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Feat/freqai
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4
.github/workflows/ci.yml vendored
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@ -351,7 +351,7 @@ jobs:
python setup.py sdist bdist_wheel
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@v1.5.0
uses: pypa/gh-action-pypi-publish@v1.5.1
if: (github.event_name == 'release')
with:
user: __token__
@ -359,7 +359,7 @@ jobs:
repository_url: https://test.pypi.org/legacy/
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@v1.5.0
uses: pypa/gh-action-pypi-publish@v1.5.1
if: (github.event_name == 'release')
with:
user: __token__

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.gitignore vendored
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@ -111,5 +111,4 @@ target/
!config_examples/config_ftx.example.json
!config_examples/config_full.example.json
!config_examples/config_kraken.example.json
!config_examples/config_freqai_futures.example.json
!config_examples/config_freqai_spot.example.json
!config_examples/config_freqai.example.json

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.pre-commit-config.yaml
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@ -15,7 +15,7 @@ repos:
additional_dependencies:
- types-cachetools==5.2.1
- types-filelock==3.2.7
- types-requests==2.28.3
- types-requests==2.28.8
- types-tabulate==0.8.11
- types-python-dateutil==2.8.19
# stages: [push]

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Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.10.5-slim-bullseye as base
FROM python:3.10.6-slim-bullseye as base
# Setup env
ENV LANG C.UTF-8

2
README.md
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@ -194,7 +194,7 @@ Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/
The clock must be accurate, synchronized to a NTP server very frequently to avoid problems with communication to the exchanges.
### Min hardware required
### Minimum hardware required
To run this bot we recommend you a cloud instance with a minimum of:

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config_examples/config_freqai_futures.example.json → config_examples/config_freqai.example.json
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@ -52,13 +52,13 @@
}
],
"freqai": {
"enabled": true,
"startup_candles": 10000,
"purge_old_models": true,
"train_period_days": 15,
"backtest_period_days": 7,
"live_retrain_hours": 0,
"identifier": "uniqe-id6",
"live_trained_timestamp": 0,
"identifier": "uniqe-id",
"feature_parameters": {
"include_timeframes": [
"3m",
@ -84,8 +84,7 @@
"random_state": 1
},
"model_training_parameters": {
"n_estimators": 1000,
"task_type": "CPU"
"n_estimators": 1000
}
},
"bot_name": "",

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config_examples/config_freqai_spot.example.json
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@ -1,96 +0,0 @@
{
"max_open_trades": 1,
"stake_currency": "USDT",
"stake_amount": 900,
"tradable_balance_ratio": 1,
"fiat_display_currency": "USD",
"dry_run": true,
"timeframe": "5m",
"dry_run_wallet": 4000,
"dataformat_ohlcv": "json",
"cancel_open_orders_on_exit": true,
"unfilledtimeout": {
"entry": 10,
"exit": 30
},
"exchange": {
"name": "binance",
"key": "",
"secret": "",
"ccxt_config": {
"enableRateLimit": true
},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 200
},
"pair_whitelist": [
"BTC/USDT",
"ETH/USDT"
],
"pair_blacklist": []
},
"entry_pricing": {
"price_side": "same",
"use_order_book": true,
"order_book_top": 1,
"price_last_balance": 0.0,
"check_depth_of_market": {
"enabled": false,
"bids_to_ask_delta": 1
}
},
"exit_pricing": {
"price_side": "other",
"use_order_book": true,
"order_book_top": 1
},
"pairlists": [
{
"method": "StaticPairList"
}
],
"freqai": {
"startup_candles": 10000,
"train_period_days": 30,
"backtest_period_days": 7,
"live_retrain_hours": 1,
"identifier": "example",
"live_trained_timestamp": 0,
"feature_parameters": {
"include_timeframes": [
"5m",
"15m",
"4h"
],
"include_corr_pairlist": [
"BTC/USDT",
"ETH/USDT"
],
"label_period_candles": 500,
"include_shifted_candles": 1,
"DI_threshold": 0,
"weight_factor": 0.9,
"principal_component_analysis": false,
"use_SVM_to_remove_outliers": false,
"stratify_training_data": 0,
"indicator_max_period_candles": 50,
"indicator_periods_candles": [10, 20]
},
"data_split_parameters": {
"test_size": 0.33,
"random_state": 1
},
"model_training_parameters": {
"n_estimators": 1000,
"task_type": "CPU"
}
},
"bot_name": "",
"initial_state": "running",
"forcebuy_enable": false,
"internals": {
"process_throttle_secs": 5
}
}

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docs/backtesting.md
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@ -514,6 +514,7 @@ You can then load the trades to perform further analysis as shown in the [data a
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
- Exchange [trading limits](#trading-limits-in-backtesting) are respected
- Buys happen at open-price
- All orders are filled at the requested price (no slippage, no unfilled orders)
- Exit-signal exits happen at open-price of the consecutive candle
@ -543,7 +544,24 @@ Also, keep in mind that past results don't guarantee future success.
In addition to the above assumptions, strategy authors should carefully read the [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies) section, to avoid using data in backtesting which is not available in real market conditions.
### Improved backtest accuracy
### Trading limits in backtesting
Exchanges have certain trading limits, like minimum base currency, or minimum stake (quote) currency.
These limits are usually listed in the exchange documentation as "trading rules" or similar.
Backtesting (as well as live and dry-run) does honor these limits, and will ensure that a stoploss can be placed below this value - so the value will be slightly higher than what the exchange specifies.
Freqtrade has however no information about historic limits.
This can lead to situations where trading-limits are inflated by using a historic price, resulting in minimum amounts > 50$.
For example:
BTC minimum tradable amount is 0.001.
BTC trades at 22.000\$ today (0.001 BTC is related to this) - but the backtesting period includes prices as high as 50.000\$.
Today's minimum would be `0.001 * 22_000` - or 22\$.
However the limit could also be 50$ - based on `0.001 * 50_000` in some historic setting.
## 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?).
So assuming you run backtesting with a 1h timeframe, there will be 4 prices for that candle (Open, High, Low, Close).

2
docs/configuration.md
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@ -105,7 +105,7 @@ This is similar to using multiple `--config` parameters, but simpler in usage as
``` json title="Result"
{
"max_open_trades": 10,
"max_open_trades": 3,
"stake_currency": "USDT",
"stake_amount": "unlimited"
}

30
docs/developer.md
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@ -68,6 +68,36 @@ def test_method_to_test(caplog):
```
### Debug configuration
To debug freqtrade, we recommend VSCode with the following launch configuration (located in `.vscode/launch.json`).
Details will obviously vary between setups - but this should work to get you started.
``` json
{
"name": "freqtrade trade",
"type": "python",
"request": "launch",
"module": "freqtrade",
"console": "integratedTerminal",
"args": [
"trade",
// Optional:
// "--userdir", "user_data",
"--strategy",
"MyAwesomeStrategy",
]
},
```
Command line arguments can be added in the `"args"` array.
This method can also be used to debug a strategy, by setting the breakpoints within the strategy.
A similar setup can also be taken for Pycharm - using `freqtrade` as module name, and setting the command line arguments as "parameters".
!!! Note "Startup directory"
This assumes that you have the repository checked out, and the editor is started at the repository root level (so setup.py is at the top level of your repository).
## ErrorHandling
Freqtrade Exceptions all inherit from `FreqtradeException`.

206
docs/freqai.md
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@ -1,35 +1,52 @@
![freqai-logo](assets/freqai_logo_no_md.svg)
![freqai-logo](assets/freqai_doc_logo.svg)
# FreqAI
FreqAI is a module designed to automate a variety of tasks associated with
training a predictive model to provide signals based on input features.
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.
Among the the features included:
* Create large rich feature sets (10k+ features) based on simple user created strategies.
* Sweep model training and backtesting to simulate consistent model retraining through time.
* Remove outliers automatically from training and prediction sets using a Dissimilarity Index and Support Vector Machines.
* Reduce the dimensionality of the data with Principal Component Analysis.
* Store models to disk to make reloading from a crash fast and easy (and purge obsolete files automatically for sustained dry/live runs).
* Normalize the data automatically in a smart and statistically safe way.
* Automated data download and data handling.
* Clean the incoming data of NaNs in a safe way before training and prediction.
* Retrain live automatically so that the model self-adapts to the market in an unsupervised manner.
* **Self-adaptive retraining**: automatically 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 automatically from training and prediction sets using a variety of outlier detection techniques.
* **Crash resilience**: automatic model storage to disk to make reloading from a crash fast and easy (and purge obsolete files automatically for sustained dry/live runs).
* **Automated data normalization**: automatically normalize the data automatically in a smart and statistically safe way.
* **Automatic data download**: automatically compute the data download timerange and downloads data accordingly (in live deployments).
* **Clean the incoming data of NaNs in a safe way 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.
## Quick start
The easiest way to quickly test FreqAI is to run it in dry run 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 example strategy, example prediction model, and example config can all be found in
`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`,
`config_examples/config_freqai.example.json`, respectively.
## General approach
The user provides FreqAI with a set of custom indicators (created inside the strategy the same way
a typical Freqtrade strategy is created) as well as a target value (typically some price change into the future).
FreqAI trains a model to predict the target value based on the input of custom indicators.
FreqAI will train and save a new model for each pair in the config whitelist.
Users employ FreqAI to backtest a strategy (emulate reality with retraining a model as new data is introduced) and run the model live to generate entry and exit signals.
In dry/live, FreqAI works in a background thread to keep all models as updated as possible with consistent retraining.
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.
An overview of the algorithm is shown here to help users understand the data processing pipeline and the model usage.
![freqai-algo](assets/freqai_algo.png)
## Background and 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
vector of all features for a single candle. In FreqAI, the user
builds the features from anything they can construct in the strategy.
**Labels** are the target values with which the weights inside a model are trained
@ -49,23 +66,14 @@ directly influence nodal weights within the model.
## Install prerequisites
Use `pip` to install the prerequisites with:
The normal Freqtrade install process will ask the user if they wish to install `FreqAI` dependencies. The user should reply "yes" to this question if they wish to use FreqAI. If the user did not reply yes, they can manually install these dependencies after the install with:
``` bash
pip install -r requirements-freqai.txt
```
## Running from the example files
An example strategy, an example prediction model, and example config can all be found in
`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMPredictionModel.py`,
`config_examples/config_freqai_futures.example.json`, respectively.
Assuming the user has downloaded the necessary data, Freqai can be executed from these templates with:
```bash
freqtrade backtesting --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMPredictionModel --strategy-path freqtrade/templates --timerange 20220101-20220201
```
!!! Note
Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since Catboost does not provide wheels for this platform.
## Configuring the bot
@ -88,28 +96,32 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `purge_old_models` | Tell FreqAI to delete obsolete models. Otherwise, all historic models will remain on disk. Defaults to `False`. <br> **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. <br> **Datatype:** positive integer.
| | **Feature Parameters**
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples shown [here](#building-the-feature-set) <br> **Datatype:** dictionary.
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples shown [here](#feature-engineering) <br> **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. <br> **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. <br> **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. <br> **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. <br> **Datatype:** positive integer.
| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when above 0, explained more [here](#removing-outliers-with-the-dissimilarity-index). <br> **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). <br> **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). <br> **Datatype:** positive float (typically below 1).
| `principal_component_analysis` | Ask FreqAI to automatically reduce the dimensionality of the data set using PCA. <br> **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. <br> **Datatype:** boolean.
| `svm_nu` | The `nu` parameter for the support vector machine. *Very* broadly, this is the percentage of data points that should be considered outliers. <br> **Datatype:** float between 0 and 1.
| `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 reprodicibility. But these and all others can be added/changed in this dictionary. <br> **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. <br> **Datatype:** positive integer.
| `indicator_max_period_candles` | The maximum *period* used in `populate_any_indicators()` for indicator creation. 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 <br> **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. <br> **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. <br> **Datatype:** float (fraction of 1).
| | **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) <br> **Datatype:** dictionary.
| `test_size` | Fraction of data that should be used for testing instead of training. <br> **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.
| | **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 `LightGBMPredictionModel`, 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. <br> **Datatype:** dictionary.
| `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. <br> **Datatype:** dictionary.
| `n_estimators` | A common parameter among regressors which sets the number of boosted trees to fit <br> **Datatype:** integer.
| `learning_rate` | A common parameter among regressors which sets the boosting learning rate. <br> **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`. <br> **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` <br> **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 <br> **Datatype:** integer.
### Important FreqAI dataframe key patterns
@ -125,8 +137,8 @@ Here are the values the user can expect to include/use inside the typical strate
### Example config file
The user interface is isolated to the typical config file. A typical Freqai
config setup includes:
The user interface is isolated to the typical config file. A typical FreqAI
config setup could include:
```json
"freqai": {
@ -161,7 +173,7 @@ config setup includes:
}
```
### Building the feature set
### Feature engineering
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 `&`.
@ -174,7 +186,7 @@ various configuration parameters which multiply the feature set such as `include
```python
def populate_any_indicators(
self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
"""
Function designed to automatically generate, name and merge features
@ -190,6 +202,8 @@ various configuration parameters which multiply the feature set such as `include
:param coin: the name of the coin which will modify the feature names.
"""
coint = pair.split('/')[0]
with self.freqai.lock:
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
@ -257,7 +271,7 @@ various configuration parameters which multiply the feature set such as `include
return df
```
The user of the present example does not want to pass the `bb_lowerband` as a feature to the model,
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 `%`.
@ -305,7 +319,7 @@ set will include all the features from `populate_any_indicators` on all the `inc
`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.
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:
@ -318,12 +332,12 @@ Users define the backtesting timerange with the typical `--timerange` parameter
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.
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),
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
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
@ -341,16 +355,18 @@ and adding this to the `train_period_days`. The units need to be in the base can
!!! 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
## Running FreqAI
### Training and backtesting
### Backtesting
The freqai training/backtesting module can be executed with the following command:
The FreqAI backtesting module can be executed with the following command:
```bash
freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai_futures.example.json --freqaimodel LightGBMPredictionModel --timerange 20210501-20210701
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`.
@ -366,7 +382,7 @@ for each pair, for each backtesting window within the bigger `--timerange`.
### Building a freqai strategy
The Freqai strategy requires the user to include the following lines of code in the strategy:
The FreqAI strategy requires the user to include the following lines of code in the strategy:
```python
@ -385,8 +401,6 @@ The Freqai strategy requires the user to include the following lines of code in
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config["freqai"]
# All indicators must be populated by populate_any_indicators() for live functionality
# to work correctly.
@ -403,21 +417,24 @@ The Freqai strategy requires the user to include the following lines of code in
The user should also include `populate_any_indicators()` from `templates/FreqaiExampleStrategy.py` which builds
the feature set with a proper naming convention for the IFreqaiModel to use later.
### Building an IFreqaiModel
### Setting classifier targets
FreqAI includes a the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. Typically, the user would set the targets using strings:
```python
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
```
FreqAI has multiple example prediction model based libraries such as `Catboost` regression (`freqai/prediction_models/CatboostPredictionModel.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.
### 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 ExamplePredictionModel
freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor
```
By default, Freqai will not find find any existing models and will start by training a new one
By default, FreqAI will not find 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.
If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse
@ -430,7 +447,7 @@ the same `identifier` parameter
}
```
In this case, although Freqai will initiate with a
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.
@ -457,7 +474,7 @@ the user is asking for `labels` that are 24 candles in the future.
### Removing outliers with the Dissimilarity Index
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
prediction by the model. To do so, FreqAI measures the distance between each training
data point and all other training data points:
$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$
@ -512,7 +529,7 @@ variance of the data set is >= 0.999.
### Removing outliers using a Support Vector Machine (SVM)
The user can tell Freqai to remove outlier data points from the training/test data sets by setting:
The user can tell FreqAI to remove outlier data points from the training/test data sets by setting:
```json
"freqai": {
@ -522,9 +539,21 @@ The user can tell Freqai to remove outlier data points from the training/test da
}
```
Freqai will train an SVM on the training data (or components if the user activated
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.
### Clustering the training data and removing outliers with DBSCAN
The user can tell 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:
@ -612,53 +641,32 @@ If the user sets this value, FreqAI will initially use the predictions from the
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`.
<!-- ## Dynamic target expectation
## Extra returns per train
The labels used for model training have a unique statistical distribution for each separate model training.
We can use this information to know if our current prediction is in the realm of what the model was trained on,
and if so, what is the statistical probability of the current prediction. With this information, we can
make more informed prediction.
FreqAI builds this label distribution and provides a quantile to the strategy, which can be optionally used as a
dynamic threshold. The `target_quantile: X` means that X% of the labels are below this value. So setting:
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 assigining 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 databse.
The user needs to set the standard dictionary in the config so FreqAI can return proper dataframe shapes:
```json
"freqai": {
"feature_parameters" : {
"target_quantile": 0.9
}
"extra_returns_per_train": {"total_profit": 4}
}
```
Means the user will get back in the strategy the label threshold at which 90% of the labels were
below this value. An example usage in the strategy may look something like:
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.
```python
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
## Building an IFreqaiModel
# ... #
(
dataframe["prediction"],
dataframe["do_predict"],
dataframe["target_upper_quantile"],
dataframe["target_lower_quantile"],
) = self.freqai.start(dataframe, metadata, self)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
buy_conditions = [
(dataframe["prediction"] > dataframe["target_upper_quantile"]) & (dataframe["do_predict"] == 1)
]
if buy_conditions:
dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), "buy"] = 1
return dataframe
``` -->
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.
## Additional information

66
docs/strategy-callbacks.md
View File

@ -623,12 +623,13 @@ class AwesomeStrategy(IStrategy):
!!! Warning
`confirm_trade_exit()` can prevent stoploss exits, causing significant losses as this would ignore stoploss exits.
`confirm_trade_exit()` will not be called for Liquidations - as liquidations are forced by the exchange, and therefore cannot be rejected.
## Adjust trade position
The `position_adjustment_enable` strategy property enables the usage of `adjust_trade_position()` callback in the strategy.
For performance reasons, it's disabled by default and freqtrade will show a warning message on startup if enabled.
`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging).
`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging) or to increase or decrease positions.
`max_entry_position_adjustment` property is used to limit the number of additional buys per trade (on top of the first buy) that the bot can execute. By default, the value is -1 which means the bot have no limit on number of adjustment buys.
@ -636,10 +637,13 @@ The strategy is expected to return a stake_amount (in stake currency) between `m
If there are not enough funds in the wallet (the return value is above `max_stake`) then the signal will be ignored.
Additional orders also result in additional fees and those orders don't count towards `max_open_trades`.
This callback is **not** called when there is an open order (either buy or sell) waiting for execution, or when you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`.
This callback is **not** called when there is an open order (either buy or sell) waiting for execution.
`adjust_trade_position()` is called very frequently for the duration of a trade, so you must keep your implementation as performant as possible.
Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position, no matter if it's a long or short trade. Modifications to leverage are not possible.
Additional Buys are ignored once you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`, but the callback is called anyway looking for partial exits.
Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position (negative values will decrease your position), no matter if it's a long or short trade. Modifications to leverage are not possible.
!!! Note "About stake size"
Using fixed stake size means it will be the amount used for the first order, just like without position adjustment.
@ -648,12 +652,12 @@ Position adjustments will always be applied in the direction of the trade, so a
!!! Warning
Stoploss is still calculated from the initial opening price, not averaged price.
Regular stoploss rules still apply (cannot move down).
!!! Warning "/stopbuy"
While `/stopbuy` 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 performance will be affected.
During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
``` python
from freqtrade.persistence import Trade
@ -684,22 +688,41 @@ def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: f
return proposed_stake / self.max_dca_multiplier
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, min_stake: Optional[float],
max_stake: float, **kwargs):
current_rate: float, current_profit: float,
min_stake: Optional[float], max_stake: float,
current_entry_rate: float, current_exit_rate: float,
current_entry_profit: float, current_exit_profit: float,
**kwargs) -> Optional[float]:
"""
Custom trade adjustment logic, returning the stake amount that a trade should be increased.
This means extra buy orders with additional fees.
Custom trade adjustment logic, returning the stake amount that a trade should be
increased or decreased.
This means extra buy or sell orders with additional fees.
Only called when `position_adjustment_enable` is set to True.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns None
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param current_rate: Current buy rate.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param min_stake: Minimal stake size allowed by exchange.
:param max_stake: Balance available for trading.
:param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
:param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
:param current_entry_rate: Current rate using entry pricing.
:param current_exit_rate: Current rate using exit pricing.
:param current_entry_profit: Current profit using entry pricing.
:param current_exit_profit: Current profit using exit pricing.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: Stake amount to adjust your trade
:return float: Stake amount to adjust your trade,
Positive values to increase position, Negative values to decrease position.
Return None for no action.
"""
if current_profit > 0.05 and trade.nr_of_successful_exits == 0:
# Take half of the profit at +5%
return -(trade.stake_amount / 2)
if current_profit > -0.05:
return None
@ -734,6 +757,25 @@ def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: f
```
### Position adjust calculations
* Entry rates are calculated using weighted averages.
* Exits will not influence the average entry rate.
* Partial exit relative profit is relative to the average entry price at this point.
* Final exit relative profit is calculated based on the total invested capital. (See example below)
??? example "Calculation example"
*This example assumes 0 fees for simplicity, and a long position on an imaginary coin.*
* Buy 100@8\$
* Buy 100@9\$ -> Avg price: 8.5\$
* Sell 100@10\$ -> Avg price: 8.5\$, realized profit 150\$, 17.65%
* Buy 150@11\$ -> Avg price: 10\$, realized profit 150\$, 17.65%
* Sell 100@12\$ -> Avg price: 10\$, total realized profit 350\$, 20%
* Sell 150@14\$ -> Avg price: 10\$, total realized profit 950\$, 40%
The total profit for this trade was 950$ on a 3350$ investment (`100@8$ + 100@9$ + 150@11$`). As such - the final relative profit is 28.35% (`950 / 3350`).
## Adjust Entry Price
The `adjust_entry_price()` callback may be used by strategy developer to refresh/replace limit orders upon arrival of new candles.

20
docs/strategy-customization.md
View File

@ -646,6 +646,9 @@ This is where calling `self.dp.current_whitelist()` comes in handy.
return informative_pairs
```
??? Note "Plotting with current_whitelist"
Current whitelist is not supported for `plot-dataframe`, as this command is usually used by providing an explicit pairlist - and would therefore make the return values of this method misleading.
### *get_pair_dataframe(pair, timeframe)*
``` python
@ -731,6 +734,23 @@ if self.dp:
!!! Warning "Warning about backtesting"
This method will always return up-to-date values - so usage during backtesting / hyperopt will lead to wrong results.
### Send Notification
The dataprovider `.send_msg()` function allows you to send custom notifications from your strategy.
Identical notifications will only be sent once per candle, unless the 2nd argument (`always_send`) is set to True.
``` python
self.dp.send_msg(f"{metadata['pair']} just got hot!")
# Force send this notification, avoid caching (Please read warning below!)
self.dp.send_msg(f"{metadata['pair']} just got hot!", always_send=True)
```
Notifications will only be sent in trading modes (Live/Dry-run) - so this method can be called without conditions for backtesting.
!!! Warning "Spamming"
You can spam yourself pretty good by setting `always_send=True` in this method. Use this with great care and only in conditions you know will not happen throughout a candle to avoid a message every 5 seconds.
### Complete Data-provider sample
```python

4
docs/strategy_migration.md
View File

@ -18,7 +18,7 @@ Note : `forcesell`, `forcebuy`, `emergencysell` are changed to `force_exit`, `fo
* [`check_buy_timeout()` -> `check_entry_timeout()`](#custom_entry_timeout)
* [`check_sell_timeout()` -> `check_exit_timeout()`](#custom_entry_timeout)
* New `side` argument to callbacks without trade object
* [`custom_stake_amount`](#custom-stake-amount)
* [`custom_stake_amount`](#custom_stake_amount)
* [`confirm_trade_entry`](#confirm_trade_entry)
* [`custom_entry_price`](#custom_entry_price)
* [Changed argument name in `confirm_trade_exit`](#confirm_trade_exit)
@ -192,7 +192,7 @@ class AwesomeStrategy(IStrategy):
return False
```
### Custom-stake-amount
### `custom_stake_amount`
New string argument `side` - which can be either `"long"` or `"short"`.

4
docs/telegram-usage.md
View File

@ -98,6 +98,7 @@ Example configuration showing the different settings:
"exit_fill": "off",
"protection_trigger": "off",
"protection_trigger_global": "on",
"strategy_msg": "off",
"show_candle": "off"
},
"reload": true,
@ -109,7 +110,8 @@ Example configuration showing the different settings:
`exit` notifications are sent when the order is placed, while `exit_fill` notifications are sent when the order is filled on the exchange.
`*_fill` notifications are off by default and must be explicitly enabled.
`protection_trigger` notifications are sent when a protection triggers and `protection_trigger_global` notifications trigger when global protections are triggered.
`show_candle` - show candle values as part of entry/exit messages. Only possible value is "ohlc".
`strategy_msg` - Receive notifications from the strategy, sent via `self.dp.send_msg()` from the strategy [more details](strategy-customization.md#send-notification).
`show_candle` - show candle values as part of entry/exit messages. Only possible values are `"ohlc"` or `"off"`.
`balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown.
`reload` allows you to disable reload-buttons on selected messages.

2
freqtrade/__init__.py
View File

@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = 'develop'
__version__ = '2022.8.dev'
if 'dev' in __version__:
try:

6
freqtrade/commands/build_config_commands.py
View File

@ -67,7 +67,7 @@ def ask_user_config() -> Dict[str, Any]:
"type": "text",
"name": "stake_amount",
"message": f"Please insert your stake amount (Number or '{UNLIMITED_STAKE_AMOUNT}'):",
"default": "100",
"default": "unlimited",
"validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_float(val),
"filter": lambda val: '"' + UNLIMITED_STAKE_AMOUNT + '"'
if val == UNLIMITED_STAKE_AMOUNT
@ -164,7 +164,7 @@ def ask_user_config() -> Dict[str, Any]:
"when": lambda x: x['telegram']
},
{
"type": "text",
"type": "password",
"name": "telegram_chat_id",
"message": "Insert Telegram chat id",
"when": lambda x: x['telegram']
@ -191,7 +191,7 @@ def ask_user_config() -> Dict[str, Any]:
"when": lambda x: x['api_server']
},
{
"type": "text",
"type": "password",
"name": "api_server_password",
"message": "Insert api-server password",
"when": lambda x: x['api_server']

1
freqtrade/configuration/__init__.py
View File

@ -4,5 +4,4 @@ from freqtrade.configuration.check_exchange import check_exchange
from freqtrade.configuration.config_setup import setup_utils_configuration
from freqtrade.configuration.config_validation import validate_config_consistency
from freqtrade.configuration.configuration import Configuration
from freqtrade.configuration.PeriodicCache import PeriodicCache
from freqtrade.configuration.timerange import TimeRange

17
freqtrade/configuration/config_validation.py
View File

@ -85,7 +85,6 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
_validate_unlimited_amount(conf)
_validate_ask_orderbook(conf)
validate_migrated_strategy_settings(conf)
_validate_freqai(conf)
# validate configuration before returning
logger.info('Validating configuration ...')
@ -164,22 +163,6 @@ def _validate_edge(conf: Dict[str, Any]) -> None:
)
def _validate_freqai(conf: Dict[str, Any]) -> None:
"""
Freqai param validator
"""
if not conf.get('freqai', {}):
return
for param in constants.SCHEMA_FREQAI_REQUIRED:
if param not in conf.get('freqai', {}):
if param not in conf.get('freqai', {}).get('feature_parameters', {}):
raise OperationalException(
f'{param} not found in Freqai config'
)
def _validate_whitelist(conf: Dict[str, Any]) -> None:
"""
Dynamic whitelist does not require pair_whitelist to be set - however StaticWhitelist does.

50
freqtrade/constants.py
View File

@ -241,6 +241,7 @@ CONF_SCHEMA = {
},
'exchange': {'$ref': '#/definitions/exchange'},
'edge': {'$ref': '#/definitions/edge'},
'freqai': {'$ref': '#/definitions/freqai'},
'experimental': {
'type': 'object',
'properties': {
@ -318,6 +319,10 @@ CONF_SCHEMA = {
'type': 'string',
'enum': ['off', 'ohlc'],
},
'strategy_msg': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
},
}
},
'reload': {'type': 'boolean'},
@ -481,21 +486,31 @@ CONF_SCHEMA = {
"freqai": {
"type": "object",
"properties": {
"enabled": {"type": "boolean", "default": False},
"keras": {"type": "boolean", "default": False},
"conv_width": {"type": "integer", "default": 2},
"train_period_days": {"type": "integer", "default": 0},
"backtest_period_days": {"type": "float", "default": 7},
"identifier": {"type": "str", "default": "example"},
"backtest_period_days": {"type": "number", "default": 7},
"identifier": {"type": "string", "default": "example"},
"feature_parameters": {
"type": "object",
"properties": {
"include_corr_pairlist": {"type": "list"},
"include_timeframes": {"type": "list"},
"include_corr_pairlist": {"type": "array"},
"include_timeframes": {"type": "array"},
"label_period_candles": {"type": "integer"},
"include_shifted_candles": {"type": "integer", "default": 0},
"DI_threshold": {"type": "float", "default": 0},
"DI_threshold": {"type": "number", "default": 0},
"weight_factor": {"type": "number", "default": 0},
"principal_component_analysis": {"type": "boolean", "default": False},
"use_SVM_to_remove_outliers": {"type": "boolean", "default": False},
"svm_params": {"type": "object",
"properties": {
"shuffle": {"type": "boolean", "default": False},
"nu": {"type": "number", "default": 0.1}
},
}
},
"required": ["include_timeframes", "include_corr_pairlist", ]
},
"data_split_parameters": {
"type": "object",
@ -507,13 +522,19 @@ CONF_SCHEMA = {
"model_training_parameters": {
"type": "object",
"properties": {
"n_estimators": {"type": "integer", "default": 2000},
"random_state": {"type": "integer", "default": 1},
"learning_rate": {"type": "number", "default": 0.02},
"task_type": {"type": "string", "default": "CPU"},
"n_estimators": {"type": "integer", "default": 1000}
},
},
},
"required": [
"enabled",
"train_period_days",
"backtest_period_days",
"identifier",
"feature_parameters",
"data_split_parameters",
"model_training_parameters"
]
},
},
}
@ -558,17 +579,6 @@ SCHEMA_MINIMAL_REQUIRED = [
'dataformat_trades',
]
SCHEMA_FREQAI_REQUIRED = [
'include_timeframes',
'train_period_days',
'backtest_period_days',
'identifier',
'include_corr_pairlist',
'feature_parameters',
'data_split_parameters',
'model_training_parameters'
]
CANCEL_REASON = {
"TIMEOUT": "cancelled due to timeout",
"PARTIALLY_FILLED_KEEP_OPEN": "partially filled - keeping order open",

23
freqtrade/data/dataprovider.py
View File

@ -5,6 +5,7 @@ including ticker and orderbook data, live and historical candle (OHLCV) data
Common Interface for bot and strategy to access data.
"""
import logging
from collections import deque
from datetime import datetime, timezone
from typing import Any, Dict, List, Optional, Tuple
@ -16,6 +17,7 @@ from freqtrade.data.history import load_pair_history
from freqtrade.enums import CandleType, RunMode
from freqtrade.exceptions import ExchangeError, OperationalException
from freqtrade.exchange import Exchange, timeframe_to_seconds
from freqtrade.util import PeriodicCache
logger = logging.getLogger(__name__)
@ -33,6 +35,10 @@ class DataProvider:
self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
self.__slice_index: Optional[int] = None
self.__cached_pairs_backtesting: Dict[PairWithTimeframe, DataFrame] = {}
self._msg_queue: deque = deque()
self.__msg_cache = PeriodicCache(
maxsize=1000, ttl=timeframe_to_seconds(self._config.get('timeframe', '1h')))
def _set_dataframe_max_index(self, limit_index: int):
"""
@ -265,3 +271,20 @@ class DataProvider:
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return self._exchange.fetch_l2_order_book(pair, maximum)
def send_msg(self, message: str, *, always_send: bool = False) -> None:
"""
Send custom RPC Notifications from your bot.
Will not send any bot in modes other than Dry-run or Live.
:param message: Message to be sent. Must be below 4096.
:param always_send: If False, will send the message only once per candle, and surpress
identical messages.
Careful as this can end up spaming your chat.
Defaults to False
"""
if self.runmode not in (RunMode.DRY_RUN, RunMode.LIVE):
return
if always_send or message not in self.__msg_cache:
self._msg_queue.append(message)
self.__msg_cache[message] = True

2
freqtrade/enums/exittype.py
View File

@ -9,10 +9,12 @@ class ExitType(Enum):
STOP_LOSS = "stop_loss"
STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange"
TRAILING_STOP_LOSS = "trailing_stop_loss"
LIQUIDATION = "liquidation"
EXIT_SIGNAL = "exit_signal"
FORCE_EXIT = "force_exit"
EMERGENCY_EXIT = "emergency_exit"
CUSTOM_EXIT = "custom_exit"
PARTIAL_EXIT = "partial_exit"
NONE = ""
def __str__(self):

2
freqtrade/enums/rpcmessagetype.py
View File

@ -17,6 +17,8 @@ class RPCMessageType(Enum):
PROTECTION_TRIGGER = 'protection_trigger'
PROTECTION_TRIGGER_GLOBAL = 'protection_trigger_global'
STRATEGY_MSG = 'strategy_msg'
def __repr__(self):
return self.value

71
freqtrade/exchange/exchange.py
View File

@ -16,7 +16,7 @@ import arrow
import ccxt
import ccxt.async_support as ccxt_async
from cachetools import TTLCache
from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, Precise, decimal_to_precision
from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision
from pandas import DataFrame
from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
@ -32,6 +32,7 @@ from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGE
retrier_async)
from freqtrade.misc import chunks, deep_merge_dicts, safe_value_fallback2
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.util import FtPrecise
CcxtModuleType = Any
@ -708,10 +709,10 @@ class Exchange:
# counting_mode=self.precisionMode,
# ))
if self.precisionMode == TICK_SIZE:
precision = Precise(str(self.markets[pair]['precision']['price']))
price_str = Precise(str(price))
precision = FtPrecise(self.markets[pair]['precision']['price'])
price_str = FtPrecise(price)
missing = price_str % precision
if not missing == Precise("0"):
if not missing == FtPrecise("0"):
price = round(float(str(price_str - missing + precision)), 14)
else:
symbol_prec = self.markets[pair]['precision']['price']
@ -849,6 +850,7 @@ class Exchange:
dry_order.update({
'average': average,
'filled': _amount,
'remaining': 0.0,
'cost': (dry_order['amount'] * average) / leverage
})
# market orders will always incurr taker fees
@ -1332,11 +1334,19 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def fetch_positions(self) -> List[Dict]:
def fetch_positions(self, pair: str = None) -> List[Dict]:
"""
Fetch positions from the exchange.
If no pair is given, all positions are returned.
:param pair: Pair for the query
"""
if self._config['dry_run'] or self.trading_mode != TradingMode.FUTURES:
return []
try:
positions: List[Dict] = self._api.fetch_positions()
symbols = []
if pair:
symbols.append(pair)
positions: List[Dict] = self._api.fetch_positions(symbols)
self._log_exchange_response('fetch_positions', positions)
return positions
except ccxt.DDoSProtection as e:
@ -1499,7 +1509,8 @@ class Exchange:
return price_side
def get_rate(self, pair: str, refresh: bool,
side: EntryExit, is_short: bool) -> float:
side: EntryExit, is_short: bool,
order_book: Optional[dict] = None, ticker: Optional[dict] = None) -> float:
"""
Calculates bid/ask target
bid rate - between current ask price and last price
@ -1531,6 +1542,7 @@ class Exchange:
if conf_strategy.get('use_order_book', False):
order_book_top = conf_strategy.get('order_book_top', 1)
if order_book is None:
order_book = self.fetch_l2_order_book(pair, order_book_top)
logger.debug('order_book %s', order_book)
# top 1 = index 0
@ -1538,14 +1550,15 @@ class Exchange:
rate = order_book[f"{price_side}s"][order_book_top - 1][0]
except (IndexError, KeyError) as e:
logger.warning(
f"{name} Price at location {order_book_top} from orderbook could not be "
f"determined. Orderbook: {order_book}"
f"{pair} - {name} Price at location {order_book_top} from orderbook "
f"could not be determined. Orderbook: {order_book}"
)
raise PricingError from e
logger.debug(f"{name} price from orderbook {price_side_word}"
logger.debug(f"{pair} - {name} price from orderbook {price_side_word}"
f"side - top {order_book_top} order book {side} rate {rate:.8f}")
else:
logger.debug(f"Using Last {price_side_word} / Last Price")
if ticker is None:
ticker = self.fetch_ticker(pair)
ticker_rate = ticker[price_side]
if ticker['last'] and ticker_rate:
@ -1563,6 +1576,33 @@ class Exchange:
return rate
def get_rates(self, pair: str, refresh: bool, is_short: bool) -> Tuple[float, float]:
entry_rate = None
exit_rate = None
if not refresh:
entry_rate = self._entry_rate_cache.get(pair)
exit_rate = self._exit_rate_cache.get(pair)
if entry_rate:
logger.debug(f"Using cached buy rate for {pair}.")
if exit_rate:
logger.debug(f"Using cached sell rate for {pair}.")
entry_pricing = self._config.get('entry_pricing', {})
exit_pricing = self._config.get('exit_pricing', {})
order_book = ticker = None
if not entry_rate and entry_pricing.get('use_order_book', False):
order_book_top = max(entry_pricing.get('order_book_top', 1),
exit_pricing.get('order_book_top', 1))
order_book = self.fetch_l2_order_book(pair, order_book_top)
entry_rate = self.get_rate(pair, refresh, 'entry', is_short, order_book=order_book)
elif not entry_rate:
ticker = self.fetch_ticker(pair)
entry_rate = self.get_rate(pair, refresh, 'entry', is_short, ticker=ticker)
if not exit_rate:
exit_rate = self.get_rate(pair, refresh, 'exit',
is_short, order_book=order_book, ticker=ticker)
return entry_rate, exit_rate
# Fee handling
@retrier
@ -2539,7 +2579,6 @@ class Exchange:
else:
return 0.0
@retrier
def get_or_calculate_liquidation_price(
self,
pair: str,
@ -2573,20 +2612,12 @@ class Exchange:
upnl_ex_1=upnl_ex_1
)
else:
try:
positions = self._api.fetch_positions([pair])
positions = self.fetch_positions(pair)
if len(positions) > 0:
pos = positions[0]
isolated_liq = pos['liquidationPrice']
else:
return None
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set margin mode due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
if isolated_liq:
buffer_amount = abs(open_rate - isolated_liq) * self.liquidation_buffer

14
freqtrade/exchange/ftx.py
View File

@ -1,6 +1,6 @@
""" FTX exchange subclass """
import logging
from typing import Any, Dict, List, Tuple
from typing import Any, Dict, List, Optional, Tuple
import ccxt
@ -116,9 +116,17 @@ class Ftx(Exchange):
if len(order) == 1:
if order[0].get('status') == 'closed':
# Trigger order was triggered ...
real_order_id = order[0].get('info', {}).get('orderId')
real_order_id: Optional[str] = order[0].get('info', {}).get('orderId')
# OrderId may be None for stoploss-market orders
# But contains "average" in these cases.
# So we need to get it through the endpoint
# /conditional_orders/{conditional_order_id}/triggers
if not real_order_id:
res = self._api.privateGetConditionalOrdersConditionalOrderIdTriggers(
params={'conditional_order_id': order_id})
self._log_exchange_response('fetch_stoploss_order2', res)
real_order_id = res['result'][0]['orderId'] if res.get(
'result', []) else None
if real_order_id:
order1 = self._api.fetch_order(real_order_id, pair)
self._log_exchange_response('fetch_stoploss_order1', order1)

168
freqtrade/freqai/data_drawer.py
View File

@ -5,13 +5,14 @@ import re
import shutil
import threading
from pathlib import Path
from typing import Any, Dict, Tuple
from typing import Any, Dict, Tuple, TypedDict
import numpy as np
import pandas as pd
import rapidjson
from joblib import dump, load
from joblib.externals import cloudpickle
from numpy.typing import ArrayLike
from numpy.typing import ArrayLike, NDArray
from pandas import DataFrame
from freqtrade.configuration import TimeRange
@ -24,6 +25,15 @@ from freqtrade.strategy.interface import IStrategy
logger = logging.getLogger(__name__)
class pair_info(TypedDict):
model_filename: str
first: bool
trained_timestamp: int
priority: int
data_path: str
extras: dict
class FreqaiDataDrawer:
"""
Class aimed at holding all pair models/info in memory for better inferencing/retrainig/saving
@ -39,7 +49,7 @@ class FreqaiDataDrawer:
Robert Caulk @robcaulk
Theoretical brainstorming:
Elin Törnquist @thorntwig
Elin Törnquist @th0rntwig
Code review, software architecture brainstorming:
@xmatthias
@ -54,14 +64,13 @@ class FreqaiDataDrawer:
self.config = config
self.freqai_info = config.get("freqai", {})
# dictionary holding all pair metadata necessary to load in from disk
self.pair_dict: Dict[str, Any] = {}
self.pair_dict: Dict[str, pair_info] = {}
# dictionary holding all actively inferenced models in memory given a model filename
self.model_dictionary: Dict[str, Any] = {}
self.model_return_values: Dict[str, Any] = {}
self.pair_data_dict: Dict[str, Any] = {}
self.historic_data: Dict[str, Any] = {}
self.historic_predictions: Dict[str, Any] = {}
self.follower_dict: Dict[str, Any] = {}
self.model_return_values: Dict[str, DataFrame] = {}
self.historic_data: Dict[str, Dict[str, DataFrame]] = {}
self.historic_predictions: Dict[str, DataFrame] = {}
self.follower_dict: Dict[str, pair_info] = {}
self.full_path = full_path
self.follower_name: str = self.config.get("bot_name", "follower1")
self.follower_dict_path = Path(
@ -76,6 +85,10 @@ class FreqaiDataDrawer:
self.load_historic_predictions_from_disk()
self.training_queue: Dict[str, int] = {}
self.history_lock = threading.Lock()
self.old_DBSCAN_eps: Dict[str, float] = {}
self.empty_pair_dict: pair_info = {
"model_filename": "", "trained_timestamp": 0,
"priority": 1, "first": True, "data_path": "", "extras": {}}
def load_drawer_from_disk(self):
"""
@ -132,15 +145,17 @@ class FreqaiDataDrawer:
"""
Save data drawer full of all pair model metadata in present model folder.
"""
with open(self.pair_dictionary_path, "w") as fp:
json.dump(self.pair_dict, fp, default=self.np_encoder)
with open(self.pair_dictionary_path, 'w') as fp:
rapidjson.dump(self.pair_dict, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def save_follower_dict_to_disk(self):
"""
Save follower dictionary to disk (used by strategy for persistent prediction targets)
"""
with open(self.follower_dict_path, "w") as fp:
json.dump(self.follower_dict, fp, default=self.np_encoder)
rapidjson.dump(self.follower_dict, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def create_follower_dict(self):
"""
@ -174,18 +189,19 @@ class FreqaiDataDrawer:
trained_timestamp: int = the last time the coin was trained
return_null_array: bool = Follower could not find pair metadata
"""
pair_dict = self.pair_dict.get(pair)
data_path_set = self.pair_dict.get(pair, {}).get("data_path", None)
data_path_set = self.pair_dict.get(pair, self.empty_pair_dict).get("data_path", "")
return_null_array = False
if pair_dict:
model_filename = pair_dict["model_filename"]
trained_timestamp = pair_dict["trained_timestamp"]
elif not self.follow_mode:
pair_dict = self.pair_dict[pair] = {}
model_filename = pair_dict["model_filename"] = ""
trained_timestamp = pair_dict["trained_timestamp"] = 0
pair_dict["priority"] = len(self.pair_dict)
self.pair_dict[pair] = self.empty_pair_dict.copy()
model_filename = ""
trained_timestamp = 0
self.pair_dict[pair]["priority"] = len(self.pair_dict)
if not data_path_set and self.follow_mode:
logger.warning(
@ -204,11 +220,9 @@ class FreqaiDataDrawer:
if pair_in_dict:
return
else:
self.pair_dict[metadata["pair"]] = {}
self.pair_dict[metadata["pair"]]["model_filename"] = ""
self.pair_dict[metadata["pair"]]["first"] = True
self.pair_dict[metadata["pair"]]["trained_timestamp"] = 0
self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
self.pair_dict[metadata["pair"]]["priority"] = len(self.pair_dict)
return
def pair_to_end_of_training_queue(self, pair: str) -> None:
@ -225,25 +239,59 @@ class FreqaiDataDrawer:
historical candles, and also stores historical predictions despite retrainings (so stored
predictions are true predictions, not just inferencing on trained data)
"""
# dynamic df returned to strategy and plotted in frequi
mrv_df = self.model_return_values[pair] = pd.DataFrame()
for label in dk.label_list:
# if user reused `identifier` in config and has historical predictions collected, load them
# so that frequi remains uninterrupted after a crash
hist_df = self.historic_predictions
if pair in hist_df:
len_diff = len(hist_df[pair].index) - len(pred_df.index)
if len_diff < 0:
df_concat = pd.concat([pred_df.iloc[:abs(len_diff)], hist_df[pair]],
ignore_index=True, keys=hist_df[pair].keys())
else:
df_concat = hist_df[pair].tail(len(pred_df.index)).reset_index(drop=True)
df_concat = df_concat.fillna(0)
self.model_return_values[pair] = df_concat
logger.info(f'Setting initial FreqUI plots from historical data for {pair}.')
else:
for label in pred_df.columns:
mrv_df[label] = pred_df[label]
if mrv_df[label].dtype == object:
continue
mrv_df[f"{label}_mean"] = dk.data["labels_mean"][label]
mrv_df[f"{label}_std"] = dk.data["labels_std"][label]
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
mrv_df["DI_values"] = dk.DI_values
mrv_df["do_predict"] = do_preds
def append_model_predictions(self, pair: str, predictions: DataFrame, do_preds: ArrayLike,
if dk.data['extra_returns_per_train']:
rets = dk.data['extra_returns_per_train']
for return_str in rets:
mrv_df[return_str] = rets[return_str]
# for keras type models, the conv_window needs to be prepended so
# viewing is correct in frequi
if self.freqai_info.get('keras', False):
n_lost_points = self.freqai_info.get('conv_width', 2)
zeros_df = DataFrame(np.zeros((n_lost_points, len(mrv_df.columns))),
columns=mrv_df.columns)
self.model_return_values[pair] = pd.concat(
[zeros_df, mrv_df], axis=0, ignore_index=True)
def append_model_predictions(self, pair: str, predictions: DataFrame,
do_preds: NDArray[np.int_],
dk: FreqaiDataKitchen, len_df: int) -> None:
# strat seems to feed us variable sized dataframes - and since we are trying to build our
# own return array in the same shape, we need to figure out how the size has changed
# and adapt our stored/returned info accordingly.
length_difference = len(self.model_return_values[pair]) - len_df
i = 0
@ -262,19 +310,28 @@ class FreqaiDataDrawer:
hp_df = pd.concat([hp_df, nan_df], ignore_index=True, axis=0)
self.historic_predictions[pair] = hp_df[:-1]
for label in dk.label_list:
# incase user adds additional "predictions" e.g. predict_proba output:
for label in predictions.columns:
df[label].iloc[-1] = predictions[label].iloc[-1]
if df[label].dtype == object:
continue
df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label]
df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label]
# df['prediction'].iloc[-1] = predictions[-1]
df["do_predict"].iloc[-1] = do_preds[-1]
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
df["DI_values"].iloc[-1] = dk.DI_values[-1]
if dk.data['extra_returns_per_train']:
rets = dk.data['extra_returns_per_train']
for return_str in rets:
df[return_str].iloc[-1] = rets[return_str]
# append the new predictions to persistent storage
if pair in self.historic_predictions:
self.historic_predictions[pair].iloc[-1] = df[label].iloc[-1]
for key in df.keys():
self.historic_predictions[pair][key].iloc[-1] = df[key].iloc[-1]
if length_difference < 0:
prepend_df = pd.DataFrame(
@ -301,16 +358,25 @@ class FreqaiDataDrawer:
dk.find_features(dataframe)
for label in dk.label_list:
if self.freqai_info.get('predict_proba', []):
full_labels = dk.label_list + self.freqai_info['predict_proba']
else:
full_labels = dk.label_list
for label in full_labels:
dataframe[label] = 0
dataframe[f"{label}_mean"] = 0
dataframe[f"{label}_std"] = 0
# dataframe['prediction'] = 0
dataframe["do_predict"] = 0
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
dataframe["DI_value"] = 0
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
dataframe["DI_values"] = 0
if dk.data['extra_returns_per_train']:
rets = dk.data['extra_returns_per_train']
for return_str in rets:
dataframe[return_str] = 0
dk.return_dataframe = dataframe
@ -379,24 +445,28 @@ class FreqaiDataDrawer:
model.save(save_path / f"{dk.model_filename}_model.h5")
if dk.svm_model is not None:
dump(dk.svm_model, save_path / str(dk.model_filename + "_svm_model.joblib"))
dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib")
dk.data["data_path"] = str(dk.data_path)
dk.data["model_filename"] = str(dk.model_filename)
dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
dk.data["label_list"] = dk.label_list
# store the metadata
with open(save_path / str(dk.model_filename + "_metadata.json"), "w") as fp:
json.dump(dk.data, fp, default=dk.np_encoder)
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
# save the train data to file so we can check preds for area of applicability later
dk.data_dictionary["train_features"].to_pickle(
save_path / str(dk.model_filename + "_trained_df.pkl")
save_path / f"{dk.model_filename}_trained_df.pkl"
)
if self.freqai_info.get("feature_parameters", {}).get("principal_component_analysis"):
dk.data_dictionary["train_dates"].to_pickle(
save_path / f"{dk.model_filename}_trained_dates_df.pkl"
)
if self.freqai_info["feature_parameters"].get("principal_component_analysis"):
cloudpickle.dump(
dk.pca, open(dk.data_path / str(dk.model_filename + "_pca_object.pkl"), "wb")
dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
)
# if self.live:
@ -429,27 +499,27 @@ class FreqaiDataDrawer:
/ dk.data_path.parts[-1]
)
with open(dk.data_path / str(dk.model_filename + "_metadata.json"), "r") as fp:
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
dk.data = json.load(fp)
dk.training_features_list = dk.data["training_features_list"]
dk.label_list = dk.data["label_list"]
dk.data_dictionary["train_features"] = pd.read_pickle(
dk.data_path / str(dk.model_filename + "_trained_df.pkl")
dk.data_path / f"{dk.model_filename}_trained_df.pkl"
)
# 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]
elif not dk.keras:
model = load(dk.data_path / str(dk.model_filename + "_model.joblib"))
model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
else:
from tensorflow import keras
model = keras.models.load_model(dk.data_path / str(dk.model_filename + "_model.h5"))
model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
if Path(dk.data_path / str(dk.model_filename + "_svm_model.joblib")).resolve().exists():
dk.svm_model = load(dk.data_path / str(dk.model_filename + "_svm_model.joblib"))
if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")
if not model:
raise OperationalException(
@ -458,7 +528,7 @@ class FreqaiDataDrawer:
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
dk.pca = cloudpickle.load(
open(dk.data_path / str(dk.model_filename + "_pca_object.pkl"), "rb")
open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
)
return model
@ -471,7 +541,7 @@ class FreqaiDataDrawer:
:params:
dataframe: DataFrame = strategy provided dataframe
"""
feat_params = self.freqai_info.get("feature_parameters", {})
feat_params = self.freqai_info["feature_parameters"]
with self.history_lock:
history_data = self.historic_data
@ -524,7 +594,7 @@ class FreqaiDataDrawer:
for pair in dk.all_pairs:
if pair not in history_data:
history_data[pair] = {}
for tf in self.freqai_info.get("feature_parameters", {}).get("include_timeframes"):
for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
history_data[pair][tf] = load_pair_history(
datadir=self.config["datadir"],
timeframe=tf,
@ -550,11 +620,11 @@ class FreqaiDataDrawer:
corr_dataframes: Dict[Any, Any] = {}
base_dataframes: Dict[Any, Any] = {}
historic_data = self.historic_data
pairs = self.freqai_info.get("feature_parameters", {}).get(
pairs = self.freqai_info["feature_parameters"].get(
"include_corr_pairlist", []
)
for tf in self.freqai_info.get("feature_parameters", {}).get("include_timeframes"):
for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
base_dataframes[tf] = dk.slice_dataframe(timerange, historic_data[pair][tf])
if pairs:
for p in pairs:

461
freqtrade/freqai/data_kitchen.py
View File

@ -10,13 +10,16 @@ import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from sklearn import linear_model
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.model_selection import train_test_split
from sklearn.neighbors import NearestNeighbors
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
from freqtrade.exceptions import OperationalException
from freqtrade.resolvers import ExchangeResolver
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.strategy.interface import IStrategy
@ -39,7 +42,7 @@ class FreqaiDataKitchen:
Robert Caulk @robcaulk
Theoretical brainstorming:
Elin Törnquist @thorntwig
Elin Törnquist @th0rntwig
Code review, software architecture brainstorming:
@xmatthias
@ -55,10 +58,10 @@ class FreqaiDataKitchen:
live: bool = False,
pair: str = "",
):
self.data: Dict[Any, Any] = {}
self.data_dictionary: Dict[Any, Any] = {}
self.data: Dict[str, Any] = {}
self.data_dictionary: Dict[str, DataFrame] = {}
self.config = config
self.freqai_config = config["freqai"]
self.freqai_config: Dict[str, Any] = config["freqai"]
self.full_df: DataFrame = DataFrame()
self.append_df: DataFrame = DataFrame()
self.data_path = Path()
@ -68,14 +71,14 @@ class FreqaiDataKitchen:
self.live = live
self.pair = pair
self.svm_model: linear_model.SGDOneClassSVM = None
self.keras = self.freqai_config.get("keras", False)
self.keras: bool = self.freqai_config.get("keras", False)
self.set_all_pairs()
if not self.live:
if not self.config["timerange"]:
raise OperationalException(
'Please pass --timerange if you intend to use FreqAI for backtesting.')
self.full_timerange = self.create_fulltimerange(
self.config["timerange"], self.freqai_config.get("train_period_days")
self.config["timerange"], self.freqai_config.get("train_period_days", 0)
)
(self.training_timeranges, self.backtesting_timeranges) = self.split_timerange(
@ -84,6 +87,10 @@ class FreqaiDataKitchen:
config["freqai"]["backtest_period_days"],
)
self.data['extra_returns_per_train'] = self.freqai_config.get('extra_returns_per_train', {})
self.thread_count = self.freqai_config.get("data_kitchen_thread_count", -1)
self.train_dates: DataFrame = pd.DataFrame()
def set_paths(
self,
pair: str,
@ -101,7 +108,7 @@ class FreqaiDataKitchen:
self.data_path = Path(
self.full_path
/ str("sub-train" + "-" + pair.split("/")[0] + "_" + str(trained_timestamp))
/ f"sub-train-{pair.split('/')[0]}_{trained_timestamp}"
)
return
@ -116,7 +123,7 @@ class FreqaiDataKitchen:
:filtered_dataframe: cleaned dataframe ready to be split.
:labels: cleaned labels ready to be split.
"""
feat_dict = self.freqai_config.get("feature_parameters", {})
feat_dict = self.freqai_config["feature_parameters"]
weights: npt.ArrayLike
if feat_dict.get("weight_factor", 0) > 0:
@ -188,20 +195,23 @@ class FreqaiDataKitchen:
drop_index = pd.isnull(filtered_dataframe).any(1) # get the rows that have NaNs,
drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
if (
training_filter
): # we don't care about total row number (total no. datapoints) in training, we only care
if (training_filter):
# we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple models), we detect here
# if labels has multiple columns (user wants to train multiple modelEs), we detect here
labels = unfiltered_dataframe.filter(label_list, axis=1)
drop_index_labels = pd.isnull(labels).any(1)
drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0)
dates = unfiltered_dataframe['date']
filtered_dataframe = filtered_dataframe[
(drop_index == 0) & (drop_index_labels == 0)
] # dropping values
labels = labels[
(drop_index == 0) & (drop_index_labels == 0)
] # assuming the labels depend entirely on the dataframe here.
self.train_dates = dates[
(drop_index == 0) & (drop_index_labels == 0)
]
logger.info(
f"dropped {len(unfiltered_dataframe) - len(filtered_dataframe)} training points"
f" due to NaNs in populated dataset {len(unfiltered_dataframe)}."
@ -252,6 +262,7 @@ class FreqaiDataKitchen:
"test_labels": test_labels,
"train_weights": train_weights,
"test_weights": test_weights,
"train_dates": self.train_dates
}
return self.data_dictionary
@ -279,7 +290,7 @@ class FreqaiDataKitchen:
self.data[item + "_min"] = train_min[item]
for item in data_dictionary["train_labels"].keys():
if data_dictionary["train_labels"][item].dtype == str:
if data_dictionary["train_labels"][item].dtype == object:
continue
train_labels_max = data_dictionary["train_labels"][item].max()
train_labels_min = data_dictionary["train_labels"][item].min()
@ -305,8 +316,7 @@ class FreqaiDataKitchen:
"""
Normalize a set of data using the mean and standard deviation from
the associated training data.
:params:
:df: Dataframe to be standardized
:param df: Dataframe to be standardized
"""
for item in df.keys():
@ -323,12 +333,11 @@ class FreqaiDataKitchen:
"""
Normalize a set of data using the mean and standard deviation from
the associated training data.
:params:
:df: Dataframe of predictions to be denormalized
:param df: Dataframe of predictions to be denormalized
"""
for label in self.label_list:
if df[label].dtype == str:
for label in df.columns:
if df[label].dtype == object:
continue
df[label] = (
(df[label] + 1)
@ -339,7 +348,7 @@ class FreqaiDataKitchen:
return df
def split_timerange(
self, tr: str, train_split: int = 28, bt_split: int = 7
self, tr: str, train_split: int = 28, bt_split: float = 7
) -> Tuple[list, list]:
"""
Function which takes a single time range (tr) and splits it
@ -347,12 +356,12 @@ class FreqaiDataKitchen:
tr: str, full timerange to train on
train_split: the period length for the each training (days). Specified in user
configuration file
bt_split: the backtesting length (dats). Specified in user configuration file
bt_split: the backtesting length (days). Specified in user configuration file
"""
if not isinstance(train_split, int) or train_split < 1:
raise OperationalException(
"train_period_days must be an integer greater than 0. " f"Got {train_split}."
f"train_period_days must be an integer greater than 0. Got {train_split}."
)
train_period_days = train_split * SECONDS_IN_DAY
bt_period = bt_split * SECONDS_IN_DAY
@ -374,7 +383,7 @@ class FreqaiDataKitchen:
while True:
if not first:
timerange_train.startts = timerange_train.startts + bt_period
timerange_train.startts = timerange_train.startts + int(bt_period)
timerange_train.stopts = timerange_train.startts + train_period_days
first = False
@ -387,7 +396,7 @@ class FreqaiDataKitchen:
timerange_backtest.startts = timerange_train.stopts
timerange_backtest.stopts = timerange_backtest.startts + bt_period
timerange_backtest.stopts = timerange_backtest.startts + int(bt_period)
if timerange_backtest.stopts > config_timerange.stopts:
timerange_backtest.stopts = config_timerange.stopts
@ -408,9 +417,8 @@ class FreqaiDataKitchen:
def slice_dataframe(self, timerange: TimeRange, df: DataFrame) -> DataFrame:
"""
Given a full dataframe, extract the user desired window
:params:
:tr: timerange string that we wish to extract from df
:df: Dataframe containing all candles to run the entire backtest. Here
:param tr: timerange string that we wish to extract from df
:param df: Dataframe containing all candles to run the entire backtest. Here
it is sliced down to just the present training period.
"""
@ -489,11 +497,10 @@ class FreqaiDataKitchen:
point. This metric defines the neighborhood of trained data and is used
for prediction confidence in the Dissimilarity Index
"""
logger.info("computing average mean distance for all training points")
tc = self.freqai_config.get("model_training_parameters", {}).get("thread_count", -1)
pairwise = pairwise_distances(self.data_dictionary["train_features"], n_jobs=tc)
# 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()
logger.info(f"avg_mean_dist {avg_mean_dist:.2f}")
return avg_mean_dist
@ -515,21 +522,22 @@ class FreqaiDataKitchen:
return
if predict:
assert self.svm_model, "No svm model available for outlier removal"
if not self.svm_model:
logger.warning("No svm model available for outlier removal")
return
y_pred = self.svm_model.predict(self.data_dictionary["prediction_features"])
do_predict = np.where(y_pred == -1, 0, y_pred)
if (len(do_predict) - do_predict.sum()) > 0:
logger.info(
f"svm_remove_outliers() tossed {len(do_predict) - do_predict.sum()} predictions"
)
logger.info(f"SVM tossed {len(do_predict) - do_predict.sum()} predictions.")
self.do_predict += do_predict
self.do_predict -= 1
else:
# use SGDOneClassSVM to increase speed?
nu = self.freqai_config.get("feature_parameters", {}).get("svm_nu", 0.2)
self.svm_model = linear_model.SGDOneClassSVM(nu=nu).fit(
svm_params = self.freqai_config["feature_parameters"].get(
"svm_params", {"shuffle": False, "nu": 0.1})
self.svm_model = linear_model.SGDOneClassSVM(**svm_params).fit(
self.data_dictionary["train_features"]
)
y_pred = self.svm_model.predict(self.data_dictionary["train_features"])
@ -546,12 +554,14 @@ class FreqaiDataKitchen:
]
logger.info(
f"svm_remove_outliers() tossed {len(y_pred) - dropped_points.sum()}"
f" train points from {len(y_pred)}"
f"SVM tossed {len(y_pred) - dropped_points.sum()}"
f" train points from {len(y_pred)} total points."
)
# same for test data
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
# TODO: This (and the part above) could be refactored into a separate function
# 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)
self.data_dictionary["test_features"] = self.data_dictionary["test_features"][
@ -564,8 +574,77 @@ class FreqaiDataKitchen:
]
logger.info(
f"svm_remove_outliers() tossed {len(y_pred) - dropped_points.sum()}"
f" test points from {len(y_pred)}"
f"SVM tossed {len(y_pred) - dropped_points.sum()}"
f" test points from {len(y_pred)} total points."
)
return
def use_DBSCAN_to_remove_outliers(self, predict: bool, eps=None) -> None:
"""
Use DBSCAN to cluster training data and remove "noisy" data (read outliers).
User controls this via the config param `DBSCAN_outlier_pct` which indicates the
pct of training data that they want to be considered outliers.
:params:
predict: bool = If False (training), iterate to find the best hyper parameters to match
user requested outlier percent target. If True (prediction), use the parameters
determined from the previous training to estimate if the current prediction point
is an outlier.
"""
if predict:
train_ft_df = self.data_dictionary['train_features']
pred_ft_df = self.data_dictionary['prediction_features']
num_preds = len(pred_ft_df)
df = pd.concat([train_ft_df, pred_ft_df], axis=0, ignore_index=True)
clustering = DBSCAN(eps=self.data['DBSCAN_eps'],
min_samples=self.data['DBSCAN_min_samples'],
n_jobs=self.thread_count
).fit(df)
do_predict = np.where(clustering.labels_[-num_preds:] == -1, 0, 1)
if (len(do_predict) - do_predict.sum()) > 0:
logger.info(f"DBSCAN tossed {len(do_predict) - do_predict.sum()} predictions")
self.do_predict += do_predict
self.do_predict -= 1
else:
MinPts = len(self.data_dictionary['train_features'].columns) * 2
# measure pairwise distances to train_features.shape[1]*2 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]
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}.')
self.data['DBSCAN_eps'] = epsilon
self.data['DBSCAN_min_samples'] = MinPts
dropped_points = np.where(clustering.labels_ == -1, 1, 0)
self.data_dictionary['train_features'] = self.data_dictionary['train_features'][
(clustering.labels_ != -1)
]
self.data_dictionary["train_labels"] = self.data_dictionary["train_labels"][
(clustering.labels_ != -1)
]
self.data_dictionary["train_weights"] = self.data_dictionary["train_weights"][
(clustering.labels_ != -1)
]
logger.info(
f"DBSCAN tossed {dropped_points.sum()}"
f" train points from {len(clustering.labels_)}"
)
return
@ -573,9 +652,8 @@ class FreqaiDataKitchen:
def find_features(self, dataframe: DataFrame) -> None:
"""
Find features in the strategy provided dataframe
:params:
dataframe: DataFrame = strategy provided dataframe
:returns:
:param dataframe: DataFrame = strategy provided dataframe
:return:
features: list = the features to be used for training/prediction
"""
column_names = dataframe.columns
@ -586,7 +664,6 @@ class FreqaiDataKitchen:
self.training_features_list = features
self.label_list = labels
# return features, labels
def check_if_pred_in_training_spaces(self) -> None:
"""
@ -599,13 +676,13 @@ class FreqaiDataKitchen:
distance = pairwise_distances(
self.data_dictionary["train_features"],
self.data_dictionary["prediction_features"],
n_jobs=-1,
n_jobs=self.thread_count,
)
self.DI_values = distance.min(axis=0) / self.data["avg_mean_dist"]
do_predict = np.where(
self.DI_values < self.freqai_config.get("feature_parameters", {}).get("DI_threshold"),
self.DI_values < self.freqai_config["feature_parameters"]["DI_threshold"],
1,
0,
)
@ -628,25 +705,27 @@ class FreqaiDataKitchen:
weights = np.exp(-np.arange(num_weights) / (wfactor * num_weights))[::-1]
return weights
def append_predictions(self, predictions, do_predict, len_dataframe):
def append_predictions(self, predictions: DataFrame, do_predict: npt.ArrayLike) -> None:
"""
Append backtest prediction from current backtest period to all previous periods
"""
self.append_df = DataFrame()
for label in self.label_list:
self.append_df[label] = predictions[label]
self.append_df[f"{label}_mean"] = self.data["labels_mean"][label]
self.append_df[f"{label}_std"] = self.data["labels_std"][label]
append_df = DataFrame()
for label in predictions.columns:
append_df[label] = predictions[label]
if append_df[label].dtype == object:
continue
append_df[f"{label}_mean"] = self.data["labels_mean"][label]
append_df[f"{label}_std"] = self.data["labels_std"][label]
self.append_df["do_predict"] = do_predict
if self.freqai_config.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
self.append_df["DI_values"] = self.DI_values
append_df["do_predict"] = do_predict
if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
append_df["DI_values"] = self.DI_values
if self.full_df.empty:
self.full_df = self.append_df
self.full_df = append_df
else:
self.full_df = pd.concat([self.full_df, self.append_df], axis=0)
self.full_df = pd.concat([self.full_df, append_df], axis=0)
return
@ -666,7 +745,6 @@ class FreqaiDataKitchen:
to_keep = [col for col in dataframe.columns if not col.startswith("&")]
self.return_dataframe = pd.concat([dataframe[to_keep], self.full_df], axis=1)
self.append_df = DataFrame()
self.full_df = DataFrame()
return
@ -683,7 +761,7 @@ class FreqaiDataKitchen:
if backtest_timerange.stopts == 0:
# typically open ended time ranges do work, however, there are some edge cases where
# it does not. accomodating these kinds of edge cases just to allow open-ended
# it does not. accommodating these kinds of edge cases just to allow open-ended
# timerange is not high enough priority to warrant the effort. It is safer for now
# to simply ask user to add their end date
raise OperationalException("FreqAI backtesting does not allow open ended timeranges. "
@ -701,7 +779,7 @@ class FreqaiDataKitchen:
full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")
self.full_path = Path(
self.config["user_data_dir"] / "models" / str(self.freqai_config.get("identifier"))
self.config["user_data_dir"] / "models" / f"{self.freqai_config['identifier']}"
)
config_path = Path(self.config["config_files"][0])
@ -719,9 +797,8 @@ class FreqaiDataKitchen:
"""
A model age checker to determine if the model is trustworthy based on user defined
`expiration_hours` in the configuration file.
:params:
trained_timestamp: int = The time of training for the most recent model.
:returns:
:param trained_timestamp: int = The time of training for the most recent model.
:return:
bool = If the model is expired or not.
"""
time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
@ -740,30 +817,21 @@ class FreqaiDataKitchen:
trained_timerange = TimeRange()
data_load_timerange = TimeRange()
# find the max indicator length required
max_timeframe_chars = self.freqai_config.get("feature_parameters", {}).get(
"include_timeframes"
)[-1]
max_period = self.freqai_config.get("feature_parameters", {}).get(
"indicator_max_period_candles", 50
)
additional_seconds = 0
if max_timeframe_chars[-1] == "d":
additional_seconds = max_period * SECONDS_IN_DAY * int(max_timeframe_chars[-2])
elif max_timeframe_chars[-1] == "h":
additional_seconds = max_period * 3600 * int(max_timeframe_chars[-2])
elif max_timeframe_chars[-1] == "m":
if len(max_timeframe_chars) == 2:
additional_seconds = max_period * 60 * int(max_timeframe_chars[-2])
elif len(max_timeframe_chars) == 3:
additional_seconds = max_period * 60 * int(float(max_timeframe_chars[0:2]))
else:
logger.warning(
"FreqAI could not detect max timeframe and therefore may not "
"download the proper amount of data for training"
)
timeframes = self.freqai_config["feature_parameters"].get("include_timeframes")
# logger.info(f'Extending data download by {additional_seconds/SECONDS_IN_DAY:.2f} days')
max_tf_seconds = 0
for tf in timeframes:
secs = timeframe_to_seconds(tf)
if secs > max_tf_seconds:
max_tf_seconds = secs
# We notice that users like to use exotic indicators where
# they do not know the required timeperiod. Here we include a factor
# of safety by multiplying the user considered "max" by 2.
max_period = self.freqai_config["feature_parameters"].get(
"indicator_max_period_candles", 20
) * 2
additional_seconds = max_period * max_tf_seconds
if trained_timestamp != 0:
elapsed_time = (time - trained_timestamp) / SECONDS_IN_HOUR
@ -784,7 +852,7 @@ class FreqaiDataKitchen:
data_load_timerange.stopts = int(time)
else: # user passed no live_trained_timerange in config
trained_timerange.startts = int(
time - self.freqai_config.get("train_period_days") * SECONDS_IN_DAY
time - self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
)
trained_timerange.stopts = int(time)
@ -815,24 +883,22 @@ class FreqaiDataKitchen:
self.model_filename = f"cb_{coin.lower()}_{int(trained_timerange.stopts)}"
def download_all_data_for_training(self, timerange: TimeRange) -> None:
def download_all_data_for_training(self, timerange: TimeRange, dp: DataProvider) -> None:
"""
Called only once upon start of bot to download the necessary data for
populating indicators and training the model.
:params:
timerange: TimeRange = The full data timerange for populating the indicators
:param timerange: TimeRange = The full data timerange for populating the indicators
and training the model.
:param dp: DataProvider instance attached to the strategy
"""
exchange = ExchangeResolver.load_exchange(
self.config["exchange"]["name"], self.config, validate=False, load_leverage_tiers=False
)
new_pairs_days = int((timerange.stopts - timerange.startts) / SECONDS_IN_DAY)
if not dp._exchange:
# Not realistic - this is only called in live mode.
raise OperationalException("Dataprovider did not have an exchange attached.")
refresh_backtest_ohlcv_data(
exchange,
dp._exchange,
pairs=self.all_pairs,
timeframes=self.freqai_config.get("feature_parameters", {}).get("include_timeframes"),
timeframes=self.freqai_config["feature_parameters"].get("include_timeframes"),
datadir=self.config["datadir"],
timerange=timerange,
new_pairs_days=new_pairs_days,
@ -845,7 +911,7 @@ class FreqaiDataKitchen:
def set_all_pairs(self) -> None:
self.all_pairs = copy.deepcopy(
self.freqai_config.get("feature_parameters", {}).get("include_corr_pairlist", [])
self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
)
for pair in self.config.get("exchange", "").get("pair_whitelist"):
if pair not in self.all_pairs:
@ -876,8 +942,8 @@ class FreqaiDataKitchen:
# for prediction dataframe creation, we let dataprovider handle everything in the strategy
# so we create empty dictionaries, which allows us to pass None to
# `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe.
tfs = self.freqai_config.get("feature_parameters", {}).get("include_timeframes")
pairs = self.freqai_config.get("feature_parameters", {}).get("include_corr_pairlist", [])
tfs = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
if not prediction_dataframe.empty:
dataframe = prediction_dataframe.copy()
for tf in tfs:
@ -889,29 +955,26 @@ class FreqaiDataKitchen:
else:
dataframe = base_dataframes[self.config["timeframe"]].copy()
sgi = True
sgi = False
for tf in tfs:
if tf == tfs[-1]:
sgi = True # doing this last allows user to use all tf raw prices in labels
dataframe = strategy.populate_any_indicators(
pair,
pair,
dataframe.copy(),
tf,
informative=base_dataframes[tf],
coin=pair.split("/")[0] + "-",
set_generalized_indicators=sgi,
set_generalized_indicators=sgi
)
sgi = False
if pairs:
for i in pairs:
if pair in i:
continue # dont repeat anything from whitelist
dataframe = strategy.populate_any_indicators(
pair,
i,
dataframe.copy(),
tf,
informative=corr_dataframes[i][tf],
coin=i.split("/")[0] + "-",
informative=corr_dataframes[i][tf]
)
return dataframe
@ -923,17 +986,12 @@ class FreqaiDataKitchen:
import scipy as spy
self.data["labels_mean"], self.data["labels_std"] = {}, {}
for label in self.label_list:
for label in self.data_dictionary["train_labels"].columns:
if self.data_dictionary["train_labels"][label].dtype == object:
continue
f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label])
self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
# KEEPME incase we want to let user start to grab quantiles.
# upper_q = spy.stats.norm.ppf(self.freqai_config['feature_parameters'][
# 'target_quantile'], *f)
# lower_q = spy.stats.norm.ppf(1 - self.freqai_config['feature_parameters'][
# 'target_quantile'], *f)
# self.data["upper_quantile"] = upper_q
# self.data["lower_quantile"] = lower_q
return
def remove_features_from_df(self, dataframe: DataFrame) -> DataFrame:
@ -945,168 +1003,3 @@ class FreqaiDataKitchen:
col for col in dataframe.columns if not col.startswith("%") or col.startswith("%%")
]
return dataframe[to_keep]
def np_encoder(self, object):
if isinstance(object, np.generic):
return object.item()
# Functions containing useful data manpulation examples. but not actively in use.
# Possibly phasing these outlier removal methods below out in favor of
# use_SVM_to_remove_outliers (computationally more efficient and apparently higher performance).
# But these have good data manipulation examples, so keep them commented here for now.
# def determine_statistical_distributions(self) -> None:
# from fitter import Fitter
# logger.info('Determining best model for all features, may take some time')
# def compute_quantiles(ft):
# f = Fitter(self.data_dictionary["train_features"][ft],
# distributions=['gamma', 'cauchy', 'laplace',
# 'beta', 'uniform', 'lognorm'])
# f.fit()
# # f.summary()
# dist = list(f.get_best().items())[0][0]
# params = f.get_best()[dist]
# upper_q = getattr(spy.stats, list(f.get_best().items())[0][0]).ppf(0.999, **params)
# lower_q = getattr(spy.stats, list(f.get_best().items())[0][0]).ppf(0.001, **params)
# return ft, upper_q, lower_q, dist
# quantiles_tuple = Parallel(n_jobs=-1)(
# delayed(compute_quantiles)(ft) for ft in self.data_dictionary[
# 'train_features'].columns)
# df = pd.DataFrame(quantiles_tuple, columns=['features', 'upper_quantiles',
# 'lower_quantiles', 'dist'])
# self.data_dictionary['upper_quantiles'] = df['upper_quantiles']
# self.data_dictionary['lower_quantiles'] = df['lower_quantiles']
# return
# def remove_outliers(self, predict: bool) -> None:
# """
# Remove data that looks like an outlier based on the distribution of each
# variable.
# :params:
# :predict: boolean which tells the function if this is prediction data or
# training data coming in.
# """
# lower_quantile = self.data_dictionary["lower_quantiles"].to_numpy()
# upper_quantile = self.data_dictionary["upper_quantiles"].to_numpy()
# if predict:
# df = self.data_dictionary["prediction_features"][
# (self.data_dictionary["prediction_features"] < upper_quantile)
# & (self.data_dictionary["prediction_features"] > lower_quantile)
# ]
# drop_index = pd.isnull(df).any(1)
# self.data_dictionary["prediction_features"].fillna(0, inplace=True)
# drop_index = ~drop_index
# do_predict = np.array(drop_index.replace(True, 1).replace(False, 0))
# logger.info(
# "remove_outliers() tossed %s predictions",
# len(do_predict) - do_predict.sum(),
# )
# self.do_predict += do_predict
# self.do_predict -= 1
# else:
# filter_train_df = self.data_dictionary["train_features"][
# (self.data_dictionary["train_features"] < upper_quantile)
# & (self.data_dictionary["train_features"] > lower_quantile)
# ]
# drop_index = pd.isnull(filter_train_df).any(1)
# drop_index = drop_index.replace(True, 1).replace(False, 0)
# self.data_dictionary["train_features"] = self.data_dictionary["train_features"][
# (drop_index == 0)
# ]
# self.data_dictionary["train_labels"] = self.data_dictionary["train_labels"][
# (drop_index == 0)
# ]
# self.data_dictionary["train_weights"] = self.data_dictionary["train_weights"][
# (drop_index == 0)
# ]
# logger.info(
# f'remove_outliers() tossed {drop_index.sum()}'
# f' training points from {len(filter_train_df)}'
# )
# # do the same for the test data
# filter_test_df = self.data_dictionary["test_features"][
# (self.data_dictionary["test_features"] < upper_quantile)
# & (self.data_dictionary["test_features"] > lower_quantile)
# ]
# drop_index = pd.isnull(filter_test_df).any(1)
# drop_index = drop_index.replace(True, 1).replace(False, 0)
# self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][
# (drop_index == 0)
# ]
# self.data_dictionary["test_features"] = self.data_dictionary["test_features"][
# (drop_index == 0)
# ]
# self.data_dictionary["test_weights"] = self.data_dictionary["test_weights"][
# (drop_index == 0)
# ]
# logger.info(
# f'remove_outliers() tossed {drop_index.sum()}'
# f' test points from {len(filter_test_df)}'
# )
# return
# def standardize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
# """
# standardize all data in the data_dictionary according to the training dataset
# :params:
# :data_dictionary: dictionary containing the cleaned and split training/test data/labels
# :returns:
# :data_dictionary: updated dictionary with standardized values.
# """
# # standardize the data by training stats
# train_mean = data_dictionary["train_features"].mean()
# train_std = data_dictionary["train_features"].std()
# data_dictionary["train_features"] = (
# data_dictionary["train_features"] - train_mean
# ) / train_std
# data_dictionary["test_features"] = (
# data_dictionary["test_features"] - train_mean
# ) / train_std
# train_labels_std = data_dictionary["train_labels"].std()
# train_labels_mean = data_dictionary["train_labels"].mean()
# data_dictionary["train_labels"] = (
# data_dictionary["train_labels"] - train_labels_mean
# ) / train_labels_std
# data_dictionary["test_labels"] = (
# data_dictionary["test_labels"] - train_labels_mean
# ) / train_labels_std
# for item in train_std.keys():
# self.data[item + "_std"] = train_std[item]
# self.data[item + "_mean"] = train_mean[item]
# self.data["labels_std"] = train_labels_std
# self.data["labels_mean"] = train_labels_mean
# return data_dictionary
# def standardize_data_from_metadata(self, df: DataFrame) -> DataFrame:
# """
# Normalizes a set of data using the mean and standard deviation from
# the associated training data.
# :params:
# :df: Dataframe to be standardized
# """
# for item in df.keys():
# df[item] = (df[item] - self.data[item + "_mean"]) / self.data[item + "_std"]
# return df

121
freqtrade/freqai/freqai_interface.py
View File

@ -1,7 +1,5 @@
# import contextlib
import copy
import datetime
import gc
import logging
import shutil
import threading
@ -12,7 +10,7 @@ from typing import Any, Dict, Tuple
import numpy as np
import pandas as pd
from numpy.typing import ArrayLike
from numpy.typing import NDArray
from pandas import DataFrame
from freqtrade.configuration import TimeRange
@ -47,7 +45,7 @@ class IFreqaiModel(ABC):
Robert Caulk @robcaulk
Theoretical brainstorming:
Elin Törnquist @thorntwig
Elin Törnquist @th0rntwig
Code review, software architecture brainstorming:
@xmatthias
@ -82,6 +80,8 @@ class IFreqaiModel(ABC):
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
self.pair_it = 0
self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
self.last_trade_database_summary: DataFrame = {}
self.current_trade_database_summary: DataFrame = {}
def assert_config(self, config: Dict[str, Any]) -> None:
@ -123,7 +123,7 @@ class IFreqaiModel(ABC):
dataframe = dk.remove_features_from_df(dk.return_dataframe)
del dk
return self.return_values(dataframe)
return dataframe
@threaded
def start_scanning(self, strategy: IStrategy) -> None:
@ -183,8 +183,6 @@ class IFreqaiModel(ABC):
(_, _, _) = self.dd.get_pair_dict_info(metadata["pair"])
train_it += 1
total_trains = len(dk.backtesting_timeranges)
gc.collect()
dk.data = {} # clean the pair specific data between training window sliding
self.training_timerange = tr_train
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
@ -204,13 +202,8 @@ class IFreqaiModel(ABC):
dk.data_path = Path(
dk.full_path
/ str(
"sub-train"
+ "-"
+ metadata["pair"].split("/")[0]
+ "_"
+ str(int(trained_timestamp.stopts))
)
/
f"sub-train-{metadata['pair'].split('/')[0]}_{int(trained_timestamp.stopts)}"
)
if not self.model_exists(
metadata["pair"], dk, trained_timestamp=int(trained_timestamp.stopts)
@ -228,7 +221,7 @@ class IFreqaiModel(ABC):
pred_df, do_preds = self.predict(dataframe_backtest, dk)
dk.append_predictions(pred_df, do_preds, len(dataframe_backtest))
dk.append_predictions(pred_df, do_preds)
dk.fill_predictions(dataframe)
@ -280,7 +273,7 @@ class IFreqaiModel(ABC):
"corr_pairlist, this may take a while if you do not have the "
"data saved"
)
dk.download_all_data_for_training(data_load_timerange)
dk.download_all_data_for_training(data_load_timerange, strategy.dp)
self.dd.load_all_pair_histories(data_load_timerange, dk)
if not self.scanning:
@ -331,7 +324,8 @@ class IFreqaiModel(ABC):
return
elif self.dk.check_if_model_expired(trained_timestamp):
pred_df = DataFrame(np.zeros((2, len(dk.label_list))), columns=dk.label_list)
do_preds, dk.DI_values = np.ones(2) * 2, np.zeros(2)
do_preds = np.ones(2, dtype=np.int_) * 2
dk.DI_values = np.zeros(2)
logger.warning(
f"Model expired for {pair}, returning null values to strategy. Strategy "
"construction should take care to consider this event with "
@ -379,17 +373,25 @@ class IFreqaiModel(ABC):
example of how outlier data points are dropped from the dataframe used for training.
"""
if self.freqai_info.get("feature_parameters", {}).get(
if self.freqai_info["feature_parameters"].get(
"principal_component_analysis", False
):
dk.principal_component_analysis()
if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers", False):
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=False)
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
dk.data["avg_mean_dist"] = dk.compute_distances()
if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
if dk.pair in self.dd.old_DBSCAN_eps:
eps = self.dd.old_DBSCAN_eps[dk.pair]
else:
eps = None
dk.use_DBSCAN_to_remove_outliers(predict=False, eps=eps)
self.dd.old_DBSCAN_eps[dk.pair] = dk.data['DBSCAN_eps']
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
"""
Base data cleaning method for predict.
@ -401,17 +403,20 @@ class IFreqaiModel(ABC):
of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
for buy signals.
"""
if self.freqai_info.get("feature_parameters", {}).get(
if self.freqai_info["feature_parameters"].get(
"principal_component_analysis", False
):
dk.pca_transform(dataframe)
if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers", False):
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=True)
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
dk.check_if_pred_in_training_spaces()
if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
dk.use_DBSCAN_to_remove_outliers(predict=True)
def model_exists(
self,
pair: str,
@ -430,9 +435,9 @@ class IFreqaiModel(ABC):
coin, _ = pair.split("/")
if not self.live:
dk.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
dk.model_filename = model_filename = f"cb_{coin.lower()}_{trained_timestamp}"
path_to_modelfile = Path(dk.data_path / str(model_filename + "_model.joblib"))
path_to_modelfile = Path(dk.data_path / f"{model_filename}_model.joblib")
file_exists = path_to_modelfile.is_file()
if file_exists and not scanning:
logger.info("Found model at %s", dk.data_path / dk.model_filename)
@ -442,7 +447,7 @@ class IFreqaiModel(ABC):
def set_full_path(self) -> None:
self.full_path = Path(
self.config["user_data_dir"] / "models" / str(self.freqai_info.get("identifier"))
self.config["user_data_dir"] / "models" / f"{self.freqai_info['identifier']}"
)
self.full_path.mkdir(parents=True, exist_ok=True)
shutil.copy(
@ -500,13 +505,54 @@ class IFreqaiModel(ABC):
def set_initial_historic_predictions(
self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
) -> None:
trained_predictions = model.predict(df)
"""
This function is called only if the datadrawer failed to load an
existing set of historic predictions. In this case, it builds
the structure and sets fake predictions off the first training
data. After that, FreqAI will append new real predictions to the
set of historic predictions.
These values are used to generate live statistics which can be used
in the strategy for adaptive values. E.g. &*_mean/std are quantities
that can computed based on live predictions from the set of historical
predictions. Those values can be used in the user strategy to better
assess prediction rarity, and thus wait for probabilistically favorable
entries relative to the live historical predictions.
If the user reuses an identifier on a subsequent instance,
this function will not be called. In that case, "real" predictions
will be appended to the loaded set of historic predictions.
:param: df: DataFrame = the dataframe containing the training feature data
:param: model: Any = A model which was `fit` using a common library such as
catboost or lightgbm
:param: dk: FreqaiDataKitchen = object containing methods for data analysis
:param: pair: str = current pair
"""
num_candles = self.freqai_info.get('fit_live_predictions_candles', 600)
if not num_candles:
num_candles = 600
df_tail = df.tail(num_candles)
trained_predictions = model.predict(df_tail)
pred_df = DataFrame(trained_predictions, columns=dk.label_list)
pred_df = dk.denormalize_labels_from_metadata(pred_df)
self.dd.historic_predictions[pair] = pd.DataFrame()
self.dd.historic_predictions[pair] = copy.deepcopy(pred_df)
self.dd.historic_predictions[pair] = pred_df
hist_preds_df = self.dd.historic_predictions[pair]
for label in hist_preds_df.columns:
if hist_preds_df[label].dtype == object:
continue
hist_preds_df[f'{label}_mean'] = 0
hist_preds_df[f'{label}_std'] = 0
hist_preds_df['do_predict'] = 0
if self.freqai_info['feature_parameters'].get('DI_threshold', 0) > 0:
hist_preds_df['DI_values'] = 0
for return_str in dk.data['extra_returns_per_train']:
hist_preds_df[return_str] = 0
def fit_live_predictions(self, dk: FreqaiDataKitchen) -> None:
"""
@ -517,13 +563,15 @@ class IFreqaiModel(ABC):
num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
for label in dk.label_list:
if self.dd.historic_predictions[dk.pair][label].dtype == object:
continue
f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
return
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModlel.py for an example.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.
@abstractmethod
def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any:
@ -550,7 +598,7 @@ class IFreqaiModel(ABC):
@abstractmethod
def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True
) -> Tuple[DataFrame, ArrayLike]:
) -> Tuple[DataFrame, NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param unfiltered_dataframe: Full dataframe for the current backtest period.
@ -561,14 +609,3 @@ class IFreqaiModel(ABC):
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (i.e. SVM and/or DI index)
"""
@abstractmethod
def return_values(self, dataframe: DataFrame) -> DataFrame:
"""
User defines the dataframe to be returned to strategy here.
:param dataframe: DataFrame = the full dataframe for the current prediction (live)
or --timerange (backtesting)
:return: dataframe: DataFrame = dataframe filled with user defined data
"""
return

16
freqtrade/freqai/prediction_models/BaseRegressionModel.py
View File

@ -1,6 +1,7 @@
import logging
from typing import Any, Tuple
import numpy as np
import numpy.typing as npt
from pandas import DataFrame
@ -18,15 +19,6 @@ class BaseRegressionModel(IFreqaiModel):
such as prediction_models/CatboostPredictionModel.py for guidance.
"""
def return_values(self, dataframe: DataFrame) -> DataFrame:
"""
User uses this function to add any additional return values to the dataframe.
e.g.
dataframe['volatility'] = dk.volatility_values
"""
return dataframe
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
) -> Any:
@ -55,6 +47,8 @@ class BaseRegressionModel(IFreqaiModel):
f"{end_date}--------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
@ -74,8 +68,6 @@ class BaseRegressionModel(IFreqaiModel):
if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
self.fit_live_predictions(dk)
else:
dk.fit_labels()
self.dd.save_historic_predictions_to_disk()
@ -85,7 +77,7 @@ class BaseRegressionModel(IFreqaiModel):
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
) -> Tuple[DataFrame, npt.ArrayLike]:
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.

9
freqtrade/freqai/prediction_models/BaseTensorFlowModel.py
View File

@ -16,15 +16,6 @@ class BaseTensorFlowModel(IFreqaiModel):
User *must* inherit from this class and set fit() and predict().
"""
def return_values(self, dataframe: DataFrame) -> DataFrame:
"""
User uses this function to add any additional return values to the dataframe.
e.g.
dataframe['volatility'] = dk.volatility_values
"""
return dataframe
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
) -> Any:

41
freqtrade/freqai/prediction_models/CatboostClassifier.py Normal file
View File

@ -0,0 +1,41 @@
import logging
from typing import Any, Dict
from catboost import CatBoostClassifier, Pool
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
logger = logging.getLogger(__name__)
class CatboostClassifier(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
train_data = Pool(
data=data_dictionary["train_features"],
label=data_dictionary["train_labels"],
weight=data_dictionary["train_weights"],
)
cbr = CatBoostClassifier(
allow_writing_files=False,
loss_function='MultiClass',
**self.model_training_parameters,
)
cbr.fit(train_data)
return cbr

5
freqtrade/freqai/prediction_models/CatboostPredictionModel.py → freqtrade/freqai/prediction_models/CatboostRegressor.py
View File

@ -1,6 +1,7 @@
import gc
import logging
from typing import Any, Dict
import gc
from catboost import CatBoostRegressor, Pool
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
@ -9,7 +10,7 @@ from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressio
logger = logging.getLogger(__name__)
class CatboostPredictionModel(BaseRegressionModel):
class CatboostRegressor(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which

2
freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py → freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py
View File

@ -10,7 +10,7 @@ from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressio
logger = logging.getLogger(__name__)
class CatboostPredictionMultiModel(BaseRegressionModel):
class CatboostRegressorMultiTarget(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which

38
freqtrade/freqai/prediction_models/LightGBMClassifier.py Normal file
View File

@ -0,0 +1,38 @@
import logging
from typing import Any, Dict
from lightgbm import LGBMClassifier
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
logger = logging.getLogger(__name__)
class LightGBMClassifier(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
eval_set = None
else:
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
model = LGBMClassifier(**self.model_training_parameters)
model.fit(X=X, y=y, eval_set=eval_set)
return model

2
freqtrade/freqai/prediction_models/LightGBMPredictionModel.py → freqtrade/freqai/prediction_models/LightGBMRegressor.py
View File

@ -9,7 +9,7 @@ from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressio
logger = logging.getLogger(__name__)
class LightGBMPredictionModel(BaseRegressionModel):
class LightGBMRegressor(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which

2
freqtrade/freqai/prediction_models/LightGBMPredictionMultiModel.py → freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py
View File

@ -10,7 +10,7 @@ from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressio
logger = logging.getLogger(__name__)
class LightGBMPredictionMultiModel(BaseRegressionModel):
class LightGBMRegressorMultiTarget(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which

212
freqtrade/freqtradebot.py
View File

@ -5,6 +5,7 @@ import copy
import logging
import traceback
from datetime import datetime, time, timedelta, timezone
from decimal import Decimal
from math import isclose
from threading import Lock
from typing import Any, Dict, List, Optional, Tuple
@ -25,7 +26,7 @@ from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.exchange.exchange import timeframe_to_next_date
from freqtrade.misc import safe_value_fallback, safe_value_fallback2
from freqtrade.mixins import LoggingMixin
from freqtrade.persistence import Order, PairLocks, Trade, cleanup_db, init_db
from freqtrade.persistence import Order, PairLocks, Trade, init_db
from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@ -149,7 +150,7 @@ class FreqtradeBot(LoggingMixin):
self.check_for_open_trades()
self.rpc.cleanup()
cleanup_db()
Trade.commit()
self.exchange.close()
def startup(self) -> None:
@ -214,6 +215,7 @@ class FreqtradeBot(LoggingMixin):
if self.trading_mode == TradingMode.FUTURES:
self._schedule.run_pending()
Trade.commit()
self.rpc.process_msg_queue(self.dataprovider._msg_queue)
self.last_process = datetime.now(timezone.utc)
def process_stopped(self) -> None:
@ -524,39 +526,61 @@ class FreqtradeBot(LoggingMixin):
If the strategy triggers the adjustment, a new order gets issued.
Once that completes, the existing trade is modified to match new data.
"""
if self.strategy.max_entry_position_adjustment > -1:
count_of_buys = trade.nr_of_successful_entries
if count_of_buys > self.strategy.max_entry_position_adjustment:
logger.debug(f"Max adjustment entries for {trade.pair} has been reached.")
return
else:
logger.debug("Max adjustment entries is set to unlimited.")
current_rate = self.exchange.get_rate(
trade.pair, side='entry', is_short=trade.is_short, refresh=True)
current_profit = trade.calc_profit_ratio(current_rate)
current_entry_rate, current_exit_rate = self.exchange.get_rates(
trade.pair, True, trade.is_short)
min_stake_amount = self.exchange.get_min_pair_stake_amount(trade.pair,
current_rate,
current_entry_profit = trade.calc_profit_ratio(current_entry_rate)
current_exit_profit = trade.calc_profit_ratio(current_exit_rate)
min_entry_stake = self.exchange.get_min_pair_stake_amount(trade.pair,
current_entry_rate,
self.strategy.stoploss)
max_stake_amount = self.exchange.get_max_pair_stake_amount(trade.pair, current_rate)
min_exit_stake = self.exchange.get_min_pair_stake_amount(trade.pair,
current_exit_rate,
self.strategy.stoploss)
max_entry_stake = self.exchange.get_max_pair_stake_amount(trade.pair, current_entry_rate)
stake_available = self.wallets.get_available_stake_amount()
logger.debug(f"Calling adjust_trade_position for pair {trade.pair}")
stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
default_retval=None)(
trade=trade, current_time=datetime.now(timezone.utc), current_rate=current_rate,
current_profit=current_profit, min_stake=min_stake_amount,
max_stake=min(max_stake_amount, stake_available))
trade=trade,
current_time=datetime.now(timezone.utc), current_rate=current_entry_rate,
current_profit=current_entry_profit, min_stake=min_entry_stake,
max_stake=min(max_entry_stake, stake_available),
current_entry_rate=current_entry_rate, current_exit_rate=current_exit_rate,
current_entry_profit=current_entry_profit, current_exit_profit=current_exit_profit
)
if stake_amount is not None and stake_amount > 0.0:
# We should increase our position
self.execute_entry(trade.pair, stake_amount, price=current_rate,
if self.strategy.max_entry_position_adjustment > -1:
count_of_entries = trade.nr_of_successful_entries
if count_of_entries > self.strategy.max_entry_position_adjustment:
logger.debug(f"Max adjustment entries for {trade.pair} has been reached.")
return
else:
logger.debug("Max adjustment entries is set to unlimited.")
self.execute_entry(trade.pair, stake_amount, price=current_entry_rate,
trade=trade, is_short=trade.is_short)
if stake_amount is not None and stake_amount < 0.0:
# We should decrease our position
# TODO: Selling part of the trade not implemented yet.
logger.error(f"Unable to decrease trade position / sell partially"
f" for pair {trade.pair}, feature not implemented.")
amount = abs(float(Decimal(stake_amount) / Decimal(current_exit_rate)))
if amount > trade.amount:
# This is currently ineffective as remaining would become < min tradable
# Fixing this would require checking for 0.0 there -
# if we decide that this callback is allowed to "fully exit"
logger.info(
f"Adjusting amount to trade.amount as it is higher. {amount} > {trade.amount}")
amount = trade.amount
remaining = (trade.amount - amount) * current_exit_rate
if remaining < min_exit_stake:
logger.info(f'Remaining amount of {remaining} would be too small.')
return
self.execute_trade_exit(trade, current_exit_rate, exit_check=ExitCheckTuple(
exit_type=ExitType.PARTIAL_EXIT), sub_trade_amt=amount)
def _check_depth_of_market(self, pair: str, conf: Dict, side: SignalDirection) -> bool:
"""
@ -600,7 +624,8 @@ class FreqtradeBot(LoggingMixin):
ordertype: Optional[str] = None,
enter_tag: Optional[str] = None,
trade: Optional[Trade] = None,
order_adjust: bool = False
order_adjust: bool = False,
leverage_: Optional[float] = None,
) -> bool:
"""
Executes a limit buy for the given pair
@ -616,7 +641,7 @@ class FreqtradeBot(LoggingMixin):
pos_adjust = trade is not None
enter_limit_requested, stake_amount, leverage = self.get_valid_enter_price_and_stake(
pair, price, stake_amount, trade_side, enter_tag, trade, order_adjust)
pair, price, stake_amount, trade_side, enter_tag, trade, order_adjust, leverage_)
if not stake_amount:
return False
@ -730,7 +755,7 @@ class FreqtradeBot(LoggingMixin):
# Updating wallets
self.wallets.update()
self._notify_enter(trade, order, order_type)
self._notify_enter(trade, order_obj, order_type, sub_trade=pos_adjust)
if pos_adjust:
if order_status == 'closed':
@ -739,8 +764,8 @@ class FreqtradeBot(LoggingMixin):
else:
logger.info(f"DCA order {order_status}, will wait for resolution: {trade}")
# Update fees if order is closed
if order_status == 'closed':
# Update fees if order is non-opened
if order_status in constants.NON_OPEN_EXCHANGE_STATES:
self.update_trade_state(trade, order_id, order)
return True
@ -763,6 +788,7 @@ class FreqtradeBot(LoggingMixin):
entry_tag: Optional[str],
trade: Optional[Trade],
order_adjust: bool,
leverage_: Optional[float],
) -> Tuple[float, float, float]:
if price:
@ -785,8 +811,11 @@ class FreqtradeBot(LoggingMixin):
if not enter_limit_requested:
raise PricingError('Could not determine entry price.')
if trade is None:
if self.trading_mode != TradingMode.SPOT and trade is None:
max_leverage = self.exchange.get_max_leverage(pair, stake_amount)
if leverage_:
leverage = leverage_
else:
leverage = strategy_safe_wrapper(self.strategy.leverage, default_retval=1.0)(
pair=pair,
current_time=datetime.now(timezone.utc),
@ -794,7 +823,7 @@ class FreqtradeBot(LoggingMixin):
proposed_leverage=1.0,
max_leverage=max_leverage,
side=trade_side, entry_tag=entry_tag,
) if self.trading_mode != TradingMode.SPOT else 1.0
)
# Cap leverage between 1.0 and max_leverage.
leverage = min(max(leverage, 1.0), max_leverage)
else:
@ -829,13 +858,14 @@ class FreqtradeBot(LoggingMixin):
return enter_limit_requested, stake_amount, leverage
def _notify_enter(self, trade: Trade, order: Dict, order_type: Optional[str] = None,
fill: bool = False) -> None:
def _notify_enter(self, trade: Trade, order: Order, order_type: Optional[str] = None,
fill: bool = False, sub_trade: bool = False) -> None:
"""
Sends rpc notification when a entry order occurred.
"""
msg_type = RPCMessageType.ENTRY_FILL if fill else RPCMessageType.ENTRY
open_rate = safe_value_fallback(order, 'average', 'price')
open_rate = order.safe_price
if open_rate is None:
open_rate = trade.open_rate
@ -859,15 +889,17 @@ class FreqtradeBot(LoggingMixin):
'stake_amount': trade.stake_amount,
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
'amount': safe_value_fallback(order, 'filled', 'amount') or trade.amount,
'amount': order.safe_amount_after_fee,
'open_date': trade.open_date or datetime.utcnow(),
'current_rate': current_rate,
'sub_trade': sub_trade,
}
# Send the message
self.rpc.send_msg(msg)
def _notify_enter_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
def _notify_enter_cancel(self, trade: Trade, order_type: str, reason: str,
sub_trade: bool = False) -> None:
"""
Sends rpc notification when a entry order cancel occurred.
"""
@ -892,6 +924,7 @@ class FreqtradeBot(LoggingMixin):
'open_date': trade.open_date,
'current_rate': current_rate,
'reason': reason,
'sub_trade': sub_trade,
}
# Send the message
@ -1015,7 +1048,7 @@ class FreqtradeBot(LoggingMixin):
trade.stoploss_order_id = None
logger.error(f'Unable to place a stoploss order on exchange. {e}')
logger.warning('Exiting the trade forcefully')
self.execute_trade_exit(trade, trade.stop_loss, exit_check=ExitCheckTuple(
self.execute_trade_exit(trade, stop_price, exit_check=ExitCheckTuple(
exit_type=ExitType.EMERGENCY_EXIT))
except ExchangeError:
@ -1085,7 +1118,7 @@ class FreqtradeBot(LoggingMixin):
if (trade.is_open
and stoploss_order
and stoploss_order['status'] in ('canceled', 'cancelled')):
if self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss):
if self.create_stoploss_order(trade=trade, stop_price=trade.stoploss_or_liquidation):
return False
else:
trade.stoploss_order_id = None
@ -1114,7 +1147,7 @@ class FreqtradeBot(LoggingMixin):
:param order: Current on exchange stoploss order
:return: None
"""
stoploss_norm = self.exchange.price_to_precision(trade.pair, trade.stop_loss)
stoploss_norm = self.exchange.price_to_precision(trade.pair, trade.stoploss_or_liquidation)
if self.exchange.stoploss_adjust(stoploss_norm, order, side=trade.exit_side):
# we check if the update is necessary
@ -1132,7 +1165,7 @@ class FreqtradeBot(LoggingMixin):
f"for pair {trade.pair}")
# Create new stoploss order
if not self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss):
if not self.create_stoploss_order(trade=trade, stop_price=stoploss_norm):
logger.warning(f"Could not create trailing stoploss order "
f"for pair {trade.pair}.")
@ -1365,16 +1398,22 @@ class FreqtradeBot(LoggingMixin):
trade.open_order_id = None
trade.exit_reason = None
cancelled = True
self.wallets.update()
else:
# TODO: figure out how to handle partially complete sell orders
reason = constants.CANCEL_REASON['PARTIALLY_FILLED_KEEP_OPEN']
cancelled = False
self.wallets.update()
order_obj = trade.select_order_by_order_id(order['id'])
if not order_obj:
raise DependencyException(
f"Order_obj not found for {order['id']}. This should not have happened.")
sub_trade = order_obj.amount != trade.amount
self._notify_exit_cancel(
trade,
order_type=self.strategy.order_types['exit'],
reason=reason
reason=reason, order=order_obj, sub_trade=sub_trade
)
return cancelled
@ -1415,6 +1454,7 @@ class FreqtradeBot(LoggingMixin):
*,
exit_tag: Optional[str] = None,
ordertype: Optional[str] = None,
sub_trade_amt: float = None,
) -> bool:
"""
Executes a trade exit for the given trade and limit
@ -1431,14 +1471,15 @@ class FreqtradeBot(LoggingMixin):
)
exit_type = 'exit'
exit_reason = exit_tag or exit_check.exit_reason
if exit_check.exit_type in (ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS):
if exit_check.exit_type in (
ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS, ExitType.LIQUIDATION):
exit_type = 'stoploss'
# if stoploss is on exchange and we are on dry_run mode,
# we consider the sell price stop price
if (self.config['dry_run'] and exit_type == 'stoploss'
and self.strategy.order_types['stoploss_on_exchange']):
limit = trade.stop_loss
limit = trade.stoploss_or_liquidation
# set custom_exit_price if available
proposed_limit_rate = limit
@ -1460,14 +1501,17 @@ class FreqtradeBot(LoggingMixin):
# Emergency sells (default to market!)
order_type = self.strategy.order_types.get("emergency_exit", "market")
amount = self._safe_exit_amount(trade.pair, trade.amount)
amount = self._safe_exit_amount(trade.pair, sub_trade_amt or trade.amount)
time_in_force = self.strategy.order_time_in_force['exit']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
if (exit_check.exit_type != ExitType.LIQUIDATION
and not sub_trade_amt
and not strategy_safe_wrapper(
self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair, trade=trade, order_type=order_type, amount=amount, rate=limit,
time_in_force=time_in_force, exit_reason=exit_reason,
sell_reason=exit_reason, # sellreason -> compatibility
current_time=datetime.now(timezone.utc)):
current_time=datetime.now(timezone.utc))):
logger.info(f"User denied exit for {trade.pair}.")
return False
@ -1501,7 +1545,7 @@ class FreqtradeBot(LoggingMixin):
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock')
self._notify_exit(trade, order_type)
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
if order.get('status', 'unknown') in ('closed', 'expired'):
self.update_trade_state(trade, trade.open_order_id, order)
@ -1509,16 +1553,27 @@ class FreqtradeBot(LoggingMixin):
return True
def _notify_exit(self, trade: Trade, order_type: str, fill: bool = False) -> None:
def _notify_exit(self, trade: Trade, order_type: str, fill: bool = False,
sub_trade: bool = False, order: Order = None) -> None:
"""
Sends rpc notification when a sell occurred.
"""
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
# Use cached rates here - it was updated seconds ago.
current_rate = self.exchange.get_rate(
trade.pair, side='exit', is_short=trade.is_short, refresh=False) if not fill else None
# second condition is for mypy only; order will always be passed during sub trade
if sub_trade and order is not None:
amount = order.safe_filled if fill else order.amount
profit_rate = order.safe_price
profit = trade.calc_profit(rate=profit_rate, amount=amount, open_rate=trade.open_rate)
profit_ratio = trade.calc_profit_ratio(profit_rate, amount, trade.open_rate)
else:
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit = trade.calc_profit(rate=profit_rate) + (0.0 if fill else trade.realized_profit)
profit_ratio = trade.calc_profit_ratio(profit_rate)
amount = trade.amount
gain = "profit" if profit_ratio > 0 else "loss"
msg = {
@ -1532,11 +1587,11 @@ class FreqtradeBot(LoggingMixin):
'gain': gain,
'limit': profit_rate,
'order_type': order_type,
'amount': trade.amount,
'amount': amount,
'open_rate': trade.open_rate,
'close_rate': trade.close_rate,
'close_rate': profit_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_amount': profit,
'profit_ratio': profit_ratio,
'buy_tag': trade.enter_tag,
'enter_tag': trade.enter_tag,
@ -1544,19 +1599,18 @@ class FreqtradeBot(LoggingMixin):
'exit_reason': trade.exit_reason,
'open_date': trade.open_date,
'close_date': trade.close_date or datetime.utcnow(),
'stake_amount': trade.stake_amount,
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency'),
'sub_trade': sub_trade,
'cumulative_profit': trade.realized_profit,
}
if 'fiat_display_currency' in self.config:
msg.update({
'fiat_currency': self.config['fiat_display_currency'],
})
# Send the message
self.rpc.send_msg(msg)
def _notify_exit_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
def _notify_exit_cancel(self, trade: Trade, order_type: str, reason: str,
order: Order, sub_trade: bool = False) -> None:
"""
Sends rpc notification when a sell cancel occurred.
"""
@ -1582,7 +1636,7 @@ class FreqtradeBot(LoggingMixin):
'gain': gain,
'limit': profit_rate or 0,
'order_type': order_type,
'amount': trade.amount,
'amount': order.safe_amount_after_fee,
'open_rate': trade.open_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
@ -1596,6 +1650,8 @@ class FreqtradeBot(LoggingMixin):
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
'reason': reason,
'sub_trade': sub_trade,
'stake_amount': trade.stake_amount,
}
if 'fiat_display_currency' in self.config:
@ -1650,40 +1706,50 @@ class FreqtradeBot(LoggingMixin):
self.handle_order_fee(trade, order_obj, order)
trade.update_trade(order_obj)
# TODO: is the below necessary? it's already done in update_trade for filled buys
trade.recalc_trade_from_orders()
Trade.commit()
if order['status'] in constants.NON_OPEN_EXCHANGE_STATES:
if order.get('status') in constants.NON_OPEN_EXCHANGE_STATES:
# If a entry order was closed, force update on stoploss on exchange
if order.get('side') == trade.entry_side:
trade = self.cancel_stoploss_on_exchange(trade)
if not self.edge:
# TODO: should shorting/leverage be supported by Edge,
# then this will need to be fixed.
trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
if order.get('side') == trade.entry_side or trade.amount > 0:
# Must also run for partial exits
# TODO: Margin will need to use interest_rate as well.
# interest_rate = self.exchange.get_interest_rate()
trade.set_isolated_liq(self.exchange.get_liquidation_price(
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
))
if not self.edge:
# TODO: should shorting/leverage be supported by Edge,
# then this will need to be fixed.
trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
# Updating wallets when order is closed
self.wallets.update()
Trade.commit()
if not trade.is_open:
self.order_close_notify(trade, order_obj, stoploss_order, send_msg)
return False
def order_close_notify(
self, trade: Trade, order: Order, stoploss_order: bool, send_msg: bool):
"""send "fill" notifications"""
sub_trade = not isclose(order.safe_amount_after_fee,
trade.amount, abs_tol=constants.MATH_CLOSE_PREC)
if order.ft_order_side == trade.exit_side:
# Exit notification
if send_msg and not stoploss_order and not trade.open_order_id:
self._notify_exit(trade, '', True)
self._notify_exit(trade, '', fill=True, sub_trade=sub_trade, order=order)
if not trade.is_open:
self.handle_protections(trade.pair, trade.trade_direction)
elif send_msg and not trade.open_order_id and not stoploss_order:
# Enter fill
self._notify_enter(trade, order, fill=True)
return False
self._notify_enter(trade, order, fill=True, sub_trade=sub_trade)
def handle_protections(self, pair: str, side: LongShort) -> None:
prot_trig = self.protections.stop_per_pair(pair, side=side)

91
freqtrade/optimize/backtesting.py Executable file → Normal file
View File

@ -89,6 +89,9 @@ class Backtesting:
self.dataprovider = DataProvider(self.config, self.exchange)
if self.config.get('strategy_list'):
if self.config.get('freqai', {}).get('enabled', False):
raise OperationalException(
"You can't use strategy_list and freqai at the same time.")
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
@ -207,8 +210,12 @@ class Backtesting:
"""
self.progress.init_step(BacktestState.DATALOAD, 1)
if self.config.get('freqai') is not None:
self.required_startup += int(self.config.get('freqai', {}).get('startup_candles', 1000))
if self.config.get('freqai', {}).get('enabled', False):
startup_candles = int(self.config.get('freqai', {}).get('startup_candles', 0))
if not startup_candles:
raise OperationalException('FreqAI backtesting module requires user set '
'startup_candles in config.')
self.required_startup += int(self.config.get('freqai', {}).get('startup_candles', 0))
logger.info(f'Increasing startup_candle_count for freqai to {self.required_startup}')
self.config['startup_candle_count'] = self.required_startup
@ -386,7 +393,8 @@ class Backtesting:
Get close rate for backtesting result
"""
# Special handling if high or low hit STOP_LOSS or ROI
if exit.exit_type in (ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS):
if exit.exit_type in (
ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS, ExitType.LIQUIDATION):
return self._get_close_rate_for_stoploss(row, trade, exit, trade_dur)
elif exit.exit_type == (ExitType.ROI):
return self._get_close_rate_for_roi(row, trade, exit, trade_dur)
@ -401,11 +409,16 @@ class Backtesting:
is_short = trade.is_short or False
leverage = trade.leverage or 1.0
side_1 = -1 if is_short else 1
if exit.exit_type == ExitType.LIQUIDATION and trade.liquidation_price:
stoploss_value = trade.liquidation_price
else:
stoploss_value = trade.stop_loss
if is_short:
if trade.stop_loss < row[LOW_IDX]:
if stoploss_value < row[LOW_IDX]:
return row[OPEN_IDX]
else:
if trade.stop_loss > row[HIGH_IDX]:
if stoploss_value > row[HIGH_IDX]:
return row[OPEN_IDX]
# Special case: trailing triggers within same candle as trade opened. Assume most
@ -438,7 +451,7 @@ class Backtesting:
return max(row[LOW_IDX], stop_rate)
# Set close_rate to stoploss
return trade.stop_loss
return stoploss_value
def _get_close_rate_for_roi(self, row: Tuple, trade: LocalTrade, exit: ExitCheckTuple,
trade_dur: int) -> float:
@ -502,16 +515,20 @@ class Backtesting:
def _get_adjust_trade_entry_for_candle(self, trade: LocalTrade, row: Tuple
) -> LocalTrade:
current_profit = trade.calc_profit_ratio(row[OPEN_IDX])
min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, row[OPEN_IDX], -0.1)
max_stake = self.exchange.get_max_pair_stake_amount(trade.pair, row[OPEN_IDX])
current_rate = row[OPEN_IDX]
current_date = row[DATE_IDX].to_pydatetime()
current_profit = trade.calc_profit_ratio(current_rate)
min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, current_rate, -0.1)
max_stake = self.exchange.get_max_pair_stake_amount(trade.pair, current_rate)
stake_available = self.wallets.get_available_stake_amount()
stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
default_retval=None)(
trade=trade, # type: ignore[arg-type]
current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
current_time=current_date, current_rate=current_rate,
current_profit=current_profit, min_stake=min_stake,
max_stake=min(max_stake, stake_available))
max_stake=min(max_stake, stake_available),
current_entry_rate=current_rate, current_exit_rate=current_rate,
current_entry_profit=current_profit, current_exit_profit=current_profit)
# Check if we should increase our position
if stake_amount is not None and stake_amount > 0.0:
@ -522,6 +539,24 @@ class Backtesting:
self.wallets.update()
return pos_trade
if stake_amount is not None and stake_amount < 0.0:
amount = abs(stake_amount) / current_rate
if amount > trade.amount:
# This is currently ineffective as remaining would become < min tradable
amount = trade.amount
remaining = (trade.amount - amount) * current_rate
if remaining < min_stake:
# Remaining stake is too low to be sold.
return trade
pos_trade = self._exit_trade(trade, row, current_rate, amount)
if pos_trade is not None:
order = pos_trade.orders[-1]
if self._get_order_filled(order.price, row):
order.close_bt_order(current_date, trade)
trade.recalc_trade_from_orders()
self.wallets.update()
return pos_trade
return trade
def _get_order_filled(self, rate: float, row: Tuple) -> bool:
@ -597,21 +632,30 @@ class Backtesting:
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['exit']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
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='limit',
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):
current_time=exit_candle_time)):
return None
trade.exit_reason = exit_reason
return self._exit_trade(trade, row, close_rate, trade.amount)
return None
def _exit_trade(self, trade: LocalTrade, sell_row: Tuple,
close_rate: float, amount: float = None) -> Optional[LocalTrade]:
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
order = Order(
id=self.order_id_counter,
ft_trade_id=trade.id,
@ -627,16 +671,14 @@ class Backtesting:
status="open",
price=close_rate,
average=close_rate,
amount=trade.amount,
amount=amount,
filled=0,
remaining=trade.amount,
cost=trade.amount * close_rate,
remaining=amount,
cost=amount * close_rate,
)
trade.orders.append(order)
return trade
return None
def _get_exit_trade_entry(self, trade: LocalTrade, row: Tuple) -> Optional[LocalTrade]:
exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
@ -812,7 +854,7 @@ class Backtesting:
trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
trade.set_isolated_liq(self.exchange.get_liquidation_price(
trade.set_liquidation_price(self.exchange.get_liquidation_price(
pair=pair,
open_rate=propose_rate,
amount=amount,
@ -863,6 +905,8 @@ class Backtesting:
# Ignore trade if entry-order did not fill yet
continue
exit_row = data[pair][-1]
self._exit_trade(trade, exit_row, exit_row[OPEN_IDX], trade.amount)
trade.orders[-1].close_bt_order(exit_row[DATE_IDX].to_pydatetime(), trade)
trade.close_date = exit_row[DATE_IDX].to_pydatetime()
trade.exit_reason = ExitType.FORCE_EXIT.value
@ -1004,7 +1048,7 @@ class Backtesting:
return None
return row
def backtest(self, processed: Dict,
def backtest(self, processed: Dict, # noqa: max-complexity: 13
start_date: datetime, end_date: datetime,
max_open_trades: int = 0, position_stacking: bool = False,
enable_protections: bool = False) -> Dict[str, Any]:
@ -1106,6 +1150,11 @@ class Backtesting:
if order and self._get_order_filled(order.price, row):
order.close_bt_order(current_time, trade)
trade.open_order_id = None
sub_trade = order.safe_amount_after_fee != trade.amount
if sub_trade:
order.close_bt_order(current_time, trade)
trade.recalc_trade_from_orders()
else:
trade.close_date = current_time
trade.close(order.price, show_msg=False)

2
freqtrade/persistence/__init__.py
View File

@ -1,5 +1,5 @@
# flake8: noqa: F401
from freqtrade.persistence.models import cleanup_db, init_db
from freqtrade.persistence.models import init_db
from freqtrade.persistence.pairlock_middleware import PairLocks
from freqtrade.persistence.trade_model import LocalTrade, Order, Trade

10
freqtrade/persistence/migrations.py
View File

@ -95,6 +95,7 @@ def migrate_trades_and_orders_table(
exit_reason = get_column_def(cols, 'sell_reason', get_column_def(cols, 'exit_reason', 'null'))
strategy = get_column_def(cols, 'strategy', 'null')
enter_tag = get_column_def(cols, 'buy_tag', get_column_def(cols, 'enter_tag', 'null'))
realized_profit = get_column_def(cols, 'realized_profit', '0.0')
trading_mode = get_column_def(cols, 'trading_mode', 'null')
@ -155,7 +156,7 @@ def migrate_trades_and_orders_table(
max_rate, min_rate, exit_reason, exit_order_status, strategy, enter_tag,
timeframe, open_trade_value, close_profit_abs,
trading_mode, leverage, liquidation_price, is_short,
interest_rate, funding_fees
interest_rate, funding_fees, realized_profit
)
select id, lower(exchange), pair, {base_currency} base_currency,
{stake_currency} stake_currency,
@ -181,7 +182,7 @@ def migrate_trades_and_orders_table(
{open_trade_value} open_trade_value, {close_profit_abs} close_profit_abs,
{trading_mode} trading_mode, {leverage} leverage, {liquidation_price} liquidation_price,
{is_short} is_short, {interest_rate} interest_rate,
{funding_fees} funding_fees
{funding_fees} funding_fees, {realized_profit} realized_profit
from {trade_back_name}
"""))
@ -297,8 +298,9 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
# Check if migration necessary
# Migrates both trades and orders table!
if not has_column(cols_orders, 'stop_price'):
# if not has_column(cols_trades, 'base_currency'):
# if ('orders' not in previous_tables
# or not has_column(cols_orders, 'stop_price')):
if not has_column(cols_trades, 'realized_profit'):
logger.info(f"Running database migration for trades - "
f"backup: {table_back_name}, {order_table_bak_name}")
migrate_trades_and_orders_table(

8
freqtrade/persistence/models.py
View File

@ -61,11 +61,3 @@ def init_db(db_url: str) -> None:
previous_tables = inspect(engine).get_table_names()
_DECL_BASE.metadata.create_all(engine)
check_migrate(engine, decl_base=_DECL_BASE, previous_tables=previous_tables)
def cleanup_db() -> None:
"""
Flushes all pending operations to disk.
:return: None
"""
Trade.commit()

201
freqtrade/persistence/trade_model.py
View File

@ -4,13 +4,15 @@ This module contains the class to persist trades into SQLite
import logging
from datetime import datetime, timedelta, timezone
from decimal import Decimal
from math import isclose
from typing import Any, Dict, List, Optional
from sqlalchemy import (Boolean, Column, DateTime, Enum, Float, ForeignKey, Integer, String,
UniqueConstraint, desc, func)
from sqlalchemy.orm import Query, lazyload, relationship
from freqtrade.constants import DATETIME_PRINT_FORMAT, NON_OPEN_EXCHANGE_STATES, BuySell, LongShort
from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPEN_EXCHANGE_STATES,
BuySell, LongShort)
from freqtrade.enums import ExitType, TradingMode
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.leverage import interest
@ -176,10 +178,9 @@ class Order(_DECL_BASE):
self.remaining = 0
self.status = 'closed'
self.ft_is_open = False
if (self.ft_order_side == trade.entry_side
and len(trade.select_filled_orders(trade.entry_side)) == 1):
if (self.ft_order_side == trade.entry_side):
trade.open_rate = self.price
trade.recalc_open_trade_value()
trade.recalc_trade_from_orders()
trade.adjust_stop_loss(trade.open_rate, trade.stop_loss_pct, refresh=True)
@staticmethod
@ -195,7 +196,7 @@ class Order(_DECL_BASE):
if filtered_orders:
oobj = filtered_orders[0]
oobj.update_from_ccxt_object(order)
Order.query.session.commit()
Trade.commit()
else:
logger.warning(f"Did not find order for {order}.")
@ -237,6 +238,7 @@ class LocalTrade():
trades: List['LocalTrade'] = []
trades_open: List['LocalTrade'] = []
total_profit: float = 0
realized_profit: float = 0
id: int = 0
@ -302,6 +304,16 @@ class LocalTrade():
# Futures properties
funding_fees: Optional[float] = None
@property
def stoploss_or_liquidation(self) -> float:
if self.liquidation_price:
if self.is_short:
return min(self.stop_loss, self.liquidation_price)
else:
return max(self.stop_loss, self.liquidation_price)
return self.stop_loss
@property
def buy_tag(self) -> Optional[str]:
"""
@ -437,6 +449,7 @@ class LocalTrade():
if self.close_date else None),
'close_timestamp': int(self.close_date.replace(
tzinfo=timezone.utc).timestamp() * 1000) if self.close_date else None,
'realized_profit': self.realized_profit or 0.0,
'close_rate': self.close_rate,
'close_rate_requested': self.close_rate_requested,
'close_profit': self.close_profit, # Deprecated
@ -497,7 +510,7 @@ class LocalTrade():
self.max_rate = max(current_price, self.max_rate or self.open_rate)
self.min_rate = min(current_price_low, self.min_rate or self.open_rate)
def set_isolated_liq(self, liquidation_price: Optional[float]):
def set_liquidation_price(self, liquidation_price: Optional[float]):
"""
Method you should use to set self.liquidation price.
Assures stop_loss is not passed the liquidation price
@ -506,22 +519,13 @@ class LocalTrade():
return
self.liquidation_price = liquidation_price
def _set_stop_loss(self, stop_loss: float, percent: float):
def __set_stop_loss(self, stop_loss: float, percent: float):
"""
Method you should use to set self.stop_loss.
Assures stop_loss is not passed the liquidation price
Method used internally to set self.stop_loss.
"""
if self.liquidation_price is not None:
if self.is_short:
sl = min(stop_loss, self.liquidation_price)
else:
sl = max(stop_loss, self.liquidation_price)
else:
sl = stop_loss
if not self.stop_loss:
self.initial_stop_loss = sl
self.stop_loss = sl
self.initial_stop_loss = stop_loss
self.stop_loss = stop_loss
self.stop_loss_pct = -1 * abs(percent)
self.stoploss_last_update = datetime.utcnow()
@ -543,18 +547,12 @@ class LocalTrade():
leverage = self.leverage or 1.0
if self.is_short:
new_loss = float(current_price * (1 + abs(stoploss / leverage)))
# If trading with leverage, don't set the stoploss below the liquidation price
if self.liquidation_price:
new_loss = min(self.liquidation_price, new_loss)
else:
new_loss = float(current_price * (1 - abs(stoploss / leverage)))
# If trading with leverage, don't set the stoploss below the liquidation price
if self.liquidation_price:
new_loss = max(self.liquidation_price, new_loss)
# no stop loss assigned yet
if self.initial_stop_loss_pct is None or refresh:
self._set_stop_loss(new_loss, stoploss)
self.__set_stop_loss(new_loss, stoploss)
self.initial_stop_loss = new_loss
self.initial_stop_loss_pct = -1 * abs(stoploss)
@ -569,7 +567,7 @@ class LocalTrade():
# ? decreasing the minimum stoploss
if (higher_stop and not self.is_short) or (lower_stop and self.is_short):
logger.debug(f"{self.pair} - Adjusting stoploss...")
self._set_stop_loss(new_loss, stoploss)
self.__set_stop_loss(new_loss, stoploss)
else:
logger.debug(f"{self.pair} - Keeping current stoploss...")
@ -601,14 +599,28 @@ class LocalTrade():
if self.is_open:
payment = "SELL" if self.is_short else "BUY"
logger.info(f'{order.order_type.upper()}_{payment} has been fulfilled for {self}.')
# condition to avoid reset value when updating fees
if self.open_order_id == order.order_id:
self.open_order_id = None
else:
logger.warning(
f'Got different open_order_id {self.open_order_id} != {order.order_id}')
self.recalc_trade_from_orders()
elif order.ft_order_side == self.exit_side:
if self.is_open:
payment = "BUY" if self.is_short else "SELL"
# * On margin shorts, you buy a little bit more than the amount (amount + interest)
logger.info(f'{order.order_type.upper()}_{payment} has been fulfilled for {self}.')
# condition to avoid reset value when updating fees
if self.open_order_id == order.order_id:
self.open_order_id = None
else:
logger.warning(
f'Got different open_order_id {self.open_order_id} != {order.order_id}')
if isclose(order.safe_amount_after_fee, self.amount, abs_tol=MATH_CLOSE_PREC):
self.close(order.safe_price)
else:
self.recalc_trade_from_orders()
elif order.ft_order_side == 'stoploss':
self.stoploss_order_id = None
self.close_rate_requested = self.stop_loss
@ -627,11 +639,11 @@ class LocalTrade():
"""
self.close_rate = rate
self.close_date = self.close_date or datetime.utcnow()
self.close_profit = self.calc_profit_ratio(rate)
self.close_profit_abs = self.calc_profit(rate)
self.close_profit_abs = self.calc_profit(rate) + self.realized_profit
self.is_open = False
self.exit_order_status = 'closed'
self.open_order_id = None
self.recalc_trade_from_orders(is_closing=True)
if show_msg:
logger.info(
'Marking %s as closed as the trade is fulfilled and found no open orders for it.',
@ -677,12 +689,12 @@ class LocalTrade():
"""
return len([o for o in self.orders if o.ft_order_side == self.exit_side])
def _calc_open_trade_value(self) -> float:
def _calc_open_trade_value(self, amount: float, open_rate: float) -> float:
"""
Calculate the open_rate including open_fee.
:return: Price in of the open trade incl. Fees
"""
open_trade = Decimal(self.amount) * Decimal(self.open_rate)
open_trade = Decimal(amount) * Decimal(open_rate)
fees = open_trade * Decimal(self.fee_open)
if self.is_short:
return float(open_trade - fees)
@ -694,7 +706,7 @@ class LocalTrade():
Recalculate open_trade_value.
Must be called whenever open_rate, fee_open is changed.
"""
self.open_trade_value = self._calc_open_trade_value()
self.open_trade_value = self._calc_open_trade_value(self.amount, self.open_rate)
def calculate_interest(self) -> Decimal:
"""
@ -726,7 +738,7 @@ class LocalTrade():
else:
return close_trade - fees
def calc_close_trade_value(self, rate: float) -> float:
def calc_close_trade_value(self, rate: float, amount: float = None) -> float:
"""
Calculate the Trade's close value including fees
:param rate: rate to compare with.
@ -735,96 +747,143 @@ class LocalTrade():
if rate is None and not self.close_rate:
return 0.0
amount = Decimal(self.amount)
amount1 = Decimal(amount or self.amount)
trading_mode = self.trading_mode or TradingMode.SPOT
if trading_mode == TradingMode.SPOT:
return float(self._calc_base_close(amount, rate, self.fee_close))
return float(self._calc_base_close(amount1, rate, self.fee_close))
elif (trading_mode == TradingMode.MARGIN):
total_interest = self.calculate_interest()
if self.is_short:
amount = amount + total_interest
return float(self._calc_base_close(amount, rate, self.fee_close))
amount1 = amount1 + total_interest
return float(self._calc_base_close(amount1, rate, self.fee_close))
else:
# Currency already owned for longs, no need to purchase
return float(self._calc_base_close(amount, rate, self.fee_close) - total_interest)
return float(self._calc_base_close(amount1, rate, self.fee_close) - total_interest)
elif (trading_mode == TradingMode.FUTURES):
funding_fees = self.funding_fees or 0.0
# Positive funding_fees -> Trade has gained from fees.
# Negative funding_fees -> Trade had to pay the fees.
if self.is_short:
return float(self._calc_base_close(amount, rate, self.fee_close)) - funding_fees
return float(self._calc_base_close(amount1, rate, self.fee_close)) - funding_fees
else:
return float(self._calc_base_close(amount, rate, self.fee_close)) + funding_fees
return float(self._calc_base_close(amount1, rate, self.fee_close)) + funding_fees
else:
raise OperationalException(
f"{self.trading_mode.value} trading is not yet available using freqtrade")
def calc_profit(self, rate: float) -> float:
def calc_profit(self, rate: float, amount: float = None, open_rate: float = None) -> float:
"""
Calculate the absolute profit in stake currency between Close and Open trade
:param rate: close rate to compare with.
:param amount: Amount to use for the calculation. Falls back to trade.amount if not set.
:param open_rate: open_rate to use. Defaults to self.open_rate if not provided.
:return: profit in stake currency as float
"""
close_trade_value = self.calc_close_trade_value(rate)
close_trade_value = self.calc_close_trade_value(rate, amount)
if amount is None or open_rate is None:
open_trade_value = self.open_trade_value
else:
open_trade_value = self._calc_open_trade_value(amount, open_rate)
if self.is_short:
profit = self.open_trade_value - close_trade_value
profit = open_trade_value - close_trade_value
else:
profit = close_trade_value - self.open_trade_value
profit = close_trade_value - open_trade_value
return float(f"{profit:.8f}")
def calc_profit_ratio(self, rate: float) -> float:
def calc_profit_ratio(
self, rate: float, amount: float = None, open_rate: float = None) -> float:
"""
Calculates the profit as ratio (including fee).
:param rate: rate to compare with.
:param amount: Amount to use for the calculation. Falls back to trade.amount if not set.
:param open_rate: open_rate to use. Defaults to self.open_rate if not provided.
:return: profit ratio as float
"""
close_trade_value = self.calc_close_trade_value(rate)
close_trade_value = self.calc_close_trade_value(rate, amount)
if amount is None or open_rate is None:
open_trade_value = self.open_trade_value
else:
open_trade_value = self._calc_open_trade_value(amount, open_rate)
short_close_zero = (self.is_short and close_trade_value == 0.0)
long_close_zero = (not self.is_short and self.open_trade_value == 0.0)
long_close_zero = (not self.is_short and open_trade_value == 0.0)
leverage = self.leverage or 1.0
if (short_close_zero or long_close_zero):
return 0.0
else:
if self.is_short:
profit_ratio = (1 - (close_trade_value / self.open_trade_value)) * leverage
profit_ratio = (1 - (close_trade_value / open_trade_value)) * leverage
else:
profit_ratio = ((close_trade_value / self.open_trade_value) - 1) * leverage
profit_ratio = ((close_trade_value / open_trade_value) - 1) * leverage
return float(f"{profit_ratio:.8f}")
def recalc_trade_from_orders(self):
def recalc_trade_from_orders(self, is_closing: bool = False):
current_amount = 0.0
current_stake = 0.0
total_stake = 0.0 # Total stake after all buy orders (does not subtract!)
avg_price = 0.0
close_profit = 0.0
close_profit_abs = 0.0
total_amount = 0.0
total_stake = 0.0
for o in self.orders:
if (o.ft_is_open or
(o.ft_order_side != self.entry_side) or
(o.status not in NON_OPEN_EXCHANGE_STATES)):
if o.ft_is_open or not o.filled:
continue
tmp_amount = o.safe_amount_after_fee
tmp_price = o.average or o.price
if tmp_amount > 0.0 and tmp_price is not None:
total_amount += tmp_amount
total_stake += tmp_price * tmp_amount
tmp_price = o.safe_price
if total_amount > 0:
is_exit = o.ft_order_side != self.entry_side
side = -1 if is_exit else 1
if tmp_amount > 0.0 and tmp_price is not None:
current_amount += tmp_amount * side
price = avg_price if is_exit else tmp_price
current_stake += price * tmp_amount * side
if current_amount > 0:
avg_price = current_stake / current_amount
if is_exit:
# Process partial exits
exit_rate = o.safe_price
exit_amount = o.safe_amount_after_fee
profit = self.calc_profit(rate=exit_rate, amount=exit_amount, open_rate=avg_price)
close_profit_abs += profit
close_profit = self.calc_profit_ratio(
exit_rate, amount=exit_amount, open_rate=avg_price)
if current_amount <= 0:
profit = close_profit_abs
else:
total_stake = total_stake + self._calc_open_trade_value(tmp_amount, price)
if close_profit:
self.close_profit = close_profit
self.realized_profit = close_profit_abs
self.close_profit_abs = profit
if current_amount > 0:
# Trade is still open
# Leverage not updated, as we don't allow changing leverage through DCA at the moment.
self.open_rate = total_stake / total_amount
self.stake_amount = total_stake / (self.leverage or 1.0)
self.amount = total_amount
self.fee_open_cost = self.fee_open * total_stake
self.open_rate = current_stake / current_amount
self.stake_amount = current_stake / (self.leverage or 1.0)
self.amount = current_amount
self.fee_open_cost = self.fee_open * current_stake
self.recalc_open_trade_value()
if self.stop_loss_pct is not None and self.open_rate is not None:
self.adjust_stop_loss(self.open_rate, self.stop_loss_pct)
elif is_closing and total_stake > 0:
# 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
def select_order_by_order_id(self, order_id: str) -> Optional[Order]:
"""
@ -846,7 +905,7 @@ class LocalTrade():
"""
orders = self.orders
if order_side:
orders = [o for o in self.orders if o.ft_order_side == order_side]
orders = [o for o in orders if o.ft_order_side == order_side]
if is_open is not None:
orders = [o for o in orders if o.ft_is_open == is_open]
if len(orders) > 0:
@ -861,9 +920,9 @@ class LocalTrade():
:return: array of Order objects
"""
return [o for o in self.orders if ((o.ft_order_side == order_side) or (order_side is None))
and o.ft_is_open is False and
(o.filled or 0) > 0 and
o.status in NON_OPEN_EXCHANGE_STATES]
and o.ft_is_open is False
and o.filled
and o.status in NON_OPEN_EXCHANGE_STATES]
def select_filled_or_open_orders(self) -> List['Order']:
"""
@ -1028,6 +1087,7 @@ class Trade(_DECL_BASE, LocalTrade):
open_trade_value = Column(Float)
close_rate: Optional[float] = Column(Float)
close_rate_requested = Column(Float)
realized_profit = Column(Float, default=0.0)
close_profit = Column(Float)
close_profit_abs = Column(Float)
stake_amount = Column(Float, nullable=False)
@ -1073,6 +1133,7 @@ class Trade(_DECL_BASE, LocalTrade):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.realized_profit = 0
self.recalc_open_trade_value()
def delete(self) -> None:
@ -1087,6 +1148,10 @@ class Trade(_DECL_BASE, LocalTrade):
def commit():
Trade.query.session.commit()
@staticmethod
def rollback():
Trade.query.session.rollback()
@staticmethod
def get_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,

2
freqtrade/plugins/pairlist/AgeFilter.py
View File

@ -8,11 +8,11 @@ from typing import Any, Dict, List, Optional
import arrow
from pandas import DataFrame
from freqtrade.configuration import PeriodicCache
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural
from freqtrade.plugins.pairlist.IPairList import IPairList
from freqtrade.util import PeriodicCache
logger = logging.getLogger(__name__)

10
freqtrade/plugins/pairlist/pairlist_helpers.py
View File

@ -43,12 +43,10 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
def dynamic_expand_pairlist(config: dict, markets: list) -> List[str]:
if config.get('freqai', {}):
corr_pairlist = config['freqai']['feature_parameters']['include_corr_pairlist']
full_pairs = config['pairs'] + [pair for pair in corr_pairlist
if pair not in config['pairs']]
expanded_pairs = expand_pairlist(full_pairs, markets)
else:
expanded_pairs = expand_pairlist(config['pairs'], markets)
if config.get('freqai', {}).get('enabled', False):
corr_pairlist = config['freqai']['feature_parameters']['include_corr_pairlist']
expanded_pairs += [pair for pair in corr_pairlist
if pair not in config['pairs']]
return expanded_pairs

2
freqtrade/plugins/protections/stoploss_guard.py
View File

@ -49,7 +49,7 @@ class StoplossGuard(IProtection):
trades1 = Trade.get_trades_proxy(pair=pair, is_open=False, close_date=look_back_until)
trades = [trade for trade in trades1 if (str(trade.exit_reason) in (
ExitType.TRAILING_STOP_LOSS.value, ExitType.STOP_LOSS.value,
ExitType.STOPLOSS_ON_EXCHANGE.value)
ExitType.STOPLOSS_ON_EXCHANGE.value, ExitType.LIQUIDATION.value)
and trade.close_profit and trade.close_profit < self._profit_limit)]
if self._only_per_side:

6
freqtrade/rpc/api_server/api_schemas.py
View File

@ -194,11 +194,11 @@ class OrderSchema(BaseModel):
pair: str
order_id: str
status: str
remaining: float
remaining: Optional[float]
amount: float
safe_price: float
cost: float
filled: float
filled: Optional[float]
ft_order_side: str
order_type: str
is_open: bool
@ -325,11 +325,13 @@ class ForceEnterPayload(BaseModel):
ordertype: Optional[OrderTypeValues]
stakeamount: Optional[float]
entry_tag: Optional[str]
leverage: Optional[float]
class ForceExitPayload(BaseModel):
tradeid: str
ordertype: Optional[OrderTypeValues]
amount: Optional[float]
class BlacklistPayload(BaseModel):

12
freqtrade/rpc/api_server/api_v1.py
View File

@ -37,7 +37,8 @@ logger = logging.getLogger(__name__)
# 2.14: Add entry/exit orders to trade response
# 2.15: Add backtest history endpoints
# 2.16: Additional daily metrics
API_VERSION = 2.16
# 2.17: Forceentry - leverage, partial force_exit
API_VERSION = 2.17
# Public API, requires no auth.
router_public = APIRouter()
@ -142,12 +143,11 @@ def show_config(rpc: Optional[RPC] = Depends(get_rpc_optional), config=Depends(g
@router.post('/forcebuy', response_model=ForceEnterResponse, tags=['trading'])
def force_entry(payload: ForceEnterPayload, rpc: RPC = Depends(get_rpc)):
ordertype = payload.ordertype.value if payload.ordertype else None
stake_amount = payload.stakeamount if payload.stakeamount else None
entry_tag = payload.entry_tag if payload.entry_tag else 'force_entry'
trade = rpc._rpc_force_entry(payload.pair, payload.price, order_side=payload.side,
order_type=ordertype, stake_amount=stake_amount,
enter_tag=entry_tag)
order_type=ordertype, stake_amount=payload.stakeamount,
enter_tag=payload.entry_tag or 'force_entry',
leverage=payload.leverage)
if trade:
return ForceEnterResponse.parse_obj(trade.to_json())
@ -161,7 +161,7 @@ def force_entry(payload: ForceEnterPayload, rpc: RPC = Depends(get_rpc)):
@router.post('/forcesell', response_model=ResultMsg, tags=['trading'])
def forceexit(payload: ForceExitPayload, rpc: RPC = Depends(get_rpc)):
ordertype = payload.ordertype.value if payload.ordertype else None
return rpc._rpc_force_exit(payload.tradeid, ordertype)
return rpc._rpc_force_exit(payload.tradeid, ordertype, amount=payload.amount)
@router.get('/blacklist', response_model=BlacklistResponse, tags=['info', 'pairlist'])

4
freqtrade/rpc/api_server/deps.py
View File

@ -18,9 +18,9 @@ def get_rpc_optional() -> Optional[RPC]:
def get_rpc() -> Optional[Iterator[RPC]]:
_rpc = get_rpc_optional()
if _rpc:
Trade.query.session.rollback()
Trade.rollback()
yield _rpc
Trade.query.session.rollback()
Trade.rollback()
else:
raise RPCException('Bot is not in the correct state')

7
freqtrade/rpc/api_server/web_ui.py
View File

@ -1,4 +1,5 @@
from pathlib import Path
from typing import Optional
from fastapi import APIRouter
from fastapi.exceptions import HTTPException
@ -50,8 +51,12 @@ async def index_html(rest_of_path: str):
filename = uibase / rest_of_path
# It's security relevant to check "relative_to".
# Without this, Directory-traversal is possible.
media_type: Optional[str] = None
if filename.suffix == '.js':
# Force text/javascript for .js files - Circumvent faulty system configuration
media_type = 'application/javascript'
if filename.is_file() and is_relative_to(filename, uibase):
return FileResponse(str(filename))
return FileResponse(str(filename), media_type=media_type)
index_file = uibase / 'index.html'
if not index_file.is_file():

8
freqtrade/rpc/fiat_convert.py
View File

@ -12,6 +12,7 @@ from pycoingecko import CoinGeckoAPI
from requests.exceptions import RequestException
from freqtrade.constants import SUPPORTED_FIAT
from freqtrade.mixins.logging_mixin import LoggingMixin
logger = logging.getLogger(__name__)
@ -27,7 +28,7 @@ coingecko_mapping = {
}
class CryptoToFiatConverter:
class CryptoToFiatConverter(LoggingMixin):
"""
Main class to initiate Crypto to FIAT.
This object contains a list of pair Crypto, FIAT
@ -54,6 +55,7 @@ class CryptoToFiatConverter:
# Timeout: 6h
self._pair_price: TTLCache = TTLCache(maxsize=500, ttl=6 * 60 * 60)
LoggingMixin.__init__(self, logger, 3600)
self._load_cryptomap()
def _load_cryptomap(self) -> None:
@ -177,7 +179,9 @@ class CryptoToFiatConverter:
if not _gekko_id:
# return 0 for unsupported stake currencies (fiat-convert should not break the bot)
logger.warning("unsupported crypto-symbol %s - returning 0.0", crypto_symbol)
self.log_once(
f"unsupported crypto-symbol {crypto_symbol.upper()} - returning 0.0",
logger.warning)
return 0.0
try:

70
freqtrade/rpc/rpc.py
View File

@ -179,8 +179,10 @@ class RPC:
else:
current_rate = trade.close_rate
if len(trade.select_filled_orders(trade.entry_side)) > 0:
current_profit = trade.calc_profit_ratio(current_rate)
current_profit_abs = trade.calc_profit(current_rate)
current_profit = trade.calc_profit_ratio(
current_rate) if not isnan(current_rate) else NAN
current_profit_abs = trade.calc_profit(
current_rate) if not isnan(current_rate) else NAN
current_profit_fiat: Optional[float] = None
# Calculate fiat profit
if self._fiat_converter:
@ -201,7 +203,7 @@ class RPC:
trade_dict = trade.to_json()
trade_dict.update(dict(
close_profit=trade.close_profit if trade.close_profit is not None else None,
close_profit=trade.close_profit if not trade.is_open else None,
current_rate=current_rate,
current_profit=current_profit, # Deprecated
current_profit_pct=round(current_profit * 100, 2), # Deprecated
@ -239,7 +241,10 @@ class RPC:
trade.pair, side='exit', is_short=trade.is_short, refresh=False)
except (PricingError, ExchangeError):
current_rate = NAN
if len(trade.select_filled_orders(trade.entry_side)) > 0:
trade_profit = NAN
profit_str = f'{NAN:.2%}'
else:
if trade.nr_of_successful_entries > 0:
trade_profit = trade.calc_profit(current_rate)
profit_str = f'{trade.calc_profit_ratio(current_rate):.2%}'
else:
@ -424,21 +429,20 @@ class RPC:
for trade in trades:
current_rate: float = 0.0
if not trade.open_rate:
continue
if trade.close_date:
durations.append((trade.close_date - trade.open_date).total_seconds())
if not trade.is_open:
profit_ratio = trade.close_profit
profit_closed_coin.append(trade.close_profit_abs)
profit_abs = trade.close_profit_abs
profit_closed_coin.append(profit_abs)
profit_closed_ratio.append(profit_ratio)
if trade.close_profit >= 0:
winning_trades += 1
winning_profit += trade.close_profit_abs
winning_profit += profit_abs
else:
losing_trades += 1
losing_profit += trade.close_profit_abs
losing_profit += profit_abs
else:
# Get current rate
try:
@ -446,11 +450,15 @@ class RPC:
trade.pair, side='exit', is_short=trade.is_short, refresh=False)
except (PricingError, ExchangeError):
current_rate = NAN
if isnan(current_rate):
profit_ratio = NAN
profit_abs = NAN
else:
profit_ratio = trade.calc_profit_ratio(rate=current_rate)
profit_abs = trade.calc_profit(
rate=trade.close_rate or current_rate) + trade.realized_profit
profit_all_coin.append(
trade.calc_profit(rate=trade.close_rate or current_rate)
)
profit_all_coin.append(profit_abs)
profit_all_ratio.append(profit_ratio)
best_pair = Trade.get_best_pair(start_date)
@ -659,12 +667,8 @@ class RPC:
return {'status': 'No more buy will occur from now. Run /reload_config to reset.'}
def _rpc_force_exit(self, trade_id: str, ordertype: Optional[str] = None) -> Dict[str, str]:
"""
Handler for forceexit <id>.
Sells the given trade at current price
"""
def _exec_force_exit(trade: Trade) -> None:
def __exec_force_exit(self, trade: Trade, ordertype: Optional[str],
amount: Optional[float] = None) -> None:
# Check if there is there is an open order
fully_canceled = False
if trade.open_order_id:
@ -685,10 +689,26 @@ class RPC:
exit_check = ExitCheckTuple(exit_type=ExitType.FORCE_EXIT)
order_type = ordertype or self._freqtrade.strategy.order_types.get(
"force_exit", self._freqtrade.strategy.order_types["exit"])
sub_amount: Optional[float] = None
if amount and amount < trade.amount:
# Partial exit ...
min_exit_stake = self._freqtrade.exchange.get_min_pair_stake_amount(
trade.pair, current_rate, trade.stop_loss_pct)
remaining = (trade.amount - amount) * current_rate
if remaining < min_exit_stake:
raise RPCException(f'Remaining amount of {remaining} would be too small.')
sub_amount = amount
self._freqtrade.execute_trade_exit(
trade, current_rate, exit_check, ordertype=order_type)
# ---- EOF def _exec_forcesell ----
trade, current_rate, exit_check, ordertype=order_type,
sub_trade_amt=sub_amount)
def _rpc_force_exit(self, trade_id: str, ordertype: Optional[str] = None, *,
amount: Optional[float] = None) -> Dict[str, str]:
"""
Handler for forceexit <id>.
Sells the given trade at current price
"""
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
@ -697,7 +717,7 @@ class RPC:
if trade_id == 'all':
# Execute sell for all open orders
for trade in Trade.get_open_trades():
_exec_force_exit(trade)
self.__exec_force_exit(trade, ordertype)
Trade.commit()
self._freqtrade.wallets.update()
return {'result': 'Created sell orders for all open trades.'}
@ -710,7 +730,7 @@ class RPC:
logger.warning('force_exit: Invalid argument received')
raise RPCException('invalid argument')
_exec_force_exit(trade)
self.__exec_force_exit(trade, ordertype, amount)
Trade.commit()
self._freqtrade.wallets.update()
return {'result': f'Created sell order for trade {trade_id}.'}
@ -719,7 +739,8 @@ class RPC:
order_type: Optional[str] = None,
order_side: SignalDirection = SignalDirection.LONG,
stake_amount: Optional[float] = None,
enter_tag: Optional[str] = 'force_entry') -> Optional[Trade]:
enter_tag: Optional[str] = 'force_entry',
leverage: Optional[float] = None) -> Optional[Trade]:
"""
Handler for forcebuy <asset> <price>
Buys a pair trade at the given or current price
@ -761,6 +782,7 @@ class RPC:
ordertype=order_type, trade=trade,
is_short=is_short,
enter_tag=enter_tag,
leverage_=leverage,
):
Trade.commit()
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
@ -875,7 +897,7 @@ class RPC:
lock.active = False
lock.lock_end_time = datetime.now(timezone.utc)
PairLock.query.session.commit()
Trade.commit()
return self._rpc_locks()

12
freqtrade/rpc/rpc_manager.py
View File

@ -2,6 +2,7 @@
This module contains class to manage RPC communications (Telegram, API, ...)
"""
import logging
from collections import deque
from typing import Any, Dict, List
from freqtrade.enums import RPCMessageType
@ -77,6 +78,17 @@ class RPCManager:
except NotImplementedError:
logger.error(f"Message type '{msg['type']}' not implemented by handler {mod.name}.")
def process_msg_queue(self, queue: deque) -> None:
"""
Process all messages in the queue.
"""
while queue:
msg = queue.popleft()
self.send_msg({
'type': RPCMessageType.STRATEGY_MSG,
'msg': msg,
})
def startup_messages(self, config: Dict[str, Any], pairlist, protections) -> None:
if config['dry_run']:
self.send_msg({

159
freqtrade/rpc/telegram.py
View File

@ -16,8 +16,8 @@ from typing import Any, Callable, Dict, List, Optional, Union
import arrow
from tabulate import tabulate
from telegram import (CallbackQuery, InlineKeyboardButton, InlineKeyboardMarkup, KeyboardButton,
ParseMode, ReplyKeyboardMarkup, Update)
from telegram import (MAX_MESSAGE_LENGTH, CallbackQuery, InlineKeyboardButton, InlineKeyboardMarkup,
KeyboardButton, ParseMode, ReplyKeyboardMarkup, Update)
from telegram.error import BadRequest, NetworkError, TelegramError
from telegram.ext import CallbackContext, CallbackQueryHandler, CommandHandler, Updater
from telegram.utils.helpers import escape_markdown
@ -35,8 +35,6 @@ logger = logging.getLogger(__name__)
logger.debug('Included module rpc.telegram ...')
MAX_TELEGRAM_MESSAGE_LENGTH = 4096
@dataclass
class TimeunitMappings:
@ -72,7 +70,7 @@ def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]:
)
return wrapper
# Rollback session to avoid getting data stored in a transaction.
Trade.query.session.rollback()
Trade.rollback()
logger.debug(
'Executing handler: %s for chat_id: %s',
command_handler.__name__,
@ -315,20 +313,36 @@ class Telegram(RPCHandler):
msg['profit_fiat'] = self._rpc._fiat_converter.convert_amount(
msg['profit_amount'], msg['stake_currency'], msg['fiat_currency'])
msg['profit_extra'] = (
f" ({msg['gain']}: {msg['profit_amount']:.8f} {msg['stake_currency']}"
f" / {msg['profit_fiat']:.3f} {msg['fiat_currency']})")
f" / {msg['profit_fiat']:.3f} {msg['fiat_currency']}")
else:
msg['profit_extra'] = ''
msg['profit_extra'] = (
f" ({msg['gain']}: {msg['profit_amount']:.8f} {msg['stake_currency']}"
f"{msg['profit_extra']})")
is_fill = msg['type'] == RPCMessageType.EXIT_FILL
is_sub_trade = msg.get('sub_trade')
is_sub_profit = msg['profit_amount'] != msg.get('cumulative_profit')
profit_prefix = ('Sub ' if is_sub_profit
else 'Cumulative ') if is_sub_trade else ''
cp_extra = ''
if is_sub_profit and is_sub_trade:
if self._rpc._fiat_converter:
cp_fiat = self._rpc._fiat_converter.convert_amount(
msg['cumulative_profit'], msg['stake_currency'], msg['fiat_currency'])
cp_extra = f" / {cp_fiat:.3f} {msg['fiat_currency']}"
else:
cp_extra = ''
cp_extra = f"*Cumulative Profit:* (`{msg['cumulative_profit']:.8f} " \
f"{msg['stake_currency']}{cp_extra}`)\n"
message = (
f"{msg['emoji']} *{self._exchange_from_msg(msg)}:* "
f"{'Exited' if is_fill else 'Exiting'} {msg['pair']} (#{msg['trade_id']})\n"
f"{self._add_analyzed_candle(msg['pair'])}"
f"*{'Profit' if is_fill else 'Unrealized Profit'}:* "
f"*{f'{profit_prefix}Profit' if is_fill else f'Unrealized {profit_prefix}Profit'}:* "
f"`{msg['profit_ratio']:.2%}{msg['profit_extra']}`\n"
f"{cp_extra}"
f"*Enter Tag:* `{msg['enter_tag']}`\n"
f"*Exit Reason:* `{msg['exit_reason']}`\n"
f"*Duration:* `{msg['duration']} ({msg['duration_min']:.1f} min)`\n"
f"*Direction:* `{msg['direction']}`\n"
f"{msg['leverage_text']}"
f"*Amount:* `{msg['amount']:.8f}`\n"
@ -336,11 +350,25 @@ class Telegram(RPCHandler):
)
if msg['type'] == RPCMessageType.EXIT:
message += (f"*Current Rate:* `{msg['current_rate']:.8f}`\n"
f"*Close Rate:* `{msg['limit']:.8f}`")
f"*Exit Rate:* `{msg['limit']:.8f}`")
elif msg['type'] == RPCMessageType.EXIT_FILL:
message += f"*Close Rate:* `{msg['close_rate']:.8f}`"
message += f"*Exit Rate:* `{msg['close_rate']:.8f}`"
if msg.get('sub_trade'):
if self._rpc._fiat_converter:
msg['stake_amount_fiat'] = self._rpc._fiat_converter.convert_amount(
msg['stake_amount'], msg['stake_currency'], msg['fiat_currency'])
else:
msg['stake_amount_fiat'] = 0
rem = round_coin_value(msg['stake_amount'], msg['stake_currency'])
message += f"\n*Remaining:* `({rem}"
if msg.get('fiat_currency', None):
message += f", {round_coin_value(msg['stake_amount_fiat'], msg['fiat_currency'])}"
message += ")`"
else:
message += f"\n*Duration:* `{msg['duration']} ({msg['duration_min']:.1f} min)`"
return message
def compose_message(self, msg: Dict[str, Any], msg_type: RPCMessageType) -> str:
@ -353,7 +381,8 @@ class Telegram(RPCHandler):
elif msg_type in (RPCMessageType.ENTRY_CANCEL, RPCMessageType.EXIT_CANCEL):
msg['message_side'] = 'enter' if msg_type in [RPCMessageType.ENTRY_CANCEL] else 'exit'
message = (f"\N{WARNING SIGN} *{self._exchange_from_msg(msg)}:* "
f"Cancelling {msg['message_side']} Order for {msg['pair']} "
f"Cancelling {'partial ' if msg.get('sub_trade') else ''}"
f"{msg['message_side']} Order for {msg['pair']} "
f"(#{msg['trade_id']}). Reason: {msg['reason']}.")
elif msg_type == RPCMessageType.PROTECTION_TRIGGER:
@ -376,7 +405,8 @@ class Telegram(RPCHandler):
elif msg_type == RPCMessageType.STARTUP:
message = f"{msg['status']}"
elif msg_type == RPCMessageType.STRATEGY_MSG:
message = f"{msg['msg']}"
else:
raise NotImplementedError(f"Unknown message type: {msg_type}")
return message
@ -423,54 +453,63 @@ class Telegram(RPCHandler):
else:
return "\N{CROSS MARK}"
def _prepare_entry_details(self, filled_orders: List, quote_currency: str, is_open: bool):
def _prepare_order_details(self, filled_orders: List, quote_currency: str, is_open: bool):
"""
Prepare details of trade with entry adjustment enabled
"""
lines: List[str] = []
lines_detail: List[str] = []
if len(filled_orders) > 0:
first_avg = filled_orders[0]["safe_price"]
for x, order in enumerate(filled_orders):
if not order['ft_is_entry'] or order['is_open'] is True:
lines: List[str] = []
if order['is_open'] is True:
continue
wording = 'Entry' if order['ft_is_entry'] else 'Exit'
cur_entry_datetime = arrow.get(order["order_filled_date"])
cur_entry_amount = order["amount"]
cur_entry_amount = order["filled"] or order["amount"]
cur_entry_average = order["safe_price"]
lines.append(" ")
if x == 0:
lines.append(f"*Entry #{x+1}:*")
lines.append(f"*{wording} #{x+1}:*")
lines.append(
f"*Entry Amount:* {cur_entry_amount} ({order['cost']:.8f} {quote_currency})")
lines.append(f"*Average Entry Price:* {cur_entry_average}")
f"*Amount:* {cur_entry_amount} ({order['cost']:.8f} {quote_currency})")
lines.append(f"*Average Price:* {cur_entry_average}")
else:
sumA = 0
sumB = 0
for y in range(x):
sumA += (filled_orders[y]["amount"] * filled_orders[y]["safe_price"])
sumB += filled_orders[y]["amount"]
amount = filled_orders[y]["filled"] or filled_orders[y]["amount"]
sumA += amount * filled_orders[y]["safe_price"]
sumB += amount
prev_avg_price = sumA / sumB
# TODO: This calculation ignores fees.
price_to_1st_entry = ((cur_entry_average - first_avg) / first_avg)
minus_on_entry = 0
if prev_avg_price:
minus_on_entry = (cur_entry_average - prev_avg_price) / prev_avg_price
dur_entry = cur_entry_datetime - arrow.get(
filled_orders[x - 1]["order_filled_date"])
days = dur_entry.days
hours, remainder = divmod(dur_entry.seconds, 3600)
minutes, seconds = divmod(remainder, 60)
lines.append(f"*Entry #{x+1}:* at {minus_on_entry:.2%} avg profit")
lines.append(f"*{wording} #{x+1}:* at {minus_on_entry:.2%} avg profit")
if is_open:
lines.append("({})".format(cur_entry_datetime
.humanize(granularity=["day", "hour", "minute"])))
lines.append(
f"*Entry Amount:* {cur_entry_amount} ({order['cost']:.8f} {quote_currency})")
lines.append(f"*Average Entry Price:* {cur_entry_average} "
f"*Amount:* {cur_entry_amount} ({order['cost']:.8f} {quote_currency})")
lines.append(f"*Average {wording} Price:* {cur_entry_average} "
f"({price_to_1st_entry:.2%} from 1st entry rate)")
lines.append(f"*Order filled at:* {order['order_filled_date']}")
lines.append(f"({days}d {hours}h {minutes}m {seconds}s from previous entry)")
return lines
lines.append(f"*Order filled:* {order['order_filled_date']}")
# TODO: is this really useful?
# dur_entry = cur_entry_datetime - arrow.get(
# filled_orders[x - 1]["order_filled_date"])
# days = dur_entry.days
# hours, remainder = divmod(dur_entry.seconds, 3600)
# minutes, seconds = divmod(remainder, 60)
# lines.append(
# f"({days}d {hours}h {minutes}m {seconds}s from previous {wording.lower()})")
lines_detail.append("\n".join(lines))
return lines_detail
@authorized_only
def _status(self, update: Update, context: CallbackContext) -> None:
@ -485,7 +524,14 @@ class Telegram(RPCHandler):
if context.args and 'table' in context.args:
self._status_table(update, context)
return
else:
self._status_msg(update, context)
def _status_msg(self, update: Update, context: CallbackContext) -> None:
"""
handler for `/status` and `/status <id>`.
"""
try:
# Check if there's at least one numerical ID provided.
@ -497,7 +543,6 @@ class Telegram(RPCHandler):
results = self._rpc._rpc_trade_status(trade_ids=trade_ids)
position_adjust = self._config.get('position_adjustment_enable', False)
max_entries = self._config.get('max_entry_position_adjustment', -1)
messages = []
for r in results:
r['open_date_hum'] = arrow.get(r['open_date']).humanize()
r['num_entries'] = len([o for o in r['orders'] if o['ft_is_entry']])
@ -528,6 +573,8 @@ class Telegram(RPCHandler):
])
if r['is_open']:
if r.get('realized_profit'):
lines.append("*Realized Profit:* `{realized_profit:.8f}`")
if (r['stop_loss_abs'] != r['initial_stop_loss_abs']
and r['initial_stop_loss_ratio'] is not None):
# Adding initial stoploss only if it is different from stoploss
@ -540,24 +587,34 @@ class Telegram(RPCHandler):
lines.append("*Stoploss distance:* `{stoploss_current_dist:.8f}` "
"`({stoploss_current_dist_ratio:.2%})`")
if r['open_order']:
if r['exit_order_status']:
lines.append("*Open Order:* `{open_order}` - `{exit_order_status}`")
else:
lines.append("*Open Order:* `{open_order}`")
lines.append(
"*Open Order:* `{open_order}`"
+ "- `{exit_order_status}`" if r['exit_order_status'] else "")
lines_detail = self._prepare_entry_details(
lines_detail = self._prepare_order_details(
r['orders'], r['quote_currency'], r['is_open'])
lines.extend(lines_detail if lines_detail else "")
# Filter empty lines using list-comprehension
messages.append("\n".join([line for line in lines if line]).format(**r))
for msg in messages:
self._send_msg(msg)
self.__send_status_msg(lines, r)
except RPCException as e:
self._send_msg(str(e))
def __send_status_msg(self, lines: List[str], r: Dict[str, Any]) -> None:
"""
Send status message.
"""
msg = ''
for line in lines:
if line:
if (len(msg) + len(line) + 1) < MAX_MESSAGE_LENGTH:
msg += line + '\n'
else:
self._send_msg(msg.format(**r))
msg = "*Trade ID:* `{trade_id}` - continued\n" + line + '\n'
self._send_msg(msg.format(**r))
@authorized_only
def _status_table(self, update: Update, context: CallbackContext) -> None:
"""
@ -860,7 +917,7 @@ class Telegram(RPCHandler):
total_dust_currencies += 1
# Handle overflowing message length
if len(output + curr_output) >= MAX_TELEGRAM_MESSAGE_LENGTH:
if len(output + curr_output) >= MAX_MESSAGE_LENGTH:
self._send_msg(output)
output = curr_output
else:
@ -1123,7 +1180,7 @@ class Telegram(RPCHandler):
f"({trade['profit_ratio']:.2%}) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
@ -1158,7 +1215,7 @@ class Telegram(RPCHandler):
f"({trade['profit_ratio']:.2%}) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
@ -1193,7 +1250,7 @@ class Telegram(RPCHandler):
f"({trade['profit_ratio']:.2%}) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
@ -1228,7 +1285,7 @@ class Telegram(RPCHandler):
f"({trade['profit']:.2%}) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
@ -1367,7 +1424,7 @@ class Telegram(RPCHandler):
escape_markdown(logrec[2], version=2),
escape_markdown(logrec[3], version=2),
escape_markdown(logrec[4], version=2))
if len(msgs + msg) + 10 >= MAX_TELEGRAM_MESSAGE_LENGTH:
if len(msgs + msg) + 10 >= MAX_MESSAGE_LENGTH:
# Send message immediately if it would become too long
self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2)
msgs = msg + '\n'

54
freqtrade/strategy/interface.py
View File

@ -146,11 +146,19 @@ class IStrategy(ABC, HyperStrategyMixin):
self._ft_informative.append((informative_data, cls_method))
def load_freqAI_model(self) -> None:
if self.config.get('freqai', None):
if self.config.get('freqai', {}).get('enabled', False):
# Import here to avoid importing this if freqAI is disabled
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
self.freqai = FreqaiModelResolver.load_freqaimodel(self.config)
self.freqai_info = self.config["freqai"]
else:
# Gracious failures if freqAI is disabled but "start" is called.
class DummyClass():
def start(self, *args, **kwargs):
raise OperationalException(
'freqAI is not enabled. Please enable it in your config to use this strategy.')
self.freqai = DummyClass() # type: ignore
def ft_bot_start(self, **kwargs) -> None:
"""
@ -472,10 +480,13 @@ class IStrategy(ABC, HyperStrategyMixin):
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float,
min_stake: Optional[float], max_stake: float,
current_entry_rate: float, current_exit_rate: float,
current_entry_profit: float, current_exit_profit: float,
**kwargs) -> Optional[float]:
"""
Custom trade adjustment logic, returning the stake amount that a trade should be increased.
This means extra buy orders with additional fees.
Custom trade adjustment logic, returning the stake amount that a trade should be
increased or decreased.
This means extra buy or sell orders with additional fees.
Only called when `position_adjustment_enable` is set to True.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
@ -486,10 +497,16 @@ class IStrategy(ABC, HyperStrategyMixin):
:param current_time: datetime object, containing the current datetime
:param current_rate: Current buy rate.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param min_stake: Minimal stake size allowed by exchange.
:param max_stake: Balance available for trading.
:param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
:param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
:param current_entry_rate: Current rate using entry pricing.
:param current_exit_rate: Current rate using exit pricing.
:param current_entry_profit: Current profit using entry pricing.
:param current_exit_profit: Current profit using exit pricing.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: Stake amount to adjust your trade
:return float: Stake amount to adjust your trade,
Positive values to increase position, Negative values to decrease position.
Return None for no action.
"""
return None
@ -557,8 +574,8 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
return None
def populate_any_indicators(self, basepair: str, pair: str, df: DataFrame, tf: str,
informative: DataFrame = None, coin: str = "",
def populate_any_indicators(self, pair: str, df: DataFrame, tf: str,
informative: DataFrame = None,
set_generalized_indicators: bool = False) -> DataFrame:
"""
Function designed to automatically generate, name and merge features
@ -570,7 +587,6 @@ class IStrategy(ABC, HyperStrategyMixin):
: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.
"""
return df
@ -989,7 +1005,7 @@ class IStrategy(ABC, HyperStrategyMixin):
# ROI
# Trailing stoploss
if stoplossflag.exit_type == ExitType.STOP_LOSS:
if stoplossflag.exit_type in (ExitType.STOP_LOSS, ExitType.LIQUIDATION):
logger.debug(f"{trade.pair} - Stoploss hit. exit_type={stoplossflag.exit_type}")
exits.append(stoplossflag)
@ -1061,6 +1077,17 @@ class IStrategy(ABC, HyperStrategyMixin):
sl_higher_long = (trade.stop_loss >= (low or current_rate) and not trade.is_short)
sl_lower_short = (trade.stop_loss <= (high or current_rate) and trade.is_short)
liq_higher_long = (trade.liquidation_price
and trade.liquidation_price >= (low or current_rate)
and not trade.is_short)
liq_lower_short = (trade.liquidation_price
and trade.liquidation_price <= (high or current_rate)
and trade.is_short)
if (liq_higher_long or liq_lower_short):
logger.debug(f"{trade.pair} - Liquidation price hit. exit_type=ExitType.LIQUIDATION")
return ExitCheckTuple(exit_type=ExitType.LIQUIDATION)
# evaluate if the stoploss was hit if stoploss is not on exchange
# in Dry-Run, this handles stoploss logic as well, as the logic will not be different to
# regular stoploss handling.
@ -1078,13 +1105,6 @@ class IStrategy(ABC, HyperStrategyMixin):
f"stoploss is {trade.stop_loss:.6f}, "
f"initial stoploss was at {trade.initial_stop_loss:.6f}, "
f"trade opened at {trade.open_rate:.6f}")
new_stoploss = (
trade.stop_loss + trade.initial_stop_loss
if trade.is_short else
trade.stop_loss - trade.initial_stop_loss
)
logger.debug(f"{trade.pair} - Trailing stop saved "
f"{new_stoploss:.6f}")
return ExitCheckTuple(exit_type=exit_type)

45
freqtrade/templates/FreqaiExampleStrategy.py
View File

@ -65,7 +65,7 @@ class FreqaiExampleStrategy(IStrategy):
return informative_pairs
def populate_any_indicators(
self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
"""
Function designed to automatically generate, name and merge features
@ -78,9 +78,10 @@ class FreqaiExampleStrategy(IStrategy):
: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]
with self.freqai.lock:
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
@ -92,11 +93,8 @@ class FreqaiExampleStrategy(IStrategy):
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}20sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"{coin}21ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{coin}close_over_20sma-period_{t}"] = (
informative["close"] / informative[f"{coin}20sma-period_{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}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
@ -148,8 +146,6 @@ class FreqaiExampleStrategy(IStrategy):
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"])
@ -159,12 +155,31 @@ class FreqaiExampleStrategy(IStrategy):
- 1
)
# Classifiers are typically set up with strings as targets:
# df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
# df["close"], 'up', 'down')
# If user wishes to use multiple targets, they can add more by
# appending more columns with '&'. User should keep in mind that multi targets
# requires a multioutput prediction model such as
# templates/CatboostPredictionMultiModel.py,
# df["&-s_range"] = (
# df["close"]
# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
# .max()
# -
# df["close"]
# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
# .min()
# )
return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config["freqai"]
# All indicators must be populated by populate_any_indicators() for live functionality
# to work correctly.
@ -235,16 +250,16 @@ class FreqaiExampleStrategy(IStrategy):
if (
"prediction" + entry_tag not in pair_dict[pair]
or pair_dict[pair]["prediction" + entry_tag] > 0
or pair_dict[pair]['extras']["prediction" + entry_tag] == 0
):
with self.freqai.lock:
pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&-s_close"])
pair_dict[pair]['extras']["prediction" + entry_tag] = abs(trade_candle["&-s_close"])
if not follow_mode:
self.freqai.dd.save_drawer_to_disk()
else:
self.freqai.dd.save_follower_dict_to_disk()
roi_price = pair_dict[pair]["prediction" + entry_tag]
roi_price = pair_dict[pair]['extras']["prediction" + entry_tag]
roi_time = self.max_roi_time_long.value
roi_decay = roi_price * (
@ -282,7 +297,7 @@ class FreqaiExampleStrategy(IStrategy):
pair_dict = self.freqai.dd.follower_dict
with self.freqai.lock:
pair_dict[pair]["prediction" + entry_tag] = 0
pair_dict[pair]['extras']["prediction" + entry_tag] = 0
if not follow_mode:
self.freqai.dd.save_drawer_to_disk()
else:

1
freqtrade/templates/base_config.json.j2
View File

@ -12,6 +12,7 @@
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "{{ fiat_display_currency }}",{{ ('\n "timeframe": "' + timeframe + '",') if timeframe else '' }}
"dry_run": {{ dry_run | lower }},
"dry_run_wallet": 1000,
"cancel_open_orders_on_exit": false,
"trading_mode": "{{ trading_mode }}",
"margin_mode": "{{ margin_mode }}",

26
freqtrade/templates/subtemplates/strategy_methods_advanced.j2
View File

@ -247,12 +247,16 @@ def check_exit_timeout(self, pair: str, trade: 'Trade', order: 'Order',
"""
return False
def adjust_trade_position(self, trade: 'Trade', current_time: 'datetime',
current_rate: float, current_profit: float, min_stake: Optional[float],
max_stake: float, **kwargs) -> 'Optional[float]':
def adjust_trade_position(self, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float,
min_stake: Optional[float], max_stake: float,
current_entry_rate: float, current_exit_rate: float,
current_entry_profit: float, current_exit_profit: float,
**kwargs) -> Optional[float]:
"""
Custom trade adjustment logic, returning the stake amount that a trade should be increased.
This means extra buy orders with additional fees.
Custom trade adjustment logic, returning the stake amount that a trade should be
increased or decreased.
This means extra buy or sell orders with additional fees.
Only called when `position_adjustment_enable` is set to True.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
@ -263,10 +267,16 @@ def adjust_trade_position(self, trade: 'Trade', current_time: 'datetime',
:param current_time: datetime object, containing the current datetime
:param current_rate: Current buy rate.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param min_stake: Minimal stake size allowed by exchange.
:param max_stake: Balance available for trading.
:param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
:param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
:param current_entry_rate: Current rate using entry pricing.
:param current_exit_rate: Current rate using exit pricing.
:param current_entry_profit: Current profit using entry pricing.
:param current_exit_profit: Current profit using exit pricing.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: Stake amount to adjust your trade
:return float: Stake amount to adjust your trade,
Positive values to increase position, Negative values to decrease position.
Return None for no action.
"""
return None

3
freqtrade/util/__init__.py Normal file
View File

@ -0,0 +1,3 @@
# flake8: noqa: F401
from freqtrade.util.ft_precise import FtPrecise
from freqtrade.util.periodic_cache import PeriodicCache

12
freqtrade/util/ft_precise.py Normal file
View File

@ -0,0 +1,12 @@
"""
Slim wrapper around ccxt's Precise (string math)
To have imports from freqtrade - and support float initializers
"""
from ccxt import Precise
class FtPrecise(Precise):
def __init__(self, number, decimals=None):
if not isinstance(number, str):
number = str(number)
super().__init__(number, decimals)

0
freqtrade/configuration/PeriodicCache.py → freqtrade/util/periodic_cache.py
View File

4
requirements-dev.txt
View File

@ -6,7 +6,7 @@
-r docs/requirements-docs.txt
coveralls==3.3.1
flake8==4.0.1
flake8==5.0.4
flake8-tidy-imports==4.8.0
mypy==0.971
pre-commit==2.20.0
@ -25,6 +25,6 @@ nbconvert==6.5.0
# mypy types
types-cachetools==5.2.1
types-filelock==3.2.7
types-requests==2.28.3
types-requests==2.28.8
types-tabulate==0.8.11
types-python-dateutil==2.8.19

5
requirements-freqai.txt
View File

@ -2,8 +2,7 @@
-r requirements.txt
# Required for freqai
scikit-learn==1.1.1
scikit-optimize==0.9.0
scikit-learn==1.1.2
joblib==1.1.0
catboost==1.0.4
catboost==1.0.6; platform_machine != 'aarch64'
lightgbm==3.3.2

4
requirements-hyperopt.txt
View File

@ -2,8 +2,8 @@
-r requirements.txt
# Required for hyperopt
scipy==1.8.1
scikit-learn==1.1.1
scipy==1.9.0
scikit-learn==1.1.2
scikit-optimize==0.9.0
filelock==3.7.1
progressbar2==4.0.0

8
requirements.txt
View File

@ -2,7 +2,7 @@ numpy==1.23.1
pandas==1.4.3
pandas-ta==0.3.14b
ccxt==1.91.29
ccxt==1.91.93
# 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==1.26.10
jsonschema==4.7.2
urllib3==1.26.11
jsonschema==4.9.1
TA-Lib==0.4.24
technical==1.3.0
tabulate==0.8.10
@ -28,7 +28,7 @@ py_find_1st==1.1.5
# Load ticker files 30% faster
python-rapidjson==1.8
# Properly format api responses
orjson==3.7.8
orjson==3.7.11
# Notify systemd
sdnotify==0.3.2

10
scripts/rest_client.py
View File

@ -275,14 +275,20 @@ class FtRestClient():
}
return self._post("forceenter", data=data)
def forceexit(self, tradeid):
def forceexit(self, tradeid, ordertype=None, amount=None):
"""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
:return: json object
"""
return self._post("forceexit", data={"tradeid": tradeid})
return self._post("forceexit", data={
"tradeid": tradeid,
"ordertype": ordertype,
"amount": amount,
})
def strategies(self):
"""Lists available strategies

10
setup.py
View File

@ -12,6 +12,13 @@ hyperopt = [
'progressbar2',
]
freqai = [
'scikit-learn',
'joblib',
'catboost; platform_machine != "aarch64"',
'lightgbm',
]
develop = [
'coveralls',
'flake8',
@ -31,7 +38,7 @@ jupyter = [
'nbconvert',
]
all_extra = plot + develop + jupyter + hyperopt
all_extra = plot + develop + jupyter + hyperopt + freqai
setup(
tests_require=[
@ -79,6 +86,7 @@ setup(
'plot': plot,
'jupyter': jupyter,
'hyperopt': hyperopt,
'freqai': freqai,
'all': all_extra,
},
)

3
tests/conftest.py
View File

@ -1627,8 +1627,8 @@ def limit_buy_order_open():
'timestamp': arrow.utcnow().int_timestamp * 1000,
'datetime': arrow.utcnow().isoformat(),
'price': 0.00001099,
'average': 0.00001099,
'amount': 90.99181073,
'average': None,
'filled': 0.0,
'cost': 0.0009999,
'remaining': 90.99181073,
@ -2817,6 +2817,7 @@ def limit_buy_order_usdt_open():
'datetime': arrow.utcnow().isoformat(),
'timestamp': arrow.utcnow().int_timestamp * 1000,
'price': 2.00,
'average': 2.00,
'amount': 30.0,
'filled': 0.0,
'cost': 60.0,

6
tests/conftest_trades.py
View File

@ -214,7 +214,8 @@ def mock_trade_4(fee, is_short: bool):
open_order_id=f'prod_buy_{direc(is_short)}_12345',
strategy='StrategyTestV3',
timeframe=5,
is_short=is_short
is_short=is_short,
stop_loss_pct=0.10
)
o = Order.parse_from_ccxt_object(mock_order_4(is_short), 'ETC/BTC', entry_side(is_short))
trade.orders.append(o)
@ -270,7 +271,8 @@ def mock_trade_5(fee, is_short: bool):
enter_tag='TEST1',
stoploss_order_id=f'prod_stoploss_{direc(is_short)}_3455',
timeframe=5,
is_short=is_short
is_short=is_short,
stop_loss_pct=0.10,
)
o = Order.parse_from_ccxt_object(mock_order_5(is_short), 'XRP/BTC', entry_side(is_short))
trade.orders.append(o)

4
tests/conftest_trades_usdt.py
View File

@ -63,7 +63,7 @@ def mock_trade_usdt_1(fee, is_short: bool):
open_rate=10.0,
close_rate=8.0,
close_profit=-0.2,
close_profit_abs=-4.0,
close_profit_abs=-4.09,
exchange='binance',
strategy='SampleStrategy',
open_order_id=f'prod_exit_1_{direc(is_short)}',
@ -183,7 +183,7 @@ def mock_trade_usdt_3(fee, is_short: bool):
open_rate=1.0,
close_rate=1.1,
close_profit=0.1,
close_profit_abs=9.8425,
close_profit_abs=2.8425,
exchange='binance',
is_open=False,
strategy='StrategyTestV2',

24
tests/data/test_dataprovider.py
View File

@ -311,3 +311,27 @@ def test_no_exchange_mode(default_conf):
with pytest.raises(OperationalException, match=message):
dp.available_pairs()
def test_dp_send_msg(default_conf):
default_conf["runmode"] = RunMode.DRY_RUN
default_conf["timeframe"] = '1h'
dp = DataProvider(default_conf, None)
msg = 'Test message'
dp.send_msg(msg)
assert msg in dp._msg_queue
dp._msg_queue.pop()
assert msg not in dp._msg_queue
# Message is not resent due to caching
dp.send_msg(msg)
assert msg not in dp._msg_queue
dp.send_msg(msg, always_send=True)
assert msg in dp._msg_queue
default_conf["runmode"] = RunMode.BACKTEST
dp = DataProvider(default_conf, None)
dp.send_msg(msg, always_send=True)
assert msg not in dp._msg_queue

57
tests/exchange/test_ccxt_precise.py
View File

@ -1,14 +1,14 @@
from ccxt import Precise
from freqtrade.util import FtPrecise
ws = Precise('-1.123e-6')
ws = Precise('-1.123e-6')
xs = Precise('0.00000002')
ys = Precise('69696900000')
zs = Precise('0')
ws = FtPrecise('-1.123e-6')
ws = FtPrecise('-1.123e-6')
xs = FtPrecise('0.00000002')
ys = FtPrecise('69696900000')
zs = FtPrecise('0')
def test_precise():
def test_FtPrecise():
assert ys * xs == '1393.938'
assert xs * ys == '1393.938'
@ -45,31 +45,36 @@ def test_precise():
assert xs + zs == '0.00000002'
assert ys + zs == '69696900000'
assert abs(Precise('-500.1')) == '500.1'
assert abs(Precise('213')) == '213'
assert abs(FtPrecise('-500.1')) == '500.1'
assert abs(FtPrecise('213')) == '213'
assert abs(Precise('-500.1')) == '500.1'
assert -Precise('213') == '-213'
assert abs(FtPrecise('-500.1')) == '500.1'
assert -FtPrecise('213') == '-213'
assert Precise('10.1') % Precise('0.5') == '0.1'
assert Precise('5550') % Precise('120') == '30'
assert FtPrecise('10.1') % FtPrecise('0.5') == '0.1'
assert FtPrecise('5550') % FtPrecise('120') == '30'
assert Precise('-0.0') == Precise('0')
assert Precise('5.534000') == Precise('5.5340')
assert FtPrecise('-0.0') == FtPrecise('0')
assert FtPrecise('5.534000') == FtPrecise('5.5340')
assert min(Precise('-3.1415'), Precise('-2')) == '-3.1415'
assert min(FtPrecise('-3.1415'), FtPrecise('-2')) == '-3.1415'
assert max(Precise('3.1415'), Precise('-2')) == '3.1415'
assert max(FtPrecise('3.1415'), FtPrecise('-2')) == '3.1415'
assert Precise('2') > Precise('1.2345')
assert not Precise('-3.1415') > Precise('-2')
assert not Precise('3.1415') > Precise('3.1415')
assert Precise.string_gt('3.14150000000000000000001', '3.1415')
assert FtPrecise('2') > FtPrecise('1.2345')
assert not FtPrecise('-3.1415') > FtPrecise('-2')
assert not FtPrecise('3.1415') > FtPrecise('3.1415')
assert FtPrecise.string_gt('3.14150000000000000000001', '3.1415')
assert Precise('3.1415') >= Precise('3.1415')
assert Precise('3.14150000000000000000001') >= Precise('3.1415')
assert FtPrecise('3.1415') >= FtPrecise('3.1415')
assert FtPrecise('3.14150000000000000000001') >= FtPrecise('3.1415')
assert not Precise('3.1415') < Precise('3.1415')
assert not FtPrecise('3.1415') < FtPrecise('3.1415')
assert Precise('3.1415') <= Precise('3.1415')
assert Precise('3.1415') <= Precise('3.14150000000000000000001')
assert FtPrecise('3.1415') <= FtPrecise('3.1415')
assert FtPrecise('3.1415') <= FtPrecise('3.14150000000000000000001')
assert FtPrecise(213) == '213'
assert FtPrecise(-213) == '-213'
assert str(FtPrecise(-213)) == '-213'
assert FtPrecise(213.2) == '213.2'

213
tests/exchange/test_exchange.py
View File

@ -27,6 +27,57 @@ from tests.conftest import get_mock_coro, get_patched_exchange, log_has, log_has
# Make sure to always keep one exchange here which is NOT subclassed!!
EXCHANGES = ['bittrex', 'binance', 'kraken', 'ftx', 'gateio']
get_entry_rate_data = [
('other', 20, 19, 10, 0.0, 20), # Full ask side
('ask', 20, 19, 10, 0.0, 20), # Full ask side
('ask', 20, 19, 10, 1.0, 10), # Full last side
('ask', 20, 19, 10, 0.5, 15), # Between ask and last
('ask', 20, 19, 10, 0.7, 13), # Between ask and last
('ask', 20, 19, 10, 0.3, 17), # Between ask and last
('ask', 5, 6, 10, 1.0, 5), # last bigger than ask
('ask', 5, 6, 10, 0.5, 5), # last bigger than ask
('ask', 20, 19, 10, None, 20), # price_last_balance missing
('ask', 10, 20, None, 0.5, 10), # last not available - uses ask
('ask', 4, 5, None, 0.5, 4), # last not available - uses ask
('ask', 4, 5, None, 1, 4), # last not available - uses ask
('ask', 4, 5, None, 0, 4), # last not available - uses ask
('same', 21, 20, 10, 0.0, 20), # Full bid side
('bid', 21, 20, 10, 0.0, 20), # Full bid side
('bid', 21, 20, 10, 1.0, 10), # Full last side
('bid', 21, 20, 10, 0.5, 15), # Between bid and last
('bid', 21, 20, 10, 0.7, 13), # Between bid and last
('bid', 21, 20, 10, 0.3, 17), # Between bid and last
('bid', 6, 5, 10, 1.0, 5), # last bigger than bid
('bid', 21, 20, 10, None, 20), # price_last_balance missing
('bid', 6, 5, 10, 0.5, 5), # last bigger than bid
('bid', 21, 20, None, 0.5, 20), # last not available - uses bid
('bid', 6, 5, None, 0.5, 5), # last not available - uses bid
('bid', 6, 5, None, 1, 5), # last not available - uses bid
('bid', 6, 5, None, 0, 5), # last not available - uses bid
]
get_sell_rate_data = [
('bid', 12.0, 11.0, 11.5, 0.0, 11.0), # full bid side
('bid', 12.0, 11.0, 11.5, 1.0, 11.5), # full last side
('bid', 12.0, 11.0, 11.5, 0.5, 11.25), # between bid and lat
('bid', 12.0, 11.2, 10.5, 0.0, 11.2), # Last smaller than bid
('bid', 12.0, 11.2, 10.5, 1.0, 11.2), # Last smaller than bid - uses bid
('bid', 12.0, 11.2, 10.5, 0.5, 11.2), # Last smaller than bid - uses bid
('bid', 0.003, 0.002, 0.005, 0.0, 0.002),
('bid', 0.003, 0.002, 0.005, None, 0.002),
('ask', 12.0, 11.0, 12.5, 0.0, 12.0), # full ask side
('ask', 12.0, 11.0, 12.5, 1.0, 12.5), # full last side
('ask', 12.0, 11.0, 12.5, 0.5, 12.25), # between bid and lat
('ask', 12.2, 11.2, 10.5, 0.0, 12.2), # Last smaller than ask
('ask', 12.0, 11.0, 10.5, 1.0, 12.0), # Last smaller than ask - uses ask
('ask', 12.0, 11.2, 10.5, 0.5, 12.0), # Last smaller than ask - uses ask
('ask', 10.0, 11.0, 11.0, 0.0, 10.0),
('ask', 10.11, 11.2, 11.0, 0.0, 10.11),
('ask', 0.001, 0.002, 11.0, 0.0, 0.001),
('ask', 0.006, 1.0, 11.0, 0.0, 0.006),
('ask', 0.006, 1.0, 11.0, None, 0.006),
]
def ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name,
fun, mock_ccxt_fun, retries=API_RETRY_COUNT + 1, **kwargs):
@ -2360,34 +2411,7 @@ def test_fetch_l2_order_book_exception(default_conf, mocker, exchange_name):
exchange.fetch_l2_order_book(pair='ETH/BTC', limit=50)
@pytest.mark.parametrize("side,ask,bid,last,last_ab,expected", [
('other', 20, 19, 10, 0.0, 20), # Full ask side
('ask', 20, 19, 10, 0.0, 20), # Full ask side
('ask', 20, 19, 10, 1.0, 10), # Full last side
('ask', 20, 19, 10, 0.5, 15), # Between ask and last
('ask', 20, 19, 10, 0.7, 13), # Between ask and last
('ask', 20, 19, 10, 0.3, 17), # Between ask and last
('ask', 5, 6, 10, 1.0, 5), # last bigger than ask
('ask', 5, 6, 10, 0.5, 5), # last bigger than ask
('ask', 20, 19, 10, None, 20), # price_last_balance missing
('ask', 10, 20, None, 0.5, 10), # last not available - uses ask
('ask', 4, 5, None, 0.5, 4), # last not available - uses ask
('ask', 4, 5, None, 1, 4), # last not available - uses ask
('ask', 4, 5, None, 0, 4), # last not available - uses ask
('same', 21, 20, 10, 0.0, 20), # Full bid side
('bid', 21, 20, 10, 0.0, 20), # Full bid side
('bid', 21, 20, 10, 1.0, 10), # Full last side
('bid', 21, 20, 10, 0.5, 15), # Between bid and last
('bid', 21, 20, 10, 0.7, 13), # Between bid and last
('bid', 21, 20, 10, 0.3, 17), # Between bid and last
('bid', 6, 5, 10, 1.0, 5), # last bigger than bid
('bid', 21, 20, 10, None, 20), # price_last_balance missing
('bid', 6, 5, 10, 0.5, 5), # last bigger than bid
('bid', 21, 20, None, 0.5, 20), # last not available - uses bid
('bid', 6, 5, None, 0.5, 5), # last not available - uses bid
('bid', 6, 5, None, 1, 5), # last not available - uses bid
('bid', 6, 5, None, 0, 5), # last not available - uses bid
])
@pytest.mark.parametrize("side,ask,bid,last,last_ab,expected", get_entry_rate_data)
def test_get_entry_rate(mocker, default_conf, caplog, side, ask, bid,
last, last_ab, expected) -> None:
caplog.set_level(logging.DEBUG)
@ -2411,27 +2435,7 @@ def test_get_entry_rate(mocker, default_conf, caplog, side, ask, bid,
assert not log_has("Using cached entry rate for ETH/BTC.", caplog)
@pytest.mark.parametrize('side,ask,bid,last,last_ab,expected', [
('bid', 12.0, 11.0, 11.5, 0.0, 11.0), # full bid side
('bid', 12.0, 11.0, 11.5, 1.0, 11.5), # full last side
('bid', 12.0, 11.0, 11.5, 0.5, 11.25), # between bid and lat
('bid', 12.0, 11.2, 10.5, 0.0, 11.2), # Last smaller than bid
('bid', 12.0, 11.2, 10.5, 1.0, 11.2), # Last smaller than bid - uses bid
('bid', 12.0, 11.2, 10.5, 0.5, 11.2), # Last smaller than bid - uses bid
('bid', 0.003, 0.002, 0.005, 0.0, 0.002),
('bid', 0.003, 0.002, 0.005, None, 0.002),
('ask', 12.0, 11.0, 12.5, 0.0, 12.0), # full ask side
('ask', 12.0, 11.0, 12.5, 1.0, 12.5), # full last side
('ask', 12.0, 11.0, 12.5, 0.5, 12.25), # between bid and lat
('ask', 12.2, 11.2, 10.5, 0.0, 12.2), # Last smaller than ask
('ask', 12.0, 11.0, 10.5, 1.0, 12.0), # Last smaller than ask - uses ask
('ask', 12.0, 11.2, 10.5, 0.5, 12.0), # Last smaller than ask - uses ask
('ask', 10.0, 11.0, 11.0, 0.0, 10.0),
('ask', 10.11, 11.2, 11.0, 0.0, 10.11),
('ask', 0.001, 0.002, 11.0, 0.0, 0.001),
('ask', 0.006, 1.0, 11.0, 0.0, 0.006),
('ask', 0.006, 1.0, 11.0, None, 0.006),
])
@pytest.mark.parametrize('side,ask,bid,last,last_ab,expected', get_sell_rate_data)
def test_get_exit_rate(default_conf, mocker, caplog, side, bid, ask,
last, last_ab, expected) -> None:
caplog.set_level(logging.DEBUG)
@ -2481,14 +2485,14 @@ def test_get_ticker_rate_error(mocker, entry, default_conf, caplog, side, is_sho
@pytest.mark.parametrize('is_short,side,expected', [
(False, 'bid', 0.043936), # Value from order_book_l2 fitxure - bids side
(False, 'ask', 0.043949), # Value from order_book_l2 fitxure - asks side
(False, 'other', 0.043936), # Value from order_book_l2 fitxure - bids side
(False, 'same', 0.043949), # Value from order_book_l2 fitxure - asks side
(True, 'bid', 0.043936), # Value from order_book_l2 fitxure - bids side
(True, 'ask', 0.043949), # Value from order_book_l2 fitxure - asks side
(True, 'other', 0.043949), # Value from order_book_l2 fitxure - asks side
(True, 'same', 0.043936), # Value from order_book_l2 fitxure - bids side
(False, 'bid', 0.043936), # Value from order_book_l2 fixture - bids side
(False, 'ask', 0.043949), # Value from order_book_l2 fixture - asks side
(False, 'other', 0.043936), # Value from order_book_l2 fixture - bids side
(False, 'same', 0.043949), # Value from order_book_l2 fixture - asks side
(True, 'bid', 0.043936), # Value from order_book_l2 fixture - bids side
(True, 'ask', 0.043949), # Value from order_book_l2 fixture - asks side
(True, 'other', 0.043949), # Value from order_book_l2 fixture - asks side
(True, 'same', 0.043936), # Value from order_book_l2 fixture - bids side
])
def test_get_exit_rate_orderbook(
default_conf, mocker, caplog, is_short, side, expected, order_book_l2):
@ -2521,7 +2525,8 @@ def test_get_exit_rate_orderbook_exception(default_conf, mocker, caplog):
exchange = get_patched_exchange(mocker, default_conf)
with pytest.raises(PricingError):
exchange.get_rate(pair, refresh=True, side="exit", is_short=False)
assert log_has_re(r"Exit Price at location 1 from orderbook could not be determined\..*",
assert log_has_re(rf"{pair} - Exit Price at location 1 from orderbook "
rf"could not be determined\..*",
caplog)
@ -2548,6 +2553,84 @@ def test_get_exit_rate_exception(default_conf, mocker, is_short):
assert exchange.get_rate(pair, refresh=True, side="exit", is_short=is_short) == 0.13
@pytest.mark.parametrize("side,ask,bid,last,last_ab,expected", get_entry_rate_data)
@pytest.mark.parametrize("side2", ['bid', 'ask'])
@pytest.mark.parametrize("use_order_book", [True, False])
def test_get_rates_testing_buy(mocker, default_conf, caplog, side, ask, bid,
last, last_ab, expected,
side2, use_order_book, order_book_l2) -> None:
caplog.set_level(logging.DEBUG)
if last_ab is None:
del default_conf['entry_pricing']['price_last_balance']
else:
default_conf['entry_pricing']['price_last_balance'] = last_ab
default_conf['entry_pricing']['price_side'] = side
default_conf['exit_pricing']['price_side'] = side2
default_conf['exit_pricing']['use_order_book'] = use_order_book
api_mock = MagicMock()
api_mock.fetch_l2_order_book = order_book_l2
api_mock.fetch_ticker = MagicMock(
return_value={'ask': ask, 'last': last, 'bid': bid})
exchange = get_patched_exchange(mocker, default_conf, api_mock)
assert exchange.get_rates('ETH/BTC', refresh=True, is_short=False)[0] == expected
assert not log_has("Using cached buy rate for ETH/BTC.", caplog)
api_mock.fetch_l2_order_book.reset_mock()
api_mock.fetch_ticker.reset_mock()
assert exchange.get_rates('ETH/BTC', refresh=False, is_short=False)[0] == expected
assert log_has("Using cached buy rate for ETH/BTC.", caplog)
assert api_mock.fetch_l2_order_book.call_count == 0
assert api_mock.fetch_ticker.call_count == 0
# Running a 2nd time with Refresh on!
caplog.clear()
assert exchange.get_rates('ETH/BTC', refresh=True, is_short=False)[0] == expected
assert not log_has("Using cached buy rate for ETH/BTC.", caplog)
assert api_mock.fetch_l2_order_book.call_count == int(use_order_book)
assert api_mock.fetch_ticker.call_count == 1
@pytest.mark.parametrize('side,ask,bid,last,last_ab,expected', get_sell_rate_data)
@pytest.mark.parametrize("side2", ['bid', 'ask'])
@pytest.mark.parametrize("use_order_book", [True, False])
def test_get_rates_testing_sell(default_conf, mocker, caplog, side, bid, ask,
last, last_ab, expected,
side2, use_order_book, order_book_l2) -> None:
caplog.set_level(logging.DEBUG)
default_conf['exit_pricing']['price_side'] = side
if last_ab is not None:
default_conf['exit_pricing']['price_last_balance'] = last_ab
default_conf['entry_pricing']['price_side'] = side2
default_conf['entry_pricing']['use_order_book'] = use_order_book
api_mock = MagicMock()
api_mock.fetch_l2_order_book = order_book_l2
api_mock.fetch_ticker = MagicMock(
return_value={'ask': ask, 'last': last, 'bid': bid})
exchange = get_patched_exchange(mocker, default_conf, api_mock)
pair = "ETH/BTC"
# Test regular mode
rate = exchange.get_rates(pair, refresh=True, is_short=False)[1]
assert not log_has("Using cached sell rate for ETH/BTC.", caplog)
assert isinstance(rate, float)
assert rate == expected
# Use caching
api_mock.fetch_l2_order_book.reset_mock()
api_mock.fetch_ticker.reset_mock()
rate = exchange.get_rates(pair, refresh=False, is_short=False)[1]
assert rate == expected
assert log_has("Using cached sell rate for ETH/BTC.", caplog)
assert api_mock.fetch_l2_order_book.call_count == 0
assert api_mock.fetch_ticker.call_count == 0
@pytest.mark.parametrize("exchange_name", EXCHANGES)
@pytest.mark.asyncio
async def test___async_get_candle_history_sort(default_conf, mocker, exchange_name):
@ -4099,20 +4182,6 @@ def test_get_or_calculate_liquidation_price(mocker, default_conf):
)
assert liq_price == 17.540699999999998
ccxt_exceptionhandlers(
mocker,
default_conf,
api_mock,
"binance",
"get_or_calculate_liquidation_price",
"fetch_positions",
pair="XRP/USDT",
open_rate=0.0,
is_short=False,
position=0.0,
wallet_balance=0.0,
)
@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),

18
tests/exchange/test_ftx.py
View File

@ -203,7 +203,7 @@ def test_fetch_stoploss_order_ftx(default_conf, mocker, limit_sell_order, limit_
'info': {
'orderId': 'mocked_limit_sell',
}}])
api_mock.fetch_order = MagicMock(return_value=limit_sell_order)
api_mock.fetch_order = MagicMock(return_value=limit_sell_order.copy())
# No orderId field - no call to fetch_order
resp = exchange.fetch_stoploss_order('X', 'TKN/BTC')
@ -219,11 +219,23 @@ def test_fetch_stoploss_order_ftx(default_conf, mocker, limit_sell_order, limit_
order = {'id': 'X', 'status': 'closed', 'info': {'orderId': None}, 'average': 0.254}
api_mock.fetch_orders = MagicMock(return_value=[order])
api_mock.fetch_order.reset_mock()
api_mock.privateGetConditionalOrdersConditionalOrderIdTriggers = MagicMock(
return_value={'result': [
{'orderId': 'mocked_market_sell', 'type': 'market', 'side': 'sell', 'price': 0.254}
]})
resp = exchange.fetch_stoploss_order('X', 'TKN/BTC')
assert resp
# fetch_order not called (no regular order ID)
assert api_mock.fetch_order.call_count == 0
assert order == order
assert api_mock.fetch_order.call_count == 1
api_mock.privateGetConditionalOrdersConditionalOrderIdTriggers.call_count == 1
expected_resp = limit_sell_order.copy()
expected_resp.update({
'id_stop': 'X',
'id': 'X',
'type': 'stop',
'status_stop': 'triggered',
})
assert expected_resp == resp
with pytest.raises(InvalidOrderException):
api_mock.fetch_orders = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))

8
tests/freqai/conftest.py
View File

@ -21,10 +21,11 @@ def freqai_conf(default_conf, tmpdir):
"strategy": "freqai_test_strat",
"user_data_dir": Path(tmpdir),
"strategy-path": "freqtrade/tests/strategy/strats",
"freqaimodel": "LightGBMPredictionModel",
"freqaimodel": "LightGBMRegressor",
"freqaimodel_path": "freqai/prediction_models",
"timerange": "20180110-20180115",
"freqai": {
"enabled": True,
"startup_candles": 10000,
"purge_old_models": True,
"train_period_days": 5,
@ -47,9 +48,9 @@ def freqai_conf(default_conf, tmpdir):
"indicator_periods_candles": [10],
},
"data_split_parameters": {"test_size": 0.33, "random_state": 1},
"model_training_parameters": {"n_estimators": 100, "verbosity": 0},
"model_training_parameters": {"n_estimators": 100},
},
"config_files": [Path('config_examples', 'config_freqai_futures.example.json')]
"config_files": [Path('config_examples', 'config_freqai.example.json')]
}
)
freqaiconf['exchange'].update({'pair_whitelist': ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC']})
@ -57,7 +58,6 @@ def freqai_conf(default_conf, tmpdir):
def get_patched_data_kitchen(mocker, freqaiconf):
# dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
dk = FreqaiDataKitchen(freqaiconf)
return dk

33
tests/freqai/test_freqai_backtsting.py Normal file
View File

@ -0,0 +1,33 @@
from pathlib import Path
from unittest.mock import PropertyMock
import pytest
from freqtrade.commands.optimize_commands import start_backtesting
from freqtrade.exceptions import OperationalException
from tests.conftest import (CURRENT_TEST_STRATEGY, get_args, patch_exchange,
patched_configuration_load_config_file)
def test_backtest_start_backtest_list_freqai(freqai_conf, mocker, testdatadir):
# Tests detail-data loading
patch_exchange(mocker)
mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
PropertyMock(return_value=['HULUMULU/USDT', 'XRP/USDT']))
# mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
patched_configuration_load_config_file(mocker, freqai_conf)
args = [
'backtesting',
'--config', 'config.json',
'--datadir', str(testdatadir),
'--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
'--timeframe', '1h',
'--strategy-list', CURRENT_TEST_STRATEGY
]
args = get_args(args)
with pytest.raises(OperationalException,
match=r"You can't use strategy_list and freqai at the same time\."):
start_backtesting(args)

77
tests/freqai/test_freqai_interface.py
View File

@ -12,6 +12,11 @@ from tests.conftest import get_patched_exchange, log_has_re
from tests.freqai.conftest import get_patched_freqai_strategy
def is_arm() -> bool:
machine = platform.machine()
return "arm" in machine or "aarch64" in machine
def test_train_model_in_series_LightGBM(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
@ -43,7 +48,7 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf):
def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
freqai_conf.update({"freqaimodel": "LightGBMPredictionMultiModel"})
freqai_conf.update({"freqaimodel": "LightGBMRegressorMultiTarget"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
@ -70,11 +75,75 @@ def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.skipif("arm" in platform.uname()[-1], reason="no ARM for Catboost ...")
@pytest.mark.skipif(is_arm(), reason="no ARM for Catboost ...")
def test_train_model_in_series_Catboost(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "CatboostPredictionModel"})
del freqai_conf['freqai']['model_training_parameters']['verbosity']
freqai_conf.update({"freqaimodel": "CatboostRegressor"})
# freqai_conf.get('freqai', {}).update(
# {'model_training_parameters': {"n_estimators": 100, "verbose": 0}})
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)
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")
freqai.train_model_in_series(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists()
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.skipif(is_arm(), reason="no ARM for Catboost ...")
def test_train_model_in_series_CatboostClassifier(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "CatboostClassifier"})
freqai_conf.update({"strategy": "freqai_test_classifier"})
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)
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")
freqai.train_model_in_series(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists()
shutil.rmtree(Path(freqai.dk.full_path))
def test_train_model_in_series_LightGBMClassifier(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "LightGBMClassifier"})
freqai_conf.update({"strategy": "freqai_test_classifier"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)

86
tests/optimize/test_backtesting_adjust_position.py
View File

@ -1,8 +1,10 @@
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
from copy import deepcopy
from unittest.mock import MagicMock
import pandas as pd
import pytest
from arrow import Arrow
from freqtrade.configuration import TimeRange
@ -87,3 +89,87 @@ def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) ->
assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
round(ln.iloc[0]["low"], 6) < round(
t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))
def test_backtest_position_adjustment_detailed(default_conf, fee, mocker) -> None:
default_conf['use_exit_signal'] = False
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=10)
mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf'))
patch_exchange(mocker)
default_conf.update({
"stake_amount": 100.0,
"dry_run_wallet": 1000.0,
"strategy": "StrategyTestV3"
})
backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
pair = 'XRP/USDT'
row = [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0),
2.1, # Open
2.2, # High
1.9, # Low
2.1, # Close
1, # enter_long
0, # exit_long
0, # enter_short
0, # exit_short
'', # enter_tag
'', # exit_tag
]
trade = backtesting._enter_trade(pair, row=row, direction='long')
trade.orders[0].close_bt_order(row[0], trade)
assert trade
assert pytest.approx(trade.stake_amount) == 100.0
assert pytest.approx(trade.amount) == 47.61904762
assert len(trade.orders) == 1
backtesting.strategy.adjust_trade_position = MagicMock(return_value=None)
trade = backtesting._get_adjust_trade_entry_for_candle(trade, row)
assert trade
assert pytest.approx(trade.stake_amount) == 100.0
assert pytest.approx(trade.amount) == 47.61904762
assert len(trade.orders) == 1
# Increase position by 100
backtesting.strategy.adjust_trade_position = MagicMock(return_value=100)
trade = backtesting._get_adjust_trade_entry_for_candle(trade, row)
assert trade
assert pytest.approx(trade.stake_amount) == 200.0
assert pytest.approx(trade.amount) == 95.23809524
assert len(trade.orders) == 2
# Reduce by more than amount - no change to trade.
backtesting.strategy.adjust_trade_position = MagicMock(return_value=-500)
trade = backtesting._get_adjust_trade_entry_for_candle(trade, row)
assert trade
assert pytest.approx(trade.stake_amount) == 200.0
assert pytest.approx(trade.amount) == 95.23809524
assert len(trade.orders) == 2
assert trade.nr_of_successful_entries == 2
# Reduce position by 50
backtesting.strategy.adjust_trade_position = MagicMock(return_value=-100)
trade = backtesting._get_adjust_trade_entry_for_candle(trade, row)
assert trade
assert pytest.approx(trade.stake_amount) == 100.0
assert pytest.approx(trade.amount) == 47.61904762
assert len(trade.orders) == 3
assert trade.nr_of_successful_entries == 2
assert trade.nr_of_successful_exits == 1
# Adjust below minimum
backtesting.strategy.adjust_trade_position = MagicMock(return_value=-99)
trade = backtesting._get_adjust_trade_entry_for_candle(trade, row)
assert trade
assert pytest.approx(trade.stake_amount) == 100.0
assert pytest.approx(trade.amount) == 47.61904762
assert len(trade.orders) == 3
assert trade.nr_of_successful_entries == 2
assert trade.nr_of_successful_exits == 1

7
tests/plugins/test_protections.py
View File

@ -305,6 +305,7 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side):
min_ago_open=800, min_ago_close=450, profit_rate=0.9,
))
Trade.commit()
# Not locked with 1 trade
assert not freqtrade.protections.global_stop()
assert not freqtrade.protections.stop_per_pair('XRP/BTC')
@ -316,6 +317,7 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side):
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)
assert not freqtrade.protections.global_stop()
assert not freqtrade.protections.stop_per_pair('XRP/BTC')
@ -327,14 +329,16 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side):
'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(
'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
min_ago_open=110, min_ago_close=20, profit_rate=0.8,
min_ago_open=110, min_ago_close=21, profit_rate=0.8,
))
Trade.commit()
# Locks due to 2nd trade
assert freqtrade.protections.global_stop() != only_per_side
@ -342,6 +346,7 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side):
assert PairLocks.is_pair_locked('XRP/BTC', side='long')
assert PairLocks.is_pair_locked('XRP/BTC', side='*') != only_per_side
assert not PairLocks.is_global_lock()
Trade.commit()
@pytest.mark.usefixtures("init_persistence")

77
tests/rpc/test_rpc.py
View File

@ -111,6 +111,7 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None:
'stoploss_entry_dist': -0.00010475,
'stoploss_entry_dist_ratio': -0.10448878,
'open_order': None,
'realized_profit': 0.0,
'exchange': 'binance',
'leverage': 1.0,
'interest_rate': 0.0,
@ -196,6 +197,7 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None:
'stoploss_entry_dist_ratio': -0.10448878,
'open_order': None,
'exchange': 'binance',
'realized_profit': 0.0,
'leverage': 1.0,
'interest_rate': 0.0,
'liquidation_price': None,
@ -312,10 +314,10 @@ def test__rpc_timeunit_profit(default_conf_usdt, ticker, fee,
# {'date': datetime.date(2022, 6, 11), 'abs_profit': 13.8299999,
# 'starting_balance': 1055.37, 'rel_profit': 0.0131044,
# 'fiat_value': 0.0, 'trade_count': 2}
assert day['abs_profit'] in (0.0, pytest.approx(13.8299999), pytest.approx(-4.0))
assert day['rel_profit'] in (0.0, pytest.approx(0.01310441), pytest.approx(-0.00377583))
assert day['abs_profit'] in (0.0, pytest.approx(6.83), pytest.approx(-4.09))
assert day['rel_profit'] in (0.0, pytest.approx(0.00642902), pytest.approx(-0.00383512))
assert day['trade_count'] in (0, 1, 2)
assert day['starting_balance'] in (pytest.approx(1059.37), pytest.approx(1055.37))
assert day['starting_balance'] in (pytest.approx(1062.37), pytest.approx(1066.46))
assert day['fiat_value'] in (0.0, )
# ensure first day is current date
assert str(days['data'][0]['date']) == str(datetime.utcnow().date())
@ -433,9 +435,9 @@ def test_rpc_trade_statistics(default_conf_usdt, ticker, fee, mocker) -> None:
create_mock_trades_usdt(fee)
stats = rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
assert pytest.approx(stats['profit_closed_coin']) == 9.83
assert pytest.approx(stats['profit_closed_coin']) == 2.74
assert pytest.approx(stats['profit_closed_percent_mean']) == -1.67
assert pytest.approx(stats['profit_closed_fiat']) == 10.813
assert pytest.approx(stats['profit_closed_fiat']) == 3.014
assert pytest.approx(stats['profit_all_coin']) == -77.45964918
assert pytest.approx(stats['profit_all_percent_mean']) == -57.86
assert pytest.approx(stats['profit_all_fiat']) == -85.205614098
@ -459,46 +461,6 @@ def test_rpc_trade_statistics(default_conf_usdt, ticker, fee, mocker) -> None:
assert isnan(stats['profit_all_coin'])
# Test that rpc_trade_statistics can handle trades that lacks
# trade.open_rate (it is set to None)
def test_rpc_trade_statistics_closed(mocker, default_conf_usdt, ticker, fee):
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price',
return_value=1.1)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker,
get_fee=fee,
)
freqtradebot = get_patched_freqtradebot(mocker, default_conf_usdt)
patch_get_signal(freqtradebot)
stake_currency = default_conf_usdt['stake_currency']
fiat_display_currency = default_conf_usdt['fiat_display_currency']
rpc = RPC(freqtradebot)
# Create some test data
create_mock_trades_usdt(fee)
for trade in Trade.query.order_by(Trade.id).all():
trade.open_rate = None
stats = rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
assert stats['profit_closed_coin'] == 0
assert stats['profit_closed_percent_mean'] == 0
assert stats['profit_closed_fiat'] == 0
assert stats['profit_all_coin'] == 0
assert stats['profit_all_percent_mean'] == 0
assert stats['profit_all_fiat'] == 0
assert stats['trade_count'] == 7
assert stats['first_trade_date'] == '2 days ago'
assert stats['latest_trade_date'] == '17 minutes ago'
assert stats['avg_duration'] == '0:00:00'
assert stats['best_pair'] == 'XRP/USDT'
assert stats['best_rate'] == 10.0
def test_rpc_balance_handle_error(default_conf, mocker):
mock_balance = {
'BTC': {
@ -841,7 +803,8 @@ def test_rpc_force_exit(default_conf, ticker, fee, mocker) -> None:
'side': 'sell',
'amount': amount,
'remaining': amount,
'filled': 0.0
'filled': 0.0,
'id': trade.orders[0].order_id,
}
)
msg = rpc._rpc_force_exit('3')
@ -867,9 +830,9 @@ def test_performance_handle(default_conf_usdt, ticker, fee, mocker) -> None:
res = rpc._rpc_performance()
assert len(res) == 3
assert res[0]['pair'] == 'XRP/USDT'
assert res[0]['pair'] == 'ETC/USDT'
assert res[0]['count'] == 1
assert res[0]['profit_pct'] == 10.0
assert res[0]['profit_pct'] == 5.0
def test_enter_tag_performance_handle(default_conf, ticker, fee, mocker) -> None:
@ -893,16 +856,16 @@ def test_enter_tag_performance_handle(default_conf, ticker, fee, mocker) -> None
res = rpc._rpc_enter_tag_performance(None)
assert len(res) == 3
assert res[0]['enter_tag'] == 'TEST3'
assert res[0]['enter_tag'] == 'TEST1'
assert res[0]['count'] == 1
assert res[0]['profit_pct'] == 10.0
assert res[0]['profit_pct'] == 5.0
res = rpc._rpc_enter_tag_performance(None)
assert len(res) == 3
assert res[0]['enter_tag'] == 'TEST3'
assert res[0]['enter_tag'] == 'TEST1'
assert res[0]['count'] == 1
assert res[0]['profit_pct'] == 10.0
assert res[0]['profit_pct'] == 5.0
def test_enter_tag_performance_handle_2(mocker, default_conf, markets, fee):
@ -953,11 +916,11 @@ def test_exit_reason_performance_handle(default_conf_usdt, ticker, fee, mocker)
res = rpc._rpc_exit_reason_performance(None)
assert len(res) == 3
assert res[0]['exit_reason'] == 'roi'
assert res[0]['exit_reason'] == 'exit_signal'
assert res[0]['count'] == 1
assert res[0]['profit_pct'] == 10.0
assert res[0]['profit_pct'] == 5.0
assert res[1]['exit_reason'] == 'exit_signal'
assert res[1]['exit_reason'] == 'roi'
assert res[2]['exit_reason'] == 'Other'
@ -1009,9 +972,9 @@ def test_mix_tag_performance_handle(default_conf, ticker, fee, mocker) -> None:
res = rpc._rpc_mix_tag_performance(None)
assert len(res) == 3
assert res[0]['mix_tag'] == 'TEST3 roi'
assert res[0]['mix_tag'] == 'TEST1 exit_signal'
assert res[0]['count'] == 1
assert res[0]['profit_pct'] == 10.0
assert res[0]['profit_pct'] == 5.0
def test_mix_tag_performance_handle_2(mocker, default_conf, markets, fee):

83
tests/rpc/test_rpc_apiserver.py
View File

@ -109,6 +109,9 @@ def test_api_ui_fallback(botclient, mocker):
rc = client_get(client, "/something")
assert rc.status_code == 200
rc = client_get(client, "/something.js")
assert rc.status_code == 200
# Test directory traversal without mock
rc = client_get(client, '%2F%2F%2Fetc/passwd')
assert rc.status_code == 200
@ -717,11 +720,11 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
(
True,
{'best_pair': 'ETC/BTC', 'best_rate': -0.5, 'best_pair_profit_ratio': -0.005,
'profit_all_coin': 43.61269123,
'profit_all_fiat': 538398.67323435, 'profit_all_percent_mean': 66.41,
'profit_all_coin': 45.561959,
'profit_all_fiat': 562462.39126200, 'profit_all_percent_mean': 66.41,
'profit_all_ratio_mean': 0.664109545, 'profit_all_percent_sum': 398.47,
'profit_all_ratio_sum': 3.98465727, 'profit_all_percent': 4.36,
'profit_all_ratio': 0.043612222872799825, 'profit_closed_coin': -0.00673913,
'profit_all_ratio_sum': 3.98465727, 'profit_all_percent': 4.56,
'profit_all_ratio': 0.04556147, 'profit_closed_coin': -0.00673913,
'profit_closed_fiat': -83.19455985, 'profit_closed_ratio_mean': -0.0075,
'profit_closed_percent_mean': -0.75, 'profit_closed_ratio_sum': -0.015,
'profit_closed_percent_sum': -1.5, 'profit_closed_ratio': -6.739057628404269e-06,
@ -732,11 +735,11 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
(
False,
{'best_pair': 'XRP/BTC', 'best_rate': 1.0, 'best_pair_profit_ratio': 0.01,
'profit_all_coin': -44.0631579,
'profit_all_fiat': -543959.6842755, 'profit_all_percent_mean': -66.41,
'profit_all_coin': -45.79641127,
'profit_all_fiat': -565356.69712815, 'profit_all_percent_mean': -66.41,
'profit_all_ratio_mean': -0.6641100666666667, 'profit_all_percent_sum': -398.47,
'profit_all_ratio_sum': -3.9846604, 'profit_all_percent': -4.41,
'profit_all_ratio': -0.044063014216106644, 'profit_closed_coin': 0.00073913,
'profit_all_ratio_sum': -3.9846604, 'profit_all_percent': -4.58,
'profit_all_ratio': -0.045796261934205953, 'profit_closed_coin': 0.00073913,
'profit_closed_fiat': 9.124559849999999, 'profit_closed_ratio_mean': 0.0075,
'profit_closed_percent_mean': 0.75, 'profit_closed_ratio_sum': 0.015,
'profit_closed_percent_sum': 1.5, 'profit_closed_ratio': 7.391275897987988e-07,
@ -747,11 +750,11 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
(
None,
{'best_pair': 'XRP/BTC', 'best_rate': 1.0, 'best_pair_profit_ratio': 0.01,
'profit_all_coin': -14.43790415,
'profit_all_fiat': -178235.92673175, 'profit_all_percent_mean': 0.08,
'profit_all_coin': -14.94732578,
'profit_all_fiat': -184524.7367541, 'profit_all_percent_mean': 0.08,
'profit_all_ratio_mean': 0.000835751666666662, 'profit_all_percent_sum': 0.5,
'profit_all_ratio_sum': 0.005014509999999972, 'profit_all_percent': -1.44,
'profit_all_ratio': -0.014437768014451796, 'profit_closed_coin': -0.00542913,
'profit_all_ratio_sum': 0.005014509999999972, 'profit_all_percent': -1.49,
'profit_all_ratio': -0.014947184841095841, 'profit_closed_coin': -0.00542913,
'profit_closed_fiat': -67.02260985, 'profit_closed_ratio_mean': 0.0025,
'profit_closed_percent_mean': 0.25, 'profit_closed_ratio_sum': 0.005,
'profit_closed_percent_sum': 0.5, 'profit_closed_ratio': -5.429078808526421e-06,
@ -790,22 +793,22 @@ def test_api_profit(botclient, mocker, ticker, fee, markets, is_short, expected)
'first_trade_timestamp': ANY,
'latest_trade_date': '5 minutes ago',
'latest_trade_timestamp': ANY,
'profit_all_coin': expected['profit_all_coin'],
'profit_all_fiat': expected['profit_all_fiat'],
'profit_all_percent_mean': expected['profit_all_percent_mean'],
'profit_all_ratio_mean': expected['profit_all_ratio_mean'],
'profit_all_percent_sum': expected['profit_all_percent_sum'],
'profit_all_ratio_sum': expected['profit_all_ratio_sum'],
'profit_all_percent': expected['profit_all_percent'],
'profit_all_ratio': expected['profit_all_ratio'],
'profit_closed_coin': expected['profit_closed_coin'],
'profit_closed_fiat': expected['profit_closed_fiat'],
'profit_closed_ratio_mean': expected['profit_closed_ratio_mean'],
'profit_closed_percent_mean': expected['profit_closed_percent_mean'],
'profit_closed_ratio_sum': expected['profit_closed_ratio_sum'],
'profit_closed_percent_sum': expected['profit_closed_percent_sum'],
'profit_closed_ratio': expected['profit_closed_ratio'],
'profit_closed_percent': expected['profit_closed_percent'],
'profit_all_coin': pytest.approx(expected['profit_all_coin']),
'profit_all_fiat': pytest.approx(expected['profit_all_fiat']),
'profit_all_percent_mean': pytest.approx(expected['profit_all_percent_mean']),
'profit_all_ratio_mean': pytest.approx(expected['profit_all_ratio_mean']),
'profit_all_percent_sum': pytest.approx(expected['profit_all_percent_sum']),
'profit_all_ratio_sum': pytest.approx(expected['profit_all_ratio_sum']),
'profit_all_percent': pytest.approx(expected['profit_all_percent']),
'profit_all_ratio': pytest.approx(expected['profit_all_ratio']),
'profit_closed_coin': pytest.approx(expected['profit_closed_coin']),
'profit_closed_fiat': pytest.approx(expected['profit_closed_fiat']),
'profit_closed_ratio_mean': pytest.approx(expected['profit_closed_ratio_mean']),
'profit_closed_percent_mean': pytest.approx(expected['profit_closed_percent_mean']),
'profit_closed_ratio_sum': pytest.approx(expected['profit_closed_ratio_sum']),
'profit_closed_percent_sum': pytest.approx(expected['profit_closed_percent_sum']),
'profit_closed_ratio': pytest.approx(expected['profit_closed_ratio']),
'profit_closed_percent': pytest.approx(expected['profit_closed_percent']),
'trade_count': 6,
'closed_trade_count': 2,
'winning_trades': expected['winning_trades'],
@ -1202,7 +1205,7 @@ def test_api_forceexit(botclient, mocker, ticker, fee, markets):
fetch_ticker=ticker,
get_fee=fee,
markets=PropertyMock(return_value=markets),
_is_dry_limit_order_filled=MagicMock(return_value=False),
_is_dry_limit_order_filled=MagicMock(return_value=True),
)
patch_get_signal(ftbot)
@ -1212,12 +1215,27 @@ def test_api_forceexit(botclient, mocker, ticker, fee, markets):
assert rc.json() == {"error": "Error querying /api/v1/forceexit: invalid argument"}
Trade.query.session.rollback()
ftbot.enter_positions()
create_mock_trades(fee)
trade = Trade.get_trades([Trade.id == 5]).first()
assert pytest.approx(trade.amount) == 123
rc = client_post(client, f"{BASE_URI}/forceexit",
data='{"tradeid": "5", "ordertype": "market", "amount": 23}')
assert_response(rc)
assert rc.json() == {'result': 'Created sell order for trade 5.'}
Trade.query.session.rollback()
trade = Trade.get_trades([Trade.id == 5]).first()
assert pytest.approx(trade.amount) == 100
assert trade.is_open is True
rc = client_post(client, f"{BASE_URI}/forceexit",
data='{"tradeid": "1"}')
data='{"tradeid": "5"}')
assert_response(rc)
assert rc.json() == {'result': 'Created sell order for trade 1.'}
assert rc.json() == {'result': 'Created sell order for trade 5.'}
Trade.query.session.rollback()
trade = Trade.get_trades([Trade.id == 5]).first()
assert trade.is_open is False
def test_api_pair_candles(botclient, ohlcv_history):
@ -1403,6 +1421,7 @@ def test_api_strategies(botclient):
'StrategyTestV2',
'StrategyTestV3',
'StrategyTestV3Futures',
'freqai_test_classifier',
'freqai_test_multimodel_strat',
'freqai_test_strat'
]}

21
tests/rpc/test_rpc_manager.py
View File

@ -1,6 +1,7 @@
# pragma pylint: disable=missing-docstring, C0103
import logging
import time
from collections import deque
from unittest.mock import MagicMock
from freqtrade.enums import RPCMessageType
@ -81,9 +82,25 @@ def test_send_msg_telegram_disabled(mocker, default_conf, caplog) -> None:
assert telegram_mock.call_count == 0
def test_process_msg_queue(mocker, default_conf, caplog) -> None:
telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg')
mocker.patch('freqtrade.rpc.telegram.Telegram._init')
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
rpc_manager = RPCManager(freqtradebot)
queue = deque()
queue.append('Test message')
queue.append('Test message 2')
rpc_manager.process_msg_queue(queue)
assert log_has("Sending rpc message: {'type': strategy_msg, 'msg': 'Test message'}", caplog)
assert log_has("Sending rpc message: {'type': strategy_msg, 'msg': 'Test message 2'}", caplog)
assert telegram_mock.call_count == 2
def test_send_msg_telegram_enabled(mocker, default_conf, caplog) -> None:
telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock())
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg')
mocker.patch('freqtrade.rpc.telegram.Telegram._init')
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
rpc_manager = RPCManager(freqtradebot)

172
tests/rpc/test_rpc_telegram.py
View File

@ -272,7 +272,7 @@ def test_telegram_status_multi_entry(default_conf, update, mocker, fee) -> None:
msg = msg_mock.call_args_list[0][0][0]
assert re.search(r'Number of Entries.*2', msg)
assert re.search(r'Average Entry Price', msg)
assert re.search(r'Order filled at', msg)
assert re.search(r'Order filled', msg)
assert re.search(r'Close Date:', msg) is None
assert re.search(r'Close Profit:', msg) is None
@ -342,7 +342,7 @@ def test_status_handle(default_conf, update, ticker, fee, mocker) -> None:
# close_rate should not be included in the message as the trade is not closed
# and no line should be empty
lines = msg_mock.call_args_list[0][0][0].split('\n')
assert '' not in lines
assert '' not in lines[:-1]
assert 'Close Rate' not in ''.join(lines)
assert 'Close Profit' not in ''.join(lines)
@ -357,13 +357,29 @@ def test_status_handle(default_conf, update, ticker, fee, mocker) -> None:
telegram._status(update=update, context=context)
lines = msg_mock.call_args_list[0][0][0].split('\n')
assert '' not in lines
assert '' not in lines[:-1]
assert 'Close Rate' not in ''.join(lines)
assert 'Close Profit' not in ''.join(lines)
assert msg_mock.call_count == 2
assert 'LTC/BTC' in msg_mock.call_args_list[0][0][0]
mocker.patch('freqtrade.rpc.telegram.MAX_MESSAGE_LENGTH', 500)
msg_mock.reset_mock()
context = MagicMock()
context.args = ["2"]
telegram._status(update=update, context=context)
assert msg_mock.call_count == 2
msg1 = msg_mock.call_args_list[0][0][0]
msg2 = msg_mock.call_args_list[1][0][0]
assert 'Close Rate' not in msg1
assert 'Trade ID:* `2`' in msg1
assert 'Trade ID:* `2` - continued' in msg2
def test_status_table_handle(default_conf, update, ticker, fee, mocker) -> None:
mocker.patch.multiple(
@ -433,10 +449,10 @@ def test_daily_handle(default_conf_usdt, update, ticker, fee, mocker, time_machi
assert "Daily Profit over the last 2 days</b>:" in msg_mock.call_args_list[0][0][0]
assert 'Day ' in msg_mock.call_args_list[0][0][0]
assert str(datetime.utcnow().date()) in msg_mock.call_args_list[0][0][0]
assert ' 13.83 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 15.21 USD' in msg_mock.call_args_list[0][0][0]
assert ' 6.83 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 7.51 USD' in msg_mock.call_args_list[0][0][0]
assert '(2)' in msg_mock.call_args_list[0][0][0]
assert '(2) 13.83 USDT 15.21 USD 1.31%' in msg_mock.call_args_list[0][0][0]
assert '(2) 6.83 USDT 7.51 USD 0.64%' in msg_mock.call_args_list[0][0][0]
assert '(0)' in msg_mock.call_args_list[0][0][0]
# Reset msg_mock
@ -447,8 +463,8 @@ def test_daily_handle(default_conf_usdt, update, ticker, fee, mocker, time_machi
assert "Daily Profit over the last 7 days</b>:" in msg_mock.call_args_list[0][0][0]
assert str(datetime.utcnow().date()) in msg_mock.call_args_list[0][0][0]
assert str((datetime.utcnow() - timedelta(days=5)).date()) in msg_mock.call_args_list[0][0][0]
assert ' 13.83 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 15.21 USD' in msg_mock.call_args_list[0][0][0]
assert ' 6.83 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 7.51 USD' in msg_mock.call_args_list[0][0][0]
assert '(2)' in msg_mock.call_args_list[0][0][0]
assert '(1)' in msg_mock.call_args_list[0][0][0]
assert '(0)' in msg_mock.call_args_list[0][0][0]
@ -460,8 +476,8 @@ def test_daily_handle(default_conf_usdt, update, ticker, fee, mocker, time_machi
context = MagicMock()
context.args = ["1"]
telegram._daily(update=update, context=context)
assert ' 13.83 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 15.21 USD' in msg_mock.call_args_list[0][0][0]
assert ' 6.83 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 7.51 USD' in msg_mock.call_args_list[0][0][0]
assert '(2)' in msg_mock.call_args_list[0][0][0]
@ -523,8 +539,8 @@ def test_weekly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mach
today = datetime.utcnow().date()
first_iso_day_of_current_week = today - timedelta(days=today.weekday())
assert str(first_iso_day_of_current_week) in msg_mock.call_args_list[0][0][0]
assert ' 9.83 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 10.81 USD' in msg_mock.call_args_list[0][0][0]
assert ' 2.74 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 3.01 USD' in msg_mock.call_args_list[0][0][0]
assert '(3)' in msg_mock.call_args_list[0][0][0]
assert '(0)' in msg_mock.call_args_list[0][0][0]
@ -536,8 +552,8 @@ def test_weekly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mach
assert "Weekly Profit over the last 8 weeks (starting from Monday)</b>:" \
in msg_mock.call_args_list[0][0][0]
assert 'Weekly' in msg_mock.call_args_list[0][0][0]
assert ' 9.83 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 10.81 USD' in msg_mock.call_args_list[0][0][0]
assert ' 2.74 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 3.01 USD' in msg_mock.call_args_list[0][0][0]
assert '(3)' in msg_mock.call_args_list[0][0][0]
assert '(0)' in msg_mock.call_args_list[0][0][0]
@ -592,8 +608,8 @@ def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mac
today = datetime.utcnow().date()
current_month = f"{today.year}-{today.month:02} "
assert current_month in msg_mock.call_args_list[0][0][0]
assert ' 9.83 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 10.81 USD' in msg_mock.call_args_list[0][0][0]
assert ' 2.74 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 3.01 USD' in msg_mock.call_args_list[0][0][0]
assert '(3)' in msg_mock.call_args_list[0][0][0]
assert '(0)' in msg_mock.call_args_list[0][0][0]
@ -606,8 +622,8 @@ def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mac
assert 'Monthly Profit over the last 6 months</b>:' in msg_mock.call_args_list[0][0][0]
assert 'Month ' in msg_mock.call_args_list[0][0][0]
assert current_month in msg_mock.call_args_list[0][0][0]
assert ' 9.83 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 10.81 USD' in msg_mock.call_args_list[0][0][0]
assert ' 2.74 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 3.01 USD' in msg_mock.call_args_list[0][0][0]
assert '(3)' in msg_mock.call_args_list[0][0][0]
assert '(0)' in msg_mock.call_args_list[0][0][0]
@ -620,8 +636,8 @@ def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mac
telegram._monthly(update=update, context=context)
assert msg_mock.call_count == 1
assert 'Monthly Profit over the last 12 months</b>:' in msg_mock.call_args_list[0][0][0]
assert ' 9.83 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 10.81 USD' in msg_mock.call_args_list[0][0][0]
assert ' 2.74 USDT' in msg_mock.call_args_list[0][0][0]
assert ' 3.01 USD' in msg_mock.call_args_list[0][0][0]
assert '(3)' in msg_mock.call_args_list[0][0][0]
# The one-digit months should contain a zero, Eg: September 2021 = "2021-09"
@ -959,6 +975,9 @@ def test_telegram_forceexit_handle(default_conf, update, ticker, fee,
'open_date': ANY,
'close_date': ANY,
'close_rate': ANY,
'stake_amount': 0.0009999999999054,
'sub_trade': False,
'cumulative_profit': 0.0,
} == last_msg
@ -1028,6 +1047,9 @@ def test_telegram_force_exit_down_handle(default_conf, update, ticker, fee,
'open_date': ANY,
'close_date': ANY,
'close_rate': ANY,
'stake_amount': 0.0009999999999054,
'sub_trade': False,
'cumulative_profit': 0.0,
} == last_msg
@ -1087,6 +1109,9 @@ def test_forceexit_all_handle(default_conf, update, ticker, fee, mocker) -> None
'open_date': ANY,
'close_date': ANY,
'close_rate': ANY,
'stake_amount': 0.0009999999999054,
'sub_trade': False,
'cumulative_profit': 0.0,
} == msg
@ -1259,7 +1284,7 @@ def test_telegram_performance_handle(default_conf_usdt, update, ticker, fee, moc
telegram._performance(update=update, context=MagicMock())
assert msg_mock.call_count == 1
assert 'Performance' in msg_mock.call_args_list[0][0][0]
assert '<code>XRP/USDT\t9.842 USDT (10.00%) (1)</code>' in msg_mock.call_args_list[0][0][0]
assert '<code>XRP/USDT\t2.842 USDT (10.00%) (1)</code>' in msg_mock.call_args_list[0][0][0]
def test_telegram_entry_tag_performance_handle(
@ -1309,7 +1334,7 @@ def test_telegram_exit_reason_performance_handle(default_conf_usdt, update, tick
telegram._exit_reason_performance(update=update, context=context)
assert msg_mock.call_count == 1
assert 'Exit Reason Performance' in msg_mock.call_args_list[0][0][0]
assert '<code>roi\t9.842 USDT (10.00%) (1)</code>' in msg_mock.call_args_list[0][0][0]
assert '<code>roi\t2.842 USDT (10.00%) (1)</code>' in msg_mock.call_args_list[0][0][0]
context.args = ['XRP/USDT']
telegram._exit_reason_performance(update=update, context=context)
@ -1341,7 +1366,7 @@ def test_telegram_mix_tag_performance_handle(default_conf_usdt, update, ticker,
telegram._mix_tag_performance(update=update, context=context)
assert msg_mock.call_count == 1
assert 'Mix Tag Performance' in msg_mock.call_args_list[0][0][0]
assert ('<code>TEST3 roi\t9.842 USDT (10.00%) (1)</code>'
assert ('<code>TEST3 roi\t2.842 USDT (10.00%) (1)</code>'
in msg_mock.call_args_list[0][0][0])
context.args = ['XRP/USDT']
@ -1507,7 +1532,7 @@ def test_telegram_logs(default_conf, update, mocker) -> None:
msg_mock.reset_mock()
# Test with changed MaxMessageLength
mocker.patch('freqtrade.rpc.telegram.MAX_TELEGRAM_MESSAGE_LENGTH', 200)
mocker.patch('freqtrade.rpc.telegram.MAX_MESSAGE_LENGTH', 200)
context = MagicMock()
context.args = []
telegram._logs(update=update, context=context)
@ -1789,7 +1814,6 @@ def test_send_msg_entry_fill_notification(default_conf, mocker, message_type, en
'leverage': leverage,
'stake_amount': 0.01465333,
'direction': entered,
# 'stake_amount_fiat': 0.0,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'open_rate': 1.099e-05,
@ -1806,6 +1830,33 @@ def test_send_msg_entry_fill_notification(default_conf, mocker, message_type, en
'*Total:* `(0.01465333 BTC, 180.895 USD)`'
)
msg_mock.reset_mock()
telegram.send_msg({
'type': message_type,
'trade_id': 1,
'enter_tag': enter_signal,
'exchange': 'Binance',
'pair': 'ETH/BTC',
'leverage': leverage,
'stake_amount': 0.01465333,
'sub_trade': True,
'direction': entered,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'open_rate': 1.099e-05,
'amount': 1333.3333333333335,
'open_date': arrow.utcnow().shift(hours=-1)
})
assert msg_mock.call_args[0][0] == (
f'\N{CHECK MARK} *Binance (dry):* {entered}ed ETH/BTC (#1)\n'
f'*Enter Tag:* `{enter_signal}`\n'
'*Amount:* `1333.33333333`\n'
f"{leverage_text}"
'*Open Rate:* `0.00001099`\n'
'*Total:* `(0.01465333 BTC, 180.895 USD)`'
)
def test_send_msg_sell_notification(default_conf, mocker) -> None:
@ -1840,12 +1891,51 @@ def test_send_msg_sell_notification(default_conf, mocker) -> None:
'*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n'
'*Enter Tag:* `buy_signal1`\n'
'*Exit Reason:* `stop_loss`\n'
'*Duration:* `1:00:00 (60.0 min)`\n'
'*Direction:* `Long`\n'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Current Rate:* `0.00003201`\n'
'*Close Rate:* `0.00003201`'
'*Exit Rate:* `0.00003201`\n'
'*Duration:* `1:00:00 (60.0 min)`'
)
msg_mock.reset_mock()
telegram.send_msg({
'type': RPCMessageType.EXIT,
'trade_id': 1,
'exchange': 'Binance',
'pair': 'KEY/ETH',
'direction': 'Long',
'gain': 'loss',
'limit': 3.201e-05,
'amount': 1333.3333333333335,
'order_type': 'market',
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'cumulative_profit': -0.15746268,
'profit_amount': -0.05746268,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD',
'enter_tag': 'buy_signal1',
'exit_reason': ExitType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
'close_date': arrow.utcnow(),
'stake_amount': 0.01,
'sub_trade': True,
})
assert msg_mock.call_args[0][0] == (
'\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
'*Unrealized Sub Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n'
'*Cumulative Profit:* (`-0.15746268 ETH / -24.812 USD`)\n'
'*Enter Tag:* `buy_signal1`\n'
'*Exit Reason:* `stop_loss`\n'
'*Direction:* `Long`\n'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Current Rate:* `0.00003201`\n'
'*Exit Rate:* `0.00003201`\n'
'*Remaining:* `(0.01 ETH, -24.812 USD)`'
)
msg_mock.reset_mock()
@ -1871,15 +1961,15 @@ def test_send_msg_sell_notification(default_conf, mocker) -> None:
})
assert msg_mock.call_args[0][0] == (
'\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
'*Unrealized Profit:* `-57.41%`\n'
'*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH)`\n'
'*Enter Tag:* `buy_signal1`\n'
'*Exit Reason:* `stop_loss`\n'
'*Duration:* `1 day, 2:30:00 (1590.0 min)`\n'
'*Direction:* `Long`\n'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Current Rate:* `0.00003201`\n'
'*Close Rate:* `0.00003201`'
'*Exit Rate:* `0.00003201`\n'
'*Duration:* `1 day, 2:30:00 (1590.0 min)`'
)
# Reset singleton function to avoid random breaks
telegram._rpc._fiat_converter.convert_amount = old_convamount
@ -1954,15 +2044,15 @@ def test_send_msg_sell_fill_notification(default_conf, mocker, direction,
leverage_text = f'*Leverage:* `{leverage}`\n' if leverage and leverage != 1.0 else ''
assert msg_mock.call_args[0][0] == (
'\N{WARNING SIGN} *Binance (dry):* Exited KEY/ETH (#1)\n'
'*Profit:* `-57.41%`\n'
'*Profit:* `-57.41% (loss: -0.05746268 ETH)`\n'
f'*Enter Tag:* `{enter_signal}`\n'
'*Exit Reason:* `stop_loss`\n'
'*Duration:* `1 day, 2:30:00 (1590.0 min)`\n'
f"*Direction:* `{direction}`\n"
f"{leverage_text}"
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Close Rate:* `0.00003201`'
'*Exit Rate:* `0.00003201`\n'
'*Duration:* `1 day, 2:30:00 (1590.0 min)`'
)
@ -1994,6 +2084,16 @@ def test_startup_notification(default_conf, mocker) -> None:
assert msg_mock.call_args[0][0] == '*Custom:* `Hello World`'
def test_send_msg_strategy_msg_notification(default_conf, mocker) -> None:
telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
telegram.send_msg({
'type': RPCMessageType.STRATEGY_MSG,
'msg': 'hello world, Test msg'
})
assert msg_mock.call_args[0][0] == 'hello world, Test msg'
def test_send_msg_unknown_type(default_conf, mocker) -> None:
telegram, _, _ = get_telegram_testobject(mocker, default_conf)
with pytest.raises(NotImplementedError, match=r'Unknown message type: None'):
@ -2080,16 +2180,16 @@ def test_send_msg_sell_notification_no_fiat(
leverage_text = f'*Leverage:* `{leverage}`\n' if leverage and leverage != 1.0 else ''
assert msg_mock.call_args[0][0] == (
'\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
'*Unrealized Profit:* `-57.41%`\n'
'*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH)`\n'
f'*Enter Tag:* `{enter_signal}`\n'
'*Exit Reason:* `stop_loss`\n'
'*Duration:* `2:35:03 (155.1 min)`\n'
f'*Direction:* `{direction}`\n'
f'{leverage_text}'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Current Rate:* `0.00003201`\n'
'*Close Rate:* `0.00003201`'
'*Exit Rate:* `0.00003201`\n'
'*Duration:* `2:35:03 (155.1 min)`'
)

139
tests/strategy/strats/freqai_test_classifier.py Normal file
View File

@ -0,0 +1,139 @@
import logging
from functools import reduce
import numpy as np
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
logger = logging.getLogger(__name__)
class freqai_test_classifier(IStrategy):
"""
Test strategy - used for testing freqAI functionalities.
DO not use in production.
"""
minimal_roi = {"0": 0.1, "240": -1}
plot_config = {
"main_plot": {},
"subplots": {
"prediction": {"prediction": {"color": "blue"}},
"target_roi": {
"target_roi": {"color": "brown"},
},
"do_predict": {
"do_predict": {"color": "brown"},
},
},
}
process_only_new_candles = True
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
can_short = False
linear_roi_offset = DecimalParameter(
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
)
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
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
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
coin = pair.split('/')[0]
with self.freqai.lock:
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}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
informative[f"%-{coin}raw_price"] = informative["close"]
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-up_or_down'] = np.where(df["close"].shift(-100) > df["close"], 'up', 'down')
return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config["freqai"]
dataframe = self.freqai.start(dataframe, metadata, self)
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df['&s-up_or_down'] == 'up']
if enter_long_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
] = (1, "long")
enter_short_conditions = [df['&s-up_or_down'] == 'down']
if enter_short_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
] = (1, "short")
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
return df

32
tests/strategy/strats/freqai_test_multimodel_strat.py
View File

@ -13,13 +13,8 @@ logger = logging.getLogger(__name__)
class freqai_test_multimodel_strat(IStrategy):
"""
Example strategy showing how the user connects their own
IFreqaiModel to the strategy. Namely, the user uses:
self.freqai.start(dataframe, metadata)
to make predictions on their data. populate_any_indicators() automatically
generates the variety of features indicated by the user in the
canonical freqtrade configuration file under config['freqai'].
Test strategy - used for testing freqAI multimodel functionalities.
DO not use in production.
"""
minimal_roi = {"0": 0.1, "240": -1}
@ -62,22 +57,10 @@ class freqai_test_multimodel_strat(IStrategy):
return informative_pairs
def populate_any_indicators(
self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
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.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
"""
coin = pair.split('/')[0]
with self.freqai.lock:
if informative is None:
@ -147,11 +130,6 @@ class freqai_test_multimodel_strat(IStrategy):
self.freqai_info = self.config["freqai"]
# All indicators must be populated by populate_any_indicators() for live functionality
# to work correctly.
# the model will return 4 values, its prediction, an indication of whether or not the
# prediction should be accepted, the target mean/std values from the labels used during
# each training period.
dataframe = self.freqai.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25

32
tests/strategy/strats/freqai_test_strat.py
View File

@ -13,13 +13,8 @@ logger = logging.getLogger(__name__)
class freqai_test_strat(IStrategy):
"""
Example strategy showing how the user connects their own
IFreqaiModel to the strategy. Namely, the user uses:
self.freqai.start(dataframe, metadata)
to make predictions on their data. populate_any_indicators() automatically
generates the variety of features indicated by the user in the
canonical freqtrade configuration file under config['freqai'].
Test strategy - used for testing freqAI functionalities.
DO not use in production.
"""
minimal_roi = {"0": 0.1, "240": -1}
@ -62,22 +57,10 @@ class freqai_test_strat(IStrategy):
return informative_pairs
def populate_any_indicators(
self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
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.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
"""
coin = pair.split('/')[0]
with self.freqai.lock:
if informative is None:
@ -135,11 +118,6 @@ class freqai_test_strat(IStrategy):
self.freqai_info = self.config["freqai"]
# All indicators must be populated by populate_any_indicators() for live functionality
# to work correctly.
# the model will return 4 values, its prediction, an indication of whether or not the
# prediction should be accepted, the target mean/std values from the labels used during
# each training period.
dataframe = self.freqai.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25

9
tests/strategy/strats/strategy_test_v3.py
View File

@ -185,9 +185,12 @@ class StrategyTestV3(IStrategy):
return 3.0
def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float,
min_stake: Optional[float], max_stake: float, **kwargs):
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float,
min_stake: Optional[float], max_stake: float,
current_entry_rate: float, current_exit_rate: float,
current_entry_profit: float, current_exit_profit: float,
**kwargs) -> Optional[float]:
if current_profit < -0.0075:
orders = trade.select_filled_orders(trade.entry_side)

49
tests/strategy/test_interface.py
View File

@ -290,6 +290,25 @@ def test_advise_all_indicators(default_conf, testdatadir) -> None:
assert len(processed['UNITTEST/BTC']) == 102 # partial candle was removed
def test_populate_any_indicators(default_conf, testdatadir) -> None:
strategy = StrategyResolver.load_strategy(default_conf)
timerange = TimeRange.parse_timerange('1510694220-1510700340')
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
fill_up_missing=True)
processed = strategy.populate_any_indicators('UNITTEST/BTC', data, '5m')
assert processed == data
assert id(processed) == id(data)
assert len(processed['UNITTEST/BTC']) == 102 # partial candle was removed
def test_freqai_not_initialized(default_conf) -> None:
strategy = StrategyResolver.load_strategy(default_conf)
strategy.ft_bot_start()
with pytest.raises(OperationalException, match=r'freqAI is not enabled\.'):
strategy.freqai.start()
def test_advise_all_indicators_copy(mocker, default_conf, testdatadir) -> None:
strategy = StrategyResolver.load_strategy(default_conf)
aimock = mocker.patch('freqtrade.strategy.interface.IStrategy.advise_indicators')
@ -408,28 +427,31 @@ def test_min_roi_reached3(default_conf, fee) -> None:
@pytest.mark.parametrize(
'profit,adjusted,expected,trailing,custom,profit2,adjusted2,expected2,custom_stop', [
'profit,adjusted,expected,liq,trailing,custom,profit2,adjusted2,expected2,custom_stop', [
# Profit, adjusted stoploss(absolute), profit for 2nd call, enable trailing,
# enable custom stoploss, expected after 1st call, expected after 2nd call
(0.2, 0.9, ExitType.NONE, False, False, 0.3, 0.9, ExitType.NONE, None),
(0.2, 0.9, ExitType.NONE, False, False, -0.2, 0.9, ExitType.STOP_LOSS, None),
(0.2, 1.14, ExitType.NONE, True, False, 0.05, 1.14, ExitType.TRAILING_STOP_LOSS, None),
(0.01, 0.96, ExitType.NONE, True, False, 0.05, 1, ExitType.NONE, None),
(0.05, 1, ExitType.NONE, True, False, -0.01, 1, ExitType.TRAILING_STOP_LOSS, None),
(0.2, 0.9, ExitType.NONE, None, False, False, 0.3, 0.9, ExitType.NONE, None),
(0.2, 0.9, ExitType.NONE, None, False, False, -0.2, 0.9, ExitType.STOP_LOSS, None),
(0.2, 0.9, ExitType.NONE, 0.8, False, False, -0.2, 0.9, ExitType.LIQUIDATION, None),
(0.2, 1.14, ExitType.NONE, None, True, False, 0.05, 1.14, ExitType.TRAILING_STOP_LOSS,
None),
(0.01, 0.96, ExitType.NONE, None, True, False, 0.05, 1, ExitType.NONE, None),
(0.05, 1, ExitType.NONE, None, True, False, -0.01, 1, ExitType.TRAILING_STOP_LOSS, None),
# Default custom case - trails with 10%
(0.05, 0.95, ExitType.NONE, False, True, -0.02, 0.95, ExitType.NONE, None),
(0.05, 0.95, ExitType.NONE, False, True, -0.06, 0.95, ExitType.TRAILING_STOP_LOSS, None),
(0.05, 1, ExitType.NONE, False, True, -0.06, 1, ExitType.TRAILING_STOP_LOSS,
(0.05, 0.95, ExitType.NONE, None, False, True, -0.02, 0.95, ExitType.NONE, None),
(0.05, 0.95, ExitType.NONE, None, False, True, -0.06, 0.95, ExitType.TRAILING_STOP_LOSS,
None),
(0.05, 1, ExitType.NONE, None, False, True, -0.06, 1, ExitType.TRAILING_STOP_LOSS,
lambda **kwargs: -0.05),
(0.05, 1, ExitType.NONE, False, True, 0.09, 1.04, ExitType.NONE,
(0.05, 1, ExitType.NONE, None, False, True, 0.09, 1.04, ExitType.NONE,
lambda **kwargs: -0.05),
(0.05, 0.95, ExitType.NONE, False, True, 0.09, 0.98, ExitType.NONE,
(0.05, 0.95, ExitType.NONE, None, False, True, 0.09, 0.98, ExitType.NONE,
lambda current_profit, **kwargs: -0.1 if current_profit < 0.6 else -(current_profit * 2)),
# Error case - static stoploss in place
(0.05, 0.9, ExitType.NONE, False, True, 0.09, 0.9, ExitType.NONE,
(0.05, 0.9, ExitType.NONE, None, False, True, 0.09, 0.9, ExitType.NONE,
lambda **kwargs: None),
])
def test_stop_loss_reached(default_conf, fee, profit, adjusted, expected, trailing, custom,
def test_stop_loss_reached(default_conf, fee, profit, adjusted, expected, liq, trailing, custom,
profit2, adjusted2, expected2, custom_stop) -> None:
strategy = StrategyResolver.load_strategy(default_conf)
@ -442,6 +464,7 @@ def test_stop_loss_reached(default_conf, fee, profit, adjusted, expected, traili
fee_close=fee.return_value,
exchange='binance',
open_rate=1,
liquidation_price=liq,
)
trade.adjust_min_max_rates(trade.open_rate, trade.open_rate)
strategy.trailing_stop = trailing

6
tests/strategy/test_strategy_loading.py
View File

@ -34,7 +34,7 @@ def test_search_all_strategies_no_failed():
directory = Path(__file__).parent / "strats"
strategies = StrategyResolver.search_all_objects(directory, enum_failed=False)
assert isinstance(strategies, list)
assert len(strategies) == 8
assert len(strategies) == 9
assert isinstance(strategies[0], dict)
@ -42,10 +42,10 @@ def test_search_all_strategies_with_failed():
directory = Path(__file__).parent / "strats"
strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
assert isinstance(strategies, list)
assert len(strategies) == 9
assert len(strategies) == 10
# with enum_failed=True search_all_objects() shall find 2 good strategies
# and 1 which fails to load
assert len([x for x in strategies if x['class'] is not None]) == 8
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

428
tests/test_freqtradebot.py
View File

@ -68,8 +68,14 @@ def test_process_stopped(mocker, default_conf_usdt) -> None:
assert coo_mock.call_count == 1
def test_process_calls_sendmsg(mocker, default_conf_usdt) -> None:
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
freqtrade.process()
assert freqtrade.rpc.process_msg_queue.call_count == 1
def test_bot_cleanup(mocker, default_conf_usdt, caplog) -> None:
mock_cleanup = mocker.patch('freqtrade.freqtradebot.cleanup_db')
mock_cleanup = mocker.patch('freqtrade.freqtradebot.Trade.commit')
coo_mock = mocker.patch('freqtrade.freqtradebot.FreqtradeBot.cancel_all_open_orders')
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
freqtrade.cleanup()
@ -837,8 +843,8 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order,
# In case of closed order
order['status'] = 'closed'
order['price'] = 10
order['cost'] = 100
order['average'] = 10
order['cost'] = 300
order['id'] = '444'
mocker.patch('freqtrade.exchange.Exchange.create_order',
@ -849,7 +855,7 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order,
assert trade
assert trade.open_order_id is None
assert trade.open_rate == 10
assert trade.stake_amount == round(order['price'] * order['filled'] / leverage, 8)
assert trade.stake_amount == round(order['average'] * order['filled'] / leverage, 8)
assert pytest.approx(trade.liquidation_price) == liq_price
# In case of rejected or expired order and partially filled
@ -857,8 +863,8 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order,
order['amount'] = 30.0
order['filled'] = 20.0
order['remaining'] = 10.00
order['price'] = 0.5
order['cost'] = 15.0
order['average'] = 0.5
order['cost'] = 10.0
order['id'] = '555'
mocker.patch('freqtrade.exchange.Exchange.create_order',
MagicMock(return_value=order))
@ -866,9 +872,9 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order,
trade = Trade.query.all()[3]
trade.is_short = is_short
assert trade
assert trade.open_order_id == '555'
assert trade.open_order_id is None
assert trade.open_rate == 0.5
assert trade.stake_amount == round(order['price'] * order['filled'] / leverage, 8)
assert trade.stake_amount == round(order['average'] * order['filled'] / leverage, 8)
# Test with custom stake
order['status'] = 'open'
@ -895,7 +901,7 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order,
order['amount'] = 30.0 * leverage
order['filled'] = 0.0
order['remaining'] = 30.0
order['price'] = 0.5
order['average'] = 0.5
order['cost'] = 0.0
order['id'] = '66'
mocker.patch('freqtrade.exchange.Exchange.create_order',
@ -967,6 +973,14 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order,
trade.is_short = is_short
assert pytest.approx(trade.stake_amount) == 500
order['id'] = '55673'
freqtrade.strategy.leverage.reset_mock()
assert freqtrade.execute_entry(pair, 200, leverage_=3)
assert freqtrade.strategy.leverage.call_count == 0
trade = Trade.query.all()[10]
assert trade.leverage == 1 if trading_mode == 'spot' else 3
@pytest.mark.parametrize("is_short", [False, True])
def test_execute_entry_confirm_error(mocker, default_conf_usdt, fee, limit_order, is_short) -> None:
@ -1077,7 +1091,7 @@ def test_handle_stoploss_on_exchange(mocker, default_conf_usdt, fee, caplog, is_
'last': 1.9
}),
create_order=MagicMock(side_effect=[
{'id': enter_order['id']},
enter_order,
exit_order,
]),
get_fee=fee,
@ -1103,20 +1117,20 @@ def test_handle_stoploss_on_exchange(mocker, default_conf_usdt, fee, caplog, is_
# should do nothing and return false
trade.is_open = True
trade.open_order_id = None
trade.stoploss_order_id = 100
trade.stoploss_order_id = "100"
hanging_stoploss_order = MagicMock(return_value={'status': 'open'})
mocker.patch('freqtrade.exchange.Exchange.fetch_stoploss_order', hanging_stoploss_order)
assert freqtrade.handle_stoploss_on_exchange(trade) is False
assert trade.stoploss_order_id == 100
assert trade.stoploss_order_id == "100"
# Third case: when stoploss was set but it was canceled for some reason
# should set a stoploss immediately and return False
caplog.clear()
trade.is_open = True
trade.open_order_id = None
trade.stoploss_order_id = 100
trade.stoploss_order_id = "100"
canceled_stoploss_order = MagicMock(return_value={'status': 'canceled'})
mocker.patch('freqtrade.exchange.Exchange.fetch_stoploss_order', canceled_stoploss_order)
@ -2033,6 +2047,7 @@ def test_update_trade_state_exception(mocker, default_conf_usdt, is_short, limit
trade = MagicMock()
trade.open_order_id = '123'
trade.amount = 123
# Test raise of OperationalException exception
mocker.patch(
@ -2346,9 +2361,9 @@ def test_close_trade(
trade.is_short = is_short
assert trade
oobj = Order.parse_from_ccxt_object(enter_order, enter_order['symbol'], 'buy')
oobj = Order.parse_from_ccxt_object(enter_order, enter_order['symbol'], trade.enter_side)
trade.update_trade(oobj)
oobj = Order.parse_from_ccxt_object(exit_order, exit_order['symbol'], 'sell')
oobj = Order.parse_from_ccxt_object(exit_order, exit_order['symbol'], trade.exit_side)
trade.update_trade(oobj)
assert trade.is_open is False
@ -2391,8 +2406,8 @@ def test_manage_open_orders_entry_usercustom(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker_usdt,
fetch_order=MagicMock(return_value=old_order),
cancel_order_with_result=cancel_order_wr_mock,
cancel_order=cancel_order_mock,
cancel_order_with_result=cancel_order_wr_mock,
get_fee=fee
)
freqtrade = FreqtradeBot(default_conf_usdt)
@ -2440,7 +2455,9 @@ def test_manage_open_orders_entry(
) -> None:
old_order = limit_sell_order_old if is_short else limit_buy_order_old
rpc_mock = patch_RPCManager(mocker)
old_order['id'] = open_trade.open_order_id
open_trade.open_order_id = old_order['id']
order = Order.parse_from_ccxt_object(old_order, 'mocked', 'buy')
open_trade.orders[0] = order
limit_buy_cancel = deepcopy(old_order)
limit_buy_cancel['status'] = 'canceled'
cancel_order_mock = MagicMock(return_value=limit_buy_cancel)
@ -2631,7 +2648,9 @@ def test_manage_open_orders_exit_usercustom(
is_short, open_trade_usdt, caplog
) -> None:
default_conf_usdt["unfilledtimeout"] = {"entry": 1440, "exit": 1440, "exit_timeout_count": 1}
limit_sell_order_old['id'] = open_trade_usdt.open_order_id
open_trade_usdt.open_order_id = limit_sell_order_old['id']
order = Order.parse_from_ccxt_object(limit_sell_order_old, 'mocked', 'sell')
open_trade_usdt.orders[0] = order
if is_short:
limit_sell_order_old['side'] = 'buy'
open_trade_usdt.is_short = is_short
@ -3244,6 +3263,9 @@ def test_execute_trade_exit_up(default_conf_usdt, ticker_usdt, fee, ticker_usdt_
'open_date': ANY,
'close_date': ANY,
'close_rate': ANY,
'sub_trade': False,
'cumulative_profit': 0.0,
'stake_amount': pytest.approx(60),
} == last_msg
@ -3304,6 +3326,9 @@ def test_execute_trade_exit_down(default_conf_usdt, ticker_usdt, fee, ticker_usd
'open_date': ANY,
'close_date': ANY,
'close_rate': ANY,
'sub_trade': False,
'cumulative_profit': 0.0,
'stake_amount': pytest.approx(60),
} == last_msg
@ -3385,6 +3410,9 @@ def test_execute_trade_exit_custom_exit_price(
'open_date': ANY,
'close_date': ANY,
'close_rate': ANY,
'sub_trade': False,
'cumulative_profit': 0.0,
'stake_amount': pytest.approx(60),
} == last_msg
@ -3453,6 +3481,9 @@ def test_execute_trade_exit_down_stoploss_on_exchange_dry_run(
'open_date': ANY,
'close_date': ANY,
'close_rate': ANY,
'sub_trade': False,
'cumulative_profit': 0.0,
'stake_amount': pytest.approx(60),
} == last_msg
@ -3684,7 +3715,7 @@ def test_execute_trade_exit_market_order(
)
assert not trade.is_open
assert trade.close_profit == profit_ratio
assert pytest.approx(trade.close_profit) == profit_ratio
assert rpc_mock.call_count == 4
last_msg = rpc_mock.call_args_list[-2][0][0]
@ -3712,6 +3743,9 @@ def test_execute_trade_exit_market_order(
'open_date': ANY,
'close_date': ANY,
'close_rate': ANY,
'sub_trade': False,
'cumulative_profit': 0.0,
'stake_amount': pytest.approx(60),
} == last_msg
@ -3783,7 +3817,7 @@ def test_exit_profit_only(
'last': bid
}),
create_order=MagicMock(side_effect=[
limit_order_open[eside],
limit_order[eside],
{'id': 1234553382},
]),
get_fee=fee,
@ -4075,7 +4109,7 @@ def test_trailing_stop_loss_positive(
'last': enter_price - (-0.01 if is_short else 0.01),
}),
create_order=MagicMock(side_effect=[
limit_order_open[eside],
limit_order[eside],
{'id': 1234553382},
]),
get_fee=fee,
@ -4626,7 +4660,7 @@ def test_order_book_entry_pricing1(mocker, default_conf_usdt, order_book_l2, exc
with pytest.raises(PricingError):
freqtrade.exchange.get_rate('ETH/USDT', side="entry", is_short=False, refresh=True)
assert log_has_re(
r'Entry Price at location 1 from orderbook could not be determined.', caplog)
r'ETH/USDT - Entry Price at location 1 from orderbook could not be determined.', caplog)
else:
assert freqtrade.exchange.get_rate(
'ETH/USDT', side="entry", is_short=False, refresh=True) == 0.043935
@ -4705,7 +4739,8 @@ def test_order_book_exit_pricing(
return_value={'bids': [[]], 'asks': [[]]})
with pytest.raises(PricingError):
freqtrade.handle_trade(trade)
assert log_has_re(r'Exit Price at location 1 from orderbook could not be determined\..*',
assert log_has_re(
r"ETH/USDT - Exit Price at location 1 from orderbook could not be determined\..*",
caplog)
@ -5379,7 +5414,7 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None:
'status': None,
'price': 9,
'amount': 12,
'cost': 100,
'cost': 108,
'ft_is_open': True,
'id': '651',
'order_id': '651'
@ -5474,7 +5509,7 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None:
assert trade.open_order_id is None
assert pytest.approx(trade.open_rate) == 9.90909090909
assert trade.amount == 22
assert trade.stake_amount == 218
assert pytest.approx(trade.stake_amount) == 218
orders = Order.query.all()
assert orders
@ -5527,6 +5562,329 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None:
# Make sure the closed order is found as the second order.
order = trade.select_order('buy', False)
assert order.order_id == '652'
closed_sell_dca_order_1 = {
'ft_pair': pair,
'status': 'closed',
'ft_order_side': 'sell',
'side': 'sell',
'type': 'limit',
'price': 8,
'average': 8,
'amount': 15,
'filled': 15,
'cost': 120,
'ft_is_open': False,
'id': '653',
'order_id': '653'
}
mocker.patch('freqtrade.exchange.Exchange.create_order',
MagicMock(return_value=closed_sell_dca_order_1))
mocker.patch('freqtrade.exchange.Exchange.fetch_order',
MagicMock(return_value=closed_sell_dca_order_1))
mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order',
MagicMock(return_value=closed_sell_dca_order_1))
assert freqtrade.execute_trade_exit(trade=trade, limit=8,
exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT),
sub_trade_amt=15)
# Assert trade is as expected (averaged dca)
trade = Trade.query.first()
assert trade
assert trade.open_order_id is None
assert trade.is_open
assert trade.amount == 22
assert trade.stake_amount == 192.05405405405406
assert pytest.approx(trade.open_rate) == 8.729729729729
orders = Order.query.all()
assert orders
assert len(orders) == 4
# Make sure the closed order is found as the second order.
order = trade.select_order('sell', False)
assert order.order_id == '653'
def test_position_adjust2(mocker, default_conf_usdt, fee) -> None:
"""
TODO: Should be adjusted to test both long and short
buy 100 @ 11
sell 50 @ 8
sell 50 @ 16
"""
patch_RPCManager(mocker)
patch_exchange(mocker)
patch_wallet(mocker, free=10000)
default_conf_usdt.update({
"position_adjustment_enable": True,
"dry_run": False,
"stake_amount": 200.0,
"dry_run_wallet": 1000.0,
})
freqtrade = FreqtradeBot(default_conf_usdt)
freqtrade.strategy.confirm_trade_entry = MagicMock(return_value=True)
bid = 11
amount = 100
buy_rate_mock = MagicMock(return_value=bid)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_rate=buy_rate_mock,
fetch_ticker=MagicMock(return_value={
'bid': 10,
'ask': 12,
'last': 11
}),
get_min_pair_stake_amount=MagicMock(return_value=1),
get_fee=fee,
)
pair = 'ETH/USDT'
# Initial buy
closed_successful_buy_order = {
'pair': pair,
'ft_pair': pair,
'ft_order_side': 'buy',
'side': 'buy',
'type': 'limit',
'status': 'closed',
'price': bid,
'average': bid,
'cost': bid * amount,
'amount': amount,
'filled': amount,
'ft_is_open': False,
'id': '600',
'order_id': '600'
}
mocker.patch('freqtrade.exchange.Exchange.create_order',
MagicMock(return_value=closed_successful_buy_order))
mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order',
MagicMock(return_value=closed_successful_buy_order))
assert freqtrade.execute_entry(pair, amount)
# Should create an closed trade with an no open order id
# Order is filled and trade is open
orders = Order.query.all()
assert orders
assert len(orders) == 1
trade = Trade.query.first()
assert trade
assert trade.is_open is True
assert trade.open_order_id is None
assert trade.open_rate == bid
assert trade.stake_amount == bid * amount
# Assume it does nothing since order is closed and trade is open
freqtrade.update_closed_trades_without_assigned_fees()
trade = Trade.query.first()
assert trade
assert trade.is_open is True
assert trade.open_order_id is None
assert trade.open_rate == bid
assert trade.stake_amount == bid * amount
assert not trade.fee_updated(trade.entry_side)
freqtrade.manage_open_orders()
trade = Trade.query.first()
assert trade
assert trade.is_open is True
assert trade.open_order_id is None
assert trade.open_rate == bid
assert trade.stake_amount == bid * amount
assert not trade.fee_updated(trade.entry_side)
amount = 50
ask = 8
closed_sell_dca_order_1 = {
'ft_pair': pair,
'status': 'closed',
'ft_order_side': 'sell',
'side': 'sell',
'type': 'limit',
'price': ask,
'average': ask,
'amount': amount,
'filled': amount,
'cost': amount * ask,
'ft_is_open': False,
'id': '601',
'order_id': '601'
}
mocker.patch('freqtrade.exchange.Exchange.create_order',
MagicMock(return_value=closed_sell_dca_order_1))
mocker.patch('freqtrade.exchange.Exchange.fetch_order',
MagicMock(return_value=closed_sell_dca_order_1))
mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order',
MagicMock(return_value=closed_sell_dca_order_1))
assert freqtrade.execute_trade_exit(trade=trade, limit=ask,
exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT),
sub_trade_amt=amount)
trades: List[Trade] = trade.get_open_trades_without_assigned_fees()
assert len(trades) == 1
# Assert trade is as expected (averaged dca)
trade = Trade.query.first()
assert trade
assert trade.open_order_id is None
assert trade.amount == 50
assert trade.open_rate == 11
assert trade.stake_amount == 550
assert pytest.approx(trade.realized_profit) == -152.375
assert pytest.approx(trade.close_profit_abs) == -152.375
orders = Order.query.all()
assert orders
assert len(orders) == 2
# Make sure the closed order is found as the second order.
order = trade.select_order('sell', False)
assert order.order_id == '601'
amount = 50
ask = 16
closed_sell_dca_order_2 = {
'ft_pair': pair,
'status': 'closed',
'ft_order_side': 'sell',
'side': 'sell',
'type': 'limit',
'price': ask,
'average': ask,
'amount': amount,
'filled': amount,
'cost': amount * ask,
'ft_is_open': False,
'id': '602',
'order_id': '602'
}
mocker.patch('freqtrade.exchange.Exchange.create_order',
MagicMock(return_value=closed_sell_dca_order_2))
mocker.patch('freqtrade.exchange.Exchange.fetch_order',
MagicMock(return_value=closed_sell_dca_order_2))
mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order',
MagicMock(return_value=closed_sell_dca_order_2))
assert freqtrade.execute_trade_exit(trade=trade, limit=ask,
exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT),
sub_trade_amt=amount)
# Assert trade is as expected (averaged dca)
trade = Trade.query.first()
assert trade
assert trade.open_order_id is None
assert trade.amount == 50
assert trade.open_rate == 11
assert trade.stake_amount == 550
# Trade fully realized
assert pytest.approx(trade.realized_profit) == 94.25
assert pytest.approx(trade.close_profit_abs) == 94.25
orders = Order.query.all()
assert orders
assert len(orders) == 3
# Make sure the closed order is found as the second order.
order = trade.select_order('sell', False)
assert order.order_id == '602'
assert trade.is_open is False
@pytest.mark.parametrize('data', [
(
# tuple 1 - side amount, price
# tuple 2 - amount, open_rate, stake_amount, cumulative_profit, realized_profit, rel_profit
(('buy', 100, 10), (100.0, 10.0, 1000.0, 0.0, None, None)),
(('buy', 100, 15), (200.0, 12.5, 2500.0, 0.0, None, None)),
(('sell', 50, 12), (150.0, 12.5, 1875.0, -28.0625, -28.0625, -0.044788)),
(('sell', 100, 20), (50.0, 12.5, 625.0, 713.8125, 741.875, 0.59201995)),
(('sell', 50, 5), (50.0, 12.5, 625.0, 336.625, 336.625, 0.1343142)), # final profit (sum)
),
(
(('buy', 100, 3), (100.0, 3.0, 300.0, 0.0, None, None)),
(('buy', 100, 7), (200.0, 5.0, 1000.0, 0.0, None, None)),
(('sell', 100, 11), (100.0, 5.0, 500.0, 596.0, 596.0, 1.189027)),
(('buy', 150, 15), (250.0, 11.0, 2750.0, 596.0, 596.0, 1.189027)),
(('sell', 100, 19), (150.0, 11.0, 1650.0, 1388.5, 792.5, 0.7186579)),
(('sell', 150, 23), (150.0, 11.0, 1650.0, 3175.75, 3175.75, 0.9747170)), # final profit
)
])
def test_position_adjust3(mocker, default_conf_usdt, fee, data) -> None:
default_conf_usdt.update({
"position_adjustment_enable": True,
"dry_run": False,
"stake_amount": 200.0,
"dry_run_wallet": 1000.0,
})
patch_RPCManager(mocker)
patch_exchange(mocker)
patch_wallet(mocker, free=10000)
freqtrade = FreqtradeBot(default_conf_usdt)
trade = None
freqtrade.strategy.confirm_trade_entry = MagicMock(return_value=True)
for idx, (order, result) in enumerate(data):
amount = order[1]
price = order[2]
price_mock = MagicMock(return_value=price)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_rate=price_mock,
fetch_ticker=MagicMock(return_value={
'bid': 10,
'ask': 12,
'last': 11
}),
get_min_pair_stake_amount=MagicMock(return_value=1),
get_fee=fee,
)
pair = 'ETH/USDT'
closed_successful_order = {
'pair': pair,
'ft_pair': pair,
'ft_order_side': order[0],
'side': order[0],
'type': 'limit',
'status': 'closed',
'price': price,
'average': price,
'cost': price * amount,
'amount': amount,
'filled': amount,
'ft_is_open': False,
'id': f'60{idx}',
'order_id': f'60{idx}'
}
mocker.patch('freqtrade.exchange.Exchange.create_order',
MagicMock(return_value=closed_successful_order))
mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order',
MagicMock(return_value=closed_successful_order))
if order[0] == 'buy':
assert freqtrade.execute_entry(pair, amount, trade=trade)
else:
assert freqtrade.execute_trade_exit(
trade=trade, limit=price,
exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT),
sub_trade_amt=amount)
orders1 = Order.query.all()
assert orders1
assert len(orders1) == idx + 1
trade = Trade.query.first()
assert trade
if idx < len(data) - 1:
assert trade.is_open is True
assert trade.open_order_id is None
assert trade.amount == result[0]
assert trade.open_rate == result[1]
assert trade.stake_amount == result[2]
assert pytest.approx(trade.realized_profit) == result[3]
assert pytest.approx(trade.close_profit_abs) == result[4]
assert pytest.approx(trade.close_profit) == result[5]
order_obj = trade.select_order(order[0], False)
assert order_obj.order_id == f'60{idx}'
trade = Trade.query.first()
assert trade
assert trade.open_order_id is None
assert trade.is_open is False
def test_process_open_trade_positions_exception(mocker, default_conf_usdt, fee, caplog) -> None:
@ -5550,9 +5908,25 @@ def test_check_and_call_adjust_trade_position(mocker, default_conf_usdt, fee, ca
"max_entry_position_adjustment": 0,
})
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
buy_rate_mock = MagicMock(return_value=10)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_rate=buy_rate_mock,
fetch_ticker=MagicMock(return_value={
'bid': 10,
'ask': 12,
'last': 11
}),
get_min_pair_stake_amount=MagicMock(return_value=1),
get_fee=fee,
)
create_mock_trades(fee)
caplog.set_level(logging.DEBUG)
freqtrade.strategy.adjust_trade_position = MagicMock(return_value=10)
freqtrade.process_open_trade_positions()
assert log_has_re(r"Max adjustment entries for .* has been reached\.", caplog)
caplog.clear()
freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-10)
freqtrade.process_open_trade_positions()
assert log_has_re(r"LIMIT_SELL has been fulfilled.*", caplog)

67
tests/test_integration.py
View File

@ -6,7 +6,7 @@ from freqtrade.enums import ExitCheckTuple, ExitType
from freqtrade.persistence import Trade
from freqtrade.persistence.models import Order
from freqtrade.rpc.rpc import RPC
from tests.conftest import get_patched_freqtradebot, patch_get_signal
from tests.conftest import get_patched_freqtradebot, log_has_re, patch_get_signal
def test_may_execute_exit_stoploss_on_exchange_multi(default_conf, ticker, fee,
@ -291,7 +291,7 @@ def test_dca_short(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
'freqtrade.exchange.Exchange',
fetch_ticker=ticker_usdt,
get_fee=fee,
amount_to_precision=lambda s, x, y: y,
amount_to_precision=lambda s, x, y: round(y, 4),
price_to_precision=lambda s, x, y: y,
)
@ -303,6 +303,7 @@ def test_dca_short(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
assert len(trade.orders) == 1
assert pytest.approx(trade.stake_amount) == 60
assert trade.open_rate == 2.02
assert trade.orders[0].amount == trade.amount
# No adjustment
freqtrade.process()
trade = Trade.get_trades().first()
@ -331,8 +332,7 @@ def test_dca_short(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
trade = Trade.get_trades().first()
assert len(trade.orders) == 2
assert pytest.approx(trade.stake_amount) == 120
# assert trade.orders[0].amount == 30
assert trade.orders[1].amount == 60 / ticker_usdt_modif['ask']
assert trade.orders[1].amount == round(60 / ticker_usdt_modif['ask'], 4)
assert trade.amount == trade.orders[0].amount + trade.orders[1].amount
assert trade.nr_of_successful_entries == 2
@ -344,7 +344,7 @@ def test_dca_short(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
assert trade.is_open is False
# assert trade.orders[0].amount == 30
assert trade.orders[0].side == 'sell'
assert trade.orders[1].amount == 60 / ticker_usdt_modif['ask']
assert trade.orders[1].amount == round(60 / ticker_usdt_modif['ask'], 4)
# Sold everything
assert trade.orders[-1].side == 'buy'
assert trade.orders[2].amount == trade.amount
@ -455,3 +455,60 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
# Check the 2 filled orders equal the above amount
assert pytest.approx(trade.orders[1].amount) == 30.150753768
assert pytest.approx(trade.orders[-1].amount) == 61.538461232
def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog) -> None:
default_conf_usdt['position_adjustment_enable'] = True
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker_usdt,
get_fee=fee,
amount_to_precision=lambda s, x, y: y,
price_to_precision=lambda s, x, y: y,
get_min_pair_stake_amount=MagicMock(return_value=10),
)
patch_get_signal(freqtrade)
freqtrade.enter_positions()
assert len(Trade.get_trades().all()) == 1
trade = Trade.get_trades().first()
assert len(trade.orders) == 1
assert pytest.approx(trade.stake_amount) == 60
assert pytest.approx(trade.amount) == 30.0
assert trade.open_rate == 2.0
# Too small size
freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-59)
freqtrade.process()
trade = Trade.get_trades().first()
assert len(trade.orders) == 1
assert pytest.approx(trade.stake_amount) == 60
assert pytest.approx(trade.amount) == 30.0
assert log_has_re("Remaining amount of 1.6.* would be too small.", caplog)
freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-20)
freqtrade.process()
trade = Trade.get_trades().first()
assert len(trade.orders) == 2
assert trade.orders[-1].ft_order_side == 'sell'
assert pytest.approx(trade.stake_amount) == 40.198
assert pytest.approx(trade.amount) == 20.099
assert trade.open_rate == 2.0
assert trade.is_open
caplog.clear()
# Sell more than what we got (we got ~20 coins left)
# First adjusts the amount to 20 - then rejects.
freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-50)
freqtrade.process()
assert log_has_re("Adjusting amount to trade.amount as it is higher.*", caplog)
assert log_has_re("Remaining amount of 0.0 would be too small.", caplog)
trade = Trade.get_trades().first()
assert len(trade.orders) == 2
assert trade.orders[-1].ft_order_side == 'sell'
assert pytest.approx(trade.stake_amount) == 40.198
assert trade.is_open

2
tests/test_periodiccache.py
View File

@ -1,6 +1,6 @@
import time_machine
from freqtrade.configuration import PeriodicCache
from freqtrade.util import PeriodicCache
def test_ttl_cache():

306
tests/test_persistence.py
View File

@ -99,7 +99,7 @@ def test_enter_exit_side(fee, is_short):
@pytest.mark.usefixtures("init_persistence")
def test_set_stop_loss_isolated_liq(fee):
def test_set_stop_loss_liquidation(fee):
trade = Trade(
id=2,
pair='ADA/USDT',
@ -115,73 +115,94 @@ def test_set_stop_loss_isolated_liq(fee):
leverage=2.0,
trading_mode=margin
)
trade.set_isolated_liq(0.09)
trade.set_liquidation_price(0.09)
assert trade.liquidation_price == 0.09
assert trade.stop_loss is None
assert trade.initial_stop_loss is None
trade._set_stop_loss(0.1, (1.0 / 9.0))
trade.adjust_stop_loss(2.0, 0.2, True)
assert trade.liquidation_price == 0.09
assert trade.stop_loss == 0.1
assert trade.initial_stop_loss == 0.1
assert trade.stop_loss == 1.8
assert trade.initial_stop_loss == 1.8
trade.set_isolated_liq(0.08)
trade.set_liquidation_price(0.08)
assert trade.liquidation_price == 0.08
assert trade.stop_loss == 0.1
assert trade.initial_stop_loss == 0.1
assert trade.stop_loss == 1.8
assert trade.initial_stop_loss == 1.8
trade.set_isolated_liq(0.11)
trade._set_stop_loss(0.1, 0)
trade.set_liquidation_price(0.11)
trade.adjust_stop_loss(2.0, 0.2)
assert trade.liquidation_price == 0.11
assert trade.stop_loss == 0.11
assert trade.initial_stop_loss == 0.1
# Stoploss does not change from liquidation price
assert trade.stop_loss == 1.8
assert trade.initial_stop_loss == 1.8
# lower stop doesn't move stoploss
trade._set_stop_loss(0.1, 0)
trade.adjust_stop_loss(1.8, 0.2)
assert trade.liquidation_price == 0.11
assert trade.stop_loss == 0.11
assert trade.initial_stop_loss == 0.1
assert trade.stop_loss == 1.8
assert trade.initial_stop_loss == 1.8
# higher stop does move stoploss
trade.adjust_stop_loss(2.1, 0.1)
assert trade.liquidation_price == 0.11
assert pytest.approx(trade.stop_loss) == 1.994999
assert trade.initial_stop_loss == 1.8
assert trade.stoploss_or_liquidation == trade.stop_loss
trade.stop_loss = None
trade.liquidation_price = None
trade.initial_stop_loss = None
trade.initial_stop_loss_pct = None
trade._set_stop_loss(0.07, 0)
trade.adjust_stop_loss(2.0, 0.1, True)
assert trade.liquidation_price is None
assert trade.stop_loss == 0.07
assert trade.initial_stop_loss == 0.07
assert trade.stop_loss == 1.9
assert trade.initial_stop_loss == 1.9
assert trade.stoploss_or_liquidation == 1.9
trade.is_short = True
trade.recalc_open_trade_value()
trade.stop_loss = None
trade.initial_stop_loss = None
trade.initial_stop_loss_pct = None
trade.set_isolated_liq(0.09)
assert trade.liquidation_price == 0.09
trade.set_liquidation_price(3.09)
assert trade.liquidation_price == 3.09
assert trade.stop_loss is None
assert trade.initial_stop_loss is None
trade._set_stop_loss(0.08, (1.0 / 9.0))
assert trade.liquidation_price == 0.09
assert trade.stop_loss == 0.08
assert trade.initial_stop_loss == 0.08
trade.adjust_stop_loss(2.0, 0.2)
assert trade.liquidation_price == 3.09
assert trade.stop_loss == 2.2
assert trade.initial_stop_loss == 2.2
assert trade.stoploss_or_liquidation == 2.2
trade.set_isolated_liq(0.1)
assert trade.liquidation_price == 0.1
assert trade.stop_loss == 0.08
assert trade.initial_stop_loss == 0.08
trade.set_liquidation_price(3.1)
assert trade.liquidation_price == 3.1
assert trade.stop_loss == 2.2
assert trade.initial_stop_loss == 2.2
assert trade.stoploss_or_liquidation == 2.2
trade.set_isolated_liq(0.07)
trade._set_stop_loss(0.1, (1.0 / 8.0))
assert trade.liquidation_price == 0.07
assert trade.stop_loss == 0.07
assert trade.initial_stop_loss == 0.08
trade.set_liquidation_price(3.8)
assert trade.liquidation_price == 3.8
# Stoploss does not change from liquidation price
assert trade.stop_loss == 2.2
assert trade.initial_stop_loss == 2.2
# Stop doesn't move stop higher
trade._set_stop_loss(0.1, (1.0 / 9.0))
assert trade.liquidation_price == 0.07
assert trade.stop_loss == 0.07
assert trade.initial_stop_loss == 0.08
trade.adjust_stop_loss(2.0, 0.3)
assert trade.liquidation_price == 3.8
assert trade.stop_loss == 2.2
assert trade.initial_stop_loss == 2.2
# Stoploss does move lower
trade.set_liquidation_price(1.5)
trade.adjust_stop_loss(1.8, 0.1)
assert trade.liquidation_price == 1.5
assert pytest.approx(trade.stop_loss) == 1.89
assert trade.initial_stop_loss == 2.2
assert trade.stoploss_or_liquidation == 1.5
@pytest.mark.parametrize('exchange,is_short,lev,minutes,rate,interest,trading_mode', [
@ -479,7 +500,7 @@ def test_update_limit_order(fee, caplog, limit_buy_order_usdt, limit_sell_order_
assert trade.close_profit is None
assert trade.close_date is None
trade.open_order_id = 'something'
trade.open_order_id = enter_order['id']
oobj = Order.parse_from_ccxt_object(enter_order, 'ADA/USDT', entry_side)
trade.orders.append(oobj)
trade.update_trade(oobj)
@ -494,7 +515,7 @@ def test_update_limit_order(fee, caplog, limit_buy_order_usdt, limit_sell_order_
caplog)
caplog.clear()
trade.open_order_id = 'something'
trade.open_order_id = enter_order['id']
time_machine.move_to("2022-03-31 21:45:05 +00:00")
oobj = Order.parse_from_ccxt_object(exit_order, 'ADA/USDT', exit_side)
trade.orders.append(oobj)
@ -529,7 +550,7 @@ def test_update_market_order(market_buy_order_usdt, market_sell_order_usdt, fee,
leverage=1.0,
)
trade.open_order_id = 'something'
trade.open_order_id = 'mocked_market_buy'
oobj = Order.parse_from_ccxt_object(market_buy_order_usdt, 'ADA/USDT', 'buy')
trade.orders.append(oobj)
trade.update_trade(oobj)
@ -544,7 +565,7 @@ def test_update_market_order(market_buy_order_usdt, market_sell_order_usdt, fee,
caplog.clear()
trade.is_open = True
trade.open_order_id = 'something'
trade.open_order_id = 'mocked_market_sell'
oobj = Order.parse_from_ccxt_object(market_sell_order_usdt, 'ADA/USDT', 'sell')
trade.orders.append(oobj)
trade.update_trade(oobj)
@ -609,14 +630,14 @@ 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(), open_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_profit_ratio(trade.close_rate)) == profit_ratio
@pytest.mark.usefixtures("init_persistence")
def test_trade_close(limit_buy_order_usdt, limit_sell_order_usdt, fee):
def test_trade_close(fee):
trade = Trade(
pair='ADA/USDT',
stake_amount=60.0,
@ -794,7 +815,7 @@ def test_calc_open_trade_value(
trade.update_trade(oobj) # Buy @ 2.0
# Get the open rate price with the standard fee rate
assert trade._calc_open_trade_value() == result
assert trade._calc_open_trade_value(trade.amount, trade.open_rate) == result
@pytest.mark.parametrize(
@ -884,7 +905,7 @@ def test_calc_close_trade_price(
('binance', False, 1, 1.9, 0.003, -3.3209999, -0.055211970, spot, 0),
('binance', False, 1, 2.2, 0.003, 5.6520000, 0.093965087, spot, 0),
# # FUTURES, funding_fee=1
# FUTURES, funding_fee=1
('binance', False, 1, 2.1, 0.0025, 3.6925, 0.06138819, futures, 1),
('binance', False, 3, 2.1, 0.0025, 3.6925, 0.18416458, futures, 1),
('binance', True, 1, 2.1, 0.0025, -2.3074999, -0.03855472, futures, 1),
@ -1170,6 +1191,11 @@ def test_calc_profit(
assert pytest.approx(trade.calc_profit(rate=close_rate)) == round(profit, 8)
assert pytest.approx(trade.calc_profit_ratio(rate=close_rate)) == round(profit_ratio, 8)
assert pytest.approx(trade.calc_profit(close_rate, trade.amount,
trade.open_rate)) == round(profit, 8)
assert pytest.approx(trade.calc_profit_ratio(close_rate, trade.amount,
trade.open_rate)) == round(profit_ratio, 8)
def test_migrate_new(mocker, default_conf, fee, caplog):
"""
@ -1361,7 +1387,7 @@ 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 trade.open_trade_value == trade._calc_open_trade_value()
assert trade.open_trade_value == trade._calc_open_trade_value(trade.amount, trade.open_rate)
assert trade.close_profit_abs is None
orders = trade.orders
@ -1537,26 +1563,26 @@ def test_adjust_stop_loss(fee):
# Get percent of profit with a custom rate (Higher than open rate)
trade.adjust_stop_loss(1.3, -0.1)
assert round(trade.stop_loss, 8) == 1.17
assert pytest.approx(trade.stop_loss) == 1.17
assert trade.stop_loss_pct == -0.1
assert trade.initial_stop_loss == 0.95
assert trade.initial_stop_loss_pct == -0.05
# current rate lower again ... should not change
trade.adjust_stop_loss(1.2, 0.1)
assert round(trade.stop_loss, 8) == 1.17
assert pytest.approx(trade.stop_loss) == 1.17
assert trade.initial_stop_loss == 0.95
assert trade.initial_stop_loss_pct == -0.05
# current rate higher... should raise stoploss
trade.adjust_stop_loss(1.4, 0.1)
assert round(trade.stop_loss, 8) == 1.26
assert pytest.approx(trade.stop_loss) == 1.26
assert trade.initial_stop_loss == 0.95
assert trade.initial_stop_loss_pct == -0.05
# Initial is true but stop_loss set - so doesn't do anything
trade.adjust_stop_loss(1.7, 0.1, True)
assert round(trade.stop_loss, 8) == 1.26
assert pytest.approx(trade.stop_loss) == 1.26
assert trade.initial_stop_loss == 0.95
assert trade.initial_stop_loss_pct == -0.05
assert trade.stop_loss_pct == -0.1
@ -1609,9 +1635,10 @@ def test_adjust_stop_loss_short(fee):
assert trade.initial_stop_loss == 1.05
assert trade.initial_stop_loss_pct == -0.05
assert trade.stop_loss_pct == -0.1
trade.set_isolated_liq(0.63)
# Liquidation price is lower than stoploss - so liquidation would trigger first.
trade.set_liquidation_price(0.63)
trade.adjust_stop_loss(0.59, -0.1)
assert trade.stop_loss == 0.63
assert trade.stop_loss == 0.649
assert trade.liquidation_price == 0.63
@ -1722,6 +1749,7 @@ def test_to_json(fee):
'stake_amount': 0.001,
'trade_duration': None,
'trade_duration_s': None,
'realized_profit': 0.0,
'close_profit': None,
'close_profit_pct': None,
'close_profit_abs': None,
@ -1798,6 +1826,7 @@ def test_to_json(fee):
'initial_stop_loss_abs': None,
'initial_stop_loss_pct': None,
'initial_stop_loss_ratio': None,
'realized_profit': 0.0,
'close_profit': None,
'close_profit_pct': None,
'close_profit_abs': None,
@ -2009,10 +2038,10 @@ def test_stoploss_reinitialization_short(default_conf, fee):
assert trade_adj.initial_stop_loss == 1.01
assert trade_adj.initial_stop_loss_pct == -0.05
# Stoploss can't go above liquidation price
trade_adj.set_isolated_liq(0.985)
trade_adj.set_liquidation_price(0.985)
trade.adjust_stop_loss(0.9799, -0.05)
assert trade_adj.stop_loss == 0.985
assert trade_adj.stop_loss == 0.985
assert trade_adj.stop_loss == 0.989699
assert trade_adj.liquidation_price == 0.985
def test_update_fee(fee):
@ -2346,6 +2375,7 @@ def test_Trade_object_idem():
'delete',
'session',
'commit',
'rollback',
'query',
'open_date',
'get_best_pair',
@ -2399,7 +2429,7 @@ def test_recalc_trade_from_orders(fee):
)
assert fee.return_value == 0.0025
assert trade._calc_open_trade_value() == o1_trade_val
assert trade._calc_open_trade_value(trade.amount, trade.open_rate) == o1_trade_val
assert trade.amount == o1_amount
assert trade.stake_amount == o1_cost
assert trade.open_rate == o1_rate
@ -2511,7 +2541,8 @@ def test_recalc_trade_from_orders(fee):
assert pytest.approx(trade.fee_open_cost) == o1_fee_cost + o2_fee_cost + o3_fee_cost
assert pytest.approx(trade.open_trade_value) == o1_trade_val + o2_trade_val + o3_trade_val
# Just to make sure sell orders are ignored, let's calculate one more time.
# Just to make sure full sell orders are ignored, let's calculate one more time.
sell1 = Order(
ft_order_side='sell',
ft_pair=trade.pair,
@ -2673,7 +2704,7 @@ def test_recalc_trade_from_orders_ignores_bad_orders(fee, is_short):
assert trade.open_trade_value == 2 * o1_trade_val
assert trade.nr_of_successful_entries == 2
# Just to make sure exit orders are ignored, let's calculate one more time.
# Reduce position - this will reduce amount again.
sell1 = Order(
ft_order_side=exit_side,
ft_pair=trade.pair,
@ -2684,7 +2715,7 @@ def test_recalc_trade_from_orders_ignores_bad_orders(fee, is_short):
side=exit_side,
price=4,
average=3,
filled=2,
filled=o1_amount,
remaining=1,
cost=5,
order_date=trade.open_date,
@ -2693,11 +2724,11 @@ def test_recalc_trade_from_orders_ignores_bad_orders(fee, is_short):
trade.orders.append(sell1)
trade.recalc_trade_from_orders()
assert trade.amount == 2 * o1_amount
assert trade.stake_amount == 2 * o1_amount
assert trade.amount == o1_amount
assert trade.stake_amount == o1_amount
assert trade.open_rate == o1_rate
assert trade.fee_open_cost == 2 * o1_fee_cost
assert trade.open_trade_value == 2 * o1_trade_val
assert trade.fee_open_cost == o1_fee_cost
assert trade.open_trade_value == o1_trade_val
assert trade.nr_of_successful_entries == 2
# Check with 1 order
@ -2721,11 +2752,11 @@ def test_recalc_trade_from_orders_ignores_bad_orders(fee, is_short):
trade.recalc_trade_from_orders()
# Calling recalc with single initial order should not change anything
assert trade.amount == 3 * o1_amount
assert trade.stake_amount == 3 * o1_amount
assert trade.amount == 2 * o1_amount
assert trade.stake_amount == 2 * o1_amount
assert trade.open_rate == o1_rate
assert trade.fee_open_cost == 3 * o1_fee_cost
assert trade.open_trade_value == 3 * o1_trade_val
assert trade.fee_open_cost == 2 * o1_fee_cost
assert trade.open_trade_value == 2 * o1_trade_val
assert trade.nr_of_successful_entries == 3
@ -2793,3 +2824,144 @@ def test_order_to_ccxt(limit_buy_order_open):
del raw_order['stopPrice']
del limit_buy_order_open['datetime']
assert raw_order == limit_buy_order_open
@pytest.mark.usefixtures("init_persistence")
@pytest.mark.parametrize('data', [
{
# tuple 1 - side, amount, price
# tuple 2 - amount, open_rate, stake_amount, cumulative_profit, realized_profit, rel_profit
'orders': [
(('buy', 100, 10), (100.0, 10.0, 1000.0, 0.0, None, None)),
(('buy', 100, 15), (200.0, 12.5, 2500.0, 0.0, None, None)),
(('sell', 50, 12), (150.0, 12.5, 1875.0, -25.0, -25.0, -0.04)),
(('sell', 100, 20), (50.0, 12.5, 625.0, 725.0, 750.0, 0.60)),
(('sell', 50, 5), (50.0, 12.5, 625.0, 350.0, -375.0, -0.60)),
],
'end_profit': 350.0,
'end_profit_ratio': 0.14,
'fee': 0.0,
},
{
'orders': [
(('buy', 100, 10), (100.0, 10.0, 1000.0, 0.0, None, None)),
(('buy', 100, 15), (200.0, 12.5, 2500.0, 0.0, None, None)),
(('sell', 50, 12), (150.0, 12.5, 1875.0, -28.0625, -28.0625, -0.044788)),
(('sell', 100, 20), (50.0, 12.5, 625.0, 713.8125, 741.875, 0.59201995)),
(('sell', 50, 5), (50.0, 12.5, 625.0, 336.625, -377.1875, -0.60199501)),
],
'end_profit': 336.625,
'end_profit_ratio': 0.1343142,
'fee': 0.0025,
},
{
'orders': [
(('buy', 100, 3), (100.0, 3.0, 300.0, 0.0, None, None)),
(('buy', 100, 7), (200.0, 5.0, 1000.0, 0.0, None, None)),
(('sell', 100, 11), (100.0, 5.0, 500.0, 596.0, 596.0, 1.189027)),
(('buy', 150, 15), (250.0, 11.0, 2750.0, 596.0, 596.0, 1.189027)),
(('sell', 100, 19), (150.0, 11.0, 1650.0, 1388.5, 792.5, 0.7186579)),
(('sell', 150, 23), (150.0, 11.0, 1650.0, 3175.75, 1787.25, 1.08048062)),
],
'end_profit': 3175.75,
'end_profit_ratio': 0.9747170,
'fee': 0.0025,
},
{
# Test above without fees
'orders': [
(('buy', 100, 3), (100.0, 3.0, 300.0, 0.0, None, None)),
(('buy', 100, 7), (200.0, 5.0, 1000.0, 0.0, None, None)),
(('sell', 100, 11), (100.0, 5.0, 500.0, 600.0, 600.0, 1.2)),
(('buy', 150, 15), (250.0, 11.0, 2750.0, 600.0, 600.0, 1.2)),
(('sell', 100, 19), (150.0, 11.0, 1650.0, 1400.0, 800.0, 0.72727273)),
(('sell', 150, 23), (150.0, 11.0, 1650.0, 3200.0, 1800.0, 1.09090909)),
],
'end_profit': 3200.0,
'end_profit_ratio': 0.98461538,
'fee': 0.0,
},
{
'orders': [
(('buy', 100, 8), (100.0, 8.0, 800.0, 0.0, None, None)),
(('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)),
],
'end_profit': 950.0,
'end_profit_ratio': 0.283582,
'fee': 0.0,
},
])
def test_recalc_trade_from_orders_dca(data) -> None:
pair = 'ETH/USDT'
trade = Trade(
id=2,
pair=pair,
stake_amount=1000,
open_rate=data['orders'][0][0][2],
amount=data['orders'][0][0][1],
is_open=True,
open_date=arrow.utcnow().datetime,
fee_open=data['fee'],
fee_close=data['fee'],
exchange='binance',
is_short=False,
leverage=1.0,
trading_mode=TradingMode.SPOT
)
Trade.query.session.add(trade)
for idx, (order, result) in enumerate(data['orders']):
amount = order[1]
price = order[2]
order_obj = Order(
ft_order_side=order[0],
ft_pair=trade.pair,
order_id=f"order_{order[0]}_{idx}",
ft_is_open=False,
status="closed",
symbol=trade.pair,
order_type="market",
side=order[0],
price=price,
average=price,
filled=amount,
remaining=0,
cost=amount * price,
order_date=arrow.utcnow().shift(hours=-10 + idx).datetime,
order_filled_date=arrow.utcnow().shift(hours=-10 + idx).datetime,
)
trade.orders.append(order_obj)
trade.recalc_trade_from_orders()
Trade.commit()
orders1 = Order.query.all()
assert orders1
assert len(orders1) == idx + 1
trade = Trade.query.first()
assert trade
assert len(trade.orders) == idx + 1
if idx < len(data) - 1:
assert trade.is_open is True
assert trade.open_order_id is 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) == result[5]
trade.close(price)
assert pytest.approx(trade.close_profit_abs) == data['end_profit']
assert pytest.approx(trade.close_profit) == data['end_profit_ratio']
assert not trade.is_open
trade = Trade.query.first()
assert trade
assert trade.open_order_id is None