Add: STL with multiple frequency (1d and 1h)

STL.py

@@ -11,7 +11,7 @@
 warnings.filterwarnings("ignore")
 
 # ========== 配置参数 ==========
-START_DATE = "2024-08-01"
+START_DATE = "2023-01-01"
 DATA_DIR = "./data/BTC_factors"
 
 # 频率到周期的映射（日周期）
@@ -165,30 +165,63 @@ def process_single_factor(
             trend_strength = trend_var / (trend_var + resid_var + eps)
             seasonal_strength = seasonal_var / (seasonal_var + resid_var + eps)
 
-        # === 保存图像 ===
+        # === 保存图像（含 ACF/PACF）===
+        from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
+
         decomposition_type = "multiplicative" if use_multiplicative else "additive"
         image_path = os.path.join(
             IMAGE_DIR, f"{factor_name}_{original_frequency}_{decomposition_type}.png"
         )
-        fig, axes = plt.subplots(4, 1, figsize=(12, 10))
 
+        # 创建 6 个子图：observed, trend, seasonal, resid, ACF, PACF
+        fig, axes = plt.subplots(6, 1, figsize=(12, 14))
+
+        # 1. Observed
         if use_multiplicative:
             observed.plot(
                 ax=axes[0],
-                title=f"{factor_name} ({original_frequency}) - Multiplicative Decomposition",
+                title=f"{factor_name} ({original_frequency}) - {decomposition_type.capitalize()} Decomposition",
             )
-            trend.plot(ax=axes[1], title="Trend")
-            seasonal.plot(ax=axes[2], title=f"Seasonal (Period={period})")
-            resid.plot(ax=axes[3], title="Residuals")
         else:
             result.observed.plot(
                 ax=axes[0],
-                title=f"{factor_name} ({original_frequency}) - Additive Decomposition",
+                title=f"{factor_name} ({original_frequency}) - {decomposition_type.capitalize()} Decomposition",
             )
+
+        # 2. Trend
+        if use_multiplicative:
+            trend.plot(ax=axes[1], title="Trend")
+        else:
             result.trend.plot(ax=axes[1], title="Trend")
+
+        # 3. Seasonal
+        if use_multiplicative:
+            seasonal.plot(ax=axes[2], title=f"Seasonal (Period={period})")
+        else:
             result.seasonal.plot(ax=axes[2], title=f"Seasonal (Period={period})")
+
+        # 4. Residuals
+        if use_multiplicative:
+            resid.plot(ax=axes[3], title="Residuals")
+        else:
             result.resid.plot(ax=axes[3], title="Residuals")
 
+        # 5. ACF of residuals
+        plot_acf(
+            resid,
+            lags=min(30, len(resid) // 2 - 1),
+            ax=axes[4],
+            title="ACF of Residuals",
+        )
+
+        # 6. PACF of residuals
+        plot_pacf(
+            resid,
+            lags=min(30, len(resid) // 2 - 1),
+            ax=axes[5],
+            title="PACF of Residuals",
+        )
+
         plt.tight_layout()
         plt.savefig(image_path, dpi=150, bbox_inches="tight")
         plt.close(fig)
@@ -356,22 +389,17 @@ def process_selected_factors(factor_list, use_multiplicative=False):
 if __name__ == "__main__":
 
     # 批量处理所有因子（加法分解）
-    print("=== Processing all factors with additive decomposition ===")
-    summary_additive = batch_process_factors(use_multiplicative=False)
+    # print("=== Processing all factors with additive decomposition ===")
+    # summary_additive = batch_process_factors(use_multiplicative=False)
 
     # # 批量处理所有因子（乘法分解）
     # print("\n=== Processing all factors with multiplicative decomposition ===")
     # summary_multiplicative = batch_process_factors(use_multiplicative=True)
 
     print("\n=== Processing selected factors ===")
-    selected_factors = [
-        "active_1m_3m",
-        "count",
-        "supply_balance_less_0001",
-        "liquid_sum",
-    ]  # 示例因子列表
+    selected_factors = ["active_3m_6m"]  # 示例因子列表
     selected_summary = process_selected_factors(
-        selected_factors, use_multiplicative=True
+        selected_factors, use_multiplicative=False
     )
 
     print("\n✅ All done!")