Python statsmodels.tsa.stattools.coint() Examples

The following are 4 code examples of statsmodels.tsa.stattools.coint(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module statsmodels.tsa.stattools , or try the search function .
Example #1
Source File: Cointegration.py    From Pair-Trading-Reinforcement-Learning with MIT License 5 votes vote down vote up 
def get_p_value(x, y):
        _, p_val, _ = coint(x, y)
        return p_val
Example #2
Source File: ch4_pairs_correlation_init.py    From Learn-Algorithmic-Trading---Fundamentals-of-Algorithmic-Trading with MIT License 5 votes vote down vote up 
def find_cointegrated_pairs(data):
    n = data.shape[1]
    pvalue_matrix = np.ones((n, n))
    keys = data.keys()
    pairs = []
    for i in range(n):
        for j in range(i+1, n):
            result = coint(data[keys[i]], data[keys[j]])
            pvalue_matrix[i, j] = result[1]
            if result[1] < 0.02:
                pairs.append((keys[i], keys[j]))
    return pvalue_matrix, pairs
Example #3
Source File: ch4_pairs_correlation_init.py    From Learn-Algorithmic-Trading with MIT License 5 votes vote down vote up 
def find_cointegrated_pairs(data):
    n = data.shape[1]
    pvalue_matrix = np.ones((n, n))
    keys = data.keys()
    pairs = []
    for i in range(n):
        for j in range(i+1, n):
            result = coint(data[keys[i]], data[keys[j]])
            pvalue_matrix[i, j] = result[1]
            if result[1] < 0.02:
                pairs.append((keys[i], keys[j]))
    return pvalue_matrix, pairs
Example #4
Source File: pair_selector.py    From pairstrade-fyp-2019 with MIT License 4 votes vote down vote up 
def coint(df, intercept = True, sig_level = 0.01):
    """
    Find pairs (of 2 time series) that passes the cointegration test.

    Parameters
    ----------
    df: pandas dataframe
        each column is the time series of a certain stock
    
    intercept: boolean
        if True, OLS and ADF test are done manually
        if False, the coint() function from statsmodels.tsa.stattools, which 
        does not include intercept term while doing OLS regression, is used.
    
    sig_level: if p_value of cointegration test is below this level, then we
        can reject the NULL hypothesis, which says that the two series are not
        cointegrated
    
    Return
    ------
    A list of tuples of the form (name of stock 1, name of stock 2, p_value of
    cointegration test).
    
    """
    cointegrated_pairs = []
    
    stock_names = df.columns.values.tolist()
    N = len(stock_names)
    
    stock_pairs = list(itertools.combinations(stock_names, 2))
    
    for pair in stock_pairs:
        stock_1, stock_2 = pair

        p_value = 0

        if not intercept:
            p_value = smts.coint(df[stock_1].values.astype(float), df[stock_2].values.astype(float), trend='c')[1]
        else:
            Y = df[stock_1].values.astype(float)
            X = df[stock_2].values.astype(float)
            X = sm.add_constant(X)

            model = sm.OLS(Y, X)
            results = model.fit()
            intercept, slope = results.params

            p_value = smts.adfuller(results.resid)[1]

        if p_value < sig_level and slope > 0:
            cointegrated_pairs.append(tuple([stock_1, stock_2, p_value]))

    return cointegrated_pairs