diff --git a/docs/hyperopt.md b/docs/hyperopt.md index 2ad94896a..f4b69b632 100644 --- a/docs/hyperopt.md +++ b/docs/hyperopt.md @@ -1,155 +1,114 @@ # Hyperopt -This page explains how to tune your strategy by finding the optimal -parameters with Hyperopt. +This page explains how to tune your strategy by finding the optimal +parameters, a process called hyperparameter optimization. The bot uses several +algorithms included in the `scikit-optimize` package to accomplish this. The +search will burn all your CPU cores, make your laptop sound like a fighter jet +and still take a long time. ## Table of Contents - [Prepare your Hyperopt](#prepare-hyperopt) - - [1. Configure your Guards and Triggers](#1-configure-your-guards-and-triggers) - - [2. Update the hyperopt config file](#2-update-the-hyperopt-config-file) -- [Advanced Hyperopt notions](#advanced-notions) - - [Understand the Guards and Triggers](#understand-the-guards-and-triggers) +- [Configure your Guards and Triggers](#configure-your-guards-and-triggers) +- [Solving a Mystery](#solving-a-mystery) +- [Adding New Indicators](#adding-new-indicators) - [Execute Hyperopt](#execute-hyperopt) - [Understand the hyperopts result](#understand-the-backtesting-result) -## Prepare Hyperopt -Before we start digging in Hyperopt, we recommend you to take a look at -your strategy file located into [user_data/strategies/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py) +## Prepare Hyperopting +We recommend you start by taking a look at `hyperopt.py` file located in [freqtrade/optimize](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py) -### 1. Configure your Guards and Triggers -There are two places you need to change in your strategy file to add a -new buy strategy for testing: -- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L278-L294). -- Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297) known as `SPACE`. +### Configure your Guards and Triggers +There are two places you need to change to add a new buy strategy for testing: +- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L278-L294). +- Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L218-L229) +and the associated methods `indicator_space`, `roi_space`, `stoploss_space`. -There you have two different type of indicators: 1. `guards` and 2. -`triggers`. -1. Guards are conditions like "never buy if ADX < 10", or never buy if -current price is over EMA10. +There you have two different type of indicators: 1. `guards` and 2. `triggers`. +1. Guards are conditions like "never buy if ADX < 10", or "never buy if +current price is over EMA10". 2. Triggers are ones that actually trigger buy in specific moment, like -"buy when EMA5 crosses over EMA10" or buy when close price touches lower -bollinger band. +"buy when EMA5 crosses over EMA10" or "buy when close price touches lower +bollinger band". -HyperOpt will, for each eval round, pick just ONE trigger, and possibly -multiple guards. So that the constructed strategy will be something like +Hyperoptimization will, for each eval round, pick one trigger and possibly +multiple guards. The constructed strategy will be something like "*buy exactly when close price touches lower bollinger band, BUT only if ADX > 10*". - -If you have updated the buy strategy, means change the content of +If you have updated the buy strategy, ie. changed the contents of `populate_buy_trend()` method you have to update the `guards` and -`triggers` hyperopts must used. +`triggers` hyperopts must use. -As for an example if your `populate_buy_trend()` method is: -```python -def populate_buy_trend(dataframe: DataFrame) -> DataFrame: - dataframe.loc[ - (dataframe['rsi'] < 35) & - (dataframe['adx'] > 65), - 'buy'] = 1 +## Solving a Mystery - return dataframe -``` +Let's say you are curious: should you use MACD crossings or lower Bollinger +Bands to trigger your buys. And you also wonder should you use RSI or ADX to +help with those buy decisions. If you decide to use RSI or ADX, which values +should I use for them? So let's use hyperparameter optimization to solve this +mystery. -Your hyperopt file must contain `guards` to find the right value for -`(dataframe['adx'] > 65)` & and `(dataframe['plus_di'] > 0.5)`. That -means you will need to enable/disable triggers. - -In our case the `SPACE` and `populate_buy_trend` in your strategy file -will look like: -```python -space = { - 'rsi': hp.choice('rsi', [ - {'enabled': False}, - {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)} - ]), - 'adx': hp.choice('adx', [ - {'enabled': False}, - {'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)} - ]), - 'trigger': hp.choice('trigger', [ - {'type': 'lower_bb'}, - {'type': 'faststoch10'}, - {'type': 'ao_cross_zero'}, - {'type': 'ema5_cross_ema10'}, - {'type': 'macd_cross_signal'}, - {'type': 'sar_reversal'}, - {'type': 'stochf_cross'}, - {'type': 'ht_sine'}, - ]), -} - -... - -def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: - conditions = [] - # GUARDS AND TRENDS - if params['adx']['enabled']: - conditions.append(dataframe['adx'] > params['adx']['value']) - if params['rsi']['enabled']: - conditions.append(dataframe['rsi'] < params['rsi']['value']) - - # TRIGGERS - triggers = { - 'lower_bb': dataframe['tema'] <= dataframe['blower'], - 'faststoch10': (crossed_above(dataframe['fastd'], 10.0)), - 'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)), - 'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])), - 'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])), - 'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])), - 'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])), - 'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])), - } - ... -``` - - -### 2. Update the hyperopt config file -Hyperopt is using a dedicated config file. Currently hyperopt -cannot use your config file. It is also made on purpose to allow you -testing your strategy with different configurations. - -The Hyperopt configuration is located in -[user_data/hyperopt_conf.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopt_conf.py). - - -## Advanced notions -### Understand the Guards and Triggers -When you need to add the new guards and triggers to be hyperopt -parameters, you do this by adding them into the [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297). - -If it's a trigger, you add one line to the 'trigger' choice group and that's it. - -If it's a guard, you will add a line like this: -``` -'rsi': hp.choice('rsi', [ - {'enabled': False}, - {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)} -]), -``` -This says, "*one of the guards is RSI, it can have two values, enabled or -disabled. If it is enabled, try different values for it between 20 and 40*". - -So, the part of the strategy builder using the above setting looks like -this: +We will start by defining a search space: ``` -if params['rsi']['enabled']: - conditions.append(dataframe['rsi'] < params['rsi']['value']) + def indicator_space() -> List[Dimension]: + """ + Define your Hyperopt space for searching strategy parameters + """ + return [ + Integer(20, 40, name='adx-value'), + Integer(20, 40, name='rsi-value'), + Categorical([True, False], name='adx-enabled'), + Categorical([True, False], name='rsi-enabled'), + Categorical(['bb_lower', 'macd_cross_signal'], name='trigger') + ] ``` -It checks if Hyperopt wants the RSI guard to be enabled for this -round `params['rsi']['enabled']` and if it is, then it will add a -condition that says RSI must be smaller than the value hyperopt picked -for this evaluation, which is given in the `params['rsi']['value']`. +Above definition says: I have five parameters I want you to randomly combine +to find the best combination. Two of them are integer values (`adx-value` +and `rsi-value`) and I want you test in the range of values 20 to 40. +Then we have three category variables. First two are either `True` or `False`. +We use these to either enable or disable the ADX and RSI guards. The last +one we call `trigger` and use it to decide which buy trigger we want to use. -That's it. Now you can add new parts of strategies to Hyperopt and it -will try all the combinations with all different values in the search -for best working algo. +So let's write the buy strategy using these values: +``` + def populate_buy_trend(dataframe: DataFrame) -> DataFrame: + conditions = [] + # GUARDS AND TRENDS + if 'adx-enabled' in params and params['adx-enabled']: + conditions.append(dataframe['adx'] > params['adx-value']) + if 'rsi-enabled' in params and params['rsi-enabled']: + conditions.append(dataframe['rsi'] < params['rsi-value']) -### Add a new Indicators -If you want to test an indicator that isn't used by the bot currently, -you need to add it to the `populate_indicators()` method in `hyperopt.py`. + # TRIGGERS + if params['trigger'] == 'bb_lower': + conditions.append(dataframe['close'] < dataframe['bb_lowerband']) + if params['trigger'] == 'macd_cross_signal': + conditions.append(qtpylib.crossed_above( + dataframe['macd'], dataframe['macdsignal'] + )) + + dataframe.loc[ + reduce(lambda x, y: x & y, conditions), + 'buy'] = 1 + + return dataframe + + return populate_buy_trend +``` + +Hyperopting will now call this `populate_buy_trend` as many times you ask it (`epochs`) +with different value combinations. It will then use the given historical data and make +buys based on the buy signals generated with the above function and based on the results +it will end with telling you which paramter combination produced the best profits. + +The search for best parameters starts with a few random combinations and then uses a +regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination +that minimizes the value of the objective function `calculate_loss` in `hyperopt.py`. + +The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators. +When you want to test an indicator that isn't used by the bot currently, remember to +add it to the `populate_indicators()` method in `hyperopt.py`. ## Execute Hyperopt Once you have updated your hyperopt configuration you can run it. @@ -164,12 +123,12 @@ python3 ./freqtrade/main.py -c config.json hyperopt -e 5000 The `-e` flag will set how many evaluations hyperopt will do. We recommend running at least several thousand evaluations. -### Execute hyperopt with different ticker-data source +### Execute Hyperopt with Different Ticker-Data Source If you would like to hyperopt parameters using an alternate ticker data that you have on-disk, use the `--datadir PATH` option. Default hyperopt will use data from directory `user_data/data`. -### Running hyperopt with smaller testset +### Running Hyperopt with Smaller Testset Use the `--timeperiod` argument to change how much of the testset you want to use. The last N ticks/timeframes will be used. Example: @@ -178,7 +137,7 @@ Example: python3 ./freqtrade/main.py hyperopt --timeperiod -200 ``` -### Running hyperopt with smaller search space +### Running Hyperopt with Smaller Search Space Use the `--spaces` argument to limit the search space used by hyperopt. Letting Hyperopt optimize everything is a huuuuge search space. Often it might make more sense to start by just searching for initial buy algorithm. @@ -193,87 +152,44 @@ Legal values are: - `stoploss`: search for the best stoploss value - space-separated list of any of the above values for example `--spaces roi stoploss` -## Understand the hyperopts result -Once Hyperopt is completed you can use the result to adding new buy -signal. Given following result from hyperopt: -``` -Best parameters: -{ - "adx": { - "enabled": true, - "value": 15.0 - }, - "fastd": { - "enabled": true, - "value": 40.0 - }, - "green_candle": { - "enabled": true - }, - "mfi": { - "enabled": false - }, - "over_sar": { - "enabled": false - }, - "rsi": { - "enabled": true, - "value": 37.0 - }, - "trigger": { - "type": "lower_bb" - }, - "uptrend_long_ema": { - "enabled": true - }, - "uptrend_short_ema": { - "enabled": false - }, - "uptrend_sma": { - "enabled": false - } -} +## Understand the Hyperopts Result +Once Hyperopt is completed you can use the result to create a new strategy. +Given the following result from hyperopt: -Best Result: - 2197 trades. Avg profit 1.84%. Total profit 0.79367541 BTC. Avg duration 241.0 mins. +``` +Best result: + 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. +with values: +{'adx-value': 44, 'rsi-value': 29, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'bb_lower'} ``` You should understand this result like: -- You should **consider** the guard "adx" (`"adx"` is `"enabled": true`) -and the best value is `15.0` (`"value": 15.0,`) -- You should **consider** the guard "fastd" (`"fastd"` is `"enabled": -true`) and the best value is `40.0` (`"value": 40.0,`) -- You should **consider** to enable the guard "green_candle" -(`"green_candle"` is `"enabled": true`) but this guards as no -customizable value. -- You should **ignore** the guard "mfi" (`"mfi"` is `"enabled": false`) -- and so on... +- The buy trigger that worked best was `bb_lower`. +- You should not use ADX because `adx-enabled: False`) +- You should **consider** using the RSI indicator (`rsi-enabled: True` and the best value is `29.0` (`rsi-value: 29.0`) You have to look inside your strategy file into `buy_strategy_generator()` method, what those values match to. -So for example you had `adx:` with the `value: 15.0` so we would look -at `adx`-block, that translates to the following code block: +So for example you had `rsi-value: 29.0` so we would look +at `rsi`-block, that translates to the following code block: ``` -(dataframe['adx'] > 15.0) +(dataframe['rsi'] < 29.0) ``` -Translating your whole hyperopt result to as the new buy-signal -would be the following: +Translating your whole hyperopt result as the new buy-signal +would then look like: ``` def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: dataframe.loc[ ( - (dataframe['adx'] > 15.0) & # adx-value - (dataframe['fastd'] < 40.0) & # fastd-value - (dataframe['close'] > dataframe['open']) & # green_candle - (dataframe['rsi'] < 37.0) & # rsi-value - (dataframe['ema50'] > dataframe['ema100']) # uptrend_long_ema + (dataframe['rsi'] < 29.0) & # rsi-value + dataframe['close'] < dataframe['bb_lowerband'] # trigger ), 'buy'] = 1 return dataframe ``` -## Next step +## Next Step Now you have a perfect bot and want to control it from Telegram. Your next step is to learn the [Telegram usage](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md). diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index 7a313a3ac..7138e2601 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -4,22 +4,21 @@ This module contains the hyperopt logic """ -import json import logging +import multiprocessing import os -import pickle -import signal import sys from argparse import Namespace from functools import reduce from math import exp from operator import itemgetter -from typing import Dict, Any, Callable, Optional +from typing import Any, Callable, Dict, List -import numpy import talib.abstract as ta -from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe from pandas import DataFrame +from sklearn.externals.joblib import Parallel, delayed, dump, load +from skopt import Optimizer +from skopt.space import Categorical, Dimension, Integer, Real import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.arguments import Arguments @@ -29,6 +28,9 @@ from freqtrade.optimize.backtesting import Backtesting logger = logging.getLogger(__name__) +MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization +TICKERDATA_PICKLE = os.path.join('user_data', 'hyperopt_tickerdata.pkl') + class Hyperopt(Backtesting): """ @@ -44,7 +46,6 @@ class Hyperopt(Backtesting): # to the number of days self.target_trades = 600 self.total_tries = config.get('epochs', 0) - self.current_tries = 0 self.current_best_loss = 100 # max average trade duration in minutes @@ -56,130 +57,38 @@ class Hyperopt(Backtesting): # check that the reported Σ% values do not exceed this! self.expected_max_profit = 3.0 - # Configuration and data used by hyperopt - self.processed: Optional[Dict[str, Any]] = None + # Previous evaluations + self.trials_file = os.path.join('user_data', 'hyperopt_results.pickle') + self.trials: List = [] - # Hyperopt Trials - self.trials_file = os.path.join('user_data', 'hyperopt_trials.pickle') - self.trials = Trials() + def get_args(self, params): + dimensions = self.hyperopt_space() + # Ensure the number of dimensions match + # the number of parameters in the list x. + if len(params) != len(dimensions): + raise ValueError('Mismatch in number of search-space dimensions. ' + f'len(dimensions)=={len(dimensions)} and len(x)=={len(params)}') + + # Create a dict where the keys are the names of the dimensions + # and the values are taken from the list of parameters x. + arg_dict = {dim.name: value for dim, value in zip(dimensions, params)} + return arg_dict @staticmethod def populate_indicators(dataframe: DataFrame) -> DataFrame: - """ - Adds several different TA indicators to the given DataFrame - """ dataframe['adx'] = ta.ADX(dataframe) - dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) - dataframe['cci'] = ta.CCI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] - dataframe['macdhist'] = macd['macdhist'] dataframe['mfi'] = ta.MFI(dataframe) - dataframe['minus_dm'] = ta.MINUS_DM(dataframe) - dataframe['minus_di'] = ta.MINUS_DI(dataframe) - dataframe['plus_dm'] = ta.PLUS_DM(dataframe) - dataframe['plus_di'] = ta.PLUS_DI(dataframe) - dataframe['roc'] = ta.ROC(dataframe) dataframe['rsi'] = ta.RSI(dataframe) - # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) - rsi = 0.1 * (dataframe['rsi'] - 50) - dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) - # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) - dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) - # Stoch - stoch = ta.STOCH(dataframe) - dataframe['slowd'] = stoch['slowd'] - dataframe['slowk'] = stoch['slowk'] - # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] - dataframe['fastk'] = stoch_fast['fastk'] - # Stoch RSI - stoch_rsi = ta.STOCHRSI(dataframe) - dataframe['fastd_rsi'] = stoch_rsi['fastd'] - dataframe['fastk_rsi'] = stoch_rsi['fastk'] + dataframe['minus_di'] = ta.MINUS_DI(dataframe) # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] - dataframe['bb_middleband'] = bollinger['mid'] - dataframe['bb_upperband'] = bollinger['upper'] - # EMA - Exponential Moving Average - dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) - dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) - dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) - dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) - dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) - # SAR Parabolic dataframe['sar'] = ta.SAR(dataframe) - # SMA - Simple Moving Average - dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) - # TEMA - Triple Exponential Moving Average - dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) - # Hilbert Transform Indicator - SineWave - hilbert = ta.HT_SINE(dataframe) - dataframe['htsine'] = hilbert['sine'] - dataframe['htleadsine'] = hilbert['leadsine'] - - # Pattern Recognition - Bullish candlestick patterns - # ------------------------------------ - """ - # Hammer: values [0, 100] - dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) - # Inverted Hammer: values [0, 100] - dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) - # Dragonfly Doji: values [0, 100] - dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) - # Piercing Line: values [0, 100] - dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] - # Morningstar: values [0, 100] - dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] - # Three White Soldiers: values [0, 100] - dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] - """ - - # Pattern Recognition - Bearish candlestick patterns - # ------------------------------------ - """ - # Hanging Man: values [0, 100] - dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) - # Shooting Star: values [0, 100] - dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) - # Gravestone Doji: values [0, 100] - dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) - # Dark Cloud Cover: values [0, 100] - dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) - # Evening Doji Star: values [0, 100] - dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) - # Evening Star: values [0, 100] - dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) - """ - - # Pattern Recognition - Bullish/Bearish candlestick patterns - # ------------------------------------ - """ - # Three Line Strike: values [0, -100, 100] - dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) - # Spinning Top: values [0, -100, 100] - dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] - # Engulfing: values [0, -100, 100] - dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] - # Harami: values [0, -100, 100] - dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] - # Three Outside Up/Down: values [0, -100, 100] - dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] - # Three Inside Up/Down: values [0, -100, 100] - dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] - """ - - # Chart type - # ------------------------------------ - # Heikinashi stategy - heikinashi = qtpylib.heikinashi(dataframe) - dataframe['ha_open'] = heikinashi['open'] - dataframe['ha_close'] = heikinashi['close'] - dataframe['ha_high'] = heikinashi['high'] - dataframe['ha_low'] = heikinashi['low'] return dataframe @@ -187,15 +96,16 @@ class Hyperopt(Backtesting): """ Save hyperopt trials to file """ - logger.info('Saving Trials to \'%s\'', self.trials_file) - pickle.dump(self.trials, open(self.trials_file, 'wb')) + if self.trials: + logger.info('Saving %d evaluations to \'%s\'', len(self.trials), self.trials_file) + dump(self.trials, self.trials_file) - def read_trials(self) -> Trials: + def read_trials(self) -> List: """ Read hyperopt trials file """ logger.info('Reading Trials from \'%s\'', self.trials_file) - trials = pickle.load(open(self.trials_file, 'rb')) + trials = load(self.trials_file) os.remove(self.trials_file) return trials @@ -203,9 +113,15 @@ class Hyperopt(Backtesting): """ Display Best hyperopt result """ - vals = json.dumps(self.trials.best_trial['misc']['vals'], indent=4) - results = self.trials.best_trial['result']['result'] - logger.info('Best result:\n%s\nwith values:\n%s', results, vals) + results = sorted(self.trials, key=itemgetter('loss')) + best_result = results[0] + logger.info( + 'Best result:\n%s\nwith values:\n%s', + best_result['result'], + best_result['params'] + ) + if 'roi_t1' in best_result['params']: + logger.info('ROI table:\n%s', self.generate_roi_table(best_result['params'])) def log_results(self, results) -> None: """ @@ -231,7 +147,8 @@ class Hyperopt(Backtesting): trade_loss = 1 - 0.25 * exp(-(trade_count - self.target_trades) ** 2 / 10 ** 5.8) profit_loss = max(0, 1 - total_profit / self.expected_max_profit) duration_loss = 0.4 * min(trade_duration / self.max_accepted_trade_duration, 1) - return trade_loss + profit_loss + duration_loss + result = trade_loss + profit_loss + duration_loss + return result @staticmethod def generate_roi_table(params: Dict) -> Dict[int, float]: @@ -247,87 +164,44 @@ class Hyperopt(Backtesting): return roi_table @staticmethod - def roi_space() -> Dict[str, Any]: + def roi_space() -> List[Dimension]: """ Values to search for each ROI steps """ - return { - 'roi_t1': hp.quniform('roi_t1', 10, 120, 20), - 'roi_t2': hp.quniform('roi_t2', 10, 60, 15), - 'roi_t3': hp.quniform('roi_t3', 10, 40, 10), - 'roi_p1': hp.quniform('roi_p1', 0.01, 0.04, 0.01), - 'roi_p2': hp.quniform('roi_p2', 0.01, 0.07, 0.01), - 'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01), - } + return [ + Integer(10, 120, name='roi_t1'), + Integer(10, 60, name='roi_t2'), + Integer(10, 40, name='roi_t3'), + Real(0.01, 0.04, name='roi_p1'), + Real(0.01, 0.07, name='roi_p2'), + Real(0.01, 0.20, name='roi_p3'), + ] @staticmethod - def stoploss_space() -> Dict[str, Any]: + def stoploss_space() -> List[Dimension]: """ - Stoploss Value to search + Stoploss search space """ - return { - 'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02), - } + return [ + Real(-0.5, -0.02, name='stoploss'), + ] @staticmethod - def indicator_space() -> Dict[str, Any]: + def indicator_space() -> List[Dimension]: """ Define your Hyperopt space for searching strategy parameters """ - return { - 'macd_below_zero': hp.choice('macd_below_zero', [ - {'enabled': False}, - {'enabled': True} - ]), - 'mfi': hp.choice('mfi', [ - {'enabled': False}, - {'enabled': True, 'value': hp.quniform('mfi-value', 10, 25, 5)} - ]), - 'fastd': hp.choice('fastd', [ - {'enabled': False}, - {'enabled': True, 'value': hp.quniform('fastd-value', 15, 45, 5)} - ]), - 'adx': hp.choice('adx', [ - {'enabled': False}, - {'enabled': True, 'value': hp.quniform('adx-value', 20, 50, 5)} - ]), - 'rsi': hp.choice('rsi', [ - {'enabled': False}, - {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 5)} - ]), - 'uptrend_long_ema': hp.choice('uptrend_long_ema', [ - {'enabled': False}, - {'enabled': True} - ]), - 'uptrend_short_ema': hp.choice('uptrend_short_ema', [ - {'enabled': False}, - {'enabled': True} - ]), - 'over_sar': hp.choice('over_sar', [ - {'enabled': False}, - {'enabled': True} - ]), - 'green_candle': hp.choice('green_candle', [ - {'enabled': False}, - {'enabled': True} - ]), - 'uptrend_sma': hp.choice('uptrend_sma', [ - {'enabled': False}, - {'enabled': True} - ]), - 'trigger': hp.choice('trigger', [ - {'type': 'lower_bb'}, - {'type': 'lower_bb_tema'}, - {'type': 'faststoch10'}, - {'type': 'ao_cross_zero'}, - {'type': 'ema3_cross_ema10'}, - {'type': 'macd_cross_signal'}, - {'type': 'sar_reversal'}, - {'type': 'ht_sine'}, - {'type': 'heiken_reversal_bull'}, - {'type': 'di_cross'}, - ]), - } + return [ + Integer(10, 25, name='mfi-value'), + Integer(15, 45, name='fastd-value'), + Integer(20, 50, name='adx-value'), + Integer(20, 40, name='rsi-value'), + Categorical([True, False], name='mfi-enabled'), + Categorical([True, False], name='fastd-enabled'), + Categorical([True, False], name='adx-enabled'), + Categorical([True, False], name='rsi-enabled'), + Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger') + ] def has_space(self, space: str) -> bool: """ @@ -337,17 +211,17 @@ class Hyperopt(Backtesting): return True return False - def hyperopt_space(self) -> Dict[str, Any]: + def hyperopt_space(self) -> List[Dimension]: """ Return the space to use during Hyperopt """ - spaces: Dict = {} + spaces: List[Dimension] = [] if self.has_space('buy'): - spaces = {**spaces, **Hyperopt.indicator_space()} + spaces += Hyperopt.indicator_space() if self.has_space('roi'): - spaces = {**spaces, **Hyperopt.roi_space()} + spaces += Hyperopt.roi_space() if self.has_space('stoploss'): - spaces = {**spaces, **Hyperopt.stoploss_space()} + spaces += Hyperopt.stoploss_space() return spaces @staticmethod @@ -361,63 +235,26 @@ class Hyperopt(Backtesting): """ conditions = [] # GUARDS AND TRENDS - if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']: - conditions.append(dataframe['ema50'] > dataframe['ema100']) - if 'macd_below_zero' in params and params['macd_below_zero']['enabled']: - conditions.append(dataframe['macd'] < 0) - if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']: - conditions.append(dataframe['ema5'] > dataframe['ema10']) - if 'mfi' in params and params['mfi']['enabled']: - conditions.append(dataframe['mfi'] < params['mfi']['value']) - if 'fastd' in params and params['fastd']['enabled']: - conditions.append(dataframe['fastd'] < params['fastd']['value']) - if 'adx' in params and params['adx']['enabled']: - conditions.append(dataframe['adx'] > params['adx']['value']) - if 'rsi' in params and params['rsi']['enabled']: - conditions.append(dataframe['rsi'] < params['rsi']['value']) - if 'over_sar' in params and params['over_sar']['enabled']: - conditions.append(dataframe['close'] > dataframe['sar']) - if 'green_candle' in params and params['green_candle']['enabled']: - conditions.append(dataframe['close'] > dataframe['open']) - if 'uptrend_sma' in params and params['uptrend_sma']['enabled']: - prevsma = dataframe['sma'].shift(1) - conditions.append(dataframe['sma'] > prevsma) + if 'mfi-enabled' in params and params['mfi-enabled']: + conditions.append(dataframe['mfi'] < params['mfi-value']) + if 'fastd-enabled' in params and params['fastd-enabled']: + conditions.append(dataframe['fastd'] < params['fastd-value']) + if 'adx-enabled' in params and params['adx-enabled']: + conditions.append(dataframe['adx'] > params['adx-value']) + if 'rsi-enabled' in params and params['rsi-enabled']: + conditions.append(dataframe['rsi'] < params['rsi-value']) # TRIGGERS - triggers = { - 'lower_bb': ( - dataframe['close'] < dataframe['bb_lowerband'] - ), - 'lower_bb_tema': ( - dataframe['tema'] < dataframe['bb_lowerband'] - ), - 'faststoch10': (qtpylib.crossed_above( - dataframe['fastd'], 10.0 - )), - 'ao_cross_zero': (qtpylib.crossed_above( - dataframe['ao'], 0.0 - )), - 'ema3_cross_ema10': (qtpylib.crossed_above( - dataframe['ema3'], dataframe['ema10'] - )), - 'macd_cross_signal': (qtpylib.crossed_above( + if params['trigger'] == 'bb_lower': + conditions.append(dataframe['close'] < dataframe['bb_lowerband']) + if params['trigger'] == 'macd_cross_signal': + conditions.append(qtpylib.crossed_above( dataframe['macd'], dataframe['macdsignal'] - )), - 'sar_reversal': (qtpylib.crossed_above( + )) + if params['trigger'] == 'sar_reversal': + conditions.append(qtpylib.crossed_above( dataframe['close'], dataframe['sar'] - )), - 'ht_sine': (qtpylib.crossed_above( - dataframe['htleadsine'], dataframe['htsine'] - )), - 'heiken_reversal_bull': ( - (qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) & - (dataframe['ha_low'] == dataframe['ha_open']) - ), - 'di_cross': (qtpylib.crossed_above( - dataframe['plus_di'], dataframe['minus_di'] - )), - } - conditions.append(triggers.get(params['trigger']['type'])) + )) dataframe.loc[ reduce(lambda x, y: x & y, conditions), @@ -427,7 +264,9 @@ class Hyperopt(Backtesting): return populate_buy_trend - def generate_optimizer(self, params: Dict) -> Dict: + def generate_optimizer(self, _params) -> Dict: + params = self.get_args(_params) + if self.has_space('roi'): self.analyze.strategy.minimal_roi = self.generate_roi_table(params) @@ -437,10 +276,11 @@ class Hyperopt(Backtesting): if self.has_space('stoploss'): self.analyze.strategy.stoploss = params['stoploss'] + processed = load(TICKERDATA_PICKLE) results = self.backtest( { 'stake_amount': self.config['stake_amount'], - 'processed': self.processed, + 'processed': processed, 'realistic': self.config.get('realistic_simulation', False), } ) @@ -450,30 +290,18 @@ class Hyperopt(Backtesting): trade_count = len(results.index) trade_duration = results.trade_duration.mean() - if trade_count == 0 or trade_duration > self.max_accepted_trade_duration: - print('.', end='') - sys.stdout.flush() + if trade_count == 0: return { - 'status': STATUS_FAIL, - 'loss': float('inf') + 'loss': MAX_LOSS, + 'params': params, + 'result': result_explanation, } loss = self.calculate_loss(total_profit, trade_count, trade_duration) - self.current_tries += 1 - - self.log_results( - { - 'loss': loss, - 'current_tries': self.current_tries, - 'total_tries': self.total_tries, - 'result': result_explanation, - } - ) - return { 'loss': loss, - 'status': STATUS_OK, + 'params': params, 'result': result_explanation, } @@ -491,6 +319,27 @@ class Hyperopt(Backtesting): results.trade_duration.mean(), ) + def get_optimizer(self, cpu_count) -> Optimizer: + return Optimizer( + self.hyperopt_space(), + base_estimator="ET", + acq_optimizer="auto", + n_initial_points=30, + acq_optimizer_kwargs={'n_jobs': cpu_count} + ) + + def run_optimizer_parallel(self, parallel, asked) -> List: + return parallel(delayed(self.generate_optimizer)(v) for v in asked) + + def load_previous_results(self): + """ read trials file if we have one """ + if os.path.exists(self.trials_file) and os.path.getsize(self.trials_file) > 0: + self.trials = self.read_trials() + logger.info( + 'Loaded %d previous evaluations from disk.', + len(self.trials) + ) + def start(self) -> None: timerange = Arguments.parse_timerange(None if self.config.get( 'timerange') is None else str(self.config.get('timerange'))) @@ -503,67 +352,35 @@ class Hyperopt(Backtesting): if self.has_space('buy'): self.analyze.populate_indicators = Hyperopt.populate_indicators # type: ignore - self.processed = self.tickerdata_to_dataframe(data) + dump(self.tickerdata_to_dataframe(data), TICKERDATA_PICKLE) + self.exchange = None # type: ignore + self.load_previous_results() - logger.info('Preparing Trials..') - signal.signal(signal.SIGINT, self.signal_handler) - # read trials file if we have one - if os.path.exists(self.trials_file) and os.path.getsize(self.trials_file) > 0: - self.trials = self.read_trials() - - self.current_tries = len(self.trials.results) - self.total_tries += self.current_tries - logger.info( - 'Continuing with trials. Current: %d, Total: %d', - self.current_tries, - self.total_tries - ) + cpus = multiprocessing.cpu_count() + logger.info(f'Found {cpus} CPU cores. Let\'s make them scream!') + opt = self.get_optimizer(cpus) + EVALS = max(self.total_tries//cpus, 1) try: - best_parameters = fmin( - fn=self.generate_optimizer, - space=self.hyperopt_space(), - algo=tpe.suggest, - max_evals=self.total_tries, - trials=self.trials - ) + with Parallel(n_jobs=cpus) as parallel: + for i in range(EVALS): + asked = opt.ask(n_points=cpus) + f_val = self.run_optimizer_parallel(parallel, asked) + opt.tell(asked, [i['loss'] for i in f_val]) - results = sorted(self.trials.results, key=itemgetter('loss')) - best_result = results[0]['result'] - - except ValueError: - best_parameters = {} - best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \ - 'try with more epochs (param: -e).' - - # Improve best parameter logging display - if best_parameters: - best_parameters = space_eval( - self.hyperopt_space(), - best_parameters - ) - - logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4)) - if 'roi_t1' in best_parameters: - logger.info('ROI table:\n%s', self.generate_roi_table(best_parameters)) - - logger.info('Best Result:\n%s', best_result) - - # Store trials result to file to resume next time - self.save_trials() - - def signal_handler(self, sig, frame) -> None: - """ - Hyperopt SIGINT handler - """ - logger.info( - 'Hyperopt received %s', - signal.Signals(sig).name - ) + self.trials += f_val + for j in range(cpus): + self.log_results({ + 'loss': f_val[j]['loss'], + 'current_tries': i * cpus + j, + 'total_tries': self.total_tries, + 'result': f_val[j]['result'], + }) + except KeyboardInterrupt: + print('User interrupted..') self.save_trials() self.log_trials_result() - sys.exit(0) def start(args: Namespace) -> None: diff --git a/freqtrade/tests/optimize/test_hyperopt.py b/freqtrade/tests/optimize/test_hyperopt.py index 8ad1932af..72a102c22 100644 --- a/freqtrade/tests/optimize/test_hyperopt.py +++ b/freqtrade/tests/optimize/test_hyperopt.py @@ -1,6 +1,5 @@ # pragma pylint: disable=missing-docstring,W0212,C0103 import os -import signal from copy import deepcopy from unittest.mock import MagicMock @@ -40,21 +39,11 @@ def create_trials(mocker) -> None: mocker.patch('freqtrade.optimize.hyperopt.os.path.exists', return_value=False) mocker.patch('freqtrade.optimize.hyperopt.os.path.getsize', return_value=1) mocker.patch('freqtrade.optimize.hyperopt.os.remove', return_value=True) - mocker.patch('freqtrade.optimize.hyperopt.pickle.dump', return_value=None) + mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None) - return mocker.Mock( - results=[ - { - 'loss': 1, - 'result': 'foo', - 'status': 'ok' - } - ], - best_trial={'misc': {'vals': {'adx': 999}}} - ) + return [{'loss': 1, 'result': 'foo', 'params': {}}] -# Unit tests def test_start(mocker, default_conf, caplog) -> None: """ Test start() function @@ -148,155 +137,18 @@ def test_no_log_if_loss_does_not_improve(init_hyperopt, caplog) -> None: assert caplog.record_tuples == [] -def test_fmin_best_results(mocker, init_hyperopt, default_conf, caplog) -> None: - fmin_result = { - "macd_below_zero": 0, - "adx": 1, - "adx-value": 15.0, - "fastd": 1, - "fastd-value": 40.0, - "green_candle": 1, - "mfi": 0, - "over_sar": 0, - "rsi": 1, - "rsi-value": 37.0, - "trigger": 2, - "uptrend_long_ema": 1, - "uptrend_short_ema": 0, - "uptrend_sma": 0, - "stoploss": -0.1, - "roi_t1": 1, - "roi_t2": 2, - "roi_t3": 3, - "roi_p1": 1, - "roi_p2": 2, - "roi_p3": 3, - } - - conf = deepcopy(default_conf) - conf.update({'config': 'config.json.example'}) - conf.update({'epochs': 1}) - conf.update({'timerange': None}) - conf.update({'spaces': 'all'}) - - mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock()) - mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value=fmin_result) - patch_exchange(mocker) - - StrategyResolver({'strategy': 'DefaultStrategy'}) - hyperopt = Hyperopt(conf) - hyperopt.trials = create_trials(mocker) - hyperopt.tickerdata_to_dataframe = MagicMock() - hyperopt.start() - - exists = [ - 'Best parameters:', - '"adx": {\n "enabled": true,\n "value": 15.0\n },', - '"fastd": {\n "enabled": true,\n "value": 40.0\n },', - '"green_candle": {\n "enabled": true\n },', - '"macd_below_zero": {\n "enabled": false\n },', - '"mfi": {\n "enabled": false\n },', - '"over_sar": {\n "enabled": false\n },', - '"roi_p1": 1.0,', - '"roi_p2": 2.0,', - '"roi_p3": 3.0,', - '"roi_t1": 1.0,', - '"roi_t2": 2.0,', - '"roi_t3": 3.0,', - '"rsi": {\n "enabled": true,\n "value": 37.0\n },', - '"stoploss": -0.1,', - '"trigger": {\n "type": "faststoch10"\n },', - '"uptrend_long_ema": {\n "enabled": true\n },', - '"uptrend_short_ema": {\n "enabled": false\n },', - '"uptrend_sma": {\n "enabled": false\n }', - 'ROI table:\n{0: 6.0, 3.0: 3.0, 5.0: 1.0, 6.0: 0}', - 'Best Result:\nfoo' - ] - for line in exists: - assert line in caplog.text - - -def test_fmin_throw_value_error(mocker, init_hyperopt, default_conf, caplog) -> None: - mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock()) - mocker.patch('freqtrade.optimize.hyperopt.fmin', side_effect=ValueError()) - - conf = deepcopy(default_conf) - conf.update({'config': 'config.json.example'}) - conf.update({'epochs': 1}) - conf.update({'timerange': None}) - conf.update({'spaces': 'all'}) - patch_exchange(mocker) - - StrategyResolver({'strategy': 'DefaultStrategy'}) - hyperopt = Hyperopt(conf) - hyperopt.trials = create_trials(mocker) - hyperopt.tickerdata_to_dataframe = MagicMock() - - hyperopt.start() - - exists = [ - 'Best Result:', - 'Sorry, Hyperopt was not able to find good parameters. Please try with more epochs ' - '(param: -e).', - ] - - for line in exists: - assert line in caplog.text - - -def test_resuming_previous_hyperopt_results_succeeds(mocker, init_hyperopt, default_conf) -> None: - trials = create_trials(mocker) - - conf = deepcopy(default_conf) - conf.update({'config': 'config.json.example'}) - conf.update({'epochs': 1}) - conf.update({'timerange': None}) - conf.update({'spaces': 'all'}) - - mocker.patch('freqtrade.optimize.hyperopt.os.path.exists', return_value=True) - mocker.patch('freqtrade.optimize.hyperopt.len', return_value=len(trials.results)) - mock_read = mocker.patch( - 'freqtrade.optimize.hyperopt.Hyperopt.read_trials', - return_value=trials - ) - mock_save = mocker.patch( - 'freqtrade.optimize.hyperopt.Hyperopt.save_trials', - return_value=None - ) - mocker.patch('freqtrade.optimize.hyperopt.sorted', return_value=trials.results) - mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock()) - mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={}) - patch_exchange(mocker) - - StrategyResolver({'strategy': 'DefaultStrategy'}) - hyperopt = Hyperopt(conf) - hyperopt.trials = trials - hyperopt.tickerdata_to_dataframe = MagicMock() - - hyperopt.start() - - mock_read.assert_called_once() - mock_save.assert_called_once() - - current_tries = hyperopt.current_tries - total_tries = hyperopt.total_tries - - assert current_tries == len(trials.results) - assert total_tries == (current_tries + len(trials.results)) - - def test_save_trials_saves_trials(mocker, init_hyperopt, caplog) -> None: - create_trials(mocker) - mock_dump = mocker.patch('freqtrade.optimize.hyperopt.pickle.dump', return_value=None) + trials = create_trials(mocker) + mock_dump = mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None) hyperopt = _HYPEROPT - mocker.patch('freqtrade.optimize.hyperopt.open', return_value=hyperopt.trials_file) + _HYPEROPT.trials = trials hyperopt.save_trials() trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle') assert log_has( - 'Saving Trials to \'{}\''.format(trials_file), + 'Saving 1 evaluations to \'{}\''.format(trials_file), caplog.record_tuples ) mock_dump.assert_called_once() @@ -304,8 +156,7 @@ def test_save_trials_saves_trials(mocker, init_hyperopt, caplog) -> None: def test_read_trials_returns_trials_file(mocker, init_hyperopt, caplog) -> None: trials = create_trials(mocker) - mock_load = mocker.patch('freqtrade.optimize.hyperopt.pickle.load', return_value=trials) - mock_open = mocker.patch('freqtrade.optimize.hyperopt.open', return_value=mock_load) + mock_load = mocker.patch('freqtrade.optimize.hyperopt.load', return_value=trials) hyperopt = _HYPEROPT hyperopt_trial = hyperopt.read_trials() @@ -315,7 +166,6 @@ def test_read_trials_returns_trials_file(mocker, init_hyperopt, caplog) -> None: caplog.record_tuples ) assert hyperopt_trial == trials - mock_open.assert_called_once() mock_load.assert_called_once() @@ -333,12 +183,15 @@ def test_roi_table_generation(init_hyperopt) -> None: assert hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0} -def test_start_calls_fmin(mocker, init_hyperopt, default_conf) -> None: - trials = create_trials(mocker) - mocker.patch('freqtrade.optimize.hyperopt.sorted', return_value=trials.results) +def test_start_calls_optimizer(mocker, init_hyperopt, default_conf, caplog) -> None: + dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock()) mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock()) + mocker.patch('freqtrade.optimize.hyperopt.multiprocessing.cpu_count', MagicMock(return_value=1)) + parallel = mocker.patch( + 'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel', + MagicMock(return_value=[{'loss': 1, 'result': 'foo result', 'params': {}}]) + ) patch_exchange(mocker) - mock_fmin = mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={}) conf = deepcopy(default_conf) conf.update({'config': 'config.json.example'}) @@ -347,11 +200,13 @@ def test_start_calls_fmin(mocker, init_hyperopt, default_conf) -> None: conf.update({'spaces': 'all'}) hyperopt = Hyperopt(conf) - hyperopt.trials = trials hyperopt.tickerdata_to_dataframe = MagicMock() hyperopt.start() - mock_fmin.assert_called_once() + parallel.assert_called_once() + + assert 'Best result:\nfoo result\nwith values:\n{}' in caplog.text + assert dumper.called def test_format_results(init_hyperopt): @@ -384,20 +239,6 @@ def test_format_results(init_hyperopt): assert result.find('Total profit 1.00000000 EUR') -def test_signal_handler(mocker, init_hyperopt): - """ - Test Hyperopt.signal_handler() - """ - m = MagicMock() - mocker.patch('sys.exit', m) - mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.save_trials', m) - mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.log_trials_result', m) - - hyperopt = _HYPEROPT - hyperopt.signal_handler(signal.SIGTERM, None) - assert m.call_count == 3 - - def test_has_space(init_hyperopt): """ Test Hyperopt.has_space() method @@ -422,8 +263,8 @@ def test_populate_indicators(init_hyperopt) -> None: # Check if some indicators are generated. We will not test all of them assert 'adx' in dataframe - assert 'ao' in dataframe - assert 'cci' in dataframe + assert 'mfi' in dataframe + assert 'rsi' in dataframe def test_buy_strategy_generator(init_hyperopt) -> None: @@ -437,44 +278,15 @@ def test_buy_strategy_generator(init_hyperopt) -> None: populate_buy_trend = _HYPEROPT.buy_strategy_generator( { - 'uptrend_long_ema': { - 'enabled': True - }, - 'macd_below_zero': { - 'enabled': True - }, - 'uptrend_short_ema': { - 'enabled': True - }, - 'mfi': { - 'enabled': True, - 'value': 20 - }, - 'fastd': { - 'enabled': True, - 'value': 20 - }, - 'adx': { - 'enabled': True, - 'value': 20 - }, - 'rsi': { - 'enabled': True, - 'value': 20 - }, - 'over_sar': { - 'enabled': True, - }, - 'green_candle': { - 'enabled': True, - }, - 'uptrend_sma': { - 'enabled': True, - }, - - 'trigger': { - 'type': 'lower_bb' - } + 'adx-value': 20, + 'fastd-value': 20, + 'mfi-value': 20, + 'rsi-value': 20, + 'adx-enabled': True, + 'fastd-enabled': True, + 'mfi-enabled': True, + 'rsi-enabled': True, + 'trigger': 'bb_lower' } ) result = populate_buy_trend(dataframe) @@ -503,35 +315,34 @@ def test_generate_optimizer(mocker, init_hyperopt, default_conf) -> None: MagicMock(return_value=backtest_result) ) patch_exchange(mocker) + mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock()) optimizer_param = { - 'adx': {'enabled': False}, - 'fastd': {'enabled': True, 'value': 35.0}, - 'green_candle': {'enabled': True}, - 'macd_below_zero': {'enabled': True}, - 'mfi': {'enabled': False}, - 'over_sar': {'enabled': False}, - 'roi_p1': 0.01, - 'roi_p2': 0.01, - 'roi_p3': 0.1, + 'adx-value': 0, + 'fastd-value': 35, + 'mfi-value': 0, + 'rsi-value': 0, + 'adx-enabled': False, + 'fastd-enabled': True, + 'mfi-enabled': False, + 'rsi-enabled': False, + 'trigger': 'macd_cross_signal', 'roi_t1': 60.0, 'roi_t2': 30.0, 'roi_t3': 20.0, - 'rsi': {'enabled': False}, + 'roi_p1': 0.01, + 'roi_p2': 0.01, + 'roi_p3': 0.1, 'stoploss': -0.4, - 'trigger': {'type': 'macd_cross_signal'}, - 'uptrend_long_ema': {'enabled': False}, - 'uptrend_short_ema': {'enabled': True}, - 'uptrend_sma': {'enabled': True} } response_expected = { 'loss': 1.9840569076926293, 'result': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC ' '(0.0231Σ%). Avg duration 100.0 mins.', - 'status': 'ok' + 'params': optimizer_param } hyperopt = Hyperopt(conf) - generate_optimizer_value = hyperopt.generate_optimizer(optimizer_param) + generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values())) assert generate_optimizer_value == response_expected diff --git a/requirements.txt b/requirements.txt index 8ab13d394..ca1575d7a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,11 +15,11 @@ TA-Lib==0.4.17 pytest==3.6.2 pytest-mock==1.10.0 pytest-cov==2.5.1 -hyperopt==0.1 -# do not upgrade networkx before this is fixed https://github.com/hyperopt/hyperopt/issues/325 -networkx==1.11 # pyup: ignore tabulate==0.8.2 coinmarketcap==5.0.3 +# Required for hyperopt +scikit-optimize==0.5.2 + # Required for plotting data #plotly==2.7.0 diff --git a/setup.py b/setup.py index ee6b7ae38..cd0574fa2 100644 --- a/setup.py +++ b/setup.py @@ -36,6 +36,7 @@ setup(name='freqtrade', 'tabulate', 'cachetools', 'coinmarketcap', + 'scikit-optimize', ], include_package_data=True, zip_safe=False,