diff --git a/docs/bot-usage.md b/docs/bot-usage.md index e856755d2..56e6008a1 100644 --- a/docs/bot-usage.md +++ b/docs/bot-usage.md @@ -337,8 +337,8 @@ optional arguments: generate completely different results, since the target for optimization is different. Built-in Hyperopt-loss-functions are: DefaultHyperOptLoss, - OnlyProfitHyperOptLoss, SharpeHyperOptLoss (default: - `DefaultHyperOptLoss`). + OnlyProfitHyperOptLoss, SharpeHyperOptLoss, + SharpeHyperOptLossDaily (default: `DefaultHyperOptLoss`). Common arguments: -v, --verbose Verbose mode (-vv for more, -vvv to get all messages). diff --git a/docs/hyperopt.md b/docs/hyperopt.md index f399fe816..3e10f66da 100644 --- a/docs/hyperopt.md +++ b/docs/hyperopt.md @@ -57,12 +57,12 @@ Rarely you may also need to override: !!! Tip "Quickly optimize ROI, stoploss and trailing stoploss" You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything (i.e. without creation of a "complete" Hyperopt class with dimensions, parameters, triggers and guards, as described in this document) from the default hyperopt template by relying on your strategy to do most of the calculations. - ``` python + ```python # Have a working strategy at hand. freqtrade new-hyperopt --hyperopt EmptyHyperopt freqtrade hyperopt --hyperopt EmptyHyperopt --spaces roi stoploss trailing --strategy MyWorkingStrategy --config config.json -e 100 - ``` + ``` ### 1. Install a Custom Hyperopt File @@ -75,8 +75,8 @@ Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing: -- Inside `indicator_space()` - the parameters hyperopt shall be optimizing. -- Inside `populate_buy_trend()` - applying the parameters. +* Inside `indicator_space()` - the parameters hyperopt shall be optimizing. +* Inside `populate_buy_trend()` - applying the parameters. There you have two different types of indicators: 1. `guards` and 2. `triggers`. @@ -141,7 +141,7 @@ one we call `trigger` and use it to decide which buy trigger we want to use. So let's write the buy strategy using these values: -``` python +```python def populate_buy_trend(dataframe: DataFrame) -> DataFrame: conditions = [] # GUARDS AND TRENDS @@ -192,6 +192,7 @@ Currently, the following loss functions are builtin: * `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function) * `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration) * `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on the trade returns) +* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on daily trade returns) Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation. @@ -206,7 +207,7 @@ We strongly recommend to use `screen` or `tmux` to prevent any connection loss. freqtrade hyperopt --config config.json --hyperopt -e 5000 --spaces all ``` -Use `` as the name of the custom hyperopt used. +Use `` as the name of the custom hyperopt used. The `-e` option will set how many evaluations hyperopt will do. We recommend running at least several thousand evaluations. @@ -265,23 +266,23 @@ The default Hyperopt Search Space, used when no `--space` command line option is ### Position stacking and disabling max market positions -In some situations, you may need to run Hyperopt (and Backtesting) with the +In some situations, you may need to run Hyperopt (and Backtesting) with the `--eps`/`--enable-position-staking` and `--dmmp`/`--disable-max-market-positions` arguments. By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one -open trade is allowed for every traded pair. The total number of trades open for all pairs +open trade is allowed for every traded pair. The total number of trades open for all pairs is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to some potential trades to be hidden (or masked) by previosly open trades. The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times, -while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades` +while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades` during Hyperopt/Backtesting (which is equal to setting `max_open_trades` to a very high number). !!! Note Dry/live runs will **NOT** use position stacking - therefore it does make sense to also validate the strategy without this as it's closer to reality. -You can also enable position stacking in the configuration file by explicitly setting +You can also enable position stacking in the configuration file by explicitly setting `"position_stacking"=true`. ### Reproducible results @@ -323,7 +324,7 @@ method, what those values match to. So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block: -``` python +```python (dataframe['rsi'] < 29.0) ``` @@ -372,18 +373,19 @@ In order to use this best ROI table found by Hyperopt in backtesting and for liv 118: 0 } ``` + As stated in the comment, you can also use it as the value of the `minimal_roi` setting in the configuration file. #### Default ROI Search Space If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point): -| # step | 1m | | 5m | | 1h | | 1d | | -|---|---|---|---|---|---|---|---|---| -| 1 | 0 | 0.01161...0.11992 | 0 | 0.03...0.31 | 0 | 0.06883...0.71124 | 0 | 0.12178...1.25835 | -| 2 | 2...8 | 0.00774...0.04255 | 10...40 | 0.02...0.11 | 120...480 | 0.04589...0.25238 | 2880...11520 | 0.08118...0.44651 | -| 3 | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 | -| 4 | 6...44 | 0.0 | 30...220 | 0.0 | 360...2640 | 0.0 | 8640...63360 | 0.0 | +| # step | 1m | | 5m | | 1h | | 1d | | +| ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- | +| 1 | 0 | 0.01161...0.11992 | 0 | 0.03...0.31 | 0 | 0.06883...0.71124 | 0 | 0.12178...1.25835 | +| 2 | 2...8 | 0.00774...0.04255 | 10...40 | 0.02...0.11 | 120...480 | 0.04589...0.25238 | 2880...11520 | 0.08118...0.44651 | +| 3 | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 | +| 4 | 6...44 | 0.0 | 30...220 | 0.0 | 360...2640 | 0.0 | 8640...63360 | 0.0 | These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the ticker interval used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the ticker interval used. @@ -416,6 +418,7 @@ In order to use this best stoploss value found by Hyperopt in backtesting and fo # This attribute will be overridden if the config file contains "stoploss" stoploss = -0.27996 ``` + As stated in the comment, you can also use it as the value of the `stoploss` setting in the configuration file. #### Default Stoploss Search Space @@ -452,6 +455,7 @@ In order to use these best trailing stop parameters found by Hyperopt in backtes trailing_stop_positive_offset = 0.06038 trailing_only_offset_is_reached = True ``` + As stated in the comment, you can also use it as the values of the corresponding settings in the configuration file. #### Default Trailing Stop Search Space diff --git a/freqtrade/commands/cli_options.py b/freqtrade/commands/cli_options.py index 490f26cfa..6d8d13129 100644 --- a/freqtrade/commands/cli_options.py +++ b/freqtrade/commands/cli_options.py @@ -256,7 +256,7 @@ AVAILABLE_CLI_OPTIONS = { help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). ' 'Different functions can generate completely different results, ' 'since the target for optimization is different. Built-in Hyperopt-loss-functions are: ' - 'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss.' + 'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily.' '(default: `%(default)s`).', metavar='NAME', default=constants.DEFAULT_HYPEROPT_LOSS, diff --git a/freqtrade/optimize/hyperopt_loss_sharpe_daily.py b/freqtrade/optimize/hyperopt_loss_sharpe_daily.py new file mode 100644 index 000000000..d8ea3c5fe --- /dev/null +++ b/freqtrade/optimize/hyperopt_loss_sharpe_daily.py @@ -0,0 +1,61 @@ +""" +SharpeHyperOptLossDaily + +This module defines the alternative HyperOptLoss class which can be used for +Hyperoptimization. +""" +import math +from datetime import datetime + +from pandas import DataFrame, date_range + +from freqtrade.optimize.hyperopt import IHyperOptLoss + + +class SharpeHyperOptLossDaily(IHyperOptLoss): + """ + Defines the loss function for hyperopt. + + This implementation uses the Sharpe Ratio calculation. + """ + + @staticmethod + def hyperopt_loss_function(results: DataFrame, trade_count: int, + min_date: datetime, max_date: datetime, + *args, **kwargs) -> float: + """ + Objective function, returns smaller number for more optimal results. + + Uses Sharpe Ratio calculation. + """ + resample_freq = '1D' + slippage_per_trade_ratio = 0.0005 + days_in_year = 365 + annual_risk_free_rate = 0.0 + risk_free_rate = annual_risk_free_rate / days_in_year + + # apply slippage per trade to profit_percent + results.loc[:, 'profit_percent_after_slippage'] = \ + results['profit_percent'] - slippage_per_trade_ratio + + # create the index within the min_date and end max_date + t_index = date_range(start=min_date, end=max_date, freq=resample_freq) + + sum_daily = ( + results.resample(resample_freq, on='close_time').agg( + {"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0) + ) + + total_profit = sum_daily["profit_percent_after_slippage"] - risk_free_rate + expected_returns_mean = total_profit.mean() + up_stdev = total_profit.std() + + if (up_stdev != 0.): + sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year) + else: + # Define high (negative) sharpe ratio to be clear that this is NOT optimal. + sharp_ratio = -20. + + # print(t_index, sum_daily, total_profit) + # print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio) + return -sharp_ratio diff --git a/tests/optimize/test_hyperopt.py b/tests/optimize/test_hyperopt.py index 69d110649..b3356bd6d 100644 --- a/tests/optimize/test_hyperopt.py +++ b/tests/optimize/test_hyperopt.py @@ -42,7 +42,13 @@ def hyperopt_results(): 'profit_percent': [-0.1, 0.2, 0.3], 'profit_abs': [-0.2, 0.4, 0.6], 'trade_duration': [10, 30, 10], - 'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI] + 'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI], + 'close_time': + [ + datetime(2019, 1, 1, 9, 26, 3, 478039), + datetime(2019, 2, 1, 9, 26, 3, 478039), + datetime(2019, 3, 1, 9, 26, 3, 478039) + ] } ) @@ -336,6 +342,24 @@ def test_sharpe_loss_prefers_higher_profits(default_conf, hyperopt_results) -> N assert under > correct +def test_sharpe_loss_daily_prefers_higher_profits(default_conf, hyperopt_results) -> None: + results_over = hyperopt_results.copy() + results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2 + results_under = hyperopt_results.copy() + results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2 + + default_conf.update({'hyperopt_loss': 'SharpeHyperOptLossDaily'}) + hl = HyperOptLossResolver.load_hyperoptloss(default_conf) + correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results), + datetime(2019, 1, 1), datetime(2019, 5, 1)) + over = hl.hyperopt_loss_function(results_over, len(hyperopt_results), + datetime(2019, 1, 1), datetime(2019, 5, 1)) + under = hl.hyperopt_loss_function(results_under, len(hyperopt_results), + datetime(2019, 1, 1), datetime(2019, 5, 1)) + assert over < correct + assert under > correct + + def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None: results_over = hyperopt_results.copy() results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2