cfdaily
95 lines
2.7 KiB
Python
95 lines
2.7 KiB
Python
"""
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ZYJ-20260401-001 - edict集成测试任务
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获取沪深300指数(000300.SH)最近5个交易日收盘价
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计算每日涨跌幅和5日平均收盘价
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"""
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import akshare as ak
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import pandas as pd
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from datetime import datetime
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# 获取沪深300指数历史数据
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print("🚀 开始获取沪深300指数(000300.SH)数据...")
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# 用akshare获取
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df = ak.stock_zh_index_daily(symbol="sh000300")
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# 将date列转为datetime并设置为索引
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df['date'] = pd.to_datetime(df['date'])
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df = df.set_index('date')
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# 获取最近5个交易日
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df_last5 = df.tail(5).copy()
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# 计算每日涨跌幅
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df_last5['pct_change'] = df_last5['close'].pct_change() * 100
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# 计算5日平均收盘价
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avg_close_5d = df_last5['close'].mean()
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# 格式化输出
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print("\n📊 沪深300指数最近5个交易日数据:")
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print("=" * 80)
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print(f"{'日期':<12} {'收盘价':>12} {'涨跌幅(%)':>10}")
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print("-" * 80)
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result_data = []
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for idx, row in df_last5.iterrows():
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date_str = idx.strftime("%Y-%m-%d")
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close = float(row['close'])
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pct = float(row['pct_change']) if not pd.isna(row['pct_change']) else None
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pct_str = f"{pct:.2f}" if pct is not None else "-"
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print(f"{date_str:<12} {close:>12.2f} {pct_str:>10}")
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result_data.append({
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'date': date_str,
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'close': close,
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'pct_change': pct
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})
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print("-" * 80)
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print(f"{'5日平均收盘价':<12} {avg_close_5d:>12.2f}")
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print("=" * 80)
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# 分析趋势
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last_close = result_data[-1]['close']
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first_close = result_data[0]['close']
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total_change = (last_close - first_close) / first_close * 100
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print(f"\n📈 趋势分析:")
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print(f"- 起始日期: {result_data[0]['date']}")
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print(f"- 起始收盘价: {first_close:.2f}")
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print(f"- 最新日期: {result_data[-1]['date']}")
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print(f"- 最新收盘价: {last_close:.2f}")
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print(f"- 5日累计涨跌幅: {total_change:.2f}%")
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if total_change > 1:
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trend = "明显上行趋势"
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elif total_change < -1:
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trend = "明显下行趋势"
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else:
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trend = "横盘震荡整理"
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print(f"- 趋势判断: {trend}")
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# 保存结果到文件
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output_file = "/Users/chufeng/.openclaw/sanguo_projects/sanguo_quant_live/zhaoyun-data/research/task-20260401-edict-test/result.csv"
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df_save = pd.DataFrame(result_data)
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df_save.to_csv(output_file, index=False)
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print(f"\n💾 结果已保存到: {output_file}")
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# 输出json格式结果
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result_json = {
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'task_id': 'ZYJ-20260401-001',
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'symbol': '000300.SH',
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'name': '沪深300指数',
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'data': result_data,
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'avg_close_5d': round(avg_close_5d, 2),
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'total_change_pct': round(total_change, 2),
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'trend': trend,
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'generated_at': datetime.now().isoformat()
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}
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print("\n📋 JSON结果:")
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import json
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print(json.dumps(result_json, indent=2, ensure_ascii=False))
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