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+*.png

README.md

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+# Bitcoin Price Model
+
+<p align="center">
+  <img src="https://img.izismile.com/img/img5/20120417/640/i_have_no_idea_what_im_doing_meme_640_07.jpg" alt="I have no idea what I'm doing" />
+</p>
+
+**Don't take this seriously. It's all in good fun.**
+
+I decided to have fun and ask Anthropic's Claude AI (3.6 Sonnet) to help build
+a Bitcoin price model, using data from [Investing.com](https://www.investing.com/crypto/bitcoin/historical-data).
+
+The model is still a mess: lots of redundant code as we went through various
+methods for projecting future prices; the plots' colors don't render correctly.
+
+I feel like I'm dangerous enough to know what to ask for out of a model, but
+not knowledgeable enough evaluate whether what Claude produced actually makes
+any sense. (Stats class in college was a long time ago...)
+
+## tl;dr show me the projection!
+
+As of writing (2024-11-14), here is what the model generates:
+
+![moooooon](./moneyshot.png)

model.py

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+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import matplotlib.pyplot as plt
+import seaborn as sns
+from scipy.stats import norm
+
+def analyze_bitcoin_prices(csv_path):
+    """
+    Analyze Bitcoin price data to calculate volatility and growth rates.
+    """
+    # Read CSV with proper data types
+    df = pd.read_csv(csv_path, parse_dates=[0])
+
+    # Print first few rows of raw data to inspect
+    print("\nFirst few rows of raw data:")
+    print(df.head())
+
+    # Print data info to see types and non-null counts
+    print("\nDataset Info:")
+    print(df.info())
+
+    # Convert price columns to float and handle any potential formatting issues
+    price_columns = ['Price', 'Open', 'High', 'Low']
+    for col in price_columns:
+        # Remove any commas in numbers
+        df[col] = df[col].astype(str).str.replace(',', '')
+        df[col] = pd.to_numeric(df[col], errors='coerce')
+
+    # Rename columns for clarity
+    df.columns = ['Date', 'Close', 'Open', 'High', 'Low', 'Volume', 'Change']
+
+    # Sort by date in ascending order
+    df = df.sort_values('Date')
+
+    # Print summary statistics after conversion
+    print("\nPrice Summary After Conversion:")
+    print(df[['Close', 'Open', 'High', 'Low']].describe())
+
+    # Calculate daily returns
+    df['Daily_Return'] = df['Close'].pct_change()
+
+    # Print first few daily returns to verify calculation
+    print("\nFirst few daily returns:")
+    print(df[['Date', 'Close', 'Daily_Return']].head())
+
+    # Check for any infinite or NaN values
+    print("\nInfinite or NaN value counts:")
+    print(df.isna().sum())
+
+    # Calculate metrics using 365 days for annualization
+    analysis = {
+        'period_start': df['Date'].min().strftime('%Y-%m-%d'),
+        'period_end': df['Date'].max().strftime('%Y-%m-%d'),
+        'total_days': len(df),
+        'daily_volatility': df['Daily_Return'].std(),
+        'annualized_volatility': df['Daily_Return'].std() * np.sqrt(365),
+        'total_return': (df['Close'].iloc[-1] / df['Close'].iloc[0] - 1) * 100,
+        'average_daily_return': df['Daily_Return'].mean() * 100,
+        'average_annual_return': ((1 + df['Daily_Return'].mean()) ** 365 - 1) * 100,
+        'min_price': df['Low'].min(),
+        'max_price': df['High'].max(),
+        'avg_price': df['Close'].mean(),
+        'start_price': df['Close'].iloc[0],
+        'end_price': df['Close'].iloc[-1]
+    }
+
+    # Calculate rolling metrics
+    df['Rolling_Volatility_30d'] = df['Daily_Return'].rolling(window=30).std() * np.sqrt(365)
+    df['Rolling_Return_30d'] = df['Close'].pct_change(periods=30) * 100
+
+    return analysis, df
+
+def visualize_cycle_patterns(df, cycle_returns, cycle_volatility):
+    """
+    Create enhanced visualization of Bitcoin's behavior across halving cycles.
+    """
+    plt.style.use('seaborn-v0_8')
+    fig = plt.figure(figsize=(15, 15))
+
+    # Create a 3x1 subplot grid with different heights
+    gs = plt.GridSpec(3, 1, height_ratios=[2, 1, 2], hspace=0.3)
+
+    # Plot 1: Returns across cycle with confidence bands
+    ax1 = plt.subplot(gs[0])
+
+    # Convert days to percentage through cycle
+    x_points = np.array(cycle_returns.index) / (4 * 365) * 100
+
+    # Calculate rolling mean and standard deviation for confidence bands
+    window = 30  # 30-day window
+    rolling_mean = pd.Series(cycle_returns.values).rolling(window=window).mean()
+    rolling_std = pd.Series(cycle_returns.values).rolling(window=window).std()
+
+    # Plot confidence bands
+    ax1.fill_between(x_points,
+                     (rolling_mean - 2*rolling_std) * 100,
+                     (rolling_mean + 2*rolling_std) * 100,
+                     alpha=0.2, color='blue', label='95% Confidence')
+    ax1.fill_between(x_points,
+                     (rolling_mean - rolling_std) * 100,
+                     (rolling_mean + rolling_std) * 100,
+                     alpha=0.3, color='blue', label='68% Confidence')
+
+    # Plot average returns
+    ax1.plot(x_points, cycle_returns.values * 100, 'b-',
+             label='Average Daily Return', linewidth=2)
+    ax1.axhline(y=0, color='gray', linestyle='--', alpha=0.5)
+
+    # Add vertical lines for each year in cycle
+    for year in range(1, 4):
+        ax1.axvline(x=year*25, color='gray', linestyle=':', alpha=0.3)
+        ax1.text(year*25, ax1.get_ylim()[1], f'Year {year}',
+                rotation=90, va='top', ha='right', alpha=0.7)
+
+    # Highlight halving points
+    ax1.axvline(x=0, color='red', linestyle='--', alpha=0.5, label='Halving Event')
+    ax1.axvline(x=100, color='red', linestyle='--', alpha=0.5)
+
+    ax1.set_title('Bitcoin Return Patterns Across Halving Cycle', pad=20)
+    ax1.set_xlabel('Position in Cycle (%)')
+    ax1.set_ylabel('Average Daily Return (%)')
+    ax1.grid(True, alpha=0.3)
+    ax1.legend(loc='upper right')
+
+    # Plot 2: Volatility across cycle
+    ax2 = plt.subplot(gs[1])
+
+    # Calculate rolling volatility confidence bands
+    vol_mean = pd.Series(cycle_volatility.values).rolling(window=window).mean()
+    vol_std = pd.Series(cycle_volatility.values).rolling(window=window).std()
+
+    # Plot volatility with confidence bands
+    annualized_factor = np.sqrt(365) * 100
+    ax2.fill_between(x_points,
+                     (vol_mean - 2*vol_std) * annualized_factor,
+                     (vol_mean + 2*vol_std) * annualized_factor,
+                     alpha=0.2, color='red', label='95% Confidence')
+    ax2.plot(x_points, cycle_volatility.values * annualized_factor, 'r-',
+             label='Annualized Volatility', linewidth=2)
+
+    # Add year markers
+    for year in range(1, 4):
+        ax2.axvline(x=year*25, color='gray', linestyle=':', alpha=0.3)
+
+    ax2.axvline(x=0, color='red', linestyle='--', alpha=0.5)
+    ax2.axvline(x=100, color='red', linestyle='--', alpha=0.5)
+
+    ax2.set_xlabel('Position in Cycle (%)')
+    ax2.set_ylabel('Volatility (%)')
+    ax2.grid(True, alpha=0.3)
+    ax2.legend(loc='upper right')
+
+    # Plot 3: Average price trajectory within cycles
+    ax3 = plt.subplot(gs[2])
+
+    # Define a color scheme for cycles
+    cycle_colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']
+
+    # Calculate average price path for each cycle
+    halving_dates = get_halving_dates()
+    cycles = []
+
+    for i in range(len(halving_dates)-1):
+        cycle_start = halving_dates[i]
+        cycle_end = halving_dates[i+1]
+        cycle_data = df[(df['Date'] >= cycle_start) & (df['Date'] < cycle_end)].copy()
+
+        if len(cycle_data) > 0:
+            cycle_data['Cycle_Pct'] = ((cycle_data['Date'] - cycle_start).dt.total_seconds() /
+                                     (cycle_end - cycle_start).total_seconds() * 100)
+            cycle_data['Normalized_Price'] = cycle_data['Close'] / cycle_data['Close'].iloc[0]
+            cycles.append(cycle_data)
+
+    # Plot each historical cycle with distinct colors
+    for i, cycle in enumerate(cycles):
+        ax3.semilogy(cycle['Cycle_Pct'], cycle['Normalized_Price'],
+                    color=cycle_colors[i], alpha=0.7,
+                    label=f'Cycle {i+1} ({cycle["Date"].iloc[0].strftime("%Y")}-{cycle["Date"].iloc[-1].strftime("%Y")})')
+
+    # Calculate and plot average cycle
+    if cycles:
+        avg_cycle = pd.concat([c.set_index('Cycle_Pct')['Normalized_Price'] for c in cycles], axis=1)
+        avg_cycle_mean = avg_cycle.mean(axis=1)
+        avg_cycle_std = avg_cycle.std(axis=1)
+
+        ax3.semilogy(avg_cycle_mean.index, avg_cycle_mean.values, 'k-',
+                     linewidth=2, label='Average Cycle')
+        ax3.fill_between(avg_cycle_mean.index,
+                        avg_cycle_mean * np.exp(-2*avg_cycle_std),
+                        avg_cycle_mean * np.exp(2*avg_cycle_std),
+                        alpha=0.2, color='gray')
+
+    # Add year markers
+    for year in range(1, 4):
+        ax3.axvline(x=year*25, color='gray', linestyle=':', alpha=0.3)
+
+    ax3.axvline(x=0, color='red', linestyle='--', alpha=0.5)
+    ax3.axvline(x=100, color='red', linestyle='--', alpha=0.5)
+
+    ax3.set_title('Price Performance Across Cycles (Normalized)', pad=20)
+    ax3.set_xlabel('Position in Cycle (%)')
+    ax3.set_ylabel('Price (Relative to Cycle Start)')
+    ax3.grid(True, alpha=0.3)
+    ax3.legend(loc='center left', bbox_to_anchor=(1.02, 0.5))
+
+    # Add current cycle position marker on all plots
+    current_position = get_cycle_position(df['Date'].max(), halving_dates) * 100
+    for ax in [ax1, ax2, ax3]:
+        ax.axvline(x=current_position, color='green', linestyle='-', alpha=0.5,
+                   label='Current Position')
+
+    # Main title for the figure
+    fig.suptitle('Bitcoin Halving Cycle Analysis', fontsize=16, y=0.95)
+
+    # Adjust layout to prevent legend cutoff
+    plt.tight_layout()
+
+    # Save the plot
+    plt.savefig('bitcoin_cycle_patterns.png', dpi=300, bbox_inches='tight')
+    plt.close()
+
+def create_plots(df, start=None, end=None, project_days=365):
+    """
+    Create plots including historical data and future projections.
+    """
+    # Filter data based on date range
+    mask = pd.Series(True, index=df.index)
+    if start:
+        mask &= df['Date'] >= pd.to_datetime(start)
+    if end:
+        mask &= df['Date'] <= pd.to_datetime(end)
+
+    plot_df = df[mask].copy()
+
+    if len(plot_df) == 0:
+        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)
+
+    # Create cycle visualization
+    visualize_cycle_patterns(plot_df, cycle_returns, cycle_volatility)
+
+    # Set up the style
+    plt.style.use('seaborn-v0_8')
+
+    # Create figure
+    fig = plt.figure(figsize=(15, 15))
+
+    # Date range for titles
+    hist_date_range = f" ({plot_df['Date'].min().strftime('%Y-%m-%d')} to {plot_df['Date'].max().strftime('%Y-%m-%d')})"
+
+    # 1. Price history and projections (log scale)
+    ax1 = plt.subplot(4, 1, 1)
+
+    # Plot historical prices
+    ax1.semilogy(plot_df['Date'], plot_df['Close'], 'b-', label='Historical Price')
+
+    # Plot projections
+    ax1.semilogy(projections.index, projections['Expected_Trend'], '--',
+                 color='purple', label='Expected Trend')
+    ax1.semilogy(projections.index, projections['Median'], ':',
+                 color='green', label='Simulated Median')
+    ax1.fill_between(projections.index,
+                    projections['Lower_95'], projections['Upper_95'],
+                    alpha=0.2, color='orange', label='95% Confidence Interval')
+    ax1.fill_between(projections.index,
+                    projections['Lower_68'], projections['Upper_68'],
+                    alpha=0.3, color='green', label='68% Confidence Interval')
+
+    # Customize y-axis
+    ax1.yaxis.set_major_formatter(plt.FuncFormatter(format_price))
+
+    # Set custom y-axis ticks at meaningful price points
+    min_price = min(plot_df['Low'].min(), projections['Lower_95'].min())
+    max_price = max(plot_df['High'].max(), projections['Upper_95'].max())
+
+    price_points = get_nice_price_points(min_price, max_price)
+    ax1.set_yticks(price_points)
+
+    # Adjust y-axis label properties
+    ax1.tick_params(axis='y', labelsize=8)  # Smaller font size
+
+    # Add some padding to prevent label cutoff
+    ax1.margins(y=0.02)
+
+    # Adjust label padding to prevent overlap
+    ax1.yaxis.set_tick_params(pad=1)
+
+    # Add grid lines with adjusted opacity
+    ax1.grid(True, which='major', linestyle='-', alpha=0.5)
+    ax1.grid(True, which='minor', linestyle=':', alpha=0.2)
+
+    ax1.set_title('Bitcoin Price History and Projections (Log Scale)' + hist_date_range)
+    # Make legend font size smaller too for consistency
+    ax1.legend(fontsize=8)
+
+    # 2. Rolling volatility
+    ax2 = plt.subplot(4, 1, 2)
+    ax2.plot(plot_df['Date'], plot_df['Rolling_Volatility_30d'], 'r-', label='30-Day Rolling Volatility')
+    ax2.set_title('30-Day Rolling Volatility (Annualized)' + hist_date_range)
+    ax2.set_xlabel('Date')
+    ax2.set_ylabel('Volatility')
+    ax2.grid(True)
+    ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: '{:.0%}'.format(y)))
+    ax2.legend()
+
+    # 3. Returns distribution
+    ax3 = plt.subplot(4, 1, 3)
+    returns_mean = plot_df['Daily_Return'].mean()
+    returns_std = plot_df['Daily_Return'].std()
+    filtered_returns = plot_df['Daily_Return'][
+        (plot_df['Daily_Return'] > returns_mean - 5 * returns_std) &
+        (plot_df['Daily_Return'] < returns_mean + 5 * returns_std)
+    ]
+
+    sns.histplot(filtered_returns, bins=100, ax=ax3)
+    ax3.set_title('Distribution of Daily Returns (Excluding Extreme Outliers)' + hist_date_range)
+    ax3.set_xlabel('Daily Return')
+    ax3.set_ylabel('Count')
+    ax3.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: '{:.0%}'.format(x)))
+
+    # Add a vertical line for mean return
+    ax3.axvline(filtered_returns.mean(), color='r', linestyle='dashed', linewidth=1)
+    ax3.text(filtered_returns.mean(), ax3.get_ylim()[1], 'Mean',
+             rotation=90, va='top', ha='right')
+
+# 4. Projection ranges
+    ax4 = plt.subplot(4, 1, 4)
+
+    # Calculate and plot price ranges at different future points
+    timepoints = np.array([30, 90, 180, 365])
+    timepoints = timepoints[timepoints <= project_days]
+
+    ranges = []
+    labels = []
+    positions = []
+
+    for t in timepoints:
+        idx = t - 1  # Convert to 0-based index
+        ranges.extend([
+            projections['Lower_95'].iloc[idx],
+            projections['Lower_68'].iloc[idx],
+            projections['Median'].iloc[idx],
+            projections['Upper_68'].iloc[idx],
+            projections['Upper_95'].iloc[idx]
+        ])
+        labels.extend([
+            '95% Lower',
+            '68% Lower',
+            'Median',
+            '68% Upper',
+            '95% Upper'
+        ])
+        positions.extend([t] * 5)
+
+    # Plot ranges (removed violin plot)
+    ax4.scatter(positions, ranges, alpha=0.6)
+
+    # Add lines connecting the ranges
+    for t in timepoints:
+        idx = positions.index(t)
+        ax4.plot([t] * 5, ranges[idx:idx+5], 'k-', alpha=0.3)
+
+    # Set log scale first
+    ax4.set_yscale('log')
+
+    # Get the current order of magnitude for setting appropriate ticks
+    min_price = min(ranges)
+    max_price = max(ranges)
+
+    # Create price points at regular intervals on log scale
+    log_min = np.floor(np.log10(min_price))
+    log_max = np.ceil(np.log10(max_price))
+    price_points = []
+    for exp in range(int(log_min), int(log_max + 1)):
+        for mult in [1, 2, 5]:
+            point = mult * 10**exp
+            if min_price <= point <= max_price:
+                price_points.append(point)
+
+    ax4.set_yticks(price_points)
+
+    def price_formatter(x, p):
+        if x >= 1e6:
+            return f'${x/1e6:.1f}M'
+        if x >= 1e3:
+            return f'${x/1e3:.0f}K'
+        return f'${x:.0f}'
+
+    # Apply formatter to major ticks
+    ax4.yaxis.set_major_formatter(plt.FuncFormatter(price_formatter))
+
+    # Customize the plot
+    ax4.set_title('Projected Price Ranges at Future Timepoints')
+    ax4.set_xlabel('Days Forward')
+    ax4.set_ylabel('Price (USD)')
+    ax4.grid(True, alpha=0.3)
+
+    # Set x-axis to show only our timepoints
+    ax4.set_xticks(timepoints)
+
+    # Adjust layout
+    plt.tight_layout()
+
+    # Save the plot
+    start_str = start if start else plot_df['Date'].min().strftime('%Y-%m-%d')
+    end_str = end if end else plot_df['Date'].max().strftime('%Y-%m-%d')
+    filename = f'bitcoin_analysis_{start_str}_to_{end_str}_with_projections.png'
+    plt.savefig(filename, dpi=300, bbox_inches='tight')
+    plt.close()
+
+    return projections
+
+def analyze_cycles(df, cycle_period=4*365):
+    """Analyze Bitcoin market cycles to understand return patterns"""
+    df = df.copy()
+
+    # Calculate rolling 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 where we are in the supposed 4-year cycle
+    df['Days_From_Start'] = (df['Date'] - df['Date'].min()).dt.days
+    df['Cycle_Position'] = df['Days_From_Start'] % cycle_period
+
+    # Group by cycle position and calculate average returns
+    cycle_returns = df.groupby(df['Cycle_Position'])['Daily_Return'].mean()
+    cycle_volatility = df.groupby(df['Cycle_Position'])['Daily_Return'].std()
+
+    return cycle_returns, cycle_volatility
+
+def get_halving_dates():
+    """Return known and projected Bitcoin halving dates"""
+    return pd.to_datetime([
+        '2008-01-03',  # Bitcoin genesis block (treat as cycle start)
+        '2012-11-28',  # First halving
+        '2016-07-09',  # Second halving
+        '2020-05-11',  # Third halving
+        '2024-04-17',  # Fourth halving (projected)
+        '2028-04-17',  # Fifth halving (projected)
+    ])
+
+def get_cycle_position(date, halving_dates):
+    """
+    Calculate position in halving cycle (0 to 1) for a given date.
+    0 represents a halving event, 1 represents just before the next halving.
+    """
+    # Convert date to datetime if it's not already
+    date = pd.to_datetime(date)
+
+    # Find the most recent halving before this date
+    prev_halving = halving_dates[halving_dates <= date].max()
+    if pd.isna(prev_halving):
+        return 0.0  # For dates before first halving
+
+    # Find next halving
+    future_halvings = halving_dates[halving_dates > date]
+    if len(future_halvings) == 0:
+        # For dates after last known halving, use same cycle length as last known cycle
+        last_cycle_length = (halving_dates[-1] - halving_dates[-2]).days
+        days_since_halving = (date - halving_dates[-1]).days
+        return min(days_since_halving / last_cycle_length, 1.0)
+
+    next_halving = future_halvings.min()
+
+    # Calculate position as fraction between halvings
+    days_since_halving = (date - prev_halving).days
+    cycle_length = (next_halving - prev_halving).days
+    return min(days_since_halving / cycle_length, 1.0)
+
+def analyze_cycles_with_halvings(df):
+    """Analyze Bitcoin market cycles aligned with halving events"""
+    df = df.copy()
+
+    # Get halving dates
+    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)
+
+    # 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
+    window = 91  # About 3 months
+    if len(cycle_returns) > window:
+        cycle_returns = pd.Series(
+            savgol_filter(cycle_returns, window, 3),
+            index=cycle_returns.index
+        )
+
+    return cycle_returns, cycle_volatility
+
+
+def project_prices_with_cycles(df, days_forward=365, simulations=1000, confidence_levels=[0.95, 0.68]):
+    """
+    Project future Bitcoin prices using Monte Carlo simulation with halving-aligned cycles.
+    """
+    # Analyze historical cycles
+    cycle_returns, cycle_volatility = analyze_cycles_with_halvings(df)
+
+    # Get current position in halving cycle
+    halving_dates = get_halving_dates()
+    current_date = df['Date'].max()
+    cycle_position = get_cycle_position(current_date, halving_dates)
+    current_cycle_days = int(cycle_position * 4 * 365)
+
+    # Current price (last known price)
+    last_price = df['Close'].iloc[-1]
+    last_date = df['Date'].iloc[-1]
+
+    # Generate dates for projection
+    future_dates = pd.date_range(
+        start=last_date + timedelta(days=1),
+        periods=days_forward,
+        freq='D'
+    )
+
+    # Calculate expected returns for future dates based on cycle position
+    future_cycle_days = [
+        (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
+    ])
+
+    # 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
+
+    # Run Monte Carlo simulation
+    np.random.seed(42)  # For reproducibility
+    simulated_paths = np.zeros((days_forward, simulations))
+
+    for sim in range(simulations):
+        # Generate random returns using cycle-aware expected returns
+        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))
+        simulated_paths[:, sim] = price_path
+
+    # Calculate percentiles for confidence intervals
+    results = pd.DataFrame(index=future_dates)
+    results['Median'] = np.percentile(simulated_paths, 50, axis=1)
+
+    for level in confidence_levels:
+        lower_percentile = (1 - level) * 100 / 2
+        upper_percentile = 100 - lower_percentile
+
+        results[f'Lower_{int(level*100)}'] = np.percentile(simulated_paths, lower_percentile, axis=1)
+        results[f'Upper_{int(level*100)}'] = np.percentile(simulated_paths, upper_percentile, axis=1)
+
+    # Add expected trend line (without randomness)
+    results['Expected_Trend'] = last_price * np.exp(np.cumsum(expected_returns))
+
+    return results
+
+def calculate_rolling_metrics(df, window=365):
+    """Calculate rolling returns and volatility metrics"""
+    df = df.copy()
+    df['Rolling_Daily_Return'] = df['Daily_Return'].rolling(window=window).mean()
+    df['Rolling_Daily_Volatility'] = df['Daily_Return'].rolling(window=window).std()
+    return df
+
+def fit_return_trend(df):
+    """Fit an exponential decay trend to the rolling returns"""
+    # Calculate days from start
+    df = df.copy()
+    df['Days'] = (df['Date'] - df['Date'].min()).dt.days
+
+    # Calculate rolling metrics
+    df = calculate_rolling_metrics(df)
+
+    # Remove NaN values for fitting
+    clean_data = df.dropna()
+
+    # Fit exponential decay: y = a * exp(-bx) + c
+    from scipy.optimize import curve_fit
+
+    def exp_decay(x, a, b, c):
+        return a * np.exp(-b * x) + c
+
+    popt, _ = curve_fit(
+        exp_decay,
+        clean_data['Days'],
+        clean_data['Rolling_Daily_Return'],
+        p0=[0.01, 0.001, 0.0001],  # Initial guess for parameters
+        bounds=([0, 0, 0], [1, 1, 0.01])  # Constraints to keep parameters positive
+    )
+
+    return popt
+
+def project_prices_with_trend(df, days_forward=365, simulations=1000, confidence_levels=[0.95, 0.68]):
+    """
+    Project future Bitcoin prices using Monte Carlo simulation with trend adjustment.
+    """
+    # Fit return trend
+    trend_params = fit_return_trend(df)
+
+    # Calculate days from start for projection
+    days_from_start = (df['Date'].max() - df['Date'].min()).days
+
+    # Current price (last known price)
+    last_price = df['Close'].iloc[-1]
+    last_date = df['Date'].iloc[-1]
+
+    # Generate dates for projection
+    future_dates = pd.date_range(
+        start=last_date + timedelta(days=1),
+        periods=days_forward,
+        freq='D'
+    )
+
+    # Calculate expected returns for future dates using fitted trend
+    def exp_decay(x, a, b, c):
+        return a * np.exp(-b * x) + c
+
+    future_days = np.arange(days_from_start + 1, days_from_start + days_forward + 1)
+    expected_returns = exp_decay(future_days, *trend_params)
+
+    # Use recent volatility for projections
+    recent_volatility = df['Daily_Return'].tail(365).std()
+
+    # Run Monte Carlo simulation
+    np.random.seed(42)  # For reproducibility
+    simulated_paths = np.zeros((days_forward, simulations))
+
+    for sim in range(simulations):
+        # Generate random returns using trending expected return
+        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))
+        simulated_paths[:, sim] = price_path
+
+    # Calculate percentiles for confidence intervals
+    results = pd.DataFrame(index=future_dates)
+    results['Median'] = np.percentile(simulated_paths, 50, axis=1)
+
+    for level in confidence_levels:
+        lower_percentile = (1 - level) * 100 / 2
+        upper_percentile = 100 - lower_percentile
+
+        results[f'Lower_{int(level*100)}'] = np.percentile(simulated_paths, lower_percentile, axis=1)
+        results[f'Upper_{int(level*100)}'] = np.percentile(simulated_paths, upper_percentile, axis=1)
+
+    # Add expected trend line (without randomness)
+    results['Expected_Trend'] = last_price * np.exp(np.cumsum(expected_returns))
+
+    return results
+
+def get_nice_price_points(min_price, max_price):
+    """
+    Generate a reasonable set of price points for the y-axis that look clean
+    and cover the range without cluttering the chart.
+    """
+    log_min = np.floor(np.log10(min_price))
+    log_max = np.ceil(np.log10(max_price))
+    price_points = []
+
+    # For very large ranges (spanning more than 4 orders of magnitude),
+    # only use powers of 10 and mid-points
+    if log_max - log_min > 4:
+        for exp in range(int(log_min), int(log_max + 1)):
+            base = 10**exp
+            # Add main power of 10
+            if min_price <= base <= max_price:
+                price_points.append(base)
+            # Add mid-point if range is large enough
+            if min_price <= base * 5 <= max_price and exp > log_min:
+                price_points.append(base * 5)
+    else:
+        # For smaller ranges, use 1, 2, 5 sequence
+        for exp in range(int(log_min), int(log_max + 1)):
+            for mult in [1, 2, 5]:
+                point = mult * 10**exp
+                if min_price <= point <= max_price:
+                    price_points.append(point)
+
+    return np.array(price_points)
+
+def format_price(x, p):
+    """Format large numbers in K, M, B format with appropriate precision"""
+    if abs(x) >= 1e9:
+        return f'${x/1e9:.1f}B'
+    if abs(x) >= 1e6:
+        return f'${x/1e6:.1f}M'
+    if abs(x) >= 1e3:
+        return f'${x/1e3:.1f}K'
+    if abs(x) >= 1:
+        return f'${x:.0f}'
+    return f'${x:.2f}'  # For values less than $1, show cents
+
+def project_prices(df, days_forward=365, simulations=1000, confidence_levels=[0.95, 0.68]):
+    """
+    Project future Bitcoin prices using Monte Carlo simulation.
+
+    Parameters:
+    df: DataFrame with historical price data
+    days_forward: Number of days to project forward
+    simulations: Number of Monte Carlo simulations to run
+    confidence_levels: List of confidence levels for the projection intervals
+
+    Returns:
+    DataFrame with projection results
+    """
+    # Calculate daily return parameters
+    daily_return = df['Daily_Return'].mean()
+    daily_volatility = df['Daily_Return'].std()
+
+    # Current price (last known price)
+    last_price = df['Close'].iloc[-1]
+    last_date = df['Date'].iloc[-1]
+
+    # Generate dates for projection
+    future_dates = pd.date_range(
+        start=last_date + timedelta(days=1),
+        periods=days_forward,
+        freq='D'
+    )
+
+    # Run Monte Carlo simulation
+    np.random.seed(42)  # For reproducibility
+    simulated_paths = np.zeros((days_forward, simulations))
+
+    for sim in range(simulations):
+        # Generate random returns using historical parameters
+        returns = np.random.normal(
+            loc=daily_return,
+            scale=daily_volatility,
+            size=days_forward
+        )
+
+        # Calculate price path
+        price_path = last_price * np.exp(np.cumsum(returns))
+        simulated_paths[:, sim] = price_path
+
+    # Calculate percentiles for confidence intervals
+    results = pd.DataFrame(index=future_dates)
+    results['Median'] = np.percentile(simulated_paths, 50, axis=1)
+
+    for level in confidence_levels:
+        lower_percentile = (1 - level) * 100 / 2
+        upper_percentile = 100 - lower_percentile
+
+        results[f'Lower_{int(level*100)}'] = np.percentile(simulated_paths, lower_percentile, axis=1)
+        results[f'Upper_{int(level*100)}'] = np.percentile(simulated_paths, upper_percentile, axis=1)
+
+    return results
+
+def print_analysis(analysis):
+    print(f"\nBitcoin Price Analysis ({analysis['period_start']} to {analysis['period_end']})")
+    print("-" * 50)
+    print(f"Total Days Analyzed: {analysis['total_days']}")
+    print(f"\nPrice Range:")
+    print(f"Starting Price: ${analysis['start_price']:,.2f}")
+    print(f"Ending Price: ${analysis['end_price']:,.2f}")
+    print(f"Minimum Price: ${analysis['min_price']:,.2f}")
+    print(f"Maximum Price: ${analysis['max_price']:,.2f}")
+    print(f"Average Price: ${analysis['avg_price']:,.2f}")
+    print(f"\nVolatility Metrics:")
+    print(f"Daily Volatility: {analysis['daily_volatility']:.2%}")
+    print(f"Annualized Volatility: {analysis['annualized_volatility']:.2%}")
+    print(f"\nReturn Metrics:")
+    print(f"Total Return: {analysis['total_return']:,.2f}%")
+    print(f"Average Daily Return: {analysis['average_daily_return']:.2f}%")
+    print(f"Average Annual Return: {analysis['average_annual_return']:,.2f}%")
+
+if __name__ == "__main__":
+    analysis, df = analyze_bitcoin_prices("prices.csv")
+    #create_plots(df)  # Full history
+    #create_plots(df, start='2022-01-01')  # From 2022 onwards
+    #create_plots(df, start='2023-01-01', end='2023-12-31')  # Just 2023
+    # Create plots with different time ranges and projections
+    projections = create_plots(df, start='2011-01-01', project_days=365*4)
+    print("\nProjected Prices at Key Points:")
+    print(projections.iloc[[29, 89, 179, 364]].round(2))  # 30, 90, 180, 365 days
+    print_analysis(analysis)

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+matplotlib = ">=3.4,<3.6.1 || >3.6.1"
+numpy = ">=1.20,<1.24.0 || >1.24.0"
+pandas = ">=1.2"
+
+[package.extras]
+dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"]
+docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx (<6.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
+stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
+
+[[package]]
+name = "six"
+version = "1.16.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
+    {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
+    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+]
+
+[[package]]
+name = "tzdata"
+version = "2024.2"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
+    {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
+]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.13"
+content-hash = "376b98659bcfe622090d838b23f142eaa76c6441ace6ca751db716db53d9af63"

pyproject.toml

@@ -0,0 +1,18 @@
+[tool.poetry]
+name = "bitcoin-model"
+version = "0.1.0"
+description = ""
+authors = ["Sam Fredrickson <samfredrickson@gmail.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.13"
+pandas = "^2.2.3"
+matplotlib = "^3.9.2"
+seaborn = "^0.13.2"
+scipy = "^1.14.1"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"