diff --git a/.gitignore b/.gitignore
index 9ed046c40..3a9df9852 100644
--- a/.gitignore
+++ b/.gitignore
@@ -81,7 +81,6 @@ target/
 
 # Jupyter Notebook
 .ipynb_checkpoints
-*.ipynb
 
 # pyenv
 .python-version
@@ -93,3 +92,6 @@ target/
 
 .pytest_cache/
 .mypy_cache/
+
+#exceptions
+!user_data/noteboks/*example.ipynb
diff --git a/docs/data-analysis.md b/docs/data-analysis.md
index 2b6d6ed58..ecd94445b 100644
--- a/docs/data-analysis.md
+++ b/docs/data-analysis.md
@@ -1,164 +1,114 @@
 # Analyzing bot data
 
-After performing backtests, or after running the bot for some time, it will be interesting to analyze the results your bot generated.
+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).
 
-A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.
+*Pro tip - Don't forget to start a jupyter notbook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
 
-The following helpers will help you loading the data into Pandas DataFrames, and may also give you some starting points in analyzing the results.
+## Example snippets
 
-## Strategy development problem analysis
-
-Debugging a strategy (are there no buy signals, ...) can be very time-consuming.
-FreqTrade tries to help you by exposing a few helper-functions, which can be very handy.
-
-It's recommended using Juptyer Notebooks for analysis, since it offers a dynamic way to rerun certain parts of the code.
-
-The following is a full code-snippet, which will be explained by both comments, and step by step below.
+### Load backtest results into a pandas dataframe
 
 ```python
-# Some necessary imports
-from pathlib import Path
-
-from freqtrade.data.history import load_pair_history
-from freqtrade.resolvers import StrategyResolver
-# Define some constants
-ticker_interval = "5m"
-
-# Name of the strategy class
-strategyname = 'Awesomestrategy'
-# Location of the strategy
-strategy_location = '../xmatt/strategies'
-# Location of the data
-data_location = '../freqtrade/user_data/data/binance/'
-# Only use one pair here
-pair = "XRP_ETH"
-
-### End constants
-
-# Load data
-bt_data = load_pair_history(datadir=Path(data_location),
-                            ticker_interval = ticker_interval,
-                            pair=pair)
-print(len(bt_data))
-
-### Start strategy reload
-# Load strategy - best done in a new cell
-# Rerun each time the strategy-file is changed.
-strategy = StrategyResolver({'strategy': strategyname,
-                            'user_data_dir': Path.cwd(),
-                            'strategy_path': location}).strategy
-
-# Run strategy (just like in backtesting)
-df = strategy.analyze_ticker(bt_data, {'pair': pair})
-print(f"Generated {df['buy'].sum()} buy signals")
-
-# Reindex data to be "nicer" and show data
-data = df.set_index('date', drop=True)
-data.tail()
-
-```
-
-### Explanation
-
-#### Imports and constant definition
-
-``` python
-# Some necessary imports
-from pathlib import Path
-
-from freqtrade.data.history import load_pair_history
-from freqtrade.resolvers import StrategyResolver
-# Define some constants
-ticker_interval = "5m"
-
-# Name of the strategy class
-strategyname = 'Awesomestrategy'
-# Location of the strategy
-strategy_location = 'user_data/strategies'
-# Location of the data
-data_location = 'user_data/data/binance'
-# Only use one pair here
-pair = "XRP_ETH"
-```
-
-This first section imports necessary modules, and defines some constants you'll probably need to adjust for your case.
-
-#### Load candles
-
-``` python
-# Load data
-bt_data = load_pair_history(datadir=Path(data_location),
-                            ticker_interval = ticker_interval,
-                            pair=pair)
-print(len(bt_data))
-```
-
-This second section loads the historic data and prints the amount of candles in the DataFrame.
-You can also inspect this dataframe by using `bt_data.head()` or `bt_data.tail()`.
-
-#### Run strategy and analyze results
-
-Now, it's time to load and run your strategy.
-For this, I recommend using a new cell in your notebook, since you'll want to repeat this until you're satisfied with your strategy.
-
-``` python
-# Load strategy - best done in a new cell
-# Needs to be ran each time the strategy-file is changed.
-strategy = StrategyResolver({'strategy': strategyname,
-                            'user_data_dir': Path.cwd(),
-                            'strategy_path': location}).strategy
-
-# Run strategy (just like in backtesting)
-df = strategy.analyze_ticker(bt_data, {'pair': pair})
-print(f"Generated {df['buy'].sum()} buy signals")
-
-# Reindex data to be "nicer" and show data
-data = df.set_index('date', drop=True)
-data.tail()
-```
-
-The code snippet loads and analyzes the strategy, calculates and prints the number of buy signals.
-
-The last 2 lines serve to analyze the dataframe in detail.
-This can be important if your strategy did not generate any buy signals.
-Note that using `data.head()` would also work, however this is misleading since most indicators have some "startup" time at the start of a backtested dataframe.
-
-There can be many things wrong, some signs to look for are:
-
-* Columns with NaN values at the end of the dataframe
-* Columns used in `crossed*()` functions with completely different units
-
-## Backtesting
-
-To analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
-You can then load the trades to perform further analysis.
-
-Freqtrade provides the `load_backtest_data()` helper function to easily load the backtest results, which takes the path to the the backtest-results file as parameter.
-
-``` python
 from freqtrade.data.btanalysis import load_backtest_data
-df = load_backtest_data("user_data/backtest-result.json")
+# 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()
-
 ```
 
 This will allow you to drill deeper into your backtest results, and perform analysis which otherwise would make the regular backtest-output very difficult to digest due to information overload.
 
-If you have some ideas for interesting / helpful backtest data analysis ideas, please submit a Pull Request so the community can benefit from it.
-
-## Live data
-
-To analyze the trades your bot generated, you can load them to a DataFrame as follows:
+### Load live trading results into a pandas dataframe
 
 ``` python
 from freqtrade.data.btanalysis import load_trades_from_db
 
+# 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 analyses
+
+```python
+# Imports
+from pathlib import Path
+import os
+from freqtrade.data.history import load_pair_history
+from freqtrade.resolvers import StrategyResolver
+
+# You can override strategy settings as demonstrated below.
+# Customize these according to your needs.
+
+# Define some constants
+ticker_interval = "5m"
+# Name of the strategy class
+strategy_name = 'AwesomeStrategy'
+# Path to user data
+user_data_dir = 'user_data'
+# Location of the strategy
+strategy_location = Path(user_data_dir, 'strategies')
+# Location of the data
+data_location = Path(user_data_dir, 'data', 'binance')
+# Pair to analyze 
+# Only use one pair here
+pair = "BTC_USDT"
+```
+
+### Load exchange data
+
+```python
+# Load data using values set above
+bt_data = load_pair_history(datadir=Path(data_location),
+                            ticker_interval=ticker_interval,
+                            pair=pair)
+
+# Confirm success
+print(f"Loaded {len(bt_data)} rows of data for {pair} from {data_location}")
+```
+
+### Load and run strategy  
+
+* Rerun each time the strategy file is changed
+
+```python
+# Load strategy using values set above
+strategy = StrategyResolver({'strategy': strategy_name,
+                            'user_data_dir': user_data_dir,
+                            'strategy_path': strategy_location}).strategy
+
+# 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()
 ```
 
 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/environment.yml b/environment.yml
index 8f43b6991..cd3350fd5 100644
--- a/environment.yml
+++ b/environment.yml
@@ -37,6 +37,7 @@ dependencies:
   - coveralls
   - mypy
   # Useful for jupyter
+  - jupyter
   - ipykernel
   - isort
   - yapf
diff --git a/setup.py b/setup.py
index 202e3fd0d..41e1b8f45 100644
--- a/setup.py
+++ b/setup.py
@@ -25,7 +25,13 @@ develop = [
     'pytest-random-order',
 ]
 
-all_extra = api + plot + develop
+jupyter = [
+    'jupyter',
+    'nbstripout',
+    'ipykernel',
+    ]
+
+all_extra = api + plot + develop + jupyter
 
 setup(name='freqtrade',
       version=__version__,
@@ -68,6 +74,8 @@ setup(name='freqtrade',
           'dev': all_extra,
           'plot': plot,
           'all': all_extra,
+          'jupyter': jupyter,
+          
       },
       include_package_data=True,
       zip_safe=False,
diff --git a/user_data/notebooks/.gitkeep b/user_data/notebooks/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/user_data/notebooks/analysis_example.ipynb b/user_data/notebooks/analysis_example.ipynb
new file mode 100644
index 000000000..f5e2c12d7
--- /dev/null
+++ b/user_data/notebooks/analysis_example.ipynb
@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyzing bot data\n",
+    "\n",
+    "You can analyze the results of backtests and trading history easily using Jupyter notebooks.  \n",
+    "**Copy this file so your changes don't get clobbered with the next freqtrade update!**  \n",
+    "For usage instructions, see [jupyter.org](https://jupyter.org/documentation).  \n",
+    "*Pro tip - Don't forget to start a jupyter notbook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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"
+   ]
+  },
+  {
+   "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",
+    "    os.chdir(Path(Path.cwd(), '../..'))\n",
+    "    print(Path.cwd())\n",
+    "except:\n",
+    "    pass"
+   ]
+  },
+  {
+   "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 = \"5m\"\n",
+    "# Name of the strategy class\n",
+    "strategy_name = 'AwesomeStrategy'\n",
+    "# Path to user data\n",
+    "user_data_dir = 'user_data'\n",
+    "# Location of the strategy\n",
+    "strategy_location = Path(user_data_dir, 'strategies')\n",
+    "# Location of the data\n",
+    "data_location = Path(user_data_dir, 'data', 'binance')\n",
+    "# Pair to analyze \n",
+    "# Only use one pair here\n",
+    "pair = \"BTC_USDT\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load exchange data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data using values set above\n",
+    "bt_data = load_pair_history(datadir=Path(data_location),\n",
+    "                            ticker_interval=ticker_interval,\n",
+    "                            pair=pair)\n",
+    "\n",
+    "# Confirm success\n",
+    "print(\"Loaded \" + str(len(bt_data)) + f\" rows of data for {pair} from {data_location}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load and run strategy\n",
+    "* Rerun each time the strategy file is changed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load strategy using values set above\n",
+    "strategy = StrategyResolver({'strategy': strategy_name,\n",
+    "                            'user_data_dir': user_data_dir,\n",
+    "                            'strategy_path': strategy_location}).strategy\n",
+    "\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": [
+    "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."
+   ]
+  }
+ ],
+ "metadata": {
+  "file_extension": ".py",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "mimetype": "text/x-python",
+  "name": "python",
+  "npconvert_exporter": "python",
+  "pygments_lexer": "ipython3",
+  "version": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}