refactor: remove legacy trading engine, streamline project to weather-only bot
This commit is contained in:
AmandaloveYang
@@ -1,95 +0,0 @@
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from loguru import logger
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class OrderbookAnalyzer:
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"""
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分析目标: 评估市场供需平衡和流动性
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"""
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def __init__(self, config=None):
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self.config = config or {}
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self.wall_threshold = self.config.get("wall_threshold", 500) # 单笔订单超过此值为墙
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logger.info("Initializing Orderbook Analyzer...")
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def assess_liquidity(self, orderbook, side="ask"):
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"""
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分析流动性深度 (基于前 3 档)
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"""
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orders = orderbook.get('asks' if side == "ask" else 'bids', [])
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if not orders:
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return "枯竭", 0
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# 前 3 档总量 (Polymarket 通常返回价格字符串)
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depth = sum(float(o.get("size", 0)) for o in orders[:3])
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if depth < 50:
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return "稀薄", depth
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elif depth < 500:
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return "正常", depth
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else:
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return "充裕", depth
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def analyze(self, orderbook):
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"""
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增强版订单簿分析：集成深度与 Spread 评估
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"""
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bids = orderbook.get('bids', [])
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asks = orderbook.get('asks', [])
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if not bids or not asks:
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return {
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"signal": "NEUTRAL",
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"confidence": 0.0,
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"tradeable": False,
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"reason": "缺乏双边报价",
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"liquidity": "枯竭",
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"spread": 1.0
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}
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# 1. 计算核心指标
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best_bid = float(bids[0].get('price', 0))
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best_ask = float(asks[0].get('price', 0))
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spread = abs(best_ask - best_bid)
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mid_price = (best_ask + best_bid) / 2
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# 2. 评估流动性
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ask_liq, ask_depth = self.assess_liquidity(orderbook, "ask")
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bid_liq, bid_depth = self.assess_liquidity(orderbook, "bid")
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# 3. 交易可行性判定 (Spread <= 10c 且 深度 >= $50)
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is_tradeable = (spread <= 0.10) and (ask_depth >= 50 or bid_depth >= 50)
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# 4. Imbalance 计算
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bid_volume = sum([float(b.get('size', 0)) for b in bids])
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ask_volume = sum([float(a.get('size', 0)) for a in asks])
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imbalance = bid_volume / ask_volume if ask_volume > 0 else 0
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result = {
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"best_bid": best_bid,
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"best_ask": best_ask,
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"mid_price": mid_price,
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"spread": round(spread, 4),
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"ask_depth": round(ask_depth, 2),
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"bid_depth": round(bid_depth, 2),
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"liquidity": ask_liq if ask_depth < bid_depth else bid_liq,
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"tradeable": is_tradeable,
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"imbalance": imbalance,
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"signal": "NEUTRAL",
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"confidence": 0.5
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}
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# 5. 信号修正
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if is_tradeable:
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if imbalance > 2.5:
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result["signal"] = "BULLISH"
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result["confidence"] = 0.75
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elif imbalance < 0.4:
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result["signal"] = "BEARISH"
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result["confidence"] = 0.75
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else:
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result["confidence"] = 0.1 # 不建议交易
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return result
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def analyze_orderbook(orderbook):
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"""兼容旧接口的便捷函数"""
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analyzer = OrderbookAnalyzer()
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return analyzer.analyze(orderbook)
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@@ -1,146 +0,0 @@
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import numpy as np
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from loguru import logger
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class TechnicalIndicators:
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"""
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技术指标计算 - RSI, 布林带等
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"""
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def __init__(self):
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logger.info("Initializing Technical Indicators...")
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def calculate_rsi(self, prices: list, period: int = 14) -> float:
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"""
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计算相对强弱指标 (RSI)
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Args:
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prices: 价格历史列表
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period: RSI周期，默认14
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Returns:
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float: RSI值 (0-100)
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"""
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if len(prices) < period + 1:
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logger.debug("Insufficient data for RSI calculation")
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return 50.0 # 返回中性值
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prices = np.array(prices)
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deltas = np.diff(prices)
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gains = np.where(deltas > 0, deltas, 0)
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losses = np.where(deltas < 0, -deltas, 0)
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avg_gain = np.mean(gains[-period:])
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avg_loss = np.mean(losses[-period:])
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if avg_loss == 0:
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return 100.0
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rs = avg_gain / avg_loss
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rsi = 100 - (100 / (1 + rs))
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logger.debug(f"RSI({period}): {rsi:.2f}")
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return rsi
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def calculate_bollinger_bands(self, prices: list, period: int = 20, std_dev: float = 2.0) -> dict:
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"""
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计算布林带
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Args:
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prices: 价格历史列表
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period: 移动平均周期
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std_dev: 标准差倍数
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Returns:
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dict: 包含上轨、中轨、下轨
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"""
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if len(prices) < period:
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logger.debug("Insufficient data for Bollinger Bands")
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return {"upper": None, "middle": None, "lower": None}
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prices = np.array(prices[-period:])
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middle = np.mean(prices)
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std = np.std(prices)
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upper = middle + std_dev * std
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lower = middle - std_dev * std
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return {
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"upper": upper,
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"middle": middle,
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"lower": lower,
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"std": std
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}
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def calculate_momentum(self, prices: list, period: int = 10) -> float:
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"""
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计算价格动量
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Args:
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prices: 价格历史
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period: 动量周期
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Returns:
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float: 动量值 (当前价格 / N周期前价格 - 1)
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"""
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if len(prices) < period + 1:
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return 0.0
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current = prices[-1]
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past = prices[-period - 1]
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if past == 0:
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return 0.0
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momentum = (current / past) - 1
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return momentum
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def get_signal(self, prices: list) -> dict:
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"""
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综合技术指标信号
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Returns:
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dict: 包含信号和分数
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"""
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rsi = self.calculate_rsi(prices)
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bb = self.calculate_bollinger_bands(prices)
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momentum = self.calculate_momentum(prices)
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# RSI信号
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if rsi > 70:
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rsi_signal = "OVERBOUGHT"
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rsi_score = 0.3 # 超买，看跌
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elif rsi < 30:
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rsi_signal = "OVERSOLD"
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rsi_score = 0.8 # 超卖，看涨
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else:
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rsi_signal = "NEUTRAL"
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rsi_score = 0.5
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# 布林带信号
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if bb["upper"] and len(prices) > 0:
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current_price = prices[-1]
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if current_price > bb["upper"]:
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bb_signal = "ABOVE_UPPER"
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bb_score = 0.7 # 突破上轨，强势
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elif current_price < bb["lower"]:
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bb_signal = "BELOW_LOWER"
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bb_score = 0.3 # 跌破下轨，弱势
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else:
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bb_signal = "WITHIN_BANDS"
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bb_score = 0.5
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else:
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bb_signal = "NO_DATA"
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bb_score = 0.5
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# 综合分数
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combined_score = (rsi_score * 0.5 + bb_score * 0.3 +
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(0.5 + momentum * 2) * 0.2) # momentum 转换为 0-1
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combined_score = max(0, min(1, combined_score))
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return {
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"rsi": {"value": rsi, "signal": rsi_signal, "score": rsi_score},
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"bollinger": {"bands": bb, "signal": bb_signal, "score": bb_score},
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"momentum": momentum,
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"combined_score": combined_score
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}
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@@ -1,135 +0,0 @@
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import numpy as np
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from loguru import logger
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class VolumeAnalyzer:
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"""
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交易量异常检测 - 识别聪明钱和市场转折点
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"""
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def __init__(self, config=None):
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self.config = config or {}
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self.volume_threshold = self.config.get("volume_threshold", 2.0) # 2倍标准差
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self.large_order_threshold = self.config.get("large_order_threshold", 1000) # $1000
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logger.info("Initializing Volume Analyzer...")
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def detect_volume_spike(self, volume_history: list) -> dict:
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"""
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检测成交量异常放大
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Args:
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volume_history: 历史成交量列表
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Returns:
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dict: 包含信号和置信度
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"""
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if len(volume_history) < 24:
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return {"signal": "INSUFFICIENT_DATA", "score": 0.5}
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recent_volume = np.array(volume_history[-24:]) # 最近24小时
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historical_volume = np.array(volume_history[:-24])
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if len(historical_volume) == 0:
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return {"signal": "INSUFFICIENT_DATA", "score": 0.5}
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avg_volume = np.mean(historical_volume)
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std_volume = np.std(historical_volume)
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recent_avg = np.mean(recent_volume)
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# 计算Z-score
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if std_volume > 0:
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z_score = (recent_avg - avg_volume) / std_volume
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else:
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z_score = 0
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logger.debug(f"Volume Z-score: {z_score:.2f}")
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if z_score > self.volume_threshold:
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return {
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"signal": "VOLUME_SPIKE",
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"score": min(0.9, 0.5 + z_score * 0.1),
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"z_score": z_score,
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"interpretation": "成交量异常放大，可能有新信息进入市场"
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}
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elif z_score < -self.volume_threshold:
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return {
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"signal": "VOLUME_DRY",
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"score": 0.3,
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"z_score": z_score,
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"interpretation": "成交量萎缩，市场观望"
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}
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return {"signal": "NORMAL", "score": 0.5, "z_score": z_score}
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def detect_large_orders(self, transactions: list) -> dict:
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"""
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检测大额订单 (聪明钱信号)
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Args:
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transactions: 交易列表，每个包含 size, side, price
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Returns:
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dict: 大额订单分析结果
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"""
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large_buys = []
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large_sells = []
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for tx in transactions:
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size = tx.get("size", 0)
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side = tx.get("side", "").upper()
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if size >= self.large_order_threshold:
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if side == "BUY":
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large_buys.append(tx)
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elif side == "SELL":
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large_sells.append(tx)
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total_large_buy = sum(t.get("size", 0) for t in large_buys)
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total_large_sell = sum(t.get("size", 0) for t in large_sells)
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logger.debug(f"Large buys: ${total_large_buy:.2f}, Large sells: ${total_large_sell:.2f}")
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if total_large_buy > total_large_sell * 2:
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return {
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"signal": "SMART_MONEY_BUY",
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"score": 0.8,
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"large_buy_volume": total_large_buy,
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"large_sell_volume": total_large_sell,
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"interpretation": "大户在积极买入，跟随机会"
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}
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elif total_large_sell > total_large_buy * 2:
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return {
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"signal": "SMART_MONEY_SELL",
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"score": 0.2,
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"large_buy_volume": total_large_buy,
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"large_sell_volume": total_large_sell,
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"interpretation": "大户在抛售，风险警告"
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}
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return {
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"signal": "NEUTRAL",
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"score": 0.5,
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"large_buy_volume": total_large_buy,
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"large_sell_volume": total_large_sell
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}
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def analyze(self, volume_history: list, transactions: list = None) -> dict:
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"""
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综合分析交易量
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"""
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volume_signal = self.detect_volume_spike(volume_history)
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if transactions:
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order_signal = self.detect_large_orders(transactions)
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else:
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order_signal = {"signal": "NO_DATA", "score": 0.5}
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# 综合评分
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combined_score = (volume_signal.get("score", 0.5) * 0.6 +
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order_signal.get("score", 0.5) * 0.4)
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return {
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"volume_signal": volume_signal,
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"order_signal": order_signal,
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"combined_score": combined_score
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}
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@@ -1,59 +0,0 @@
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from loguru import logger
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from typing import List, Dict
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from src.data_collection.onchain_tracker import OnchainTracker
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class WhaleTracker:
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"""
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大户行为分析模块
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"""
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def __init__(self, config: dict, tracker: OnchainTracker):
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self.config = config
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self.tracker = tracker
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logger.info("Initializing Whale Tracker...")
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def analyze_market_whales(self, market_id: str) -> Dict:
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"""
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分析特定市场的鲸鱼行为
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"""
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large_trades = self.tracker.get_large_transactions(market_id)
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if not large_trades:
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return {"bullish": False, "signal": "NEUTRAL", "reason": "No whale activity detected"}
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buy_value = 0
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sell_value = 0
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for trade in large_trades:
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side = trade.get("side", "").upper()
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value = trade.get("value", 0)
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if side == "BUY":
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buy_value += value
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else:
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sell_value += value
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# 判断情绪
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if buy_value > sell_value * 2:
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return {
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"bullish": True,
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"signal": "STRONG_ACCUMULATION",
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"buy_value": buy_value,
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"sell_value": sell_value,
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"reason": "Whales are heavily buying"
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}
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elif sell_value > buy_value * 2:
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return {
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"bullish": False,
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"signal": "STRONG_DISTRIBUTION",
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"buy_value": buy_value,
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"sell_value": sell_value,
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"reason": "Whales are heavily selling"
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}
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return {
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"bullish": buy_value > sell_value,
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"signal": "MODERATE",
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"buy_value": buy_value,
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"sell_value": sell_value,
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"reason": "Mixed whale activity"
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}
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@@ -1,56 +0,0 @@
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from loguru import logger
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from typing import List, Dict, Optional
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from src.data_collection.polymarket_api import PolymarketClient
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class OnchainTracker:
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"""
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追踪 Polymarket 上的大额交易和钱包动向
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主要通过 Polymarket API 获取交易历史，并模拟链上分析逻辑
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"""
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def __init__(self, config: dict, client: PolymarketClient):
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self.config = config
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self.client = client
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self.whale_threshold = self.config.get("whale_threshold", 5000) # $5000 以上视为鲸鱼
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logger.info(f"Initializing Onchain Tracker (Whale Threshold: ${self.whale_threshold})")
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def get_large_transactions(self, market_id: str, limit: int = 100) -> List[Dict]:
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"""
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获取特定市场的历史大额交易
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"""
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trades = self.client.get_trades(market_id=market_id, limit=limit)
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if not trades:
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return []
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# 过滤大额交易 (Polymarket API 返回的格式可能需要根据实际调整)
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# 假设格式: [{"price": 0.9, "size": 10000, "side": "BUY", "maker": "0x...", "taker": "0x..."}]
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large_trades = []
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for trade in trades:
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size = float(trade.get("size", 0))
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price = float(trade.get("price", 0))
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value = size * price
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if value >= self.whale_threshold:
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||||
trade["value"] = value
|
||||
large_trades.append(trade)
|
||||
|
||||
return large_trades
|
||||
|
||||
def get_whale_positions(self, market_id: str) -> Dict[str, float]:
|
||||
"""
|
||||
估算大户在某个市场的持仓情况
|
||||
注意：这只是基于最近交易的估算，真实持仓需要查询链上合约
|
||||
"""
|
||||
trades = self.get_large_transactions(market_id, limit=500)
|
||||
whale_holdings = {}
|
||||
|
||||
for trade in trades:
|
||||
wallet = trade.get("proxyWallet") or trade.get("maker") or "unknown"
|
||||
side = trade.get("side", "").upper()
|
||||
size = float(trade.get("size", 0))
|
||||
|
||||
if side == "BUY":
|
||||
whale_holdings[wallet] = whale_holdings.get(wallet, 0) + size
|
||||
else:
|
||||
whale_holdings[wallet] = whale_holdings.get(wallet, 0) - size
|
||||
|
||||
return whale_holdings
|
||||
@@ -1,291 +0,0 @@
|
||||
import os
|
||||
import requests
|
||||
import time
|
||||
import re
|
||||
from typing import Dict, List, Optional
|
||||
from loguru import logger
|
||||
from datetime import datetime
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
class PolymarketClient:
|
||||
"""
|
||||
Polymarket API Client (Pure REST API version)
|
||||
Directly uses Gamma API and CLOB REST API without py-clob-client dependency.
|
||||
"""
|
||||
|
||||
def __init__(self, config: Dict):
|
||||
self.clob_url = config.get("base_url", "https://clob.polymarket.com")
|
||||
self.gamma_url = "https://gamma-api.polymarket.com"
|
||||
self.timeout = config.get("timeout", 20)
|
||||
self.session = requests.Session()
|
||||
|
||||
# Cache mechanism
|
||||
self._weather_markets_cache = []
|
||||
self._last_discovery_time = 0
|
||||
self._cache_ttl = 300 # 5 minutes cache
|
||||
|
||||
# Proxy settings (automatically read from environment)
|
||||
proxy = os.getenv("HTTPS_PROXY") or os.getenv("HTTP_PROXY")
|
||||
if proxy:
|
||||
self.session.proxies = {"http": proxy, "https": proxy}
|
||||
logger.info(f"Requests session using proxy: {proxy}")
|
||||
|
||||
# Set common User-Agent and headers
|
||||
self.session.headers.update(
|
||||
{
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
|
||||
"Accept": "application/json",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
)
|
||||
|
||||
self.api_key = config.get("api_key")
|
||||
if self.api_key:
|
||||
self.session.headers.update({"POLY_API_KEY": self.api_key})
|
||||
|
||||
logger.info(f"Polymarket REST Client initialized. CLOB: {self.clob_url}, Gamma: {self.gamma_url}")
|
||||
|
||||
def get_markets(self, next_cursor: str = None) -> Optional[Dict]:
|
||||
"""Fetch markets list via CLOB REST API"""
|
||||
try:
|
||||
params = {}
|
||||
if next_cursor:
|
||||
params["next_cursor"] = next_cursor
|
||||
resp = self.session.get(f"{self.clob_url}/markets", params=params, timeout=self.timeout)
|
||||
return resp.json() if resp.status_code == 200 else None
|
||||
except Exception as e:
|
||||
logger.debug(f"get_markets failed: {e}")
|
||||
return None
|
||||
|
||||
def get_market(self, market_id: str) -> Optional[Dict]:
|
||||
"""Fetch market details via CLOB REST API"""
|
||||
try:
|
||||
resp = self.session.get(f"{self.clob_url}/markets/{market_id}", timeout=self.timeout)
|
||||
return resp.json() if resp.status_code == 200 else None
|
||||
except Exception as e:
|
||||
logger.debug(f"get_market failed: {e}")
|
||||
return None
|
||||
|
||||
def get_price(self, token_id: str, side: str = "ask") -> Optional[float]:
|
||||
"""Fetch real-time price for a token via CLOB REST API"""
|
||||
try:
|
||||
# Correct CLOB Mapping:
|
||||
# 'sell' side price is the ASK (price you pay to BUY)
|
||||
# 'buy' side price is the BID (price you get to SELL)
|
||||
clob_side = "sell" if side.lower() in ["ask", "buy"] else "buy"
|
||||
resp = self.session.get(
|
||||
f"{self.clob_url}/price",
|
||||
params={"token_id": token_id, "side": clob_side},
|
||||
timeout=10
|
||||
)
|
||||
data = resp.json()
|
||||
return float(data.get("price", 0)) if resp.status_code == 200 else None
|
||||
except Exception as e:
|
||||
logger.debug(f"get_price failed ({token_id}): {e}")
|
||||
return None
|
||||
|
||||
def get_orderbook(self, token_id: str) -> Optional[Dict]:
|
||||
"""Fetch orderbook for a token via CLOB REST API"""
|
||||
try:
|
||||
resp = self.session.get(
|
||||
f"{self.clob_url}/book", params={"token_id": token_id}, timeout=10
|
||||
)
|
||||
return resp.json() if resp.status_code == 200 else None
|
||||
except Exception as e:
|
||||
logger.debug(f"get_orderbook failed ({token_id}): {e}")
|
||||
return None
|
||||
|
||||
def get_buy_prices(self, yes_token_id: str, no_token_id: str) -> Optional[Dict]:
|
||||
"""Fetch buy prices for both YES and NO tokens"""
|
||||
try:
|
||||
# Buy Yes = Ask price of YES token
|
||||
buy_yes = self.get_price(yes_token_id, "BUY")
|
||||
# Buy No = Ask price of NO token
|
||||
buy_no = self.get_price(no_token_id, "BUY")
|
||||
|
||||
if buy_yes is not None and buy_no is not None:
|
||||
return {"buy_yes": buy_yes, "buy_no": buy_no}
|
||||
except Exception as e:
|
||||
logger.debug(f"get_buy_prices failed: {e}")
|
||||
return None
|
||||
|
||||
def get_multiple_prices(self, token_requests: List[Dict]) -> Dict[str, float]:
|
||||
"""Batch fetch prices for multiple tokens using ThreadPoolExecutor"""
|
||||
if not token_requests:
|
||||
return {}
|
||||
|
||||
all_prices = {}
|
||||
|
||||
def robust_float(val):
|
||||
try: return float(val)
|
||||
except: return 0.0
|
||||
|
||||
def fetch_single(req):
|
||||
tid = req["token_id"]
|
||||
side = req.get("side", "ask").lower()
|
||||
# To get ASK (price to buy), request 'sell' side
|
||||
# To get BID (price to sell), request 'buy' side
|
||||
api_side = "sell" if side == "ask" else "buy"
|
||||
val = self.get_price(tid, api_side)
|
||||
if val:
|
||||
return f"{tid}:{side.lower()}", val
|
||||
return None
|
||||
|
||||
with ThreadPoolExecutor(max_workers=5) as executor:
|
||||
results = list(executor.map(fetch_single, token_requests))
|
||||
|
||||
for res in results:
|
||||
if res:
|
||||
key, val = res
|
||||
all_prices[key] = val
|
||||
|
||||
return all_prices
|
||||
|
||||
def get_midpoint(self, token_id: str) -> Optional[float]:
|
||||
"""Fetch midpoint price via CLOB REST API"""
|
||||
try:
|
||||
resp = self.session.get(f"{self.clob_url}/midpoint", params={"token_id": token_id}, timeout=10)
|
||||
data = resp.json()
|
||||
return float(data.get("mid", 0)) if resp.status_code == 200 else None
|
||||
except:
|
||||
return None
|
||||
|
||||
def discover_weather_markets(self) -> list:
|
||||
"""Scan Gamma API for all weather-related markets with prioritized search and city targeting"""
|
||||
# Cache check
|
||||
current_time = time.time()
|
||||
if self._weather_markets_cache and (current_time - self._last_discovery_time < self._cache_ttl):
|
||||
logger.debug(f"Using cached market list ({len(self._weather_markets_cache)} items)")
|
||||
return self._weather_markets_cache
|
||||
|
||||
logger.info("📡 Scanning Polymarket via Gamma API for weather markets...")
|
||||
all_weather_markets = []
|
||||
seen_keys = set()
|
||||
|
||||
# 1. Target newest markets by query and ID sorting
|
||||
search_queries = ["highest temperature", "temperature in", "daily weather"]
|
||||
|
||||
try:
|
||||
# Use multiple offsets to find more historical/diverse markets
|
||||
for offset in [0, 500, 1000]:
|
||||
for query in search_queries:
|
||||
logger.debug(f"Searching with query: {query} (offset {offset})")
|
||||
params = {
|
||||
"query": query,
|
||||
"active": "true",
|
||||
"limit": 500,
|
||||
"offset": offset,
|
||||
"order": "id",
|
||||
"ascending": "false"
|
||||
}
|
||||
resp = self.session.get(f"{self.gamma_url}/markets", params=params, timeout=self.timeout)
|
||||
if resp.status_code == 200:
|
||||
markets = resp.json()
|
||||
logger.debug(f"Query '{query}' returned {len(markets)} markets")
|
||||
for m in markets:
|
||||
q = m.get("question", "").lower()
|
||||
slug = m.get("slug", "").lower()
|
||||
|
||||
# Filter for weather markets (Broadened)
|
||||
is_weather = any(k in q or k in slug for k in [
|
||||
"highest temperature", "highest-temperature",
|
||||
"temperature in", "temperature-in",
|
||||
"daily weather", "daily-weather",
|
||||
"weather", "气温", "温度"
|
||||
])
|
||||
if is_weather:
|
||||
c_id = m.get("conditionId")
|
||||
t_ids = m.get("clobTokenIds")
|
||||
active_id = m.get("activeTokenId")
|
||||
|
||||
# Robust JSON parsing for clobTokenIds string
|
||||
if isinstance(t_ids, str) and t_ids.startswith("["):
|
||||
try:
|
||||
import json
|
||||
t_ids = json.loads(t_ids)
|
||||
except:
|
||||
pass
|
||||
|
||||
# For Neg Risk markets, activeTokenId might be missing in list view
|
||||
# If we have clobTokenIds, we can work with it
|
||||
if not t_ids:
|
||||
continue
|
||||
|
||||
if not active_id and isinstance(t_ids, list) and len(t_ids) > 0:
|
||||
active_id = t_ids[0] # Assume first is YES
|
||||
|
||||
if not active_id:
|
||||
continue
|
||||
|
||||
unique_key = f"{c_id}_{active_id}"
|
||||
if unique_key not in seen_keys:
|
||||
logger.debug(f"Found weather segment: {q}")
|
||||
all_weather_markets.append({
|
||||
"condition_id": c_id,
|
||||
"question": m.get("question"),
|
||||
"active_token_id": active_id,
|
||||
"outcome_index": t_ids.index(active_id) if isinstance(t_ids, list) and active_id in t_ids else 0,
|
||||
"tokens": t_ids,
|
||||
"prices": m.get("outcomePrices"),
|
||||
"event_title": m.get("description", "")[:100],
|
||||
"slug": m.get("slug"),
|
||||
"group_id": m.get("negRiskMarketID")
|
||||
})
|
||||
seen_keys.add(unique_key)
|
||||
else:
|
||||
logger.debug(f"Query '{query}' failed with status {resp.status_code}")
|
||||
|
||||
if len(all_weather_markets) > 50:
|
||||
break
|
||||
|
||||
logger.info(f"Discovery complete: Found {len(all_weather_markets)} weather segments.")
|
||||
self._weather_markets_cache = all_weather_markets
|
||||
self._last_discovery_time = current_time
|
||||
return all_weather_markets
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Market discovery failed: {e}")
|
||||
return []
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Market discovery failed: {e}")
|
||||
return []
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Market discovery failed: {e}")
|
||||
return []
|
||||
|
||||
def get_weather_markets(self) -> list:
|
||||
return self.discover_weather_markets()
|
||||
|
||||
def find_weather_market(self, city: str, date_str: str = None) -> Optional[Dict]:
|
||||
markets = self.get_weather_markets()
|
||||
for m in markets:
|
||||
# Match against question, title AND slug
|
||||
content = (str(m.get("question", "")) + str(m.get("event_title", "")) + str(m.get("slug", ""))).lower()
|
||||
if city.lower() in content:
|
||||
if date_str:
|
||||
if date_str.lower() in content: return m
|
||||
else:
|
||||
return m
|
||||
return None
|
||||
|
||||
def get_weather_event_markets(self, city: str) -> list:
|
||||
all_markets = self.get_weather_markets()
|
||||
return [
|
||||
m for m in all_markets
|
||||
if city.lower() in (str(m.get("question", "")) + str(m.get("event_title", "")) + str(m.get("slug", ""))).lower()
|
||||
]
|
||||
|
||||
# --- Trading Stubs (Real trading requires signing, which is disabled in pure REST mode) ---
|
||||
def create_order(self, *args, **kwargs) -> Optional[Dict]:
|
||||
logger.warning("create_order: Real trading is disabled in pure REST mode. Please use paper trading.")
|
||||
return None
|
||||
|
||||
def cancel_order(self, *args, **kwargs) -> Optional[Dict]:
|
||||
logger.warning("cancel_order: Real trading is disabled in pure REST mode.")
|
||||
return None
|
||||
|
||||
def get_orders(self, *args, **kwargs) -> Optional[Dict]:
|
||||
logger.warning("get_orders: Real trading is disabled in pure REST mode.")
|
||||
return None
|
||||
@@ -1,285 +0,0 @@
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
from datetime import datetime
|
||||
from loguru import logger
|
||||
|
||||
try:
|
||||
from statsmodels.tsa.arima.model import ARIMA
|
||||
HAS_STATSMODELS = True
|
||||
except ImportError:
|
||||
HAS_STATSMODELS = False
|
||||
logger.debug("statsmodels not installed, ARIMA model unavailable")
|
||||
|
||||
try:
|
||||
from sklearn.ensemble import RandomForestRegressor
|
||||
from sklearn.model_selection import train_test_split
|
||||
HAS_SKLEARN = True
|
||||
except ImportError:
|
||||
HAS_SKLEARN = False
|
||||
logger.debug("scikit-learn not installed, ML models unavailable")
|
||||
|
||||
|
||||
class TemperaturePredictor:
|
||||
"""
|
||||
Temperature prediction model using statistical and ML methods
|
||||
|
||||
Supports:
|
||||
- ARIMA for time series prediction
|
||||
- Random Forest for feature-based prediction
|
||||
- Ensemble of both methods
|
||||
"""
|
||||
|
||||
def __init__(self, config: dict = None):
|
||||
self.config = config or {}
|
||||
self.arima_order = self.config.get("arima_order", (5, 1, 2))
|
||||
self.rf_estimators = self.config.get("rf_estimators", 100)
|
||||
|
||||
self.arima_model = None
|
||||
self.rf_model = None
|
||||
self.is_trained = False
|
||||
|
||||
logger.info("Temperature Predictor initialized")
|
||||
|
||||
def prepare_features(self, data: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
Prepare features for ML model
|
||||
|
||||
Args:
|
||||
data: DataFrame with temperature history
|
||||
|
||||
Returns:
|
||||
DataFrame: Feature-engineered data
|
||||
"""
|
||||
df = data.copy()
|
||||
|
||||
# Time-based features
|
||||
if 'date' in df.columns:
|
||||
df['date'] = pd.to_datetime(df['date'])
|
||||
df['day_of_year'] = df['date'].dt.dayofyear
|
||||
df['month'] = df['date'].dt.month
|
||||
df['day_of_week'] = df['date'].dt.dayofweek
|
||||
|
||||
# Lag features
|
||||
if 'temp' in df.columns:
|
||||
for lag in [1, 2, 3, 7, 14]:
|
||||
df[f'temp_lag_{lag}'] = df['temp'].shift(lag)
|
||||
|
||||
# Rolling statistics
|
||||
df['temp_rolling_mean_7'] = df['temp'].rolling(window=7).mean()
|
||||
df['temp_rolling_std_7'] = df['temp'].rolling(window=7).std()
|
||||
df['temp_rolling_mean_14'] = df['temp'].rolling(window=14).mean()
|
||||
|
||||
# Drop NaN rows created by lag features
|
||||
df = df.dropna()
|
||||
|
||||
return df
|
||||
|
||||
def train_arima(self, temperature_series: List[float]) -> bool:
|
||||
"""
|
||||
Train ARIMA model on temperature time series
|
||||
|
||||
Args:
|
||||
temperature_series: List of historical temperatures
|
||||
|
||||
Returns:
|
||||
bool: Success status
|
||||
"""
|
||||
if not HAS_STATSMODELS:
|
||||
logger.error("statsmodels required for ARIMA training")
|
||||
return False
|
||||
|
||||
if len(temperature_series) < 30:
|
||||
logger.warning("Insufficient data for ARIMA training (need 30+ points)")
|
||||
return False
|
||||
|
||||
try:
|
||||
series = np.array(temperature_series)
|
||||
model = ARIMA(series, order=self.arima_order)
|
||||
self.arima_model = model.fit()
|
||||
logger.info(f"ARIMA model trained. AIC: {self.arima_model.aic:.2f}")
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"ARIMA training failed: {e}")
|
||||
return False
|
||||
|
||||
def train_random_forest(self,
|
||||
features: pd.DataFrame,
|
||||
target_col: str = 'temp') -> bool:
|
||||
"""
|
||||
Train Random Forest model
|
||||
|
||||
Args:
|
||||
features: Feature DataFrame
|
||||
target_col: Target column name
|
||||
|
||||
Returns:
|
||||
bool: Success status
|
||||
"""
|
||||
if not HAS_SKLEARN:
|
||||
logger.error("scikit-learn required for Random Forest training")
|
||||
return False
|
||||
|
||||
if len(features) < 50:
|
||||
logger.warning("Insufficient data for RF training (need 50+ rows)")
|
||||
return False
|
||||
|
||||
try:
|
||||
# Prepare data
|
||||
feature_cols = [c for c in features.columns
|
||||
if c not in [target_col, 'date', 'datetime']]
|
||||
|
||||
X = features[feature_cols].values
|
||||
y = features[target_col].values
|
||||
|
||||
# Train-test split
|
||||
X_train, X_test, y_train, y_test = train_test_split(
|
||||
X, y, test_size=0.2, random_state=42
|
||||
)
|
||||
|
||||
# Train model
|
||||
self.rf_model = RandomForestRegressor(
|
||||
n_estimators=self.rf_estimators,
|
||||
random_state=42,
|
||||
n_jobs=-1
|
||||
)
|
||||
self.rf_model.fit(X_train, y_train)
|
||||
|
||||
# Evaluate
|
||||
train_score = self.rf_model.score(X_train, y_train)
|
||||
test_score = self.rf_model.score(X_test, y_test)
|
||||
|
||||
logger.info(f"Random Forest trained. Train R²: {train_score:.4f}, Test R²: {test_score:.4f}")
|
||||
|
||||
# Store feature names
|
||||
self.feature_names = feature_cols
|
||||
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"Random Forest training failed: {e}")
|
||||
return False
|
||||
|
||||
def predict_arima(self, steps: int = 1) -> Optional[Dict]:
|
||||
"""
|
||||
Make prediction using ARIMA model
|
||||
|
||||
Args:
|
||||
steps: Number of steps to forecast
|
||||
|
||||
Returns:
|
||||
dict: Prediction with confidence interval
|
||||
"""
|
||||
if self.arima_model is None:
|
||||
logger.warning("ARIMA model not trained")
|
||||
return None
|
||||
|
||||
try:
|
||||
forecast = self.arima_model.forecast(steps=steps)
|
||||
conf_int = self.arima_model.get_forecast(steps=steps).conf_int()
|
||||
|
||||
return {
|
||||
"method": "ARIMA",
|
||||
"predicted_temp": float(forecast[0]) if steps == 1 else [float(f) for f in forecast],
|
||||
"confidence_interval": [float(conf_int.iloc[0, 0]), float(conf_int.iloc[0, 1])] if steps == 1 else conf_int.values.tolist()
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"ARIMA prediction failed: {e}")
|
||||
return None
|
||||
|
||||
def predict_rf(self, features: np.ndarray) -> Optional[Dict]:
|
||||
"""
|
||||
Make prediction using Random Forest model
|
||||
|
||||
Args:
|
||||
features: Feature array for prediction
|
||||
|
||||
Returns:
|
||||
dict: Prediction result
|
||||
"""
|
||||
if self.rf_model is None:
|
||||
logger.warning("Random Forest model not trained")
|
||||
return None
|
||||
|
||||
try:
|
||||
prediction = self.rf_model.predict(features.reshape(1, -1))[0]
|
||||
|
||||
# Estimate confidence using tree variance
|
||||
tree_predictions = [tree.predict(features.reshape(1, -1))[0]
|
||||
for tree in self.rf_model.estimators_]
|
||||
std = np.std(tree_predictions)
|
||||
|
||||
return {
|
||||
"method": "RandomForest",
|
||||
"predicted_temp": float(prediction),
|
||||
"confidence_interval": [float(prediction - 1.96 * std),
|
||||
float(prediction + 1.96 * std)],
|
||||
"std": float(std)
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"Random Forest prediction failed: {e}")
|
||||
return None
|
||||
|
||||
def predict_ensemble(self,
|
||||
temperature_history: List[float],
|
||||
feature_data: pd.DataFrame = None,
|
||||
arima_weight: float = 0.4,
|
||||
rf_weight: float = 0.6) -> Dict:
|
||||
"""
|
||||
Make ensemble prediction combining ARIMA and Random Forest
|
||||
|
||||
Args:
|
||||
temperature_history: Historical temperature series
|
||||
feature_data: Feature data for RF prediction
|
||||
arima_weight: Weight for ARIMA prediction
|
||||
rf_weight: Weight for RF prediction
|
||||
|
||||
Returns:
|
||||
dict: Ensemble prediction
|
||||
"""
|
||||
predictions = []
|
||||
weights = []
|
||||
|
||||
# ARIMA prediction
|
||||
if self.arima_model is not None:
|
||||
arima_pred = self.predict_arima(steps=1)
|
||||
if arima_pred:
|
||||
predictions.append(arima_pred["predicted_temp"])
|
||||
weights.append(arima_weight)
|
||||
|
||||
# Random Forest prediction
|
||||
if self.rf_model is not None and feature_data is not None:
|
||||
# Get latest features
|
||||
prepared = self.prepare_features(feature_data)
|
||||
if len(prepared) > 0 and hasattr(self, 'feature_names'):
|
||||
latest_features = prepared[self.feature_names].iloc[-1].values
|
||||
rf_pred = self.predict_rf(latest_features)
|
||||
if rf_pred:
|
||||
predictions.append(rf_pred["predicted_temp"])
|
||||
weights.append(rf_weight)
|
||||
|
||||
if not predictions:
|
||||
logger.debug("No predictions available (Model not trained)")
|
||||
return {
|
||||
"predicted_temp": None,
|
||||
"confidence": 0.5,
|
||||
"error": "No models available for prediction"
|
||||
}
|
||||
|
||||
# Weighted average
|
||||
weights = np.array(weights) / np.sum(weights) # Normalize weights
|
||||
ensemble_pred = np.average(predictions, weights=weights)
|
||||
|
||||
# Estimate confidence based on model agreement
|
||||
if len(predictions) > 1:
|
||||
spread = abs(predictions[0] - predictions[1])
|
||||
confidence = max(0.5, 1.0 - spread / 5.0) # Lower confidence if predictions differ
|
||||
else:
|
||||
confidence = 0.7
|
||||
|
||||
return {
|
||||
"predicted_temp": float(ensemble_pred),
|
||||
"confidence": confidence,
|
||||
"confidence_interval": [ensemble_pred - 2.0, ensemble_pred + 2.0], # Approximate
|
||||
"individual_predictions": predictions,
|
||||
"weights": weights.tolist()
|
||||
}
|
||||
@@ -1,198 +0,0 @@
|
||||
from loguru import logger
|
||||
from src.analysis.volume_analyzer import VolumeAnalyzer
|
||||
from src.analysis.orderbook_analyzer import analyze_orderbook
|
||||
from src.analysis.technical_indicators import TechnicalIndicators
|
||||
|
||||
class DecisionEngine:
|
||||
"""
|
||||
综合决策引擎 - 多因子加权评分系统
|
||||
"""
|
||||
|
||||
def __init__(self, config: dict = None):
|
||||
self.config = config or {}
|
||||
|
||||
# 因子权重
|
||||
self.weights = self.config.get("weights", {
|
||||
"statistical_prediction": 0.50,
|
||||
"data_source_consensus": 0.15,
|
||||
"market_volume_signal": 0.15,
|
||||
"orderbook_analysis": 0.10,
|
||||
"technical_indicators": 0.05,
|
||||
"onchain_whale_signal": 0.05
|
||||
})
|
||||
|
||||
# 初始化分析器
|
||||
self.volume_analyzer = VolumeAnalyzer(config)
|
||||
self.tech_indicators = TechnicalIndicators()
|
||||
|
||||
logger.info("决策引擎初始化完成。")
|
||||
logger.debug(f"权重配置: {self.weights}")
|
||||
|
||||
def calculate_signal(self,
|
||||
model_prediction: dict,
|
||||
market_data: dict,
|
||||
weather_consensus: dict = None,
|
||||
whale_activity: dict = None) -> dict:
|
||||
"""
|
||||
综合多因子计算交易信号
|
||||
|
||||
Args:
|
||||
model_prediction: 统计模型预测结果
|
||||
market_data: 市场数据 (价格历史、订单簿、交易量等)
|
||||
weather_consensus: 天气数据源一致性检查结果
|
||||
whale_activity: 链上大户活动数据
|
||||
|
||||
Returns:
|
||||
dict: 综合评分和交易建议
|
||||
"""
|
||||
scores = {}
|
||||
details = {}
|
||||
|
||||
# 1. 统计模型预测得分 (权重: 50%)
|
||||
stat_confidence = model_prediction.get("confidence", 0.5)
|
||||
scores["statistical"] = stat_confidence
|
||||
details["statistical"] = {
|
||||
"score": stat_confidence,
|
||||
"prediction": model_prediction.get("predicted_temp"),
|
||||
"confidence_interval": model_prediction.get("confidence_interval")
|
||||
}
|
||||
|
||||
# 2. 多源数据一致性 (权重: 15%)
|
||||
if weather_consensus:
|
||||
is_consensus = weather_consensus.get("consensus", False)
|
||||
consensus_score = 1.0 if is_consensus else 0.3
|
||||
else:
|
||||
consensus_score = 0.5
|
||||
scores["consensus"] = consensus_score
|
||||
details["consensus"] = weather_consensus
|
||||
|
||||
# 3. 交易量信号 (权重: 15%)
|
||||
volume_history = market_data.get("volume_history", [])
|
||||
transactions = market_data.get("transactions", [])
|
||||
volume_analysis = self.volume_analyzer.analyze(volume_history, transactions)
|
||||
scores["volume"] = volume_analysis.get("combined_score", 0.5)
|
||||
details["volume"] = volume_analysis
|
||||
|
||||
# 4. 订单簿分析 (权重: 10%)
|
||||
orderbook = market_data.get("orderbook", {})
|
||||
orderbook_signal = analyze_orderbook(orderbook)
|
||||
scores["orderbook"] = orderbook_signal.get("confidence", 0.5)
|
||||
details["orderbook"] = orderbook_signal
|
||||
|
||||
# 5. 技术指标 (权重: 5%)
|
||||
price_history = market_data.get("price_history", [])
|
||||
if price_history:
|
||||
tech_signal = self.tech_indicators.get_signal(price_history)
|
||||
scores["technical"] = tech_signal.get("combined_score", 0.5)
|
||||
details["technical"] = tech_signal
|
||||
else:
|
||||
scores["technical"] = 0.5
|
||||
details["technical"] = {"message": "No price history available"}
|
||||
|
||||
# 6. 链上鲸鱼信号 (权重: 5%)
|
||||
if whale_activity:
|
||||
is_bullish = whale_activity.get("bullish", False)
|
||||
whale_score = 0.8 if is_bullish else 0.2
|
||||
else:
|
||||
whale_score = 0.5
|
||||
scores["whale"] = whale_score
|
||||
details["whale"] = whale_activity
|
||||
|
||||
# 加权计算最终分数
|
||||
final_score = (
|
||||
scores["statistical"] * self.weights["statistical_prediction"] +
|
||||
scores["consensus"] * self.weights["data_source_consensus"] +
|
||||
scores["volume"] * self.weights["market_volume_signal"] +
|
||||
scores["orderbook"] * self.weights["orderbook_analysis"] +
|
||||
scores["technical"] * self.weights["technical_indicators"] +
|
||||
scores["whale"] * self.weights["onchain_whale_signal"]
|
||||
)
|
||||
|
||||
# 生成建议
|
||||
recommendation = self._get_recommendation(final_score)
|
||||
|
||||
result = {
|
||||
"final_score": round(final_score, 4),
|
||||
"recommendation": recommendation,
|
||||
"factor_scores": scores,
|
||||
"factor_details": details,
|
||||
"weights": self.weights
|
||||
}
|
||||
|
||||
logger.info(f"Decision: {recommendation} (score: {final_score:.4f})")
|
||||
return result
|
||||
|
||||
def _get_recommendation(self, score: float) -> str:
|
||||
"""
|
||||
根据评分生成交易建议
|
||||
|
||||
Args:
|
||||
score: 综合评分 (0-1)
|
||||
|
||||
Returns:
|
||||
str: 交易建议
|
||||
"""
|
||||
if score > 0.80:
|
||||
return "STRONG_BUY"
|
||||
elif score > 0.65:
|
||||
return "BUY"
|
||||
elif score > 0.50:
|
||||
return "WEAK_BUY"
|
||||
elif score > 0.35:
|
||||
return "HOLD"
|
||||
elif score > 0.20:
|
||||
return "WEAK_SELL"
|
||||
else:
|
||||
return "NO_ACTION"
|
||||
|
||||
def should_trade(self,
|
||||
signal: dict,
|
||||
current_price: float,
|
||||
min_confidence: float = 0.65) -> dict:
|
||||
"""
|
||||
判断是否应该执行交易
|
||||
|
||||
Args:
|
||||
signal: calculate_signal返回的信号
|
||||
current_price: 当前市场价格
|
||||
min_confidence: 最低置信度阈值
|
||||
|
||||
Returns:
|
||||
dict: 交易决策
|
||||
"""
|
||||
final_score = signal.get("final_score", 0)
|
||||
recommendation = signal.get("recommendation", "NO_ACTION")
|
||||
|
||||
# 检查是否满足交易条件
|
||||
should_buy = (
|
||||
final_score >= min_confidence and
|
||||
recommendation in ["STRONG_BUY", "BUY"] and
|
||||
current_price >= 0.85 # 价格阈值
|
||||
)
|
||||
|
||||
should_sell = (
|
||||
final_score < 0.35 or
|
||||
recommendation in ["WEAK_SELL", "NO_ACTION"]
|
||||
)
|
||||
|
||||
if should_buy:
|
||||
return {
|
||||
"action": "BUY",
|
||||
"confidence": final_score,
|
||||
"price": current_price,
|
||||
"reason": f"Score {final_score:.2f} >= threshold {min_confidence}"
|
||||
}
|
||||
elif should_sell:
|
||||
return {
|
||||
"action": "SELL",
|
||||
"confidence": final_score,
|
||||
"price": current_price,
|
||||
"reason": f"Score {final_score:.2f} below threshold or bearish signal"
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"action": "HOLD",
|
||||
"confidence": final_score,
|
||||
"price": current_price,
|
||||
"reason": "Conditions not met for trading"
|
||||
}
|
||||
@@ -1,153 +0,0 @@
|
||||
from loguru import logger
|
||||
|
||||
class PositionManager:
|
||||
"""
|
||||
仓位管理 - Kelly公式动态仓位计算
|
||||
"""
|
||||
|
||||
def __init__(self, config=None):
|
||||
self.config = config or {}
|
||||
self.max_position_ratio = self.config.get("max_position_ratio", 0.25) # 最大单笔25%
|
||||
self.max_total_exposure = self.config.get("max_total_exposure", 0.80) # 最大总仓位80%
|
||||
self.min_trade_size = self.config.get("min_trade_size", 10) # 最小交易额$10
|
||||
logger.info("Initializing Position Manager...")
|
||||
|
||||
def kelly_criterion(self, win_prob: float, odds: float) -> float:
|
||||
"""
|
||||
Kelly公式计算最优投资比例
|
||||
|
||||
f = (bp - q) / b
|
||||
f: 应投资的资金比例
|
||||
b: 赔率 (盈利/亏损)
|
||||
p: 胜率
|
||||
q: 败率 (1-p)
|
||||
|
||||
Args:
|
||||
win_prob: 预测胜率 (0-1)
|
||||
odds: 赔率
|
||||
|
||||
Returns:
|
||||
float: 建议投资比例 (0-1)
|
||||
"""
|
||||
if win_prob <= 0 or win_prob >= 1 or odds <= 0:
|
||||
return 0.0
|
||||
|
||||
q = 1 - win_prob
|
||||
f = (win_prob * odds - q) / odds
|
||||
|
||||
# 限制最大仓位
|
||||
f = max(0, min(f, self.max_position_ratio))
|
||||
|
||||
logger.debug(f"Kelly ratio: {f:.4f} (win_prob={win_prob:.2f}, odds={odds:.2f})")
|
||||
return f
|
||||
|
||||
def calculate_position_size(self,
|
||||
total_capital: float,
|
||||
win_prob: float,
|
||||
market_price: float,
|
||||
current_exposure: float = 0) -> dict:
|
||||
"""
|
||||
计算建议仓位大小
|
||||
|
||||
Args:
|
||||
total_capital: 总资金
|
||||
win_prob: 模型预测胜率
|
||||
market_price: 当前市场价格 (0-1)
|
||||
current_exposure: 当前已有仓位占比
|
||||
|
||||
Returns:
|
||||
dict: 包含建议仓位大小和相关信息
|
||||
"""
|
||||
# 计算赔率
|
||||
if market_price <= 0 or market_price >= 1:
|
||||
return {"size": 0, "error": "Invalid market price"}
|
||||
|
||||
odds = (1 - market_price) / market_price
|
||||
|
||||
# Kelly计算
|
||||
kelly_ratio = self.kelly_criterion(win_prob, odds)
|
||||
|
||||
# 检查总仓位限制
|
||||
available_ratio = self.max_total_exposure - current_exposure
|
||||
if available_ratio <= 0:
|
||||
return {
|
||||
"size": 0,
|
||||
"kelly_ratio": kelly_ratio,
|
||||
"reason": "Max exposure reached"
|
||||
}
|
||||
|
||||
# 实际使用比例
|
||||
actual_ratio = min(kelly_ratio, available_ratio)
|
||||
|
||||
# 计算金额
|
||||
position_size = total_capital * actual_ratio
|
||||
|
||||
# 检查最小交易额
|
||||
if position_size < self.min_trade_size:
|
||||
return {
|
||||
"size": 0,
|
||||
"kelly_ratio": kelly_ratio,
|
||||
"reason": f"Below minimum trade size (${self.min_trade_size})"
|
||||
}
|
||||
|
||||
return {
|
||||
"size": position_size,
|
||||
"kelly_ratio": kelly_ratio,
|
||||
"actual_ratio": actual_ratio,
|
||||
"odds": odds,
|
||||
"expected_return": (win_prob * odds - (1 - win_prob)) * position_size
|
||||
}
|
||||
|
||||
def should_exit(self,
|
||||
entry_price: float,
|
||||
current_price: float,
|
||||
current_prediction: float,
|
||||
stop_loss: float = 0.15,
|
||||
take_profit: float = 0.30) -> dict:
|
||||
"""
|
||||
判断是否应该平仓
|
||||
|
||||
Args:
|
||||
entry_price: 入场价格
|
||||
current_price: 当前价格
|
||||
current_prediction: 当前模型预测
|
||||
stop_loss: 止损比例
|
||||
take_profit: 止盈比例
|
||||
|
||||
Returns:
|
||||
dict: 退出建议
|
||||
"""
|
||||
if entry_price <= 0:
|
||||
return {"should_exit": False}
|
||||
|
||||
pnl_ratio = (current_price - entry_price) / entry_price
|
||||
|
||||
# 止损
|
||||
if pnl_ratio < -stop_loss:
|
||||
return {
|
||||
"should_exit": True,
|
||||
"reason": "STOP_LOSS",
|
||||
"pnl_ratio": pnl_ratio
|
||||
}
|
||||
|
||||
# 止盈
|
||||
if pnl_ratio > take_profit:
|
||||
return {
|
||||
"should_exit": True,
|
||||
"reason": "TAKE_PROFIT",
|
||||
"pnl_ratio": pnl_ratio
|
||||
}
|
||||
|
||||
# 模型预测反转
|
||||
if current_prediction < 0.4: # 预测胜率下降
|
||||
return {
|
||||
"should_exit": True,
|
||||
"reason": "PREDICTION_REVERSAL",
|
||||
"pnl_ratio": pnl_ratio,
|
||||
"current_prediction": current_prediction
|
||||
}
|
||||
|
||||
return {
|
||||
"should_exit": False,
|
||||
"pnl_ratio": pnl_ratio
|
||||
}
|
||||
@@ -1,99 +0,0 @@
|
||||
from loguru import logger
|
||||
|
||||
|
||||
class RiskManager:
|
||||
"""
|
||||
风险控制系统
|
||||
"""
|
||||
|
||||
def __init__(self, config=None):
|
||||
self.config = config or {}
|
||||
# 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.is_trading_paused = False
|
||||
|
||||
logger.info("Initializing Pro Risk Manager...")
|
||||
|
||||
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 = 0,
|
||||
hours_to_settle: float = 24,
|
||||
is_high_relative_volume: bool = False,
|
||||
) -> tuple[float, str]:
|
||||
"""
|
||||
仓位计算方法 (简化版，移除流动性过滤):
|
||||
仓位 = base_position(置信度)
|
||||
× time_decay(离结算衰减)
|
||||
× budget_limit
|
||||
"""
|
||||
self._reset_daily_exposure()
|
||||
|
||||
final_pos = base_confidence_usd
|
||||
reason = "Normal"
|
||||
|
||||
# 1. 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%)"
|
||||
|
||||
final_pos *= time_factor
|
||||
if final_pos <= 0:
|
||||
return 0.0, reason
|
||||
|
||||
# 2. 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)"
|
||||
|
||||
if final_pos > remaining_daily:
|
||||
final_pos = remaining_daily
|
||||
reason = "🛑触及日风控上限"
|
||||
|
||||
# 3. 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": []}
|
||||
@@ -1,219 +0,0 @@
|
||||
from loguru import logger
|
||||
from typing import Optional, Dict
|
||||
from src.data_collection.polymarket_api import PolymarketClient
|
||||
|
||||
class OrderExecutor:
|
||||
"""
|
||||
交易执行器 - 负责订单生成、提交和管理
|
||||
"""
|
||||
|
||||
def __init__(self, config: dict, client: PolymarketClient):
|
||||
self.config = config
|
||||
self.client = client
|
||||
self.pending_orders = {}
|
||||
self.executed_orders = []
|
||||
|
||||
logger.info("Order Executor initialized")
|
||||
|
||||
def execute_trade(self,
|
||||
token_id: str,
|
||||
side: str,
|
||||
amount: float,
|
||||
price: float,
|
||||
order_type: str = "GTC") -> Dict:
|
||||
"""
|
||||
执行交易
|
||||
|
||||
Args:
|
||||
token_id: Token ID
|
||||
side: "BUY" 或 "SELL"
|
||||
amount: 交易金额
|
||||
price: 价格
|
||||
order_type: 订单类型 (GTC, GTD, FOK)
|
||||
|
||||
Returns:
|
||||
dict: 订单结果
|
||||
"""
|
||||
logger.info(f"Executing {side} order: ${amount:.2f} @ {price:.4f}")
|
||||
|
||||
# 计算数量
|
||||
if price <= 0:
|
||||
return {"status": "error", "message": "Invalid price"}
|
||||
|
||||
size = amount / price
|
||||
|
||||
# 提交订单
|
||||
try:
|
||||
result = self.client.create_order(
|
||||
token_id=token_id,
|
||||
side=side,
|
||||
price=price,
|
||||
size=size,
|
||||
order_type=order_type
|
||||
)
|
||||
|
||||
if result:
|
||||
order_id = result.get("orderID", "unknown")
|
||||
self.executed_orders.append({
|
||||
"order_id": order_id,
|
||||
"token_id": token_id,
|
||||
"side": side,
|
||||
"price": price,
|
||||
"size": size,
|
||||
"amount": amount,
|
||||
"result": result
|
||||
})
|
||||
|
||||
logger.info(f"Order executed successfully: {order_id}")
|
||||
return {
|
||||
"status": "success",
|
||||
"order_id": order_id,
|
||||
"side": side,
|
||||
"price": price,
|
||||
"size": size,
|
||||
"amount": amount
|
||||
}
|
||||
else:
|
||||
return {"status": "error", "message": "Order submission failed"}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Order execution failed: {e}")
|
||||
return {"status": "error", "message": str(e)}
|
||||
|
||||
def cancel_order(self, order_id: str) -> Dict:
|
||||
"""
|
||||
取消订单
|
||||
|
||||
Args:
|
||||
order_id: 订单ID
|
||||
|
||||
Returns:
|
||||
dict: 取消结果
|
||||
"""
|
||||
try:
|
||||
result = self.client.cancel_order(order_id)
|
||||
if result:
|
||||
logger.info(f"Order {order_id} cancelled")
|
||||
return {"status": "success", "order_id": order_id}
|
||||
else:
|
||||
return {"status": "error", "message": "Cancel failed"}
|
||||
except Exception as e:
|
||||
logger.error(f"Cancel order failed: {e}")
|
||||
return {"status": "error", "message": str(e)}
|
||||
|
||||
def get_open_orders(self, market_id: str = None) -> Optional[Dict]:
|
||||
"""
|
||||
获取当前挂单
|
||||
|
||||
Args:
|
||||
market_id: 可选的市场过滤
|
||||
|
||||
Returns:
|
||||
dict: 挂单列表
|
||||
"""
|
||||
return self.client.get_orders(market_id)
|
||||
|
||||
def get_execution_history(self) -> list:
|
||||
"""
|
||||
获取执行历史
|
||||
|
||||
Returns:
|
||||
list: 已执行订单列表
|
||||
"""
|
||||
return self.executed_orders
|
||||
|
||||
|
||||
class PortfolioTracker:
|
||||
"""
|
||||
持仓追踪器
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.positions = {}
|
||||
self.total_invested = 0
|
||||
self.total_pnl = 0
|
||||
|
||||
logger.info("Portfolio Tracker initialized")
|
||||
|
||||
def add_position(self,
|
||||
token_id: str,
|
||||
side: str,
|
||||
size: float,
|
||||
entry_price: float,
|
||||
amount: float):
|
||||
"""
|
||||
添加持仓
|
||||
"""
|
||||
if token_id not in self.positions:
|
||||
self.positions[token_id] = {
|
||||
"side": side,
|
||||
"size": size,
|
||||
"entry_price": entry_price,
|
||||
"amount": amount,
|
||||
"current_price": entry_price,
|
||||
"unrealized_pnl": 0
|
||||
}
|
||||
else:
|
||||
# 加仓
|
||||
existing = self.positions[token_id]
|
||||
total_size = existing["size"] + size
|
||||
avg_price = (existing["size"] * existing["entry_price"] + size * entry_price) / total_size
|
||||
existing["size"] = total_size
|
||||
existing["entry_price"] = avg_price
|
||||
existing["amount"] += amount
|
||||
|
||||
self.total_invested += amount
|
||||
logger.info(f"Position added: {token_id}, size={size}, price={entry_price}")
|
||||
|
||||
def update_price(self, token_id: str, current_price: float):
|
||||
"""
|
||||
更新持仓价格
|
||||
"""
|
||||
if token_id in self.positions:
|
||||
pos = self.positions[token_id]
|
||||
pos["current_price"] = current_price
|
||||
|
||||
# 计算未实现盈亏
|
||||
if pos["side"] == "BUY":
|
||||
pos["unrealized_pnl"] = (current_price - pos["entry_price"]) * pos["size"]
|
||||
else:
|
||||
pos["unrealized_pnl"] = (pos["entry_price"] - current_price) * pos["size"]
|
||||
|
||||
def close_position(self, token_id: str, exit_price: float) -> Dict:
|
||||
"""
|
||||
平仓
|
||||
"""
|
||||
if token_id not in self.positions:
|
||||
return {"status": "error", "message": "Position not found"}
|
||||
|
||||
pos = self.positions[token_id]
|
||||
|
||||
if pos["side"] == "BUY":
|
||||
realized_pnl = (exit_price - pos["entry_price"]) * pos["size"]
|
||||
else:
|
||||
realized_pnl = (pos["entry_price"] - exit_price) * pos["size"]
|
||||
|
||||
self.total_pnl += realized_pnl
|
||||
self.total_invested -= pos["amount"]
|
||||
|
||||
del self.positions[token_id]
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"realized_pnl": realized_pnl,
|
||||
"exit_price": exit_price
|
||||
}
|
||||
|
||||
def get_summary(self) -> Dict:
|
||||
"""
|
||||
获取持仓汇总
|
||||
"""
|
||||
total_unrealized = sum(p["unrealized_pnl"] for p in self.positions.values())
|
||||
|
||||
return {
|
||||
"positions_count": len(self.positions),
|
||||
"total_invested": self.total_invested,
|
||||
"total_unrealized_pnl": total_unrealized,
|
||||
"total_realized_pnl": self.total_pnl,
|
||||
"positions": self.positions
|
||||
}
|
||||
@@ -1,158 +0,0 @@
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from datetime import datetime, timedelta
|
||||
from loguru import logger
|
||||
|
||||
|
||||
class PaperTrader:
|
||||
"""
|
||||
模拟交易系统 (Paper Trading System)
|
||||
"""
|
||||
|
||||
def __init__(self, storage_path="data/paper_positions.json", total_capital=1000.0):
|
||||
self.storage_path = storage_path
|
||||
self.initial_capital = total_capital
|
||||
data = self._load_data()
|
||||
self.positions = data.get("positions", {})
|
||||
self.history = data.get("history", []) # 历史结项记录
|
||||
self.trades = data.get("trades", []) # 原始买入/卖出记录
|
||||
self.balance = data.get("balance", total_capital)
|
||||
logger.info(f"模拟交易系统初始化。累计成交: {len(self.history)} 笔, 买入记录: {len(self.trades)} 笔")
|
||||
|
||||
def _load_data(self):
|
||||
if os.path.exists(self.storage_path):
|
||||
try:
|
||||
with open(self.storage_path, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
except:
|
||||
return {"positions": {}, "history": [], "trades": [], "balance": self.initial_capital}
|
||||
return {"positions": {}, "history": [], "trades": [], "balance": self.initial_capital}
|
||||
|
||||
def _save_data(self):
|
||||
with open(self.storage_path, "w", encoding="utf-8") as f:
|
||||
json.dump(
|
||||
{
|
||||
"positions": self.positions,
|
||||
"history": self.history,
|
||||
"trades": self.trades,
|
||||
"balance": round(self.balance, 2),
|
||||
},
|
||||
f,
|
||||
ensure_ascii=False,
|
||||
indent=2,
|
||||
)
|
||||
|
||||
def open_position(self, market_id: str, city: str, option: str, price: int, side: str, amount_usd: float = 5.0, target_date: str = None, predicted_temp: float = None):
|
||||
"""
|
||||
开仓进入模拟仓位
|
||||
"""
|
||||
# 价格以美分计，转换为 0-1 比例
|
||||
price_decimal = price / 100.0
|
||||
|
||||
# 检查余额
|
||||
if self.balance < amount_usd:
|
||||
logger.warning(f"余额不足，无法开仓 (余额: ${self.balance:.2f})")
|
||||
return False
|
||||
|
||||
# 计算持仓份额
|
||||
shares = amount_usd / price_decimal if price_decimal > 0 else 0
|
||||
|
||||
position_id = f"{market_id}_{side}"
|
||||
|
||||
# 如果已经有相同方向的仓位，可以选择加仓或忽略（这里简单起见，不重复开仓）
|
||||
if position_id in self.positions:
|
||||
return False
|
||||
|
||||
new_pos = {
|
||||
"market_id": market_id,
|
||||
"city": city,
|
||||
"option": option,
|
||||
"side": side,
|
||||
"entry_price": price,
|
||||
"shares": shares,
|
||||
"cost_usd": amount_usd,
|
||||
"current_price": price,
|
||||
"pnl_usd": 0.0,
|
||||
"pnl_pct": 0.0,
|
||||
"status": "OPEN",
|
||||
"target_date": target_date,
|
||||
"predicted_temp": predicted_temp,
|
||||
"opened_at": (datetime.utcnow() + timedelta(hours=8)).strftime("%Y-%m-%d %H:%M:%S")
|
||||
}
|
||||
|
||||
self.positions[position_id] = new_pos
|
||||
self.balance -= amount_usd
|
||||
|
||||
# 记录交易流水
|
||||
self.trades.append({
|
||||
"type": "BUY",
|
||||
"city": city,
|
||||
"option": option,
|
||||
"side": side,
|
||||
"price": price,
|
||||
"amount": amount_usd,
|
||||
"time": new_pos["opened_at"]
|
||||
})
|
||||
|
||||
self._save_data()
|
||||
|
||||
logger.success(f"【模拟开仓】{city} | {option} | {side} | 价格: {price}¢ | 投入: ${amount_usd}")
|
||||
return True
|
||||
|
||||
def update_pnl(self, current_prices: dict):
|
||||
updated_report = []
|
||||
finished_ids = []
|
||||
|
||||
for pid, pos in self.positions.items():
|
||||
if pos["status"] != "OPEN":
|
||||
continue
|
||||
m_id = pos["market_id"]
|
||||
|
||||
if m_id in current_prices:
|
||||
curr_price = current_prices[m_id].get("price", 50)
|
||||
if pos["side"] == "NO":
|
||||
curr_price = 100 - curr_price
|
||||
|
||||
# 更新当前价值
|
||||
value = pos["shares"] * (curr_price / 100.0)
|
||||
pnl = value - pos["cost_usd"]
|
||||
pnl_pct = (pnl / pos["cost_usd"]) * 100 if pos["cost_usd"] > 0 else 0
|
||||
|
||||
pos["current_price"] = curr_price
|
||||
pos["pnl_usd"] = round(pnl, 2)
|
||||
pos["pnl_pct"] = round(pnl_pct, 2)
|
||||
|
||||
# --- 自动结项检测：如果价格变为 0 或 100 (Polymarket 已结算) ---
|
||||
if curr_price >= 99.5 or curr_price <= 0.5:
|
||||
pos["status"] = "CLOSED"
|
||||
pos["closed_at"] = (
|
||||
datetime.utcnow() + timedelta(hours=8)
|
||||
).strftime("%Y-%m-%d %H:%M:%S")
|
||||
self.balance += value # 资金回笼
|
||||
self.history.append(pos)
|
||||
finished_ids.append(pid)
|
||||
logger.success(
|
||||
f"【模拟结项】{pos['city']} | {pos['option']} | 最终价格: {curr_price}¢ | 获利: ${pnl:+.2f}"
|
||||
)
|
||||
else:
|
||||
updated_report.append(pos)
|
||||
|
||||
# 从活跃仓位中移除已结项的
|
||||
for pid in finished_ids:
|
||||
# 在流水中添加卖出（结项）记录
|
||||
pos = self.positions[pid]
|
||||
self.trades.append({
|
||||
"type": "SELL",
|
||||
"city": pos["city"],
|
||||
"option": pos["option"],
|
||||
"side": pos["side"],
|
||||
"price": pos["current_price"],
|
||||
"amount": round(pos["shares"] * (pos["current_price"] / 100.0), 2),
|
||||
"time": pos.get("closed_at")
|
||||
})
|
||||
del self.positions[pid]
|
||||
|
||||
self._save_data()
|
||||
|
||||
return updated_report
|
||||
@@ -1,285 +0,0 @@
|
||||
import requests
|
||||
import html
|
||||
from loguru import logger
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class TelegramNotifier:
|
||||
"""
|
||||
Telegram 消息推送模块
|
||||
支持信号推送、预警推送和市场异常提醒
|
||||
"""
|
||||
|
||||
def __init__(self, config: dict):
|
||||
self.config = config
|
||||
self.token = config.get("bot_token")
|
||||
self.chat_id = config.get("chat_id")
|
||||
self.proxy = config.get("proxy")
|
||||
|
||||
self.session = requests.Session()
|
||||
if self.proxy:
|
||||
if not self.proxy.startswith("http"):
|
||||
self.proxy = f"http://{self.proxy}"
|
||||
self.session.proxies = {"http": self.proxy, "https": self.proxy}
|
||||
|
||||
logger.info("Telegram 通知器初始化完成。")
|
||||
|
||||
@staticmethod
|
||||
def _escape_html(text: str) -> str:
|
||||
"""Escape HTML special characters"""
|
||||
if not isinstance(text, str):
|
||||
text = str(text)
|
||||
return html.escape(text, quote=False)
|
||||
|
||||
def _send_message(self, text: str):
|
||||
"""发送 Telegram 消息的主函数 (支持多个 ID)"""
|
||||
if not self.token or not self.chat_id:
|
||||
logger.warning("未配置 Telegram Token 或 Chat ID，无法发送消息。")
|
||||
return False
|
||||
|
||||
# 支持逗号分隔的多个 ID
|
||||
chat_ids = str(self.chat_id).replace(" ", "").split(",")
|
||||
url = f"https://api.telegram.org/bot{self.token}/sendMessage"
|
||||
|
||||
all_successful = True
|
||||
for cid in chat_ids:
|
||||
if not cid:
|
||||
continue
|
||||
|
||||
payload = {
|
||||
"chat_id": cid,
|
||||
"text": text,
|
||||
"parse_mode": "HTML",
|
||||
"disable_web_page_preview": True,
|
||||
}
|
||||
|
||||
try:
|
||||
response = self.session.post(url, json=payload, timeout=10)
|
||||
if response.status_code != 200:
|
||||
error_msg = response.text
|
||||
if "chat not found" in error_msg.lower():
|
||||
logger.error(
|
||||
f"Telegram 消息发送给 {cid} 失败 (400): Chat ID {cid} 无效或机器人尚未被加入该聊天。请在 Telegram 中发送 /id 给机器人确认正确的 Chat ID。"
|
||||
)
|
||||
else:
|
||||
logger.error(
|
||||
f"Telegram 消息发送给 {cid} 失败 ({response.status_code}): {error_msg}"
|
||||
)
|
||||
all_successful = False
|
||||
else:
|
||||
logger.info(f"Telegram 消息发送给 {cid} 成功。")
|
||||
except Exception as e:
|
||||
logger.error(f"Telegram 消息发送给 {cid} 异常: {e}")
|
||||
all_successful = False
|
||||
return all_successful
|
||||
|
||||
def send_signal(
|
||||
self,
|
||||
market_name: str,
|
||||
full_title: str,
|
||||
option: str,
|
||||
score: float,
|
||||
prediction: str,
|
||||
confidence: int,
|
||||
analysis_list: list,
|
||||
price: float,
|
||||
market_url: str,
|
||||
local_time: str = None,
|
||||
target_date: str = None,
|
||||
):
|
||||
"""发送交易信号推送"""
|
||||
stars = "⭐" * int(score) + "☆" * (5 - int(score))
|
||||
timestamp_utc = datetime.utcnow().strftime("%H:%M")
|
||||
|
||||
analysis_text = "\n".join(
|
||||
[
|
||||
f"✅ {self._escape_html(item)}" if "✅" not in item else item
|
||||
for item in analysis_list
|
||||
]
|
||||
)
|
||||
|
||||
local_time_text = (
|
||||
f"🕒 当地时间: <b>{self._escape_html(local_time)}</b>\n"
|
||||
if local_time
|
||||
else ""
|
||||
)
|
||||
target_date_text = self._escape_html(target_date) if target_date else "待定"
|
||||
|
||||
text = (
|
||||
f"🎯 <b>交易信号 #{self._escape_html(market_name.split(' ')[0])}</b>\n\n"
|
||||
f"📍 城市: <b>{self._escape_html(market_name)}</b>\n"
|
||||
f"🏆 市场: <i>{self._escape_html(full_title)}</i>\n"
|
||||
f"📝 选项: <b>{self._escape_html(option)}</b>\n"
|
||||
f"💰 当前价格: <b>{price}¢</b>\n"
|
||||
f"═══════════════════\n"
|
||||
f"📊 信号评分: {stars} ({score}/5)\n"
|
||||
f"🤖 模型预测: {self._escape_html(prediction)}\n"
|
||||
f"📈 置信度: {confidence}%\n\n"
|
||||
f"分析汇总:\n"
|
||||
f"{analysis_text}\n"
|
||||
f"═══════════════════\n"
|
||||
f"{local_time_text}"
|
||||
f"📅 结算日期: <b>{target_date_text}</b>\n"
|
||||
f"🔗 <a href='{market_url}'>点击进入市场</a>\n\n"
|
||||
f"⏰ 信号时间: {timestamp_utc} UTC"
|
||||
)
|
||||
return self._send_message(text)
|
||||
|
||||
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,
|
||||
metar_data: dict = None,
|
||||
):
|
||||
"""发送简约版合并预警 (含 METAR 航空气象数据)"""
|
||||
if not alerts:
|
||||
return
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# UTC+8 北京时间
|
||||
now_bj = datetime.utcnow() + timedelta(hours=8)
|
||||
timestamp_bj = now_bj.strftime("%H:%M")
|
||||
|
||||
# 1. METAR 航空气象数据区块
|
||||
metar_text = ""
|
||||
if metar_data and metar_data.get("current", {}).get("temp") is not None:
|
||||
icao = metar_data.get("icao", "N/A")
|
||||
temp = metar_data["current"]["temp"]
|
||||
unit = "°F" if metar_data.get("unit") == "fahrenheit" else "°C"
|
||||
|
||||
# 解析观测时间 (格式: 2026-02-07T11:00:00.000Z)
|
||||
obs_time_raw = metar_data.get("observation_time", "")
|
||||
if "T" in obs_time_raw:
|
||||
obs_time = obs_time_raw.split("T")[1][:5] + " UTC"
|
||||
else:
|
||||
obs_time = obs_time_raw or "N/A"
|
||||
|
||||
# 可选：风速信息
|
||||
wind_kt = metar_data["current"].get("wind_speed_kt")
|
||||
wind_text = f" | 风速:{wind_kt}kt" if wind_kt else ""
|
||||
|
||||
metar_text = (
|
||||
f"✈️ <b>机场实测 ({icao}):</b>\n"
|
||||
f" 🌡️ {temp:.1f}{unit}{wind_text}\n"
|
||||
f" 🕐 观测: {obs_time}\n\n"
|
||||
)
|
||||
|
||||
# 2. 信号详情构建
|
||||
items_text = ""
|
||||
for a in alerts:
|
||||
items_text += f"{a['msg']}\n\n"
|
||||
|
||||
# 3. 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"
|
||||
)
|
||||
|
||||
# 4. 总体布局
|
||||
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"{metar_text}"
|
||||
f"📊 <b>实时异动:</b>\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)"
|
||||
)
|
||||
return self._send_message(text)
|
||||
|
||||
def send_anomaly(
|
||||
self,
|
||||
city_tag: str,
|
||||
market_name: str,
|
||||
detected_anomaly: str,
|
||||
stats: dict,
|
||||
whales: list,
|
||||
current_price: float,
|
||||
local_time: str = None,
|
||||
):
|
||||
"""发送市场异常推送"""
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# UTC+8 北京时间
|
||||
timestamp_bj = (datetime.utcnow() + timedelta(hours=8)).strftime("%H:%M")
|
||||
|
||||
whale_text = "\n".join([f"- {self._escape_html(w)}" for w in whales])
|
||||
stats_text = "\n".join(
|
||||
[
|
||||
f"{self._escape_html(k)}: {self._escape_html(v)}"
|
||||
for k, v in stats.items()
|
||||
]
|
||||
)
|
||||
local_time_text = (
|
||||
f"🕒 当地时间: <b>{self._escape_html(local_time)}</b>\n"
|
||||
if local_time
|
||||
else ""
|
||||
)
|
||||
|
||||
text = (
|
||||
f"👀 <b>市场异常 #{self._escape_html(city_tag)}</b>\n\n"
|
||||
f"📍 城市: {self._escape_html(city_tag)}\n"
|
||||
f"🏆 市场: {self._escape_html(market_name)}\n\n"
|
||||
f"🚨 <b>检测到异常:</b>\n"
|
||||
f"{self._escape_html(detected_anomaly)}\n"
|
||||
f"{stats_text}\n\n"
|
||||
f"🐋 <b>大户动向:</b>\n"
|
||||
f"{whale_text}\n\n"
|
||||
f"💰 当前价格: <b>{current_price}¢</b>\n"
|
||||
f"═══════════════════\n"
|
||||
f"{local_time_text}"
|
||||
f"⏰ 信号时间: {timestamp_bj} (北京时间)"
|
||||
)
|
||||
return self._send_message(text)
|
||||
|
||||
def send_alert(
|
||||
self,
|
||||
city_tag: str,
|
||||
market_name: str,
|
||||
price: float,
|
||||
trigger: str,
|
||||
prev_price: float,
|
||||
change: str,
|
||||
quick_analysis: list,
|
||||
local_time: str = None,
|
||||
):
|
||||
"""发送价格预警推送"""
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# UTC+8 北京时间
|
||||
timestamp_bj = (datetime.utcnow() + timedelta(hours=8)).strftime("%H:%M")
|
||||
|
||||
analysis_text = "\n".join(
|
||||
[f"- {self._escape_html(item)}" for item in quick_analysis]
|
||||
)
|
||||
local_time_text = (
|
||||
f"🕒 当地时间: <b>{self._escape_html(local_time)}</b>\n"
|
||||
if local_time
|
||||
else ""
|
||||
)
|
||||
|
||||
text = (
|
||||
f"⚡ <b>价格预警 #{self._escape_html(city_tag)}</b>\n\n"
|
||||
f"📍 城市: {self._escape_html(city_tag)}\n"
|
||||
f"🏆 市场: {self._escape_html(market_name)}\n"
|
||||
f"💰 报价: <b>{price}¢ ↗️</b>\n\n"
|
||||
f"触发条件: {self._escape_html(trigger)}\n"
|
||||
f"变动详情: {prev_price}¢ -> {price}¢ ({self._escape_html(change)})\n\n"
|
||||
f"📊 <b>快速分析:</b>\n"
|
||||
f"{analysis_text}\n\n"
|
||||
f"═══════════════════\n"
|
||||
f"{local_time_text}"
|
||||
f"⏰ 预警时间: {timestamp_bj} (北京时间)"
|
||||
)
|
||||
return self._send_message(text)
|
||||
Reference in New Issue
Block a user