# -*- coding: utf-8 -*-
"""
factor_detail_charts.py — 因子走势交互图表 v3
- 主图：价格+总分默认显示，因子按需叠加
- 开仓/平仓/止盈/止损标注在价格线上（竖线+标签）
- 时间段：快捷按钮 + datetime框 + 鼠标拖选
- 相关系数面板随时间段实时更新
"""
import re, math, json, os, sys, subprocess, requests
from datetime import datetime, timezone
_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, _ROOT)
_SYMBOL = sys.argv[1] if len(sys.argv) > 1 else "BTC"
LOG = os.path.join(_ROOT, "my_trader.log")
OUT = os.path.join(_ROOT, "dashboard", {"BTC":"strategy_a.html","XAU":"strategy_b.html"}.get(_SYMBOL,"strategy_"+_SYMBOL.lower()+".html"))
MAXN = 6000
if _SYMBOL == "XAU":
FK = ['XTR','XMF','XMA','XMO','XVL','XOI','XCF','XVX','XRD','XDX','XEF','XCR','XRSI','XBB','XEX']
FN = {'XTR':'趋势','XMF':'多周期共振','XMA':'均线排列','XMO':'动量(ROC)','XVL':'成交量','XOI':'持仓变化','XCF':'COT持仓','XVX':'量价背离','XRD':'实际利率','XDX':'美元反向','XEF':'ETF资金流','XCR':'信用风险','XRSI':'RSI回归','XBB':'布林带','XEX':'量能衰竭'}
FC = {'XTR':'#ff6b6b','XMF':'#ff9f43','XMA':'#ffa502','XMO':'#f368e0','XVL':'#2ed573','XOI':'#1e90ff','XCF':'#a55eea','XVX':'#7bed9f','XRD':'#ff4757','XDX':'#e056fd','XEF':'#3ae374','XCR':'#ff6348','XRSI':'#18dcff','XBB':'#7d5fff','XEX':'#00d2d3'}
FW = {'XTR':0.06,'XMF':0.06,'XMA':0.04,'XMO':0.04,'XVL':0.08,'XOI':0.06,'XCF':0.06,'XVX':0.04,'XRD':0.10,'XDX':0.10,'XEF':0.06,'XCR':0.06,'XRSI':0.10,'XBB':0.08,'XEX':0.06}
FD = {'XTR':'1H+4H+日线EMA三重确认','XMF':'15m/1H/4H均线共振','XMA':'MA20/50/200多头排列','XMO':'ROC(12)价格变化率','XVL':'COMEX期货量vs滚动均值','XOI':'OI变化方向+速率','XCF':'CFTC管理基金净持仓(极端反转)','XVX':'缩量涨/放量滞涨检测','XRD':'TIPS实际利率倒数(金价核心驱动)','XDX':'DXY美元指数负相关','XEF':'GLD ETF资金流入流出方向','XCR':'信用利差→避险需求','XRSI':'RSI(14)超买>70/超卖<30回归','XBB':'布林带%B位置+带宽','XEX':'连续缩量+波幅收窄衰竭'}
else:
FK = ['TR','OB','TK','OI','FR','MP','VD','BTC','GM','IV','EX','LC','MR','SM','MTF','OBL','LL','LEX']
FN = {'TR':'趋势','OB':'挂单','TK':'成交','OI':'持仓量','FR':'费率','MP':'痛点','VD':'量变','BTC':'BTC','GM':'Gamma','IV':'波动率','EX':'衰竭','LC':'清算','MR':'回归','SM':'聪明钱','MTF':'MTF共振','OBL':'清算占比','LL':'低杠杆','LEX':'清算衰竭'}
FC = {'TR':'#ff6b6b','OB':'#ffa502','TK':'#2ed573','OI':'#1e90ff','FR':'#a55eea','MP':'#ff4757','VD':'#7bed9f','BTC':'#eccc68','GM':'#e056fd','IV':'#686de0','EX':'#ff6348','LC':'#3ae374','MR':'#18dcff','SM':'#7d5fff','MTF':'#ff9f43','OBL':'#00d2d3','LL':'#f368e0','LEX':'#1dd1a1'}
FW = {'TR':0.04,'OB':0.10,'TK':0.02,'OI':0.01,'FR':0.10,'MP':0.08,'VD':0.03,'BTC':0.02,'GM':0.02,'IV':0.02,'EX':0.18,'LC':0.04,'MR':0.01,'SM':0.03,'MTF':0.10,'OBL':0.08,'LL':0.04,'LEX':0.06}
FD = {'TR':'1H+4H EMA交叉趋势方向','OB':'订单簿买卖挂单深度失衡','TK':'主动成交买/卖比例','OI':'持仓量变化方向','FR':'资金费率多空拥挤度','MP':'期权最大痛点磁吸效应','VD':'成交量Delta变化','BTC':'宏观联动(ETH/黄金反相关)','GM':'Gamma做市商行为','IV':'隐含波动率变化','EX':'量能衰竭检测','LC':'清算地图方向(+多/-空)','MR':'短期过热均值回归','SM':'聪明钱(链上+合约现货分歧)','MTF':'多时间框架共振(1m/5m/15m)','OBL':'订单簿vs清算挂单占比','LL':'低杠杆积累检测','LEX':'清算衰竭信号'}
R_PX = re.compile(r'(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}),\d+ \[MyTrader\] INFO: \[ETH\] \$([0-9,.]+) \|')
R_F = re.compile(
r'(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}),\d+ \[MyTrader\] INFO: \[ETH\] '
r'TR:([+-]?\d+\.?\d*) OB:([+-]?\d+\.?\d*) FR:([+-]?\d+\.?\d*) '
r'TK:([+-]?\d+\.?\d*) OI:([+-]?\d+\.?\d*) MP:([+-]?\d+\.?\d*) '
r'VD:([+-]?\d+\.?\d*) BTC:([+-]?\d+\.?\d*) GM:([+-]?\d+\.?\d*) '
r'IV:([+-]?\d+\.?\d*) EX:([+-]?\d+\.?\d*) LC:([+-]?\d+\.?\d*) '
r'MR:([+-]?\d+\.?\d*) SM:([+-]?\d+\.?\d*) '
r'mom:[+-]?\d+\.?\d* flip:[+-]?\d+\.?\d* => ([+-]?\d+\.?\d*) -> (\w+)')
R_NEWS = re.compile(r'(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}),\d+ \[MyTrader\] INFO: \[News\] DeepSeek: AI判断:([+-]?\d+\.?\d*)\(把握(\d+)%[^)]*\) 恐贪:(\d+) (.+?) \[(.+?)\]')
R_OPEN = re.compile(r'(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}).*ETH (LONG|SHORT) [\d.]+\S* @ \$([\d.]+) TP:\$([\d.]+) SL:\$([\d.]+)')
R_TP = re.compile(r'(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}).*\[止盈\].*\$([-\d.]+)')
R_SL = re.compile(r'(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}).*\[止损\].*\$([-\d.]+)')
R_LOCK = re.compile(r'(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}).*(锁利|因子反向.*锁|强制平仓|决策树.*平)')
def parse():
if _SYMBOL == "XAU":
import pymysql
from pymysql.cursors import DictCursor
conn = pymysql.connect(host='127.0.0.1',port=3306,user='admin',password='8ggtjny',
database='coinglass_trader',charset='utf8mb4',cursorclass=DictCursor)
rows = []
events = []
try:
with conn.cursor() as cur:
cur.execute("""SELECT price,total_score,direction,
trend,orderbook,funding,taker,oi,maxpain,vol_delta,btc_corr,
gamma,iv,exhaust,liq_cool,mean_revert,news,smart_money,
mtf,ob_liq,low_lev,liq_ex,snapshot_at
FROM factor_snapshots WHERE symbol='XAU'
ORDER BY snapshot_at ASC LIMIT 6000""")
for r in cur:
ts = r['snapshot_at']
if hasattr(ts,'strftime'):
from datetime import timezone, timedelta
if ts.tzinfo is None: ts = ts.replace(tzinfo=timezone.utc)
ts = ts.astimezone(timezone(timedelta(hours=8))).strftime('%Y-%m-%d %H:%M:%S')
else: ts = str(ts)[:19]
row = {'ts':ts, 'price':float(r['price']or 0),
'total':float(r['total_score']or 0),'dir':r['direction']or'WAIT',
'XTR':float(r['trend']or 0),'XMF':float(r['mtf']or 0),
'XMA':float(r['low_lev']or 0),'XMO':0,
'XVL':float(r['taker']or 0),'XOI':float(r['oi']or 0),
'XCF':float(r['smart_money']or 0),'XVX':float(r['vol_delta']or 0),
'XRD':float(r['liq_cool']or 0),'XDX':float(r['mean_revert']or 0),
'XEF':float(r['funding']or 0),'XCR':float(r['btc_corr']or 0),
'XRSI':float(r['mean_revert']or 0),'XBB':float(r['ob_liq']or 0),
'XEX':float(r['exhaust']or 0)}
rows.append(row)
with conn.cursor() as cur:
cur.execute("""SELECT direction,entry_price,exit_price,pnl,exit_reason,
DATE_FORMAT(opened_at,'%%Y-%%m-%%d %%H:%%i:%%s') as opened,
DATE_FORMAT(closed_at,'%%Y-%%m-%%d %%H:%%i:%%s') as closed
FROM trades ORDER BY opened_at ASC LIMIT 200""")
for t in cur:
if t['opened']:
d = t['direction']
events.append({'ts':t['opened'],'type':'open','price':float(t['entry_price']),
'label':f"{chr(0x25b2) if d=='LONG' else chr(0x25bc)}{d[0]} @{float(t['entry_price']):.0f}",
'color':'#2ed573' if d=='LONG' else '#ff4757','dir':d,'tp':0,'sl':0})
if t['closed'] and t['exit_reason']:
pnl = float(t.get('pnl',0)or 0)
events.append({'ts':t['closed'],'type':'close','price':float(t.get('exit_price',0)or 0),
'label':t['exit_reason'][:10],'color':'#2ed573' if pnl>0 else '#ff4757','dir':''})
finally:
conn.close()
prev_dir = None
for r in rows:
d = r.get('dir','WAIT')
if d != prev_dir and d in ('LONG','SHORT'):
events.append({'ts':r['ts'],'type':'signal','price':r['price'],
'label':f"{chr(0x25b2) if d=='LONG' else chr(0x25bc)}{d[0]} @{r['price']:.0f}",
'color':'#2ed573' if d=='LONG' else '#ff4757','dir':d})
prev_dir = d
return rows[:MAXN], events
# BTC: original log-based parse
lines = open(LOG, encoding='utf-8', errors='replace').readlines()
prices_map, rows = {}, []
events = [] # {ts, type, price, label}
last_news = None # 最近一条 News 行
for l in lines:
m = R_PX.search(l)
if m: prices_map[m.group(1)] = float(m.group(2).replace(',',''))
m = R_NEWS.search(l)
if m:
last_news = {
'ai': float(m.group(2)),
'conf': int(m.group(3)),
'fg': int(m.group(4)),
'summary': m.group(5).strip()[:80],
'headline': m.group(6).strip()[:100],
}
m = R_F.search(l)
if m:
ts = m.group(1)
rows.append({
'ts':ts, 'price':prices_map.get(ts),
'TR':float(m.group(2)),'OB':float(m.group(3)),'FR':float(m.group(4)),
'TK':float(m.group(5)),'OI':float(m.group(6)),'MP':float(m.group(7)),
'VD':float(m.group(8)),'BTC':float(m.group(9)),'GM':float(m.group(10)),
'IV':float(m.group(11)),'EX':float(m.group(12)),'LC':float(m.group(13)),
'MR':float(m.group(14)),'SM':float(m.group(15)),
'total':float(m.group(16)),'dir':m.group(17),
'news': last_news, # 该 cycle 对应的最新 News
})
m = R_OPEN.search(l)
if m:
d = m.group(2)
events.append({'ts':m.group(1),'type':'open','price':float(m.group(3)),
'label':f"{'▲' if d=='LONG' else '▼'}{d[0]} @{float(m.group(3)):.0f}",
'color':'#2ed573' if d=='LONG' else '#ff4757','dir':d,
'tp':float(m.group(4)),'sl':float(m.group(5))})
m = R_TP.search(l)
if m:
events.append({'ts':m.group(1),'type':'tp','price':0,
'label':f"止盈 ${float(m.group(2)):+.4f}",
'color':'#f0c040','dir':''})
m = R_SL.search(l)
if m:
events.append({'ts':m.group(1),'type':'sl','price':0,
'label':f"止损 ${float(m.group(2)):+.4f}",
'color':'#ff6348','dir':''})
m = R_LOCK.search(l)
if m:
events.append({'ts':m.group(1),'type':'close','price':0,
'label':f"平仓:{m.group(2)[:6]}",
'color':'#aaa','dir':''})
# fill prices
last = None
for r in rows:
if r['price'] is None: r['price'] = last
else: last = r['price']
rows = [r for r in rows if r['price'] is not None][-MAXN:]
# fill event prices from nearest row
ts_set = {r['ts']:r['price'] for r in rows}
all_ts = sorted(ts_set.keys())
for ev in events:
if ev['price'] == 0 or ev['price'] is None:
# find closest
best = None
for t in all_ts:
if t <= ev['ts']: best = t
if best: ev['price'] = ts_set[best]
else: ev['price'] = ts_set.get(all_ts[0], 0) if all_ts else 0
# filter events to data range
if rows:
t0, t1 = rows[0]['ts'], rows[-1]['ts']
events = [e for e in events if t0 <= e['ts'] <= t1]
# 断点处理：检测 >5分钟的空缺，从OKX拉历史1分钟K线补充真实价格
# 已抓取的价格缓存到 gap_prices.json，避免重复请求
from datetime import datetime as _dt, timedelta
GAP_FILL = 5 * 60 # >5分钟才补充
CACHE_FILE = 'gap_prices.json'
# 缓存格式：{"prices": {"YYYY-MM-DD HH:MM:SS": price, ...}}
# 统一 ts->price 字典，不依赖精确 key 匹配
price_cache = {}
if os.path.exists(CACHE_FILE):
try:
raw = json.load(open(CACHE_FILE, encoding='utf-8'))
# 兼容旧格式 {key: [{ts,price},...]}
if 'prices' in raw:
price_cache = raw['prices']
else:
for v in raw.values():
if isinstance(v, list):
for item in v:
if isinstance(item, dict) and 'ts' in item:
price_cache[item['ts']] = item['price']
except:
price_cache = {}
# 检测空缺
gaps = []
for i in range(1, len(rows)):
try:
t0g = _dt.strptime(rows[i-1]['ts'], '%Y-%m-%d %H:%M:%S')
t1g = _dt.strptime(rows[i]['ts'], '%Y-%m-%d %H:%M:%S')
gap = (t1g - t0g).total_seconds()
if gap > GAP_FILL:
gaps.append((rows[i-1]['ts'], rows[i]['ts'], int(gap)))
except:
pass
# 对每个空缺，先从缓存取，缺少的才去拉
fill_rows = []
needs_fetch = [] # (t0_ms, t1_ms, label)
for g_start, g_end, g_sec in gaps:
t0g = _dt.strptime(g_start, '%Y-%m-%d %H:%M:%S')
t1g = _dt.strptime(g_end, '%Y-%m-%d %H:%M:%S')
# 从下一个整分钟开始枚举（对齐秒=0，与缓存key格式一致）
cur = t0g.replace(second=0) + timedelta(minutes=1)
missing_ranges = []
seg_miss_start = None
while cur < t1g:
ts_str = cur.strftime('%Y-%m-%d %H:%M:%S')
if ts_str in price_cache:
fill_rows.append({'ts': ts_str, 'price': price_cache[ts_str]})
if seg_miss_start:
missing_ranges.append((seg_miss_start, cur))
seg_miss_start = None
else:
if seg_miss_start is None:
seg_miss_start = cur
cur += timedelta(minutes=1)
if seg_miss_start:
missing_ranges.append((seg_miss_start, t1g))
for ms, me in missing_ranges:
# 日志 ts 是北京时间（UTC+8），转 UTC 再乘1000
t0_ms = int((ms - timedelta(hours=8)).replace(tzinfo=timezone.utc).timestamp() * 1000)
t1_ms = int((me - timedelta(hours=8)).replace(tzinfo=timezone.utc).timestamp() * 1000)
needs_fetch.append((t0_ms, t1_ms, f"{ms.strftime('%H:%M')}~{me.strftime('%H:%M')}"))
if needs_fetch:
print(f"发现 {len(needs_fetch)} 个未缓存区段，正在从OKX补充...", flush=True)
try:
from config import OKX_BASE_URL, PROXIES
sess = requests.Session()
if PROXIES:
sess.proxies = PROXIES
sess.headers['User-Agent'] = 'Mozilla/5.0'
new_count = 0
for t0_ms, t1_ms, label in needs_fetch:
after_ms = t1_ms
for _ in range(15):
r = sess.get(
f'{OKX_BASE_URL}/api/v5/market/history-candles',
params={'instId':'ETH-USDT-SWAP','bar':'1m',
'after':str(after_ms),'before':str(t0_ms),'limit':'100'},
timeout=8
)
data = r.json().get('data', [])
if not data:
break
for c in data:
ts_ms_c = int(c[0])
if t0_ms <= ts_ms_c <= t1_ms:
ts_local = (_dt.utcfromtimestamp(ts_ms_c/1000) + timedelta(hours=8)).strftime('%Y-%m-%d %H:%M:%S')
p = float(c[4])
if ts_local not in price_cache:
price_cache[ts_local] = p
fill_rows.append({'ts': ts_local, 'price': p})
new_count += 1
after_ms = int(data[-1][0]) - 1
if after_ms <= t0_ms:
break
print(f" 补充 {new_count} 条新价格数据", flush=True)
# 持久化（统一格式）
with open(CACHE_FILE, 'w', encoding='utf-8') as cf:
json.dump({'prices': price_cache}, cf, ensure_ascii=False)
print(f" 已保存到 {CACHE_FILE}（共 {len(price_cache)} 条）", flush=True)
except Exception as e:
print(f"价格补充失败（继续生成）: {e}", flush=True)
elif gaps:
print(f"使用缓存价格数据（共 {len(fill_rows)} 条）", flush=True)
if fill_rows:
null_factors = {k: None for k in FK}
null_factors['total'] = None
null_factors['dir'] = None
full_fill = [{**null_factors, 'ts': fr['ts'], 'price': fr['price'], '_filled': True}
for fr in fill_rows]
rows = sorted(rows + full_fill, key=lambda r: r['ts'])
print(f" 共补充 {len(fill_rows)} 条价格数据", flush=True)
return rows, events
def pearson(x, y):
n = len(x)
if n < 3: return 0.0
mx, my = sum(x)/n, sum(y)/n
num = sum((a-mx)*(b-my) for a,b in zip(x,y))
dx = math.sqrt(sum((a-mx)**2 for a in x))
dy = math.sqrt(sum((b-my)**2 for b in y))
return round(num/(dx*dy),4) if dx>1e-10 and dy>1e-10 else 0.0
def overview(rows):
real = [r for r in rows if not r.get('_filled') and r.get('total') is not None]
n=len(real); prices=[r['price'] for r in real]
pmin,pmax=min(prices),max(prices); pr=(pmax-pmin) or 1; pm=(pmax+pmin)/2
pn=[(p-pm)/(pr/2) for p in prices]; tot=[r['total'] for r in real]
res={}
for k in FK:
fv=[r[k] for r in real]; d={'vt':pearson(fv,tot),'vp':pearson(fv,pn)}
for lag in [1,3,5,10,20]:
if n<=lag: d[f'l{lag}']=0.0; continue
fr=[math.log(prices[i+lag]/prices[i]) if prices[i]>0 else 0 for i in range(n-lag)]
d[f'l{lag}']=pearson(fv[:n-lag],fr)
res[k]=d
return res
def html(rows, events, ov):
n=len(rows); t0=rows[0]['ts']; t1=rows[-1]['ts']
now=datetime.now().strftime('%Y-%m-%d %H:%M:%S')
# 热力图行
def cc(v):
a=abs(v)
if a<0.05: return '#333'
return f'rgb(0,{int(80+a*175)},50)' if v>0 else f'rgb({int(80+a*175)},30,30)'
ovr=''
for k in FK:
d=ov[k]; c=FC[k]
cells=f'
{d["vt"]:+.3f}
'
cells+=f'
{d["vp"]:+.3f}
'
for lag in [1,3,5,10,20]:
v=d[f'l{lag}']; cells+=f'
{v:+.3f}
'
ovr+=f'
{k}
{FN[k]}
{cells}
\n'
af_data = json.dumps({k:[round(r[k],4) if r[k] is not None else None for r in rows] for k in FK})
# 数据 JSON（注入到 \n'
'\n'
'\n'
'\n'
'\n\n'
f'