From a4fc5afb66bc6adf3e638469b2c1196ca1194ac2 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 27 Oct 2018 17:35:08 +0200 Subject: [PATCH 1/2] Add hyperopt ROI documentation, add note on methology for hyperopt --- docs/hyperopt.md | 62 ++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 52 insertions(+), 10 deletions(-) diff --git a/docs/hyperopt.md b/docs/hyperopt.md index d1f363733..51fcbfaad 100644 --- a/docs/hyperopt.md +++ b/docs/hyperopt.md @@ -1,4 +1,5 @@ # 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 @@ -8,17 +9,20 @@ and still take a long time. *Note:* Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133) ## Table of Contents + - [Prepare your Hyperopt](#prepare-hyperopt) - [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) +- [Understand the hyperopt result](#understand-the-hyperopt-result) ## 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) ### 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#L231-L264). - Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L213-L224) @@ -113,11 +117,12 @@ When you want to test an indicator that isn't used by the bot currently, remembe add it to the `populate_indicators()` method in `hyperopt.py`. ## Execute Hyperopt -Once you have updated your hyperopt configuration you can run it. -Because hyperopt tries a lot of combination to find the best parameters -it will take time you will have the result (more than 30 mins). -We strongly recommend to use `screen` to prevent any connection loss. +Once you have updated your hyperopt configuration you can run it. +Because hyperopt tries a lot of combinations to find the best parameters it will take time you will have the result (more than 30 mins). + +We strongly recommend to use `screen` or `tmux` to prevent any connection loss. + ```bash python3 ./freqtrade/main.py -c config.json hyperopt -e 5000 ``` @@ -126,11 +131,13 @@ 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 + 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 + Use the `--timerange` argument to change how much of the testset you want to use. The last N ticks/timeframes will be used. Example: @@ -140,6 +147,7 @@ python3 ./freqtrade/main.py hyperopt --timerange -200 ``` ### 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. @@ -154,7 +162,8 @@ 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 +## Understand the Hyperopt Result + Once Hyperopt is completed you can use the result to create a new strategy. Given the following result from hyperopt: @@ -166,22 +175,24 @@ with values: ``` You should understand this result like: + - 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. +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: +So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block: + ``` (dataframe['rsi'] < 29.0) ``` Translating your whole hyperopt result as the new buy-signal would then look like: -``` + +```python def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: dataframe.loc[ ( @@ -192,6 +203,37 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: return dataframe ``` +### Understand Hyperopt ROI results + +If you are optimizing ROI, you're result will look as follows and include a ROI table. + +``` +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', 'roi_t1': 40, 'roi_t2': 57, 'roi_t3': 21, 'roi_p1': 0.03634636907306948, 'roi_p2': 0.055237357937802885, 'roi_p3': 0.015163796015548354, 'stoploss': -0.37996664668703606} +ROI table: +{0: 0.10674752302642071, 21: 0.09158372701087236, 78: 0.03634636907306948, 118: 0} +``` + +This would translate to the following ROI table: + +``` python + minimal_roi = { + "118": 0, + "78": 0.0363463, + "21": 0.0915, + "0": 0.106 + } +``` + +### Validate backtest result + +Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected. +To archive the same results (number of trades, ...) than during hyperopt, please use the command line flag `--disable-max-market-positions`. +This setting is the default for hyperopt for speed reasons. you can overwrite this in the configuration by setting `"position_stacking"=false` or by changing the relevant line in your hyperopt file [here](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L283) + ## 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). From 7e4a0baef252d24d50564061e25433a80a8b0594 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 27 Oct 2018 17:38:15 +0200 Subject: [PATCH 2/2] improve hyperopt.md --- docs/hyperopt.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/hyperopt.md b/docs/hyperopt.md index 51fcbfaad..e2dcf3e95 100644 --- a/docs/hyperopt.md +++ b/docs/hyperopt.md @@ -231,7 +231,9 @@ This would translate to the following ROI table: Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected. To archive the same results (number of trades, ...) than during hyperopt, please use the command line flag `--disable-max-market-positions`. -This setting is the default for hyperopt for speed reasons. you can overwrite this in the configuration by setting `"position_stacking"=false` or by changing the relevant line in your hyperopt file [here](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L283) +This setting is the default for hyperopt for speed reasons. You can overwrite this in the configuration by setting `"position_stacking"=false` or by changing the relevant line in your hyperopt file [here](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L283). + +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. ## Next Step