def create_round_trip_tear_sheet(returns, positions, transactions,
                                 sector_mappings=None,
                                 estimate_intraday='infer', return_fig=False):
    """
    Generate a number of figures and plots describing the duration,
    frequency, and profitability of trade "round trips."
    A round trip is started when a new long or short position is
    opened and is only completed when the number of shares in that
    position returns to or crosses zero.

    Parameters
    ----------
    returns : pd.Series
        Daily returns of the strategy, noncumulative.
         - See full explanation in create_full_tear_sheet.
    positions : pd.DataFrame
        Daily net position values.
         - See full explanation in create_full_tear_sheet.
    transactions : pd.DataFrame
        Prices and amounts of executed trades. One row per trade.
         - See full explanation in create_full_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
        Approximate returns for intraday strategies.
        See description in create_full_tear_sheet.
    return_fig : boolean, optional
        If True, returns the figure that was plotted on.
    """

    positions = utils.check_intraday(estimate_intraday, returns,
                                     positions, transactions)

    transactions_closed = round_trips.add_closing_transactions(positions,
                                                               transactions)
    # extract_round_trips requires BoD portfolio_value
    trades = round_trips.extract_round_trips(
        transactions_closed,
        portfolio_value=positions.sum(axis='columns') / (1 + returns)
    )

    if len(trades) < 5:
        warnings.warn(
            """Fewer than 5 round-trip trades made.
               Skipping round trip tearsheet.""", UserWarning)
        return

    round_trips.print_round_trip_stats(trades)

    plotting.show_profit_attribution(trades)

    if sector_mappings is not None:
        sector_trades = round_trips.apply_sector_mappings_to_round_trips(
            trades, sector_mappings)
        plotting.show_profit_attribution(sector_trades)

    fig = plt.figure(figsize=(14, 3 * 6))

    gs = gridspec.GridSpec(3, 2, wspace=0.5, hspace=0.5)

    ax_trade_lifetimes = plt.subplot(gs[0, :])
    ax_prob_profit_trade = plt.subplot(gs[1, 0])
    ax_holding_time = plt.subplot(gs[1, 1])
    ax_pnl_per_round_trip_dollars = plt.subplot(gs[2, 0])
    ax_pnl_per_round_trip_pct = plt.subplot(gs[2, 1])

    plotting.plot_round_trip_lifetimes(trades, ax=ax_trade_lifetimes)

    plotting.plot_prob_profit_trade(trades, ax=ax_prob_profit_trade)

    trade_holding_times = [x.days for x in trades['duration']]
    sns.distplot(trade_holding_times, kde=False, ax=ax_holding_time)
    ax_holding_time.set(xlabel='Holding time in days')

    sns.distplot(trades.pnl, kde=False, ax=ax_pnl_per_round_trip_dollars)
    ax_pnl_per_round_trip_dollars.set(xlabel='PnL per round-trip trade in $')

    sns.distplot(trades.returns.dropna() * 100, kde=False,
                 ax=ax_pnl_per_round_trip_pct)
    ax_pnl_per_round_trip_pct.set(
        xlabel='Round-trip returns in %')

    gs.tight_layout(fig)

    if return_fig:
        return fig
def create_round_trip_tear_sheet(returns, positions, transactions,
                                 sector_mappings=None,
                                 estimate_intraday='infer', return_fig=False):
    """
    Generate a number of figures and plots describing the duration,
    frequency, and profitability of trade "round trips."
    A round trip is started when a new long or short position is
    opened and is only completed when the number of shares in that
    position returns to or crosses zero.

    Parameters
    ----------
    returns : pd.Series
        Daily returns of the strategy, noncumulative.
         - See full explanation in create_full_tear_sheet.
    positions : pd.DataFrame
        Daily net position values.
         - See full explanation in create_full_tear_sheet.
    transactions : pd.DataFrame
        Prices and amounts of executed trades. One row per trade.
         - See full explanation in create_full_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
        Approximate returns for intraday strategies.
        See description in create_full_tear_sheet.
    return_fig : boolean, optional
        If True, returns the figure that was plotted on.
    """

    positions = utils.check_intraday(estimate_intraday, returns,
                                     positions, transactions)

    transactions_closed = round_trips.add_closing_transactions(positions,
                                                               transactions)
    # extract_round_trips requires BoD portfolio_value
    trades = round_trips.extract_round_trips(
        transactions_closed,
        portfolio_value=positions.sum(axis='columns') / (1 + returns)
    )

    if len(trades) < 5:
        warnings.warn(
            """Fewer than 5 round-trip trades made.
               Skipping round trip tearsheet.""", UserWarning)
        return

    round_trips.print_round_trip_stats(trades)

    plotting.show_profit_attribution(trades)

    if sector_mappings is not None:
        sector_trades = round_trips.apply_sector_mappings_to_round_trips(
            trades, sector_mappings)
        plotting.show_profit_attribution(sector_trades)

    fig = plt.figure(figsize=(14, 3 * 6))

    gs = gridspec.GridSpec(3, 2, wspace=0.5, hspace=0.5)

    ax_trade_lifetimes = plt.subplot(gs[0, :])
    ax_prob_profit_trade = plt.subplot(gs[1, 0])
    ax_holding_time = plt.subplot(gs[1, 1])
    ax_pnl_per_round_trip_dollars = plt.subplot(gs[2, 0])
    ax_pnl_per_round_trip_pct = plt.subplot(gs[2, 1])

    plotting.plot_round_trip_lifetimes(trades, ax=ax_trade_lifetimes)

    plotting.plot_prob_profit_trade(trades, ax=ax_prob_profit_trade)

    trade_holding_times = [x.days for x in trades['duration']]
    sns.distplot(trade_holding_times, kde=False, ax=ax_holding_time)
    ax_holding_time.set(xlabel='Holding time in days')

    sns.distplot(trades.pnl, kde=False, ax=ax_pnl_per_round_trip_dollars)
    ax_pnl_per_round_trip_dollars.set(xlabel='PnL per round-trip trade in $')

    sns.distplot(trades.returns.dropna() * 100, kde=False,
                 ax=ax_pnl_per_round_trip_pct)
    ax_pnl_per_round_trip_pct.set(
        xlabel='Round-trip returns in %')

    gs.tight_layout(fig)

    if return_fig:
        return fig
def create_round_trip_tear_sheet ( returns , positions , transactions , sector_mappings = None , estimate_intraday = 'infer' , return_fig = False ) : positions = utils . check_intraday ( estimate_intraday , returns , positions , transactions ) transactions_closed = round_trips . add_closing_transactions ( positions , transactions ) # extract_round_trips requires BoD portfolio_value trades = round_trips . extract_round_trips ( transactions_closed , portfolio_value = positions . sum ( axis = 'columns' ) / ( 1 + returns ) ) if len ( trades ) < 5 : warnings . warn ( """Fewer than 5 round-trip trades made.
               Skipping round trip tearsheet.""" , UserWarning ) return round_trips . print_round_trip_stats ( trades ) plotting . show_profit_attribution ( trades ) if sector_mappings is not None : sector_trades = round_trips . apply_sector_mappings_to_round_trips ( trades , sector_mappings ) plotting . show_profit_attribution ( sector_trades ) fig = plt . figure ( figsize = ( 14 , 3 * 6 ) ) gs = gridspec . GridSpec ( 3 , 2 , wspace = 0.5 , hspace = 0.5 ) ax_trade_lifetimes = plt . subplot ( gs [ 0 , : ] ) ax_prob_profit_trade = plt . subplot ( gs [ 1 , 0 ] ) ax_holding_time = plt . subplot ( gs [ 1 , 1 ] ) ax_pnl_per_round_trip_dollars = plt . subplot ( gs [ 2 , 0 ] ) ax_pnl_per_round_trip_pct = plt . subplot ( gs [ 2 , 1 ] ) plotting . plot_round_trip_lifetimes ( trades , ax = ax_trade_lifetimes ) plotting . plot_prob_profit_trade ( trades , ax = ax_prob_profit_trade ) trade_holding_times = [ x . days for x in trades [ 'duration' ] ] sns . distplot ( trade_holding_times , kde = False , ax = ax_holding_time ) ax_holding_time . set ( xlabel = 'Holding time in days' ) sns . distplot ( trades . pnl , kde = False , ax = ax_pnl_per_round_trip_dollars ) ax_pnl_per_round_trip_dollars . set ( xlabel = 'PnL per round-trip trade in $' ) sns . distplot ( trades . returns . dropna ( ) * 100 , kde = False , ax = ax_pnl_per_round_trip_pct ) ax_pnl_per_round_trip_pct . set ( xlabel = 'Round-trip returns in %' ) gs . tight_layout ( fig ) if return_fig : return fig
Generate a number of figures and plots describing the duration,
    frequency, and profitability of trade "round trips."
    A round trip is started when a new long or short position is
    opened and is only completed when the number of shares in that
    position returns to or crosses zero.

    Parameters
    ----------
    returns : pd.Series
        Daily returns of the strategy, noncumulative.
         - See full explanation in create_full_tear_sheet.
    positions : pd.DataFrame
        Daily net position values.
         - See full explanation in create_full_tear_sheet.
    transactions : pd.DataFrame
        Prices and amounts of executed trades. One row per trade.
         - See full explanation in create_full_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
        Approximate returns for intraday strategies.
        See description in create_full_tear_sheet.
    return_fig : boolean, optional
        If True, returns the figure that was plotted on.
Generate a number of figures and plots describing the duration frequency and profitability of trade round trips . A round trip is started when a new long or short position is opened and is only completed when the number of shares in that position returns to or crosses zero .