From e964c97eacc28d89e88dd4b1493d19fa59777392 Mon Sep 17 00:00:00 2001 From: Sam Fredrickson Date: Thu, 14 Nov 2024 16:53:12 -0800 Subject: [PATCH] Initial commit. --- .gitignore | 1 + README.md | 23 + model.py | 808 ++++++++ moneyshot.png | Bin 0 -> 759576 bytes poetry.lock | 734 +++++++ prices.csv | 5236 ++++++++++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 18 + 7 files changed, 6820 insertions(+) create mode 100644 .gitignore create mode 100644 README.md create mode 100644 model.py create mode 100644 moneyshot.png create mode 100644 poetry.lock create mode 100644 prices.csv create mode 100644 pyproject.toml diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e33609d --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +*.png diff --git a/README.md b/README.md new file mode 100644 index 0000000..e672fa0 --- /dev/null +++ b/README.md @@ -0,0 +1,23 @@ +# Bitcoin Price Model + +

+ I have no idea what I'm doing +

+ +**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) diff --git a/model.py b/model.py new file mode 100644 index 0000000..48afa21 --- /dev/null +++ b/model.py @@ -0,0 +1,808 @@ +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) diff --git a/moneyshot.png b/moneyshot.png new file 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