diff --git a/WHATSNEW.md b/WHATSNEW.md
index b4560f79..95584829 100644
--- a/WHATSNEW.md
+++ b/WHATSNEW.md
@@ -2,6 +2,14 @@
 
 These are new features and improvements of note in each release.
 
+## v0.5.0 (upcoming)
+
+### New features
+
+* Refactored round-trip analysis to be more general and have better output. Now does full portfolio reconstruction to match trades [PR293](https://github.com/quantopian/pyfolio/pull/293). Thomas, Andrew
+
+### Bug fixes
+
 ## v0.4.0 (Dec 10, 2015)
 
 This is a major release from 0.3.1 that includes new features and quite a few bug fixes. We recommend that all users upgrade to this new version.
diff --git a/pyfolio/examples/round_trip_example.ipynb b/pyfolio/examples/round_trip_example.ipynb
index 08ffd9be..7bae4baa 100644
--- a/pyfolio/examples/round_trip_example.ipynb
+++ b/pyfolio/examples/round_trip_example.ipynb
@@ -11,13 +11,11 @@
    "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",
+    "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 then later completely or partially closed out.\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."
+    "To identify round trips, pyfolio reconstructs the complete portfolio based on the transactions that you pass in. When you make a trade, pyfolio checks if shares are already present in your portfolio purchased at a certain price. If there are, we compute the PnL, returns and duration of that round trip trade. 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."
    ]
   },
   {
@@ -48,7 +46,7 @@
     "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)"
+    "                      index_col=0, parse_dates=0, header=None)[1]"
    ]
   },
   {
@@ -71,51 +69,337 @@
    "metadata": {
     "collapsed": false
    },
+   "outputs": [],
+   "source": [
+    "rts = pf.round_trips.extract_round_trips(transactions, portfolio_value=positions.sum(axis='columns') / (returns + 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>close_dt</th>\n",
+       "      <th>long</th>\n",
+       "      <th>open_dt</th>\n",
+       "      <th>pnl</th>\n",
+       "      <th>rt_returns</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>returns</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2004-01-13</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2004-01-09</td>\n",
+       "      <td>-126.000000</td>\n",
+       "      <td>-0.022523</td>\n",
+       "      <td>AMD</td>\n",
+       "      <td>4 days</td>\n",
+       "      <td>-0.001249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2004-01-16</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2004-01-09</td>\n",
+       "      <td>50.020000</td>\n",
+       "      <td>0.078507</td>\n",
+       "      <td>AMD</td>\n",
+       "      <td>7 days</td>\n",
+       "      <td>0.000503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2004-01-20</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2004-01-09</td>\n",
+       "      <td>1540.099065</td>\n",
+       "      <td>0.104696</td>\n",
+       "      <td>AMD</td>\n",
+       "      <td>11 days</td>\n",
+       "      <td>0.015257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2004-01-21</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2004-01-20</td>\n",
+       "      <td>287.119806</td>\n",
+       "      <td>0.085155</td>\n",
+       "      <td>AMD</td>\n",
+       "      <td>1 days</td>\n",
+       "      <td>0.002861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2004-01-22</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2004-01-20</td>\n",
+       "      <td>103.349947</td>\n",
+       "      <td>0.112198</td>\n",
+       "      <td>AMD</td>\n",
+       "      <td>2 days</td>\n",
+       "      <td>0.001032</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    close_dt   long    open_dt          pnl  rt_returns symbol  duration  \\\n",
+       "0 2004-01-13   True 2004-01-09  -126.000000   -0.022523    AMD    4 days   \n",
+       "1 2004-01-16   True 2004-01-09    50.020000    0.078507    AMD    7 days   \n",
+       "2 2004-01-20   True 2004-01-09  1540.099065    0.104696    AMD   11 days   \n",
+       "3 2004-01-21  False 2004-01-20   287.119806    0.085155    AMD    1 days   \n",
+       "4 2004-01-22  False 2004-01-20   103.349947    0.112198    AMD    2 days   \n",
+       "\n",
+       "    returns  \n",
+       "0 -0.001249  \n",
+       "1  0.000503  \n",
+       "2  0.015257  \n",
+       "3  0.002861  \n",
+       "4  0.001032  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rts.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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  0.523077\n",
-      "std    22 days 02:16:41.165898   1616.537844     0.031288  0.499642\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/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Summary stats</th>\n",
+       "      <th>All trades</th>\n",
+       "      <th>Long trades</th>\n",
+       "      <th>Short trades</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Total number of round_trips</th>\n",
+       "      <td>5819.00</td>\n",
+       "      <td>4664.00</td>\n",
+       "      <td>1155.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Percent profitable</th>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.49</td>\n",
+       "      <td>0.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Winning round_trips</th>\n",
+       "      <td>2888.00</td>\n",
+       "      <td>2292.00</td>\n",
+       "      <td>596.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Losing round_trips</th>\n",
+       "      <td>2914.00</td>\n",
+       "      <td>2361.00</td>\n",
+       "      <td>553.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Even round_trips</th>\n",
+       "      <td>17.00</td>\n",
+       "      <td>11.00</td>\n",
+       "      <td>6.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Summary stats                All trades  Long trades  Short trades\n",
+       "Total number of round_trips     5819.00      4664.00       1155.00\n",
+       "Percent profitable                 0.50         0.49          0.52\n",
+       "Winning round_trips             2888.00      2292.00        596.00\n",
+       "Losing round_trips              2914.00      2361.00        553.00\n",
+       "Even round_trips                  17.00        11.00          6.00"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>PnL stats</th>\n",
+       "      <th>All trades</th>\n",
+       "      <th>Long trades</th>\n",
+       "      <th>Short trades</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Total profit</th>\n",
+       "      <td>$67003.94</td>\n",
+       "      <td>$63472.61</td>\n",
+       "      <td>$3531.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gross profit</th>\n",
+       "      <td>$448674.42</td>\n",
+       "      <td>$428094.75</td>\n",
+       "      <td>$20579.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gross loss</th>\n",
+       "      <td>$-381670.48</td>\n",
+       "      <td>$-364622.13</td>\n",
+       "      <td>$-17048.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Profit factor</th>\n",
+       "      <td>$1.18</td>\n",
+       "      <td>$1.17</td>\n",
+       "      <td>$1.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Avg. trade net profit</th>\n",
+       "      <td>$11.51</td>\n",
+       "      <td>$13.61</td>\n",
+       "      <td>$3.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Avg. winning trade</th>\n",
+       "      <td>$155.36</td>\n",
+       "      <td>$186.78</td>\n",
+       "      <td>$34.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Avg. losing trade</th>\n",
+       "      <td>$-130.98</td>\n",
+       "      <td>$-154.44</td>\n",
+       "      <td>$-30.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ratio Avg. Win:Avg. Loss</th>\n",
+       "      <td>$1.19</td>\n",
+       "      <td>$1.21</td>\n",
+       "      <td>$1.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Largest winning trade</th>\n",
+       "      <td>$9500.14</td>\n",
+       "      <td>$9500.14</td>\n",
+       "      <td>$1623.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Largest losing trade</th>\n",
+       "      <td>$-22902.83</td>\n",
+       "      <td>$-22902.83</td>\n",
+       "      <td>$-661.29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "PnL stats                 All trades  Long trades  Short trades\n",
+       "Total profit               $67003.94    $63472.61      $3531.32\n",
+       "Gross profit              $448674.42   $428094.75     $20579.67\n",
+       "Gross loss               $-381670.48  $-364622.13    $-17048.35\n",
+       "Profit factor                  $1.18        $1.17         $1.21\n",
+       "Avg. trade net profit         $11.51       $13.61         $3.06\n",
+       "Avg. winning trade           $155.36      $186.78        $34.53\n",
+       "Avg. losing trade           $-130.98     $-154.44       $-30.83\n",
+       "Ratio Avg. Win:Avg. Loss       $1.19        $1.21         $1.12\n",
+       "Largest winning trade       $9500.14     $9500.14      $1623.24\n",
+       "Largest losing trade      $-22902.83   $-22902.83      $-661.29"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Duration stats</th>\n",
+       "      <th>All trades</th>\n",
+       "      <th>Long trades</th>\n",
+       "      <th>Short trades</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Avg duration</th>\n",
+       "      <td>13 days 03:27:07.702354</td>\n",
+       "      <td>15 days 19:02:40.548885</td>\n",
+       "      <td>2 days 10:39:35.064935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Median duration</th>\n",
+       "      <td>8 days 00:00:00</td>\n",
+       "      <td>12 days 00:00:00</td>\n",
+       "      <td>2 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Avg # round_trips per day</th>\n",
+       "      <td>70.11</td>\n",
+       "      <td>56.19</td>\n",
+       "      <td>96.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Avg # round_trips per month</th>\n",
+       "      <td>1472.28</td>\n",
+       "      <td>1180.05</td>\n",
+       "      <td>2021.25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f334aae1f28>"
+       "Duration stats                            All trades              Long trades  \\\n",
+       "Avg duration                 13 days 03:27:07.702354  15 days 19:02:40.548885   \n",
+       "Median duration                      8 days 00:00:00         12 days 00:00:00   \n",
+       "Avg # round_trips per day                      70.11                    56.19   \n",
+       "Avg # round_trips per month                  1472.28                  1180.05   \n",
+       "\n",
+       "Duration stats                         Short trades  \n",
+       "Avg duration                 2 days 10:39:35.064935  \n",
+       "Median duration                     2 days 00:00:00  \n",
+       "Avg # round_trips per day                     96.25  \n",
+       "Avg # round_trips per month                 2021.25  "
       ]
      },
      "metadata": {},
@@ -123,9 +407,207 @@
     },
     {
      "data": {
-      "image/png": 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XIEkHAAAAAMAlSNIBAAAAAHAJknQAAAAAAFyCJB0AAAAA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\n",
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Return stats</th>\n",
+       "      <th>All trades</th>\n",
+       "      <th>Long trades</th>\n",
+       "      <th>Short trades</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Avg returns all round_trips</th>\n",
+       "      <td>0.01%</td>\n",
+       "      <td>0.01%</td>\n",
+       "      <td>0.00%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Avg returns winning</th>\n",
+       "      <td>0.13%</td>\n",
+       "      <td>0.15%</td>\n",
+       "      <td>0.03%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Avg returns losing</th>\n",
+       "      <td>-0.11%</td>\n",
+       "      <td>-0.13%</td>\n",
+       "      <td>-0.03%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Median returns all round_trips</th>\n",
+       "      <td>-0.00%</td>\n",
+       "      <td>-0.00%</td>\n",
+       "      <td>0.00%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Median returns winning</th>\n",
+       "      <td>0.02%</td>\n",
+       "      <td>0.03%</td>\n",
+       "      <td>0.01%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Median returns losing</th>\n",
+       "      <td>-0.01%</td>\n",
+       "      <td>-0.02%</td>\n",
+       "      <td>-0.00%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Largest winning trade</th>\n",
+       "      <td>6.78%</td>\n",
+       "      <td>6.78%</td>\n",
+       "      <td>1.37%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Largest losing trade</th>\n",
+       "      <td>-17.23%</td>\n",
+       "      <td>-17.23%</td>\n",
+       "      <td>-0.72%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f334aafbf28>"
+       "Return stats                    All trades  Long trades  Short trades\n",
+       "Avg returns all round_trips          0.01%        0.01%         0.00%\n",
+       "Avg returns winning                  0.13%        0.15%         0.03%\n",
+       "Avg returns losing                  -0.11%       -0.13%        -0.03%\n",
+       "Median returns all round_trips      -0.00%       -0.00%         0.00%\n",
+       "Median returns winning               0.02%        0.03%         0.01%\n",
+       "Median returns losing               -0.01%       -0.02%        -0.00%\n",
+       "Largest winning trade                6.78%        6.78%         1.37%\n",
+       "Largest losing trade               -17.23%      -17.23%        -0.72%"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Symbol stats</th>\n",
+       "      <th>AMD</th>\n",
+       "      <th>CERN</th>\n",
+       "      <th>COST</th>\n",
+       "      <th>DELL</th>\n",
+       "      <th>GPS</th>\n",
+       "      <th>INTC</th>\n",
+       "      <th>MMM</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Avg returns all round_trips</th>\n",
+       "      <td>-0.00%</td>\n",
+       "      <td>0.02%</td>\n",
+       "      <td>0.02%</td>\n",
+       "      <td>-0.03%</td>\n",
+       "      <td>0.00%</td>\n",
+       "      <td>0.02%</td>\n",
+       "      <td>0.01%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Avg returns winning</th>\n",
+       "      <td>0.20%</td>\n",
+       "      <td>0.15%</td>\n",
+       "      <td>0.10%</td>\n",
+       "      <td>0.11%</td>\n",
+       "      <td>0.10%</td>\n",
+       "      <td>0.11%</td>\n",
+       "      <td>0.10%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Avg returns losing</th>\n",
+       "      <td>-0.19%</td>\n",
+       "      <td>-0.13%</td>\n",
+       "      <td>-0.07%</td>\n",
+       "      <td>-0.15%</td>\n",
+       "      <td>-0.09%</td>\n",
+       "      <td>-0.06%</td>\n",
+       "      <td>-0.09%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Median returns all round_trips</th>\n",
+       "      <td>-0.00%</td>\n",
+       "      <td>0.00%</td>\n",
+       "      <td>0.00%</td>\n",
+       "      <td>-0.00%</td>\n",
+       "      <td>-0.00%</td>\n",
+       "      <td>-0.00%</td>\n",
+       "      <td>0.00%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Median returns winning</th>\n",
+       "      <td>0.03%</td>\n",
+       "      <td>0.02%</td>\n",
+       "      <td>0.02%</td>\n",
+       "      <td>0.02%</td>\n",
+       "      <td>0.02%</td>\n",
+       "      <td>0.01%</td>\n",
+       "      <td>0.02%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Median returns losing</th>\n",
+       "      <td>-0.02%</td>\n",
+       "      <td>-0.01%</td>\n",
+       "      <td>-0.01%</td>\n",
+       "      <td>-0.02%</td>\n",
+       "      <td>-0.01%</td>\n",
+       "      <td>-0.01%</td>\n",
+       "      <td>-0.01%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Largest winning trade</th>\n",
+       "      <td>6.78%</td>\n",
+       "      <td>6.14%</td>\n",
+       "      <td>3.96%</td>\n",
+       "      <td>2.78%</td>\n",
+       "      <td>1.80%</td>\n",
+       "      <td>2.40%</td>\n",
+       "      <td>2.45%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Largest losing trade</th>\n",
+       "      <td>-17.23%</td>\n",
+       "      <td>-3.92%</td>\n",
+       "      <td>-2.32%</td>\n",
+       "      <td>-6.39%</td>\n",
+       "      <td>-6.86%</td>\n",
+       "      <td>-4.45%</td>\n",
+       "      <td>-1.79%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Symbol stats                       AMD   CERN   COST   DELL    GPS   INTC  \\\n",
+       "Avg returns all round_trips     -0.00%  0.02%  0.02% -0.03%  0.00%  0.02%   \n",
+       "Avg returns winning              0.20%  0.15%  0.10%  0.11%  0.10%  0.11%   \n",
+       "Avg returns losing              -0.19% -0.13% -0.07% -0.15% -0.09% -0.06%   \n",
+       "Median returns all round_trips  -0.00%  0.00%  0.00% -0.00% -0.00% -0.00%   \n",
+       "Median returns winning           0.03%  0.02%  0.02%  0.02%  0.02%  0.01%   \n",
+       "Median returns losing           -0.02% -0.01% -0.01% -0.02% -0.01% -0.01%   \n",
+       "Largest winning trade            6.78%  6.14%  3.96%  2.78%  1.80%  2.40%   \n",
+       "Largest losing trade           -17.23% -3.92% -2.32% -6.39% -6.86% -4.45%   \n",
+       "\n",
+       "Symbol stats                      MMM  \n",
+       "Avg returns all round_trips     0.01%  \n",
+       "Avg returns winning             0.10%  \n",
+       "Avg returns losing             -0.09%  \n",
+       "Median returns all round_trips  0.00%  \n",
+       "Median returns winning          0.02%  \n",
+       "Median returns losing          -0.01%  \n",
+       "Largest winning trade           2.45%  \n",
+       "Largest losing trade           -1.79%  "
       ]
      },
      "metadata": {},
@@ -133,7 +615,14 @@
     }
    ],
    "source": [
-    "pf.tears.create_round_trip_tear_sheet(positions, transactions, sector_mappings=sect_map)"
+    "pf.round_trips.print_round_trip_stats(rts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, when calling `pyfolio.create_full_tear_sheet()` you can set `round_trips=True` to create the last tables automatically."
    ]
   }
  ],
@@ -153,7 +642,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.5.0"
   }
  },
  "nbformat": 4,
diff --git a/pyfolio/round_trips.py b/pyfolio/round_trips.py
index 3eb6b521..85646aee 100644
--- a/pyfolio/round_trips.py
+++ b/pyfolio/round_trips.py
@@ -13,168 +13,259 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from __future__ import division
-from collections import defaultdict
+from math import copysign
+import warnings
+from collections import deque, OrderedDict
 
 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'
+from .utils import print_table, APPROX_BDAYS_PER_MONTH
+
+PNL_STATS = OrderedDict(
+    [('Total profit', lambda x: x.sum()),
+     ('Gross profit', lambda x: x[x > 0].sum()),
+     ('Gross loss', lambda x: x[x < 0].sum()),
+     ('Profit factor', lambda x: x[x > 0].sum() / x[x < 0].abs().sum()
+      if x[x < 0].abs().sum() != 0 else np.nan),
+     ('Avg. trade net profit', 'mean'),
+     ('Avg. winning trade', lambda x: x[x > 0].mean()),
+     ('Avg. losing trade', lambda x: x[x < 0].mean()),
+     ('Ratio Avg. Win:Avg. Loss', lambda x: x[x > 0].mean() /
+      x[x < 0].abs().mean() if x[x < 0].abs().mean() != 0 else np.nan),
+     ('Largest winning trade', 'max'),
+     ('Largest losing trade', 'min'),
+     ])
+
+SUMMARY_STATS = OrderedDict(
+    [('Total number of round_trips', 'count'),
+     ('Percent profitable', lambda x: len(x[x > 0]) / float(len(x))),
+     ('Winning round_trips', lambda x: len(x[x > 0])),
+     ('Losing round_trips', lambda x: len(x[x < 0])),
+     ('Even round_trips', lambda x: len(x[x == 0])),
+     ])
+
+RETURN_STATS = OrderedDict(
+    [('Avg returns all round_trips', lambda x: x.mean()),
+     ('Avg returns winning', lambda x: x[x > 0].mean()),
+     ('Avg returns losing', lambda x: x[x < 0].mean()),
+     ('Median returns all round_trips', lambda x: x.median()),
+     ('Median returns winning', lambda x: x[x > 0].median()),
+     ('Median returns losing', lambda x: x[x < 0].median()),
+     ('Largest winning trade', 'max'),
+     ('Largest losing trade', 'min'),
+     ])
+
+DURATION_STATS = OrderedDict(
+    [('Avg duration', lambda x: x.mean()),
+     ('Median duration', lambda x: x.median()),
+     ('Avg # round_trips per day', lambda x: float(len(x)) /
+      (x.max() - x.min()).days),
+     ('Avg # round_trips per month', lambda x: float(len(x)) /
+      (((x.max() - x.min()).days) / APPROX_BDAYS_PER_MONTH)),
+     ])
+
+
+def agg_all_long_short(round_trips, col, stats_dict):
+    stats_all = (round_trips
+                 .assign(ones=1)
+                 .groupby('ones')[col]
+                 .agg(stats_dict)
+                 .T
+                 .rename_axis({1.0: 'All trades'},
+                              axis='columns'))
+    stats_long_short = (round_trips
+                        .groupby('long')[col]
+                        .agg(stats_dict)
+                        .T
+                        .rename_axis({False: 'Short trades',
+                                      True: 'Long trades'},
+                                     axis='columns'))
+
+    return stats_all.join(stats_long_short)[['All trades',
+                                             'Long trades',
+                                             'Short trades']]
+
+
+def _groupby_consecutive(txn, max_delta=pd.Timedelta('8h')):
+    """Merge transactions of the same direction separated by less than
+    max_delta time duration.
 
     Parameters
     ----------
     transactions : pd.DataFrame
-        Prices and amounts of executed trades. One row per trade.
+        Prices and amounts of executed round_trips. 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']
+    max_delta : pandas.Timedelta (optional)
+        Merge transactions in the same direction separated by less
+        than max_delta time duration.
 
-    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
 
+    Returns
+    -------
+    transactions : pd.DataFrame
 
-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'
+    def vwap(transaction):
+        if transaction.amount.sum() == 0:
+            warnings.warn('Zero transacted shares, setting vwap to nan.')
+            return np.nan
+        return (transaction.amount * transaction.price).sum() / \
+            transaction.amount.sum()
+
+    out = []
+    for sym, t in txn.groupby('symbol'):
+        t = t.sort_index()
+        t.index.name = 'dt'
+        t = t.reset_index()
+
+        t['order_sign'] = t.amount > 0
+        t['block_dir'] = (t.order_sign.shift(
+            1) != t.order_sign).astype(int).cumsum()
+        t['block_time'] = ((t.dt - t.dt.shift(1)) >
+                           max_delta).astype(int).cumsum()
+        grouped_price = t.groupby(['block_dir',
+                                   'block_time'])[['price', 'amount']]\
+                         .apply(vwap)
+        grouped_price.name = 'price'
+        grouped_rest = t.groupby(['block_dir', 'block_time']).agg({
+            'amount': 'sum',
+            'symbol': 'first',
+            'dt': 'first'})
+
+        grouped = grouped_rest.join(grouped_price)
+
+        out.append(grouped)
+
+    out = pd.concat(out)
+    out = out.set_index('dt')
+    return out
+
+
+def extract_round_trips(transactions,
+                        portfolio_value=None):
+    """Group transactions into "round trips". First, transactions are
+    grouped by day and directionality. Then, long and short
+    transactions are matched to create round-trip round_trips for which
+    PnL, duration and returns are computed. Crossings where a position
+    changes from long to short and vice-versa are handled correctly.
+
+    Under the hood, we reconstruct the individual shares in a
+    portfolio over time and match round_trips in a FIFO-order.
 
+    For example, the following transactions would constitute one round trip:
     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'
+    2004-01-09 12:18:01    10       50      'AAPL'
+    2004-01-09 15:12:53    10       100      'AAPL'
+    2004-01-13 14:41:23    -10      100      'AAPL'
+    2004-01-13 15:23:34    -10      200       'AAPL'
+
+    First, the first two and last two round_trips will be merged into a two
+    single transactions (computing the price via vwap). Then, during
+    the portfolio reconstruction, the two resulting transactions will
+    be merged and result in 1 round-trip trade with a PnL of
+    (150 * 20) - (75 * 20) = 1500.
+
+    Note, that round trips do not have to close out positions
+    completely. For example, we could have removed the last
+    transaction in the example above and still generated a round-trip
+    over 10 shares with 10 shares left in the portfolio to be matched
+    with a later transaction.
 
     Parameters
     ----------
     transactions : pd.DataFrame
-        Prices and amounts of executed trades. One row per trade.
+        Prices and amounts of executed round_trips. One row per trade.
         - See full explanation in tears.create_full_tear_sheet
 
+    portfolio_value : pd.Series (optional)
+        Portfolio value (all net assets including cash) over time.
+        Note that portfolio_value needs to beginning of day, so either
+        use .shift() or positions.sum(axis='columns') / (1+returns).
+
     Returns
     -------
-    transactions_split : pd.DataFrame
-        Prices and amounts of executed trades. Trades that cause
-        total position amount to cross zero are divided.
+    round_trips : pd.DataFrame
+        DataFrame with one row per round trip.  The returns column
+        contains returns in respect to the portfolio value while
+        rt_returns are the returns in regards to the invested capital
+        into that partiulcar round-trip.
     """
 
-    trans_split = []
+    transactions = _groupby_consecutive(transactions)
+    roundtrips = []
 
     for sym, trans_sym in transactions.groupby('symbol'):
         trans_sym = trans_sym.sort_index()
+        price_stack = deque()
+        dt_stack = deque()
+        trans_sym['signed_price'] = trans_sym.price * \
+            np.sign(trans_sym.amount)
+        trans_sym['abs_amount'] = trans_sym.amount.abs().astype(int)
+        for dt, t in trans_sym.iterrows():
+            if t.price < 0:
+                warnings.warn('Negative price detected, ignoring for'
+                              'round-trip.')
+                continue
 
-        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
+            indiv_prices = [t.signed_price] * t.abs_amount
+            if (len(price_stack) == 0) or \
+               (copysign(1, price_stack[-1]) == copysign(1, t.amount)):
+                price_stack.extend(indiv_prices)
+                dt_stack.extend([dt] * len(indiv_prices))
+            else:
+                # Close round-trip
+                pnl = 0
+                invested = 0
+                cur_open_dts = []
+
+                for price in indiv_prices:
+                    if len(price_stack) != 0 and \
+                       (copysign(1, price_stack[-1]) != copysign(1, price)):
+                        # Retrieve first dt, stock-price pair from
+                        # stack
+                        prev_price = price_stack.popleft()
+                        prev_dt = dt_stack.popleft()
+
+                        pnl += -(price + prev_price)
+                        cur_open_dts.append(prev_dt)
+                        invested += abs(prev_price)
+
+                    else:
+                        # Push additional stock-prices onto stack
+                        price_stack.append(price)
+                        dt_stack.append(dt)
+
+                roundtrips.append({'pnl': pnl,
+                                   'open_dt': cur_open_dts[0],
+                                   'close_dt': dt,
+                                   'long': price < 0,
+                                   'rt_returns': pnl / invested,
+                                   'symbol': sym,
+                                   })
+
+    roundtrips = pd.DataFrame(roundtrips)
+
+    roundtrips['duration'] = roundtrips['close_dt'] - roundtrips['open_dt']
+
+    if portfolio_value is not None:
+        # Need to normalize so that we can join
+        pv = pd.DataFrame(portfolio_value,
+                          columns=['portfolio_value'])\
+            .assign(date=portfolio_value.index)
+        roundtrips['date'] = roundtrips.close_dt.apply(lambda x:
+                                                       x.replace(hour=0,
+                                                                 minute=0,
+                                                                 second=0))
+
+        tmp = roundtrips.assign(date=roundtrips.close_dt)\
+                        .join(pv, on='date', lsuffix='_')
+
+        roundtrips['returns'] = tmp.pnl / tmp.portfolio_value
+        roundtrips = roundtrips.drop('date', axis='columns')
+
+    return roundtrips
 
 
 def add_closing_transactions(positions, transactions):
@@ -188,7 +279,7 @@ def add_closing_transactions(positions, transactions):
     positions : pd.DataFrame
         The positions that the strategy takes over time.
     transactions : pd.DataFrame
-        Prices and amounts of executed trades. One row per trade.
+        Prices and amounts of executed round_trips. One row per trade.
         - See full explanation in tears.create_full_tear_sheet
 
     Returns
@@ -201,8 +292,8 @@ def add_closing_transactions(positions, transactions):
 
     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.
+    # Add closing round_trips one second after the close to be sure
+    # they don't conflict with other round_trips executed at that time.
     end_dt = open_pos.name + pd.Timedelta(seconds=1)
 
     for sym, ending_val in open_pos.iteritems():
@@ -248,3 +339,67 @@ def apply_sector_mappings_to_round_trips(round_trips, sector_mappings):
     sector_round_trips = sector_round_trips.dropna(axis=0)
 
     return sector_round_trips
+
+
+def gen_round_trip_stats(round_trips):
+    """Generate various round-trip statistics.
+
+    Parameters
+    ----------
+    round_trips : pd.DataFrame
+        DataFrame with one row per round trip trade.
+        - See full explanation in round_trips.extract_round_trips
+
+    Returns
+    -------
+    stats : dict
+       A dictionary where each value is a pandas DataFrame containing
+       various round-trip statistics.
+
+    See also
+    --------
+    round_trips.print_round_trip_stats
+    """
+
+    stats = {}
+    stats['pnl'] = agg_all_long_short(round_trips, 'pnl', PNL_STATS)
+    stats['summary'] = agg_all_long_short(round_trips, 'pnl',
+                                          SUMMARY_STATS)
+    stats['duration'] = agg_all_long_short(round_trips, 'duration',
+                                           DURATION_STATS)
+    stats['returns'] = agg_all_long_short(round_trips, 'returns',
+                                          RETURN_STATS)
+
+    stats['symbols'] = \
+        round_trips.groupby('symbol')['returns'].agg(RETURN_STATS).T
+
+    return stats
+
+
+def print_round_trip_stats(round_trips, hide_pos=False):
+    """Print various round-trip statistics. Tries to pretty-print tables
+    with HTML output if run inside IPython NB.
+
+    Parameters
+    ----------
+    round_trips : pd.DataFrame
+        DataFrame with one row per round trip trade.
+        - See full explanation in round_trips.extract_round_trips
+
+    See also
+    --------
+    round_trips.gen_round_trip_stats
+    """
+
+    stats = gen_round_trip_stats(round_trips)
+
+    print_table(stats['summary'], fmt='{:.2f}', name='Summary stats')
+    print_table(stats['pnl'], fmt='${:.2f}', name='PnL stats')
+    print_table(stats['duration'], fmt='{:.2f}',
+                name='Duration stats')
+    print_table(stats['returns'] * 100, fmt='{:.2f}%',
+                name='Return stats')
+
+    if not hide_pos:
+        print_table(stats['symbols'] * 100,
+                    fmt='{:.2f}%', name='Symbol stats')
diff --git a/pyfolio/tears.py b/pyfolio/tears.py
index e04ab1fb..fe142261 100644
--- a/pyfolio/tears.py
+++ b/pyfolio/tears.py
@@ -64,6 +64,7 @@ def create_full_tear_sheet(returns,
                            hide_positions=False,
                            cone_std=(1.0, 1.5, 2.0),
                            bootstrap=False,
+                           unadjusted_returns=None,
                            set_context=True):
     """
     Generate a number of tear sheets that are useful
@@ -153,13 +154,12 @@ def create_full_tear_sheet(returns,
     if returns.index[0] < benchmark_rets.index[0]:
         returns = returns[returns.index > benchmark_rets.index[0]]
 
-    if slippage is not None and transactions is not None:
+    if (unadjusted_returns is None) and (slippage is not None) and\
+       (transactions is not None):
         turnover = txn.get_turnover(positions, transactions,
                                     period=None, average=False)
         unadjusted_returns = returns.copy()
         returns = txn.adjust_returns_for_slippage(returns, turnover, slippage)
-    else:
-        unadjusted_returns = None
 
     create_returns_tear_sheet(
         returns,
@@ -186,6 +186,7 @@ def create_full_tear_sheet(returns,
                                   set_context=set_context)
             if round_trips:
                 create_round_trip_tear_sheet(
+                    returns=returns,
                     positions=positions,
                     transactions=transactions,
                     sector_mappings=sector_mappings)
@@ -517,7 +518,7 @@ def create_txn_tear_sheet(returns, positions, transactions,
 
 
 @plotting_context
-def create_round_trip_tear_sheet(positions, transactions,
+def create_round_trip_tear_sheet(returns, positions, transactions,
                                  sector_mappings=None,
                                  return_fig=False):
     """
@@ -529,6 +530,9 @@ def create_round_trip_tear_sheet(positions, transactions,
 
     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.
@@ -544,7 +548,11 @@ def create_round_trip_tear_sheet(positions, transactions,
 
     transactions_closed = round_trips.add_closing_transactions(positions,
                                                                transactions)
-    trades = round_trips.extract_round_trips(transactions_closed)
+    # 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(
@@ -552,24 +560,7 @@ def create_round_trip_tear_sheet(positions, transactions,
                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)))
+    round_trips.print_round_trip_stats(trades)
 
     plotting.show_profit_attribution(trades)
 
@@ -600,7 +591,7 @@ def create_round_trip_tear_sheet(positions, transactions,
     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 * 100, kde=False,
+    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 %')
@@ -760,16 +751,16 @@ def create_capacity_tear_sheet(returns, positions, transactions,
 
     llt = capacity.get_low_liquidity_transactions(transactions, market_data)
     print('Tickers with daily transactions consuming >{}% of daily bar \n'
-          'all backtest:'.format(trade_daily_vol_limit*100))
+          'all backtest:'.format(trade_daily_vol_limit * 100))
     utils.print_table(
-        llt[llt['max_pct_bar_consumed'] > trade_daily_vol_limit*100])
+        llt[llt['max_pct_bar_consumed'] > trade_daily_vol_limit * 100])
 
     llt = capacity.get_low_liquidity_transactions(
         transactions, market_data, last_n_days=last_n_days)
 
     print("last {} trading days:".format(last_n_days))
     utils.print_table(
-        llt[llt['max_pct_bar_consumed'] > trade_daily_vol_limit*100])
+        llt[llt['max_pct_bar_consumed'] > trade_daily_vol_limit * 100])
 
     bt_starting_capital = positions.iloc[0].sum() / (1 + returns.iloc[0])
     fig, ax_capacity_sweep = plt.subplots(figsize=(14, 6))
diff --git a/pyfolio/tests/test_round_trips.py b/pyfolio/tests/test_round_trips.py
index 058f3458..cfac4eb5 100644
--- a/pyfolio/tests/test_round_trips.py
+++ b/pyfolio/tests/test_round_trips.py
@@ -3,6 +3,7 @@
 from unittest import TestCase
 
 from pandas import (
+    Series,
     DataFrame,
     DatetimeIndex,
     date_range,
@@ -15,60 +16,134 @@
 import gzip
 
 from pyfolio.round_trips import (extract_round_trips,
-                                 add_closing_transactions)
+                                 add_closing_transactions,
+                                 _groupby_consecutive,
+                                 )
 
 
 class RoundTripTestCase(TestCase):
     dates = date_range(start='2015-01-01', freq='D', periods=20)
+    dates_intraday = date_range(start='2015-01-01',
+                                freq='2BH', periods=8)
 
     @parameterized.expand([
-        (DataFrame(data=[[2, 10, 'A'],
-                         [-2, 15, 'A']],
+        (DataFrame(data=[[2, 10., 'A'],
+                         [2, 20., 'A'],
+                         [-2, 20., 'A'],
+                         [-2, 10., 'A'],
+                         ],
+                   columns=['amount', 'price', 'symbol'],
+                   index=dates_intraday[:4]),
+         DataFrame(data=[[4, 15., 'A'],
+                         [-4, 15., 'A'],
+                         ],
+                   columns=['amount', 'price', 'symbol'],
+                   index=dates_intraday[[0, 2]])
+         .rename_axis('dt', axis='index')
+         ),
+        (DataFrame(data=[[2, 10., 'A'],
+                         [2, 20., 'A'],
+                         [2, 20., 'A'],
+                         [2, 10., 'A'],
+                         ],
+                   columns=['amount', 'price', 'symbol'],
+                   index=dates_intraday[[0, 1, 4, 5]]),
+         DataFrame(data=[[4, 15., 'A'],
+                         [4, 15., 'A'],
+                         ],
+                   columns=['amount', 'price', 'symbol'],
+                   index=dates_intraday[[0, 4]])
+         .rename_axis('dt', axis='index')
+         ),
+    ])
+    def test_groupby_consecutive(self, transactions, expected):
+        grouped_txn = _groupby_consecutive(transactions)
+        assert_frame_equal(grouped_txn.sort(axis=1), expected.sort(axis=1))
+
+    @parameterized.expand([
+        # Simple round-trip
+        (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,
+                          Timedelta(days=1), 10., .5,
                           True, 'A']],
                    columns=['open_dt', 'close_dt',
-                            'duration', 'pnl', 'returns',
+                            'duration', 'pnl', 'rt_returns',
                             'long', 'symbol'],
                    index=[0])
          ),
-        (DataFrame(data=[[2, 10, 'A'],
-                         [2, 15, 'A'],
-                         [-9, 10, 'A']],
+        # Round-trip with left-over txn that shouldn't be counted
+        (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,
+                          Timedelta(days=2), -10., -.2,
                           True, 'A']],
                    columns=['open_dt', 'close_dt',
-                            'duration', 'pnl', 'returns',
+                            'duration', 'pnl', 'rt_returns',
                             'long', 'symbol'],
                    index=[0])
          ),
-        (DataFrame(data=[[2, 10, 'A'],
-                         [-4, 15, 'A'],
-                         [3, 20, 'A']],
+        # Round-trip with sell that crosses 0 and should be split
+        (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,
+                          Timedelta(days=1), 10., .5,
                           True, 'A'],
-                         [dates[1] + Timedelta(seconds=1), dates[2],
-                          Timedelta(days=1) - Timedelta(seconds=1),
+                         [dates[1], dates[2],
+                          Timedelta(days=1),
                           -10, (-1. / 3),
                           False, 'A']],
                    columns=['open_dt', 'close_dt',
-                            'duration', 'pnl', 'returns',
+                            'duration', 'pnl', 'rt_returns',
                             'long', 'symbol'],
                    index=[0, 1])
-         )
+         ),
+        # Round-trip that does not cross 0
+        (DataFrame(data=[[4, 10., 'A'],
+                         [-2, 15., 'A'],
+                         [2, 20., 'A']],
+                   columns=['amount', 'price', 'symbol'],
+                   index=dates[:3]),
+         DataFrame(data=[[dates[0], dates[1],
+                          Timedelta(days=1), 10., .5,
+                          True, 'A']],
+                   columns=['open_dt', 'close_dt',
+                            'duration', 'pnl', 'rt_returns',
+                            'long', 'symbol'],
+                   index=[0])
+         ),
+        # Round-trip that does not cross 0 and has portfolio value
+        (DataFrame(data=[[4, 10., 'A'],
+                         [-2, 15., 'A'],
+                         [2, 20., 'A']],
+                   columns=['amount', 'price', 'symbol'],
+                   index=dates[:3]),
+         DataFrame(data=[[dates[0], dates[1],
+                          Timedelta(days=1), 10., .5,
+                          True, 'A', 0.1]],
+                   columns=['open_dt', 'close_dt',
+                            'duration', 'pnl', 'rt_returns',
+                            'long', 'symbol', 'returns'],
+                   index=[0]),
+         Series([100., 100., 100.], index=dates[:3]),
+         ),
+
     ])
-    def test_extract_round_trips(self, transactions, expected):
-        round_trips = extract_round_trips(transactions)
+    def test_extract_round_trips(self, transactions, expected,
+                                 portfolio_value=None):
+        round_trips = extract_round_trips(transactions,
+                                          portfolio_value=portfolio_value)
 
-        assert_frame_equal(round_trips, expected)
+        assert_frame_equal(round_trips.sort(axis=1),
+                           expected.sort(axis=1))
 
     def test_add_closing_trades(self):
         dates = date_range(start='2015-01-01', periods=20)
@@ -84,7 +159,7 @@ def test_add_closing_trades(self):
                               index=[dates[:3]])
 
         expected_ix = dates[:3].append(DatetimeIndex([dates[2] +
-                                       Timedelta(seconds=1)]))
+                                                      Timedelta(seconds=1)]))
         expected = DataFrame(data=[[2, 10, 'A'],
                                    [-5, 10, 'A'],
                                    [-1, 10., 'B'],