stable/user_data/notebooks/analysis_example.ipynb
2019-08-09 11:41:05 -04:00

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{
"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. A sample notebook is located at `user_data/notebooks/analysis_example.ipynb`. For usage instructions, see [jupyter.org](https://jupyter.org/documentation)."
]
},
{
"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 = 'NewStrategy'\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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