Merge pull request #511 from gcarq/hyperopt_selectable_spaces
Allow selecting Hyperopt search space
This commit is contained in:
Samuel Husso
GitHub
commit
e3d222912d
@ -51,12 +51,12 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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return dataframe
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```
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Your hyperopt file must contains `guards` to find the right value for
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Your hyperopt file must contain `guards` to find the right value for
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`(dataframe['adx'] > 65)` & and `(dataframe['plus_di'] > 0.5)`. That
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means you will need to enable/disable triggers.
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In our case the `SPACE` and `populate_buy_trend` in your strategy file
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will be look like:
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will look like:
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```python
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space = {
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'rsi': hp.choice('rsi', [
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@ -105,7 +105,7 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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### 2. Update the hyperopt config file
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Hyperopt is using a dedicated config file. At this moment hyperopt
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Hyperopt is using a dedicated config file. Currently hyperopt
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cannot use your config file. It is also made on purpose to allow you
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testing your strategy with different configurations.
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@ -127,19 +127,21 @@ If it's a guard, you will add a line like this:
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{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
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]),
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```
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This says, "*one of guards is RSI, it can have two values, enabled or
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This says, "*one of the guards is RSI, it can have two values, enabled or
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disabled. If it is enabled, try different values for it between 20 and 40*".
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So, the part of the strategy builder using the above setting looks like
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this:
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```
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if params['rsi']['enabled']:
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conditions.append(dataframe['rsi'] < params['rsi']['value'])
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```
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It checks if Hyperopt wants the RSI guard to be enabled for this
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round `params['rsi']['enabled']` and if it is, then it will add a
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condition that says RSI must be < than the value hyperopt picked
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for this evaluation, that is given in the `params['rsi']['value']`.
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condition that says RSI must be smaller than the value hyperopt picked
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for this evaluation, which is given in the `params['rsi']['value']`.
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That's it. Now you can add new parts of strategies to Hyperopt and it
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will try all the combinations with all different values in the search
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@ -148,8 +150,7 @@ for best working algo.
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### Add a new Indicators
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If you want to test an indicator that isn't used by the bot currently,
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you need to add it to your strategy file (example: [user_data/strategies/test_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py))
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inside the `populate_indicators()` method.
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you need to add it to the `populate_indicators()` method in `hyperopt.py`.
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## Execute Hyperopt
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Once you have updated your hyperopt configuration you can run it.
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@ -158,17 +159,19 @@ it will take time you will have the result (more than 30 mins).
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We strongly recommend to use `screen` to prevent any connection loss.
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```bash
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python3 ./freqtrade/main.py -c config.json hyperopt
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python3 ./freqtrade/main.py -c config.json hyperopt -e 5000
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```
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The `-e` flag will set how many evaluations hyperopt will do. We recommend
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running at least several thousand evaluations.
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### Execute hyperopt with different ticker-data source
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If you would like to learn parameters using an alternate ticke-data that
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If you would like to hyperopt parameters using an alternate ticker data that
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you have on-disk, use the `--datadir PATH` option. Default hyperopt will
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use data from directory `user_data/data`.
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### Running hyperopt with smaller testset
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Use the --timeperiod argument to change how much of the testset
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Use the `--timeperiod` argument to change how much of the testset
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you want to use. The last N ticks/timeframes will be used.
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Example:
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@ -176,6 +179,21 @@ Example:
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python3 ./freqtrade/main.py hyperopt --timeperiod -200
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```
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### Running hyperopt with smaller search space
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Use the `--spaces` argument to limit the search space used by hyperopt.
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Letting Hyperopt optimize everything is a huuuuge search space. Often it
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might make more sense to start by just searching for initial buy algorithm.
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Or maybe you just want to optimize your stoploss or roi table for that awesome
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new buy strategy you have.
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Legal values are:
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- `all`: optimize everything
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- `buy`: just search for a new buy strategy
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- `roi`: just optimize the minimal profit table for your strategy
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- `stoploss`: search for the best stoploss value
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- space-separated list of any of the above values for example `--spaces roi stoploss`
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### Hyperopt with MongoDB
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Hyperopt with MongoDB, is like Hyperopt under steroids. As you saw by
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executing the previous command is the execution takes a long time.
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@ -267,7 +285,6 @@ customizable value.
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- You should **ignore** the guard "mfi" (`"mfi"` is `"enabled": false`)
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- and so on...
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You have to look inside your strategy file into `buy_strategy_generator()`
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method, what those values match to.
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@ -277,7 +294,7 @@ at `adx`-block, that translates to the following code block:
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(dataframe['adx'] > 15.0)
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```
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So translating your whole hyperopt result to as the new buy-signal
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Translating your whole hyperopt result to as the new buy-signal
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would be the following:
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```
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def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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@ -280,6 +280,15 @@ def hyperopt_options(parser: argparse.ArgumentParser) -> None:
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type=str,
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dest='timerange',
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)
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parser.add_argument(
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'-s', '--spaces',
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help='Specify which parameters to hyperopt. Space separate list. \
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Default: %(default)s',
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choices=['all', 'buy', 'roi', 'stoploss'],
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default='all',
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nargs='+',
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dest='spaces',
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)
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def parse_timerange(text):
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@ -312,8 +312,21 @@ def indicator_space() -> Dict[str, Any]:
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}
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def hyperopt_space() -> Dict[str, Any]:
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return {**indicator_space(), **roi_space(), **stoploss_space()}
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def has_space(spaces, space):
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if space in spaces or 'all' in spaces:
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return True
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return False
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def hyperopt_space(selected_spaces: str) -> Dict[str, Any]:
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spaces = {}
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if has_space(selected_spaces, 'buy'):
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spaces = {**spaces, **indicator_space()}
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if has_space(selected_spaces, 'roi'):
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spaces = {**spaces, **roi_space()}
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if has_space(selected_spaces, 'stoploss'):
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spaces = {**spaces, **stoploss_space()}
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return spaces
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def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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@ -393,15 +406,21 @@ def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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def optimizer(params):
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global _CURRENT_TRIES
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strategy = Strategy()
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if 'roi_t1' in params:
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strategy = Strategy()
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strategy.minimal_roi = generate_roi_table(params)
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backtesting.populate_buy_trend = buy_strategy_generator(params)
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if 'trigger' in params:
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backtesting.populate_buy_trend = buy_strategy_generator(params)
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if 'stoploss' in params:
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stoploss = params['stoploss']
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else:
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stoploss = strategy.stoploss
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results = backtest({'stake_amount': OPTIMIZE_CONFIG['stake_amount'],
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'processed': PROCESSED,
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'stoploss': params['stoploss']})
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'stoploss': stoploss})
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result_explanation = format_results(results)
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total_profit = results.profit_percent.sum()
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@ -475,7 +494,8 @@ def start(args):
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data = optimize.load_data(args.datadir, pairs=pairs,
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ticker_interval=strategy.ticker_interval,
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timerange=timerange)
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optimize.populate_indicators = populate_indicators
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if has_space(args.spaces, 'buy'):
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optimize.populate_indicators = populate_indicators
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PROCESSED = optimize.tickerdata_to_dataframe(data)
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if args.mongodb:
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@ -500,7 +520,7 @@ def start(args):
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try:
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best_parameters = fmin(
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fn=optimizer,
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space=hyperopt_space(),
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space=hyperopt_space(args.spaces),
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algo=tpe.suggest,
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max_evals=TOTAL_TRIES,
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trials=TRIALS
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@ -517,7 +537,7 @@ def start(args):
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# Improve best parameter logging display
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if best_parameters:
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best_parameters = space_eval(
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hyperopt_space(),
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hyperopt_space(args.spaces),
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best_parameters
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)
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@ -6,7 +6,7 @@ from unittest.mock import MagicMock
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import pandas as pd
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from freqtrade.optimize.hyperopt import calculate_loss, TARGET_TRADES, EXPECTED_MAX_PROFIT, start, \
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log_results, save_trials, read_trials, generate_roi_table
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log_results, save_trials, read_trials, generate_roi_table, has_space
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import freqtrade.optimize.hyperopt as hyperopt
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@ -71,7 +71,7 @@ def test_start_calls_fmin(mocker):
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mock_fmin = mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
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args = mocker.Mock(epochs=1, config='config.json.example', mongodb=False,
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timerange=None)
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timerange=None, spaces='all')
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start(args)
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mock_fmin.assert_called_once()
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@ -85,7 +85,7 @@ def test_start_uses_mongotrials(mocker):
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mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
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args = mocker.Mock(epochs=1, config='config.json.example', mongodb=True,
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timerange=None)
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timerange=None, spaces='all')
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start(args)
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mock_mongotrials.assert_called_once()
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@ -148,7 +148,7 @@ def test_fmin_best_results(mocker, caplog):
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mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value=fmin_result)
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args = mocker.Mock(epochs=1, config='config.json.example',
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timerange=None)
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timerange=None, spaces='all')
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start(args)
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exists = [
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@ -172,7 +172,7 @@ def test_fmin_throw_value_error(mocker, caplog):
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mocker.patch('freqtrade.optimize.hyperopt.fmin', side_effect=ValueError())
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args = mocker.Mock(epochs=1, config='config.json.example',
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timerange=None)
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timerange=None, spaces='all')
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start(args)
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exists = [
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@ -207,7 +207,8 @@ def test_resuming_previous_hyperopt_results_succeeds(mocker):
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args = mocker.Mock(epochs=1,
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config='config.json.example',
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mongodb=False,
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timerange=None)
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timerange=None,
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spaces='all')
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start(args)
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@ -279,3 +280,10 @@ def test_signal_handler(mocker):
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mocker.patch('freqtrade.optimize.hyperopt.log_trials_result', m)
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hyperopt.signal_handler(9, None)
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assert m.call_count == 3
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def test_has_space():
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assert has_space(['buy', 'roi'], 'roi')
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assert has_space(['buy', 'roi'], 'buy')
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assert not has_space(['buy', 'roi'], 'stoploss')
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assert has_space(['all'], 'buy')
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