cross-day-mainforce-600809 / scripts /run_local_test.py
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#!/usr/bin/env python3
"""
本地最小验证:5 个交易日,验证 pipeline 正确性。
用法:
python scripts/run_local_test.py
"""
import os
import sys
from pathlib import Path
# 保证项目根在 path 里
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from src.data.loader import load_l2_day, BLACKLIST_DATES
from src.features.passive_orders import (
compute_vwap,
extract_passive_orders,
prepare_features,
select_candidates,
)
from src.clustering.daily_cluster import cluster_candidates
from src.matching.cross_day_match import match_clusters, match_multi_window
from src.tracking.entity_tracker import EntityTracker
# 测试日期:2024年3月第二周(避开黑名单)
TEST_DATES = [20240311, 20240312, 20240313, 20240314, 20240315]
OUTPUT_DIR = os.path.join(
os.path.dirname(__file__), "..", "outputs", "local_test"
)
def main():
os.makedirs(OUTPUT_DIR, exist_ok=True)
print(f"本地测试: {len(TEST_DATES)} 天, 输出目录: {OUTPUT_DIR}\n")
tracker = EntityTracker(inactive_threshold=5)
recent_history = {}
for i, date in enumerate(TEST_DATES):
print(f"--- {date} ---")
# 1. 加载
try:
data = load_l2_day(date)
except Exception as e:
print(f" SKIP: 加载失败 ({e})")
continue
trades = data["trades"]
orders = data["orders"]
if "is_cancellation" in trades.columns:
trades = trades[~trades["is_cancellation"]]
print(f" trades={len(trades):,}, orders={len(orders):,}")
# 2. VWAP + 被动单
vwap = compute_vwap(trades)
passive = extract_passive_orders(trades, vwap)
candidates = select_candidates(passive, top_n=150)
print(f" passive_orders={len(passive):,}, candidates={len(candidates)}")
print(f" VWAP={vwap:.2f}, bid_candidates={len(candidates[candidates['side']=='bid'])}, ask_candidates={len(candidates[candidates['side']=='ask'])}")
if candidates.empty:
recent_history[date] = {}
continue
# 3. 聚类
feats = prepare_features(candidates)
labeled, centroids = cluster_candidates(candidates, feats)
n_clusters = len(centroids)
n_noise = (labeled["cluster_id"] == -1).sum()
print(f" clusters={n_clusters}, noise={n_noise}")
# 4. 跨日匹配
prev_dates = sorted(
[d for d in recent_history.keys() if d < date]
)[-2:]
prev_c_for_match = {}
for pd_ in prev_dates:
if recent_history.get(pd_):
prev_c_for_match[pd_] = recent_history[pd_]
matches = match_multi_window(date, centroids, prev_c_for_match)
print(f" matches={len(matches)}")
for m in matches:
print(f" {m[0]} c{m[1]}{m[2]} cost={m[3]:.3f}")
# 5. 实体追踪
cid_to_eid = tracker.process_day(date, centroids, matches)
print(f" entity mapping: {cid_to_eid}")
# 6. 仓位推断
signal = tracker.compute_position_signal(date)
print(f" signal: score={signal['score']:.4f}, bid_entities={signal['bid_entities']}, ask_entities={signal['ask_entities']}")
recent_history[date] = centroids
# ---- 导出 ----
print("\n===== 导出 =====")
entity_df = tracker.get_entity_timeline()
print(f"实体总数: {len(entity_df)}")
print(entity_df.to_string())
entity_path = os.path.join(OUTPUT_DIR, "entity_timeline.parquet")
entity_df.to_parquet(entity_path)
print(f"实体表 → {entity_path}")
signals_df = tracker.get_daily_signals()
signals_path = os.path.join(OUTPUT_DIR, "position_signal_daily.parquet")
signals_df.to_parquet(signals_path)
print(f"信号表 → {signals_path}")
print(signals_df[["date", "score", "score_z", "n_active_entities"]].to_string())
# 被动单样本(第一天)
passive_path = os.path.join(OUTPUT_DIR, "sample_passive_orders.parquet")
passive.to_parquet(passive_path)
print(f"被动单样本 → {passive_path}")
# 聚类样本
cluster_path = os.path.join(OUTPUT_DIR, "sample_clusters.parquet")
labeled.to_parquet(cluster_path)
print(f"聚类样本 → {cluster_path}")
print("\n===== 本地验证完成 =====")
if __name__ == "__main__":
main()