232 lines
8.3 KiB
Python
232 lines
8.3 KiB
Python
"""
|
||
均线交叉检测优化算法测试脚本
|
||
|
||
测试优化后的ma5102030方法，验证多均线同时交叉的检测效果
|
||
"""
|
||
|
||
import pandas as pd
|
||
import numpy as np
|
||
import matplotlib.pyplot as plt
|
||
from core.biz.metrics_calculation import MetricsCalculation
|
||
import logging
|
||
import os
|
||
|
||
# plt支持中文
|
||
plt.rcParams['font.family'] = ['SimHei']
|
||
|
||
# 设置日志
|
||
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
|
||
|
||
def generate_test_data_with_crosses(n=200):
|
||
"""生成包含多个均线交叉的测试数据"""
|
||
np.random.seed(42)
|
||
|
||
# 生成价格数据，包含明显的趋势变化
|
||
price = 100
|
||
prices = []
|
||
|
||
for i in range(n):
|
||
if i < 50:
|
||
# 第一阶段：下跌趋势
|
||
price -= 0.5 + np.random.normal(0, 0.3)
|
||
elif i < 100:
|
||
# 第二阶段：震荡
|
||
price += np.random.normal(0, 0.5)
|
||
elif i < 150:
|
||
# 第三阶段：强势上涨
|
||
price += 1.0 + np.random.normal(0, 0.3)
|
||
else:
|
||
# 第四阶段：回调
|
||
price -= 0.3 + np.random.normal(0, 0.4)
|
||
|
||
prices.append(max(price, 50)) # 确保价格不会太低
|
||
|
||
# 创建DataFrame
|
||
data = pd.DataFrame({
|
||
'timestamp': pd.date_range('2023-01-01', periods=n, freq='H'),
|
||
'close': prices,
|
||
'open': [p * (1 + np.random.normal(0, 0.01)) for p in prices],
|
||
'high': [p * (1 + abs(np.random.normal(0, 0.02))) for p in prices],
|
||
'low': [p * (1 - abs(np.random.normal(0, 0.02))) for p in prices],
|
||
'volume': np.random.randint(1000, 10000, n)
|
||
})
|
||
|
||
return data
|
||
|
||
def test_ma_cross_optimization():
|
||
"""测试优化后的均线交叉检测"""
|
||
print("=== 均线交叉检测优化测试 ===\n")
|
||
|
||
# 生成测试数据
|
||
data = generate_test_data_with_crosses(200)
|
||
print(f"生成测试数据: {len(data)} 条记录")
|
||
|
||
# 初始化指标计算器
|
||
metrics = MetricsCalculation()
|
||
|
||
# 计算均线
|
||
data = metrics.ma5102030(data)
|
||
|
||
data = metrics.calculate_ma_price_percent(data)
|
||
|
||
# 分析交叉信号
|
||
cross_signals = data[data['ma_cross'] != '']
|
||
print(f"\n检测到 {len(cross_signals)} 个交叉信号")
|
||
|
||
if len(cross_signals) > 0:
|
||
print("\n交叉信号详情:")
|
||
for idx, row in cross_signals.iterrows():
|
||
print(f"时间: {row['timestamp']}, 信号: {row['ma_cross']}")
|
||
|
||
# 统计不同类型的交叉
|
||
cross_types = {}
|
||
for signal in data['ma_cross'].unique():
|
||
if signal != '':
|
||
count = (data['ma_cross'] == signal).sum()
|
||
cross_types[signal] = count
|
||
|
||
print(f"\n交叉类型统计:")
|
||
for cross_type, count in sorted(cross_types.items()):
|
||
print(f"{cross_type}: {count} 次")
|
||
|
||
return data
|
||
|
||
def visualize_ma_crosses(data):
|
||
"""可视化均线交叉信号"""
|
||
print("\n=== 生成均线交叉可视化图表 ===")
|
||
|
||
# 选择数据
|
||
plot_data = data.copy()
|
||
|
||
# 创建图表
|
||
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10))
|
||
fig.suptitle('均线交叉检测优化效果', fontsize=16)
|
||
|
||
# 价格和均线图
|
||
ax1.plot(plot_data.index, plot_data['close'], label='价格', alpha=0.7, linewidth=1)
|
||
ax1.plot(plot_data.index, plot_data['ma5'], label='MA5', alpha=0.8, linewidth=1.5)
|
||
ax1.plot(plot_data.index, plot_data['ma10'], label='MA10', alpha=0.8, linewidth=1.5)
|
||
ax1.plot(plot_data.index, plot_data['ma20'], label='MA20', alpha=0.8, linewidth=1.5)
|
||
ax1.plot(plot_data.index, plot_data['ma30'], label='MA30', alpha=0.8, linewidth=1.5)
|
||
|
||
# 标记交叉点
|
||
cross_points = plot_data[plot_data['ma_cross'] != '']
|
||
for idx, row in cross_points.iterrows():
|
||
ax1.scatter(idx, row['close'], color='red', s=100, alpha=0.8, zorder=5)
|
||
ax1.annotate(row['ma_cross'], (idx, row['close']),
|
||
xytext=(10, 10), textcoords='offset points',
|
||
fontsize=8, bbox=dict(boxstyle='round,pad=0.3', facecolor='yellow', alpha=0.7))
|
||
|
||
ax1.set_title('价格、均线和交叉信号')
|
||
ax1.legend()
|
||
ax1.grid(True, alpha=0.3)
|
||
|
||
# 均线偏离度图
|
||
ax2.plot(plot_data.index, plot_data['ma5_close_diff'], label='MA5偏离度', alpha=0.7)
|
||
ax2.plot(plot_data.index, plot_data['ma10_close_diff'], label='MA10偏离度', alpha=0.7)
|
||
ax2.plot(plot_data.index, plot_data['ma20_close_diff'], label='MA20偏离度', alpha=0.7)
|
||
ax2.plot(plot_data.index, plot_data['ma30_close_diff'], label='MA30偏离度', alpha=0.7)
|
||
ax2.axhline(y=0, color='black', linestyle='--', alpha=0.5)
|
||
|
||
# 标记交叉点
|
||
for idx, row in cross_points.iterrows():
|
||
ax2.scatter(idx, 0, color='red', s=100, alpha=0.8, zorder=5)
|
||
|
||
ax2.set_title('均线偏离度和交叉信号')
|
||
ax2.legend()
|
||
ax2.grid(True, alpha=0.3)
|
||
|
||
plt.tight_layout()
|
||
folder = "./output/algorithm/"
|
||
os.makedirs(folder, exist_ok=True)
|
||
file_name = f"{folder}/ma_cross_optimization.png"
|
||
plt.savefig(file_name, dpi=300, bbox_inches='tight')
|
||
print("图表已保存为: ma_cross_optimization.png")
|
||
plt.show()
|
||
|
||
def analyze_cross_combinations(data):
|
||
"""分析交叉组合的效果"""
|
||
print("\n=== 交叉组合分析 ===")
|
||
|
||
# 获取所有交叉信号
|
||
cross_data = data[data['ma_cross'] != ''].copy()
|
||
|
||
if len(cross_data) == 0:
|
||
print("未检测到交叉信号")
|
||
return
|
||
|
||
# 分析组合信号
|
||
combination_signals = cross_data[cross_data['ma_cross'].str.contains('，')]
|
||
single_signals = cross_data[~cross_data['ma_cross'].str.contains('，')]
|
||
|
||
print(f"组合交叉信号: {len(combination_signals)} 个")
|
||
print(f"单一交叉信号: {len(single_signals)} 个")
|
||
|
||
if len(combination_signals) > 0:
|
||
print("\n组合交叉信号详情:")
|
||
for idx, row in combination_signals.iterrows():
|
||
print(f"时间: {row['timestamp']}, 组合信号: {row['ma_cross']}")
|
||
|
||
# 分析上穿和下穿信号
|
||
up_cross_signals = cross_data[cross_data['ma_cross'].str.contains('上穿')]
|
||
down_cross_signals = cross_data[cross_data['ma_cross'].str.contains('下穿')]
|
||
|
||
print(f"\n上穿信号: {len(up_cross_signals)} 个")
|
||
print(f"下穿信号: {len(down_cross_signals)} 个")
|
||
|
||
# 统计各种交叉类型
|
||
print(f"\n详细交叉类型统计:")
|
||
cross_type_counts = {}
|
||
for signal in cross_data['ma_cross'].unique():
|
||
if signal != '':
|
||
count = (cross_data['ma_cross'] == signal).sum()
|
||
cross_type_counts[signal] = count
|
||
|
||
# 按类型分组显示
|
||
up_cross_types = {k: v for k, v in cross_type_counts.items() if '上穿' in k}
|
||
down_cross_types = {k: v for k, v in cross_type_counts.items() if '下穿' in k}
|
||
|
||
print(f"\n上穿信号类型:")
|
||
for cross_type, count in sorted(up_cross_types.items()):
|
||
print(f" {cross_type}: {count} 次")
|
||
|
||
print(f"\n下穿信号类型:")
|
||
for cross_type, count in sorted(down_cross_types.items()):
|
||
print(f" {cross_type}: {count} 次")
|
||
|
||
# 分析信号强度
|
||
print(f"\n信号强度分析:")
|
||
print(f"总交叉信号: {len(cross_data)}")
|
||
print(f"组合信号占比: {len(combination_signals)/len(cross_data)*100:.1f}%")
|
||
print(f"单一信号占比: {len(single_signals)/len(cross_data)*100:.1f}%")
|
||
print(f"上穿信号占比: {len(up_cross_signals)/len(cross_data)*100:.1f}%")
|
||
print(f"下穿信号占比: {len(down_cross_signals)/len(cross_data)*100:.1f}%")
|
||
|
||
def main():
|
||
"""主函数"""
|
||
print("开始测试均线交叉检测优化...")
|
||
|
||
# 测试优化算法
|
||
data = test_ma_cross_optimization()
|
||
|
||
# 分析交叉组合
|
||
analyze_cross_combinations(data)
|
||
|
||
# 可视化结果
|
||
try:
|
||
visualize_ma_crosses(data)
|
||
except Exception as e:
|
||
print(f"可视化失败: {e}")
|
||
|
||
print("\n=== 测试完成 ===")
|
||
print("\n优化效果:")
|
||
print("1. 能够检测多个均线同时交叉的情况")
|
||
print("2. 更好地识别趋势转变的关键时刻")
|
||
print("3. 提供更丰富的技术分析信息")
|
||
print("4. 减少信号噪音，提高信号质量")
|
||
print("5. 支持完整的均线交叉类型：5上穿10/20/30，10上穿20/30，20上穿30")
|
||
print("6. 支持对应的下穿信号：10下穿5，20下穿10/5，30下穿20/10/5")
|
||
print("7. 使用更清晰的上穿/下穿命名规范")
|
||
|
||
if __name__ == "__main__":
|
||
main() |