To backtest the approach outlined by Giovanni in the X post (combining the Log-Periodic Power Law Singularity (LPPLS) model with a "Local Slopes" indicator for Bitcoin price prediction), we need to break this down into actionable steps. The LPPLS model is a well-documented method in econophysics for detecting financial bubbles, and the "Local Slopes" indicator appears to be a custom or less formalized metric (possibly related to linear regression slopes over short timeframes). Since the exact definition of "Local Slopes" isn't standardized, I'll interpret it as the slope of a linear regression over a moving window, a common technical analysis technique, and align it with the post's context. Below, I'll provide a practical explanation and a Python code framework to backtest this strategy, assuming you have historical Bitcoin price data.

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Conceptual Breakdown

1. LPPLS Model Overview:
2. The LPPLS model fits a price time series to a function that captures super-exponential growth (indicative of bubbles) with log-periodic oscillations. It’s designed to predict a critical time (tc) where a bubble might peak or crash.
3. The general form of the LPPLS model is:

\[

p(t) = A + B(t_c - t)^m (1 + C \cos(\omega \log(t_c - t) + \phi))

\]

4. p(t): Price at time t.
5. A: Long-term price trend after the bubble.
6. B: Amplitude of the power-law decay.
7. t_c: Critical time of the bubble peak/crash.
8. m: Power-law exponent (typically 0 < m < 1).
9. C: Amplitude of the log-periodic oscillations.
10. ω: Angular frequency of oscillations.
11. φ: Phase of the oscillations.
12. The model is fitted to historical data using optimization techniques (e.g., least squares) to estimate these parameters.

1. Local Slopes Indicator:
2. Interpreted as the slope of a linear regression over a short moving window (e.g., 5-20 days), this indicator measures the trend direction and strength.
3. A rising slope might indicate bullish momentum, while a falling slope could signal a potential reversal, aligning with the post's claim of combining it with LPPLS for prediction.

1. Backtesting Strategy:
2. Use LPPLS to identify a critical time (t_c) and assess if the current price trajectory suggests an imminent move.
3. Use the Local Slopes indicator to confirm the trend direction and magnitude.
4. Generate a signal (e.g., buy/sell) based on the coincidence of LPPLS predictions and slope conditions, then simulate trades to evaluate performance.

1. Data Requirements:
2. Historical Bitcoin price data (e.g., daily OHLC from exchanges like Coinbase or Binance).
3. Timeframe: At least 1-2 years to capture multiple bubble cycles.

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Practical Implementation in Python

Below is a step-by-step Python script using popular libraries (pandas, numpy, scipy for optimization, and yfinance for data). This code assumes you want to backtest the strategy from January 2020 to September 2025 (up to today’s date, 03:32 PM HKT, September 17, 2025).

Prerequisites

• Install required libraries: pip install pandas numpy scipy yfinance matplotlib.
• Ensure you have an internet connection for fetching data via yfinance.

Code

import pandas as pd
import numpy as np
from scipy.optimize import minimize
import yfinance as yf
import matplotlib.pyplot as plt

# 1. Fetch Bitcoin Data
def get_bitcoin_data():
    btc = yf.download('BTC-USD', start='2020-01-01', end='2025-09-17')
    return btc['Close']  # Use closing prices

# 2. LPPLS Model Function
def lppls_function(t, params):
    A, B, tc, m, C, omega, phi = params
    t = tc - t  # Time to critical point
    return A + B * (t ** m) * (1 + C * np.cos(omega * np.log(t) + phi))

# 3. Objective Function for Optimization
def objective_function(params, t, price):
    predicted = lppls_function(t, params)
    return np.sum((price - predicted) ** 2)  # Least squares

# 4. Fit LPPLS Model
def fit_lppls(t, price, initial_params):
    bounds = [(0, None), (-np.inf, 0), (t[-1], t[-1] + 365), (0, 1), (0, 1), (0, 10), (-np.pi, np.pi)]
    result = minimize(objective_function, initial_params, args=(t, price), bounds=bounds)
    return result.x  # Optimized parameters

# 5. Calculate Local Slopes (Linear Regression Slope over Window)
def calculate_local_slopes(prices, window=10):
    slopes = []
    for i in range(len(prices) - window + 1):
        window_data = prices[i:i + window]
        x = np.arange(window)
        slope, _ = np.polyfit(x, window_data, 1)  # Linear regression slope
        slopes.append(slope)
    return np.array(slopes + [np.nan] * (window - 1))  # Pad with NaN for alignment

# 6. Generate Trading Signals
def generate_signals(prices, lppls_params, slopes, t):
    signals = np.zeros(len(prices))
    tc = lppls_params[2]
    for i in range(len(prices)):
        if t[i] >= tc - 1 and slopes[i] > 0:  # Predict move if near tc and slope is positive
            signals[i] = 1  # Buy signal
        elif t[i] >= tc - 1 and slopes[i] < 0:
            signals[i] = -1  # Sell signal
    return signals

# 7. Backtest Strategy
def backtest_strategy(prices, signals):
    initial_capital = 10000
    position = 0
    portfolio = initial_capital
    holdings = []

    for i in range(1, len(prices)):
        if signals[i] == 1 and position == 0:  # Buy
            position = portfolio / prices[i]
            portfolio -= position * prices[i]
        elif signals[i] == -1 and position > 0:  # Sell
            portfolio += position * prices[i]
            position = 0

        holdings.append(position * prices[i] + portfolio)

    return np.array(holdings)

# Main Execution
if __name__ == "__main__":
    # Get data
    prices = get_bitcoin_data()
    t = np.arange(len(prices))

    # Initial parameters for LPPLS (rough estimates)
    initial_params = [prices[-1], -100, t[-1] + 30, 0.5, 0.1, 6.0, 0.0]

    # Fit LPPLS model
    lppls_params = fit_lppls(t, prices, initial_params)
    print("LPPLS Parameters:", lppls_params)

    # Calculate local slopes
    slopes = calculate_local_slopes(prices)

    # Generate signals
    signals = generate_signals(prices, lppls_params, slopes, t)

    # Backtest
    portfolio_value = backtest_strategy(prices, signals)

    # Plotting
    plt.figure(figsize=(12, 6))
    plt.plot(prices, label='Bitcoin Price')
    plt.plot(prices[signals == 1], 'go', label='Buy Signal')
    plt.plot(prices[signals == -1], 'ro', label='Sell Signal')
    plt.plot(portfolio_value, label='Portfolio Value', linestyle='--')
    plt.title('Bitcoin Price with LPPLS + Local Slopes Backtest')
    plt.legend()
    plt.show()

    # Performance
    final_value = portfolio_value[-1]
    print(f"Initial Capital: $10,000")
    print(f"Final Portfolio Value: ${final_value:.2f}")
    print(f"Return: {(final_value - 10000) / 10000 * 100:.2f}%")

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Explanation of the Code

1. Data Fetching:
2. Uses yfinance to download Bitcoin (BTC-USD) closing prices from January 2020 to September 17, 2025.

1. LPPLS Model:
2. The lppls_function implements the LPPLS equation.
3. fit_lppls optimizes the parameters using scipy.optimize.minimize with initial guesses and bounds to ensure realistic values (e.g., tc is in the future).

1. Local Slopes:
2. calculate_local_slopes computes the slope of a linear regression over a 10-day window (adjustable). This is a proxy for the "Local Slopes" indicator mentioned by Giovanni.

1. Signal Generation:
2. A buy signal is generated if the current time is within 1 day of the predicted critical time (tc) and the slope is positive.
3. A sell signal is generated if the slope is negative near tc.

1. Backtesting:
2. Simulates a simple trading strategy with $10,000 initial capital, buying/selling based on signals.
3. Tracks portfolio value over time.

1. Visualization:
2. Plots the Bitcoin price, buy/sell signals, and portfolio value for analysis.

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How to Use and Customize

• Data: Replace yfinance with your own dataset (e.g., CSV file) if you have higher-resolution data or a different source.
• Parameters:
• Adjust initial_params based on visual inspection or prior fits.
• Tune the window in calculate_local_slopes (e.g., 5, 15, 20) to match Giovanni’s implied timeframe.
• Modify the signal logic (e.g., add a slope threshold like 0.1% change/day) based on the post’s “% threshold to classify as a ‘move’” query.
• Validation: Compare tc predictions with historical peaks (e.g., 2021 bull run) to assess accuracy.
• Risk Management: Add stop-loss or position sizing for realism.

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Limitations and Next Steps

• LPPLS Fit: The model’s accuracy depends on the quality of the fit. Real-world noise may require regularization or multiple fits.
• Local Slopes: Without Giovanni’s exact definition, the 10-day window is an assumption. Experiment with different windows or consult his follow-up posts.
• Prediction Horizon: The post claims 1-day advance prediction; test this by shifting tc logic (e.g., signal 1 day before tc).
• Skepticism: As noted in the X thread and web results, predictive claims lack universal scientific rigor. Validate against out-of-sample data (e.g., 2025 data if available).

Run this code, tweak the parameters, and analyze the results. If you have access to Giovanni’s follow-up predictions (e.g., from the thread asking for tomorrow’s move), incorporate them to refine the model. Let me know if you need help interpreting the output or extending the strategy!