Instruction:
Can you create a Python 3 function that
downloads report for the given token and queryId.

Response:
def download(self, token, queryId):
        """
        Download report for the given ``token`` and ``queryId``.
        """
        url = (
                'https://gdcdyn.interactivebrokers.com'
                f'/Universal/servlet/FlexStatementService.SendRequest?'
                f't={token}&q={queryId}&v=3')
        resp = urlopen(url)
        data = resp.read()

        root = et.fromstring(data)
        if root.find('Status').text == 'Success':
            code = root.find('ReferenceCode').text
            baseUrl = root.find('Url').text
            _logger.info('Statement is being prepared...')
        else:
            errorCode = root.find('ErrorCode').text
            errorMsg = root.find('ErrorMessage').text
            raise FlexError(f'{errorCode}: {errorMsg}')

        while True:
            time.sleep(1)
            url = f'{baseUrl}?q={code}&t={token}'
            resp = urlopen(url)
            self.data = resp.read()
            self.root = et.fromstring(self.data)
            if self.root[0].tag == 'code':
                msg = self.root[0].text
                if msg.startswith('Statement generation in progress'):
                    _logger.info('still working...')
                    continue
                else:
                    raise FlexError(msg)
            break
        _logger.info('Statement retrieved.')


Instruction:
Create a Python 3 function for
loading report from XML file.

Response:
def load(self, path):
        """
        Load report from XML file.
        """
        with open(path, 'rb') as f:
            self.data = f.read()
            self.root = et.fromstring(self.data)


Instruction:
Can you write a function in Python 3 where it
saves report to XML file.

Response:
def save(self, path):
        """
        Save report to XML file.
        """
        with open(path, 'wb') as f:
            f.write(self.data)


Instruction:
Write a Python 3 function for
creating and a return a specialized contract based on the given secType.

Response:
def create(**kwargs):
        """
        Create and a return a specialized contract based on the given secType,
        or a general Contract if secType is not given.
        """
        secType = kwargs.get('secType', '')
        cls = {
            '': Contract,
            'STK': Stock,
            'OPT': Option,
            'FUT': Future,
            'CONTFUT': ContFuture,
            'CASH': Forex,
            'IND': Index,
            'CFD': CFD,
            'BOND': Bond,
            'CMDTY': Commodity,
            'FOP': FuturesOption,
            'FUND': MutualFund,
            'WAR': Warrant,
            'IOPT': Warrant,
            'BAG': Bag,
            'NEWS': Contract
        }.get(secType, Contract)
        if cls is not Contract:
            kwargs.pop('secType', '')
        return cls(**kwargs)


Instruction:
Can you tell what is the following Python 3 function doing
def hasBidAsk(self) -> bool:
        return (
            self.bid != -1 and not isNan(self.bid) and self.bidSize > 0 and
            self.ask != -1 and not isNan(self.ask) and self.askSize > 0)

Response: See if this ticker has a valid bid and ask.


Instruction:
Create a Python 3 function for
returning the midpoint of the current session.

Response:
def midpoint(self) -> float:
        """
        Return average of bid and ask, or NaN if no valid bid and ask
        are available.
        """
        return (self.bid + self.ask) / 2 if self.hasBidAsk() else nan


Instruction:
Can you implement a function in Python 3 that
returns the first available one of the current order and the last price if within current bid and ask.

Response:
def marketPrice(self) -> float:
        """
        Return the first available one of

        * last price if within current bid/ask;
        * average of bid and ask (midpoint);
        * close price.
        """
        price = self.last if (
            self.hasBidAsk() and self.bid <= self.last <= self.ask) else \
            self.midpoint()
        if isNan(price):
            price = self.close
        return price


Instruction:
Can you tell what is the following Python 3 function doing
def add_keywords_from_list(self, keyword_list):
        if not isinstance(keyword_list, list):
            raise AttributeError("keyword_list should be a list")

        for keyword in keyword_list:
            self.add_keyword(keyword)

Response: To add keywords from a list of keywords to the current object


Instruction:
Can you generate the documentation for the following Python 3 function
def remove_keywords_from_list(self, keyword_list):
        if not isinstance(keyword_list, list):
                raise AttributeError("keyword_list should be a list")

        for keyword in keyword_list:
            self.remove_keyword(keyword)

Response: To remove keywords present in list


Instruction:
Write a Python 3 function for
searching in the string for all keywords present in corpus and returns a list of lists of all keywords present in the corpus.

Response:
def extract_keywords(self, sentence, span_info=False):
        """Searches in the string for all keywords present in corpus.
        Keywords present are added to a list `keywords_extracted` and returned.

        Args:
            sentence (str): Line of text where we will search for keywords

        Returns:
            keywords_extracted (list(str)): List of terms/keywords found in sentence that match our corpus

        Examples:
            >>> from flashtext import KeywordProcessor
            >>> keyword_processor = KeywordProcessor()
            >>> keyword_processor.add_keyword('Big Apple', 'New York')
            >>> keyword_processor.add_keyword('Bay Area')
            >>> keywords_found = keyword_processor.extract_keywords('I love Big Apple and Bay Area.')
            >>> keywords_found
            >>> ['New York', 'Bay Area']

        """
        keywords_extracted = []
        if not sentence:
            # if sentence is empty or none just return empty list
            return keywords_extracted
        if not self.case_sensitive:
            sentence = sentence.lower()
        current_dict = self.keyword_trie_dict
        sequence_start_pos = 0
        sequence_end_pos = 0
        reset_current_dict = False
        idx = 0
        sentence_len = len(sentence)
        while idx < sentence_len:
            char = sentence[idx]
            # when we reach a character that might denote word end
            if char not in self.non_word_boundaries:

                # if end is present in current_dict
                if self._keyword in current_dict or char in current_dict:
                    # update longest sequence found
                    sequence_found = None
                    longest_sequence_found = None
                    is_longer_seq_found = False
                    if self._keyword in current_dict:
                        sequence_found = current_dict[self._keyword]
                        longest_sequence_found = current_dict[self._keyword]
                        sequence_end_pos = idx

                    # re look for longest_sequence from this position
                    if char in current_dict:
                        current_dict_continued = current_dict[char]

                        idy = idx + 1
                        while idy < sentence_len:
                            inner_char = sentence[idy]
                            if inner_char not in self.non_word_boundaries and self._keyword in current_dict_continued:
                                # update longest sequence found
                                longest_sequence_found = current_dict_continued[self._keyword]
                                sequence_end_pos = idy
                                is_longer_seq_found = True
                            if inner_char in current_dict_continued:
                                current_dict_continued = current_dict_continued[inner_char]
                            else:
                                break
                            idy += 1
                        else:
                            # end of sentence reached.
                            if self._keyword in current_dict_continued:
                                # update longest sequence found
                                longest_sequence_found = current_dict_continued[self._keyword]
                                sequence_end_pos = idy
                                is_longer_seq_found = True
                        if is_longer_seq_found:
                            idx = sequence_end_pos
                    current_dict = self.keyword_trie_dict
                    if longest_sequence_found:
                        keywords_extracted.append((longest_sequence_found, sequence_start_pos, idx))
                    reset_current_dict = True
                else:
                    # we reset current_dict
                    current_dict = self.keyword_trie_dict
                    reset_current_dict = True
            elif char in current_dict:
                # we can continue from this char
                current_dict = current_dict[char]
            else:
                # we reset current_dict
                current_dict = self.keyword_trie_dict
                reset_current_dict = True
                # skip to end of word
                idy = idx + 1
                while idy < sentence_len:
                    char = sentence[idy]
                    if char not in self.non_word_boundaries:
                        break
                    idy += 1
                idx = idy
            # if we are end of sentence and have a sequence discovered
            if idx + 1 >= sentence_len:
                if self._keyword in current_dict:
                    sequence_found = current_dict[self._keyword]
                    keywords_extracted.append((sequence_found, sequence_start_pos, sentence_len))
            idx += 1
            if reset_current_dict:
                reset_current_dict = False
                sequence_start_pos = idx
        if span_info:
            return keywords_extracted
        return [value[0] for value in keywords_extracted]


Instruction:
Write a Python 3 function for
searching in the string for all keywords present in corpus and replaces them with the clean name.

Response:
def replace_keywords(self, sentence):
        """Searches in the string for all keywords present in corpus.
        Keywords present are replaced by the clean name and a new string is returned.

        Args:
            sentence (str): Line of text where we will replace keywords

        Returns:
            new_sentence (str): Line of text with replaced keywords

        Examples:
            >>> from flashtext import KeywordProcessor
            >>> keyword_processor = KeywordProcessor()
            >>> keyword_processor.add_keyword('Big Apple', 'New York')
            >>> keyword_processor.add_keyword('Bay Area')
            >>> new_sentence = keyword_processor.replace_keywords('I love Big Apple and bay area.')
            >>> new_sentence
            >>> 'I love New York and Bay Area.'

        """
        if not sentence:
            # if sentence is empty or none just return the same.
            return sentence
        new_sentence = []
        orig_sentence = sentence
        if not self.case_sensitive:
            sentence = sentence.lower()
        current_word = ''
        current_dict = self.keyword_trie_dict
        current_white_space = ''
        sequence_end_pos = 0
        idx = 0
        sentence_len = len(sentence)
        while idx < sentence_len:
            char = sentence[idx]
            current_word += orig_sentence[idx]
            # when we reach whitespace
            if char not in self.non_word_boundaries:
                current_white_space = char
                # if end is present in current_dict
                if self._keyword in current_dict or char in current_dict:
                    # update longest sequence found
                    sequence_found = None
                    longest_sequence_found = None
                    is_longer_seq_found = False
                    if self._keyword in current_dict:
                        sequence_found = current_dict[self._keyword]
                        longest_sequence_found = current_dict[self._keyword]
                        sequence_end_pos = idx

                    # re look for longest_sequence from this position
                    if char in current_dict:
                        current_dict_continued = current_dict[char]
                        current_word_continued = current_word
                        idy = idx + 1
                        while idy < sentence_len:
                            inner_char = sentence[idy]
                            current_word_continued += orig_sentence[idy]
                            if inner_char not in self.non_word_boundaries and self._keyword in current_dict_continued:
                                # update longest sequence found
                                current_white_space = inner_char
                                longest_sequence_found = current_dict_continued[self._keyword]
                                sequence_end_pos = idy
                                is_longer_seq_found = True
                            if inner_char in current_dict_continued:
                                current_dict_continued = current_dict_continued[inner_char]
                            else:
                                break
                            idy += 1
                        else:
                            # end of sentence reached.
                            if self._keyword in current_dict_continued:
                                # update longest sequence found
                                current_white_space = ''
                                longest_sequence_found = current_dict_continued[self._keyword]
                                sequence_end_pos = idy
                                is_longer_seq_found = True
                        if is_longer_seq_found:
                            idx = sequence_end_pos
                            current_word = current_word_continued
                    current_dict = self.keyword_trie_dict
                    if longest_sequence_found:
                        new_sentence.append(longest_sequence_found + current_white_space)
                        current_word = ''
                        current_white_space = ''
                    else:
                        new_sentence.append(current_word)
                        current_word = ''
                        current_white_space = ''
                else:
                    # we reset current_dict
                    current_dict = self.keyword_trie_dict
                    new_sentence.append(current_word)
                    current_word = ''
                    current_white_space = ''
            elif char in current_dict:
                # we can continue from this char
                current_dict = current_dict[char]
            else:
                # we reset current_dict
                current_dict = self.keyword_trie_dict
                # skip to end of word
                idy = idx + 1
                while idy < sentence_len:
                    char = sentence[idy]
                    current_word += orig_sentence[idy]
                    if char not in self.non_word_boundaries:
                        break
                    idy += 1
                idx = idy
                new_sentence.append(current_word)
                current_word = ''
                current_white_space = ''
            # if we are end of sentence and have a sequence discovered
            if idx + 1 >= sentence_len:
                if self._keyword in current_dict:
                    sequence_found = current_dict[self._keyword]
                    new_sentence.append(sequence_found)
                else:
                    new_sentence.append(current_word)
            idx += 1
        return "".join(new_sentence)


Instruction:
How would you explain what the following Python 3 function does
def factor_information_coefficient(factor_data,
                                   group_adjust=False,
                                   by_group=False):
    """
    Computes the Spearman Rank Correlation based Information Coefficient (IC)
    between factor values and N period forward returns for each period in
    the factor index.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to, and
        (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    group_adjust : bool
        Demean forward returns by group before computing IC.
    by_group : bool
        If True, compute period wise IC separately for each group.

    Returns
    -------
    ic : pd.DataFrame
        Spearman Rank correlation between factor and
        provided forward returns.
    """

    def src_ic(group):
        f = group['factor']
        _ic = group[utils.get_forward_returns_columns(factor_data.columns)] \
            .apply(lambda x: stats.spearmanr(x, f)[0])
        return _ic

    factor_data = factor_data.copy()

    grouper = [factor_data.index.get_level_values('date')]

    if group_adjust:
        factor_data = utils.demean_forward_returns(factor_data,
                                                   grouper + ['group'])
    if by_group:
        grouper.append('group')

    ic = factor_data.groupby(grouper).apply(src_ic)

    return ic

Response: Computes the Spearman Rank Correlation based Information Coefficient between factor values and N period forward returns for each group.


Instruction:
Write a Python 3 script for
calculating the mean information coefficient between two groups of the same type.

Response:
def mean_information_coefficient(factor_data,
                                 group_adjust=False,
                                 by_group=False,
                                 by_time=None):
    """
    Get the mean information coefficient of specified groups.
    Answers questions like:
    What is the mean IC for each month?
    What is the mean IC for each group for our whole timerange?
    What is the mean IC for for each group, each week?

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to, and
        (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    group_adjust : bool
        Demean forward returns by group before computing IC.
    by_group : bool
        If True, take the mean IC for each group.
    by_time : str (pd time_rule), optional
        Time window to use when taking mean IC.
        See http://pandas.pydata.org/pandas-docs/stable/timeseries.html
        for available options.

    Returns
    -------
    ic : pd.DataFrame
        Mean Spearman Rank correlation between factor and provided
        forward price movement windows.
    """

    ic = factor_information_coefficient(factor_data, group_adjust, by_group)

    grouper = []
    if by_time is not None:
        grouper.append(pd.Grouper(freq=by_time))
    if by_group:
        grouper.append('group')

    if len(grouper) == 0:
        ic = ic.mean()

    else:
        ic = (ic.reset_index().set_index('date').groupby(grouper).mean())

    return ic


Instruction:
Can you generate a brief explanation for the following Python 3 code
def factor_weights(factor_data,
                   demeaned=True,
                   group_adjust=False,
                   equal_weight=False):
    """
    Computes asset weights by factor values and dividing by the sum of their
    absolute value (achieving gross leverage of 1). Positive factor values will
    results in positive weights and negative values in negative weights.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to, and
        (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    demeaned : bool
        Should this computation happen on a long short portfolio? if True,
        weights are computed by demeaning factor values and dividing by the sum
        of their absolute value (achieving gross leverage of 1). The sum of
        positive weights will be the same as the negative weights (absolute
        value), suitable for a dollar neutral long-short portfolio
    group_adjust : bool
        Should this computation happen on a group neutral portfolio? If True,
        compute group neutral weights: each group will weight the same and
        if 'demeaned' is enabled the factor values demeaning will occur on the
        group level.
    equal_weight : bool, optional
        if True the assets will be equal-weighted instead of factor-weighted
        If demeaned is True then the factor universe will be split in two
        equal sized groups, top assets with positive weights and bottom assets
        with negative weights

    Returns
    -------
    returns : pd.Series
        Assets weighted by factor value.
    """

    def to_weights(group, _demeaned, _equal_weight):

        if _equal_weight:
            group = group.copy()

            if _demeaned:
                # top assets positive weights, bottom ones negative
                group = group - group.median()

            negative_mask = group < 0
            group[negative_mask] = -1.0
            positive_mask = group > 0
            group[positive_mask] = 1.0

            if _demeaned:
                # positive weights must equal negative weights
                if negative_mask.any():
                    group[negative_mask] /= negative_mask.sum()
                if positive_mask.any():
                    group[positive_mask] /= positive_mask.sum()

        elif _demeaned:
            group = group - group.mean()

        return group / group.abs().sum()

    grouper = [factor_data.index.get_level_values('date')]
    if group_adjust:
        grouper.append('group')

    weights = factor_data.groupby(grouper)['factor'] \
        .apply(to_weights, demeaned, equal_weight)

    if group_adjust:
        weights = weights.groupby(level='date').apply(to_weights, False, False)

    return weights

Response: Computes the asset weights for a single alpha factor and forward returns for a single group neutral portfolio.


Instruction:
Implement a Python 3 function for
returning a DataFrame containing the period wise factor returns for a single alpha factor and a factor value for each period.

Response:
def factor_returns(factor_data,
                   demeaned=True,
                   group_adjust=False,
                   equal_weight=False,
                   by_asset=False):
    """
    Computes period wise returns for portfolio weighted by factor
    values.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to, and
        (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    demeaned : bool
        Control how to build factor weights
        -- see performance.factor_weights for a full explanation
    group_adjust : bool
        Control how to build factor weights
        -- see performance.factor_weights for a full explanation
    equal_weight : bool, optional
        Control how to build factor weights
        -- see performance.factor_weights for a full explanation
    by_asset: bool, optional
        If True, returns are reported separately for each esset.

    Returns
    -------
    returns : pd.DataFrame
        Period wise factor returns
    """

    weights = \
        factor_weights(factor_data, demeaned, group_adjust, equal_weight)

    weighted_returns = \
        factor_data[utils.get_forward_returns_columns(factor_data.columns)] \
        .multiply(weights, axis=0)

    if by_asset:
        returns = weighted_returns
    else:
        returns = weighted_returns.groupby(level='date').sum()

    return returns


Instruction:
Write a Python 3 function for
computing the alpha beta for a given factor and returns.

Response:
def factor_alpha_beta(factor_data,
                      returns=None,
                      demeaned=True,
                      group_adjust=False,
                      equal_weight=False):
    """
    Compute the alpha (excess returns), alpha t-stat (alpha significance),
    and beta (market exposure) of a factor. A regression is run with
    the period wise factor universe mean return as the independent variable
    and mean period wise return from a portfolio weighted by factor values
    as the dependent variable.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to, and
        (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    returns : pd.DataFrame, optional
        Period wise factor returns. If this is None then it will be computed
        with 'factor_returns' function and the passed flags: 'demeaned',
        'group_adjust', 'equal_weight'
    demeaned : bool
        Control how to build factor returns used for alpha/beta computation
        -- see performance.factor_return for a full explanation
    group_adjust : bool
        Control how to build factor returns used for alpha/beta computation
        -- see performance.factor_return for a full explanation
    equal_weight : bool, optional
        Control how to build factor returns used for alpha/beta computation
        -- see performance.factor_return for a full explanation

    Returns
    -------
    alpha_beta : pd.Series
        A list containing the alpha, beta, a t-stat(alpha)
        for the given factor and forward returns.
    """

    if returns is None:
        returns = \
            factor_returns(factor_data, demeaned, group_adjust, equal_weight)

    universe_ret = factor_data.groupby(level='date')[
        utils.get_forward_returns_columns(factor_data.columns)] \
        .mean().loc[returns.index]

    if isinstance(returns, pd.Series):
        returns.name = universe_ret.columns.values[0]
        returns = pd.DataFrame(returns)

    alpha_beta = pd.DataFrame()
    for period in returns.columns.values:
        x = universe_ret[period].values
        y = returns[period].values
        x = add_constant(x)

        reg_fit = OLS(y, x).fit()
        try:
            alpha, beta = reg_fit.params
        except ValueError:
            alpha_beta.loc['Ann. alpha', period] = np.nan
            alpha_beta.loc['beta', period] = np.nan
        else:
            freq_adjust = pd.Timedelta('252Days') / pd.Timedelta(period)

            alpha_beta.loc['Ann. alpha', period] = \
                (1 + alpha) ** freq_adjust - 1
            alpha_beta.loc['beta', period] = beta

    return alpha_beta


Instruction:
How would you implement a function in Python 3 that
builds the cumulative returns from the given period returns.

Response:
def cumulative_returns(returns, period, freq=None):
    """
    Builds cumulative returns from 'period' returns. This function simulates
    the cumulative effect that a series of gains or losses (the 'returns')
    have on an original amount of capital over a period of time.

    if F is the frequency at which returns are computed (e.g. 1 day if
    'returns' contains daily values) and N is the period for which the retuns
    are computed (e.g. returns after 1 day, 5 hours or 3 days) then:
    - if N <= F the cumulative retuns are trivially computed as Compound Return
    - if N > F (e.g. F 1 day, and N is 3 days) then the returns overlap and the
      cumulative returns are computed building and averaging N interleaved sub
      portfolios (started at subsequent periods 1,2,..,N) each one rebalancing
      every N periods. This correspond to an algorithm which trades the factor
      every single time it is computed, which is statistically more robust and
      with a lower volatity compared to an algorithm that trades the factor
      every N periods and whose returns depend on the specific starting day of
      trading.

    Also note that when the factor is not computed at a specific frequency, for
    exaple a factor representing a random event, it is not efficient to create
    multiples sub-portfolios as it is not certain when the factor will be
    traded and this would result in an underleveraged portfolio. In this case
    the simulated portfolio is fully invested whenever an event happens and if
    a subsequent event occur while the portfolio is still invested in a
    previous event then the portfolio is rebalanced and split equally among the
    active events.

    Parameters
    ----------
    returns: pd.Series
        pd.Series containing factor 'period' forward returns, the index
        contains timestamps at which the trades are computed and the values
        correspond to returns after 'period' time
    period: pandas.Timedelta or string
        Length of period for which the returns are computed (1 day, 2 mins,
        3 hours etc). It can be a Timedelta or a string in the format accepted
        by Timedelta constructor ('1 days', '1D', '30m', '3h', '1D1h', etc)
    freq : pandas DateOffset, optional
        Used to specify a particular trading calendar. If not present
        returns.index.freq will be used

    Returns
    -------
    Cumulative returns series : pd.Series
        Example:
            2015-07-16 09:30:00  -0.012143
            2015-07-16 12:30:00   0.012546
            2015-07-17 09:30:00   0.045350
            2015-07-17 12:30:00   0.065897
            2015-07-20 09:30:00   0.030957
    """

    if not isinstance(period, pd.Timedelta):
        period = pd.Timedelta(period)

    if freq is None:
        freq = returns.index.freq

    if freq is None:
        freq = BDay()
        warnings.warn("'freq' not set, using business day calendar",
                      UserWarning)

    #
    # returns index contains factor computation timestamps, then add returns
    # timestamps too (factor timestamps + period) and save them to 'full_idx'
    # Cumulative returns will use 'full_idx' index,because we want a cumulative
    # returns value for each entry in 'full_idx'
    #
    trades_idx = returns.index.copy()
    returns_idx = utils.add_custom_calendar_timedelta(trades_idx, period, freq)
    full_idx = trades_idx.union(returns_idx)

    #
    # Build N sub_returns from the single returns Series. Each sub_retuns
    # stream will contain non-overlapping returns.
    # In the next step we'll compute the portfolio returns averaging the
    # returns happening on those overlapping returns streams
    #
    sub_returns = []
    while len(trades_idx) > 0:

        #
        # select non-overlapping returns starting with first timestamp in index
        #
        sub_index = []
        next = trades_idx.min()
        while next <= trades_idx.max():
            sub_index.append(next)
            next = utils.add_custom_calendar_timedelta(next, period, freq)
            # make sure to fetch the next available entry after 'period'
            try:
                i = trades_idx.get_loc(next, method='bfill')
                next = trades_idx[i]
            except KeyError:
                break

        sub_index = pd.DatetimeIndex(sub_index, tz=full_idx.tz)
        subret = returns[sub_index]

        # make the index to have all entries in 'full_idx'
        subret = subret.reindex(full_idx)

        #
        # compute intermediate returns values for each index in subret that are
        # in between the timestaps at which the factors are computed and the
        # timestamps at which the 'period' returns actually happen
        #
        for pret_idx in reversed(sub_index):

            pret = subret[pret_idx]

            # get all timestamps between factor computation and period returns
            pret_end_idx = \
                utils.add_custom_calendar_timedelta(pret_idx, period, freq)
            slice = subret[(subret.index > pret_idx) & (
                subret.index <= pret_end_idx)].index

            if pd.isnull(pret):
                continue

            def rate_of_returns(ret, period):
                return ((np.nansum(ret) + 1)**(1. / period)) - 1

            # compute intermediate 'period' returns values, note that this also
            # moves the final 'period' returns value from trading timestamp to
            # trading timestamp + 'period'
            for slice_idx in slice:
                sub_period = utils.diff_custom_calendar_timedeltas(
                    pret_idx, slice_idx, freq)
                subret[slice_idx] = rate_of_returns(pret, period / sub_period)

            subret[pret_idx] = np.nan

            # transform returns as percentage change from previous value
            subret[slice[1:]] = (subret[slice] + 1).pct_change()[slice[1:]]

        sub_returns.append(subret)
        trades_idx = trades_idx.difference(sub_index)

    #
    # Compute portfolio cumulative returns averaging the returns happening on
    # overlapping returns streams.
    #
    sub_portfolios = pd.concat(sub_returns, axis=1)
    portfolio = pd.Series(index=sub_portfolios.index)

    for i, (index, row) in enumerate(sub_portfolios.iterrows()):

        # check the active portfolios, count() returns non-nans elements
        active_subfolios = row.count()

        # fill forward portfolio value
        portfolio.iloc[i] = portfolio.iloc[i - 1] if i > 0 else 1.

        if active_subfolios <= 0:
            continue

        # current portfolio is the average of active sub_portfolios
        portfolio.iloc[i] *= (row + 1).mean(skipna=True)

    return portfolio


Instruction:
Make a summary of the following Python 3 code
def positions(weights, period, freq=None):

    weights = weights.unstack()

    if not isinstance(period, pd.Timedelta):
        period = pd.Timedelta(period)

    if freq is None:
        freq = weights.index.freq

    if freq is None:
        freq = BDay()
        warnings.warn("'freq' not set, using business day calendar",
                      UserWarning)

    #
    # weights index contains factor computation timestamps, then add returns
    # timestamps too (factor timestamps + period) and save them to 'full_idx'
    # 'full_idx' index will contain an entry for each point in time the weights
    # change and hence they have to be re-computed
    #
    trades_idx = weights.index.copy()
    returns_idx = utils.add_custom_calendar_timedelta(trades_idx, period, freq)
    weights_idx = trades_idx.union(returns_idx)

    #
    # Compute portfolio weights for each point in time contained in the index
    #
    portfolio_weights = pd.DataFrame(index=weights_idx,
                                     columns=weights.columns)
    active_weights = []

    for curr_time in weights_idx:

        #
        # fetch new weights that become available at curr_time and store them
        # in active weights
        #
        if curr_time in weights.index:
            assets_weights = weights.loc[curr_time]
            expire_ts = utils.add_custom_calendar_timedelta(curr_time,
                                                            period, freq)
            active_weights.append((expire_ts, assets_weights))

        #
        # remove expired entry in active_weights (older than 'period')
        #
        if active_weights:
            expire_ts, assets_weights = active_weights[0]
            if expire_ts <= curr_time:
                active_weights.pop(0)

        if not active_weights:
            continue
        #
        # Compute total weights for curr_time and store them
        #
        tot_weights = [w for (ts, w) in active_weights]
        tot_weights = pd.concat(tot_weights, axis=1)
        tot_weights = tot_weights.sum(axis=1)
        tot_weights /= tot_weights.abs().sum()

        portfolio_weights.loc[curr_time] = tot_weights

    return portfolio_weights.fillna(0)

Response: Builds net position values time series for each asset in the next position.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def mean_return_by_quantile(factor_data,
                            by_date=False,
                            by_group=False,
                            demeaned=True,
                            group_adjust=False):
    """
    Computes mean returns for factor quantiles across
    provided forward returns columns.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to, and
        (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    by_date : bool
        If True, compute quantile bucket returns separately for each date.
    by_group : bool
        If True, compute quantile bucket returns separately for each group.
    demeaned : bool
        Compute demeaned mean returns (long short portfolio)
    group_adjust : bool
        Returns demeaning will occur on the group level.

    Returns
    -------
    mean_ret : pd.DataFrame
        Mean period wise returns by specified factor quantile.
    std_error_ret : pd.DataFrame
        Standard error of returns by specified quantile.
    """

    if group_adjust:
        grouper = [factor_data.index.get_level_values('date')] + ['group']
        factor_data = utils.demean_forward_returns(factor_data, grouper)
    elif demeaned:
        factor_data = utils.demean_forward_returns(factor_data)
    else:
        factor_data = factor_data.copy()

    grouper = ['factor_quantile', factor_data.index.get_level_values('date')]

    if by_group:
        grouper.append('group')

    group_stats = factor_data.groupby(grouper)[
        utils.get_forward_returns_columns(factor_data.columns)] \
        .agg(['mean', 'std', 'count'])

    mean_ret = group_stats.T.xs('mean', level=1).T

    if not by_date:
        grouper = [mean_ret.index.get_level_values('factor_quantile')]
        if by_group:
            grouper.append(mean_ret.index.get_level_values('group'))
        group_stats = mean_ret.groupby(grouper)\
            .agg(['mean', 'std', 'count'])
        mean_ret = group_stats.T.xs('mean', level=1).T

    std_error_ret = group_stats.T.xs('std', level=1).T \
        / np.sqrt(group_stats.T.xs('count', level=1).T)

    return mean_ret, std_error_ret

Response: Compute mean returns for a single alpha factor and a single group of returns.


Instruction:
Implement a function in Python 3 to
compute the difference between the mean returns of the current weather and the standard error of the two quantiles.

Response:
def compute_mean_returns_spread(mean_returns,
                                upper_quant,
                                lower_quant,
                                std_err=None):
    """
    Computes the difference between the mean returns of
    two quantiles. Optionally, computes the standard error
    of this difference.

    Parameters
    ----------
    mean_returns : pd.DataFrame
        DataFrame of mean period wise returns by quantile.
        MultiIndex containing date and quantile.
        See mean_return_by_quantile.
    upper_quant : int
        Quantile of mean return from which we
        wish to subtract lower quantile mean return.
    lower_quant : int
        Quantile of mean return we wish to subtract
        from upper quantile mean return.
    std_err : pd.DataFrame, optional
        Period wise standard error in mean return by quantile.
        Takes the same form as mean_returns.

    Returns
    -------
    mean_return_difference : pd.Series
        Period wise difference in quantile returns.
    joint_std_err : pd.Series
        Period wise standard error of the difference in quantile returns.
        if std_err is None, this will be None
    """

    mean_return_difference = mean_returns.xs(upper_quant,
                                             level='factor_quantile') \
        - mean_returns.xs(lower_quant, level='factor_quantile')

    if std_err is None:
        joint_std_err = None
    else:
        std1 = std_err.xs(upper_quant, level='factor_quantile')
        std2 = std_err.xs(lower_quant, level='factor_quantile')
        joint_std_err = np.sqrt(std1**2 + std2**2)

    return mean_return_difference, joint_std_err


Instruction:
Explain what the following Python 3 code does
def quantile_turnover(quantile_factor, quantile, period=1):

    quant_names = quantile_factor[quantile_factor == quantile]
    quant_name_sets = quant_names.groupby(level=['date']).apply(
        lambda x: set(x.index.get_level_values('asset')))

    if isinstance(period, int):
        name_shifted = quant_name_sets.shift(period)
    else:
        shifted_idx = utils.add_custom_calendar_timedelta(
                quant_name_sets.index, -pd.Timedelta(period),
                quantile_factor.index.levels[0].freq)
        name_shifted = quant_name_sets.reindex(shifted_idx)
        name_shifted.index = quant_name_sets.index

    new_names = (quant_name_sets - name_shifted).dropna()
    quant_turnover = new_names.apply(
        lambda x: len(x)) / quant_name_sets.apply(lambda x: len(x))
    quant_turnover.name = quantile
    return quant_turnover

Response: Computes the proportion of names in a factor quantile that were not in that quantile in the previous period.


Instruction:
Can you tell what is the following Python 3 function doing
def factor_rank_autocorrelation(factor_data, period=1):

    grouper = [factor_data.index.get_level_values('date')]

    ranks = factor_data.groupby(grouper)['factor'].rank()

    asset_factor_rank = ranks.reset_index().pivot(index='date',
                                                  columns='asset',
                                                  values='factor')

    if isinstance(period, int):
        asset_shifted = asset_factor_rank.shift(period)
    else:
        shifted_idx = utils.add_custom_calendar_timedelta(
                asset_factor_rank.index, -pd.Timedelta(period),
                factor_data.index.levels[0].freq)
        asset_shifted = asset_factor_rank.reindex(shifted_idx)
        asset_shifted.index = asset_factor_rank.index

    autocorr = asset_factor_rank.corrwith(asset_shifted, axis=1)
    autocorr.name = period
    return autocorr

Response: Calculates autocorrelation of factor values in specified time spans.


Instruction:
Can you tell what is the following Python 3 function doing
def average_cumulative_return_by_quantile(factor_data,
                                          prices,
                                          periods_before=10,
                                          periods_after=15,
                                          demeaned=True,
                                          group_adjust=False,
                                          by_group=False):
    """
    Plots average cumulative returns by factor quantiles in the period range
    defined by -periods_before to periods_after

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to, and
        (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    prices : pd.DataFrame
        A wide form Pandas DataFrame indexed by date with assets
        in the columns. Pricing data should span the factor
        analysis time period plus/minus an additional buffer window
        corresponding to periods_after/periods_before parameters.
    periods_before : int, optional
        How many periods before factor to plot
    periods_after  : int, optional
        How many periods after factor to plot
    demeaned : bool, optional
        Compute demeaned mean returns (long short portfolio)
    group_adjust : bool
        Returns demeaning will occur on the group level (group
        neutral portfolio)
    by_group : bool
        If True, compute cumulative returns separately for each group
    Returns
    -------
    cumulative returns and std deviation : pd.DataFrame
        A MultiIndex DataFrame indexed by quantile (level 0) and mean/std
        (level 1) and the values on the columns in range from
        -periods_before to periods_after
        If by_group=True the index will have an additional 'group' level
        ::
            ---------------------------------------------------
                        |       | -2  | -1  |  0  |  1  | ...
            ---------------------------------------------------
              quantile  |       |     |     |     |     |
            ---------------------------------------------------
                        | mean  |  x  |  x  |  x  |  x  |
                 1      ---------------------------------------
                        | std   |  x  |  x  |  x  |  x  |
            ---------------------------------------------------
                        | mean  |  x  |  x  |  x  |  x  |
                 2      ---------------------------------------
                        | std   |  x  |  x  |  x  |  x  |
            ---------------------------------------------------
                ...     |                 ...
            ---------------------------------------------------
    """

    def cumulative_return(q_fact, demean_by):
        return common_start_returns(q_fact, prices,
                                    periods_before,
                                    periods_after,
                                    True, True, demean_by)

    def average_cumulative_return(q_fact, demean_by):
        q_returns = cumulative_return(q_fact, demean_by)
        return pd.DataFrame({'mean': q_returns.mean(axis=1),
                             'std': q_returns.std(axis=1)}).T

    if by_group:
        #
        # Compute quantile cumulative returns separately for each group
        # Deman those returns accordingly to 'group_adjust' and 'demeaned'
        #
        returns_bygroup = []

        for group, g_data in factor_data.groupby('group'):
            g_fq = g_data['factor_quantile']
            if group_adjust:
                demean_by = g_fq  # demeans at group level
            elif demeaned:
                demean_by = factor_data['factor_quantile']  # demean by all
            else:
                demean_by = None
            #
            # Align cumulative return from different dates to the same index
            # then compute mean and std
            #
            avgcumret = g_fq.groupby(g_fq).apply(average_cumulative_return,
                                                 demean_by)
            if len(avgcumret) == 0:
                continue

            avgcumret['group'] = group
            avgcumret.set_index('group', append=True, inplace=True)
            returns_bygroup.append(avgcumret)

        return pd.concat(returns_bygroup, axis=0)

    else:
        #
        # Compute quantile cumulative returns for the full factor_data
        # Align cumulative return from different dates to the same index
        # then compute mean and std
        # Deman those returns accordingly to 'group_adjust' and 'demeaned'
        #
        if group_adjust:
            all_returns = []
            for group, g_data in factor_data.groupby('group'):
                g_fq = g_data['factor_quantile']
                avgcumret = g_fq.groupby(g_fq).apply(cumulative_return, g_fq)
                all_returns.append(avgcumret)
            q_returns = pd.concat(all_returns, axis=1)
            q_returns = pd.DataFrame({'mean': q_returns.mean(axis=1),
                                      'std': q_returns.std(axis=1)})
            return q_returns.unstack(level=1).stack(level=0)
        elif demeaned:
            fq = factor_data['factor_quantile']
            return fq.groupby(fq).apply(average_cumulative_return, fq)
        else:
            fq = factor_data['factor_quantile']
            return fq.groupby(fq).apply(average_cumulative_return, None)

Response: Calculates the average cumulative returns for a single factor and a forward factor.


Instruction:
Can you generate the documentation for the following Python 3 function
def factor_cumulative_returns(factor_data,
                              period,
                              long_short=True,
                              group_neutral=False,
                              equal_weight=False,
                              quantiles=None,
                              groups=None):
    """
    Simulate a portfolio using the factor in input and returns the cumulative
    returns of the simulated portfolio

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to,
        and (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    period : string
        'factor_data' column name corresponding to the 'period' returns to be
        used in the computation of porfolio returns
    long_short : bool, optional
        if True then simulates a dollar neutral long-short portfolio
        - see performance.create_pyfolio_input for more details
    group_neutral : bool, optional
        If True then simulates a group neutral portfolio
        - see performance.create_pyfolio_input for more details
    equal_weight : bool, optional
        Control the assets weights:
        - see performance.create_pyfolio_input for more details
    quantiles: sequence[int], optional
        Use only specific quantiles in the computation. By default all
        quantiles are used
    groups: sequence[string], optional
        Use only specific groups in the computation. By default all groups
        are used

    Returns
    -------
    Cumulative returns series : pd.Series
        Example:
            2015-07-16 09:30:00  -0.012143
            2015-07-16 12:30:00   0.012546
            2015-07-17 09:30:00   0.045350
            2015-07-17 12:30:00   0.065897
            2015-07-20 09:30:00   0.030957
    """
    fwd_ret_cols = utils.get_forward_returns_columns(factor_data.columns)

    if period not in fwd_ret_cols:
        raise ValueError("Period '%s' not found" % period)

    todrop = list(fwd_ret_cols)
    todrop.remove(period)
    portfolio_data = factor_data.drop(todrop, axis=1)

    if quantiles is not None:
        portfolio_data = portfolio_data[portfolio_data['factor_quantile'].isin(
            quantiles)]

    if groups is not None:
        portfolio_data = portfolio_data[portfolio_data['group'].isin(groups)]

    returns = \
        factor_returns(portfolio_data, long_short, group_neutral, equal_weight)

    return cumulative_returns(returns[period], period)

Response: Simulate a portfolio using a factor in input and returns the cumulative returns of the simulated portfolio.


Instruction:
How would you code a function in Python 3 to
simulate a portfolio using a factor in input and returns the assets positions as percentage of the total portfolio.

Response:
def factor_positions(factor_data,
                     period,
                     long_short=True,
                     group_neutral=False,
                     equal_weight=False,
                     quantiles=None,
                     groups=None):
    """
    Simulate a portfolio using the factor in input and returns the assets
    positions as percentage of the total portfolio.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to,
        and (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    period : string
        'factor_data' column name corresponding to the 'period' returns to be
        used in the computation of porfolio returns
    long_short : bool, optional
        if True then simulates a dollar neutral long-short portfolio
        - see performance.create_pyfolio_input for more details
    group_neutral : bool, optional
        If True then simulates a group neutral portfolio
        - see performance.create_pyfolio_input for more details
    equal_weight : bool, optional
        Control the assets weights:
        - see performance.create_pyfolio_input for more details.
    quantiles: sequence[int], optional
        Use only specific quantiles in the computation. By default all
        quantiles are used
    groups: sequence[string], optional
        Use only specific groups in the computation. By default all groups
        are used

    Returns
    -------
    assets positions : pd.DataFrame
        Assets positions series, datetime on index, assets on columns.
        Example:
            index                 'AAPL'         'MSFT'          cash
            2004-01-09 10:30:00   13939.3800     -14012.9930     711.5585
            2004-01-09 15:30:00       0.00       -16012.9930     411.5585
            2004-01-12 10:30:00   14492.6300     -14624.8700       0.0
            2004-01-12 15:30:00   14874.5400     -15841.2500       0.0
            2004-01-13 10:30:00   -13853.2800    13653.6400      -43.6375
    """
    fwd_ret_cols = utils.get_forward_returns_columns(factor_data.columns)

    if period not in fwd_ret_cols:
        raise ValueError("Period '%s' not found" % period)

    todrop = list(fwd_ret_cols)
    todrop.remove(period)
    portfolio_data = factor_data.drop(todrop, axis=1)

    if quantiles is not None:
        portfolio_data = portfolio_data[portfolio_data['factor_quantile'].isin(
            quantiles)]

    if groups is not None:
        portfolio_data = portfolio_data[portfolio_data['group'].isin(groups)]

    weights = \
        factor_weights(portfolio_data, long_short, group_neutral, equal_weight)

    return positions(weights, period)


Instruction:
Write a Python 3 script to
create a new portfolio from a factor data set and return it.

Response:
def create_pyfolio_input(factor_data,
                         period,
                         capital=None,
                         long_short=True,
                         group_neutral=False,
                         equal_weight=False,
                         quantiles=None,
                         groups=None,
                         benchmark_period='1D'):
    """
    Simulate a portfolio using the input factor and returns the portfolio
    performance data properly formatted for Pyfolio analysis.

    For more details on how this portfolio is built see:
    - performance.cumulative_returns (how the portfolio returns are computed)
    - performance.factor_weights (how assets weights are computed)

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to,
        and (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    period : string
        'factor_data' column name corresponding to the 'period' returns to be
        used in the computation of porfolio returns
    capital : float, optional
        If set, then compute 'positions' in dollar amount instead of percentage
    long_short : bool, optional
        if True enforce a dollar neutral long-short portfolio: asset weights
        will be computed by demeaning factor values and dividing by the sum of
        their absolute value (achieving gross leverage of 1) which will cause
        the portfolio to hold both long and short positions and the total
        weights of both long and short positions will be equal.
        If False the portfolio weights will be computed dividing the factor
        values and  by the sum of their absolute value (achieving gross
        leverage of 1). Positive factor values will generate long positions and
        negative factor values will produce short positions so that a factor
        with only posive values will result in a long only portfolio.
    group_neutral : bool, optional
        If True simulates a group neutral portfolio: the portfolio weights
        will be computed so that each group will weigh the same.
        if 'long_short' is enabled the factor values demeaning will occur on
        the group level resulting in a dollar neutral, group neutral,
        long-short portfolio.
        If False group information will not be used in weights computation.
    equal_weight : bool, optional
        if True the assets will be equal-weighted. If long_short is True then
        the factor universe will be split in two equal sized groups with the
        top assets in long positions and bottom assets in short positions.
        if False the assets will be factor-weighed, see 'long_short' argument
    quantiles: sequence[int], optional
        Use only specific quantiles in the computation. By default all
        quantiles are used
    groups: sequence[string], optional
        Use only specific groups in the computation. By default all groups
        are used
    benchmark_period : string, optional
        By default benchmark returns are computed as the factor universe mean
        daily returns but 'benchmark_period' allows to choose a 'factor_data'
        column corresponding to the returns to be used in the computation of
        benchmark returns. More generally benchmark returns are computed as the
        factor universe returns traded at 'benchmark_period' frequency, equal
        weighting and long only

    Returns
    -------
     returns : pd.Series
        Daily returns of the strategy, noncumulative.
         - Time series with decimal returns.
         - Example:
            2015-07-16    -0.012143
            2015-07-17    0.045350
            2015-07-20    0.030957
            2015-07-21    0.004902

     positions : pd.DataFrame
        Time series of dollar amount (or percentage when 'capital' is not
        provided) invested in each position and cash.
         - Days where stocks are not held can be represented by 0.
         - Non-working capital is labelled 'cash'
         - Example:
            index         'AAPL'         'MSFT'          cash
            2004-01-09    13939.3800     -14012.9930     711.5585
            2004-01-12    14492.6300     -14624.8700     27.1821
            2004-01-13    -13853.2800    13653.6400      -43.6375

     benchmark : pd.Series
        Benchmark returns computed as the factor universe mean daily returns.

    """

    #
    # Build returns:
    # we don't know the frequency at which the factor returns are computed but
    # pyfolio wants daily returns. So we compute the cumulative returns of the
    # factor, then resample it at 1 day frequency and finally compute daily
    # returns
    #
    cumrets = factor_cumulative_returns(factor_data,
                                        period,
                                        long_short,
                                        group_neutral,
                                        equal_weight,
                                        quantiles,
                                        groups)
    cumrets = cumrets.resample('1D').last().fillna(method='ffill')
    returns = cumrets.pct_change().fillna(0)

    #
    # Build positions. As pyfolio asks for daily position we have to resample
    # the positions returned by 'factor_positions' at 1 day frequency and
    # recompute the weights so that the sum of daily weights is 1.0
    #
    positions = factor_positions(factor_data,
                                 period,
                                 long_short,
                                 group_neutral,
                                 equal_weight,
                                 quantiles,
                                 groups)
    positions = positions.resample('1D').sum().fillna(method='ffill')
    positions = positions.div(positions.abs().sum(axis=1), axis=0).fillna(0)
    positions['cash'] = 1. - positions.sum(axis=1)

    # transform percentage positions to dollar positions
    if capital is not None:
        positions = positions.mul(
            cumrets.reindex(positions.index) * capital, axis=0)

    #
    #
    #
    # Build benchmark returns as the factor universe mean returns traded at
    # 'benchmark_period' frequency
    #
    fwd_ret_cols = utils.get_forward_returns_columns(factor_data.columns)
    if benchmark_period in fwd_ret_cols:
        benchmark_data = factor_data.copy()
        # make sure no negative positions
        benchmark_data['factor'] = benchmark_data['factor'].abs()
        benchmark_rets = factor_cumulative_returns(benchmark_data,
                                                   benchmark_period,
                                                   long_short=False,
                                                   group_neutral=False,
                                                   equal_weight=True)
        benchmark_rets = benchmark_rets.resample(
            '1D').last().fillna(method='ffill')
        benchmark_rets = benchmark_rets.pct_change().fillna(0)
        benchmark_rets.name = 'benchmark'
    else:
        benchmark_rets = None

    return returns, positions, benchmark_rets


Instruction:
Write a Python 3 function for
creating a small summary tear sheet with returns information and turnover analysis.

Response:
def create_summary_tear_sheet(factor_data,
                              long_short=True,
                              group_neutral=False):
    """
    Creates a small summary tear sheet with returns, information, and turnover
    analysis.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to, and
        (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    long_short : bool
        Should this computation happen on a long short portfolio? if so, then
        mean quantile returns will be demeaned across the factor universe.
    group_neutral : bool
        Should this computation happen on a group neutral portfolio? if so,
        returns demeaning will occur on the group level.
    """

    # Returns Analysis
    mean_quant_ret, std_quantile = \
        perf.mean_return_by_quantile(factor_data,
                                     by_group=False,
                                     demeaned=long_short,
                                     group_adjust=group_neutral)

    mean_quant_rateret = \
        mean_quant_ret.apply(utils.rate_of_return, axis=0,
                             base_period=mean_quant_ret.columns[0])

    mean_quant_ret_bydate, std_quant_daily = \
        perf.mean_return_by_quantile(factor_data,
                                     by_date=True,
                                     by_group=False,
                                     demeaned=long_short,
                                     group_adjust=group_neutral)

    mean_quant_rateret_bydate = mean_quant_ret_bydate.apply(
        utils.rate_of_return,
        axis=0,
        base_period=mean_quant_ret_bydate.columns[0]
    )

    compstd_quant_daily = std_quant_daily.apply(
        utils.std_conversion, axis=0,
        base_period=std_quant_daily.columns[0]
    )

    alpha_beta = perf.factor_alpha_beta(factor_data,
                                        demeaned=long_short,
                                        group_adjust=group_neutral)

    mean_ret_spread_quant, std_spread_quant = perf.compute_mean_returns_spread(
        mean_quant_rateret_bydate,
        factor_data['factor_quantile'].max(),
        factor_data['factor_quantile'].min(),
        std_err=compstd_quant_daily)

    periods = utils.get_forward_returns_columns(factor_data.columns)

    fr_cols = len(periods)
    vertical_sections = 2 + fr_cols * 3
    gf = GridFigure(rows=vertical_sections, cols=1)

    plotting.plot_quantile_statistics_table(factor_data)

    plotting.plot_returns_table(alpha_beta,
                                mean_quant_rateret,
                                mean_ret_spread_quant)

    plotting.plot_quantile_returns_bar(mean_quant_rateret,
                                       by_group=False,
                                       ylim_percentiles=None,
                                       ax=gf.next_row())

    # Information Analysis
    ic = perf.factor_information_coefficient(factor_data)
    plotting.plot_information_table(ic)

    # Turnover Analysis
    quantile_factor = factor_data['factor_quantile']

    quantile_turnover = \
        {p: pd.concat([perf.quantile_turnover(quantile_factor, q, p)
                       for q in range(1, int(quantile_factor.max()) + 1)],
                      axis=1)
            for p in periods}

    autocorrelation = pd.concat(
        [perf.factor_rank_autocorrelation(factor_data, period) for period in
         periods], axis=1)

    plotting.plot_turnover_table(autocorrelation, quantile_turnover)

    plt.show()
    gf.close()


Instruction:
Write a Python 3 script to
create a tear sheet for the returns analysis of a factor.

Response:
def create_returns_tear_sheet(factor_data,
                              long_short=True,
                              group_neutral=False,
                              by_group=False):
    """
    Creates a tear sheet for returns analysis of a factor.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to,
        and (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    long_short : bool
        Should this computation happen on a long short portfolio? if so, then
        mean quantile returns will be demeaned across the factor universe.
        Additionally factor values will be demeaned across the factor universe
        when factor weighting the portfolio for cumulative returns plots
    group_neutral : bool
        Should this computation happen on a group neutral portfolio? if so,
        returns demeaning will occur on the group level.
        Additionally each group will weight the same in cumulative returns
        plots
    by_group : bool
        If True, display graphs separately for each group.
    """

    factor_returns = perf.factor_returns(factor_data,
                                         long_short,
                                         group_neutral)

    mean_quant_ret, std_quantile = \
        perf.mean_return_by_quantile(factor_data,
                                     by_group=False,
                                     demeaned=long_short,
                                     group_adjust=group_neutral)

    mean_quant_rateret = \
        mean_quant_ret.apply(utils.rate_of_return, axis=0,
                             base_period=mean_quant_ret.columns[0])

    mean_quant_ret_bydate, std_quant_daily = \
        perf.mean_return_by_quantile(factor_data,
                                     by_date=True,
                                     by_group=False,
                                     demeaned=long_short,
                                     group_adjust=group_neutral)

    mean_quant_rateret_bydate = mean_quant_ret_bydate.apply(
        utils.rate_of_return, axis=0,
        base_period=mean_quant_ret_bydate.columns[0]
    )

    compstd_quant_daily = \
        std_quant_daily.apply(utils.std_conversion, axis=0,
                              base_period=std_quant_daily.columns[0])

    alpha_beta = perf.factor_alpha_beta(factor_data,
                                        factor_returns,
                                        long_short,
                                        group_neutral)

    mean_ret_spread_quant, std_spread_quant = \
        perf.compute_mean_returns_spread(mean_quant_rateret_bydate,
                                         factor_data['factor_quantile'].max(),
                                         factor_data['factor_quantile'].min(),
                                         std_err=compstd_quant_daily)

    fr_cols = len(factor_returns.columns)
    vertical_sections = 2 + fr_cols * 3
    gf = GridFigure(rows=vertical_sections, cols=1)

    plotting.plot_returns_table(alpha_beta,
                                mean_quant_rateret,
                                mean_ret_spread_quant)

    plotting.plot_quantile_returns_bar(mean_quant_rateret,
                                       by_group=False,
                                       ylim_percentiles=None,
                                       ax=gf.next_row())

    plotting.plot_quantile_returns_violin(mean_quant_rateret_bydate,
                                          ylim_percentiles=(1, 99),
                                          ax=gf.next_row())

    trading_calendar = factor_data.index.levels[0].freq
    if trading_calendar is None:
        trading_calendar = pd.tseries.offsets.BDay()
        warnings.warn(
            "'freq' not set in factor_data index: assuming business day",
            UserWarning
        )

    for p in factor_returns:

        title = ('Factor Weighted '
                 + ('Group Neutral ' if group_neutral else '')
                 + ('Long/Short ' if long_short else '')
                 + "Portfolio Cumulative Return ({} Period)".format(p))

        plotting.plot_cumulative_returns(
            factor_returns[p],
            period=p,
            freq=trading_calendar,
            title=title,
            ax=gf.next_row()
        )

        plotting.plot_cumulative_returns_by_quantile(
            mean_quant_ret_bydate[p],
            period=p,
            freq=trading_calendar,
            ax=gf.next_row()
        )

    ax_mean_quantile_returns_spread_ts = [gf.next_row()
                                          for x in range(fr_cols)]
    plotting.plot_mean_quantile_returns_spread_time_series(
        mean_ret_spread_quant,
        std_err=std_spread_quant,
        bandwidth=0.5,
        ax=ax_mean_quantile_returns_spread_ts
    )

    plt.show()
    gf.close()

    if by_group:
        mean_return_quantile_group, mean_return_quantile_group_std_err = \
            perf.mean_return_by_quantile(factor_data,
                                         by_date=False,
                                         by_group=True,
                                         demeaned=long_short,
                                         group_adjust=group_neutral)

        mean_quant_rateret_group = mean_return_quantile_group.apply(
            utils.rate_of_return, axis=0,
            base_period=mean_return_quantile_group.columns[0]
        )

        num_groups = len(mean_quant_rateret_group.index
                         .get_level_values('group').unique())

        vertical_sections = 1 + (((num_groups - 1) // 2) + 1)
        gf = GridFigure(rows=vertical_sections, cols=2)

        ax_quantile_returns_bar_by_group = [gf.next_cell()
                                            for _ in range(num_groups)]
        plotting.plot_quantile_returns_bar(mean_quant_rateret_group,
                                           by_group=True,
                                           ylim_percentiles=(5, 95),
                                           ax=ax_quantile_returns_bar_by_group)
        plt.show()
        gf.close()


Instruction:
Can you write a function in Python 3 where it
creates a tear sheet for information analysis of a factor.

Response:
def create_information_tear_sheet(factor_data,
                                  group_neutral=False,
                                  by_group=False):
    """
    Creates a tear sheet for information analysis of a factor.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to, and
        (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    group_neutral : bool
        Demean forward returns by group before computing IC.
    by_group : bool
        If True, display graphs separately for each group.
    """

    ic = perf.factor_information_coefficient(factor_data, group_neutral)

    plotting.plot_information_table(ic)

    columns_wide = 2
    fr_cols = len(ic.columns)
    rows_when_wide = (((fr_cols - 1) // columns_wide) + 1)
    vertical_sections = fr_cols + 3 * rows_when_wide + 2 * fr_cols
    gf = GridFigure(rows=vertical_sections, cols=columns_wide)

    ax_ic_ts = [gf.next_row() for _ in range(fr_cols)]
    plotting.plot_ic_ts(ic, ax=ax_ic_ts)

    ax_ic_hqq = [gf.next_cell() for _ in range(fr_cols * 2)]
    plotting.plot_ic_hist(ic, ax=ax_ic_hqq[::2])
    plotting.plot_ic_qq(ic, ax=ax_ic_hqq[1::2])

    if not by_group:

        mean_monthly_ic = \
            perf.mean_information_coefficient(factor_data,
                                              group_adjust=group_neutral,
                                              by_group=False,
                                              by_time="M")
        ax_monthly_ic_heatmap = [gf.next_cell() for x in range(fr_cols)]
        plotting.plot_monthly_ic_heatmap(mean_monthly_ic,
                                         ax=ax_monthly_ic_heatmap)

    if by_group:
        mean_group_ic = \
            perf.mean_information_coefficient(factor_data,
                                              group_adjust=group_neutral,
                                              by_group=True)

        plotting.plot_ic_by_group(mean_group_ic, ax=gf.next_row())

    plt.show()
    gf.close()


Instruction:
Here you have a function in Python 3, explain what it does
def create_turnover_tear_sheet(factor_data, turnover_periods=None):

    if turnover_periods is None:
        turnover_periods = utils.get_forward_returns_columns(
            factor_data.columns)

    quantile_factor = factor_data['factor_quantile']

    quantile_turnover = \
        {p: pd.concat([perf.quantile_turnover(quantile_factor, q, p)
                       for q in range(1, int(quantile_factor.max()) + 1)],
                      axis=1)
            for p in turnover_periods}

    autocorrelation = pd.concat(
        [perf.factor_rank_autocorrelation(factor_data, period) for period in
         turnover_periods], axis=1)

    plotting.plot_turnover_table(autocorrelation, quantile_turnover)

    fr_cols = len(turnover_periods)
    columns_wide = 1
    rows_when_wide = (((fr_cols - 1) // 1) + 1)
    vertical_sections = fr_cols + 3 * rows_when_wide + 2 * fr_cols
    gf = GridFigure(rows=vertical_sections, cols=columns_wide)

    for period in turnover_periods:
        if quantile_turnover[period].isnull().all().all():
            continue
        plotting.plot_top_bottom_quantile_turnover(quantile_turnover[period],
                                                   period=period,
                                                   ax=gf.next_row())

    for period in autocorrelation:
        if autocorrelation[period].isnull().all():
            continue
        plotting.plot_factor_rank_auto_correlation(autocorrelation[period],
                                                   period=period,
                                                   ax=gf.next_row())

    plt.show()
    gf.close()

Response: Creates a tear sheet for analyzing the turnover properties of a factor.


Instruction:
Implement a function in Python 3 to
create a full tear sheet for analysis and evaluating a single alpha factor.

Response:
def create_full_tear_sheet(factor_data,
                           long_short=True,
                           group_neutral=False,
                           by_group=False):
    """
    Creates a full tear sheet for analysis and evaluating single
    return predicting (alpha) factor.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to, and
        (optionally) the group the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    long_short : bool
        Should this computation happen on a long short portfolio?
        - See tears.create_returns_tear_sheet for details on how this flag
        affects returns analysis
    group_neutral : bool
        Should this computation happen on a group neutral portfolio?
        - See tears.create_returns_tear_sheet for details on how this flag
        affects returns analysis
        - See tears.create_information_tear_sheet for details on how this
        flag affects information analysis
    by_group : bool
        If True, display graphs separately for each group.
    """

    plotting.plot_quantile_statistics_table(factor_data)
    create_returns_tear_sheet(factor_data,
                              long_short,
                              group_neutral,
                              by_group,
                              set_context=False)
    create_information_tear_sheet(factor_data,
                                  group_neutral,
                                  by_group,
                                  set_context=False)
    create_turnover_tear_sheet(factor_data, set_context=False)


Instruction:
Explain what the following Python 3 code does
def create_event_returns_tear_sheet(factor_data,
                                    prices,
                                    avgretplot=(5, 15),
                                    long_short=True,
                                    group_neutral=False,
                                    std_bar=True,
                                    by_group=False):
    """
    Creates a tear sheet to view the average cumulative returns for a
    factor within a window (pre and post event).

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex Series indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, the factor
        quantile/bin that factor value belongs to and (optionally) the group
        the asset belongs to.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    prices : pd.DataFrame
        A DataFrame indexed by date with assets in the columns containing the
        pricing data.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    avgretplot: tuple (int, int) - (before, after)
        If not None, plot quantile average cumulative returns
    long_short : bool
        Should this computation happen on a long short portfolio? if so then
        factor returns will be demeaned across the factor universe
    group_neutral : bool
        Should this computation happen on a group neutral portfolio? if so,
        returns demeaning will occur on the group level.
    std_bar : boolean, optional
        Show plots with standard deviation bars, one for each quantile
    by_group : bool
        If True, display graphs separately for each group.
    """

    before, after = avgretplot

    avg_cumulative_returns = \
        perf.average_cumulative_return_by_quantile(
            factor_data,
            prices,
            periods_before=before,
            periods_after=after,
            demeaned=long_short,
            group_adjust=group_neutral)

    num_quantiles = int(factor_data['factor_quantile'].max())

    vertical_sections = 1
    if std_bar:
        vertical_sections += (((num_quantiles - 1) // 2) + 1)
    cols = 2 if num_quantiles != 1 else 1
    gf = GridFigure(rows=vertical_sections, cols=cols)
    plotting.plot_quantile_average_cumulative_return(avg_cumulative_returns,
                                                     by_quantile=False,
                                                     std_bar=False,
                                                     ax=gf.next_row())
    if std_bar:
        ax_avg_cumulative_returns_by_q = [gf.next_cell()
                                          for _ in range(num_quantiles)]
        plotting.plot_quantile_average_cumulative_return(
            avg_cumulative_returns,
            by_quantile=True,
            std_bar=True,
            ax=ax_avg_cumulative_returns_by_q)

    plt.show()
    gf.close()

    if by_group:
        groups = factor_data['group'].unique()
        num_groups = len(groups)
        vertical_sections = ((num_groups - 1) // 2) + 1
        gf = GridFigure(rows=vertical_sections, cols=2)

        avg_cumret_by_group = \
            perf.average_cumulative_return_by_quantile(
                factor_data,
                prices,
                periods_before=before,
                periods_after=after,
                demeaned=long_short,
                group_adjust=group_neutral,
                by_group=True)

        for group, avg_cumret in avg_cumret_by_group.groupby(level='group'):
            avg_cumret.index = avg_cumret.index.droplevel('group')
            plotting.plot_quantile_average_cumulative_return(
                avg_cumret,
                by_quantile=False,
                std_bar=False,
                title=group,
                ax=gf.next_cell())

        plt.show()
        gf.close()

Response: Creates a tear sheet to view the average cumulative returns for a single alpha factor and a single group of assets.


Instruction:
Can you create a Python 3 function that
creates an event study tear sheet for analysis of a specific event.

Response:
def create_event_study_tear_sheet(factor_data,
                                  prices=None,
                                  avgretplot=(5, 15),
                                  rate_of_ret=True,
                                  n_bars=50):
    """
    Creates an event study tear sheet for analysis of a specific event.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single event, forward returns for each
        period, the factor quantile/bin that factor value belongs to, and
        (optionally) the group the asset belongs to.
    prices : pd.DataFrame, required only if 'avgretplot' is provided
        A DataFrame indexed by date with assets in the columns containing the
        pricing data.
        - See full explanation in utils.get_clean_factor_and_forward_returns
    avgretplot: tuple (int, int) - (before, after), optional
        If not None, plot event style average cumulative returns within a
        window (pre and post event).
    rate_of_ret : bool, optional
        Display rate of return instead of simple return in 'Mean Period Wise
        Return By Factor Quantile' and 'Period Wise Return By Factor Quantile'
        plots
    n_bars : int, optional
        Number of bars in event distribution plot
    """

    long_short = False

    plotting.plot_quantile_statistics_table(factor_data)

    gf = GridFigure(rows=1, cols=1)
    plotting.plot_events_distribution(events=factor_data['factor'],
                                      num_bars=n_bars,
                                      ax=gf.next_row())
    plt.show()
    gf.close()

    if prices is not None and avgretplot is not None:

        create_event_returns_tear_sheet(factor_data=factor_data,
                                        prices=prices,
                                        avgretplot=avgretplot,
                                        long_short=long_short,
                                        group_neutral=False,
                                        std_bar=True,
                                        by_group=False)

    factor_returns = perf.factor_returns(factor_data,
                                         demeaned=False,
                                         equal_weight=True)

    mean_quant_ret, std_quantile = \
        perf.mean_return_by_quantile(factor_data,
                                     by_group=False,
                                     demeaned=long_short)
    if rate_of_ret:
        mean_quant_ret = \
            mean_quant_ret.apply(utils.rate_of_return, axis=0,
                                 base_period=mean_quant_ret.columns[0])

    mean_quant_ret_bydate, std_quant_daily = \
        perf.mean_return_by_quantile(factor_data,
                                     by_date=True,
                                     by_group=False,
                                     demeaned=long_short)
    if rate_of_ret:
        mean_quant_ret_bydate = mean_quant_ret_bydate.apply(
            utils.rate_of_return, axis=0,
            base_period=mean_quant_ret_bydate.columns[0]
        )

    fr_cols = len(factor_returns.columns)
    vertical_sections = 2 + fr_cols * 1
    gf = GridFigure(rows=vertical_sections, cols=1)

    plotting.plot_quantile_returns_bar(mean_quant_ret,
                                       by_group=False,
                                       ylim_percentiles=None,
                                       ax=gf.next_row())

    plotting.plot_quantile_returns_violin(mean_quant_ret_bydate,
                                          ylim_percentiles=(1, 99),
                                          ax=gf.next_row())

    trading_calendar = factor_data.index.levels[0].freq
    if trading_calendar is None:
        trading_calendar = pd.tseries.offsets.BDay()
        warnings.warn(
            "'freq' not set in factor_data index: assuming business day",
            UserWarning
        )

    for p in factor_returns:

        plotting.plot_cumulative_returns(
            factor_returns[p],
            period=p,
            freq=trading_calendar,
            ax=gf.next_row()
        )

    plt.show()
    gf.close()


Instruction:
Explain what the following Python 3 code does
def non_unique_bin_edges_error(func):
    message = """

    An error occurred while computing bins/quantiles on the input provided.
    This usually happens when the input contains too many identical
    values and they span more than one quantile. The quantiles are choosen
    to have the same number of records each, but the same value cannot span
    multiple quantiles. Possible workarounds are:
    1 - Decrease the number of quantiles
    2 - Specify a custom quantiles range, e.g. [0, .50, .75, 1.] to get unequal
        number of records per quantile
    3 - Use 'bins' option instead of 'quantiles', 'bins' chooses the
        buckets to be evenly spaced according to the values themselves, while
        'quantiles' forces the buckets to have the same number of records.
    4 - for factors with discrete values use the 'bins' option with custom
        ranges and create a range for each discrete value
    Please see utils.get_clean_factor_and_forward_returns documentation for
    full documentation of 'bins' and 'quantiles' options.

"""

    def dec(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except ValueError as e:
            if 'Bin edges must be unique' in str(e):
                rethrow(e, message)
            raise
    return dec

Response: Decorator to handle non - unique bin edges on a single resource table.


Instruction:
Explain what the following Python 3 code does
def quantize_factor(factor_data,
                    quantiles=5,
                    bins=None,
                    by_group=False,
                    no_raise=False,
                    zero_aware=False):
    """
    Computes period wise factor quantiles.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
        containing the values for a single alpha factor, forward returns for
        each period, the factor quantile/bin that factor value belongs to, and
        (optionally) the group the asset belongs to.

        - See full explanation in utils.get_clean_factor_and_forward_returns

    quantiles : int or sequence[float]
        Number of equal-sized quantile buckets to use in factor bucketing.
        Alternately sequence of quantiles, allowing non-equal-sized buckets
        e.g. [0, .10, .5, .90, 1.] or [.05, .5, .95]
        Only one of 'quantiles' or 'bins' can be not-None
    bins : int or sequence[float]
        Number of equal-width (valuewise) bins to use in factor bucketing.
        Alternately sequence of bin edges allowing for non-uniform bin width
        e.g. [-4, -2, -0.5, 0, 10]
        Only one of 'quantiles' or 'bins' can be not-None
    by_group : bool, optional
        If True, compute quantile buckets separately for each group.
    no_raise: bool, optional
        If True, no exceptions are thrown and the values for which the
        exception would have been thrown are set to np.NaN
    zero_aware : bool, optional
        If True, compute quantile buckets separately for positive and negative
        signal values. This is useful if your signal is centered and zero is
        the separation between long and short signals, respectively.

    Returns
    -------
    factor_quantile : pd.Series
        Factor quantiles indexed by date and asset.
    """
    if not ((quantiles is not None and bins is None) or
            (quantiles is None and bins is not None)):
        raise ValueError('Either quantiles or bins should be provided')

    if zero_aware and not (isinstance(quantiles, int)
                           or isinstance(bins, int)):
        msg = ("zero_aware should only be True when quantiles or bins is an"
               " integer")
        raise ValueError(msg)

    def quantile_calc(x, _quantiles, _bins, _zero_aware, _no_raise):
        try:
            if _quantiles is not None and _bins is None and not _zero_aware:
                return pd.qcut(x, _quantiles, labels=False) + 1
            elif _quantiles is not None and _bins is None and _zero_aware:
                pos_quantiles = pd.qcut(x[x >= 0], _quantiles // 2,
                                        labels=False) + _quantiles // 2 + 1
                neg_quantiles = pd.qcut(x[x < 0], _quantiles // 2,
                                        labels=False) + 1
                return pd.concat([pos_quantiles, neg_quantiles]).sort_index()
            elif _bins is not None and _quantiles is None and not _zero_aware:
                return pd.cut(x, _bins, labels=False) + 1
            elif _bins is not None and _quantiles is None and _zero_aware:
                pos_bins = pd.cut(x[x >= 0], _bins // 2,
                                  labels=False) + _bins // 2 + 1
                neg_bins = pd.cut(x[x < 0], _bins // 2,
                                  labels=False) + 1
                return pd.concat([pos_bins, neg_bins]).sort_index()
        except Exception as e:
            if _no_raise:
                return pd.Series(index=x.index)
            raise e

    grouper = [factor_data.index.get_level_values('date')]
    if by_group:
        grouper.append('group')

    factor_quantile = factor_data.groupby(grouper)['factor'] \
        .apply(quantile_calc, quantiles, bins, zero_aware, no_raise)
    factor_quantile.name = 'factor_quantile'

    return factor_quantile.dropna()

Response: Quantize a factor by factor data.


Instruction:
Write a Python 3 function for
inferring the trading calendar from factor and price information.

Response:
def infer_trading_calendar(factor_idx, prices_idx):
    """
    Infer the trading calendar from factor and price information.

    Parameters
    ----------
    factor_idx : pd.DatetimeIndex
        The factor datetimes for which we are computing the forward returns
    prices_idx : pd.DatetimeIndex
        The prices datetimes associated withthe factor data

    Returns
    -------
    calendar : pd.DateOffset
    """
    full_idx = factor_idx.union(prices_idx)

    traded_weekdays = []
    holidays = []

    days_of_the_week = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
    for day, day_str in enumerate(days_of_the_week):

        weekday_mask = (full_idx.dayofweek == day)

        # drop days of the week that are not traded at all
        if not weekday_mask.any():
            continue
        traded_weekdays.append(day_str)

        # look for holidays
        used_weekdays = full_idx[weekday_mask].normalize()
        all_weekdays = pd.date_range(full_idx.min(), full_idx.max(),
                                     freq=CustomBusinessDay(weekmask=day_str)
                                     ).normalize()
        _holidays = all_weekdays.difference(used_weekdays)
        _holidays = [timestamp.date() for timestamp in _holidays]
        holidays.extend(_holidays)

    traded_weekdays = ' '.join(traded_weekdays)
    return CustomBusinessDay(weekmask=traded_weekdays, holidays=holidays)