def create_full_tear_sheet(returns,<EOL>positions=None,<EOL>transactions=None,<EOL>market_data=None,<EOL>benchmark_rets=None,<EOL>slippage=None,<EOL>live_start_date=None,<EOL>sector_mappings=None,<EOL>bayesian=False,<EOL>round_trips=False,<EOL>estimate_intraday='<STR_LIT>',<EOL>hide_positions=False,<EOL>cone_std=(<NUM_LIT:1.0>, <NUM_LIT>, <NUM_LIT>),<EOL>bootstrap=False,<EOL>unadjusted_returns=None,<EOL>style_factor_panel=None,<EOL>sectors=None,<EOL>caps=None,<EOL>shares_held=None,<EOL>volumes=None,<EOL>percentile=None,<EOL>turnover_denom='<STR_LIT>',<EOL>set_context=True,<EOL>factor_returns=None,<EOL>factor_loadings=None,<EOL>pos_in_dollars=True,<EOL>header_rows=None,<EOL>factor_partitions=FACTOR_PARTITIONS):
if (unadjusted_returns is None) and (slippage is not None) and(transactions is not None):<EOL><INDENT>unadjusted_returns = returns.copy()<EOL>returns = txn.adjust_returns_for_slippage(returns, positions,<EOL>transactions, slippage)<EOL><DEDENT>positions = utils.check_intraday(estimate_intraday, returns,<EOL>positions, transactions)<EOL>create_returns_tear_sheet(<EOL>returns,<EOL>positions=positions,<EOL>transactions=transactions,<EOL>live_start_date=live_start_date,<EOL>cone_std=cone_std,<EOL>benchmark_rets=benchmark_rets,<EOL>bootstrap=bootstrap,<EOL>turnover_denom=turnover_denom,<EOL>header_rows=header_rows,<EOL>set_context=set_context)<EOL>create_interesting_times_tear_sheet(returns,<EOL>benchmark_rets=benchmark_rets,<EOL>set_context=set_context)<EOL>if positions is not None:<EOL><INDENT>create_position_tear_sheet(returns, positions,<EOL>hide_positions=hide_positions,<EOL>set_context=set_context,<EOL>sector_mappings=sector_mappings,<EOL>estimate_intraday=False)<EOL>if transactions is not None:<EOL><INDENT>create_txn_tear_sheet(returns, positions, transactions,<EOL>unadjusted_returns=unadjusted_returns,<EOL>estimate_intraday=False,<EOL>set_context=set_context)<EOL>if round_trips:<EOL><INDENT>create_round_trip_tear_sheet(<EOL>returns=returns,<EOL>positions=positions,<EOL>transactions=transactions,<EOL>sector_mappings=sector_mappings,<EOL>estimate_intraday=False)<EOL><DEDENT>if market_data is not None:<EOL><INDENT>create_capacity_tear_sheet(returns, positions, transactions,<EOL>market_data,<EOL>liquidation_daily_vol_limit=<NUM_LIT>,<EOL>last_n_days=<NUM_LIT>,<EOL>estimate_intraday=False)<EOL><DEDENT><DEDENT>if style_factor_panel is not None:<EOL><INDENT>create_risk_tear_sheet(positions, style_factor_panel, sectors,<EOL>caps, shares_held, volumes, percentile)<EOL><DEDENT>if factor_returns is not None and factor_loadings is not None:<EOL><INDENT>create_perf_attrib_tear_sheet(returns, positions, factor_returns,<EOL>factor_loadings, transactions,<EOL>pos_in_dollars=pos_in_dollars,<EOL>factor_partitions=factor_partitions)<EOL><DEDENT><DEDENT>if bayesian:<EOL><INDENT>create_bayesian_tear_sheet(returns,<EOL>live_start_date=live_start_date,<EOL>benchmark_rets=benchmark_rets,<EOL>set_context=set_context)<EOL><DEDENT>
Generate a number of tear sheets that are useful
for analyzing a strategy's performance.

- Fetches benchmarks if needed.
- Creates tear sheets for returns, and significant events.
    If possible, also creates tear sheets for position analysis,
    transaction analysis, and Bayesian analysis.

Parameters
----------
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, optional
    Daily net position values.
     - Time series of dollar amount invested in each position and cash.
     - Days where stocks are not held can be represented by 0 or NaN.
     - 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
transactions : pd.DataFrame, optional
    Executed trade volumes and fill prices.
    - One row per trade.
    - Trades on different names that occur at the
      same time will have identical indicies.
    - Example:
        index                  amount   price    symbol
        2004-01-09 12:18:01    483      324.12   'AAPL'
        2004-01-09 12:18:01    122      83.10    'MSFT'
        2004-01-13 14:12:23    -75      340.43   'AAPL'
market_data : pd.Panel, optional
    Panel with items axis of 'price' and 'volume' DataFrames.
    The major and minor axes should match those of the
    the passed positions DataFrame (same dates and symbols).
slippage : int/float, optional
    Basis points of slippage to apply to returns before generating
    tearsheet stats and plots.
    If a value is provided, slippage parameter sweep
    plots will be generated from the unadjusted returns.
    Transactions and positions must also be passed.
    - See txn.adjust_returns_for_slippage for more details.
live_start_date : datetime, optional
    The point in time when the strategy began live trading,
    after its backtest period. This datetime should be normalized.
hide_positions : bool, optional
    If True, will not output any symbol names.
bayesian: boolean, optional
    If True, causes the generation of a Bayesian tear sheet.
round_trips: boolean, optional
    If True, causes the generation of a round trip tear sheet.
sector_mappings : dict or pd.Series, optional
    Security identifier to sector mapping.
    Security ids as keys, sectors as values.
estimate_intraday: boolean or str, optional
    Instead of using the end-of-day positions, use the point in the day
    where we have the most $ invested. This will adjust positions to
    better approximate and represent how an intraday strategy behaves.
    By default, this is 'infer', and an attempt will be made to detect
    an intraday strategy. Specifying this value will prevent detection.
cone_std : float, or tuple, optional
    If float, The standard deviation to use for the cone plots.
    If tuple, Tuple of standard deviation values to use for the cone plots
     - The cone is a normal distribution with this standard deviation
         centered around a linear regression.
bootstrap : boolean (optional)
    Whether to perform bootstrap analysis for the performance
    metrics. Takes a few minutes longer.
turnover_denom : str
    Either AGB or portfolio_value, default AGB.
    - See full explanation in txn.get_turnover.
factor_returns : pd.Dataframe, optional
    Returns by factor, with date as index and factors as columns
factor_loadings : pd.Dataframe, optional
    Factor loadings for all days in the date range, with date and
    ticker as index, and factors as columns.
pos_in_dollars : boolean, optional
    indicates whether positions is in dollars
header_rows : dict or OrderedDict, optional
    Extra rows to display at the top of the perf stats table.
set_context : boolean, optional
    If True, set default plotting style context.
     - See plotting.context().
factor_partitions : dict, optional
    dict specifying how factors should be separated in perf attrib
    factor returns and risk exposures plots
    - See create_perf_attrib_tear_sheet().