diff --git a/bot_listener.py b/bot_listener.py
index 2cbf4e3c..bf8eeabf 100644
--- a/bot_listener.py
+++ b/bot_listener.py
@@ -66,7 +66,7 @@ def start_bot():
# 过滤掉已结束的市场（价格接近0或100）和无日期的
active_signals = []
- for s in signals.values():
+ for s in signals:
price = s.get("price", 50)
if 5 <= price <= 95 and s.get("target_date"):
active_signals.append(s)
@@ -370,113 +370,6 @@ def start_bot():
return html_path
- @bot.message_handler(func=lambda m: True)
- def handle_city_query(message):
- """输入城市名直查当日天气市场"""
- import re
- from datetime import datetime
-
- query = message.text.strip()
- if len(query) < 2 or query.startswith("/"):
- return
-
- bot.send_chat_action(message.chat.id, "typing")
-
- try:
- # 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)
- cache_path = "data/all_markets.json"
- if not os.path.exists(cache_path):
- # 扫码还没完成的情形
- 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。")
- return
-
- with open(cache_path, "r", encoding="utf-8") as f:
- cached_data = json.load(f)
-
- pm = PolymarketClient(config["polymarket"])
-
- # 2. 筛选匹配城市及日期的市场
- today_str = datetime.now().strftime("%Y-%m-%d")
- city_markets = []
-
- for m_id, m in cached_data.items():
- title = m.get("event_title", "") + m.get("question", "") + m.get("full_title", "")
- if query.lower() in title.lower():
- # 提取并验证日期
- target_date = m.get("target_date")
- if not target_date:
- date_match = re.search(r'(\d{4}-\d{2}-\d{2})', title)
- target_date = date_match.group(1) if date_match else "Unknown"
-
- if target_date != "Unknown" and target_date < today_str:
- continue
-
- m["target_date"] = target_date
- city_markets.append(m)
-
- if not city_markets:
- if message.chat.type == "private":
- 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）。")
- return
-
- # 获取最早日期
- valid_dates = [m["target_date"] for m in city_markets if m["target_date"] != "Unknown"]
- if not valid_dates:
- bot.reply_to(message, "❌ 该城市目前没有已标明结算日期的活跃市场。")
- return
-
- earliest_date = min(valid_dates)
- target_markets = [m for m in city_markets if m["target_date"] == earliest_date]
-
- # 3. 构建报告
- msg_lines = [
- f"🌡️ {query.upper()} 概率报告 ({earliest_date})\n",
- "隐含概率 (Midpoint) 及买入报价：\n"
- ]
-
- # 批量获取实时价格 (确保报价最新)
- price_reqs = []
- for m in target_markets:
- t_ids = m.get("tokens", [])
- if len(t_ids) >= 1:
- price_reqs.append({"token_id": t_ids[0], "side": "ask"})
- price_reqs.append({"token_id": t_ids[0], "side": "bid"})
-
- price_map = pm.get_multiple_prices(price_reqs)
-
- for m in target_markets:
- tid = m.get("active_token_id") or (m.get("tokens", [])[0] if m.get("tokens") else None)
- if not tid: continue
-
- # 获取中点价 (概率)
- mid = pm.get_midpoint(tid)
- prob = f"{mid*100:.1f}%" if mid is not None else "N/A"
-
- # 获取报价
- buy_yes = price_map.get(f"{tid}:ask")
- bid_yes = price_map.get(f"{tid}:bid")
- buy_no = (1.0 - bid_yes) if bid_yes is not None else None
-
- yes_str = f"{int(buy_yes*100)}¢" if buy_yes else "??¢"
- no_str = f"{int(buy_no*100)}¢" if buy_no else "??¢"
-
- opt = m.get("option") or m.get("question") or ""
- # 简化选项显示
- opt = re.sub(r'.*temperature in.*be ', '', opt, flags=re.I)
-
- msg_lines.append(
- f"🔹 {opt}\n"
- f" └ 隐含概率: {prob}\n"
- f" └ 买入 是:{yes_str} | 买入 否:{no_str}\n"
- )
-
- msg_lines.append(f"\n🔗 在 Polymarket 查看")
- bot.send_message(message.chat.id, "\n".join(msg_lines), parse_mode="HTML", disable_web_page_preview=True)
-
- except Exception as e:
- logger.error(f"城市直查失败: {e}")
- if message.chat.type == "private":
- bot.reply_to(message, "❌ 抱歉，数据处理出现异常。")
@bot.message_handler(commands=["status"])
def get_status(message):
diff --git a/main.py b/main.py
index f2425665..c89ed59e 100644
--- a/main.py
+++ b/main.py
@@ -37,6 +37,8 @@ def main():
# 3. 初始化分析与交易组件
predictor = TemperaturePredictor()
+ risk_manager = RiskManager(config_data.get("config", {}))
+ orderbook_analyzer = OrderbookAnalyzer(config_data.get("config", {}))
decision_engine = DecisionEngine(config_data.get("config", {}))
whale_tracker = WhaleTracker(config_data.get("config", {}), onchain)
paper_trader = PaperTrader()
@@ -130,14 +132,16 @@ def main():
for m in all_weather_markets:
ts = m.get("tokens", [])
if isinstance(ts, str):
- try: ts = json.loads(ts)
- except: ts = []
-
+ try:
+ ts = json.loads(ts)
+ except:
+ ts = []
+
active_tid = m.get("active_token_id")
-
+
# 如果是多选一市场（比如 Dallas 76-77°F）
if len(ts) > 2 and active_tid:
- # 获取该档位的买入价 (Ask)
+ # 获取该档位的买入价 (Ask)
price_requests.append({"token_id": active_tid, "side": "ask"})
# 获取该档位的买入“否”价所需的 Bid 价
price_requests.append({"token_id": active_tid, "side": "bid"})
@@ -164,11 +168,13 @@ def main():
# 注入实时批量价格
ts = m.get("tokens", [])
if isinstance(ts, str):
- try: ts = json.loads(ts)
- except: ts = []
-
+ try:
+ ts = json.loads(ts)
+ except:
+ ts = []
+
active_tid = m.get("active_token_id")
-
+
# 多选一市场逻辑
if len(ts) > 2 and active_tid:
m["buy_yes_live"] = token_price_map.get(f"{active_tid}:ask")
@@ -229,13 +235,21 @@ def main():
if not consensus.get("consensus"):
continue
+ temp_unit = weather_data.get("open-meteo", {}).get(
+ "unit", "celsius"
+ )
+ temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
logger.info(
- f"☁️ {city} 当前气温: {consensus['average_temp']}°C | 监控合约: {len(city_markets)}"
+ f"☁️ {city} 当前气温: {consensus['average_temp']}{temp_symbol} | 监控合约: {len(city_markets)}"
)
# --- 本城市汇总预警缓存 ---
city_alerts = []
city_local_time = None
+ city_total_vol = 0
+ city_pred_high = None
+ city_target_date = None
+ city_strategy_tips = []
# B. 遍历该城市所有合约
for market in city_markets:
@@ -243,6 +257,17 @@ def main():
question = market.get("question", "未知市场")
event_title = market.get("event_title", "")
+ # 累计城市总成交量
+ vol_raw = market.get("volume", 0)
+ if isinstance(vol_raw, str):
+ try:
+ vol_raw = float(
+ vol_raw.replace("$", "").replace(",", "")
+ )
+ except:
+ vol_raw = 0
+ city_total_vol += vol_raw
+
# 识别该合约的目标日期
target_date = weather.extract_date_from_title(
event_title
@@ -261,60 +286,168 @@ def main():
break
# --- 价格获取逻辑 (增强版) ---
- buy_yes_price = market.get("buy_yes_live")
- buy_no_price = market.get("buy_no_live")
- ts = market.get("tokens", [])
- if isinstance(ts, str): ts = json.loads(ts)
+ # 使用 token_price_map 获取实时数据
active_tid = market.get("active_token_id")
+ ts = market.get("tokens", [])
+ if isinstance(ts, str):
+ ts = json.loads(ts)
- # 特殊逻辑：针对多选一型市场（Dallas/Chicago 等温度段）
- if len(ts) > 2 and active_tid:
- # 1. 尝试从批量映射中重新获取该档位的精确买入价(Ask)
- buy_yes_price = polymarket.get_price(active_tid, side="ask") if buy_yes_price is None else buy_yes_price
- # 2. 计算“否”的价格（在多选一里是 1 - 最高买入意愿价Bid）
- bid_price = polymarket.get_price(active_tid, side="bid")
- if bid_price:
- buy_no_price = 1.0 - bid_price
-
- if i < 3: # 仅对前几个档位打印调试
- 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})")
+ buy_yes_price = None
+ buy_no_price = None
+ bid_yes_price = None
- # 通用回退逻辑 (二选一)
- if buy_yes_price is None or buy_no_price is None:
- if ts and len(ts) >= 2:
- prices = polymarket.get_buy_prices(ts[0], ts[1])
- if prices:
- buy_yes_price = prices.get("buy_yes")
- buy_no_price = prices.get("buy_no")
-
- # 最后的回退：使用原始 Gamma 概率 (利用 outcome_index 获取正确的那一个)
- if buy_yes_price is None or buy_no_price is None:
- gamma_prices = market.get("prices", [])
- if isinstance(gamma_prices, str): gamma_prices = json.loads(gamma_prices)
-
- # 尝试获取该档位相对应的概率索引
- idx = market.get("outcome_index", 0)
- prob = float(gamma_prices[idx]) if (gamma_prices and idx < len(gamma_prices)) else 0.5
-
- if buy_yes_price is None: buy_yes_price = prob
- if buy_no_price is None: buy_no_price = 1.0 - prob
+ if len(ts) == 2:
+ # 传统二选一市场 (Yes/No Token 独立)
+ buy_yes_price = token_price_map.get(f"{ts[0]}:ask")
+ buy_no_price = token_price_map.get(f"{ts[1]}:ask")
+ bid_yes_price = token_price_map.get(f"{ts[0]}:bid")
+ elif active_tid:
+ # 多选一市场 (单 Token 对应一个档位)
+ buy_yes_price = token_price_map.get(f"{active_tid}:ask")
+ bid_yes_price = token_price_map.get(f"{active_tid}:bid")
+ if bid_yes_price is not None:
+ buy_no_price = 1.0 - bid_yes_price
+
+ # 兜底概率计算
+ current_prob = (
+ (buy_yes_price + bid_yes_price) / 2
+ if (buy_yes_price and bid_yes_price)
+ else (buy_yes_price or 0.5)
+ )
+ if buy_no_price is None:
+ buy_no_price = 1.0 - current_prob
+
+ # 计算价格趋势
+ prev_data = price_history.get(market_id, {})
+ prev_prob = prev_data.get("price", current_prob)
+ prob_change = (current_prob - prev_prob) * 100
+ trend_str = (
+ f"▲{abs(prob_change):.0f}%"
+ if prob_change > 0.5
+ else (
+ f"▼{abs(prob_change):.0f}%"
+ if prob_change < -0.5
+ else ""
+ )
+ )
+
+ # 更新历史缓存
+ price_history[market_id] = {
+ "price": current_prob,
+ "timestamp": datetime.now().isoformat(),
+ }
+
+ # --- 预警收集 (自动推送逻辑) ---
+ # 触发阈值: 价格处于 85-95 锁死区间，或者概率异动 > 10%
+ is_price_locked = (
+ current_prob >= 0.85 or (1 - current_prob) >= 0.85
+ )
+ is_big_move = abs(prob_change) >= 10
+
+ if is_price_locked or is_big_move:
+ alert_key = f"alert_{market_id}_{int(current_prob * 100)}"
+ if alert_key not in pushed_signals:
+ # 深度分析订单簿
+ ob_data = (
+ polymarket.get_orderbook(active_tid)
+ if active_tid
+ else None
+ )
+ ob_analysis = (
+ orderbook_analyzer.analyze(ob_data)
+ if ob_data
+ else {
+ "tradeable": False,
+ "liquidity": "枯竭",
+ "spread": 0,
+ "mid_price": current_prob,
+ }
+ )
+
+ # 预测偏差分析
+ if ref_temp:
+ city_pred_high = ref_temp # 记录到城市概览
+ temp_match = re.search(
+ r"(\d+)(?:-(\d+))?°[FC]", question
+ )
+ if temp_match:
+ low_b = int(temp_match.group(1))
+ high_b = (
+ int(temp_match.group(2))
+ if temp_match.group(2)
+ else low_b
+ )
+ diff = ref_temp - ((low_b + high_b) / 2)
+ msg += f"\n📐 预测偏差: {diff:+.1f}{temp_symbol} (预测 {ref_temp}{temp_symbol})"
+
+ # 生成策略建议：仅保留模型一致提示
+ if abs(diff) < 2 and current_prob > 0.7:
+ city_strategy_tips.append(
+ f"预测温度{ref_temp}{temp_symbol}落在{question}区间，市场与模型一致"
+ )
+
+ # 模拟下单 - 使用 Ask 价格（实际可成交价格）
+ if buy_yes_price and buy_yes_price > 0.5:
+ trigger_side = "Buy Yes"
+ trigger_price = int(buy_yes_price * 100)
+ else:
+ trigger_side = "Buy No"
+ trigger_price = (
+ int(buy_no_price * 100)
+ if buy_no_price
+ else int((1 - current_prob) * 100)
+ )
+
+ success = paper_trader.open_position(
+ market_id=market_id,
+ city=city,
+ option=question,
+ price=trigger_price,
+ side="YES" if trigger_side == "Buy Yes" else "NO",
+ amount_usd=5.0,
+ target_date=target_date,
+ predicted_temp=ref_temp,
+ )
+
+ city_alerts.append(
+ {
+ "market": target_date or "今日",
+ "msg": msg,
+ "bought": success,
+ "amount": 5.0,
+ "confidence": "动态",
+ }
+ )
+ pushed_signals[alert_key] = time.time()
+ if target_date:
+ city_target_date = target_date
# C. 准备缓存数据
- temp_unit = weather_data.get("open-meteo", {}).get("unit", "celsius")
+ temp_unit = weather_data.get("open-meteo", {}).get(
+ "unit", "celsius"
+ )
temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
- city_local_time = weather_data.get("open-meteo", {}).get("current", {}).get("local_time")
-
+ city_local_time = (
+ weather_data.get("open-meteo", {})
+ .get("current", {})
+ .get("local_time")
+ )
+
current_price = buy_yes_price if buy_yes_price else 0.5
-
+
# 计算价格趋势
prev_data = price_history.get(market_id, {})
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"📊 {question}\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"📊 {question}\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:
diff --git a/src/analysis/orderbook_analyzer.py b/src/analysis/orderbook_analyzer.py
index d0abf2b2..4783d1d5 100644
--- a/src/analysis/orderbook_analyzer.py
+++ b/src/analysis/orderbook_analyzer.py
@@ -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
diff --git a/src/data_collection/polymarket_api.py b/src/data_collection/polymarket_api.py
index 4c53effb..136a2527 100644
--- a/src/data_collection/polymarket_api.py
+++ b/src/data_collection/polymarket_api.py
@@ -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:
diff --git a/src/data_collection/weather_sources.py b/src/data_collection/weather_sources.py
index 748a5ee4..47d01798 100644
--- a/src/data_collection/weather_sources.py
+++ b/src/data_collection/weather_sources.py
@@ -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:
diff --git a/src/models/statistical_model.py b/src/models/statistical_model.py
index 56d325df..df31e956 100644
--- a/src/models/statistical_model.py
+++ b/src/models/statistical_model.py
@@ -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"
}
diff --git a/src/strategy/risk_manager.py b/src/strategy/risk_manager.py
index 29169817..967960e3 100644
--- a/src/strategy/risk_manager.py
+++ b/src/strategy/risk_manager.py
@@ -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": []}
diff --git a/src/utils/notifier.py b/src/utils/notifier.py
index 0765e4d1..5fdc7136 100644
--- a/src/utils/notifier.py
+++ b/src/utils/notifier.py
@@ -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} {a['market']}: {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 = (
+ "💡 策略建议:\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"🔔 城市监控报告 #{self._escape_html(city)}\n\n"
f"📍 城市: {self._escape_html(city)}\n"
f"📊 实时异动:\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)"
diff --git a/test_template.py b/test_template.py
new file mode 100644
index 00000000..186e1caf
--- /dev/null
+++ b/test_template.py
@@ -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": (
+ "🟢 26°F+\n"
+ "执行动作: BUY YES ⬆️\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": (
+ "🔴 18-19°F\n"
+ "执行动作: SELL YES ⬇️\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()