#!/usr/bin/env python3 """ 玉米期货投研PPT图表生成脚本 """ import pandas as pd import matplotlib.pyplot as plt import matplotlib matplotlib.use('Agg') import numpy as np # 设置中文字体 plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans', 'WenQuanYi Micro Hei', 'Arial Unicode MS'] plt.rcParams['axes.unicode_minus'] = False # 投研风格配色 COLORS = { 'blue': '#1a365d', 'gold': '#d69e2e', 'red': '#c53030', 'green': '#2f855a', 'gray': '#718096', 'light_blue': '#3182ce', 'bg': '#f7fafc' } def load_data(): """加载数据""" corn = pd.read_csv('/root/.openclaw/workspace/corn_data.csv') meal = pd.read_csv('/root/.openclaw/workspace/meal_data.csv') starch = pd.read_csv('/root/.openclaw/workspace/starch_data.csv') hog = pd.read_csv('/root/.openclaw/workspace/hog_data.csv') return corn, meal, starch, hog def create_price_chart(corn): """生成玉米价格走势图""" fig, ax = plt.subplots(figsize=(12, 6)) ax.set_facecolor(COLORS['bg']) dates = pd.to_datetime(corn['date']) closes = corn['close'].values # 绘制K线图简化版（收盘价曲线） ax.plot(dates, closes, color=COLORS['blue'], linewidth=2, label='收盘价') ax.fill_between(dates, closes, min(closes)*0.98, alpha=0.3, color=COLORS['light_blue']) # 添加均线 corn['ma5'] = corn['close'].rolling(5).mean() corn['ma10'] = corn['close'].rolling(10).mean() corn['ma20'] = corn['close'].rolling(20).mean() ax.plot(dates, corn['ma5'], color=COLORS['gold'], linewidth=1, linestyle='--', label='MA5') ax.plot(dates, corn['ma10'], color=COLORS['red'], linewidth=1, linestyle='--', label='MA10') ax.plot(dates, corn['ma20'], color=COLORS['green'], linewidth=1, linestyle='--', label='MA20') ax.set_title('玉米期货主力合约价格走势（60日）', fontsize=16, fontweight='bold', color=COLORS['blue']) ax.set_xlabel('日期', fontsize=12) ax.set_ylabel('价格（元/吨）', fontsize=12) ax.legend(loc='upper left') ax.grid(True, alpha=0.3) plt.tight_layout() plt.savefig('/root/.openclaw/workspace/ppt_images/chart1_price.png', dpi=150, bbox_inches='tight') plt.close() print("✅ 图表1: 价格走势图 完成") def create_volume_chart(corn): """生成持仓量变化图""" fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8), gridspec_kw={'height_ratios': [2, 1]}) dates = pd.to_datetime(corn['date']) # 持仓量 ax1.bar(dates, corn['hold']/10000, color=COLORS['light_blue'], alpha=0.7) ax1.set_title('持仓量变化（60日）', fontsize=14, fontweight='bold', color=COLORS['blue']) ax1.set_ylabel('持仓量（万手）', fontsize=11) ax1.grid(True, alpha=0.3, axis='y') # 成交量 ax2.bar(dates, corn['volume']/10000, color=COLORS['gold'], alpha=0.7) ax2.set_title('成交量变化（60日）', fontsize=14, fontweight='bold', color=COLORS['blue']) ax2.set_xlabel('日期', fontsize=11) ax2.set_ylabel('成交量（万手）', fontsize=11) ax2.grid(True, alpha=0.3, axis='y') plt.tight_layout() plt.savefig('/root/.openclaw/workspace/ppt_images/chart2_volume.png', dpi=150, bbox_inches='tight') plt.close() print("✅ 图表2: 持仓量/成交量图 完成") def create_correlation_chart(corn, meal, starch, hog): """生成相关品种对比图""" fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # 归一化数据（以第一个值为基准） def normalize(series): return series / series.iloc[0] * 100 dates = pd.to_datetime(corn['date']) # 玉米 ax1 = axes[0, 0] ax1.plot(dates, normalize(corn['close']), color=COLORS['blue'], linewidth=2) ax1.set_title('玉米期货', fontsize=14, fontweight='bold') ax1.set_ylabel('价格指数', fontsize=11) ax1.grid(True, alpha=0.3) ax1.axhline(y=100, color='gray', linestyle='--', alpha=0.5) # 豆粕 ax2 = axes[0, 1] ax2.plot(dates, normalize(meal['close']), color=COLORS['red'], linewidth=2) ax2.set_title('豆粕期货', fontsize=14, fontweight='bold') ax2.set_ylabel('价格指数', fontsize=11) ax2.grid(True, alpha=0.3) ax2.axhline(y=100, color='gray', linestyle='--', alpha=0.5) # 淀粉 ax3 = axes[1, 0] ax3.plot(dates, normalize(starch['close']), color=COLORS['gold'], linewidth=2) ax3.set_title('淀粉期货', fontsize=14, fontweight='bold') ax3.set_xlabel('日期', fontsize=11) ax3.set_ylabel('价格指数', fontsize=11) ax3.grid(True, alpha=0.3) ax3.axhline(y=100, color='gray', linestyle='--', alpha=0.5) # 生猪 ax4 = axes[1, 1] ax4.plot(dates, normalize(hog['close']), color=COLORS['green'], linewidth=2) ax4.set_title('生猪期货', fontsize=14, fontweight='bold') ax4.set_xlabel('日期', fontsize=11) ax4.set_ylabel('价格指数', fontsize=11) ax4.grid(True, alpha=0.3) ax4.axhline(y=100, color='gray', linestyle='--', alpha=0.5) plt.suptitle('相关品种价格走势对比（归一化）', fontsize=16, fontweight='bold', color=COLORS['blue'], y=1.02) plt.tight_layout() plt.savefig('/root/.openclaw/workspace/ppt_images/chart3_correlation.png', dpi=150, bbox_inches='tight') plt.close() print("✅ 图表3: 相关品种对比图 完成") def create_summary_table(corn): """生成关键数据汇总""" latest = corn.iloc[-1] prev = corn.iloc[-5] if len(corn) >= 5 else corn.iloc[0] data = { '指标': ['最新收盘价', '60日最高', '60日最低', 'MA5', 'MA10', 'MA20', '最新持仓', '周涨幅', '月涨幅'], '数值': [ f'{latest["close"]:.0f}元/吨', f'{corn["high"].max():.0f}元/吨', f'{corn["low"].min():.0f}元/吨', f'{corn["close"].tail(5).mean():.1f}元/吨', f'{corn["close"].tail(10).mean():.1f}元/吨', f'{corn["close"].tail(20).mean():.1f}元/吨', f'{latest["hold"]/10000:.1f}万手', f'{(latest["close"]/prev["close"]-1)*100:.2f}%', f'{(latest["close"]/corn.iloc[0]["close"]-1)*100:.2f}%' ] } df = pd.DataFrame(data) fig, ax = plt.subplots(figsize=(10, 4)) ax.axis('off') table = ax.table(cellText=df.values, colLabels=df.columns, cellLoc='center', loc='center', colColours=[COLORS['blue']]*2) table.auto_set_font_size(False) table.set_fontsize(12) table.scale(1.2, 2) # 设置表头颜色 for i in range(2): table[(0, i)].set_text_props(color='white', fontweight='bold') plt.title('玉米期货关键数据汇总', fontsize=16, fontweight='bold', color=COLORS['blue'], pad=20) plt.tight_layout() plt.savefig('/root/.openclaw/workspace/ppt_images/chart4_summary.png', dpi=150, bbox_inches='tight') plt.close() print("✅ 图表4: 关键数据汇总表 完成") def main(): import os os.makedirs('/root/.openclaw/workspace/ppt_images', exist_ok=True) print("📊 开始生成图表...") corn, meal, starch, hog = load_data() create_price_chart(corn) create_volume_chart(corn) create_correlation_chart(corn, meal, starch, hog) create_summary_table(corn) print("\n✅ 所有图表生成完成！") print("📁 图表保存在: /root/.openclaw/workspace/ppt_images/") if __name__ == '__main__': main()