diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a205f24ec..86c4ec1ad 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,7 +15,7 @@ repos: additional_dependencies: - types-cachetools==5.2.1 - types-filelock==3.2.7 - - types-requests==2.28.8 + - types-requests==2.28.9 - types-tabulate==0.8.11 - types-python-dateutil==2.8.19 # stages: [push] diff --git a/Dockerfile b/Dockerfile index 14a67edc8..e84a4d095 100644 --- a/Dockerfile +++ b/Dockerfile @@ -11,7 +11,7 @@ ENV FT_APP_ENV="docker" # Prepare environment RUN mkdir /freqtrade \ && apt-get update \ - && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev \ + && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev libgomp1 \ && apt-get clean \ && useradd -u 1000 -G sudo -U -m -s /bin/bash ftuser \ && chown ftuser:ftuser /freqtrade \ diff --git a/README.md b/README.md index 059e80cd9..0cc2364e5 100644 --- a/README.md +++ b/README.md @@ -130,7 +130,7 @@ Telegram is not mandatory. However, this is a great way to control your bot. Mor - `/start`: Starts the trader. - `/stop`: Stops the trader. -- `/stopbuy`: Stop entering new trades. +- `/stopentry`: Stop entering new trades. - `/status |[table]`: Lists all or specific open trades. - `/profit []`: Lists cumulative profit from all finished trades, over the last n days. - `/forceexit |all`: Instantly exits the given trade (Ignoring `minimum_roi`). diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index 093e11b2a..7112fc225 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -75,7 +75,6 @@ "weight_factor": 0.9, "principal_component_analysis": false, "use_SVM_to_remove_outliers": true, - "stratify_training_data": 0, "indicator_max_period_candles": 20, "indicator_periods_candles": [10, 20] }, diff --git a/config_examples/config_full.example.json b/config_examples/config_full.example.json index 74457d2b6..8155cb145 100644 --- a/config_examples/config_full.example.json +++ b/config_examples/config_full.example.json @@ -64,8 +64,8 @@ "stoploss_on_exchange_limit_ratio": 0.99 }, "order_time_in_force": { - "entry": "gtc", - "exit": "gtc" + "entry": "GTC", + "exit": "GTC" }, "pairlists": [ {"method": "StaticPairList"}, diff --git a/docs/assets/freqai_DI.jpg b/docs/assets/freqai_DI.jpg new file mode 100644 index 000000000..5e2aead34 Binary files /dev/null and b/docs/assets/freqai_DI.jpg differ diff --git a/docs/assets/freqai_algo.jpg b/docs/assets/freqai_algo.jpg new file mode 100644 index 000000000..44600e71a Binary files /dev/null and b/docs/assets/freqai_algo.jpg differ diff --git a/docs/assets/freqai_algo.png b/docs/assets/freqai_algo.png deleted file mode 100644 index 13813e129..000000000 Binary files a/docs/assets/freqai_algo.png and /dev/null differ diff --git a/docs/assets/freqai_dbscan.jpg b/docs/assets/freqai_dbscan.jpg new file mode 100644 index 000000000..0974550d2 Binary files /dev/null and b/docs/assets/freqai_dbscan.jpg differ diff --git a/docs/assets/freqai_moving-window.jpg b/docs/assets/freqai_moving-window.jpg new file mode 100644 index 000000000..9361479ac Binary files /dev/null and b/docs/assets/freqai_moving-window.jpg differ diff --git a/docs/assets/freqai_weight-factor.jpg b/docs/assets/freqai_weight-factor.jpg new file mode 100644 index 000000000..4f8b23e18 Binary files /dev/null and b/docs/assets/freqai_weight-factor.jpg differ diff --git a/docs/assets/weights_factor.png b/docs/assets/weights_factor.png deleted file mode 100644 index 1171a49ba..000000000 Binary files a/docs/assets/weights_factor.png and /dev/null differ diff --git a/docs/backtesting.md b/docs/backtesting.md index a7baf6932..8b2fdc345 100644 --- a/docs/backtesting.md +++ b/docs/backtesting.md @@ -561,6 +561,14 @@ BTC trades at 22.000\$ today (0.001 BTC is related to this) - but the backtestin Today's minimum would be `0.001 * 22_000` - or 22\$. However the limit could also be 50$ - based on `0.001 * 50_000` in some historic setting. +#### Trading precision limits + +Most exchanges pose precision limits on both price and amounts, so you cannot buy 1.0020401 of a pair, or at a price of 1.24567123123. +Instead, these prices and amounts will be rounded or truncated (based on the exchange definition) to the defined trading precision. +The above values may for example be rounded to an amount of 1.002, and a price of 1.24567. + +These precision values are based on current exchange limits (as described in the [above section](#trading-limits-in-backtesting)), as historic precision limits are not available. + ## Improved backtest accuracy One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or viceversa?). diff --git a/docs/bot-basics.md b/docs/bot-basics.md index 14823722e..3df926371 100644 --- a/docs/bot-basics.md +++ b/docs/bot-basics.md @@ -70,7 +70,7 @@ This loop will be repeated again and again until the bot is stopped. * Determine stake size by calling the `custom_stake_amount()` callback. * Check position adjustments for open trades if enabled and call `adjust_trade_position()` to determine if an additional order is requested. * Call `custom_stoploss()` and `custom_exit()` to find custom exit points. - * For exits based on exit-signal and custom-exit: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle). + * For exits based on exit-signal, custom-exit and partial exits: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle). * Generate backtest report output !!! Note diff --git a/docs/configuration.md b/docs/configuration.md index d5c0b3d8b..f8a600e76 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -57,10 +57,21 @@ You can specify additional configuration files in `add_config_files`. Files spec This is similar to using multiple `--config` parameters, but simpler in usage as you don't have to specify all files for all commands. !!! Tip "Use multiple configuration files to keep secrets secret" - You can use a 2nd configuration file containing your secrets. That way you can share your "primary" configuration file, while still keeping your API keys for yourself. + You can use a 2nd configuration file containing your secrets. That way you can share your "primary" configuration file, while still keeping your API keys for yourself. + The 2nd file should only specify what you intend to override. + If a key is in more than one of the configurations, then the "last specified configuration" wins (in the above example, `config-private.json`). + + For one-off commands, you can also use the below syntax by specifying multiple "--config" parameters. + + ``` bash + freqtrade trade --config user_data/config1.json --config user_data/config-private.json <...> + ``` + + The below is equivalent to the example above - but having 2 configuration files in the configuration, for easier reuse. ``` json title="user_data/config.json" "add_config_files": [ + "config1.json", "config-private.json" ] ``` @@ -69,17 +80,6 @@ This is similar to using multiple `--config` parameters, but simpler in usage as freqtrade trade --config user_data/config.json <...> ``` - The 2nd file should only specify what you intend to override. - If a key is in more than one of the configurations, then the "last specified configuration" wins (in the above example, `config-private.json`). - - For one-off commands, you can also use the below syntax by specifying multiple "--config" parameters. - - ``` bash - freqtrade trade --config user_data/config.json --config user_data/config-private.json <...> - ``` - - This is equivalent to the example above - but `config-private.json` is specified as cli argument. - ??? Note "config collision handling" If the same configuration setting takes place in both `config.json` and `config-import.json`, then the parent configuration wins. In the below case, `max_open_trades` would be 3 after the merging - as the reusable "import" configuration has this key overwritten. @@ -110,6 +110,8 @@ This is similar to using multiple `--config` parameters, but simpler in usage as "stake_amount": "unlimited" } ``` + + If multiple files are in the `add_config_files` section, then they will be assumed to be at identical levels, having the last occurrence override the earlier config (unless a parent already defined such a key). ## Configuration parameters @@ -525,21 +527,28 @@ It means if the order is not executed immediately AND fully then it is cancelled It is the same as FOK (above) except it can be partially fulfilled. The remaining part is automatically cancelled by the exchange. -The `order_time_in_force` parameter contains a dict with buy and sell time in force policy values. +**PO (Post only):** + +Post only order. The order is either placed as a maker order, or it is canceled. +This means the order must be placed on orderbook for at at least time in an unfilled state. + +#### time_in_force config + +The `order_time_in_force` parameter contains a dict with entry and exit time in force policy values. This can be set in the configuration file or in the strategy. Values set in the configuration file overwrites values set in the strategy. -The possible values are: `gtc` (default), `fok` or `ioc`. +The possible values are: `GTC` (default), `FOK` or `IOC`. ``` python "order_time_in_force": { - "entry": "gtc", - "exit": "gtc" + "entry": "GTC", + "exit": "GTC" }, ``` !!! Warning - This is ongoing work. For now, it is supported only for binance and kucoin. + This is ongoing work. For now, it is supported only for binance, gate, ftx and kucoin. Please don't change the default value unless you know what you are doing and have researched the impact of using different values for your particular exchange. ### What values can be used for fiat_display_currency? diff --git a/docs/data-download.md b/docs/data-download.md index 681fb717d..b72e7f337 100644 --- a/docs/data-download.md +++ b/docs/data-download.md @@ -63,7 +63,7 @@ optional arguments: `jsongz`). --trading-mode {spot,margin,futures} Select Trading mode - --prepend Allow data prepending. + --prepend Allow data prepending. (Data-appending is disabled) Common arguments: -v, --verbose Verbose mode (-vv for more, -vvv to get all messages). @@ -186,7 +186,7 @@ Freqtrade currently supports 3 data-formats for both OHLCV and trades data: By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data. This can be changed via the `--data-format-ohlcv` and `--data-format-trades` command line arguments respectively. -To persist this change, you can should also add the following snippet to your configuration, so you don't have to insert the above arguments each time: +To persist this change, you should also add the following snippet to your configuration, so you don't have to insert the above arguments each time: ``` jsonc // ... @@ -374,6 +374,7 @@ usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--data-format-ohlcv {json,jsongz,hdf5}] [-p PAIRS [PAIRS ...]] [--trading-mode {spot,margin,futures}] + [--show-timerange] optional arguments: -h, --help show this help message and exit @@ -387,6 +388,8 @@ optional arguments: separated. --trading-mode {spot,margin,futures} Select Trading mode + --show-timerange Show timerange available for available data. (May take + a while to calculate). Common arguments: -v, --verbose Verbose mode (-vv for more, -vvv to get all messages). diff --git a/docs/developer.md b/docs/developer.md index aca4ce4ed..f88754c50 100644 --- a/docs/developer.md +++ b/docs/developer.md @@ -409,8 +409,9 @@ Determine if crucial bugfixes have been made between this commit and the current * Merge the release branch (stable) into this branch. * Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi. -* Commit this part -* push that branch to the remote and create a PR against the stable branch +* Commit this part. +* push that branch to the remote and create a PR against the stable branch. +* Update develop version to next version following the pattern `2019.8-dev`. ### Create changelog from git commits diff --git a/docs/exchanges.md b/docs/exchanges.md index 50ebf9e0a..407a67d70 100644 --- a/docs/exchanges.md +++ b/docs/exchanges.md @@ -278,7 +278,7 @@ For example, to test the order type `FOK` with Kraken, and modify candle limit t "exchange": { "name": "kraken", "_ft_has_params": { - "order_time_in_force": ["gtc", "fok"], + "order_time_in_force": ["GTC", "FOK"], "ohlcv_candle_limit": 200 } //... diff --git a/docs/faq.md b/docs/faq.md index f1542d08e..381bbceb5 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -77,9 +77,9 @@ Freqtrade will not provide incomplete candles to strategies. Using incomplete ca You can use "current" market data by using the [dataprovider](strategy-customization.md#orderbookpair-maximum)'s orderbook or ticker methods - which however cannot be used during backtesting. -### Is there a setting to only SELL the coins being held and not perform anymore BUYS? +### Is there a setting to only Exit the trades being held and not perform any new Entries? -You can use the `/stopbuy` command in Telegram to prevent future buys, followed by `/forceexit all` (sell all open trades). +You can use the `/stopentry` command in Telegram to prevent future trade entry, followed by `/forceexit all` (sell all open trades). ### I want to run multiple bots on the same machine diff --git a/docs/freqai.md b/docs/freqai.md index bd746c984..482a56d2b 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -2,67 +2,63 @@ # FreqAI -FreqAI is a module designed to automate a variety of tasks associated with training a predictive model to generate market forecasts given a set of input features. +FreqAI is a module designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input features. -Among the the features included: +Features include: -* **Self-adaptive retraining**: retrain models during live deployments to self-adapt to the market in an unsupervised manner. -* **Rapid feature engineering**: create large rich feature sets (10k+ features) based on simple user created strategies. -* **High performance**: adaptive retraining occurs on separate thread (or on GPU if available) from inferencing and bot trade operations. Keep newest models and data in memory for rapid inferencing. -* **Realistic backtesting**: emulate self-adaptive retraining with backtesting module that automates past retraining. -* **Modifiable**: use the generalized and robust architecture for incorporating any machine learning library/method available in Python. Seven examples available. -* **Smart outlier removal**: remove outliers from training and prediction sets using a variety of outlier detection techniques. -* **Crash resilience**: model storage to disk to make reloading from a crash fast and easy (and purge obsolete files for sustained dry/live runs). -* **Automated data normalization**: normalize the data in a smart and statistically safe way. +* **Self-adaptive retraining**: retrain models during [live deployments](#running-the-model-live) to self-adapt to the market in an unsupervised manner. +* **Rapid feature engineering**: create large rich [feature sets](#feature-engineering) (10k+ features) based on simple user-created strategies. +* **High performance**: adaptive retraining occurs on a separate thread (or on GPU if available) from inferencing and bot trade operations. Newest models and data are kept in memory for rapid inferencing. +* **Realistic backtesting**: emulate self-adaptive retraining with a [backtesting module](#backtesting) that automates past retraining. +* **Modifiability**: use the generalized and robust architecture for incorporating any [machine learning library/method](#building-a-custom-prediction-model) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network. +* **Smart outlier removal**: remove outliers from training and prediction data sets using a variety of [outlier detection techniques](#outlier-removal). +* **Crash resilience**: store model to disk to make reloading from a crash fast and easy, and [purge obsolete files](#purging-old-model-data) for sustained dry/live runs. +* **Automatic data normalization**: [normalize the data](#feature-normalization) in a smart and statistically safe way. * **Automatic data download**: compute the data download timerange and update historic data (in live deployments). -* **Clean incoming data** safe NaN handling before training and prediction. -* **Dimensionality reduction**: reduce the size of the training data via Principal Component Analysis. -* **Deploy bot fleets**: set one bot to train models while a fleet of other bots inference into the models and handle trades. +* **Cleaning of incoming data**: handle NaNs safely before training and prediction. +* **Dimensionality reduction**: reduce the size of the training data via [Principal Component Analysis](#reducing-data-dimensionality-with-principal-component-analysis). +* **Deploying bot fleets**: set one bot to train models while a fleet of [follower bots](#setting-up-a-follower) inference the models and handle trades. ## Quick start -The easiest way to quickly test FreqAI is to run it in dry run with the following command +The easiest way to quickly test FreqAI is to run it in dry mode with the following command ```bash freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates ``` -where the user will see the boot-up process of auto-data downloading, followed by simultaneous training and trading. +The user will see the boot-up process of automatic data downloading, followed by simultaneous training and trading. -The example strategy, example prediction model, and example config can all be found in -`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, +The example strategy, example prediction model, and example config can be found in +`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, and `config_examples/config_freqai.example.json`, respectively. ## General approach -The user provides FreqAI with a set of custom *base* indicators (created inside the strategy the same way -a typical Freqtrade strategy is created) as well as target values which look into the future. -FreqAI trains a model to predict the target value based on the input of custom indicators for each pair in the whitelist. These models are consistently retrained to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining) and deploy dry/live. In dry/live conditions, FreqAI can be set to constant retraining in a background thread in an effort to keep models as young as possible. +The user provides FreqAI with a set of custom *base* indicators (the same way as in a typical Freqtrade strategy) as well as target values (*labels*). +FreqAI trains a model to predict the target values based on the input of custom indicators, for each pair in the whitelist. These models are consistently retrained to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining) and deploy dry/live runs. In dry/live conditions, FreqAI can be set to constant retraining in a background thread in an effort to keep models as up to date as possible. -An overview of the algorithm is shown here to help users understand the data processing pipeline and the model usage. +An overview of the algorithm is shown below, explaining the data processing pipeline and the model usage. -![freqai-algo](assets/freqai_algo.png) +![freqai-algo](assets/freqai_algo.jpg) -## Background and vocabulary +### Important machine learning vocabulary -**Features** are the quantities with which a model is trained. $X_i$ represents the -vector of all features for a single candle. In FreqAI, the user -builds the features from anything they can construct in the strategy. +**Features** - the quantities with which a model is trained. All features for a single candle is stored as a vector. In FreqAI, the user +builds the feature sets from anything they can construct in the strategy. -**Labels** are the target values with which the weights inside a model are trained -toward. Each set of features is associated with a single label, which is also -defined within the strategy by the user. These labels intentionally look into the -future, and are not available to the model during dryrun/live/backtesting. +**Labels** - the target values that a model is trained +toward. Each set of features is associated with a single label that is +defined by the user within the strategy. These labels intentionally look into the +future, and are not available to the model during dry/live/backtesting. -**Training** refers to the process of feeding individual feature sets into the -model with associated labels with the goal of matching input feature sets to associated labels. +**Training** - the process of feeding individual feature sets, composed of historic data, with associated labels into the +model with the goal of matching input feature sets to associated labels. -**Train data** is a subset of the historic data which is fed to the model during -training to adjust weights. This data directly influences weight connections in the model. +**Train data** - a subset of the historic data that is fed to the model during +training. This data directly influences weight connections in the model. -**Test data** is a subset of the historic data which is used to evaluate the -intermediate performance of the model during training. This data does not -directly influence nodal weights within the model. +**Test data** - a subset of the historic data that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model. ## Install prerequisites @@ -81,72 +77,82 @@ For docker users, a dedicated tag with freqAI dependencies is available as `:fre As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular freqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. -## Configuring FreqAI +## Setting up FreqAI ### Parameter table -The table below will list all configuration parameters available for FreqAI. +The table below will list all configuration parameters available for FreqAI, presented in the same order as `config_examples/config_freqai.example.json`. Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways. | Parameter | Description | |------------|-------------| -| `freqai` | **Required.** The parent dictionary containing all the parameters below for controlling FreqAI.
**Datatype:** dictionary. -| `identifier` | **Required.** A unique name for the current model. This can be reused to reload pre-trained models/data.
**Datatype:** string. -| `train_period_days` | **Required.** Number of days to use for the training data (width of the sliding window).
**Datatype:** positive integer. -| `backtest_period_days` | **Required.** Number of days to inference into the trained model before sliding the window and retraining. This can be fractional days, but beware that the user provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest.
**Datatype:** Float. -| `live_retrain_hours` | Frequency of retraining during dry/live runs. Default set to 0, which means it will retrain as often as possible.
**Datatype:** Float > 0. -| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. `False` by default.
**Datatype:** boolean. -| `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period.
**Datatype:** positive integer. -| `fit_live_predictions_candles` | Computes target (label) statistics from prediction data, instead of from the training data set. Number of candles is the number of historical candles it uses to generate the statistics.
**Datatype:** positive integer. -| `purge_old_models` | Tell FreqAI to delete obsolete models. Otherwise, all historic models will remain on disk. Defaults to `False`.
**Datatype:** boolean. -| `expiration_hours` | Ask FreqAI to avoid making predictions if a model is more than `expiration_hours` old. Defaults to 0 which means models never expire.
**Datatype:** positive integer. -| | **Feature Parameters** -| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples shown [here](#feature-engineering)
**Datatype:** dictionary. -| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` will be created for each coin in this list, and that set of features is added to the base asset feature set.
**Datatype:** list of assets (strings). -| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for and added as features to the base asset feature set.
**Datatype:** list of timeframes (strings). -| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators`, refer to `templates/FreqaiExampleStrategy.py` for detailed usage. The user can create custom labels, making use of this parameter not.
**Datatype:** positive integer. -| `include_shifted_candles` | Parameter used to add a sense of temporal recency to flattened regression type input data. `include_shifted_candles` takes all features, duplicates and shifts them by the number indicated by user.
**Datatype:** positive integer. -| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when above 0, explained in detail [here](#removing-outliers-with-the-dissimilarity-index).
**Datatype:** positive float (typically below 1). -| `weight_factor` | Used to set weights for training data points according to their recency, see details and a figure of how it works [here](#controlling-the-model-learning-process).
**Datatype:** positive float (typically below 1). -| `principal_component_analysis` | Ask FreqAI to automatically reduce the dimensionality of the data set using PCA.
**Datatype:** boolean. -| `use_SVM_to_remove_outliers` | Ask FreqAI to train a support vector machine to detect and remove outliers from the training data set as well as from incoming data points.
**Datatype:** boolean. -| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. E.g. `nu` *Very* broadly, is the percentage of data points that should be considered outliers. `shuffle` is by default false to maintain reproducibility. But these and all others can be added/changed in this dictionary.
**Datatype:** dictionary. -| `stratify_training_data` | This value is used to indicate the stratification of the data. e.g. 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing.
**Datatype:** positive integer. +| | **General configuration parameters** +| `freqai` | **Required.**
The parent dictionary containing all the parameters for controlling FreqAI.
**Datatype:** Dictionary. +| `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period.
**Datatype:** Positive integer. +| `purge_old_models` | Delete obsolete models (otherwise, all historic models will remain on disk).
**Datatype:** Boolean. Default: `False`. +| `train_period_days` | **Required.**
Number of days to use for the training data (width of the sliding window).
**Datatype:** Positive integer. +| `backtest_period_days` | **Required.**
Number of days to inference from the trained model before sliding the window defined above, and retraining the model. This can be fractional days, but beware that the user-provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest.
**Datatype:** Float. +| `identifier` | **Required.**
A unique name for the current model. This can be reused to reload pre-trained models/data.
**Datatype:** String. +| `live_retrain_hours` | Frequency of retraining during dry/live runs.
Default set to 0, which means the model will retrain as often as possible.
**Datatype:** Float > 0. +| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old.
Defaults set to 0, which means models never expire.
**Datatype:** Positive integer. +| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training data set.
**Datatype:** Positive integer. +| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models.
**Datatype:** Boolean. Default: `False`. +| | **Feature parameters** +| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](#feature-engineering).
**Datatype:** Dictionary. +| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base asset feature set.
**Datatype:** List of timeframes (strings). +| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](#feature-engineering)) will be created for each coin in this list, and that set of features is added to the base asset feature set.
**Datatype:** List of assets (strings). +| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). The user can create custom labels, making use of this parameter or not.
**Datatype:** Positive integer. +| `include_shifted_candles` | Add features from previous candles to subsequent candles to add historical information. FreqAI takes all features from the `include_shifted_candles` previous candles, duplicates and shifts them so that the information is available for the subsequent candle.
**Datatype:** Positive integer. +| `weight_factor` | Used to set weights for training data points according to their recency. See details about how it works [here](#controlling-the-model-learning-process).
**Datatype:** Positive float (typically < 1). | `indicator_max_period_candles` | **No longer used**. User must use the strategy set `startup_candle_count` which defines the maximum *period* used in `populate_any_indicators()` for indicator creation (timeframe independent). FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN
**Datatype:** positive integer. -| `indicator_periods_candles` | A list of integers used to duplicate all indicators according to a set of periods and add them to the feature set.
**Datatype:** list of positive integers. -| `use_DBSCAN_to_remove_outliers` | Inactive by default. If true, FreqAI clusters data using DBSCAN to identify and remove outliers from training and prediction data.
**Datatype:** float (fraction of 1). +| `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set.
**Datatype:** List of positive integers. +| `stratify_training_data` | This value is used to indicate the grouping of the data. For example, 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](#stratifying-the-data-for-training-and-testing-the-model)
**Datatype:** Positive integer. +| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis)
**Datatype:** Boolean. +| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index).
**Datatype:** Positive float (typically < 1). +| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training data set, as well as from incoming data points. See details about how it works [here](#removing-outliers-using-a-support-vector-machine-svm).
**Datatype:** Boolean. +| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm).
**Datatype:** Dictionary. +| `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan).
**Datatype:** Boolean. +| `outlier_protection_percentage` | If more than `outlier_protection_percentage` fraction of points are removed as outliers, FreqAI will log a warning message and ignore outlier detection while keeping the original dataset intact.
**Datatype:** float. Default: `30` +| `reverse_train_test_order` | If true, FreqAI will train on the latest data split and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, users should be careful to understand unorthodox nature of this parameter before employing it.
**Datatype:** bool. Default: False | | **Data split parameters** -| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)
**Datatype:** dictionary. -| `test_size` | Fraction of data that should be used for testing instead of training.
**Datatype:** positive float below 1. -| `shuffle` | Shuffle the training data points during training. Typically for time-series forecasting, this is set to False.
**Datatype:** boolean. +| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
**Datatype:** Dictionary. +| `test_size` | Fraction of data that should be used for testing instead of training.
**Datatype:** Positive float < 1. +| `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`.
| | **Model training parameters** -| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected library. For example, if the user uses `LightGBMRegressor`, then this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html). If the user selects a different model, then this dictionary can contain any parameter from that different model.
**Datatype:** dictionary. -| `n_estimators` | A common parameter among regressors which sets the number of boosted trees to fit
**Datatype:** integer. -| `learning_rate` | A common parameter among regressors which sets the boosting learning rate.
**Datatype:** float. -| `n_jobs`, `thread_count`, `task_type` | Different libraries use different parameter names to control the number of threads used for parallel processing or whether or not it is a `task_type` of `gpu` or `cpu`.
**Datatype:** float. +| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected model library. For example, if the user uses `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If the user selects a different model, this dictionary can contain any parameter from that model.
**Datatype:** Dictionary.**Datatype:** Boolean. +| `n_estimators` | The number of boosted trees to fit in regression.
**Datatype:** Integer. +| `learning_rate` | Boosting learning rate during regression.
**Datatype:** Float. +| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names.
**Datatype:** Float. | | **Extraneous parameters** -| `keras` | If your model makes use of keras (typical of Tensorflow based prediction models), activate this flag so that the model save/loading follows keras standards. Default value `false`
**Datatype:** boolean. -| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for `shift` by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. Default value, 2
**Datatype:** integer. +| `keras` | If your model makes use of Keras (typical for Tensorflow-based prediction models), activate this flag so that the model save/loading follows Keras standards.
**Datatype:** Boolean. Default: `False`. +| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction.
**Datatype:** Integer. Default: 2. -### Important FreqAI dataframe key patterns +### Important dataframe key patterns -Here are the values the user can expect to include/use inside the typical strategy dataframe (`df[]`): +Below are the values the user can expect to include/use inside a typical strategy dataframe (`df[]`): | DataFrame Key | Description | |------------|-------------| -| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target inside FreqAI (typically following the naming convention `&-s*`). These same dataframe columns names are fed back to the user as the predictions. For example, the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['&-s_close']`. FreqAI makes the predictions and gives them back to the user under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
**Datatype:** depends on the output of the model. -| `df['&*_std/mean']` | The standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand rarity of prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training (or historically with `fit_live_predictions_candles`)
**Datatype:** float. -| `df['do_predict']` | An indication of an outlier, this return value is integer between -1 and 2 which lets the user understand if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the [Dissimilarity Index](#removing-outliers-with-the-dissimilarity-index) is above the user defined threshold, it will subtract 1 from `do_predict`. If `use_SVM_to_remove_outliers()` is active, then the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract one from `do_predict`. A particular case is when `do_predict == 2`, it means that the model has expired due to `expired_hours`.
**Datatype:** integer between -1 and 2. -| `df['DI_values']` | The raw Dissimilarity Index values to give the user a sense of confidence in the prediction. Lower DI means the data point is closer to the trained parameter space.
**Datatype:** float. -| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature inside FreqAI. For example, the user can include the rsi in the training feature set (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#building-the-feature-set).
**Note**: since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table.) these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, you can prepend it with `%%`.
**Datatype:** depends on the output of the model. +| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back to the user as the predictions. For example, if the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), they set `df['&-s_close']`. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
**Datatype:** Depends on the output of the model. +| `df['&*_std/mean']` | Standard deviation and mean values of the user-defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`).
**Datatype:** Float. +| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets the user know if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](#removing-outliers-with-the-dissimilarity-index)) of the input data point is above the user-defined threshold, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`.
**Datatype:** Integer between -1 and 2. +| `df['DI_values']` | Dissimilarity Index values are proxies to the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence.
**Datatype:** Float. +| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, the user can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#feature-engineering).
**Note**: Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, they can prepend it with `%%`.
**Datatype:** Depends on the output of the model. + +### File structure + +`user_data_dir/models/` contains all the data associated with the trainings and backtests. +This file structure is heavily controlled and inferenced by the `FreqaiDataKitchen()` +and should therefore not be modified. ### Example config file -The user interface is isolated to the typical config file. A typical FreqAI config setup could include: +The user interface is isolated to the typical Freqtrade config file. A FreqAI config should include: ```json "freqai": { + "enabled": true, "startup_candles": 10000, "purge_old_models": true, "train_period_days": 30, @@ -161,232 +167,23 @@ The user interface is isolated to the typical config file. A typical FreqAI conf ], "label_period_candles": 24, "include_shifted_candles": 2, - "weight_factor": 0, "indicator_periods_candles": [10, 20] }, "data_split_parameters" : { - "test_size": 0.25, - "random_state": 42 + "test_size": 0.25 }, "model_training_parameters" : { - "n_estimators": 100, - "random_state": 42, - "learning_rate": 0.02, - "task_type": "CPU", + "n_estimators": 100 }, } ``` -### Feature engineering +## Building a FreqAI strategy -Features are added by the user inside the `populate_any_indicators()` method of the strategy -by prepending indicators with `%` and labels are added by prepending `&`. -There are some important components/structures that the user *must* include when building their feature set. -Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`). -This is where the user will add single features and labels to their feature set to avoid duplication from -various configuration parameters which multiply the feature set such as `include_timeframes`. +The FreqAI strategy requires the user to include the following lines of code in the standard Freqtrade strategy: ```python - def populate_any_indicators( - self, pair, df, tf, informative=None, set_generalized_indicators=False - ): - """ - Function designed to automatically generate, name and merge features - from user indicated timeframes in the configuration file. User controls the indicators - passed to the training/prediction by prepending indicators with `'%-' + coin ` - (see convention below). I.e. user should not prepend any supporting metrics - (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the - model. - :param pair: pair to be used as informative - :param df: strategy dataframe which will receive merges from informatives - :param tf: timeframe of the dataframe which will modify the feature names - :param informative: the dataframe associated with the informative pair - :param coin: the name of the coin which will modify the feature names. - """ - - coint = pair.split('/')[0] - - if informative is None: - informative = self.dp.get_pair_dataframe(pair, tf) - - # first loop is automatically duplicating indicators for time periods - for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: - t = int(t) - informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) - informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) - informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) - - bollinger = qtpylib.bollinger_bands( - qtpylib.typical_price(informative), window=t, stds=2.2 - ) - informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"] - informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"] - informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"] - - informative[f"%-{coin}bb_width-period_{t}"] = ( - informative[f"{coin}bb_upperband-period_{t}"] - - informative[f"{coin}bb_lowerband-period_{t}"] - ) / informative[f"{coin}bb_middleband-period_{t}"] - informative[f"%-{coin}close-bb_lower-period_{t}"] = ( - informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"] - ) - - informative[f"%-{coin}relative_volume-period_{t}"] = ( - informative["volume"] / informative["volume"].rolling(t).mean() - ) - - indicators = [col for col in informative if col.startswith("%")] - # This loop duplicates and shifts all indicators to add a sense of recency to data - for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): - if n == 0: - continue - informative_shift = informative[indicators].shift(n) - informative_shift = informative_shift.add_suffix("_shift-" + str(n)) - informative = pd.concat((informative, informative_shift), axis=1) - - df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) - skip_columns = [ - (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] - ] - df = df.drop(columns=skip_columns) - - # Add generalized indicators here (because in live, it will call this - # function to populate indicators during training). Notice how we ensure not to - # add them multiple times - if set_generalized_indicators: - df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 - df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 - - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) - - return df -``` - -The user of the present example does not wish to pass the `bb_lowerband` as a feature to the model, -and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the -model for training/prediction and has therefore prepended it with `%`. - -The `include_timeframes` from the example config above are the timeframes (`tf`) of each call to `populate_any_indicators()` -included metric for inclusion in the feature set. In the present case, the user is asking for the -`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set. - -In addition, the user can ask for each of these features to be included from -informative pairs using the `include_corr_pairlist`. This means that the present feature -set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of -`ETH/USD`, `LINK/USD`, and `BNB/USD`. - -`include_shifted_candles` is another user controlled parameter which indicates the number of previous -candles to include in the present feature set. In other words, `include_shifted_candles: 2`, tells -FreqAI to include the the past 2 candles for each of the features included in the dataset. - -In total, the number of features the present user has created is: - -length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles` -$3 * 3 * 3 * 2 * 2 = 108$. - -!!! Note - Features **must** be defined in `populate_any_indicators()`. Making features in `populate_indicators()` - will fail in live/dry mode. If the user wishes to add generalized features that are not associated with - a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` - (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`: - - ```python - def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False): - - ... - - # Add generalized indicators here (because in live, it will call only this function to populate - # indicators for retraining). Notice how we ensure not to add them multiple times by associating - # these generalized indicators to the basepair/timeframe - if set_generalized_indicators: - df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 - df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 - - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) - ``` - - (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`) - -### Deciding the sliding training window and backtesting duration - -Users define the backtesting timerange with the typical `--timerange` parameter in the user -configuration file. `train_period_days` is the duration of the sliding training window, while -`backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be -a float to indicate sub daily retraining in live/dry mode). In the present example, -the user is asking FreqAI to use a training period of 30 days and backtest the subsequent 7 days. -This means that if the user sets `--timerange 20210501-20210701`, -FreqAI will train 8 separate models (because the full range comprises 8 weeks), -and then backtest the subsequent week associated with each of the 8 training -data set timerange months. Users can think of this as a "sliding window" which -emulates FreqAI retraining itself once per week in live using the previous -month of data. - -In live, the required training data is automatically computed and downloaded. However, in backtesting -the user must manually enter the required number of `startup_candles` in the config. This value -is used to increase the available data to FreqAI and should be sufficient to enable all indicators -to be NaN free at the beginning of the first training timerange. This boils down to identifying the -highest timeframe (`4h` in present example) and the longest indicator period (25 in present example) -and adding this to the `train_period_days`. The units need to be in the base candle time frame: - -`startup_candles` = ( 4 hours * 25 max period * 60 minutes/hour + 30 day train_period_days * 1440 minutes per day ) / 5 min (base time frame) = 1488. - -!!! Note - In dry/live, this is all precomputed and handled automatically. Thus, `startup_candle` has no influence on dry/live. - -!!! Note - Although fractional `backtest_period_days` is allowed, the user should be ware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. This is why it is physically impossible to truly backtest FreqAI adaptive training. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run. - -## Running FreqAI - -### Backtesting - -The FreqAI backtesting module can be executed with the following command: - -```bash -freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701 -``` - -Backtesting mode requires the user to have the data pre-downloaded (unlike dry/live, where FreqAI automatically downloads the necessary data). The user should be careful to consider that the range of the downloaded data is more than the backtesting range. This is because FreqAI needs data prior to the desired backtesting range in order to train a model to be ready to make predictions on the first candle of the user set backtesting range. More details on how to calculate the data download timerange can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration). - -If this command has never been executed with the existing config file, then it will train a new model -for each pair, for each backtesting window within the bigger `--timerange`. - -!!! Note "Model reuse" - Once the training is completed, the user can execute this again with the same config file and - FreqAI will find the trained models and load them instead of spending time training. This is useful - if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. IF the user - *wants* to retrain a new model with the same config file, then he/she should simply change the `identifier`. - This way, the user can return to using any model they wish by simply changing the `identifier`. - ---- - -### Building a freqai strategy - -The FreqAI strategy requires the user to include the following lines of code in the strategy: - -```python - - # user should define the maximum startup candle count (the largest number of candles + # user should define the maximum startup candle count (the largest number of candles # passed to any single indicator) startup_candle_count: int = 20 @@ -405,12 +202,9 @@ The FreqAI strategy requires the user to include the following lines of code in def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - # All indicators must be populated by populate_any_indicators() for live functionality - # to work correctly. - - # the model will return all labels created by user in `populate_any_indicators` - # (& appended targets), an indication of whether or not the prediction should be accepted, - # the target mean/std values for each of the labels created by user in + # the model will return all labels created by user in `populate_any_indicators` + # (& appended targets), an indication of whether or not the prediction should be accepted, + # the target mean/std values for each of the labels created by user in # `populate_any_indicators()` for each training period. dataframe = self.freqai.start(dataframe, metadata, self) @@ -483,160 +277,316 @@ The FreqAI strategy requires the user to include the following lines of code in ``` -Notice how the `populate_any_indicators()` is where the user adds their own features and labels ([more information](#feature-engineering)). See a full example at `templates/FreqaiExampleStrategy.py`. +Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](#feature-engineering)) and labels ([more information](#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`. -### Setting classifier targets +## Creating a dynamic target -FreqAI includes a the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. Typically, the user would set the targets using strings: +The `&*_std/mean` return values describe the statistical fit of the user defined label *during the most recent training*. This value allows the user to know the rarity of a given prediction. For example, `templates/FreqaiExampleStrategy.py`, creates a `target_roi` which is based on filtering out predictions that are below a given z-score of 1.25. + +```python +dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 +dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 +``` + +If the user wishes to consider the population +of *historical predictions* for creating the dynamic target instead of the trained labels, (as discussed above) the user +can do so by setting `fit_live_prediction_candles` in the config to the number of historical prediction candles +the user wishes to use to generate target statistics. + +```json + "freqai": { + "fit_live_prediction_candles": 300, + } +``` + +If the user sets this value, FreqAI will initially use the predictions from the training data +and subsequently begin introducing real prediction data as it is generated. FreqAI will save +this historical data to be reloaded if the user stops and restarts a model with the same `identifier`. + +## Building a custom prediction model + +FreqAI has multiple example prediction model libraries, such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression. +However, the user can customize and create their own prediction models using the `IFreqaiModel` class. +The user is encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures. + +## Feature engineering + +Features are added by the user inside the `populate_any_indicators()` method of the strategy +by prepending indicators with `%`, and labels with `&`. + +There are some important components/structures that the user *must* include when building their feature set; the use of these is shown below: + +```python + def populate_any_indicators( + self, pair, df, tf, informative=None, set_generalized_indicators=False + ): + """ + Function designed to automatically generate, name, and merge features + from user-indicated timeframes in the configuration file. The user controls the indicators + passed to the training/prediction by prepending indicators with `'%-' + coin ` + (see convention below). I.e., the user should not prepend any supporting metrics + (e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the + model. + :param pair: pair to be used as informative + :param df: strategy dataframe which will receive merges from informatives + :param tf: timeframe of the dataframe which will modify the feature names + :param informative: the dataframe associated with the informative pair + :param coin: the name of the coin which will modify the feature names. + """ + + coin = pair.split('/')[0] + + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) + + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + + bollinger = qtpylib.bollinger_bands( + qtpylib.typical_price(informative), window=t, stds=2.2 + ) + informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"] + informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"] + informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"] + + informative[f"%-{coin}bb_width-period_{t}"] = ( + informative[f"{coin}bb_upperband-period_{t}"] + - informative[f"{coin}bb_lowerband-period_{t}"] + ) / informative[f"{coin}bb_middleband-period_{t}"] + informative[f"%-{coin}close-bb_lower-period_{t}"] = ( + informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"] + ) + + informative[f"%-{coin}relative_volume-period_{t}"] = ( + informative["volume"] / informative["volume"].rolling(t).mean() + ) + + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) + + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) + + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: + df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 + df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 + + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) + + return df +``` + +In the presented example strategy, the user does not wish to pass the `bb_lowerband` as a feature to the model, +and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the +model for training/prediction and has therefore prepended it with `%`. + +The `include_timeframes` in the example config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the present case, the user is asking for the +`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set. + +The user can ask for each of the defined features to be included also from +informative pairs using the `include_corr_pairlist`. This means that the feature +set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD`). + +`include_shifted_candles` indicates the number of previous +candles to include in the feature set. For example, `include_shifted_candles: 2` tells +FreqAI to include the past 2 candles for each of the features in the feature set. + +In total, the number of features the user of the presented example strat has created is: +length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles` + $= 3 * 3 * 3 * 2 * 2 = 108$. + +Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`). +This is where the user will add single features and labels to their feature set to avoid duplication of them from +various configuration parameters that multiply the feature set, such as `include_timeframes`. + +!!! Note + Features **must** be defined in `populate_any_indicators()`. Definining FreqAI features in `populate_indicators()` + will cause the algorithm to fail in live/dry mode. If the user wishes to add generalized features that are not associated with + a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` + (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`): + + ```python + def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False): + + ... + + # Add generalized indicators here (because in live, it will call only this function to populate + # indicators for retraining). Notice how we ensure not to add them multiple times by associating + # these generalized indicators to the basepair/timeframe + if set_generalized_indicators: + df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 + df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 + + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) + ``` + + (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.) + +## Setting classifier targets + +FreqAI includes the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. The user should take care to set the classes using strings: ```python df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down') ``` +Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column. + +## Running FreqAI + +There are two ways to train and deploy an adaptive machine learning model. FreqAI enables live deployment as well as backtesting analyses. In both cases, a model is trained periodically, as shown in the following figure. + +![freqai-window](assets/freqai_moving-window.jpg) + ### Running the model live -FreqAI can be run dry/live using the following command +FreqAI can be run dry/live using the following command: ```bash freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor ``` By default, FreqAI will not find any existing models and will start by training a new one -given the user configuration settings. Following training, it will use that model to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the pairs to try and keep all models equally "young." FreqAI will always use the newest trained model to make predictions on incoming live data. If users do not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before retraining a new model. Additionally, users can set `expired_hours` to tell FreqAI to avoid making predictions on models aged over this number of hours. +based on the user's configuration settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If the user does not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, the user can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours. -If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse -the same `identifier` parameter +If the user wishes to start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), the user only needs to reuse +the same `identifier` parameter: ```json "freqai": { "identifier": "example", - "live_retrain_hours": 1 + "live_retrain_hours": 0.5 } ``` In this case, although FreqAI will initiate with a pre-trained model, it will still check to see how much time has elapsed since the model was trained, -and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will self retrain. +and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will retrain. -## Data analysis techniques +### Backtesting -### Controlling the model learning process +The FreqAI backtesting module can be executed with the following command: -Model training parameters are unique to the ML library used by the user. FreqAI allows users to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration files show some of the example parameters associated with `Catboost` and `LightGBM`, but users can add any parameters available in those libraries. +```bash +freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701 +``` -Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function. FreqAI includes some additional parameters such `weight_factor` which allows the user to weight more recent data more strongly -than past data via an exponential function: +Backtesting mode requires the user to have the data pre-downloaded (unlike in dry/live mode where FreqAI automatically downloads the necessary data). The user should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the user-set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration). -$$ W_i = \exp(\frac{-i}{\alpha*n}) $$ +If this command has never been executed with the existing config file, it will train a new model +for each pair, for each backtesting window within the expanded `--timerange`. -where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. +!!! Note "Model reuse" + Once the training is completed, the user can execute the backtesting again with the same config file and + FreqAI will find the trained models and load them instead of spending time training. This is useful + if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. If the user + *wants* to retrain a new model with the same config file, then they should simply change the `identifier`. + This way, the user can return to using any model they wish by simply specifying the `identifier`. -![weight-factor](assets/weights_factor.png) +--- -`train_test_split()` has a parameters called `shuffle`, which users also have access to in FreqAI, that allows them to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. +### Deciding the size of the sliding training window and backtesting duration -Finally, `label_period_candles` defines the offset used for the `labels`. In the present example, -the user is asking for `labels` that are 24 candles in the future. +The user defines the backtesting timerange with the typical `--timerange` parameter in the +configuration file. The duration of the sliding training window is set by `train_period_days`, whilst +`backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be +a float to indicate sub-daily retraining in live/dry mode). In the presented example config, +the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. +This means that if the user sets `--timerange 20210501-20210701`, +FreqAI will train have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks). After the training of the model, FreqAI will backtest the subsequent 7 days. The "sliding window" then moves one week forward (emulating FreqAI retraining once per week in live mode) and the new model uses the previous 30 days (including the 7 days used for backtesting by the previous model) to train. This is repeated until the end of `--timerange`. -### Removing outliers with the Dissimilarity Index +In live mode, the required training data is automatically computed and downloaded. However, in backtesting mode, +the user must manually enter the required number of `startup_candles` in the config. This value +is used to increase the data to FreqAI, which should be sufficient to enable all indicators +to be NaN free at the beginning of the first training. This is done by identifying the +longest timeframe (`4h` in presented example config) and the longest indicator period (`20` days in presented example config) +and adding this to the `train_period_days`. The units need to be in the base candle time frame: +`startup_candles` = ( 4 hours * 20 max period * 60 minutes/hour + 30 day train_period_days * 1440 minutes per day ) / 5 min (base time frame) = 9360. -The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each -prediction by the model. To do so, FreqAI measures the distance between each training -data point and all other training data points: +!!! Note + In dry/live mode, this is all precomputed and handled automatically. Thus, `startup_candle` has no influence on dry/live mode. -$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ +!!! Note + Although fractional `backtest_period_days` is allowed, the user should be aware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. Because of this, a true backtest of FreqAI adaptive training would take a *very* long time. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run. -where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ -is the number of features i.e. the length of the vector $X$. -The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean -of the average distances: +### Defining model expirations -$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ - -$\overline{d}$ quantifies the spread of the training data, which is compared to -the distance between the new prediction feature vectors, $X_k$ and all the training -data: - -$$ d_k = \arg \min d_{k,i} $$ - -which enables the estimation of a Dissimilarity Index: - -$$ DI_k = d_k/\overline{d} $$ - -Equity and crypto markets suffer from a high level of non-patterned noise in the -form of outlier data points. The dissimilarity index allows predictions which -are outliers and not existent in the model feature space, to be thrown out due -to low levels of certainty. Activating the Dissimilarity Index can be achieved with: +During dry/live mode, FreqAI trains each coin pair sequentially (on separate threads/GPU from the main Freqtrade bot). This means that there is always an age discrepancy between models. If a user is training on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. This may be undesirable if the characteristic time scale (the trade duration target) for a strategy is less than 4 hours. The user can decide to only make trade entries if the model is less than +a certain number of hours old by setting the `expiration_hours` in the config file: ```json "freqai": { - "feature_parameters" : { - "DI_threshold": 1 - } + "expiration_hours": 0.5, } ``` -The user can tweak the DI with `DI_threshold` to increase or decrease the extrapolation of the trained model. +In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old. -### Reducing data dimensionality with Principal Component Analysis +### Purging old model data -Users can reduce the dimensionality of their features by activating the `principal_component_analysis`: +FreqAI stores new model files each time it retrains. These files become obsolete as new models are trained and FreqAI adapts to new market conditions. Users planning to leave FreqAI running for extended periods of time with high frequency retraining should enable `purge_old_models` in their config: ```json "freqai": { - "feature_parameters" : { - "principal_component_analysis": true - } + "purge_old_models": true, } ``` -Which will perform PCA on the features and reduce the dimensionality of the data so that the explained -variance of the data set is >= 0.999. +This will automatically purge all models older than the two most recently trained ones. -### Removing outliers using a Support Vector Machine (SVM) +### Returning additional info from training -The user can tell FreqAI to remove outlier data points from the training/test data sets by setting: +The user may find that there are some important metrics that they'd like to return to the strategy at the end of each model training. +The user can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy. +The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is +the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of the particular target (label) during the most recent training. +An example, where the user wants to use live metrics from the trade database, is shown below: ```json "freqai": { - "feature_parameters" : { - "use_SVM_to_remove_outliers": true - } + "extra_returns_per_train": {"total_profit": 4} } ``` -FreqAI will train an SVM on the training data (or components if the user activated -`principal_component_analysis`) and remove any data point that it deems to be sitting beyond the feature space. +The user needs to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, this is the value that will be returned. -### Clustering the training data and removing outliers with DBSCAN - -The user can configure FreqAI to use DBSCAN to cluster training data and remove outliers from the training data set. The user activates `use_DBSCAN_to_remove_outliers` to cluster training data for identification of outliers. Also used to detect incoming outliers for prediction data points. - -```json - "freqai": { - "feature_parameters" : { - "use_DBSCAN_to_remove_outliers": true - } - } -``` - -### Stratifying the data - -The user can stratify the training/testing data using: - -```json - "freqai": { - "feature_parameters" : { - "stratify_training_data": 3 - } - } -``` - -which will split the data chronologically so that every Xth data points is a testing data point. In the -present example, the user is asking for every third data point in the dataframe to be used for -testing, the other points are used for training. - -## Setting up a follower +### Setting up a follower The user can define: @@ -647,112 +597,152 @@ The user can define: } ``` -to indicate to the bot that it should not train models, but instead should look for models trained -by a leader with the same `identifier`. In this example, the user has a leader bot with the -`identifier: "example"` already running or launching simultaneously as the present follower. +to indicate to the bot that it should not train models, but instead should look for models trained by a leader with the same `identifier`. In this example, the user has a leader bot with the `identifier: "example"`. The leader bot is already running or launching simultaneously as the follower. The follower will load models created by the leader and inference them to obtain predictions. -## Purging old model data +## Data manipulation techniques -FreqAI stores new model files each time it retrains. These files become obsolete as new models -are trained and FreqAI adapts to the new market conditions. Users planning to leave FreqAI running -for extended periods of time with high frequency retraining should set `purge_old_models` in their -config: +### Feature normalization + +The feature set created by the user is automatically normalized to the training data. This includes all test data and unseen prediction data (dry/live/backtest). + +### Reducing data dimensionality with Principal Component Analysis + +Users can reduce the dimensionality of their features by activating the `principal_component_analysis` in the config: ```json "freqai": { - "purge_old_models": true, + "feature_parameters" : { + "principal_component_analysis": true + } } ``` -which will automatically purge all models older than the two most recently trained ones. +This will perform PCA on the features and reduce the dimensionality of the data so that the explained variance of the data set is >= 0.999. -## Defining model expirations +### Stratifying the data for training and testing the model -During dry/live, FreqAI trains each pair sequentially (on separate threads/GPU from the main -Freqtrade bot). This means there is always an age discrepancy between models. If a user is training -on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. -This may be undesirable if the characteristic time scale (read trade duration target) for a strategy -is much less than 4 hours. The user can decide to only make trade entries if the model is less than -a certain number of hours in age by setting the `expiration_hours` in the config file: +The user can stratify (group) the training/testing data using: ```json "freqai": { - "expiration_hours": 0.5, + "feature_parameters" : { + "stratify_training_data": 3 + } } ``` -In the present example, the user will only allow predictions on models that are less than 1/2 hours -old. +This will split the data chronologically so that every Xth data point is used to test the model after training. In the +example above, the user is asking for every third data point in the dataframe to be used for +testing; the other points are used for training. -## Choosing the calculation of the `target_roi` +The test data is used to evaluate the performance of the model after training. If the test score is high, the model is able to capture the behavior of the data well. If the test score is low, either the model either does not capture the complexity of the data, the test data is significantly different from the train data, or a different model should be used. -As shown in `templates/FreqaiExampleStrategy.py`, the `target_roi` is based on two metrics computed -by FreqAI: `label_mean` and `label_std`. These are the statistics associated with the labels used -*during the most recent training*. -This allows the model to know what magnitude of a target to be expecting since it is directly stemming from the training data. -By default, FreqAI computes this based on training data and it assumes the labels are Gaussian distributed. -These are big assumptions that the user should consider when creating their labels. If the user wants to consider the population -of *historical predictions* for creating the dynamic target instead of the trained labels, the user -can do so by setting `fit_live_prediction_candles` to the number of historical prediction candles -the user wishes to use to generate target statistics. +### Controlling the model learning process + +Model training parameters are unique to the machine learning library selected by the user. FreqAI allows the user to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration file (found in `config_examples/config_freqai.example.json`) show some of the example parameters associated with `Catboost` and `LightGBM`, but the user can add any parameters available in those libraries. + +Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function. + +FreqAI includes some additional parameters such as `weight_factor`, which allows the user to weight more recent data more strongly +than past data via an exponential function: + +$$ W_i = \exp(\frac{-i}{\alpha*n}) $$ + +where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points (candles) in a feature set. + +![weight-factor](assets/freqai_weight-factor.jpg) + +`train_test_split()` has a parameters called `shuffle` that allows the user to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. + +Finally, `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented example config, +the user is asking for `labels` that are 24 candles in the future. + +### Outlier removal + +#### Removing outliers with the Dissimilarity Index + +The user can tell FreqAI to remove outlier data points from the training/test data sets using a Dissimilarity Index by including the following statement in the config: ```json "freqai": { - "fit_live_prediction_candles": 300, + "feature_parameters" : { + "DI_threshold": 1 + } } ``` -If the user sets this value, FreqAI will initially use the predictions from the training data set -and then subsequently begin introducing real prediction data as it is generated. FreqAI will save -this historical data to be reloaded if the user stops and restarts with the same `identifier`. +Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. -## Extra returns per train +To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points: -Users may find that there are some important metrics that they'd like to return to the strategy at the end of each retrain. -Users can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction -model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy. -The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is -the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of that particular label during the most recent training. -Another example is shown below if the user wants to use live metrics from the trade database. +$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ -The user needs to set the standard dictionary in the config so FreqAI can return proper dataframe shapes: +where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean of the average distances: + +$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ + +$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data: + +$$ d_k = \arg \min d_{k,i} $$ + +which enables the estimation of the Dissimilarity Index as: + +$$ DI_k = d_k/\overline{d} $$ + +The user can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model. + +Below is a figure that describes the DI for a 3D data set. + +![DI](assets/freqai_DI.jpg) + +#### Removing outliers using a Support Vector Machine (SVM) + +The user can tell FreqAI to remove outlier data points from the training/test data sets using a SVM by setting: ```json "freqai": { - "extra_returns_per_train": {"total_profit": 4} + "feature_parameters" : { + "use_SVM_to_remove_outliers": true + } } ``` -These values will likely be overridden by the user prediction model, but in the case where the user model has yet to set them, or needs -a default initial value - this is the value that will be returned. +FreqAI will train an SVM on the training data (or components of it if the user activated +`principal_component_analysis`) and remove any data point that the SVM deems to be beyond the feature space. -## Building an IFreqaiModel +The parameter `shuffle` is by default set to `False` to ensure consistent results. If it is set to `True`, running the SVM multiple times on the same data set might result in different outcomes due to `max_iter` being to low for the algorithm to reach the demanded `tol`. Increasing `max_iter` solves this issue but causes the procedure to take longer time. -FreqAI has multiple example prediction model based libraries such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression. -However, users can customize and create their own prediction models using the `IFreqaiModel` class. -Users are encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures. +The parameter `nu`, *very* broadly, is the amount of data points that should be considered outliers. + +#### Removing outliers with DBSCAN + +The user can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config: + +```json + "freqai": { + "feature_parameters" : { + "use_DBSCAN_to_remove_outliers": true + } + } +``` + +DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be. + +Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$. + +![dbscan](assets/freqai_dbscan.jpg) + +FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](#https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html)) with `min_samples` ($N$) taken as double the no. of user-defined features, and `eps` ($\varepsilon$) taken as the longest distance in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set. ## Additional information ### Common pitfalls -FreqAI cannot be combined with `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically). +FreqAI cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically). This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively, it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends -new candles automatically for future retrains. But this means that if new pairs arrive later in the dry run due -to a volume pairlist, it will not have the data ready. FreqAI does work, however, with the `ShufflePairlist`. - -### Feature normalization - -The feature set created by the user is automatically normalized to the training data only. -This includes all test data and unseen prediction data (dry/live/backtest). - -### File structure - -`user_data_dir/models/` contains all the data associated with the trainings and backtests. -This file structure is heavily controlled and read by the `FreqaiDataKitchen()` -and should therefore not be modified. +new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume). ## Credits @@ -761,8 +751,8 @@ FreqAI was developed by a group of individuals who all contributed specific skil Conception and software development: Robert Caulk @robcaulk -Theoretical brainstorming: -Elin Törnquist @thorntwig +Theoretical brainstorming, data analysis: +Elin Törnquist @th0rntwig Code review, software architecture brainstorming: @xmatthias diff --git a/docs/hyperopt.md b/docs/hyperopt.md index c9ec30056..6b6c2a772 100644 --- a/docs/hyperopt.md +++ b/docs/hyperopt.md @@ -40,7 +40,8 @@ pip install -r requirements-hyperopt.txt ``` usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-s NAME] [--strategy-path PATH] - [--recursive-strategy-search] [-i TIMEFRAME] + [--recursive-strategy-search] [--freqaimodel NAME] + [--freqaimodel-path PATH] [-i TIMEFRAME] [--timerange TIMERANGE] [--data-format-ohlcv {json,jsongz,hdf5}] [--max-open-trades INT] @@ -53,7 +54,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--print-all] [--no-color] [--print-json] [-j JOBS] [--random-state INT] [--min-trades INT] [--hyperopt-loss NAME] [--disable-param-export] - [--ignore-missing-spaces] + [--ignore-missing-spaces] [--analyze-per-epoch] optional arguments: -h, --help show this help message and exit @@ -129,6 +130,7 @@ optional arguments: --ignore-missing-spaces, --ignore-unparameterized-spaces Suppress errors for any requested Hyperopt spaces that do not contain any parameters. + --analyze-per-epoch Run populate_indicators once per epoch. Common arguments: -v, --verbose Verbose mode (-vv for more, -vvv to get all messages). @@ -154,6 +156,10 @@ Strategy arguments: --recursive-strategy-search Recursively search for a strategy in the strategies folder. + --freqaimodel NAME Specify a custom freqaimodels. + --freqaimodel-path PATH + Specify additional lookup path for freqaimodels. + ``` ### Hyperopt checklist @@ -185,7 +191,7 @@ Rarely you may also need to create a [nested class](advanced-hyperopt.md#overrid ### Hyperopt execution logic -Hyperopt will first load your data into memory and will then run `populate_indicators()` once per Pair to generate all indicators. +Hyperopt will first load your data into memory and will then run `populate_indicators()` once per Pair to generate all indicators, unless `--analyze-per-epoch` is specified. Hyperopt will then spawn into different processes (number of processors, or `-j `), and run backtesting over and over again, changing the parameters that are part of the `--spaces` defined. @@ -426,9 +432,10 @@ While this strategy is most likely too simple to provide consistent profit, it s `range` property may also be used with `DecimalParameter` and `CategoricalParameter`. `RealParameter` does not provide this property due to infinite search space. ??? Hint "Performance tip" - By doing the calculation of all possible indicators in `populate_indicators()`, the calculation of the indicator happens only once for every parameter. - While this may slow down the hyperopt startup speed, the overall performance will increase as the Hyperopt execution itself may pick the same value for multiple epochs (changing other values). - You should however try to use space ranges as small as possible. Every new column will require more memory, and every possibility hyperopt can try will increase the search space. + During normal hyperopting, indicators are calculated once and supplied to each epoch, linearly increasing RAM usage as a factor of increasing cores. As this also has performance implications, hyperopt provides `--analyze-per-epoch` which will move the execution of `populate_indicators()` to the epoch process, calculating a single value per parameter per epoch instead of using the `.range` functionality. In this case, `.range` functionality will only return the actually used value. This will reduce RAM usage, but increase CPU usage. However, your hyperopting run will be less likely to fail due to Out Of Memory (OOM) issues. + + In either case, you should try to use space ranges as small as possible this will improve CPU/RAM usage in both scenarios. + ## Optimizing protections @@ -879,6 +886,7 @@ To combat these, you have multiple options: * Avoid using `--timeframe-detail` (this loads a lot of additional data into memory). * Reduce the number of parallel processes (`-j `). * Increase the memory of your machine. +* Use `--analyze-per-epoch` if you're using a lot of parameters with `.range` functionality. ## The objective has been evaluated at this point before. diff --git a/docs/requirements-docs.txt b/docs/requirements-docs.txt index a53e909e0..5a6c46471 100644 --- a/docs/requirements-docs.txt +++ b/docs/requirements-docs.txt @@ -1,6 +1,6 @@ markdown==3.3.7 mkdocs==1.3.1 -mkdocs-material==8.4.0 +mkdocs-material==8.4.2 mdx_truly_sane_lists==1.3 pymdown-extensions==9.5 jinja2==3.1.2 diff --git a/docs/rest-api.md b/docs/rest-api.md index 1ec9b6c12..cc82aadda 100644 --- a/docs/rest-api.md +++ b/docs/rest-api.md @@ -163,6 +163,8 @@ python3 scripts/rest_client.py --config rest_config.json [optional par | `strategy ` | Get specific Strategy content. **Alpha** | `available_pairs` | List available backtest data. **Alpha** | `version` | Show version. +| `sysinfo` | Show informations about the system load. +| `health` | Show bot health (last bot loop). !!! Warning "Alpha status" Endpoints labeled with *Alpha status* above may change at any time without notice. @@ -227,6 +229,11 @@ forceexit Force-exit a trade. :param tradeid: Id of the trade (can be received via status command) + :param ordertype: Order type to use (must be market or limit) + :param amount: Amount to sell. Full sell if not given + +health + Provides a quick health check of the running bot. locks Return current locks @@ -312,12 +319,13 @@ version whitelist Show the current whitelist. + ``` ### OpenAPI interface To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration. -This will enable the Swagger UI at the `/docs` endpoint. By default, that's running at http://localhost:8080/docs/ - but it'll depend on your settings. +This will enable the Swagger UI at the `/docs` endpoint. By default, that's running at http://localhost:8080/docs - but it'll depend on your settings. ### Advanced API usage using JWT tokens diff --git a/docs/strategy-callbacks.md b/docs/strategy-callbacks.md index a9b032818..0b8403414 100644 --- a/docs/strategy-callbacks.md +++ b/docs/strategy-callbacks.md @@ -75,7 +75,7 @@ class AwesomeStrategy(IStrategy): ``` -### Stake size management +## Stake size management Called before entering a trade, makes it possible to manage your position size when placing a new trade. @@ -423,7 +423,7 @@ class AwesomeStrategy(IStrategy): !!! Warning "Backtesting" Custom prices are supported in backtesting (starting with 2021.12), and orders will fill if the price falls within the candle's low/high range. Orders that don't fill immediately are subject to regular timeout handling, which happens once per (detail) candle. - `custom_exit_price()` is only called for sells of type exit_signal and Custom exit. All other exit-types will use regular backtesting prices. + `custom_exit_price()` is only called for sells of type exit_signal, Custom exit and partial exits. All other exit-types will use regular backtesting prices. ## Custom order timeout rules @@ -654,7 +654,7 @@ Position adjustments will always be applied in the direction of the trade, so a Stoploss is still calculated from the initial opening price, not averaged price. Regular stoploss rules still apply (cannot move down). - While `/stopbuy` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades. + While `/stopentry` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades. !!! Warning "Backtesting" During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected. diff --git a/docs/strategy_analysis_example.md b/docs/strategy_analysis_example.md index fbfce37d1..1526ea038 100644 --- a/docs/strategy_analysis_example.md +++ b/docs/strategy_analysis_example.md @@ -14,7 +14,7 @@ from freqtrade.configuration import Configuration # Initialize empty configuration object config = Configuration.from_files([]) -# Optionally, use existing configuration file +# Optionally (recommended), use existing configuration file # config = Configuration.from_files(["config.json"]) # Define some constants @@ -22,7 +22,7 @@ config["timeframe"] = "5m" # Name of the strategy class config["strategy"] = "SampleStrategy" # Location of the data -data_location = Path(config['user_data_dir'], 'data', 'binance') +data_location = config['datadir'] # Pair to analyze - Only use one pair here pair = "BTC/USDT" ``` diff --git a/docs/strategy_migration.md b/docs/strategy_migration.md index 064e7a59d..ac65abff4 100644 --- a/docs/strategy_migration.md +++ b/docs/strategy_migration.md @@ -332,8 +332,8 @@ After: ``` python hl_lines="2 3" order_time_in_force: Dict = { - "entry": "gtc", - "exit": "gtc", + "entry": "GTC", + "exit": "GTC", } ``` diff --git a/docs/telegram-usage.md b/docs/telegram-usage.md index add889681..ece8700de 100644 --- a/docs/telegram-usage.md +++ b/docs/telegram-usage.md @@ -149,7 +149,7 @@ You can create your own keyboard in `config.json`: !!! Note "Supported Commands" Only the following commands are allowed. Command arguments are not supported! - `/start`, `/stop`, `/status`, `/status table`, `/trades`, `/profit`, `/performance`, `/daily`, `/stats`, `/count`, `/locks`, `/balance`, `/stopbuy`, `/reload_config`, `/show_config`, `/logs`, `/whitelist`, `/blacklist`, `/edge`, `/help`, `/version` + `/start`, `/stop`, `/status`, `/status table`, `/trades`, `/profit`, `/performance`, `/daily`, `/stats`, `/count`, `/locks`, `/balance`, `/stopentry`, `/reload_config`, `/show_config`, `/logs`, `/whitelist`, `/blacklist`, `/edge`, `/help`, `/version` ## Telegram commands @@ -161,7 +161,7 @@ official commands. You can ask at any moment for help with `/help`. |----------|-------------| | `/start` | Starts the trader | `/stop` | Stops the trader -| `/stopbuy` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules. +| `/stopbuy | /stopentry` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules. | `/reload_config` | Reloads the configuration file | `/show_config` | Shows part of the current configuration with relevant settings to operation | `/logs [limit]` | Show last log messages. diff --git a/docs/utils.md b/docs/utils.md index 0dd88b242..5646365e4 100644 --- a/docs/utils.md +++ b/docs/utils.md @@ -611,6 +611,26 @@ Common arguments: ``` +### Webserver mode - docker + +You can also use webserver mode via docker. +Starting a one-off container requires the configuration of the port explicitly, as ports are not exposed by default. +You can use `docker-compose run --rm -p 127.0.0.1:8080:8080 freqtrade webserver` to start a one-off container that'll be removed once you stop it. This assumes that port 8080 is still available and no other bot is running on that port. + +Alternatively, you can reconfigure the docker-compose file to have the command updated: + +``` yml + command: > + webserver + --config /freqtrade/user_data/config.json +``` + +You can now use `docker-compose up` to start the webserver. +This assumes that the configuration has a webserver enabled and configured for docker (listening port = `0.0.0.0`). + +!!! Tip + Don't forget to reset the command back to the trade command if you want to start a live or dry-run bot. + ## Show previous Backtest results Allows you to show previous backtest results. diff --git a/freqtrade/__init__.py b/freqtrade/__init__.py index 2572c03f1..77c305c66 100644 --- a/freqtrade/__init__.py +++ b/freqtrade/__init__.py @@ -1,5 +1,5 @@ """ Freqtrade bot """ -__version__ = '2022.8.dev' +__version__ = '2022.9.dev' if 'dev' in __version__: try: diff --git a/freqtrade/commands/arguments.py b/freqtrade/commands/arguments.py index 48a423be4..37ce17f21 100644 --- a/freqtrade/commands/arguments.py +++ b/freqtrade/commands/arguments.py @@ -34,7 +34,7 @@ ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path", "print_colorized", "print_json", "hyperopt_jobs", "hyperopt_random_state", "hyperopt_min_trades", "hyperopt_loss", "disableparamexport", - "hyperopt_ignore_missing_space"] + "hyperopt_ignore_missing_space", "analyze_per_epoch"] ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"] @@ -69,7 +69,7 @@ ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes", "exchange", "tradin ARGS_CONVERT_TRADES = ["pairs", "timeframes", "exchange", "dataformat_ohlcv", "dataformat_trades"] -ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs", "trading_mode"] +ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs", "trading_mode", "show_timerange"] ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "new_pairs_days", "include_inactive", "timerange", "download_trades", "exchange", "timeframes", diff --git a/freqtrade/commands/cli_options.py b/freqtrade/commands/cli_options.py index f85b75af1..3d094da36 100644 --- a/freqtrade/commands/cli_options.py +++ b/freqtrade/commands/cli_options.py @@ -255,6 +255,13 @@ AVAILABLE_CLI_OPTIONS = { nargs='+', default='default', ), + "analyze_per_epoch": Arg( + '--analyze-per-epoch', + help='Run populate_indicators once per epoch.', + action='store_true', + default=False, + ), + "print_all": Arg( '--print-all', help='Print all results, not only the best ones.', @@ -367,7 +374,7 @@ AVAILABLE_CLI_OPTIONS = { metavar='BASE_CURRENCY', ), "trading_mode": Arg( - '--trading-mode', + '--trading-mode', '--tradingmode', help='Select Trading mode', choices=constants.TRADING_MODES, ), @@ -434,6 +441,11 @@ AVAILABLE_CLI_OPTIONS = { help='Storage format for downloaded trades data. (default: `jsongz`).', choices=constants.AVAILABLE_DATAHANDLERS, ), + "show_timerange": Arg( + '--show-timerange', + help='Show timerange available for available data. (May take a while to calculate).', + action='store_true', + ), "exchange": Arg( '--exchange', help=f'Exchange name (default: `{constants.DEFAULT_EXCHANGE}`). ' @@ -450,7 +462,7 @@ AVAILABLE_CLI_OPTIONS = { ), "prepend_data": Arg( '--prepend', - help='Allow data prepending.', + help='Allow data prepending. (Data-appending is disabled)', action='store_true', ), "erase": Arg( diff --git a/freqtrade/commands/data_commands.py b/freqtrade/commands/data_commands.py index 311590e64..360387aa6 100644 --- a/freqtrade/commands/data_commands.py +++ b/freqtrade/commands/data_commands.py @@ -5,13 +5,13 @@ from datetime import datetime, timedelta from typing import Any, Dict, List from freqtrade.configuration import TimeRange, setup_utils_configuration +from freqtrade.constants import DATETIME_PRINT_FORMAT from freqtrade.data.converter import convert_ohlcv_format, convert_trades_format from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_ohlcv_data, refresh_backtest_trades_data) from freqtrade.enums import CandleType, RunMode, TradingMode from freqtrade.exceptions import OperationalException -from freqtrade.exchange import timeframe_to_minutes -from freqtrade.exchange.exchange import market_is_active +from freqtrade.exchange import market_is_active, timeframe_to_minutes from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist from freqtrade.resolvers import ExchangeResolver @@ -80,7 +80,7 @@ def start_download_data(args: Dict[str, Any]) -> None: data_format_trades=config['dataformat_trades'], ) else: - if not exchange._ft_has.get('ohlcv_has_history', True): + if not exchange.get_option('ohlcv_has_history', True): raise OperationalException( f"Historic klines not available for {exchange.name}. " "Please use `--dl-trades` instead for this exchange " @@ -177,17 +177,31 @@ def start_list_data(args: Dict[str, Any]) -> None: paircombs = [comb for comb in paircombs if comb[0] in args['pairs']] print(f"Found {len(paircombs)} pair / timeframe combinations.") - groupedpair = defaultdict(list) - for pair, timeframe, candle_type in sorted( - paircombs, - key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2]) - ): - groupedpair[(pair, candle_type)].append(timeframe) + if not config.get('show_timerange'): + groupedpair = defaultdict(list) + for pair, timeframe, candle_type in sorted( + paircombs, + key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2]) + ): + groupedpair[(pair, candle_type)].append(timeframe) - if groupedpair: + if groupedpair: + print(tabulate([ + (pair, ', '.join(timeframes), candle_type) + for (pair, candle_type), timeframes in groupedpair.items() + ], + headers=("Pair", "Timeframe", "Type"), + tablefmt='psql', stralign='right')) + else: + paircombs1 = [( + pair, timeframe, candle_type, + *dhc.ohlcv_data_min_max(pair, timeframe, candle_type) + ) for pair, timeframe, candle_type in paircombs] print(tabulate([ - (pair, ', '.join(timeframes), candle_type) - for (pair, candle_type), timeframes in groupedpair.items() - ], - headers=("Pair", "Timeframe", "Type"), + (pair, timeframe, candle_type, + start.strftime(DATETIME_PRINT_FORMAT), + end.strftime(DATETIME_PRINT_FORMAT)) + for pair, timeframe, candle_type, start, end in paircombs1 + ], + headers=("Pair", "Timeframe", "Type", 'From', 'To'), tablefmt='psql', stralign='right')) diff --git a/freqtrade/configuration/configuration.py b/freqtrade/configuration/configuration.py index b4f36aa3c..7c68ac46c 100644 --- a/freqtrade/configuration/configuration.py +++ b/freqtrade/configuration/configuration.py @@ -302,6 +302,9 @@ class Configuration: self._args_to_config(config, argname='spaces', logstring='Parameter -s/--spaces detected: {}') + self._args_to_config(config, argname='analyze_per_epoch', + logstring='Parameter --analyze-per-epoch detected.') + self._args_to_config(config, argname='print_all', logstring='Parameter --print-all detected ...') @@ -426,6 +429,9 @@ class Configuration: self._args_to_config(config, argname='dataformat_trades', logstring='Using "{}" to store trades data.') + self._args_to_config(config, argname='show_timerange', + logstring='Detected --show-timerange') + def _process_data_options(self, config: Dict[str, Any]) -> None: self._args_to_config(config, argname='new_pairs_days', logstring='Detected --new-pairs-days: {}') diff --git a/freqtrade/constants.py b/freqtrade/constants.py index ddbc84fa9..bab8c4816 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -23,7 +23,8 @@ REQUIRED_ORDERTIF = ['entry', 'exit'] REQUIRED_ORDERTYPES = ['entry', 'exit', 'stoploss', 'stoploss_on_exchange'] PRICING_SIDES = ['ask', 'bid', 'same', 'other'] ORDERTYPE_POSSIBILITIES = ['limit', 'market'] -ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc'] +_ORDERTIF_POSSIBILITIES = ['GTC', 'FOK', 'IOC', 'PO'] +ORDERTIF_POSSIBILITIES = _ORDERTIF_POSSIBILITIES + [t.lower() for t in _ORDERTIF_POSSIBILITIES] HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss', 'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily', 'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily', diff --git a/freqtrade/data/history/hdf5datahandler.py b/freqtrade/data/history/hdf5datahandler.py index dadc9c7e6..135d97c79 100644 --- a/freqtrade/data/history/hdf5datahandler.py +++ b/freqtrade/data/history/hdf5datahandler.py @@ -7,9 +7,8 @@ import numpy as np import pandas as pd from freqtrade.configuration import TimeRange -from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, - ListPairsWithTimeframes, TradeList) -from freqtrade.enums import CandleType, TradingMode +from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList +from freqtrade.enums import CandleType from .idatahandler import IDataHandler @@ -21,29 +20,6 @@ class HDF5DataHandler(IDataHandler): _columns = DEFAULT_DATAFRAME_COLUMNS - @classmethod - def ohlcv_get_available_data( - cls, datadir: Path, trading_mode: TradingMode) -> ListPairsWithTimeframes: - """ - Returns a list of all pairs with ohlcv data available in this datadir - :param datadir: Directory to search for ohlcv files - :param trading_mode: trading-mode to be used - :return: List of Tuples of (pair, timeframe) - """ - if trading_mode == TradingMode.FUTURES: - datadir = datadir.joinpath('futures') - _tmp = [ - re.search( - cls._OHLCV_REGEX, p.name - ) for p in datadir.glob("*.h5") - ] - return [ - ( - cls.rebuild_pair_from_filename(match[1]), - cls.rebuild_timeframe_from_filename(match[2]), - CandleType.from_string(match[3]) - ) for match in _tmp if match and len(match.groups()) > 1] - @classmethod def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]: """ diff --git a/freqtrade/data/history/history_utils.py b/freqtrade/data/history/history_utils.py index c972c841c..7a3fa4e0c 100644 --- a/freqtrade/data/history/history_utils.py +++ b/freqtrade/data/history/history_utils.py @@ -56,7 +56,7 @@ def load_pair_history(pair: str, fill_missing=fill_up_missing, drop_incomplete=drop_incomplete, startup_candles=startup_candles, - candle_type=candle_type + candle_type=candle_type, ) @@ -97,14 +97,15 @@ def load_data(datadir: Path, fill_up_missing=fill_up_missing, startup_candles=startup_candles, data_handler=data_handler, - candle_type=candle_type + candle_type=candle_type, ) if not hist.empty: result[pair] = hist else: if candle_type is CandleType.FUNDING_RATE and user_futures_funding_rate is not None: logger.warn(f"{pair} using user specified [{user_futures_funding_rate}]") - result[pair] = DataFrame(columns=["open", "close", "high", "low", "volume"]) + elif candle_type not in (CandleType.SPOT, CandleType.FUTURES): + result[pair] = DataFrame(columns=["date", "open", "close", "high", "low", "volume"]) if fail_without_data and not result: raise OperationalException("No data found. Terminating.") @@ -301,8 +302,8 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes if trading_mode == 'futures': # Predefined candletype (and timeframe) depending on exchange # Downloads what is necessary to backtest based on futures data. - tf_mark = exchange._ft_has['mark_ohlcv_timeframe'] - fr_candle_type = CandleType.from_string(exchange._ft_has['mark_ohlcv_price']) + tf_mark = exchange.get_option('mark_ohlcv_timeframe') + fr_candle_type = CandleType.from_string(exchange.get_option('mark_ohlcv_price')) # All exchanges need FundingRate for futures trading. # The timeframe is aligned to the mark-price timeframe. for funding_candle_type in (CandleType.FUNDING_RATE, fr_candle_type): @@ -329,13 +330,12 @@ def _download_trades_history(exchange: Exchange, try: until = None + since = 0 if timerange: if timerange.starttype == 'date': since = timerange.startts * 1000 if timerange.stoptype == 'date': until = timerange.stopts * 1000 - else: - since = arrow.utcnow().shift(days=-new_pairs_days).int_timestamp * 1000 trades = data_handler.trades_load(pair) @@ -348,6 +348,9 @@ def _download_trades_history(exchange: Exchange, logger.info(f"Start earlier than available data. Redownloading trades for {pair}...") trades = [] + if not since: + since = arrow.utcnow().shift(days=-new_pairs_days).int_timestamp * 1000 + from_id = trades[-1][1] if trades else None if trades and since < trades[-1][0]: # Reset since to the last available point diff --git a/freqtrade/data/history/idatahandler.py b/freqtrade/data/history/idatahandler.py index 07dc7c763..846bcc607 100644 --- a/freqtrade/data/history/idatahandler.py +++ b/freqtrade/data/history/idatahandler.py @@ -9,7 +9,7 @@ from abc import ABC, abstractmethod from copy import deepcopy from datetime import datetime, timezone from pathlib import Path -from typing import List, Optional, Type +from typing import List, Optional, Tuple, Type from pandas import DataFrame @@ -39,15 +39,26 @@ class IDataHandler(ABC): raise NotImplementedError() @classmethod - @abstractmethod def ohlcv_get_available_data( cls, datadir: Path, trading_mode: TradingMode) -> ListPairsWithTimeframes: """ Returns a list of all pairs with ohlcv data available in this datadir :param datadir: Directory to search for ohlcv files :param trading_mode: trading-mode to be used - :return: List of Tuples of (pair, timeframe) + :return: List of Tuples of (pair, timeframe, CandleType) """ + if trading_mode == TradingMode.FUTURES: + datadir = datadir.joinpath('futures') + _tmp = [ + re.search( + cls._OHLCV_REGEX, p.name + ) for p in datadir.glob(f"*.{cls._get_file_extension()}")] + return [ + ( + cls.rebuild_pair_from_filename(match[1]), + cls.rebuild_timeframe_from_filename(match[2]), + CandleType.from_string(match[3]) + ) for match in _tmp if match and len(match.groups()) > 1] @classmethod @abstractmethod @@ -73,6 +84,18 @@ class IDataHandler(ABC): :return: None """ + def ohlcv_data_min_max(self, pair: str, timeframe: str, + candle_type: CandleType) -> Tuple[datetime, datetime]: + """ + Returns the min and max timestamp for the given pair and timeframe. + :param pair: Pair to get min/max for + :param timeframe: Timeframe to get min/max for + :param candle_type: Any of the enum CandleType (must match trading mode!) + :return: (min, max) + """ + data = self._ohlcv_load(pair, timeframe, None, candle_type) + return data.iloc[0]['date'].to_pydatetime(), data.iloc[-1]['date'].to_pydatetime() + @abstractmethod def _ohlcv_load(self, pair: str, timeframe: str, timerange: Optional[TimeRange], candle_type: CandleType diff --git a/freqtrade/data/history/jsondatahandler.py b/freqtrade/data/history/jsondatahandler.py index 83ec183df..a62e5e381 100644 --- a/freqtrade/data/history/jsondatahandler.py +++ b/freqtrade/data/history/jsondatahandler.py @@ -8,9 +8,9 @@ from pandas import DataFrame, read_json, to_datetime from freqtrade import misc from freqtrade.configuration import TimeRange -from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, ListPairsWithTimeframes, TradeList +from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList from freqtrade.data.converter import trades_dict_to_list -from freqtrade.enums import CandleType, TradingMode +from freqtrade.enums import CandleType from .idatahandler import IDataHandler @@ -23,28 +23,6 @@ class JsonDataHandler(IDataHandler): _use_zip = False _columns = DEFAULT_DATAFRAME_COLUMNS - @classmethod - def ohlcv_get_available_data( - cls, datadir: Path, trading_mode: TradingMode) -> ListPairsWithTimeframes: - """ - Returns a list of all pairs with ohlcv data available in this datadir - :param datadir: Directory to search for ohlcv files - :param trading_mode: trading-mode to be used - :return: List of Tuples of (pair, timeframe) - """ - if trading_mode == 'futures': - datadir = datadir.joinpath('futures') - _tmp = [ - re.search( - cls._OHLCV_REGEX, p.name - ) for p in datadir.glob(f"*.{cls._get_file_extension()}")] - return [ - ( - cls.rebuild_pair_from_filename(match[1]), - cls.rebuild_timeframe_from_filename(match[2]), - CandleType.from_string(match[3]) - ) for match in _tmp if match and len(match.groups()) > 1] - @classmethod def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]: """ diff --git a/freqtrade/edge/edge_positioning.py b/freqtrade/edge/edge_positioning.py index 2fe41a17b..af20e1645 100644 --- a/freqtrade/edge/edge_positioning.py +++ b/freqtrade/edge/edge_positioning.py @@ -15,7 +15,7 @@ from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT from freqtrade.data.history import get_timerange, load_data, refresh_data from freqtrade.enums import CandleType, ExitType, RunMode from freqtrade.exceptions import OperationalException -from freqtrade.exchange.exchange import timeframe_to_seconds +from freqtrade.exchange import timeframe_to_seconds from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist from freqtrade.strategy.interface import IStrategy diff --git a/freqtrade/enums/__init__.py b/freqtrade/enums/__init__.py index e50ebc4a4..d2f5474fc 100644 --- a/freqtrade/enums/__init__.py +++ b/freqtrade/enums/__init__.py @@ -3,6 +3,7 @@ from freqtrade.enums.backteststate import BacktestState from freqtrade.enums.candletype import CandleType from freqtrade.enums.exitchecktuple import ExitCheckTuple from freqtrade.enums.exittype import ExitType +from freqtrade.enums.hyperoptstate import HyperoptState from freqtrade.enums.marginmode import MarginMode from freqtrade.enums.ordertypevalue import OrderTypeValues from freqtrade.enums.rpcmessagetype import RPCMessageType diff --git a/freqtrade/enums/hyperoptstate.py b/freqtrade/enums/hyperoptstate.py new file mode 100644 index 000000000..6716e123a --- /dev/null +++ b/freqtrade/enums/hyperoptstate.py @@ -0,0 +1,12 @@ +from enum import Enum + + +class HyperoptState(Enum): + """ Hyperopt states """ + STARTUP = 1 + DATALOAD = 2 + INDICATORS = 3 + OPTIMIZE = 4 + + def __str__(self): + return f"{self.name.lower()}" diff --git a/freqtrade/exchange/__init__.py b/freqtrade/exchange/__init__.py index cb63c6b9a..ff7ec7e04 100644 --- a/freqtrade/exchange/__init__.py +++ b/freqtrade/exchange/__init__.py @@ -9,10 +9,11 @@ from freqtrade.exchange.bitpanda import Bitpanda from freqtrade.exchange.bittrex import Bittrex from freqtrade.exchange.bybit import Bybit from freqtrade.exchange.coinbasepro import Coinbasepro -from freqtrade.exchange.exchange import (amount_to_precision, available_exchanges, ccxt_exchanges, - date_minus_candles, is_exchange_known_ccxt, - is_exchange_officially_supported, market_is_active, - price_to_precision, timeframe_to_minutes, +from freqtrade.exchange.exchange import (amount_to_contract_precision, amount_to_contracts, + amount_to_precision, available_exchanges, ccxt_exchanges, + contracts_to_amount, date_minus_candles, + is_exchange_known_ccxt, is_exchange_officially_supported, + market_is_active, price_to_precision, timeframe_to_minutes, timeframe_to_msecs, timeframe_to_next_date, timeframe_to_prev_date, timeframe_to_seconds, validate_exchange, validate_exchanges) diff --git a/freqtrade/exchange/binance.py b/freqtrade/exchange/binance.py index 37a3c419d..026ba1c65 100644 --- a/freqtrade/exchange/binance.py +++ b/freqtrade/exchange/binance.py @@ -23,8 +23,7 @@ class Binance(Exchange): _ft_has: Dict = { "stoploss_on_exchange": True, "stoploss_order_types": {"limit": "stop_loss_limit"}, - "order_time_in_force": ['gtc', 'fok', 'ioc'], - "time_in_force_parameter": "timeInForce", + "order_time_in_force": ['GTC', 'FOK', 'IOC'], "ohlcv_candle_limit": 1000, "trades_pagination": "id", "trades_pagination_arg": "fromId", @@ -137,23 +136,27 @@ class Binance(Exchange): pair: str, open_rate: float, # Entry price of position is_short: bool, - position: float, # Absolute value of position size + amount: float, + stake_amount: float, wallet_balance: float, # Or margin balance mm_ex_1: float = 0.0, # (Binance) Cross only upnl_ex_1: float = 0.0, # (Binance) Cross only ) -> Optional[float]: """ + Important: Must be fetching data from cached values as this is used by backtesting! MARGIN: https://www.binance.com/en/support/faq/f6b010588e55413aa58b7d63ee0125ed PERPETUAL: https://www.binance.com/en/support/faq/b3c689c1f50a44cabb3a84e663b81d93 :param exchange_name: - :param open_rate: (EP1) Entry price of position + :param open_rate: Entry price of position :param is_short: True if the trade is a short, false otherwise - :param position: Absolute value of position size (in base currency) - :param wallet_balance: (WB) + :param amount: Absolute value of position size incl. leverage (in base currency) + :param stake_amount: Stake amount - Collateral in settle currency. + :param trading_mode: SPOT, MARGIN, FUTURES, etc. + :param margin_mode: Either ISOLATED or CROSS + :param wallet_balance: Amount of margin_mode in the wallet being used to trade Cross-Margin Mode: crossWalletBalance Isolated-Margin Mode: isolatedWalletBalance - :param maintenance_amt: # * Only required for Cross :param mm_ex_1: (TMM) @@ -165,12 +168,11 @@ class Binance(Exchange): """ side_1 = -1 if is_short else 1 - position = abs(position) cross_vars = upnl_ex_1 - mm_ex_1 if self.margin_mode == MarginMode.CROSS else 0.0 # mm_ratio: Binance's formula specifies maintenance margin rate which is mm_ratio * 100% # maintenance_amt: (CUM) Maintenance Amount of position - mm_ratio, maintenance_amt = self.get_maintenance_ratio_and_amt(pair, position) + mm_ratio, maintenance_amt = self.get_maintenance_ratio_and_amt(pair, stake_amount) if (maintenance_amt is None): raise OperationalException( @@ -182,9 +184,9 @@ class Binance(Exchange): return ( ( (wallet_balance + cross_vars + maintenance_amt) - - (side_1 * position * open_rate) + (side_1 * amount * open_rate) ) / ( - (position * mm_ratio) - (side_1 * position) + (amount * mm_ratio) - (side_1 * amount) ) ) else: diff --git a/freqtrade/exchange/binance_leverage_tiers.json b/freqtrade/exchange/binance_leverage_tiers.json index 1cf6ba079..eace16c05 100644 --- a/freqtrade/exchange/binance_leverage_tiers.json +++ b/freqtrade/exchange/binance_leverage_tiers.json @@ -81,6 +81,88 @@ } } ], + "1000SHIB/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "1000SHIB/USDT": [ { "tier": 1.0, @@ -1991,6 +2073,88 @@ } } ], + "AUCTION/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "AUDIO/USDT": [ { "tier": 1.0, @@ -5333,6 +5497,88 @@ } } ], + "CVX/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "DAR/USDT": [ { "tier": 1.0, @@ -7013,6 +7259,88 @@ } } ], + "ETC/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "ETC/USDT": [ { "tier": 1.0, @@ -7164,13 +7492,13 @@ "tier": 1.0, "currency": "BUSD", "minNotional": 0.0, - "maxNotional": 25000.0, + "maxNotional": 50000.0, "maintenanceMarginRate": 0.004, "maxLeverage": 50.0, "info": { "bracket": "1", "initialLeverage": "50", - "notionalCap": "25000", + "notionalCap": "50000", "notionalFloor": "0", "maintMarginRatio": "0.004", "cum": "0.0" @@ -7179,7 +7507,7 @@ { "tier": 2.0, "currency": "BUSD", - "minNotional": 25000.0, + "minNotional": 50000.0, "maxNotional": 100000.0, "maintenanceMarginRate": 0.005, "maxLeverage": 25.0, @@ -7187,111 +7515,111 @@ "bracket": "2", "initialLeverage": "25", "notionalCap": "100000", - "notionalFloor": "25000", + "notionalFloor": "50000", "maintMarginRatio": "0.005", - "cum": "25.0" + "cum": "50.0" } }, { "tier": 3.0, "currency": "BUSD", "minNotional": 100000.0, - "maxNotional": 500000.0, + "maxNotional": 1000000.0, "maintenanceMarginRate": 0.01, "maxLeverage": 20.0, "info": { "bracket": "3", "initialLeverage": "20", - "notionalCap": "500000", + "notionalCap": "1000000", "notionalFloor": "100000", "maintMarginRatio": "0.01", - "cum": "525.0" + "cum": "550.0" } }, { "tier": 4.0, "currency": "BUSD", - "minNotional": 500000.0, - "maxNotional": 1500000.0, + "minNotional": 1000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.025, "maxLeverage": 10.0, "info": { "bracket": "4", "initialLeverage": "10", - "notionalCap": "1500000", - "notionalFloor": "500000", + "notionalCap": "5000000", + "notionalFloor": "1000000", "maintMarginRatio": "0.025", - "cum": "8025.0" + "cum": "15550.0" } }, { "tier": 5.0, "currency": "BUSD", - "minNotional": 1500000.0, - "maxNotional": 4000000.0, + "minNotional": 5000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 6.0, "info": { "bracket": "5", "initialLeverage": "6", - "notionalCap": "4000000", - "notionalFloor": "1500000", + "notionalCap": "10000000", + "notionalFloor": "5000000", "maintMarginRatio": "0.05", - "cum": "45525.0" + "cum": "140550.0" } }, { "tier": 6.0, "currency": "BUSD", - "minNotional": 4000000.0, - "maxNotional": 10000000.0, + "minNotional": 10000000.0, + "maxNotional": 20000000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "10000000", - "notionalFloor": "4000000", + "notionalCap": "20000000", + "notionalFloor": "10000000", "maintMarginRatio": "0.1", - "cum": "245525.0" + "cum": "640550.0" } }, { "tier": 7.0, "currency": "BUSD", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 20000000.0, + "maxNotional": 40000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "40000000", + "notionalFloor": "20000000", "maintMarginRatio": "0.125", - "cum": "495525.0" + "cum": "1140550.0" } }, { "tier": 8.0, "currency": "BUSD", - "minNotional": 20000000.0, - "maxNotional": 40000000.0, + "minNotional": 40000000.0, + "maxNotional": 80000000.0, "maintenanceMarginRate": 0.15, "maxLeverage": 3.0, "info": { "bracket": "8", "initialLeverage": "3", - "notionalCap": "40000000", - "notionalFloor": "20000000", + "notionalCap": "80000000", + "notionalFloor": "40000000", "maintMarginRatio": "0.15", - "cum": "995525.0" + "cum": "2140550.0" } }, { "tier": 9.0, "currency": "BUSD", - "minNotional": 40000000.0, + "minNotional": 80000000.0, "maxNotional": 150000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, @@ -7299,25 +7627,25 @@ "bracket": "9", "initialLeverage": "2", "notionalCap": "150000000", - "notionalFloor": "40000000", + "notionalFloor": "80000000", "maintMarginRatio": "0.25", - "cum": "4995525.0" + "cum": "1.014055E7" } }, { "tier": 10.0, "currency": "BUSD", "minNotional": 150000000.0, - "maxNotional": 500000000.0, + "maxNotional": 300000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "10", "initialLeverage": "1", - "notionalCap": "500000000", + "notionalCap": "300000000", "notionalFloor": "150000000", "maintMarginRatio": "0.5", - "cum": "4.2495525E7" + "cum": "4.764055E7" } } ], @@ -7326,13 +7654,13 @@ "tier": 1.0, "currency": "USDT", "minNotional": 0.0, - "maxNotional": 10000.0, + "maxNotional": 100000.0, "maintenanceMarginRate": 0.005, "maxLeverage": 100.0, "info": { "bracket": "1", "initialLeverage": "100", - "notionalCap": "10000", + "notionalCap": "100000", "notionalFloor": "0", "maintMarginRatio": "0.005", "cum": "0.0" @@ -7341,119 +7669,119 @@ { "tier": 2.0, "currency": "USDT", - "minNotional": 10000.0, - "maxNotional": 100000.0, + "minNotional": 100000.0, + "maxNotional": 250000.0, "maintenanceMarginRate": 0.0065, "maxLeverage": 75.0, "info": { "bracket": "2", "initialLeverage": "75", - "notionalCap": "100000", - "notionalFloor": "10000", + "notionalCap": "250000", + "notionalFloor": "100000", "maintMarginRatio": "0.0065", - "cum": "15.0" + "cum": "150.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 500000.0, + "minNotional": 250000.0, + "maxNotional": 1000000.0, "maintenanceMarginRate": 0.01, "maxLeverage": 50.0, "info": { "bracket": "3", "initialLeverage": "50", - "notionalCap": "500000", - "notionalFloor": "100000", + "notionalCap": "1000000", + "notionalFloor": "250000", "maintMarginRatio": "0.01", - "cum": "365.0" + "cum": "1025.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 500000.0, - "maxNotional": 1500000.0, + "minNotional": 1000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.02, "maxLeverage": 25.0, "info": { "bracket": "4", "initialLeverage": "25", - "notionalCap": "1500000", - "notionalFloor": "500000", + "notionalCap": "5000000", + "notionalFloor": "1000000", "maintMarginRatio": "0.02", - "cum": "5365.0" + "cum": "11025.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 1500000.0, - "maxNotional": 4000000.0, + "minNotional": 5000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "5", "initialLeverage": "10", - "notionalCap": "4000000", - "notionalFloor": "1500000", + "notionalCap": "10000000", + "notionalFloor": "5000000", "maintMarginRatio": "0.05", - "cum": "50365.0" + "cum": "161025.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 4000000.0, - "maxNotional": 10000000.0, + "minNotional": 10000000.0, + "maxNotional": 20000000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "10000000", - "notionalFloor": "4000000", + "notionalCap": "20000000", + "notionalFloor": "10000000", "maintMarginRatio": "0.1", - "cum": "250365.0" + "cum": "661025.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 20000000.0, + "maxNotional": 40000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "40000000", + "notionalFloor": "20000000", "maintMarginRatio": "0.125", - "cum": "500365.0" + "cum": "1161025.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 20000000.0, - "maxNotional": 40000000.0, + "minNotional": 40000000.0, + "maxNotional": 80000000.0, "maintenanceMarginRate": 0.15, "maxLeverage": 3.0, "info": { "bracket": "8", "initialLeverage": "3", - "notionalCap": "40000000", - "notionalFloor": "20000000", + "notionalCap": "80000000", + "notionalFloor": "40000000", "maintMarginRatio": "0.15", - "cum": "1000365.0" + "cum": "2161025.0" } }, { "tier": 9.0, "currency": "USDT", - "minNotional": 40000000.0, + "minNotional": 80000000.0, "maxNotional": 150000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, @@ -7461,25 +7789,25 @@ "bracket": "9", "initialLeverage": "2", "notionalCap": "150000000", - "notionalFloor": "40000000", + "notionalFloor": "80000000", "maintMarginRatio": "0.25", - "cum": "5000365.0" + "cum": "1.0161025E7" } }, { "tier": 10.0, "currency": "USDT", "minNotional": 150000000.0, - "maxNotional": 500000000.0, + "maxNotional": 300000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "10", "initialLeverage": "1", - "notionalCap": "500000000", + "notionalCap": "300000000", "notionalFloor": "150000000", "maintMarginRatio": "0.5", - "cum": "4.2500365E7" + "cum": "4.7661025E7" } } ], @@ -7597,6 +7925,88 @@ } } ], + "FIL/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "FIL/USDT": [ { "tier": 1.0, @@ -9737,6 +10147,104 @@ } } ], + "INJ/USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.01, + "maxLeverage": 25.0, + "info": { + "bracket": "1", + "initialLeverage": "25", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.01", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "2", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "75.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "3", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "700.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "4", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5700.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "5", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11950.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "6", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386950.0" + } + } + ], "IOST/USDT": [ { "tier": 1.0, @@ -10521,6 +11029,170 @@ } } ], + "LDO/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], + "LEVER/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "LINA/USDT": [ { "tier": 1.0, @@ -11663,6 +12335,88 @@ } } ], + "MATIC/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "MATIC/USDT": [ { "tier": 1.0, @@ -16055,6 +16809,88 @@ } } ], + "UNI/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "UNI/USDT": [ { "tier": 1.0, diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index dbc3447be..9d08d3d19 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -17,6 +17,7 @@ import ccxt import ccxt.async_support as ccxt_async from cachetools import TTLCache from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision +from dateutil import parser from pandas import DataFrame from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell, @@ -30,7 +31,8 @@ from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGE EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED, SUPPORTED_EXCHANGES, remove_credentials, retrier, retrier_async) -from freqtrade.misc import chunks, deep_merge_dicts, safe_value_fallback2 +from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json, + safe_value_fallback2) from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist from freqtrade.util import FtPrecise @@ -52,15 +54,15 @@ class Exchange: # Parameters to add directly to buy/sell calls (like agreeing to trading agreement) _params: Dict = {} - # Additional headers - added to the ccxt object - _headers: Dict = {} + # Additional parameters - added to the ccxt object + _ccxt_params: Dict = {} # Dict to specify which options each exchange implements # This defines defaults, which can be selectively overridden by subclasses using _ft_has # or by specifying them in the configuration. _ft_has_default: Dict = { "stoploss_on_exchange": False, - "order_time_in_force": ["gtc"], + "order_time_in_force": ["GTC"], "time_in_force_parameter": "timeInForce", "ohlcv_params": {}, "ohlcv_candle_limit": 500, @@ -240,9 +242,9 @@ class Exchange: } if ccxt_kwargs: logger.info('Applying additional ccxt config: %s', ccxt_kwargs) - if self._headers: - # Inject static headers after the above output to not confuse users. - ccxt_kwargs = deep_merge_dicts({'headers': self._headers}, ccxt_kwargs) + if self._ccxt_params: + # Inject static options after the above output to not confuse users. + ccxt_kwargs = deep_merge_dicts(self._ccxt_params, ccxt_kwargs) if ccxt_kwargs: ex_config.update(ccxt_kwargs) try: @@ -406,7 +408,7 @@ class Exchange: else: return DataFrame() - def _get_contract_size(self, pair: str) -> float: + def get_contract_size(self, pair: str) -> float: if self.trading_mode == TradingMode.FUTURES: market = self.markets[pair] contract_size: float = 1.0 @@ -419,7 +421,7 @@ class Exchange: def _trades_contracts_to_amount(self, trades: List) -> List: if len(trades) > 0 and 'symbol' in trades[0]: - contract_size = self._get_contract_size(trades[0]['symbol']) + contract_size = self.get_contract_size(trades[0]['symbol']) if contract_size != 1: for trade in trades: trade['amount'] = trade['amount'] * contract_size @@ -427,7 +429,7 @@ class Exchange: def _order_contracts_to_amount(self, order: Dict) -> Dict: if 'symbol' in order and order['symbol'] is not None: - contract_size = self._get_contract_size(order['symbol']) + contract_size = self.get_contract_size(order['symbol']) if contract_size != 1: for prop in self._ft_has.get('order_props_in_contracts', []): if prop in order and order[prop] is not None: @@ -436,19 +438,13 @@ class Exchange: def _amount_to_contracts(self, pair: str, amount: float) -> float: - contract_size = self._get_contract_size(pair) - if contract_size and contract_size != 1: - return amount / contract_size - else: - return amount + contract_size = self.get_contract_size(pair) + return amount_to_contracts(amount, contract_size) def _contracts_to_amount(self, pair: str, num_contracts: float) -> float: - contract_size = self._get_contract_size(pair) - if contract_size and contract_size != 1: - return num_contracts * contract_size - else: - return num_contracts + contract_size = self.get_contract_size(pair) + return contracts_to_amount(num_contracts, contract_size) def set_sandbox(self, api: ccxt.Exchange, exchange_config: dict, name: str) -> None: if exchange_config.get('sandbox'): @@ -615,7 +611,7 @@ class Exchange: """ Checks if order time in force configured in strategy/config are supported """ - if any(v not in self._ft_has["order_time_in_force"] + if any(v.upper() not in self._ft_has["order_time_in_force"] for k, v in order_time_in_force.items()): raise OperationalException( f'Time in force policies are not supported for {self.name} yet.') @@ -672,6 +668,12 @@ class Exchange: f"Freqtrade does not support {mm_value} {trading_mode.value} on {self.name}" ) + def get_option(self, param: str, default: Any = None) -> Any: + """ + Get parameter value from _ft_has + """ + return self._ft_has.get(param, default) + def exchange_has(self, endpoint: str) -> bool: """ Checks if exchange implements a specific API endpoint. @@ -987,12 +989,12 @@ class Exchange: ordertype: str, leverage: float, reduceOnly: bool, - time_in_force: str = 'gtc', + time_in_force: str = 'GTC', ) -> Dict: params = self._params.copy() - if time_in_force != 'gtc' and ordertype != 'market': + if time_in_force != 'GTC' and ordertype != 'market': param = self._ft_has.get('time_in_force_parameter', '') - params.update({param: time_in_force}) + params.update({param: time_in_force.upper()}) if reduceOnly: params.update({'reduceOnly': True}) return params @@ -1007,7 +1009,7 @@ class Exchange: rate: float, leverage: float, reduceOnly: bool = False, - time_in_force: str = 'gtc', + time_in_force: str = 'GTC', ) -> Dict: if self._config['dry_run']: dry_order = self.create_dry_run_order( @@ -2207,6 +2209,7 @@ class Exchange: @retrier_async async def get_market_leverage_tiers(self, symbol: str) -> Tuple[str, List[Dict]]: + """ Leverage tiers per symbol """ try: tier = await self._api_async.fetch_market_leverage_tiers(symbol) return symbol, tier @@ -2238,12 +2241,21 @@ class Exchange: tiers: Dict[str, List[Dict]] = {} - # Be verbose here, as this delays startup by ~1 minute. - logger.info( - f"Initializing leverage_tiers for {len(symbols)} markets. " - "This will take about a minute.") + tiers_cached = self.load_cached_leverage_tiers(self._config['stake_currency']) + if tiers_cached: + tiers = tiers_cached - coros = [self.get_market_leverage_tiers(symbol) for symbol in sorted(symbols)] + coros = [ + self.get_market_leverage_tiers(symbol) + for symbol in sorted(symbols) if symbol not in tiers] + + # Be verbose here, as this delays startup by ~1 minute. + if coros: + logger.info( + f"Initializing leverage_tiers for {len(symbols)} markets. " + "This will take about a minute.") + else: + logger.info("Using cached leverage_tiers.") async def gather_results(): return await asyncio.gather(*input_coro, return_exceptions=True) @@ -2255,7 +2267,8 @@ class Exchange: for symbol, res in results: tiers[symbol] = res - + if len(coros) > 0: + self.cache_leverage_tiers(tiers, self._config['stake_currency']) logger.info(f"Done initializing {len(symbols)} markets.") return tiers @@ -2264,6 +2277,30 @@ class Exchange: else: return {} + def cache_leverage_tiers(self, tiers: Dict[str, List[Dict]], stake_currency: str) -> None: + + filename = self._config['datadir'] / "futures" / f"leverage_tiers_{stake_currency}.json" + if not filename.parent.is_dir(): + filename.parent.mkdir(parents=True) + data = { + "updated": datetime.now(timezone.utc), + "data": tiers, + } + file_dump_json(filename, data) + + def load_cached_leverage_tiers(self, stake_currency: str) -> Optional[Dict[str, List[Dict]]]: + filename = self._config['datadir'] / "futures" / f"leverage_tiers_{stake_currency}.json" + if filename.is_file(): + tiers = file_load_json(filename) + updated = tiers.get('updated') + if updated: + updated_dt = parser.parse(updated) + if updated_dt < datetime.now(timezone.utc) - timedelta(days=1): + logger.info("Cached leverage tiers are outdated. Will update.") + return None + return tiers['data'] + return None + def fill_leverage_tiers(self) -> None: """ Assigns property _leverage_tiers to a dictionary of information about the leverage @@ -2279,10 +2316,10 @@ class Exchange: def parse_leverage_tier(self, tier) -> Dict: info = tier.get('info', {}) return { - 'min': tier['minNotional'], - 'max': tier['maxNotional'], - 'mmr': tier['maintenanceMarginRate'], - 'lev': tier['maxLeverage'], + 'minNotional': tier['minNotional'], + 'maxNotional': tier['maxNotional'], + 'maintenanceMarginRate': tier['maintenanceMarginRate'], + 'maxLeverage': tier['maxLeverage'], 'maintAmt': float(info['cum']) if 'cum' in info else None, } @@ -2311,18 +2348,18 @@ class Exchange: pair_tiers = self._leverage_tiers[pair] if stake_amount == 0: - return self._leverage_tiers[pair][0]['lev'] # Max lev for lowest amount + return self._leverage_tiers[pair][0]['maxLeverage'] # Max lev for lowest amount for tier_index in range(len(pair_tiers)): tier = pair_tiers[tier_index] - lev = tier['lev'] + lev = tier['maxLeverage'] if tier_index < len(pair_tiers) - 1: next_tier = pair_tiers[tier_index + 1] - next_floor = next_tier['min'] / next_tier['lev'] + next_floor = next_tier['minNotional'] / next_tier['maxLeverage'] if next_floor > stake_amount: # Next tier min too high for stake amount - return min((tier['max'] / stake_amount), lev) + return min((tier['maxNotional'] / stake_amount), lev) # # With the two leverage tiers below, # - a stake amount of 150 would mean a max leverage of (10000 / 150) = 66.66 @@ -2343,10 +2380,11 @@ class Exchange: # else: # if on the last tier - if stake_amount > tier['max']: # If stake is > than max tradeable amount + if stake_amount > tier['maxNotional']: + # If stake is > than max tradeable amount raise InvalidOrderException(f'Amount {stake_amount} too high for {pair}') else: - return tier['lev'] + return tier['maxLeverage'] raise OperationalException( 'Looped through all tiers without finding a max leverage. Should never be reached' @@ -2377,7 +2415,8 @@ class Exchange: return try: - self._api.set_leverage(symbol=pair, leverage=leverage) + res = self._api.set_leverage(symbol=pair, leverage=leverage) + self._log_exchange_response('set_leverage', res) except ccxt.DDoSProtection as e: raise DDosProtection(e) from e except (ccxt.NetworkError, ccxt.ExchangeError) as e: @@ -2393,36 +2432,6 @@ class Exchange: """ return 0.0 - def get_liquidation_price( - self, - pair: str, - open_rate: float, - amount: float, # quote currency, includes leverage - leverage: float, - is_short: bool - ) -> Optional[float]: - - if self.trading_mode in TradingMode.SPOT: - return None - elif ( - self.margin_mode == MarginMode.ISOLATED and - self.trading_mode == TradingMode.FUTURES - ): - wallet_balance = (amount * open_rate) / leverage - isolated_liq = self.get_or_calculate_liquidation_price( - pair=pair, - open_rate=open_rate, - is_short=is_short, - position=amount, - wallet_balance=wallet_balance, - mm_ex_1=0.0, - upnl_ex_1=0.0, - ) - return isolated_liq - else: - raise OperationalException( - "Freqtrade only supports isolated futures for leverage trading") - def funding_fee_cutoff(self, open_date: datetime): """ :param open_date: The open date for a trade @@ -2441,7 +2450,8 @@ class Exchange: return try: - self._api.set_margin_mode(margin_mode.value, pair, params) + res = self._api.set_margin_mode(margin_mode.value, pair, params) + self._log_exchange_response('set_margin_mode', res) except ccxt.DDoSProtection as e: raise DDosProtection(e) from e except (ccxt.NetworkError, ccxt.ExchangeError) as e: @@ -2582,34 +2592,36 @@ class Exchange: else: return 0.0 - def get_or_calculate_liquidation_price( + def get_liquidation_price( self, pair: str, # Dry-run open_rate: float, # Entry price of position is_short: bool, - position: float, # Absolute value of position size - wallet_balance: float, # Or margin balance + amount: float, # Absolute value of position size + stake_amount: float, + wallet_balance: float = 0.0, mm_ex_1: float = 0.0, # (Binance) Cross only upnl_ex_1: float = 0.0, # (Binance) Cross only ) -> Optional[float]: """ Set's the margin mode on the exchange to cross or isolated for a specific pair - :param pair: base/quote currency pair (e.g. "ADA/USDT") """ if self.trading_mode == TradingMode.SPOT: return None - elif (self.trading_mode != TradingMode.FUTURES and self.margin_mode != MarginMode.ISOLATED): + elif (self.trading_mode != TradingMode.FUTURES): raise OperationalException( - f"{self.name} does not support {self.margin_mode.value} {self.trading_mode.value}") + f"{self.name} does not support {self.margin_mode} {self.trading_mode}") + isolated_liq = None if self._config['dry_run'] or not self.exchange_has("fetchPositions"): isolated_liq = self.dry_run_liquidation_price( pair=pair, open_rate=open_rate, is_short=is_short, - position=position, + amount=amount, + stake_amount=stake_amount, wallet_balance=wallet_balance, mm_ex_1=mm_ex_1, upnl_ex_1=upnl_ex_1 @@ -2619,8 +2631,6 @@ class Exchange: if len(positions) > 0: pos = positions[0] isolated_liq = pos['liquidationPrice'] - else: - return None if isolated_liq: buffer_amount = abs(open_rate - isolated_liq) * self.liquidation_buffer @@ -2638,22 +2648,24 @@ class Exchange: pair: str, open_rate: float, # Entry price of position is_short: bool, - position: float, # Absolute value of position size + amount: float, + stake_amount: float, wallet_balance: float, # Or margin balance mm_ex_1: float = 0.0, # (Binance) Cross only upnl_ex_1: float = 0.0, # (Binance) Cross only ) -> Optional[float]: """ + Important: Must be fetching data from cached values as this is used by backtesting! PERPETUAL: gateio: https://www.gate.io/help/futures/perpetual/22160/calculation-of-liquidation-price okex: https://www.okex.com/support/hc/en-us/articles/ 360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin - Important: Must be fetching data from cached values as this is used by backtesting! :param exchange_name: :param open_rate: Entry price of position :param is_short: True if the trade is a short, false otherwise - :param position: Absolute value of position size incl. leverage (in base currency) + :param amount: Absolute value of position size incl. leverage (in base currency) + :param stake_amount: Stake amount - Collateral in settle currency. :param trading_mode: SPOT, MARGIN, FUTURES, etc. :param margin_mode: Either ISOLATED or CROSS :param wallet_balance: Amount of margin_mode in the wallet being used to trade @@ -2667,7 +2679,7 @@ class Exchange: market = self.markets[pair] taker_fee_rate = market['taker'] - mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, position) + mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, stake_amount) if self.trading_mode == TradingMode.FUTURES and self.margin_mode == MarginMode.ISOLATED: @@ -2675,7 +2687,7 @@ class Exchange: raise OperationalException( "Freqtrade does not yet support inverse contracts") - value = wallet_balance / position + value = wallet_balance / amount mm_ratio_taker = (mm_ratio + taker_fee_rate) if is_short: @@ -2711,8 +2723,8 @@ class Exchange: pair_tiers = self._leverage_tiers[pair] for tier in reversed(pair_tiers): - if nominal_value >= tier['min']: - return (tier['mmr'], tier['maintAmt']) + if nominal_value >= tier['minNotional']: + return (tier['maintenanceMarginRate'], tier['maintAmt']) raise OperationalException("nominal value can not be lower than 0") # The lowest notional_floor for any pair in fetch_leverage_tiers is always 0 because it @@ -2854,6 +2866,33 @@ def market_is_active(market: Dict) -> bool: return market.get('active', True) is not False +def amount_to_contracts(amount: float, contract_size: Optional[float]) -> float: + """ + Convert amount to contracts. + :param amount: amount to convert + :param contract_size: contract size - taken from exchange.get_contract_size(pair) + :return: num-contracts + """ + if contract_size and contract_size != 1: + return amount / contract_size + else: + return amount + + +def contracts_to_amount(num_contracts: float, contract_size: Optional[float]) -> float: + """ + Takes num-contracts and converts it to contract size + :param num_contracts: number of contracts + :param contract_size: contract size - taken from exchange.get_contract_size(pair) + :return: Amount + """ + + if contract_size and contract_size != 1: + return num_contracts * contract_size + else: + return num_contracts + + def amount_to_precision(amount: float, amount_precision: Optional[float], precisionMode: Optional[int]) -> float: """ @@ -2878,6 +2917,29 @@ def amount_to_precision(amount: float, amount_precision: Optional[float], return amount +def amount_to_contract_precision( + amount, amount_precision: Optional[float], precisionMode: Optional[int], + contract_size: Optional[float]) -> float: + """ + Returns the amount to buy or sell to a precision the Exchange accepts + including calculation to and from contracts. + Re-implementation of ccxt internal methods - ensuring we can test the result is correct + based on our definitions. + :param amount: amount to truncate + :param amount_precision: amount precision to use. + should be retrieved from markets[pair]['precision']['amount'] + :param precisionMode: precision mode to use. Should be used from precisionMode + one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE + :param contract_size: contract size - taken from exchange.get_contract_size(pair) + :return: truncated amount + """ + if amount_precision is not None and precisionMode is not None: + contracts = amount_to_contracts(amount, contract_size) + amount_p = amount_to_precision(contracts, amount_precision, precisionMode) + return contracts_to_amount(amount_p, contract_size) + return amount + + def price_to_precision(price: float, price_precision: Optional[float], precisionMode: Optional[int]) -> float: """ diff --git a/freqtrade/exchange/ftx.py b/freqtrade/exchange/ftx.py index b3c219542..6a43ab302 100644 --- a/freqtrade/exchange/ftx.py +++ b/freqtrade/exchange/ftx.py @@ -19,6 +19,7 @@ logger = logging.getLogger(__name__) class Ftx(Exchange): _ft_has: Dict = { + "order_time_in_force": ['GTC', 'IOC', 'PO'], "stoploss_on_exchange": True, "ohlcv_candle_limit": 1500, "ohlcv_require_since": True, diff --git a/freqtrade/exchange/gateio.py b/freqtrade/exchange/gateio.py index 6df3425d2..ab127a036 100644 --- a/freqtrade/exchange/gateio.py +++ b/freqtrade/exchange/gateio.py @@ -25,9 +25,7 @@ class Gateio(Exchange): _ft_has: Dict = { "ohlcv_candle_limit": 1000, - "ohlcv_volume_currency": "quote", - "time_in_force_parameter": "timeInForce", - "order_time_in_force": ['gtc', 'ioc'], + "order_time_in_force": ['GTC', 'IOC'], "stoploss_order_types": {"limit": "limit"}, "stoploss_on_exchange": True, } @@ -58,7 +56,7 @@ class Gateio(Exchange): ordertype: str, leverage: float, reduceOnly: bool, - time_in_force: str = 'gtc', + time_in_force: str = 'GTC', ) -> Dict: params = super()._get_params( side=side, @@ -70,7 +68,7 @@ class Gateio(Exchange): if ordertype == 'market' and self.trading_mode == TradingMode.FUTURES: params['type'] = 'market' param = self._ft_has.get('time_in_force_parameter', '') - params.update({param: 'ioc'}) + params.update({param: 'IOC'}) return params def get_trades_for_order(self, order_id: str, pair: str, since: datetime, diff --git a/freqtrade/exchange/kraken.py b/freqtrade/exchange/kraken.py index 0103e2702..6c9b88eae 100644 --- a/freqtrade/exchange/kraken.py +++ b/freqtrade/exchange/kraken.py @@ -171,7 +171,7 @@ class Kraken(Exchange): ordertype: str, leverage: float, reduceOnly: bool, - time_in_force: str = 'gtc' + time_in_force: str = 'GTC' ) -> Dict: params = super()._get_params( side=side, diff --git a/freqtrade/exchange/kucoin.py b/freqtrade/exchange/kucoin.py index 21eaa4bc3..f05fd3f56 100644 --- a/freqtrade/exchange/kucoin.py +++ b/freqtrade/exchange/kucoin.py @@ -23,8 +23,7 @@ class Kucoin(Exchange): "stoploss_order_types": {"limit": "limit", "market": "market"}, "l2_limit_range": [20, 100], "l2_limit_range_required": False, - "order_time_in_force": ['gtc', 'fok', 'ioc'], - "time_in_force_parameter": "timeInForce", + "order_time_in_force": ['GTC', 'FOK', 'IOC'], "ohlcv_candle_limit": 1500, } diff --git a/freqtrade/exchange/okx.py b/freqtrade/exchange/okx.py index 540e76fca..9340dd0e4 100644 --- a/freqtrade/exchange/okx.py +++ b/freqtrade/exchange/okx.py @@ -39,6 +39,8 @@ class Okx(Exchange): net_only = True + _ccxt_params: Dict = {'options': {'brokerId': 'ffb5405ad327SUDE'}} + def ohlcv_candle_limit( self, timeframe: str, candle_type: CandleType, since_ms: Optional[int] = None) -> int: """ @@ -96,7 +98,7 @@ class Okx(Exchange): ordertype: str, leverage: float, reduceOnly: bool, - time_in_force: str = 'gtc', + time_in_force: str = 'GTC', ) -> Dict: params = super()._get_params( side=side, @@ -144,4 +146,4 @@ class Okx(Exchange): return float('inf') pair_tiers = self._leverage_tiers[pair] - return pair_tiers[-1]['max'] / leverage + return pair_tiers[-1]['maxNotional'] / leverage diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index c8dbdf5e5..b6a1a15d7 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -421,7 +421,7 @@ class FreqaiDataDrawer: ) # if self.live: - self.model_dictionary[dk.model_filename] = model + self.model_dictionary[coin] = model self.pair_dict[coin]["model_filename"] = dk.model_filename self.pair_dict[coin]["data_path"] = str(dk.data_path) self.save_drawer_to_disk() @@ -460,8 +460,8 @@ class FreqaiDataDrawer: ) # try to access model in memory instead of loading object from disk to save time - if dk.live and dk.model_filename in self.model_dictionary: - model = self.model_dictionary[dk.model_filename] + if dk.live and coin in self.model_dictionary: + model = self.model_dictionary[coin] elif not dk.keras: model = load(dk.data_path / f"{dk.model_filename}_model.joblib") else: @@ -566,7 +566,6 @@ class FreqaiDataDrawer: for training according to user defined train_period_days metadata: dict = strategy furnished pair metadata """ - with self.history_lock: corr_dataframes: Dict[Any, Any] = {} base_dataframes: Dict[Any, Any] = {} diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 1b88405c1..f38c69fae 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -166,9 +166,17 @@ class FreqaiDataKitchen: train_labels = labels train_weights = weights - return self.build_data_dictionary( - train_features, test_features, train_labels, test_labels, train_weights, test_weights - ) + # Simplest way to reverse the order of training and test data: + if self.freqai_config['feature_parameters'].get('reverse_train_test_order', False): + return self.build_data_dictionary( + test_features, train_features, test_labels, + train_labels, test_weights, train_weights + ) + else: + return self.build_data_dictionary( + train_features, test_features, train_labels, + test_labels, train_weights, test_weights + ) def filter_features( self, @@ -452,7 +460,6 @@ class FreqaiDataKitchen: logger.info("reduced feature dimension by %s", n_components - n_keep_components) logger.info("explained variance %f", np.sum(pca2.explained_variance_ratio_)) train_components = pca2.transform(self.data_dictionary["train_features"]) - test_components = pca2.transform(self.data_dictionary["test_features"]) self.data_dictionary["train_features"] = pd.DataFrame( data=train_components, @@ -466,6 +473,7 @@ class FreqaiDataKitchen: self.training_features_list = self.data_dictionary["train_features"].columns if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0: + test_components = pca2.transform(self.data_dictionary["test_features"]) self.data_dictionary["test_features"] = pd.DataFrame( data=test_components, columns=["PC" + str(i) for i in range(0, n_keep_components)], @@ -504,10 +512,25 @@ class FreqaiDataKitchen: # logger.info("computing average mean distance for all training points") pairwise = pairwise_distances( self.data_dictionary["train_features"], n_jobs=self.thread_count) - avg_mean_dist = pairwise.mean(axis=1).mean() + # remove the diagonal distances which are itself distances ~0 + np.fill_diagonal(pairwise, np.NaN) + pairwise = pairwise.reshape(-1, 1) + avg_mean_dist = pairwise[~np.isnan(pairwise)].mean() return avg_mean_dist + def get_outlier_percentage(self, dropped_pts: npt.NDArray) -> float: + """ + Check if more than X% of points werer dropped during outlier detection. + """ + outlier_protection_pct = self.freqai_config["feature_parameters"].get( + "outlier_protection_percentage", 30) + outlier_pct = (dropped_pts.sum() / len(dropped_pts)) * 100 + if outlier_pct >= outlier_protection_pct: + return outlier_pct + else: + return 0.0 + def use_SVM_to_remove_outliers(self, predict: bool) -> None: """ Build/inference a Support Vector Machine to detect outliers @@ -545,8 +568,17 @@ class FreqaiDataKitchen: self.data_dictionary["train_features"] ) y_pred = self.svm_model.predict(self.data_dictionary["train_features"]) - dropped_points = np.where(y_pred == -1, 0, y_pred) + kept_points = np.where(y_pred == -1, 0, y_pred) # keep_index = np.where(y_pred == 1) + outlier_pct = self.get_outlier_percentage(1 - kept_points) + if outlier_pct: + logger.warning( + f"SVM detected {outlier_pct:.2f}% of the points as outliers. " + f"Keeping original dataset." + ) + self.svm_model = None + return + self.data_dictionary["train_features"] = self.data_dictionary["train_features"][ (y_pred == 1) ] @@ -558,7 +590,7 @@ class FreqaiDataKitchen: ] logger.info( - f"SVM tossed {len(y_pred) - dropped_points.sum()}" + f"SVM tossed {len(y_pred) - kept_points.sum()}" f" train points from {len(y_pred)} total points." ) @@ -567,7 +599,7 @@ class FreqaiDataKitchen: # to reduce code duplication if self.freqai_config['data_split_parameters'].get('test_size', 0.1) != 0: y_pred = self.svm_model.predict(self.data_dictionary["test_features"]) - dropped_points = np.where(y_pred == -1, 0, y_pred) + kept_points = np.where(y_pred == -1, 0, y_pred) self.data_dictionary["test_features"] = self.data_dictionary["test_features"][ (y_pred == 1) ] @@ -578,7 +610,7 @@ class FreqaiDataKitchen: ] logger.info( - f"SVM tossed {len(y_pred) - dropped_points.sum()}" + f"SVM tossed {len(y_pred) - kept_points.sum()}" f" test points from {len(y_pred)} total points." ) @@ -596,7 +628,11 @@ class FreqaiDataKitchen: is an outlier. """ + from math import cos, sin + if predict: + if not self.data['DBSCAN_eps']: + return train_ft_df = self.data_dictionary['train_features'] pred_ft_df = self.data_dictionary['prediction_features'] num_preds = len(pred_ft_df) @@ -614,28 +650,61 @@ class FreqaiDataKitchen: else: - MinPts = len(self.data_dictionary['train_features'].columns) * 2 - # measure pairwise distances to train_features.shape[1]*2 nearest neighbours + def normalise_distances(distances): + normalised_distances = (distances - distances.min()) / \ + (distances.max() - distances.min()) + return normalised_distances + + def rotate_point(origin, point, angle): + # rotate a point counterclockwise by a given angle (in radians) + # around a given origin + x = origin[0] + cos(angle) * (point[0] - origin[0]) - \ + sin(angle) * (point[1] - origin[1]) + y = origin[1] + sin(angle) * (point[0] - origin[0]) + \ + cos(angle) * (point[1] - origin[1]) + return (x, y) + + MinPts = int(len(self.data_dictionary['train_features'].index) * 0.25) + # measure pairwise distances to nearest neighbours neighbors = NearestNeighbors( n_neighbors=MinPts, n_jobs=self.thread_count) neighbors_fit = neighbors.fit(self.data_dictionary['train_features']) distances, _ = neighbors_fit.kneighbors(self.data_dictionary['train_features']) - distances = np.sort(distances, axis=0) - index_ten_pct = int(len(distances[:, 1]) * 0.1) - distances = distances[index_ten_pct:, 1] - epsilon = distances[-1] + distances = np.sort(distances, axis=0).mean(axis=1) + + normalised_distances = normalise_distances(distances) + x_range = np.linspace(0, 1, len(distances)) + line = np.linspace(normalised_distances[0], + normalised_distances[-1], len(normalised_distances)) + deflection = np.abs(normalised_distances - line) + max_deflection_loc = np.where(deflection == deflection.max())[0][0] + origin = x_range[max_deflection_loc], line[max_deflection_loc] + point = x_range[max_deflection_loc], normalised_distances[max_deflection_loc] + rot_angle = np.pi / 4 + elbow_loc = rotate_point(origin, point, rot_angle) + + epsilon = elbow_loc[1] * (distances[-1] - distances[0]) + distances[0] clustering = DBSCAN(eps=epsilon, min_samples=MinPts, n_jobs=int(self.thread_count)).fit( self.data_dictionary['train_features'] ) - logger.info(f'DBSCAN found eps of {epsilon}.') + logger.info(f'DBSCAN found eps of {epsilon:.2f}.') self.data['DBSCAN_eps'] = epsilon self.data['DBSCAN_min_samples'] = MinPts dropped_points = np.where(clustering.labels_ == -1, 1, 0) + outlier_pct = self.get_outlier_percentage(dropped_points) + if outlier_pct: + logger.warning( + f"DBSCAN detected {outlier_pct:.2f}% of the points as outliers. " + f"Keeping original dataset." + ) + self.data['DBSCAN_eps'] = 0 + return + self.data_dictionary['train_features'] = self.data_dictionary['train_features'][ (clustering.labels_ != -1) ] @@ -693,8 +762,8 @@ class FreqaiDataKitchen: if (len(do_predict) - do_predict.sum()) > 0: logger.info( - f"DI tossed {len(do_predict) - do_predict.sum():.2f} predictions for " - "being too far from training data" + f"DI tossed {len(do_predict) - do_predict.sum()} predictions for " + "being too far from training data." ) self.do_predict += do_predict @@ -866,13 +935,6 @@ class FreqaiDataKitchen: data_load_timerange.stopts = int(time) retrain = True - # logger.info( - # f"downloading data for " - # f"{(data_load_timerange.stopts-data_load_timerange.startts)/SECONDS_IN_DAY:.2f} " - # " days. " - # f"Extension of {additional_seconds/SECONDS_IN_DAY:.2f} days" - # ) - return retrain, trained_timerange, data_load_timerange def set_new_model_names(self, pair: str, trained_timerange: TimeRange): diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 1a9e549f6..6b4ac183a 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -80,12 +80,15 @@ class IFreqaiModel(ABC): logger.warning("DI threshold is not configured for Keras models yet. Deactivating.") self.CONV_WIDTH = self.freqai_info.get("conv_width", 2) self.pair_it = 0 + self.pair_it_train = 0 self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist")) self.last_trade_database_summary: DataFrame = {} self.current_trade_database_summary: DataFrame = {} self.analysis_lock = Lock() self.inference_time: float = 0 + self.train_time: float = 0 self.begin_time: float = 0 + self.begin_time_train: float = 0 self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe']) def assert_config(self, config: Dict[str, Any]) -> None: @@ -128,11 +131,20 @@ class IFreqaiModel(ABC): dk = self.start_backtesting(dataframe, metadata, self.dk) dataframe = dk.remove_features_from_df(dk.return_dataframe) - del dk + self.clean_up() if self.live: self.inference_timer('stop') return dataframe + def clean_up(self): + """ + Objects that should be handled by GC already between coins, but + are explicitly shown here to help demonstrate the non-persistence of these + objects. + """ + self.model = None + self.dk = None + @threaded def start_scanning(self, strategy: IStrategy) -> None: """ @@ -159,9 +171,11 @@ class IFreqaiModel(ABC): dk.set_paths(pair, new_trained_timerange.stopts) if retrain: + self.train_timer('start') self.train_model_in_series( new_trained_timerange, pair, strategy, dk, data_load_timerange ) + self.train_timer('stop') self.dd.save_historic_predictions_to_disk() @@ -474,8 +488,7 @@ class IFreqaiModel(ABC): data_load_timerange: TimeRange, ): """ - Retrieve data and train model in single threaded mode (only used if model directory is empty - upon startup for dry/live ) + Retrieve data and train model. :param new_trained_timerange: TimeRange = the timerange to train the model on :param metadata: dict = strategy provided metadata :param strategy: IStrategy = user defined strategy object @@ -606,6 +619,24 @@ class IFreqaiModel(ABC): self.inference_time = 0 return + def train_timer(self, do='start'): + """ + Timer designed to track the cumulative time spent training the full pairlist in + FreqAI. + """ + if do == 'start': + self.pair_it_train += 1 + self.begin_time_train = time.time() + elif do == 'stop': + end = time.time() + self.train_time += (end - self.begin_time_train) + if self.pair_it_train == self.total_pairs: + logger.info( + f'Total time spent training pairlist {self.train_time:.2f} seconds') + self.pair_it_train = 0 + self.train_time = 0 + return + # Following methods which are overridden by user made prediction models. # See freqai/prediction_models/CatboostPredictionModel.py for an example. diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index a214efd76..5393e3d39 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -21,8 +21,7 @@ from freqtrade.enums import (ExitCheckTuple, ExitType, RPCMessageType, RunMode, State, TradingMode) from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError, InvalidOrderException, PricingError) -from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds -from freqtrade.exchange.exchange import timeframe_to_next_date +from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds from freqtrade.misc import safe_value_fallback, safe_value_fallback2 from freqtrade.mixins import LoggingMixin from freqtrade.persistence import Order, PairLocks, Trade, init_db @@ -240,7 +239,7 @@ class FreqtradeBot(LoggingMixin): 'status': f"{len(open_trades)} open trades active.\n\n" f"Handle these trades manually on {self.exchange.name}, " - f"or '/start' the bot again and use '/stopbuy' " + f"or '/start' the bot again and use '/stopentry' " f"to handle open trades gracefully. \n" f"{'Note: Trades are simulated (dry run).' if self.config['dry_run'] else ''}", } @@ -271,7 +270,7 @@ class FreqtradeBot(LoggingMixin): Return the number of free open trades slots or 0 if max number of open trades reached """ - open_trades = len(Trade.get_open_trades()) + open_trades = Trade.get_open_trade_count() return max(0, self.config['max_open_trades'] - open_trades) def update_funding_fees(self): @@ -290,13 +289,14 @@ class FreqtradeBot(LoggingMixin): def startup_backpopulate_precision(self): - trades = Trade.get_trades([Trade.precision_mode.is_(None)]) + trades = Trade.get_trades([Trade.contract_size.is_(None)]) for trade in trades: if trade.exchange != self.exchange.id: continue trade.precision_mode = self.exchange.precisionMode trade.amount_precision = self.exchange.get_precision_amount(trade.pair) trade.price_precision = self.exchange.get_precision_price(trade.pair) + trade.contract_size = self.exchange.get_contract_size(trade.pair) Trade.commit() def startup_update_open_orders(self): @@ -418,7 +418,7 @@ class FreqtradeBot(LoggingMixin): whitelist = copy.deepcopy(self.active_pair_whitelist) if not whitelist: - logger.info("Active pair whitelist is empty.") + self.log_once("Active pair whitelist is empty.", logger.info) return trades_created # Remove pairs for currently opened trades from the whitelist for trade in Trade.get_open_trades(): @@ -427,8 +427,8 @@ class FreqtradeBot(LoggingMixin): logger.debug('Ignoring %s in pair whitelist', trade.pair) if not whitelist: - logger.info("No currency pair in active pair whitelist, " - "but checking to exit open trades.") + self.log_once("No currency pair in active pair whitelist, " + "but checking to exit open trades.", logger.info) return trades_created if PairLocks.is_global_lock(side='*'): # This only checks for total locks (both sides). @@ -755,6 +755,7 @@ class FreqtradeBot(LoggingMixin): amount_precision=self.exchange.get_precision_amount(pair), price_precision=self.exchange.get_precision_price(pair), precision_mode=self.exchange.precisionMode, + contract_size=self.exchange.get_contract_size(pair), ) else: # This is additional buy, we reset fee_open_currency so timeout checking can work @@ -1551,9 +1552,10 @@ class FreqtradeBot(LoggingMixin): trade.close_rate_requested = limit trade.exit_reason = exit_reason - # Lock pair for one candle to prevent immediate re-trading - self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc), - reason='Auto lock') + if not sub_trade_amt: + # Lock pair for one candle to prevent immediate re-trading + self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc), + reason='Auto lock') self._notify_exit(trade, order_type, sub_trade=bool(sub_trade_amt), order=order_obj) # In case of market sell orders the order can be closed immediately @@ -1730,11 +1732,12 @@ class FreqtradeBot(LoggingMixin): # TODO: Margin will need to use interest_rate as well. # interest_rate = self.exchange.get_interest_rate() trade.set_liquidation_price(self.exchange.get_liquidation_price( - leverage=trade.leverage, pair=trade.pair, - amount=trade.amount, open_rate=trade.open_rate, - is_short=trade.is_short + is_short=trade.is_short, + amount=trade.amount, + stake_amount=trade.stake_amount, + wallet_balance=trade.stake_amount, )) # Updating wallets when order is closed @@ -1775,7 +1778,7 @@ class FreqtradeBot(LoggingMixin): self.rpc.send_msg(msg) def apply_fee_conditional(self, trade: Trade, trade_base_currency: str, - amount: float, fee_abs: float) -> float: + amount: float, fee_abs: float) -> Optional[float]: """ Applies the fee to amount (either from Order or from Trades). Can eat into dust if more than the required asset is available. @@ -1788,35 +1791,32 @@ class FreqtradeBot(LoggingMixin): logger.info(f"Fee amount for {trade} was in base currency - " f"Eating Fee {fee_abs} into dust.") elif fee_abs != 0: - real_amount = self.exchange.amount_to_precision(trade.pair, amount - fee_abs) - logger.info(f"Applying fee on amount for {trade} " - f"(from {amount} to {real_amount}).") - return real_amount - return amount + logger.info(f"Applying fee on amount for {trade}, fee={fee_abs}.") + return fee_abs + return None def handle_order_fee(self, trade: Trade, order_obj: Order, order: Dict[str, Any]) -> None: # Try update amount (binance-fix) try: - new_amount = self.get_real_amount(trade, order, order_obj) - if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount, - abs_tol=constants.MATH_CLOSE_PREC): - order_obj.ft_fee_base = trade.amount - new_amount + fee_abs = self.get_real_amount(trade, order, order_obj) + if fee_abs is not None: + order_obj.ft_fee_base = fee_abs except DependencyException as exception: logger.warning("Could not update trade amount: %s", exception) - def get_real_amount(self, trade: Trade, order: Dict, order_obj: Order) -> float: + def get_real_amount(self, trade: Trade, order: Dict, order_obj: Order) -> Optional[float]: """ Detect and update trade fee. Calls trade.update_fee() upon correct detection. Returns modified amount if the fee was taken from the destination currency. Necessary for exchanges which charge fees in base currency (e.g. binance) - :return: identical (or new) amount for the trade + :return: Absolute fee to apply for this order or None """ # Init variables order_amount = safe_value_fallback(order, 'filled', 'amount') # Only run for closed orders if trade.fee_updated(order.get('side', '')) or order['status'] == 'open': - return order_amount + return None trade_base_currency = self.exchange.get_pair_base_currency(trade.pair) # use fee from order-dict if possible @@ -1834,12 +1834,12 @@ class FreqtradeBot(LoggingMixin): # Apply fee to amount return self.apply_fee_conditional(trade, trade_base_currency, amount=order_amount, fee_abs=fee_cost) - return order_amount + return None return self.fee_detection_from_trades( trade, order, order_obj, order_amount, order.get('trades', [])) def fee_detection_from_trades(self, trade: Trade, order: Dict, order_obj: Order, - order_amount: float, trades: List) -> float: + order_amount: float, trades: List) -> Optional[float]: """ fee-detection fallback to Trades. Either uses provided trades list or the result of fetch_my_trades to get correct fee. @@ -1850,7 +1850,7 @@ class FreqtradeBot(LoggingMixin): if len(trades) == 0: logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade) - return order_amount + return None fee_currency = None amount = 0 fee_abs = 0.0 @@ -1894,8 +1894,7 @@ class FreqtradeBot(LoggingMixin): if fee_abs != 0: return self.apply_fee_conditional(trade, trade_base_currency, amount=amount, fee_abs=fee_abs) - else: - return amount + return None def get_valid_price(self, custom_price: float, proposed_price: float) -> float: """ diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 3d715c82d..77bf3d8ad 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -23,7 +23,8 @@ from freqtrade.data.dataprovider import DataProvider from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType, RunMode, TradingMode) from freqtrade.exceptions import DependencyException, OperationalException -from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds +from freqtrade.exchange import (amount_to_contract_precision, price_to_precision, + timeframe_to_minutes, timeframe_to_seconds) from freqtrade.mixins import LoggingMixin from freqtrade.optimize.backtest_caching import get_strategy_run_id from freqtrade.optimize.bt_progress import BTProgress @@ -257,7 +258,7 @@ class Backtesting: funding_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, - timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'], + timeframe=self.exchange.get_option('mark_ohlcv_timeframe'), timerange=self.timerange, startup_candles=0, fail_without_data=True, @@ -269,12 +270,12 @@ class Backtesting: mark_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, - timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'], + timeframe=self.exchange.get_option('mark_ohlcv_timeframe'), timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), - candle_type=CandleType.from_string(self.exchange._ft_has["mark_ohlcv_price"]) + candle_type=CandleType.from_string(self.exchange.get_option("mark_ohlcv_price")) ) # Combine data to avoid combining the data per trade. unavailable_pairs = [] @@ -524,12 +525,16 @@ class Backtesting: # Check if we should increase our position if stake_amount is not None and stake_amount > 0.0: - - pos_trade = self._enter_trade( - trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade) - if pos_trade is not None: - self.wallets.update() - return pos_trade + check_adjust_entry = True + if self.strategy.max_entry_position_adjustment > -1: + entry_count = trade.nr_of_successful_entries + check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment) + if check_adjust_entry: + pos_trade = self._enter_trade( + trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade) + if pos_trade is not None: + self.wallets.update() + return pos_trade if stake_amount is not None and stake_amount < 0.0: amount = abs(stake_amount) / current_rate @@ -540,7 +545,8 @@ class Backtesting: if remaining < min_stake: # Remaining stake is too low to be sold. return trade - pos_trade = self._exit_trade(trade, row, current_rate, amount) + exit_ = ExitCheckTuple(ExitType.PARTIAL_EXIT) + pos_trade = self._get_exit_for_signal(trade, row, exit_, amount) if pos_trade is not None: order = pos_trade.orders[-1] if self._get_order_filled(order.price, row): @@ -560,12 +566,7 @@ class Backtesting: # Check if we need to adjust our current positions if self.strategy.position_adjustment_enable: - check_adjust_entry = True - if self.strategy.max_entry_position_adjustment > -1: - entry_count = trade.nr_of_successful_entries - check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment) - if check_adjust_entry: - trade = self._get_adjust_trade_entry_for_candle(trade, row) + trade = self._get_adjust_trade_entry_for_candle(trade, row) enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX] exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX] @@ -580,14 +581,15 @@ class Backtesting: return t return None - def _get_exit_for_signal(self, trade: LocalTrade, row: Tuple, - exit_: ExitCheckTuple) -> Optional[LocalTrade]: + def _get_exit_for_signal( + self, trade: LocalTrade, row: Tuple, exit_: ExitCheckTuple, + amount: Optional[float] = None) -> Optional[LocalTrade]: exit_candle_time: datetime = row[DATE_IDX].to_pydatetime() if exit_.exit_flag: trade.close_date = exit_candle_time exit_reason = exit_.exit_reason - + amount_ = amount if amount is not None else trade.amount trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60) try: close_rate = self._get_close_rate(row, trade, exit_, trade_dur) @@ -596,7 +598,8 @@ class Backtesting: # call the custom exit price,with default value as previous close_rate current_profit = trade.calc_profit_ratio(close_rate) order_type = self.strategy.order_types['exit'] - if exit_.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT): + if exit_.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT, + ExitType.PARTIAL_EXIT): # Checks and adds an exit tag, after checking that the length of the # row has the length for an exit tag column if ( @@ -624,22 +627,23 @@ class Backtesting: # Confirm trade exit: time_in_force = self.strategy.order_time_in_force['exit'] - if (exit_.exit_type != ExitType.LIQUIDATION and not strategy_safe_wrapper( - self.strategy.confirm_trade_exit, default_retval=True)( - pair=trade.pair, - trade=trade, # type: ignore[arg-type] - order_type=order_type, - amount=trade.amount, - rate=close_rate, - time_in_force=time_in_force, - sell_reason=exit_reason, # deprecated - exit_reason=exit_reason, - current_time=exit_candle_time)): + if (exit_.exit_type not in (ExitType.LIQUIDATION, ExitType.PARTIAL_EXIT) + and not strategy_safe_wrapper( + self.strategy.confirm_trade_exit, default_retval=True)( + pair=trade.pair, + trade=trade, # type: ignore[arg-type] + order_type=order_type, + amount=amount_, + rate=close_rate, + time_in_force=time_in_force, + sell_reason=exit_reason, # deprecated + exit_reason=exit_reason, + current_time=exit_candle_time)): return None trade.exit_reason = exit_reason - return self._exit_trade(trade, row, close_rate, trade.amount) + return self._exit_trade(trade, row, close_rate, amount_) return None def _exit_trade(self, trade: LocalTrade, sell_row: Tuple, @@ -647,7 +651,10 @@ class Backtesting: self.order_id_counter += 1 exit_candle_time = sell_row[DATE_IDX].to_pydatetime() order_type = self.strategy.order_types['exit'] - amount = amount or trade.amount + # amount = amount or trade.amount + amount = amount_to_contract_precision(amount or trade.amount, trade.amount_precision, + self.precision_mode, trade.contract_size) + rate = price_to_precision(close_rate, trade.price_precision, self.precision_mode) order = Order( id=self.order_id_counter, ft_trade_id=trade.id, @@ -661,12 +668,12 @@ class Backtesting: side=trade.exit_side, order_type=order_type, status="open", - price=close_rate, - average=close_rate, + price=rate, + average=rate, amount=amount, filled=0, remaining=amount, - cost=amount * close_rate, + cost=amount * rate, ) trade.orders.append(order) return trade @@ -812,7 +819,17 @@ class Backtesting: if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount): self.order_id_counter += 1 base_currency = self.exchange.get_pair_base_currency(pair) - amount = round((stake_amount / propose_rate) * leverage, 8) + precision_price = self.exchange.get_precision_price(pair) + propose_rate = price_to_precision(propose_rate, precision_price, self.precision_mode) + amount_p = (stake_amount / propose_rate) * leverage + + contract_size = self.exchange.get_contract_size(pair) + precision_amount = self.exchange.get_precision_amount(pair) + amount = amount_to_contract_precision(amount_p, precision_amount, self.precision_mode, + contract_size) + # Backcalculate actual stake amount. + stake_amount = amount * propose_rate / leverage + is_short = (direction == 'short') # Necessary for Margin trading. Disabled until support is enabled. # interest_rate = self.exchange.get_interest_rate() @@ -841,9 +858,10 @@ class Backtesting: trading_mode=self.trading_mode, leverage=leverage, # interest_rate=interest_rate, - amount_precision=self.exchange.get_precision_amount(pair), - price_precision=self.exchange.get_precision_price(pair), + amount_precision=precision_amount, + price_precision=precision_price, precision_mode=self.precision_mode, + contract_size=contract_size, orders=[], ) @@ -853,7 +871,8 @@ class Backtesting: pair=pair, open_rate=propose_rate, amount=amount, - leverage=leverage, + stake_amount=trade.stake_amount, + wallet_balance=trade.stake_amount, is_short=is_short, )) diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index cbcf39131..fea2a672f 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -24,13 +24,15 @@ from pandas import DataFrame from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN from freqtrade.data.converter import trim_dataframes from freqtrade.data.history import get_timerange +from freqtrade.enums import HyperoptState from freqtrade.exceptions import OperationalException from freqtrade.misc import deep_merge_dicts, file_dump_json, plural from freqtrade.optimize.backtesting import Backtesting # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules from freqtrade.optimize.hyperopt_auto import HyperOptAuto from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss -from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer +from freqtrade.optimize.hyperopt_tools import (HyperoptStateContainer, HyperoptTools, + hyperopt_serializer) from freqtrade.optimize.optimize_reports import generate_strategy_stats from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver @@ -74,10 +76,14 @@ class Hyperopt: self.dimensions: List[Dimension] = [] self.config = config + self.min_date: datetime + self.max_date: datetime self.backtesting = Backtesting(self.config) self.pairlist = self.backtesting.pairlists.whitelist self.custom_hyperopt: HyperOptAuto + self.analyze_per_epoch = self.config.get('analyze_per_epoch', False) + HyperoptStateContainer.set_state(HyperoptState.STARTUP) if not self.config.get('hyperopt'): self.custom_hyperopt = HyperOptAuto(self.config) @@ -290,6 +296,7 @@ class Hyperopt: Called once per epoch to optimize whatever is configured. Keep this function as optimized as possible! """ + HyperoptStateContainer.set_state(HyperoptState.OPTIMIZE) backtest_start_time = datetime.now(timezone.utc) params_dict = self._get_params_dict(self.dimensions, raw_params) @@ -321,6 +328,10 @@ class Hyperopt: with self.data_pickle_file.open('rb') as f: processed = load(f, mmap_mode='r') + if self.analyze_per_epoch: + # Data is not yet analyzed, rerun populate_indicators. + processed = self.advise_and_trim(processed) + bt_results = self.backtesting.backtest( processed=processed, start_date=self.min_date, @@ -406,22 +417,33 @@ class Hyperopt: def _set_random_state(self, random_state: Optional[int]) -> int: return random_state or random.randint(1, 2**16 - 1) - def prepare_hyperopt_data(self) -> None: - data, timerange = self.backtesting.load_bt_data() - self.backtesting.load_bt_data_detail() - logger.info("Dataload complete. Calculating indicators") - + def advise_and_trim(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]: preprocessed = self.backtesting.strategy.advise_all_indicators(data) # Trim startup period from analyzed dataframe to get correct dates for output. - processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup) + processed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup) self.min_date, self.max_date = get_timerange(processed) + return processed - logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} ' - f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} ' - f'({(self.max_date - self.min_date).days} days)..') - # Store non-trimmed data - will be trimmed after signal generation. - dump(preprocessed, self.data_pickle_file) + def prepare_hyperopt_data(self) -> None: + HyperoptStateContainer.set_state(HyperoptState.DATALOAD) + data, self.timerange = self.backtesting.load_bt_data() + self.backtesting.load_bt_data_detail() + logger.info("Dataload complete. Calculating indicators") + + if not self.analyze_per_epoch: + HyperoptStateContainer.set_state(HyperoptState.INDICATORS) + + preprocessed = self.advise_and_trim(data) + + logger.info(f'Hyperopting with data from ' + f'{self.min_date.strftime(DATETIME_PRINT_FORMAT)} ' + f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} ' + f'({(self.max_date - self.min_date).days} days)..') + # Store non-trimmed data - will be trimmed after signal generation. + dump(preprocessed, self.data_pickle_file) + else: + dump(data, self.data_pickle_file) def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]: """ diff --git a/freqtrade/optimize/hyperopt_tools.py b/freqtrade/optimize/hyperopt_tools.py index ab6ef013b..9b022d519 100755 --- a/freqtrade/optimize/hyperopt_tools.py +++ b/freqtrade/optimize/hyperopt_tools.py @@ -13,6 +13,7 @@ from colorama import Fore, Style from pandas import isna, json_normalize from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES +from freqtrade.enums import HyperoptState from freqtrade.exceptions import OperationalException from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2 from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs @@ -32,6 +33,15 @@ def hyperopt_serializer(x): return str(x) +class HyperoptStateContainer(): + """ Singleton class to track state of hyperopt""" + state: HyperoptState = HyperoptState.OPTIMIZE + + @classmethod + def set_state(cls, value: HyperoptState): + cls.state = value + + class HyperoptTools(): @staticmethod diff --git a/freqtrade/persistence/migrations.py b/freqtrade/persistence/migrations.py index e54675f16..1131c88b4 100644 --- a/freqtrade/persistence/migrations.py +++ b/freqtrade/persistence/migrations.py @@ -133,6 +133,7 @@ def migrate_trades_and_orders_table( amount_precision = get_column_def(cols, 'amount_precision', 'null') price_precision = get_column_def(cols, 'price_precision', 'null') precision_mode = get_column_def(cols, 'precision_mode', 'null') + contract_size = get_column_def(cols, 'contract_size', 'null') # Schema migration necessary with engine.begin() as connection: @@ -161,7 +162,7 @@ def migrate_trades_and_orders_table( timeframe, open_trade_value, close_profit_abs, trading_mode, leverage, liquidation_price, is_short, interest_rate, funding_fees, realized_profit, - amount_precision, price_precision, precision_mode + amount_precision, price_precision, precision_mode, contract_size ) select id, lower(exchange), pair, {base_currency} base_currency, {stake_currency} stake_currency, @@ -189,7 +190,7 @@ def migrate_trades_and_orders_table( {is_short} is_short, {interest_rate} interest_rate, {funding_fees} funding_fees, {realized_profit} realized_profit, {amount_precision} amount_precision, {price_precision} price_precision, - {precision_mode} precision_mode + {precision_mode} precision_mode, {contract_size} contract_size from {trade_back_name} """)) @@ -307,7 +308,9 @@ def check_migrate(engine, decl_base, previous_tables) -> None: # Migrates both trades and orders table! # if ('orders' not in previous_tables # or not has_column(cols_orders, 'stop_price')): - if not has_column(cols_trades, 'precision_mode'): + migrating = False + if not has_column(cols_trades, 'contract_size'): + migrating = True logger.info(f"Running database migration for trades - " f"backup: {table_back_name}, {order_table_bak_name}") migrate_trades_and_orders_table( @@ -315,6 +318,7 @@ def check_migrate(engine, decl_base, previous_tables) -> None: order_table_bak_name, cols_orders) if not has_column(cols_pairlocks, 'side'): + migrating = True logger.info(f"Running database migration for pairlocks - " f"backup: {pairlock_table_bak_name}") @@ -329,3 +333,6 @@ def check_migrate(engine, decl_base, previous_tables) -> None: set_sqlite_to_wal(engine) fix_old_dry_orders(engine) + + if migrating: + logger.info("Database migration finished.") diff --git a/freqtrade/persistence/models.py b/freqtrade/persistence/models.py index f0fa05343..7f851322e 100644 --- a/freqtrade/persistence/models.py +++ b/freqtrade/persistence/models.py @@ -53,7 +53,7 @@ def init_db(db_url: str) -> None: # https://docs.sqlalchemy.org/en/13/orm/contextual.html#thread-local-scope # Scoped sessions proxy requests to the appropriate thread-local session. # We should use the scoped_session object - not a seperately initialized version - Trade._session = scoped_session(sessionmaker(bind=engine, autoflush=True)) + Trade._session = scoped_session(sessionmaker(bind=engine, autoflush=False)) Trade.query = Trade._session.query_property() Order.query = Trade._session.query_property() PairLock.query = Trade._session.query_property() diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index b954fee20..23997f835 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -14,7 +14,7 @@ from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPE BuySell, LongShort) from freqtrade.enums import ExitType, TradingMode from freqtrade.exceptions import DependencyException, OperationalException -from freqtrade.exchange import amount_to_precision, price_to_precision +from freqtrade.exchange import amount_to_contract_precision, price_to_precision from freqtrade.leverage import interest from freqtrade.persistence.base import _DECL_BASE from freqtrade.util import FtPrecise @@ -296,6 +296,7 @@ class LocalTrade(): amount_precision: Optional[float] = None price_precision: Optional[float] = None precision_mode: Optional[int] = None + contract_size: Optional[float] = None # Leverage trading properties liquidation_price: Optional[float] = None @@ -623,7 +624,8 @@ class LocalTrade(): else: logger.warning( f'Got different open_order_id {self.open_order_id} != {order.order_id}') - amount_tr = amount_to_precision(self.amount, self.amount_precision, self.precision_mode) + amount_tr = amount_to_contract_precision(self.amount, self.amount_precision, + self.precision_mode, self.contract_size) if isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC): self.close(order.safe_price) else: @@ -841,7 +843,7 @@ class LocalTrade(): avg_price = FtPrecise(0.0) close_profit = 0.0 close_profit_abs = 0.0 - + profit = None for o in self.orders: if o.ft_is_open or not o.filled: continue @@ -868,8 +870,6 @@ class LocalTrade(): close_profit_abs += profit close_profit = self.calc_profit_ratio( exit_rate, amount=exit_amount, open_rate=avg_price) - if current_amount <= ZERO: - profit = close_profit_abs else: total_stake = total_stake + self._calc_open_trade_value(tmp_amount, price) @@ -878,8 +878,8 @@ class LocalTrade(): self.realized_profit = close_profit_abs self.close_profit_abs = profit - current_amount_tr = amount_to_precision(float(current_amount), - self.amount_precision, self.precision_mode) + current_amount_tr = amount_to_contract_precision( + float(current_amount), self.amount_precision, self.precision_mode, self.contract_size) if current_amount_tr > 0.0: # Trade is still open # Leverage not updated, as we don't allow changing leverage through DCA at the moment. @@ -894,6 +894,7 @@ class LocalTrade(): # Close profit abs / maximum owned # Fees are considered as they are part of close_profit_abs self.close_profit = (close_profit_abs / total_stake) * self.leverage + self.close_profit_abs = close_profit_abs def select_order_by_order_id(self, order_id: str) -> Optional[Order]: """ @@ -1044,6 +1045,16 @@ class LocalTrade(): """ return Trade.get_trades_proxy(is_open=True) + @staticmethod + def get_open_trade_count() -> int: + """ + get open trade count + """ + if Trade.use_db: + return Trade.query.filter(Trade.is_open.is_(True)).count() + else: + return len(LocalTrade.trades_open) + @staticmethod def stoploss_reinitialization(desired_stoploss): """ @@ -1132,6 +1143,7 @@ class Trade(_DECL_BASE, LocalTrade): amount_precision = Column(Float, nullable=True) price_precision = Column(Float, nullable=True) precision_mode = Column(Integer, nullable=True) + contract_size = Column(Float, nullable=True) # Leverage trading properties leverage = Column(Float, nullable=True, default=1.0) diff --git a/freqtrade/plugins/pairlist/PrecisionFilter.py b/freqtrade/plugins/pairlist/PrecisionFilter.py index 521f38635..dcd153d8e 100644 --- a/freqtrade/plugins/pairlist/PrecisionFilter.py +++ b/freqtrade/plugins/pairlist/PrecisionFilter.py @@ -51,6 +51,11 @@ class PrecisionFilter(IPairList): :param ticker: ticker dict as returned from ccxt.fetch_tickers() :return: True if the pair can stay, false if it should be removed """ + if ticker.get('last', None) is None: + self.log_once(f"Removed {ticker['symbol']} from whitelist, because " + "ticker['last'] is empty (Usually no trade in the last 24h).", + logger.info) + return False stop_price = ticker['last'] * self._stoploss # Adjust stop-prices to precision diff --git a/freqtrade/plugins/pairlist/VolumePairList.py b/freqtrade/plugins/pairlist/VolumePairList.py index e364e1a69..8138a5fb6 100644 --- a/freqtrade/plugins/pairlist/VolumePairList.py +++ b/freqtrade/plugins/pairlist/VolumePairList.py @@ -73,7 +73,7 @@ class VolumePairList(IPairList): if (not self._use_range and not ( self._exchange.exchange_has('fetchTickers') - and self._exchange._ft_has["tickers_have_quoteVolume"])): + and self._exchange.get_option("tickers_have_quoteVolume"))): raise OperationalException( "Exchange does not support dynamic whitelist in this configuration. " "Please edit your config and either remove Volumepairlist, " @@ -193,7 +193,7 @@ class VolumePairList(IPairList): ) in candles else None # in case of candle data calculate typical price and quoteVolume for candle if pair_candles is not None and not pair_candles.empty: - if self._exchange._ft_has["ohlcv_volume_currency"] == "base": + if self._exchange.get_option("ohlcv_volume_currency") == "base": pair_candles['typical_price'] = (pair_candles['high'] + pair_candles['low'] + pair_candles['close']) / 3 diff --git a/freqtrade/resolvers/iresolver.py b/freqtrade/resolvers/iresolver.py index 74b28dffe..b99e7a94b 100644 --- a/freqtrade/resolvers/iresolver.py +++ b/freqtrade/resolvers/iresolver.py @@ -193,7 +193,10 @@ class IResolver: :return: List of dicts containing 'name', 'class' and 'location' entries """ logger.debug(f"Searching for {cls.object_type.__name__} '{directory}'") - objects = [] + objects: List[Dict[str, Any]] = [] + if not directory.is_dir(): + logger.info(f"'{directory}' is not a directory, skipping.") + return objects for entry in directory.iterdir(): if ( recursive and entry.is_dir() diff --git a/freqtrade/rpc/api_server/api_v1.py b/freqtrade/rpc/api_server/api_v1.py index e0fef7be8..bf21715b7 100644 --- a/freqtrade/rpc/api_server/api_v1.py +++ b/freqtrade/rpc/api_server/api_v1.py @@ -216,9 +216,10 @@ def stop(rpc: RPC = Depends(get_rpc)): return rpc._rpc_stop() +@router.post('/stopentry', response_model=StatusMsg, tags=['botcontrol']) @router.post('/stopbuy', response_model=StatusMsg, tags=['botcontrol']) def stop_buy(rpc: RPC = Depends(get_rpc)): - return rpc._rpc_stopbuy() + return rpc._rpc_stopentry() @router.post('/reload_config', response_model=StatusMsg, tags=['botcontrol']) diff --git a/freqtrade/rpc/rpc.py b/freqtrade/rpc/rpc.py index ed7f13a96..11311f671 100644 --- a/freqtrade/rpc/rpc.py +++ b/freqtrade/rpc/rpc.py @@ -657,7 +657,7 @@ class RPC: self._freqtrade.state = State.RELOAD_CONFIG return {'status': 'Reloading config ...'} - def _rpc_stopbuy(self) -> Dict[str, str]: + def _rpc_stopentry(self) -> Dict[str, str]: """ Handler to stop buying, but handle open trades gracefully. """ @@ -665,7 +665,7 @@ class RPC: # Set 'max_open_trades' to 0 self._freqtrade.config['max_open_trades'] = 0 - return {'status': 'No more buy will occur from now. Run /reload_config to reset.'} + return {'status': 'No more entries will occur from now. Run /reload_config to reset.'} def __exec_force_exit(self, trade: Trade, ordertype: Optional[str], amount: Optional[float] = None) -> None: diff --git a/freqtrade/rpc/telegram.py b/freqtrade/rpc/telegram.py index 8ce2fa2e4..8c988d570 100644 --- a/freqtrade/rpc/telegram.py +++ b/freqtrade/rpc/telegram.py @@ -114,18 +114,20 @@ class Telegram(RPCHandler): # TODO: DRY! - its not good to list all valid cmds here. But otherwise # this needs refactoring of the whole telegram module (same # problem in _help()). - valid_keys: List[str] = [r'/start$', r'/stop$', r'/status$', r'/status table$', - r'/trades$', r'/performance$', r'/buys', r'/entries', - r'/sells', r'/exits', r'/mix_tags', - r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+', - r'/stats$', r'/count$', r'/locks$', r'/balance$', - r'/stopbuy$', r'/reload_config$', r'/show_config$', - r'/logs$', r'/whitelist$', r'/whitelist(\ssorted|\sbaseonly)+$', - r'/blacklist$', r'/bl_delete$', - r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$', - r'/forcebuy$', r'/forcelong$', r'/forceshort$', - r'/forcesell$', r'/forceexit$', - r'/edge$', r'/health$', r'/help$', r'/version$'] + valid_keys: List[str] = [ + r'/start$', r'/stop$', r'/status$', r'/status table$', + r'/trades$', r'/performance$', r'/buys', r'/entries', + r'/sells', r'/exits', r'/mix_tags', + r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+', + r'/stats$', r'/count$', r'/locks$', r'/balance$', + r'/stopbuy$', r'/stopentry$', r'/reload_config$', r'/show_config$', + r'/logs$', r'/whitelist$', r'/whitelist(\ssorted|\sbaseonly)+$', + r'/blacklist$', r'/bl_delete$', + r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$', + r'/forcebuy$', r'/forcelong$', r'/forceshort$', + r'/forcesell$', r'/forceexit$', + r'/edge$', r'/health$', r'/help$', r'/version$' + ] # Create keys for generation valid_keys_print = [k.replace('$', '') for k in valid_keys] @@ -182,7 +184,7 @@ class Telegram(RPCHandler): CommandHandler(['unlock', 'delete_locks'], self._delete_locks), CommandHandler(['reload_config', 'reload_conf'], self._reload_config), CommandHandler(['show_config', 'show_conf'], self._show_config), - CommandHandler('stopbuy', self._stopbuy), + CommandHandler(['stopbuy', 'stopentry'], self._stopentry), CommandHandler('whitelist', self._whitelist), CommandHandler('blacklist', self._blacklist), CommandHandler(['blacklist_delete', 'bl_delete'], self._blacklist_delete), @@ -984,7 +986,7 @@ class Telegram(RPCHandler): self._send_msg(f"Status: `{msg['status']}`") @authorized_only - def _stopbuy(self, update: Update, context: CallbackContext) -> None: + def _stopentry(self, update: Update, context: CallbackContext) -> None: """ Handler for /stop_buy. Sets max_open_trades to 0 and gracefully sells all open trades @@ -992,7 +994,7 @@ class Telegram(RPCHandler): :param update: message update :return: None """ - msg = self._rpc._rpc_stopbuy() + msg = self._rpc._rpc_stopentry() self._send_msg(f"Status: `{msg['status']}`") @authorized_only @@ -1488,7 +1490,7 @@ class Telegram(RPCHandler): "------------\n" "*/start:* `Starts the trader`\n" "*/stop:* Stops the trader\n" - "*/stopbuy:* `Stops buying, but handles open trades gracefully` \n" + "*/stopentry:* `Stops entering, but handles open trades gracefully` \n" "*/forceexit |all:* `Instantly exits the given trade or all trades, " "regardless of profit`\n" "*/fx |all:* `Alias to /forceexit`\n" diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index 3ea1a3fae..c7ea95bda 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -78,8 +78,8 @@ class IStrategy(ABC, HyperStrategyMixin): # Optional time in force order_time_in_force: Dict = { - 'entry': 'gtc', - 'exit': 'gtc', + 'entry': 'GTC', + 'exit': 'GTC', } # run "populate_indicators" only for new candle diff --git a/freqtrade/strategy/parameters.py b/freqtrade/strategy/parameters.py index 83dd41de9..c6037ae0b 100644 --- a/freqtrade/strategy/parameters.py +++ b/freqtrade/strategy/parameters.py @@ -7,6 +7,9 @@ from abc import ABC, abstractmethod from contextlib import suppress from typing import Any, Optional, Sequence, Union +from freqtrade.enums.hyperoptstate import HyperoptState +from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer + with suppress(ImportError): from skopt.space import Integer, Real, Categorical @@ -57,6 +60,13 @@ class BaseParameter(ABC): Get-space - will be used by Hyperopt to get the hyperopt Space """ + def can_optimize(self): + return ( + self.in_space + and self.optimize + and HyperoptStateContainer.state != HyperoptState.OPTIMIZE + ) + class NumericParameter(BaseParameter): """ Internal parameter used for Numeric purposes """ @@ -133,7 +143,7 @@ class IntParameter(NumericParameter): Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid calculating 100ds of indicators. """ - if self.in_space and self.optimize: + if self.can_optimize(): # Scikit-optimize ranges are "inclusive", while python's "range" is exclusive return range(self.low, self.high + 1) else: @@ -212,7 +222,7 @@ class DecimalParameter(NumericParameter): Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid calculating 100ds of indicators. """ - if self.in_space and self.optimize: + if self.can_optimize(): low = int(self.low * pow(10, self._decimals)) high = int(self.high * pow(10, self._decimals)) + 1 return [round(n * pow(0.1, self._decimals), self._decimals) for n in range(low, high)] @@ -261,7 +271,7 @@ class CategoricalParameter(BaseParameter): Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid calculating 100ds of indicators. """ - if self.in_space and self.optimize: + if self.can_optimize(): return self.opt_range else: return [self.value] diff --git a/freqtrade/templates/FreqaiHybridExampleStrategy.py b/freqtrade/templates/FreqaiHybridExampleStrategy.py new file mode 100644 index 000000000..0a91455f5 --- /dev/null +++ b/freqtrade/templates/FreqaiHybridExampleStrategy.py @@ -0,0 +1,259 @@ +import logging + +import numpy as np +import pandas as pd +import talib.abstract as ta +from pandas import DataFrame +from technical import qtpylib + +from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair + + +logger = logging.getLogger(__name__) + + +class FreqaiExampleHybridStrategy(IStrategy): + """ + Example of a hybrid FreqAI strat, designed to illustrate how a user may employ + FreqAI to bolster a typical Freqtrade strategy. + + Launching this strategy would be: + + freqtrade trade --strategy FreqaiExampleHyridStrategy --strategy-path freqtrade/templates + --freqaimodel CatboostClassifier --config config_examples/config_freqai.example.json + + or the user simply adds this to their config: + + "freqai": { + "enabled": true, + "purge_old_models": true, + "train_period_days": 15, + "identifier": "uniqe-id", + "feature_parameters": { + "include_timeframes": [ + "3m", + "15m", + "1h" + ], + "include_corr_pairlist": [ + "BTC/USDT", + "ETH/USDT" + ], + "label_period_candles": 20, + "include_shifted_candles": 2, + "DI_threshold": 0.9, + "weight_factor": 0.9, + "principal_component_analysis": false, + "use_SVM_to_remove_outliers": true, + "indicator_max_period_candles": 20, + "indicator_periods_candles": [10, 20] + }, + "data_split_parameters": { + "test_size": 0, + "random_state": 1 + }, + "model_training_parameters": { + "n_estimators": 800 + } + }, + + Thanks to @smarmau and @johanvulgt for developing and sharing the strategy. + """ + + minimal_roi = { + "60": 0.01, + "30": 0.02, + "0": 0.04 + } + + plot_config = { + 'main_plot': { + 'tema': {}, + }, + 'subplots': { + "MACD": { + 'macd': {'color': 'blue'}, + 'macdsignal': {'color': 'orange'}, + }, + "RSI": { + 'rsi': {'color': 'red'}, + }, + "Up_or_down": { + '&s-up_or_down': {'color': 'green'}, + } + } + } + + process_only_new_candles = True + stoploss = -0.05 + use_exit_signal = True + startup_candle_count: int = 300 + can_short = True + + # Hyperoptable parameters + buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) + sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True) + short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True) + exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) + + # FreqAI required function, leave as is or add additional informatives to existing structure. + def informative_pairs(self): + whitelist_pairs = self.dp.current_whitelist() + corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"] + informative_pairs = [] + for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]: + for pair in whitelist_pairs: + informative_pairs.append((pair, tf)) + for pair in corr_pairs: + if pair in whitelist_pairs: + continue # avoid duplication + informative_pairs.append((pair, tf)) + return informative_pairs + + # FreqAI required function, user can add or remove indicators, but general structure + # must stay the same. + def populate_any_indicators( + self, pair, df, tf, informative=None, set_generalized_indicators=False + ): + """ + User feeds these indicators to FreqAI to train a classifier to decide + if the market will go up or down. + + :param pair: pair to be used as informative + :param df: strategy dataframe which will receive merges from informatives + :param tf: timeframe of the dataframe which will modify the feature names + :param informative: the dataframe associated with the informative pair + """ + + coin = pair.split('/')[0] + + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) + + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t) + informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t) + informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t) + informative[f"%-{coin}relative_volume-period_{t}"] = ( + informative["volume"] / informative["volume"].rolling(t).mean() + ) + + # FreqAI needs the following lines in order to detect features and automatically + # expand upon them. + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) + + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) + + # User can set the "target" here (in present case it is the + # "up" or "down") + if set_generalized_indicators: + # User "looks into the future" here to figure out if the future + # will be "up" or "down". This same column name is available to + # the user + df['&s-up_or_down'] = np.where(df["close"].shift(-50) > + df["close"], 'up', 'down') + + return df + + # flake8: noqa: C901 + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + + # User creates their own custom strat here. Present example is a supertrend + # based strategy. + + dataframe = self.freqai.start(dataframe, metadata, self) + + # TA indicators to combine with the Freqai targets + # RSI + dataframe['rsi'] = ta.RSI(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'] + dataframe["bb_percent"] = ( + (dataframe["close"] - dataframe["bb_lowerband"]) / + (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) + ) + dataframe["bb_width"] = ( + (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] + ) + + # TEMA - Triple Exponential Moving Average + dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) + + return dataframe + + def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: + + df.loc[ + ( + # Signal: RSI crosses above 30 + (qtpylib.crossed_above(df['rsi'], self.buy_rsi.value)) & + (df['tema'] <= df['bb_middleband']) & # Guard: tema below BB middle + (df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising + (df['volume'] > 0) & # Make sure Volume is not 0 + (df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction + # Only enter trade if Freqai thinks the trend is in this direction + (df['&s-up_or_down'] == 'up') + ), + 'enter_long'] = 1 + + df.loc[ + ( + # Signal: RSI crosses above 70 + (qtpylib.crossed_above(df['rsi'], self.short_rsi.value)) & + (df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle + (df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling + (df['volume'] > 0) & # Make sure Volume is not 0 + (df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction + # Only enter trade if Freqai thinks the trend is in this direction + (df['&s-up_or_down'] == 'down') + ), + 'enter_short'] = 1 + + return df + + def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: + + df.loc[ + ( + # Signal: RSI crosses above 70 + (qtpylib.crossed_above(df['rsi'], self.sell_rsi.value)) & + (df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle + (df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling + (df['volume'] > 0) # Make sure Volume is not 0 + ), + + 'exit_long'] = 1 + + df.loc[ + ( + # Signal: RSI crosses above 30 + (qtpylib.crossed_above(df['rsi'], self.exit_short_rsi.value)) & + # Guard: tema below BB middle + (df['tema'] <= df['bb_middleband']) & + (df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising + (df['volume'] > 0) # Make sure Volume is not 0 + ), + 'exit_short'] = 1 + + return df diff --git a/freqtrade/templates/base_strategy.py.j2 b/freqtrade/templates/base_strategy.py.j2 index 610a7a96e..5a4504687 100644 --- a/freqtrade/templates/base_strategy.py.j2 +++ b/freqtrade/templates/base_strategy.py.j2 @@ -88,8 +88,8 @@ class {{ strategy }}(IStrategy): # Optional order time in force. order_time_in_force = { - 'entry': 'gtc', - 'exit': 'gtc' + 'entry': 'GTC', + 'exit': 'GTC' } {{ plot_config | indent(4) }} diff --git a/freqtrade/templates/sample_strategy.py b/freqtrade/templates/sample_strategy.py index 1b375714a..1eb3d4256 100644 --- a/freqtrade/templates/sample_strategy.py +++ b/freqtrade/templates/sample_strategy.py @@ -88,8 +88,8 @@ class SampleStrategy(IStrategy): # Optional order time in force. order_time_in_force = { - 'entry': 'gtc', - 'exit': 'gtc' + 'entry': 'GTC', + 'exit': 'GTC' } plot_config = { diff --git a/freqtrade/templates/strategy_analysis_example.ipynb b/freqtrade/templates/strategy_analysis_example.ipynb index a7430c225..77444a023 100644 --- a/freqtrade/templates/strategy_analysis_example.ipynb +++ b/freqtrade/templates/strategy_analysis_example.ipynb @@ -30,7 +30,7 @@ "\n", "# Initialize empty configuration object\n", "config = Configuration.from_files([])\n", - "# Optionally, use existing configuration file\n", + "# Optionally (recommended), use existing configuration file\n", "# config = Configuration.from_files([\"config.json\"])\n", "\n", "# Define some constants\n", @@ -38,7 +38,7 @@ "# Name of the strategy class\n", "config[\"strategy\"] = \"SampleStrategy\"\n", "# Location of the data\n", - "data_location = Path(config['user_data_dir'], 'data', 'binance')\n", + "data_location = config['datadir']\n", "# Pair to analyze - Only use one pair here\n", "pair = \"BTC/USDT\"" ] @@ -365,7 +365,7 @@ "metadata": { "file_extension": ".py", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 64-bit ('trade_397')", "language": "python", "name": "python3" }, @@ -379,7 +379,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" }, "mimetype": "text/x-python", "name": "python", @@ -427,7 +427,12 @@ ], "window_display": false }, - "version": 3 + "version": 3, + "vscode": { + "interpreter": { + "hash": "675f32a300d6d26767470181ad0b11dd4676bcce7ed1dd2ffe2fbc370c95fc7c" + } + } }, "nbformat": 4, "nbformat_minor": 4 diff --git a/freqtrade/wallets.py b/freqtrade/wallets.py index 14e5a6743..41115c72e 100644 --- a/freqtrade/wallets.py +++ b/freqtrade/wallets.py @@ -148,7 +148,7 @@ class Wallets: # Position is not open ... continue size = self._exchange._contracts_to_amount(symbol, position['contracts']) - collateral = position['collateral'] + collateral = position['collateral'] or 0.0 leverage = position['leverage'] self._positions[symbol] = PositionWallet( symbol, position=size, diff --git a/requirements-dev.txt b/requirements-dev.txt index 0cd4a6a6c..26df7115c 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -17,14 +17,14 @@ pytest-mock==3.8.2 pytest-random-order==1.0.4 isort==5.10.1 # For datetime mocking -time-machine==2.7.1 +time-machine==2.8.1 # Convert jupyter notebooks to markdown documents -nbconvert==6.5.3 +nbconvert==7.0.0 # mypy types types-cachetools==5.2.1 types-filelock==3.2.7 -types-requests==2.28.8 +types-requests==2.28.9 types-tabulate==0.8.11 types-python-dateutil==2.8.19 diff --git a/requirements-hyperopt.txt b/requirements-hyperopt.txt index 020ccdda8..efa31272a 100644 --- a/requirements-hyperopt.txt +++ b/requirements-hyperopt.txt @@ -2,7 +2,7 @@ -r requirements.txt # Required for hyperopt -scipy==1.9.0 +scipy==1.9.1 scikit-learn==1.1.2 scikit-optimize==0.9.0 filelock==3.8.0 diff --git a/requirements.txt b/requirements.txt index 77925f98b..cbd5e31ba 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ numpy==1.23.2 pandas==1.4.3 pandas-ta==0.3.14b -ccxt==1.92.20 +ccxt==1.92.84 # Pin cryptography for now due to rust build errors with piwheels cryptography==37.0.4 aiohttp==3.8.1 @@ -11,8 +11,8 @@ python-telegram-bot==13.13 arrow==1.2.2 cachetools==4.2.2 requests==2.28.1 -urllib3==1.26.11 -jsonschema==4.9.1 +urllib3==1.26.12 +jsonschema==4.14.0 TA-Lib==0.4.24 technical==1.3.0 tabulate==0.8.10 @@ -28,14 +28,14 @@ py_find_1st==1.1.5 # Load ticker files 30% faster python-rapidjson==1.8 # Properly format api responses -orjson==3.7.12 +orjson==3.8.0 # Notify systemd sdnotify==0.3.2 # API Server -fastapi==0.79.0 -uvicorn==0.18.2 +fastapi==0.81.0 +uvicorn==0.18.3 pyjwt==2.4.0 aiofiles==0.8.0 psutil==5.9.1 diff --git a/scripts/rest_client.py b/scripts/rest_client.py index 989e6a50d..ac6d97133 100755 --- a/scripts/rest_client.py +++ b/scripts/rest_client.py @@ -361,6 +361,13 @@ class FtRestClient(): """ return self._get("sysinfo") + def health(self): + """Provides a quick health check of the running bot. + + :return: json object + """ + return self._get("health") + def add_arguments(): parser = argparse.ArgumentParser() diff --git a/tests/commands/test_commands.py b/tests/commands/test_commands.py index 9df6acf75..28515a28a 100644 --- a/tests/commands/test_commands.py +++ b/tests/commands/test_commands.py @@ -1430,6 +1430,27 @@ def test_start_list_data(testdatadir, capsys): assert "\n| XRP/USDT | 1h | futures |\n" in captured.out assert "\n| XRP/USDT | 1h, 8h | mark |\n" in captured.out + args = [ + "list-data", + "--data-format-ohlcv", + "json", + "--pairs", "XRP/ETH", + "--datadir", + str(testdatadir), + "--show-timerange", + ] + pargs = get_args(args) + pargs['config'] = None + start_list_data(pargs) + captured = capsys.readouterr() + assert "Found 2 pair / timeframe combinations." in captured.out + assert ("\n| Pair | Timeframe | Type | From | To |\n" + in captured.out) + assert "UNITTEST/BTC" not in captured.out + assert ( + "\n| XRP/ETH | 1m | spot | 2019-10-11 00:00:00 | 2019-10-13 11:19:00 |\n" + in captured.out) + @pytest.mark.usefixtures("init_persistence") def test_show_trades(mocker, fee, capsys, caplog): diff --git a/tests/conftest.py b/tests/conftest.py index a02fc4566..fffac8e0a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -3085,416 +3085,416 @@ def leverage_tiers(): return { "1000SHIB/USDT": [ { - 'min': 0, - 'max': 50000, - 'mmr': 0.01, - 'lev': 50, + 'minNotional': 0, + 'maxNotional': 50000, + 'maintenanceMarginRate': 0.01, + 'maxLeverage': 50, 'maintAmt': 0.0 }, { - 'min': 50000, - 'max': 150000, - 'mmr': 0.025, - 'lev': 20, + 'minNotional': 50000, + 'maxNotional': 150000, + 'maintenanceMarginRate': 0.025, + 'maxLeverage': 20, 'maintAmt': 750.0 }, { - 'min': 150000, - 'max': 250000, - 'mmr': 0.05, - 'lev': 10, + 'minNotional': 150000, + 'maxNotional': 250000, + 'maintenanceMarginRate': 0.05, + 'maxLeverage': 10, 'maintAmt': 4500.0 }, { - 'min': 250000, - 'max': 500000, - 'mmr': 0.1, - 'lev': 5, + 'minNotional': 250000, + 'maxNotional': 500000, + 'maintenanceMarginRate': 0.1, + 'maxLeverage': 5, 'maintAmt': 17000.0 }, { - 'min': 500000, - 'max': 1000000, - 'mmr': 0.125, - 'lev': 4, + 'minNotional': 500000, + 'maxNotional': 1000000, + 'maintenanceMarginRate': 0.125, + 'maxLeverage': 4, 'maintAmt': 29500.0 }, { - 'min': 1000000, - 'max': 2000000, - 'mmr': 0.25, - 'lev': 2, + 'minNotional': 1000000, + 'maxNotional': 2000000, + 'maintenanceMarginRate': 0.25, + 'maxLeverage': 2, 'maintAmt': 154500.0 }, { - 'min': 2000000, - 'max': 30000000, - 'mmr': 0.5, - 'lev': 1, + 'minNotional': 2000000, + 'maxNotional': 30000000, + 'maintenanceMarginRate': 0.5, + 'maxLeverage': 1, 'maintAmt': 654500.0 }, ], "1INCH/USDT": [ { - 'min': 0, - 'max': 5000, - 'mmr': 0.012, - 'lev': 50, + 'minNotional': 0, + 'maxNotional': 5000, + 'maintenanceMarginRate': 0.012, + 'maxLeverage': 50, 'maintAmt': 0.0 }, { - 'min': 5000, - 'max': 25000, - 'mmr': 0.025, - 'lev': 20, + 'minNotional': 5000, + 'maxNotional': 25000, + 'maintenanceMarginRate': 0.025, + 'maxLeverage': 20, 'maintAmt': 65.0 }, { - 'min': 25000, - 'max': 100000, - 'mmr': 0.05, - 'lev': 10, + 'minNotional': 25000, + 'maxNotional': 100000, + 'maintenanceMarginRate': 0.05, + 'maxLeverage': 10, 'maintAmt': 690.0 }, { - 'min': 100000, - 'max': 250000, - 'mmr': 0.1, - 'lev': 5, + 'minNotional': 100000, + 'maxNotional': 250000, + 'maintenanceMarginRate': 0.1, + 'maxLeverage': 5, 'maintAmt': 5690.0 }, { - 'min': 250000, - 'max': 1000000, - 'mmr': 0.125, - 'lev': 2, + 'minNotional': 250000, + 'maxNotional': 1000000, + 'maintenanceMarginRate': 0.125, + 'maxLeverage': 2, 'maintAmt': 11940.0 }, { - 'min': 1000000, - 'max': 100000000, - 'mmr': 0.5, - 'lev': 1, + 'minNotional': 1000000, + 'maxNotional': 100000000, + 'maintenanceMarginRate': 0.5, + 'maxLeverage': 1, 'maintAmt': 386940.0 }, ], "AAVE/USDT": [ { - 'min': 0, - 'max': 5000, - 'mmr': 0.01, - 'lev': 50, + 'minNotional': 0, + 'maxNotional': 5000, + 'maintenanceMarginRate': 0.01, + 'maxLeverage': 50, 'maintAmt': 0.0 }, { - 'min': 5000, - 'max': 25000, - 'mmr': 0.02, - 'lev': 25, + 'minNotional': 5000, + 'maxNotional': 25000, + 'maintenanceMarginRate': 0.02, + 'maxLeverage': 25, 'maintAmt': 75.0 }, { - 'min': 25000, - 'max': 100000, - 'mmr': 0.05, - 'lev': 10, + 'minNotional': 25000, + 'maxNotional': 100000, + 'maintenanceMarginRate': 0.05, + 'maxLeverage': 10, 'maintAmt': 700.0 }, { - 'min': 100000, - 'max': 250000, - 'mmr': 0.1, - 'lev': 5, + 'minNotional': 100000, + 'maxNotional': 250000, + 'maintenanceMarginRate': 0.1, + 'maxLeverage': 5, 'maintAmt': 5700.0 }, { - 'min': 250000, - 'max': 1000000, - 'mmr': 0.125, - 'lev': 2, + 'minNotional': 250000, + 'maxNotional': 1000000, + 'maintenanceMarginRate': 0.125, + 'maxLeverage': 2, 'maintAmt': 11950.0 }, { - 'min': 10000000, - 'max': 50000000, - 'mmr': 0.5, - 'lev': 1, + 'minNotional': 10000000, + 'maxNotional': 50000000, + 'maintenanceMarginRate': 0.5, + 'maxLeverage': 1, 'maintAmt': 386950.0 }, ], "ADA/BUSD": [ { - "min": 0, - "max": 100000, - "mmr": 0.025, - "lev": 20, + "minNotional": 0, + "maxNotional": 100000, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20, "maintAmt": 0.0 }, { - "min": 100000, - "max": 500000, - "mmr": 0.05, - "lev": 10, + "minNotional": 100000, + "maxNotional": 500000, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10, "maintAmt": 2500.0 }, { - "min": 500000, - "max": 1000000, - "mmr": 0.1, - "lev": 5, + "minNotional": 500000, + "maxNotional": 1000000, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5, "maintAmt": 27500.0 }, { - "min": 1000000, - "max": 2000000, - "mmr": 0.15, - "lev": 3, + "minNotional": 1000000, + "maxNotional": 2000000, + "maintenanceMarginRate": 0.15, + "maxLeverage": 3, "maintAmt": 77500.0 }, { - "min": 2000000, - "max": 5000000, - "mmr": 0.25, - "lev": 2, + "minNotional": 2000000, + "maxNotional": 5000000, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2, "maintAmt": 277500.0 }, { - "min": 5000000, - "max": 30000000, - "mmr": 0.5, - "lev": 1, + "minNotional": 5000000, + "maxNotional": 30000000, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1, "maintAmt": 1527500.0 }, ], 'BNB/BUSD': [ { - "min": 0, # stake(before leverage) = 0 - "max": 100000, # max stake(before leverage) = 5000 - "mmr": 0.025, - "lev": 20, + "minNotional": 0, # stake(before leverage) = 0 + "maxNotional": 100000, # max stake(before leverage) = 5000 + "maintenanceMarginRate": 0.025, + "maxLeverage": 20, "maintAmt": 0.0 }, { - "min": 100000, # stake = 10000.0 - "max": 500000, # max_stake = 50000.0 - "mmr": 0.05, - "lev": 10, + "minNotional": 100000, # stake = 10000.0 + "maxNotional": 500000, # max_stake = 50000.0 + "maintenanceMarginRate": 0.05, + "maxLeverage": 10, "maintAmt": 2500.0 }, { - "min": 500000, # stake = 100000.0 - "max": 1000000, # max_stake = 200000.0 - "mmr": 0.1, - "lev": 5, + "minNotional": 500000, # stake = 100000.0 + "maxNotional": 1000000, # max_stake = 200000.0 + "maintenanceMarginRate": 0.1, + "maxLeverage": 5, "maintAmt": 27500.0 }, { - "min": 1000000, # stake = 333333.3333333333 - "max": 2000000, # max_stake = 666666.6666666666 - "mmr": 0.15, - "lev": 3, + "minNotional": 1000000, # stake = 333333.3333333333 + "maxNotional": 2000000, # max_stake = 666666.6666666666 + "maintenanceMarginRate": 0.15, + "maxLeverage": 3, "maintAmt": 77500.0 }, { - "min": 2000000, # stake = 1000000.0 - "max": 5000000, # max_stake = 2500000.0 - "mmr": 0.25, - "lev": 2, + "minNotional": 2000000, # stake = 1000000.0 + "maxNotional": 5000000, # max_stake = 2500000.0 + "maintenanceMarginRate": 0.25, + "maxLeverage": 2, "maintAmt": 277500.0 }, { - "min": 5000000, # stake = 5000000.0 - "max": 30000000, # max_stake = 30000000.0 - "mmr": 0.5, - "lev": 1, + "minNotional": 5000000, # stake = 5000000.0 + "maxNotional": 30000000, # max_stake = 30000000.0 + "maintenanceMarginRate": 0.5, + "maxLeverage": 1, "maintAmt": 1527500.0 } ], 'BNB/USDT': [ { - "min": 0, # stake = 0.0 - "max": 10000, # max_stake = 133.33333333333334 - "mmr": 0.0065, - "lev": 75, + "minNotional": 0, # stake = 0.0 + "maxNotional": 10000, # max_stake = 133.33333333333334 + "maintenanceMarginRate": 0.0065, + "maxLeverage": 75, "maintAmt": 0.0 }, { - "min": 10000, # stake = 200.0 - "max": 50000, # max_stake = 1000.0 - "mmr": 0.01, - "lev": 50, + "minNotional": 10000, # stake = 200.0 + "maxNotional": 50000, # max_stake = 1000.0 + "maintenanceMarginRate": 0.01, + "maxLeverage": 50, "maintAmt": 35.0 }, { - "min": 50000, # stake = 2000.0 - "max": 250000, # max_stake = 10000.0 - "mmr": 0.02, - "lev": 25, + "minNotional": 50000, # stake = 2000.0 + "maxNotional": 250000, # max_stake = 10000.0 + "maintenanceMarginRate": 0.02, + "maxLeverage": 25, "maintAmt": 535.0 }, { - "min": 250000, # stake = 25000.0 - "max": 1000000, # max_stake = 100000.0 - "mmr": 0.05, - "lev": 10, + "minNotional": 250000, # stake = 25000.0 + "maxNotional": 1000000, # max_stake = 100000.0 + "maintenanceMarginRate": 0.05, + "maxLeverage": 10, "maintAmt": 8035.0 }, { - "min": 1000000, # stake = 200000.0 - "max": 2000000, # max_stake = 400000.0 - "mmr": 0.1, - "lev": 5, + "minNotional": 1000000, # stake = 200000.0 + "maxNotional": 2000000, # max_stake = 400000.0 + "maintenanceMarginRate": 0.1, + "maxLeverage": 5, "maintAmt": 58035.0 }, { - "min": 2000000, # stake = 500000.0 - "max": 5000000, # max_stake = 1250000.0 - "mmr": 0.125, - "lev": 4, + "minNotional": 2000000, # stake = 500000.0 + "maxNotional": 5000000, # max_stake = 1250000.0 + "maintenanceMarginRate": 0.125, + "maxLeverage": 4, "maintAmt": 108035.0 }, { - "min": 5000000, # stake = 1666666.6666666667 - "max": 10000000, # max_stake = 3333333.3333333335 - "mmr": 0.15, - "lev": 3, + "minNotional": 5000000, # stake = 1666666.6666666667 + "maxNotional": 10000000, # max_stake = 3333333.3333333335 + "maintenanceMarginRate": 0.15, + "maxLeverage": 3, "maintAmt": 233035.0 }, { - "min": 10000000, # stake = 5000000.0 - "max": 20000000, # max_stake = 10000000.0 - "mmr": 0.25, - "lev": 2, + "minNotional": 10000000, # stake = 5000000.0 + "maxNotional": 20000000, # max_stake = 10000000.0 + "maintenanceMarginRate": 0.25, + "maxLeverage": 2, "maintAmt": 1233035.0 }, { - "min": 20000000, # stake = 20000000.0 - "max": 50000000, # max_stake = 50000000.0 - "mmr": 0.5, - "lev": 1, + "minNotional": 20000000, # stake = 20000000.0 + "maxNotional": 50000000, # max_stake = 50000000.0 + "maintenanceMarginRate": 0.5, + "maxLeverage": 1, "maintAmt": 6233035.0 }, ], 'BTC/USDT': [ { - "min": 0, # stake = 0.0 - "max": 50000, # max_stake = 400.0 - "mmr": 0.004, - "lev": 125, + "minNotional": 0, # stake = 0.0 + "maxNotional": 50000, # max_stake = 400.0 + "maintenanceMarginRate": 0.004, + "maxLeverage": 125, "maintAmt": 0.0 }, { - "min": 50000, # stake = 500.0 - "max": 250000, # max_stake = 2500.0 - "mmr": 0.005, - "lev": 100, + "minNotional": 50000, # stake = 500.0 + "maxNotional": 250000, # max_stake = 2500.0 + "maintenanceMarginRate": 0.005, + "maxLeverage": 100, "maintAmt": 50.0 }, { - "min": 250000, # stake = 5000.0 - "max": 1000000, # max_stake = 20000.0 - "mmr": 0.01, - "lev": 50, + "minNotional": 250000, # stake = 5000.0 + "maxNotional": 1000000, # max_stake = 20000.0 + "maintenanceMarginRate": 0.01, + "maxLeverage": 50, "maintAmt": 1300.0 }, { - "min": 1000000, # stake = 50000.0 - "max": 7500000, # max_stake = 375000.0 - "mmr": 0.025, - "lev": 20, + "minNotional": 1000000, # stake = 50000.0 + "maxNotional": 7500000, # max_stake = 375000.0 + "maintenanceMarginRate": 0.025, + "maxLeverage": 20, "maintAmt": 16300.0 }, { - "min": 7500000, # stake = 750000.0 - "max": 40000000, # max_stake = 4000000.0 - "mmr": 0.05, - "lev": 10, + "minNotional": 7500000, # stake = 750000.0 + "maxNotional": 40000000, # max_stake = 4000000.0 + "maintenanceMarginRate": 0.05, + "maxLeverage": 10, "maintAmt": 203800.0 }, { - "min": 40000000, # stake = 8000000.0 - "max": 100000000, # max_stake = 20000000.0 - "mmr": 0.1, - "lev": 5, + "minNotional": 40000000, # stake = 8000000.0 + "maxNotional": 100000000, # max_stake = 20000000.0 + "maintenanceMarginRate": 0.1, + "maxLeverage": 5, "maintAmt": 2203800.0 }, { - "min": 100000000, # stake = 25000000.0 - "max": 200000000, # max_stake = 50000000.0 - "mmr": 0.125, - "lev": 4, + "minNotional": 100000000, # stake = 25000000.0 + "maxNotional": 200000000, # max_stake = 50000000.0 + "maintenanceMarginRate": 0.125, + "maxLeverage": 4, "maintAmt": 4703800.0 }, { - "min": 200000000, # stake = 66666666.666666664 - "max": 400000000, # max_stake = 133333333.33333333 - "mmr": 0.15, - "lev": 3, + "minNotional": 200000000, # stake = 66666666.666666664 + "maxNotional": 400000000, # max_stake = 133333333.33333333 + "maintenanceMarginRate": 0.15, + "maxLeverage": 3, "maintAmt": 9703800.0 }, { - "min": 400000000, # stake = 200000000.0 - "max": 600000000, # max_stake = 300000000.0 - "mmr": 0.25, - "lev": 2, + "minNotional": 400000000, # stake = 200000000.0 + "maxNotional": 600000000, # max_stake = 300000000.0 + "maintenanceMarginRate": 0.25, + "maxLeverage": 2, "maintAmt": 4.97038E7 }, { - "min": 600000000, # stake = 600000000.0 - "max": 1000000000, # max_stake = 1000000000.0 - "mmr": 0.5, - "lev": 1, + "minNotional": 600000000, # stake = 600000000.0 + "maxNotional": 1000000000, # max_stake = 1000000000.0 + "maintenanceMarginRate": 0.5, + "maxLeverage": 1, "maintAmt": 1.997038E8 }, ], "ZEC/USDT": [ { - 'min': 0, - 'max': 50000, - 'mmr': 0.01, - 'lev': 50, + 'minNotional': 0, + 'maxNotional': 50000, + 'maintenanceMarginRate': 0.01, + 'maxLeverage': 50, 'maintAmt': 0.0 }, { - 'min': 50000, - 'max': 150000, - 'mmr': 0.025, - 'lev': 20, + 'minNotional': 50000, + 'maxNotional': 150000, + 'maintenanceMarginRate': 0.025, + 'maxLeverage': 20, 'maintAmt': 750.0 }, { - 'min': 150000, - 'max': 250000, - 'mmr': 0.05, - 'lev': 10, + 'minNotional': 150000, + 'maxNotional': 250000, + 'maintenanceMarginRate': 0.05, + 'maxLeverage': 10, 'maintAmt': 4500.0 }, { - 'min': 250000, - 'max': 500000, - 'mmr': 0.1, - 'lev': 5, + 'minNotional': 250000, + 'maxNotional': 500000, + 'maintenanceMarginRate': 0.1, + 'maxLeverage': 5, 'maintAmt': 17000.0 }, { - 'min': 500000, - 'max': 1000000, - 'mmr': 0.125, - 'lev': 4, + 'minNotional': 500000, + 'maxNotional': 1000000, + 'maintenanceMarginRate': 0.125, + 'maxLeverage': 4, 'maintAmt': 29500.0 }, { - 'min': 1000000, - 'max': 2000000, - 'mmr': 0.25, - 'lev': 2, + 'minNotional': 1000000, + 'maxNotional': 2000000, + 'maintenanceMarginRate': 0.25, + 'maxLeverage': 2, 'maintAmt': 154500.0 }, { - 'min': 2000000, - 'max': 30000000, - 'mmr': 0.5, - 'lev': 1, + 'minNotional': 2000000, + 'maxNotional': 30000000, + 'maintenanceMarginRate': 0.5, + 'maxLeverage': 1, 'maintAmt': 654500.0 }, ] diff --git a/tests/data/test_btanalysis.py b/tests/data/test_btanalysis.py index 977140ebb..72084d067 100644 --- a/tests/data/test_btanalysis.py +++ b/tests/data/test_btanalysis.py @@ -1,4 +1,3 @@ -from math import isclose from pathlib import Path from unittest.mock import MagicMock @@ -269,7 +268,7 @@ def test_create_cum_profit(testdatadir): "cum_profits", timeframe="5m") assert "cum_profits" in cum_profits.columns assert cum_profits.iloc[0]['cum_profits'] == 0 - assert isclose(cum_profits.iloc[-1]['cum_profits'], 8.723007518796964e-06) + assert pytest.approx(cum_profits.iloc[-1]['cum_profits']) == 8.723007518796964e-06 def test_create_cum_profit1(testdatadir): @@ -287,7 +286,7 @@ def test_create_cum_profit1(testdatadir): "cum_profits", timeframe="5m") assert "cum_profits" in cum_profits.columns assert cum_profits.iloc[0]['cum_profits'] == 0 - assert isclose(cum_profits.iloc[-1]['cum_profits'], 8.723007518796964e-06) + assert pytest.approx(cum_profits.iloc[-1]['cum_profits']) == 8.723007518796964e-06 with pytest.raises(ValueError, match='Trade dataframe empty.'): create_cum_profit(df.set_index('date'), bt_data[bt_data["pair"] == 'NOTAPAIR'], diff --git a/tests/exchange/test_binance.py b/tests/exchange/test_binance.py index 45f8a3817..4d1c40647 100644 --- a/tests/exchange/test_binance.py +++ b/tests/exchange/test_binance.py @@ -376,96 +376,96 @@ def test_fill_leverage_tiers_binance(default_conf, mocker): assert exchange._leverage_tiers == { 'ADA/BUSD': [ { - "min": 0, - "max": 100000, - "mmr": 0.025, - "lev": 20, + "minNotional": 0, + "maxNotional": 100000, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20, "maintAmt": 0.0 }, { - "min": 100000, - "max": 500000, - "mmr": 0.05, - "lev": 10, + "minNotional": 100000, + "maxNotional": 500000, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10, "maintAmt": 2500.0 }, { - "min": 500000, - "max": 1000000, - "mmr": 0.1, - "lev": 5, + "minNotional": 500000, + "maxNotional": 1000000, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5, "maintAmt": 27500.0 }, { - "min": 1000000, - "max": 2000000, - "mmr": 0.15, - "lev": 3, + "minNotional": 1000000, + "maxNotional": 2000000, + "maintenanceMarginRate": 0.15, + "maxLeverage": 3, "maintAmt": 77500.0 }, { - "min": 2000000, - "max": 5000000, - "mmr": 0.25, - "lev": 2, + "minNotional": 2000000, + "maxNotional": 5000000, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2, "maintAmt": 277500.0 }, { - "min": 5000000, - "max": 30000000, - "mmr": 0.5, - "lev": 1, + "minNotional": 5000000, + "maxNotional": 30000000, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1, "maintAmt": 1527500.0 } ], "ZEC/USDT": [ { - 'min': 0, - 'max': 50000, - 'mmr': 0.01, - 'lev': 50, + 'minNotional': 0, + 'maxNotional': 50000, + 'maintenanceMarginRate': 0.01, + 'maxLeverage': 50, 'maintAmt': 0.0 }, { - 'min': 50000, - 'max': 150000, - 'mmr': 0.025, - 'lev': 20, + 'minNotional': 50000, + 'maxNotional': 150000, + 'maintenanceMarginRate': 0.025, + 'maxLeverage': 20, 'maintAmt': 750.0 }, { - 'min': 150000, - 'max': 250000, - 'mmr': 0.05, - 'lev': 10, + 'minNotional': 150000, + 'maxNotional': 250000, + 'maintenanceMarginRate': 0.05, + 'maxLeverage': 10, 'maintAmt': 4500.0 }, { - 'min': 250000, - 'max': 500000, - 'mmr': 0.1, - 'lev': 5, + 'minNotional': 250000, + 'maxNotional': 500000, + 'maintenanceMarginRate': 0.1, + 'maxLeverage': 5, 'maintAmt': 17000.0 }, { - 'min': 500000, - 'max': 1000000, - 'mmr': 0.125, - 'lev': 4, + 'minNotional': 500000, + 'maxNotional': 1000000, + 'maintenanceMarginRate': 0.125, + 'maxLeverage': 4, 'maintAmt': 29500.0 }, { - 'min': 1000000, - 'max': 2000000, - 'mmr': 0.25, - 'lev': 2, + 'minNotional': 1000000, + 'maxNotional': 2000000, + 'maintenanceMarginRate': 0.25, + 'maxLeverage': 2, 'maintAmt': 154500.0 }, { - 'min': 2000000, - 'max': 30000000, - 'mmr': 0.5, - 'lev': 1, + 'minNotional': 2000000, + 'maxNotional': 30000000, + 'maintenanceMarginRate': 0.5, + 'maxLeverage': 1, 'maintAmt': 654500.0 }, ] diff --git a/tests/exchange/test_ccxt_compat.py b/tests/exchange/test_ccxt_compat.py index 7bb52ccaf..49b7684f8 100644 --- a/tests/exchange/test_ccxt_compat.py +++ b/tests/exchange/test_ccxt_compat.py @@ -137,6 +137,10 @@ def exchange_futures(request, exchange_conf, class_mocker): 'freqtrade.exchange.binance.Binance.fill_leverage_tiers') class_mocker.patch('freqtrade.exchange.exchange.Exchange.fetch_trading_fees') class_mocker.patch('freqtrade.exchange.okx.Okx.additional_exchange_init') + class_mocker.patch('freqtrade.exchange.exchange.Exchange.load_cached_leverage_tiers', + return_value=None) + class_mocker.patch('freqtrade.exchange.exchange.Exchange.cache_leverage_tiers') + exchange = ExchangeResolver.load_exchange( request.param, exchange_conf, validate=True, load_leverage_tiers=True) @@ -405,14 +409,14 @@ class TestCCXTExchange(): assert (isinstance(futures_leverage, float) or isinstance(futures_leverage, int)) assert futures_leverage >= 1.0 - def test_ccxt__get_contract_size(self, exchange_futures): + def test_ccxt_get_contract_size(self, exchange_futures): futures, futures_name = exchange_futures if futures: futures_pair = EXCHANGES[futures_name].get( 'futures_pair', EXCHANGES[futures_name]['pair'] ) - contract_size = futures._get_contract_size(futures_pair) + contract_size = futures.get_contract_size(futures_pair) assert (isinstance(contract_size, float) or isinstance(contract_size, int)) assert contract_size >= 0.0 @@ -464,6 +468,7 @@ class TestCCXTExchange(): False, 100, 100, + 100, ) assert (isinstance(liquidation_price, float)) assert liquidation_price >= 0.0 @@ -474,6 +479,7 @@ class TestCCXTExchange(): False, 100, 100, + 100, ) assert (isinstance(liquidation_price, float)) assert liquidation_price >= 0.0 diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index 6ad4a72c6..5456b3098 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -2,7 +2,6 @@ import copy import logging from copy import deepcopy from datetime import datetime, timedelta, timezone -from math import isclose from random import randint from unittest.mock import MagicMock, Mock, PropertyMock, patch @@ -181,11 +180,11 @@ def test_init_ccxt_kwargs(default_conf, mocker, caplog): assert log_has("Applying additional ccxt config: {'TestKWARG': 11, 'TestKWARG44': 11}", caplog) assert log_has(asynclogmsg, caplog) # Test additional headers case - Exchange._headers = {'hello': 'world'} + Exchange._ccxt_params = {'hello': 'world'} ex = Exchange(conf) assert log_has("Applying additional ccxt config: {'TestKWARG': 11, 'TestKWARG44': 11}", caplog) - assert ex._api.headers == {'hello': 'world'} + assert ex._api.hello == 'world' assert ex._ccxt_config == {} Exchange._headers = {} @@ -275,7 +274,7 @@ def test_validate_order_time_in_force(default_conf, mocker, caplog): ex.validate_order_time_in_force(tif2) # Patch to see if this will pass if the values are in the ft dict - ex._ft_has.update({"order_time_in_force": ["gtc", "fok", "ioc"]}) + ex._ft_has.update({"order_time_in_force": ["GTC", "FOK", "IOC"]}) ex.validate_order_time_in_force(tif2) @@ -407,10 +406,10 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None: # min result = exchange.get_min_pair_stake_amount('ETH/BTC', 1, stoploss) expected_result = 2 * (1 + 0.05) / (1 - abs(stoploss)) - assert isclose(result, expected_result) + assert pytest.approx(result) == expected_result # With Leverage result = exchange.get_min_pair_stake_amount('ETH/BTC', 1, stoploss, 3.0) - assert isclose(result, expected_result / 3) + assert pytest.approx(result) == expected_result / 3 # max result = exchange.get_max_pair_stake_amount('ETH/BTC', 2) assert result == 10000 @@ -426,10 +425,10 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None: ) result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss) expected_result = 2 * 2 * (1 + 0.05) / (1 - abs(stoploss)) - assert isclose(result, expected_result) + assert pytest.approx(result) == expected_result # With Leverage result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss, 5.0) - assert isclose(result, expected_result / 5) + assert pytest.approx(result) == expected_result / 5 # max result = exchange.get_max_pair_stake_amount('ETH/BTC', 2) assert result == 20000 @@ -445,10 +444,10 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None: ) result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss) expected_result = max(2, 2 * 2) * (1 + 0.05) / (1 - abs(stoploss)) - assert isclose(result, expected_result) + assert pytest.approx(result) == expected_result # With Leverage result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss, 10) - assert isclose(result, expected_result / 10) + assert pytest.approx(result) == expected_result / 10 # min amount and cost are set (amount is minial) markets["ETH/BTC"]["limits"] = { @@ -461,20 +460,20 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None: ) result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss) expected_result = max(8, 2 * 2) * (1 + 0.05) / (1 - abs(stoploss)) - assert isclose(result, expected_result) + assert pytest.approx(result) == expected_result # With Leverage result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss, 7.0) - assert isclose(result, expected_result / 7.0) + assert pytest.approx(result) == expected_result / 7.0 # Max result = exchange.get_max_pair_stake_amount('ETH/BTC', 2) assert result == 1000 result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -0.4) expected_result = max(8, 2 * 2) * 1.5 - assert isclose(result, expected_result) + assert pytest.approx(result) == expected_result # With Leverage result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -0.4, 8.0) - assert isclose(result, expected_result / 8.0) + assert pytest.approx(result) == expected_result / 8.0 # Max result = exchange.get_max_pair_stake_amount('ETH/BTC', 2) assert result == 1000 @@ -482,10 +481,10 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None: # Really big stoploss result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -1) expected_result = max(8, 2 * 2) * 1.5 - assert isclose(result, expected_result) + assert pytest.approx(result) == expected_result # With Leverage result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -1, 12.0) - assert isclose(result, expected_result / 12) + assert pytest.approx(result) == expected_result / 12 # Max result = exchange.get_max_pair_stake_amount('ETH/BTC', 2) assert result == 1000 @@ -501,7 +500,7 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None: # Contract size 0.01 result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -1) - assert isclose(result, expected_result * 0.01) + assert pytest.approx(result) == expected_result * 0.01 # Max result = exchange.get_max_pair_stake_amount('ETH/BTC', 2) assert result == 10 @@ -513,7 +512,7 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None: ) # With Leverage, Contract size 10 result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -1, 12.0) - assert isclose(result, (expected_result / 12) * 10.0) + assert pytest.approx(result) == (expected_result / 12) * 10.0 # Max result = exchange.get_max_pair_stake_amount('ETH/BTC', 2) assert result == 10000 @@ -1503,7 +1502,7 @@ def test_buy_considers_time_in_force(default_conf, mocker, exchange_name): assert api_mock.create_order.call_args[0][3] == 1 assert api_mock.create_order.call_args[0][4] == 200 assert "timeInForce" in api_mock.create_order.call_args[0][5] - assert api_mock.create_order.call_args[0][5]["timeInForce"] == time_in_force + assert api_mock.create_order.call_args[0][5]["timeInForce"] == time_in_force.upper() order_type = 'market' time_in_force = 'ioc' @@ -1642,10 +1641,10 @@ def test_sell_considers_time_in_force(default_conf, mocker, exchange_name): assert api_mock.create_order.call_args[0][3] == 1 assert api_mock.create_order.call_args[0][4] == 200 assert "timeInForce" in api_mock.create_order.call_args[0][5] - assert api_mock.create_order.call_args[0][5]["timeInForce"] == time_in_force + assert api_mock.create_order.call_args[0][5]["timeInForce"] == time_in_force.upper() order_type = 'market' - time_in_force = 'ioc' + time_in_force = 'IOC' order = exchange.create_order(pair='ETH/BTC', ordertype=order_type, side="sell", amount=1, rate=200, leverage=1.0, time_in_force=time_in_force) @@ -2352,10 +2351,11 @@ def test_fetch_l2_order_book(default_conf, mocker, order_book_l2, exchange_name) order_book = exchange.fetch_l2_order_book(pair='ETH/BTC', limit=val) assert api_mock.fetch_l2_order_book.call_args_list[0][0][0] == 'ETH/BTC' # Not all exchanges support all limits for orderbook - if not exchange._ft_has['l2_limit_range'] or val in exchange._ft_has['l2_limit_range']: + if (not exchange.get_option('l2_limit_range') + or val in exchange.get_option('l2_limit_range')): assert api_mock.fetch_l2_order_book.call_args_list[0][0][1] == val else: - next_limit = exchange.get_next_limit_in_list(val, exchange._ft_has['l2_limit_range']) + next_limit = exchange.get_next_limit_in_list(val, exchange.get_option('l2_limit_range')) assert api_mock.fetch_l2_order_book.call_args_list[0][0][1] == next_limit @@ -3238,7 +3238,7 @@ def test_get_trades_for_order(default_conf, mocker, exchange_name, trading_mode, orders = exchange.get_trades_for_order(order_id, 'ETH/USDT:USDT', since) assert len(orders) == 1 assert orders[0]['price'] == 165 - assert isclose(orders[0]['amount'], amount) + assert pytest.approx(orders[0]['amount']) == amount assert api_mock.fetch_my_trades.call_count == 1 # since argument should be assert isinstance(api_mock.fetch_my_trades.call_args[0][1], int) @@ -3311,16 +3311,16 @@ def test_merge_ft_has_dict(default_conf, mocker): ex = Kraken(default_conf) assert ex._ft_has != Exchange._ft_has_default - assert ex._ft_has['trades_pagination'] == 'id' - assert ex._ft_has['trades_pagination_arg'] == 'since' + assert ex.get_option('trades_pagination') == 'id' + assert ex.get_option('trades_pagination_arg') == 'since' # Binance defines different values ex = Binance(default_conf) assert ex._ft_has != Exchange._ft_has_default - assert ex._ft_has['stoploss_on_exchange'] - assert ex._ft_has['order_time_in_force'] == ['gtc', 'fok', 'ioc'] - assert ex._ft_has['trades_pagination'] == 'id' - assert ex._ft_has['trades_pagination_arg'] == 'fromId' + assert ex.get_option('stoploss_on_exchange') + assert ex.get_option('order_time_in_force') == ['GTC', 'FOK', 'IOC'] + assert ex.get_option('trades_pagination') == 'id' + assert ex.get_option('trades_pagination_arg') == 'fromId' conf = copy.deepcopy(default_conf) conf['exchange']['_ft_has_params'] = {"DeadBeef": 20, @@ -3775,8 +3775,8 @@ def test__get_funding_fees_from_exchange(default_conf, mocker, exchange_name): since=unix_time ) - assert (isclose(expected_fees, fees_from_datetime)) - assert (isclose(expected_fees, fees_from_unix_time)) + assert pytest.approx(expected_fees) == fees_from_datetime + assert pytest.approx(expected_fees) == fees_from_unix_time ccxt_exceptionhandlers( mocker, @@ -4088,66 +4088,6 @@ def test_combine_funding_and_mark( assert len(df) == 0 -def test_get_or_calculate_liquidation_price(mocker, default_conf): - - api_mock = MagicMock() - positions = [ - { - 'info': {}, - 'symbol': 'NEAR/USDT:USDT', - 'timestamp': 1642164737148, - 'datetime': '2022-01-14T12:52:17.148Z', - 'initialMargin': 1.51072, - 'initialMarginPercentage': 0.1, - 'maintenanceMargin': 0.38916147, - 'maintenanceMarginPercentage': 0.025, - 'entryPrice': 18.884, - 'notional': 15.1072, - 'leverage': 9.97, - 'unrealizedPnl': 0.0048, - 'contracts': 8, - 'contractSize': 0.1, - 'marginRatio': None, - 'liquidationPrice': 17.47, - 'markPrice': 18.89, - 'margin_mode': 1.52549075, - 'marginType': 'isolated', - 'side': 'buy', - 'percentage': 0.003177292946409658 - } - ] - api_mock.fetch_positions = MagicMock(return_value=positions) - mocker.patch.multiple( - 'freqtrade.exchange.Exchange', - exchange_has=MagicMock(return_value=True), - ) - default_conf['dry_run'] = False - default_conf['trading_mode'] = 'futures' - default_conf['margin_mode'] = 'isolated' - default_conf['liquidation_buffer'] = 0.0 - - exchange = get_patched_exchange(mocker, default_conf, api_mock) - liq_price = exchange.get_or_calculate_liquidation_price( - pair='NEAR/USDT:USDT', - open_rate=18.884, - is_short=False, - position=0.8, - wallet_balance=0.8, - ) - assert liq_price == 17.47 - - default_conf['liquidation_buffer'] = 0.05 - exchange = get_patched_exchange(mocker, default_conf, api_mock) - liq_price = exchange.get_or_calculate_liquidation_price( - pair='NEAR/USDT:USDT', - open_rate=18.884, - is_short=False, - position=0.8, - wallet_balance=0.8, - ) - assert liq_price == 17.540699999999998 - - @pytest.mark.parametrize('exchange,rate_start,rate_end,d1,d2,amount,expected_fees', [ ('binance', 0, 2, "2021-09-01 01:00:00", "2021-09-01 04:00:00", 30.0, 0.0), ('binance', 0, 2, "2021-09-01 00:00:00", "2021-09-01 08:00:00", 30.0, -0.00091409999), @@ -4287,7 +4227,7 @@ def test__fetch_and_calculate_funding_fees_datetime_called( ('XLTCUSDT', 0.01, 'futures'), ('ETH/USDT:USDT', 10, 'futures') ]) -def test__get_contract_size(mocker, default_conf, pair, expected_size, trading_mode): +def est__get_contract_size(mocker, default_conf, pair, expected_size, trading_mode): api_mock = MagicMock() default_conf['trading_mode'] = trading_mode default_conf['margin_mode'] = 'isolated' @@ -4306,7 +4246,7 @@ def test__get_contract_size(mocker, default_conf, pair, expected_size, trading_m 'contractSize': '10', } }) - size = exchange._get_contract_size(pair) + size = exchange.get_contract_size(pair) assert expected_size == size @@ -4538,11 +4478,12 @@ def test_liquidation_price_is_none( default_conf['trading_mode'] = trading_mode default_conf['margin_mode'] = margin_mode exchange = get_patched_exchange(mocker, default_conf, id=exchange_name) - assert exchange.get_or_calculate_liquidation_price( + assert exchange.get_liquidation_price( pair='DOGE/USDT', open_rate=open_rate, is_short=is_short, - position=71200.81144, + amount=71200.81144, + stake_amount=open_rate * 71200.81144, wallet_balance=-56354.57, mm_ex_1=0.10, upnl_ex_1=0.0 @@ -4551,7 +4492,7 @@ def test_liquidation_price_is_none( @pytest.mark.parametrize( 'exchange_name, is_short, trading_mode, margin_mode, wallet_balance, ' - 'mm_ex_1, upnl_ex_1, maintenance_amt, position, open_rate, ' + 'mm_ex_1, upnl_ex_1, maintenance_amt, amount, open_rate, ' 'mm_ratio, expected', [ ("binance", False, 'futures', 'isolated', 1535443.01, 0.0, @@ -4565,22 +4506,23 @@ def test_liquidation_price_is_none( ]) def test_liquidation_price( mocker, default_conf, exchange_name, open_rate, is_short, trading_mode, - margin_mode, wallet_balance, mm_ex_1, upnl_ex_1, maintenance_amt, position, mm_ratio, expected + margin_mode, wallet_balance, mm_ex_1, upnl_ex_1, maintenance_amt, amount, mm_ratio, expected ): default_conf['trading_mode'] = trading_mode default_conf['margin_mode'] = margin_mode default_conf['liquidation_buffer'] = 0.0 exchange = get_patched_exchange(mocker, default_conf, id=exchange_name) exchange.get_maintenance_ratio_and_amt = MagicMock(return_value=(mm_ratio, maintenance_amt)) - assert isclose(round(exchange.get_or_calculate_liquidation_price( + assert pytest.approx(round(exchange.get_liquidation_price( pair='DOGE/USDT', open_rate=open_rate, is_short=is_short, wallet_balance=wallet_balance, mm_ex_1=mm_ex_1, upnl_ex_1=upnl_ex_1, - position=position, - ), 2), expected) + amount=amount, + stake_amount=open_rate * amount, + ), 2)) == expected def test_get_max_pair_stake_amount( @@ -4791,6 +4733,20 @@ def test_load_leverage_tiers(mocker, default_conf, leverage_tiers, exchange_name ) +@pytest.mark.asyncio +@pytest.mark.parametrize('exchange_name', EXCHANGES) +async def test_get_market_leverage_tiers(mocker, default_conf, exchange_name): + default_conf['exchange']['name'] = exchange_name + await async_ccxt_exception( + mocker, + default_conf, + MagicMock(), + "get_market_leverage_tiers", + "fetch_market_leverage_tiers", + symbol='BTC/USDT:USDT' + ) + + def test_parse_leverage_tier(mocker, default_conf): exchange = get_patched_exchange(mocker, default_conf) @@ -4811,10 +4767,10 @@ def test_parse_leverage_tier(mocker, default_conf): } assert exchange.parse_leverage_tier(tier) == { - "min": 0, - "max": 100000, - "mmr": 0.025, - "lev": 20, + "minNotional": 0, + "maxNotional": 100000, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20, "maintAmt": 0.0, } @@ -4840,10 +4796,10 @@ def test_parse_leverage_tier(mocker, default_conf): } assert exchange.parse_leverage_tier(tier2) == { - 'min': 0, - 'max': 2000, - 'mmr': 0.01, - 'lev': 75, + 'minNotional': 0, + 'maxNotional': 2000, + 'maintenanceMarginRate': 0.01, + 'maxLeverage': 75, "maintAmt": None, } @@ -4911,8 +4867,8 @@ def test_get_max_leverage_futures(default_conf, mocker, leverage_tiers): assert exchange.get_max_leverage("BNB/BUSD", 1.0) == 20.0 assert exchange.get_max_leverage("BNB/USDT", 100.0) == 75.0 assert exchange.get_max_leverage("BTC/USDT", 170.30) == 125.0 - assert isclose(exchange.get_max_leverage("BNB/BUSD", 99999.9), 5.000005) - assert isclose(exchange.get_max_leverage("BNB/USDT", 1500), 33.333333333333333) + assert pytest.approx(exchange.get_max_leverage("BNB/BUSD", 99999.9)) == 5.000005 + assert pytest.approx(exchange.get_max_leverage("BNB/USDT", 1500)) == 33.333333333333333 assert exchange.get_max_leverage("BTC/USDT", 300000000) == 2.0 assert exchange.get_max_leverage("BTC/USDT", 600000000) == 1.0 # Last tier @@ -4935,7 +4891,7 @@ def test__get_params(mocker, default_conf, exchange_name): params1 = {'test': True} params2 = { 'test': True, - 'timeInForce': 'ioc', + 'timeInForce': 'IOC', 'reduceOnly': True, } @@ -4950,7 +4906,7 @@ def test__get_params(mocker, default_conf, exchange_name): side="buy", ordertype='market', reduceOnly=False, - time_in_force='gtc', + time_in_force='GTC', leverage=1.0, ) == params1 @@ -4958,7 +4914,7 @@ def test__get_params(mocker, default_conf, exchange_name): side="buy", ordertype='market', reduceOnly=False, - time_in_force='ioc', + time_in_force='IOC', leverage=1.0, ) == params1 @@ -4966,7 +4922,7 @@ def test__get_params(mocker, default_conf, exchange_name): side="buy", ordertype='limit', reduceOnly=False, - time_in_force='gtc', + time_in_force='GTC', leverage=1.0, ) == params1 @@ -4979,11 +4935,93 @@ def test__get_params(mocker, default_conf, exchange_name): side="buy", ordertype='limit', reduceOnly=True, - time_in_force='ioc', + time_in_force='IOC', leverage=3.0, ) == params2 +def test_get_liquidation_price1(mocker, default_conf): + + api_mock = MagicMock() + positions = [ + { + 'info': {}, + 'symbol': 'NEAR/USDT:USDT', + 'timestamp': 1642164737148, + 'datetime': '2022-01-14T12:52:17.148Z', + 'initialMargin': 1.51072, + 'initialMarginPercentage': 0.1, + 'maintenanceMargin': 0.38916147, + 'maintenanceMarginPercentage': 0.025, + 'entryPrice': 18.884, + 'notional': 15.1072, + 'leverage': 9.97, + 'unrealizedPnl': 0.0048, + 'contracts': 8, + 'contractSize': 0.1, + 'marginRatio': None, + 'liquidationPrice': 17.47, + 'markPrice': 18.89, + 'margin_mode': 1.52549075, + 'marginType': 'isolated', + 'side': 'buy', + 'percentage': 0.003177292946409658 + } + ] + api_mock.fetch_positions = MagicMock(return_value=positions) + mocker.patch.multiple( + 'freqtrade.exchange.Exchange', + exchange_has=MagicMock(return_value=True), + ) + default_conf['dry_run'] = False + default_conf['trading_mode'] = 'futures' + default_conf['margin_mode'] = 'isolated' + default_conf['liquidation_buffer'] = 0.0 + + exchange = get_patched_exchange(mocker, default_conf, api_mock) + liq_price = exchange.get_liquidation_price( + pair='NEAR/USDT:USDT', + open_rate=18.884, + is_short=False, + amount=0.8, + stake_amount=18.884 * 0.8, + ) + assert liq_price == 17.47 + + default_conf['liquidation_buffer'] = 0.05 + exchange = get_patched_exchange(mocker, default_conf, api_mock) + liq_price = exchange.get_liquidation_price( + pair='NEAR/USDT:USDT', + open_rate=18.884, + is_short=False, + amount=0.8, + stake_amount=18.884 * 0.8, + ) + assert liq_price == 17.540699999999998 + + api_mock.fetch_positions = MagicMock(return_value=[]) + exchange = get_patched_exchange(mocker, default_conf, api_mock) + liq_price = exchange.get_liquidation_price( + pair='NEAR/USDT:USDT', + open_rate=18.884, + is_short=False, + amount=0.8, + stake_amount=18.884 * 0.8, + ) + assert liq_price is None + default_conf['trading_mode'] = 'margin' + + exchange = get_patched_exchange(mocker, default_conf, api_mock) + with pytest.raises(OperationalException, match=r'.*does not support .* margin'): + exchange.get_liquidation_price( + pair='NEAR/USDT:USDT', + open_rate=18.884, + is_short=False, + amount=0.8, + stake_amount=18.884 * 0.8, + ) + + @pytest.mark.parametrize('liquidation_buffer', [0.0, 0.05]) @pytest.mark.parametrize( "is_short,trading_mode,exchange_name,margin_mode,leverage,open_rate,amount,expected_liq", [ @@ -4997,22 +5035,22 @@ def test__get_params(mocker, default_conf, exchange_name): (True, 'futures', 'binance', 'isolated', 5.0, 10.0, 1.0, 11.89108910891089), (True, 'futures', 'binance', 'isolated', 3.0, 10.0, 1.0, 13.211221122079207), (True, 'futures', 'binance', 'isolated', 5.0, 8.0, 1.0, 9.514851485148514), - (True, 'futures', 'binance', 'isolated', 5.0, 10.0, 0.6, 12.557755775577558), + (True, 'futures', 'binance', 'isolated', 5.0, 10.0, 0.6, 11.897689768976898), # Binance, long (False, 'futures', 'binance', 'isolated', 5, 10, 1.0, 8.070707070707071), (False, 'futures', 'binance', 'isolated', 5, 8, 1.0, 6.454545454545454), - (False, 'futures', 'binance', 'isolated', 3, 10, 1.0, 6.717171717171718), - (False, 'futures', 'binance', 'isolated', 5, 10, 0.6, 7.39057239057239), + (False, 'futures', 'binance', 'isolated', 3, 10, 1.0, 6.723905723905723), + (False, 'futures', 'binance', 'isolated', 5, 10, 0.6, 8.063973063973064), # Gateio/okx, short (True, 'futures', 'gateio', 'isolated', 5, 10, 1.0, 11.87413417771621), (True, 'futures', 'gateio', 'isolated', 5, 10, 2.0, 11.87413417771621), - (True, 'futures', 'gateio', 'isolated', 3, 10, 1.0, 13.476180850346978), + (True, 'futures', 'gateio', 'isolated', 3, 10, 1.0, 13.193482419684678), (True, 'futures', 'gateio', 'isolated', 5, 8, 1.0, 9.499307342172967), + (True, 'futures', 'okx', 'isolated', 3, 10, 1.0, 13.193482419684678), # Gateio/okx, long (False, 'futures', 'gateio', 'isolated', 5.0, 10.0, 1.0, 8.085708510208207), (False, 'futures', 'gateio', 'isolated', 3.0, 10.0, 1.0, 6.738090425173506), - # (True, 'futures', 'okx', 'isolated', 11.87413417771621), - # (False, 'futures', 'okx', 'isolated', 8.085708510208207), + (False, 'futures', 'okx', 'isolated', 3.0, 10.0, 1.0, 6.738090425173506), ] ) def test_get_liquidation_price( @@ -5085,7 +5123,7 @@ def test_get_liquidation_price( default_conf_usdt['exchange']['name'] = exchange_name default_conf_usdt['margin_mode'] = margin_mode mocker.patch('freqtrade.exchange.Gateio.validate_ordertypes') - exchange = get_patched_exchange(mocker, default_conf_usdt) + exchange = get_patched_exchange(mocker, default_conf_usdt, id=exchange_name) exchange.get_maintenance_ratio_and_amt = MagicMock(return_value=(0.01, 0.01)) exchange.name = exchange_name @@ -5096,7 +5134,9 @@ def test_get_liquidation_price( pair='ETH/USDT:USDT', open_rate=open_rate, amount=amount, - leverage=leverage, + stake_amount=amount * open_rate / leverage, + wallet_balance=amount * open_rate / leverage, + # leverage=leverage, is_short=is_short, ) if expected_liq is None: @@ -5104,7 +5144,7 @@ def test_get_liquidation_price( else: buffer_amount = liquidation_buffer * abs(open_rate - expected_liq) expected_liq = expected_liq - buffer_amount if is_short else expected_liq + buffer_amount - isclose(expected_liq, liq) + assert pytest.approx(expected_liq) == liq @pytest.mark.parametrize('contract_size,order_amount', [ @@ -5131,7 +5171,7 @@ def test_stoploss_contract_size(mocker, default_conf, contract_size, order_amoun mocker.patch('freqtrade.exchange.Exchange.price_to_precision', lambda s, x, y: y) exchange = get_patched_exchange(mocker, default_conf, api_mock) - exchange._get_contract_size = MagicMock(return_value=contract_size) + exchange.get_contract_size = MagicMock(return_value=contract_size) api_mock.create_order.reset_mock() order = exchange.stoploss( diff --git a/tests/exchange/test_kraken.py b/tests/exchange/test_kraken.py index 02df60990..66006f2fe 100644 --- a/tests/exchange/test_kraken.py +++ b/tests/exchange/test_kraken.py @@ -50,7 +50,7 @@ def test_buy_kraken_trading_agreement(default_conf, mocker): assert api_mock.create_order.call_args[0][2] == 'buy' assert api_mock.create_order.call_args[0][3] == 1 assert api_mock.create_order.call_args[0][4] == 200 - assert api_mock.create_order.call_args[0][5] == {'timeInForce': 'ioc', + assert api_mock.create_order.call_args[0][5] == {'timeInForce': 'IOC', 'trading_agreement': 'agree'} diff --git a/tests/exchange/test_okx.py b/tests/exchange/test_okx.py index 91c4a3368..b475b84ff 100644 --- a/tests/exchange/test_okx.py +++ b/tests/exchange/test_okx.py @@ -1,4 +1,5 @@ from datetime import datetime, timedelta, timezone +from pathlib import Path from unittest.mock import MagicMock, PropertyMock import pytest @@ -6,7 +7,7 @@ import pytest from freqtrade.enums import MarginMode, TradingMode from freqtrade.enums.candletype import CandleType from freqtrade.exchange.exchange import timeframe_to_minutes -from tests.conftest import get_mock_coro, get_patched_exchange +from tests.conftest import get_mock_coro, get_patched_exchange, log_has from tests.exchange.test_exchange import ccxt_exceptionhandlers @@ -267,7 +268,10 @@ def test_additional_exchange_init_okx(default_conf, mocker): "additional_exchange_init", "fetch_accounts") -def test_load_leverage_tiers_okx(default_conf, mocker, markets): +def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmpdir, caplog, time_machine): + + default_conf['datadir'] = Path(tmpdir) + # fd_mock = mocker.patch('freqtrade.exchange.exchange.file_dump_json') api_mock = MagicMock() type(api_mock).has = PropertyMock(return_value={ 'fetchLeverageTiers': False, @@ -410,48 +414,66 @@ def test_load_leverage_tiers_okx(default_conf, mocker, markets): assert exchange._leverage_tiers == { 'ADA/USDT:USDT': [ { - 'min': 0, - 'max': 500, - 'mmr': 0.02, - 'lev': 75, + 'minNotional': 0, + 'maxNotional': 500, + 'maintenanceMarginRate': 0.02, + 'maxLeverage': 75, 'maintAmt': None }, { - 'min': 501, - 'max': 1000, - 'mmr': 0.025, - 'lev': 50, + 'minNotional': 501, + 'maxNotional': 1000, + 'maintenanceMarginRate': 0.025, + 'maxLeverage': 50, 'maintAmt': None }, { - 'min': 1001, - 'max': 2000, - 'mmr': 0.03, - 'lev': 20, + 'minNotional': 1001, + 'maxNotional': 2000, + 'maintenanceMarginRate': 0.03, + 'maxLeverage': 20, 'maintAmt': None }, ], 'ETH/USDT:USDT': [ { - 'min': 0, - 'max': 2000, - 'mmr': 0.01, - 'lev': 75, + 'minNotional': 0, + 'maxNotional': 2000, + 'maintenanceMarginRate': 0.01, + 'maxLeverage': 75, 'maintAmt': None }, { - 'min': 2001, - 'max': 4000, - 'mmr': 0.015, - 'lev': 50, + 'minNotional': 2001, + 'maxNotional': 4000, + 'maintenanceMarginRate': 0.015, + 'maxLeverage': 50, 'maintAmt': None }, { - 'min': 4001, - 'max': 8000, - 'mmr': 0.02, - 'lev': 20, + 'minNotional': 4001, + 'maxNotional': 8000, + 'maintenanceMarginRate': 0.02, + 'maxLeverage': 20, 'maintAmt': None }, ], } + filename = (default_conf['datadir'] / + f"futures/leverage_tiers_{default_conf['stake_currency']}.json") + assert filename.is_file() + + logmsg = 'Cached leverage tiers are outdated. Will update.' + assert not log_has(logmsg, caplog) + + api_mock.fetch_market_leverage_tiers.reset_mock() + + exchange.load_leverage_tiers() + assert not log_has(logmsg, caplog) + + api_mock.fetch_market_leverage_tiers.call_count == 0 + # 2 day passes ... + time_machine.move_to(datetime.now() + timedelta(days=2)) + exchange.load_leverage_tiers() + + assert log_has(logmsg, caplog) diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index 113cb3a79..db499631b 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -1,5 +1,6 @@ from copy import deepcopy from pathlib import Path +from unittest.mock import MagicMock import pytest @@ -80,6 +81,51 @@ def get_patched_freqaimodel(mocker, freqaiconf): return freqaimodel +def make_data_dictionary(mocker, freqai_conf): + freqai_conf.update({"timerange": "20180110-20180130"}) + + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) + freqai = strategy.freqai + freqai.live = True + freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.pair = "ADA/BTC" + timerange = TimeRange.parse_timerange("20180110-20180130") + freqai.dd.load_all_pair_histories(timerange, freqai.dk) + + freqai.dd.pair_dict = MagicMock() + + data_load_timerange = TimeRange.parse_timerange("20180110-20180130") + new_timerange = TimeRange.parse_timerange("20180120-20180130") + + corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes( + data_load_timerange, freqai.dk.pair, freqai.dk + ) + + unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators( + strategy, corr_dataframes, base_dataframes, freqai.dk.pair + ) + + unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe) + + freqai.dk.find_features(unfiltered_dataframe) + + features_filtered, labels_filtered = freqai.dk.filter_features( + unfiltered_dataframe, + freqai.dk.training_features_list, + freqai.dk.label_list, + training_filter=True, + ) + + data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered) + + data_dictionary = freqai.dk.normalize_data(data_dictionary) + + return freqai + + def get_freqai_live_analyzed_dataframe(mocker, freqaiconf): strategy = get_patched_freqai_strategy(mocker, freqaiconf) exchange = get_patched_exchange(mocker, freqaiconf) diff --git a/tests/freqai/test_freqai_datakitchen.py b/tests/freqai/test_freqai_datakitchen.py index 9f2a2f71e..9ef955695 100644 --- a/tests/freqai/test_freqai_datakitchen.py +++ b/tests/freqai/test_freqai_datakitchen.py @@ -5,7 +5,8 @@ from pathlib import Path import pytest from freqtrade.exceptions import OperationalException -from tests.freqai.conftest import get_patched_data_kitchen +from tests.conftest import log_has_re +from tests.freqai.conftest import get_patched_data_kitchen, make_data_dictionary @pytest.mark.parametrize( @@ -66,3 +67,30 @@ def test_check_if_model_expired(mocker, freqai_conf, timestamp, expected): dk = get_patched_data_kitchen(mocker, freqai_conf) assert dk.check_if_model_expired(timestamp) == expected shutil.rmtree(Path(dk.full_path)) + + +def test_use_DBSCAN_to_remove_outliers(mocker, freqai_conf, caplog): + freqai = make_data_dictionary(mocker, freqai_conf) + # freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 1}) + freqai.dk.use_DBSCAN_to_remove_outliers(predict=False) + assert log_has_re( + "DBSCAN found eps of 2.42.", + caplog, + ) + + +def test_compute_distances(mocker, freqai_conf): + freqai = make_data_dictionary(mocker, freqai_conf) + freqai_conf['freqai']['feature_parameters'].update({"DI_threshold": 1}) + avg_mean_dist = freqai.dk.compute_distances() + assert round(avg_mean_dist, 2) == 2.56 + + +def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf, caplog): + freqai = make_data_dictionary(mocker, freqai_conf) + freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 0.1}) + freqai.dk.use_SVM_to_remove_outliers(predict=False) + assert log_has_re( + "SVM detected 8.46%", + caplog, + ) diff --git a/tests/leverage/test_interest.py b/tests/leverage/test_interest.py index 7bdf4c2f0..64e99b6b4 100644 --- a/tests/leverage/test_interest.py +++ b/tests/leverage/test_interest.py @@ -1,5 +1,3 @@ -from math import isclose - import pytest from freqtrade.leverage import interest @@ -30,9 +28,9 @@ twentyfive_hours = FtPrecise(25.0) def test_interest(exchange, interest_rate, hours, expected): borrowed = FtPrecise(60.0) - assert isclose(interest( + assert pytest.approx(float(interest( exchange_name=exchange, borrowed=borrowed, rate=FtPrecise(interest_rate), hours=hours - ), expected) + ))) == expected diff --git a/tests/optimize/test_backtesting.py b/tests/optimize/test_backtesting.py index 0b964c54a..368e368c5 100644 --- a/tests/optimize/test_backtesting.py +++ b/tests/optimize/test_backtesting.py @@ -550,6 +550,7 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None: mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001) mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf')) mocker.patch("freqtrade.exchange.Exchange.get_max_leverage", return_value=100) + mocker.patch("freqtrade.optimize.backtesting.price_to_precision", lambda p, *args: p) patch_exchange(mocker) default_conf_usdt['stake_amount'] = 300 default_conf_usdt['max_open_trades'] = 2 @@ -559,13 +560,13 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None: default_conf_usdt['exchange']['pair_whitelist'] = ['.*'] backtesting = Backtesting(default_conf_usdt) backtesting._set_strategy(backtesting.strategylist[0]) - pair = 'UNITTEST/USDT:USDT' + pair = 'ETH/USDT:USDT' row = [ pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0), - 0.001, # Open - 0.0012, # High - 0.00099, # Low - 0.0011, # Close + 0.1, # Open + 0.12, # High + 0.099, # Low + 0.11, # Close 1, # enter_long 0, # exit_long 1, # enter_short @@ -580,8 +581,8 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None: return_value=(0.01, 0.01)) # leverage = 5 - # ep1(trade.open_rate) = 0.001 - # position(trade.amount) = 1500000 + # ep1(trade.open_rate) = 0.1 + # position(trade.amount) = 15000 # stake_amount = 300 -> wb = 300 / 5 = 60 # mmr = 0.01 # cum_b = 0.01 @@ -591,26 +592,26 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None: # Binance, Long # liquidation_price # = ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position)) - # = ((300 + 0.01) - (1 * 1500000 * 0.001)) / ((1500000 * 0.01) - (1 * 1500000)) + # = ((300 + 0.01) - (1 * 15000 * 0.1)) / ((15000 * 0.01) - (1 * 15000)) # = 0.0008080740740740741 # freqtrade_liquidation_price = liq + (abs(open_rate - liq) * liq_buffer * side_1) - # = 0.0008080740740740741 + ((0.001 - 0.0008080740740740741) * 0.05 * 1) - # = 0.0008176703703703704 + # = 0.08080740740740741 + ((0.1 - 0.08080740740740741) * 0.05 * 1) + # = 0.08176703703703704 trade = backtesting._enter_trade(pair, row=row, direction='long') - assert pytest.approx(trade.liquidation_price) == 0.00081767037 + assert pytest.approx(trade.liquidation_price) == 0.081767037 # Binance, Short # liquidation_price # = ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position)) - # = ((300 + 0.01) - ((-1) * 1500000 * 0.001)) / ((1500000 * 0.01) - ((-1) * 1500000)) + # = ((300 + 0.01) - ((-1) * 15000 * 0.1)) / ((15000 * 0.01) - ((-1) * 15000)) # = 0.0011881254125412541 # freqtrade_liquidation_price = liq + (abs(open_rate - liq) * liq_buffer * side_1) - # = 0.0011881254125412541 + (abs(0.001 - 0.0011881254125412541) * 0.05 * -1) - # = 0.0011787191419141915 + # = 0.11881254125412541 + (abs(0.1 - 0.11881254125412541) * 0.05 * -1) + # = 0.11787191419141915 trade = backtesting._enter_trade(pair, row=row, direction='short') - assert pytest.approx(trade.liquidation_price) == 0.0011787191 + assert pytest.approx(trade.liquidation_price) == 0.11787191 # Stake-amount too high! mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=600.0) diff --git a/tests/optimize/test_backtesting_adjust_position.py b/tests/optimize/test_backtesting_adjust_position.py index 2bb7de574..71f8cdcea 100644 --- a/tests/optimize/test_backtesting_adjust_position.py +++ b/tests/optimize/test_backtesting_adjust_position.py @@ -18,6 +18,8 @@ from tests.conftest import patch_exchange def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) -> None: default_conf['use_exit_signal'] = False mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) + mocker.patch('freqtrade.optimize.backtesting.amount_to_contract_precision', + lambda x, *args, **kwargs: round(x, 8)) mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001) mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf')) patch_exchange(mocker) diff --git a/tests/plugins/test_pairlist.py b/tests/plugins/test_pairlist.py index 5974bee89..48a0f81cb 100644 --- a/tests/plugins/test_pairlist.py +++ b/tests/plugins/test_pairlist.py @@ -366,6 +366,9 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf): ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}, {"method": "PrecisionFilter"}], "BTC", ['ETH/BTC', 'TKN/BTC', 'LTC/BTC', 'XRP/BTC']), + ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}, + {"method": "PrecisionFilter"}], + "USDT", ['ETH/USDT', 'NANO/USDT']), # PriceFilter and VolumePairList ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}, {"method": "PriceFilter", "low_price_ratio": 0.03}], diff --git a/tests/plugins/test_protections.py b/tests/plugins/test_protections.py index 8a5356b3e..acfe124a8 100644 --- a/tests/plugins/test_protections.py +++ b/tests/plugins/test_protections.py @@ -67,6 +67,8 @@ def generate_mock_trade(pair: str, fee: float, is_open: bool, trade.close(open_rate * (2 - profit_rate if is_short else profit_rate)) trade.exit_reason = exit_reason + Trade.query.session.add(trade) + Trade.commit() return trade @@ -125,33 +127,33 @@ def test_stoploss_guard(mocker, default_conf, fee, caplog, is_short): assert not log_has_re(message, caplog) caplog.clear() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=200, min_ago_close=30, is_short=is_short, - )) + ) assert not freqtrade.protections.global_stop() assert not log_has_re(message, caplog) caplog.clear() # This trade does not count, as it's closed too long ago - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'BCH/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=250, min_ago_close=100, is_short=is_short, - )) + ) - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'ETH/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=240, min_ago_close=30, is_short=is_short, - )) + ) # 3 Trades closed - but the 2nd has been closed too long ago. assert not freqtrade.protections.global_stop() assert not log_has_re(message, caplog) caplog.clear() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'LTC/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=180, min_ago_close=30, is_short=is_short, - )) + ) assert freqtrade.protections.global_stop() assert log_has_re(message, caplog) @@ -186,25 +188,25 @@ def test_stoploss_guard_perpair(mocker, default_conf, fee, caplog, only_per_pair assert not log_has_re(message, caplog) caplog.clear() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( pair, fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=200, min_ago_close=30, profit_rate=0.9, is_short=is_short - )) + ) assert not freqtrade.protections.stop_per_pair(pair) assert not freqtrade.protections.global_stop() assert not log_has_re(message, caplog) caplog.clear() # This trade does not count, as it's closed too long ago - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( pair, fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=250, min_ago_close=100, profit_rate=0.9, is_short=is_short - )) + ) # Trade does not count for per pair stop as it's the wrong pair. - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'ETH/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=240, min_ago_close=30, profit_rate=0.9, is_short=is_short - )) + ) # 3 Trades closed - but the 2nd has been closed too long ago. assert not freqtrade.protections.stop_per_pair(pair) assert freqtrade.protections.global_stop() != only_per_pair @@ -216,10 +218,10 @@ def test_stoploss_guard_perpair(mocker, default_conf, fee, caplog, only_per_pair caplog.clear() # Trade does not count potentially, as it's in the wrong direction - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( pair, fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=150, min_ago_close=25, profit_rate=0.9, is_short=not is_short - )) + ) freqtrade.protections.stop_per_pair(pair) assert freqtrade.protections.global_stop() != only_per_pair assert PairLocks.is_pair_locked(pair, side=check_side) != (only_per_side and only_per_pair) @@ -231,10 +233,10 @@ def test_stoploss_guard_perpair(mocker, default_conf, fee, caplog, only_per_pair caplog.clear() # 2nd Trade that counts with correct pair - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( pair, fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=180, min_ago_close=30, profit_rate=0.9, is_short=is_short - )) + ) freqtrade.protections.stop_per_pair(pair) assert freqtrade.protections.global_stop() != only_per_pair @@ -259,20 +261,20 @@ def test_CooldownPeriod(mocker, default_conf, fee, caplog): assert not log_has_re(message, caplog) caplog.clear() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=200, min_ago_close=30, - )) + ) assert not freqtrade.protections.global_stop() assert freqtrade.protections.stop_per_pair('XRP/BTC') assert PairLocks.is_pair_locked('XRP/BTC') assert not PairLocks.is_global_lock() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'ETH/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value, min_ago_open=205, min_ago_close=35, - )) + ) assert not freqtrade.protections.global_stop() assert not PairLocks.is_pair_locked('ETH/BTC') @@ -300,10 +302,10 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side): assert not log_has_re(message, caplog) caplog.clear() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=800, min_ago_close=450, profit_rate=0.9, - )) + ) Trade.commit() # Not locked with 1 trade @@ -312,10 +314,10 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side): assert not PairLocks.is_pair_locked('XRP/BTC') assert not PairLocks.is_global_lock() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=200, min_ago_close=120, profit_rate=0.9, - )) + ) Trade.commit() # Not locked with 1 trade (first trade is outside of lookback_period) @@ -325,19 +327,19 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side): assert not PairLocks.is_global_lock() # Add positive trade - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value, min_ago_open=20, min_ago_close=10, profit_rate=1.15, is_short=True - )) + ) Trade.commit() assert freqtrade.protections.stop_per_pair('XRP/BTC') != only_per_side assert not PairLocks.is_pair_locked('XRP/BTC', side='*') assert PairLocks.is_pair_locked('XRP/BTC', side='long') == only_per_side - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=110, min_ago_close=21, profit_rate=0.8, - )) + ) Trade.commit() # Locks due to 2nd trade @@ -365,36 +367,38 @@ def test_MaxDrawdown(mocker, default_conf, fee, caplog): assert not freqtrade.protections.stop_per_pair('XRP/BTC') caplog.clear() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=1000, min_ago_close=900, profit_rate=1.1, - )) - Trade.query.session.add(generate_mock_trade( + ) + generate_mock_trade( 'ETH/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=1000, min_ago_close=900, profit_rate=1.1, - )) - Trade.query.session.add(generate_mock_trade( + ) + generate_mock_trade( 'NEO/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=1000, min_ago_close=900, profit_rate=1.1, - )) + ) + Trade.commit() # No losing trade yet ... so max_drawdown will raise exception assert not freqtrade.protections.global_stop() assert not freqtrade.protections.stop_per_pair('XRP/BTC') - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=500, min_ago_close=400, profit_rate=0.9, - )) + ) # Not locked with one trade assert not freqtrade.protections.global_stop() assert not freqtrade.protections.stop_per_pair('XRP/BTC') assert not PairLocks.is_pair_locked('XRP/BTC') assert not PairLocks.is_global_lock() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=1200, min_ago_close=1100, profit_rate=0.5, - )) + ) + Trade.commit() # Not locked with 1 trade (2nd trade is outside of lookback_period) assert not freqtrade.protections.global_stop() @@ -404,20 +408,22 @@ def test_MaxDrawdown(mocker, default_conf, fee, caplog): assert not log_has_re(message, caplog) # Winning trade ... (should not lock, does not change drawdown!) - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value, min_ago_open=320, min_ago_close=410, profit_rate=1.5, - )) + ) + Trade.commit() assert not freqtrade.protections.global_stop() assert not PairLocks.is_global_lock() caplog.clear() # Add additional negative trade, causing a loss of > 15% - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value, min_ago_open=20, min_ago_close=10, profit_rate=0.8, - )) + ) + Trade.commit() assert not freqtrade.protections.stop_per_pair('XRP/BTC') # local lock not supported assert not PairLocks.is_pair_locked('XRP/BTC') diff --git a/tests/rpc/test_rpc.py b/tests/rpc/test_rpc.py index 7b42bf083..8bbf75a32 100644 --- a/tests/rpc/test_rpc.py +++ b/tests/rpc/test_rpc.py @@ -663,7 +663,7 @@ def test_rpc_stop(mocker, default_conf) -> None: assert freqtradebot.state == State.STOPPED -def test_rpc_stopbuy(mocker, default_conf) -> None: +def test_rpc_stopentry(mocker, default_conf) -> None: mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock()) mocker.patch.multiple( 'freqtrade.exchange.Exchange', @@ -676,8 +676,8 @@ def test_rpc_stopbuy(mocker, default_conf) -> None: freqtradebot.state = State.RUNNING assert freqtradebot.config['max_open_trades'] != 0 - result = rpc._rpc_stopbuy() - assert {'status': 'No more buy will occur from now. Run /reload_config to reset.'} == result + result = rpc._rpc_stopentry() + assert {'status': 'No more entries will occur from now. Run /reload_config to reset.'} == result assert freqtradebot.config['max_open_trades'] == 0 diff --git a/tests/rpc/test_rpc_apiserver.py b/tests/rpc/test_rpc_apiserver.py index 9aa965da2..5dfa77d8b 100644 --- a/tests/rpc/test_rpc_apiserver.py +++ b/tests/rpc/test_rpc_apiserver.py @@ -422,13 +422,20 @@ def test_api_reloadconf(botclient): assert ftbot.state == State.RELOAD_CONFIG -def test_api_stopbuy(botclient): +def test_api_stopentry(botclient): ftbot, client = botclient assert ftbot.config['max_open_trades'] != 0 rc = client_post(client, f"{BASE_URI}/stopbuy") assert_response(rc) - assert rc.json() == {'status': 'No more buy will occur from now. Run /reload_config to reset.'} + assert rc.json() == { + 'status': 'No more entries will occur from now. Run /reload_config to reset.'} + assert ftbot.config['max_open_trades'] == 0 + + rc = client_post(client, f"{BASE_URI}/stopentry") + assert_response(rc) + assert rc.json() == { + 'status': 'No more entries will occur from now. Run /reload_config to reset.'} assert ftbot.config['max_open_trades'] == 0 diff --git a/tests/rpc/test_rpc_telegram.py b/tests/rpc/test_rpc_telegram.py index a30115bd9..cde7025a7 100644 --- a/tests/rpc/test_rpc_telegram.py +++ b/tests/rpc/test_rpc_telegram.py @@ -103,7 +103,8 @@ def test_telegram_init(default_conf, mocker, caplog) -> None: "['stats'], ['daily'], ['weekly'], ['monthly'], " "['count'], ['locks'], ['unlock', 'delete_locks'], " "['reload_config', 'reload_conf'], ['show_config', 'show_conf'], " - "['stopbuy'], ['whitelist'], ['blacklist'], ['blacklist_delete', 'bl_delete'], " + "['stopbuy', 'stopentry'], ['whitelist'], ['blacklist'], " + "['blacklist_delete', 'bl_delete'], " "['logs'], ['edge'], ['health'], ['help'], ['version']" "]") @@ -896,10 +897,10 @@ def test_stopbuy_handle(default_conf, update, mocker) -> None: telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) assert freqtradebot.config['max_open_trades'] != 0 - telegram._stopbuy(update=update, context=MagicMock()) + telegram._stopentry(update=update, context=MagicMock()) assert freqtradebot.config['max_open_trades'] == 0 assert msg_mock.call_count == 1 - assert 'No more buy will occur from now. Run /reload_config to reset.' \ + assert 'No more entries will occur from now. Run /reload_config to reset.' \ in msg_mock.call_args_list[0][0][0] diff --git a/tests/strategy/test_interface.py b/tests/strategy/test_interface.py index 83f7d19b7..65ee05d71 100644 --- a/tests/strategy/test_interface.py +++ b/tests/strategy/test_interface.py @@ -12,7 +12,9 @@ from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider from freqtrade.data.history import load_data from freqtrade.enums import ExitCheckTuple, ExitType, SignalDirection +from freqtrade.enums.hyperoptstate import HyperoptState from freqtrade.exceptions import OperationalException, StrategyError +from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer from freqtrade.optimize.space import SKDecimal from freqtrade.persistence import PairLocks, Trade from freqtrade.resolvers import StrategyResolver @@ -859,7 +861,9 @@ def test_strategy_safe_wrapper_trade_copy(fee): def test_hyperopt_parameters(): + HyperoptStateContainer.set_state(HyperoptState.INDICATORS) from skopt.space import Categorical, Integer, Real + with pytest.raises(OperationalException, match=r"Name is determined.*"): IntParameter(low=0, high=5, default=1, name='hello') @@ -937,6 +941,12 @@ def test_hyperopt_parameters(): assert list(boolpar.range) == [True, False] + HyperoptStateContainer.set_state(HyperoptState.OPTIMIZE) + assert len(list(intpar.range)) == 1 + assert len(list(fltpar.range)) == 1 + assert len(list(catpar.range)) == 1 + assert len(list(boolpar.range)) == 1 + def test_auto_hyperopt_interface(default_conf): default_conf.update({'strategy': 'HyperoptableStrategyV2'}) diff --git a/tests/strategy/test_strategy_helpers.py b/tests/strategy/test_strategy_helpers.py index 244fd3919..a7c2da26a 100644 --- a/tests/strategy/test_strategy_helpers.py +++ b/tests/strategy/test_strategy_helpers.py @@ -1,5 +1,3 @@ -from math import isclose - import numpy as np import pandas as pd import pytest @@ -165,7 +163,7 @@ def test_stoploss_from_open(): or (side == 'short' and expected_stop_price < current_price)): assert stoploss == 0 else: - assert isclose(stop_price, expected_stop_price, rel_tol=0.00001) + assert pytest.approx(stop_price) == expected_stop_price def test_stoploss_from_absolute(): diff --git a/tests/strategy/test_strategy_loading.py b/tests/strategy/test_strategy_loading.py index 5b6f15d11..bf81cd068 100644 --- a/tests/strategy/test_strategy_loading.py +++ b/tests/strategy/test_strategy_loading.py @@ -48,6 +48,10 @@ def test_search_all_strategies_with_failed(): assert len([x for x in strategies if x['class'] is not None]) == 9 assert len([x for x in strategies if x['class'] is None]) == 1 + directory = Path(__file__).parent / "strats_nonexistingdir" + strategies = StrategyResolver.search_all_objects(directory, enum_failed=True) + assert len(strategies) == 0 + def test_load_strategy(default_conf, result): default_conf.update({'strategy': 'SampleStrategy', @@ -271,8 +275,8 @@ def test_strategy_override_order_tif(caplog, default_conf): caplog.set_level(logging.INFO) order_time_in_force = { - 'entry': 'fok', - 'exit': 'gtc', + 'entry': 'FOK', + 'exit': 'GTC', } default_conf.update({ @@ -286,11 +290,11 @@ def test_strategy_override_order_tif(caplog, default_conf): assert strategy.order_time_in_force[method] == order_time_in_force[method] assert log_has("Override strategy 'order_time_in_force' with value in config file:" - " {'entry': 'fok', 'exit': 'gtc'}.", caplog) + " {'entry': 'FOK', 'exit': 'GTC'}.", caplog) default_conf.update({ 'strategy': CURRENT_TEST_STRATEGY, - 'order_time_in_force': {'entry': 'fok'} + 'order_time_in_force': {'entry': 'FOK'} }) # Raise error for invalid configuration with pytest.raises(ImportError, diff --git a/tests/test_configuration.py b/tests/test_configuration.py index db87c405f..2825ede5c 100644 --- a/tests/test_configuration.py +++ b/tests/test_configuration.py @@ -973,17 +973,17 @@ def test_validate_time_in_force(default_conf, caplog) -> None: conf = deepcopy(default_conf) conf['order_time_in_force'] = { 'buy': 'gtc', - 'sell': 'gtc', + 'sell': 'GTC', } validate_config_consistency(conf) assert log_has_re(r"DEPRECATED: Using 'buy' and 'sell' for time_in_force is.*", caplog) assert conf['order_time_in_force']['entry'] == 'gtc' - assert conf['order_time_in_force']['exit'] == 'gtc' + assert conf['order_time_in_force']['exit'] == 'GTC' conf = deepcopy(default_conf) conf['order_time_in_force'] = { - 'buy': 'gtc', - 'sell': 'gtc', + 'buy': 'GTC', + 'sell': 'GTC', } conf['trading_mode'] = 'futures' with pytest.raises(OperationalException, diff --git a/tests/test_freqtradebot.py b/tests/test_freqtradebot.py index ace77a3b6..e6c6e7978 100644 --- a/tests/test_freqtradebot.py +++ b/tests/test_freqtradebot.py @@ -473,8 +473,6 @@ def test_create_trade_no_signal(default_conf_usdt, fee, mocker) -> None: freqtrade = FreqtradeBot(default_conf_usdt) patch_get_signal(freqtrade, enter_long=False, exit_long=False) - Trade.query = MagicMock() - Trade.query.filter = MagicMock() assert not freqtrade.create_trade('ETH/USDT') @@ -677,6 +675,7 @@ def test_process_trade_no_whitelist_pair(default_conf_usdt, ticker_usdt, limit_b open_rate=0.001, exchange='binance', )) + Trade.commit() assert pair not in freqtrade.active_pair_whitelist freqtrade.process() @@ -1052,8 +1051,6 @@ def test_add_stoploss_on_exchange(mocker, default_conf_usdt, limit_order, is_sho mocker.patch('freqtrade.freqtradebot.FreqtradeBot.handle_trade', MagicMock(return_value=True)) mocker.patch('freqtrade.exchange.Exchange.fetch_order', return_value=order) mocker.patch('freqtrade.exchange.Exchange.get_trades_for_order', return_value=[]) - mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', - return_value=order['amount']) stoploss = MagicMock(return_value={'id': 13434334}) mocker.patch('freqtrade.exchange.Binance.stoploss', stoploss) @@ -1876,8 +1873,6 @@ def test_exit_positions(mocker, default_conf_usdt, limit_order, is_short, caplog mocker.patch('freqtrade.exchange.Exchange.fetch_order', return_value=limit_order[entry_side(is_short)]) mocker.patch('freqtrade.exchange.Exchange.get_trades_for_order', return_value=[]) - mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', - return_value=limit_order[entry_side(is_short)]['amount']) trade = MagicMock() trade.is_short = is_short @@ -1887,14 +1882,13 @@ def test_exit_positions(mocker, default_conf_usdt, limit_order, is_short, caplog n = freqtrade.exit_positions(trades) assert n == 0 # Test amount not modified by fee-logic - assert not log_has( - 'Applying fee to amount for Trade {} from 30.0 to 90.81'.format(trade), caplog - ) + assert not log_has_re(r'Applying fee to amount for Trade .*', caplog) - mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', return_value=90.81) + gra = mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', return_value=0.0) # test amount modified by fee-logic n = freqtrade.exit_positions(trades) assert n == 0 + assert gra.call_count == 0 @pytest.mark.parametrize("is_short", [False, True]) @@ -1928,8 +1922,7 @@ def test_update_trade_state(mocker, default_conf_usdt, limit_order, is_short, ca mocker.patch('freqtrade.freqtradebot.FreqtradeBot.handle_trade', MagicMock(return_value=True)) mocker.patch('freqtrade.exchange.Exchange.fetch_order', return_value=order) mocker.patch('freqtrade.exchange.Exchange.get_trades_for_order', return_value=[]) - mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', - return_value=order['amount']) + mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', return_value=0.0) order_id = order['id'] trade = Trade( @@ -1961,11 +1954,11 @@ def test_update_trade_state(mocker, default_conf_usdt, limit_order, is_short, ca assert trade.amount == order['amount'] trade.open_order_id = order_id - mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', return_value=90.81) - assert trade.amount != 90.81 + mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', return_value=0.01) + assert trade.amount == 30.0 # test amount modified by fee-logic freqtrade.update_trade_state(trade, order_id) - assert trade.amount == 90.81 + assert trade.amount == 29.99 assert trade.open_order_id is None trade.is_open = True @@ -2414,6 +2407,7 @@ def test_manage_open_orders_entry_usercustom( open_trade.orders[0].side = 'sell' if is_short else 'buy' open_trade.orders[0].ft_order_side = 'sell' if is_short else 'buy' Trade.query.session.add(open_trade) + Trade.commit() # Ensure default is to return empty (so not mocked yet) freqtrade.manage_open_orders() @@ -2472,6 +2466,7 @@ def test_manage_open_orders_entry( open_trade.is_short = is_short Trade.query.session.add(open_trade) + Trade.commit() freqtrade.strategy.check_entry_timeout = MagicMock(return_value=False) freqtrade.strategy.adjust_entry_price = MagicMock(return_value=1234) @@ -2509,6 +2504,7 @@ def test_adjust_entry_cancel( open_trade.is_short = is_short Trade.query.session.add(open_trade) + Trade.commit() # Timeout to not interfere freqtrade.strategy.ft_check_timed_out = MagicMock(return_value=False) @@ -2549,6 +2545,7 @@ def test_adjust_entry_maintain_replace( open_trade.is_short = is_short Trade.query.session.add(open_trade) + Trade.commit() # Timeout to not interfere freqtrade.strategy.ft_check_timed_out = MagicMock(return_value=False) @@ -2601,6 +2598,7 @@ def test_check_handle_cancelled_buy( open_trade.orders = [] open_trade.is_short = is_short Trade.query.session.add(open_trade) + Trade.commit() # check it does cancel buy orders over the time limit freqtrade.manage_open_orders() @@ -2631,6 +2629,7 @@ def test_manage_open_orders_buy_exception( open_trade.is_short = is_short Trade.query.session.add(open_trade) + Trade.commit() # check it does cancel buy orders over the time limit freqtrade.manage_open_orders() @@ -2672,6 +2671,7 @@ def test_manage_open_orders_exit_usercustom( open_trade_usdt.is_open = False Trade.query.session.add(open_trade_usdt) + Trade.commit() # Ensure default is false freqtrade.manage_open_orders() assert cancel_order_mock.call_count == 0 @@ -2754,6 +2754,7 @@ def test_manage_open_orders_exit( open_trade_usdt.is_short = is_short Trade.query.session.add(open_trade_usdt) + Trade.commit() freqtrade.strategy.check_exit_timeout = MagicMock(return_value=False) freqtrade.strategy.check_entry_timeout = MagicMock(return_value=False) @@ -2794,6 +2795,7 @@ def test_check_handle_cancelled_exit( open_trade_usdt.is_short = is_short Trade.query.session.add(open_trade_usdt) + Trade.commit() # check it does cancel sell orders over the time limit freqtrade.manage_open_orders() @@ -2830,6 +2832,7 @@ def test_manage_open_orders_partial( freqtrade = FreqtradeBot(default_conf_usdt) prior_stake = open_trade.stake_amount Trade.query.session.add(open_trade) + Trade.commit() # check it does cancel buy orders over the time limit # note this is for a partially-complete buy order @@ -2874,6 +2877,7 @@ def test_manage_open_orders_partial_fee( open_trade.fee_open = fee() open_trade.fee_close = fee() Trade.query.session.add(open_trade) + Trade.commit() # cancelling a half-filled order should update the amount to the bought amount # and apply fees if necessary. freqtrade.manage_open_orders() @@ -2923,6 +2927,7 @@ def test_manage_open_orders_partial_except( open_trade.fee_open = fee() open_trade.fee_close = fee() Trade.query.session.add(open_trade) + Trade.commit() # cancelling a half-filled order should update the amount to the bought amount # and apply fees if necessary. freqtrade.manage_open_orders() @@ -2961,6 +2966,7 @@ def test_manage_open_orders_exception(default_conf_usdt, ticker_usdt, open_trade freqtrade = FreqtradeBot(default_conf_usdt) Trade.query.session.add(open_trade_usdt) + Trade.commit() caplog.clear() freqtrade.manage_open_orders() @@ -4256,10 +4262,10 @@ def test_get_real_amount_quote(default_conf_usdt, trades_for_order, buy_order_fe caplog.clear() order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy') # Amount is reduced by "fee" - assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) == amount - (amount * 0.001) + assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) == (amount * 0.001) assert log_has( 'Applying fee on amount for Trade(id=None, pair=LTC/ETH, amount=8.00000000, is_short=False,' - ' leverage=1.0, open_rate=0.24544100, open_since=closed) (from 8.0 to 7.992).', + ' leverage=1.0, open_rate=0.24544100, open_since=closed), fee=0.008.', caplog ) @@ -4284,7 +4290,7 @@ def test_get_real_amount_quote_dust(default_conf_usdt, trades_for_order, buy_ord walletmock.reset_mock() order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy') # Amount is kept as is - assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) == amount + assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) is None assert walletmock.call_count == 1 assert log_has_re(r'Fee amount for Trade.* was in base currency ' '- Eating Fee 0.008 into dust', caplog) @@ -4307,7 +4313,7 @@ def test_get_real_amount_no_trade(default_conf_usdt, buy_order_fee, caplog, mock order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy') # Amount is reduced by "fee" - assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) == amount + assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) is None assert log_has( 'Applying fee on amount for Trade(id=None, pair=LTC/ETH, amount=8.00000000, ' 'is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed) failed: ' @@ -4331,8 +4337,7 @@ def test_get_real_amount_no_trade(default_conf_usdt, buy_order_fee, caplog, mock # from order ({'cost': 0.004, 'currency': 'LTC'}, 0.004, False, ( 'Applying fee on amount for Trade(id=None, pair=LTC/ETH, amount=8.00000000, ' - 'is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed) (from' - ' 8.0 to 7.996).' + 'is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed), fee=0.004.' )), # invalid, no currency in from fee dict ({'cost': 0.008, 'currency': None}, 0, True, None), @@ -4364,7 +4369,11 @@ def test_get_real_amount( caplog.clear() order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy') - assert freqtrade.get_real_amount(trade, buy_order, order_obj) == amount - fee_reduction_amount + res = freqtrade.get_real_amount(trade, buy_order, order_obj) + if fee_reduction_amount == 0: + assert res is None + else: + assert res == fee_reduction_amount if expected_log: assert log_has(expected_log, caplog) @@ -4410,14 +4419,14 @@ def test_get_real_amount_multi( return_value={'ask': 0.19, 'last': 0.2}) # Amount is reduced by "fee" - expected_amount = amount - (amount * fee_reduction_amount) + expected_amount = amount * fee_reduction_amount order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy') assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) == expected_amount assert log_has( ( 'Applying fee on amount for Trade(id=None, pair=LTC/ETH, amount=8.00000000, ' - 'is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed) ' - f'(from 8.0 to {expected_log_amount}).' + 'is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed), ' + f'fee={expected_amount}.' ), caplog ) @@ -4450,7 +4459,7 @@ def test_get_real_amount_invalid_order(default_conf_usdt, trades_for_order, buy_ order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy') # Amount does not change - assert freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj) == amount + assert freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj) is None def test_get_real_amount_fees_order(default_conf_usdt, market_buy_order_usdt_doublefee, @@ -4473,7 +4482,7 @@ def test_get_real_amount_fees_order(default_conf_usdt, market_buy_order_usdt_dou # Amount does not change assert trade.fee_open == 0.0025 order_obj = Order.parse_from_ccxt_object(market_buy_order_usdt_doublefee, 'LTC/ETH', 'buy') - assert freqtrade.get_real_amount(trade, market_buy_order_usdt_doublefee, order_obj) == 30.0 + assert freqtrade.get_real_amount(trade, market_buy_order_usdt_doublefee, order_obj) is None assert tfo_mock.call_count == 0 # Fetch fees from trades dict if available to get "proper" values assert round(trade.fee_open, 4) == 0.001 @@ -4525,7 +4534,7 @@ def test_get_real_amount_wrong_amount_rounding(default_conf_usdt, trades_for_ord order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy') # Amount changes by fee amount. assert pytest.approx(freqtrade.get_real_amount( - trade, limit_buy_order_usdt, order_obj)) == amount - (amount * 0.001) + trade, limit_buy_order_usdt, order_obj)) == (amount * 0.001) def test_get_real_amount_open_trade_usdt(default_conf_usdt, fee, mocker): @@ -4547,7 +4556,7 @@ def test_get_real_amount_open_trade_usdt(default_conf_usdt, fee, mocker): } freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt) order_obj = Order.parse_from_ccxt_object(order, 'LTC/ETH', 'buy') - assert freqtrade.get_real_amount(trade, order, order_obj) == amount + assert freqtrade.get_real_amount(trade, order, order_obj) is None def test_get_real_amount_in_point(default_conf_usdt, buy_order_fee, fee, mocker, caplog): @@ -4604,7 +4613,7 @@ def test_get_real_amount_in_point(default_conf_usdt, buy_order_fee, fee, mocker, order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy') res = freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj) - assert res == amount + assert res is None assert trade.fee_open_currency is None assert trade.fee_open_cost is None message = "Not updating buy-fee - rate: None, POINT." @@ -4612,7 +4621,7 @@ def test_get_real_amount_in_point(default_conf_usdt, buy_order_fee, fee, mocker, caplog.clear() freqtrade.config['exchange']['unknown_fee_rate'] = 1 res = freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj) - assert res == amount + assert res is None assert trade.fee_open_currency == 'POINT' assert pytest.approx(trade.fee_open_cost) == 0.3046651026 assert trade.fee_open == 0.002 @@ -4621,12 +4630,12 @@ def test_get_real_amount_in_point(default_conf_usdt, buy_order_fee, fee, mocker, @pytest.mark.parametrize('amount,fee_abs,wallet,amount_exp', [ - (8.0, 0.0, 10, 8), - (8.0, 0.0, 0, 8), - (8.0, 0.1, 0, 7.9), - (8.0, 0.1, 10, 8), - (8.0, 0.1, 8.0, 8.0), - (8.0, 0.1, 7.9, 7.9), + (8.0, 0.0, 10, None), + (8.0, 0.0, 0, None), + (8.0, 0.1, 0, 0.1), + (8.0, 0.1, 10, None), + (8.0, 0.1, 8.0, None), + (8.0, 0.1, 7.9, 0.1), ]) def test_apply_fee_conditional(default_conf_usdt, fee, mocker, amount, fee_abs, wallet, amount_exp): diff --git a/tests/test_persistence.py b/tests/test_persistence.py index f68791b72..f16c8b054 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -1,7 +1,6 @@ # pragma pylint: disable=missing-docstring, C0103 import logging from datetime import datetime, timedelta, timezone -from math import isclose from pathlib import Path from types import FunctionType from unittest.mock import MagicMock @@ -630,9 +629,9 @@ def test_calc_open_close_trade_price( trade.open_rate = 2.0 trade.close_rate = 2.2 trade.recalc_open_trade_value() - assert isclose(trade._calc_open_trade_value(trade.amount, trade.open_rate), open_value) - assert isclose(trade.calc_close_trade_value(trade.close_rate), close_value) - assert isclose(trade.calc_profit(trade.close_rate), round(profit, 8)) + assert pytest.approx(trade._calc_open_trade_value(trade.amount, trade.open_rate)) == open_value + assert pytest.approx(trade.calc_close_trade_value(trade.close_rate)) == close_value + assert pytest.approx(trade.calc_profit(trade.close_rate)) == round(profit, 8) assert pytest.approx(trade.calc_profit_ratio(trade.close_rate)) == profit_ratio @@ -1387,6 +1386,7 @@ def test_migrate_new(mocker, default_conf, fee, caplog): assert log_has("trying trades_bak2", caplog) assert log_has("Running database migration for trades - backup: trades_bak2, orders_bak0", caplog) + assert log_has("Database migration finished.", caplog) assert pytest.approx(trade.open_trade_value) == trade._calc_open_trade_value( trade.amount, trade.open_rate) assert trade.close_profit_abs is None @@ -1688,6 +1688,7 @@ def test_get_open(fee, is_short, use_db): create_mock_trades(fee, is_short, use_db) assert len(Trade.get_open_trades()) == 4 + assert Trade.get_open_trade_count() == 4 Trade.use_db = True @@ -1700,6 +1701,7 @@ def test_get_open_lev(fee, use_db): create_mock_trades_with_leverage(fee, use_db) assert len(Trade.get_open_trades()) == 5 + assert Trade.get_open_trade_count() == 5 Trade.use_db = True @@ -1885,6 +1887,7 @@ def test_stoploss_reinitialization(default_conf, fee): assert trade.initial_stop_loss == 0.95 assert trade.initial_stop_loss_pct == -0.05 Trade.query.session.add(trade) + Trade.commit() # Lower stoploss Trade.stoploss_reinitialization(0.06) @@ -1946,6 +1949,7 @@ def test_stoploss_reinitialization_leverage(default_conf, fee): assert trade.initial_stop_loss == 0.98 assert trade.initial_stop_loss_pct == -0.1 Trade.query.session.add(trade) + Trade.commit() # Lower stoploss Trade.stoploss_reinitialization(0.15) @@ -2007,6 +2011,7 @@ def test_stoploss_reinitialization_short(default_conf, fee): assert trade.initial_stop_loss == 1.02 assert trade.initial_stop_loss_pct == -0.1 Trade.query.session.add(trade) + Trade.commit() # Lower stoploss Trade.stoploss_reinitialization(-0.15) trades = Trade.get_open_trades() @@ -2888,8 +2893,8 @@ def test_order_to_ccxt(limit_buy_order_open): (('buy', 100, 9), (200.0, 8.5, 1700.0, 0.0, None, None)), (('sell', 100, 10), (100.0, 8.5, 850.0, 150.0, 150.0, 0.17647059)), (('buy', 150, 11), (250.0, 10, 2500.0, 150.0, 150.0, 0.17647059)), - (('sell', 100, 12), (150.0, 10.0, 1500.0, 350.0, 350.0, 0.2)), - (('sell', 150, 14), (150.0, 10.0, 1500.0, 950.0, 950.0, 0.40)), + (('sell', 100, 12), (150.0, 10.0, 1500.0, 350.0, 200.0, 0.2)), + (('sell', 150, 14), (150.0, 10.0, 1500.0, 950.0, 600.0, 0.40)), ], 'end_profit': 950.0, 'end_profit_ratio': 0.283582, @@ -2954,9 +2959,8 @@ def test_recalc_trade_from_orders_dca(data) -> None: assert trade.amount == result[0] assert trade.open_rate == result[1] assert trade.stake_amount == result[2] - # TODO: enable the below. assert pytest.approx(trade.realized_profit) == result[3] - # assert pytest.approx(trade.close_profit_abs) == result[4] + assert pytest.approx(trade.close_profit_abs) == result[4] assert pytest.approx(trade.close_profit) == result[5] trade.close(price)