235 lines
9.7 KiB
Python
235 lines
9.7 KiB
Python
"""Strategy attribution read model based on opportunity and paper-trading conversion."""
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import json
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import re
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from app.db.schema import get_conn
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def safe_list_json(value):
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try:
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if isinstance(value, list):
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return value
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if isinstance(value, str) and value.strip():
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parsed = json.loads(value)
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return parsed if isinstance(parsed, list) else []
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except Exception:
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pass
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return []
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def safe_dict_json(value):
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try:
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if isinstance(value, dict):
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return value
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if isinstance(value, str) and value.strip():
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parsed = json.loads(value)
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return parsed if isinstance(parsed, dict) else {}
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except Exception:
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pass
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return {}
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def get_strategy_insights():
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"""Strategy attribution based on opportunity and paper-trading conversion.
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Recommendation rows are opportunities/signals, not an execution ledger.
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Therefore this read model does not use recommendation.pnl_pct as strategy
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PnL. Paper-trading PnL is exposed only as an execution-conversion metric.
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"""
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conn = get_conn()
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rows = conn.execute(
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"""
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SELECT
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r.*,
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pt.id AS paper_trade_id,
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pt.status AS paper_status,
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pt.realized_pnl_pct AS paper_realized_pnl_pct,
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pt.realized_pnl_usdt AS paper_realized_pnl_usdt,
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pt.pnl_pct AS paper_pnl_pct,
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pt.exit_reason AS paper_exit_reason
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FROM recommendation r
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LEFT JOIN paper_trades pt ON pt.recommendation_id = r.id
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ORDER BY r.rec_time DESC, r.id DESC
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"""
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).fetchall()
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conn.close()
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items = [dict(r) for r in rows]
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actionable_statuses = {"buy_now", "wait_pullback"}
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total = len(items)
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actionable = [x for x in items if (x.get("execution_status") or "") in actionable_statuses]
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buy_now = [x for x in items if (x.get("execution_status") or "") == "buy_now"]
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paper_items = [x for x in items if x.get("paper_trade_id")]
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closed_paper = [x for x in paper_items if x.get("paper_status") == "closed"]
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paper_wins = [x for x in closed_paper if float(x.get("paper_realized_pnl_pct") or 0) > 0]
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paper_realized_usdt = round(sum(float(x.get("paper_realized_pnl_usdt") or 0) for x in closed_paper), 4)
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overview = {
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"total_opportunities": total,
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"actionable_count": len(actionable),
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"buy_now_count": len(buy_now),
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"paper_trade_count": len(paper_items),
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"closed_paper_trade_count": len(closed_paper),
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"paper_win_count": len(paper_wins),
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"paper_win_rate_pct": round(len(paper_wins) / len(closed_paper) * 100, 1) if closed_paper else 0,
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"paper_realized_pnl_usdt": paper_realized_usdt,
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"actionable_conversion_pct": round(len(actionable) / total * 100, 1) if total else 0,
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"paper_conversion_pct": round(len(paper_items) / len(buy_now) * 100, 1) if buy_now else 0,
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"definition": "策略归因只看机会转化和模拟交易转化；收益只来自 paper_trades，不读取 recommendation.pnl_pct。",
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}
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def add_bucket(bucket_map, key, item):
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if not key:
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return
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b = bucket_map.setdefault(key, {
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"opportunity_count": 0,
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"actionable_count": 0,
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"buy_now_count": 0,
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"paper_trade_count": 0,
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"closed_paper_trade_count": 0,
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"paper_win_count": 0,
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"paper_realized_pnl_usdt": 0.0,
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})
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execution_status = item.get("execution_status") or ""
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paper_status = item.get("paper_status") or ""
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b["opportunity_count"] += 1
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if execution_status in actionable_statuses:
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b["actionable_count"] += 1
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if execution_status == "buy_now":
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b["buy_now_count"] += 1
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if item.get("paper_trade_id"):
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b["paper_trade_count"] += 1
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if paper_status == "closed":
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b["closed_paper_trade_count"] += 1
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pnl_pct = float(item.get("paper_realized_pnl_pct") or 0)
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if pnl_pct > 0:
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b["paper_win_count"] += 1
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b["paper_realized_pnl_usdt"] += float(item.get("paper_realized_pnl_usdt") or 0)
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factor_map = {}
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env_map = {}
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version_map = {}
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evidence_map = {}
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for item in items:
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labels = safe_list_json(item.get("signal_labels_json")) or safe_list_json(item.get("signals"))
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codes = safe_list_json(item.get("signal_codes_json"))
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for factor in labels:
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add_bucket(factor_map, str(factor).strip(), item)
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for code in codes:
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text = str(code or "").strip()
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if text.startswith(("sentiment_", "listing_", "ecosystem_")):
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add_bucket(evidence_map, "舆情:" + text, item)
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elif text.startswith(("dex_", "liquidity_", "exchange_", "whale_", "smart_money", "holder_")):
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add_bucket(evidence_map, "链上:" + text, item)
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mc = safe_dict_json(item.get("market_context_json"))
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for key in ("btc_trend", "market_regime", "altcoin_regime", "sentiment"):
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if mc.get(key):
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add_bucket(env_map, f"{key}:{mc.get(key)}", item)
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for bucket in env_buckets_from_market_context(mc):
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add_bucket(env_map, bucket, item)
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if item.get("strategy_version"):
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add_bucket(version_map, str(item.get("strategy_version")).strip(), item)
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return {
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"overview": overview,
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"metric_definition": {
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"opportunity_count": "进入 opportunity/recommendation 表的机会样本数，不代表交易。",
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"actionable_count": "确认层输出 buy_now 或 wait_pullback 的样本数。",
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"paper_trade_count": "已经被模拟交易账本执行的样本数。",
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"paper_realized_pnl_usdt": "仅来自 paper_trades 的已平仓模拟收益。",
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},
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"factor_attribution": serialize_buckets("factor", factor_map)[:30],
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"market_environment": serialize_buckets("environment", env_map)[:20],
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"evidence_attribution": serialize_buckets("evidence", evidence_map)[:20],
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"version_performance": serialize_buckets("strategy_version", version_map, sort_by_version=True)[:20],
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}
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def env_buckets_from_market_context(mc):
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"""Convert market_context_json numeric fields into attribution buckets."""
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buckets = []
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try:
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change_24h = float(mc.get("change_24h", 0) or 0)
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turn_1h = float(mc.get("turnover_acceleration_1h", 0) or 0)
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turn_4h = float(mc.get("turnover_acceleration_4h", 0) or 0)
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volume_24h = float(mc.get("volume_24h") or mc.get("quote_volume_24h") or 0)
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funding = float(mc.get("funding_rate", 0) or 0)
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except Exception:
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change_24h = turn_1h = turn_4h = volume_24h = funding = 0
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if change_24h >= 8:
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buckets.append("24h涨幅:强势拉升≥8%")
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elif change_24h >= 3:
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buckets.append("24h涨幅:温和上涨3-8%")
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elif change_24h <= -3:
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buckets.append("24h涨幅:回撤≤-3%")
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else:
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buckets.append("24h涨幅:震荡-3~3%")
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if turn_1h >= 3:
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buckets.append("1h成交加速:爆量≥3x")
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elif turn_1h >= 1.5:
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buckets.append("1h成交加速:放量1.5-3x")
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elif turn_1h > 0:
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buckets.append("1h成交加速:平量<1.5x")
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if turn_4h >= 3:
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buckets.append("4h成交加速:爆量≥3x")
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elif turn_4h >= 1.5:
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buckets.append("4h成交加速:放量1.5-3x")
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elif turn_4h > 0:
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buckets.append("4h成交加速:平量<1.5x")
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if volume_24h >= 100_000_000:
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buckets.append("24h成交额:高流动性≥1亿")
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elif volume_24h >= 10_000_000:
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buckets.append("24h成交额:中等流动性1千万-1亿")
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elif volume_24h > 0:
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buckets.append("24h成交额:低流动性<1千万")
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if funding >= 0.0005:
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buckets.append("资金费率:多头拥挤")
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elif funding <= -0.0005:
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buckets.append("资金费率:空头拥挤")
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return buckets
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def serialize_buckets(name_key, bucket_map, sort_by_version=False):
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rows = []
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for key, bucket in bucket_map.items():
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rows.append({
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name_key: key,
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"opportunity_count": bucket["opportunity_count"],
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"actionable_count": bucket["actionable_count"],
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"buy_now_count": bucket["buy_now_count"],
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"paper_trade_count": bucket["paper_trade_count"],
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"closed_paper_trade_count": bucket["closed_paper_trade_count"],
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"paper_win_count": bucket["paper_win_count"],
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"actionable_conversion_pct": round(bucket["actionable_count"] / bucket["opportunity_count"] * 100, 1) if bucket["opportunity_count"] else 0,
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"paper_conversion_pct": round(bucket["paper_trade_count"] / bucket["buy_now_count"] * 100, 1) if bucket["buy_now_count"] else 0,
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"paper_win_rate_pct": round(bucket["paper_win_count"] / bucket["closed_paper_trade_count"] * 100, 1) if bucket["closed_paper_trade_count"] else 0,
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"paper_realized_pnl_usdt": round(bucket["paper_realized_pnl_usdt"], 4),
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})
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if sort_by_version:
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rows.sort(key=lambda x: (version_sort_key(x[name_key]), x["opportunity_count"], x["actionable_conversion_pct"]), reverse=True)
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else:
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rows.sort(key=lambda x: (-x["opportunity_count"], -x["actionable_conversion_pct"], x[name_key]))
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return rows
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def version_sort_key(version: str):
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text = str(version or '').strip()
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if text.startswith('v') or text.startswith('V'):
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text = text[1:]
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parts = []
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for chunk in text.replace('-', '.').split('.'):
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if chunk.isdigit():
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parts.append(int(chunk))
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else:
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match = re.match(r'^(\d+)', chunk)
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if match:
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parts.append(int(match.group(1)))
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else:
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parts.append(chunk)
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return tuple(parts)
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