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)