bitcoin-model/model.py

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
        + (volume_growth_365d / volume_growth_std_365).clip(-2, 2) * 0.6
    ).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)

    # Add stronger early-market adjustment
    if df["Date"].min() < pd.Timestamp("2013-01-01"):
        historical_period *= 1.5  # Increase uncertainty for pre-2013 data

    # 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

    # Reduce conservatism in high-maturity periods
    if final_maturity > 0.7:
        ci_adjustment *= 1.2  # Widen intervals less in very mature periods

    # 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 era-specific adjustments.
    """
    # Calculate market maturity score
    maturity_score = calculate_market_maturity_score(df)
    final_maturity = maturity_score.iloc[-1]

    df = df.copy()
    df["Log_Price"] = np.log(df["Close"])
    df["Log_Return"] = df["Log_Price"].diff()

    # Define market eras and their characteristics
    min_date = df["Date"].min()
    max_date = df["Date"].max()

    era_adjustments = {
        "early": {
            "start_date": pd.Timestamp("2013-01-01"),
            "end_date": pd.Timestamp("2017-12-10"),  # Futures introduction
            "volatility_scale": 1.5,  # Balanced for post-2013 early market
            "trend_scale": 0.85,  # Moderately conservative trends
            "skew_scale": 1.2,  # Moderate trend following
        },
        "transition": {
            "start_date": pd.Timestamp("2017-12-10"),
            "end_date": pd.Timestamp("2020-01-01"),
            "volatility_scale": 1.2,  # Slightly elevated uncertainty
            "trend_scale": 0.95,  # Near-normal trends
            "skew_scale": 1.05,  # Light trend following
        },
        "mature": {
            "start_date": pd.Timestamp("2020-01-01"),
            "end_date": pd.Timestamp("2100-01-01"),
            "volatility_scale": 0.9,  # Slightly reduced volatility for mature market
            "trend_scale": 1.0,  # Base case
            "skew_scale": 0.95,  # Slight reduction in trend following
        },
    }

    # Determine which era we're in
    current_era = None
    for era, params in era_adjustments.items():
        if min_date >= params["start_date"] and min_date < params["end_date"]:
            current_era = era
            break

    if current_era is None:
        current_era = "mature"  # Default to mature era if no match

    # Get era-specific adjustment factors
    vol_scale = era_adjustments[current_era]["volatility_scale"]
    trend_scale = era_adjustments[current_era]["trend_scale"]
    skew_scale = era_adjustments[current_era]["skew_scale"]

    # Get cycle trends and position
    cycle_trends = analyze_trends(df)
    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 projection dates
    future_dates = pd.date_range(
        start=last_date + timedelta(days=1), periods=days_forward, freq="D"
    )

    # Calculate expected returns
    future_cycle_days = [
        (current_cycle_days + i) % (4 * 365) for i in range(days_forward)
    ]
    expected_returns = []

    for day in future_cycle_days:
        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 and adjust volatility
    base_volatility = calculate_volatility(df)
    volatility = base_volatility * vol_scale

    # Adjust expected returns
    adjusted_expected_returns = expected_returns * trend_scale

    # Run Monte Carlo simulation
    np.random.seed(42)
    simulated_paths = np.zeros((days_forward, simulations))

    for sim in range(simulations):
        # Calculate skew with era-specific scaling
        base_skew = 0.087 * skew_scale
        skew = np.sign(adjusted_expected_returns) * base_skew

        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 results
    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
        )

    results["Expected_Trend"] = last_price * np.exp(
        np.cumsum(adjusted_expected_returns)
    )
    results["Market_Maturity"] = final_maturity
    results["Era"] = current_era

    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
    heading_label = f'Historical Price (Training: {start_date.strftime("%Y-%m-%d")} to {backtest_date.strftime("%Y-%m-%d")})'

    ax.semilogy(
        training_df["Date"],
        training_df["Close"],
        "b-",
        label=heading_label,
        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}%"
            )
            with open(
                f'bitcoin_backtest_{start_date.strftime("%Y%m%d")}_to_{backtest_date.strftime("%Y%m%d")}.txt',
                "w",
            ) as f:
                f.write(f"{heading_label}\n")
                f.write(metrics_text)
            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, start):
    projections = create_plots(df, start=start, 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
    projection_starts = [
        "2013-01-01",
        "2014-01-01",
        "2015-01-01",
        "2016-07-09",
    ]
    for start in projection_starts:
        proc = Process(target=run_projection, args=(df, start))
        proc.start()
        procs.append(proc)

    # Create multiple backtests for different periods
    # Create multiple backtests for different periods
    backtests = [
        # Base case: Second until fourth halving
        {
            "start_date": "2016-07-09",
            "backtest_date": "2024-04-19",
            "project_days": 1460,
        },
        # Post-Futures Window with two cycles of training
        {
            "start_date": "2013-01-01",  # Includes pre-futures for cycle learning
            "backtest_date": "2020-05-11",
            "project_days": 1460,
        },
        # Cross-Regime Test with two cycles of training
        {
            "start_date": "2014-01-01",
            "backtest_date": "2021-12-31",
            "project_days": 1460,
        },
        # Recent Window focusing on post-2022 behavior
        {
            "start_date": "2015-01-01",  # About two cycles before 2022
            "backtest_date": "2022-01-01",
            "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()