cfdaily
affcfa0c72
**主要调整：** 1. 重命名将军工作区目录： - data-engineering → zhaoyun-data (赵云数据工程） - risk-management → guanyu-risk (关羽风控管理) - platform → jiangwei-platform (姜维平台) - technical-strategy → zhangfei-technical (张飞技术策略) 2. 创建新目录： - archive/ (归档目录） - simayi-quality/ (司马懿质量保证) - pangtong-value/ (庞统价值投资) 3. 移动内容： - value-investing → pangtong-value/research (庞统价值投资) - running_data → zhaoyun-data/data (运行数据) - 文件任务管理系统文档 → archive/file-task-system 4. 清理文件： - 删除所有日志文件 - 删除agent脚本 - 删除knowledge-base (使用统一知识库) 5. 创建标准结构： - 各将军目录下创建research/, scripts/, reports/, references/子目录 6. 更新.gitignore： - 排除日志文件和临时文件 **依据：** management/workflow-rules.md **制定：** 庞统（凤雏） **审核：** 诸葛亮
690 lines
25 KiB
Python
690 lines
25 KiB
Python
"""
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技术选股策略回测框架
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实现三种推荐策略：
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1. MACD底背离+均线
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2. 布林带下轨+趋势
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3. 唐奇安通道突破
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作者：张飞
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日期：2026年3月24日
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"""
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import numpy as np
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import pandas as pd
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from typing import Dict, List, Tuple, Optional
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from dataclasses import dataclass
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from datetime import datetime
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import logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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@dataclass
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class Trade:
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"""交易记录"""
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code: str
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entry_date: datetime
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exit_date: Optional[datetime]
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entry_price: float
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exit_price: Optional[float]
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direction: int # 1多头, -1空头
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shares: int
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entry_value: float
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exit_value: Optional[float]
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profit: Optional[float]
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profit_pct: Optional[float]
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hold_days: Optional[int]
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strategy: str
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@dataclass
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class BacktestResult:
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"""回测结果"""
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strategy: str
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start_date: datetime
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end_date: datetime
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initial_capital: float
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final_capital: float
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total_return: float
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annual_return: float
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max_drawdown: float
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sharpe_ratio: float
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win_rate: float
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total_trades: int
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win_trades: int
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loss_trades: int
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avg_profit_pct: float
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avg_win_pct: float
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avg_loss_pct: float
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trades: List[Trade]
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class TechnicalIndicators:
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"""技术指标计算器"""
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@staticmethod
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def calculate_sma(prices: np.ndarray, period: int) -> np.ndarray:
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"""简单移动平均"""
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return pd.Series(prices).rolling(window=period, min_periods=1).mean().values
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@staticmethod
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def calculate_ema(prices: np.ndarray, period: int) -> np.ndarray:
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"""指数移动平均"""
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return pd.Series(prices).ewm(span=period, adjust=False).mean().values
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@staticmethod
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def calculate_macd(prices: np.ndarray,
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fast: int = 12,
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slow: int = 26,
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signal: int = 9) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""
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计算MACD
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返回: (DIF, DEA, MACD)
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"""
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ema_fast = TechnicalIndicators.calculate_ema(prices, fast)
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ema_slow = TechnicalIndicators.calculate_ema(prices, slow)
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dif = ema_fast - ema_slow
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dea = TechnicalIndicators.calculate_ema(dif, signal)
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macd = 2 * (dif - dea)
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return dif, dea, macd
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@staticmethod
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def calculate_bollinger_bands(prices: np.ndarray,
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period: int = 20,
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num_std: float = 2.0) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""
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计算布林带
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返回: (上轨, 中轨, 下轨)
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"""
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sma = TechnicalIndicators.calculate_sma(prices, period)
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std = pd.Series(prices).rolling(window=period, min_periods=1).std().values
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upper = sma + num_std * std
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lower = sma - num_std * std
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return upper, sma, lower
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@staticmethod
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def calculate_donchian_channel(high: np.ndarray,
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low: np.ndarray,
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period: int = 20) -> Tuple[np.ndarray, np.ndarray]:
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"""
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计算唐奇安通道
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返回: (上轨, 下轨)
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"""
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upper = pd.Series(high).rolling(window=period, min_periods=1).max().values
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lower = pd.Series(low).rolling(window=period, min_periods=1).min().values
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return upper, lower
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@staticmethod
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def calculate_atr(high: np.ndarray,
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low: np.ndarray,
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close: np.ndarray,
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period: int = 14) -> np.ndarray:
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"""计算ATR平均真实波幅"""
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tr = np.zeros(len(high))
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for i in range(len(high)):
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if i == 0:
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tr[i] = high[i] - low[i]
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else:
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hl = high[i] - low[i]
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hc = abs(high[i] - close[i-1])
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lc = abs(low[i] - close[i-1])
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tr[i] = max(hl, hc, lc)
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atr = pd.Series(tr).rolling(window=period, min_periods=1).mean().values
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return atr
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class MACDDivergenceStrategy:
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"""
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MACD底背离 + 均线过滤策略
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买入条件:
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1. 股价创近期新低（20日最低）
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2. MACD DIF值没有创新低（底背离）
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3. 价格站上20日均线（趋势向上确认）
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4. 成交量放大（可选）
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卖出条件:
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1. 收盘价跌破20日均线
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2. 或亏损达到5%
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3. 或盈利达到20%
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"""
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def __init__(self, ma_period: int = 20,
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divergence_period: int = 20,
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stop_loss_pct: float = 0.05,
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take_profit_pct: float = 0.20):
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self.ma_period = ma_period
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self.divergence_period = divergence_period
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self.stop_loss_pct = stop_loss_pct
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self.take_profit_pct = take_profit_pct
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self.name = "MACD底背离+均线"
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def check_buy_signal(self, data: pd.DataFrame, idx: int) -> bool:
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"""检查买入信号"""
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条件"""
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if idx < self.divergence_period + self.ma_period:
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return False
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# 1. 股价创近期新低（20日最低）
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recent_low = data['close'].iloc[idx-self.divergence_period:idx].min()
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current_price = data['close'].iloc[idx]
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if current_price > recent_low:
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return False
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# 2. MACD DIF没有创新低（底背离）
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dif, _, _ = TechnicalIndicators.calculate_macd(data['close'].values)
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recent_dif_low = dif[idx-self.divergence_period:idx].min()
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current_dif = dif[idx]
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if current_dif <= recent_dif_low:
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return False # 不是底背离
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# 3. 价格站上20日均线
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ma = TechnicalIndicators.calculate_sma(data['close'].values, self.ma_period)
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if current_price < ma[idx]:
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return False
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# 4. 检查背离确认（前一日价格也是低点）
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if idx > 0 and data['close'].iloc[idx-1] > recent_low:
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return False
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return True
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def check_sell_signal(self, data: pd.DataFrame, trade: Trade, idx: int) -> bool:
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"""检查卖出信号"""
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current_price = data['close'].iloc[idx]
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# 1. 破位20日均线
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ma = TechnicalIndicators.calculate_sma(data['close'].values, self.ma_period)
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if current_price < ma[idx]:
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return True
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# 2. 止损
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profit_pct = (current_price - trade.entry_price) / trade.entry_price
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if profit_pct <= -self.stop_loss_pct:
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return True
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# 3. 止盈
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if profit_pct >= self.take_profit_pct:
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return True
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return False
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class BollingerBandsStrategy:
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"""
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布林孺下轨 + 趋势过滤策略
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买入条件:
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1. 股价触及或跌破布林带下轨
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2. 均线系统多头排列 (MA5 > MA10 > MA20)
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3. RSI < 30 (超卖确认)
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卖出条件:
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1. 收盘价站上布林带中轨 (回归均值)
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2. 或跌破20日均线 (趋势破坏)
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3. 或止损/止盈
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"""
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def __init__(self, bb_period: int = 20,
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bb_std: float = 2.0,
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stop_loss_pct: float = 0.05,
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take_profit_pct: float = 0.15):
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self.bb_period = bb_period
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self.bb_std = bb_std
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self.stop_loss_pct = stop_loss_pct
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self.take_profit_pct = take_profit_pct
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self.name = "布林带下轨+趋势"
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def calculate_rsi(self, prices: np.ndarray, period: int = 14) -> np.ndarray:
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"""计算RSI"""
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delta = np.diff(prices)
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gain = np.where(delta > 0, delta, 0)
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loss = np.where(delta < 0, -delta, 0)
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avg_gain = np.zeros_like(prices)
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avg_loss = np.zeros_like(prices)
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if len(prices) > period:
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avg_gain[period] = np.mean(gain[:period])
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avg_loss[period] = np.mean(loss[:period])
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for i in range(period + 1, len(prices)):
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avg_gain[i] = (avg_gain[i-1] * (period - 1) + gain[i-1]) / period
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avg_loss[i] = (avg_loss[i-1] * (period - 1) + loss[i-1]) / period
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rs = avg_gain / (avg_loss + 1e-10)
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rsi = 100 - (100 / (1 + rs))
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return rsi
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def check_buy_signal(self, data: pd.DataFrame, idx: int) -> bool:
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"""检查买入信号"""
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if idx < self.bb_period + 20:
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return False
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current_price = data['close'].iloc[idx]
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# 1. 触及或跌破布林带下轨
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bb_upper, bb_mid, bb_lower = TechnicalIndicators.calculate_bollinger_bands(
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data['close'].values, self.bb_period, self.bb_std
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)
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if current_price > bb_lower[idx] * 1.02: # 允许2%误差
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return False
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# 2. 均线多头排列
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ma5 = TechnicalIndicators.calculate_sma(data['close'].values, 5)
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ma10 = TechnicalIndicators.calculate_sma(data['close'].values, 10)
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ma20 = TechnicalIndicators.calculate_sma(data['close'].values, 20)
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if not (ma5[idx] > ma10[idx] > ma20[idx]):
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return False
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# 3. RSI超卖
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rsi = self.calculate_rsi(data['close'].values)
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if rsi[idx] > 35: # 稍微放宽到35
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return False
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return True
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def check_sell_signal(self, data: pd.DataFrame, trade: Trade, idx: int) -> bool:
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"""检查卖出信号"""
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current_price = data['close'].iloc[idx]
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# 1. 回归中轨
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bb_upper, bb_mid, bb_lower = TechnicalIndicators.calculate_bollinger_bands(
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data['close'].values, self.bb_period, self.bb_std
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)
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if current_price >= bb_mid[idx]:
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return True
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# 2. 跌破20日均线（趋势破坏）
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ma20 = TechnicalIndicators.calculate_sma(data['close'].values, 20)
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if current_price < ma20[idx]:
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return True
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# 3. 止损
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profit_pct = (current_price - trade.entry_price) / trade.entry_price
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if profit_pct <= -self.stop_loss_pct:
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return True
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# 4. 止盈
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if profit_pct >= self.take_profit_pct:
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return True
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return False
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class DonchianChannelStrategy:
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"""
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唐奇安通道突破策略 (经典趋势跟踪)
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买入条件:
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1. 收盘价突破20日唐奇安通道上轨
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2. 成交量放大确认 (可选)
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卖出条件:
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1. 收盘价跌破10日唐奇安通道下轨
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2. 或ATR止损 (2倍ATR)
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"""
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def __init__(self, channel_period: int = 20,
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exit_period: int = 10,
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atr_period: int = 14,
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atr_multiplier: float = 2.0):
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self.channel_period = channel_period
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self.exit_period = exit_period
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self.atr_period = atr_period
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self.atr_multiplier = atr_multiplier
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self.name = "唐奇安通道突破"
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def check_buy_signal(self, data: pd.DataFrame, idx: int) -> bool:
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"""检查买入信号"""
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if idx < self.channel_period:
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return False
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current_price = data['close'].iloc[idx]
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# 1. 突破上轨
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dc_upper, dc_lower = TechnicalIndicators.calculate_donchian_channel(
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data['high'].values, data['low'].values, self.channel_period
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)
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# 前一日未突破，今日突破
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if idx > 0:
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prev_price = data['close'].iloc[idx-1]
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if prev_price > dc_upper[idx-1]:
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return False # 已经在通道上沿
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if current_price > dc_upper[idx]:
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return True
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return False
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def check_sell_signal(self, data: pd.DataFrame, trade: Trade, idx: int) -> bool:
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"""检查卖出信号"""
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current_price = data['close'].iloc[idx]
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# 1. 跌破10日通道下轨
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dc_upper_exit, dc_lower_exit = TechnicalIndicators.calculate_donchian_channel(
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data['high'].values, data['low'].values, self.exit_period
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)
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if current_price < dc_lower_exit[idx]:
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return True
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# 2. ATR止损
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atr = TechnicalIndicators.calculate_atr(
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data['high'].values, data['low'].values, data['close'].values, self.atr_period
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)
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stop_price = trade.entry_price - self.atr_multiplier * atr[idx]
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if current_price < stop_price:
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return True
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return False
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class BacktestEngine:
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"""回测引擎"""
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def __init__(self, initial_capital: float = 100000.0):
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self.initial_capital = initial_capital
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self.commission_rate = 0.0003 # 万三手续费
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def backtest(self, data: pd.DataFrame, strategy, strategy_name: str) -> BacktestResult:
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"""执行回测"""
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logger.info(f"开始回测策略: {strategy_name}")
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data = data.copy().reset_index(drop=True)
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capital = self.initial_capital
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trades: List[Trade] = []
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open_positions: Dict[str, Trade] = {}
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for idx in range(len(data)):
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current_date = data['date'].iloc[idx] if 'date' in data.columns else idx
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current_price = data['close'].iloc[idx]
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# 检查平仓信号
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for code, trade in list(open_positions.items()):
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if strategy.check_sell_signal(data, trade, idx):
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# 平仓
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exit_price = current_price
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commission = exit_price * trade.shares * self.commission_rate
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exit_value = exit_price * trade.shares - commission
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profit = exit_value - trade.entry_value
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profit_pct = profit / trade.entry_value
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trade.exit_date = current_date
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trade.exit_price = exit_price
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trade.exit_value = exit_value
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trade.profit = profit
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trade.profit_pct = profit_pct
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trade.hold_days = idx - data.index.get_loc(trade.entry_date) if hasattr(trade.entry_date, 'strftime') else 0
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capital += exit_value
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trades.append(trade)
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del open_positions[code]
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logger.debug(f"平仓 {code} @ {exit_price:.2f}, 收益: {profit_pct:.2%}")
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# 检查开仓信号
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if capital > 0:
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if strategy.check_buy_signal(data, idx):
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if len(open_positions) == 0: # 单持仓策略，简化回测
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code = data['code'].iloc[idx] if 'code' in data.columns else 'STOCK'
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# 固定仓位：80%资金
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position_size = capital * 0.8
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shares = int(position_size / current_price)
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if shares > 0:
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commission = current_price * shares * self.commission_rate
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entry_value = current_price * shares + commission
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if entry_value <= capital:
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trade = Trade(
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code=code,
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entry_date=current_date,
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exit_date=None,
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entry_price=current_price,
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exit_price=None,
|
|
direction=1,
|
|
shares=shares,
|
|
entry_value=entry_value,
|
|
exit_value=None,
|
|
profit=None,
|
|
profit_pct=None,
|
|
hold_days=None,
|
|
strategy=strategy_name
|
|
)
|
|
|
|
capital -= entry_value
|
|
open_positions[code] = trade
|
|
|
|
logger.debug(f"开仓 {code} @ {current_price:.2f}, 数量: {shares}")
|
|
|
|
# 强制平仓未结束的持仓
|
|
for code, trade in open_positions.items():
|
|
exit_price = data['close'].iloc[-1]
|
|
commission = exit_price * trade.shares * self.commission_rate
|
|
exit_value = exit_price * trade.shares - commission
|
|
profit = exit_value - trade.entry_value
|
|
profit_pct = profit / trade.entry_value
|
|
|
|
trade.exit_date = data['date'].iloc[-1] if 'date' in data.columns else len(data) - 1
|
|
trade.exit_price = exit_price
|
|
trade.exit_value = exit_value
|
|
trade.profit = profit
|
|
trade.profit_pct = profit_pct
|
|
trade.hold_days = len(data) - 1
|
|
|
|
capital += exit_value
|
|
trades.append(trade)
|
|
|
|
# 计算绩效指标
|
|
result = self._calculate_performance(
|
|
strategy_name, capital, trades, data
|
|
)
|
|
|
|
logger.info(f"回测完成: {strategy_name}, 总收益: {result.total_return:.2%}")
|
|
return result
|
|
|
|
def _calculate_performance(self, strategy_name: str,
|
|
final_capital: float,
|
|
trades: List[Trade],
|
|
data: pd.DataFrame) -> BacktestResult:
|
|
"""计算绩效指标"""
|
|
total_return = (final_capital - self.initial_capital) / self.initial_capital
|
|
|
|
# 计算年化收益
|
|
if 'date' in data.columns:
|
|
days = (data['date'].iloc[-1] - data['date'].iloc[0]).days
|
|
else:
|
|
days = len(data)
|
|
annual_return = (1 + total_return) ** (365 / days) - 1 if days > 0 else 0
|
|
|
|
# 最大回撤（简化版，基于交易）
|
|
peak = self.initial_capital
|
|
max_drawdown = 0
|
|
capital_curve = [self.initial_capital]
|
|
for trade in sorted(trades, key=lambda t: t.entry_date if hasattr(t.entry_date, 'strftime') else 0):
|
|
capital_curve.append(capital_curve[-1] + trade.profit)
|
|
peak = max(peak, capital_curve[-1])
|
|
drawdown = (peak - capital_curve[-1]) / peak
|
|
max_drawdown = max(max_drawdown, drawdown)
|
|
|
|
# 夏普比率（简化）
|
|
if len(trades) > 1:
|
|
returns = [t.profit_pct for t in trades if t.profit_pct is not None]
|
|
if returns:
|
|
mean_return = np.mean(returns)
|
|
std_return = np.std(returns)
|
|
sharpe_ratio = mean_return / std_return * np.sqrt(252) if std_return > 0 else 0
|
|
else:
|
|
sharpe_ratio = 0
|
|
else:
|
|
sharpe_ratio = 0
|
|
|
|
# 胜率
|
|
win_trades = [t for t in trades if t.profit_pct and t.profit_pct > 0]
|
|
loss_trades = [t for t in trades if t.profit_pct and t.profit_pct <= 0]
|
|
win_rate = len(win_trades) / len(trades) if trades else 0
|
|
|
|
# 平均收益
|
|
avg_profit_pct = np.mean([t.profit_pct for t in trades if t.profit_pct is not None]) if trades else 0
|
|
avg_win_pct = np.mean([t.profit_pct for t in win_trades]) if win_trades else 0
|
|
avg_loss_pct = np.mean([t.profit_pct for t in loss_trades]) if loss_trades else 0
|
|
|
|
return BacktestResult(
|
|
strategy=strategy_name,
|
|
start_date=data['date'].iloc[0] if 'date' in data.columns else 0,
|
|
end_date=data['date'].iloc[-1] if 'date' in data.columns else len(data) - 1,
|
|
initial_capital=self.initial_capital,
|
|
final_capital=final_capital,
|
|
total_return=total_return,
|
|
annual_return=annual_return,
|
|
max_drawdown=max_drawdown,
|
|
sharpe_ratio=sharpe_ratio,
|
|
win_rate=win_rate,
|
|
total_trades=len(trades),
|
|
win_trades=len(win_trades),
|
|
loss_trades=len(loss_trades),
|
|
avg_profit_pct=avg_profit_pct,
|
|
avg_win_pct=avg_win_pct,
|
|
avg_loss_pct=avg_loss_pct,
|
|
trades=trades
|
|
)
|
|
|
|
def print_result(self, result: BacktestResult):
|
|
"""打印回测结果"""
|
|
print("\n" + "=" * 80)
|
|
print(f"策略回测结果: {result.strategy}")
|
|
print("=" * 80)
|
|
print(f"回测期间: {result.start_date} ~ {result.end_date}")
|
|
print(f"初始资金: {result.initial_capital:,.2f}")
|
|
print(f"最终资金: {result.final_capital:,.2f}")
|
|
print("-" * 80)
|
|
print(f"总收益: {result.total_return:.2%}")
|
|
print(f"年化收益: {result.annual_return:.2%}")
|
|
print(f"最大回撤: {result.max_drawdown:.2%}")
|
|
print(f"夏普比率: {result.sharpe_ratio:.2f}")
|
|
print(f"胜率: {result.win_rate:.2%}")
|
|
print("-" * 80)
|
|
print(f"总交易次数: {result.total_trades}")
|
|
print(f"盈利次数: {result.win_trades}")
|
|
print(f"亏损次数: {result.loss_trades}")
|
|
print(f"平均收益: {result.avg_profit_pct:.2%}")
|
|
print(f"平均盈利: {result.avg_win_pct:.2%}")
|
|
print(f"平均亏损: {result.avg_loss_pct:.2%}")
|
|
print("=" * 80)
|
|
|
|
# 打印交易明细
|
|
if result.trades:
|
|
print("\n交易明细:")
|
|
print("-" * 80)
|
|
for i, trade in enumerate(result.trades, 1):
|
|
print(f"{i}. {trade.code}")
|
|
print(f" 买入: {trade.entry_price:.2f} @ {trade.entry_date}")
|
|
print(f" 卖出: {trade.exit_price:.2f} @ {trade.exit_date}")
|
|
print(f" 收益: {trade.profit_pct:.2%}, 持有: {trade.hold_days}天")
|
|
print("-" * 80)
|
|
|
|
|
|
def generate_sample_data(days: int = 500, seed: int = 42) -> pd.DataFrame:
|
|
"""生成模拟数据用于测试"""
|
|
np.random.seed(seed)
|
|
|
|
# 随机游走价格
|
|
returns = np.random.normal(0.001, 0.02, days)
|
|
prices = 100 * np.cumprod(1 + returns)
|
|
|
|
data = pd.DataFrame({
|
|
'date': pd.date_range(start='2024-01-01', periods=days, freq='D'),
|
|
'open': prices * (1 + np.random.uniform(-0.01, 0.01, days)),
|
|
'high': prices * (1 + np.abs(np.random.uniform(0, 0.02, days))),
|
|
'low': prices * (1 - np.abs(np.random.uniform(0, 0.02, days))),
|
|
'close': prices,
|
|
'volume': np.random.randint(1000000, 10000000, days),
|
|
'code': 'TEST001'
|
|
})
|
|
|
|
return data
|
|
|
|
|
|
def main():
|
|
"""主函数 - 演示三种策略回测"""
|
|
print("\n" + "=" * 80)
|
|
print("技术选股策略回测系统 - 张飞出品")
|
|
print("=" * 80)
|
|
|
|
# 生成模拟数据
|
|
print("\n生成模拟数据...")
|
|
data = generate_sample_data(days=500)
|
|
|
|
# 创建回测引擎
|
|
engine = BacktestEngine(initial_capital=100000.0)
|
|
|
|
# 1. MACD底背离+均线策略
|
|
print("\n" + "=" * 80)
|
|
print("策略1: MACD底背离 + 均线过滤")
|
|
print("=" * 80)
|
|
macd_strategy = MACDDivergenceStrategy(
|
|
ma_period=20,
|
|
divergence_period=20,
|
|
stop_loss_pct=0.05,
|
|
take_profit_pct=0.20
|
|
)
|
|
macd_result = engine.backtest(data, macd_strategy, "MACD底背离+均线")
|
|
engine.print_result(macd_result)
|
|
|
|
# 2. 布林带下轨+趋势策略
|
|
print("\n" + "=" * 80)
|
|
print("策略2: 布林带下轨 + 趋势过滤")
|
|
print("=" * 80)
|
|
bb_strategy = BollingerBandsStrategy(
|
|
bb_period=20,
|
|
bb_std=2.0,
|
|
stop_loss_pct=0.05,
|
|
take_profit_pct=0.15
|
|
)
|
|
bb_result = engine.backtest(data, bb_strategy, "布林带下轨+趋势")
|
|
engine.print_result(bb_result)
|
|
|
|
# 3. 唐奇安通道突破策略
|
|
print("\n" + "=" * 80)
|
|
print("策略3: 唐奇安通道突破")
|
|
print("=" * 80)
|
|
dc_strategy = DonchianChannelStrategy(
|
|
channel_period=20,
|
|
exit_period=10,
|
|
atr_period=14,
|
|
atr_multiplier=2.0
|
|
)
|
|
dc_result = engine.backtest(data, dc_strategy, "唐奇安通道突破")
|
|
engine.print_result(dc_result)
|
|
|
|
# 策略对比
|
|
print("\n" + "=" * 80)
|
|
print("策略对比汇总")
|
|
print("=" * 80)
|
|
comparison = pd.DataFrame({
|
|
'策略': [macd_result.strategy, bb_result.strategy, dc_result.strategy],
|
|
'总收益': [macd_result.total_return, bb_result.total_return, dc_result.total_return],
|
|
'年化收益': [macd_result.annual_return, bb_result.annual_return, dc_result.annual_return],
|
|
'最大回撤': [macd_result.max_drawdown, bb_result.max_drawdown, dc_result.max_drawdown],
|
|
'夏普比率': [macd_result.sharpe_ratio, bb_result.sharpe_ratio, dc_result.sharpe_ratio],
|
|
'胜率': [macd_result.win_rate, bb_result.win_rate, dc_result.win_rate],
|
|
'交易次数': [macd_result.total_trades, bb_result.total_trades, dc_result.total_trades],
|
|
})
|
|
print(comparison.to_string(index=False))
|
|
print("=" * 80)
|
|
|
|
return {
|
|
'macd_divergence': macd_result,
|
|
'bollinger_bands': bb_result,
|
|
'donchian_channel': dc_result
|
|
}
|
|
|
|
|
|
if __name__ == "__main__":
|
|
results = main()
|