37 lines
1.9 KiB
Python
37 lines
1.9 KiB
Python
import sqlite3
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conn = sqlite3.connect('/home/hmo/MoFin/data/mofin.db')
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conn.row_factory = sqlite3.Row
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# 1. 找曾被 zhiwei 修过的 15 只（她提到的几只）当前值
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codes = ['000711', '603766', '600617', '688271']
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print('=== holding_strategies 当前值（她修过的几只）===')
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for code in codes:
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r = conn.execute(
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"SELECT code, name, entry_low, entry_high, stop_loss, take_profit, strategy_type, "
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"quality_check, created_at, updated_at FROM holding_strategies WHERE code=? AND status='active'",
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(code,)).fetchone()
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if r:
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c = (r['entry_low'] + r['entry_high']) / 2 if r['entry_low'] and r['entry_high'] else 0
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print(f" {r['code']} {r['name']}: 区{r['entry_low']}~{r['entry_high']} 中心{c:.2f} | type={r['strategy_type']} | created={r['created_at']} updated={r['updated_at']}")
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# 2. 找 candidates 里这些股票的评分/买入区（看是不是 promote 写进来的）
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print()
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print('=== candidates 对应记录 ===')
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for code in codes:
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r = conn.execute(
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"SELECT code, name, score, entry_low, entry_high, stop_loss, take_profit, promoted, created_at "
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"FROM candidates WHERE code=?", (code,)).fetchone()
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if r:
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print(f" {r['code']} {r['name']}: score={r['score']} 区{r['entry_low']}~{r['entry_high']} promoted={r['promoted']} created={r['created_at']}")
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# 3. 还有谁可能是 97 中心：找 entry 中心在 90~105 的活跃自选
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print()
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print('=== 当前活跃自选里中心 90~105 的（可能还有漏网的）===')
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rows = conn.execute(
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"SELECT code, name, entry_low, entry_high FROM holding_strategies "
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"WHERE status='active' AND decision_type='自选策略' AND entry_low > 0").fetchall()
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for r in rows:
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c = (r['entry_low'] + r['entry_high']) / 2
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if 90 <= c <= 105:
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print(f" {r['code']} {r['name']}: 区{r['entry_low']}~{r['entry_high']} 中心{c:.2f}")
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conn.close() |