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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_clean_factor_and_forward_returns(factor, prices, groupby=None, binning_by_group=False, quantiles=5, bins=None, periods=(1, 5, 10), filter_zscore=20, groupby_labels=None, max_loss=0.35, zero_aware=False, cumulative_returns=True):
 """ Formats the factor data, pricing data, and group mappings into a DataFrame that contains aligned MultiIndex indices of timestamp and asset. The returned data will be formatted to be suitable for Alphalens functions. It is safe to skip a call to this function and still make use of Alphalens functionalities as long as the factor data conforms to the format returned from get_clean_factor_and_forward_returns and documented here Parameters factor : pd.Series - MultiIndex A MultiIndex Series indexed by timestamp (level 0) and asset (level 1), containing the values for a single alpha factor. :: date | asset | | AAPL | 0.5 | BA | -1.1 2014-01-01 | CMG | 1.7 | DAL | -0.1 | LULU | 2.7 prices : pd.DataFrame A wide form Pandas DataFrame indexed by timestamp with assets in the columns. Pricing data must span the factor analysis time period plus an additional buffer window that is greater than the maximum number of expected periods in the forward returns calculations. It is important to pass the correct pricing data in depending on what time of period your signal was generated so to avoid lookahead bias, or delayed calculations. 'Prices' must contain at least an entry for each timestamp/asset combination in 'factor'. This entry should reflect the buy price for the assets and usually it is the next available price after the factor is computed but it can also be a later price if the factor is meant to be traded later (e.g. if the factor is computed at market open but traded 1 hour after market open the price information should be 1 hour after market open). 'Prices' must also contain entries for timestamps following each timestamp/asset combination in 'factor', as many more timestamps as the maximum value in 'periods'. The asset price after 'period' timestamps will be considered the sell price for that asset when computing 'period' forward returns. :: | AAPL | BA | CMG | DAL | LULU | Date | | | | | | 2014-01-01 |605.12| 24.58| 11.72| 54.43 | 37.14 | 2014-01-02 |604.35| 22.23| 12.21| 52.78 | 33.63 | 2014-01-03 |607.94| 21.68| 14.36| 53.94 | 29.37 | groupby : pd.Series - MultiIndex or dict Either A MultiIndex Series indexed by date and asset, containing the period wise group codes for each asset, or a dict of asset to group mappings. If a dict is passed, it is assumed that group mappings are unchanged for the entire time period of the passed factor data. binning_by_group : bool If True, compute quantile buckets separately for each group. This is useful when the factor values range vary considerably across gorups so that it is wise to make the binning group relative. You should probably enable this if the factor is intended to be analyzed for a group neutral portfolio 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] Chooses the buckets to be evenly spaced according to the values themselves. Useful when the factor contains discrete values. Only one of 'quantiles' or 'bins' can be not-None periods : sequence[int] periods to compute forward returns on. filter_zscore : int or float, optional Sets forward returns greater than X standard deviations from the the mean to nan. Set it to 'None' to avoid filtering. Caution: this outlier filtering incorporates lookahead bias. groupby_labels : dict A dictionary keyed by group code with values corresponding to the display name for each group. max_loss : float, optional Maximum percentage (0.00 to 1.00) of factor data dropping allowed, computed comparing the number of items in the input factor index and the number of items in the output DataFrame index. Factor data can be partially dropped due to being flawed itself (e.g. NaNs), not having provided enough price data to compute forward returns for all factor values, or because it is not possible to perform binning. Set max_loss=0 to avoid Exceptions suppression. 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. cumulative_returns : bool, optional If True, forward returns columns will contain cumulative returns. Setting this to False is useful if you want to analyze how predictive a factor is for a single forward day. Returns ------- merged_data : pd.DataFrame - MultiIndex A MultiIndex Series 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. - forward returns column names follow the format accepted by pd.Timedelta (e.g. '1D', '30m', '3h15m', '1D1h', etc) - 'date' index freq property (merged_data.index.levels[0].freq) will be set to a trading calendar (pandas DateOffset) inferred from the input data (see infer_trading_calendar for more details). This is currently used only in cumulative returns computation :: | | 1D | 5D | 10D |factor|group|factor_quantile date | asset | | | | | | | AAPL | 0.09|-0.01|-0.079| 0.5 | G1 | 3 | BA | 0.02| 0.06| 0.020| -1.1 | G2 | 5 2014-01-01 | CMG | 0.03| 0.09| 0.036| 1.7 | G2 | 1 | DAL |-0.02|-0.06|-0.029| -0.1 | G3 | 5 | LULU |-0.03| 0.05|-0.009| 2.7 | G1 | 2 """


    forward_returns = compute_forward_returns(factor, prices, periods,
                                              filter_zscore,
                                              cumulative_returns)

    factor_data = get_clean_factor(factor, forward_returns, groupby=groupby,
                                   groupby_labels=groupby_labels,
                                   quantiles=quantiles, bins=bins,
                                   binning_by_group=binning_by_group,
                                   max_loss=max_loss, zero_aware=zero_aware)

    return factor_data