diff --git a/docs/data-analysis.md b/docs/data-analysis.md
index 826c34747..3b07d77a6 100644
--- a/docs/data-analysis.md
+++ b/docs/data-analysis.md
@@ -2,11 +2,33 @@
 
 You can analyze the results of backtests and trading history easily using Jupyter notebooks. A sample notebook is located at `user_data/notebooks/analysis_example.ipynb`. For usage instructions, see [jupyter.org](https://jupyter.org/documentation).
 
+## Example snippets
+
+### Load backtest results into a pandas dataframe
+
+```python
+# Load backtest results
+df = load_backtest_data("user_data/backtest_data/backtest-result.json")
+
+# Show value-counts per pair
+df.groupby("pair")["sell_reason"].value_counts()
+```
+
+### Load live trading results into a pandas dataframe
+
+``` python
+# Fetch trades from database
+df = load_trades_from_db("sqlite:///tradesv3.sqlite")
+
+# Display results
+df.groupby("pair")["sell_reason"].value_counts()
+```
+
 ## Strategy debugging example
 
 Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
 
-### Import requirements and define variables used in the script
+### Import requirements and define variables used in analyses
 
 ```python
 # Imports
@@ -47,12 +69,6 @@ print("Loaded " + str(len(bt_data)) + f" rows of data for {pair} from {data_loca
 ### Load and run strategy  
 
 * Rerun each time the strategy file is changed
-* Display the trade details. Note that using `data.head()` would also work, however most indicators have some "startup" data at the top of the dataframe.
-
-Some possible problems:
-
-* Columns with NaN values at the end of the dataframe
-* Columns used in `crossed*()` functions with completely different units
 
 ```python
 # Load strategy using values set above
@@ -60,33 +76,31 @@ strategy = StrategyResolver({'strategy': strategy_name,
                             'user_data_dir': user_data_dir,
                             'strategy_path': strategy_location}).strategy
 
-# Run strategy (just like in backtesting)
+# Generate buy/sell signals using strategy
 df = strategy.analyze_ticker(bt_data, {'pair': pair})
+```
 
+### Display the trade details
+
+* Note that using `data.head()` would also work, however most indicators have some "startup" data at the top of the dataframe.
+
+#### Some possible problems
+
+* Columns with NaN values at the end of the dataframe
+* Columns used in `crossed*()` functions with completely different units
+
+#### Comparison with full backtest
+
+having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.
+
+Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns > 29).
+The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available.
+
+```python
 # Report results
 print(f"Generated {df['buy'].sum()} buy signals")
 data = df.set_index('date', drop=True)
 data.tail()
 ```
 
-### Load backtest results into a pandas dataframe
-
-```python
-# Load backtest results
-df = load_backtest_data("user_data/backtest_data/backtest-result.json")
-
-# Show value-counts per pair
-df.groupby("pair")["sell_reason"].value_counts()
-```
-
-### Load live trading results into a pandas dataframe
-
-``` python
-# Fetch trades from database
-df = load_trades_from_db("sqlite:///tradesv3.sqlite")
-
-# Display results
-df.groupby("pair")["sell_reason"].value_counts()
-```
-
 Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
diff --git a/setup.py b/setup.py
index b46101c0f..6ac7a117f 100644
--- a/setup.py
+++ b/setup.py
@@ -25,7 +25,15 @@ develop = [
     'pytest-random-order',
 ]
 
-all_extra = api + plot + develop
+jupyter = [
+    'jupyter',
+    'nbstripout',
+    'ipykernel',
+    'isort',
+    'yapf',
+    ]
+
+all_extra = api + plot + develop + jupyter
 
 setup(name='freqtrade',
       version=__version__,
@@ -68,7 +76,7 @@ setup(name='freqtrade',
           'dev': all_extra,
           'plot': plot,
           'all': all_extra,
-          'jupyter': [],
+          'jupyter': jupyter,
           
       },
       include_package_data=True,
diff --git a/user_data/notebooks/analysis_example.ipynb b/user_data/notebooks/analysis_example.ipynb
index d14575e97..b3a61bc51 100644
--- a/user_data/notebooks/analysis_example.ipynb
+++ b/user_data/notebooks/analysis_example.ipynb
@@ -4,31 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Strategy debugging example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Change directory\n",
-    "# Define all paths relative to the project root shown in the cell output\n",
-    "import os\n",
-    "from pathlib import Path\n",
-    "try:\n",
-    "\tos.chdir(Path(os.getcwd(), '../..'))\n",
-    "\tprint(os.getcwd())\n",
-    "except:\n",
-    "\tpass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Import requirements and define variables used in the script"
+    "# Analyzing bot data\n",
+    "\n",
+    "You can analyze the results of backtests and trading history easily using Jupyter notebooks. A sample notebook is located at `user_data/notebooks/analysis_example.ipynb`. For usage instructions, see [jupyter.org](https://jupyter.org/documentation)."
    ]
   },
   {
@@ -39,11 +17,97 @@
    "source": [
     "# Imports\n",
     "from pathlib import Path\n",
+    "import os\n",
     "from freqtrade.data.history import load_pair_history\n",
     "from freqtrade.resolvers import StrategyResolver\n",
     "from freqtrade.data.btanalysis import load_backtest_data\n",
-    "from freqtrade.data.btanalysis import load_trades_from_db\n",
+    "from freqtrade.data.btanalysis import load_trades_from_db"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change directory\n",
+    "# Define all paths relative to the project root shown in the cell output\n",
+    "try:\n",
+    "\tos.chdir(Path(Path.cwd(), '../..'))\n",
+    "\tprint(Path.cwd())\n",
+    "except:\n",
+    "\tpass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example snippets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load backtest results into a pandas dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load backtest results\n",
+    "df = load_backtest_data(\"user_data/backtest_data/backtest-result.json\")\n",
     "\n",
+    "# Show value-counts per pair\n",
+    "df.groupby(\"pair\")[\"sell_reason\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load live trading results into a pandas dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fetch trades from database\n",
+    "df = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
+    "\n",
+    "# Display results\n",
+    "df.groupby(\"pair\")[\"sell_reason\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Strategy debugging example\n",
+    "\n",
+    "Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import requirements and define variables used in analyses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Define some constants\n",
     "ticker_interval = \"1m\"\n",
     "# Name of the strategy class\n",
@@ -51,9 +115,9 @@
     "# Path to user data\n",
     "user_data_dir = 'user_data'\n",
     "# Location of the strategy\n",
-    "strategy_location = Path(user_data_dir, 'strategies')\n",
+    "strategy_location = os.path.join(user_data_dir, 'strategies')\n",
     "# Location of the data\n",
-    "data_location = Path(user_data_dir, 'data', 'binance')\n",
+    "data_location = os.path.join(user_data_dir, 'data', 'binance')\n",
     "# Pair to analyze \n",
     "# Only use one pair here\n",
     "pair = \"BTC_USDT\""
@@ -85,15 +149,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Load and run strategy  \n",
-    "\n",
-    "* Rerun each time the strategy file is changed\n",
-    "* Display the trade details. Note that using `data.head()` would also work, however most indicators have some \"startup\" data at the top of the dataframe.\n",
-    "\n",
-    "Some possible problems:\n",
-    "\n",
-    "* Columns with NaN values at the end of the dataframe\n",
-    "* Columns used in `crossed*()` functions with completely different units"
+    "### Load and run strategy\n",
+    "* Rerun each time the strategy file is changed"
    ]
   },
   {
@@ -107,53 +164,49 @@
     "                            'user_data_dir': user_data_dir,\n",
     "                            'strategy_path': strategy_location}).strategy\n",
     "\n",
-    "# Run strategy (just like in backtesting)\n",
-    "df = strategy.analyze_ticker(bt_data, {'pair': pair})\n",
+    "# Generate buy/sell signals using strategy\n",
+    "df = strategy.analyze_ticker(bt_data, {'pair': pair})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display the trade details\n",
+    "* Note that using `data.head()` would also work, however most indicators have some \"startup\" data at the top of the dataframe.\n",
     "\n",
+    "#### Some possible problems\n",
+    "\n",
+    "* Columns with NaN values at the end of the dataframe\n",
+    "* Columns used in `crossed*()` functions with completely different units\n",
+    "\n",
+    "#### Comparison with full backtest\n",
+    "\n",
+    "having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.\n",
+    "\n",
+    "Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple \"buy\" signals for each pair in sequence (until rsi returns > 29).\n",
+    "The bot will only buy on the first of these signals (and also only if a trade-slot (\"max_open_trades\") is still available), or on one of the middle signals, as soon as a \"slot\" becomes available.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Report results\n",
     "print(f\"Generated {df['buy'].sum()} buy signals\")\n",
     "data = df.set_index('date', drop=True)\n",
     "data.tail()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load backtest results into a pandas dataframe"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load backtest results\n",
-    "df = load_backtest_data(\"user_data/backtest_data/backtest-result.json\")\n",
-    "\n",
-    "# Show value-counts per pair\n",
-    "df.groupby(\"pair\")[\"sell_reason\"].value_counts()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load live trading results into a pandas dataframe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Fetch trades from database\n",
-    "df = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
-    "\n",
-    "# Display results\n",
-    "df.groupby(\"pair\")[\"sell_reason\"].value_counts()"
+    "Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data."
    ]
   }
  ],