Switch to simpler log-based projection.

model.py

@@ -4,6 +4,7 @@ from datetime import datetime, timedelta
 import matplotlib.pyplot as plt
 import seaborn as sns
 from scipy.stats import norm
+from scipy.signal import savgol_filter
 
 
 # Utility functions
@@ -102,50 +103,49 @@ def get_nice_price_points(min_price, max_price):
 # Analysis functions
 
 
-def analyze_cycles_with_halvings(df):
-    """Analyze Bitcoin market cycles aligned with halving events"""
+def analyze_trends(df):
+    """
+    Analyze Bitcoin price trends using log returns.
+    """
     df = df.copy()
 
-    # Get halving dates
+    # Get halving dates and calculate cycle position
     halving_dates = get_halving_dates()
-
-    # Calculate cycle position for each date
     df["Cycle_Position"] = df["Date"].apply(
         lambda x: get_cycle_position(x, halving_dates)
     )
-
-    # Convert to days within cycle (0 to ~1460 days)
     df["Cycle_Days"] = (df["Cycle_Position"] * 4 * 365).round().astype(int)
 
-    # Calculate returns at different scales
-    df["Returns_30d"] = df["Close"].pct_change(periods=30)
-    df["Returns_90d"] = df["Close"].pct_change(periods=90)
-    df["Returns_365d"] = df["Close"].pct_change(periods=365)
+    # Calculate log returns
+    df["Log_Price"] = np.log(df["Close"])
+    df["Log_Return"] = df["Log_Price"].diff()
 
-    # Group by position in cycle and calculate average returns
-    cycle_returns = df.groupby(df["Cycle_Days"])["Daily_Return"].mean()
-    cycle_volatility = df.groupby(df["Cycle_Days"])["Daily_Return"].std()
-
-    # Smooth the cycle returns to reduce noise
-    from scipy.signal import savgol_filter
+    # Group by position in cycle
+    position_returns = df.groupby("Cycle_Days")["Log_Return"].mean()
 
+    # Smooth the returns using Savitzky-Golay filter
     window = 91  # About 3 months
-    if len(cycle_returns) > window:
-        cycle_returns = pd.Series(
-            savgol_filter(cycle_returns, window, 3), index=cycle_returns.index
+    if len(position_returns) > window:
+        position_returns = pd.Series(
+            savgol_filter(position_returns, window, 3), index=position_returns.index
         )
 
-    return cycle_returns, cycle_volatility
+    return position_returns
 
 
-def project_prices_with_cycles(
+def project_prices(
     df, days_forward=365, simulations=1000, confidence_levels=[0.95, 0.68]
 ):
     """
-    Project future Bitcoin prices using Monte Carlo simulation with halving-aligned cycles.
+    Project future Bitcoin prices using Monte Carlo simulation.
     """
-    # Analyze historical cycles
-    cycle_returns, cycle_volatility = analyze_cycles_with_halvings(df)
+    # Calculate log returns for volatility estimation
+    df = df.copy()
+    df["Log_Price"] = np.log(df["Close"])
+    df["Log_Return"] = df["Log_Price"].diff()
+
+    # Get cycle-based trends
+    cycle_trends = analyze_trends(df)
 
     # Get current position in halving cycle
     halving_dates = get_halving_dates()
@@ -153,7 +153,7 @@ def project_prices_with_cycles(
     cycle_position = get_cycle_position(current_date, halving_dates)
     current_cycle_days = int(cycle_position * 4 * 365)
 
-    # Current price (last known price)
+    # Current price and date
     last_price = df["Close"].iloc[-1]
     last_date = df["Date"].iloc[-1]
 
@@ -167,15 +167,11 @@ def project_prices_with_cycles(
         (current_cycle_days + i) % (4 * 365) for i in range(days_forward)
     ]
     expected_returns = np.array(
-        [cycle_returns.get(day, cycle_returns.mean()) for day in future_cycle_days]
+        [cycle_trends.get(day, cycle_trends.mean()) for day in future_cycle_days]
     )
 
-    # Calculate base volatility (recent)
-    recent_volatility = df["Daily_Return"].tail(90).std()
-
-    # Add long-term trend component (very gentle decay)
-    long_term_decay = 0.9 ** (np.arange(days_forward) / 365)  # 10% reduction per year
-    expected_returns = expected_returns * long_term_decay
+    # Calculate volatility using recent data
+    recent_volatility = df["Log_Return"].tail(90).std()
 
     # Run Monte Carlo simulation
     np.random.seed(42)  # For reproducibility
@@ -186,9 +182,9 @@ def project_prices_with_cycles(
         returns = np.random.normal(
             loc=expected_returns, scale=recent_volatility, size=days_forward
         )
-
-        # Calculate price path
-        price_path = last_price * np.exp(np.cumsum(returns))
+        # Since we're using log returns, we can simply sum them
+        cumulative_returns = np.cumsum(returns)
+        price_path = last_price * np.exp(cumulative_returns)
         simulated_paths[:, sim] = price_path
 
     # Calculate percentiles for confidence intervals
@@ -206,7 +202,7 @@ def project_prices_with_cycles(
             simulated_paths, upper_percentile, axis=1
         )
 
-    # Add expected trend line (without randomness)
+    # Add expected trend line
     results["Expected_Trend"] = last_price * np.exp(np.cumsum(expected_returns))
 
     return results
@@ -301,11 +297,11 @@ def create_plots(df, start=None, end=None, project_days=365):
         raise ValueError("No data found for the specified date range")
 
     # Generate projections
-    cycle_returns, cycle_volatility = analyze_cycles_with_halvings(plot_df)
-    projections = project_prices_with_cycles(plot_df, days_forward=project_days)
+    # cycle_returns, cycle_volatility = analyze_trends(plot_df)
+    projections = project_prices(plot_df, days_forward=project_days)
 
     # Create cycle visualization
-    visualize_cycle_patterns(plot_df, cycle_returns, cycle_volatility)
+    # visualize_cycle_patterns(plot_df, cycle_returns, cycle_volatility)
 
     # Set up the style
     plt.style.use("seaborn-v0_8")
@@ -743,9 +739,7 @@ def create_backtest_plot(
         raise ValueError("Insufficient training data before backtest date")
 
     # Generate historical projections using only training data
-    historical_projections = project_prices_with_cycles(
-        training_df, days_forward=project_days
-    )
+    historical_projections = project_prices(training_df, days_forward=project_days)
 
     # Set up the plot
     plt.style.use("seaborn-v0_8")