Fix Chinese font rendering in all chart outputs

- Add src/font_config.py: centralized font detection that auto-selects
  from Noto Sans SC > Hiragino Sans GB > STHeiti > Arial Unicode MS
- Replace hardcoded font lists in all 18 modules with unified config
- Add .gitignore for __pycache__, .DS_Store, venv, etc.
- Regenerate all 70 charts with correct Chinese rendering

Previously, 7 modules (fft, wavelet, acf, fractal, hurst, indicators,
patterns) had no Chinese font config at all, causing □□□ rendering.
The remaining 11 modules used a hardcoded fallback list that didn't
prioritize the best available system font.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.gitignore

@@ -0,0 +1,31 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.egg-info/
+*.egg
+dist/
+build/
+
+# Virtual environments
+.venv/
+venv/
+env/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/
+
+# Jupyter
+.ipynb_checkpoints/

output/综合结论报告.txt

@@ -15,8 +15,23 @@ BTC/USDT 价格规律性分析 — 综合结论报告
 ----------------------------------------------------------------------
 模块                                 得分         强度      发现数
 ----------------------------------------------------------------------
+fft                              0.00       none        0
+fractal                          0.00       none        0
+power_law                        0.00       none        0
+wavelet                          0.00       none        0
+acf                              0.00       none        0
+returns                          0.00       none        0
+volatility                       0.00       none        0
+hurst                            0.00       none        0
+volume_price                     0.00       none        0
+time_series                      0.00       none        0
+causality                        0.00       none        0
+calendar                         0.00       none        0
+halving                          0.00       none        0
 indicators                       0.00       none        0
 patterns                         0.00       none        0
+clustering                       0.00       none        0
+anomaly                          0.00       none        0
 ----------------------------------------------------------------------
 
 ## 强证据规律（可重复、有经济意义）:
@@ -26,8 +41,23 @@ patterns                         0.00       none        0
   （无）
 
 ## 弱证据/不显著:
-  * indicators
+  * fft
+  * time_series
+  * clustering
   * patterns
+  * indicators
+  * halving
+  * calendar
+  * causality
+  * volume_price
+  * fractal
+  * hurst
+  * volatility
+  * returns
+  * acf
+  * wavelet
+  * power_law
+  * anomaly
 
 ======================================================================
 注: 得分基于各模块自报告的统计检验结果。

src/acf_analysis.py

@@ -10,6 +10,9 @@ import pandas as pd
 import matplotlib
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
+
+from src.font_config import configure_chinese_font
+configure_chinese_font()
 from statsmodels.tsa.stattools import acf, pacf
 from statsmodels.stats.diagnostic import acorr_ljungbox
 from pathlib import Path

src/anomaly.py

@@ -705,9 +705,8 @@ def run_anomaly_analysis(
     print(f"数据范围: {df.index.min()} ~ {df.index.max()}")
     print(f"样本数量: {len(df)}")
 
-    # 设置中文字体
-    plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
-    plt.rcParams['axes.unicode_minus'] = False
+    from src.font_config import configure_chinese_font
+    configure_chinese_font()
 
     # --- 集成异常检测 ---
     print("\n>>> [1/5] 执行集成异常检测...")

src/calendar_analysis.py

@@ -12,9 +12,8 @@ from pathlib import Path
 from itertools import combinations
 from scipy import stats
 
-# 中文显示配置
-plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
-plt.rcParams['axes.unicode_minus'] = False
+from src.font_config import configure_chinese_font
+configure_chinese_font()
 
 # 星期名称映射（中英文）
 WEEKDAY_NAMES_CN = {0: '周一', 1: '周二', 2: '周三', 3: '周四',

src/causality.py

@@ -543,9 +543,8 @@ def run_causality_analysis(
     print(f"因果变量对数: {len(CAUSALITY_PAIRS)}")
     print(f"总检验次数（含所有滞后）: {len(CAUSALITY_PAIRS) * len(TEST_LAGS)}")
 
-    # 设置中文字体
-    plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
-    plt.rcParams['axes.unicode_minus'] = False
+    from src.font_config import configure_chinese_font
+    configure_chinese_font()
 
     # --- 日线级 Granger 因果检验 ---
     print("\n>>> [1/4] 执行日线级 Granger 因果检验...")

src/clustering.py

@@ -632,9 +632,8 @@ def run_clustering_analysis(df: pd.DataFrame, output_dir: "str | Path" = "output
     output_dir = Path(output_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
 
-    # 设置中文字体（macOS）
-    plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
-    plt.rcParams['axes.unicode_minus'] = False
+    from src.font_config import configure_chinese_font
+    configure_chinese_font()
 
     print("=" * 60)
     print("  BTC 市场状态聚类与马尔可夫链分析")

src/fft_analysis.py

@@ -3,6 +3,9 @@
 import matplotlib
 matplotlib.use("Agg")
 
+from src.font_config import configure_chinese_font
+configure_chinese_font()
+
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt

src/font_config.py

@@ -0,0 +1,60 @@
+"""
+统一 matplotlib 中文字体配置。
+
+所有绘图模块在创建图表前应调用 configure_chinese_font()。
+"""
+
+import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib.font_manager as fm
+
+_configured = False
+
+# 按优先级排列的中文字体候选列表
+_CHINESE_FONT_CANDIDATES = [
+    'Noto Sans SC',       # Google 思源黑体（最佳渲染质量）
+    'Hiragino Sans GB',   # macOS 系统自带
+    'STHeiti',            # macOS 系统自带
+    'Arial Unicode MS',   # macOS/Windows 通用
+    'SimHei',             # Windows 黑体
+    'WenQuanYi Micro Hei',  # Linux 文泉驿
+    'DejaVu Sans',        # 最终回退（不支持中文，但不会崩溃）
+]
+
+
+def _find_available_chinese_fonts():
+    """检测系统中实际可用的中文字体。"""
+    available = []
+    for font_name in _CHINESE_FONT_CANDIDATES:
+        try:
+            path = fm.findfont(
+                fm.FontProperties(family=font_name),
+                fallback_to_default=False
+            )
+            if path and 'LastResort' not in path:
+                available.append(font_name)
+        except Exception:
+            continue
+    return available if available else ['DejaVu Sans']
+
+
+def configure_chinese_font():
+    """
+    配置 matplotlib 使用中文字体。
+
+    - 自动检测系统可用的中文字体
+    - 设置 sans-serif 字体族
+    - 修复负号显示问题
+    - 仅在首次调用时执行，后续调用为空操作
+    """
+    global _configured
+    if _configured:
+        return
+
+    available = _find_available_chinese_fonts()
+
+    plt.rcParams['font.sans-serif'] = available
+    plt.rcParams['axes.unicode_minus'] = False
+    plt.rcParams['font.family'] = 'sans-serif'
+
+    _configured = True

src/fractal_analysis.py

@@ -13,6 +13,9 @@
 import matplotlib
 matplotlib.use('Agg')
 
+from src.font_config import configure_chinese_font
+configure_chinese_font()
+
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt

src/halving_analysis.py

@@ -10,9 +10,8 @@ import matplotlib.ticker as mticker
 from pathlib import Path
 from scipy import stats
 
-# 中文显示配置
-plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
-plt.rcParams['axes.unicode_minus'] = False
+from src.font_config import configure_chinese_font
+configure_chinese_font()
 
 # BTC 减半日期（数据范围 2017-2026 内的两次减半）
 HALVING_DATES = [

src/hurst_analysis.py

@@ -15,6 +15,9 @@ Hurst指数分析模块
 import matplotlib
 matplotlib.use('Agg')
 
+from src.font_config import configure_chinese_font
+configure_chinese_font()
+
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt

src/indicators.py

@@ -9,6 +9,9 @@
 import matplotlib
 matplotlib.use('Agg')
 
+from src.font_config import configure_chinese_font
+configure_chinese_font()
+
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt

src/patterns.py

@@ -8,6 +8,9 @@ K线形态识别与统计验证模块
 import matplotlib
 matplotlib.use('Agg')
 
+from src.font_config import configure_chinese_font
+configure_chinese_font()
+
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt

src/power_law_analysis.py

@@ -15,9 +15,8 @@ from scipy.optimize import curve_fit
 from pathlib import Path
 from typing import Tuple, Dict
 
-# 中文显示支持
-plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
-plt.rcParams['axes.unicode_minus'] = False
+from src.font_config import configure_chinese_font
+configure_chinese_font()
 
 
 def _compute_days_since_start(df: pd.DataFrame) -> np.ndarray:

src/returns_analysis.py

@@ -446,9 +446,8 @@ def run_returns_analysis(df: pd.DataFrame, output_dir: str = "output/returns"):
     # --- 生成可视化 ---
     print("\n>>> 生成可视化图表...")
 
-    # 设置中文字体（兼容多系统）
-    plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
-    plt.rcParams['axes.unicode_minus'] = False
+    from src.font_config import configure_chinese_font
+    configure_chinese_font()
 
     plot_histogram_vs_normal(daily_returns, output_dir)
     plot_qq(daily_returns, output_dir)

src/time_series.py

@@ -643,9 +643,8 @@ def run_time_series_analysis(df: pd.DataFrame, output_dir: "str | Path" = "outpu
     output_dir = Path(output_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
 
-    # 设置中文字体（macOS）
-    plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
-    plt.rcParams['axes.unicode_minus'] = False
+    from src.font_config import configure_chinese_font
+    configure_chinese_font()
 
     print("=" * 60)
     print("  BTC 时间序列预测分析")

src/visualization.py

@@ -55,11 +55,8 @@ EVIDENCE_COLORS = {
 def apply_style():
     """应用全局matplotlib样式"""
     plt.rcParams.update(STYLE_CONFIG)
-    try:
-        plt.rcParams["font.sans-serif"] = ["Arial Unicode MS", "SimHei", "DejaVu Sans"]
-        plt.rcParams["axes.unicode_minus"] = False
-    except Exception:
-        pass
+    from src.font_config import configure_chinese_font
+    configure_chinese_font()
 
 
 def ensure_dir(path):

src/volatility_analysis.py

@@ -584,9 +584,8 @@ def run_volatility_analysis(df: pd.DataFrame, output_dir: str = "output/volatili
     daily_returns = log_returns(df['close'])
     print(f"日对数收益率样本数: {len(daily_returns)}")
 
-    # 设置中文字体（兼容多系统）
-    plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
-    plt.rcParams['axes.unicode_minus'] = False
+    from src.font_config import configure_chinese_font
+    configure_chinese_font()
 
     # 固定随机种子以保证杠杆效应散点图采样可复现
     np.random.seed(42)

src/volume_price_analysis.py

@@ -15,9 +15,8 @@ from statsmodels.tsa.stattools import grangercausalitytests
 from pathlib import Path
 from typing import Dict, List, Tuple
 
-# 中文显示支持
-plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
-plt.rcParams['axes.unicode_minus'] = False
+from src.font_config import configure_chinese_font
+configure_chinese_font()
 
 
 # =============================================================================

src/wavelet_analysis.py

@@ -3,6 +3,9 @@
 import matplotlib
 matplotlib.use('Agg')
 
+from src.font_config import configure_chinese_font
+configure_chinese_font()
+
 import numpy as np
 import pandas as pd
 import pywt