Add samples for plotting to jupyter documentation

user_data/notebooks/strategy_analysis_example.ipynb

@@ -107,10 +107,111 @@
    "source": [
     "# Report results\n",
     "print(f\"Generated {df['buy'].sum()} buy signals\")\n",
-    "data = df.set_index('date', drop=True)\n",
+    "data = df.set_index('date', drop=False)\n",
     "data.tail()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load existing objects into a Jupyter notebook\n",
+    "\n",
+    "The following cells assume that you have already generated data using the cli.  \n",
+    "They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load backtest results to pandas dataframe\n",
+    "\n",
+    "Analyze a trades dataframe (also used below for plotting)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.data.btanalysis import load_backtest_data\n",
+    "\n",
+    "# Load backtest results\n",
+    "trades = load_backtest_data(user_data_dir / \"backtest_results/backtest-result.json\")\n",
+    "\n",
+    "# Show value-counts per pair\n",
+    "trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load live trading results into a pandas dataframe\n",
+    "\n",
+    "In case you did already some trading and want to analyze your performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.data.btanalysis import load_trades_from_db\n",
+    "\n",
+    "# Fetch trades from database\n",
+    "trades = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
+    "\n",
+    "# Display results\n",
+    "trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot results\n",
+    "\n",
+    "Freqtrade offers interactive plotting capabilities based on plotly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.plot.plotting import  generate_candlestick_graph\n",
+    "# Limit graph period to keep plotly quick and reactive\n",
+    "\n",
+    "data_red = data['2019-06-01':'2019-06-10']\n",
+    "# Generate candlestick graph\n",
+    "graph = generate_candlestick_graph(pair=pair,\n",
+    "                                   data=data_red,\n",
+    "                                   trades=trades,\n",
+    "                                   indicators1=['sma20', 'ema50', 'ema55'],\n",
+    "                                   indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']\n",
+    "                                  )\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show graph inline\n",
+    "# graph.show()\n",
+    "\n",
+    "# Render graph in a seperate window\n",
+    "graph.show(renderer=\"browser\")\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},