242 lines
9.2 KiB
Plaintext
242 lines
9.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2025-09-04T03:09:51.064325Z",
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"start_time": "2025-09-04T03:09:50.789242Z"
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}
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},
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import talib as ta # Make sure TA-Lib is installed: pip install TA-Lib\n",
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"import statsmodels.api as sm\n",
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"\n",
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"import warnings\n",
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"\n",
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"# 忽略所有警告\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"\n",
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"# --- 0. Configure your file path ---\n",
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"# Please replace 'your_futures_data.csv' with the actual path to your CSV file\n",
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"file_path = '/mnt/d/PyProject/NewQuant/data/data/KQ_m@SHFE_rb/KQ_m@SHFE_rb_min15.csv'\n"
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],
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"outputs": [],
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"execution_count": 1
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-04T03:09:51.149053Z",
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"start_time": "2025-09-04T03:09:51.081682Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"# --- 1. Data Loading and Preprocessing ---\n",
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"def load_and_preprocess_data(file_path):\n",
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" \"\"\"\n",
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" Loads historical futures data and performs basic preprocessing.\n",
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" Assumes data contains 'datetime', 'open', 'high', 'low', 'close', 'volume' columns.\n",
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" \"\"\"\n",
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" try:\n",
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" df = pd.read_csv(file_path, parse_dates=['datetime'], index_col='datetime')\n",
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" # Ensure data is sorted by time\n",
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" df = df.sort_index()\n",
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" # Check and handle missing values\n",
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" initial_rows = len(df)\n",
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" df.dropna(inplace=True)\n",
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" if len(df) < initial_rows:\n",
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" print(f\"Warning: Missing values found in data, deleted {initial_rows - len(df)} rows.\")\n",
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"\n",
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" # Check if necessary columns exist\n",
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" required_columns = ['open', 'high', 'low', 'close', 'volume']\n",
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" if not all(col in df.columns for col in required_columns):\n",
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" raise ValueError(f\"CSV file is missing required columns. Please ensure it contains: {required_columns}\")\n",
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"\n",
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" print(f\"Successfully loaded {len(df)} rows of data.\")\n",
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" print(\"First 5 rows of data:\")\n",
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" print(df.head())\n",
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" return df\n",
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" except FileNotFoundError:\n",
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" print(f\"Error: File '{file_path}' not found. Please check the path.\")\n",
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" return None\n",
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" except Exception as e:\n",
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" print(f\"Error during data loading or preprocessing: {e}\")\n",
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" return None\n",
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"\n",
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"\n",
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"df_raw = load_and_preprocess_data(file_path)"
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],
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"id": "548c68daa68af8c1",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Successfully loaded 25470 rows of data.\n",
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"First 5 rows of data:\n",
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" open high low close volume open_oi \\\n",
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"datetime \n",
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"2020-12-31 14:45:00 4352.0 4400.0 4345.0 4388.0 213731.0 1221661.0 \n",
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"2021-01-04 09:00:00 4356.0 4368.0 4309.0 4336.0 338332.0 1217327.0 \n",
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"2021-01-04 09:15:00 4336.0 4342.0 4307.0 4318.0 144479.0 1197881.0 \n",
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"2021-01-04 09:30:00 4318.0 4329.0 4312.0 4317.0 85679.0 1194567.0 \n",
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"2021-01-04 09:45:00 4317.0 4338.0 4316.0 4338.0 66461.0 1194592.0 \n",
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"\n",
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" close_oi underlying_symbol \n",
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"datetime \n",
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"2020-12-31 14:45:00 1217327.0 SHFE.rb2105 \n",
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"2021-01-04 09:00:00 1197881.0 SHFE.rb2105 \n",
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"2021-01-04 09:15:00 1194567.0 SHFE.rb2105 \n",
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"2021-01-04 09:30:00 1194592.0 SHFE.rb2105 \n",
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"2021-01-04 09:45:00 1198035.0 SHFE.rb2105 \n"
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]
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}
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],
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"execution_count": 2
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-04T03:12:29.108246Z",
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"start_time": "2025-09-04T03:12:26.136815Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import talib\n",
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"from scipy.stats import jarque_bera, bootstrap\n",
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"from statsmodels.tsa.stattools import adfuller, acf\n",
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"from statsmodels.stats.diagnostic import het_arch\n",
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"\n",
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"\n",
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"def rsi_stats(df_raw: pd.DataFrame,\n",
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" period: int = 14,\n",
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" lookback: int = 252 * 4):\n",
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" \"\"\"\n",
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" 返回 RSI 多维度统计量\n",
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" \"\"\"\n",
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" rsi = talib.RSI(df_raw['close'], timeperiod=period)\n",
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" rsi = rsi[~np.isnan(rsi)]\n",
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" if len(rsi) < 100:\n",
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" raise ValueError('RSI 样本不足')\n",
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"\n",
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" # 基本描述\n",
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" stats = {\n",
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" 'period': period,\n",
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" 'mean': np.mean(rsi),\n",
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" 'std': np.std(rsi, ddof=1),\n",
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" 'skew': pd.Series(rsi).skew(),\n",
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" 'kurt': pd.Series(rsi).kurtosis(),\n",
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" 'q10': np.percentile(rsi, 10),\n",
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" 'q90': np.percentile(rsi, 90),\n",
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" }\n",
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"\n",
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" # 均值回归 / 动量\n",
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" adf_res = adfuller(rsi, regression='c')\n",
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" stats['adf_stat'] = adf_res[0]\n",
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" stats['adf_p'] = adf_res[1]\n",
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" stats['acf_1'] = acf(rsi, nlags=1, fft=False)[1]\n",
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"\n",
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" # 波动聚集（RSI 变化率的 ARCH）\n",
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" delta_rsi = np.diff(rsi)\n",
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" arch_res = het_arch(delta_rsi)\n",
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" arch_lm, arch_p = arch_res[0], arch_res[1]\n",
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"\n",
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" stats['arch_lm'] = arch_lm\n",
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" stats['arch_p'] = arch_p\n",
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"\n",
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" # 重尾指数（Hill）\n",
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" tail = sorted(rsi)[-int(np.ceil(0.05 * len(rsi))):]\n",
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" stats['hill_tail'] = len(tail) / np.sum(np.log(tail) - np.log(min(tail)))\n",
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"\n",
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" # 均值回归区间收益\n",
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" extreme_low = stats['q10']\n",
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" extreme_high = stats['q90']\n",
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" # 事件：RSI 穿越极值后 5-bar 收益\n",
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" events = []\n",
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" for i in range(1, len(rsi)):\n",
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" if rsi[i - 1] < extreme_low and rsi[i] >= extreme_low:\n",
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" events.append(i)\n",
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" elif rsi[i - 1] > extreme_high and rsi[i] <= extreme_high:\n",
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" events.append(-i)\n",
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"\n",
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" if len(events) > 20:\n",
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" rets = []\n",
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" for idx in events:\n",
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" idx_abs = abs(idx)\n",
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" if idx_abs + 5 >= len(rsi):\n",
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" continue\n",
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" direc = 1 if idx > 0 else -1\n",
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" start = df_raw['close'].iloc[idx_abs]\n",
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" end = df_raw['close'].iloc[idx_abs + 5]\n",
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" rets.append((end - start) * direc)\n",
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" rets = np.array(rets)\n",
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" stats['mean_ret_5'] = np.mean(rets)\n",
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" stats['win_rate_5'] = np.mean(rets > 0)\n",
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" stats['events_5'] = len(rets)\n",
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" else:\n",
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" stats.update({'mean_ret_5': np.nan, 'win_rate_5': np.nan, 'events_5': 0})\n",
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"\n",
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" return pd.Series(stats)\n",
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"\n",
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"\n",
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"# ---------- 一键 ----------\n",
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"for p in [6, 14, 21]:\n",
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" try:\n",
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" res = rsi_stats(df_raw, period=p)\n",
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" print(res.to_frame().T.to_string(index=False))\n",
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" except ValueError as e:\n",
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" e.with_traceback().print_exc()"
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],
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"id": "c566a4757b4f6456",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" period mean std skew kurt q10 q90 adf_stat adf_p acf_1 arch_lm arch_p hill_tail mean_ret_5 win_rate_5 events_5\n",
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" 6.0 50.076761 18.780546 -0.007016 -0.633046 25.069754 75.082648 -39.303166 0.0 0.848566 360.978412 1.861253e-71 16.95949 -36.215042 0.005014 1795.0\n",
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" period mean std skew kurt q10 q90 adf_stat adf_p acf_1 arch_lm arch_p hill_tail mean_ret_5 win_rate_5 events_5\n",
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" 14.0 50.091278 12.856068 -0.0578 -0.260539 32.889392 66.554807 -27.311287 0.0 0.933965 102.255697 1.925344e-17 16.462544 -16.821584 0.240209 1149.0\n",
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" period mean std skew kurt q10 q90 adf_stat adf_p acf_1 arch_lm arch_p hill_tail mean_ret_5 win_rate_5 events_5\n",
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" 21.0 50.067891 10.675715 -0.080938 -0.156658 35.637464 63.528908 -22.804102 0.0 0.955594 55.768629 2.268265e-08 16.935726 -13.032393 0.30721 957.0\n"
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]
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}
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],
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"execution_count": 7
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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