!pip install numpy pandas matplotlib seaborn requests --quiet

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import requests
import warnings

warnings.filterwarnings('ignore')
%matplotlib inline
plt.rcParams['figure.figsize'] = (14, 5)
plt.rcParams['axes.spines.top']   = False
plt.rcParams['axes.spines.right'] = False
print('Imports ready.')

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import requests
import warnings

warnings.filterwarnings('ignore')
%matplotlib inline
plt.rcParams['figure.figsize'] = (14, 5)
plt.rcParams['axes.spines.top']   = False
plt.rcParams['axes.spines.right'] = False
print('Imports ready.')

# --- Configuration ---
USE_SYNTHETIC      = True
GLASSNODE_API_KEY  = 'YOUR_API_KEY_HERE'
SOPR_EMA_PERIOD    = 14       # smooth SOPR to reduce noise
SOPR_BULL_SUPPORT  = 1.0      # SOPR dipping to this in bull market = buy
SOPR_PIVOT_WINDOW  = 5        # bars for local pivot detection
HOLD_BARS          = 14       # hold signal for N days after bounce
SIMULATION_DAYS    = 1460
print('Config ready.')

def generate_synthetic_sopr(
    n_days: int = 1460,
    seed: int = 42
) -> pd.DataFrame:
    """
    Generate synthetic SOPR data with realistic bull/bear cycle dynamics.

    Parameters
    ----------
    n_days : int  Number of days.
    seed : int  Random seed.

    Returns
    -------
    pd.DataFrame
        Columns: btc_price, sopr, market_phase.

    Notes
    -----
    During bull market: SOPR oscillates above 1.0 with dips to 1.0 as support.
    During bear market: SOPR oscillates below 1.0 with bounces to 1.0 as resistance.
    """
    rng = np.random.default_rng(seed)
    t = np.linspace(0, 2 * np.pi, n_days)

    # Price cycle
    cycle = np.sin(t - np.pi / 2) * 0.5 + 0.5
    btc_price = 15_000 + 75_000 * cycle**1.5 + np.cumsum(rng.normal(0, 300, n_days))
    btc_price = np.maximum(btc_price, 5_000)

    # SOPR: mean slightly above 1 in bull, below 1 in bear, with noise
    sopr_mean = 1.0 + 0.4 * (cycle - 0.3)  # >1 in bull, <1 in bear
    sopr = sopr_mean + rng.normal(0, 0.08, n_days)
    sopr = np.maximum(sopr, 0.5)

    market_phase = np.where(cycle > 0.5, 'bull', 'bear')

    index = pd.date_range('2020-01-01', periods=n_days, freq='D')
    return pd.DataFrame({'btc_price': btc_price, 'sopr': sopr, 'market_phase': market_phase}, index=index)


def fetch_sopr(
    api_key: str = None,
    use_synthetic: bool = False
) -> pd.DataFrame:
    """
    Fetch SOPR from Glassnode or return synthetic data.

    Parameters
    ----------
    api_key : str  Glassnode API key.
    use_synthetic : bool  Skip API call if True.

    Returns
    -------
    pd.DataFrame  SOPR dataset.
    """
    if use_synthetic or not api_key or api_key == 'YOUR_API_KEY_HERE':
        return generate_synthetic_sopr(SIMULATION_DAYS)
    try:
        url    = 'https://api.glassnode.com/v1/metrics/indicators/sopr'
        params = {'a': 'BTC', 'api_key': api_key, 'i': '24h', 'f': 'JSON'}
        resp   = requests.get(url, params=params, timeout=10)
        resp.raise_for_status()
        raw = pd.DataFrame(resp.json())
        raw.index = pd.to_datetime(raw['t'], unit='s')
        raw['sopr'] = raw['v'].astype(float)
        return raw[['sopr']]
    except Exception as e:
        print(f'Glassnode failed ({e}), using synthetic.')
        return generate_synthetic_sopr(SIMULATION_DAYS)


df = fetch_sopr(GLASSNODE_API_KEY, USE_SYNTHETIC)
print(f'SOPR range: {df["sopr"].min():.3f} — {df["sopr"].max():.3f}')
print(f'Days above 1.0: {(df["sopr"] > 1).sum()} / {len(df)}')

def compute_sopr_features(
    df: pd.DataFrame,
    ema_period: int = 14
) -> pd.DataFrame:
    """
    Compute EMA-smoothed SOPR and related features.

    Parameters
    ----------
    df : pd.DataFrame  DataFrame with 'sopr' column.
    ema_period : int  EMA smoothing period.

    Returns
    -------
    pd.DataFrame  Input df with sopr_ema, sopr_above_1, sopr_streak columns.
    """
    df = df.copy()
    df['sopr_ema']     = df['sopr'].ewm(span=ema_period).mean()
    df['sopr_above_1'] = (df['sopr_ema'] > 1.0).astype(int)

    # Count consecutive days above/below 1.0
    streak = []
    count = 0
    for val in df['sopr_above_1']:
        if val == 1:
            count = count + 1 if count >= 0 else 1
        else:
            count = count - 1 if count <= 0 else -1
        streak.append(count)
    df['sopr_streak'] = streak
    return df


def detect_sopr_pivots(
    df: pd.DataFrame,
    window: int = 5,
    support_level: float = 1.0
) -> pd.DataFrame:
    """
    Detect SOPR pivots: dips to and bounces from the 1.0 support level.

    Parameters
    ----------
    df : pd.DataFrame  DataFrame with 'sopr_ema' column.
    window : int  Lookback for local minimum detection.
    support_level : float  SOPR level to watch for pivot signals.

    Returns
    -------
    pd.DataFrame  Input df with 'sopr_pivot' column (1=bounce from support).
    """
    df = df.copy()
    sopr = df['sopr_ema'].values
    pivot = np.zeros(len(df))

    for i in range(window, len(df) - window):
        # Local minimum near support level
        local_min = sopr[i] <= sopr[i-window:i].min() and sopr[i] <= sopr[i+1:i+window+1].min()
        near_support = abs(sopr[i] - support_level) < 0.05
        if local_min and near_support and sopr[i] > 0.9:
            pivot[i] = 1

    df['sopr_pivot'] = pivot
    return df


def generate_sopr_signal(
    df: pd.DataFrame,
    hold_bars: int = 14
) -> pd.DataFrame:
    """
    Generate buy signals from SOPR pivot detection.

    Enter on SOPR bounce from 1.0 support; exit after hold_bars days
    or if SOPR drops significantly below 1.0 (bear market signal).

    Parameters
    ----------
    df : pd.DataFrame  DataFrame with 'sopr_pivot' and 'sopr_ema' columns.
    hold_bars : int  Days to hold position after signal.

    Returns
    -------
    pd.DataFrame  Input df with 'sopr_signal' column.
    """
    df = df.copy()
    signal = np.zeros(len(df))
    hold_count = 0

    for i in range(len(df)):
        if df['sopr_pivot'].iloc[i] == 1:
            hold_count = hold_bars
        if hold_count > 0 and df['sopr_ema'].iloc[i] > 0.92:
            signal[i] = 1
            hold_count -= 1
        else:
            hold_count = max(0, hold_count - 1)

    df['sopr_signal'] = signal
    return df


df = compute_sopr_features(df, SOPR_EMA_PERIOD)
df = detect_sopr_pivots(df, SOPR_PIVOT_WINDOW)
df = generate_sopr_signal(df, HOLD_BARS)

print(f'SOPR pivots detected: {int(df["sopr_pivot"].sum())}')
print(f'Days in position: {int(df["sopr_signal"].sum())} / {len(df)}')

def backtest_sopr_strategy(
    df: pd.DataFrame,
    initial_capital: float = 10_000.0
) -> pd.DataFrame:
    """
    Backtest SOPR pivot bounce strategy.

    Parameters
    ----------
    df : pd.DataFrame  DataFrame with 'sopr_signal' and 'btc_price' columns.
    initial_capital : float  Starting capital.

    Returns
    -------
    pd.DataFrame  Input df with 'equity' and 'bh_equity'.
    """
    df = df.copy()
    df['price_ret']    = df['btc_price'].pct_change()
    df['strategy_ret'] = df['sopr_signal'].shift(1) * df['price_ret']
    df['equity']       = initial_capital * (1 + df['strategy_ret'].fillna(0)).cumprod()
    df['bh_equity']    = initial_capital * (1 + df['price_ret'].fillna(0)).cumprod()
    return df


df = backtest_sopr_strategy(df)

# Visualization
fig, axes = plt.subplots(3, 1, figsize=(14, 13), sharex=True)

# Panel 1: BTC price with signal
axes[0].plot(df.index, df['btc_price'], color='steelblue', lw=1.0, label='BTC Price')
pivot_days = df[df['sopr_pivot'] == 1]
axes[0].scatter(pivot_days.index, pivot_days['btc_price'], color='green', s=40, zorder=5, marker='^', label='SOPR Pivot (Buy)')
axes[0].set_ylabel('Price (USD)')
axes[0].set_title('BTC Price with SOPR Pivot Signals')
axes[0].legend()

# Panel 2: SOPR
axes[1].plot(df.index, df['sopr'],     color='lightblue', lw=0.6, alpha=0.6, label='SOPR Raw')
axes[1].plot(df.index, df['sopr_ema'], color='purple',    lw=1.2, label=f'SOPR EMA({SOPR_EMA_PERIOD})')
axes[1].axhline(1.0, color='gold', lw=1.5, linestyle='--', label='SOPR = 1.0')
axes[1].scatter(pivot_days.index, pivot_days['sopr_ema'], color='green', s=40, zorder=5, marker='^')
axes[1].set_ylabel('SOPR')
axes[1].set_title('SOPR with EMA — Green markers = pivot signals')
axes[1].legend()

# Panel 3: Equity
axes[2].plot(df.index, df['equity'],    color='green',     lw=1.5, label='SOPR Strategy')
axes[2].plot(df.index, df['bh_equity'], color='steelblue', lw=1.0, linestyle='--', label='Buy & Hold')
axes[2].set_ylabel('Portfolio Value (USD)')
axes[2].set_xlabel('Date')
axes[2].set_title('SOPR Strategy vs Buy & Hold')
axes[2].legend()

plt.tight_layout()
plt.show()

final = df.iloc[-1]
print(f'SOPR Strategy: ${final["equity"]:,.0f}')
print(f'Buy & Hold:    ${final["bh_equity"]:,.0f}')

def export_sopr_data(df: pd.DataFrame) -> None:
    """
    Export SOPR signal dataset.

    Parameters
    ----------
    df : pd.DataFrame  Fully processed SOPR dataframe.
    """
    df.to_csv('sopr_signal.csv')
    print('Exported: sopr_signal.csv')


export_sopr_data(df)