Starter Code

MidasHMM/hmm/midas/analysis.py

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+# midas/analysis.py
+import matplotlib.pyplot as plt
+import seaborn as sns
+from matplotlib.lines import Line2D
+import numpy as np
+
+class MarketRegimeAnalysis:
+    def __init__(self, model, features):
+        self.model = model
+        self.features = features
+        self.states = model.predict(features)
+        
+    def plot_regimes(self, prices: pd.Series):
+        plt.figure(figsize=(15, 8))
+        palette = sns.color_palette("husl", n_colors=self.model.n_components)
+        
+        for state in range(self.model.n_components):
+            mask = self.states == state
+            plt.scatter(prices.index[mask], prices[mask], 
+                       color=palette[state], s=10, label=f'Regime {state}')
+            
+        plt.title("Market Regime Visualization")
+        plt.xlabel("Date")
+        plt.ylabel("Price")
+        plt.legend()
+        return plt
+    
+    def plot_transition_matrix(self):
+        transmat = self.model.transmat_
+        plt.figure(figsize=(10, 8))
+        sns.heatmap(transmat, annot=True, fmt=".2f", cmap="Blues",
+                   xticklabels=range(transmat.shape[0]),
+                   yticklabels=range(transmat.shape[1]))
+        plt.title("State Transition Probabilities")
+        plt.xlabel("Next State")
+        plt.ylabel("Current State")
+        return plt
+    
+    def plot_state_durations(self):
+        state_changes = np.diff(self.states, prepend=self.states[0])
+        change_points = np.where(state_changes != 0)[0]
+        durations = np.diff(np.append(change_points, len(self.states)))
+        
+        plt.figure(figsize=(10, 6))
+        sns.histplot(durations, bins=30, kde=True)
+        plt.title("Regime Duration Distribution")
+        plt.xlabel("Duration (Bars)")
+        plt.ylabel("Frequency")
+        return plt