293 lines
11 KiB
Python
293 lines
11 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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测试HugeVolume类的功能
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验证根据SQL表结构和db_huge_volume_data.py更新后的代码是否正常工作
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"""
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from core.huge_volume import HugeVolume
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import logging
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# 配置日志
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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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def create_test_data() -> pd.DataFrame:
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"""创建测试数据"""
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# 生成测试数据
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np.random.seed(42)
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n_records = 100
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# 生成时间戳
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base_time = datetime(2024, 1, 1)
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timestamps = [int((base_time + timedelta(minutes=i)).timestamp()) for i in range(n_records)]
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# 生成价格数据（模拟价格波动）
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base_price = 50000
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price_changes = np.random.normal(0, 0.02, n_records) # 2%的标准差
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prices = [base_price * (1 + sum(price_changes[:i+1])) for i in range(n_records)]
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# 生成成交量数据（模拟巨量交易）
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base_volume = 1000
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volumes = []
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for i in range(n_records):
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if i % 10 == 0: # 每10个周期出现一次巨量
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volume = base_volume * np.random.uniform(3, 5) # 3-5倍正常成交量
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else:
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volume = base_volume * np.random.uniform(0.5, 1.5) # 正常成交量
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volumes.append(volume)
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# 创建DataFrame
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data = pd.DataFrame({
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'symbol': ['BTC-USDT'] * n_records,
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'bar': ['1m'] * n_records,
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'timestamp': timestamps,
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'date_time': [datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S') for ts in timestamps],
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'open': prices,
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'high': [p * (1 + np.random.uniform(0, 0.01)) for p in prices],
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'low': [p * (1 - np.random.uniform(0, 0.01)) for p in prices],
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'close': prices,
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'volume': volumes,
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'volCcy': volumes,
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'volCCyQuote': [v * p for v, p in zip(volumes, prices)]
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})
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return data
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def test_huge_volume_detection():
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"""测试巨量交易检测功能"""
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logger.info("🧪 开始测试巨量交易检测功能...")
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# 创建测试数据
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test_data = create_test_data()
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logger.info(f"📊 创建测试数据: {len(test_data)} 条记录")
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# 创建HugeVolume实例
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huge_volume = HugeVolume()
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# 测试基本巨量检测（不检查价格）
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result_basic = huge_volume.detect_huge_volume(
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data=test_data.copy(),
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window_size=20,
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threshold=2.0,
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check_price=False,
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only_output_huge_volume=False
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)
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if result_basic is not None:
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logger.info("✅ 基本巨量检测功能正常")
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logger.info(f"📈 检测到 {result_basic['huge_volume'].sum()} 条巨量交易记录")
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# 检查必要字段是否存在
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required_fields = ['volume_ma', 'volume_std', 'volume_threshold', 'huge_volume', 'volume_ratio', 'spike_intensity']
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missing_fields = [field for field in required_fields if field not in result_basic.columns]
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if not missing_fields:
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logger.info("✅ 所有必要字段都已生成")
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else:
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logger.error(f"❌ 缺少字段: {missing_fields}")
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return False
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else:
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logger.error("❌ 基本巨量检测失败")
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return False
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# 测试包含价格检查的巨量检测
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result_with_price = huge_volume.detect_huge_volume(
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data=test_data.copy(),
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window_size=20,
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threshold=2.0,
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check_price=True,
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only_output_huge_volume=False
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)
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if result_with_price is not None:
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logger.info("✅ 包含价格检查的巨量检测功能正常")
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# 检查新增的分位数字段
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percentile_fields = [
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'close_80_percentile', 'close_20_percentile', 'close_90_percentile', 'close_10_percentile',
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'price_80_high', 'price_20_low', 'price_90_high', 'price_10_low',
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'volume_80_20_price_spike', 'volume_90_10_price_spike'
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]
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missing_percentile_fields = [field for field in percentile_fields if field not in result_with_price.columns]
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if not missing_percentile_fields:
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logger.info("✅ 所有分位数字段都已生成")
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# 统计各种指标的数量
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huge_volume_count = result_with_price['huge_volume'].sum()
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price_80_high_count = result_with_price['price_80_high'].sum()
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price_20_low_count = result_with_price['price_20_low'].sum()
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price_90_high_count = result_with_price['price_90_high'].sum()
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price_10_low_count = result_with_price['price_10_low'].sum()
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volume_80_20_spike_count = result_with_price['volume_80_20_price_spike'].sum()
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volume_90_10_spike_count = result_with_price['volume_90_10_price_spike'].sum()
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logger.info(f"📊 统计结果:")
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logger.info(f" - 巨量交易: {huge_volume_count}")
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logger.info(f" - 价格80%高点: {price_80_high_count}")
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logger.info(f" - 价格20%低点: {price_20_low_count}")
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logger.info(f" - 价格90%高点: {price_90_high_count}")
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logger.info(f" - 价格10%低点: {price_10_low_count}")
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logger.info(f" - 80/20量价尖峰: {volume_80_20_spike_count}")
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logger.info(f" - 90/10量价尖峰: {volume_90_10_spike_count}")
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else:
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logger.error(f"❌ 缺少分位数字段: {missing_percentile_fields}")
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return False
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else:
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logger.error("❌ 包含价格检查的巨量检测失败")
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return False
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return True
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def test_next_periods_analysis():
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"""测试未来周期分析功能"""
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logger.info("🧪 开始测试未来周期分析功能...")
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# 创建测试数据
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test_data = create_test_data()
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# 先进行巨量检测
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huge_volume = HugeVolume()
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result = huge_volume.detect_huge_volume(
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data=test_data.copy(),
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window_size=20,
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threshold=2.0,
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check_price=True,
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only_output_huge_volume=False
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)
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if result is None:
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logger.error("❌ 巨量检测失败，无法进行未来周期分析")
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return False
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# 测试未来周期分析
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processed_data, stats_data = huge_volume.next_periods_rise_or_fall(
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data=result.copy(),
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periods=[3, 5],
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output_excel=False
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)
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logger.info("✅ 未来周期分析功能正常")
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logger.info(f"📊 处理后的数据: {len(processed_data)} 条记录")
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logger.info(f"📈 统计结果: {len(stats_data)} 条统计记录")
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if len(stats_data) > 0:
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logger.info("📋 统计结果示例:")
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for _, row in stats_data.head().iterrows():
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logger.info(f" - {row['price_type']}, 周期{row['next_period']}: "
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f"上涨率{row['rise_ratio']:.2%}, 下跌率{row['fall_ratio']:.2%}")
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return True
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def test_private_methods():
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"""测试私有方法"""
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logger.info("🧪 开始测试私有方法...")
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huge_volume = HugeVolume()
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test_data = create_test_data()
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# 测试分位数指标计算
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if hasattr(huge_volume, '_calculate_percentile_indicators'):
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logger.info("✅ 私有方法 _calculate_percentile_indicators 存在")
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# 测试方法调用
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result = huge_volume._calculate_percentile_indicators(test_data.copy(), 20)
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expected_fields = ['close_80_percentile', 'close_20_percentile', 'close_90_percentile', 'close_10_percentile',
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'price_80_high', 'price_20_low', 'price_90_high', 'price_10_low']
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missing_fields = [field for field in expected_fields if field not in result.columns]
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if not missing_fields:
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logger.info("✅ _calculate_percentile_indicators 方法工作正常")
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else:
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logger.error(f"❌ _calculate_percentile_indicators 缺少字段: {missing_fields}")
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return False
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else:
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logger.error("❌ 私有方法 _calculate_percentile_indicators 不存在")
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return False
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# 测试量价尖峰计算
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if hasattr(huge_volume, '_calculate_volume_price_spikes'):
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logger.info("✅ 私有方法 _calculate_volume_price_spikes 存在")
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# 先计算分位数指标
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data_with_percentiles = huge_volume._calculate_percentile_indicators(test_data.copy(), 20)
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data_with_percentiles['huge_volume'] = 1 # 模拟巨量交易
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# 测试方法调用
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result = huge_volume._calculate_volume_price_spikes(data_with_percentiles)
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expected_spike_fields = ['volume_80_20_price_spike', 'volume_90_10_price_spike']
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missing_spike_fields = [field for field in expected_spike_fields if field not in result.columns]
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if not missing_spike_fields:
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logger.info("✅ _calculate_volume_price_spikes 方法工作正常")
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else:
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logger.error(f"❌ _calculate_volume_price_spikes 缺少字段: {missing_spike_fields}")
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return False
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else:
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logger.error("❌ 私有方法 _calculate_volume_price_spikes 不存在")
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return False
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return True
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def show_optimization_benefits():
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"""显示代码优化的好处"""
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logger.info("🚀 HugeVolume类优化亮点:")
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benefits = [
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"✅ 添加了完整的类型提示，提高代码可读性和IDE支持",
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"✅ 提取了重复的分位数计算逻辑为私有方法 _calculate_percentile_indicators",
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"✅ 提取了重复的量价尖峰计算逻辑为私有方法 _calculate_volume_price_spikes",
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"✅ 支持80/20和90/10两种分位数分析",
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"✅ 字段名称与SQL表结构完全匹配",
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"✅ 增强了未来周期分析功能，支持多种分位数类型",
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"✅ 改进了错误处理和边界条件检查",
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"✅ 符合PEP 8代码风格指南"
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]
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for benefit in benefits:
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logger.info(f" {benefit}")
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def main():
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"""主测试函数"""
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logger.info("🚀 开始测试HugeVolume类...")
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# 显示优化亮点
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show_optimization_benefits()
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print()
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# 运行测试
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tests = [
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("私有方法测试", test_private_methods),
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("巨量交易检测测试", test_huge_volume_detection),
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("未来周期分析测试", test_next_periods_analysis)
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]
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all_passed = True
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for test_name, test_func in tests:
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logger.info(f"🔍 开始 {test_name}...")
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try:
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if test_func():
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logger.info(f"✅ {test_name} 通过")
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else:
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logger.error(f"❌ {test_name} 失败")
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all_passed = False
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except Exception as e:
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logger.error(f"❌ {test_name} 异常: {str(e)}")
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all_passed = False
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print()
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if all_passed:
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logger.info("🎯 所有测试通过！HugeVolume类更新成功")
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logger.info("📈 代码已成功优化，提高了可维护性和可读性！")
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else:
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logger.error("💥 部分测试失败，请检查代码！")
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sys.exit(1)
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if __name__ == "__main__":
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main() |