v10: Trading OS — 5-subsystem architecture + Signal Intelligence + Dashboard Command Center
Architecture: - Restructure into 5 subsystems: data/, features/, market/, signals/, execution/, apps/ - Unified ts_code conversion in core/codes.py (idempotent, kills 4 duplicate copies) - analytics_conn() + kline_glob() — zero hardcoded DB/Parquet paths - Fixed double-suffix bug (.SZ.SZ) in backfill pipeline root cause Signal Intelligence (the moat): - 14 signal types: EMA52, Vegas, Chan, ORB, Gap, NR7, Inside Bar - 640K+ historical signal instances across 8 backfilled types - Multi-signal Expectancy Engine with breadth-similarity matching - Signal backfill CLI: ashare-dp backfill signals Market Intelligence: - 8 engines: State, Leadership, Opportunity, Flow, Sentiment, Memory, Knowledge Graph, Recommendations - Real limit-up/down sentiment via akshare (108 ZT, 19 DT, 52 broken board) - Knowledge Graph: 8 themes × 30+ concepts with keyword matching - Money-flow stock recommendations with entry/stop/target trade plans Dashboard Command Center: - Decision-first layout: COMMAND → WHERE → WHY → RISK → EXPECTANCY - Multi-signal Expectancy comparison table (8 types ranked by WR) - Theme Map visualization with rotation detection - Intraday Replay infrastructure (30min state snapshots) - RECOMMENDATIONS card with actionable trade plans Trading Memory: - trade_log table + POST/GET/PUT API for trade recording - Performance stats aggregation Code Quality: - 0 hardcoded DB paths, 0 REPLACE hacks, 0 dead ts_code copies - EMA52 screening deduplicated (CLI + scheduler share one function) - read_parquet_sql() helper for 28 duplicate patterns - 6 bugs fixed from code review (NR7 window, theme matching, column indices, etc.) Co-Authored-By: Claude <noreply@anthropic.com>
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jackyu66git
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"""Stock Recommendation Engine — money flow driven stock picks.
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Identifies top stocks in sectors with strong capital inflow,
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ranks by momentum/volume/trend, generates actionable trade plans
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with entry/stop/target levels.
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"""
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from __future__ import annotations
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from datetime import date, datetime
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import duckdb
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from loguru import logger
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from ashare_dp.data.store.database import analytics_conn, kline_glob
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def get_recommendations(
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trade_date: date,
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top_n: int = 10,
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min_amount_yi: float = 2.0, # 最低日成交额（亿）
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) -> list[dict]:
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"""Generate ranked stock recommendations based on money flow.
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1. Find sectors with strongest capital inflow
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2. Within each sector, rank stocks by momentum + volume + trend
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3. Generate trade plans (entry, stop, targets)
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Returns list of recommendation dicts sorted by composite score.
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"""
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parquet_glob = kline_glob()
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conn = analytics_conn()
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# ═══ Step 1: Find top inflow sectors ═══
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try:
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flow_sql = f"""
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WITH normalized AS (
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SELECT ts_code, trade_date, amount
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FROM read_parquet('{parquet_glob}', hive_partitioning=true, union_by_name=true)
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WHERE trade_date >= $start_date AND trade_date <= $end_date
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),
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daily AS (
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SELECT n.trade_date, n.amount, si.industry_name
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FROM normalized n
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JOIN stock_industry si ON n.ts_code = si.ts_code
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),
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industry_daily AS (
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SELECT industry_name, trade_date, SUM(amount) AS total_amount
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FROM daily GROUP BY industry_name, trade_date
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),
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ranked AS (
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SELECT *, ROW_NUMBER() OVER (PARTITION BY industry_name ORDER BY trade_date DESC) AS rn
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FROM industry_daily
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),
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recent AS (
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SELECT industry_name, SUM(total_amount) AS recent
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FROM ranked WHERE rn <= 5 GROUP BY industry_name
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),
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prior AS (
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SELECT industry_name, SUM(total_amount) AS prior
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FROM ranked WHERE rn > 5 AND rn <= 10 GROUP BY industry_name
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)
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SELECT COALESCE(r.industry_name, p.industry_name) AS industry_name,
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CASE WHEN COALESCE(p.prior,0) > 0 THEN (COALESCE(r.recent,0) - p.prior) / p.prior ELSE 0 END AS flow_change
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FROM recent r FULL OUTER JOIN prior p ON r.industry_name = p.industry_name
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WHERE COALESCE(r.recent, 0) + COALESCE(p.prior, 0) > 0
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ORDER BY flow_change DESC
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LIMIT 5
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"""
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end_str = trade_date.strftime("%Y-%m-%d")
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from datetime import timedelta
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start_str = (trade_date - timedelta(days=20)).strftime("%Y-%m-%d")
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flow_df = conn.execute(
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flow_sql.replace("$start_date", f"'{start_str}'").replace("$end_date", f"'{end_str}'")
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).fetchdf()
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top_sectors = flow_df["industry_name"].tolist() if not flow_df.empty else []
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except Exception as e:
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logger.warning(f"Flow sector query failed: {e}")
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top_sectors = []
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finally:
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conn.close()
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if not top_sectors:
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return []
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# ═══ Step 2: Find best stocks in top sectors ═══
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conn = analytics_conn()
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recommendations = []
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for sector in top_sectors[:3]: # top 3 inflow sectors
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try:
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sector_list = "', '".join(top_sectors)
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stock_sql = f"""
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WITH normalized AS (
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SELECT ts_code, trade_date, trade_time, close, volume, amount, high, low, open
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FROM read_parquet('{parquet_glob}', hive_partitioning=true, union_by_name=true)
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WHERE trade_date <= $trade_date
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),
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with_ma AS (
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SELECT *,
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AVG(close) OVER (PARTITION BY ts_code ORDER BY trade_time ROWS BETWEEN 19 PRECEDING AND CURRENT ROW) AS ma20,
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AVG(close) OVER (PARTITION BY ts_code ORDER BY trade_time ROWS BETWEEN 59 PRECEDING AND CURRENT ROW) AS ma60,
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AVG(volume) OVER (PARTITION BY ts_code ORDER BY trade_time ROWS BETWEEN 19 PRECEDING AND CURRENT ROW) AS vol_ma20,
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(high - low) AS day_range,
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ROW_NUMBER() OVER (PARTITION BY ts_code ORDER BY trade_time DESC) AS rn
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FROM normalized
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),
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with_atr AS (
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SELECT *,
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AVG(day_range) OVER (PARTITION BY ts_code ORDER BY trade_time ROWS BETWEEN 19 PRECEDING AND CURRENT ROW) AS atr20
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FROM with_ma
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),
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latest AS (
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SELECT w.*, si.industry_name
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FROM with_atr w
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JOIN stock_industry si ON w.ts_code = si.ts_code
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WHERE w.rn = 1 AND si.industry_name = $sector
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),
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prev AS (
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SELECT ts_code, close AS close_prev
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FROM with_ma WHERE rn = 6
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),
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scored AS (
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SELECT
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l.ts_code, l.industry_name, l.close, l.volume, l.amount,
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l.ma20, l.ma60, l.vol_ma20, l.atr20,
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l.high, l.low, l.open,
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p.close_prev,
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-- Momentum: 5d return
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(l.close - p.close_prev) / NULLIF(p.close_prev, 0) AS ret_5d,
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-- Volume expansion
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l.volume / NULLIF(l.vol_ma20, 0) AS vol_ratio,
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-- Trend: above MAs
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CASE WHEN l.close > l.ma20 THEN 1 ELSE 0 END + CASE WHEN l.close > l.ma60 THEN 1 ELSE 0 END AS trend_score
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FROM latest l
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LEFT JOIN prev p ON l.ts_code = p.ts_code
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WHERE l.amount > $min_amt
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)
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SELECT *,
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(COALESCE(ret_5d, 0) * 40 + LEAST(vol_ratio, 3.0) / 3.0 * 30 + trend_score * 15) AS composite
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FROM scored
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ORDER BY composite DESC
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LIMIT 4
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"""
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min_amt = min_amount_yi * 1e8
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stock_df = conn.execute(
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stock_sql,
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{"trade_date": trade_date.isoformat(), "sector": sector, "min_amt": min_amt},
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).fetchdf()
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# Look up stock names
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stock_names = {}
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try:
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nc = duckdb.connect("data/duckdb/ashare.db", read_only=True)
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for c in stock_df["ts_code"].tolist():
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r = nc.execute("SELECT name FROM stock_info WHERE ts_code=?", [c]).fetchone()
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stock_names[c] = r[0] if r else c
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nc.close()
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except Exception:
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pass
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for _, row in stock_df.iterrows():
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import math
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entry = float(row["close"])
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atr_raw = row.get("atr20")
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atr = float(atr_raw) if atr_raw and not (isinstance(atr_raw, float) and math.isnan(atr_raw)) else entry * 0.03
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# Stop: MA20 or 2 ATR below entry
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ma20 = float(row["ma20"] or entry)
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stop = round(min(ma20 * 0.97, entry - 2 * atr), 2)
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# Targets
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target1 = round(entry + 1.5 * atr, 2)
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target2 = round(entry + 3.0 * atr, 2)
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# Risk/reward
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risk = entry - stop
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reward = target1 - entry
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rr_ratio = round(reward / risk, 1) if risk > 0 else 0
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# Position sizing guidance
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position_advice = "Standard"
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if float(row["trend_score"]) >= 2:
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position_advice = "Aggressive"
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elif float(row.get("vol_ratio", 1)) < 0.8:
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position_advice = "Reduced"
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import math as _m
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def _safe(v, d):
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try:
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f = float(v)
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return d if _m.isnan(f) else f
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except (ValueError, TypeError):
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return d
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_ret5 = _safe(row.get("ret_5d"), 0.0)
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_volr = _safe(row.get("vol_ratio"), 1.0)
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_trnd = _safe(row.get("trend_score"), 0.0)
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_comp = _safe(row.get("composite"), 0.0)
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if _m.isnan(_ret5): _ret5 = 0.0
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if _m.isnan(_volr): _volr = 1.0
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if _m.isnan(_trnd): _trnd = 0.0
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if _m.isnan(_comp): _comp = 0.0
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ts = row["ts_code"]
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recommendations.append({
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"ts_code": ts,
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"name": stock_names.get(ts, ts),
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"sector": sector,
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"entry": round(entry, 2),
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"stop": stop,
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"target1": target1,
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"target2": target2,
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"rr_ratio": round(float(rr_ratio), 1) if not _m.isnan(rr_ratio) else 0.0,
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"position": position_advice,
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"score": round(_comp, 1),
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"metrics": {
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"ret_5d": round(_ret5 * 100, 1),
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"vol_ratio": round(_volr, 2),
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"trend": "Strong" if _trnd >= 2 else "Weak",
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"amount_yi": round(float(row["amount"]) / 1e8, 1),
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},
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"action": _recommend_action(_ret5, _volr, _trnd),
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})
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except Exception as e:
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logger.warning(f"Stock scoring failed for sector {sector}: {e}")
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conn.close()
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# Sort by composite score
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recommendations.sort(key=lambda r: r["score"], reverse=True)
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return recommendations[:top_n]
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def _recommend_action(ret_5d: float, vol_ratio: float, trend_score: float) -> str:
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"""Generate trading action recommendation."""
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if trend_score >= 2 and ret_5d > 0.03 and vol_ratio > 1.2:
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return "买入 — 趋势强势，放量上涨"
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elif trend_score >= 2 and ret_5d > 0:
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return "关注 — 趋势健康，等待回踩"
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elif trend_score >= 1 and vol_ratio > 1.0:
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return "观察 — 趋势形成中，可轻仓试"
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elif ret_5d < -0.03:
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return "回避 — 短期偏弱，等待企稳"
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else:
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return "观望 — 方向不明，暂不参与"
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