diff --git a/docs/backtesting.md b/docs/backtesting.md
index 5a25bc255..8d8ea8030 100644
--- a/docs/backtesting.md
+++ b/docs/backtesting.md
@@ -221,24 +221,8 @@ strategies, your configuration, and the crypto-currency you have set up.
 ### Further backtest-result analysis
 
 To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
-You can then load the trades to perform further analysis.
+You can then load the trades to perform further analysis as shown in our [data analysis](data-analysis.md#backtesting) backtesting section.
 
-A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.
-
-Freqtrade provides an easy to load the backtest results, which is `load_backtest_data` - and takes a path to the backtest-results file.
-
-``` python
-from freqtrade.data.btanalysis import load_backtest_data
-df = load_backtest_data("user_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 would make the regular backtest-output unreadable.
-
-If you have some ideas for interesting / helpful backtest data analysis ideas, please submit a PR so the community can benefit from it.
 
 ## Backtesting multiple strategies
 
diff --git a/docs/data-analysis.md b/docs/data-analysis.md
new file mode 100644
index 000000000..ef3a02355
--- /dev/null
+++ b/docs/data-analysis.md
@@ -0,0 +1,44 @@
+# Analyzing bot data
+
+After performing backtests, or after running the bot for some time, it will be interresting to analyze the results your bot generated.
+
+A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.
+
+The following helpers will help you loading the data into Pandas DataFrames, and may also give you some starting points in analyzing the results.
+
+## 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.
+
+A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.
+
+Freqtrade provides an easy to load the backtest results, which is `load_backtest_data` - and takes a path to the backtest-results file.
+
+``` python
+from freqtrade.data.btanalysis import load_backtest_data
+df = load_backtest_data("user_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 would make the regular backtest-output unreadable.
+
+If you have some ideas for interesting / helpful backtest data analysis ideas, please submit a PR so the community can benefit from it.
+
+## Live data
+
+To analyze the trades your bot generated, you can load them to a DataFrame as follwos:
+
+``` python
+from freqtrade.data.btanalysis import load_trades_from_db
+
+df = load_trades_from_db("sqlite:///tradesv3.sqlite")
+
+df.groupby("pair")["sell_reason"].value_counts()
+
+```
+
+Feel free to submit an issue or Pull Request if you would like to share ideas on how to best analyze the data.
diff --git a/mkdocs.yml b/mkdocs.yml
index 6b445ee3a..b5e759432 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -17,6 +17,7 @@ nav:
     - Plotting: plotting.md
     - Deprecated features: deprecated.md
     - FAQ: faq.md
+    - Data Analysis: data-analysis.md
     - SQL Cheatsheet: sql_cheatsheet.md
     - Sandbox testing: sandbox-testing.md
     - Contributors guide: developer.md