feat: 初始化量化交易知识库 v1.0
- 01_基础理论：量化交易基础概念、市场微观结构、加密货币特殊性 - 02_技术指标：完整指标体系（MA/EMA/MACD/RSI/KDJ/布林带/SuperTrend/DMI等） - 03_交易策略：趋势跟踪、均值回归、套利、动量策略详解 - 04_交易信号系统：多指标共振评分引擎（基于 tradehk 项目） - 05_市场品种：加密货币、XAUT黄金代币、代币化美股全览 - 06_数据流程：数据采集、清洗、存储、实时流处理 - 07_回测框架：回测方法论、偏差规避、绩效评估指标 - 08_风险管理：仓位管理、止损止盈、Kelly公式、杠杆管理 - 09_AI与机器学习：深度学习、强化学习、LLM在量化投资中的应用 - 10_链上数据分析：SOPR/MVRV/巨鲸监控/衍生品数据 - 11_参考文献：arXiv论文汇总、开源项目、数据平台资源 - samples/：Python信号计算器和回测样本代码 参考项目：tradehk（ssh://git@git.hk.hao.work:2222/hao/tradehk.git） 全部中文化，适用于加密货币（CEX/DEX）、XAUT黄金、代币化美股
这个提交包含在：
Manus Quant Agent
374
samples/signal_calculator_sample.py
普通文件
374
samples/signal_calculator_sample.py
普通文件
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"""
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量化交易信号计算器 - 完整示例
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基于 tradehk 项目的多指标共振信号系统 Python 实现
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使用方法：
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pip install pandas numpy requests
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python signal_calculator_sample.py
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"""
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import pandas as pd
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import numpy as np
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import requests
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from typing import Optional, Tuple
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from dataclasses import dataclass
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# ============================================================
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# 数据获取
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# ============================================================
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def fetch_binance_klines(symbol: str, interval: str, limit: int = 500) -> pd.DataFrame:
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"""
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从 Binance 获取 K 线数据
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参数：
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symbol: 交易对，如 'BTCUSDT'
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interval: 时间周期，如 '1h', '4h', '1d'
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limit: 获取数量（最大 1000）
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"""
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url = "https://api.binance.com/api/v3/klines"
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params = {"symbol": symbol, "interval": interval, "limit": limit}
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resp = requests.get(url, params=params, timeout=10)
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resp.raise_for_status()
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data = resp.json()
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df = pd.DataFrame(data, columns=[
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'timestamp', 'open', 'high', 'low', 'close', 'volume',
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'close_time', 'quote_volume', 'trades', 'taker_buy_base',
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'taker_buy_quote', 'ignore'
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])
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df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
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for col in ['open', 'high', 'low', 'close', 'volume']:
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df[col] = df[col].astype(float)
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return df[['timestamp', 'open', 'high', 'low', 'close', 'volume']].set_index('timestamp')
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# ============================================================
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# 技术指标计算
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# ============================================================
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def calc_rma(series: pd.Series, period: int) -> pd.Series:
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"""Wilder 平滑移动均线（RMA）"""
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alpha = 1.0 / period
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return series.ewm(alpha=alpha, adjust=False).mean()
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def calc_ema(series: pd.Series, period: int) -> pd.Series:
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"""指数移动均线（EMA）"""
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return series.ewm(span=period, adjust=False).mean()
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def calc_sma(series: pd.Series, period: int) -> pd.Series:
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"""简单移动均线（SMA）"""
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return series.rolling(period).mean()
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def calc_atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
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"""真实波动幅度（ATR）"""
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high, low, close = df['high'], df['low'], df['close']
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tr = pd.concat([
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high - low,
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(high - close.shift(1)).abs(),
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(low - close.shift(1)).abs()
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], axis=1).max(axis=1)
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return calc_rma(tr, period)
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def calc_rsi(close: pd.Series, period: int = 14) -> pd.Series:
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"""相对强弱指数（RSI）"""
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delta = close.diff()
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gain = delta.clip(lower=0)
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loss = (-delta).clip(lower=0)
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avg_gain = calc_rma(gain, period)
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avg_loss = calc_rma(loss, period)
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rs = avg_gain / avg_loss.replace(0, np.nan)
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return 100 - (100 / (1 + rs))
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def calc_macd(close: pd.Series, fast: int = 10, slow: int = 20, signal: int = 10) -> Tuple[pd.Series, pd.Series, pd.Series]:
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"""
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MACD（tradehk 参数：10, 20, 10）
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返回：(macd线, 信号线, 柱状图)
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"""
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ema_fast = calc_ema(close, fast)
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ema_slow = calc_ema(close, slow)
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macd_line = ema_fast - ema_slow
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signal_line = calc_ema(macd_line, signal)
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histogram = macd_line - signal_line
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return macd_line, signal_line, histogram
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def calc_ewo(close: pd.Series) -> pd.Series:
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"""艾略特波浪振荡器（EWO = EMA5 - EMA35）"""
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return calc_ema(close, 5) - calc_ema(close, 35)
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def calc_ao(df: pd.DataFrame) -> pd.Series:
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"""动量振荡器（AO）"""
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midpoint = (df['high'] + df['low']) / 2
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return calc_sma(midpoint, 5) - calc_sma(midpoint, 34)
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def calc_kdj(df: pd.DataFrame, period: int = 9, k_smooth: int = 3, d_smooth: int = 3) -> Tuple[pd.Series, pd.Series, pd.Series]:
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"""KDJ 指标"""
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low_min = df['low'].rolling(period).min()
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high_max = df['high'].rolling(period).max()
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rsv = (df['close'] - low_min) / (high_max - low_min).replace(0, np.nan) * 100
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k = rsv.ewm(com=k_smooth - 1, adjust=False).mean()
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d = k.ewm(com=d_smooth - 1, adjust=False).mean()
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j = 3 * k - 2 * d
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return k, d, j
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def calc_bollinger_bands(close: pd.Series, period: int = 20, multiplier: float = 2.0) -> Tuple[pd.Series, pd.Series, pd.Series]:
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"""布林带"""
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middle = calc_sma(close, period)
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std = close.rolling(period).std()
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upper = middle + multiplier * std
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lower = middle - multiplier * std
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return upper, middle, lower
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def calc_supertrend(df: pd.DataFrame, period: int = 10, multiplier: float = 3.0) -> Tuple[pd.Series, pd.Series]:
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"""
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超级趋势线（SuperTrend）
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返回：(趋势值, 方向: 1=多头, -1=空头)
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"""
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atr = calc_atr(df, period)
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hl2 = (df['high'] + df['low']) / 2
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upper_band = hl2 + multiplier * atr
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lower_band = hl2 - multiplier * atr
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supertrend = pd.Series(np.nan, index=df.index)
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direction = pd.Series(1, index=df.index)
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for i in range(1, len(df)):
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# 更新上轨
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if upper_band.iloc[i] < upper_band.iloc[i-1] or df['close'].iloc[i-1] > upper_band.iloc[i-1]:
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upper_band.iloc[i] = upper_band.iloc[i]
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else:
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upper_band.iloc[i] = upper_band.iloc[i-1]
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# 更新下轨
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if lower_band.iloc[i] > lower_band.iloc[i-1] or df['close'].iloc[i-1] < lower_band.iloc[i-1]:
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lower_band.iloc[i] = lower_band.iloc[i]
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else:
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lower_band.iloc[i] = lower_band.iloc[i-1]
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# 确定方向
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if direction.iloc[i-1] == -1:
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if df['close'].iloc[i] > upper_band.iloc[i]:
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direction.iloc[i] = 1
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else:
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direction.iloc[i] = -1
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else:
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if df['close'].iloc[i] < lower_band.iloc[i]:
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direction.iloc[i] = -1
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else:
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direction.iloc[i] = 1
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supertrend.iloc[i] = lower_band.iloc[i] if direction.iloc[i] == 1 else upper_band.iloc[i]
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return supertrend, direction
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# ============================================================
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# 信号生成（多指标共振）
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# ============================================================
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@dataclass
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class SignalResult:
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signal_type: str # 'BUY', 'SELL', 'NEUTRAL'
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strength: str # 'STRONG', 'MODERATE', 'WEAK'
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bullish_score: int
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bearish_score: int
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details: dict
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def generate_signal(df: pd.DataFrame, use_kdj: bool = True, use_supertrend: bool = True) -> SignalResult:
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"""
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多指标共振信号生成器
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参数：
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df: 包含 OHLCV 数据的 DataFrame
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use_kdj: 是否启用 KDJ 信号
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use_supertrend: 是否启用 SuperTrend 信号
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"""
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close = df['close']
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# 计算所有指标
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ma10 = calc_sma(close, 10)
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ma100 = calc_sma(close, 100)
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macd_line, signal_line, histogram = calc_macd(close)
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ao = calc_ao(df)
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rsi = calc_rsi(close)
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bb_upper, bb_middle, bb_lower = calc_bollinger_bands(close)
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# 取最后两根 K 线
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curr = df.index[-1]
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prev = df.index[-2]
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bullish_score = 0
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bearish_score = 0
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details = {}
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# ---- 核心信号：MACD ----
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macd_cross_up = macd_line[curr] > signal_line[curr] and macd_line[prev] <= signal_line[prev]
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macd_cross_down = macd_line[curr] < signal_line[curr] and macd_line[prev] >= signal_line[prev]
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if macd_cross_up:
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bullish_score += 2
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details['MACD'] = '金叉 (+2)'
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elif macd_cross_down:
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bearish_score += 2
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details['MACD'] = '死叉 (+2)'
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elif histogram[curr] > 0 and histogram[curr] > histogram[prev]:
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bullish_score += 1
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details['MACD'] = '柱状图扩大（正）(+1)'
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elif histogram[curr] < 0 and histogram[curr] < histogram[prev]:
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bearish_score += 1
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details['MACD'] = '柱状图扩大（负）(+1)'
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# ---- 核心信号：AO ----
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ao_cross_up = ao[curr] > 0 and ao[prev] <= 0
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ao_cross_down = ao[curr] < 0 and ao[prev] >= 0
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if ao_cross_up:
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bullish_score += 1
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details['AO'] = '上穿零轴 (+1)'
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elif ao_cross_down:
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bearish_score += 1
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details['AO'] = '下穿零轴 (+1)'
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# ---- 核心信号：MA 排列 ----
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if close[curr] > ma10[curr] and ma10[curr] > ma100[curr]:
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bullish_score += 1
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details['MA'] = '多头排列 (+1)'
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elif close[curr] < ma10[curr] and ma10[curr] < ma100[curr]:
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bearish_score += 1
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details['MA'] = '空头排列 (+1)'
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# ---- 可选信号：RSI ----
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if rsi[curr] < 30:
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bullish_score += 1
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details['RSI'] = f'超卖 {rsi[curr]:.1f} (+1)'
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elif rsi[curr] > 70:
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bearish_score += 1
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details['RSI'] = f'超买 {rsi[curr]:.1f} (+1)'
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# ---- 可选信号：布林带 ----
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if close[curr] <= bb_lower[curr]:
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bullish_score += 1
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details['布林带'] = '触及下轨 (+1)'
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elif close[curr] >= bb_upper[curr]:
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bearish_score += 1
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details['布林带'] = '触及上轨 (+1)'
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# ---- 可选信号：KDJ ----
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if use_kdj:
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k, d, j = calc_kdj(df)
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kdj_cross_up = k[curr] > d[curr] and k[prev] <= d[prev]
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kdj_cross_down = k[curr] < d[curr] and k[prev] >= d[prev]
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if kdj_cross_up and k[curr] < 30:
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bullish_score += 2
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details['KDJ'] = f'低位金叉 K={k[curr]:.1f} (+2)'
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elif kdj_cross_up:
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bullish_score += 1
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details['KDJ'] = f'金叉 K={k[curr]:.1f} (+1)'
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elif kdj_cross_down and k[curr] > 70:
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bearish_score += 2
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details['KDJ'] = f'高位死叉 K={k[curr]:.1f} (+2)'
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elif kdj_cross_down:
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bearish_score += 1
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details['KDJ'] = f'死叉 K={k[curr]:.1f} (+1)'
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# ---- 可选信号：SuperTrend ----
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if use_supertrend:
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st_value, st_direction = calc_supertrend(df)
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if st_direction[curr] == 1 and st_direction[prev] == -1:
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bullish_score += 2
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details['SuperTrend'] = '趋势反转看多 (+2)'
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elif st_direction[curr] == -1 and st_direction[prev] == 1:
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bearish_score += 2
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details['SuperTrend'] = '趋势反转看空 (+2)'
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elif st_direction[curr] == 1:
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bullish_score += 1
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details['SuperTrend'] = '多头趋势中 (+1)'
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elif st_direction[curr] == -1:
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bearish_score += 1
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details['SuperTrend'] = '空头趋势中 (+1)'
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# ---- 信号强度判定 ----
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active_optional = sum([True, True, use_kdj, use_supertrend]) # RSI, BB 始终启用
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strong_threshold = 5 + int(active_optional * 0.5)
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moderate_threshold = 3 + int(active_optional * 0.3)
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if bullish_score > bearish_score:
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signal_type = 'BUY'
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score = bullish_score
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elif bearish_score > bullish_score:
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signal_type = 'SELL'
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score = bearish_score
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else:
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return SignalResult('NEUTRAL', 'WEAK', bullish_score, bearish_score, details)
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if score >= strong_threshold:
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strength = 'STRONG'
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elif score >= moderate_threshold:
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strength = 'MODERATE'
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else:
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strength = 'WEAK'
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return SignalResult(signal_type, strength, bullish_score, bearish_score, details)
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# ============================================================
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# 主程序
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# ============================================================
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if __name__ == '__main__':
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print("=" * 60)
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print("量化交易信号计算器 - 多指标共振系统")
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print("=" * 60)
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# 测试品种列表
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test_symbols = [
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('BTCUSDT', '1h', 'BTC/USDT 1小时'),
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('ETHUSDT', '4h', 'ETH/USDT 4小时'),
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('XAUTUSDT', '4h', 'XAUT/USDT 4小时（黄金代币）'),
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]
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for symbol, interval, label in test_symbols:
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print(f"\n{'─' * 50}")
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print(f"品种：{label}")
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print(f"{'─' * 50}")
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try:
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# 获取数据
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df = fetch_binance_klines(symbol, interval, limit=200)
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print(f"数据获取成功：{len(df)} 根 K 线")
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print(f"最新价格：{df['close'].iloc[-1]:.4f}")
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# 生成信号
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result = generate_signal(df, use_kdj=True, use_supertrend=True)
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# 输出结果
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emoji = '🟢' if result.signal_type == 'BUY' else ('🔴' if result.signal_type == 'SELL' else '⚪')
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print(f"\n{emoji} 信号：{result.signal_type} ({result.strength})")
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print(f" 多头评分：{result.bullish_score} 空头评分：{result.bearish_score}")
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print(f"\n指标详情：")
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for indicator, detail in result.details.items():
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print(f" {indicator}: {detail}")
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except Exception as e:
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print(f"错误：{e}")
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print(f"\n{'=' * 60}")
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print("注意：以上信号仅供参考，不构成投资建议")
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print("=" * 60)
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在新工单中引用
屏蔽一个用户