diff --git a/docs/notebooks/tutorial.ipynb b/docs/notebooks/tutorial.ipynb
index 0b99896e..5718eb12 100644
--- a/docs/notebooks/tutorial.ipynb
+++ b/docs/notebooks/tutorial.ipynb
@@ -76,8 +76,6 @@
    "source": [
     "As you can see, we first have to import some functions we would like to use. All functions commonly used in your algorithm can be found in `zipline.api`. Here we are using `order()` which takes two arguments -- a security object, and a number specifying how many stocks you would like to order (if negative, `order()` will sell/short stocks). In this case we want to order 10 shares of Apple at each iteration. For more documentation on `order()`, see the [Quantopian docs](https://www.quantopian.com/help#api-order).\n",
     "\n",
-    "You don't have to use the `symbol()` function and could just pass in `AAPL` directly but it is good practice as this way your code will be Quantopian compatible.\n",
-    "\n",
     "Finally, the `record()` function allows you to save the value of a variable at each iteration. You provide it with a name for the variable together with the variable itself: `varname=var`. After the algorithm finished running you will have access to each variable value you tracked with `record()` under the name you provided (we will see this further below). You also see how we can access the current price data of the AAPL stock in the `data` event frame (for more information see [here](https://www.quantopian.com/help#api-event-properties)."
    ]
   },
@@ -105,7 +103,7 @@
      "output_type": "stream",
      "text": [
       "usage: run_algo.py [-h] [-c FILE] [--algofile ALGOFILE] [--data-frequency {minute,daily}] [--start START] [--end END]\r\n",
-      "                   [--capital_base CAPITAL_BASE] [--source {yahoo}] [--source_time_column SOURCE_TIME_COLUMN] [--symbols SYMBOLS]\r\n",
+      "                   [--capital_base CAPITAL_BASE] [--source {yahoo}] [--source_time_column SOURCE_TIME_COLUMN]\r\n",
       "                   [--output OUTPUT] [--metadata_path METADATA_PATH] [--metadata_index METADATA_INDEX] [--print-algo] [--no-print-algo]\r\n",
       "\r\n",
       "Zipline version 0.8.0rc1.\r\n",
@@ -121,7 +119,6 @@
       "  --capital_base CAPITAL_BASE\r\n",
       "  --source {yahoo}, -d {yahoo}\r\n",
       "  --source_time_column SOURCE_TIME_COLUMN, -t SOURCE_TIME_COLUMN\r\n",
-      "  --symbols SYMBOLS\r\n",
       "  --output OUTPUT, -o OUTPUT\r\n",
       "  --metadata_path METADATA_PATH, -m METADATA_PATH\r\n",
       "  --metadata_index METADATA_INDEX, -x METADATA_INDEX\r\n",
@@ -140,7 +137,7 @@
    "source": [
     "Note that you have to omit the preceding '!' when you call `run_algo.py`, this is only required by the IPython Notebook in which this tutorial was written.\n",
     "\n",
-    "As you can see there are a couple of flags that specify where to find your algorithm (`-f`) as well as parameters specifying which stock data to load from Yahoo! finance (`--symbols`) and the time-range (`--start` and `--end`). Finally, you'll want to save the performance metrics of your algorithm so that you can analyze how it performed. This is done via the `--output` flag and will cause it to write the performance `DataFrame` in the pickle Python file format. Note that you can also define a configuration file with these parameters that you can then conveniently pass to the `-c` option so that you don't have to supply the command line args all the time (see the .conf files in the examples directory).\n",
+    "As you can see there are a couple of flags that specify where to find your algorithm (`-f`) as well as parameters specifying which stock data to load from Yahoo! finance and the time-range (`--start` and `--end`). Finally, you'll want to save the performance metrics of your algorithm so that you can analyze how it performed. This is done via the `--output` flag and will cause it to write the performance `DataFrame` in the pickle Python file format. Note that you can also define a configuration file with these parameters that you can then conveniently pass to the `-c` option so that you don't have to supply the command line args all the time (see the .conf files in the examples directory).\n",
     "\n",
     "Thus, to execute our algorithm from above and save the results to `buyapple_out.pickle` we would call `run_algo.py` as follows:"
    ]
@@ -164,7 +161,7 @@
     }
    ],
    "source": [
-    "!run_algo.py -f ../../zipline/examples/buyapple.py --start 2000-1-1 --end 2014-1-1 --symbols AAPL -o buyapple_out.pickle"
+    "!run_algo.py -f ../../zipline/examples/buyapple.py --start 2000-1-1 --end 2014-1-1 -o buyapple_out.pickle"
    ]
   },
   {
@@ -410,7 +407,7 @@
    "source": [
     "import pandas as pd\n",
     "perf = pd.read_pickle('buyapple_out.pickle') # read in perf DataFrame\n",
-    "perf.head()"
+    "print perf.head()"
    ]
   },
   {
@@ -513,7 +510,7 @@
     }
    ],
    "source": [
-    "%%zipline --start 2000-1-1 --end 2014-1-1 --symbols AAPL -o perf_ipython\n",
+    "%%zipline --start 2000-1-1 --end 2014-1-1 -o perf_ipython\n",
     "\n",
     "from zipline.api import symbol, order, record\n",
     "\n",
@@ -867,7 +864,7 @@
     }
    ],
    "source": [
-    "%%zipline --start 2000-1-1 --end 2014-1-1 --symbols AAPL -o perf_dma\n",
+    "%%zipline --start 2000-1-1 --end 2014-1-1 -o perf_dma\n",
     "\n",
     "\n",
     "from zipline.api import order_target, record, symbol, history\n",