAAAAAAAA4wJnoAAAAAAAAAHxCivzMLgFeiJ3oAAAAAAAAAAA4wCI6AAAAAAAAAAAO+BmGYZhdhDtZLBazSwAAAAAAAABcIjIy0uwSvMqMgw3MLsHlut/1q9kl5Ho+eSb6jR8u8fHxiomJUUREhMLDw9PlLRZLtj6QyJubdzQmsz572j2Qd24MPfbcvKvmoMfuy5tVEz12Pu+JNdFj8+egx+bmPbEmemz+HN6Q96Yee2JN3pA3s8fumMPX8sgem8HBHMg+fmoAAAAAAAAAAHCARXQAAAAAAAAAABxgER0AAAAAAAAAAAd88kx0AAAAAAAAAL7Hxp5iOIGfGgAAAAAAAAAAHGARHQAAAAAAAAAAB1hEBwAAAAAAAADAAc5EBwAAAAAAAOATUgw/s0uAF2InOgAAAAAAAAAADvgZhmGYXYQ7WSwWs0sAAAAAAAAAXCIyMtLsErzKlP2NzS7B5fpWWWd2CbmeTx7ncuOHS3x8vGJiYhQREaHw8PB0eYvFkq0PJPLm5h2NyazPnnYP5J0bQ489N++qOeix+/Jm1USPnc97Yk302Pw56LG5eU+siR6bP4c35L2px55YkzfkzeyxO+bwtTyAnOeTi+gAAAAAAAAAfI+N063hBH5qAAAAAAAAAABwgEV0AAAAAAAAAAAcYBEdAAAAAAAAAAAHOBMdAAAAAAAAgE9IMdhTjOzjpwYAAAAAAAAAAAdYRAcAAAAAAAAAwAEW0QEAAAAAAAAAcMDPMAzD7CLcyWKxmF0CAAAAAAAA4BKRkZFml+BVPoppbnYJLjcwYrXZJeR6Pvlg0Rs/XOLj4xUTE6OIiAiFh4eny1sslmx9IJE3N+9oTGZ99rR7IO/cGHrsuXlXzUGP3Zc3qyZ67HzeE2uix+bPQY/NzXtiTfTY/Dm8Ie9NPfbEmrwhb2aP3TGHr+WRPTb5mV0CvBDHuQAAAAAAAAAA4ACL6AAAAAAAAAAAOMAiOgAAAAAAAAAADvjkmegAAAAAAAAAfE+KwZ5iZB8/NQAAAAAAAAAAOMAiOgAAAAAAAAAADrCIDgAAAAAAAACAA5yJDgAAAAAAAMAn2ORndgnwQuxEBwAAAAAAAADAAT/DMAyzi3Ani8VidgkAAAAAAACAS0RGRppdglcZs6+12SW43JC7V5hdQq7nk8e53PjhEh8fr5iYGEVERCg8PDxd3mKxZOsDiby5eUdjMuuzp90DeefG0GPPzbtqDnrsvrxZNdFj5/OeWBM9Nn8Oemxu3hNrosfmz+ENeW/qsSfW5A15M3vsjjl8LQ8g5/nkIjoAAAAAAAAA35NicLo1so+fGgAAAAAAAAAAHGARHQAAAAAAAAAAB1hEBwAAAAAAAADAAc5EBwAAAAAAAOATbJyJDifwUwMAAAAAAAAAgAMsogMAAAAAAAAA4ACL6AAAAAAAAAAAOMCZ6AAAAAAAAAB8Qor8zC4BXsjPMAzD7CLcyWKxmF0CAAAAAAAA4BKRkZFml+BV3rK0M7sEl3srcpnZJeR6PrkT/cYPl/j4eMXExCgiIkLh4eHp8haLJVsfSOTNzTsak1mfPe0eyDs3hh57bt5Vc9Bj9+XNqokeO5/3xJrosflz0GNz855YEz02fw5vyHtTjz2xJm/Im9ljd8zha3kAOY8z0QEAAAAAAAAAcMAnd6IDAAAAAAAA8D02gz3FyD5+agAAAAAAAAAAcIBFdAAAAAAAAAAAHGARHQAAAAAAAAAABzgTHQAAAAAAAIBPSDH8zC4BXoid6AAAAAAAAAAAOMAiOgAAAAAAAAAADvgZhmGYXYQkxcTE6K233tLevXsVEhKijh076rXXXpO/v782bNigsWPH6vTp02rVqpVGjBihkJAQp+axWCwurhwAAAAAAAAwR2RkpNkleJVhe9qbXYLLvVttsdkl5HoecSb65cuX9eKLL6p9+/aaPHmyDhw4oL59+6pixYqKjIxUnz591Lt3b7Vp00YffPCBJkyYoCFDhjg9340fLvHx8YqJiVFERITCw8PT5S0WS7Y+kMibm3c0JrM+e9o9kHduDD323Lyr5qDH7subVRM9dj7viTXRY/PnoMfm5j2xJnps/hzekPemHntiTd6QN7PH7pjD1/IAcp5HLKL/+eefatOmjQYNGiRJKlasmGrWrKndu3dr165dioiIUO/evSVJw4cPV6tWrTRo0CCnd6MDAAAAAAAA8D02TreGEzzip6Z69ep644037P+22Ww6fPiwypcvr3379qlBgwb214oXL65ChQrp4MGDZpQKAAAAAAAAAPAhHrGIfqN58+bpypUrat++veLi4lS6dOk0rxcoUECnT582qToAAAAAAAAAgK/wiONcrnfo0CF9+OGHGj16tPLnz6+AgIB0x7aEhoYqPj7e6TluHJuQkJDmf7MyJrtzkHdvPqMxN+uzp90D+eyPoceenXfFHPTYvXl3zEGPXZt3xxz02LV5d8xBj83Nu2MOeuzavDvm8PS8t/XYHXPktrzZPXbHHL6Uz+j5fgBcy88wDMPsIlJduHBBnTp10oMPPqgRI0ZIkp5++mm1bt1aUVFR9lybNm3Up08ftWzZMttzWCwWJSYmZmtMSEhItsaQNzfviTWRd23eE2sib/4c5F2b98SayJs/B3nX5j2xJvKuzXtiTeTNn4O8a/OeWBN58+fwtXzNmjWznIX0+u4OZpfgch/cu9DsEnI9j1lET0hIUPfu3RUaGqpp06YpMPDaJvmxY8fq1KlTmjhxoiTJarWqbt26mjt3rqpVq5bteSwWi8qXL59u7r/++ktly5ZVWFhYujFHjhxJNyYz5M3NOxqTWZ897R7IOzeGHntu3lVz0GP35c2qiR47n/fEmuix+XPQY3PznlgTPTZ/Dm/Ie1OPPbEmb8ib2WN3zOFreXaiZw+L6HCGRxznYhiGBg4cqPPnz2vWrFlKTExUYmKiAgIC1KZNG3Xs2FHbtm1T7dq1NWXKFBUqVEiRkZFOz+fowyUsLMzha9n9QCJvbj6zMY767Gn3QN75MfTYM/OunIMeuyfvjjnosWvz7piDHrs274456LG5eXfMQY9dm3fHHN6S95Yeu2OO3Jo3q8fumMPX8gBylkcsoh84cEDr16+XJDVo0MB+vU6dOpo9e7b69eunrl27qmDBgoqPj9fHH38sf3+PfCYqAAAAAAAAACAX8YhF9CpVqujAgQMOX+/Ro4dat26tAwcO6J577lHx4sXdWB0AAAAAAACA3CBFbMxF9nnMmejuYrFYzC4BAAAAAAAAcIlbOfLYF726u5PZJbjcuHvnm11CrucRO9Hd7cYPl/j4eMXExCgiIiLDM6csFku2PpDIm5t3NCazPnvaPZB3bgw99ty8q+agx+7Lm1UTPXY+74k10WPz56DH5uY9sSZ6bP4c3pD3ph57Yk3ekDezx+6Yw9fyAHIef78AAAAAAAAAAIADPrkTHQAAAAAAAIDvsRl+ZpcAL8ROdAAAAAAAAAAAHGARHQAAAAAAAAAAB1hEBwAAAAAAAADAAc5EBwAAAAAAAOATUjgTHU5gJzoAAAAAAAAAAA6wiA4AAAAAAAAAgAMsogMAAAAAAAAA4ICfYRiG2UW4k8ViMbsEAAAAAAAAwCUiIyPNLsGr9NvZ2ewSXG7yfd+YXUKu55MPFr3xwyU+Pl4xMTGKiIhQeHh4urzFYsnWBxJ5c/OOxmTWZ0+7B/LOjaHHnpt31Rz02H15s2qix87nPbEmemz+HPTY3Lwn1kSPzZ/DG/Le1GNPrMkb8mb22B1z+FoeQM7jOBcAAAAAAAAAABxgER0AAAAAAAAAAAd88jgXAAAAAAAAAL7HJj+zS4AXYic6AAAAAAAAAAAOsIgOAAAAAAAAAIADLKIDAAAAAAAAAOAAi+gAAAAAAAAAADjAg0UBAAAAAAAA+IQUgweLIvvYiQ4AAAAAAAAAgAN+hmEYZhfhThaLxewSAAAAAAAAAJeIjIw0uwSv0nNHlNkluNxnNWebXUKu55PHudz44RIfH6+YmBhFREQoPDw8Xd5isWTrA4m8uXlHYzLrs6fdA3nnxtBjz827ag567L68WTXRY+fznlgTPTZ/Dnpsbt4Ta6LH5s/hDXlv6rEn1uQNeTN77I45fC0PIOf55CI6AAAAAAAAAN+TYnC6NbKPnxoAAAAAAAAAABxgER0AAAAAAAAAAAdYRAcAAAAAAAAAwAHORAcAAAAAAADgE1LkZ3YJ8ELsRAcAAAAAAAAAwAEW0QEAAAAAAAAAcIBFdAAAAAAAAAAAHPAzDMMwuwh3slgsZpcAAAAAAAAAuERkZKTZJXiVbtu6mV2Cy31Z+0uzS8j1fPLBojd+uMTHxysmJkYREREKDw9Pl7dYLNn6QCJvbt7RmMz67Gn3QN65MfTYc/OumoMeuy9vVk302Pm8J9ZEj82fgx6bm/fEmuix+XN4Q96beuyJNXlD3sweu2MOX8sDyHkc5wIAAAAAAAAAgAMsogMAAAAAAAAA4ACL6AAAAAAAAAB8Qorhn+u+bsW5c+fUu3dv1ahRQ+3bt9f+/fuzNM5ms2ns2LGqW7euGjVqpBUrVqR5/f3331flypXTfK1Zs8b++oYNG9S6dWvVrFlTw4cPV2Ji4i3dR05jER0AAAAAAAAAfIxhGOrbt69iY2O1aNEiRUVFqXfv3rJarTcdO3HiRC1btkyTJ0/WhAkT9N5772nv3r3213ft2qV3331X27Zts3899NBDkqQDBw6oT58+evTRR7V06VJdvHhREyZMyLH7dAUW0QEAAAAAAADAx+zcuVO7du3SO++8owoVKujxxx9XuXLl0uwYz0hSUpLmzJmjvn37qk6dOqpRo4a6dOmiuXPn2l/ft2+fHnzwQeXPn9/+FRQUJEmaPXu2IiIi1Lt3b5UqVUrDhw/XwoULPXo3eqDZBQAAAAAAAAAAnNOkSZNMX1+7dm2G1/ft26cSJUqoYsWK9ms1atTQ7t271a5dO4fvd/ToUcXHx6tBgwZpxi1fvlyStGfPHknSCy+8oH/++UelS5dW79699cgjj9jnffjhh+1jixcvrkKFCungwYO65557Mr9Zk7CIDgAAAAAAAMAnpBh+ZpfgMeLi4lS6dOk01woUKKCYmJibjgsICFCpUqXs1/Lnz6/Tp09Lkg4dOqSKFStq6NChKlOmjL777ju9+uqrqlixoipXruxw3tOnT7OIDgAAAAAAAABwLUc7zW8mMDBQISEhaa6FhoYqPj7+puOCg4PTXAsLC7OPe/rpp/X000/bX3vhhRe0bt06ff/996pcubICAgKcmtdMnIkOAAAAAAAAAD6mUKFCOnfuXJprly9fTrdAntG4hIQEXb582X4tLi4u03HFihXT8ePHHc57s/Fm8zMMwzC7CHeyWCxmlwAAAAAAAAC4RGRkpNkleJWoLd3NLsHlZted4dS4Q4cOqX379tq4caPy588vSRo0aJDy58+vt956y+G4lJQUNWjQQO+9954eeughSdK8efP09ddfa8WKFRo6dKjq1auntm3bSpKuXr2q5s2bq2XLlnr99dc1duxYnTp1ShMnTpQkWa1W1a1bV3PnzlW1atWcupec5pPHudz44RIfH6+YmBhFREQoPDw8Xd5isWTrA4m8uXlHYzLrs6fdA3nnxtBjz827ag567L68WTXRY+fznlgTPTZ/Dnpsbt4Ta6LH5s/hDXlv6rEn1uQNeTN77I45fC2P7EkRZ6KnqlSpksqXL68JEyZo5MiRiomJ0erVqzV16lSlpKTo8uXLypMnjwICAtKM8/f3V+vWrTVp0iTVrFlTycnJmjVrlpo2bSrp2trr+PHjVahQIRUoUEBffvmlYmNj1bFjR0lSmzZt1LFjR23btk21a9fWlClTVKhQIY/+uffJRXQAAAAAAAAA8HVjxoxRjx49tHLlSsXFxaldu3Zq2LChjh8/riZNmmjp0qWKiIhIN+7ll1/Wiy++qIYNG8owDJUpU0Y9e/aUJD3zzDM6e/asXnvtNV25ckU1a9bUvHnzVLZsWUnS3XffrX79+qlr164qWLCg4uPj9fHHH8vf33NPHmcRHQAAAAAAAAB80N13361Vq1Zp27ZtKlSokP04lZIlS+rAgQMOx+XLl09z587Vjh07lJSUpDp16igoKMj+ev/+/dW/f3+H43v06KHWrVvrwIEDuueee1S8eHHX3VQOYBEdAAAAAAAAAHxUeHi4/Wzz7PD391ft2rWdnrdUqVIqVaqU0+PdyXP3yAMAAAAAAAAAYDJ2ogMAAAAAAADwCSkGDxZF9rETHQAAAAAAAAAAB1hEBwAAAAAAAADAARbRAQAAAAAAAABwgDPRAQAAAAAAAPiEFIM9xcg+P8MwDLOLcCeLxWJ2CQAAAAAAAIBLREZGml2CV+m0uafZJbjc/Ps/M7uEXM8nd6Lf+OESHx+vmJgYRUREKDw8PF3eYrFk6wOJvLl5R2My67On3QN558bQY8/Nu2oOeuy+vFk10WPn855YEz02fw56bG7eE2uix+bP4Q15b+qxJ9bkDXkze+yOOXwtDyDn8fcLAAAAAAAAAAA44JM70QEAAAAAAAD4nhTDz+wS4IXYiQ4AAAAAAAAAgAMsogMAAAAAAAAA4ACL6AAAAAAAAAAAOMCZ6AAAAAAAAAB8Qoo4Ex3Zx050AAAAAAAAAAAcYBEdAAAAAAAAAAAH/AzDMMwuwp0sFovZJQAAAAAAAAAuERkZaXYJXuWJTb3NLsHlvq0/1ewScj2fPBP9xg+X+Ph4xcTEKCIiQuHh4enyFoslWx9I5M3NOxqTWZ897R7IOzeGHntu3lVz0GP35c2qiR47n/fEmuix+XPQY3PznlgTPTZ/Dm/Ie1OPPbEmb8ib2WN3zOFreWRPisGZ6Mg+jnMBAAAAAAAAAMABFtEBAAAAAAAAAHCARXQAAAAAAAAAABzwyTPRAQAAAAAAAPgezkSHM9iJDgAAAAAAAACAAyyiAwAAAAAAAADgAIvoAAAAAAAAAAA4wJnoAAAAAAAAAHwCZ6LDGexEBwAAAAAAAADAAT/DMAyzi3Ani8VidgkAAAAAAACAS0RGRppdgldp82s/s0twue8aTDa7hFzPJ49zufHDJT4+XjExMYqIiFB4eHi6vMViydYHEnlz847GZNZnT7sH8s6Noceem3fVHPTYfXmzaqLHzuc9sSZ6bP4c9NjcvCfWRI/Nn8Mb8t7UY0+syRvyZvbYHXP4Wh5AzvPJRXQAAAAAAAAAvocz0eEMzkQHAAAAAAAAAMABFtEBAAAAAAAAAHCARXQAAAAAAAAAABxgER0AAAAAAAAAAAd4sCgAAAAAAAAAn5AiHiyK7GMnOgAAAAAAAAAADrCIDgAAAAAAAACAAyyiAwAAAAAAAADggJ9hGIbZRbiTxWIxuwQAAAAAAADAJSIjI80uwau02DDA7BJcbtVDE80uIdfzyQeL3vjhEh8fr5iYGEVERCg8PDxd3mKxZOsDiby5eUdjMuuzp90DeefG0GPPzbtqDnrsvrxZNdFj5/OeWBM9Nn8Oemxu3hNrosfmz+ENeW/qsSfW5A15M3vsjjl8LQ8g53GcCwAAAAAAAAAADrCIDgAAAAAAAACAAz55nAsAAAAAAAAA35Ni+JldArwQO9EBAAAAAAAAAHCARXQAAAAAAAAAABxgER0AAAAAAAAAAAc4Ex0AAAAAAACAT+BMdDjDo3aiJyUlac+ePTpw4IAMwzC7HAAAAAAAAACAj/MzPGS1es+ePerVq5duu+02nTlzRnfeeae+/PJL5c+fXxs2bNDYsWN1+vRptWrVSiNGjFBISIhT81gsFhdXDgAAAAAAAJgjMjLS7BK8SpP1g8wuweXWNppgdgm5nkcc52Kz2TRo0CC98sorat++vaxWq5544gnNnTtXjRo1Up8+fdS7d2+1adNGH3zwgSZMmKAhQ4Y4Pd+NHy7x8fGKiYlRRESEwsPD0+UtFku2PpDIm5t3NCazPnvaPZB3bgw99ty8q+agx+7Lm1UTPXY+74k10WPz56DH5uY9sSZ6bP4c3pD3ph57Yk3ekDezx+6Yw9fyAHKeRxznEhcXpy5duqh9+/aSpDx58qh8+fK6ePGiZs+erYiICPXu3VulSpXS8OHDtXDhQiUmJppcNQAAAAAAAABvkmL45bov5DyPWEQvWLCgunTpYv/3li1btHnzZrVq1Ur79u1TgwYN7K8VL15chQoV0sGDB80oFQAAAAAAAADgQzziOJfrtWnTRgcPHtSAAQNUrVo1xcXFqXTp0mkyBQoU0OnTp3XPPfeYVCUAAAAAAAAAwBd43CL6jBkztGLFCk2YMEHVq1dXQEBAuoeIhoaGKj4+3uk5bhybkJCQ5n+zMia7c5B3bz6jMTfrs6fdA/nsj6HHnp13xRz02L15d8xBj12bd8cc9Ni1eXfMQY/NzbtjDnrs2rw75vD0vLf12B1z5La82T12xxy+lM/o+X4AXMvPMAzD7CIyMnToUF2+fFlnz55V69atFRUVZX+tTZs26tOnj1q2bJnt97VYLNk+Tz0kJCRbY8ibm/fEmsi7Nu+JNZE3fw7yrs17Yk3kzZ+DvGvznlgTedfmPbEm8ubPQd61eU+sibz5c/havmbNmlnOQnp47atml+ByPzcZZ3YJuZ5HLKLv3btXn332mSZNmiQ/v2uH4b/11lu6ePGibr/9dp06dUoTJ06UJFmtVtWtW1dz585VtWrVsj2XxWJR+fLl01xLSEjQX3/9pbJlyyosLCzdmCNHjqQbkxny5uYdjcmsz552D+SdG0OPPTfvqjnosfvyZtVEj53Pe2JN9Nj8OeixuXlPrIkemz+HN+S9qceeWJM35M3ssTvm8LU8O9Gzh0V0OMMjjnMpV66cdu3apbfeeksvvfSSDh8+rO+++04ffvihbr/9dnXs2FHbtm1T7dq1NWXKFBUqVEiRkZFOz+fowyUsLMzha9n9QCJvbj6zMY767Gn3QN75MfTYM/OunIMeuyfvjjnosWvz7piDHrs274456LG5eXfMQY9dm3fHHN6S95Yeu2OO3Jo3q8fumMPX8gBylkcsooeHh2vGjBl699139eijj6p48eIaMWKEGjduLEnq16+funbtqoIFCyo+Pl4ff/yx/P39Ta4aAAAAAAAAAJDbecQiuiRVqVJFs2fPzvC1Hj16qHXr1jpw4IDuueceFS9e3M3VAQAAAAAAAPB2KfIzuwR4IY84E92dLBaL2SUAAAAAAAAALnErRx77ooZrXzO7BJf7pcmHZpeQ63nMTnR3uvHDJT4+XjExMYqIiMjwzCmLxZKtDyTy5uYdjcmsz552D+SdG0OPPTfvqjnosfvyZtVEj53Pe2JN9Nj8OeixuXlPrIkemz+HN+S9qceeWJM35M3ssTvm8LU8gJzHweIAAAAAAAAAADjAIjoAAAAAAAAAAA745HEuAAAAAAAAAHxPisGDRZF97EQHAAAAAAAAAMABFtEBAAAAAAAAAHCARXQAAAAAAAAAABzgTHQAAAAAAAAAPsHgTHQ4gZ3oAAAAAAAAAAA4wCI6AAAAAAAAAAAO+BmGYZhdhDtZLBazSwAAAAAAAABcIjIy0uwSvMoDP71hdgkut7HZWLNLyPV88kz0Gz9c4uPjFRMTo4iICIWHh6fLWyyWbH0gkTc372hMZn32tHsg79wYeuy5eVfNQY/dlzerJnrsfN4Ta6LH5s9Bj83Ne2JN9Nj8Obwh70099sSavCFvZo/dMYev5ZE9KZyJDidwnAsAAAAAAAAAAA6wiA4AAAAAAAAAgAMsogMAAAAAAAAA4IBPnokOAAAAAAAAwPcYnIkOJ7ATHQAAAAAAAAAAB1hEBwAAAAAAAADAARbRAQAAAAAAAABwgDPRAQAAAAAAAPiEFM5EhxPYiQ4AAAAAAAAAgAN+hmEYZhfhThaLxewSAAAAAAAAAJeIjIw0uwSvUmflULNLcLmtLd8zu4RczyePc7nxwyU+Pl4xMTGKiIhQeHh4urzFYsnWBxJ5c/OOxmTWZ0+7B/LOjaHHnpt31Rz02H15s2qix87nPbEmemz+HPTY3Lwn1kSPzZ/DG/Le1GNPrMkb8mb22B1z+FoeQM7zyUV0AAAAAAAAAL7Ht87kgKtwJjoAAAAAAAAAAA6wiA4AAAAAAAAAgAMsogMAAAAAAAAA4ABnogMAAAAAAADwCSnyM7sEeCF2ogMAAAAAAAAA4ACL6AAAAAAAAAAAOMAiOgAAAAAAAAAADvgZhmGYXYQ7WSwWs0sAAAAAAAAAXCIyMtLsErxKzR+HmV2Cy+1o9a7ZJeR6Pvlg0Rs/XOLj4xUTE6OIiAiFh4eny1sslmx9IJE3N+9oTGZ99rR7IO/cGHrsuXlXzUGP3Zc3qyZ67HzeE2uix+bPQY/NzXtiTfTY/Dm8Ie9NPfbEmrwhb2aP3TGHr+WRPYbBg0WRfRznAgAAAAAAAACAAyyiAwAAAAAAAADgAIvoAAAAAAAAAAA44JNnogMAAAAAAADwPSmciQ4nsBMdAAAAAAAAAAAHWEQHAAAAAAAAAMABFtEBAAAAAAAAAHCAM9EBAAAAAAAA+ATDMLsCeCN2ogMAAAAAAAAA4ICfYfjW718sFovZJQAAAAAAAAAuERkZaXYJXuXe70eYXYLL7X70bbNLyPV88jiXGz9c4uPjFRMTo4iICIWHh6fLWyyWbH0gkTc372hMZn32tHsg79wYeuy5eVfNQY/dlzerJnrsfN4Ta6LH5s9Bj83Ne2JN9Nj8Obwh70099sSavCFvZo/dMYev5QHkPJ9cRAcAAAAAAADgewzDz+wS4IU4Ex0AAAAAAAAAAAdYRAcAAAAAAAAAwAEW0QEAAAAAAAAAcIAz0QEAAAAAAAD4BM5EhzPYiQ4AAAAAAAAAgAMsogMAAAAAAAAA4ACL6AAAAAAAAAAAOMCZ6AAAAAAAAAB8QgpnosMJfoZhGGYX4U4Wi8XsEgAAAAAAAACXiIyMNLsEr1J12Vtml+Bye9u9ZXYJuZ5P7kS/8cMlPj5eMTExioiIUHh4eLq8xWLJ1gcSeXPzjsZk1mdPuwfyzo2hx56bd9Uc9Nh9ebNqosfO5z2xJnps/hz02Ny8J9ZEj82fwxvy3tRjT6zJG/Jm9tgdc/haHkDO40x0AAAAAAAAAAAc8Mmd6AAAAAAAAAB8j28dbA1XYSc6AAAAAAAAAAAOsIgOAAAAAAAAAIADLKIDAAAAAAAAAOAAi+gAAAAAAAAA4IPOnTun3r17q0aNGmrfvr3279+fpXE2m01jx45V3bp11ahRI61YsSLN65s3b1abNm1UtWpV1a1bVzNnzrS/ZhiG6tatq8qVK9u/atWq5dL7cjUeLAoAAAAAAADAJxiGn9kleAzDMNS3b19J0qJFi7Rnzx717t1b3333nfLkyZPp2IkTJ2rZsmWaPHmygoKC1K9fP5UpU0ZVq1bV8ePH1bdvX7388st69NFH9dtvv+mNN97Q3XffrXr16uno0aNKTEzU77//roCAAEmSn59n94Wd6AAAAAAAAADgY3bu3Kldu3bpnXfeUYUKFfT444+rXLlyWrNmTabjkpKSNGfOHPXt21d16tRRjRo11KVLF82dO1eSdPDgQfXs2VPPPfecbrvtNrVr105lypTR7t27JUm7du1SjRo1VKhQIeXPn1/58+dXvnz5cvx+b4WfYRiG2UW4k8ViMbsEAAAAAAAAwCUiIyPNLsGrRCwZZXYJLhfz+JtOjZs9e7Zmzpyp9evX269NmTJFsbGxGjlypMNxBw4cUNu2bbVmzRqVKlVKkrRt2zaNGjVK33//fbp8XFycHnjgAY0fP17NmjXTsGHDtHnzZiUkJCghIUH16tXTsGHD7O/liXzyOJcbP1zi4+MVExOjiIgIhYeHp8tbLJZsfSCRNzfvaExmffa0eyDv3Bh67Ll5V81Bj92XN6smeux83hNrosfmz0GPzc17Yk302Pw5vCHvTT32xJq8IW9mj90xh6/lgSZNmmT6+tq1azO8HhcXp9KlS6e5VqBAAcXExGT6fnFxcQoICEiz6J0/f36dPn06w/zUqVNVokQJNWrUSJJ09OhRNWzYUM8//7ySk5P19ttva+DAgVq0aFGm82bFnj17tHLlSh04cEBnzpxRQECAihYtqsjISLVs2VKVK1d26n19chEdAAAAAAAAgO/hTPT/CQwMVEhISJproaGhio+Pv+m44ODgNNfCwsIyHLd582bNnj1bX375pQIDry1Fpx77kurtt99W06ZNdeTIEZUvX96ZW9GBAwf09ttv68KFC2rZsqW6d++uokWLymaz6ezZs9q6dav69OmjSpUqadiwYSpZsmS23p9FdAAAAAAAAADwUo52mt9MoUKFdO7cuTTXLl++nG6BPKNxCQkJunz5svLmzSvp2u70G8cdO3ZMgwYN0qBBg1S7dm2H71esWDFJ0vHjx51aRF+4cKEmTpyogQMH6sknn0z3euXKlfXggw+qf//++vLLL/XUU0/pvffeU8OGDbM8Bw8WBQAAAAAAAAAfU716dR06dEiXLl2yX4uOjtYdd9yR6bhSpUqpSJEi2rFjh8NxsbGx6tGjhxo1aqTnn3/efv2///5Tq1at0uxaT32fEiVKZPseDh06pC+//FJz587NcAH9egEBAerevbumTJmid999N8193wyL6AAAAAAAAADgYypVqqTy5ctrwoQJSklJ0d69e7V69Wo1btxYKSkpunTpkmw2W7px/v7+at26tSZNmqTLly/r/PnzmjVrlho3bixJSkxMVPfu3VW4cGENHjxYVqtVVqtVSUlJuu2225Q3b14NGzZMFotF69ev1/Dhw1W/fn1VrFjRqXv4/vvvVaZMmSyPqV69un788Uflz58/y2NYRAcAAAAAAADgE4xc+HUrxowZo7Vr16p+/frq2LGj2rZtq4YNG+rkyZOqXbu2Dh48mOG4l19+WUFBQWrYsKEaN26s0ND/x969x9lY7v8ff68ZDAvDyCnlGDWLNQ5pSEjRgSmlA+1yyI5SDhUp1IgQkUh0stMBWwfa0WHbhdS3k5JUbpZDSe0Q2W0MaxqM9fujR/PbY6wx95p71nWvWa/n4zGPvWet93Vdn7uPx/3H5Xbd5XX77bdLkj7++GNt3LhRa9euVXp6us4991yde+65evDBByVJTzzxhA4ePKibbrpJY8eO1eWXX645c+ZEfA0JCfa3uO2O4Ux0AAAAAAAAAIhDTZs21bvvvqu1a9cqJSVFzZs3lySdeeaZ2rJlS9hxlStX1qJFi7Ru3TodOXJEbdq0UdmyZSVJXbp0KXTs6aefrnnz5jl7ISWMTXQAAAAAAAAAiFNer1edOnWyPS4hIaHQF4aWJmyiAwAAAAAAAABiVufOneXxeIqcX7Vqla352UQHAAAAAAAAEBdCoaJvtCJ2DBs2rETn94RCoeKePx9TLMsyXQIAAAAAAADgCL/fb7qEmHL26xNNl+C4rdeNNV1CqReXT6KfeHMJBoMKBALy+Xzyer0F8pZl2bohkTebDzemsD677RrIRzaGHrs379Qa9Dh6eVM10ePI826siR6bX4Mem827sSZ6bH6NWMjHUo/dWFMs5E32OBprxFseQMlLMF0AAAAAAAAAAABuFdGT6Pv27dO7776rLVu2aO/evUpMTFSNGjXk9/t16aWXqkqVKk7XCQAAAAAAAADFE1cHW+Po0aPatWuXTj/9dB08eFDVq1ePaB5bT6Lv27dP9913n7p3766NGzcqLS1NN954o6677jr5fD599tlnuuyyy/Twww/r0KFDERUEAAAAAAAAAECksrOzNWrUKJ177rnq1q2btm/frqlTp6pHjx7au3ev7fmKvIn+8ccf66qrrtIZZ5yh1atXa/LkyerZs6c6deqkzp0764YbbtBjjz2mFStW6NixY+revbs2b95suyAAAAAAAAAAACL1yCOP6LPPPtN9992n48ePS5JuvfVWJSQkaOrUqbbnK9Im+rZt2zR69Gg9+eSTuuuuu1S+fPmw2eTkZI0bN07333+/brvtNh08eNB2UQAAAAAAAAAARGLFihUaO3as+vbtm/fZ2WefreHDh+vjjz+2PV+RzkRv0qSJ/vWvf6lSpUpFnvjSSy9Vu3btbI0BAAAAAAAAgJISCnlMl4AoOHbsmJKSkgp8npOTo1DI/sH4RT7OJTExUWvWrLE1ORvoAAAAAAAAAIBouvjii/Xoo4/mO25869atmjVrli6++GLb8xXpSXRJ2rVrlwYNGqRvvvmmwHdt27ZVhQoVlJiY+P8nLlNGPXr00B133GG7KAAAAAAAAAAAInH//fdr6NCh6tGjhyTp+uuvV25urs477zyNGTPG9nxF3kRPSkpSmTInjx84cEDjxo3L99maNWs0b948NtEBAAAAAAAAAFFTpUoVLViwQF988YW2bt0q6Y8jy9u2bRvRfJ5QEQ+B2blzp6655hp98cUXmjlzppKSknT06FHdeeed8vl8+R6Nl6Rvv/1WixYt0oQJE1SuXLmIiisJlmWZLgEAAAAAAABwhN/vN11CTGn82iTTJTjuu16Zpkso9Yr8JPr/evbZZ9WlSxd98sknGjp0aN7nr776qsqWLaucnBzdeOONat68uWOFOunEm0swGFQgEJDP55PX6y2QtyzL1g2JvNl8uDGF9dlt10A+sjH02L15p9agx9HLm6qJHkeed2NN9Nj8GvTYbN6NNdFj82vEQj6WeuzGmmIhb7LH0Vgj3vIATu7jjz/WSy+9pB9//FG5ubmqX7+++vTpo86dO9ueq8gvFv1fHo9HTz75pKpUqZL3uyQ98sgj+vvf/66HH344kmkBAAAAAAAAACiWRYsWaeDAgTp06JAuvvhiXXbZZTp69KiGDBmi1157zfZ8ET2J/qc/N8//VLt2bb3++utKT08vzrQAAAAAAAAAAETk2Wef1cCBAzVy5Mh8n8+cOVPPPfecevXqZWu+Im2iv/rqq0XaoT9xUx0AAAAAAAAA3CIUYv8yHmRlZal9+/YFPj///PM1f/582/MV6TiXDRs26OjRo7YnBwAAAAAAAAAgmq699lq98MILOnz4cN5n2dnZWrhwobp162Z7viI9if7QQw9p9+7duvbaa4s0KU+kAwAAAAAAAACi4fbbb8/3eygU0qeffqpOnTopNTVVHo9HW7du1aFDh9SuXTvb8xdpEz0xMbFIG+M//vij/H6/cnNzbRcCAAAAAAAAAIBdKSkpBT7r3r17vt/PPPPMiOeP6MWioVBIQ4cO1f79+/N+l6R//etfSk5O1oEDByIuCAAAAAAAAACAopoyZUqJzh/RJvoNN9ygChUq6Prrr1dCQkLeU+p169aVJFWpUsW5CgEAAAAAAADACbxYNG4Eg0F9//33+v333/M+O3bsmNatW6ehQ4famssT+vMx8lP4+eef1aNHD3355ZcFvktNTVXPnj1POm7ixIm2Clq2bJlmzZql999/X5L0xhtvaPbs2Tp8+LB69eql4cOHKyGhSO9DPSnLsiIeCwAAAAAAALiJ3+83XUJMOeuVyaZLcNz3f7nfdAmu89lnn+nOO+/UoUOHJP3/k1Q8Ho9SUlL06aef2pqvyE+i5+Tk5Nu1/19du3ZVTk5O3lPpoVBIubm5Onr0qK1ifvnlF02aNEmVK1eWJP3f//2fHnjgAT300ENKT0/X6NGjtXDhQvXr18/WvCc68eYSDAYVCATk8/nk9XoL5C3LsnVDIm82H25MYX122zWQj2wMPXZv3qk16HH08qZqoseR591YEz02vwY9Npt3Y0302PwasZCPpR67saZYyJvscTTWiLc8gIKmTp2q7t27q0ePHrrpppv0f//3f/rvf/+rv/71rxo5cqTt+Yq8iV6vXj0tX778pN89/vjjthc+USgU0pgxY1S7dm0dPnxYkvTCCy/osssuy3vKffTo0brvvvuKvYkOAAAAAAAAACidduzYoQcffFDNmzdX/fr19dVXX+mSSy7R0KFD9fTTT+uqq66yNV+Rz0UpW7Zs3pnnJWHhwoXatWuX7rnnnrzPNm3apI4dO+b9npaWpl27dum3334rsToAAAAAAAAAlE6hUOn7QUHVq1dXIBCQJJ177rlav369JKlRo0batWuX7fkierGo03744Qc9/vjjeuGFF5SdnZ33eVZWlurXr5/3e2JioipWrKi9e/eqWrVqJkoFAAAAAAAAALhYnz59NHnyZNWsWVNdunTRiBEjVKFCBX388cc666yzbM9nfBM9NzdXo0aN0i233KLmzZvr888/z/suMTFR5cqVy5cvX7583nEvkQoGg/l+/3Pj/n838E81xu4a5KObP9mYU/XZbddA3v4YeuzuvBNr0OPo5qOxBj12Nh+NNeixs/lorEGPzeajsQY9djYfjTXcno+1HkdjjdKWN93jaKwRT/mTvd8PiHf9+/fXaaedpmrVqql169a64YYbNH/+fKWkpGjq1Km25/OEQmYf+n/yySf10Ucf6e9//7sSExP1+eefa8yYMXr//fd14YUXavz48ercuXNevnXr1nrxxReVlpYW0XqWZSknJ8fWmKSkJFtjyJvNu7Em8s7m3VgTefNrkHc278aayJtfg7yzeTfWRN7ZvBtrIm9+DfLO5t1YE3nza8RbvnXr1kXOQmr08mTTJThu+433my6h1DO+id65c2f95z//UdmyZSX98WR6dna2KlWqJJ/Pp+bNm+vee++VJG3fvl3dunXTJ598ourVq0e0nmVZatSoUb7PsrOztWPHDjVo0EAVKlQoMGb79u0FxhSGvNl8uDGF9dlt10A+sjH02L15p9agx9HLm6qJHkeed2NN9Nj8GvTYbN6NNdFj82vEQj6WeuzGmmIhb7LH0Vgj3vI8iW5Po0WlcBP9JjbRS5rx41wWLVqkY8eO5f3+zTffaOrUqVq0aJG++eYbjR8/Xtdee60aNGig2bNnq0WLFhFvoP8p3M2lQoUKYb+ze0MibzZf2JhwfXbbNZCPfAw9dmfeyTXocXTy0ViDHjubj8Ya9NjZfDTWoMdm89FYgx47m4/GGrGSj5UeR2ON0po31eNorBFveQAly/gmeu3atfP9vnPnTpUpU0ZnnnmmzjzzTK1du1ZXX321KlWqJEl6/vnnTZQJAAAAAAAAAIhDxjfRT9S2bVu9//77eb+PHz9evXv31k8//aTWrVuratWq5ooDAAAAAAAAALjKmDFjbOWnTJliK2/8TPRosyzLdAkAAAAAAACAI/x+v+kSYkrDv9vbPI0FP/S2t4FcGpX0Jnqxn0T/8ssvlZycrLPPPru4U0XNiTeXYDCoQCAgn8930jOnLMuydUMibzYfbkxhfXbbNZCPbAw9dm/eqTXocfTypmqix5Hn3VgTPTa/Bj02m3djTfTY/BqxkI+lHruxpljIm+xxNNaItzwA+5vidhVrE33Dhg0aNWqUypQpo3nz5unMM890qi4AAAAAAAAAAIyLeBN9y5YtGj58uJ544gn99ttvuvXWW7VgwQJVr17dyfoAAAAAAAAAADAmIZJBO3bs0JAhQ/TII4+oWbNm6tixo4YOHaoBAwYoKyvL6RoBAAAAAAAAoPhCpfAHJc72JvrOnTs1aNAgjR8/Xuedd17e51dccYV69eqlgQMHKjs729EiAQAAAAAAAAAwwdYm+t69ezVw4EDdc8896tChQ4Hve/furY4dO2rw4ME6evSoY0UCAAAAAAAAAGBCkTfRDx8+rAEDBui2227TZZddFjY3dOhQNWrUSPfcc48jBQIAAAAAAAAAYEqRXyxasWJFTZgwQa1atTplduzYsVq7dm2xCgMAAAAAAAAAJ4VCHtMlIAbZOs7lZBvoS5cu1cGDBwt8np6eHnlVAAAAAAAAAAC4gO0Xi/6v3NxcjRkzRrt27XKqHgAAAAAAAAAAXMMTCoVCkQ7Ozc1Vs2bNtHTpUqWmpjpZV4mxLMt0CQAAAAAAAIAj/H6/6RJiSoMFj5guwXE7+o42XUKpV+Qz0UuTE28uwWBQgUBAPp9PXq+3QN6yLFs3JPJm8+HGFNZnt10D+cjG0GP35p1agx5HL2+qJnoced6NNdFj82vQY7N5N9ZEj82vEQv5WOqxG2uKhbzJHkdjjXjLw6aIHydGPCvWcS4AAAAAAAAAALjN0aNHlZOTI0k6fPiwVq5cqc2bN0c0F5voAAAAAAAAAIBS44svvlD79u21Zs0aHT58WFdffbWGDh2qa665Rv/4xz9sz1fsTXSPx1PcKQAAAAAAAAAAcMQjjzyiyy67TK1bt9a7776rnJwcvfvuu+rbt6/mzp1re75ib6IX472kAAAAAAAAAAA4avv27brqqqtUqVIlrV+/Xpdffrnq16+vbt26affu3bbnK9YmemJiojZv3qzU1NTiTAMAAAAAAAAAUeAphT84UbVq1bR582b9/vvv+vTTT9W6dWtJ0i+//KLq1avbno8z0QEAAAAAAAAApcZf/vIXTZ48Wenp6crJyVHHjh21atUqTZ06VVdccYXt+cqUQI0AAAAAAAAAABhx2223qVGjRvr55591+eWXq1KlSvrtt9/0l7/8Rbfeeqvt+dhEBwAAAAAAAACUKpdcckm+33v27BnxXBznAgAAAAAAACA+hErhDwr47rvvHJ3PEwqF4uo/tWVZpksAAAAAAAAAHOH3+02XEFMavDTVdAmO23HzKNMluE5qaqqaNGmijIwMXXHFFapXr16x5ovL41xOvLkEg0EFAgH5fD55vd4CecuybN2QyJvNhxtTWJ/ddg3kIxtDj92bd2oNehy9vKma6HHkeTfWRI/Nr0GPzebdWBM9Nr9GLORjqcdurCkW8iZ7HI014i0PoKC3335bH3zwgT744APNmTNH55xzjq644gp169ZNderUsT1fXG6iAwAAAAAAAABKp8aNG6tx48YaOHCgDhw4oI8++kirV6/W3/72NzVs2FAvv/yyrfnYRAcAAAAAAAAQH+LqYGtIUpUqVdS0aVPt2bNHP//8s7Zu3Wp7jog30X/99Vf9+OOP+u9//6vff/9dFSpUUM2aNeX3+5WQwPtKAQAAAAAAAADRd+zYMa1du1arV6/Whx9+qL1796pTp04aMGCAOnXqZHs+25voy5cv15NPPqnvvvtOlStXltfrlcfj0YEDB5Sdna0qVapoyJAh6tevn+1iAAAAAAAAAAAojjZt2ujYsWPq0KGDhg0bps6dO5/0XZhFZWsTfcGCBXrqqac0cuRIXXLJJapSpUq+77///nu99NJLmjJliipVqqRrr7024sIAAAAAAAAAALBr7NixuuSSS1S5cmVH5rO1if7cc89p4sSJuuSSS076/VlnnaUJEybol19+0auvvsomOgAAAAAAAAD3CHlMV4AouOaaaxydz9bh5bm5udqzZ88pc7///ruSk5MjLgoAAAAAAAAAADew9ST69ddfr2nTpiknJ0dXXnmlatasme/7QCCguXPn6quvvtLf//53RwsFAAAAAAAAACDaPKFQKFTUcCgU0hNPPKHnn39eR44cUZUqVZScnKyEhATt2bNH2dnZatq0qUaNGqW2bduWZN0RsyzLdAkAAAAAAACAI/x+v+kSYkqDF6aZLsFxO/56n+kSSj1bT6J7PB7dddddGjBggL766ivt2bNHOTk5KleunKpVq6bU1FSdeeaZJVWrY068uQSDQQUCAfl8vpO+pdWyLFs3JPJm8+HGFNZnt10D+cjG0GP35p1agx5HL2+qJnoced6NNdFj82vQY7N5N9ZEj82vEQv5WOqxG2uKhbzJHkdjjXjLw56iP04M/H9F3kT/97//rbp160qSKlWqpAsvvNDWGAAAAAAAAAAAYk2RXiz63Xff6YYbbtCKFSuKPPGCBQvUt29fHTx4MOLiAAAAAAAAAAAwqUib6I0bN9a8efP0yCOPaNSoUfr111/DZv/973/r9ttv1+uvv65FixYpOTnZsWIBAAAAAAAAAIimIh/n4vP59M477+ipp55SRkaGmjVrplatWqlGjRoKhULau3evPv/8c/34448aMGCA+vfvrzJlbB25DgAAAAAAAAAlhzPREQFbu9zly5fXiBEjNHToUH388cfasmWLvvvuOyUkJKhGjRoaOnSozj//fDbPAQAAAAAAAADGHDhwQO+//7527NihPn36aN26dapTp46aN29ue66IdrvLlSunzp07q3PnzpEMBwAAAAAAAACgRAQCAfXv31+HDh3S8ePH1a1bN61du1avvvqqnnrqKV144YW25ivSmegAAAAAAAAAAMSCiRMnKj09XZ9++qlCoT/O8Bk7dqx69+6txx9/3PZ8bKIDAAAAAAAAiA8hT+n7QQF/PolepUqVfJ9feuml2r59u+352EQHAAAAAAAAAJQaNWrUUCAQKPD5N998o5o1a9qezxP683n2OGFZlukSAAAAAAAAAEf4/X7TJcSU+s89aroEx/048F7TJbjOokWLNGXKFN14441asGCBhg8frv/+979atGiR7rvvPvXu3dvWfBG9WDTWnXhzCQaDCgQC8vl88nq9BfKWZdm6IZE3mw83prA+u+0ayEc2hh67N+/UGvQ4enlTNdHjyPNurIkem1+DHpvNu7Ememx+jVjIx1KP3VhTLORN9jgaa8RbHkBBN910kypUqKA5c+YoFAppxowZqlOnjh588EFdd911tueLy010AAAAAAAAAEDpdc011+iaa67R4cOHFQqFVKlSpYjnYhMdAAAAAAAAQFzwxNXB1pCkihUrFnsOXiwKAAAAAAAAACg13nzzTf3www+OzccmOgAAAAAAAACg1Jg5c6Y++eQTx+ZjEx0AAAAAAAAAUGpcccUVevfddx2bj010AAAAAAAAAPEhVAp/UMCdd96pihUr6q677tKePXuKPV+Jvlj0yJEjKleuXEkuAQAAAAAAAABAnq5du0qSdu3apdWrV6tGjRr5vl+1apWt+Wxvou/bt08XXXSRvv76a5UpE374sWPH1KdPHw0fPlzt2rWzuwwAAAAAAAAAALYNGzbM0flsb6InJSXp2LFj6t69u0477TSdeeaZaty4sVq3bi2/36+yZctKkiZPnqwtW7aoevXqjhYMAAAAAAAAAEA411xzjaPzeUKhkK2Tc7KyspSenq7HHntMv/76q/bu3avt27fr66+/VigU0rXXXqujR4/qlVde0aOPPqpu3bo5WnBxWZZlugQAAAAAAADAEX6/33QJMaXBs9NNl+C4HYNGmi7B1fbv369QKKSUlJSI5yjSk+ihUEjbt2/XWWedJUnyeDy64oor8mWOHz+umTNn6m9/+5s8Ho/GjRvnug30P514cwkGgwoEAvL5fPJ6vQXylmXZuiGRN5sPN6awPrvtGshHNoYeuzfv1Br0OHp5UzXR48jzbqyJHptfgx6bzbuxJnpsfo1YyMdSj91YUyzkTfY4GmvEWx7Ayb311luaNWuWdu7cKUk644wzdNddd6l79+625yrSJvrnn3+u/v37q3HjxkpPT5f0x0tDf//9d23atElr167VihUrtHv3bv31r3/V8ePH9cwzz+jiiy9WrVq1bBcFAAAAAAAAAEAkli1bpjFjxuiqq67KOx99zZo1Gj16tCTZ3kgv0iZ6WlqaXnzxRW3cuFGffvqpkpKSdN555+no0aOqVq2aLrzwQt122226+OKLVbFiRUnS3r17dffdd+vll1+2VRAAAAAAAAAAAJF66qmnNGjQIN111115n1199dWqXbu25syZUzKb6BUrVpTf79f555+vc889V5s3b5bX69U//vEPrV27VjVq1NCVV16Zl8/JydHIkSPVs2dPLV68WD179rRVFAAAAAAAAAA4ztbbIRGrdu/erTZt2hT4vE2bNpo3b57t+Yq0iR4MBtW5c2dlZGSoSpUq+u677/Tkk0/KsixdfPHFSklJUffu3ZWRkaHevXvrH//4hz766CMNHTpUZ5xxhu2iAAAAAAAAAACIRNOmTfXKK6+oTZs2SkxMlPTHOz1feeUVNW3a1PZ8RdpE93q9Wrx4sV588UUdOXJEiYmJmjJlitatW6eBAwdKkh544AF9+umneu6555SUlKTMzExlZGTYLggAAAAAAAAAgEiNGTNG/fv3V7du3dS6dWtJ0ldffaW9e/fqpZdesj1fQlFCmzdv1po1a5SWlqb//ve/2r17t8qXL6+rrrpKP/30kySpXLlyWrBggcaPH6/ffvtNGzZssF0MAAAAAAAAAADF0aJFCy1evFitWrXShg0b9O2336pVq1ZasmSJmjdvbnu+Ij2JblmWXnrpJZUpU0ZZWVn65Zdf9OuvvyolJUWbN2/Wjz/+mJfNzc1V27ZttXz5cp1//vnq1KmT7aIAAAAAAAAAwHGciR43GjdurKlTpzoyV5E20a+//npdf/31Wrt2rb7//nvNnDlTv//+uyZNmiSv16uZM2fq66+/Vq9evZSUlKSrrrpK1apV05QpU3ThhRfK4/E4UiwAAAAAAAAAAKdy4MAB7d69W6mpqdq5c6c++OADXX755apevbrtuTyhUOiUf/9y/PhxjRs3Th988IH+8pe/yLIsdenSRTNnztSSJUt07Ngxde/eXSNHjtTcuXP1yiuvqE6dOurWrZtGjBihSy+9NKILLQmWZZkuAQAAAAAAAHCE3+83XUJMafD0dNMlOG7HHSNNl+A6Gzdu1MCBA9WmTRvNmjVLGzZsUO/eveX1evXiiy8qNTXV1nxFehI9FAqpYsWKWrZsmd58803l5OTo+uuv14YNGzRhwgSNHDlSR48eVZ8+fdSzZ08lJSVJkrp166Zt27a5ahNdKnhzCQaDCgQC8vl88nq9BfKWZdm6IZE3mw83prA+u+0ayEc2hh67N+/UGvQ4enlTNdHjyPNurIkem1+DHpvNu7Ememx+jVjIx1KP3VhTLORN9jgaa8RbHkBBjzzyiFq3bq2xY8dKktLS0rR27Vrdd999mjp1ql544QVb8xVpEz0xMVGjR4+WJPXs2VNXXXWVJOmee+7Rzz//rNq1a+vJJ5+UpLwNdEm6+eabVaVKFVsFAQAAAAAAAECJ4Ez0uLBx40Y9++yz+Y5uSUpK0k033aTBgwfbni/B7oCKFSuqWrVqkqTk5GQ1bdpUFStW1EUXXVQgywY6AAAAAAAAACCakpOTtXXr1gKfb926VZUqVbI9X5GeRP/T0aNH9dprr+nqq68udLEjR46oW7duevrpp3X22WfbLgoAAAAAAAAAgEjceOONmj59ug4dOqRzzz1XkvTll19q7ty5uv32223PZ2sTPSEhQZMmTdIll1xS6CZ6uXLltHPnTiUmJtouCAAAAAAAAACASA0aNEiHDh3Sk08+qaNHjyoUCqls2bLq16+fBg0aZHs+W5voiYmJCoVCKlOmjObPn68NGzaoc+fOOv/885WSklIgn5Bg+7QYAAAAAAAAACgZIY/pChAl99xzj+644w599913kqSzzjpLFStWjGguW5vokuTx/PEH7eeff9a7776rt956SwkJCWrcuLHatGmj9PT0vEfkAQAAAAAAAAAwwev1qnnz5sWep8ib6Pv371dWVlbe7/fff79GjRqlHTt2aNOmTdq0aZO+/vprvfbaazp69GjeZjsAAAAAAAAAANFy8OBBzZgxQz169FDLli01depULVmyRHXr1tX06dPVqFEjW/Od8ryV48eP68UXX9Tll1+uWbNm5fsuMTFRZ511lrp3765Ro0bp5Zdf1pdffqnnn3/e3lUBAAAAAAAAAOCAhx56SJ9//rkqV66szz//XPPnz9dNN90kSXr44Ydtz3fKTfSEhAT985//VI8ePTRq1CiFQqGT5nbs2KFXXnlFY8eO1XnnnRc2BwAAAAAAAAAmeEKl7wcFffTRRxo5cqTOOussffzxx+rSpYuGDx+uESNG6Ouvv7Y9nydUhN3uY8eOqUyZP05+SU1N1SeffKLTTjtNX375pVatWqUVK1Zo586dSkpKUqtWrTRhwgRdeumlWr58uRo2bGi7qJJkWZbpEgAAAAAAAABH+P1+0yXElIZzHjNdguN+GHqP6RJcp23btnrsscfUoUMH9e3bV127dlXv3r21Zs0a3X333VqzZo2t+Yp0JvqfG+jHjx+Xx+NRbm6uvvjiC/Xr10+NGjVSRkaGOnXqpObNm6ts2bL2ryrKTry5BINBBQIB+Xw+eb3eAnnLsmzdkMibzYcbU1if3XYN5CMbQ4/dm3dqDXocvbypmuhx5Hk31kSPza9Bj83m3VgTPTa/RizkY6nHbqwpFvImexyNNeItD6CgNm3aaMKECWrevLm++uorTZgwQb/++qtefvlltWzZ0vZ8RX6xqCRlZ2crFAopJydH6enpWrp0qVJTUwvk/ny4PTc313ZBAAAAAAAAAABE6sEHH9S4ceO0bds2ZWZmqmHDhpo8ebI2b96sp59+2vZ8RdpE37Bhg84880ylpKRo7dq1qlSpkjwez0k30CXp6NGj6t69u5KSkmwXBAAAAAAAAABApGrUqKGnnnoq32cjRozQ/fffH9F8RdpEHzdunLZv365LL71UXbt2VUpKyinH3HjjjdqzZ4/+/e9/64ILLoioOAAAAAAAAABwDC/iLHUOHTqkSpUqnTJXvnx522P+dMpN9FAopBEjRmj16tV677339NZbb8nj8eT7vjAej0eBQKDIBQEAAAAAAAAAcCrbtm3TLbfcojlz5qhFixZFGrNy5UpNnDhRb731lpKTk4s05pSb6B6PRx06dFCHDh00ZswYvffee5o/f76++eYbXXXVVRo+fLhq165dYFxubq6OHTumrKysIhUCAAAAAAAAAEBRNWnSRJMnT9Ydd9yhG2+8UYMGDVK5cuVOmj106JBmzpyp999/X08//XSRN9Almy8WLVOmjDIyMpSRkaEPP/xQkydP1pVXXqlFixbp7LPPzpdNTExUYmIi56IDAAAAAAAAAEpEx44d9cYbb2jatGm68MIL1aVLF7Vq1Uo1atRQKBTS3r179fnnn+vjjz9WRkaGli1bZmsDXbK5if6/OnXqpLZt22rz5s0FNtABAAAAAAAAAIiGWrVq6bHHHtOePXv03nvv6euvv9avv/4qj8ejmjVrqk2bNnrggQdUrVq1iOaPeBNdkv7zn/+oZcuWxZkCAAAAAAAAAIBiq1Wrlvr27ev4vAmRDjx+/LjuvPNO3X333Q6WAwAAAAAAAACAe3hCoVAokoFPPvmkZs+erRkzZigjI8PpukqMZVmmSwAAAAAAAAAc4ff7TZcQUxrOfsx0CY77Ydg9pkso9SI6zmX58uV68skn1bNnT3k8Hn399deqXbu2atasqYSEiB9uj5oTby7BYFCBQEA+n09er7dA3rIsWzck8mbz4cYU1me3XQP5yMbQY/fmnVqDHkcvb6omehx53o010WPza9Bjs3k31kSPza8RC/lY6rEba4qFvMkeR2ONeMvDHk9EjxOXXvv27dODDz6ozz77TA0bNtTkyZOVmpp6ynG5ubmaPn26/vGPf8jr9eree+/N96D1jh07NHbsWFmWpbS0ND3yyCOqU6dO3vdvvPGGZs+ercOHD6tXr14aPny4q/eVbW+iL1iwQI888oiuvvpq9e/fX1dccYU8Ho8kKSEhQTVq1FDt2rXVsmVLDR06VJUqVXK8aAAAAAAAAABA5EKhkIYOHSpJWrJkib799lsNHjxYb731lipWrFjo2Mcff1zLli3T7NmzVbZsWQ0bNkz169dXs2bNlJOTowEDBig1NVXLli3T8uXLNXToUC1ZskQJCQn6v//7Pz3wwAN66KGHlJ6ertGjR2vhwoXq169fNC47IkXe3t+xY4cGDBigKVOmaODAgZoyZUre5vnq1av18ssva+bMmRowYICaNm2qV199VTNmzCixwgEAAAAAAAAAkfnqq6+0fv16TZo0SWeddZauueYaNWzYUCtXrix03JEjR7Rw4UINHTpUbdq0UatWrdSvXz8tWrRIkvTuu+9q3759mjRpkurVq6dBgwYpOztbX331lSTphRde0GWXXaaePXuqQYMGeZvoblakJ9H/8Y9/KDMzU36/X6+88oqaN2+e953H41Ht2rVVu3btfGMSEhL0/vvv68EHH3S2YgAAAAAAAACAJKlLly6Ffr9q1aqTfr5p0ybVqVNHjRs3zvusVatW+uabb3T11VeHne+HH35QMBhUx44d841788038+ZNS0tTSkpK3vctW7bUN998o/POO0+bNm3Sfffdl/ddWlqadu3apd9++03VqlUr/GINKdImenp6up566ilddNFFRZ64Xr166tWrV6R1AQAAAAAAAICzQh7TFbhGVlaW6tWrl++zKlWqKBAInHJcYmKi6tatm/dZcnKy9uzZU+i8//t9/fr1875LTExUxYoVtXfv3tjeRK9bt26+/yhF4eYzbAAAAAAAAACgNAj3pPmplClTRklJSfk+K1++vILB4CnHlStXLt9nFSpUyBuXmJioxMTEAvPu27cv7/sTx5cvX16HDx+O6Dqiochnon///fcn/TwUCqlVq1bq1auXZsyYoR9++MGx4gAAAAAAAAAAzktJScnb2P7ToUOHCmxwn2xcdna2Dh06lPdZVlZW3rhTzRvpuiYVaRN97969uu6669SnTx+tX7++wPdjxoxR8+bN9c477+jKK6/U9OnTFQqFHC8WAAAAAAAAAFB8LVu21LZt23Tw4MG8zzZs2KDTTz+90HF169ZV9erVtW7dupOOa9WqldavX6/c3NyTft+yZct8Y7dv365Dhw6dcl2TirSJXrNmTc2YMUPHjh3TTTfdpHvuuUf79+9Xbm6uPB6PevbsqczMTK1YsUL333+/XnrpJY0ePbqkawcAAAAAAACAoguVwp8INWnSRI0aNdKMGTN0/Phxbdy4Ue+99546d+6s48eP6+DBg/k2wv+UkJCgjIwMPfHEEzp06JD++9//6sUXX1Tnzp0lSe3bt9exY8c0b948SdLq1av17bff5n1/1VVX6bXXXtP333+v3NxczZ49Wy1atFD16tUjv5gS5gnZfGR85cqVGj9+vDwej4YNG6bly5fr2Wefzfe4/ZtvvqlRo0Zp/PjxuuGGG04554IFCzRp0qR8n40ZM0b9+/fXt99+q4ceekjbt29X+/btNWnSJFWtWtVOyflYlhXxWAAAAAAAAMBN/H6/6RJiSqPHZ5guwXHb7x4R8dhNmzZp0KBBOnr0qLKysnT11Vdr8uTJ+vnnn9WlSxctXbpUPp+vwLisrCzdeuut2rp1q0KhkOrXr6+FCxeqUqVKkqSPP/5Yw4cPV9myZbV//34NHjxYQ4cOzRs/fvx4LVmyJC///PPPq2nTphFfx5++//57fffdd2rRooVq164t6Y/97CNHjqhZs2b5Xmhqh+1NdEk6cOCAxo4dq/fff1/jx4/X9ddfXyAzYcIEvfPOO1q+fPkp36o6YsQINWzYUDfffHPeZ+XLl9fBgwfVrVs3XXHFFRowYIBeeOEF7d27V3PmzLFbch7LsgrcXILBoAKBgHw+n7xeb5HG2F2DfPTy4cYU1me3XQP5yMbQY/fmnVqDHkcvb6omehx53o010WPza9Bjs3k31kSPza8RC/lY6rEba4qFvMkeR2ONeMvDHjbRCwoGg1q7dq1SUlLUvHnzIo87fvy41q1bpyNHjqhNmzYqW7Zsvu/379+vdevWqV69emrSpEmB8du2bdNPP/2k1q1bF+uhaUk6ePCgxowZo1WrVsnj8ehvf/ubOnToIEm66aab9NVXX8nj8ahTp0567LHHVLFiRVvzl4mkqCpVquiJJ57Q1KlTNW7cOLVp00b16tXLl7ntttu0c+fOAm94PZn169erd+/eSk5Ozvf54sWLVaFCBT3wwAMqW7asRo0apY4dO+qXX37J+5sEAAAAAAAAAEBkvF6vOnXqZHtcQkKC0tPTw35ftWpVdenSJez3TZo0OenmeiTGjx+v7du367nnnlNaWpqqVKmS993ChQu1f/9+ffnll5o4caImTZqkKVOm2Jq/SGeihzNq1Cg999xzBTbQJal27dp65plnTrmr/8svv2jXrl2aMGGC0tLS1KVLF82fP1/SH/+coF27dnl/i5GUlKTU1FR9/fXXxSkbAAAAAAAAQDwyfX65i85EL01Wr16tMWPGqEOHDvk20KU/NvurVaumyy67TJmZmXr33Xdtzx/Rk+j/q127dmG/83g8pxy/efNm1atXTyNGjJDP59Onn36qzMxM1a9fX1lZWUpNTc2Xr1Klivbs2VPcsgEAAAAAAAAApUDlypX166+/njKXlZV10uO8T6XYm+jFddFFF+miiy7K+71Hjx769NNPtWzZMiUmJhY4DqZ8+fIKBoPFWvPE8dnZ2fn+tyhj7K5BPrr5k405VZ/ddg3k7Y+hx+7OO7EGPY5uPhpr0GNn89FYgx47m4/GGvTYbD4aa9BjZ/PRWMPt+VjrcTTWKG150z2OxhrxlI9kQxAobfr06aNJkybp4MGD6tq1q04//fR83x86dEgrV67UlClT1KdPH9vzR/Ri0ZI2ffp0ffHFF6pXr56qV6+u0aNH5313++23Kz09XQMGDIhobsuylJOTY2tMUlKSrTHkzebdWBN5Z/NurIm8+TXIO5t3Y03kza9B3tm8G2si72zejTWRN78GeWfzbqyJvPk14i3funXrImchNZpZCl8sOrx4LxYtLZ599lk988wz+v3331W+fHklJycrMTFRhw8f1sGDBxUKhdSzZ0899NBDSkiwd8q58U302bNnKykpSbfddlveZzfeeKNq1qypNm3aaOnSpVq8eLEkKRQK6cILL9SYMWOUkZER0XqWZalRo0b5PsvOztaOHTvUoEEDVahQocCY7du3FxhTGPJm8+HGFNZnt10D+cjG0GP35p1agx5HL2+qJnoced6NNdFj82vQY7N5N9ZEj82vEQv5WOqxG2uKhbzJHkdjjXjL8yS6PWfNKH2b6N+PYBP9T4cOHdLHH3+sbdu26bffftORI0fk9XrVoEEDdejQQfXr149oXuPHuTRv3lz33HOP6tevrzPPPFNvvPGGvv76a82fP18NGzbUlClT9M477+iKK67QokWLdPDgQbVv375Ya4a7uVSoUCHsd3ZvSOTN5gsbE67PbrsG8pGPocfuzDu5Bj2OTj4aa9BjZ/PRWIMeO5uPxhr02Gw+GmvQY2fz0VgjVvKx0uNorFFa86Z6HI014i0P4A+VKlVS165d1bVrV0fnNb6J3qlTJw0fPlwPP/yw9u/fr6ZNm2r+/PlKT0+XJE2aNEljxozRlClTtH//fo0fP77AG1YBAAAAAAAAACgJxjfRJal3797q3bv3Sb/r0aOHLrjgAn377bc655xzVLdu3ShXBwAAAAAAAACIV8bPRI82y7JMlwAAAAAAAAA4wu/3my4hpnAmeum0atUqW/kuXbrYyrviSfRoO/HmEgwGFQgE5PP5TnrmlGVZtm5I5M3mw40prM9uuwbykY2hx+7NO7UGPY5e3lRN9DjyvBtrosfm16DHZvNurIkem18jFvKx1GM31hQLeZM9jsYa8ZaHTXH1OHH8GDJkSJGzHo9HgUDA1vxxuYkOAAAAAAAAACgd7D6JblexNtE3btyocePGafPmzcrNzS3wvd0dfQAAAAAAAAAA7DjjjDNKdP5ibaKPGTNGkjR9+nRVq1bNkYIAAAAAAAAAAHCLYm2i//TTT3r66afVrl07p+oBAAAAAAAAgJLBmeiIQEJxBvv9fm3YsMGpWgAAAAAAAAAAcJVibaJPnDhRr732ml566SUdOXLEqZoAAAAAAAAAAHCFYh3nMmDAAAWDQU2ZMkXTpk1TzZo1lZDw//flS/qtqAAAAAAAAAAAlKRibaIPGzbMqToAAAAAAAAAoER5OBMdESjWJvo111zjVB0AAAAAAAAAALiOJxQKxdXfv1iWZboEAAAAAAAAwBF+v990CTGl8aMzTJfguO/uHWG6hFKvWE+id+/eXQMHDtTVV1/tVD1RceLNJRgMKhAIyOfzyev1FshblmXrhkTebD7cmML67LZrIB/ZGHrs3rxTa9Dj6OVN1USPI8+7sSZ6bH4Nemw278aa6LH5NWIhH0s9dmNNsZA32eNorBFveQAlr1ib6CkpKdq2bZtTtQAAAAAAAABAyQl5TFeAGJRQnMF33XWXXn/9dY5IAQAAAAAAAAC43ocffmh7TLGeRP/3v/+tSy+9VDfddJN69uyptLS0fN/36NGjONMDAAAAAAAAAGDLyJEjNXXqVCUmJuZ99t1332nKlCn67LPPtGnTJlvzFWsT/YknnpAkVa9eXatXr9bq1avzvvN4PGyiAwAAAAAAAACi6ttvv9WQIUP0xBNP6NChQ5o1a5aWLFmi888/X0uWLLE9X7E20d9///3iDAcAAAAAAACA6AmZLgDRsGjRIt16663q1auXdu7cqQYNGmjevHk6//zzI5qvWGeiAwAAAAAAAADgJtWrV9eCBQuUnJys8uXL69lnn414A10q5pPoS5cuLfR7jnMBAAAAAAAAAJSktWvXnvTz2267TZMmTVLfvn2VmZmpMmX+2A5PT0+3Nb8jZ6JLUigU0q+//qrc3FxVqFBBKSkpbKIDAAAAAAAAAEpU3759T5n561//KumPd3kGAgFb83tCoZBjJwHl5ubqvffe04wZMzRt2jS1atXKqakdY1mW6RIAAAAAAAAAR/j9ftMlxJQmj8w0XYLjto0ebrqEUq9YT6KfKDExUd26ddPpp5+uSZMmRfSm02g48eYSDAYVCATk8/nk9XoL5C3LsnVDIm82H25MYX122zWQj2wMPXZv3qk16HH08qZqoseR591YEz02vwY9Npt3Y0302PwasZCPpR67saZYyJvscTTWiLc8gJLn6Cb6n/x+v3744YeSmBoAAAAAAAAAgLAsy9L48eO1efNm5ebmFvje7nEuxdpEP9mB7dnZ2XrjjTd0xhlnFGdqAAAAAAAAAABsu//++yVJ06dPV7Vq1Yo9X7E20U92YHtiYqLOOeccTZ48uThTAwAAAAAAAICzHHs7JNzsp59+0tNPP6127do5Ml+xNtE3b97sSBEAAAAAAAAAADjB7/drw4YNjm2iJzgyCwAAAAAAAAAALjBx4kS99tpreumll3TkyJFiz1esTfSlS5fqwIEDBT6fP3++Bg4cWJypAQAAAAAAAACwbcCAATp06JCmTJmiVq1a6eKLL1aXLl3yfuwq1nEuY8aM0ZIlS1SlSpV8n/t8Pj366KPFmRoAAAAAAAAAANuGDRvm6HzF2kQPhULyeDwFPt+/f3+BjXUAAAAAAAAAMMnDi0XjwjXXXOPofLY30d944w298cYbeb+PHTtWFStWzPs9NzdXmzZt0u233+5MhQAAAAAAAAAA2PDzzz/Lsiz9/vvveZ8dO3ZM69at05QpU2zN5QmFQrb+/uWLL77QF198IUmaM2eOevXqpZo1a+Z9X65cObVo0UJt27a1VUi0WJZlugQAAAAAAADAEX6/33QJMeXsyTNNl+C4rfcPN12C67zzzjsaNWqUQqGQQqGQateurQMHDigYDMrv92vx4sW25rP9JHqbNm3Upk0bSX9sot9www1q1qyZ3WmMOvHmEgwGFQgE5PP55PV6C+Qty7J1QyJvNh9uTGF9dts1kI9sDD12b96pNehx9PKmaqLHkefdWBM9Nr8GPTabd2NN9Nj8GrGQj6Ueu7GmWMib7HE01oi3PICC5syZozvuuEM9e/ZU586d9fe//11JSUnq3bu3MjIybM+XUJxi0tPT8x3lAgAAAAAAAACuFSqFPyhg9+7dateunWrWrKkmTZro66+/VrVq1XTHHXdowYIFtucr1ib6ggUL1KBBg+JMAQAAAAAAAACAY8444wx98sknkqTWrVtr7dq1kqQaNWrot99+sz2f7eNcTrRmzRotW7ZMP/74o6ZMmaK33npLlStX1s0331zcqQEAAAAAAAAAsGXQoEEaNWqU6tatq65du6p///7Kzs7W+vXrIzouqVib6O+8847uueceNW3aVIFAQNnZ2UpJSdG0adOUm5urW265pTjTAwAAAAAAAABgy1VXXaUzzjhDFStWVGpqqkaPHq3XX39dderUUWZmpu35irWJ/uSTT2rw4MG68847lZqaKknq3bu3EhMT9dxzz7GJDgAAAAAAAMA9OEM8brRu3Trv//fp00d9+vSJeK5inYm+c+dOtW/fvsDnjRo10t69e4szNQAAAAAAAAAARZKVlaVly5Zp7ty5Wrx4sfbs2VMgs3v3bk2YMMH23MV6Ev2cc87RW2+9lber7/F4JElvv/123pPpAAAAAAAAAACUlO3bt6tPnz767bffVKFCBWVnZ6tcuXKaMWOGLrnkEv3000969tlntWzZMlWrVk0PPvigrfmLtYk+cuRIDRgwQN988408Ho/mzJmjXbt2aevWrXruueeKMzUAAAAAAAAAAKc0ffp0Va1aVfPnz1fjxo2VlZWlhx56SOPGjdO//vUvLV++XNWrV9eoUaN0ww032J6/WJvobdq00Ztvvqm5c+fK4/Fo586dOvvss/Xoo4+qUaNGxZkaAAAAAAAAABzl4Uz0Uumrr77SuHHj1LhxY0lS5cqVdf/99+uCCy7QV199pfvvv189e/ZUuXLlIprfEwqF4uqPjmVZpksAAAAAAAAAHOH3+02XEFPOmTjTdAmO2zJ2uOkSjEtNTdXixYuVlpZW4POlS5cW++jxYj2JHqtOvLkEg0EFAgH5fD55vd4CecuybN2QyJvNhxtTWJ/ddg3kIxtDj92bd2oNehy9vKma6HHkeTfWRI/Nr0GPzebdWBM9Nr9GLORjqcdurCkW8iZ7HI014i0P4A87duxQQkJCgc+3b9+u3NzcfJ81a9bM1tzF2kR/8MEHlZGRofPPP7840wAAAAAAAAAAELH77rvvpJ+PGDFCHo9HkhQKheTxeBQIBGzNXaxN9PXr1+vMM89kEx0AAAAAAAAAYMT8+fNLdP5ibaLffPPNmjt3rv7yl78oOTnZqZoAAAAAAAAAACiSNm3alOj8xdpEv+CCC7Rp0yb17NlTI0aMKHBwe506dYpVHAAAAAAAAAAAJhVrE71z5855//+uu+4q9tkyAAAAAAAAAAC4SbE20VetWuVUHQAAAAAAAABQskKmC0AsKtYm+hlnnOFUHQAAAAAAAAAAuE5CSU5+/PhxtWnTRtu2bSvJZQAAAAAAAAAAKBEluokeCoV08OBB5ebmluQyAAAAAAAAAACUCE8oFCqxk4Byc3PVrFkzLV26VKmpqSW1jC2WZZkuAQAAAAAAAHCE3+83XUJMSX1opukSHLd53HDTJZR6xToTPVadeHMJBoMKBALy+Xzyer0F8pZl2bohkTebDzemsD677RrIRzaGHrs379Qa9Dh6eVM10ePI826siR6bX4Mem827sSZ6bH6NWMjHUo/dWFMs5E32OBprxFseQMmLy010AAAAAAAAAEDpdOTIES1evFhbtmzR0aNHC3w/ZcoUW/OV6JnoAAAAAAAAAABE05gxYzRlyhTt2bPHkfl4Eh0AAAAAAAAAUGp88MEHGj9+vK6//npH5mMTHQAAAAAAAEB8CJkuANFQpUoVpaSkODZfiR7nkpiYqPnz56t+/foluQwAAAAAAAAAAJKkIUOG6NFHH9XevXsdma/YT6KvWbNGy5Yt048//qgpU6borbfeUuXKlXXzzTdLktq0aVPsIgEAAAAAAAAAKIrdu3erfPnyuvzyy9WlSxfVq1dPCQn//3nyoUOH2pqvWJvo77zzju655x41bdpUgUBA2dnZSklJ0bRp05Sbm6tbbrmlONMDAAAAAAAAAGDL559/rsqVK8vv92vPnj35XjDq8Xhsz1esTfQnn3xSgwcP1p133qnU1FRJUu/evZWYmKjnnnuOTXQAAAAAAAAA7sGZ6HFhwYIFjs7nCYVCEf/RadGihZ5//nm1bt1aqampWrp0qVJTU/XFF19o4MCB+vbbb52s1RGWZZkuAQAAAAAAAHCE3+83XUJMSR0303QJjtv80HDTJcSM48ePa926dUpPT7c1rlhPop9zzjl666231Lp1a0n//1H4t99+O+/JdDc68eYSDAYVCATk8/nk9XoL5C3LsnVDIm82H25MYX122zWQj2wMPXZv3qk16HH08qZqoseR591YEz02vwY9Npt3Y0302PwasZCPpR67saZYyJvscTTWiLc8gIL+/e9/68EHH9TXX3+t33//vcD3gUDA1nwJp46EN3LkSL3++uu65ppr5PF4NGfOHF133XV64403dM899xRnagAAAAAAAAAAbHvooYd05MgRDRkyRB6PR88884wefPBBlS1bVpMnT7Y9X7E20du0aaM333xTqamp8vl82rlzp5o0aaJly5apbdu2xZkaAAAAAAAAABzlCZW+HxT09ddf66677tLAgQNVo0YNlSlTRjfeeKMGDhyoJUuW2J6vWMe5SFLDhg01ZcqU4k4DAAAAAAAAAECxJSUlKSsrS5LUsmVLbdy4Ue3bt9cFF1ygefPm2Z6v2JvoAAAAAAAAAAC4RZcuXTRu3Diddtppat++vZ577jm1bNlSb7/9tk477TTb8xXrOJcHH3xQa9asKc4UAAAAAAAAAAA4ZsyYMbr44ov1yy+/qEePHqpatar69eunxYsXa/DgwbbnK9aT6OvXr9eZZ56p888/vzjTAAAAAAAAAEDJ4wzxuFChQgVNnDgx7/dXXnlFW7duVdWqVVW7dm3b8xVrE/3mm2/W3Llz9Ze//EXJycnFmQoAAAAAAAAAgGLbsGGDli5dqmAweNLv7b7js1ib6BdccIE2bdqknj17asSIEUpLS8v3fZ06dYozPQAAAAAAAAAAtgwePFhVq1ZVs2bN5PF4ij1fsTbRO3funPf/77rrrryCQqGQPB6PAoFA8aoDAAAAAAAAAMCGsmXLauTIkerUqZMj83lCoVDEJwHt3Lmz0O/POOOMSKcuMZZlmS4BAAAAAAAAcITf7zddQkxpmjnTdAmO2zRpuOkSXOfDDz/U2LFj1adPH9WsWbPA9z169LA1X7GeRHfjJnlRnHhzCQaDCgQC8vl88nq9BfKWZdm6IZE3mw83prA+u+0ayEc2hh67N+/UGvQ4enlTNdHjyPNurIkem1+DHpvNu7Ememx+jVjIx1KP3VhTLORN9jgaa8RbHkBBU6dO1W+//aZXXnmlwHcejye6m+i7du0q9HvORAcAAAAAAAAARNP+/fv1zDPPqEOHDo7MV+wz0Qs7mJ0z0QEAAAAAAAAA0XTbbbfp2WefVeXKlVWjRo0C39t9+LtYm+irVq3K+//Hjx/XL7/8on/+859atWqVZs2aVZypAQAAAAAAAMBZEb8dErHkkUcekSTdcMMNeZ95PB6FQiF5PB7bD387eiZ63bp1lZ6errS0NP3tb3/TU089VZzpAQAAAAAAAACw5X8f/nZCsTbRw7n66qs1ZcqUkpgaAAAAAAAAAICwTnz4u7gSHJ1NUk5OjhYtWlTg7c4AAAAAAAAAAJS02267TStXrnRsvmI9iZ6amnrSF4uWLVtWEyZMKM7UAAAAAAAAAOAszkSPC/v379fmzZt1ySWXODJfsTbR58+fX+CzxMRENWrUSCkpKcWZGgAAAAAAAAAA2wYNGqTx48erR48eOvPMM4s9X7E20du0aVPsAgAAAAAAAAAAcEpycrIuueQS9ezZU7feeqvS0tLyfZ+enm5rPk8oFIr4HzHs2rVLNWvWVJky+ffi33nnHe3YsUNDhgyJdOoSY1mW6RIAAAAAAAAAR/j9ftMlxJSm9880XYLjNk0ebroE10lNTQ37ncfjUSAQsDVfsZ5E79Kli5YsWaJmzZrl+7xKlSqaN2+eKzfRpYI3l2AwqEAgIJ/Pd9IXolqWZeuGRN5sPtyYwvrstmsgH9kYeuzevFNr0OPo5U3VRI8jz7uxJnpsfg16bDbvxprosfk1YiEfSz12Y02xkDfZ42isEW952OPhTPS4sHnzZkfnSyjO4HAPsZcrV04JCcWaGgAAAAAAAAAA42w/if7FF1/oiy++yPv91VdfVc2aNfN+z83N1YoVK3TxxRc7UyEAAAAAAAAAAA74+eefbb9s1PYm+s6dO/X555/n/W5ZlipWrJj3e1JSki6//HINHDjQ7tQAAAAAAAAAABTL/v37NX36dH399dfKzs7O+zw3N1e//vqrNm7caGs+25vo11xzja655hpJfxzQPnHixAJnogMAAAAAAAAAYML48eO1bds2nX/++XrllVc0btw4/fTTT3r++ec1evRo2/MV68WiAAAAAAAAABAzeLFoXPjss880Y8YMtW/fXsuXL1fDhg3Vq1cv/f777/roo4/Ur18/W/MV6+2fmzdv5il0AAAAAAAAAICrhEJ//I1JixYt8o5vufzyy/Xll1/anqtYT6LPmTOn0O+HDh1anOkBAAAAAAAAALDlggsu0OTJk/X444/r/PPP19KlS3XZZZdpzZo1+d7vWVTF2kT/3xeMhkIh7d69Wzt37lRKSoqaNGlSnKkBAAAAAAAAALAtMzNT9957rzZu3KjrrrtO8+fPV5cuXSRJd911l+35PKE/n2t3yLfffqv7779fw4cPzyvMjm3btqlnz5566aWX1KJFC0nShx9+qKlTp2rPnj3q1q2bxo4dq6SkpIjqsywronEAAAAAAACA2/j9ftMlxJRmo2aaLsFxG6cON12C6x0+fFhr1qxRSkqKzj33XNvjHd9El6Tt27frzjvv1Ntvv21r3NGjR3XDDTcoPT1dY8aMkSRt2bJF1113nQYPHqzu3btr2rRpqlOnTt73dlmWVeDmEgwGFQgE5PP55PV6izTG7hrko5cPN6awPrvtGshHNoYeuzfv1Br0OHp5UzXR48jzbqyJHptfgx6bzbuxJnpsfo1YyMdSj91YUyzkTfY4GmvEWx72sImOSBTrxaLhnH766dq9e7ftcU8//bQOHjyou+++O++zBQsWyOfzafDgwapbt64yMzO1ePFi5eTkOFgxAAAAAAAAAKC0eP3119W3b1916tRJ3333nSZOnKjJkyfr2LFjtucq1pnoS5cuLfBZdna23n77bZ199tm25rIsS88++6z69eun5cuX69xzz1WDBg20adMmXXTRRXm5WrVqKSUlRVu3blVaWlpxygcAAAAAAAAAlDIvvfSSpk2bps6dO+vLL7/UsWPH1Lp1a02YMEHly5fXiBEjbM1XrE30J554ouCEZcooNTVV9957b5HnCYVCGjdunCpWrCiPx6MtW7Zo6tSpuuOOO5SVlaV69erly1epUkV79uxhEx0AAAAAAABAkXkcP9gabrRgwQKNGTNGffr0UWpqqiQpIyNDx48f16OPPhrdTfT333+/OMPzrFu3TpZl6ZlnntHFF18sSTrvvPN01113qU6dOgVeIlq+fHkFg8GI1ztxbHZ2dr7/LcoYu2uQj27+ZGNO1We3XQN5+2PosbvzTqxBj6Obj8Ya9NjZfDTWoMfO5qOxBj02m4/GGvTY2Xw01nB7PtZ6HI01SlvedI+jsUY85U/2fj8g3u3bty9v8/x/nXbaaTpw4IDt+Wy9WPSjjz5ScnKyWrRoYXuhwrz11lsaPXq0vv32WyUmJkqS9uzZowsvvFAJCQm6//771bdv37x89+7dNWTIEHXt2tX2WpZl2T5PPSkpydYY8mbzbqyJvLN5N9ZE3vwa5J3Nu7Em8ubXIO9s3o01kXc278aayJtfg7yzeTfWRN78GvGWb926dZGzkPz3lb4Xi1rTeLHoifr376/k5GTNmDFDfr9fy5YtU6NGjTR8+HAdOnRIL774oq35bG2it2/fXuPHj9ell14qSerSpYueeeYZNWnSxNaiJ1q3bp369OmjL7/8UhUrVsz77KabbtK1116r7OxsPf7445Kkw4cPq23btlq0aJGaN29uey3LstSoUaN8n2VnZ2vHjh1q0KCBKlSoUGDM9u3bC4wpDHmz+XBjCuuz266BfGRj6LF7806tQY+jlzdVEz2OPO/Gmuix+TXosdm8G2uix+bXiIV8LPXYjTXFQt5kj6OxRrzleRLdHjbR48PmzZvVv39/JSYm6j//+Y9atGihXbt26ciRI3rppZdO+pR6YWwd55KVlaUaNWrk/b5z504dOXLE1oIn06JFCzVs2FAPPvig7r77bh08eFAPP/ywLrjgAvXt21e9evXS2rVrlZ6erjlz5iglJUV+vz/i9cLdXCpUqBD2O7s3JPJm84WNCddnt10D+cjH0GN35p1cgx5HJx+NNeixs/lorEGPnc1HYw16bDYfjTXosbP5aKwRK/lY6XE01iiteVM9jsYa8ZaHDZyJHhdSU1P1r3/9SwsXLtTWrVslSR06dFCfPn2UkpJiez5bm+jNmjXTwoULlZOTo4SEBEnSpk2bwp7TlJ6eXrQiypTRvHnzNHXqVF1//fU6cuSI2rVrp4kTJ+q0007TsGHD1L9/f1WtWlXBYFCzZs3KWx8AAAAAAAAAgP9VtWpVDR061JG5bG2iT5w4Uffff78GDBigY8eOyePxaOzYsSfNejweBQKBIs99+umn5x3ZcqJBgwYpIyNDW7ZsUVpammrVqmWnbAAAAAAAAABAHNm3b59ef/11/fjjj0pISFCDBg107bXXqlq1arbnsnUm+olSU1P1+uuvq1mzZpFOEXWWZZkuAQAAAAAAAHBEcY48jkf+e0vhmeiPcib6idavX69bbrlFktSoUSOFQiFt375diYmJeuaZZ4p8gsqfbD2JXlqceHMJBoMKBALy+XwnPXPKsixbNyTyZvPhxhTWZ7ddA/nIxtBj9+adWoMeRy9vqiZ6HHnejTXRY/Nr0GOzeTfWRI/NrxEL+VjqsRtrioW8yR5HY414y8MeD2eix4WHH35Y7dq106OPPqqKFStK+uN9nyNHjtTEiRP15ptv2pqvWAeLz58/Xw0bNizOFAAAAAAAAAAAOOa7775T37598zbQJaly5crq37+/duzYYXu+Ym2it2nThrcFAwAAAAAAAABco3nz5lqzZk2Bzz/66CPbR7lIcXqcCwAAAAAAAACgdEpNTdXf/vY3bdiwQa1atVIoFNLatWu1fv163XzzzXrhhRckSX/961+LNF+xnkQHAAAAAAAAgJgRKoU/JeDDDz9URkaGWrdurczMTOXk5BR57I4dO9S3b1+1atVK/fr1065du/K+C4VCmjlzps4//3w1a9ZMPXr00MaNG/O+f//993XOOefk+3n44Ydt179y5UrVrl1bO3bs0BtvvKGlS5dq586dqlmzppYvX64FCxZo4cKFRZ6PJ9EBAAAAAAAAAJKkLVu2aMiQIRo8eLC6d++uadOmacaMGRozZswpx+bk5GjAgAFKTU3VsmXLtHz5cg0dOlRLlixRQkKC5s2bp3/961+aM2eO6tWrp0mTJmnYsGFatWqVPB6P1q9fr2uvvTbfWuXKlbN9De+//77tMYXhSXQAAAAAAAAAgCRpwYIF8vl8Gjx4sOrWravMzEwtXry4SE+jv/vuu9q3b58mTZqkevXqadCgQcrOztZXX30lSfrll1/02GOP6bzzzlPNmjXVv39/7dy5U7/++qskaf369WrXrp2Sk5PzfsqXL1/k2nft2qVgMJjvsxUrVmjWrFlasmSJsrKybPyX+P94Eh0AAAAAAAAAYlSXLl0K/X7VqlW25tu0aZMuuuiivN9r1aqllJQUbd26VWlpaaccm5aWppSUlLzPWrZsqW+++UbnnXeeMjMz8+W3bdumqlWr6rTTTtPRo0e1YcMGHThwQGPHjlVycrKuu+463XnnnUpIKPxZ8D179ujee+/V2rVr9fe//13nnnuuQqGQ7rrrLq1YsUIVK1ZUTk6OZs2apfnz56thw4a2/puwiQ4AAAAAAAAgPpTQGeKxZvjw4froo49O+l2VKlVUr169Ap/t2bPnlJvoWVlZYceeKDs7W88++6z69eunxMREbdu2TcnJybr99tuVnp6ujRs36t5771WNGjXUu3fvQtfNzMzU7t27NWvWLPl8PknSvHnz9N577+nWW2/ViBEjdPjwYd19992aOnWqnnnmmULnOxGb6AAAAAAAAAAQo+w+aS79semcnZ190u9uueUWJSUl5fusfPnyBY5JOZnExEQlJiYWGLtv374C2YceekgVK1bUwIEDJUmpqan5NvZr1qypvn376s033zzlJvratWs1a9YsderUSZJ06NAhzZ07V+eee67uueceSVKlSpX017/+Ne93OzyhUCiu/v7FsizTJQAAAAAAAACO8Pv9pkuIKWkjZpouwXEbZgx3dL4bb7xRGRkZ6tu3b95n3bt315AhQ9S1a9dCx86cOVPbt2/X7Nmz8z6bNGmSjh8/rgcffDDvs4ULF+qJJ57Q4sWLVb9+/bDzvfzyy5ozZ44++eSTQte96KKL9MADD+jSSy+VJM2ePVtPPfWUXnzxRbVt2zYv995772nChAn6+OOPC53vRHH5JPqJN5dgMKhAICCfzyev11sgb1mWrRsSebP5cGMK67PbroF8ZGPosXvzTq1Bj6OXN1UTPY4878aa6LH5Neix2bwba6LH5teIhXws9diNNcVC3mSPo7FGvOUBp7Vs2VLr1q3L20Q/fPiwfvjhB9WpU+eUY1u1aqXXX39dubm5eU+kb9iwQZdcckleZuXKlZo2bZqeffbZfBvoixcvViAQyLfZvm7dOp1++umnXLdHjx56+OGHtWvXLv3nP//R888/rw4dOuRtoB86dEibN2/WY489lm9TvagKP5EdAAAAAAAAABA3unfvrpUrV2rt2rWSpDlz5iglJSXvL3dycnJ06NChk45t3769jh07pnnz5kmSVq9erW+//VadO3eW9Mem+N13361Ro0apefPmOnz4sA4fPqzc3FylpaXp9ddfz9tMf+qpp/T222+rX79+p6x56NChuvLKK/Xss8/q+eefV8eOHfXoo4/mfd+nTx/16dNHXq9Xo0ePtv3fJC6fRAcAAAAAAAAQfzymC4gBTZs21bBhw9S/f39VrVpVwWBQs2bNUkLCH89jz507VytXrtSyZcsKjC1btqymT5+u4cOH68UXX9T+/fs1ZMgQnXXWWZKkF154QUePHtWECRM0YcKEvHHz589X27ZtNW3aND322GPatWuXGjVqpNmzZ+cd0VKYMmXKaOTIkRo5cqSOHz+eV+ufhg8frsqVK6tFixYFzmwvCjbRAQAAAAAAAAB5Bg0apIyMDG3ZskVpaWmqVatW3nfDhg3TsGHDwo7t0KGDVqxYoXXr1qlevXpq0qRJ3ndz5swpdN3LL79cl19+ebFqP3EDXVLeC0cjxSY6AAAAAAAAACCfunXrqm7duhGNrVq1qrp06eJwReZwJjoAAAAAAAAAAGHwJDoAAAAAAACA+BAyXQBiEU+iAwAAAAAAAAAQBpvoAAAAAAAAAACE4QmFQnH1jxgsyzJdAgAAAAAAAOAIv99vuoSY0nz4TNMlOO7bmcNNl1DqxeWZ6CfeXILBoAKBgHw+n7xeb4G8ZVm2bkjkzebDjSmsz267BvKRjaHH7s07tQY9jl7eVE30OPK8G2uix+bXoMdm826siR6bXyMW8rHUYzfWFAt5kz2Oxhrxloc9nrh6nBhO4TgXAAAAAAAAAADCYBMdAAAAAAAAAIAw2EQHAAAAAAAAACCMuDwTHQAAAAAAAEAc4kx0RIAn0QEAAAAAAAAACINNdAAAAAAAAAAAwmATHQAAAAAAAACAMDgTHQAAAAAAAEB84Ex0RIAn0QEAAAAAAAAACMMTCoXi6u9fLMsyXQIAAAAAAADgCL/fb7qEmNLizpmmS3DcN08MN11CqReXx7mceHMJBoMKBALy+Xzyer0F8pZl2bohkTebDzemsD677RrIRzaGHrs379Qa9Dh6eVM10ePI826siR6bX4Mem827sSZ6bH6NWMjHUo/dWFMs5E32OBprxFseQMmLy010AAAAAAAAAPHHE1dncsApnIkOAAAAAAAAAEAYbKIDAAAAAAAAABAGm+gAAAAAAAAAAITBmegAAAAAAAAA4gNnoiMCPIkOAAAAAAAAAEAYbKIDAAAAAAAAABAGm+gAAAAAAAAAAITBJjoAAAAAAAAAAGF4QqFQXB2nb1mW6RIAAAAAAAAAR/j9ftMlxJRWQ2aaLsFx658cbrqEUq+M6QJMOPHmEgwGFQgE5PP55PV6C+Qty7J1QyJvNh9uTGF9dts1kI9sDD12b96pNehx9PKmaqLHkefdWBM9Nr8GPTabd2NN9Nj8GrGQj6Ueu7GmWMib7HE01oi3PICSx3EuAAAAAAAAAACEwSY6AAAAAAAAAABhxOVxLgAAAAAAAADiUFy9HRJO4Ul0AAAAAAAAAADCYBMdAAAAAAAAAIAw2EQHAAAAAAAAACAMzkQHAAAAAAAAEBc8nImOCPAkOgAAAAAAAAAAYbCJDgAAAAAAAABAGJ5QKBRX/4jBsizTJQAAAAAAAACO8Pv9pkuIKefeMdN0CY776unhpkso9eLyTPQTby7BYFCBQEA+n09er7dA3rIsWzck8mbz4cYU1me3XQP5yMbQY/fmnVqDHkcvb6omehx53o010WPza9Bjs3k31kSPza8RC/lY6rEba4qFvMkeR2ONeMvDprh6nBhO4TgXAAAAAAAAAADCYBMdAAAAAAAAAIAw2EQHAAAAAAAAACCMuDwTHQAAAAAAAEAc4kx0RIAn0QEAAAAAAAAACINNdAAAAAAAAAAAwmATHQAAAAAAAACAMDgTHQAAAAAAAEBc8HAmOiLgCYVCcfVHx7Is0yUAAAAAAAAAjvD7/aZLiCmtb5tpugTHrZs73HQJpV5cPol+4s0lGAwqEAjI5/PJ6/UWyFuWZeuGRN5sPtyYwvrstmsgH9kYeuzevFNr0OPo5U3VRI8jz7uxJnpsfg16bDbvxprosfk1YiEfSz12Y02xkDfZ42isEW95ACWPM9EBAAAAAAAAAAgjLp9EBwAAAAAAABCH4upgaziFJ9EBAAAAAAAAAAiDTXQAAAAAAAAAAMJgEx0AAAAAAAAAgDA4Ex0AAAAAAABAXPCEOBQd9vEkOgAAAAAAAAAAYbCJDgAAAAAAAABAGGyiAwAAAAAAAAAQhicUiq+DgCzLMl0CAAAAAAAA4Ai/32+6hJhy3sAZpktw3JfPjTBdQqkXly8WPfHmEgwGFQgE5PP55PV6C+Qty7J1QyJvNh9uTGF9dts1kI9sDD12b96pNehx9PKmaqLHkefdWBM9Nr8GPTabd2NN9Nj8GrGQj6Ueu7GmWMib7HE01oi3PGyKq8eJ4RSOcwEAAAAAAAAAIAw20QEAAAAAAAAACINNdAAAAAAAAAAAwojLM9EBAAAAAAAAxB8PZ6IjAjyJDgAAAAAAAABAGGyiAwAAAAAAAAAQBpvoAAAAAAAAAACEwZnoAAAAAAAAAOIDZ6IjAjyJDgAAAAAAAABAGJ5QKBRXf/9iWZbpEgAAAAAAAABH+P1+0yXElPS/zjBdguPWvjDCdAmlXlwe53LizSUYDCoQCMjn88nr9RbIW5Zl64ZE3mw+3JjC+uy2ayAf2Rh67N68U2vQ4+jlTdVEjyPPu7Ememx+DXpsNu/Gmuix+TViIR9LPXZjTbGQN9njaKwRb3kAJS8uN9EBAAAAAAAAxB9PXJ3JAadwJjoAAAAAAAAAAGGwiQ4AAAAAAAAAQBhsogMAAAAAAAAAEAZnogMAAAAAAACID5yJjgjwJDoAAAAAAAAAAGGwiQ4AAAAAAAAAQBhsogMAAAAAAAAAEIYnFAoZPwnoH//4h8aMGXPS77Zs2aIPP/xQU6dO1Z49e9StWzeNHTtWSUlJEa1lWVZxSgUAAAAAAABcw+/3my4hprTtN8N0CY77fP4I0yWUeq54seiVV16pSy65JN9nTz/9tLZt26YtW7ZoyJAhGjx4sLp3765p06ZpxowZYTfdi+LEm0swGFQgEJDP55PX6y2QtyzL1g2JvNl8uDGF9dlt10A+sjH02L15p9agx9HLm6qJHkeed2NN9Nj8GvTYbN6NNdFj82vEQj6WeuzGmmIhb7LH0Vgj3vIASp4rjnMpV66ckpOT835+//13LV68WGPGjNGCBQvk8/k0ePBg1a1bV5mZmVq8eLFycnJMlw0AAAAAAAAAKOVcsYl+oieffFJdu3bVWWedpU2bNqljx45539WqVUspKSnaunWrwQoBAAAAAAAAAPHAdZvo+/bt07JlyzRgwABJUlZWlurVq5cvU6VKFe3Zs8dEeQAAAAAAAABiVagU/qDEueJM9P+1cOFCtWvXTg0bNpQkJSYmFniJaPny5RUMBiNe48Sx2dnZ+f63KGPsrkE+uvmTjTlVn912DeTtj6HH7s47sQY9jm4+GmvQY2fz0ViDHjubj8Ya9NhsPhpr0GNn89FYw+35WOtxNNYobXnTPY7GGvGUP9n7/QA4yxMKhVzz9xXHjx/XxRdfrAceeECXXXaZJOnGG29URkaG+vbtm5fr3r27hgwZoq5du9pew7Is2+epJyUl2RpD3mzejTWRdzbvxprIm1+DvLN5N9ZE3vwa5J3Nu7Em8s7m3VgTefNrkHc278aayJtfI97yrVu3LnIWUtu+M0yX4LjPF4wwXUKp56pN9E8++UR33XWXPv30U5UrV06SNHXqVO3evVuPP/64JOnw4cNq27atFi1apObNm9tew7IsNWrUKN9n2dnZ2rFjhxo0aKAKFSoUGLN9+/YCYwpD3mw+3JjC+uy2ayAf2Rh67N68U2vQ4+jlTdVEjyPPu7Ememx+DXpsNu/Gmuix+TViIR9LPXZjTbGQN9njaKwRb3meRLeHTXREwlXHuaxatUpt2rTJ20CX/njqvFevXlq7dq3S09M1Z84cpaSkyO/3R7xOuJtLhQoVwn5n94ZE3my+sDHh+uy2ayAf+Rh67M68k2vQ4+jko7EGPXY2H4016LGz+WisQY/N5qOxBj12Nh+NNWIlHys9jsYapTVvqsfRWCPe8gBKlqs20T/66CP17t0732dNmzbVsGHD1L9/f1WtWlXBYFCzZs1SQoLr3okKAAAAAAAAwMU8rjmTA7HEVZvoK1asOOnngwYNUkZGhrZs2aK0tDTVqlUrypUBAAAAAAAAAOKRq85EjwbLskyXAAAAAAAAADiiOEcex6Pz+5S+M9HXLORM9JLmqifRo+XEm0swGFQgEJDP5zvpmVOWZdm6IZE3mw83prA+u+0ayEc2hh67N+/UGvQ4enlTNdHjyPNurIkem1+DHpvNu7Ememx+jVjIx1KP3VhTLORN9jgaa8RbHkDJi8tNdAAAAAAAAABxKL4O5YBDeDsnAAAAAAAAAABhsIkOAAAAAAAAAEAYbKIDAAAAAAAAABAGZ6IDAAAAAAAAiAsejkRHBHgSHQAAAAAAAACAMNhEBwAAAAAAAAAgDDbRAQAAAAAAAAAIgzPRAQAAAAAAAMQHzkRHBDyhUCiu/uhYlmW6BAAAAAAAAMARfr/fdAkxpd2Nj5kuwXGfvXyP6RJKvbh8Ev3Em0swGFQgEJDP55PX6y2QtyzL1g2JvNl8uDGF9dlt10A+sjH02L15p9agx9HLm6qJHkeed2NN9Nj8GvTYbN6NNdFj82vEQj6WeuzGmmIhb7LH0Vgj3vIASh5nogMAAAAAAAAAEEZcPokOAAAAAAAAIP54jpuuALGIJ9EBAAAAAAAAAAiDTXQAAAAAAAAAAMJgEx0AAAAAAAAAgDA4Ex0AAAAAAABAfAiZLgCxiCfRAQAAAAAAAAAIg010AAAAAAAAAADC8IRCobj6RwyWZZkuAQAAAAAAAHCE3+83XUJMuaDXY6ZLcNynr91juoRSLy7PRD/x5hIMBhUIBOTz+eT1egvkLcuydUMibzYfbkxhfXbbNZCPbAw9dm/eqTXocfTypmqix5Hn3VgTPTa/Bj02m3djTfTY/BqxkI+lHruxpljIm+xxNNaItzzs8cTV48RwCse5AAAAAAAAAAAQBpvoAAAAAAAAAACEwSY6AAAAAAAAAABhsIkOAAAAAAAAAEAYcfliUQAAAAAAAABxKMSbRWEfT6IDAAAAAAAAABAGm+gAAAAAAAAAAITBJjoAAAAAAAAAIM+HH36ojIwMtW7dWpmZmcrJySny2B07dqhv375q1aqV+vXrp127duV9FwqF1LZtW51zzjl5P+edd17e97m5uZo6daratm2riy++WP/85z8dva5IsYkOAAAAAAAAIC54QqXvx2lbtmzRkCFDdOWVV2rp0qU6cOCAZsyYUaSxOTk5GjBggJKTk7Vs2TK1b99eQ4cO1fHjxyVJP/zwg3JycrRmzRqtXbtWa9eu1erVq/PGP/7441q2bJlmz56tGTNmaPLkydq4caPzF2mTJxSKr9P0LcsyXQIAAAAAAADgCL/fb7qEmNLhuummS3Dcx6+PdHS+zMxMbdmyRYsXL5Yk7dmzR926ddNnn32mpKSkQse++eabGjt2rD744AOlpKRIkrp166aJEyfqvPPO0+uvv663335bL7zwQoGxR44cUdu2bXXvvffqpptukiTNnTtXP/74ox5++GFHr9GuMkZXN+TEm0swGFQgEJDP55PX6y2QtyzL1g2JvNl8uDGF9dlt10A+sjH02L15p9agx9HLm6qJHkeed2NN9Nj8GvTYbN6NNdFj82vEQj6WeuzGmmIhb7LH0Vgj3vKA0zZt2qSLLroo7/datWopJSVFW7duVVpa2inHpqWl5W2gS1LLli31zTff6LzzztNXX32lH3/8Ue3atVN2drbOP/98PfDAA6pbt65++OEHBYNBdezYMW9sq1at9Oabbzp+jXbF5SY6AAAAAAAAAJQGXbp0KfT7VatWFfhs+PDh+uijj06ar1KliurVq1fgsz179pxyEz0rKyvsWOmP41wuvPBC3XLLLTp69KgmTpyo4cOHa8mSJcrKylJiYqLq1q2bNzY5OTlvrElsogMAAAAAAACID3F1sHV4mZmZys7OPul3t9xyS4FjW8qXL69gMHjKeRMTE5WYmFhg7L59+yRJixYtyvfdxIkTdckll2j79u0qU6aMypUrl+/7ChUqFGndksYmOgAAAAAAAADEqJM9aX4qp512WqHf/bnp/aesrKwCG9wnk5KSou3bt+f77NChQ2HH1qxZU5L0888/q379+srOztahQ4dUqVIlW+uWtATTBQAAAAAAAAAA3KFly5Zat25d3u+HDx/WDz/8oDp16pxybKtWrbR+/Xrl5ubmfbZhwwadfvrp+s9//qNu3brle7L8z3Xq1KmjunXrqnr16vnW/nOsaWyiAwAAAAAAAAAkSd27d9fKlSu1du1aSdKcOXOUkpKS98LbnJwcHTp06KRj27dvr2PHjmnevHmSpNWrV+vbb79V586dddppp6lSpUp64IEHZFmWVq9erczMTF1wwQVq3LixEhISlJGRoSeeeEKHDh3Sf//7X7344ovq3LlzdC68EBznAgAAAAAAACAueDgT/ZSaNm2qYcOGqX///qpataqCwaBmzZqlhIQ/nseeO3euVq5cqWXLlhUYW7ZsWU2fPl3Dhw/Xiy++qP3792vIkCE666yzJElPPPGEMjMzddNNNyk5OVndu3fX0KFD88bfeeeduvXWW3XhhRcqFAqpfv36uv3226Nz4YVgEx0AAAAAAAAAkGfQoEHKyMjQli1blJaWplq1auV9N2zYMA0bNizs2A4dOmjFihVat26d6tWrpyZNmuR9d/rpp+c9pX4ylStX1qJFi7Ru3TodOXJEbdq0UdmyZZ25qGJgEx0AAAAAAAAAkE/dunVVt27diMZWrVpVXbp0iWhsQkKC0tPTIxpbUjgTHQAAAAAAAACAMDyhUCiuTgKyLMt0CQAAAAAAAIAj/nzZI4qmY49HTZfguI+W3mu6hFIvLo9zOfHmEgwGFQgE5PP55PV6C+Qty7J1QyJvNh9uTGF9dts1kI9sDD12b96pNehx9PKmaqLHkefdWBM9Nr8GPTabd2NN9Nj8GrGQj6Ueu7GmWMib7HE01oi3PICSx3EuAAAAAAAAAACEwSY6AAAAAAAAAABhxOVxLgAAAAAAAADijyeu3g4Jp/AkOgAAAAAAAAAAYbCJDgAAAAAAAABAGGyiAwAAAAAAAAAQBmeiAwAAAAAAAIgPnImOCPAkOgAAAAAAAAAAYbCJDgAAAAAAAABAGJ5QKBRX/4jBsizTJQAAAAAAAACO8Pv9pkuIKRde9ajpEhz3f2/ea7qEUi8uz0Q/8eYSDAYVCATk8/nk9XoL5C3LsnVDIm82H25MYX122zWQj2wMPXZv3qk16HH08qZqoseR591YEz02vwY9Npt3Y0302PwasZCPpR67saZYyJvscTTWiLc87PHE1ePEcArHuQAAAAAAAAAAEAab6AAAAAAAAAAAhMEmOgAAAAAAAAAAYbCJDgAAAAAAAABAGHH5YlEAAAAAAAAAceg4bxaFfTyJDgAAAAAAAABAGGyiAwAAAAAAAAAQBpvoAAAAAAAAAACE4QmFQnF1EJBlWaZLAAAAAAAAABzh9/tNlxBTOmVMM12C4z78532mSyj14vLFoifeXILBoAKBgHw+n7xeb4G8ZVm2bkjkzebDjSmsz267BvKRjaHH7s07tQY9jl7eVE30OPK8G2uix+bXoMdm826siR6bXyMW8rHUYzfWFAt5kz2OxhrxlgdQ8jjOBQAAAAAAAACAMNhEBwAAAAAAAAAgjLg8zgUAAAAAAABA/PHE1dsh4RSeRAcAAAAAAAAAIAw20QEAAAAAAAAACINNdAAAAAAAAAAAwuBMdAAAAAAAAADxIcSh6LCPJ9EBAAAAAAAAAAiDTXQAAAAAAAAAAMLwhELx9W8YLMsyXQIAAAAAAADgCL/fb7qEmHJR16mmS3DcB/8aZbqEUi8uz0Q/8eYSDAYVCATk8/nk9XoL5C3LsnVDIm82H25MYX122zWQj2wMPXZv3qk16HH08qZqoseR591YEz02vwY9Npt3Y0302PwasZCPpR67saZYyJvscTTWiLc87PHE1ePEcArHuQAAAAAAAAAAEAab6AAAAAAAAAAAhMEmOgAAAAAAAAAAYcTlmegAAAAAAAAA4hBnoiMCPIkOAAAAAAAAAEAYbKIDAAAAAAAAABAGm+gAAAAAAAAAAITBmegAAAAAAAAA4oInxKHosI8n0QEAAAAAAAAACMMTCsXXX79YlmW6BAAAAAAAAMARfr/fdAkxpfOlj5guwXHvrxhtuoRSLy6Pcznx5hIMBhUIBOTz+eT1egvkLcuydUMibzYfbkxhfXbbNZCPbAw9dm/eqTXocfTypmqix5Hn3VgTPTa/Bj02m3djTfTY/BqxkI+lHruxpljIm+xxNNaItzyAksdxLgAAAAAAAAAAhBGXT6IDAAAAAAAAiEPHTReAWMST6AAAAAAAAAAAhMEmOgAAAAAAAAAAYbCJDgAAAAAAAABAGJyJDgAAAAAAACAueEIh0yUgBvEkOgAAAAAAAAAAYbCJDgAAAAAAAABAGJ5QyPy/YXjvvff02GOPadeuXapRo4b69++vfv36SZI+/PBDTZ06VXv27FG3bt00duxYJSUlRbyWZVlOlQ0AAAAAAAAY5ff7TZcQU7p0nmK6BMeten+M6RJKPeNnov/888964IEHNGPGDKWmpmr9+vW699571aBBA9WqVUtDhgzR4MGD1b17d02bNk0zZszQmDHF+4Nx4s0lGAwqEAjI5/PJ6/UWyFuWZeuGRN5sPtyYwvrstmsgH9kYeuzevFNr0OPo5U3VRI8jz7uxJnpsfg16bDbvxprosfk1YiEfSz12Y02xkDfZ42isEW952GT8cWLEIuPHuWzYsEH16tVTx44dVaNGDV122WU666yztH37di1YsEA+n0+DBw9W3bp1lZmZqcWLFysnJ8d02QAAAAAAAACAOGB8E71Jkybatm2bVq9erezsbL333nv67rvv1KFDB23atEkdO3bMy9aqVUspKSnaunWrwYoBAAAAAAAAAPHC+HEujRs31m233abbb78977Nx48apcePGysrKUr169fLlq1Spoj179igtLS3apQIAAAAAAAAA4ozxTfRAIKDnn39ec+bMUceOHWVZlkaOHKmUlBQlJiYWeIlo+fLlFQwGi7XmieOzs7Pz/W9Rxthdg3x08ycbc6o+u+0ayNsfQ4/dnXdiDXoc3Xw01qDHzuajsQY9djYfjTXosdl8NNagx87mo7GG2/Ox1uNorFHa8qZ7HI014il/svf7oRAhDkWHfZ5QyOyfnEceeUQ///yz5syZk/fZ3Llz9dFHH+nYsWPKyMhQ3759877r3r27hgwZoq5du0a0nmVZts9UT0pKsjWGvNm8G2si72zejTWRN78GeWfzbqyJvPk1yDubd2NN5J3Nu7Em8ubXIO9s3o01kTe/RrzlW7duXeQspC4XTTZdguNWfXC/6RJKPeNPoh87dkz79u3L99m+fft0/PhxtWzZUuvWrcvbRD98+LB++OEH1alTp1hr+ny+fL9nZ2drx44datCggSpUqFAgv3379gJjCkPebD7cmML67LZrIB/ZGHrs3rxTa9Dj6OVN1USPI8+7sSZ6bH4Nemw278aa6LH5NWIhH0s9dmNNsZA32eNorBFveQAlz/gmesuWLbVw4UJNnz5dTZs21datW7Vo0SLdd999Ou+889SrVy+tXbtW6enpmjNnjlJSUuT3+4u1Zrh/5lKhQoWw39n9pzHkzeYLGxOuz267BvKRj6HH7sw7uQY9jk4+GmvQY2fz0ViDHjubj8Ya9NhsPhpr0GNn89FYI1bysdLjaKxRWvOmehyNNeItD6BkGd9Ev/LKK/Xbb79p0aJFeumll1S5cmX17dtXffr0UUJCgoYNG6b+/furatWqCgaDmjVrlhISEkyXDQAAAAAAACDGeDgSHREwvokuSf369VO/fv1O+t2gQYOUkZGhLVu2KC0tTbVq1YpydQAAAAAAAACAeGX8xaLRZlmW6RIAAAAAAAAARxT32ON4c0mn0vdi0ZUf8mLRkuaKJ9Gj7cSbSzAYVCAQkM/nO+mZU5Zl2bohkTebDzemsD677RrIRzaGHrs379Qa9Dh6eVM10ePI826siR6bX4Mem827sSZ6bH6NWMjHUo/dWFMs5E32OBprxFseQMmLy010AAAAAAAAAHEovg7lgEN4QycAAAAAAAAAAGGwiQ4AAAAAAAAAQBhsogMAAAAAAAAAEAZnogMAAAAAAACIC57jpitALOJJdAAAAAAAAAAAwmATHQAAAAAAAACAMNhEBwAAAAAAAAAgDDbRAQAAAAAAAAAIwxMKhUKmi4gmy7JMlwAAAAAAAAA4wu/3my4hplzafpLpEhy34pNM0yWUemVMF2DCiTeXYDCoQCAgn88nr9dbIG9Zlq0bEnmz+XBjCuuz266BfGRj6LF7806tQY+jlzdVEz2OPO/Gmuix+TXosdm8G2uix+bXiIV8LPXYjTXFQt5kj6OxRrzlAZQ8jnMBAAAAAAAAACAMNtEBAAAAAAAAAAgjLo9zAQAAAAAAABCH4urtkHAKT6IDAAAAAAAAABAGm+gAAAAAAAAAAITBJjoAAAAAAAAAAGFwJjoAAAAAAACAuOAJcSg67ONJdAAAAAAAAAAAwvCEQvH11y+WZZkuAQAAAAAAAHCE3+83XUJMuazdRNMlOO69z8aaLqHUi8vjXE68uQSDQQUCAfl8Pnm93gJ5y7Js3ZDIm82HG1NYn912DeQjG0OP3Zt3ag16HL28qZroceR5N9ZEj82vQY/N5t1YEz02v0Ys5GOpx26sKRbyJnscjTXiLQ+g5MXlJjoAAAAAAACAOBRfh3LAIZyJDgAAAAAAAABAGGyiAwAAAAAAAAAQBpvoAAAAAAAAAACEwZnoAAAAAAAAAOLDcdMFIBbxJDoAAAAAAAAAAGGwiQ4AAAAAAAAAQBhsogMAAAAAAAAAEAZnogMAAAAAAACIC55QyHQJiEGeUCi+/uRYlmW6BAAAAAAAAMARfr/fdAkx5fL0h0yX4Lh3144zXUKpF5dPop94cwkGgwoEAvL5fPJ6vQXylmXZuiGRN5sPN6awPrvtGshHNoYeuzfv1Br0OHp5UzXR48jzbqyJHptfgx6bzbuxJnpsfo1YyMdSj91YUyzkTfY4GmvEWx5AyeNMdAAAAAAAAAAAwojLJ9EBAAAAAAAAxKH4OtkaDuFJdAAAAAAAAAAAwmATHQAAAAAAAACAMNhEBwAAAAAAAAAgDDbRAQAAAAAAAAAIgxeLAgAAAAAAAIgPvFgUEeBJdAAAAAAAAAAAwmATHQAAAAAAAACAMDyhUHz9GwbLskyXAAAAAAAAADjC7/ebLiGmXN56nOkSHPfuuodMl1DqxeWZ6CfeXILBoAKBgHw+n7xeb4G8ZVm2bkjkzebDjSmsz267BvKRjaHH7s07tQY9jl7eVE30OPK8G2uix+bXoMdm826siR6bXyMW8rHUYzfWFAt5kz2OxhrxlodNx00XgFjEcS4AAAAAAAAAAITBJjoAAAAAAAAAAGGwiQ4AAAAAAAAAQBhxeSY6AAAAAAAAgPjjCYVMl4AYxJPoAAAAAAAAAACEwSY6AAAAAAAAAABhsIkOAAAAAAAAAEAYnIkOAAAAAAAAID5wJjoiwJPoAAAAAAAAAACE4QmF4uuvXyzLMl0CAAAAAAAA4Ai/32+6hJjStcVY0yU47l/fTDRdQqkXl8e5nHhzCQaDCgQC8vl88nq9BfKWZdm6IZE3mw83prA+u+0ayEc2hh67N+/UGvQ4enlTNdHjyPNurIkem1+DHpvNu7Ememx+jVjIx1KP3VhTLORN9jgaa8RbHkDJ4zgXAAAAAAAAAPEhFCp9PyXgww8/VEZGhlq3bq3MzEzl5OQUeeyOHTvUt29ftWrVSv369dOuXbvyvuvcubPOOeecAj+zZ8+WJL3//vsFvnv44Ycdvz672EQHAAAAAAAAAEiStmzZoiFDhujKK6/U0qVLdeDAAc2YMaNIY3NycjRgwAAlJydr2bJlat++vYYOHarjx49Lkt58802tXbs272flypWqXLmyOnbsKElav369rr322nyZe+65p8SutajYRAcAAAAAAAAASJIWLFggn8+nwYMHq27dusrMzNTixYuL9DT6u+++q3379mnSpEmqV6+eBg0apOzsbH311VeSpEqVKik5OTnvZ+HCherUqZNatmwp6Y9N9Hbt2uXLlC9fviQvt0ji8kx0AAAAAAAAACgNunTpUuj3q1atsjXfpk2bdNFFF+X9XqtWLaWkpGjr1q1KS0s75di0tDSlpKTkfdayZUt98803Ou+88/Jld+/erVdffVXvvPOOJOno0aPasGGDDhw4oLFjxyo5OVnXXXed7rzzTiUkmH0WnE10AAAAAAAAAPGhhM4QjzXDhw/XRx99dNLvqlSponr16hX4bM+ePafcRM/Kygo79kQvvPCCLrnkEp1xxhmSpO+//17Jycm6/fbblZ6ero0bN+ree+9VjRo11Lt3bzuX5zg20QEAAAAAAAAgRtl90lySMjMzlZ2dfdLvbrnlFiUlJeX7rHz58goGg6ecNzExUYmJiQXG7tu3L99nWVlZWrx4sf7+97/nfZaamppvY79mzZrq27ev3nzzTTbRAQAAAAAAAADRc9pppxX63ck2vcuVK3fKeVNSUrR9+/Z8nx06dKjA2H/+858688wz1bRp00Lnq1mzpn7++edTrlvSeLEoAAAAAAAAAEDSH2eYr1u3Lu/3w4cP64cfflCdOnVOObZVq1Zav369cnNz8z7bsGGDTj/99Hy5N954Q927d8/32eLFizVhwoR8n61bt67AWBM8oVB8HQRkWZbpEgAAAAAAAABH+P1+0yXElK7NHjBdguP+tfFhR+fbtGmTevXqpRdeeEHp6emaOnWq3n77bX344YdKSEhQTk6Ojh49qkqVKhUYe/ToUXXs2FG33HKLbrvtNq1evVqDBw/W22+/rbPOOkuS9Ntvv+mCCy7QG2+8IZ/Plzd28+bNuuGGG5SZmSm/36/Vq1friSee0LRp03TVVVc5eo12xeVxLifeXILBoAKBgHw+n7xeb4G8ZVm2bkjkzebDjSmsz267BvKRjaHH7s07tQY9jl7eVE30OPK8G2uix+bXoMdm826siR6bXyMW8rHUYzfWFAt5kz2Oxhrxlgec1rRpUw0bNkz9+/dX1apVFQwGNWvWLCUk/HGoydy5c7Vy5UotW7aswNiyZctq+vTpGj58uF588UXt379fQ4YMydtAl6SPPvpIycnJOuecc/KNTU1N1bRp0/TYY49p165datSokWbPnq1LL720ZC+4COJyEx0AAAAAAAAAcHKDBg1SRkaGtmzZorS0NNWqVSvvu2HDhmnYsGFhx3bo0EErVqzQunXrVK9ePTVp0iTf91dffbWuvvrqk469/PLLdfnllztzEQ5iEx0AAAAAAAAAkE/dunVVt27diMZWrVpVXbp0cbgic9hEBwAAAAAAABAXPPH1ekg4JMF0AQAAAAAAAAAAuBWb6AAAAAAAAAAAhMEmOgAAAAAAAAAAYbCJDgAAAAAAAABAGLxYFAAAAAAAAEB84MWiiABPogMAAAAAAAAAEIYnFIqvv36xLMt0CQAAAAAAAIAj/H6/6RJiSjffGNMlOG55YIrpEkq9uDzO5cSbSzAYVCAQkM/nk9frLZC3LMvWDYm82Xy4MYX12W3XQD6yMfTYvXmn1qDH0cubqokeR553Y0302Pwa9Nhs3o010WPza8RCPpZ67MaaYiFvssfRWCPe8gBKXlxuogMAAAAAAACIQ8fj6lAOOIQz0QEAAAAAAAAACINNdAAAAAAAAAAAwmATHQAAAAAAAACAMDgTHQAAAAAAAEB8CHEmOuzjSXQAAAAAAAAAAMJgEx0AAAAAAAAAgDDYRAcAAAAAAAAAIAzORAcAAAAAAAAQHzgTHRHwhELx9SfHsizTJQAAAAAAAACO8Pv9pkuIKd2a3Ge6BMct3zbNdAmlXlw+iX7izSUYDCoQCMjn88nr9RbIW5Zl64ZE3mw+3JjC+uy2ayAf2Rh67N68U2vQ4+jlTdVEjyPPu7Ememx+DXpsNu/Gmuix+TViIR9LPXZjTbGQN9njaKwRb3kAJY8z0QEAAAAAAAAACCMun0QHAAAAAAAAEIfi62RrOIQn0QEAAAAAAAAACINNdAAAAAAAAAAAwmATHQAAAAAAAACAMDgTHQAAAAAAAEB8OM6Z6LCPJ9EBAAAAAAAAAAiDTXQAAAAAAAAAAMLwhEIh4/+G4eOPP9ZTTz2lzZs3q06dOrrjjjt0xRVXSJI+/PBDTZ06VXv27FG3bt00duxYJSUlRbyWZVlOlQ0AAAAAAAAY5ff7TZcQU7o1Gmm6BMct3z7ddAmlnvEz0QOBgG6//XaNHj1as2fP1scff6xRo0bp+PHjOvvsszVkyBANHjxY3bt317Rp0zRjxgyNGTOmWGueeHMJBoMKBALy+Xzyer0F8pZl2bohkTebDzemsD677RrIRzaGHrs379Qa9Dh6eVM10ePI826siR6bX4Mem827sSZ6bH6NWMjHUo/dWFMs5E32OBprxFseNoWOm64AMcj4cS6vv/66WrdurT59+ui0007T1Vdfrc6dO+uf//ynFixYIJ/Pp8GDB6tu3brKzMzU4sWLlZOTY7ps4P+1d/fxNdf/H8efZzNjG7O5WC4TuRgzSkhC5iLGXNSXUJbim2tSFPL9+vlWlKRCLhYqEiVymRK5jCS5OrMNy6LMXMzYbLOLc35/uDnf75ojm2OfnbPH/Xbbrfb5vD6f9+vTq3129jrv8/4AAAAAAAAAKAIMb6InJiaqcuXKObZ5eHjI3d1dR48eVcuWLW3bAwIC5Ofnp2PHjhV0mgAAAAAAAACAIsjwJnpQUJB+/PFHXblyRZJ05swZbd++XY899piSk5NVrVq1HPG+vr5KSEgwIlUAAAAAAAAAQBFj+Jroffv21cGDB9WtWzfVr19f+/btk4+Pj8LCwrRgwYJcDxEtUaKEUlNT72jMvx6flpaW45+3c0xexyC+YONvdszf1bmwXQPxeT+GGhfueEeMQY0LNr4gxqDGjo0viDGosWPjC2IMamxsfEGMQY0dG18QYxT2eGercUGM4WrxRte4IMYoSvE3e74fAMcyWa1Wq9FJSFJCQoLMZrOGDh2q6dOnKywsTH369FFoaKj69etniwsLC9OwYcPUsWPHfI1jNpvzvKa6p6dnno4h3tj4wpgT8Y6NL4w5EW/8GMQ7Nr4w5kS88WMQ79j4wpgT8Y6NL4w5EW/8GMQ7NoKZjCYAAC+SSURBVL4w5kS88WMUtfjGjRvfdiykTtVHG52Cw22Me8/oFFxeoWmiS9LYsWMVHx+vzz77TJL09ttvKz4+Xu+//74k6erVq2rWrJk+//xzBQcH52sMs9msGjVq5NiWlpamuLg4Va9eXSVLlsx1zG+//ZbrmFsh3th4e8fcqs6F7RqIz98x1LjwxjtqDGpccPFG5USN8x9fGHOixsaPQY2NjS+MOVFj48dwhnhnqnFhzMkZ4o2scUGMUdTimYmeNzTRkR+GL+dyw5EjR7Rx40atWrXKti0sLEy9evXSvn371KRJE82ePVt+fn4KCgq6o7Hs3VxKlixpd19eb0jEGxt/q2Ps1bmwXQPx+T+GGhfOeEeOQY0LJr4gxqDGjo0viDGosWPjC2IMamxsfEGMQY0dG18QYzhLvLPUuCDGcNV4o2pcEGMUtXgAd1ehaKJbrVa98cYbCg8PV+3atW3b69WrpxEjRqh///4qU6aMUlNT9cEHH8jNzfDnoQIAAAAAAAAAioBC0UQ3mUz64osvbrpv0KBBCg0NVUxMjBo0aKCAgIACzg4AAAAAAACAS7AUmpWt4UQK1ZroBcFsNhudAgAAAAAAAOAQd7rscVHTqdqLRqfgcBtPvW90Ci6vUMxEL2h/vbmkpqYqKipKgYGBN11zymw25+mGRLyx8faOuVWdC9s1EJ+/Y6hx4Y131BjUuODijcqJGuc/vjDmRI2NH4MaGxtfGHOixsaP4QzxzlTjwpiTM8QbWeOCGKOoxQO4+1hcHAAAAAAAAAAAO4rkTHQAAAAAAAAARVDRWtkaDsJMdAAAAAAAAAAA7KCJDgAAAAAAAACAHTTRAQAAAAAAAACwgzXRAQAAAAAAABQNrImOfGAmOgAAAAAAAAAAdtBEBwAAAAAAAADADproAAAAAAAAAADYYbJai9ZCQGaz2egUAAAAAAAAAIcICgoyOgWn0qnyCKNTcLiNf84yOgWXVyQfLPrXm0tqaqqioqIUGBgoLy+vXPFmszlPNyTijY23d8yt6lzYroH4/B1DjQtvvKPGoMYFF29UTtQ4//GFMSdqbPwY1NjY+MKYEzU2fgxniHemGhfGnJwh3sgaF8QYRS0ewN3Hci4AAAAAAAAAANhBEx0AAAAAAAAAADuK5HIuAAAAAAAAAIogi8XoDOCEmIkOAAAAAAAAAIAdNNEBAAAAAAAAALCDJjoAAAAAAAAAAHawJjoAAAAAAACAosFqNToDOCFmogMAAAAAAAAAYIfJai1ab7+YzWajUwAAAAAAAAAcIigoyOgUnEqne4YanYLDbTw7x+gUXF6RXM7lrzeX1NRURUVFKTAwUF5eXrnizWZznm5IxBsbb++YW9W5sF0D8fk7hhoX3nhHjUGNCy7eqJyocf7jC2NO1Nj4MaixsfGFMSdqbPwYzhDvTDUujDk5Q7yRNS6IMYpaPIC7j+VcAAAAAAAAAACwo0jORAcAAAAAAABQBBWtla3hIMxEBwAAAAAAAADADproAAAAAAAAAADYQRMdAAAAAAAAAAA7WBMdAAAAAAAAQNFgYU105B0z0QEAAAAAAAAAsIMmOgAAAAAAAAAAdtBEBwAAAAAAAADADpPVai1SCwGZzWajUwAAAAAAAAAcIigoyOgUnErHci8YnYLDfXshwugUXF6RfLDoX28uqampioqKUmBgoLy8vHLFm83mPN2QiDc23t4xt6pzYbsG4vN3DDUuvPGOGoMaF1y8UTlR4/zHF8acqLHxY1BjY+MLY07U2PgxnCHemWpcGHNyhngja1wQYxS1eAB3H8u5AAAAAAAAAABgB010AAAAAAAAAADsKJLLuQAAAAAAAAAogixF6vGQcBBmogMAAAAAAAAAYAdNdAAAAAAAAAAA7KCJDgAAAAAAAACAHayJDgAAAAAAAKBosLImOvKOmegAAAAAAAAAANhBEx0AAAAAAAAAADtMVmvR+gyD2Ww2OgUAAAAAAADAIYKCgoxOwal09BtodAoO9+2lBUan4PKK5Jrof725pKamKioqSoGBgfLy8soVbzab83RDIt7YeHvH3KrOhe0aiM/fMdS48MY7agxqXHDxRuVEjfMfXxhzosbGj0GNjY0vjDlRY+PHcIZ4Z6pxYczJGeKNrHFBjFHU4pFHFovRGcAJsZwLAAAAAAAAAAB20EQHAAAAAAAAAMAOmugAAAAAAAAAANhRJNdEBwAAAAAAAFAEWa1GZwAnxEx0AAAAAAAAAADsoIkOAAAAAAAAAIAdNNEBAAAAAAAAALCDNdEBAAAAAAAAFAlWi8XoFOCETFZr0VpN32w2G50CAAAAAAAA4BBBQUFGp+BUHvd51ugUHO67lE+NTsHlFcmZ6H+9uaSmpioqKkqBgYHy8vLKFW82m/N0QyLe2Hh7x9yqzoXtGojP3zHUuPDGO2oMalxw8UblRI3zH18Yc6LGxo9BjY2NL4w5UWPjx3CGeGeqcWHMyRnijaxxQYxR1OIB3H2siQ4AAAAAAAAAgB000QEAAAAAAAAAsKNILucCAAAAAAAAoAgqWo+HhIMwEx0AAAAAAAAAADtoogMAAAAAAAAAYAdNdAAAAAAAAAAA7GBNdAAAAAAAAABFg4U10ZF3zEQHAAAAAAAAAMAOmugAAAAAAAAAANhhslqtReozDGaz2egUAAAAAAAAAIcICgoyOgWn8njJfkan4HDfpS0xOgWXVyTXRP/rzSU1NVVRUVEKDAyUl5dXrniz2ZynGxLxxsbbO+ZWdS5s10B8/o6hxoU33lFjUOOCizcqJ2qc//jCmBM1Nn4MamxsfGHMiRobP4YzxDtTjQtjTs4Qb2SNC2KMohaPPLJajM4ATojlXAAAAAAAAAAAsIMmOgAAAAAAAAAAdtBEBwAAAAAAAADAjiK5JjoAAAAAAACAosdqsRqdApwQM9EBAAAAAAAAALCDJjoAAAAAAAAAAHbQRAcAAAAAAAAAwA7WRAcAAAAAAABQNFgtRmcAJ8RMdAAAAAAAAAAA7DBZrdYi9Uhas9lsdAoAAAAAAACAQwQFBRmdglPp4NHb6BQcblPmcqNTcHlFcjmXv95cUlNTFRUVpcDAQHl5eeWKN5vNebohEW9svL1jblXnwnYNxOfvGGpceOMdNQY1Lrh4o3KixvmPL4w5UWPjx6DGxsYXxpyosfFjOEO8M9W4MObkDPFG1rggxihq8QDuviLZRAcAAAAAAABQ9FgtRWpRDjgIa6IDAAAAAAAAAGAHTXQAAAAAAAAAAOygiQ4AAAAAAAAAgB000QEAAAAAAAAUDVaL633dBdu3b1doaKgaN26siRMn6tq1a3k6Pj09Xb1799aqVaty7fv6668VEhKiZs2a6d1335XF8t9rSE1N1bhx4/TQQw+pU6dO+umnn+74WhyBJjoAAAAAAAAAQJIUExOjYcOGqUuXLlq9erUuX76sGTNm3PbxV65c0bBhw3TgwIFc+3bs2KHXXntNQ4YM0RdffKF9+/bps88+s+2fOHGiDhw4oMWLF2vcuHF6+eWXdfbsWYdc152giQ4AAAAAAAAAkCQtWbJEgYGBGjp0qKpWraqJEydqxYoVtz0bfeTIkQoODlalSpVy7fv444/VoUMH9ezZU9WrV9e4ceNsTfSEhARt2LBBEyZMUL169dS6dWu1bdv2prPZCxpNdAAAAAAAAACAJOno0aNq2bKl7fuAgAD5+fnp2LFjt3X85MmTNWrUKJlMpr89d4MGDXTmzBklJiYqMjJSHh4eat68uW3/gw8+qEOHDt3B1ThGMaMTAAAAAAAAAADkT9u2bW+5f8uWLbm2jR49Wjt37rxpvK+vr6pVq5ZrW0JCgho0aPC3+dx777129yUnJ+fY7+7uLm9vb507d07Jycm65557VLx4cdv+0qVLKyEh4W/HvNtMVqvVanQSAAAAAAAAAIC8y08T/eLFi0pLS7tp/PPPP6/Ro0erU6dOtm19+/ZV79691bVr19vOKyQkRMOHD9cTTzxh29agQQMtXbpUwcHBtm2tW7fWjBkzdPbsWc2ZM0cbNmyw7duzZ48mTZqkTZs23fa4dwMz0QEAAAAAAADASd2sSf53ypYte8t9Fy5cyLEtOTk5xwzx/PLz88t17pSUFBUvXvym+xw17p1iTXQAAAAAAAAAgCSpUaNG2r9/v+37q1ev6uTJkzd9UOidnvu3335TSkqKKlasqPr16+vq1auKjY217T9y5IgqVqx4x+PeKZroAAAAAAAAAABJUlhYmDZv3qx9+/ZJkmbPni0/Pz8FBQVJkq5du6aUlJR8nbtr16768ssvFRsbq+zsbM2aNUsNGzZUuXLl5Ovrq1atWumdd95RRkaG/vjjD61YsUIhISEOu7b8YjkXAAAAAAAAAIAkqV69ehoxYoT69++vMmXKKDU1VR988IHc3K7Px46IiNDmzZu1Zs2aPJ+7Xbt22rVrl7p16yYfHx9J0qJFi2z7X3vtNQ0cOFCPPvqo0tLS1LRpU/Xs2dMxF3YHeLAoAAAAAAAAACCH06dPKyYmRg0aNFBAQIBDz338+HGdOnVKjRs3VpkyZXLsy8jI0L59+1S8eHE99NBDMplMDh07P2iiAwAAAAAAAABgB2uiAwAAAAAAAABgB010AAAAAAAAAADsoIkOAAAAAAAAAIAdNNEBAAAAAAAAALCDJjoAAAAAAAAAAHbQRAcAAAAAAAAAwA6a6AAAAAAAAAAA2FGkmugWi8XoFADkk9VqNToFFADq7Nqob9FAnV0fNXZ91Nj1UWPXR40BwLFcvol++fJlXblyRcnJyXJzc/nLLbJuvEDghYLryczMzPE9b4a5Jurs2m7U02QySZKys7ONTAd3QUZGhqTrv4dv1Bmuh3u166PGru+vNeZ3suuhxgBwdxQzOoG7KSYmRiNHjtS9996rkydP6umnn1bjxo3VoEEDo1ODg1y+fFmSdO3aNVWoUIE/3F3M8ePHNW/ePPn6+spisWjkyJHy9/c3Oi04GHV2bbGxsVq2bJlKlCihsmXLqnfv3ipZsqTRacGBjh07pmnTpsnf31/JyckaM2aMqlSpIk9PT6NTgwNxr3Z91Nj1UWPXR40B4O5x2anZqampmjRpktq1a6f33ntPo0ePVlxcnBYuXKitW7canR4cICYmRuHh4Ro5cqRGjRqlt99+W9euXTM6LTjIuXPn1L9/fwUEBKhu3bpKSUlRnz59tHXrVqWkpBidHhyEOru2+Ph49e3bVx4eHrJarTp8+LA6d+6sY8eOGZ0aHCQxMVGDBw9WYGCgwsLCVLFiRY0fP15fffWV4uPjjU4PDsK92vVRY9dHjV0fNQaAu8tlZ6J7eHgoIyNDtWrVkre3t0JDQ1WrVi19++23+vjjj5Wdna127doZnSbyKS0tTf/+97/VokULPfXUU0pLS9PYsWOVmJioZ599VoGBgcxKd3Lx8fEqX768hg8fLi8vL/Xq1Utz5szR3LlzdeHCBXXo0EG+vr5Gp4k7RJ1d040lPaKjo1W9enW9+uqrtn2TJ0/W0KFDNXnyZD3yyCPcq53clStX5OXlpT59+qhSpUpq2bKlVqxYoc2bN+vixYvq0aOHqlatanSauEPx8fGqUKEC92oXdvbsWZUtW5YauzB+jl0fr6sB4O5yyZnoVqtVGRkZunLlik6ePGnbXqtWLT3xxBNq1qyZli9frgMHDhiYJe5ERkaGUlNT1aRJE917772qW7euFi9erKSkJC1cuFAHDx40OkXcoaysLEVHR+v48eO2bUOHDlXnzp21cuVK7dy5UxLr4Ds7q9VKnV3QjTV009PTFR0drdjYWNu+SZMmKTQ0VP/3f/9nu1ezVqfzOX/+vM6cOSOLxaITJ07keL3Vs2dPdevWTVFRUdq4cSOz31xAZmamoqKidOLECds27tWu4fjx41q9erXKlCmjY8eOUWMXduPn+O9ec7EOvvO68ffT/37ijxoDgOO4ZBPdZDLJ29tbzz33nD7//PMcy7dUrlxZ7du3V7FixbR7925J/BJxRt7e3vLw8NCOHTts2/z8/DRt2jSlp6dr4cKFSkxMlMSLfWfyxx9/aOXKlfrhhx8kSY899pjWrVunixcv2mKeffZZtWzZUm+++aYSEhKYxepkrFarfv75Z40bN07Lly/XlStX1Lp1a61bt872MytRZ2d24cIFDRgwQOfPn1fjxo1VqVIl7dy50/bgSUl66aWX1KJFC40cOVIpKSlyd3c3MGPkVWRkpLp166a4uDhVr15d7dq105IlS3T69GlbTGhoqNq0aaPly5fr1KlTBmaL/Przzz915swZSVK1atXUuHFjrV27lnu1C4mOjlaPHj00btw4/f7779TYBSUkJGjHjh365ZdfVLp0abVo0eJvX3O5ublki8BlXbx4UUeOHNGePXtkMpkUEhKi9evX68KFC7YYagwAjuHSd8+uXbuqU6dOWrRokX766SdJ1xs4tWvXVoMGDbR27VplZGTwS8RJJCUl2V7wmUwmtWjRQlFRUdqzZ48txtfXV1OnTtWRI0cUERFhi0XhFx0drW7dumnx4sUaP368VqxYodKlS2vv3r368ccflZaWZosdNmyYKlSooM8//9zAjJEf69ev16uvvqoLFy5o+fLl2rFjh+rWratdu3Zp165dSk1NtcVSZ+eUlJSkn376SRMmTFDp0qXVvXt3zZ07V0eOHMkRN27cOPn4+GjdunUGZYr8iIqK0jPPPKPu3bvrkUcekZubm7p06aKkpCRt2LBBCQkJtthevXqpcuXK+vTTTw3MGPk1cuRIffbZZ5KkChUqqH379tqzZ4927dqV49MF3KudU1RUlHr16qXevXurc+fOKlWqlLp37277fczrLucXHR2tnj17aubMmRo6dKg2b96sxo0b85rLhcTExKh379564403NGzYMM2dO1dXrlzR4cOHtXv3bmoMAA7m0t3jUqVKadiwYbr//vs1e/Zsbdq0ydZQ9fb2VrFixZSZmWlwlvg7VqtVqampmjx5spYsWaLExES5u7vrmWeekSQtW7Ysx0fWSpcureeff14HDx7M8QcACq+kpCS9+OKLCg8P15o1azR58mRFRkaqQ4cO6ty5syIiIvTdd9/lmFHh7+/PEgFO5ty5c3rrrbc0duxYLViwQMOGDdN3332n/v37q2PHjlq4cKE2bdqkc+fO2Y6hzs6nevXquu+++3T48GH1799f/fv3V9euXTVq1Cj9/PPPtj/oSpQooRIlSuT4pAkKt9OnT6tHjx7q37+/XnnlFWVmZio6OlqNGjVSs2bNdOjQIX311Vc5lnapUKGCMjMz+VSYEwoODlaxYv99fFL79u11zz33aO3atbY172/gXu1cIiMj1bdvX73wwguaOHGi/P39tWTJEvXs2VOPPfaYFixYwOsuJ5eQkKCBAwfqySef1BdffKGxY8dq8eLF6t+/vzp06KCPP/5YmzZtyvHGJzV2LomJiRozZoy6d++uRYsW6f3331epUqVUs2ZN1apVSxEREdq0aRM/xwDgQC77YNEbAgICNHz4cC1btkyvvPKKVq1aJTc3N+3fv19Dhw6Vt7e30Snib5hMJnl5eSk2NlZHjx6Vp6enevTooYCAAE2fPl0vvvii5s6dq7CwMIWEhEi6/qLi4sWLLNXjJDIzM+Xj46MePXpIkjp27Kg9e/bo008/1ZIlS5SWlqavvvpKO3fu1KOPPqqUlBRFR0erb9++BmeOvLBYLKpWrZratm0r6XpD5uOPP9b27dvVunVrHT9+XJs3b9bOnTv1yCOPKDU1lTo7maysLJlMJpUtW1ZdunTRpUuX1L9/fy1atEgVKlTQ66+/rpCQEDVu3FgXLlzQmTNn1LBhQ6PTxm0ym82qV6+eevfuLYvFon79+ik5OVkXL15UmzZtlJaWpvj4eI0fP14hISHKyMjQ7t27NWHCBD4V5oRq166tJUuWKDg4WDt27NDOnTtltVp19uxZJSQkaOfOnWrevLnS0tK4VzuR8+fPq3///goPD9fw4cMlSY888ogWLVok6fqnhN577z1edzm5+Ph4BQYGatSoUZKuP6ti2bJl2rNnjx566CEdPnxYP/zwA6+5nNj58+dVunRphYeHy9vbW61atVJSUpLeeustrVmzRmvWrNGKFSv4OQYAB3L5Jrok25PmW7Vqpe+//15JSUl6++231bx5c6NTw22wWCxyc3NT1apVlZmZqR07dshkMqlHjx6qUqWKZs6cqXfeeUdLly7VnDlzFBgYqI0bN+rFF1/kTRInkZmZqYsXL+rKlSu2bU2bNrUtwzR69Ght3bpV+/bt07x582QymTRixAi1bNnSqJSRT+np6Tp16pRq1aqlpUuX6tdff1VycrIkqVKlSqpatap8fX0VEREhd3d36uxkbsxafeCBB5SYmKgXXnhBU6ZM0eDBgzVgwABZrVadOXNGa9askZubm4YMGaKmTZsanDVuV9OmTbVu3TotWrRIv//+u0qVKqV33nlHZrNZ+/btU2pqqmrUqKHAwECtXr1aJpNJL7/8stq3b2906siHqlWr6sqVKzpw4IBq1aqlvn37Kjk5WS+99JIsFovq16+vjz76iHu1kylRooRmzZqlhx9+2LatRYsWmjp1qhYuXKgBAwbYXnft379f8+bNo8ZOqESJEjp8+LC2bNmitm3b6t1339XRo0e1bt06JScnKyMjQ97e3qpatSqvuZxURkaGDh06pMjISNvPc/PmzZWUlKS1a9dq4MCBql69ug4ePMjPMQA4iMnK52vhBNLT0zVhwgSNGTNG69ev1+bNm9W2bVv16NFDFSpU0JUrV3Ty5EmtW7dOmZmZatWqlVq2bKnixYsbnTpu0+bNm9WgQQMFBARIks6cOaOnn35aCxYsUM2aNSVdXw6kQoUKunr1Km+QOKmTJ0/K399fvr6+OnjwoCwWi4KDg3X8+HF98sknqlSpkkaNGqXLly+rWLFi1NlJRUREaNu2bfr8889lsVj0/PPP6+eff9bQoUM1fPhwJSQkyNPTU2XKlDE6VeRRTEyM+vXrpzJlyujLL7+01fDw4cOaNGmSOnTooCFDhigtLU3u7u78HnZiV69eVUhIiOrXr6+ZM2fKx8dHkvTNN99oypQpWrFihby8vLhXO7ns7Gy5u7trwYIFio2N1b/+9S95eXnZ9icnJ8vNzY0aOxmr1aqIiAjNmzdPderU0cGDB7V27VrVrl1bZ86c0ezZs5WVlaVp06YpKSlJHh4e1NjJpKena+zYsfL29tZTTz2l+++/X1OmTNG3336rBx98UAsXLrTF8nMMAI5RJGaiw/mVKFFCEyZMULly5fTCCy8oIyNDW7ZskSQ98cQTKl++vBo2bMiyAE6sTZs2cnd3l3T90wcmk0lXr15VVlaWpOtNuY0bN2rJkiW2P+ThfO677z5J1/9ob9SokW17YGCgKlSooAMHDshiscjX19egDOEIjz32mLZt2yZJ2rt3ryIjI9WgQQOtWbNGPXv2tL1ZBudTp04djRkzRuvWrVOxYsVsDbjg4GD5+voqKipKklSyZEmDM8WdsFqt8vDwUJUqVeTp6SkfHx9brX18fOTt7S03Nzfu1S7gxmuvxo0ba/78+eratauaN29ue45BqVKljEwP+WQymTRw4EC1bt1au3btUtmyZVW7dm1J1z/55+bmpsjISF27do03tJ1UiRIlNGrUKE2bNk0jRoyQh4eHnnzySU2ZMkWzZs1SQkKCypUrJzc3N36OAcBBaKLDaZQrV05Wq1Umk8m2huOWLVvk5uamHj16qFy5cgZniDtx44846fof76VLl1bp0qXl4+OjTz75RDNnztTy5ctpoLuIG/XOyMiwzVR1c3NTtWrVeAChC/D19VVGRoY+/vhjzZ8/X8OGDVNoaKjmzJmjjIwMo9PDHQoLC1OnTp3k4+Nje6MzPT1dnp6eqlevnsHZwRFMJpOKFy+uZ555RhMmTNDq1avVvXt3Sdc/deDu7i4PDw9jk4RDPfDAA3ryySf14Ycfqnr16qpYsaLRKeEOubu7q27dujp79qxWrFih6Oho1a1bV3FxcTpz5owCAgKUlZUlT09Po1NFPt1///2aPn26Tp8+rfT0dDVu3FixsbE6f/684uLimLQAAA5GEx1OxWQy2dZIHz58uNzc3LRy5Up5eHgoPDxcbm5uRqcIB3B3d5e3t7fKlCmj0aNH6+jRo1q2bJmCgoKMTg0OdOnSJb3++utKS0uTm5ubfvnlF3366ac53lCBc/L391exYsX07rvv6tVXX1W/fv0kSRMnTrStmw7ndWOW+enTp/XNN9/Iw8ND8fHxOnDggF599VWDs4MjdezYUSdPntSECRO0cuVK+fj46MiRI4qIiJC/v7/R6cHB2rVrp23btumnn35S9+7deSCwi2jUqJFq1aqliRMnqnbt2rpw4YIiIyO1ZMkSlvdwAaVLl1b9+vVt39esWVPBwcEym81q1qyZgZkBgOthTXQ4pRsz0iXpo48+UqdOnVSlShWDs4KjWK1WXbt2Te3bt9fFixf19ddfq06dOkanBQfLysrSnj179O2336pKlSrq0KGDbf17OL9Dhw4pJiZGvXr1MjoV3CV//vmnFi9erF9//VXlypXTyJEjFRgYaHRacLDs7Gzt379fP/74o6pUqaJmzZqpWrVqRqeFu2Tq1Knq27ev7r33XqNTgQP98ccfmj9/vsxms6pXr65hw4bp/vvvNzot3CWzZs1SWFiYqlevbnQqAOBSaKLDad2YkQ7XtWnTJtWsWZPGKgAUYjeW6OEhooDz+t8JKnBNWVlZslgsslqtLOHiovg5BoC7iyY6AAAAAAAAAAB2MI0XAAAAAAAAAAA7aKIDAAAAAAAAAGAHTXQAAAAAAAAAAOygiQ4AAAAAAAAAgB000QEAAAAAAAAAsIMmOgAAAP5WVlZWnrYDAAAAgKugiQ4AAFBEJCQk2P7dYrFo/fr1io2Nva1jn3/+eb311ls5tn3zzTfq2LGjUlNTb3nstWvXJEmxsbFaunSpJOnq1au2/QcOHMiRmyNkZGQoLi7Ooec0yg8//KDBgwcrLS3tlnHnzp1TUlJSwSQFAAAAFCE00QEAAIqIIUOG6JVXXpEkubm5adGiRZozZ87fHpeYmKh9+/bJZDLl2N64cWNdvHhRERERdo9NSEjQ448/ruPHjysmJsY23qRJkzRr1ixJ0rvvvqspU6bk97Ju6rXXXtPChQsdes5bSUlJueNznD17VnFxcfrjjz9yfCUmJmrr1q367rvvcu37/fffdfr0aUnSqlWrNGLECGVmZt5xLgAAAAD+q5jRCQAAABhh7969Cg8PV0xMjNGpFIidO3cqMjJSY8aMsW0bNWqUBg8erN69e6tJkyZ2j924caMsFot69eql9PR0mUwmeXp6KiAgQIMGDVKZMmVssVarVdeuXVOJEiUkSQEBAfrHP/6h//znP+rXr588PDwUHx+vLVu2aMOGDcrOzlZUVJRef/11h13r0qVLFRcXpyVLlti2RUdHa/LkyYqOjlbDhg01depUVaxY0WFjjho1Si1btlT//v3zfY4ZM2bou+++k4eHR659pUqV0htvvJFre3Z2toKCgrRkyRK98MILOnTokN577z3bmyUAAAAA7pzJarVajU4CAACgoKWkpOjkyZNq0KCB0ancddnZ2erVq5dKlSqlTz75JMe+QYMG6cSJE1q1apV8fX1venznzp3l7u6utWvX6s0339TixYtvOV6ZMmW0d+9eSdKuXbt0/PhxZWRkKD4+Xhs3btSwYcMUGRmpwMBANWzYUH369NH3339vGz87O1seHh7y8fHJ87VevHhRXbp00WeffaaaNWvatnXu3Fl16tTRwIED9c0338hsNuvrr79WsWKOmVOSlJSkgQMHqk2bNho2bJhDzpkfiYmJ6tKli5YsWWK7fgAAAAB3hiY6AACAi5s/f75mzpyplStXqm7dujn2JSQkqHv37rrvvvsUERGRq3G9a9cuDRgwQM2bN9cnn3yixMREpaWlqXjx4jcdy2q1KisrS5UqVZIkLVq0SLt371ZKSooOHDigkiVLqmXLlrJYLMrIyFD9+vU1d+7cXOcZNWqUhg4dmudrnTt3ruLj4/Wf//zHtm3GjBlasWKFtmzZIi8vL2VnZ6t9+/YaO3asOnXqlOcx7ElJSdGQIUMUHByssWPH5vs8Fy5cUIsWLf427siRIzetQ0REhP78809Nnjw53zkAAAAA+C/WRAcAAHBhR44c0ezZszV06NBcDXTp+nIr8+fP14kTJ9S7d2+dOHHCts9qteqDDz7IsRa6v7+/PD09dfHiRZUvX972FRcXp7lz5yopKcnWQJeuP5B02rRpysjIUO3ateXn56caNWpoyJAh+uijj7RhwwYNGTJEMTExiomJUfXq1fXOO+/on//8Z76ud9OmTQoLC8uxbc+ePWrbtq28vLwkSe7u7goJCdGePXvyNYY9Pj4+WrBggY4fP67Jkycrv3NVbiyFs3r1att/l//9Wr16tUwmk903Mjp37qzNmzfLYrHk+1oAAAAA/BdNdAAA4FIyMjL09ttvq3nz5nrooYc0aNAgnTp1Klfc3r17VadOnZueY9y4cRo3bpzOnj2rl156Sc2aNdOff/6Z5zHsqVOnjubOnas2bdqoVatW2r59u7p06aKmTZvqhx9+0OnTp1WnTh0dOHDAdozValWLFi302Wef3fY4cXFxGjJkiOrXr6/WrVvrxIkTio2NzfXl7e2tKVOmKDU1VT169LA9KPTLL7/U0aNHFRoamuO8kyZNyvUg0H379mnp0qW5Grs7d+7UP/7xDwUGBmr48OHKyspSzZo1NWDAAH311Vc6depUjodynj9/XuXLl7/puuB/x2q16vjx42rUqFGO7QkJCblqXaVKFcXFxd3WeS0Wi7Kysm76lZ2dnSPW09NTH374oS5duqTx48fn2n87bjwYdMCAAWrVqlWurwEDBshqtdp9gGjlypXl7u6u8+fP53lsAAAAALnxYFEAAOBSxo0bp927d+uVV17RPffco9mzZ2vgwIHasGFDnhqzSUlJ6tOnj5o0aaIRI0bkWC/cEWOsX79er7/+ul555RW9+OKLevPNN7VixQotX75cERERCg4O1rZt2/TAAw9Iuj6j/NKlS+rYseNtnd9qtepf//qX/Pz8NGjQID355JO3jK9Ro4a++uorjR8/3jbm5s2b1bt3b5UpU0aJiYm22M6dO2vs2LFKSkqyPVT02LFjuu+++1S9enVbXFZWlrZt26bnnntO/fr100cffaQOHTqoa9euKlmypJYuXapy5crp6NGjkqTU1FRdvXpVVapUua1r/KtLly7Jx8cnVw2uXbumUqVK5djm7e2tS5cu3dZ5P/zwQ82ePfum+ypXrqwffvghxzYPDw/NmDFDEydO1OjRo/Xuu+/m6f89Pz8/RUZG/m3crdZzL1++vM6dO6eAgIDbHhcAAADAzdFEBwAALiMuLk4bNmzQtGnT1K1bN0nXlx+ZM2eOLl68qHvuuee2z7V161ZNmDBBzz777F0ZY/DgwXr00UdVo0YN3XfffQoNDdWJEye0b98+Sdcb1atWrdLo0aNt+TRp0kTlypW7rfObTCbNnTtX6enp8vPz0+HDh+Xh4aFWrVopPDxcL7zwgi12woQJOnv2rPz9/TV//nzb9smTJ9/0YaStWrWSyWTS1q1b1aNHD0nS0aNH9eijj+aIK1asmJ599llZLBb98ssvmj59ut5//32dPXtW9erVU9myZRUeHq6RI0cqPT1dsbGx8vLyyrEcTF6YTKabLqHi4eEhd3f3XNvT09Nv67y9e/dWu3btbrrPXnM8IyNDiYmJqly58m0/vDQ2NjbXrP/b8dprryk8PDzHNovFctNrBgAAAJB3NNEBAIDLuDGjuXHjxrZtdevW1cyZM/N8rlq1aqlfv353bYwKFSpIut74/d9/v6FTp056++23FR8fr4oVK2rbtm3q06dPnsbw8fGxPSjU3d1dly5d0vnz53MtbZKQkHDTGcv2mtk+Pj566KGHtHPnTvXo0UMpKSk6deqUmjVrlit29OjROnbsmLKzs+Xh4aEJEyZIuj7r/P3331dISIgqV66s7du36+zZs2rUqFG+m79+fn5KTU3VtWvX5OnpadtetmxZnT17NkdsUlKSSpYseVvnvbHu++1KTk7W4MGD1aRJE7344ou3fdyNhvz27dtv+82Yjh073vQ6biyLAwAAAODOsSY6AABwaVarVb/88kue14cOCgqSm9vtvVTK7xi3EhAQoAcffFBbt25VQkKCjh8/rvbt29/RObds2SIPD48cbwBI0rlz51SxYsU8natp06bau3evJOnQoUOyWq1q0qRJrriVK1dq//79Klu2rGbPnq0DBw5o3rx58vb2VuvWrSVJYWFhWrx4sTZu3KhHHnkkn1d3Xb169Wyz+W+oW7eu9u/fn2NbZGSk7c0LRzp//rz69eun9u3b56mBLuV8E0WSXn31VT3yyCPq2LGj7euxxx7LNeP/r286/Pbbb3J3d7/tTy0AAAAAuDWa6AAAwGXUrVtXkvTLL7/Ytv3xxx96+umndeTIEacZ44bQ0FBt27ZN27ZtU/PmzeXn55fvc129elVz587V448/bpudfkNCQkK+mugXLlxQXFycfv31V9WsWVP+/v43jV22bJkuXboks9msn3/+WTNnzlS/fv3k5eUlSerbt6+OHDmiyMjIv127/e88/vjjWrNmTa5tu3btUlRUlKTr9dqyZcsdN+z/6vTp03rmmWcUHh6u/v375/n4vzbRS5UqpWeeeUbffvut7WvSpEl/uzzM2rVr9fjjj+c6HwAAAID8YTkXAADgMmrUqKGOHTvqrbfeksVi0T333KO5c+eqWrVqevjhh51mjBs6duyo6dOnKy0tTU888US+z3P58mUNGTJEycnJGjNmTI59iYmJunz58m0tH2KxWJSVlaXixYsrODhY33//vapVq6Y9e/bkmN2enZ0tq9Vqa/b26dNHtWvX1u7duzVo0CClpqaqVKlSOnLkiBo0aKCrV6/Ky8tLVqtVSUlJdpvxt6Nnz54KDQ3V4cOHFRwcLElq06aNmjZtqmeffVYhISHatWuXypYtq169euV7nL+Kjo7W0KFDNX78+Hx/YuCv67knJSXpyy+/1KJFi2zbsrOzVaJECbvn+PPPP/XFF19o5cqV+coBAAAAQG400QEAgEuZNm2aZsyYoWnTpik7O1tNmjTRlClTbLOenWUM6fpa3g0bNtT+/fs1Z86cPB9vsVi0adMmvffee7p06ZIiIiJsM85jYmL0zTffaN++fSpWrJjq169v9zxZWVmSrs+07tChw01j9u/fry+//NL2/dSpU22N/+LFi6t27dpat26dSpcurffee08//vij9u/fr/Pnz2vChAnq2rWrYmNjFR4ervnz598yn1spVaqUxo8fr7Fjx2rZsmXy9/eXyWTSvHnz9OGHH2rHjh1q1qyZxowZk2tG/p344IMP9Oabb6p58+b5PofFYsnx/fTp0+3Gnj9/XseOHdO5c+dsy7mkp6fr5Zdf1vPPP5/vh7MCAAAAyM1k/euUFwAAAOSbxWLJ1Qz9X25ubre91vqd2rdvn5577jk9/PDDmjRpkqpWrWrbl56errZt26patWr65z//qZCQELvneeedd3T48GEtWLBAJ06ckKen5y2XCsnMzFT58uVVtmxZbd++XV9//bW2b9+uLl266OWXX1aZMmV0+vRpvfnmm9q9e7fGjBmj8PBwpaSkaPDgwTp48KCmTp2qsLCwfF/7jBkzdO7cOb311lv5PkdBO3HihDp37nxbDxb9/fffFRoaqpo1a2rOnDmqUqWKZs2apbi4OE2fPp2lXAAAAAAHookOAADgQOPGjdPXX39td394eLhee+21Asvn999/17333ntH5/j3v/+tY8eOafny5Xk+NiYmRuvXr9dTTz2lKlWq2LZnZmZq9uzZevLJJ1WtWjXb9uzsbC1fvly9evWSh4dHvnO2Wq26cuWKfH19832Owi4tLU0lS5bM8b27u7uKFy9uYFYAAACA66GJDgAA4EBnzpzR5cuX7e739/dXQEBAAWYEAAAAALgTNNEBAAAAAAAAALCjYBbkBAAAAAAAAADACdFEBwAAAAAAAADADproAAAAAAAAAADYQRMdAAAAAAAAAAA7aKIDAAAAAAAAAGAHTXQAAAAAAAAAAOygiQ4AAAAAAAAAgB000QEAAAAAAAAAsOP/AfQGabMFBGdIAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 1600x1200 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"from scipy.stats import spearmanr\n",
|
||
"from tqdm import tqdm # 用于显示进度条 (可选)\n",
|
||
"\n",
|
||
"# 设置 Matplotlib/Seaborn 样式 (可选)\n",
|
||
"sns.set_theme(style=\"whitegrid\")\n",
|
||
"plt.rcParams['font.sans-serif'] = ['SimHei'] # 或者其他支持中文的字体\n",
|
||
"plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题\n",
|
||
"\n",
|
||
"def analyze_score_performance_2d(score_df: pd.DataFrame,\n",
|
||
" score_col: str = 'score',\n",
|
||
" label_col: str = 'label',\n",
|
||
" condition1_col: str = 'circ_mv',\n",
|
||
" condition2_col: str = 'future_return',\n",
|
||
" n_bins: int = 100,\n",
|
||
" min_samples_per_bin: int = 30): # 每个格子最少样本数\n",
|
||
" \"\"\"\n",
|
||
" 分析 score 在两个条件下 (如市值、未来收益) 的二维分箱表现。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" score_df (pd.DataFrame): 包含分数、标签和条件列的 DataFrame。\n",
|
||
" score_col (str): 预测分数所在的列名。\n",
|
||
" label_col (str): 目标标签所在的列名 (应为数值或可排序类别)。\n",
|
||
" condition1_col (str): 第一个条件列名 (例如 'circ_mv')。\n",
|
||
" condition2_col (str): 第二个条件列名 (例如 'future_return')。\n",
|
||
" n_bins (int): 每个条件划分的箱数 (分位数数量)。\n",
|
||
" min_samples_per_bin (int): 计算指标所需的最小样本数，小于此数目的格子结果将被屏蔽。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" tuple: 包含 (performance_pivot, count_pivot, fig)\n",
|
||
" performance_pivot: 以二维分箱为索引/列的 Spearman 相关系数矩阵。\n",
|
||
" count_pivot: 每个二维分箱的样本数量矩阵。\n",
|
||
" fig: 生成的热力图 Matplotlib Figure 对象。\n",
|
||
" \"\"\"\n",
|
||
" print(f\"开始分析 '{score_col}' 在 '{condition1_col}' 和 '{condition2_col}' 下的表现...\")\n",
|
||
"\n",
|
||
" required_cols = [score_col, label_col, condition1_col, condition2_col]\n",
|
||
" if not all(col in score_df.columns for col in required_cols):\n",
|
||
" missing = [col for col in required_cols if col not in score_df.columns]\n",
|
||
" raise ValueError(f\"输入 DataFrame 缺少必需列: {missing}\")\n",
|
||
"\n",
|
||
" # --- 1. 数据准备和清洗 ---\n",
|
||
" print(\"准备数据，处理 NaN 值...\")\n",
|
||
" # 只保留需要的列，并移除包含 NaN 的行，避免影响分箱和计算\n",
|
||
" analysis_df = score_df[required_cols].dropna().copy()\n",
|
||
" n_original = len(score_df)\n",
|
||
" n_after_drop = len(analysis_df)\n",
|
||
" print(f\"原始数据 {n_original} 行，移除 NaN 后剩余 {n_after_drop} 行用于分析。\")\n",
|
||
"\n",
|
||
" if n_after_drop < min_samples_per_bin * n_bins: # 检查数据量是否过少\n",
|
||
" print(f\"警告: 清理 NaN 后数据量 ({n_after_drop}) 可能不足以支持 {n_bins}x{n_bins} 的精细分箱分析。\")\n",
|
||
" if n_after_drop < min_samples_per_bin:\n",
|
||
" print(\"错误: 有效数据过少，无法进行分析。\")\n",
|
||
" return None, None, None\n",
|
||
"\n",
|
||
" # --- 2. 二维分箱 ---\n",
|
||
" print(f\"对 '{condition1_col}' 和 '{condition2_col}' 进行 {n_bins} 分位数分箱...\")\n",
|
||
" bin1_col = f'{condition1_col}_bin'\n",
|
||
" bin2_col = f'{condition2_col}_bin'\n",
|
||
"\n",
|
||
" try:\n",
|
||
" # 使用 qcut 进行分位数分箱，labels=False 返回 0 到 n_bins-1 的整数标签\n",
|
||
" # duplicates='drop' 会丢弃导致边界不唯一的重复值所在的箱子，可能导致某些箱号缺失\n",
|
||
" # 对于可视化，这通常可以接受，但如果需要严格的等分，需先 rank\n",
|
||
" analysis_df[bin1_col] = pd.qcut(analysis_df[condition1_col], q=n_bins, labels=False, duplicates='drop')\n",
|
||
" analysis_df[bin2_col] = pd.qcut(analysis_df[condition2_col], q=n_bins, labels=False, duplicates='drop')\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"错误: 分箱失败，请检查数据分布或减少 n_bins。错误信息: {e}\")\n",
|
||
" # 可以尝试先 rank 再 qcut\n",
|
||
" # analysis_df[bin1_col] = pd.qcut(analysis_df[condition1_col].rank(method='first'), q=n_bins, labels=False, duplicates='raise')\n",
|
||
" # analysis_df[bin2_col] = pd.qcut(analysis_df[condition2_col].rank(method='first'), q=n_bins, labels=False, duplicates='raise')\n",
|
||
" return None, None, None\n",
|
||
"\n",
|
||
" # --- 3. 分组计算表现指标 (Spearman Rank IC) ---\n",
|
||
" print(\"按二维分箱分组计算 Spearman Rank IC...\")\n",
|
||
"\n",
|
||
" def safe_spearmanr(x, y):\n",
|
||
" \"\"\"安全计算 Spearman 相关性，处理数据量过少的情况\"\"\"\n",
|
||
" if len(x) < max(2, min_samples_per_bin): # 要求至少有 min_samples_per_bin 个点才计算\n",
|
||
" return np.nan\n",
|
||
" corr, p_value = spearmanr(x, y)\n",
|
||
" return corr if not np.isnan(corr) else np.nan # 确保返回 NaN 而不是 None 或其他\n",
|
||
"\n",
|
||
" # 按两个分箱列分组\n",
|
||
" grouped = analysis_df.groupby([bin1_col, bin2_col])\n",
|
||
"\n",
|
||
" # 计算每个格子的 Spearman 相关系数\n",
|
||
" # apply 可能较慢，但计算相关性通常需要 apply\n",
|
||
" performance_series = grouped.apply(lambda sub: safe_spearmanr(sub[score_col], sub[label_col]))\n",
|
||
"\n",
|
||
" # 计算每个格子的样本数量\n",
|
||
" count_series = grouped.size()\n",
|
||
"\n",
|
||
" # --- 4. 结果整理成 Pivot Table (用于绘图) ---\n",
|
||
" print(\"整理结果用于绘图...\")\n",
|
||
" try:\n",
|
||
" # 将 performance_series 转换成二维矩阵\n",
|
||
" # index 为 condition1_bin, columns 为 condition2_bin\n",
|
||
" performance_pivot = performance_series.unstack(level=0) # level=0 对应第一个 groupby key (bin1_col)\n",
|
||
" count_pivot = count_series.unstack(level=0)\n",
|
||
"\n",
|
||
" # 可选：按列和索引排序，确保顺序正确\n",
|
||
" performance_pivot = performance_pivot.sort_index(axis=0).sort_index(axis=1)\n",
|
||
" count_pivot = count_pivot.sort_index(axis=0).sort_index(axis=1)\n",
|
||
" \n",
|
||
" print(performance_pivot)\n",
|
||
"\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"错误: 无法将结果转换为二维矩阵，可能因为分箱不均匀或数据问题: {e}\")\n",
|
||
" return None, None, None\n",
|
||
"\n",
|
||
" # --- 5. 可视化：绘制热力图 ---\n",
|
||
" print(\"生成热力图...\")\n",
|
||
" fig, ax = plt.subplots(figsize=(16, 12)) # 调整图像大小\n",
|
||
"\n",
|
||
" # 使用 count_pivot 创建一个 mask，屏蔽掉样本量过小的格子\n",
|
||
" mask = count_pivot < min_samples_per_bin\n",
|
||
"\n",
|
||
" # 绘制热力图\n",
|
||
" sns.heatmap(performance_pivot,\n",
|
||
" annot=False, # 100x100 个格子加注释会太密集\n",
|
||
" fmt=\".2f\",\n",
|
||
" cmap=\"viridis\", # 选择颜色映射, 'viridis', 'coolwarm', 'RdYlGn' 等都不错\n",
|
||
" linewidths=.5,\n",
|
||
" linecolor='lightgray',\n",
|
||
" # mask=mask, # 应用 mask\n",
|
||
" ax=ax,\n",
|
||
" cbar_kws={'label': f'Spearman Rank IC ({score_col} vs {label_col})'}) # 颜色条标签\n",
|
||
"\n",
|
||
" # 设置标题和轴标签\n",
|
||
" ax.set_title(f'{score_col} 表现分析 (Rank IC vs {label_col})\\n基于 {condition1_col} 和 {condition2_col} {n_bins}x{n_bins} 分箱', fontsize=16)\n",
|
||
" ax.set_xlabel(f'{condition1_col} 分位数 (0 -> 高)', fontsize=12)\n",
|
||
" ax.set_ylabel(f'{condition2_col} 分位数 (0 -> 高)', fontsize=12)\n",
|
||
"\n",
|
||
" # 可选：调整刻度标签，避免显示所有 100 个刻度\n",
|
||
" if n_bins > 20:\n",
|
||
" tick_interval = n_bins // 10 # 大约显示 10 个刻度\n",
|
||
" ax.set_xticks(np.arange(0, n_bins, tick_interval) + 0.5)\n",
|
||
" ax.set_yticks(np.arange(0, n_bins, tick_interval) + 0.5)\n",
|
||
" ax.set_xticklabels(np.arange(0, n_bins, tick_interval))\n",
|
||
" ax.set_yticklabels(np.arange(0, n_bins, tick_interval))\n",
|
||
"\n",
|
||
" plt.xticks(rotation=45, ha='right')\n",
|
||
" plt.yticks(rotation=0)\n",
|
||
" plt.tight_layout() # 调整布局\n",
|
||
"\n",
|
||
" print(\"分析完成。\")\n",
|
||
" return performance_pivot, count_pivot, fig\n",
|
||
"\n",
|
||
"# --- 如何使用 ---\n",
|
||
"# 假设你的包含预测结果和所需列的 DataFrame 是 final_predictions_df\n",
|
||
"# 确保它包含 'score', 'label', 'circ_mv', 'future_return'\n",
|
||
"\n",
|
||
"# # 示例调用 (你需要有实际的 score_df)\n",
|
||
"try:\n",
|
||
" # 确保数据类型正确\n",
|
||
" cols_to_numeric = ['score', 'label', 'circ_mv', 'future_return']\n",
|
||
" for col in cols_to_numeric:\n",
|
||
" if col in score_df.columns:\n",
|
||
" score_df[col] = pd.to_numeric(score_df[col], errors='coerce')\n",
|
||
"\n",
|
||
" # 调用分析函数\n",
|
||
" performance_matrix, count_matrix, heatmap_figure = analyze_score_performance_2d(\n",
|
||
" score_df,\n",
|
||
" n_bins=100, # 你要求的100分箱\n",
|
||
" min_samples_per_bin=50 # 每个格子至少需要50个样本才显示IC，可以调整\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 显示图像\n",
|
||
" if heatmap_figure:\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" # 可以查看具体的 performance_matrix 和 count_matrix\n",
|
||
" # print(\"\\nPerformance Matrix (Spearman IC):\")\n",
|
||
" # print(performance_matrix)\n",
|
||
" # print(\"\\nCount Matrix:\")\n",
|
||
" # print(count_matrix)\n",
|
||
"\n",
|
||
"except ValueError as ve:\n",
|
||
" print(f\"数据错误: {ve}\")\n",
|
||
"except Exception as e:\n",
|
||
" print(f\"发生未知错误: {e}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "a436dba4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Empty DataFrame\n",
|
||
"Columns: [ts_code, trade_date, is_st]\n",
|
||
"Index: []\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(df[(df['ts_code'] == '600242.SH') & (df['trade_date'] >= '2023-06-01')][['ts_code', 'trade_date', 'is_st']])"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "new_trader",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.11.11"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|