feat: 策略研究Tab全流程回测框架 + API端点 + 前端渲染 + 盘中大跌分析日志
This commit is contained in:
@@ -310,3 +310,24 @@ cron prompt for 系统健康检查-开盘前 (job_id: 37c02f4d7df9)
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- **总资产**: 896,562
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- **仓位**: 73.08%
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- **截图头部659,354与xls的655,231差异来源**: 截图是13:01价格，xls是23:30价格，中间价格波动导致
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## 2026-07-28 13:51 盘中系统性大跌应对 — 韩国二次熔断+亚太科技股暴跌
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**发现了什么：**
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今天KOSPI两度触发熔断（一度跌超8%），日经-4%，MSCI亚太较6月高点跌10%，科创50-5.52%/创业板-6.55%。但上证仅-1.35%、恒指-0.34%、比亚迪/腾讯逆势上涨。
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**关键判断：**
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- 定性为板块冲击(B类)，非全面系统性。上证/恒指有韧性是关键区分信号
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- 比亚迪+0.51%、腾讯+0.23%逆势上涨验证了A/H股科技脱钩叙事
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- 信号id=1441已处理(processed=1)，自愈执行器报警的是状态文件过期导致的重复触发
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**止损距扫描结果：**
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- 中芯国际(688981)现价138.41，跌破旧止损141.21(-2.0%)，但策略状态为closed，需发布新策略
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- 丘钛科技(01478)距止损仅1.2%，临界
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- 中国神华(01088)距止损仅1.6%，临界
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- 中际旭创(300308)距止损5.2%，策略已closed
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**萃取知识：**
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1. KOSPI熔断+日韩暴跌传导至A股科技股，但上证/恒指未破位=非系统性+板块冲击。判断A股的不可一概而论。
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2. 比亚迪、腾讯在科技暴跌日逆势上涨，说明个股基本面>板块情绪。不能简单用"科技股暴跌"标签覆盖所有科技持仓。
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3. 自愈执行器重复报警的根本原因是state文件expired后未及时更新，导致管道认为这是未处理的新问题。修复措施：更新state文件。
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@@ -0,0 +1,488 @@
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#!/usr/bin/env python3
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"""MoFin 回测框架 v2 — 从 stock_daily 原始行情计算全部指标，模拟全流程"""
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import sqlite3, json, math
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from datetime import datetime, timedelta
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from collections import defaultdict
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DB_PATH = "/home/hmo/MoFin/data/mofin.db"
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# ── 技术指标计算（纯 Python，不依赖第三方库）──
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def calc_ma(series, n):
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"""简单移动平均"""
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result = []
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for i in range(len(series)):
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if i < n - 1:
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result.append(None)
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else:
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result.append(sum(series[i-n+1:i+1]) / n)
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return result
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def calc_ema(series, n):
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"""指数移动平均"""
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result = []
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k = 2 / (n + 1)
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for i in range(len(series)):
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if i == 0:
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result.append(series[i])
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else:
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result.append(series[i] * k + result[-1] * (1 - k))
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return result
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def calc_rsi(series, n=14):
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"""RSI"""
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deltas = [series[i] - series[i-1] for i in range(1, len(series))]
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gains = [d if d > 0 else 0 for d in deltas]
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losses = [-d if d < 0 else 0 for d in deltas]
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result = [None] * (n + 1)
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avg_gain = sum(gains[:n]) / n
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avg_loss = sum(losses[:n]) / n
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if avg_loss == 0:
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result.append(100)
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else:
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rs = avg_gain / avg_loss
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result.append(100 - 100 / (1 + rs))
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for i in range(n, len(gains)):
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avg_gain = (avg_gain * (n-1) + gains[i]) / n
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avg_loss = (avg_loss * (n-1) + losses[i]) / n
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if avg_loss == 0:
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result.append(100)
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else:
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rs = avg_gain / avg_loss
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result.append(100 - 100 / (1 + rs))
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# Pad front to match original length
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while len(result) < len(series):
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result.insert(0, None)
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return result[:len(series)]
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def calc_macd(series, fast=12, slow=26, signal=9):
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"""MACD: 返回 macd_line, signal_line, histogram"""
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ema_fast = calc_ema(series, fast)
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ema_slow = calc_ema(series, slow)
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macd_line = [ema_fast[i] - ema_slow[i] if ema_fast[i] is not None and ema_slow[i] is not None else None for i in range(len(series))]
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# Recompute signal line from macd_line (only non-None values)
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macd_clean = [v for v in macd_line if v is not None]
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if not macd_clean:
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return [None]*len(series), [None]*len(series), [None]*len(series)
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signal_raw = calc_ema(macd_clean, signal)
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signal_line = [None] * (len(series) - len(macd_clean)) + signal_raw
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histogram = [macd_line[i] - signal_line[i] if macd_line[i] is not None and signal_line[i] is not None else None for i in range(len(series))]
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return macd_line, signal_line, histogram
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def calc_tr(highs, lows, closes):
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"""True Range"""
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tr = [highs[0] - lows[0]]
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for i in range(1, len(highs)):
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hl = highs[i] - lows[i]
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hc = abs(highs[i] - closes[i-1])
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lc = abs(lows[i] - closes[i-1])
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tr.append(max(hl, hc, lc))
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return tr
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def calc_atr(highs, lows, closes, n=14):
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"""ATR"""
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tr = calc_tr(highs, lows, closes)
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return calc_ema(tr, n)
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def calc_trend_strength(highs, lows, closes, n=14):
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"""简易趋势强度（替代 ADX）: (DI+ - DI-) / (DI+ + DI-) 归一化"""
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tr = calc_tr(highs, lows, closes)
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up = [highs[i] - highs[i-1] for i in range(1, len(highs))]
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down = [lows[i-1] - lows[i] for i in range(1, len(lows))]
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# Directional indicators (raw)
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di_plus_raw = [0.0] * len(up)
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di_minus_raw = [0.0] * len(up)
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for i in range(len(up)):
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if up[i] > down[i] and up[i] > 0:
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di_plus_raw[i] = up[i]
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if down[i] > up[i] and down[i] > 0:
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di_minus_raw[i] = down[i]
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# Normalize by TR
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tr_val = tr[i+1] if (i+1) < len(tr) else tr[-1]
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if tr_val > 0:
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di_plus_raw[i] = di_plus_raw[i] / tr_val * 100
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di_minus_raw[i] = di_minus_raw[i] / tr_val * 100
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# SMA of DMI
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result = []
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for i in range(len(di_plus_raw)):
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if i < n - 1:
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result.append(None)
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else:
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avg_plus = sum(di_plus_raw[i-n+1:i+1]) / n
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avg_minus = sum(di_minus_raw[i-n+1:i+1]) / n
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if avg_plus + avg_minus > 0:
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dx = abs(avg_plus - avg_minus) / (avg_plus + avg_minus) * 100
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else:
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dx = 0
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result.append(dx)
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# Pad front to match original highs length
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return [None] * (len(highs) - len(result)) + result
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def calc_obv(closes, volumes):
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"""OBV"""
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obv = [volumes[0]]
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for i in range(1, len(closes)):
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if closes[i] > closes[i-1]:
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obv.append(obv[-1] + volumes[i])
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elif closes[i] < closes[i-1]:
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obv.append(obv[-1] - volumes[i])
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else:
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obv.append(obv[-1])
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return obv
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def calc_roc(series, n=10):
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"""Rate of Change"""
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result = []
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for i in range(len(series)):
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if i < n:
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result.append(None)
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else:
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result.append((series[i] - series[i-n]) / series[i-n] * 100 if series[i-n] != 0 else 0)
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return result
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def prepare_bars(code, start_date, end_date):
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"""获取原始数据并计算全部指标"""
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conn = sqlite3.connect(DB_PATH)
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rows = conn.execute("""
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SELECT date, open, close, high, low, volume, amount
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FROM stock_daily WHERE code=? AND date>=? AND date<=?
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ORDER BY date
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""", (code, start_date, end_date)).fetchall()
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conn.close()
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if not rows or len(rows) < 30:
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return None
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dates = [r[0] for r in rows]
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opens = [r[1] for r in rows]
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closes = [r[2] for r in rows]
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highs = [r[3] for r in rows]
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lows = [r[4] for r in rows]
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volumes = [r[5] for r in rows]
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# 计算全部指标
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ma5 = calc_ma(closes, 5)
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ma10 = calc_ma(closes, 10)
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ma20 = calc_ma(closes, 20)
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ma60 = calc_ma(closes, 60)
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rsi = calc_rsi(closes)
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macd_line, signal_line, macd_hist = calc_macd(closes)
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atr = calc_atr(highs, lows, closes)
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trend_strength = calc_trend_strength(highs, lows, closes)
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obv = calc_obv(closes, volumes)
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roc = calc_roc(closes)
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bars = []
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for i in range(len(dates)):
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bars.append({
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'date': dates[i],
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'open': opens[i],
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'close': closes[i],
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'high': highs[i],
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'low': lows[i],
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'volume': volumes[i],
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'ma5': ma5[i] if i < len(ma5) else None,
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'ma10': ma10[i] if i < len(ma10) else None,
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'ma20': ma20[i] if i < len(ma20) else None,
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'ma60': ma60[i] if i < len(ma60) else None,
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'rsi': rsi[i] if i < len(rsi) else None,
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'macd': macd_line[i] if i < len(macd_line) else None,
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'macd_signal': signal_line[i] if i < len(signal_line) else None,
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'macd_hist': macd_hist[i] if i < len(macd_hist) else None,
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'atr': atr[i] if i < len(atr) else None,
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'adx': trend_strength[i] if i < len(trend_strength) else None,
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'obv': obv[i] if i < len(obv) else None,
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'roc': roc[i] if i < len(roc) else None,
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})
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return bars
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def compute_single_score(bars):
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"""对单个股票计算多因子评分（与 MoFin 现有一致）"""
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if not bars or len(bars) < 15:
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return None
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last = bars[-1]
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prev5 = bars[-5] if len(bars) >= 5 else bars[-2] if len(bars) >= 2 else last
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prev10 = bars[-10] if len(bars) >= 10 else bars[0]
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def v(val): return val if val is not None else 0
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score = {'trend': 0, 'momentum': 0, 'volume': 0, 'volatility': 0, 'risk': 0}
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# 趋势分 (0-25)
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ts = 0
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adx = v(last.get('adx'))
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if adx > 25: ts += 10
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elif adx > 20: ts += 5
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mh = v(last.get('macd_hist'))
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pmh = v(prev5.get('macd_hist'))
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if mh > 0 and mh > pmh: ts += 10
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elif mh > 0: ts += 5
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elif mh < 0 and mh < pmh: ts -= 5
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ma5 = v(last.get('ma5'))
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ma10 = v(last.get('ma10'))
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ma20 = v(last.get('ma20'))
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if ma5 > ma10 > ma20 and ma5 > 0 and ma10 > 0: ts += 5
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elif ma5 < ma10 < ma20 and ma5 > 0 and ma10 > 0: ts -= 5
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score['trend'] = max(0, min(25, ts))
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# 动量分 (0-20)
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ms = 0
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roc = v(last.get('roc'))
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if roc > 5: ms += 5
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elif roc > 2: ms += 2
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elif roc < -5: ms -= 5
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elif roc < -2: ms -= 2
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rsi = v(last.get('rsi'))
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if 30 <= rsi <= 70: ms += 5
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if 50 < rsi <= 65: ms += 3
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elif rsi > 70: ms -= 3
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elif rsi < 30: ms -= 3
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close = v(last.get('close'))
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if ma20 > 0:
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pct = (close - ma20) / ma20 * 100
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if -3 <= pct <= 5: ms += 7
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elif pct < -10: ms -= 3
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score['momentum'] = max(0, min(20, ms))
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# 量能分 (0-20)
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vs = 0
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obv_cur = v(last.get('obv'))
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obv_prev = v(prev5.get('obv'))
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vol_cur = v(last.get('volume'))
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vol_prev = v(prev5.get('volume'))
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if obv_cur > obv_prev and vol_cur > 0 and vol_prev > 0:
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vr = vol_cur / vol_prev
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if vr > 1.5: vs += 10
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elif vr > 1.2: vs += 5
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if obv_cur > obv_prev: vs += 5
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if vol_prev > 0 and vol_cur / vol_prev < 0.5: vs -= 5
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score['volume'] = max(0, min(20, vs))
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# 波动率分 (0-15)
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vls = 5
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atr = v(last.get('atr'))
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if close > 0 and atr > 0:
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atr_pct = atr / close * 100
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if 1.5 <= atr_pct <= 3.5: vls += 5
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elif atr_pct < 1: vls += 3
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elif atr_pct > 5: vls -= 5
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if len(bars) >= 20:
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cls = [v(b.get('close')) for b in bars[-20:]]
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if min(cls) > 0:
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vol_range = (max(cls) - min(cls)) / min(cls) * 100
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if vol_range < 10: vls += 5
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elif vol_range > 30: vls -= 5
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score['volatility'] = max(0, min(15, vls))
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# 风险分 (0-20)
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rs = 10
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if len(bars) >= 10:
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low5 = min(bars[-5:], key=lambda x: v(x.get('low')))
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low10 = min(bars[-10:-5], key=lambda x: v(x.get('low'))) if len(bars) >= 10 else low5
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if v(low5.get('close')) < v(low10.get('close')) and v(low5.get('macd_hist')) > v(low10.get('macd_hist')):
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rs += 5
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if v(low5.get('close')) > v(low10.get('close')) and v(low5.get('macd_hist')) < v(low10.get('macd_hist')):
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rs -= 5
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if ma20 > 0:
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dist = (close - ma20) / ma20 * 100
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if dist < 2: rs += 5
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elif dist > 15: rs -= 5
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score['risk'] = max(0, min(20, rs))
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total = sum(score.values())
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return total, score
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def compute_kelly(total_score, target_pct, stop_pct):
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"""半 Kelly 仓位"""
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p = min(0.6, total_score / 100)
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if stop_pct <= 0:
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return 0
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b = target_pct / stop_pct
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if b <= 1:
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return 0
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kelly = (p * b - (1 - p)) / b
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return max(0, min(0.5, kelly)) * 0.5 # 半 Kelly
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def run_strategy_research(start_date, end_date, capital=1000000):
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"""主回测入口"""
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conn = sqlite3.connect(DB_PATH)
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# 1. 获取所有有历史数据的股票
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stocks = conn.execute("""
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SELECT DISTINCT sd.code, COALESCE(s.name, sd.code) as name
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FROM stock_daily sd
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LEFT JOIN stocks s ON sd.code = s.code
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WHERE sd.date>=? AND sd.date<=?
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""", (start_date, end_date)).fetchall()
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results = {
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'period': f"{start_date} ~ {end_date}",
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'capital': capital,
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'total_stocks_screened': len(stocks),
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'scored_stocks': 0,
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'buy_signals': 0,
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'trades': [],
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'summary': {}
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}
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trades = []
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code_names = {}
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eval_dates = [] # collect evaluation dates
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for code, name in stocks:
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code_names[code] = name
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bars = prepare_bars(code, start_date, end_date)
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if not bars or len(bars) < 20:
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continue
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# 2. 滚动评估：每周评分一次
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window_start = 20 # need at least 20 bars for indicators
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while window_start < len(bars):
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window = bars[:window_start]
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score_result = compute_single_score(window)
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if score_result is None:
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window_start += 5
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continue
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total_score, score_components = score_result
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results['scored_stocks'] += 1
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last = window[-1]
|
||||
close = v_close = last.get('close', 0) or 0
|
||||
ma20 = last.get('ma20', 0) or 0
|
||||
|
||||
# 3. 买入条件：总评分 >= 45 且 momentum >= 8
|
||||
buy_signal = total_score >= 45 and score_components['momentum'] >= 8
|
||||
|
||||
if buy_signal:
|
||||
results['buy_signals'] += 1
|
||||
entry_price = close
|
||||
atr_val = last.get('atr', 0) or 0
|
||||
if atr_val > 0:
|
||||
target_price = entry_price * 1.10
|
||||
stop_price = entry_price - atr_val * 2.0
|
||||
else:
|
||||
target_price = entry_price * 1.10
|
||||
stop_price = entry_price * 0.93
|
||||
|
||||
kelly = compute_kelly(total_score, 0.10, (entry_price - stop_price) / entry_price)
|
||||
|
||||
# 4. 模拟未来走势
|
||||
future = bars[window_start:min(window_start+20, len(bars))]
|
||||
exit_price = None
|
||||
exit_reason = None
|
||||
max_high = entry_price
|
||||
min_low = entry_price
|
||||
|
||||
for fb in future:
|
||||
fb_high = fb.get('high', 0) or 0
|
||||
fb_low = fb.get('low', 0) or 0
|
||||
fb_close = fb.get('close', 0) or 0
|
||||
max_high = max(max_high, fb_high)
|
||||
min_low = min(min_low, fb_low)
|
||||
|
||||
if fb_high >= target_price:
|
||||
exit_price = target_price
|
||||
exit_reason = 'target'
|
||||
break
|
||||
elif fb_low <= stop_price:
|
||||
exit_price = fb_close
|
||||
exit_reason = 'stop'
|
||||
break
|
||||
|
||||
if exit_price is None:
|
||||
if future:
|
||||
exit_price = future[-1].get('close', entry_price)
|
||||
else:
|
||||
exit_price = entry_price
|
||||
exit_reason = 'keep'
|
||||
|
||||
hold_days = 0
|
||||
if exit_reason and exit_reason != 'keep' and future:
|
||||
for idx, fb in enumerate(future):
|
||||
fb_high = fb.get('high', 0) or 0
|
||||
fb_low = fb.get('low', 0) or 0
|
||||
if (exit_reason == 'target' and fb_high >= target_price) or \
|
||||
(exit_reason == 'stop' and fb_low <= stop_price):
|
||||
hold_days = idx + 1
|
||||
break
|
||||
elif exit_reason == 'keep':
|
||||
hold_days = len(future)
|
||||
|
||||
profit_pct = (exit_price - entry_price) / entry_price * 100 if entry_price > 0 else 0
|
||||
|
||||
trades.append({
|
||||
'code': code,
|
||||
'name': name,
|
||||
'entry_date': last.get('date'),
|
||||
'entry_price': round(entry_price, 2),
|
||||
'exit_price': round(exit_price, 2),
|
||||
'profit_pct': round(profit_pct, 2),
|
||||
'exit_reason': exit_reason,
|
||||
'hold_days': hold_days,
|
||||
'score': total_score,
|
||||
'kelly': round(kelly, 3),
|
||||
'stop_loss': round(stop_price, 2),
|
||||
'target': round(target_price, 2),
|
||||
'max_high': round(max_high, 2),
|
||||
'min_low': round(min_low, 2)
|
||||
})
|
||||
|
||||
window_start += 5 # 每 5 个交易日评估一次
|
||||
|
||||
conn.close()
|
||||
|
||||
# 5. 汇总
|
||||
if trades:
|
||||
profits = [t['profit_pct'] for t in trades]
|
||||
wins = [t for t in trades if t['profit_pct'] > 0]
|
||||
losses = [t for t in trades if t['profit_pct'] <= 0]
|
||||
|
||||
win_rate = len(wins) / len(trades) * 100 if trades else 0
|
||||
avg_profit = sum(profits) / len(profits) if profits else 0
|
||||
avg_win = sum(t['profit_pct'] for t in wins) / len(wins) if wins else 0
|
||||
avg_loss = sum(t['profit_pct'] for t in losses) / len(losses) if losses else 0
|
||||
|
||||
mean_ret = avg_profit / 100
|
||||
if len(profits) > 1:
|
||||
variance = sum((p/100 - mean_ret)**2 for p in profits) / (len(profits) - 1)
|
||||
std_ret = math.sqrt(variance)
|
||||
else:
|
||||
std_ret = 0
|
||||
sharpe = mean_ret / std_ret * math.sqrt(252) if std_ret > 0 else 0
|
||||
|
||||
# 最大回撤：用实际资本曲线计算
|
||||
capital_curve = [capital]
|
||||
for t in trades:
|
||||
new_cap = capital_curve[-1] * (1 + t['profit_pct'] / 100)
|
||||
capital_curve.append(new_cap)
|
||||
peak_cap = capital
|
||||
max_dd = 0
|
||||
for c in capital_curve:
|
||||
peak_cap = max(peak_cap, c)
|
||||
dd = (peak_cap - c) / peak_cap * 100
|
||||
max_dd = max(max_dd, dd)
|
||||
|
||||
results['trades'] = sorted(trades, key=lambda x: abs(x['profit_pct']), reverse=True)[:50]
|
||||
results['summary'] = {
|
||||
'total_trades': len(trades),
|
||||
'win_rate': round(win_rate, 1),
|
||||
'avg_profit_pct': round(avg_profit, 2),
|
||||
'avg_win_pct': round(avg_win, 2),
|
||||
'avg_loss_pct': round(avg_loss, 2),
|
||||
'sharpe_ratio': round(sharpe, 2),
|
||||
'max_drawdown_pct': round(max_dd, 2),
|
||||
'profit_factor': round(abs(avg_win / avg_loss), 2) if avg_loss != 0 else float('inf'),
|
||||
'wins': len(wins),
|
||||
'losses': len(losses),
|
||||
'capital_end': round(capital * (1 + avg_profit/100), 2)
|
||||
}
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
end = datetime.now().strftime('%Y-%m-%d')
|
||||
start = (datetime.now() - timedelta(days=180)).strftime('%Y-%m-%d')
|
||||
r = run_strategy_research(start, end)
|
||||
print(json.dumps(r, indent=2, ensure_ascii=False))
|
||||
@@ -0,0 +1,316 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
mo_data.py — MoFin 统一数据层（纯 DB）
|
||||
|
||||
所有数据从 SQLite 读取。不做 JSON fallback。
|
||||
JSON 文件已弃用，仅保留为历史备份。
|
||||
|
||||
用法:
|
||||
from mo_data import read_portfolio, read_decisions, read_watchlist
|
||||
|
||||
pf = read_portfolio() # 返回和 portfolio.json 一样的 dict 结构
|
||||
dec = read_decisions() # 返回和 decisions.json 一样的 dict 结构
|
||||
wl = read_watchlist() # 返回和 watchlist.json 一样的 dict 结构
|
||||
"""
|
||||
|
||||
import sqlite3, json, sys
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
DB_PATH = '/home/hmo/MoFin/data/mofin.db'
|
||||
SCRIPT_DIR = Path('/home/hmo/MoFin/scripts')
|
||||
|
||||
|
||||
def _get_db():
|
||||
db = sqlite3.connect(DB_PATH)
|
||||
db.row_factory = sqlite3.Row
|
||||
return db
|
||||
|
||||
|
||||
# ── portfolio ─────────────────────────────────────────────────────
|
||||
|
||||
def read_portfolio():
|
||||
"""返回 portfolio.json 等价 dict。纯 DB。"""
|
||||
db = _get_db()
|
||||
rows = db.execute(
|
||||
"SELECT code, name, shares, cost, price, market_value, "
|
||||
"change_pct, currency, position_pct "
|
||||
"FROM holdings WHERE is_active=1"
|
||||
).fetchall()
|
||||
holdings = []
|
||||
for r in rows:
|
||||
h = dict(r)
|
||||
h['_currency'] = h.get('currency', 'CNY')
|
||||
holdings.append(h)
|
||||
|
||||
sum_row = db.execute("SELECT * FROM portfolio_summary WHERE id=1").fetchone()
|
||||
summary = dict(sum_row) if sum_row else {}
|
||||
|
||||
db.close()
|
||||
|
||||
return {
|
||||
"holdings": holdings,
|
||||
"total_assets": summary.get("total_assets", 0),
|
||||
"total_mv": summary.get("total_mv", 0),
|
||||
"stock_value": summary.get("stock_value", summary.get("total_mv", 0)),
|
||||
"cash": summary.get("cash", 0),
|
||||
"frozen_cash": summary.get("frozen_cash", 0),
|
||||
"position_pct": summary.get("position_pct", 0),
|
||||
"currency": summary.get("currency", "CNY"),
|
||||
"updated_at": summary.get("updated_at", ""),
|
||||
}
|
||||
|
||||
|
||||
# ── decisions ─────────────────────────────────────────────────────
|
||||
|
||||
def _parse_json(val, default):
|
||||
if val:
|
||||
try: return json.loads(val)
|
||||
except: pass
|
||||
return default
|
||||
|
||||
|
||||
def read_decisions():
|
||||
"""返回 decisions.json 等价 dict。纯 DB。"""
|
||||
db = _get_db()
|
||||
rows = db.execute(
|
||||
"SELECT code, name, version, price, cost, shares, "
|
||||
"stop_loss, take_profit, entry_low, entry_high, "
|
||||
"currency, strategy_type, action, timing_signal, "
|
||||
"rr_ratio, tech_snapshot, stock_category, sector_context, "
|
||||
"status, trigger_json, changelog_json, source, reason, "
|
||||
"created_at, updated_at, "
|
||||
"avg_price, decision_timestamp, note, quality_check, "
|
||||
"quality_checked_at, quality_issues_json, position_advice, "
|
||||
"signal_factors_json, time_horizon, decision_type, tag "
|
||||
"FROM holding_strategies WHERE status IN ('active','updated') "
|
||||
"ORDER BY code"
|
||||
).fetchall()
|
||||
|
||||
decisions = []
|
||||
for r in rows:
|
||||
d = dict(r)
|
||||
d['trigger'] = _parse_json(r['trigger_json'], {})
|
||||
d['changelog'] = _parse_json(r['changelog_json'], [])
|
||||
d['quality_issues'] = _parse_json(r['quality_issues_json'], {})
|
||||
d['signal_factors'] = _parse_json(r['signal_factors_json'], [])
|
||||
d['timestamp'] = r['decision_timestamp'] or r['created_at'] or ''
|
||||
d['type'] = r['decision_type'] or r['strategy_type'] or '持仓策略'
|
||||
decisions.append(d)
|
||||
|
||||
db.close()
|
||||
|
||||
return {
|
||||
"decisions": decisions,
|
||||
"total": len(decisions),
|
||||
"regenerated_at": datetime.now().strftime('%Y-%m-%d %H:%M'),
|
||||
}
|
||||
|
||||
|
||||
# ── watchlist ─────────────────────────────────────────────────────
|
||||
|
||||
def read_watchlist():
|
||||
"""返回 watchlist 等价 dict。纯 DB。
|
||||
从 holding_strategies（自选策略）读取，watchlist_stocks 已废弃。"""
|
||||
db = _get_db()
|
||||
# 主数据源：holding_strategies 自选策略
|
||||
rows = db.execute(
|
||||
"SELECT code, name, price, entry_low, entry_high, "
|
||||
"stop_loss, currency, updated_at "
|
||||
"FROM holding_strategies WHERE status='active' AND decision_type='自选策略'"
|
||||
).fetchall()
|
||||
|
||||
stocks = []
|
||||
seen = set()
|
||||
for r in rows:
|
||||
code = str(r["code"])
|
||||
if code in seen:
|
||||
continue
|
||||
seen.add(code)
|
||||
stocks.append({
|
||||
"code": code,
|
||||
"name": r["name"] or "",
|
||||
"price": r["price"] or 0,
|
||||
"entry_low": r["entry_low"] or 0,
|
||||
"entry_high": r["entry_high"] or 0,
|
||||
"stop_loss": r["stop_loss"] or 0,
|
||||
"currency": r["currency"] or "CNY",
|
||||
"added_at": r["updated_at"] or "",
|
||||
"analysis": {},
|
||||
})
|
||||
|
||||
return {"stocks": stocks, "total": len(stocks)}
|
||||
|
||||
db.close()
|
||||
|
||||
return {
|
||||
"stocks": stocks,
|
||||
"updated_at": datetime.now().strftime('%Y-%m-%d %H:%M'),
|
||||
}
|
||||
|
||||
|
||||
# ── 便捷别名 ───────────────────────────────────────────────────────
|
||||
|
||||
def read_portfolio_json():
|
||||
return read_portfolio()
|
||||
|
||||
def read_decisions_json():
|
||||
return read_decisions()
|
||||
|
||||
def read_watchlist_json():
|
||||
return read_watchlist()
|
||||
|
||||
|
||||
# ── 统一价格获取（唯一入口，禁止各脚本自拉API）──
|
||||
|
||||
def get_price(code, max_age_minutes=5, use_stale_fallback=True):
|
||||
"""获取单只股票最新价格。
|
||||
|
||||
优先级: live_prices(DB) → stock_quote(API兜底)
|
||||
- live_prices 有且不超过 max_age_minutes → 直接返回
|
||||
- 没有或过期 → 调 stock_quote 拉，写回 live_prices
|
||||
- 都失败 → 返回 (None, None)
|
||||
|
||||
返回 (price, change_pct)，两值都是 float 或 None。
|
||||
"""
|
||||
from mofin_db import get_price_from_db
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# 1. 先读 DB
|
||||
try:
|
||||
db_price, db_chg = get_price_from_db(code)
|
||||
if db_price is not None and db_price > 0:
|
||||
# 检查时效性
|
||||
conn = __import__('sqlite3').connect(str(DB_PATH))
|
||||
row = conn.execute(
|
||||
"SELECT updated_at FROM live_prices WHERE code=?",
|
||||
(str(code).strip(),)
|
||||
).fetchone()
|
||||
conn.close()
|
||||
if row and row[0]:
|
||||
try:
|
||||
updated = datetime.strptime(row[0], "%Y-%m-%d %H:%M:%S")
|
||||
age = (datetime.now() - updated).total_seconds() / 60
|
||||
if age <= max_age_minutes:
|
||||
return (db_price, db_chg)
|
||||
except:
|
||||
pass
|
||||
else:
|
||||
return (db_price, db_chg)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 2. DB 没有或过期 → 调 stock_quote
|
||||
if not use_stale_fallback:
|
||||
return (None, None)
|
||||
|
||||
try:
|
||||
import subprocess, json
|
||||
r = subprocess.run(
|
||||
[sys.executable, str(SCRIPT_DIR / "stock_quote.py"), str(code)],
|
||||
capture_output=True, text=True, timeout=15
|
||||
)
|
||||
if r.returncode == 0:
|
||||
data = json.loads(r.stdout.strip())
|
||||
price = float(data.get("price", 0))
|
||||
chg = float(data.get("change_pct", 0))
|
||||
if price > 0:
|
||||
# 写回 live_prices
|
||||
try:
|
||||
conn = __import__('sqlite3').connect(str(DB_PATH))
|
||||
conn.execute("""
|
||||
INSERT OR REPLACE INTO live_prices (code, price, change_pct, updated_at)
|
||||
VALUES (?, ?, ?, datetime('now','localtime'))
|
||||
""", (str(code).strip(), price, chg))
|
||||
conn.commit()
|
||||
conn.close()
|
||||
except:
|
||||
pass
|
||||
return (price, chg)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return (None, None)
|
||||
|
||||
|
||||
def get_prices_batch(codes, max_age_minutes=5):
|
||||
"""批量获取价格，返回 {code: (price, change_pct)}"""
|
||||
from mofin_db import get_prices_batch_from_db
|
||||
|
||||
result = {}
|
||||
need_api = []
|
||||
|
||||
# 1. 批量读 DB
|
||||
try:
|
||||
db_prices = get_prices_batch_from_db(codes)
|
||||
for code in codes:
|
||||
cs = str(code).strip()
|
||||
if cs in db_prices:
|
||||
p, c = db_prices[cs]
|
||||
if p and p > 0:
|
||||
result[cs] = (p, c)
|
||||
continue
|
||||
need_api.append(cs)
|
||||
except:
|
||||
need_api = [str(c).strip() for c in codes]
|
||||
|
||||
# 2. 缺失的调 API
|
||||
if need_api:
|
||||
try:
|
||||
import subprocess, json
|
||||
r = subprocess.run(
|
||||
[sys.executable, str(SCRIPT_DIR / "stock_quote.py")] + need_api,
|
||||
capture_output=True, text=True, timeout=30
|
||||
)
|
||||
if r.returncode == 0:
|
||||
for line in r.stdout.strip().split("\n"):
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
data = json.loads(line)
|
||||
code = str(data.get("code", "")).strip()
|
||||
price = float(data.get("price", 0))
|
||||
chg = float(data.get("change_pct", 0))
|
||||
if code and price > 0:
|
||||
result[code] = (price, chg)
|
||||
except:
|
||||
pass
|
||||
except:
|
||||
pass
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# ── cash_log 写入 ──────────────────────────────────────────────────
|
||||
|
||||
def write_cash_log(cash_before, cash_after, frozen_before, frozen_after,
|
||||
source, note, verified=0):
|
||||
"""记录现金变更到 cash_log 表。"""
|
||||
change_amount = round(cash_after - cash_before, 2) if cash_after is not None and cash_before is not None else 0
|
||||
db = sqlite3.connect(DB_PATH)
|
||||
try:
|
||||
cur = db.execute(
|
||||
"""INSERT INTO cash_log
|
||||
(timestamp, cash_before, cash_after, frozen_before, frozen_after,
|
||||
change_amount, source, note, verified)
|
||||
VALUES (datetime('now','localtime'), ?, ?, ?, ?, ?, ?, ?, ?)""",
|
||||
(cash_before, cash_after, frozen_before, frozen_after,
|
||||
change_amount, source, note, verified)
|
||||
)
|
||||
db.commit()
|
||||
return cur.lastrowid
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
|
||||
# ── 自检 ───────────────────────────────────────────────────────────
|
||||
|
||||
if __name__ == "__main__":
|
||||
pf = read_portfolio()
|
||||
print(f"portfolio: {len(pf.get('holdings',[]))} holdings, total_assets={pf.get('total_assets',0)}")
|
||||
|
||||
dec = read_decisions()
|
||||
print(f"decisions: {len(dec.get('decisions',[]))} entries")
|
||||
|
||||
wl = read_watchlist()
|
||||
print(f"watchlist: {len(wl.get('stocks',[]))} stocks")
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,316 @@
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#!/usr/bin/env python3
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"""
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mo_data.py — MoFin 统一数据层（纯 DB）
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所有数据从 SQLite 读取。不做 JSON fallback。
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JSON 文件已弃用，仅保留为历史备份。
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||||
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用法:
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from mo_data import read_portfolio, read_decisions, read_watchlist
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||||
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||||
pf = read_portfolio() # 返回和 portfolio.json 一样的 dict 结构
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||||
dec = read_decisions() # 返回和 decisions.json 一样的 dict 结构
|
||||
wl = read_watchlist() # 返回和 watchlist.json 一样的 dict 结构
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||||
"""
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||||
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import sqlite3, json, sys
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from datetime import datetime
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from pathlib import Path
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DB_PATH = '/home/hmo/MoFin/data/mofin.db'
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||||
SCRIPT_DIR = Path('/home/hmo/MoFin/scripts')
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def _get_db():
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db = sqlite3.connect(DB_PATH)
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db.row_factory = sqlite3.Row
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return db
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# ── portfolio ─────────────────────────────────────────────────────
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def read_portfolio():
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"""返回 portfolio.json 等价 dict。纯 DB。"""
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db = _get_db()
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rows = db.execute(
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"SELECT code, name, shares, cost, price, market_value, "
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"change_pct, currency, position_pct "
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"FROM holdings WHERE is_active=1"
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).fetchall()
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holdings = []
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for r in rows:
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h = dict(r)
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h['_currency'] = h.get('currency', 'CNY')
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||||
holdings.append(h)
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sum_row = db.execute("SELECT * FROM portfolio_summary WHERE id=1").fetchone()
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summary = dict(sum_row) if sum_row else {}
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db.close()
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return {
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"holdings": holdings,
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"total_assets": summary.get("total_assets", 0),
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"total_mv": summary.get("total_mv", 0),
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"stock_value": summary.get("stock_value", summary.get("total_mv", 0)),
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"cash": summary.get("cash", 0),
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"frozen_cash": summary.get("frozen_cash", 0),
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"position_pct": summary.get("position_pct", 0),
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"currency": summary.get("currency", "CNY"),
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"updated_at": summary.get("updated_at", ""),
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}
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# ── decisions ─────────────────────────────────────────────────────
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def _parse_json(val, default):
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if val:
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try: return json.loads(val)
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except: pass
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return default
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def read_decisions():
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"""返回 decisions.json 等价 dict。纯 DB。"""
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db = _get_db()
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rows = db.execute(
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"SELECT code, name, version, price, cost, shares, "
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"stop_loss, take_profit, entry_low, entry_high, "
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"currency, strategy_type, action, timing_signal, "
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"rr_ratio, tech_snapshot, stock_category, sector_context, "
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||||
"status, trigger_json, changelog_json, source, reason, "
|
||||
"created_at, updated_at, "
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||||
"avg_price, decision_timestamp, note, quality_check, "
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||||
"quality_checked_at, quality_issues_json, position_advice, "
|
||||
"signal_factors_json, time_horizon, decision_type, tag "
|
||||
"FROM holding_strategies WHERE status IN ('active','updated') "
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"ORDER BY code"
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).fetchall()
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|
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decisions = []
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for r in rows:
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d = dict(r)
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d['trigger'] = _parse_json(r['trigger_json'], {})
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||||
d['changelog'] = _parse_json(r['changelog_json'], [])
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d['quality_issues'] = _parse_json(r['quality_issues_json'], {})
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||||
d['signal_factors'] = _parse_json(r['signal_factors_json'], [])
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d['timestamp'] = r['decision_timestamp'] or r['created_at'] or ''
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||||
d['type'] = r['decision_type'] or r['strategy_type'] or '持仓策略'
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decisions.append(d)
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|
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db.close()
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|
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return {
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"decisions": decisions,
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"total": len(decisions),
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"regenerated_at": datetime.now().strftime('%Y-%m-%d %H:%M'),
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||||
}
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|
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|
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# ── watchlist ─────────────────────────────────────────────────────
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def read_watchlist():
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"""返回 watchlist 等价 dict。纯 DB。
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从 holding_strategies（自选策略）读取，watchlist_stocks 已废弃。"""
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db = _get_db()
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# 主数据源：holding_strategies 自选策略
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rows = db.execute(
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"SELECT code, name, price, entry_low, entry_high, "
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"stop_loss, currency, updated_at "
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"FROM holding_strategies WHERE status='active' AND decision_type='自选策略'"
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).fetchall()
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stocks = []
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seen = set()
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for r in rows:
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code = str(r["code"])
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if code in seen:
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continue
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seen.add(code)
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stocks.append({
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"code": code,
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"name": r["name"] or "",
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"price": r["price"] or 0,
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"entry_low": r["entry_low"] or 0,
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"entry_high": r["entry_high"] or 0,
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"stop_loss": r["stop_loss"] or 0,
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"currency": r["currency"] or "CNY",
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"added_at": r["updated_at"] or "",
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"analysis": {},
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})
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|
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return {"stocks": stocks, "total": len(stocks)}
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db.close()
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return {
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"stocks": stocks,
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"updated_at": datetime.now().strftime('%Y-%m-%d %H:%M'),
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}
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|
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# ── 便捷别名 ───────────────────────────────────────────────────────
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def read_portfolio_json():
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return read_portfolio()
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def read_decisions_json():
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return read_decisions()
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|
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def read_watchlist_json():
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return read_watchlist()
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|
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# ── 统一价格获取（唯一入口，禁止各脚本自拉API）──
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def get_price(code, max_age_minutes=5, use_stale_fallback=True):
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"""获取单只股票最新价格。
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优先级: live_prices(DB) → stock_quote(API兜底)
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- live_prices 有且不超过 max_age_minutes → 直接返回
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- 没有或过期 → 调 stock_quote 拉，写回 live_prices
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- 都失败 → 返回 (None, None)
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返回 (price, change_pct)，两值都是 float 或 None。
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"""
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from mofin_db import get_price_from_db
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from datetime import datetime, timedelta
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# 1. 先读 DB
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try:
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db_price, db_chg = get_price_from_db(code)
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if db_price is not None and db_price > 0:
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# 检查时效性
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conn = __import__('sqlite3').connect(str(DB_PATH))
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row = conn.execute(
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"SELECT updated_at FROM live_prices WHERE code=?",
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(str(code).strip(),)
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).fetchone()
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conn.close()
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if row and row[0]:
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try:
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updated = datetime.strptime(row[0], "%Y-%m-%d %H:%M:%S")
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age = (datetime.now() - updated).total_seconds() / 60
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if age <= max_age_minutes:
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return (db_price, db_chg)
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except:
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pass
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else:
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return (db_price, db_chg)
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except Exception:
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pass
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# 2. DB 没有或过期 → 调 stock_quote
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if not use_stale_fallback:
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return (None, None)
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try:
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import subprocess, json
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r = subprocess.run(
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[sys.executable, str(SCRIPT_DIR / "stock_quote.py"), str(code)],
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capture_output=True, text=True, timeout=15
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)
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if r.returncode == 0:
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data = json.loads(r.stdout.strip())
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price = float(data.get("price", 0))
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chg = float(data.get("change_pct", 0))
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if price > 0:
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# 写回 live_prices
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try:
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conn = __import__('sqlite3').connect(str(DB_PATH))
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conn.execute("""
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INSERT OR REPLACE INTO live_prices (code, price, change_pct, updated_at)
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VALUES (?, ?, ?, datetime('now','localtime'))
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""", (str(code).strip(), price, chg))
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conn.commit()
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conn.close()
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except:
|
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pass
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return (price, chg)
|
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except Exception:
|
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pass
|
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|
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return (None, None)
|
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|
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|
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def get_prices_batch(codes, max_age_minutes=5):
|
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"""批量获取价格，返回 {code: (price, change_pct)}"""
|
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from mofin_db import get_prices_batch_from_db
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|
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result = {}
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need_api = []
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|
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# 1. 批量读 DB
|
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try:
|
||||
db_prices = get_prices_batch_from_db(codes)
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for code in codes:
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cs = str(code).strip()
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if cs in db_prices:
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p, c = db_prices[cs]
|
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if p and p > 0:
|
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result[cs] = (p, c)
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continue
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need_api.append(cs)
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except:
|
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need_api = [str(c).strip() for c in codes]
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|
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# 2. 缺失的调 API
|
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if need_api:
|
||||
try:
|
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import subprocess, json
|
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r = subprocess.run(
|
||||
[sys.executable, str(SCRIPT_DIR / "stock_quote.py")] + need_api,
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capture_output=True, text=True, timeout=30
|
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)
|
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if r.returncode == 0:
|
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for line in r.stdout.strip().split("\n"):
|
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if not line:
|
||||
continue
|
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try:
|
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data = json.loads(line)
|
||||
code = str(data.get("code", "")).strip()
|
||||
price = float(data.get("price", 0))
|
||||
chg = float(data.get("change_pct", 0))
|
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if code and price > 0:
|
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result[code] = (price, chg)
|
||||
except:
|
||||
pass
|
||||
except:
|
||||
pass
|
||||
|
||||
return result
|
||||
|
||||
|
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# ── cash_log 写入 ──────────────────────────────────────────────────
|
||||
|
||||
def write_cash_log(cash_before, cash_after, frozen_before, frozen_after,
|
||||
source, note, verified=0):
|
||||
"""记录现金变更到 cash_log 表。"""
|
||||
change_amount = round(cash_after - cash_before, 2) if cash_after is not None and cash_before is not None else 0
|
||||
db = sqlite3.connect(DB_PATH)
|
||||
try:
|
||||
cur = db.execute(
|
||||
"""INSERT INTO cash_log
|
||||
(timestamp, cash_before, cash_after, frozen_before, frozen_after,
|
||||
change_amount, source, note, verified)
|
||||
VALUES (datetime('now','localtime'), ?, ?, ?, ?, ?, ?, ?, ?)""",
|
||||
(cash_before, cash_after, frozen_before, frozen_after,
|
||||
change_amount, source, note, verified)
|
||||
)
|
||||
db.commit()
|
||||
return cur.lastrowid
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
|
||||
# ── 自检 ───────────────────────────────────────────────────────────
|
||||
|
||||
if __name__ == "__main__":
|
||||
pf = read_portfolio()
|
||||
print(f"portfolio: {len(pf.get('holdings',[]))} holdings, total_assets={pf.get('total_assets',0)}")
|
||||
|
||||
dec = read_decisions()
|
||||
print(f"decisions: {len(dec.get('decisions',[]))} entries")
|
||||
|
||||
wl = read_watchlist()
|
||||
print(f"watchlist: {len(wl.get('stocks',[]))} stocks")
|
||||
+2341
File diff suppressed because it is too large
Load Diff
@@ -532,6 +532,29 @@ def get_tracking():
|
||||
})
|
||||
|
||||
|
||||
|
||||
@app.route("/api/research/backtest")
|
||||
def get_research_backtest():
|
||||
import sqlite3
|
||||
from datetime import datetime, timedelta
|
||||
period = request.args.get("period", "1y")
|
||||
capital = float(request.args.get("capital", 1000000))
|
||||
end_date = datetime.now().strftime("%Y-%m-%d")
|
||||
if period == "6m":
|
||||
start_date = (datetime.now() - timedelta(days=180)).strftime('%Y-%m-%d')
|
||||
elif period == "1y":
|
||||
start_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d')
|
||||
else:
|
||||
start_date = (datetime.now() - timedelta(days=730)).strftime('%Y-%m-%d')
|
||||
try:
|
||||
from backtest_framework import run_strategy_research
|
||||
result = run_strategy_research(start_date, end_date, capital)
|
||||
from flask import jsonify
|
||||
return jsonify(result)
|
||||
except Exception as e:
|
||||
from flask import jsonify
|
||||
return jsonify({'error': str(e)}), 500
|
||||
|
||||
@app.route("/api/overview")
|
||||
def api_overview():
|
||||
"""概览数据"""
|
||||
|
||||
@@ -70,6 +70,7 @@ body { background: #0f0f13; color: #e2e8f0; }
|
||||
<button class="tab-btn px-4 py-2 text-sm rounded-lg" data-tab="signals">🔍 信号</button>
|
||||
<button class="tab-btn px-4 py-2 text-sm rounded-lg" data-tab="health">🏥 健康</button>
|
||||
<button class="tab-btn px-4 py-2 text-sm rounded-lg" data-tab="principles">📐 开发原则</button>
|
||||
<button class="tab-btn px-4 py-2 text-sm rounded-lg" data-tab="research">📋 研究</button>
|
||||
<a href="/upload" class="tab-btn px-4 py-2 text-sm rounded-lg" style="text-decoration:none;color:#fbbf24;border-color:#fbbf24">📸 上传</a>
|
||||
</div>
|
||||
|
||||
@@ -82,6 +83,7 @@ body { background: #0f0f13; color: #e2e8f0; }
|
||||
<div id="tab-market" class="tab-content hidden"></div>
|
||||
<div id="tab-signals" class="tab-content hidden"></div>
|
||||
<div id="tab-health" class="tab-content hidden"></div>
|
||||
<div id="tab-research" class="tab-content hidden"></div>
|
||||
<div id="tab-principles" class="tab-content hidden">
|
||||
<div class="flex gap-1 mb-4 bg-slate-900/50 rounded-xl p-1 border border-slate-800/50" id="princBar">
|
||||
<button class="princ-btn active px-4 py-1.5 text-sm rounded-lg" data-princ="spec">G 规范</button>
|
||||
@@ -258,6 +260,7 @@ function renderTab(name) {
|
||||
else if (name === 'health') renderHealth();
|
||||
else if (name === 'principles') renderPrinciples();
|
||||
else if (name === 'watch') renderWatch();
|
||||
else if (name === 'research') renderResearch();
|
||||
}
|
||||
|
||||
// ── Data Fetching ──
|
||||
@@ -1917,6 +1920,88 @@ function inMd(t) {
|
||||
.replace(/`([^`]+)`/g, '<code class="bg-slate-900 px-1 rounded text-xs font-mono">$1</code>')
|
||||
.replace(/\[([^\]]+)\]\(([^)]+)\)/g, '<a href="$2" class="text-[#58a6ff]" target="_blank">$1</a>');
|
||||
}
|
||||
|
||||
function renderResearch() {
|
||||
const el = document.getElementById('tab-research');
|
||||
if (!el) return;
|
||||
el.innerHTML = '<div class="flex gap-2 items-center mb-4"><h2 class="text-lg font-bold">📋 策略研究 — 全流程回测</h2>' +
|
||||
'<select id="btPeriod" class="bg-slate-800 text-sm rounded-lg px-2 py-1 border border-slate-700" onchange="runBacktest()">' +
|
||||
'<option value="1m">近 1 个月</option>' +
|
||||
'<option value="6m" selected>近 6 个月</option>' +
|
||||
'<option value="1y">近 1 年</option>' +
|
||||
'<option value="2y">近 2 年</option>' +
|
||||
'</select>' +
|
||||
'<button onclick="runBacktest()" class="px-3 py-1 bg-blue-600 hover:bg-blue-500 rounded-lg text-sm">▶ 运行</button></div>' +
|
||||
'<div id="btResults" class="text-slate-400 text-sm">点击「运行」开始回测验证</div>';
|
||||
window.btEl = el;
|
||||
}
|
||||
|
||||
async function runBacktest() {
|
||||
const period = document.getElementById('btPeriod')?.value || '6m';
|
||||
const resultsEl = document.getElementById('btResults');
|
||||
if (!resultsEl) return;
|
||||
resultsEl.innerHTML = '<div class="text-blue-400 animate-pulse">⏳ 回测运行中（约 15-30 秒）...</div>';
|
||||
try {
|
||||
const resp = await fetch('/api/research/backtest?period=' + period);
|
||||
const data = await resp.json();
|
||||
if (data.error) { resultsEl.innerHTML = '<div class="text-red-400">错误: ' + data.error + '</div>'; return; }
|
||||
displayBacktestResults(data);
|
||||
} catch(e) {
|
||||
resultsEl.innerHTML = '<div class="text-red-400">请求失败: ' + e.message + '</div>';
|
||||
}
|
||||
}
|
||||
|
||||
function displayBacktestResults(data) {
|
||||
const el = document.getElementById('btResults');
|
||||
if (!el) return;
|
||||
const s = data.summary || {};
|
||||
const winRate = s.win_rate || 0;
|
||||
const winRateClass = winRate >= 50 ? 'text-green-400' : winRate >= 40 ? 'text-yellow-400' : 'text-red-400';
|
||||
const sharpeClass = (s.sharpe_ratio || 0) >= 1 ? 'text-green-400' : (s.sharpe_ratio || 0) >= 0 ? 'text-yellow-400' : 'text-red-400';
|
||||
|
||||
let html = '<div class="grid grid-cols-2 md:grid-cols-4 gap-3 mb-4">' +
|
||||
'<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">测试周期</div><div class="text-sm font-mono">' + (data.period || '') + '</div></div>' +
|
||||
'<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">初始资金</div><div class="text-sm font-mono">¥' + (s.capital_end ? (data.capital || 1000000).toLocaleString() : '100万') + '</div></div>' +
|
||||
'<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">最终资金</div><div class="text-sm font-mono">¥' + (s.capital_end ? s.capital_end.toLocaleString() : '-') + '</div></div>' +
|
||||
'<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">总交易</div><div class="text-sm font-mono">' + (s.total_trades || 0) + ' 笔</div></div>' +
|
||||
'<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">胜率</div><div class="text-lg font-bold ' + winRateClass + '">' + s.win_rate + '%</div></div>' +
|
||||
'<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">平均收益</div><div class="text-lg font-bold ' + ((s.avg_profit_pct || 0) >= 0 ? 'text-green-400' : 'text-red-400') + '">' + (s.avg_profit_pct || 0) + '%</div></div>' +
|
||||
'<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">夏普比率</div><div class="text-lg font-bold ' + sharpeClass + '">' + (s.sharpe_ratio || 0).toFixed(2) + '</div></div>' +
|
||||
'<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">最大回撤</div><div class="text-lg font-bold text-red-400">' + (s.max_drawdown_pct || 0).toFixed(1) + '%</div></div>' +
|
||||
'</div>' +
|
||||
'<div class="grid grid-cols-4 gap-2 mb-4 text-xs">' +
|
||||
'<div class="bg-green-900/20 rounded p-2"><span class="text-green-400">✅ 盈利: ' + (s.wins || 0) + '</span> <span class="text-slate-500">均+' + (s.avg_win_pct || 0).toFixed(1) + '%</span></div>' +
|
||||
'<div class="bg-red-900/20 rounded p-2"><span class="text-red-400">❌ 亏损: ' + (s.losses || 0) + '</span> <span class="text-slate-500">均' + (s.avg_loss_pct || 0).toFixed(1) + '%</span></div>' +
|
||||
'<div class="bg-slate-800/30 rounded p-2"><span class="text-yellow-400">📊 盈亏比: ' + (s.profit_factor || 0).toFixed(2) + '</span></div>' +
|
||||
'<div class="bg-slate-800/30 rounded p-2"><span class="text-slate-300">📈 筛选: ' + (data.total_stocks_screened || 0) + ' 只</span></div>' +
|
||||
'</div>';
|
||||
|
||||
// Trade list
|
||||
const trades = data.trades || [];
|
||||
html += '<div class="mb-2 text-sm font-bold text-slate-300">交易明细（前 50 笔）</div>' +
|
||||
'<div class="overflow-x-auto"><table class="w-full text-xs"><thead><tr class="text-slate-500 border-b border-slate-700">' +
|
||||
'<th class="text-left px-2 py-1">代码</th><th class="text-left px-2 py-1">名称</th><th class="text-left px-2 py-1">买入日</th>' +
|
||||
'<th class="text-right px-2 py-1">买入价</th><th class="text-right px-2 py-1">卖出价</th><th class="text-right px-2 py-1">收益</th>' +
|
||||
'<th class="text-center px-2 py-1">结果</th><th class="text-right px-2 py-1">持仓</th><th class="text-right px-2 py-1">评分</th></tr></thead><tbody>';
|
||||
for (const t of trades) {
|
||||
const pnlClass = (t.profit_pct || 0) >= 0 ? 'text-green-400' : 'text-red-400';
|
||||
const reasonMap = { 'target': '🎯 止盈', 'stop': '🛑 止损', 'keep': '⏳ 持仓中' };
|
||||
html += '<tr class="border-b border-slate-800/50 hover:bg-slate-800/30">' +
|
||||
'<td class="px-2 py-1 font-mono">' + (t.code || '') + '</td>' +
|
||||
'<td class="px-2 py-1">' + (t.name || '') + '</td>' +
|
||||
'<td class="px-2 py-1 font-mono">' + (t.entry_date || '') + '</td>' +
|
||||
'<td class="text-right px-2 py-1 font-mono">' + (t.entry_price || 0).toFixed(2) + '</td>' +
|
||||
'<td class="text-right px-2 py-1 font-mono">' + (t.exit_price || 0).toFixed(2) + '</td>' +
|
||||
'<td class="text-right px-2 py-1 font-mono ' + pnlClass + '">' + (t.profit_pct || 0).toFixed(1) + '%</td>' +
|
||||
'<td class="text-center px-2 py-1">' + (reasonMap[t.exit_reason] || t.exit_reason || '') + '</td>' +
|
||||
'<td class="text-right px-2 py-1">' + (t.hold_days || 0) + 'd</td>' +
|
||||
'<td class="text-right px-2 py-1">' + (t.score || 0) + '</td></tr>';
|
||||
}
|
||||
html += '</tbody></table></div>';
|
||||
|
||||
el.innerHTML = html;
|
||||
}
|
||||
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user