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74
docs/backtesting.md
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@ -107,7 +107,7 @@ Strategy arguments:
## Test your strategy with Backtesting ## Test your strategy with Backtesting
Now you have good Buy and Sell strategies and some historic data, you want to test it against Now you have good Entry and exit strategies and some historic data, you want to test it against
real data. This is what we call [backtesting](https://en.wikipedia.org/wiki/Backtesting). real data. This is what we call [backtesting](https://en.wikipedia.org/wiki/Backtesting).
Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHLCV) data from `user_data/data/<exchange>` by default. Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHLCV) data from `user_data/data/<exchange>` by default.
@ -215,7 +215,7 @@ Sometimes your account has certain fee rebates (fee reductions starting with a c
To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting. To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
This fee must be a ratio, and will be applied twice (once for trade entry, and once for trade exit). This fee must be a ratio, and will be applied twice (once for trade entry, and once for trade exit).
For example, if the buying and selling commission fee is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following: For example, if the commission fee per order is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
```bash ```bash
freqtrade backtesting --fee 0.001 freqtrade backtesting --fee 0.001
@ -252,41 +252,41 @@ The most important in the backtesting is to understand the result.
A backtesting result will look like that: A backtesting result will look like that:
``` ```
========================================================= BACKTESTING REPORT ========================================================== ========================================================= BACKTESTING REPORT =========================================================
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins Draws Loss Win% | | Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins Draws Loss Win% |
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:-------------|-------------------------:| |:---------|--------:|---------------:|---------------:|-----------------:|---------------:|:-------------|-------------------------:|
| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 0 21 40.0 | | ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 0 21 40.0 |
| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 0 8 27.3 | | ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 0 8 27.3 |
| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 0 14 56.2 | | BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 0 14 56.2 |
| DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 0 7 46.2 | | DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 0 7 46.2 |
| ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 0 10 44.4 | | ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 0 10 44.4 |
| EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 0 20 44.4 | | EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 0 20 44.4 |
| ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 0 15 42.3 | | ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 0 15 42.3 |
| ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 0 17 48.5 | | ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 0 17 48.5 |
| IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 0 18 43.8 | | IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 0 18 43.8 |
| LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 0 9 40.0 | | LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 0 9 40.0 |
| LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 0 21 34.4 | | LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 0 21 34.4 |
| NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 0 7 58.5 | | NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 0 7 58.5 |
| NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 0 13 43.5 | | NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 0 13 43.5 |
| REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 0 5 44.4 | | REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 0 5 44.4 |
| XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 0 9 43.8 | | XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 0 9 43.8 |
| XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 0 11 52.2 | | XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 0 11 52.2 |
| XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 0 23 34.3 | | XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 0 23 34.3 |
| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 0 15 31.8 | | ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 0 15 31.8 |
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 0 243 43.4 | | TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 0 243 43.4 |
========================================================= EXIT REASON STATS ========================================================== ========================================================= EXIT REASON STATS ==========================================================
| Exit Reason | Sells | Wins | Draws | Losses | | Exit Reason | Exits | Wins | Draws | Losses |
|:-------------------|--------:|------:|-------:|--------:| |:-------------------|--------:|------:|-------:|--------:|
| trailing_stop_loss | 205 | 150 | 0 | 55 | | trailing_stop_loss | 205 | 150 | 0 | 55 |
| stop_loss | 166 | 0 | 0 | 166 | | stop_loss | 166 | 0 | 0 | 166 |
| exit_signal | 56 | 36 | 0 | 20 | | exit_signal | 56 | 36 | 0 | 20 |
| force_exit | 2 | 0 | 0 | 2 | | force_exit | 2 | 0 | 0 | 2 |
====================================================== LEFT OPEN TRADES REPORT ====================================================== ====================================================== LEFT OPEN TRADES REPORT ======================================================
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Win Draw Loss Win% | | Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Win Draw Loss Win% |
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:| |:---------|---------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 0 0 100 | | ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 0 0 100 |
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 0 0 100 | | LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 0 0 100 |
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 0 0 100 | | TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 0 0 100 |
================== SUMMARY METRICS ================== ================== SUMMARY METRICS ==================
| Metric | Value | | Metric | Value |
|-----------------------------+---------------------| |-----------------------------+---------------------|
@ -356,7 +356,7 @@ The column `Avg Profit %` shows the average profit for all trades made while the
The column `Tot Profit %` shows instead the total profit % in relation to the starting balance. The column `Tot Profit %` shows instead the total profit % in relation to the starting balance.
In the above results, we have a starting balance of 0.01 BTC and an absolute profit of 0.00762792 BTC - so the `Tot Profit %` will be `(0.00762792 / 0.01) * 100 ~= 76.2%`. In the above results, we have a starting balance of 0.01 BTC and an absolute profit of 0.00762792 BTC - so the `Tot Profit %` will be `(0.00762792 / 0.01) * 100 ~= 76.2%`.
Your strategy performance is influenced by your buy strategy, your exit strategy, and also by the `minimal_roi` and `stop_loss` you have set. Your strategy performance is influenced by your entry strategy, your exit strategy, and also by the `minimal_roi` and `stop_loss` you have set.
For example, if your `minimal_roi` is only `"0": 0.01` you cannot expect the bot to make more profit than 1% (because it will exit every time a trade reaches 1%). For example, if your `minimal_roi` is only `"0": 0.01` you cannot expect the bot to make more profit than 1% (because it will exit every time a trade reaches 1%).
@ -515,7 +515,7 @@ 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: 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 - Exchange [trading limits](#trading-limits-in-backtesting) are respected
- Buys happen at open-price - Entries happen at open-price
- All orders are filled at the requested price (no slippage, no unfilled orders) - All orders are filled at the requested price (no slippage, no unfilled orders)
- Exit-signal exits happen at open-price of the consecutive candle - Exit-signal exits happen at open-price of the consecutive candle
- Exit-signal is favored over Stoploss, because exit-signals are assumed to trigger on candle's open - Exit-signal is favored over Stoploss, because exit-signals are assumed to trigger on candle's open
@ -612,11 +612,11 @@ There will be an additional table comparing win/losses of the different strategi
Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy. Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
``` ```
=========================================================== STRATEGY SUMMARY ========================================================================= =========================================================== STRATEGY SUMMARY ===========================================================================
| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | Drawdown % | | Strategy | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | Drawdown % |
|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|-----------:| |:------------|---------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|-----------:|
| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 | 45.2 | | Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 | 45.2 |
| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 825 | 241.68 | | Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 825 | 241.68 |
``` ```
## Next step ## Next step

27
docs/freqai.md
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@ -282,6 +282,8 @@ The FreqAI strategy requires the user to include the following lines of code in
Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](#feature-engineering)) and labels ([more information](#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`. Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](#feature-engineering)) and labels ([more information](#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`.
*Important*: The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
### Setting the `startup_candle_count` ### Setting the `startup_candle_count`
Users need to take care to set the `startup_candle_count` in their strategy the same way they would for any normal Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling on the `dataprovider` to avoid any NaNs at the beginning of the first training. Users can easily set this value by identifying the longest period (in candle units) that they pass to their indicator creation functions (e.g. talib functions). In the present example, the user would pass 20 to as this value (since it is the maximum value in their `indicators_periods_candles`). Users need to take care to set the `startup_candle_count` in their strategy the same way they would for any normal Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling on the `dataprovider` to avoid any NaNs at the beginning of the first training. Users can easily set this value by identifying the longest period (in candle units) that they pass to their indicator creation functions (e.g. talib functions). In the present example, the user would pass 20 to as this value (since it is the maximum value in their `indicators_periods_candles`).
@ -535,6 +537,31 @@ for each pair, for each backtesting window within the expanded `--timerange`.
--- ---
### Hyperopt
Users can hyperopt using the same command as typical [hyperopt](hyperopt.md):
```bash
freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --timerange 20220428-20220507
```
Users need to have the data pre-downloaded in the same fashion as if they were doing a FreqAI [backtest](#backtesting). In addition, users must consider some restrictions when trying to [Hyperopt](hyperopt.md) FreqAI strategies:
- The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI.
- It's not possible to hyperopt indicators in `populate_any_indicators()` function. This means that the user cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
- The [Backtesting](#backtesting) instructions also apply to Hyperopt.
The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. Users need to focus on hyperopting parameters that are not used in their FreqAI features. For example, users should not try to hyperopt rolling window lengths in their feature creation, or any of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
A good example of a hyperoptable parameter in FreqAI is a value for `DI_values` beyond which we consider outliers and below which we consider inliers:
```python
di_max = IntParameter(low=1, high=20, default=10, space='buy', optimize=True, load=True)
dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1, 0)
```
Which would help the user understand the appropriate Dissimilarity Index values for their particular parameter space.
### Deciding the size of the sliding training window and backtesting duration ### Deciding the size of the sliding training window and backtesting duration
The user defines the backtesting timerange with the typical `--timerange` parameter in the The user defines the backtesting timerange with the typical `--timerange` parameter in the

2
freqtrade/commands/db_commands.py
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@ -4,7 +4,7 @@ from typing import Any, Dict
from sqlalchemy import func from sqlalchemy import func
from freqtrade.configuration.config_setup import setup_utils_configuration from freqtrade.configuration.config_setup import setup_utils_configuration
from freqtrade.enums.runmode import RunMode from freqtrade.enums import RunMode
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)

9
freqtrade/configuration/config_validation.py
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@ -84,6 +84,7 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
_validate_protections(conf) _validate_protections(conf)
_validate_unlimited_amount(conf) _validate_unlimited_amount(conf)
_validate_ask_orderbook(conf) _validate_ask_orderbook(conf)
_validate_freqai_hyperopt(conf)
validate_migrated_strategy_settings(conf) validate_migrated_strategy_settings(conf)
# validate configuration before returning # validate configuration before returning
@ -323,6 +324,14 @@ def _validate_pricing_rules(conf: Dict[str, Any]) -> None:
del conf['ask_strategy'] del conf['ask_strategy']
def _validate_freqai_hyperopt(conf: Dict[str, Any]) -> None:
freqai_enabled = conf.get('freqai', {}).get('enabled', False)
analyze_per_epoch = conf.get('analyze_per_epoch', False)
if analyze_per_epoch and freqai_enabled:
raise OperationalException(
'Using analyze-per-epoch parameter is not supported with a FreqAI strategy.')
def _strategy_settings(conf: Dict[str, Any]) -> None: def _strategy_settings(conf: Dict[str, Any]) -> None:
process_deprecated_setting(conf, None, 'use_sell_signal', None, 'use_exit_signal') process_deprecated_setting(conf, None, 'use_sell_signal', None, 'use_exit_signal')

8
freqtrade/data/history/history_utils.py
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@ -228,9 +228,9 @@ def _download_pair_history(pair: str, *,
) )
logger.debug("Current Start: %s", logger.debug("Current Start: %s",
f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None') f"{data.iloc[0]['date']:DATETIME_PRINT_FORMAT}" if not data.empty else 'None')
logger.debug("Current End: %s", logger.debug("Current End: %s",
f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None') f"{data.iloc[-1]['date']:DATETIME_PRINT_FORMAT}" if not data.empty else 'None')
# Default since_ms to 30 days if nothing is given # Default since_ms to 30 days if nothing is given
new_data = exchange.get_historic_ohlcv(pair=pair, new_data = exchange.get_historic_ohlcv(pair=pair,
@ -254,9 +254,9 @@ def _download_pair_history(pair: str, *,
fill_missing=False, drop_incomplete=False) fill_missing=False, drop_incomplete=False)
logger.debug("New Start: %s", logger.debug("New Start: %s",
f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None') f"{data.iloc[0]['date']:DATETIME_PRINT_FORMAT}" if not data.empty else 'None')
logger.debug("New End: %s", logger.debug("New End: %s",
f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None') f"{data.iloc[-1]['date']:DATETIME_PRINT_FORMAT}" if not data.empty else 'None')
data_handler.ohlcv_store(pair, timeframe, data=data, candle_type=candle_type) data_handler.ohlcv_store(pair, timeframe, data=data, candle_type=candle_type)
return True return True

3
freqtrade/exchange/okx.py
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@ -4,8 +4,7 @@ from typing import Dict, List, Optional, Tuple
import ccxt import ccxt
from freqtrade.constants import BuySell from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, TradingMode from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.enums.candletype import CandleType
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange, date_minus_candles from freqtrade.exchange import Exchange, date_minus_candles
from freqtrade.exchange.common import retrier from freqtrade.exchange.common import retrier

10
freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py
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@ -36,9 +36,6 @@ class FreqaiMultiOutputRegressor(MultiOutputRegressor):
y = self._validate_data(X="no_validation", y=y, multi_output=True) y = self._validate_data(X="no_validation", y=y, multi_output=True)
# if is_classifier(self):
# check_classification_targets(y)
if y.ndim == 1: if y.ndim == 1:
raise ValueError( raise ValueError(
"y must have at least two dimensions for " "y must have at least two dimensions for "
@ -50,19 +47,12 @@ class FreqaiMultiOutputRegressor(MultiOutputRegressor):
): ):
raise ValueError("Underlying estimator does not support sample weights.") raise ValueError("Underlying estimator does not support sample weights.")
# fit_params_validated = _check_fit_params(X, fit_params)
if not fit_params: if not fit_params:
fit_params = [None] * y.shape[1] fit_params = [None] * y.shape[1]
# if not init_models:
# init_models = [None] * y.shape[1]
self.estimators_ = Parallel(n_jobs=self.n_jobs)( self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)( delayed(_fit_estimator)(
self.estimator, X, y[:, i], sample_weight, **fit_params[i] self.estimator, X, y[:, i], sample_weight, **fit_params[i]
# init_model=init_models[i], eval_set=eval_sets[i],
# **fit_params_validated
) )
for i in range(y.shape[1]) for i in range(y.shape[1])
) )

51
freqtrade/freqai/data_kitchen.py
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@ -184,7 +184,7 @@ class FreqaiDataKitchen:
def filter_features( def filter_features(
self, self,
unfiltered_dataframe: DataFrame, unfiltered_df: DataFrame,
training_feature_list: List, training_feature_list: List,
label_list: List = list(), label_list: List = list(),
training_filter: bool = True, training_filter: bool = True,
@ -195,31 +195,35 @@ class FreqaiDataKitchen:
0s in the prediction dataset. However, prediction dataset do_predict will reflect any 0s in the prediction dataset. However, prediction dataset do_predict will reflect any
row that had a NaN and will shield user from that prediction. row that had a NaN and will shield user from that prediction.
:params: :params:
:unfiltered_dataframe: the full dataframe for the present training period :unfiltered_df: the full dataframe for the present training period
:training_feature_list: list, the training feature list constructed by :training_feature_list: list, the training feature list constructed by
self.build_feature_list() according to user specified parameters in the configuration file. self.build_feature_list() according to user specified parameters in the configuration file.
:labels: the labels for the dataset :labels: the labels for the dataset
:training_filter: boolean which lets the function know if it is training data or :training_filter: boolean which lets the function know if it is training data or
prediction data to be filtered. prediction data to be filtered.
:returns: :returns:
:filtered_dataframe: dataframe cleaned of NaNs and only containing the user :filtered_df: dataframe cleaned of NaNs and only containing the user
requested feature set. requested feature set.
:labels: labels cleaned of NaNs. :labels: labels cleaned of NaNs.
""" """
filtered_dataframe = unfiltered_dataframe.filter(training_feature_list, axis=1) filtered_df = unfiltered_df.filter(training_feature_list, axis=1)
filtered_dataframe = filtered_dataframe.replace([np.inf, -np.inf], np.nan) filtered_df = filtered_df.replace([np.inf, -np.inf], np.nan)
drop_index = pd.isnull(filtered_dataframe).any(1) # get the rows that have NaNs, drop_index = pd.isnull(filtered_df).any(1) # get the rows that have NaNs,
drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement. drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
if (training_filter): if (training_filter):
const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
if const_cols:
filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
logger.warning(f"Removed features {const_cols} with constant values.")
# we don't care about total row number (total no. datapoints) in training, we only care # we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs # about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple modelEs), 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) labels = unfiltered_df.filter(label_list, axis=1)
drop_index_labels = pd.isnull(labels).any(1) drop_index_labels = pd.isnull(labels).any(1)
drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0) drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0)
dates = unfiltered_dataframe['date'] dates = unfiltered_df['date']
filtered_dataframe = filtered_dataframe[ filtered_df = filtered_df[
(drop_index == 0) & (drop_index_labels == 0) (drop_index == 0) & (drop_index_labels == 0)
] # dropping values ] # dropping values
labels = labels[ labels = labels[
@ -229,13 +233,13 @@ class FreqaiDataKitchen:
(drop_index == 0) & (drop_index_labels == 0) (drop_index == 0) & (drop_index_labels == 0)
] ]
logger.info( logger.info(
f"dropped {len(unfiltered_dataframe) - len(filtered_dataframe)} training points" f"dropped {len(unfiltered_df) - len(filtered_df)} training points"
f" due to NaNs in populated dataset {len(unfiltered_dataframe)}." f" due to NaNs in populated dataset {len(unfiltered_df)}."
) )
if (1 - len(filtered_dataframe) / len(unfiltered_dataframe)) > 0.1 and self.live: if (1 - len(filtered_df) / len(unfiltered_df)) > 0.1 and self.live:
worst_indicator = str(unfiltered_dataframe.count().idxmin()) worst_indicator = str(unfiltered_df.count().idxmin())
logger.warning( logger.warning(
f" {(1 - len(filtered_dataframe)/len(unfiltered_dataframe)) * 100:.0f} percent " f" {(1 - len(filtered_df)/len(unfiltered_df)) * 100:.0f} percent "
" of training data dropped due to NaNs, model may perform inconsistent " " of training data dropped due to NaNs, model may perform inconsistent "
f"with expectations. Verify {worst_indicator}" f"with expectations. Verify {worst_indicator}"
) )
@ -244,9 +248,9 @@ class FreqaiDataKitchen:
else: else:
# we are backtesting so we need to preserve row number to send back to strategy, # we are backtesting so we need to preserve row number to send back to strategy,
# so now we use do_predict to avoid any prediction based on a NaN # so now we use do_predict to avoid any prediction based on a NaN
drop_index = pd.isnull(filtered_dataframe).any(1) drop_index = pd.isnull(filtered_df).any(1)
self.data["filter_drop_index_prediction"] = drop_index self.data["filter_drop_index_prediction"] = drop_index
filtered_dataframe.fillna(0, inplace=True) filtered_df.fillna(0, inplace=True)
# replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction # replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction
# that was based on a single NaN is ultimately protected from buys with do_predict # that was based on a single NaN is ultimately protected from buys with do_predict
drop_index = ~drop_index drop_index = ~drop_index
@ -255,11 +259,11 @@ class FreqaiDataKitchen:
logger.info( logger.info(
"dropped %s of %s prediction data points due to NaNs.", "dropped %s of %s prediction data points due to NaNs.",
len(self.do_predict) - self.do_predict.sum(), len(self.do_predict) - self.do_predict.sum(),
len(filtered_dataframe), len(filtered_df),
) )
labels = [] labels = []
return filtered_dataframe, labels return filtered_df, labels
def build_data_dictionary( def build_data_dictionary(
self, self,
@ -466,10 +470,17 @@ class FreqaiDataKitchen:
) -> DataFrame: ) -> DataFrame:
""" """
Function which takes the backtesting time range and Function which takes the backtesting time range and
remove training data from dataframe remove training data from dataframe, keeping only the
startup_candle_count candles
""" """
startup_candle_count = self.config.get('startup_candle_count', 0)
tf = self.config['timeframe']
tr = self.config["timerange"] tr = self.config["timerange"]
backtesting_timerange = TimeRange.parse_timerange(tr) backtesting_timerange = TimeRange.parse_timerange(tr)
if startup_candle_count > 0 and backtesting_timerange:
backtesting_timerange.subtract_start(timeframe_to_seconds(tf) * startup_candle_count)
start = datetime.fromtimestamp(backtesting_timerange.startts, tz=timezone.utc) start = datetime.fromtimestamp(backtesting_timerange.startts, tz=timezone.utc)
df = self.return_dataframe df = self.return_dataframe
df = df.loc[df["date"] >= start, :] df = df.loc[df["date"] >= start, :]
@ -1215,7 +1226,6 @@ class FreqaiDataKitchen:
def save_backtesting_prediction( def save_backtesting_prediction(
self, append_df: DataFrame self, append_df: DataFrame
) -> None: ) -> None:
""" """
Save prediction dataframe from backtesting to h5 file format Save prediction dataframe from backtesting to h5 file format
:param append_df: dataframe for backtesting period :param append_df: dataframe for backtesting period
@ -1229,7 +1239,6 @@ class FreqaiDataKitchen:
def get_backtesting_prediction( def get_backtesting_prediction(
self self
) -> DataFrame: ) -> DataFrame:
""" """
Get prediction dataframe from h5 file format Get prediction dataframe from h5 file format
""" """

11
freqtrade/freqai/freqai_interface.py
View File

@ -14,6 +14,7 @@ from numpy.typing import NDArray
from pandas import DataFrame from pandas import DataFrame
from freqtrade.configuration import TimeRange from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.enums import RunMode from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds from freqtrade.exchange import timeframe_to_seconds
@ -92,6 +93,12 @@ class IFreqaiModel(ABC):
self._threads: List[threading.Thread] = [] self._threads: List[threading.Thread] = []
self._stop_event = threading.Event() self._stop_event = threading.Event()
def __getstate__(self):
"""
Return an empty state to be pickled in hyperopt
"""
return ({})
def assert_config(self, config: Dict[str, Any]) -> None: def assert_config(self, config: Dict[str, Any]) -> None:
if not config.get("freqai", {}): if not config.get("freqai", {}):
@ -233,10 +240,10 @@ class IFreqaiModel(ABC):
trained_timestamp = tr_train trained_timestamp = tr_train
tr_train_startts_str = datetime.fromtimestamp( tr_train_startts_str = datetime.fromtimestamp(
tr_train.startts, tr_train.startts,
tz=timezone.utc).strftime("%Y-%m-%d %H:%M:%S") tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
tr_train_stopts_str = datetime.fromtimestamp( tr_train_stopts_str = datetime.fromtimestamp(
tr_train.stopts, tr_train.stopts,
tz=timezone.utc).strftime("%Y-%m-%d %H:%M:%S") tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
logger.info( logger.info(
f"Training {metadata['pair']}, {self.pair_it}/{self.total_pairs} pairs" f"Training {metadata['pair']}, {self.pair_it}/{self.total_pairs} pairs"
f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} " f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} "

3
freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py
View File

@ -60,6 +60,9 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
{'eval_set': eval_sets[i], 'init_model': init_models[i]}) {'eval_set': eval_sets[i], 'init_model': init_models[i]})
model = FreqaiMultiOutputRegressor(estimator=cbr) model = FreqaiMultiOutputRegressor(estimator=cbr)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params) model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
return model return model

6
freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py
View File

@ -56,9 +56,9 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
'init_model': init_models[i]}) 'init_model': init_models[i]})
model = FreqaiMultiOutputRegressor(estimator=lgb) model = FreqaiMultiOutputRegressor(estimator=lgb)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params) model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
# model = FreqaiMultiOutputRegressor(estimator=lgb)
# model.fit(X=X, y=y, sample_weight=sample_weight, init_models=init_models,
# eval_sets=eval_sets, eval_sample_weight=eval_weights)
return model return model

3
freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py
View File

@ -55,6 +55,9 @@ class XGBoostRegressorMultiTarget(BaseRegressionModel):
'xgb_model': init_models[i]}) 'xgb_model': init_models[i]})
model = FreqaiMultiOutputRegressor(estimator=xgb) model = FreqaiMultiOutputRegressor(estimator=xgb)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params) model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
return model return model

5
freqtrade/optimize/optimize_reports.py
View File

@ -75,7 +75,8 @@ def _get_line_floatfmt(stake_currency: str) -> List[str]:
'.2f', 'd', 's', 's'] '.2f', 'd', 's', 's']
def _get_line_header(first_column: str, stake_currency: str, direction: str = 'Buys') -> List[str]: def _get_line_header(first_column: str, stake_currency: str,
direction: str = 'Entries') -> List[str]:
""" """
Generate header lines (goes in line with _generate_result_line()) Generate header lines (goes in line with _generate_result_line())
""" """
@ -642,7 +643,7 @@ def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_curr
if (tag_type == "enter_tag"): if (tag_type == "enter_tag"):
headers = _get_line_header("TAG", stake_currency) headers = _get_line_header("TAG", stake_currency)
else: else:
headers = _get_line_header("TAG", stake_currency, 'Sells') headers = _get_line_header("TAG", stake_currency, 'Exits')
floatfmt = _get_line_floatfmt(stake_currency) floatfmt = _get_line_floatfmt(stake_currency)
output = [ output = [
[ [

2
freqtrade/rpc/discord.py
View File

@ -1,7 +1,7 @@
import logging import logging
from typing import Any, Dict from typing import Any, Dict
from freqtrade.enums.rpcmessagetype import RPCMessageType from freqtrade.enums import RPCMessageType
from freqtrade.rpc import RPC from freqtrade.rpc import RPC
from freqtrade.rpc.webhook import Webhook from freqtrade.rpc.webhook import Webhook

5
freqtrade/strategy/interface.py
View File

@ -12,9 +12,8 @@ from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, SignalDirection, SignalTagType, from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, RunMode, SignalDirection,
SignalType, TradingMode) SignalTagType, SignalType, TradingMode)
from freqtrade.enums.runmode import RunMode
from freqtrade.exceptions import OperationalException, StrategyError from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds
from freqtrade.persistence import Order, PairLocks, Trade from freqtrade.persistence import Order, PairLocks, Trade

2
freqtrade/strategy/parameters.py
View File

@ -7,7 +7,7 @@ from abc import ABC, abstractmethod
from contextlib import suppress from contextlib import suppress
from typing import Any, Optional, Sequence, Union from typing import Any, Optional, Sequence, Union
from freqtrade.enums.hyperoptstate import HyperoptState from freqtrade.enums import HyperoptState
from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer

113
freqtrade/templates/FreqaiExampleStrategy.py
View File

@ -6,9 +6,7 @@ import talib.abstract as ta
from pandas import DataFrame from pandas import DataFrame
from technical import qtpylib from technical import qtpylib
from freqtrade.exchange import timeframe_to_prev_date from freqtrade.strategy import CategoricalParameter, IStrategy, merge_informative_pair
from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -31,9 +29,6 @@ class FreqaiExampleStrategy(IStrategy):
"main_plot": {}, "main_plot": {},
"subplots": { "subplots": {
"prediction": {"prediction": {"color": "blue"}}, "prediction": {"prediction": {"color": "blue"}},
"target_roi": {
"target_roi": {"color": "brown"},
},
"do_predict": { "do_predict": {
"do_predict": {"color": "brown"}, "do_predict": {"color": "brown"},
}, },
@ -47,10 +42,10 @@ class FreqaiExampleStrategy(IStrategy):
startup_candle_count: int = 40 startup_candle_count: int = 40
can_short = False can_short = False
linear_roi_offset = DecimalParameter( std_dev_multiplier_buy = CategoricalParameter(
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True [0.75, 1, 1.25, 1.5, 1.75], default=1.25, space="buy", optimize=True)
) std_dev_multiplier_sell = CategoricalParameter(
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True) [0.1, 0.25, 0.4], space="sell", default=0.2, optimize=True)
def informative_pairs(self): def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist() whitelist_pairs = self.dp.current_whitelist()
@ -187,21 +182,26 @@ class FreqaiExampleStrategy(IStrategy):
# `populate_any_indicators()` for each training period. # `populate_any_indicators()` for each training period.
dataframe = self.freqai.start(dataframe, metadata, self) dataframe = self.freqai.start(dataframe, metadata, self)
for val in self.std_dev_multiplier_buy.range:
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 dataframe[f'target_roi_{val}'] = dataframe["&-s_close_mean"] + \
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 dataframe["&-s_close_std"] * val
for val in self.std_dev_multiplier_sell.range:
dataframe[f'sell_roi_{val}'] = dataframe["&-s_close_mean"] - \
dataframe["&-s_close_std"] * val
return dataframe return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]] enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"]
> df[f"target_roi_{self.std_dev_multiplier_buy.value}"]]
if enter_long_conditions: if enter_long_conditions:
df.loc[ df.loc[
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"] reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
] = (1, "long") ] = (1, "long")
enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]] enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"]
< df[f"sell_roi_{self.std_dev_multiplier_sell.value}"]]
if enter_short_conditions: if enter_short_conditions:
df.loc[ df.loc[
@ -211,11 +211,13 @@ class FreqaiExampleStrategy(IStrategy):
return df return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25] exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] <
df[f"sell_roi_{self.std_dev_multiplier_sell.value}"] * 0.25]
if exit_long_conditions: if exit_long_conditions:
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1 df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25] exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] >
df[f"target_roi_{self.std_dev_multiplier_buy.value}"] * 0.25]
if exit_short_conditions: if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1 df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
@ -224,83 +226,6 @@ class FreqaiExampleStrategy(IStrategy):
def get_ticker_indicator(self): def get_ticker_indicator(self):
return int(self.config["timeframe"][:-1]) return int(self.config["timeframe"][:-1])
def custom_exit(
self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs
):
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
trade_date = timeframe_to_prev_date(self.config["timeframe"], trade.open_date_utc)
trade_candle = dataframe.loc[(dataframe["date"] == trade_date)]
if trade_candle.empty:
return None
trade_candle = trade_candle.squeeze()
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode:
pair_dict = self.freqai.dd.pair_dict
else:
pair_dict = self.freqai.dd.follower_dict
entry_tag = trade.enter_tag
if (
"prediction" + entry_tag not in pair_dict[pair]
or pair_dict[pair]['extras']["prediction" + entry_tag] == 0
):
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]['extras']["prediction" + entry_tag]
roi_time = self.max_roi_time_long.value
roi_decay = roi_price * (
1 - ((current_time - trade.open_date_utc).seconds) / (roi_time * 60)
)
if roi_decay < 0:
roi_decay = self.linear_roi_offset.value
else:
roi_decay += self.linear_roi_offset.value
if current_profit > roi_decay:
return "roi_custom_win"
if current_profit < -roi_decay:
return "roi_custom_loss"
def confirm_trade_exit(
self,
pair: str,
trade: Trade,
order_type: str,
amount: float,
rate: float,
time_in_force: str,
exit_reason: str,
current_time,
**kwargs,
) -> bool:
entry_tag = trade.enter_tag
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode:
pair_dict = self.freqai.dd.pair_dict
else:
pair_dict = self.freqai.dd.follower_dict
pair_dict[pair]['extras']["prediction" + entry_tag] = 0
if not follow_mode:
self.freqai.dd.save_drawer_to_disk()
else:
self.freqai.dd.save_follower_dict_to_disk()
return True
def confirm_trade_entry( def confirm_trade_entry(
self, self,
pair: str, pair: str,

6
tests/data/test_history.py
View File

@ -13,7 +13,7 @@ from pandas import DataFrame
from pandas.testing import assert_frame_equal from pandas.testing import assert_frame_equal
from freqtrade.configuration import TimeRange from freqtrade.configuration import TimeRange
from freqtrade.constants import AVAILABLE_DATAHANDLERS from freqtrade.constants import AVAILABLE_DATAHANDLERS, DATETIME_PRINT_FORMAT
from freqtrade.data.converter import ohlcv_to_dataframe from freqtrade.data.converter import ohlcv_to_dataframe
from freqtrade.data.history.hdf5datahandler import HDF5DataHandler from freqtrade.data.history.hdf5datahandler import HDF5DataHandler
from freqtrade.data.history.history_utils import (_download_pair_history, _download_trades_history, from freqtrade.data.history.history_utils import (_download_pair_history, _download_trades_history,
@ -386,7 +386,7 @@ def test_load_partial_missing(testdatadir, caplog) -> None:
assert td != len(data['UNITTEST/BTC']) assert td != len(data['UNITTEST/BTC'])
start_real = data['UNITTEST/BTC'].iloc[0, 0] start_real = data['UNITTEST/BTC'].iloc[0, 0]
assert log_has(f'UNITTEST/BTC, spot, 5m, ' assert log_has(f'UNITTEST/BTC, spot, 5m, '
f'data starts at {start_real.strftime("%Y-%m-%d %H:%M:%S")}', f'data starts at {start_real.strftime(DATETIME_PRINT_FORMAT)}',
caplog) caplog)
# Make sure we start fresh - test missing data at end # Make sure we start fresh - test missing data at end
caplog.clear() caplog.clear()
@ -401,7 +401,7 @@ def test_load_partial_missing(testdatadir, caplog) -> None:
# Shift endtime with +5 - as last candle is dropped (partial candle) # Shift endtime with +5 - as last candle is dropped (partial candle)
end_real = arrow.get(data['UNITTEST/BTC'].iloc[-1, 0]).shift(minutes=5) end_real = arrow.get(data['UNITTEST/BTC'].iloc[-1, 0]).shift(minutes=5)
assert log_has(f'UNITTEST/BTC, spot, 5m, ' assert log_has(f'UNITTEST/BTC, spot, 5m, '
f'data ends at {end_real.strftime("%Y-%m-%d %H:%M:%S")}', f'data ends at {end_real.strftime(DATETIME_PRINT_FORMAT)}',
caplog) caplog)

27
tests/exchange/test_ccxt_compat.py
View File

@ -267,13 +267,8 @@ class TestCCXTExchange():
now = datetime.now(timezone.utc) - timedelta(minutes=(timeframe_to_minutes(timeframe) * 2)) now = datetime.now(timezone.utc) - timedelta(minutes=(timeframe_to_minutes(timeframe) * 2))
assert exchange.klines(pair_tf).iloc[-1]['date'] >= timeframe_to_prev_date(timeframe, now) assert exchange.klines(pair_tf).iloc[-1]['date'] >= timeframe_to_prev_date(timeframe, now)
def test_ccxt__async_get_candle_history(self, exchange): def ccxt__async_get_candle_history(self, exchange, exchangename, pair, timeframe):
exchange, exchangename = exchange
# For some weired reason, this test returns random lengths for bittrex.
if not exchange._ft_has['ohlcv_has_history'] or exchangename == 'bittrex':
return
pair = EXCHANGES[exchangename]['pair']
timeframe = EXCHANGES[exchangename]['timeframe']
candle_type = CandleType.SPOT candle_type = CandleType.SPOT
timeframe_ms = timeframe_to_msecs(timeframe) timeframe_ms = timeframe_to_msecs(timeframe)
now = timeframe_to_prev_date( now = timeframe_to_prev_date(
@ -299,6 +294,24 @@ class TestCCXTExchange():
assert len(candles) >= min(candle_count, candle_count1) assert len(candles) >= min(candle_count, candle_count1)
assert candles[0][0] == since_ms or (since_ms + timeframe_ms) assert candles[0][0] == since_ms or (since_ms + timeframe_ms)
def test_ccxt__async_get_candle_history(self, exchange):
exchange, exchangename = exchange
# For some weired reason, this test returns random lengths for bittrex.
if not exchange._ft_has['ohlcv_has_history'] or exchangename in ('bittrex', 'gateio'):
return
pair = EXCHANGES[exchangename]['pair']
timeframe = EXCHANGES[exchangename]['timeframe']
self.ccxt__async_get_candle_history(exchange, exchangename, pair, timeframe)
def test_ccxt__async_get_candle_history_futures(self, exchange_futures):
exchange, exchangename = exchange_futures
if not exchange:
# exchange_futures only returns values for supported exchanges
return
pair = EXCHANGES[exchangename].get('futures_pair', EXCHANGES[exchangename]['pair'])
timeframe = EXCHANGES[exchangename]['timeframe']
self.ccxt__async_get_candle_history(exchange, exchangename, pair, timeframe)
def test_ccxt_fetch_funding_rate_history(self, exchange_futures): def test_ccxt_fetch_funding_rate_history(self, exchange_futures):
exchange, exchangename = exchange_futures exchange, exchangename = exchange_futures
if not exchange: if not exchange:

3
tests/exchange/test_okx.py
View File

@ -4,8 +4,7 @@ from unittest.mock import MagicMock, PropertyMock
import pytest import pytest
from freqtrade.enums import MarginMode, TradingMode from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.enums.candletype import CandleType
from freqtrade.exchange.exchange import timeframe_to_minutes from freqtrade.exchange.exchange import timeframe_to_minutes
from tests.conftest import get_mock_coro, get_patched_exchange, log_has from tests.conftest import get_mock_coro, get_patched_exchange, log_has
from tests.exchange.test_exchange import ccxt_exceptionhandlers from tests.exchange.test_exchange import ccxt_exceptionhandlers

250
tests/freqai/test_freqai_interface.py
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@ -17,196 +17,17 @@ def is_arm() -> bool:
return "arm" in machine or "aarch64" in machine return "arm" in machine or "aarch64" in machine
def test_extract_data_and_train_model_LightGBM(mocker, freqai_conf): @pytest.mark.parametrize('model', [
'LightGBMRegressor',
'XGBoostRegressor',
'CatboostRegressor',
])
def test_extract_data_and_train_model_Regressors(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostRegressor':
pytest.skip("CatBoost is not supported on ARM")
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"timerange": "20180110-20180130"}) freqai_conf.update({"timerange": "20180110-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
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.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
shutil.rmtree(Path(freqai.dk.full_path))
def test_extract_data_and_train_model_LightGBMMultiModel(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
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)
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.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert len(freqai.dk.label_list) == 2
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
assert len(freqai.dk.data['training_features_list']) == 26
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.skipif(is_arm(), reason="no ARM for Catboost ...")
def test_extract_data_and_train_model_Catboost(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
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.extract_data_and_train_model(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_extract_data_and_train_model_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.extract_data_and_train_model(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_extract_data_and_train_model_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)
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.extract_data_and_train_model(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_extract_data_and_train_model_XGBoostClassifier(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "XGBoostClassifier"})
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.extract_data_and_train_model(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()
def test_extract_data_and_train_model_XGBoostRegressor(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "XGBoostRegressor"})
freqai_conf.update({"strategy": "freqai_test_strat"}) freqai_conf.update({"strategy": "freqai_test_strat"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf) strategy = get_patched_freqai_strategy(mocker, freqai_conf)
@ -235,10 +56,18 @@ def test_extract_data_and_train_model_XGBoostRegressor(mocker, freqai_conf):
shutil.rmtree(Path(freqai.dk.full_path)) shutil.rmtree(Path(freqai.dk.full_path))
def test_extract_data_and_train_model_XGBoostRegressorMultiModel(mocker, freqai_conf): @pytest.mark.parametrize('model', [
'LightGBMRegressorMultiTarget',
'XGBoostRegressorMultiTarget',
'CatboostRegressorMultiTarget',
])
def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostRegressorMultiTarget':
pytest.skip("CatBoost is not supported on ARM")
freqai_conf.update({"timerange": "20180110-20180130"}) freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "XGBoostRegressorMultiTarget"})
freqai_conf.update({"strategy": "freqai_test_multimodel_strat"}) freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
freqai_conf.update({"freqaimodel": model})
strategy = get_patched_freqai_strategy(mocker, freqai_conf) strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange) strategy.dp = DataProvider(freqai_conf, exchange)
@ -267,6 +96,45 @@ def test_extract_data_and_train_model_XGBoostRegressorMultiModel(mocker, freqai_
shutil.rmtree(Path(freqai.dk.full_path)) shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.parametrize('model', [
'LightGBMClassifier',
'CatboostClassifier',
'XGBoostClassifier',
])
def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostClassifier':
pytest.skip("CatBoost is not supported on ARM")
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"strategy": "freqai_test_classifier"})
freqai_conf.update({"timerange": "20180110-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
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.extract_data_and_train_model(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_start_backtesting(mocker, freqai_conf): def test_start_backtesting(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180120-20180130"}) freqai_conf.update({"timerange": "20180120-20180130"})
freqai_conf.get("freqai", {}).update({"save_backtest_models": True}) freqai_conf.get("freqai", {}).update({"save_backtest_models": True})

24
tests/optimize/test_optimize_reports.py
View File

@ -40,14 +40,14 @@ def test_text_table_bt_results():
) )
result_str = ( result_str = (
'| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % |' '| Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | '
' Avg Duration | Win Draw Loss Win% |\n' 'Tot Profit % | Avg Duration | Win Draw Loss Win% |\n'
'|---------+--------+----------------+----------------+------------------+----------------+' '|---------+-----------+----------------+----------------+------------------+'
'----------------+-------------------------|\n' '----------------+----------------+-------------------------|\n'
'| ETH/BTC | 3 | 8.33 | 25.00 | 0.50000000 | 12.50 |' '| ETH/BTC | 3 | 8.33 | 25.00 | 0.50000000 | '
' 0:20:00 | 2 0 1 66.7 |\n' '12.50 | 0:20:00 | 2 0 1 66.7 |\n'
'| TOTAL | 3 | 8.33 | 25.00 | 0.50000000 | 12.50 |' '| TOTAL | 3 | 8.33 | 25.00 | 0.50000000 | '
' 0:20:00 | 2 0 1 66.7 |' '12.50 | 0:20:00 | 2 0 1 66.7 |'
) )
pair_results = generate_pair_metrics(['ETH/BTC'], stake_currency='BTC', pair_results = generate_pair_metrics(['ETH/BTC'], stake_currency='BTC',
@ -402,13 +402,13 @@ def test_text_table_strategy(testdatadir):
bt_res_data_comparison = bt_res_data.pop('strategy_comparison') bt_res_data_comparison = bt_res_data.pop('strategy_comparison')
result_str = ( result_str = (
'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC |' '| Strategy | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC |'
' Tot Profit % | Avg Duration | Win Draw Loss Win% | Drawdown |\n' ' Tot Profit % | Avg Duration | Win Draw Loss Win% | Drawdown |\n'
'|----------------+--------+----------------+----------------+------------------+' '|----------------+-----------+----------------+----------------+------------------+'
'----------------+----------------+-------------------------+-----------------------|\n' '----------------+----------------+-------------------------+-----------------------|\n'
'| StrategyTestV2 | 179 | 0.08 | 14.39 | 0.02608550 |' '| StrategyTestV2 | 179 | 0.08 | 14.39 | 0.02608550 |'
' 260.85 | 3:40:00 | 170 0 9 95.0 | 0.00308222 BTC 8.67% |\n' ' 260.85 | 3:40:00 | 170 0 9 95.0 | 0.00308222 BTC 8.67% |\n'
'| TestStrategy | 179 | 0.08 | 14.39 | 0.02608550 |' '| TestStrategy | 179 | 0.08 | 14.39 | 0.02608550 |'
' 260.85 | 3:40:00 | 170 0 9 95.0 | 0.00308222 BTC 8.67% |' ' 260.85 | 3:40:00 | 170 0 9 95.0 | 0.00308222 BTC 8.67% |'
) )

3
tests/strategy/test_interface.py
View File

@ -11,8 +11,7 @@ from pandas import DataFrame
from freqtrade.configuration import TimeRange from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history import load_data from freqtrade.data.history import load_data
from freqtrade.enums import ExitCheckTuple, ExitType, SignalDirection from freqtrade.enums import ExitCheckTuple, ExitType, HyperoptState, SignalDirection
from freqtrade.enums.hyperoptstate import HyperoptState
from freqtrade.exceptions import OperationalException, StrategyError from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer
from freqtrade.optimize.space import SKDecimal from freqtrade.optimize.space import SKDecimal

10
tests/test_persistence.py
View File

@ -9,7 +9,7 @@ import arrow
import pytest import pytest
from sqlalchemy import create_engine, text from sqlalchemy import create_engine, text
from freqtrade import constants from freqtrade.constants import DATETIME_PRINT_FORMAT, DEFAULT_DB_PROD_URL
from freqtrade.enums import TradingMode from freqtrade.enums import TradingMode
from freqtrade.exceptions import DependencyException, OperationalException from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.persistence import LocalTrade, Order, Trade, init_db from freqtrade.persistence import LocalTrade, Order, Trade, init_db
@ -52,7 +52,7 @@ def test_init_invalid_db_url():
def test_init_prod_db(default_conf, mocker): def test_init_prod_db(default_conf, mocker):
default_conf.update({'dry_run': False}) default_conf.update({'dry_run': False})
default_conf.update({'db_url': constants.DEFAULT_DB_PROD_URL}) default_conf.update({'db_url': DEFAULT_DB_PROD_URL})
create_engine_mock = mocker.patch('freqtrade.persistence.models.create_engine', MagicMock()) create_engine_mock = mocker.patch('freqtrade.persistence.models.create_engine', MagicMock())
@ -1739,7 +1739,7 @@ def test_to_json(fee):
'base_currency': 'ADA', 'base_currency': 'ADA',
'quote_currency': 'USDT', 'quote_currency': 'USDT',
'is_open': None, 'is_open': None,
'open_date': trade.open_date.strftime("%Y-%m-%d %H:%M:%S"), 'open_date': trade.open_date.strftime(DATETIME_PRINT_FORMAT),
'open_timestamp': int(trade.open_date.timestamp() * 1000), 'open_timestamp': int(trade.open_date.timestamp() * 1000),
'open_order_id': 'dry_run_buy_12345', 'open_order_id': 'dry_run_buy_12345',
'close_date': None, 'close_date': None,
@ -1817,9 +1817,9 @@ def test_to_json(fee):
'pair': 'XRP/BTC', 'pair': 'XRP/BTC',
'base_currency': 'XRP', 'base_currency': 'XRP',
'quote_currency': 'BTC', 'quote_currency': 'BTC',
'open_date': trade.open_date.strftime("%Y-%m-%d %H:%M:%S"), 'open_date': trade.open_date.strftime(DATETIME_PRINT_FORMAT),
'open_timestamp': int(trade.open_date.timestamp() * 1000), 'open_timestamp': int(trade.open_date.timestamp() * 1000),
'close_date': trade.close_date.strftime("%Y-%m-%d %H:%M:%S"), 'close_date': trade.close_date.strftime(DATETIME_PRINT_FORMAT),
'close_timestamp': int(trade.close_date.timestamp() * 1000), 'close_timestamp': int(trade.close_date.timestamp() * 1000),
'open_rate': 0.123, 'open_rate': 0.123,
'close_rate': 0.125, 'close_rate': 0.125,