Add first version of new-strategy generation from template
This commit is contained in:
Matthias
parent
8cf8ab089e
commit
e3cf6188a1
@ -39,6 +39,8 @@ ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one
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ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
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ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy"]
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ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
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"timeframes", "erase"]
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@ -52,7 +54,7 @@ ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
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NO_CONF_REQURIED = ["download-data", "list-timeframes", "list-markets", "list-pairs",
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"plot-dataframe", "plot-profit"]
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NO_CONF_ALLOWED = ["create-userdir", "list-exchanges"]
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NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
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class Arguments:
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@ -117,6 +119,7 @@ class Arguments:
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from freqtrade.optimize import start_backtesting, start_hyperopt, start_edge
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from freqtrade.utils import (start_create_userdir, start_download_data,
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start_list_exchanges, start_list_markets,
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start_new_strategy,
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start_list_timeframes, start_trading)
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from freqtrade.plot.plot_utils import start_plot_dataframe, start_plot_profit
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@ -158,6 +161,12 @@ class Arguments:
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create_userdir_cmd.set_defaults(func=start_create_userdir)
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self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
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# add new-strategy subcommand
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build_strategy_cmd = subparsers.add_parser('new-strategy',
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help="Create new strategy")
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build_strategy_cmd.set_defaults(func=start_new_strategy)
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self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
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# Add list-exchanges subcommand
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list_exchanges_cmd = subparsers.add_parser(
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'list-exchanges',
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@ -127,3 +127,16 @@ def round_dict(d, n):
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def plural(num, singular: str, plural: str = None) -> str:
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return singular if (num == 1 or num == -1) else plural or singular + 's'
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def render_template(template: str, arguments: dict):
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from jinja2 import Environment, PackageLoader, select_autoescape
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env = Environment(
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loader=PackageLoader('freqtrade', 'templates'),
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autoescape=select_autoescape(['html', 'xml'])
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)
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template = env.get_template(template)
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return template.render(**arguments)
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306
freqtrade/templates/base_strategy.py.j2
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306
freqtrade/templates/base_strategy.py.j2
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@ -0,0 +1,306 @@
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# --- Do not remove these libs ---
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import numpy # noqa
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# This class is a sample. Feel free to customize it.
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class {{ strategy }}(IStrategy):
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"""
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This is a strategy template to get you started..
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More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
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You can:
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:return: a Dataframe with all mandatory indicators for the strategies
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- Rename the class name (Do not forget to update class_name)
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- Add any methods you want to build your strategy
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- Add any lib you need to build your strategy
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You must keep:
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- the lib in the section "Do not remove these libs"
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- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
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populate_sell_trend, hyperopt_space, buy_strategy_generator
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"""
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# Strategy interface version - allow new iterations of the strategy interface.
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# Check the documentation or the Sample strategy to get the latest version.
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INTERFACE_VERSION = 2
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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi".
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minimal_roi = {
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"60": 0.01,
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"30": 0.02,
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"0": 0.04
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}
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# Optimal stoploss designed for the strategy.
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# This attribute will be overridden if the config file contains "stoploss".
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stoploss = -0.10
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# Trailing stoploss
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trailing_stop = False
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# trailing_stop_positive = 0.01
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# trailing_stop_positive_offset = 0.0 # Disabled / not configured
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# Optimal ticker interval for the strategy.
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ticker_interval = '5m'
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# Run "populate_indicators()" only for new candle.
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process_only_new_candles = False
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# These values can be overridden in the "ask_strategy" section in the config.
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use_sell_signal = True
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sell_profit_only = False
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ignore_roi_if_buy_signal = False
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# Number of candles the strategy requires before producing valid signals
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startup_candle_count: int = 20
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# Optional order type mapping.
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order_types = {
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'buy': 'limit',
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'sell': 'limit',
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'stoploss': 'market',
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'stoploss_on_exchange': False
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}
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# Optional order time in force.
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order_time_in_force = {
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'buy': 'gtc',
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'sell': 'gtc'
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}
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def informative_pairs(self):
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"""
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Define additional, informative pair/interval combinations to be cached from the exchange.
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These pair/interval combinations are non-tradeable, unless they are part
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of the whitelist as well.
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For more information, please consult the documentation
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:return: List of tuples in the format (pair, interval)
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Sample: return [("ETH/USDT", "5m"),
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("BTC/USDT", "15m"),
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]
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"""
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return []
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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# Momentum Indicators
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# ------------------------------------
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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"""
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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# Awesome oscillator
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dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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# Commodity Channel Index: values Oversold:<-100, Overbought:>100
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dataframe['cci'] = ta.CCI(dataframe)
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# MACD
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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# MFI
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dataframe['mfi'] = ta.MFI(dataframe)
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# Minus Directional Indicator / Movement
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dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# Plus Directional Indicator / Movement
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dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# ROC
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dataframe['roc'] = ta.ROC(dataframe)
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# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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rsi = 0.1 * (dataframe['rsi'] - 50)
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dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
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# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
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dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
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# Stoch
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stoch = ta.STOCH(dataframe)
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dataframe['slowd'] = stoch['slowd']
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dataframe['slowk'] = stoch['slowk']
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# Stoch fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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# Stoch RSI
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stoch_rsi = ta.STOCHRSI(dataframe)
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dataframe['fastd_rsi'] = stoch_rsi['fastd']
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dataframe['fastk_rsi'] = stoch_rsi['fastk']
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"""
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# Overlap Studies
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# ------------------------------------
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# Bollinger bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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"""
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# EMA - Exponential Moving Average
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dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
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dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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# SAR Parabol
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dataframe['sar'] = ta.SAR(dataframe)
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# SMA - Simple Moving Average
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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"""
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# TEMA - Triple Exponential Moving Average
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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# Cycle Indicator
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# ------------------------------------
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# Hilbert Transform Indicator - SineWave
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hilbert = ta.HT_SINE(dataframe)
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dataframe['htsine'] = hilbert['sine']
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dataframe['htleadsine'] = hilbert['leadsine']
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# Pattern Recognition - Bullish candlestick patterns
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# ------------------------------------
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"""
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# Hammer: values [0, 100]
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dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
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# Inverted Hammer: values [0, 100]
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dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
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# Dragonfly Doji: values [0, 100]
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dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
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# Piercing Line: values [0, 100]
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dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
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# Morningstar: values [0, 100]
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dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
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# Three White Soldiers: values [0, 100]
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dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
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"""
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# Pattern Recognition - Bearish candlestick patterns
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# ------------------------------------
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"""
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# Hanging Man: values [0, 100]
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dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
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# Shooting Star: values [0, 100]
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dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
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# Gravestone Doji: values [0, 100]
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dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
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# Dark Cloud Cover: values [0, 100]
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dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
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# Evening Doji Star: values [0, 100]
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dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
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# Evening Star: values [0, 100]
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dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
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"""
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# Pattern Recognition - Bullish/Bearish candlestick patterns
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# ------------------------------------
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"""
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# Three Line Strike: values [0, -100, 100]
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dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
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# Spinning Top: values [0, -100, 100]
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dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
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# Engulfing: values [0, -100, 100]
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dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
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# Harami: values [0, -100, 100]
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dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
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# Three Outside Up/Down: values [0, -100, 100]
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dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
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# Three Inside Up/Down: values [0, -100, 100]
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dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
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"""
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# Chart type
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# ------------------------------------
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"""
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# Heikinashi stategy
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heikinashi = qtpylib.heikinashi(dataframe)
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dataframe['ha_open'] = heikinashi['open']
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dataframe['ha_close'] = heikinashi['close']
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dataframe['ha_high'] = heikinashi['high']
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dataframe['ha_low'] = heikinashi['low']
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"""
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# Retrieve best bid and best ask from the orderbook
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# ------------------------------------
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"""
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# first check if dataprovider is available
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if self.dp:
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if self.dp.runmode in ('live', 'dry_run'):
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ob = self.dp.orderbook(metadata['pair'], 1)
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dataframe['best_bid'] = ob['bids'][0][0]
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dataframe['best_ask'] = ob['asks'][0][0]
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"""
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame populated with indicators
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
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(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle
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(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising
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(dataframe['volume'] > 0) # Make sure Volume is not 0
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),
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'buy'] = 1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame populated with indicators
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
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(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle
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(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling
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(dataframe['volume'] > 0) # Make sure Volume is not 0
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),
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'sell'] = 1
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return dataframe
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@ -18,7 +18,7 @@ from freqtrade.data.history import (convert_trades_to_ohlcv,
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refresh_backtest_trades_data)
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from freqtrade.exchange import (available_exchanges, ccxt_exchanges, market_is_active,
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symbol_is_pair)
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from freqtrade.misc import plural
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from freqtrade.misc import plural, render_template
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from freqtrade.resolvers import ExchangeResolver
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from freqtrade.state import RunMode
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@ -89,6 +89,27 @@ def start_create_userdir(args: Dict[str, Any]) -> None:
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sys.exit(1)
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def start_new_strategy(args: Dict[str, Any]) -> None:
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config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
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if "strategy" in args and args["strategy"]:
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new_path = config['user_data_dir'] / "strategies" / (args["strategy"] + ".py")
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if new_path.exists():
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raise OperationalException(f"`{new_path}` already exists. "
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"Please choose another Strategy Name.")
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strategy_text = render_template(template='base_strategy.py.j2',
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arguments={"strategy": args["strategy"]})
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logger.info(f"Writing strategy to `{new_path}`.")
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new_path.write_text(strategy_text)
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else:
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logger.warning("`new-strategy` requires --strategy to be set.")
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sys.exit(1)
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def start_download_data(args: Dict[str, Any]) -> None:
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"""
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Download data (former download_backtest_data.py script)
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Block a user