edits for jupyter notebook example

user_data/notebooks/analysis_example.ipynb

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+{
+ "cells": [
+  {
+   "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",
+    "try:\n",
+    "\tos.chdir(os.path.join(os.getcwd(), '../..'))\n",
+    "\tprint(os.getcwd())\n",
+    "except:\n",
+    "\tpass\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import requirements and define variables used in the script"
+   ]
+  },
+  {
+   "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\n",
+    "\n",
+    "# Define some constants\n",
+    "ticker_interval = \"1m\"\n",
+    "# Name of the strategy class\n",
+    "strategy_name = 'NewStrategy'\n",
+    "# Path to user data\n",
+    "user_data_dir = 'user_data'\n",
+    "# Location of the strategy\n",
+    "strategy_location = os.path.join(user_data_dir, 'strategies')\n",
+    "# Location of the data\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\""
+   ]
+  },
+  {
+   "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",
+    "\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"
+   ]
+  },
+  {
+   "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",
+    "# Run strategy (just like in backtesting)\n",
+    "df = strategy.analyze_ticker(bt_data, {'pair': pair})\n",
+    "\n",
+    "# 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()"
+   ]
+  }
+ ],
+ "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
+}