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#!/usr/bin/env python3
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"""
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从 CSV 导入 Alpha 数据到 SQLite（修复去重版）
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"""
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import sqlite3
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import pandas as pd
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import ast
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import re
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from pathlib import Path
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# 路径配置
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CSV_PATH = Path(__file__).parent / "alpha_list.csv"
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SQLITE_PATH = Path(__file__).parent / "alpha_analysis.db"
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# 84 个算子集合（同上，省略重复部分，你复制完整的）
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OPERATORS_SET = {
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'add', 'abs', 'log', 'subtract', 'signed_power', 'sign', 'reverse', 'power',
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'multiply', 'min', 'max', 'inverse', 'sqrt', 's_log_1p', 'densify', 'divide',
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'not', 'and', 'less', 'equal', 'or', 'not_equal', 'greater', 'greater_equal',
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'less_equal', 'is_nan', 'if_else', 'ts_sum', 'ts_scale', 'ts_mean', 'ts_zscore',
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'ts_std_dev', 'kth_element', 'inst_tvr', 'ts_corr', 'ts_count_nans',
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'ts_target_tvr_decay', 'ts_median', 'ts_covariance', 'ts_decay_linear',
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'ts_product', 'ts_regression', 'ts_delta_limit', 'ts_step', 'ts_decay_exp_window',
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'ts_quantile', 'days_from_last_change', 'hump', 'last_diff_value', 'ts_arg_max',
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'ts_arg_min', 'ts_av_diff', 'ts_backfill', 'ts_rank', 'ts_delay', 'ts_delta',
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'winsorize', 'truncate', 'regression_neut', 'scale', 'rank', 'quantile',
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'normalize', 'zscore', 'vec_min', 'vec_count', 'vec_stddev', 'vec_range',
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'vec_avg', 'vec_sum', 'vec_max', 'left_tail', 'trade_when', 'right_tail',
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'bucket', 'group_rank', 'group_cartesian_product', 'group_backfill', 'group_mean',
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'group_neutralize', 'group_normalize', 'group_median', 'group_scale', 'group_zscore'
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}
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WINDOWS = {1, 2, 5, 10, 20, 30, 60, 90, 252}
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def safe_parse_dict(s):
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"""安全解析 Python 字典字符串"""
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if pd.isna(s) or s == '':
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return {}
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try:
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return ast.literal_eval(s)
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except:
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return {}
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def extract_features(expression):
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"""从表达式提取特征"""
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if not expression:
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return [], [], []
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operators = set()
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for match in re.finditer(r'\b([a-z_][a-z0-9_]*)\s*\(', expression):
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op = match.group(1)
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if op in OPERATORS_SET:
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operators.add(op)
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candidates = set(re.findall(r'\b([a-z][a-z0-9_]*)\b', expression))
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fields = set()
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for c in candidates:
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if (c not in OPERATORS_SET and
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not c.isdigit() and
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len(c) > 2 and
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c not in ['true', 'false', 'nan', 'null', 'constant', 'filter']):
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fields.add(c)
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windows = set()
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for match in re.finditer(r'\b(\d+)\b', expression):
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num = int(match.group(1))
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if num in WINDOWS:
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windows.add(num)
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return list(operators), list(fields), list(windows)
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def main():
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print("=" * 60)
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print("开始导入 CSV 到 SQLite")
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print("=" * 60)
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# 1. 读取 CSV
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print("\n📖 读取 CSV...")
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df = pd.read_csv(CSV_PATH)
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print(f" 共 {len(df):,} 条记录")
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# 2. 解析字段
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print("\n🔍 解析字段...")
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df['regular_dict'] = df['regular'].apply(safe_parse_dict)
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df['expression'] = df['regular_dict'].apply(lambda x: x.get('code', ''))
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df['settings_dict'] = df['settings'].apply(safe_parse_dict)
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df['neutralization'] = df['settings_dict'].apply(lambda x: x.get('neutralization'))
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df['universe'] = df['settings_dict'].apply(lambda x: x.get('universe'))
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df['is_dict'] = df['is'].apply(safe_parse_dict)
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df['sharpe'] = df['is_dict'].apply(lambda x: x.get('sharpe'))
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df['fitness'] = df['is_dict'].apply(lambda x: x.get('fitness'))
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df['drawdown'] = df['is_dict'].apply(lambda x: x.get('drawdown'))
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df['turnover'] = df['is_dict'].apply(lambda x: x.get('turnover'))
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# 过滤有效数据
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df_valid = df[df['expression'].notna() & (df['expression'] != '') & df['fitness'].notna()].copy()
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print(f" 有效记录: {len(df_valid):,} 条")
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# 3. 去重：同一个 alpha_id 保留 fitness 最高的
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print("\n🔄 去重（按 alpha_id 保留最高 fitness）...")
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df_unique = df_valid.sort_values('fitness', ascending=False).drop_duplicates(subset=['id'], keep='first')
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print(f" 去重后: {len(df_unique):,} 条")
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# 4. 连接 SQLite
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conn = sqlite3.connect(str(SQLITE_PATH))
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cursor = conn.cursor()
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# 清空旧数据
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print("\n🗑️ 清空旧数据...")
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cursor.execute("DELETE FROM alpha_feature_long")
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cursor.execute("DELETE FROM alpha_success")
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conn.commit()
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# 5. 写入 alpha_success
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print("\n💾 写入 alpha_success 表...")
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for idx, row in df_unique.iterrows():
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try:
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cursor.execute("""
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INSERT INTO alpha_success
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(alpha_id, expression, sharpe, fitness, drawdown, turnover, neutralization, universe)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?)
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""", (
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row['id'],
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row['expression'],
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row['sharpe'],
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row['fitness'],
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row['drawdown'],
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row['turnover'],
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row['neutralization'],
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row['universe']
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))
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except sqlite3.IntegrityError:
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# 如果还有重复，跳过（理论上不应该）
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print(f" 跳过重复: {row['id']}")
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continue
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conn.commit()
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# 获取插入后的 id 映射
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cursor.execute("SELECT alpha_id, rowid FROM alpha_success")
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id_map = {row[0]: row[1] for row in cursor.fetchall()}
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print(f" 已写入 {len(id_map):,} 条")
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# 6. 提取特征并写入 alpha_feature_long
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print("\n🔧 提取特征并写入 alpha_feature_long...")
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feature_count = 0
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processed = 0
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for idx, row in df_unique.iterrows():
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alpha_id = row['id']
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if alpha_id not in id_map:
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continue
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sqlite_id = id_map[alpha_id]
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expression = row['expression']
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operators, fields, windows = extract_features(expression)
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# 插入算子
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for op in operators:
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cursor.execute(
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"INSERT INTO alpha_feature_long (alpha_id, feature_type, feature_name) VALUES (?, ?, ?)",
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(sqlite_id, 'operator', op)
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)
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feature_count += 1
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# 插入字段
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for field in fields:
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cursor.execute(
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"INSERT INTO alpha_feature_long (alpha_id, feature_type, feature_name) VALUES (?, ?, ?)",
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(sqlite_id, 'field', field)
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)
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feature_count += 1
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# 插入窗口
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for w in windows:
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cursor.execute(
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"INSERT INTO alpha_feature_long (alpha_id, feature_type, feature_name, feature_value) VALUES (?, ?, ?, ?)",
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(sqlite_id, 'window', str(w), w)
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)
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feature_count += 1
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# 插入中性化方式
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if pd.notna(row['neutralization']):
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cursor.execute(
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"INSERT INTO alpha_feature_long (alpha_id, feature_type, feature_name) VALUES (?, ?, ?)",
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(sqlite_id, 'neutralization', row['neutralization'])
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)
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feature_count += 1
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processed += 1
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if processed % 1000 == 0:
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print(f" 已处理 {processed:,} 条，特征数 {feature_count:,}")
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conn.commit() # 每 1000 条提交一次
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conn.commit()
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# 7. 最终统计
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cursor.execute("SELECT COUNT(*) FROM alpha_success")
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success_count = cursor.fetchone()[0]
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cursor.execute("SELECT COUNT(*) FROM alpha_feature_long")
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final_feature_count = cursor.fetchone()[0]
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print(f"\n✅ 导入完成!")
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print(f" alpha_success: {success_count:,} 条")
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print(f" alpha_feature_long: {final_feature_count:,} 条特征")
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# 8. 验证一条数据
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print("\n🔍 验证前 10 条特征:")
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cursor.execute("""
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SELECT a.alpha_id, f.feature_type, f.feature_name
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FROM alpha_success a
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JOIN alpha_feature_long f ON a.rowid = f.alpha_id
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LIMIT 10
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""")
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for row in cursor.fetchall():
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print(f" {row[0]} | {row[1]}: {row[2]}")
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conn.close()
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if __name__ == "__main__":
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main() |
