freqtrade_origin/user_data/notebooks/analysis_example.ipynb

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   "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,
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   "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."
   ]
  }
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