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Refactor market analysis and price fetching logic, remove orderbook analysis from the main loop, add new data collection and strategy modules, and update documentation.

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2569718930@qq.com
2026-02-07 22:30:19 +08:00
parent 3deca01952
commit 1ec0d6eca8
15 changed files with 1043 additions and 1009 deletions
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main.py
+81 -161
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@@ -12,9 +12,6 @@ from src.data_collection.polymarket_api import PolymarketClient
from src.data_collection.weather_sources import WeatherDataCollector
from src.data_collection.onchain_tracker import OnchainTracker
from src.models.statistical_model import TemperaturePredictor
from src.analysis.volume_analyzer import VolumeAnalyzer
from src.analysis.orderbook_analyzer import OrderbookAnalyzer
from src.analysis.technical_indicators import TechnicalIndicators
from src.analysis.whale_tracker import WhaleTracker
from src.strategy.decision_engine import DecisionEngine
from src.strategy.risk_manager import RiskManager
@@ -38,7 +35,6 @@ 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()
@@ -139,16 +135,18 @@ def main():
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:
# 智能识别买入/买否 Token
if active_tid and isinstance(ts, list):
# 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"})
# 如果是传统的 Yes/No 二选一市场
elif len(ts) == 2:
price_requests.append({"token_id": ts[0], "side": "ask"}) # Buy Yes
price_requests.append({"token_id": ts[1], "side": "ask"}) # Buy No
if len(ts) == 2:
# 传统的二选一，直接获取 No Token 的 Ask
no_tid = ts[1] if ts[0] == active_tid else ts[0]
price_requests.append({"token_id": no_tid, "side": "ask"})
else:
# 多选一，需要用 1 - Bid(Yes) 来模拟 Buy No
price_requests.append({"token_id": active_tid, "side": "bid"})
if price_requests:
logger.info(f"正在同步 {len(price_requests)} 个档位的真实盘口价格...")
@@ -157,13 +155,13 @@ def main():
# 3. 按城市分组（按condition_id去重）
markets_by_city = {}
seen_condition_ids = set()
seen_condition_ids = set() # Initialize seen_condition_ids here
for i, m in enumerate(all_weather_markets):
c_id = m.get("condition_id")
if c_id in seen_condition_ids:
continue # 跳过重复
seen_condition_ids.add(c_id)
# Use condition_id + active_token_id as unique key to support multi-bracket markets
unique_market_key = f"{m.get('condition_id')}_{m.get('active_token_id')}"
if unique_market_key in seen_condition_ids:
continue
seen_condition_ids.add(unique_market_key)
# 注入实时批量价格
ts = m.get("tokens", [])
@@ -175,24 +173,24 @@ def main():
active_tid = m.get("active_token_id")
# 多选一市场逻辑
if len(ts) > 2 and active_tid:
if active_tid and isinstance(ts, list):
m["buy_yes_live"] = token_price_map.get(f"{active_tid}:ask")
# 买入“否”的价格 = 1 - 该档位的 Bid
bid_val = token_price_map.get(f"{active_tid}:bid")
if bid_val:
m["buy_no_live"] = 1.0 - bid_val
# 二选一市场逻辑
elif len(ts) == 2:
m["buy_yes_live"] = token_price_map.get(f"{ts[0]}:ask")
m["buy_no_live"] = token_price_map.get(f"{ts[1]}:ask")
if len(ts) == 2:
no_tid = ts[1] if ts[0] == active_tid else ts[0]
m["buy_no_live"] = token_price_map.get(f"{no_tid}:ask")
else:
# 1 - Bid(Yes) = Ask(No)
bid_val = token_price_map.get(f"{active_tid}:bid")
if bid_val:
m["buy_no_live"] = 1.0 - bid_val
# 优先使用发现阶段已经识别出的城市名
city = m.get("city")
# 如果发现阶段没识别出，再尝试从问题文本提取
# 如果发现阶段没识别出，再尝试从问题文本或 Slug 提取
if not city or city == "Unknown":
full_context = f"{m.get('event_title', '')} {m.get('question', '')}"
full_context = f"{m.get('event_title', '')} {m.get('question', '')} {m.get('slug', '')}"
city = weather.extract_city_from_question(full_context)
if i < 5:
@@ -341,34 +339,21 @@ def main():
# 严格触发条件: 价格必须处于 85-95¢ 区间 (真正的高概率信号)
yes_in_range = buy_yes_price and 0.85 <= buy_yes_price <= 0.95
no_in_range = buy_no_price and 0.85 <= buy_no_price <= 0.95
# 50¢ 保护：价格接近 50% 说明市场无明确方向，跳过
is_undecided = 0.45 <= current_prob <= 0.55
if (yes_in_range or no_in_range) and not is_undecided:
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
# 获取温度符号（在此处定义以便后续使用）
temp_unit = weather_data.get("open-meteo", {}).get(
"unit", "celsius"
)
ob_analysis = (
orderbook_analyzer.analyze(ob_data)
if ob_data
else {
"tradeable": False,
"liquidity": "枯竭",
"spread": 0,
"mid_price": current_prob,
}
temp_symbol = (
"°F" if temp_unit == "fahrenheit" else "°C"
)
# 获取温度符号（在此处定义以便后续使用）
temp_unit = weather_data.get("open-meteo", {}).get("unit", "celsius")
temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
# 预测偏差分析
if ref_temp:
city_pred_high = ref_temp # 记录到城市概览
@@ -391,7 +376,6 @@ def main():
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"
@@ -405,10 +389,14 @@ def main():
)
# 构建预测文本
forecast_text = f"{ref_temp}{temp_symbol}" if ref_temp else "N/A"
forecast_text = (
f"{ref_temp}{temp_symbol}" if ref_temp else "N/A"
)
# 构建简约版消息
side_display = "Buy No" if trigger_side == "Buy No" else "Buy Yes"
side_display = (
"Buy No" if trigger_side == "Buy No" else "Buy Yes"
)
msg = f"⚡ {question} ({target_date}): {side_display} {trigger_price}¢ | 预测:{forecast_text}"
success = paper_trader.open_position(
@@ -421,7 +409,7 @@ def main():
target_date=target_date,
predicted_temp=ref_temp,
)
# 添加模拟交易标签
if success:
msg += " [🛒 $5.0 💡试探]"
@@ -534,10 +522,8 @@ def main():
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
trigger_side = "Buy No" # 直接统一为 Buy No
trigger_price = int(buy_no_price * 100)
else:
trigger_side = "Buy Yes"
trigger_price = int(buy_yes_price * 100)
@@ -551,84 +537,6 @@ def main():
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])
)
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: ${depth}\n"
f"流动性: {liq_status}"
)
# --- 智能动态仓位计算 ---
# 1. 获取 Open-Meteo 对目标日期的最高温预测
predicted_high = None
@@ -745,11 +653,10 @@ def main():
elif trigger_price >= 92:
base_pos, confidence_tag = 5.0, "📌价格锁定"
# 4. 四层过滤决策
# 4. 仓位决策
amount_usd, risk_reason = (
risk_manager.calculate_position_size(
base_confidence_usd=base_pos,
depth=depth,
hours_to_settle=hours_to_settle,
is_high_relative_volume=is_rel_high_vol,
)
@@ -758,7 +665,7 @@ def main():
logger.info(
f"【Pro仓位】{city} {question} | "
f"基础:{base_pos}$ -> 最终:{amount_usd}$ | 原因:{risk_reason} | "
f"深度:${depth} | 剩:{hours_to_settle:.1f}h"
f"剩:{hours_to_settle:.1f}h"
)
# --- 模拟交易触发逻辑 ---
@@ -798,9 +705,7 @@ def main():
)
# 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"
)
side_display = trigger_side
msg = (
f"⚡ {question} ({target_date}): {side_display} {trigger_price}¢ | "
f"预测:{forecast_text} [🛒 ${amount_usd} {confidence_tag}]"
@@ -825,17 +730,28 @@ def main():
if city_alerts:
# 去重策略建议
unique_tips = list(dict.fromkeys(city_strategy_tips))
notifier.send_combined_alert(
city=city,
alerts=city_alerts,
local_time=city_local_time,
forecast_temp=f"{city_pred_high}{temp_symbol}"
if city_pred_high
else "N/A",
total_volume=city_total_vol,
brackets_count=len(city_markets),
strategy_tips=unique_tips,
# 获取 METAR 数据（仅当天结算的市场才显示）
today_str = datetime.now().strftime("%Y-%m-%d")
# 检查是否有当天结算的市场
has_today_market = any(
a.get("market") == today_str or a.get("market") == "今日"
for a in city_alerts
)
metar_data = (
weather_data.get("metar") if has_today_market else None
)
# notifier.send_combined_alert(
# city=city,
# alerts=city_alerts,
# local_time=city_local_time,
# forecast_temp=f"{city_pred_high}{temp_symbol}"
# if city_pred_high
# else "N/A",
# total_volume=city_total_vol,
# brackets_count=len(city_markets),
# strategy_tips=unique_tips,
# metar_data=metar_data,
# )
except Exception as e:
logger.error(f"分析城市 {city} 时出错: {e}")
@@ -844,13 +760,17 @@ def main():
# --- 周期性结算：保存高价值信号 ---
active_signals = []
for mid, entry in all_markets_cache.items():
# 核心过滤：只有 ACTIVE 且 价格未锁定、日期未过期的才进入 signals 列表
if entry.get("rationale") not in ["ENDED", "EXPIRED", "ERROR"]:
# 再次双重检查日期 (硬核拦截 2026-02-06)
target_dt = entry.get("target_date")
if target_dt and target_dt < "2026-02-06":
continue
active_signals.append(entry)
# Relaxed filtering: Let the bot decide, but mark ENDED
rationale = entry.get("rationale")
if rationale == "ERROR":
continue
target_dt = entry.get("target_date")
# Only filter out truly ancient history
if target_dt and target_dt < "2026-02-01":
continue
active_signals.append(entry)
# 按分数排序
active_signals.sort(key=lambda x: x.get("score", 0), reverse=True)
@@ -926,7 +846,7 @@ def main():
report.append(
f"📈 累计浮动盈亏: <b>{total_pnl:+.2f}$</b>"
)
notifier._send_message("\n".join(report))
# notifier._send_message("\n".join(report))
pushed_signals[summary_key] = time.time()
except Exception as e: