stable/user_data/notebooks/strategy_analysis_example.ipynb
2019-08-22 15:43:39 +02:00

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{
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{
"cell_type": "markdown",
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"## 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": [
"## Setup"
]
},
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"# Change directory\n",
"# Modify this cell to insure that the output shows the correct path.\n",
"import os\n",
"from pathlib import Path\n",
"\n",
"# Define all paths relative to the project root shown in the cell output\n",
"project_root = \"somedir/freqtrade\"\n",
"i=0\n",
"try:\n",
" os.chdirdir(project_root)\n",
" assert Path('LICENSE').is_file()\n",
"except:\n",
" while i<4 and (not Path('LICENSE').is_file()):\n",
" os.chdir(Path(Path.cwd(), '../'))\n",
" i+=1\n",
" project_root = Path.cwd()\n",
"print(Path.cwd())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"source": [
"# Customize these according to your needs.\n",
"\n",
"# Define some constants\n",
"ticker_interval = \"5m\"\n",
"# Name of the strategy class\n",
"strategy_name = 'TestStrategy'\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 - Only use one pair here\n",
"pair = \"BTC_USDT\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load data using values set above\n",
"from pathlib import Path\n",
"from freqtrade.data.history import load_pair_history\n",
"\n",
"candles = load_pair_history(datadir=data_location,\n",
" ticker_interval=ticker_interval,\n",
" pair=pair)\n",
"\n",
"# Confirm success\n",
"print(\"Loaded \" + str(len(candles)) + f\" rows of data for {pair} from {data_location}\")\n",
"candles.head()"
]
},
{
"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": {
"scrolled": true
},
"outputs": [],
"source": [
"# Load strategy using values set above\n",
"from freqtrade.resolvers import StrategyResolver\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(candles, {'pair': pair})\n",
"df.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Display the trade details\n",
"\n",
"* Note that using `data.head()` would also work, however most indicators have some \"startup\" data at the top of the dataframe.\n",
"* Some possible problems\n",
" * Columns with NaN values at the end of the dataframe\n",
" * Columns used in `crossed*()` functions with completely different units\n",
"* Comparison with full backtest\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",
" * 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). 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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