Metadata-Version: 2.1
Name: lppls
Version: 0.1.7
Summary: A Python module for fitting the LPPLS model to data.
Home-page: https://github.com/Boulder-Investment-Technologies/lppls
Author: Josh Nielsen
Author-email: josh@boulderinvestment.tech
License: UNKNOWN
Description: ![Publish 🐍 📦 to PyPI](https://github.com/Boulder-Investment-Technologies/lppls/workflows/Publish%20%F0%9F%90%8D%20%F0%9F%93%A6%20to%20PyPI/badge.svg?branch=master)
        
        # Log Periodic Power Law Singularity (LPPLS) Model 
        `lppls` is a Python module for fitting the LPPLS model to data.
        
        
        ## Overview
        The LPPL model provides a flexible framework to detect bubbles and predict regime changes of a financial asset. A bubble is defined as a faster-than-exponential increase in asset price, that reflects positive feedback loop of higher return anticipations competing with negative feedback spirals of crash expectations. It models a bubble price as a power law with a finite-time singularity decorated by oscillations with a frequency increasing with time. 
        
        Here is the model:
        
        <img src="https://latex.codecogs.com/svg.latex?E[\text{ln&space;}p(t)]=A+B(t_c-t)^m&space;+C(t_c-t)^m&space;cos(\omega&space;ln(t_c-t)-\phi)" title="LPPLS Model" />
        
          where:
        
          - <img src="https://latex.codecogs.com/svg.latex?E[\text{ln&space;}p(t)]&space;:=" title="Expected Log Price" /> expected log price at the date of the termination of the bubble
          - <img src="https://latex.codecogs.com/svg.latex?t_c&space;:=" title="Critcal Time" /> critical time (date of termination of the bubble and transition in a new regime) 
          - <img src="https://latex.codecogs.com/svg.latex?A&space;:=" title="A" /> expected log price at the peak when the end of the bubble is reached at <img src="https://latex.codecogs.com/svg.latex?t_c" title="Critcal Time" />
          - <img src="https://latex.codecogs.com/svg.latex?B&space;:=" title="B" /> amplitude of the power law acceleration
          - <img src="https://latex.codecogs.com/svg.latex?C&space;:=" title="C" /> amplitude of the log-periodic oscillations
          - <img src="https://latex.codecogs.com/svg.latex?m&space;:=" title="m" /> degree of the super exponential growth
          - <img src="https://latex.codecogs.com/svg.latex?\omega&space;:=" title="Omega" /> scaling ratio of the temporal hierarchy of osciallations
          - <img src="https://latex.codecogs.com/svg.latex?\phi&space;:=" title="Phi" /> time scale of the oscillations
            
        The model has three components representing a bubble. The first, <img src="https://latex.codecogs.com/svg.latex?A+B(t_c-t)^m" title="LPPLS Term 1" />, handles the hyperbolic power law. For <img src="https://latex.codecogs.com/svg.latex?m<1" title="M less than 1" /> when the price growth becomes unsustainable, and at <img src="https://latex.codecogs.com/svg.latex?t_c" title="Critcal Time" /> the growth rate becomes infinite. The second term, <img src="https://latex.codecogs.com/svg.latex?C(t_c-t)^m" title="LPPLS Term 2" />, controls the amplitude of the oscillations. It drops to zero at the critical time <img src="https://latex.codecogs.com/svg.latex?t_c" title="Critcal Time" />. The third term, <img src="https://latex.codecogs.com/svg.latex?cos(\omega&space;ln(t_c-t)-\phi)" title="LPPLS Term 3" />, models the frequency of the osciallations. They become infinite at <img src="https://latex.codecogs.com/svg.latex?t_c" title="Critcal Time" />.
        
        ## Important links
         - Official source code repo: https://github.com/Boulder-Investment-Technologies/lppls
         - Download releases: https://pypi.org/project/lppls/
         - Issue tracker: https://github.com/Boulder-Investment-Technologies/lppls/issues
        
        ## Installation
        Dependencies
        
        `lppls` requires:
         - Pandas (>= 0.25.0)
         - Python (>= 3.6)
         - NumPy (>= 1.17.0)
         - SciPy (>= 1.3.0)
         - Matplotlib (>= 3.1.1)
        
        User installation
        If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip
        ```
        pip install -U lppls
        ```
        
        ## Example Use
        ```python
        from lppls import lppls
        import pandas as pd
        import tqdm
        import time 
        
        if __name__ == '__main__':
            start = time.time()
            data = pd.read_csv('<location>.csv', index_col='<index_col>', parse_dates=True)
            signals_list = []
            asset_list = data.columns.tolist()
            window = 126
            for seq in tqdm(range(data.shape[0] - window)):
                window_data = data.iloc[seq:seq + window].copy()
                lppl_model = lppls.LPPLS(window_data, asset_list)
                signals_list.append(lppl_model.fetch_indicators(126, 5, 21, 5))
            end = time.time()
            duration = end - start
            print(duration)
        ```
        
        ## References
         - Filimonov, V. and Sornette, D. A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model. Physica A: Statistical Mechanics and its Applications. 2013
         - Sornette, D. Why Stock Markets Crash: Critical Events in Complex Financial Systems. Princeton University Press. 2002.
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
