feat: Implement risk management, order book analysis, and a new alerting system with enhanced Polymarket batch price fetching and market data processing.
This commit is contained in:
2569718930@qq.com
+1
-108
@@ -66,7 +66,7 @@ def start_bot():
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# 过滤掉已结束的市场（价格接近0或100）和无日期的
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active_signals = []
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for s in signals.values():
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for s in signals:
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price = s.get("price", 50)
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if 5 <= price <= 95 and s.get("target_date"):
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active_signals.append(s)
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@@ -370,113 +370,6 @@ def start_bot():
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return html_path
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@bot.message_handler(func=lambda m: True)
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def handle_city_query(message):
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"""输入城市名直查当日天气市场"""
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import re
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from datetime import datetime
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query = message.text.strip()
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if len(query) < 2 or query.startswith("/"):
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return
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bot.send_chat_action(message.chat.id, "typing")
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try:
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# 1. 优先从本地全量市场缓存读取 (速度快，不依赖实时全量扫描)
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cache_path = "data/all_markets.json"
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if not os.path.exists(cache_path):
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# 扫码还没完成的情形
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bot.reply_to(message, "⏳ 系统正在进行首次数据同步（约需1分钟），请稍后再试。")
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return
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with open(cache_path, "r", encoding="utf-8") as f:
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cached_data = json.load(f)
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pm = PolymarketClient(config["polymarket"])
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# 2. 筛选匹配城市及日期的市场
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today_str = datetime.now().strftime("%Y-%m-%d")
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city_markets = []
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for m_id, m in cached_data.items():
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title = m.get("event_title", "") + m.get("question", "") + m.get("full_title", "")
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if query.lower() in title.lower():
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# 提取并验证日期
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target_date = m.get("target_date")
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if not target_date:
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date_match = re.search(r'(\d{4}-\d{2}-\d{2})', title)
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target_date = date_match.group(1) if date_match else "Unknown"
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if target_date != "Unknown" and target_date < today_str:
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continue
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m["target_date"] = target_date
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city_markets.append(m)
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if not city_markets:
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if message.chat.type == "private":
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bot.reply_to(message, f"❌ 未找到相关的活跃天气市场。\n提示：请确保输入的是城市常用名（如 Seattle, London）。")
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return
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# 获取最早日期
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valid_dates = [m["target_date"] for m in city_markets if m["target_date"] != "Unknown"]
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if not valid_dates:
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bot.reply_to(message, "❌ 该城市目前没有已标明结算日期的活跃市场。")
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return
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earliest_date = min(valid_dates)
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target_markets = [m for m in city_markets if m["target_date"] == earliest_date]
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# 3. 构建报告
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msg_lines = [
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f"🌡️ <b>{query.upper()} 概率报告 ({earliest_date})</b>\n",
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"隐含概率 (Midpoint) 及买入报价：\n"
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]
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# 批量获取实时价格 (确保报价最新)
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price_reqs = []
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for m in target_markets:
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t_ids = m.get("tokens", [])
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if len(t_ids) >= 1:
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price_reqs.append({"token_id": t_ids[0], "side": "ask"})
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price_reqs.append({"token_id": t_ids[0], "side": "bid"})
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price_map = pm.get_multiple_prices(price_reqs)
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for m in target_markets:
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tid = m.get("active_token_id") or (m.get("tokens", [])[0] if m.get("tokens") else None)
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if not tid: continue
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# 获取中点价 (概率)
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mid = pm.get_midpoint(tid)
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prob = f"{mid*100:.1f}%" if mid is not None else "N/A"
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# 获取报价
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buy_yes = price_map.get(f"{tid}:ask")
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bid_yes = price_map.get(f"{tid}:bid")
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buy_no = (1.0 - bid_yes) if bid_yes is not None else None
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yes_str = f"{int(buy_yes*100)}¢" if buy_yes else "??¢"
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no_str = f"{int(buy_no*100)}¢" if buy_no else "??¢"
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opt = m.get("option") or m.get("question") or ""
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# 简化选项显示
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opt = re.sub(r'.*temperature in.*be ', '', opt, flags=re.I)
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msg_lines.append(
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f"🔹 <b>{opt}</b>\n"
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f" └ 隐含概率: <code>{prob}</code>\n"
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f" └ 买入 是:{yes_str} | 买入 否:{no_str}\n"
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)
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msg_lines.append(f"\n🔗 <a href='https://polymarket.com/event/{target_markets[0]['slug']}'>在 Polymarket 查看</a>")
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bot.send_message(message.chat.id, "\n".join(msg_lines), parse_mode="HTML", disable_web_page_preview=True)
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except Exception as e:
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logger.error(f"城市直查失败: {e}")
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if message.chat.type == "private":
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bot.reply_to(message, "❌ 抱歉，数据处理出现异常。")
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@bot.message_handler(commands=["status"])
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def get_status(message):
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@@ -37,6 +37,8 @@ def main():
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# 3. 初始化分析与交易组件
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predictor = TemperaturePredictor()
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risk_manager = RiskManager(config_data.get("config", {}))
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orderbook_analyzer = OrderbookAnalyzer(config_data.get("config", {}))
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decision_engine = DecisionEngine(config_data.get("config", {}))
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whale_tracker = WhaleTracker(config_data.get("config", {}), onchain)
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paper_trader = PaperTrader()
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@@ -130,14 +132,16 @@ def main():
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for m in all_weather_markets:
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ts = m.get("tokens", [])
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if isinstance(ts, str):
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try: ts = json.loads(ts)
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except: ts = []
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try:
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ts = json.loads(ts)
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except:
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ts = []
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active_tid = m.get("active_token_id")
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# 如果是多选一市场（比如 Dallas 76-77°F）
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if len(ts) > 2 and active_tid:
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# 获取该档位的买入价 (Ask)
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# 获取该档位的买入价 (Ask)
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price_requests.append({"token_id": active_tid, "side": "ask"})
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# 获取该档位的买入“否”价所需的 Bid 价
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price_requests.append({"token_id": active_tid, "side": "bid"})
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@@ -164,11 +168,13 @@ def main():
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# 注入实时批量价格
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ts = m.get("tokens", [])
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if isinstance(ts, str):
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try: ts = json.loads(ts)
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except: ts = []
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try:
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ts = json.loads(ts)
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except:
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ts = []
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active_tid = m.get("active_token_id")
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# 多选一市场逻辑
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if len(ts) > 2 and active_tid:
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m["buy_yes_live"] = token_price_map.get(f"{active_tid}:ask")
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@@ -229,13 +235,21 @@ def main():
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if not consensus.get("consensus"):
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continue
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temp_unit = weather_data.get("open-meteo", {}).get(
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"unit", "celsius"
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)
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temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
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logger.info(
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f"☁️ {city} 当前气温: {consensus['average_temp']}°C | 监控合约: {len(city_markets)}"
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f"☁️ {city} 当前气温: {consensus['average_temp']}{temp_symbol} | 监控合约: {len(city_markets)}"
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)
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# --- 本城市汇总预警缓存 ---
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city_alerts = []
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city_local_time = None
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city_total_vol = 0
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city_pred_high = None
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city_target_date = None
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city_strategy_tips = []
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# B. 遍历该城市所有合约
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for market in city_markets:
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@@ -243,6 +257,17 @@ def main():
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question = market.get("question", "未知市场")
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event_title = market.get("event_title", "")
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# 累计城市总成交量
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vol_raw = market.get("volume", 0)
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if isinstance(vol_raw, str):
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try:
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vol_raw = float(
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vol_raw.replace("$", "").replace(",", "")
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)
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except:
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vol_raw = 0
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city_total_vol += vol_raw
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# 识别该合约的目标日期
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target_date = weather.extract_date_from_title(
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event_title
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@@ -261,60 +286,168 @@ def main():
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break
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# --- 价格获取逻辑 (增强版) ---
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buy_yes_price = market.get("buy_yes_live")
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buy_no_price = market.get("buy_no_live")
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ts = market.get("tokens", [])
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if isinstance(ts, str): ts = json.loads(ts)
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# 使用 token_price_map 获取实时数据
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active_tid = market.get("active_token_id")
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ts = market.get("tokens", [])
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if isinstance(ts, str):
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ts = json.loads(ts)
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# 特殊逻辑：针对多选一型市场（Dallas/Chicago 等温度段）
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if len(ts) > 2 and active_tid:
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# 1. 尝试从批量映射中重新获取该档位的精确买入价(Ask)
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buy_yes_price = polymarket.get_price(active_tid, side="ask") if buy_yes_price is None else buy_yes_price
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# 2. 计算“否”的价格（在多选一里是 1 - 最高买入意愿价Bid）
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bid_price = polymarket.get_price(active_tid, side="bid")
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if bid_price:
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buy_no_price = 1.0 - bid_price
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if i < 3: # 仅对前几个档位打印调试
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logger.debug(f"Categorical价格匹配 [{market.get('city')}-{market.get('question')}]: Yes={buy_yes_price}, No={buy_no_price} (from Bid={bid_price})")
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buy_yes_price = None
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buy_no_price = None
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bid_yes_price = None
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# 通用回退逻辑 (二选一)
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if buy_yes_price is None or buy_no_price is None:
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if ts and len(ts) >= 2:
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prices = polymarket.get_buy_prices(ts[0], ts[1])
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if prices:
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buy_yes_price = prices.get("buy_yes")
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buy_no_price = prices.get("buy_no")
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# 最后的回退：使用原始 Gamma 概率 (利用 outcome_index 获取正确的那一个)
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if buy_yes_price is None or buy_no_price is None:
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gamma_prices = market.get("prices", [])
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if isinstance(gamma_prices, str): gamma_prices = json.loads(gamma_prices)
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# 尝试获取该档位相对应的概率索引
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idx = market.get("outcome_index", 0)
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prob = float(gamma_prices[idx]) if (gamma_prices and idx < len(gamma_prices)) else 0.5
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if buy_yes_price is None: buy_yes_price = prob
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if buy_no_price is None: buy_no_price = 1.0 - prob
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if len(ts) == 2:
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# 传统二选一市场 (Yes/No Token 独立)
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buy_yes_price = token_price_map.get(f"{ts[0]}:ask")
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buy_no_price = token_price_map.get(f"{ts[1]}:ask")
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bid_yes_price = token_price_map.get(f"{ts[0]}:bid")
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elif active_tid:
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# 多选一市场 (单 Token 对应一个档位)
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buy_yes_price = token_price_map.get(f"{active_tid}:ask")
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bid_yes_price = token_price_map.get(f"{active_tid}:bid")
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if bid_yes_price is not None:
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buy_no_price = 1.0 - bid_yes_price
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# 兜底概率计算
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current_prob = (
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(buy_yes_price + bid_yes_price) / 2
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if (buy_yes_price and bid_yes_price)
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else (buy_yes_price or 0.5)
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)
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if buy_no_price is None:
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buy_no_price = 1.0 - current_prob
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# 计算价格趋势
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prev_data = price_history.get(market_id, {})
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prev_prob = prev_data.get("price", current_prob)
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prob_change = (current_prob - prev_prob) * 100
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trend_str = (
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f"▲{abs(prob_change):.0f}%"
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if prob_change > 0.5
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else (
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f"▼{abs(prob_change):.0f}%"
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if prob_change < -0.5
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else ""
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)
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)
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# 更新历史缓存
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price_history[market_id] = {
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"price": current_prob,
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"timestamp": datetime.now().isoformat(),
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}
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# --- 预警收集 (自动推送逻辑) ---
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# 触发阈值: 价格处于 85-95 锁死区间，或者概率异动 > 10%
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is_price_locked = (
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current_prob >= 0.85 or (1 - current_prob) >= 0.85
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)
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is_big_move = abs(prob_change) >= 10
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if is_price_locked or is_big_move:
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alert_key = f"alert_{market_id}_{int(current_prob * 100)}"
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if alert_key not in pushed_signals:
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# 深度分析订单簿
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ob_data = (
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polymarket.get_orderbook(active_tid)
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if active_tid
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else None
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)
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ob_analysis = (
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orderbook_analyzer.analyze(ob_data)
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if ob_data
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else {
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"tradeable": False,
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"liquidity": "枯竭",
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"spread": 0,
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"mid_price": current_prob,
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}
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)
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# 预测偏差分析
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if ref_temp:
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city_pred_high = ref_temp # 记录到城市概览
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temp_match = re.search(
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r"(\d+)(?:-(\d+))?°[FC]", question
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)
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if temp_match:
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low_b = int(temp_match.group(1))
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high_b = (
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int(temp_match.group(2))
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if temp_match.group(2)
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else low_b
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)
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diff = ref_temp - ((low_b + high_b) / 2)
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msg += f"\n📐 预测偏差: {diff:+.1f}{temp_symbol} (预测 {ref_temp}{temp_symbol})"
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# 生成策略建议：仅保留模型一致提示
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if abs(diff) < 2 and current_prob > 0.7:
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city_strategy_tips.append(
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f"预测温度{ref_temp}{temp_symbol}落在{question}区间，市场与模型一致"
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)
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# 模拟下单 - 使用 Ask 价格（实际可成交价格）
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if buy_yes_price and buy_yes_price > 0.5:
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trigger_side = "Buy Yes"
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trigger_price = int(buy_yes_price * 100)
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else:
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trigger_side = "Buy No"
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trigger_price = (
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int(buy_no_price * 100)
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if buy_no_price
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else int((1 - current_prob) * 100)
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)
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success = paper_trader.open_position(
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market_id=market_id,
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city=city,
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option=question,
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price=trigger_price,
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side="YES" if trigger_side == "Buy Yes" else "NO",
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amount_usd=5.0,
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target_date=target_date,
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predicted_temp=ref_temp,
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)
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city_alerts.append(
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{
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"market": target_date or "今日",
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"msg": msg,
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"bought": success,
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"amount": 5.0,
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"confidence": "动态",
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}
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)
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pushed_signals[alert_key] = time.time()
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if target_date:
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city_target_date = target_date
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# C. 准备缓存数据
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temp_unit = weather_data.get("open-meteo", {}).get("unit", "celsius")
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temp_unit = weather_data.get("open-meteo", {}).get(
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"unit", "celsius"
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)
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temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
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city_local_time = weather_data.get("open-meteo", {}).get("current", {}).get("local_time")
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city_local_time = (
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weather_data.get("open-meteo", {})
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.get("current", {})
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.get("local_time")
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)
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current_price = buy_yes_price if buy_yes_price else 0.5
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# 计算价格趋势
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prev_data = price_history.get(market_id, {})
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prev_price = prev_data.get("price", current_price)
|
||||
price_change_pct = ((current_price - prev_price) / prev_price * 100) if prev_price > 0 else 0
|
||||
|
||||
price_change_pct = (
|
||||
((current_price - prev_price) / prev_price * 100)
|
||||
if prev_price > 0
|
||||
else 0
|
||||
)
|
||||
|
||||
# 更新价格历史缓存
|
||||
price_history[market_id] = {
|
||||
"price": current_price,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
cache_entry = {
|
||||
@@ -334,9 +467,11 @@ def main():
|
||||
}
|
||||
|
||||
# --- 最终过滤器 (拦截垃圾信号) ---
|
||||
|
||||
|
||||
# 1. 过滤已锁定价格 (>= 98.5c)
|
||||
if (buy_yes_price and buy_yes_price >= 0.985) or (buy_no_price and buy_no_price >= 0.985):
|
||||
if (buy_yes_price and buy_yes_price >= 0.985) or (
|
||||
buy_no_price and buy_no_price >= 0.985
|
||||
):
|
||||
cache_entry["rationale"] = "ENDED"
|
||||
all_markets_cache[market_id] = cache_entry
|
||||
continue
|
||||
@@ -361,7 +496,9 @@ def main():
|
||||
whale_activity=None,
|
||||
)
|
||||
cache_entry["score"] = signal.get("final_score", 0)
|
||||
cache_entry["rationale"] = signal.get("recommendation", "ACTIVE")
|
||||
cache_entry["rationale"] = signal.get(
|
||||
"recommendation", "ACTIVE"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"计算信号失败 [{market_id}]: {e}")
|
||||
cache_entry["score"] = 0
|
||||
@@ -370,102 +507,293 @@ def main():
|
||||
all_markets_cache[market_id] = cache_entry
|
||||
|
||||
# --- 预警收集 (自动推送逻辑) ---
|
||||
if (buy_yes_price and 0.85 <= buy_yes_price <= 0.95) or (buy_no_price and 0.85 <= buy_no_price <= 0.95):
|
||||
if (buy_yes_price and 0.85 <= buy_yes_price <= 0.95) or (
|
||||
buy_no_price and 0.85 <= buy_no_price <= 0.95
|
||||
):
|
||||
alert_key = f"alert_{market_id}_range_85_95"
|
||||
if alert_key not in pushed_signals:
|
||||
trigger_side = (
|
||||
"Buy Yes" if buy_yes_price >= 0.85 else "Buy No"
|
||||
# --- 基础参数识别 ---
|
||||
is_categorical = len(ts) > 2 and active_tid
|
||||
if is_categorical:
|
||||
# 语义转换逻辑保持一致
|
||||
if buy_no_price and buy_no_price >= 0.85:
|
||||
trigger_side = "Sell Yes"
|
||||
trigger_price = int(
|
||||
buy_no_price * 100
|
||||
) # 预估价
|
||||
else:
|
||||
trigger_side = "Buy Yes"
|
||||
trigger_price = int(buy_yes_price * 100)
|
||||
else:
|
||||
trigger_side = (
|
||||
"Buy Yes" if buy_yes_price >= 0.85 else "Buy No"
|
||||
)
|
||||
trigger_price = (
|
||||
int(buy_yes_price * 100)
|
||||
if trigger_side == "Buy Yes"
|
||||
else int(buy_no_price * 100)
|
||||
)
|
||||
|
||||
# --- 深度流动性与 Spread 检查 ---
|
||||
target_tid = (
|
||||
active_tid
|
||||
if is_categorical
|
||||
else (ts[0] if trigger_side == "Buy Yes" else ts[1])
|
||||
)
|
||||
trigger_price = (
|
||||
int(buy_yes_price * 100)
|
||||
if trigger_side == "Buy Yes"
|
||||
else int(buy_no_price * 100)
|
||||
ob_data = (
|
||||
polymarket.get_orderbook(target_tid)
|
||||
if target_tid
|
||||
else None
|
||||
)
|
||||
|
||||
ob_analysis = {
|
||||
"tradeable": True,
|
||||
"liquidity": "未知",
|
||||
"spread": 0,
|
||||
"mid_price": trigger_price / 100,
|
||||
}
|
||||
if ob_data:
|
||||
ob_analysis = orderbook_analyzer.analyze(ob_data)
|
||||
|
||||
if not ob_analysis.get("tradeable", True):
|
||||
confidence_tag = (
|
||||
f"🔴不可交易 ({ob_analysis.get('liquidity')})"
|
||||
)
|
||||
if not is_categorical:
|
||||
logger.warning(
|
||||
f"跳过不可交易信号 (Spread {ob_analysis.get('spread')}): {city} {question}"
|
||||
)
|
||||
continue
|
||||
|
||||
# 更新实时数据显示
|
||||
mid_c = round(ob_analysis.get("mid_price", 0) * 100, 1)
|
||||
spr_c = round(ob_analysis.get("spread", 0) * 100, 1)
|
||||
depth = ob_analysis.get(
|
||||
"ask_depth"
|
||||
if trigger_side.startswith("Buy")
|
||||
else "bid_depth",
|
||||
0,
|
||||
)
|
||||
|
||||
# 流动性图标
|
||||
liq_map = {
|
||||
"充裕": "✅ 充裕",
|
||||
"正常": "🟡 正常",
|
||||
"稀薄": "🟠 稀薄",
|
||||
"枯竭": "🔴 枯竭",
|
||||
}
|
||||
liq_status = liq_map.get(
|
||||
ob_analysis.get("liquidity", "未知"), "❓ 未知"
|
||||
)
|
||||
|
||||
if is_categorical:
|
||||
ask_str = (
|
||||
"--"
|
||||
if trigger_side == "Sell Yes"
|
||||
else f"{trigger_price}¢"
|
||||
)
|
||||
bid_str = (
|
||||
f"{trigger_price}¢"
|
||||
if trigger_side == "Sell Yes"
|
||||
else "--"
|
||||
)
|
||||
|
||||
display_side = (
|
||||
f"📊 <b>{question}</b>\n"
|
||||
f"Ask: {ask_str} | Bid: {bid_str} | Mid: {mid_c}¢\n"
|
||||
f"Spread: {spr_c}¢ | 深度: ${depth}\n"
|
||||
f"流动性: {liq_status}"
|
||||
)
|
||||
else:
|
||||
display_side = (
|
||||
f"📊 <b>{question}</b>\n"
|
||||
f"报价: {trigger_side} {trigger_price}¢ | Mid: {mid_c}¢\n"
|
||||
f"Spread: {spr_c}¢ | 深度: ${depth}\n"
|
||||
f"流动性: {liq_status}"
|
||||
)
|
||||
|
||||
# --- 智能动态仓位计算 ---
|
||||
# 1. 获取 Open-Meteo 对目标日期的最高温预测
|
||||
predicted_high = None
|
||||
weather_supports = False
|
||||
daily_data = weather_data.get("open-meteo", {}).get("daily", {})
|
||||
daily_data = weather_data.get("open-meteo", {}).get(
|
||||
"daily", {}
|
||||
)
|
||||
if daily_data and target_date:
|
||||
dates = daily_data.get("time", [])
|
||||
max_temps = daily_data.get("temperature_2m_max", [])
|
||||
for idx, d_str in enumerate(dates):
|
||||
if target_date == d_str and idx < len(max_temps):
|
||||
if target_date == d_str and idx < len(
|
||||
max_temps
|
||||
):
|
||||
predicted_high = max_temps[idx]
|
||||
break
|
||||
|
||||
|
||||
# 2. 判断天气预测是否支持当前方向
|
||||
if predicted_high is not None:
|
||||
# 解析选项的温度范围 (例如 "40-41°F" 或 "32°F or below")
|
||||
temp_match = re.search(r'(\d+)(?:-(\d+))?°[FC]', question)
|
||||
temp_match = re.search(
|
||||
r"(\d+)(?:-(\d+))?°[FC]", question
|
||||
)
|
||||
if temp_match:
|
||||
low_bound = int(temp_match.group(1))
|
||||
high_bound = int(temp_match.group(2)) if temp_match.group(2) else low_bound
|
||||
|
||||
high_bound = (
|
||||
int(temp_match.group(2))
|
||||
if temp_match.group(2)
|
||||
else low_bound
|
||||
)
|
||||
|
||||
# 如果买 NO，天气预测应该在这个区间之外
|
||||
if trigger_side == "Buy No":
|
||||
weather_supports = (predicted_high < low_bound - 2) or (predicted_high > high_bound + 2)
|
||||
weather_supports = (
|
||||
predicted_high < low_bound - 2
|
||||
) or (predicted_high > high_bound + 2)
|
||||
else: # 买 YES
|
||||
weather_supports = (low_bound - 2 <= predicted_high <= high_bound + 2)
|
||||
|
||||
weather_supports = (
|
||||
low_bound - 2
|
||||
<= predicted_high
|
||||
<= high_bound + 2
|
||||
)
|
||||
|
||||
# 3. 获取成交量信息
|
||||
market_volume = market.get("volume", 0)
|
||||
if isinstance(market_volume, str):
|
||||
try:
|
||||
market_volume = float(market_volume.replace("$", "").replace(",", ""))
|
||||
market_volume = float(
|
||||
market_volume.replace("$", "").replace(
|
||||
",", ""
|
||||
)
|
||||
)
|
||||
except:
|
||||
market_volume = 0
|
||||
high_volume = market_volume >= 5000 # $5000+ 算高成交量
|
||||
|
||||
# 4. 动态仓位决策
|
||||
# 条件: 价格锁定程度 + 天气支持 + 成交量
|
||||
if trigger_price >= 90 and weather_supports and high_volume:
|
||||
# 三重确认：重注
|
||||
amount_usd = 10.0
|
||||
confidence_tag = "🔥高置信"
|
||||
|
||||
# --- Pro 级仓位决策系统 ---
|
||||
# 1. 计算离结算剩余小时数 (假设气温市场在目标日期晚上 23:59 结算)
|
||||
hours_to_settle = 24.0
|
||||
if target_date:
|
||||
try:
|
||||
settle_dt = datetime.strptime(
|
||||
f"{target_date} 23:59:59",
|
||||
"%Y-%m-%d %H:%M:%S",
|
||||
)
|
||||
now_utc = datetime.utcnow()
|
||||
diff = settle_dt - now_utc
|
||||
hours_to_settle = diff.total_seconds() / 3600.0
|
||||
except:
|
||||
pass
|
||||
|
||||
# 2. 计算相对成交量比例
|
||||
total_daily_vol = sum(
|
||||
[
|
||||
float(
|
||||
str(m.get("volume", 0))
|
||||
.replace("$", "")
|
||||
.replace(",", "")
|
||||
)
|
||||
for m in city_markets
|
||||
if (
|
||||
weather.extract_date_from_title(
|
||||
m.get("event_title", "")
|
||||
)
|
||||
or weather.extract_date_from_title(
|
||||
m.get("question", "")
|
||||
)
|
||||
)
|
||||
== target_date
|
||||
]
|
||||
)
|
||||
market_vol = float(
|
||||
str(market.get("volume", 0))
|
||||
.replace("$", "")
|
||||
.replace(",", "")
|
||||
)
|
||||
is_rel_high_vol = (
|
||||
(market_vol / total_daily_vol > 0.3)
|
||||
if total_daily_vol > 0
|
||||
else False
|
||||
)
|
||||
|
||||
# 3. 基础意向仓位 (基于置信度)
|
||||
base_pos = 3.0 # 默认探路
|
||||
confidence_tag = "💡试探"
|
||||
if (
|
||||
trigger_price >= 90
|
||||
and weather_supports
|
||||
and high_volume
|
||||
):
|
||||
base_pos, confidence_tag = 10.0, "🔥高置信"
|
||||
elif trigger_price >= 90 and weather_supports:
|
||||
# 双重确认：中等仓位
|
||||
amount_usd = 7.0
|
||||
confidence_tag = "⭐中置信"
|
||||
base_pos, confidence_tag = 7.0, "⭐中置信"
|
||||
elif trigger_price >= 92:
|
||||
# 价格接近锁定，即使其他条件不满足也小额参与
|
||||
amount_usd = 5.0
|
||||
confidence_tag = "📌价格锁定"
|
||||
else:
|
||||
# 普通信号：最小仓位
|
||||
amount_usd = 3.0
|
||||
confidence_tag = "💡试探"
|
||||
|
||||
base_pos, confidence_tag = 5.0, "📌价格锁定"
|
||||
|
||||
# 4. 四层过滤决策
|
||||
amount_usd, risk_reason = (
|
||||
risk_manager.calculate_position_size(
|
||||
base_confidence_usd=base_pos,
|
||||
depth=depth,
|
||||
hours_to_settle=hours_to_settle,
|
||||
is_high_relative_volume=is_rel_high_vol,
|
||||
)
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"【仓位决策】{city} {question} | "
|
||||
f"价格:{trigger_price}¢ | 预测:{predicted_high} | 天气支持:{weather_supports} | "
|
||||
f"高量:{high_volume} | 仓位:${amount_usd} ({confidence_tag})"
|
||||
f"【Pro仓位】{city} {question} | "
|
||||
f"基础:{base_pos}$ -> 最终:{amount_usd}$ | 原因:{risk_reason} | "
|
||||
f"深度:${depth} | 剩:{hours_to_settle:.1f}h"
|
||||
)
|
||||
|
||||
# --- 模拟交易触发逻辑 ---
|
||||
side = "YES" if trigger_side == "Buy Yes" else "NO"
|
||||
success = paper_trader.open_position(
|
||||
market_id=market_id,
|
||||
city=city,
|
||||
option=question,
|
||||
price=trigger_price,
|
||||
side=side,
|
||||
amount_usd=amount_usd,
|
||||
target_date=target_date,
|
||||
predicted_temp=predicted_high,
|
||||
)
|
||||
if amount_usd > 0:
|
||||
side = "YES" if trigger_side == "Buy Yes" else "NO"
|
||||
success = paper_trader.open_position(
|
||||
market_id=market_id,
|
||||
city=city,
|
||||
option=question,
|
||||
price=trigger_price,
|
||||
side=side,
|
||||
amount_usd=amount_usd,
|
||||
target_date=target_date,
|
||||
predicted_temp=predicted_high,
|
||||
)
|
||||
if success:
|
||||
risk_manager.record_trade(amount_usd)
|
||||
else:
|
||||
# 如果被风控拦截（金额为0），则不进行任何推送，避免刷屏
|
||||
success = False
|
||||
logger.info(
|
||||
f"Skipping alert for {question}: {risk_reason}"
|
||||
)
|
||||
continue
|
||||
|
||||
# 构建预测温度显示文本
|
||||
temp_unit = weather_data.get("open-meteo", {}).get("unit", "celsius")
|
||||
temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
|
||||
forecast_text = f"预测:{predicted_high}{temp_symbol}" if predicted_high else "预测:N/A"
|
||||
temp_unit = weather_data.get("open-meteo", {}).get(
|
||||
"unit", "celsius"
|
||||
)
|
||||
temp_symbol = (
|
||||
"°F" if temp_unit == "fahrenheit" else "°C"
|
||||
)
|
||||
forecast_text = (
|
||||
f"{predicted_high}{temp_symbol}"
|
||||
if predicted_high
|
||||
else "N/A"
|
||||
)
|
||||
|
||||
# 构建简约版消息: ⚡ {question} ({date}): {side} {price}¢ | 预测:{forecast} [🛒 ${amount} {tag}]
|
||||
side_display = (
|
||||
"Buy No" if trigger_side == "Buy No" else "Buy Yes"
|
||||
)
|
||||
msg = (
|
||||
f"⚡ {question} ({target_date}): {side_display} {trigger_price}¢ | "
|
||||
f"预测:{forecast_text} [🛒 ${amount_usd} {confidence_tag}]"
|
||||
)
|
||||
|
||||
city_alerts.append(
|
||||
{
|
||||
"type": "price",
|
||||
"market": f"{question} ({target_date or '今日'})",
|
||||
"msg": f"{trigger_side} {trigger_price}¢ | {forecast_text}",
|
||||
"market": f"{target_date or '今日'}",
|
||||
"msg": msg,
|
||||
"bought": success,
|
||||
"amount": amount_usd,
|
||||
"confidence": confidence_tag,
|
||||
@@ -476,15 +804,24 @@ def main():
|
||||
# 3. 信号暂存
|
||||
cached_signals[market_id] = cache_entry
|
||||
|
||||
# --- 循环结束后统一推送本城市汇总 ---
|
||||
notifier.send_combined_alert(
|
||||
city, city_alerts, local_time=city_local_time
|
||||
)
|
||||
# E. 统一发送城市汇总通知 (使用新 Pro 模板)
|
||||
if city_alerts:
|
||||
# 去重策略建议
|
||||
unique_tips = list(dict.fromkeys(city_strategy_tips))
|
||||
notifier.send_combined_alert(
|
||||
city=city,
|
||||
alerts=city_alerts,
|
||||
local_time=city_local_time,
|
||||
forecast_temp=f"{city_pred_high}{temp_symbol}"
|
||||
if city_pred_high
|
||||
else "N/A",
|
||||
total_volume=city_total_vol,
|
||||
brackets_count=len(city_markets),
|
||||
strategy_tips=unique_tips,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析城市 {city} 时出错: {e}")
|
||||
continue
|
||||
|
||||
# --- 每处理完一个城市，立即更新 JSON 文件 ---
|
||||
try:
|
||||
# --- 周期性结算：保存高价值信号 ---
|
||||
@@ -497,15 +834,17 @@ def main():
|
||||
if target_dt and target_dt < "2026-02-06":
|
||||
continue
|
||||
active_signals.append(entry)
|
||||
|
||||
|
||||
# 按分数排序
|
||||
active_signals.sort(key=lambda x: x.get("score", 0), reverse=True)
|
||||
|
||||
|
||||
with open("data/active_signals.json", "w", encoding="utf-8") as f:
|
||||
json.dump(active_signals, f, ensure_ascii=False, indent=4)
|
||||
|
||||
logger.info(f"已更新活跃信号库，包含 {len(active_signals)} 个有效信号。")
|
||||
|
||||
|
||||
logger.info(
|
||||
f"已更新活跃信号库，包含 {len(active_signals)} 个有效信号。"
|
||||
)
|
||||
|
||||
# 2. 更新全量市场缓存
|
||||
try:
|
||||
with open("data/all_markets.json", "r", encoding="utf-8") as f:
|
||||
|
||||
@@ -9,65 +9,83 @@ class OrderbookAnalyzer:
|
||||
self.wall_threshold = self.config.get("wall_threshold", 500) # 单笔订单超过此值为墙
|
||||
logger.info("Initializing Orderbook Analyzer...")
|
||||
|
||||
def assess_liquidity(self, orderbook, side="ask"):
|
||||
"""
|
||||
分析流动性深度 (基于前 3 档)
|
||||
"""
|
||||
orders = orderbook.get('asks' if side == "ask" else 'bids', [])
|
||||
if not orders:
|
||||
return "枯竭", 0
|
||||
|
||||
# 前 3 档总量 (Polymarket 通常返回价格字符串)
|
||||
depth = sum(float(o.get("size", 0)) for o in orders[:3])
|
||||
|
||||
if depth < 50:
|
||||
return "稀薄", depth
|
||||
elif depth < 500:
|
||||
return "正常", depth
|
||||
else:
|
||||
return "充裕", depth
|
||||
|
||||
def analyze(self, orderbook):
|
||||
"""
|
||||
订单簿分析决策
|
||||
|
||||
Args:
|
||||
orderbook: dict 包含 'bids' 和 'asks' 列表
|
||||
增强版订单簿分析：集成深度与 Spread 评估
|
||||
"""
|
||||
bids = orderbook.get('bids', [])
|
||||
asks = orderbook.get('asks', [])
|
||||
|
||||
if not bids or not asks:
|
||||
return {"signal": "NEUTRAL", "confidence": 0.5, "reason": "Empty orderbook"}
|
||||
return {
|
||||
"signal": "NEUTRAL",
|
||||
"confidence": 0.0,
|
||||
"tradeable": False,
|
||||
"reason": "缺乏双边报价",
|
||||
"liquidity": "枯竭",
|
||||
"spread": 1.0
|
||||
}
|
||||
|
||||
# 1. 计算买卖力量对比 (Imbalance)
|
||||
# Polymarket API 返回的通常是 [{"price": "0.90", "size": "100"}, ...]
|
||||
bid_volume = sum([float(b.get('size', 0)) for b in bids])
|
||||
ask_volume = sum([float(a.get('size', 0)) for a in asks])
|
||||
|
||||
imbalance = bid_volume / ask_volume if ask_volume > 0 else 0
|
||||
|
||||
# 2. 识别墙单
|
||||
max_bid = max([float(b.get('size', 0)) for b in bids]) if bids else 0
|
||||
max_ask = max([float(a.get('size', 0)) for a in asks]) if asks else 0
|
||||
|
||||
# 3. 计算价差 (Spread)
|
||||
# 1. 计算核心指标
|
||||
best_bid = float(bids[0].get('price', 0))
|
||||
best_ask = float(asks[0].get('price', 0))
|
||||
spread = (best_ask - best_bid) / best_ask if best_ask > 0 else 0
|
||||
spread = abs(best_ask - best_bid)
|
||||
mid_price = (best_ask + best_bid) / 2
|
||||
|
||||
# 2. 评估流动性
|
||||
ask_liq, ask_depth = self.assess_liquidity(orderbook, "ask")
|
||||
bid_liq, bid_depth = self.assess_liquidity(orderbook, "bid")
|
||||
|
||||
# 3. 交易可行性判定 (Spread <= 10c 且 深度 >= $50)
|
||||
is_tradeable = (spread <= 0.10) and (ask_depth >= 50 or bid_depth >= 50)
|
||||
|
||||
# 4. Imbalance 计算
|
||||
bid_volume = sum([float(b.get('size', 0)) for b in bids])
|
||||
ask_volume = sum([float(a.get('size', 0)) for a in asks])
|
||||
imbalance = bid_volume / ask_volume if ask_volume > 0 else 0
|
||||
|
||||
result = {
|
||||
"best_bid": best_bid,
|
||||
"best_ask": best_ask,
|
||||
"mid_price": mid_price,
|
||||
"spread": round(spread, 4),
|
||||
"ask_depth": round(ask_depth, 2),
|
||||
"bid_depth": round(bid_depth, 2),
|
||||
"liquidity": ask_liq if ask_depth < bid_depth else bid_liq,
|
||||
"tradeable": is_tradeable,
|
||||
"imbalance": imbalance,
|
||||
"bid_volume": bid_volume,
|
||||
"ask_volume": ask_volume,
|
||||
"max_bid_wall": max_bid,
|
||||
"max_ask_wall": max_ask,
|
||||
"spread": spread,
|
||||
"signal": "NEUTRAL",
|
||||
"confidence": 0.5
|
||||
}
|
||||
|
||||
# 4. 决策逻辑
|
||||
if imbalance > 2.0:
|
||||
result["signal"] = "BULLISH"
|
||||
result["confidence"] = min(0.9, 0.5 + (imbalance - 1) / 4)
|
||||
elif imbalance < 0.5:
|
||||
result["signal"] = "BEARISH"
|
||||
result["confidence"] = min(0.9, 0.5 + (1 / imbalance - 1) / 4)
|
||||
|
||||
if max_bid > self.wall_threshold and bid_volume > ask_volume:
|
||||
result["signal"] = "STRONG_BUY"
|
||||
result["confidence"] = 0.85
|
||||
elif max_ask > self.wall_threshold and ask_volume > bid_volume:
|
||||
result["signal"] = "STRONG_SELL"
|
||||
result["confidence"] = 0.85
|
||||
|
||||
# 5. 流动性警告
|
||||
if spread > 0.05: # 价差超过5%
|
||||
result["warning"] = "LOW_LIQUIDITY"
|
||||
result["confidence"] *= 0.8 # 降低置信度
|
||||
# 5. 信号修正
|
||||
if is_tradeable:
|
||||
if imbalance > 2.5:
|
||||
result["signal"] = "BULLISH"
|
||||
result["confidence"] = 0.75
|
||||
elif imbalance < 0.4:
|
||||
result["signal"] = "BEARISH"
|
||||
result["confidence"] = 0.75
|
||||
else:
|
||||
result["confidence"] = 0.1 # 不建议交易
|
||||
|
||||
return result
|
||||
|
||||
|
||||
@@ -5,9 +5,11 @@ import re
|
||||
from typing import Dict, List, Optional
|
||||
from loguru import logger
|
||||
from datetime import datetime
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
from py_clob_client.client import ClobClient
|
||||
from py_clob_client.constants import POLYGON
|
||||
from py_clob_client.clob_types import ApiCreds, BookParams, OpenOrderParams
|
||||
|
||||
|
||||
class PolymarketClient:
|
||||
@@ -19,6 +21,11 @@ class PolymarketClient:
|
||||
self.base_url = config.get("base_url", "https://clob.polymarket.com")
|
||||
self.timeout = config.get("timeout", 20)
|
||||
self.session = requests.Session()
|
||||
|
||||
# 缓存机制
|
||||
self._weather_markets_cache = []
|
||||
self._last_discovery_time = 0
|
||||
self._cache_ttl = 300 # 5 分钟缓存
|
||||
|
||||
# 统一代理设置
|
||||
proxy = os.getenv("HTTPS_PROXY") or os.getenv("HTTP_PROXY")
|
||||
@@ -40,7 +47,6 @@ class PolymarketClient:
|
||||
self.api_passphrase = config.get("api_passphrase")
|
||||
|
||||
try:
|
||||
from py_clob_client.clob_types import ApiCreds
|
||||
|
||||
# 组装凭据对象 (如果提供)
|
||||
creds = None
|
||||
@@ -102,9 +108,9 @@ class PolymarketClient:
|
||||
获取 Token 的实时盘口价格 (纯官方库实现)
|
||||
"""
|
||||
try:
|
||||
# 官方语义：BUY 对应的是我们的买入成本 (Ask)
|
||||
side_val = "BUY" if side == "ask" else "SELL"
|
||||
price_str = self.clob_client.get_price(token_id=token_id, side=side_val)
|
||||
# 用户侧语义：BUY 代表我要买 (Ask)，SELL 代表我要卖 (Bid)
|
||||
sdk_side = "BUY" if side == "ask" else "SELL"
|
||||
price_str = self.clob_client.get_price(token_id=token_id, side=sdk_side)
|
||||
if price_str:
|
||||
return float(price_str)
|
||||
except Exception as e:
|
||||
@@ -165,45 +171,59 @@ class PolymarketClient:
|
||||
|
||||
def get_multiple_prices(self, token_requests: List[Dict]) -> Dict[str, float]:
|
||||
"""
|
||||
批量获取多个 token 的价格 (官方接口实现)
|
||||
批量获取多个 token 的价格 (官方接口 + 线程池并行实现)
|
||||
"""
|
||||
if not token_requests:
|
||||
return {}
|
||||
|
||||
all_prices = {}
|
||||
try:
|
||||
# 准备官方批量请求格式
|
||||
# 我们映射 ask->BUY, bid->SELL
|
||||
batch_req = []
|
||||
for r in token_requests:
|
||||
side_val = "BUY" if r.get("side") == "ask" else "SELL"
|
||||
batch_req.append({"token_id": r["token_id"], "side": side_val})
|
||||
batch_size = 20
|
||||
|
||||
def robust_float(val):
|
||||
if isinstance(val, (int, float)): return float(val)
|
||||
if isinstance(val, str):
|
||||
try: return float(val)
|
||||
except: return 0.0
|
||||
return 0.0
|
||||
|
||||
# 使用官方批量获取接口
|
||||
results = self.clob_client.get_prices(batch_req)
|
||||
|
||||
def robust_float(val):
|
||||
if isinstance(val, (int, float)): return float(val)
|
||||
if isinstance(val, str):
|
||||
try: return float(val)
|
||||
except: return 0.0
|
||||
return 0.0
|
||||
chunks = [token_requests[i : i + batch_size] for i in range(0, len(token_requests), batch_size)]
|
||||
|
||||
def fetch_chunk(chunk):
|
||||
for attempt in range(3):
|
||||
try:
|
||||
batch_req = []
|
||||
for r in chunk:
|
||||
sdk_side = "BUY" if r.get("side") == "ask" else "SELL"
|
||||
batch_req.append(BookParams(token_id=r["token_id"], side=sdk_side))
|
||||
|
||||
results = self.clob_client.get_prices(batch_req)
|
||||
|
||||
chunk_prices = {}
|
||||
if isinstance(results, list):
|
||||
for item in results:
|
||||
tid = item.get("token_id")
|
||||
price_raw = item.get("price")
|
||||
res_side = item.get("side")
|
||||
if tid and price_raw:
|
||||
val = robust_float(price_raw)
|
||||
key_side = "ask" if res_side == "BUY" else "bid"
|
||||
chunk_prices[f"{tid}:{key_side}"] = val
|
||||
return chunk_prices
|
||||
except Exception as e:
|
||||
if attempt < 2:
|
||||
time.sleep(0.5 * (attempt + 1))
|
||||
continue
|
||||
logger.warning(f"Batch fetch failed after 3 attempts: {e}")
|
||||
return {}
|
||||
|
||||
if isinstance(results, list):
|
||||
for item in results:
|
||||
tid = item.get("token_id")
|
||||
price_raw = item.get("price")
|
||||
side = item.get("side")
|
||||
if tid and price_raw:
|
||||
val = robust_float(price_raw)
|
||||
key_side = "ask" if side == "BUY" else "bid"
|
||||
all_prices[f"{tid}:{key_side}"] = val
|
||||
all_prices[tid] = val
|
||||
# 使用更保守的线程池并发抓取
|
||||
with ThreadPoolExecutor(max_workers=3) as executor:
|
||||
future_results = list(executor.map(fetch_chunk, chunks))
|
||||
|
||||
return all_prices
|
||||
except Exception as e:
|
||||
logger.warning(f"官方库批量获取报价失败: {e}")
|
||||
return {}
|
||||
for chunk_result in future_results:
|
||||
all_prices.update(chunk_result)
|
||||
|
||||
return all_prices
|
||||
|
||||
def get_midpoint(self, token_id: str) -> Optional[float]:
|
||||
"""
|
||||
@@ -267,10 +287,28 @@ class PolymarketClient:
|
||||
logger.error(f"取消订单失败: {e}")
|
||||
return None
|
||||
|
||||
def get_orders(self, market_id: str = None) -> Optional[Dict]:
|
||||
"""
|
||||
获取当前活跃挂单
|
||||
"""
|
||||
try:
|
||||
params = OpenOrderParams(market=market_id) if market_id else None
|
||||
return self.clob_client.get_orders(params=params)
|
||||
except Exception as e:
|
||||
logger.error(f"获取挂单失败: {e}")
|
||||
return None
|
||||
|
||||
def discover_weather_markets(self) -> list:
|
||||
"""
|
||||
通过全量扫描活跃事件发现最高温天气市场。
|
||||
通过全量扫描活跃事件发现最高温天气市场 (支持缓存机制)
|
||||
"""
|
||||
# 缓存检查
|
||||
current_time = time.time()
|
||||
if self._weather_markets_cache and (current_time - self._last_discovery_time < self._cache_ttl):
|
||||
logger.debug(f"使用缓存的市场列表 (剩余寿命: {int(self._cache_ttl - (current_time - self._last_discovery_time))}s)")
|
||||
return self._weather_markets_cache
|
||||
|
||||
logger.info("📡 正在全量扫描 Polymarket 发现天气市场...")
|
||||
gamma_url = "https://gamma-api.polymarket.com/events"
|
||||
all_weather_markets = []
|
||||
seen_condition_ids = set()
|
||||
@@ -290,13 +328,23 @@ class PolymarketClient:
|
||||
for m in event.get("markets", []):
|
||||
question = m.get("groupItemTitle") or m.get("question") or ""
|
||||
|
||||
# 关键词匹配
|
||||
if not (
|
||||
is_weather_event
|
||||
or "Highest temperature" in question
|
||||
or "temperature in" in question.lower()
|
||||
):
|
||||
# 强化过滤：必须在标题中包含明确的气温气象词，且排除非气温市场
|
||||
t_lower = title.lower()
|
||||
q_lower = question.lower()
|
||||
|
||||
# 1. 标题必须像个气温市场
|
||||
if not any(k in t_lower for k in ["highest temperature", "high temperature", "will temperature", "daily temperature"]):
|
||||
continue
|
||||
|
||||
# 2. 排除干扰项
|
||||
if "climate" in t_lower or "rain" in t_lower or "snow" in t_lower:
|
||||
continue
|
||||
|
||||
# 3. 确保这个具体的 market (bracket) 是我们想要的
|
||||
if not any(k in q_lower for k in ["temperature", "be", "highest", "range"]):
|
||||
# 补充：如果是多选一市场的子项，question 可能只是一个数字或范围，此时看 title
|
||||
if not any(k in t_lower for k in ["temperature", "highest"]):
|
||||
continue
|
||||
|
||||
c_id = m.get("conditionId")
|
||||
# 识别 outcome_index
|
||||
@@ -429,6 +477,11 @@ class PolymarketClient:
|
||||
logger.info(
|
||||
f"全量发现结束，共获取 {len(all_weather_markets)} 个天气档位合约"
|
||||
)
|
||||
|
||||
# 更新缓存
|
||||
self._weather_markets_cache = all_weather_markets
|
||||
self._last_discovery_time = current_time
|
||||
|
||||
return all_weather_markets
|
||||
|
||||
except Exception as e:
|
||||
|
||||
@@ -270,6 +270,7 @@ class WeatherDataCollector:
|
||||
static_coords = {
|
||||
"london": {"lat": 51.5074, "lon": -0.1278},
|
||||
"new york": {"lat": 40.7128, "lon": -74.0060},
|
||||
"new york's central park": {"lat": 40.7812, "lon": -73.9665},
|
||||
"nyc": {"lat": 40.7128, "lon": -74.0060},
|
||||
"seattle": {"lat": 47.6062, "lon": -122.3321},
|
||||
"chicago": {"lat": 41.8781, "lon": -87.6298},
|
||||
@@ -286,6 +287,12 @@ class WeatherDataCollector:
|
||||
normalized_city = city.lower().strip()
|
||||
if normalized_city in static_coords:
|
||||
return static_coords[normalized_city]
|
||||
|
||||
# 模糊匹配映射 (针对包含城市名的情况)
|
||||
for key in static_coords:
|
||||
if key in normalized_city:
|
||||
logger.debug(f"地理编码命中模糊映射: {city} -> {key}")
|
||||
return static_coords[key]
|
||||
|
||||
try:
|
||||
url = "https://geocoding-api.open-meteo.com/v1/search"
|
||||
@@ -377,7 +384,8 @@ class WeatherDataCollector:
|
||||
"phoenix",
|
||||
"philadelphia",
|
||||
]
|
||||
use_fahrenheit = city.lower() in us_cities
|
||||
city_lower = city.lower()
|
||||
use_fahrenheit = any(uc in city_lower for uc in us_cities)
|
||||
|
||||
# Open-Meteo (Primary Free Source - No Key)
|
||||
if lat and lon:
|
||||
|
||||
@@ -258,10 +258,10 @@ class TemperaturePredictor:
|
||||
weights.append(rf_weight)
|
||||
|
||||
if not predictions:
|
||||
logger.warning("No predictions available")
|
||||
logger.debug("No predictions available (Model not trained)")
|
||||
return {
|
||||
"predicted_temp": None,
|
||||
"confidence": 0.0,
|
||||
"confidence": 0.5,
|
||||
"error": "No models available for prediction"
|
||||
}
|
||||
|
||||
|
||||
+74
-132
@@ -7,149 +7,91 @@ class RiskManager:
|
||||
|
||||
def __init__(self, config=None):
|
||||
self.config = config or {}
|
||||
self.max_single_trade = self.config.get("max_single_trade", 500) # 最大单笔$500
|
||||
self.max_drawdown = self.config.get("max_drawdown", 0.10) # 最大回撤10%
|
||||
self.min_liquidity = self.config.get("min_liquidity", 1000) # 最小流动性$1000
|
||||
self.max_slippage = self.config.get("max_slippage", 0.02) # 最大滑点2%
|
||||
self.min_confidence = self.config.get("min_confidence", 0.65) # 最小置信度65%
|
||||
|
||||
# 基础风控参数
|
||||
self.max_single_trade = self.config.get("max_single_trade", 50.0) # 最大单笔调整为 $50
|
||||
self.max_daily_exposure = 50.0 # 每日最高投入上限
|
||||
self.daily_used_exposure = 0.0
|
||||
self.last_reset_date = ""
|
||||
|
||||
self.min_confidence = 0.5
|
||||
self.peak_capital = 0
|
||||
self.current_drawdown = 0
|
||||
self.is_trading_paused = False
|
||||
|
||||
logger.info("Initializing Risk Manager...")
|
||||
logger.info("Initializing Pro Risk Manager...")
|
||||
|
||||
def check_trade_risk(self,
|
||||
trade_size: float,
|
||||
market_data: dict,
|
||||
model_confidence: float) -> dict:
|
||||
def _reset_daily_exposure(self):
|
||||
"""每日重置额度"""
|
||||
from datetime import datetime
|
||||
today = datetime.now().strftime("%Y-%m-%d")
|
||||
if self.last_reset_date != today:
|
||||
self.daily_used_exposure = 0.0
|
||||
self.last_reset_date = today
|
||||
logger.info(f"Daily exposure reset for {today}")
|
||||
|
||||
def calculate_position_size(self,
|
||||
base_confidence_usd: float,
|
||||
depth: float,
|
||||
hours_to_settle: float,
|
||||
is_high_relative_volume: bool) -> tuple[float, str]:
|
||||
"""
|
||||
检查单笔交易风险
|
||||
|
||||
Args:
|
||||
trade_size: 交易金额
|
||||
market_data: 市场数据 (包含订单簿等)
|
||||
model_confidence: 模型置信度
|
||||
|
||||
Returns:
|
||||
dict: 风险检查结果
|
||||
四层过滤仓位计算方法:
|
||||
仓位 = base_position(置信度)
|
||||
× liquidity_factor(深度/仓位 >= 5x)
|
||||
× time_decay(离结算衰减)
|
||||
× budget_limit
|
||||
"""
|
||||
risks = []
|
||||
passed = True
|
||||
self._reset_daily_exposure()
|
||||
|
||||
# 1. 检查交易金额
|
||||
if trade_size > self.max_single_trade:
|
||||
risks.append({
|
||||
"type": "TRADE_SIZE",
|
||||
"message": f"Trade size ${trade_size:.2f} exceeds max ${self.max_single_trade}"
|
||||
})
|
||||
passed = False
|
||||
|
||||
# 2. 检查置信度
|
||||
if model_confidence < self.min_confidence:
|
||||
risks.append({
|
||||
"type": "LOW_CONFIDENCE",
|
||||
"message": f"Model confidence {model_confidence:.2f} below threshold {self.min_confidence}"
|
||||
})
|
||||
passed = False
|
||||
|
||||
# 3. 检查流动性
|
||||
orderbook = market_data.get("orderbook", {})
|
||||
total_liquidity = self._calculate_liquidity(orderbook)
|
||||
if total_liquidity < self.min_liquidity:
|
||||
risks.append({
|
||||
"type": "LOW_LIQUIDITY",
|
||||
"message": f"Market liquidity ${total_liquidity:.2f} below threshold ${self.min_liquidity}"
|
||||
})
|
||||
passed = False
|
||||
|
||||
# 4. 检查滑点
|
||||
expected_slippage = self._estimate_slippage(trade_size, orderbook)
|
||||
if expected_slippage > self.max_slippage:
|
||||
risks.append({
|
||||
"type": "HIGH_SLIPPAGE",
|
||||
"message": f"Expected slippage {expected_slippage:.2%} exceeds max {self.max_slippage:.2%}"
|
||||
})
|
||||
passed = False
|
||||
|
||||
# 5. 检查是否暂停交易
|
||||
if self.is_trading_paused:
|
||||
risks.append({
|
||||
"type": "TRADING_PAUSED",
|
||||
"message": "Trading is paused due to drawdown limit"
|
||||
})
|
||||
passed = False
|
||||
|
||||
return {
|
||||
"passed": passed,
|
||||
"risks": risks,
|
||||
"liquidity": total_liquidity,
|
||||
"expected_slippage": expected_slippage
|
||||
}
|
||||
final_pos = base_confidence_usd
|
||||
reason = "Normal"
|
||||
|
||||
def _calculate_liquidity(self, orderbook: dict) -> float:
|
||||
"""计算订单簿总流动性"""
|
||||
bids = orderbook.get("bids", [])
|
||||
asks = orderbook.get("asks", [])
|
||||
# 1. 流动性过滤: 深度 < $50 强制跳过; 深度 < 仓位的 5 倍则缩减
|
||||
if depth < 50:
|
||||
return 0.0, "🚫深度不足 (min $50)"
|
||||
|
||||
bid_liquidity = sum(float(b.get("size", 0)) for b in bids)
|
||||
ask_liquidity = sum(float(a.get("size", 0)) for a in asks)
|
||||
|
||||
return bid_liquidity + ask_liquidity
|
||||
if depth < final_pos * 5:
|
||||
# 如果深度不足以承载期望仓位，按比例缩减至深度的 1/5
|
||||
final_pos = depth / 5.0
|
||||
reason = "⚠️深度限流"
|
||||
|
||||
def _estimate_slippage(self, trade_size: float, orderbook: dict) -> float:
|
||||
"""估算滑点"""
|
||||
asks = orderbook.get("asks", [])
|
||||
if not asks:
|
||||
return 0.05 # 无数据时假设5%滑点
|
||||
# 2. 时间衰减因子
|
||||
# 离结算时间越近，预测越准但也存在剧烈博弈风险
|
||||
time_factor = 1.0
|
||||
if hours_to_settle <= 1.0:
|
||||
time_factor = 0.0 # 最后 1 小时停止建仓
|
||||
reason = "🚫临近结算"
|
||||
elif hours_to_settle <= 4.0:
|
||||
time_factor = 0.4 # 1-4小时：缩小 60%
|
||||
reason = "⏱️结算冲刺 (40%)"
|
||||
elif hours_to_settle <= 12.0:
|
||||
time_factor = 0.7 # 4-12小时：缩小 30%
|
||||
reason = "⏳接近结算 (70%)"
|
||||
|
||||
best_ask = float(asks[0].get("price", 0)) if asks else 0
|
||||
if best_ask == 0:
|
||||
return 0.05
|
||||
|
||||
# 简单估算：交易额 / 流动性 * 基础滑点
|
||||
ask_liquidity = sum(float(a.get("size", 0)) for a in asks)
|
||||
if ask_liquidity == 0:
|
||||
return 0.05
|
||||
|
||||
impact_ratio = trade_size / ask_liquidity
|
||||
estimated_slippage = impact_ratio * 0.1 # 假设10%的市场冲击系数
|
||||
|
||||
return min(estimated_slippage, 0.1) # 最大10%
|
||||
final_pos *= time_factor
|
||||
if final_pos <= 0: return 0.0, reason
|
||||
|
||||
def update_drawdown(self, current_capital: float) -> dict:
|
||||
"""
|
||||
更新回撤状态
|
||||
# 3. 预算上限过滤
|
||||
remaining_daily = self.max_daily_exposure - self.daily_used_exposure
|
||||
if remaining_daily <= 0:
|
||||
return 0.0, "🚫今日总额度已满 ($50)"
|
||||
|
||||
Args:
|
||||
current_capital: 当前资金
|
||||
|
||||
Returns:
|
||||
dict: 回撤状态
|
||||
"""
|
||||
# 更新峰值
|
||||
if current_capital > self.peak_capital:
|
||||
self.peak_capital = current_capital
|
||||
|
||||
# 计算回撤
|
||||
if self.peak_capital > 0:
|
||||
self.current_drawdown = (self.peak_capital - current_capital) / self.peak_capital
|
||||
else:
|
||||
self.current_drawdown = 0
|
||||
|
||||
# 检查是否需要暂停交易
|
||||
if self.current_drawdown >= self.max_drawdown:
|
||||
self.is_trading_paused = True
|
||||
logger.warning(f"Trading PAUSED! Drawdown {self.current_drawdown:.2%} exceeds limit {self.max_drawdown:.2%}")
|
||||
|
||||
return {
|
||||
"peak_capital": self.peak_capital,
|
||||
"current_capital": current_capital,
|
||||
"drawdown": self.current_drawdown,
|
||||
"is_paused": self.is_trading_paused
|
||||
}
|
||||
if final_pos > remaining_daily:
|
||||
final_pos = remaining_daily
|
||||
reason = "🛑触及日风控上限"
|
||||
|
||||
def resume_trading(self):
|
||||
"""手动恢复交易"""
|
||||
self.is_trading_paused = False
|
||||
logger.info("Trading resumed manually")
|
||||
# 4. 高相对成交量加权 (如果是高成交量市场，且逻辑支持，可保持原状或微增)
|
||||
# 这里逻辑设定为：如果不是高成交量，再次缩减 20% 防御
|
||||
if not is_high_relative_volume:
|
||||
final_pos *= 0.8
|
||||
if reason == "Normal": reason = "📉低活缩减"
|
||||
|
||||
return round(final_pos, 2), reason
|
||||
|
||||
def record_trade(self, amount: float):
|
||||
"""记录成交额以扣除额度"""
|
||||
self.daily_used_exposure += amount
|
||||
logger.debug(f"Applied exposure: ${amount}. Daily Total: ${self.daily_used_exposure}")
|
||||
|
||||
def check_trade_risk(self, trade_size: float, market_data: dict, model_confidence: float) -> dict:
|
||||
"""保持基础接口兼容"""
|
||||
return {"passed": True, "risks": []}
|
||||
|
||||
+30
-14
@@ -45,7 +45,7 @@ class TelegramNotifier:
|
||||
for cid in chat_ids:
|
||||
if not cid:
|
||||
continue
|
||||
|
||||
|
||||
payload = {
|
||||
"chat_id": cid,
|
||||
"text": text,
|
||||
@@ -125,33 +125,49 @@ class TelegramNotifier:
|
||||
)
|
||||
return self._send_message(text)
|
||||
|
||||
def send_combined_alert(self, city: str, alerts: list, local_time: str = None):
|
||||
"""发送合并后的城市预警"""
|
||||
def send_combined_alert(
|
||||
self,
|
||||
city: str,
|
||||
alerts: list,
|
||||
local_time: str = None,
|
||||
forecast_temp: str = None,
|
||||
total_volume: float = 0,
|
||||
brackets_count: int = 0,
|
||||
strategy_tips: list = None,
|
||||
):
|
||||
"""发送简约版合并预警"""
|
||||
if not alerts:
|
||||
return
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# UTC+8 北京时间
|
||||
timestamp_bj = (datetime.utcnow() + timedelta(hours=8)).strftime("%H:%M")
|
||||
now_bj = datetime.utcnow() + timedelta(hours=8)
|
||||
timestamp_bj = now_bj.strftime(
|
||||
"%H:%M"
|
||||
) # 简化为仅显示时间，日期通常与当地一致或不重要
|
||||
|
||||
# 1. 信号详情构建
|
||||
items_text = ""
|
||||
for a in alerts:
|
||||
type_icon = "⚡" if a["type"] == "price" else "🐋"
|
||||
# 买入标签：显示金额
|
||||
if a.get("bought"):
|
||||
amount = a.get("amount", 5.0)
|
||||
confidence = a.get("confidence", "")
|
||||
buy_tag = f" [🛒 ${amount} {confidence}]"
|
||||
else:
|
||||
buy_tag = ""
|
||||
items_text += f"{type_icon} <b>{a['market']}</b>: {a['msg']}{buy_tag}\n"
|
||||
items_text += f"{a['msg']}\n\n"
|
||||
|
||||
# 2. 策略建议（如果有）
|
||||
tips_text = ""
|
||||
if strategy_tips:
|
||||
tips_text = (
|
||||
"💡 <b>策略建议:</b>\n"
|
||||
+ "\n".join([f"• {self._escape_html(tip)}" for tip in strategy_tips])
|
||||
+ "\n\n"
|
||||
)
|
||||
|
||||
# 3. 总体布局 (回归清爽风格)
|
||||
text = (
|
||||
f"🔔 <b>城市监控报告 #{self._escape_html(city)}</b>\n\n"
|
||||
f"📍 城市: {self._escape_html(city)}\n"
|
||||
f"📊 <b>实时异动:</b>\n"
|
||||
f"{items_text}\n"
|
||||
f"{items_text}"
|
||||
f"{tips_text}"
|
||||
f"═══════════════════\n"
|
||||
f"🕒 当地时间: {self._escape_html(local_time or 'N/A')}\n"
|
||||
f"⏰ 预警时间: {timestamp_bj} (北京时间)"
|
||||
|
||||
@@ -0,0 +1,65 @@
|
||||
|
||||
import os
|
||||
import json
|
||||
from src.utils.config_loader import load_config
|
||||
from src.utils.notifier import TelegramNotifier
|
||||
|
||||
def send_test_template():
|
||||
config_data = load_config()
|
||||
notifier = TelegramNotifier(config_data["telegram"])
|
||||
|
||||
city = "Nyc"
|
||||
target_date = "2026-02-07"
|
||||
|
||||
# 模拟异动信号数据
|
||||
alerts = [
|
||||
{
|
||||
"market": target_date,
|
||||
"msg": (
|
||||
"🟢 <b>26°F+</b>\n"
|
||||
"执行动作: <b>BUY YES</b> ⬆️\n"
|
||||
"Ask: 80¢ | Bid: -- | Mid: 79.5¢\n"
|
||||
"Spread: 1.2¢ | 深度: $1,847\n"
|
||||
"流动性: ✅ 充裕 | 可交易: ✅\n"
|
||||
"📐 预测偏差: -3.0°F (预测 23.0°F)"
|
||||
),
|
||||
"bought": True,
|
||||
"amount": 7.0,
|
||||
"confidence": "⭐中置信"
|
||||
},
|
||||
{
|
||||
"market": target_date,
|
||||
"msg": (
|
||||
"🔴 <b>18-19°F</b>\n"
|
||||
"执行动作: <b>SELL YES</b> ⬇️\n"
|
||||
"Ask: -- | Bid: 5.0¢ | Mid: 4.5¢\n"
|
||||
"Spread: 0.5¢ | 深度: $312\n"
|
||||
"流动性: ✅ 正常 | 可交易: ✅\n"
|
||||
"📐 预测偏差: -4.0°F (预测 23.0°F)"
|
||||
),
|
||||
"bought": False,
|
||||
"amount": 0.0,
|
||||
"confidence": ""
|
||||
}
|
||||
]
|
||||
|
||||
strategy_tips = [
|
||||
"预测温度 23.0°F 落在 22-23°F 区间，市场与模型一致",
|
||||
"26°F+ 区间出现主力大额买入，建议跟随",
|
||||
"18-19°F 流动性正常但偏差过大，已执行调仓"
|
||||
]
|
||||
|
||||
print("🚀 正在发送测试模板到 Telegram...")
|
||||
notifier.send_combined_alert(
|
||||
city=city,
|
||||
alerts=alerts,
|
||||
local_time="10:56 EST",
|
||||
forecast_temp="23.0°F",
|
||||
total_volume=40113,
|
||||
brackets_count=7,
|
||||
strategy_tips=strategy_tips
|
||||
)
|
||||
print("✅ 发送成功！请检查手机。")
|
||||
|
||||
if __name__ == "__main__":
|
||||
send_test_template()
|
||||
Reference in New Issue
Block a user