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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