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# -*- coding: utf-8 -*-
"""
量化交易监控主程序
功能：
1. 循环抓取美股数据 (支持全量/Top N)
2. 调用分析模块进行分析
3. 调用交易模块执行信号
"""
import time
import argparse
from logging_setup import init_logging
from futu import StockDataIntegrator, EastMoneyAPI
from market_analyzer import MarketAnalyzer
from trader import Trader
from datetime import datetime
from data_writer import write_symbols, append_bars_1m, append_bars_session, append_signals, append_features_1m, append_etl_run
from zoneinfo import ZoneInfo
from utils_time import now_et, fmt_et, fmt_et_hm
from signal_filter import SignalCooldownFilter
def main():
# 初始化简单日志（保持现有 print，不强制替换）
init_logging()
parser = argparse.ArgumentParser(description='AI量化交易监控系统')
parser.add_argument('--interval', type=int, default=60, help='监控间隔(秒)')
parser.add_argument('--limit', type=int, default=100, help='每次监控的股票数量')
parser.add_argument('--all', action='store_true', help='监控所有股票(速度较慢)')
parser.add_argument('--premarket', action='store_true', help='在盘前窗口抓取富途盘前价格并写入 session=pre')
parser.add_argument('--premarket-limit', type=int, default=30, help='盘前抓取的最大股票数(富途页面逐个抓取)')
parser.add_argument('--session-override', choices=['pre','regular','post'], help='测试用手动覆盖当前交易时段')
args = parser.parse_args()
print("🚀 启动 AI 量化交易监控系统...")
print(f"⏱️ 监控间隔: {args.interval} 秒")
# 初始化模块
integrator = StockDataIntegrator()
eastmoney_api = EastMoneyAPI() # 用于快速获取列表
analyzer = MarketAnalyzer()
trader = Trader()
cooldown_filter = SignalCooldownFilter(cooldown_minutes=30)
loop_count = 0
try:
while True:
loop_start = now_et()
loop_count += 1
print(f"\n🔄 第 {loop_count} 次扫描开始 - {fmt_et_hm() } ET")
# 1. 抓取数据 (常规东方财富列表)
# 为了监控效率，我们主要使用东方财富的快速列表接口
# 如果是全量监控
stock_data = []
if args.all:
print("📡 正在获取全量市场数据...")
# 这里我们简化处理，直接调用修改后的API获取所有数据
# 注意：futu.py 中的 get_us_stocks 已经支持分页获取
# 为了演示，我们这里只获取前几页，或者使用 futu.py 中新增的逻辑
# 直接使用 integrator 的逻辑，但强制 eastmoney_only 以提高速度
# 我们手动调用 eastmoney_api 来获取数据，避免 integrator 的复杂逻辑
# 获取所有数据可能需要一点时间
_, total = eastmoney_api.get_us_stocks(page_size=1)
print(f"📊 市场总股票数: {total}")
# 分页获取所有数据
page_size = 100
limit = total
total_pages = (limit + page_size - 1) // page_size
all_raw_stocks = []
for page in range(1, total_pages + 1):
stocks, _ = eastmoney_api.get_us_stocks(page_size=page_size, page_index=page)
if stocks:
all_raw_stocks.extend(stocks)
# 稍微延时
# time.sleep(0.1)
# 解析数据
for item in all_raw_stocks:
parsed = eastmoney_api.parse_stock_data(item)
if parsed:
stock_data.append({
'symbol': parsed['symbol'],
'name': parsed['name'],
'eastmoney_price': parsed['current_price'],
'eastmoney_change_ratio': parsed['change_ratio']
})
else:
print(f"📡 正在获取 Top {args.limit} 热门股票数据...")
# 获取 Top N
raw_stocks, _ = eastmoney_api.get_us_stocks(page_size=args.limit)
for item in raw_stocks:
parsed = eastmoney_api.parse_stock_data(item)
if parsed:
stock_data.append({
'symbol': parsed['symbol'],
'name': parsed['name'],
'eastmoney_price': parsed['current_price'],
'eastmoney_change_ratio': parsed['change_ratio']
})
print(f"✅ 获取到 {len(stock_data)} 条有效常规行情数据 (ET {fmt_et_hm()})")
# 1.1 盘前数据补充 (仅在盘前窗口且开启参数时，对前 N 只股票抓取富途页面)
def _get_us_market_session(now_et: datetime) -> str:
"""根据美东时间判定交易时段: pre(4:00-9:30), regular(9:30-16:00), post(16:00-20:00), off 其它。
周末直接 off。夏令时由系统 tz 数据自动处理。"""
if now_et.weekday() >= 5: # Saturday=5 Sunday=6
return 'off'
minutes = now_et.hour * 60 + now_et.minute
if 4*60 <= minutes < 9*60 + 30:
return 'pre'
if 9*60 + 30 <= minutes < 16*60:
return 'regular'
if 16*60 <= minutes < 20*60:
return 'post'
return 'off'
def _current_session() -> str:
if args.session_override:
return args.session_override
now_et = datetime.now(ZoneInfo('America/New_York'))
return _get_us_market_session(now_et)
pre_rows = []
session = _current_session()
if args.premarket and session == 'pre':
pre_candidates = stock_data[: args.premarket_limit]
print(f"🌙 盘前窗口内，准备抓取富途盘前数据 {len(pre_candidates)} 条... (ET {fmt_et_hm()})")
for i, item in enumerate(pre_candidates, 1):
symbol = item['symbol']
futu_detail = integrator.get_futu_stock_details(symbol)
if futu_detail and futu_detail.get('before_open_price'):
# 正常化盘前涨跌幅 (可能含 %)
ratio_raw = futu_detail.get('before_open_change_ratio') or ''
ratio_val = 0.0
try:
ratio_clean = str(ratio_raw).replace('%','').strip()
if ratio_clean:
ratio_f = float(ratio_clean)
# 转为小数
ratio_val = ratio_f/100.0
except Exception:
ratio_val = 0.0
item.update({
'premarket_price': futu_detail.get('before_open_price'),
'premarket_change': futu_detail.get('before_open_change'),
'premarket_change_ratio': ratio_val,
'futu_before_open_price': futu_detail.get('before_open_price'), # 兼容 append_bars_session fallback
})
pre_rows.append(item)
if i % 10 == 0:
print(f"🌙 盘前抓取进度 {i}/{len(pre_candidates)} (ET {fmt_et_hm()})")
print(f"🌙 富途盘前成功获取 {len(pre_rows)} 条 (ET {fmt_et_hm()})")
else:
if args.premarket:
print(f"🌙 当前交易时段为 {session}，未执行盘前抓取 (ET {fmt_et_hm()})")
# 2.1 将 symbols 与 1分钟线写入 CSV（data/ 下）
try:
symbol_id_map = write_symbols([
{
'symbol': s['symbol'],
'name': s['name'],
'exchange': 'US',
'currency': 'USD',
}
for s in stock_data
])
new_rows = append_bars_1m(stock_data, symbol_id_map, source='eastmoney')
append_features_1m(new_rows)
# 盘前 bars 写入 (不计算特征，避免与常规混淆)
if pre_rows:
append_bars_session(pre_rows, symbol_id_map, source='futu', session='pre')
except Exception as e:
print(f"⚠️ 数据落地失败: {e}")
# 3. 分析数据
raw_signals = analyzer.analyze(stock_data)
# 盘前可选：根据盘前涨幅单独生成预警信号（示例阈值 +3% / -3%）
premarket_signals = []
if pre_rows:
for r in pre_rows:
ratio = r.get('premarket_change_ratio') or 0.0
sym = r['symbol']
name = r.get('name', '')
price = r.get('premarket_price') or r.get('eastmoney_price')
if ratio >= 0.03:
premarket_signals.append({'symbol': sym, 'name': name, 'price': price, 'type': 'BUY', 'reason': f'盘前涨幅 {ratio:.2%} 预警'})
elif ratio <= -0.03:
premarket_signals.append({'symbol': sym, 'name': name, 'price': price, 'type': 'SELL', 'reason': f'盘前跌幅 {ratio:.2%} 预警'})
if premarket_signals:
print(f"🌙 盘前预警信号 {len(premarket_signals)} 条 (ET {fmt_et_hm()})")
raw_signals.extend(premarket_signals)
signals = cooldown_filter.filter(raw_signals)
# 3.1 写入 signals.csv
try:
if signals:
append_signals(signals, symbol_id_map)
except Exception as e:
print(f"⚠️ 写入信号失败: {e}")
# 4. 执行交易
if signals:
trader.execute_signals(signals)
else:
print("💤 当前无交易信号")
# 等待下一次扫描
# 记录ETL运行
try:
duration = (now_et() - loop_start).total_seconds()
append_etl_run(loop_count, len(stock_data), len(signals), duration, errors=0)
except Exception as e:
print(f"⚠️ 记录ETL统计失败: {e}")
print(f"⏳ 等待 {args.interval} 秒...")
time.sleep(args.interval)
except KeyboardInterrupt:
print("\n🛑 监控已停止")
if __name__ == "__main__":
main()