diff --git a/pyfolio/examples/round_trip_example.ipynb b/pyfolio/examples/round_trip_example.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Round Trip Tearsheet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When evaluating the performance of an investing strategy, it is helpful to quantify the frequency, duration, and profitability of its independent bets, or \"round trip\" trades. A round trip trade 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. \n",
+    "\n",
+    "The intent of the round trip tearsheet is to help differentiate strategies that profited off a few lucky trades from strategies that profited repeatedly from genuine alpha. Breaking down round trip profitability by traded name and sector can also help inform universe selection and identify exposure risks. For example, even if your equity curve looks robust, if only two securities in your universe of fifteen names contributed to overall profitability, you may have reason to question the logic of your strategy.\n",
+    "\n",
+    "To identify round trips, pyfolio groups transactions by symbol and identifies the points at which each position amount leaves and returns to zero. Behind the scenes, transactions that cause position amounts to flip directly from long to short or short to long are divided into separate transactions so that distinct round trips can be identified. In calculating round trips, pyfolio will also append position closing transactions at the last timestamp in the positions data. This closing transaction will cause the PnL from any open positions to realized as completed round trips.\n",
+    "\n",
+    "**Note: The round trip method of quantifying performance is not applicable to every style of strategy.** For instance, simple rebalancing algorithms make very few, if any, round trip trades. The results of the round trip tearsheet will be less informative for any strategy that doesn't entirely exit positions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import gzip\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import pyfolio as pf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "transactions = pd.read_csv(gzip.open('../tests/test_data/test_txn.csv.gz'),\n",
+    "                    index_col=0, parse_dates=0)\n",
+    "positions = pd.read_csv(gzip.open('../tests/test_data/test_pos.csv.gz'),\n",
+    "                    index_col=0, parse_dates=0)\n",
+    "returns = pd.read_csv(gzip.open('../tests/test_data/test_returns.csv.gz'),\n",
+    "                    index_col=0, parse_dates=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Optional: Sector mappings may be passed in as a dict or pd.Series. If a mapping is\n",
+    "# provided, PnL from symbols with mappings will be summed to display profitability by sector.\n",
+    "sect_map = {'COST': 'Consumer Goods', 'INTC':'Technology', 'CERN':'Healthcare', 'GPS':'Technology',\n",
+    "            'MMM': 'Construction', 'DELL': 'Technology', 'AMD':'Technology'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                      duration           pnl      returns       long\n",
+      "count                     1430   1430.000000  1430.000000       1430\n",
+      "mean    9 days 16:40:56.154545     45.737238     0.003543  0.5230769\n",
+      "std    22 days 02:16:41.165898   1616.537844     0.031288  0.4996419\n",
+      "min            0 days 00:00:00 -30697.460000    -0.218045      False\n",
+      "25%            0 days 23:59:59     -5.773144    -0.011450          0\n",
+      "50%            2 days 23:59:59      0.871629     0.003885          1\n",
+      "75%            5 days 23:59:59     40.438366     0.018126          1\n",
+      "max          286 days 00:00:00  17835.869482     0.204385       True\n",
+      "Percent of round trips profitable = 57.2%\n",
+      "Mean return per winning round trip = 0.02181\n",
+      "Mean return per losing round trip = -0.02108\n",
+      "A decision is made every 1.053 days.\n",
+      "0.9495 trading decisions per day.\n",
+      "19.94 trading decisions per month.\n",
+      "\n",
+      "Profitability (PnL / PnL total) per name:\n",
+      "symbol\n",
+      "COST    0.398964\n",
+      "INTC    0.382659\n",
+      "CERN    0.323077\n",
+      "MMM     0.221479\n",
+      "GPS     0.049385\n",
+      "AMD    -0.064091\n",
+      "DELL   -0.311473\n",
+      "Name: pnl, dtype: float64\n",
+      "\n",
+      "Profitability (PnL / PnL total) per name:\n",
+      "symbol\n",
+      "Consumer Goods    0.398964\n",
+      "Healthcare        0.323077\n",
+      "Construction      0.221479\n",
+      "Technology        0.056480\n",
+      "Name: pnl, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ad53110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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XIEkHAAAAAMAlSNIBAAAAAHAJknQAAAAAAFyCJB0AAAAA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\nJk2apIkTJ+qHH35QQkKCbrrppoteAl6V8vD9wQcfq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VBg6NChWLhw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\nsvf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r0nT1A2HUbxMgopatsbetVPy8kZERVCoVPvnkkyrz2djYVJukt2nTBkuWLEFkZCTu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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ad61750>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pf.tears.create_round_trip_tear_sheet(transactions, positions, sector_mappings=sect_map)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py
index ab9371f8..a9ce32a8 100644
--- a/pyfolio/plotting.py
+++ b/pyfolio/plotting.py
@@ -16,11 +16,13 @@
 
 import pandas as pd
 import numpy as np
+import scipy as sp
 
 import seaborn as sns
 import matplotlib
 import matplotlib.pyplot as plt
 from matplotlib.ticker import FuncFormatter
+import matplotlib.lines as mlines
 
 from sklearn import preprocessing
 
@@ -1402,3 +1404,114 @@ def cumulate_returns(x):
     ax.set_xticklabels(xticks_label)
 
     return ax
+
+
+def plot_round_trip_life_times(round_trips, ax=None):
+    """
+    Plots timespans and directions of round trip trades.
+
+    Parameters
+    ----------
+    round_trips : pd.DataFrame
+        DataFrame with one row per round trip trade.
+        - See full explanation in round_trips.extract_round_trips
+    ax : matplotlib.Axes, optional
+        Axes upon which to plot.
+
+    Returns
+    -------
+    ax : matplotlib.Axes
+        The axes that were plotted on.
+    """
+    if ax is None:
+        ax = plt.subplot()
+
+    symbols = round_trips.symbol.unique()
+    symbol_idx = pd.Series(np.arange(len(symbols)), index=symbols)
+
+    for symbol, sym_round_trips in round_trips.groupby('symbol'):
+        for _, row in sym_round_trips.iterrows():
+            c = 'b' if row.long else 'r'
+            y_ix = symbol_idx[symbol]
+            ax.plot([row['open_dt'], row['close_dt']],
+                    [y_ix, y_ix], color=c)
+
+    ax.set_yticklabels(symbols)
+
+    red_line = mlines.Line2D([], [], color='r', label='Short')
+    blue_line = mlines.Line2D([], [], color='b', label='Long')
+    ax.legend(handles=[red_line, blue_line], loc=0)
+
+    return ax
+
+
+def show_profit_attribution(round_trips):
+    """
+    Prints the share of total PnL contributed by each
+    traded name.
+
+    Parameters
+    ----------
+    round_trips : pd.DataFrame
+        DataFrame with one row per round trip trade.
+        - See full explanation in round_trips.extract_round_trips
+    ax : matplotlib.Axes, optional
+        Axes upon which to plot.
+
+    Returns
+    -------
+    ax : matplotlib.Axes
+        The axes that were plotted on.
+    """
+
+    total_pnl = round_trips['pnl'].sum()
+    pct_profit_attribution = round_trips.groupby(
+        'symbol')['pnl'].sum() / total_pnl
+
+    print('\nProfitability (PnL / PnL total) per name:')
+    print(pct_profit_attribution.sort(inplace=False, ascending=False))
+
+
+def plot_prob_profit_trade(round_trips, ax=None):
+    """
+    Plots a probability distribution for the event of making
+    a profitable trade.
+
+    Parameters
+    ----------
+    round_trips : pd.DataFrame
+        DataFrame with one row per round trip trade.
+        - See full explanation in round_trips.extract_round_trips
+    ax : matplotlib.Axes, optional
+        Axes upon which to plot.
+
+    Returns
+    -------
+    ax : matplotlib.Axes
+        The axes that were plotted on.
+    """
+
+    x = np.linspace(0, 1., 500)
+
+    round_trips['profitable'] = round_trips.pnl > 0
+
+    dist = sp.stats.beta(round_trips.profitable.sum(),
+                         (~round_trips.profitable).sum())
+    y = dist.pdf(x)
+    lower_perc = dist.ppf(.025)
+    upper_perc = dist.ppf(.975)
+
+    lower_plot = dist.ppf(.001)
+    upper_plot = dist.ppf(.999)
+
+    if ax is None:
+        ax = plt.subplot()
+
+    ax.plot(x, y)
+    ax.axvline(lower_perc, color='0.5')
+    ax.axvline(upper_perc, color='0.5')
+
+    ax.set(xlabel='Probability making a profitable decision', ylabel='Belief',
+           xlim=(lower_plot, upper_plot), ylim=(0, y.max() + 1.))
+
+    return ax
diff --git a/pyfolio/round_trips.py b/pyfolio/round_trips.py
new file mode 100644
index 00000000..58f13376
--- /dev/null
+++ b/pyfolio/round_trips.py
@@ -0,0 +1,248 @@
+#
+# Copyright 2015 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import division
+from collections import defaultdict
+
+import pandas as pd
+import numpy as np
+
+
+def extract_round_trips(transactions):
+    """
+    Group transactions into "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.
+    Computes pnl for each round trip.
+
+    For example, the following transactions would constitute one round trip:
+    index                  amount   price    symbol
+    2004-01-09 12:18:01    186      324.12   'AAPL'
+    2004-01-09 15:12:53    -10      344.54   'AAPL'
+    2004-01-13 14:41:23    24       320.21   'AAPL'
+    2004-01-30 10:23:34    -200     340.43   'AAPL'
+
+    Parameters
+    ----------
+    transactions : pd.DataFrame
+        Prices and amounts of executed trades. One row per trade.
+        - See full explanation in tears.create_full_tear_sheet
+
+    Returns
+    -------
+    round_trips : pd.DataFrame
+        DataFrame with one row per round trip.
+    """
+    # Transactions that cross zero must be split into separate
+    # long and short transactions that end/start on zero.
+    transactions_split = split_trades(transactions)
+
+    transactions_split['txn_dollars'] =  \
+        -transactions_split['amount'] * transactions_split['price']
+
+    round_trips = defaultdict(list)
+
+    for sym, trans_sym in transactions_split.groupby('symbol'):
+        trans_sym = trans_sym.sort_index()
+        amount_cumsum = trans_sym.amount.cumsum()
+        # Find indicies where the position amount returns to zero.
+        closed_idx = np.where(amount_cumsum == 0)[0] + 1
+        # Identify the first trade as the beginning of a round trip.
+        closed_idx = np.insert(closed_idx, 0, 0)
+
+        for trade_start, trade_end in zip(closed_idx, closed_idx[1:]):
+            txn = trans_sym.iloc[trade_start:trade_end]
+
+            if len(txn) == 0:
+                continue
+
+            assert txn.amount.sum() == 0
+            long_trade = txn.amount.iloc[0] > 0
+            pnl = txn.txn_dollars.sum()
+            round_trips['symbol'].append(sym)
+            round_trips['pnl'].append(pnl)
+            round_trips['duration'].append(txn.index[-1] - txn.index[0])
+            round_trips['long'].append(long_trade)
+            round_trips['open_dt'].append(txn.index[0])
+            round_trips['close_dt'].append(txn.index[-1])
+
+            # Investing txns push the position amount farther from zero.
+            # invested is always a positive value. Returned - Invested = PnL.
+            if long_trade:
+                invested = -txn.query('txn_dollars < 0').txn_dollars.sum()
+            else:
+                invested = txn.query('txn_dollars > 0').txn_dollars.sum()
+
+            if invested == 0:
+                round_trips['returns'].append(0)
+            else:
+                round_trips['returns'].append(pnl / invested)
+
+    if len(round_trips) == 0:
+        return pd.DataFrame([])
+
+    round_trips = pd.DataFrame(round_trips)
+    round_trips = round_trips[['open_dt', 'close_dt', 'duration',
+                               'pnl', 'returns', 'long', 'symbol']]
+
+    return round_trips
+
+
+def split_trades(transactions):
+    """
+    Splits transactions that cause total position amount to cross zero.
+    In other words, separates of the closing of one short/long position
+    with the opening of a new long/short position.
+
+    For example, the second transaction in this transactions DataFrame
+    would be divided as shown in the second DataFrame:
+    index                  amount   price    symbol
+    2004-01-09 12:18:01    180      324.12   'AAPL'
+    2004-01-09 15:12:53    -200     344.54   'AAPL'
+
+    index                  amount   price    symbol
+    2004-01-09 12:18:01    180      324.12   'AAPL'
+    2004-01-09 15:12:53    -180     344.54   'AAPL'
+    2004-01-09 15:12:54    -20      344.54   'AAPL'
+
+    Parameters
+    ----------
+    transactions : pd.DataFrame
+        Prices and amounts of executed trades. One row per trade.
+        - See full explanation in tears.create_full_tear_sheet
+
+    Returns
+    -------
+    transactions_split : pd.DataFrame
+        Prices and amounts of executed trades. Trades that cause
+        total position amount to cross zero are divided.
+    """
+
+    trans_split = []
+
+    for sym, trans_sym in transactions.groupby('symbol'):
+        trans_sym = trans_sym.sort_index()
+
+        while True:
+            cum_amount = trans_sym.amount.cumsum()
+            # find the indicies where position amount crosses zero
+            sign_flip = np.where(np.abs(np.diff(np.sign(cum_amount))) == 2)[0]
+
+            if len(sign_flip) == 0:
+                break  # all sign flips are converted
+
+            sign_flip = sign_flip[0] + 2
+
+            txn = trans_sym.iloc[:sign_flip]
+
+            left_over_txn_amount = txn.amount.sum()
+            assert left_over_txn_amount != 0
+
+            split_txn_1 = txn.iloc[[-1]].copy()
+            split_txn_2 = txn.iloc[[-1]].copy()
+
+            split_txn_1['amount'] -= left_over_txn_amount
+            split_txn_2['amount'] = left_over_txn_amount
+
+            # Delay 2nd trade by a second to avoid overlapping indices
+            split_txn_2.index += pd.Timedelta(seconds=1)
+
+            assert split_txn_1.amount.iloc[0] + \
+                split_txn_2.amount.iloc[0] == txn.iloc[-1].amount
+            assert trans_sym.iloc[:sign_flip - 1].amount.sum() + \
+                split_txn_1.amount.iloc[0] == 0
+
+            # Recreate transactions so far with split transaction
+            trans_sym = pd.concat([trans_sym.iloc[:sign_flip - 1],
+                                   split_txn_1,
+                                   split_txn_2,
+                                   trans_sym.iloc[sign_flip:]])
+
+        assert np.all(np.abs(np.diff(np.sign(trans_sym.amount.cumsum()))) != 2)
+        trans_split.append(trans_sym)
+
+    transactions_split = pd.concat(trans_split)
+
+    return transactions_split
+
+
+def add_closing_transactions(positions, transactions):
+    """
+    Appends transactions that close out all positions at the end of
+    the timespan covered by positions data. Utilizes pricing information
+    in the positions DataFrame to determine closing price.
+
+    Parameters
+    ----------
+    positions : pd.DataFrame
+        The positions that the strategy takes over time.
+    transactions : pd.DataFrame
+        Prices and amounts of executed trades. One row per trade.
+        - See full explanation in tears.create_full_tear_sheet
+
+    Returns
+    -------
+    closed_txns : pd.DataFrame
+        Transactions with closing transactions appended.
+    """
+
+    closed_txns = transactions.copy()
+
+    pos_at_end = positions.drop('cash', axis=1).iloc[-1]
+    open_pos = pos_at_end.replace(0, np.nan).dropna()
+    # Add closing trades one second after the close to be sure
+    # they don't conflict with other trades executed at that time.
+    end_dt = open_pos.name + pd.Timedelta(seconds=1)
+
+    for sym, ending_val in open_pos.iteritems():
+        txn_sym = transactions[transactions.symbol == sym]
+
+        ending_amount = txn_sym.amount.sum()
+
+        ending_price = ending_val / ending_amount
+        closing_txn = {'symbol': sym,
+                       'amount': -ending_amount,
+                       'price': ending_price}
+
+        closing_txn = pd.DataFrame(closing_txn, index=[end_dt])
+        closed_txns = closed_txns.append(closing_txn)
+
+    return closed_txns
+
+
+def apply_sector_mappings_to_round_trips(round_trips, sector_mappings):
+    """
+    Translates round trip symbols to sectors.
+
+    Parameters
+    ----------
+    round_trips : pd.DataFrame
+        DataFrame with one row per round trip trade.
+        - See full explanation in round_trips.extract_round_trips
+    sector_mappings : dict or pd.Series, optional
+        Security identifier to sector mapping.
+        Security ids as keys, sectors as values.
+
+    Returns
+    -------
+    sector_round_trips : pd.DataFrame
+        Round trips with symbol names replaced by sector names.
+    """
+
+    sector_round_trips = round_trips.copy()
+    sector_round_trips.symbol = sector_round_trips.symbol.apply(
+        lambda x: sector_mappings.get(x, 'No Sector Mapping'))
+    sector_round_trips = sector_round_trips.dropna(axis=0)
+
+    return sector_round_trips
diff --git a/pyfolio/tears.py b/pyfolio/tears.py
index 06a8da28..c1702e47 100644
--- a/pyfolio/tears.py
+++ b/pyfolio/tears.py
@@ -20,6 +20,7 @@
 from . import utils
 from . import pos
 from . import txn
+from . import round_trips
 from . import plotting
 from .plotting import plotting_context
 
@@ -167,6 +168,9 @@ def create_full_tear_sheet(returns, positions=None, transactions=None,
                                   unadjusted_returns=unadjusted_returns,
                                   set_context=set_context)
 
+            create_round_trip_tear_sheet(transactions, positions,
+                                         sector_mappings=sector_mappings)
+
     if bayesian:
         create_bayesian_tear_sheet(returns,
                                    live_start_date=live_start_date,
@@ -402,12 +406,12 @@ def create_position_tear_sheet(returns, positions, gross_lev=None,
 
     if sector_mappings is not None:
         sector_exposures = pos.get_sector_exposures(positions, sector_mappings)
-
-        sector_alloc = pos.get_percent_alloc(sector_exposures)
-        sector_alloc = sector_alloc.drop('cash', axis='columns')
-        ax_sector_alloc = plt.subplot(gs[4, :], sharex=ax_gross_leverage)
-        plotting.plot_sector_allocations(returns, sector_alloc,
-                                         ax=ax_sector_alloc)
+        if len(sector_exposures.columns) > 1:
+            sector_alloc = pos.get_percent_alloc(sector_exposures)
+            sector_alloc = sector_alloc.drop('cash', axis='columns')
+            ax_sector_alloc = plt.subplot(gs[4, :], sharex=ax_gross_leverage)
+            plotting.plot_sector_allocations(returns, sector_alloc,
+                                             ax=ax_sector_alloc)
 
     plt.show()
     if return_fig:
@@ -435,8 +439,6 @@ def create_txn_tear_sheet(returns, positions, transactions,
          - See full explanation in create_full_tear_sheet.
     return_fig : boolean, optional
         If True, returns the figure that was plotted on.
-    set_context : boolean, optional
-        If True, set default plotting style context.
     """
     vertical_sections = 5 if unadjusted_returns is not None else 3
 
@@ -479,6 +481,102 @@ def create_txn_tear_sheet(returns, positions, transactions,
         return fig
 
 
+@plotting_context
+def create_round_trip_tear_sheet(transactions, positions,
+                                 sector_mappings=None,
+                                 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
+    ----------
+    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.
+    return_fig : boolean, optional
+        If True, returns the figure that was plotted on.
+    """
+
+    transactions_closed = round_trips.add_closing_transactions(positions,
+                                                               transactions)
+    trades = round_trips.extract_round_trips(transactions_closed)
+
+    if len(trades) < 5:
+        warnings.warn(
+            """Fewer than 5 round-trip trades made.
+               Skipping round trip tearsheet.""", UserWarning)
+        return
+
+    ndays = len(positions)
+
+    print(trades.drop(['open_dt', 'close_dt', 'symbol'],
+                      axis='columns').describe())
+    print('Percent of round trips profitable = {:.4}%'.format(
+          (trades.pnl > 0).mean() * 100))
+
+    winning_round_trips = trades[trades.pnl > 0]
+    losing_round_trips = trades[trades.pnl < 0]
+    print('Mean return per winning round trip = {:.4}'.format(
+        winning_round_trips.returns.mean()))
+    print('Mean return per losing round trip = {:.4}').format(
+        losing_round_trips.returns.mean())
+
+    print('A decision is made every {:.4} days.'.format(ndays / len(trades)))
+    print('{:.4} trading decisions per day.'.format(len(trades) * 1. / ndays))
+    print('{:.4} trading decisions per month.'.format(
+        len(trades) * 1. / (ndays / 21)))
+
+    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))
+
+    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_life_times(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 $')
+
+    pnl_pct = trades.pnl / trades.pnl.sum() * 100
+    sns.distplot(pnl_pct, kde=False, ax=ax_pnl_per_round_trip_pct)
+    ax_pnl_per_round_trip_pct.set(
+        xlabel='PnL per round-trip trade in % of total profit')
+
+    gs.tight_layout(fig)
+
+    plt.show()
+    if return_fig:
+        return fig
+
+
 @plotting_context
 def create_interesting_times_tear_sheet(
         returns, benchmark_rets=None, legend_loc='best', return_fig=False):
diff --git a/pyfolio/tests/test_data/test_gross_lev.csv.gz b/pyfolio/tests/test_data/test_gross_lev.csv.gz
new file mode 100644
index 00000000..f0cde4ed
Binary files /dev/null and b/pyfolio/tests/test_data/test_gross_lev.csv.gz differ
diff --git a/pyfolio/tests/test_data/test_pos.csv.gz b/pyfolio/tests/test_data/test_pos.csv.gz
new file mode 100644
index 00000000..8ae0a20a
Binary files /dev/null and b/pyfolio/tests/test_data/test_pos.csv.gz differ
diff --git a/pyfolio/tests/test_data/test_returns.csv.gz b/pyfolio/tests/test_data/test_returns.csv.gz
new file mode 100644
index 00000000..6c2c2bbb
Binary files /dev/null and b/pyfolio/tests/test_data/test_returns.csv.gz differ
diff --git a/pyfolio/tests/test_data/test_txn.csv.gz b/pyfolio/tests/test_data/test_txn.csv.gz
new file mode 100644
index 00000000..739844e0
Binary files /dev/null and b/pyfolio/tests/test_data/test_txn.csv.gz differ
diff --git a/pyfolio/tests/test_round_trips.py b/pyfolio/tests/test_round_trips.py
new file mode 100644
index 00000000..058f3458
--- /dev/null
+++ b/pyfolio/tests/test_round_trips.py
@@ -0,0 +1,115 @@
+from nose_parameterized import parameterized
+
+from unittest import TestCase
+
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    date_range,
+    Timedelta,
+    read_csv
+)
+from pandas.util.testing import (assert_frame_equal)
+
+import os
+import gzip
+
+from pyfolio.round_trips import (extract_round_trips,
+                                 add_closing_transactions)
+
+
+class RoundTripTestCase(TestCase):
+    dates = date_range(start='2015-01-01', freq='D', periods=20)
+
+    @parameterized.expand([
+        (DataFrame(data=[[2, 10, 'A'],
+                         [-2, 15, 'A']],
+                   columns=['amount', 'price', 'symbol'],
+                   index=dates[:2]),
+         DataFrame(data=[[dates[0], dates[1],
+                          Timedelta(days=1), 10, .5,
+                          True, 'A']],
+                   columns=['open_dt', 'close_dt',
+                            'duration', 'pnl', 'returns',
+                            'long', 'symbol'],
+                   index=[0])
+         ),
+        (DataFrame(data=[[2, 10, 'A'],
+                         [2, 15, 'A'],
+                         [-9, 10, 'A']],
+                   columns=['amount', 'price', 'symbol'],
+                   index=dates[:3]),
+         DataFrame(data=[[dates[0], dates[2],
+                          Timedelta(days=2), -10, -.2,
+                          True, 'A']],
+                   columns=['open_dt', 'close_dt',
+                            'duration', 'pnl', 'returns',
+                            'long', 'symbol'],
+                   index=[0])
+         ),
+        (DataFrame(data=[[2, 10, 'A'],
+                         [-4, 15, 'A'],
+                         [3, 20, 'A']],
+                   columns=['amount', 'price', 'symbol'],
+                   index=dates[:3]),
+         DataFrame(data=[[dates[0], dates[1],
+                          Timedelta(days=1), 10, .5,
+                          True, 'A'],
+                         [dates[1] + Timedelta(seconds=1), dates[2],
+                          Timedelta(days=1) - Timedelta(seconds=1),
+                          -10, (-1. / 3),
+                          False, 'A']],
+                   columns=['open_dt', 'close_dt',
+                            'duration', 'pnl', 'returns',
+                            'long', 'symbol'],
+                   index=[0, 1])
+         )
+    ])
+    def test_extract_round_trips(self, transactions, expected):
+        round_trips = extract_round_trips(transactions)
+
+        assert_frame_equal(round_trips, expected)
+
+    def test_add_closing_trades(self):
+        dates = date_range(start='2015-01-01', periods=20)
+        transactions = DataFrame(data=[[2, 10, 'A'],
+                                       [-5, 10, 'A'],
+                                       [-1, 10, 'B']],
+                                 columns=['amount', 'price', 'symbol'],
+                                 index=[dates[:3]])
+        positions = DataFrame(data=[[20, 10, 0],
+                                    [-30, 10, 30],
+                                    [-60, 0, 30]],
+                              columns=['A', 'B', 'cash'],
+                              index=[dates[:3]])
+
+        expected_ix = dates[:3].append(DatetimeIndex([dates[2] +
+                                       Timedelta(seconds=1)]))
+        expected = DataFrame(data=[[2, 10, 'A'],
+                                   [-5, 10, 'A'],
+                                   [-1, 10., 'B'],
+                                   [3, 20., 'A']],
+                             columns=['amount', 'price', 'symbol'],
+                             index=expected_ix)
+
+        transactions_closed = add_closing_transactions(positions, transactions)
+        assert_frame_equal(transactions_closed, expected)
+
+    def test_txn_pnl_matches_round_trip_pnl(self):
+        __location__ = os.path.realpath(
+            os.path.join(os.getcwd(), os.path.dirname(__file__)))
+
+        test_txn = read_csv(gzip.open(
+                            __location__ + '/test_data/test_txn.csv.gz'),
+                            index_col=0, parse_dates=0)
+        test_pos = read_csv(gzip.open(
+                            __location__ + '/test_data/test_pos.csv.gz'),
+                            index_col=0, parse_dates=0)
+
+        transactions_closed = add_closing_transactions(test_pos, test_txn)
+        transactions_closed['txn_dollars'] = transactions_closed.amount * \
+            -1. * transactions_closed.price
+        round_trips = extract_round_trips(transactions_closed)
+
+        self.assertAlmostEqual(round_trips.pnl.sum(),
+                               transactions_closed.txn_dollars.sum())
diff --git a/pyfolio/txn.py b/pyfolio/txn.py
index a2c93de0..9ca12e2b 100644
--- a/pyfolio/txn.py
+++ b/pyfolio/txn.py
@@ -14,8 +14,6 @@
 # limitations under the License.
 from __future__ import division
 
-from collections import defaultdict
-
 import pandas as pd
 
 
@@ -39,7 +37,7 @@ def map_transaction(txn):
         symbol = txn['sid']['symbol']
     else:
         sid = txn['sid']
-        symbol = None
+        symbol = txn['sid']
 
     return {'sid': sid,
             'symbol': symbol,
@@ -134,47 +132,6 @@ def adjust_returns_for_slippage(returns, turnover, slippage_bps):
     return trim_returns - turnover * slippage
 
 
-def create_txn_profits(transactions):
-    """
-    Compute per-trade profits.
-
-    Generates a new transactions DataFrame with a profits column
-
-    Parameters
-    ----------
-    transactions : pd.DataFrame
-        Daily transaction volume and number of shares.
-         - See full explanation in tears.create_full_tear_sheet.
-
-    Returns
-    -------
-    profits_dts : pd.DataFrame
-        DataFrame containing transactions and their profits, datetimes,
-        amounts, current prices, prior prices, and symbols.
-    """
-
-    txn_descr = defaultdict(list)
-
-    for symbol, transactions_sym in transactions.groupby('symbol'):
-        transactions_sym = transactions_sym.reset_index()
-
-        for i, (amount, price, dt) in transactions_sym.iloc[1:][
-                ['amount', 'price', 'date_time_utc']].iterrows():
-            prev_amount, prev_price, prev_dt = transactions_sym.loc[
-                i - 1, ['amount', 'price', 'date_time_utc']]
-            profit = (price - prev_price) * -amount
-            txn_descr['profits'].append(profit)
-            txn_descr['dts'].append(dt - prev_dt)
-            txn_descr['amounts'].append(amount)
-            txn_descr['prices'].append(price)
-            txn_descr['prev_prices'].append(prev_price)
-            txn_descr['symbols'].append(symbol)
-
-    profits_dts = pd.DataFrame(txn_descr)
-
-    return profits_dts
-
-
 def get_turnover(transactions, positions, period=None, average=True):
     """
     Portfolio Turnover Rate: