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 from scipy.signal import savgol_filter from multiprocessing import Process # Utility functions 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 "2024-04-19", # Fourth halving "2028-04-20", # 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 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 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. """ # Handle zero or negative prices min_price = max(min_price, 0.0001) # Set minimum price to $0.0001 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) # Analysis functions def analyze_trends(df): """ Analyze Bitcoin price trends using log returns with simple moving average smoothing. """ df = df.copy() # Get halving dates and calculate cycle position halving_dates = get_halving_dates() df["Cycle_Position"] = df["Date"].apply( lambda x: get_cycle_position(x, halving_dates) ) df["Cycle_Days"] = (df["Cycle_Position"] * 4 * 365).round().astype(int) # Calculate log returns df["Log_Price"] = np.log(df["Close"]) df["Log_Return"] = df["Log_Price"].diff() # Group by position in cycle position_returns = df.groupby("Cycle_Days")["Log_Return"].mean() # Simple moving average smoothing window = 60 smoothed_returns = position_returns.rolling( window=window, center=True, # Center the window for better trend capture min_periods=int(window / 2), # Allow partial windows to reduce edge effects ).mean() # Fill any NaN values at the edges smoothed_returns = smoothed_returns.fillna(method="bfill").fillna(method="ffill") return smoothed_returns def calculate_volatility(df, short_window=30, medium_window=90, long_window=180): """ Calculate volatility using multiple timeframes and exponential weighting. Returns a more nuanced estimate of current market volatility. """ df = df.copy() # Calculate log returns if not already present if "Log_Return" not in df.columns: df["Log_Price"] = np.log(df["Close"]) df["Log_Return"] = df["Log_Price"].diff() # Calculate exponentially weighted volatilities for different timeframes short_vol = df["Log_Return"].ewm(span=short_window).std().iloc[-1] medium_vol = df["Log_Return"].ewm(span=medium_window).std().iloc[-1] long_vol = df["Log_Return"].ewm(span=long_window).std().iloc[-1] # Blend the estimates with more weight on recent data base_vol = 0.5 * short_vol + 0.3 * medium_vol + 0.2 * long_vol # Scale up volatility to target ~68% coverage volatility_scale = 1.2 return base_vol * volatility_scale def calculate_market_maturity_score(df): """ Calculate a market maturity score (0-1) based on multiple indicators. Higher scores indicate a more mature market. """ df = df.copy() # 1. Enhanced volume-based metrics # Use rolling median instead of mean to reduce impact of outliers df["volume_ma90"] = df["Volume"].rolling(window=90).median() df["volume_ma365"] = df["Volume"].rolling(window=365).median() # Calculate relative volume growth using log differences # This better handles exponential growth in volume over time volume_growth_90d = np.log(df["volume_ma90"] / df["volume_ma90"].shift(90)).fillna( 0 ) volume_growth_365d = np.log( df["volume_ma365"] / df["volume_ma365"].shift(365) ).fillna(0) # Normalize volume growth to rolling volatility of volume # This adapts to different market epochs volume_growth_std_90 = volume_growth_90d.rolling(window=90).std() volume_growth_std_365 = volume_growth_365d.rolling(window=365).std() normalized_volume_growth = ( (volume_growth_90d / volume_growth_std_90).clip(-2, 2) * 0.4 # Short-term component + (volume_growth_365d / volume_growth_std_365).clip(-2, 2) * 0.6 # Long-term component ).fillna(0) # Transform to 0-1 scale using sigmoid function volume_score = 1 / (1 + np.exp(-normalized_volume_growth)) # 2. Volatility maturity (lower volatility = more mature) df["rolling_vol_90"] = df["Daily_Return"].rolling(window=90).std() * np.sqrt(365) df["rolling_vol_365"] = df["Daily_Return"].rolling(window=365).std() * np.sqrt(365) # Normalize volatility relative to its historical range vol_score_90 = 1 / ( 1 + df["rolling_vol_90"] / df["rolling_vol_90"].rolling(window=365).median() ) vol_score_365 = 1 / ( 1 + df["rolling_vol_365"] / df["rolling_vol_365"].rolling(window=730).median() ) vol_maturity = vol_score_90 * 0.4 + vol_score_365 * 0.6 # 3. Market efficiency score using multiple timeframes efficiency_scores = [] for window in [30, 90]: # Calculate absolute autocorrelation at multiple lags for lag in [1, 2, 3, 5]: autocorr = ( df["Daily_Return"] .rolling(window=window) .apply(lambda x: abs(pd.Series(x).autocorr(lag))) ) efficiency_scores.append(1 - autocorr) efficiency = pd.concat(efficiency_scores, axis=1).mean(axis=1) # 4. Futures market impact (post-2017) futures_date = pd.Timestamp("2017-12-10") futures_impact = (df["Date"] > futures_date).astype(float) # Progressive futures market maturation days_since_futures = (df["Date"] - futures_date).dt.total_seconds() / (24 * 60 * 60) futures_maturity = futures_impact * (1 - np.exp(-days_since_futures / 365)) # Combine scores with dynamic weights base_weights = { "volume": 0.25, "volatility": 0.30, "efficiency": 0.25, "futures": 0.20, } # Adjust weights based on data availability lookback = pd.Timestamp("2016-01-01") historical_period = (df["Date"] < lookback).astype(float) # Reduce weight of futures impact for historical data weights = base_weights.copy() weights["futures"] = weights["futures"] * (1 - historical_period) # Redistribute futures weight to other components in historical period historical_adjustment = (weights["futures"] * historical_period) / 3 weights["volume"] += historical_adjustment weights["volatility"] += historical_adjustment weights["efficiency"] += historical_adjustment # Calculate final score maturity_score = ( weights["volume"] * volume_score + weights["volatility"] * vol_maturity + weights["efficiency"] * efficiency + weights["futures"] * futures_maturity ) # Apply non-linear transformation to better distinguish maturity levels maturity_score = 1 / (1 + np.exp(-4 * (maturity_score - 0.5))) # Final smoothing maturity_score = maturity_score.rolling( window=30, min_periods=1, center=True ).mean() return maturity_score def adjust_projections_for_maturity(df, projections, maturity_score): """ Adjust price projections based on market maturity score. More mature markets should have tighter confidence intervals and more conservative growth expectations. """ # Get final maturity score final_maturity = maturity_score.iloc[-1] # Adjust confidence intervals based on maturity # More mature markets = tighter intervals ci_adjustment = 1 - (final_maturity * 0.3) # Max 30% reduction in interval width # Adjust expected returns based on maturity # More mature markets = more conservative growth returns_adjustment = 1 - ( final_maturity * 0.2 ) # Max 20% reduction in expected returns adjusted_projections = projections.copy() # Adjust confidence intervals for ci in [68, 95]: upper_key = f"Upper_{ci}" lower_key = f"Lower_{ci}" median = adjusted_projections["Median"] # Calculate distances from median upper_distance = adjusted_projections[upper_key] - median lower_distance = median - adjusted_projections[lower_key] # Apply maturity-based adjustment adjusted_projections[upper_key] = median + (upper_distance * ci_adjustment) adjusted_projections[lower_key] = median - (lower_distance * ci_adjustment) # Adjust expected trend trend_distance = ( adjusted_projections["Expected_Trend"] - adjusted_projections["Median"] ) adjusted_projections["Expected_Trend"] = ( adjusted_projections["Median"] + trend_distance * returns_adjustment ) return adjusted_projections def project_prices( df, days_forward=365, simulations=1000, confidence_levels=[0.95, 0.68] ): """ Project future Bitcoin prices using Monte Carlo simulation with market maturity adjustments. """ # Calculate market maturity score maturity_score = calculate_market_maturity_score(df) # Original calculations df = df.copy() df["Log_Price"] = np.log(df["Close"]) df["Log_Return"] = df["Log_Price"].diff() # Get smoothed trends cycle_trends = analyze_trends(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 and date 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 with cycle boundary handling future_cycle_days = [ (current_cycle_days + i) % (4 * 365) for i in range(days_forward) ] expected_returns = [] for day in future_cycle_days: # Get base trend value base_trend = cycle_trends.get(day, cycle_trends.mean()) # Add slight mean reversion for extreme values if abs(base_trend) > 2 * cycle_trends.std(): base_trend *= 0.8 # Dampen extreme trends expected_returns.append(base_trend) expected_returns = np.array(expected_returns) # Calculate volatility with maturity adjustment base_volatility = calculate_volatility(df) final_maturity = maturity_score.iloc[-1] # Adjust volatility based on market maturity (more mature = lower volatility) volatility_adjustment = 1 - (final_maturity * 0.3) # Max 30% reduction volatility = base_volatility * volatility_adjustment # Run Monte Carlo simulation np.random.seed(42) simulated_paths = np.zeros((days_forward, simulations)) # Adjust trend expectations based on market maturity trend_adjustment = 1 - (final_maturity * 0.2) # Max 20% reduction adjusted_expected_returns = expected_returns * trend_adjustment for sim in range(simulations): # Adjust skew based on market maturity (more mature = less skew) base_skew = 0.087 skew_adjustment = 1 - (final_maturity * 0.4) # Max 40% reduction skew = np.sign(adjusted_expected_returns) * base_skew * skew_adjustment returns = np.random.normal( loc=adjusted_expected_returns + skew * volatility, scale=volatility, size=days_forward, ) # Calculate price path cumulative_returns = np.cumsum(returns) price_path = last_price * np.exp(cumulative_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 results["Expected_Trend"] = last_price * np.exp( np.cumsum(adjusted_expected_returns) ) # Add maturity score to results for analysis results["Market_Maturity"] = final_maturity return results 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 numeric_columns = ["Price", "Open", "High", "Low", "Vol."] # Added Volume for col in numeric_columns: # Remove any commas and 'K'/'M' suffixes df[col] = df[col].astype(str).str.replace(",", "") # Convert K to thousands df[col] = df[col].str.replace("K", "e3") # Convert M to millions df[col] = df[col].str.replace("M", "e6") # Convert B to billions df[col] = df[col].str.replace("B", "e9") # Convert to numeric 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", "Volume"]].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 # Main plotting functions def create_plots(df, start=None, end=None, project_days=365): """ Create enhanced plots including market maturity visualization. """ # 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") # Calculate market maturity score maturity_score = calculate_market_maturity_score(plot_df) plot_df["Market_Maturity"] = maturity_score # Generate projections projections = project_prices(plot_df, days_forward=project_days) # Set up the style plt.style.use("seaborn-v0_8") # Create figure with adjusted size for additional subplot fig = plt.figure(figsize=(15, 20)) # Increased height to accommodate new subplot # 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(5, 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)) 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) ax1.tick_params(axis="y", labelsize=8) ax1.margins(y=0.02) 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) ax1.legend(fontsize=8) # 2. Market Maturity Score ax2 = plt.subplot(5, 1, 2) ax2.plot( plot_df["Date"], plot_df["Market_Maturity"], color="purple", label="Market Maturity Score", ) ax2.set_title("Market Maturity Score" + hist_date_range) ax2.set_ylim(0, 1) ax2.grid(True, alpha=0.3) ax2.legend() # Add futures launch annotation futures_date = pd.Timestamp("2017-12-10") if futures_date >= plot_df["Date"].min() and futures_date <= plot_df["Date"].max(): ax2.axvline(futures_date, color="red", linestyle="--", alpha=0.5) ax2.text( futures_date, 0.95, "Futures\nLaunch", rotation=90, va="top", ha="right" ) # 3. Rolling volatility ax3 = plt.subplot(5, 1, 3) ax3.plot( plot_df["Date"], plot_df["Rolling_Volatility_30d"], "r-", label="30-Day Rolling Volatility", ) ax3.set_title("30-Day Rolling Volatility (Annualized)" + hist_date_range) ax3.set_ylabel("Volatility") ax3.grid(True) ax3.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: "{:.0%}".format(y))) ax3.legend() # 4. Returns distribution ax4 = plt.subplot(5, 1, 4) 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=ax4) ax4.set_title( "Distribution of Daily Returns (Excluding Extreme Outliers)" + hist_date_range ) ax4.set_xlabel("Daily Return") ax4.set_ylabel("Count") ax4.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: "{:.0%}".format(x))) # Add mean line ax4.axvline(filtered_returns.mean(), color="r", linestyle="dashed", linewidth=1) ax4.text( filtered_returns.mean(), ax4.get_ylim()[1], "Mean", rotation=90, va="top", ha="right", ) # 5. Projection ranges ax5 = plt.subplot(5, 1, 5) timepoints = np.array(range(30, project_days, 30)) timepoints = timepoints[timepoints <= project_days] ranges = [] labels = [] positions = [] for t in timepoints: idx = t - 1 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) ax5.scatter(positions, ranges, alpha=0.6) for t in timepoints: idx = positions.index(t) ax5.plot([t] * 5, ranges[idx : idx + 5], "k-", alpha=0.3) ax5.set_yscale("log") min_price = min(ranges) max_price = max(ranges) price_points = get_nice_price_points(min_price, max_price) ax5.set_yticks(price_points) ax5.yaxis.set_major_formatter(plt.FuncFormatter(format_price)) ax5.set_title("Projected Price Ranges at Future Timepoints") ax5.set_xlabel("Days Forward") ax5.set_ylabel("Price (USD)") ax5.grid(True, alpha=0.3) ax5.set_xticks(timepoints) # Adjust layout plt.tight_layout(h_pad=1.0) # Increased spacing between subplots # 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 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_backtest_plot( df, backtest_date="2020-05-11", start_date="2012-11-28", project_days=1650 ): """ Create a plot comparing actual price history against model projections from a historical date. Args: df: DataFrame with historical price data backtest_date: Date to start the backtest from (default: third halving) start_date: Date to start considering historical data (default: first halving) project_days: Number of days to project forward from backtest date """ # Convert dates to datetime backtest_date = pd.to_datetime(backtest_date) start_date = pd.to_datetime(start_date) # Validate dates if start_date >= backtest_date: raise ValueError("start_date must be earlier than backtest_date") # Clean the data: remove rows with zero or invalid prices and filter by date df = df[(df["Close"] > 0) & (df["Date"] >= start_date)].copy() # Split data into training (before backtest date) and validation (after backtest date) training_df = df[df["Date"] <= backtest_date].copy() validation_df = df[df["Date"] > backtest_date].copy() # Check if we have enough data if len(training_df) < 30: # Require at least 30 days of training data raise ValueError("Insufficient training data before backtest date") # Generate historical projections using only training data historical_projections = project_prices(training_df, days_forward=project_days) # Set up the plot plt.style.use("seaborn-v0_8") fig, ax = plt.figure(figsize=(15, 10)), plt.gca() # Plot training data ax.semilogy( training_df["Date"], training_df["Close"], "b-", label=f'Historical Price (Training: {start_date.strftime("%Y-%m-%d")} to {backtest_date.strftime("%Y-%m-%d")})', alpha=0.7, ) # Plot validation data ax.semilogy( validation_df["Date"], validation_df["Close"], "g-", label=f'Actual Price (Validation: {backtest_date.strftime("%Y-%m-%d")} onwards)', linewidth=2, ) # Plot projections ax.semilogy( historical_projections.index, historical_projections["Expected_Trend"], "--", color="purple", label="Model Projection (Expected)", ) ax.semilogy( historical_projections.index, historical_projections["Median"], ":", color="orange", label="Model Projection (Median)", ) # Add confidence intervals ax.fill_between( historical_projections.index, historical_projections["Lower_95"], historical_projections["Upper_95"], alpha=0.2, color="orange", label="95% Confidence Interval", ) ax.fill_between( historical_projections.index, historical_projections["Lower_68"], historical_projections["Upper_68"], alpha=0.3, color="green", label="68% Confidence Interval", ) # Customize y-axis ax.yaxis.set_major_formatter(plt.FuncFormatter(format_price)) # Set custom y-axis ticks min_price = min( df["Low"].min(), historical_projections["Lower_95"].min(), 0.0001, # Set minimum price floor ) max_price = max(df["High"].max(), historical_projections["Upper_95"].max()) price_points = get_nice_price_points(min_price, max_price) ax.set_yticks(price_points) # Add halving lines halving_dates = get_halving_dates() relevant_halvings = halving_dates[ (halving_dates >= start_date) & (halving_dates <= validation_df["Date"].max()) ] for date in relevant_halvings: ax.axvline(date, color="red", linestyle="--", alpha=0.3) ax.text( date, ax.get_ylim()[1], "Halving", rotation=90, va="top", ha="right", alpha=0.7, ) # Calculate and add model performance metrics if len(validation_df) > 0: # Create a common date range for comparison actual_prices = validation_df.set_index("Date")["Close"] common_dates = actual_prices.index.intersection(historical_projections.index) if len(common_dates) > 0: actual_aligned = actual_prices[common_dates] projections_aligned = historical_projections.loc[common_dates] # Calculate metrics using aligned data mape = ( np.mean( np.abs( (actual_aligned - projections_aligned["Expected_Trend"]) / actual_aligned ) ) * 100 ) coverage_95 = ( np.mean( (actual_aligned >= projections_aligned["Lower_95"]) & (actual_aligned <= projections_aligned["Upper_95"]) ) * 100 ) coverage_68 = ( np.mean( (actual_aligned >= projections_aligned["Lower_68"]) & (actual_aligned <= projections_aligned["Upper_68"]) ) * 100 ) rmse = np.sqrt( np.mean((actual_aligned - projections_aligned["Expected_Trend"]) ** 2) ) max_error = np.max( np.abs(actual_aligned - projections_aligned["Expected_Trend"]) ) # Add metrics to plot metrics_text = ( f"Model Performance Metrics:\n" f"MAPE: {mape:.1f}%\n" f"RMSE: ${rmse:,.0f}\n" f"Max Error: ${max_error:,.0f}\n" f"95% CI Coverage: {coverage_95:.1f}%\n" f"68% CI Coverage: {coverage_68:.1f}%" ) ax.text( 0.02, 0.98, metrics_text, transform=ax.transAxes, verticalalignment="top", bbox=dict(facecolor="white", alpha=0.8), ) # Customize plot ax.set_title( f'Bitcoin Price: Model Backtest\nTraining: {start_date.strftime("%Y-%m-%d")} to {backtest_date.strftime("%Y-%m-%d")}' ) ax.set_xlabel("Date") ax.set_ylabel("Price (USD)") ax.grid(True, which="major", linestyle="-", alpha=0.5) ax.grid(True, which="minor", linestyle=":", alpha=0.2) ax.legend(loc="center left", bbox_to_anchor=(1.02, 0.5)) # Adjust layout and save plt.tight_layout() filename = f'bitcoin_backtest_{start_date.strftime("%Y%m%d")}_to_{backtest_date.strftime("%Y%m%d")}.png' plt.savefig(filename, dpi=300, bbox_inches="tight") plt.close() return historical_projections def run_projection(df): projections = create_plots(df, start="2016-07-09", project_days=365 * 4) print("\nProjected Prices at Key Points:") print(projections.iloc[[29, 89, 179, 364]].round(2)) # 30, 90, 180, 365 days def run_backtest(params, df): print( f"\nRunning backtest from {params['start_date']} to {params['backtest_date']}" ) backtest_projections = create_backtest_plot(df, **params) # Print some key projection points vs actual prices print("\nBacktest Results - Projected vs Actual Prices:") validation_df = df[df["Date"] > params["backtest_date"]] actual_prices = validation_df.set_index("Date")["Close"] for days in [30, 90, 180, 365]: target_date = pd.to_datetime(params["backtest_date"]) + pd.Timedelta(days=days) if ( target_date in actual_prices.index and target_date in backtest_projections.index ): projected = backtest_projections.loc[target_date] actual = actual_prices.loc[target_date] print(f"\n{days} days out ({target_date.strftime('%Y-%m-%d')}):") print(f"Actual Price: ${actual:,.2f}") print(f"Projected (Expected): ${projected['Expected_Trend']:,.2f}") print( f"Projected Range: ${projected['Lower_95']:,.2f} - ${projected['Upper_95']:,.2f}" ) if __name__ == "__main__": analysis, df = analyze_bitcoin_prices("prices.csv") procs = [] # Create main projection proc = Process(target=run_projection, args=(df,)) proc.start() procs.append(proc) # Create multiple backtests for different periods backtests = [ # First until fourth halving { "start_date": "2012-11-28", "backtest_date": "2024-04-19", "project_days": 1460, }, # First until third halving { "start_date": "2012-11-28", "backtest_date": "2020-05-11", "project_days": 1460, }, # Second until fourth halving { "start_date": "2016-07-09", "backtest_date": "2024-04-19", "project_days": 1460, }, ] # Run all backtests for params in backtests: proc = Process( target=run_backtest, args=( params, df, ), ) procs.append(proc) proc.start() for proc in procs: proc.join()