merge develop into add-single-prec
This commit is contained in:
robcaulk
commit
790ff2a84b
@ -11,12 +11,14 @@
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"mounts": [
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"source=freqtrade-bashhistory,target=/home/ftuser/commandhistory,type=volume"
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],
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"workspaceMount": "source=${localWorkspaceFolder},target=/workspaces/freqtrade,type=bind,consistency=cached",
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// Uncomment to connect as a non-root user if you've added one. See https://aka.ms/vscode-remote/containers/non-root.
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"remoteUser": "ftuser",
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"onCreateCommand": "pip install --user -e .",
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"postCreateCommand": "freqtrade create-userdir --userdir user_data/",
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"workspaceFolder": "/freqtrade/",
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"workspaceFolder": "/workspaces/freqtrade",
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"settings": {
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"terminal.integrated.shell.linux": "/bin/bash",
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@ -39,7 +39,7 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
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- [X] [Binance](https://www.binance.com/)
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- [X] [Gate.io](https://www.gate.io/ref/6266643)
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- [X] [OKX](https://okx.com/).
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- [X] [OKX](https://okx.com/)
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Please make sure to read the [exchange specific notes](docs/exchanges.md), as well as the [trading with leverage](docs/leverage.md) documentation before diving in.
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@ -175,6 +175,10 @@ print(res)
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## FTX
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!!! Warning
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Due to the current situation, we can no longer recommend FTX.
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Please make sure to investigate the current situation before depositing any funds to FTX.
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!!! Tip "Stoploss on Exchange"
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FTX supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
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You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type of stoploss shall be used.
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@ -73,12 +73,24 @@ Backtesting mode requires [downloading the necessary data](#downloading-data-to-
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To allow for tweaking your strategy (**not** the features!), FreqAI will automatically save the predictions during backtesting so that they can be reused for future backtests and live runs using the same `identifier` model. This provides a performance enhancement geared towards enabling **high-level hyperopting** of entry/exit criteria.
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An additional directory called `predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder.
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An additional directory called `backtesting_predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder.
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To change your **features**, you **must** set a new `identifier` in the config to signal to FreqAI to train new models.
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To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config.
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### Backtest live models
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FreqAI allow you to reuse ready models through the backtest parameter `--freqai-backtest-live-models`. This can be useful when you want to reuse models generated in dry/run for comparison or other study. For that, you must set `"purge_old_models"` to `True` in the config.
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The `--timerange` parameter must not be informed, as it will be automatically calculated through the training end dates of the models.
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Each model has an identifier derived from the training end date. If you have only 1 model trained, FreqAI will backtest from the training end date until the current date. If you have more than 1 model, each model will perform the backtesting according to the training end date until the training end date of the next model and so on. For the last model, the period of the previous model will be used for the execution.
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!!! Note
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Currently, there is no checking for expired models, even if the `expired_hours` parameter is set.
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### Downloading data to cover the full backtest period
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For live/dry deployments, FreqAI will download the necessary data automatically. However, to use backtesting functionality, you need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). You need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that there is a sufficient amount of training data *before* the start of the backtesting time range. The amount of additional data can be roughly estimated by moving the start date of the time range backwards by `train_period_days` and the `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters) from the beginning of the desired backtesting time range.
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@ -32,7 +32,7 @@ Freqtrade is a free and open source crypto trading bot written in Python. It is
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- Run: Test your strategy with simulated money (Dry-Run mode) or deploy it with real money (Live-Trade mode).
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- Run using Edge (optional module): The concept is to find the best historical [trade expectancy](edge.md#expectancy) by markets based on variation of the stop-loss and then allow/reject markets to trade. The sizing of the trade is based on a risk of a percentage of your capital.
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- Control/Monitor: Use Telegram or a WebUI (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
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- Analyse: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
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- Analyze: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
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## Supported exchange marketplaces
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@ -51,7 +51,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
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- [X] [Binance](https://www.binance.com/)
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- [X] [Gate.io](https://www.gate.io/ref/6266643)
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- [X] [OKX](https://okx.com/).
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- [X] [OKX](https://okx.com/)
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Please make sure to read the [exchange specific notes](exchanges.md), as well as the [trading with leverage](leverage.md) documentation before diving in.
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@ -1,6 +1,6 @@
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markdown==3.3.7
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mkdocs==1.4.1
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mkdocs-material==8.5.7
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mkdocs==1.4.2
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mkdocs-material==8.5.8
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mdx_truly_sane_lists==1.3
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pymdown-extensions==9.7
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jinja2==3.1.2
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@ -25,7 +25,8 @@ ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
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ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
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"enable_protections", "dry_run_wallet", "timeframe_detail",
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"strategy_list", "export", "exportfilename",
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"backtest_breakdown", "backtest_cache"]
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"backtest_breakdown", "backtest_cache",
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"freqai_backtest_live_models"]
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ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
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"position_stacking", "use_max_market_positions",
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@ -668,4 +668,9 @@ AVAILABLE_CLI_OPTIONS = {
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help='Specify additional lookup path for freqaimodels.',
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metavar='PATH',
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),
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"freqai_backtest_live_models": Arg(
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'--freqai-backtest-live-models',
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help='Run backtest with ready models.',
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action='store_true'
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),
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}
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@ -86,6 +86,7 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
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_validate_unlimited_amount(conf)
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_validate_ask_orderbook(conf)
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_validate_freqai_hyperopt(conf)
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_validate_freqai_backtest(conf)
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_validate_freqai_include_timeframes(conf)
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_validate_consumers(conf)
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validate_migrated_strategy_settings(conf)
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@ -355,6 +356,26 @@ def _validate_freqai_include_timeframes(conf: Dict[str, Any]) -> None:
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f"`include_timeframes`.Offending include-timeframes: {', '.join(offending_lines)}")
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def _validate_freqai_backtest(conf: Dict[str, Any]) -> None:
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if conf.get('runmode', RunMode.OTHER) == RunMode.BACKTEST:
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freqai_enabled = conf.get('freqai', {}).get('enabled', False)
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timerange = conf.get('timerange')
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freqai_backtest_live_models = conf.get('freqai_backtest_live_models', False)
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if freqai_backtest_live_models and freqai_enabled and timerange:
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raise OperationalException(
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'Using timerange parameter is not supported with '
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'--freqai-backtest-live-models parameter.')
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if freqai_backtest_live_models and not freqai_enabled:
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raise OperationalException(
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'Using --freqai-backtest-live-models parameter is only '
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'supported with a FreqAI strategy.')
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if freqai_enabled and not freqai_backtest_live_models and not timerange:
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raise OperationalException(
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'Please pass --timerange if you intend to use FreqAI for backtesting.')
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||||
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def _validate_consumers(conf: Dict[str, Any]) -> None:
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emc_conf = conf.get('external_message_consumer', {})
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if emc_conf.get('enabled', False):
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@ -279,6 +279,9 @@ class Configuration:
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self._args_to_config(config, argname='disableparamexport',
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logstring='Parameter --disableparamexport detected: {} ...')
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self._args_to_config(config, argname='freqai_backtest_live_models',
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logstring='Parameter --freqai-backtest-live-models detected ...')
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# Edge section:
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if 'stoploss_range' in self.args and self.args["stoploss_range"]:
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txt_range = eval(self.args["stoploss_range"])
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@ -542,7 +542,7 @@ CONF_SCHEMA = {
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"keras": {"type": "boolean", "default": False},
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"write_metrics_to_disk": {"type": "boolean", "default": False},
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"purge_old_models": {"type": "boolean", "default": True},
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"conv_width": {"type": "integer", "default": 2},
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"conv_width": {"type": "integer", "default": 1},
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"train_period_days": {"type": "integer", "default": 0},
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"backtest_period_days": {"type": "number", "default": 7},
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"identifier": {"type": "string", "default": "example"},
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@ -26,7 +26,7 @@ BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
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'profit_ratio', 'profit_abs', 'exit_reason',
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'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
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'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', 'enter_tag',
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'is_short', 'open_timestamp', 'close_timestamp', 'orders'
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'leverage', 'is_short', 'open_timestamp', 'close_timestamp', 'orders'
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]
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@ -280,6 +280,8 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
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# Compatibility support for pre short Columns
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if 'is_short' not in df.columns:
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df['is_short'] = 0
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if 'leverage' not in df.columns:
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df['leverage'] = 1.0
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if 'enter_tag' not in df.columns:
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df['enter_tag'] = df['buy_tag']
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df = df.drop(['buy_tag'], axis=1)
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@ -21,7 +21,11 @@ class Bybit(Exchange):
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_ft_has: Dict = {
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"ohlcv_candle_limit": 200,
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"ccxt_futures_name": "linear"
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"ccxt_futures_name": "linear",
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"ohlcv_has_history": False,
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}
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_ft_has_futures: Dict = {
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"ohlcv_has_history": True,
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}
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_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
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@ -1689,6 +1689,17 @@ class Exchange:
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@retrier
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def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
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price: float = 1, taker_or_maker: MakerTaker = 'maker') -> float:
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"""
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Retrieve fee from exchange
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:param symbol: Pair
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:param type: Type of order (market, limit, ...)
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:param side: Side of order (buy, sell)
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:param amount: Amount of order
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:param price: Price of order
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:param taker_or_maker: 'maker' or 'taker' (ignored if "type" is provided)
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"""
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if type and type == 'market':
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taker_or_maker = 'taker'
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try:
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||||
if self._config['dry_run'] and self._config.get('fee', None) is not None:
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||||
return self._config['fee']
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||||
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@ -636,6 +636,8 @@ class FreqaiDataDrawer:
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axis=0,
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||||
)
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||||
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||||
self.current_candle = history_data[dk.pair][self.config['timeframe']].iloc[-1]['date']
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||||
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def load_all_pair_histories(self, timerange: TimeRange, dk: FreqaiDataKitchen) -> None:
|
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"""
|
||||
Load pair histories for all whitelist and corr_pairlist pairs.
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||||
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@ -1,7 +1,7 @@
|
||||
import copy
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||||
import logging
|
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import shutil
|
||||
from datetime import datetime, timezone
|
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from datetime import datetime, timedelta, timezone
|
||||
from math import cos, sin
|
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from pathlib import Path
|
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from typing import Any, Dict, List, Tuple
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@ -80,14 +80,20 @@ class FreqaiDataKitchen:
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self.svm_model: linear_model.SGDOneClassSVM = None
|
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self.keras: bool = self.freqai_config.get("keras", False)
|
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self.set_all_pairs()
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self.backtest_live_models = config.get("freqai_backtest_live_models", False)
|
||||
|
||||
if not self.live:
|
||||
if not self.config["timerange"]:
|
||||
raise OperationalException(
|
||||
'Please pass --timerange if you intend to use FreqAI for backtesting.')
|
||||
self.full_path = self.get_full_models_path(self.config)
|
||||
|
||||
if self.backtest_live_models:
|
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if self.pair:
|
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self.set_timerange_from_ready_models()
|
||||
(self.training_timeranges,
|
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self.backtesting_timeranges) = self.split_timerange_live_models()
|
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else:
|
||||
self.full_timerange = self.create_fulltimerange(
|
||||
self.config["timerange"], self.freqai_config.get("train_period_days", 0)
|
||||
)
|
||||
|
||||
(self.training_timeranges, self.backtesting_timeranges) = self.split_timerange(
|
||||
self.full_timerange,
|
||||
config["freqai"]["train_period_days"],
|
||||
@ -99,6 +105,7 @@ class FreqaiDataKitchen:
|
||||
self.train_dates: DataFrame = pd.DataFrame()
|
||||
self.unique_classes: Dict[str, list] = {}
|
||||
self.unique_class_list: list = []
|
||||
self.backtest_live_models_data: Dict[str, Any] = {}
|
||||
|
||||
def set_paths(
|
||||
self,
|
||||
@ -110,10 +117,7 @@ class FreqaiDataKitchen:
|
||||
:param metadata: dict = strategy furnished pair metadata
|
||||
:param trained_timestamp: int = timestamp of most recent training
|
||||
"""
|
||||
self.full_path = Path(
|
||||
self.config["user_data_dir"] / "models" / str(self.freqai_config.get("identifier"))
|
||||
)
|
||||
|
||||
self.full_path = self.get_full_models_path(self.config)
|
||||
self.data_path = Path(
|
||||
self.full_path
|
||||
/ f"sub-train-{pair.split('/')[0]}_{trained_timestamp}"
|
||||
@ -244,7 +248,7 @@ class FreqaiDataKitchen:
|
||||
self.data["filter_drop_index_training"] = drop_index
|
||||
|
||||
else:
|
||||
if len(self.data['constant_features_list']):
|
||||
if 'constant_features_list' in self.data and len(self.data['constant_features_list']):
|
||||
filtered_df = self.check_pred_labels(filtered_df)
|
||||
# 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
|
||||
@ -455,6 +459,29 @@ class FreqaiDataKitchen:
|
||||
# print(tr_training_list, tr_backtesting_list)
|
||||
return tr_training_list_timerange, tr_backtesting_list_timerange
|
||||
|
||||
def split_timerange_live_models(
|
||||
self
|
||||
) -> Tuple[list, list]:
|
||||
|
||||
tr_backtesting_list_timerange = []
|
||||
asset = self.pair.split("/")[0]
|
||||
if asset not in self.backtest_live_models_data["assets_end_dates"]:
|
||||
raise OperationalException(
|
||||
f"Model not available for pair {self.pair}. "
|
||||
"Please, try again after removing this pair from the configuration file."
|
||||
)
|
||||
asset_data = self.backtest_live_models_data["assets_end_dates"][asset]
|
||||
backtesting_timerange = self.backtest_live_models_data["backtesting_timerange"]
|
||||
model_end_dates = [x for x in asset_data]
|
||||
model_end_dates.append(backtesting_timerange.stopts)
|
||||
model_end_dates.sort()
|
||||
for index, item in enumerate(model_end_dates):
|
||||
if len(model_end_dates) > (index + 1):
|
||||
tr_to_add = TimeRange("date", "date", item, model_end_dates[index + 1])
|
||||
tr_backtesting_list_timerange.append(tr_to_add)
|
||||
|
||||
return tr_backtesting_list_timerange, tr_backtesting_list_timerange
|
||||
|
||||
def slice_dataframe(self, timerange: TimeRange, df: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Given a full dataframe, extract the user desired window
|
||||
@ -962,7 +989,9 @@ class FreqaiDataKitchen:
|
||||
append_df[label] = predictions[label]
|
||||
if append_df[label].dtype == object:
|
||||
continue
|
||||
if "labels_mean" in self.data:
|
||||
append_df[f"{label}_mean"] = self.data["labels_mean"][label]
|
||||
if "labels_std" in self.data:
|
||||
append_df[f"{label}_std"] = self.data["labels_std"][label]
|
||||
|
||||
for extra_col in self.data["extra_returns_per_train"]:
|
||||
@ -1031,11 +1060,6 @@ class FreqaiDataKitchen:
|
||||
start = datetime.fromtimestamp(backtest_timerange.startts, tz=timezone.utc)
|
||||
stop = datetime.fromtimestamp(backtest_timerange.stopts, tz=timezone.utc)
|
||||
full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")
|
||||
|
||||
self.full_path = Path(
|
||||
self.config["user_data_dir"] / "models" / f"{self.freqai_config['identifier']}"
|
||||
)
|
||||
|
||||
config_path = Path(self.config["config_files"][0])
|
||||
|
||||
if not self.full_path.is_dir():
|
||||
@ -1118,15 +1142,15 @@ class FreqaiDataKitchen:
|
||||
|
||||
return retrain, trained_timerange, data_load_timerange
|
||||
|
||||
def set_new_model_names(self, pair: str, trained_timerange: TimeRange):
|
||||
def set_new_model_names(self, pair: str, timestamp_id: int):
|
||||
|
||||
coin, _ = pair.split("/")
|
||||
self.data_path = Path(
|
||||
self.full_path
|
||||
/ f"sub-train-{pair.split('/')[0]}_{int(trained_timerange.stopts)}"
|
||||
/ f"sub-train-{pair.split('/')[0]}_{timestamp_id}"
|
||||
)
|
||||
|
||||
self.model_filename = f"cb_{coin.lower()}_{int(trained_timerange.stopts)}"
|
||||
self.model_filename = f"cb_{coin.lower()}_{timestamp_id}"
|
||||
|
||||
def set_all_pairs(self) -> None:
|
||||
|
||||
@ -1153,9 +1177,11 @@ class FreqaiDataKitchen:
|
||||
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
|
||||
|
||||
for pair in pairs:
|
||||
pair = pair.replace(':', '') # lightgbm doesnt like colons
|
||||
valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"]
|
||||
pair_cols = [col for col in dataframe.columns if
|
||||
any(substr in col for substr in valid_strs)]
|
||||
if pair_cols:
|
||||
pair_cols.insert(0, 'date')
|
||||
corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
|
||||
|
||||
@ -1175,8 +1201,9 @@ class FreqaiDataKitchen:
|
||||
ready for training
|
||||
"""
|
||||
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
|
||||
|
||||
current_pair = current_pair.replace(':', '')
|
||||
for pair in pairs:
|
||||
pair = pair.replace(':', '') # lightgbm doesnt work with colons
|
||||
if current_pair != pair:
|
||||
dataframe = dataframe.merge(corr_dataframes[pair], how='left', on='date')
|
||||
|
||||
@ -1246,6 +1273,8 @@ class FreqaiDataKitchen:
|
||||
|
||||
self.get_unique_classes_from_labels(dataframe)
|
||||
|
||||
dataframe = self.remove_special_chars_from_feature_names(dataframe)
|
||||
|
||||
if self.freqai_config.get('convert_df_to_float32', False):
|
||||
dataframe = self.reduce_dataframe_footprint(dataframe)
|
||||
|
||||
@ -1317,11 +1346,11 @@ class FreqaiDataKitchen:
|
||||
|
||||
def check_if_backtest_prediction_is_valid(
|
||||
self,
|
||||
length_backtesting_dataframe: int
|
||||
len_backtest_df: int
|
||||
) -> bool:
|
||||
"""
|
||||
Check if a backtesting prediction already exists and if the predictions
|
||||
to append has the same size of backtesting dataframe slice
|
||||
to append have the same size as the backtesting dataframe slice
|
||||
:param length_backtesting_dataframe: Length of backtesting dataframe slice
|
||||
:return:
|
||||
:boolean: whether the prediction file is valid.
|
||||
@ -1335,7 +1364,7 @@ class FreqaiDataKitchen:
|
||||
|
||||
if file_exists:
|
||||
append_df = self.get_backtesting_prediction()
|
||||
if len(append_df) == length_backtesting_dataframe:
|
||||
if len(append_df) == len_backtest_df:
|
||||
logger.info(f"Found backtesting prediction file at {path_to_predictionfile}")
|
||||
return True
|
||||
else:
|
||||
@ -1348,6 +1377,122 @@ class FreqaiDataKitchen:
|
||||
)
|
||||
return False
|
||||
|
||||
def set_timerange_from_ready_models(self):
|
||||
backtesting_timerange, \
|
||||
assets_end_dates = (
|
||||
self.get_timerange_and_assets_end_dates_from_ready_models(self.full_path))
|
||||
|
||||
self.backtest_live_models_data = {
|
||||
"backtesting_timerange": backtesting_timerange,
|
||||
"assets_end_dates": assets_end_dates
|
||||
}
|
||||
return
|
||||
|
||||
def get_full_models_path(self, config: Config) -> Path:
|
||||
"""
|
||||
Returns default FreqAI model path
|
||||
:param config: Configuration dictionary
|
||||
"""
|
||||
freqai_config: Dict[str, Any] = config["freqai"]
|
||||
return Path(
|
||||
config["user_data_dir"] / "models" / str(freqai_config.get("identifier"))
|
||||
)
|
||||
|
||||
def get_timerange_and_assets_end_dates_from_ready_models(
|
||||
self, models_path: Path) -> Tuple[TimeRange, Dict[str, Any]]:
|
||||
"""
|
||||
Returns timerange information based on a FreqAI model directory
|
||||
:param models_path: FreqAI model path
|
||||
|
||||
:return: a Tuple with (Timerange calculated from directory and
|
||||
a Dict with pair and model end training dates info)
|
||||
"""
|
||||
all_models_end_dates = []
|
||||
assets_end_dates: Dict[str, Any] = self.get_assets_timestamps_training_from_ready_models(
|
||||
models_path)
|
||||
for key in assets_end_dates:
|
||||
for model_end_date in assets_end_dates[key]:
|
||||
if model_end_date not in all_models_end_dates:
|
||||
all_models_end_dates.append(model_end_date)
|
||||
|
||||
if len(all_models_end_dates) == 0:
|
||||
raise OperationalException(
|
||||
'At least 1 saved model is required to '
|
||||
'run backtest with the freqai-backtest-live-models option'
|
||||
)
|
||||
|
||||
if len(all_models_end_dates) == 1:
|
||||
logger.warning(
|
||||
"Only 1 model was found. Backtesting will run with the "
|
||||
"timerange from the end of the training date to the current date"
|
||||
)
|
||||
|
||||
finish_timestamp = int(datetime.now(tz=timezone.utc).timestamp())
|
||||
if len(all_models_end_dates) > 1:
|
||||
# After last model end date, use the same period from previous model
|
||||
# to finish the backtest
|
||||
all_models_end_dates.sort(reverse=True)
|
||||
finish_timestamp = all_models_end_dates[0] + \
|
||||
(all_models_end_dates[0] - all_models_end_dates[1])
|
||||
|
||||
all_models_end_dates.append(finish_timestamp)
|
||||
all_models_end_dates.sort()
|
||||
start_date = (datetime(*datetime.fromtimestamp(min(all_models_end_dates),
|
||||
timezone.utc).timetuple()[:3], tzinfo=timezone.utc))
|
||||
end_date = (datetime(*datetime.fromtimestamp(max(all_models_end_dates),
|
||||
timezone.utc).timetuple()[:3], tzinfo=timezone.utc))
|
||||
|
||||
# add 1 day to string timerange to ensure BT module will load all dataframe data
|
||||
end_date = end_date + timedelta(days=1)
|
||||
backtesting_timerange = TimeRange(
|
||||
'date', 'date', int(start_date.timestamp()), int(end_date.timestamp())
|
||||
)
|
||||
return backtesting_timerange, assets_end_dates
|
||||
|
||||
def get_assets_timestamps_training_from_ready_models(
|
||||
self, models_path: Path) -> Dict[str, Any]:
|
||||
"""
|
||||
Scan the models path and returns all assets end training dates (timestamp)
|
||||
:param models_path: FreqAI model path
|
||||
|
||||
:return: a Dict with asset and model end training dates info
|
||||
"""
|
||||
assets_end_dates: Dict[str, Any] = {}
|
||||
if not models_path.is_dir():
|
||||
raise OperationalException(
|
||||
'Model folders not found. Saved models are required '
|
||||
'to run backtest with the freqai-backtest-live-models option'
|
||||
)
|
||||
for model_dir in models_path.iterdir():
|
||||
if str(model_dir.name).startswith("sub-train"):
|
||||
model_end_date = int(model_dir.name.split("_")[1])
|
||||
asset = model_dir.name.split("_")[0].replace("sub-train-", "")
|
||||
model_file_name = (
|
||||
f"cb_{str(model_dir.name).replace('sub-train-', '').lower()}"
|
||||
"_model.joblib"
|
||||
)
|
||||
|
||||
model_path_file = Path(model_dir / model_file_name)
|
||||
if model_path_file.is_file():
|
||||
if asset not in assets_end_dates:
|
||||
assets_end_dates[asset] = []
|
||||
assets_end_dates[asset].append(model_end_date)
|
||||
|
||||
return assets_end_dates
|
||||
|
||||
def remove_special_chars_from_feature_names(self, dataframe: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
Remove all special characters from feature strings (:)
|
||||
:param dataframe: the dataframe that just finished indicator population. (unfiltered)
|
||||
:return: dataframe with cleaned featrue names
|
||||
"""
|
||||
|
||||
spec_chars = [':']
|
||||
for c in spec_chars:
|
||||
dataframe.columns = dataframe.columns.str.replace(c, "")
|
||||
|
||||
return dataframe
|
||||
|
||||
def reduce_dataframe_footprint(self, df: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Ensure all values are float32
|
||||
|
||||
@ -68,6 +68,9 @@ class IFreqaiModel(ABC):
|
||||
if self.save_backtest_models:
|
||||
logger.info('Backtesting module configured to save all models.')
|
||||
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
|
||||
# set current candle to arbitrary historical date
|
||||
self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
|
||||
self.dd.current_candle = self.current_candle
|
||||
self.scanning = False
|
||||
self.ft_params = self.freqai_info["feature_parameters"]
|
||||
self.corr_pairlist: List[str] = self.ft_params.get("include_corr_pairlist", [])
|
||||
@ -75,7 +78,7 @@ class IFreqaiModel(ABC):
|
||||
if self.keras and self.ft_params.get("DI_threshold", 0):
|
||||
self.ft_params["DI_threshold"] = 0
|
||||
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
|
||||
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
|
||||
self.CONV_WIDTH = self.freqai_info.get('conv_width', 1)
|
||||
if self.ft_params.get("inlier_metric_window", 0):
|
||||
self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
|
||||
self.pair_it = 0
|
||||
@ -93,7 +96,6 @@ class IFreqaiModel(ABC):
|
||||
# get_corr_dataframes is controlling the caching of corr_dataframes
|
||||
# for improved performance. Careful with this boolean.
|
||||
self.get_corr_dataframes: bool = True
|
||||
|
||||
self._threads: List[threading.Thread] = []
|
||||
self._stop_event = threading.Event()
|
||||
|
||||
@ -137,6 +139,10 @@ class IFreqaiModel(ABC):
|
||||
# the concatenated results for the full backtesting period back to the strategy.
|
||||
elif not self.follow_mode:
|
||||
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
|
||||
if self.dk.backtest_live_models:
|
||||
logger.info(
|
||||
f"Backtesting {len(self.dk.backtesting_timeranges)} timeranges (live models)")
|
||||
else:
|
||||
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
|
||||
dataframe = self.dk.use_strategy_to_populate_indicators(
|
||||
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
|
||||
@ -257,23 +263,18 @@ class IFreqaiModel(ABC):
|
||||
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
|
||||
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
|
||||
|
||||
trained_timestamp = tr_train
|
||||
tr_train_startts_str = datetime.fromtimestamp(
|
||||
tr_train.startts,
|
||||
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
|
||||
tr_train_stopts_str = datetime.fromtimestamp(
|
||||
tr_train.stopts,
|
||||
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
|
||||
logger.info(
|
||||
f"Training {pair}, {self.pair_it}/{self.total_pairs} pairs"
|
||||
f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} "
|
||||
"trains"
|
||||
)
|
||||
if not self.ensure_data_exists(dataframe_backtest, tr_backtest, pair):
|
||||
continue
|
||||
|
||||
trained_timestamp_int = int(trained_timestamp.stopts)
|
||||
dk.set_paths(pair, trained_timestamp_int)
|
||||
self.log_backtesting_progress(tr_train, pair, train_it, total_trains)
|
||||
|
||||
dk.set_new_model_names(pair, trained_timestamp)
|
||||
timestamp_model_id = int(tr_train.stopts)
|
||||
if dk.backtest_live_models:
|
||||
timestamp_model_id = int(tr_backtest.startts)
|
||||
|
||||
dk.set_paths(pair, timestamp_model_id)
|
||||
|
||||
dk.set_new_model_names(pair, timestamp_model_id)
|
||||
|
||||
if dk.check_if_backtest_prediction_is_valid(len(dataframe_backtest)):
|
||||
self.dd.load_metadata(dk)
|
||||
@ -287,7 +288,7 @@ class IFreqaiModel(ABC):
|
||||
dk.find_labels(dataframe_train)
|
||||
self.model = self.train(dataframe_train, pair, dk)
|
||||
self.dd.pair_dict[pair]["trained_timestamp"] = int(
|
||||
trained_timestamp.stopts)
|
||||
tr_train.stopts)
|
||||
if self.plot_features:
|
||||
plot_feature_importance(self.model, pair, dk, self.plot_features)
|
||||
if self.save_backtest_models:
|
||||
@ -339,6 +340,7 @@ class IFreqaiModel(ABC):
|
||||
if self.dd.historic_data:
|
||||
self.dd.update_historic_data(strategy, dk)
|
||||
logger.debug(f'Updating historic data on pair {metadata["pair"]}')
|
||||
self.track_current_candle()
|
||||
|
||||
if not self.follow_mode:
|
||||
|
||||
@ -576,7 +578,7 @@ class IFreqaiModel(ABC):
|
||||
model = self.train(unfiltered_dataframe, pair, dk)
|
||||
|
||||
self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
|
||||
dk.set_new_model_names(pair, new_trained_timerange)
|
||||
dk.set_new_model_names(pair, new_trained_timerange.stopts)
|
||||
self.dd.save_data(model, pair, dk)
|
||||
|
||||
if self.plot_features:
|
||||
@ -683,8 +685,6 @@ class IFreqaiModel(ABC):
|
||||
" avoid blinding open trades and degrading performance.")
|
||||
self.pair_it = 0
|
||||
self.inference_time = 0
|
||||
if self.corr_pairlist:
|
||||
self.get_corr_dataframes = True
|
||||
return
|
||||
|
||||
def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
|
||||
@ -760,12 +760,70 @@ class IFreqaiModel(ABC):
|
||||
"is included in the column names when you are creating features "
|
||||
"in `populate_any_indicators()`.")
|
||||
self.get_corr_dataframes = not bool(self.corr_dataframes)
|
||||
else:
|
||||
elif self.corr_dataframes:
|
||||
dataframe = dk.attach_corr_pair_columns(
|
||||
dataframe, self.corr_dataframes, dk.pair)
|
||||
|
||||
return dataframe
|
||||
|
||||
def track_current_candle(self):
|
||||
"""
|
||||
Checks if the latest candle appended by the datadrawer is
|
||||
equivalent to the latest candle seen by FreqAI. If not, it
|
||||
asks to refresh the cached corr_dfs, and resets the pair
|
||||
counter.
|
||||
"""
|
||||
if self.dd.current_candle > self.current_candle:
|
||||
self.get_corr_dataframes = True
|
||||
self.pair_it = 1
|
||||
self.current_candle = self.dd.current_candle
|
||||
|
||||
def ensure_data_exists(self, dataframe_backtest: DataFrame,
|
||||
tr_backtest: TimeRange, pair: str) -> bool:
|
||||
"""
|
||||
Check if the dataframe is empty, if not, report useful information to user.
|
||||
:param dataframe_backtest: the backtesting dataframe, maybe empty.
|
||||
:param tr_backtest: current backtesting timerange.
|
||||
:param pair: current pair
|
||||
:return: if the data exists or not
|
||||
"""
|
||||
if self.config.get("freqai_backtest_live_models", False) and len(dataframe_backtest) == 0:
|
||||
tr_backtest_startts_str = datetime.fromtimestamp(
|
||||
tr_backtest.startts,
|
||||
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
|
||||
tr_backtest_stopts_str = datetime.fromtimestamp(
|
||||
tr_backtest.stopts,
|
||||
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
|
||||
logger.info(f"No data found for pair {pair} from {tr_backtest_startts_str} "
|
||||
f" from {tr_backtest_startts_str} to {tr_backtest_stopts_str}. "
|
||||
"Probably more than one training within the same candle period.")
|
||||
return False
|
||||
return True
|
||||
|
||||
def log_backtesting_progress(self, tr_train: TimeRange, pair: str,
|
||||
train_it: int, total_trains: int):
|
||||
"""
|
||||
Log the backtesting progress so user knows how many pairs have been trained and
|
||||
how many more pairs/trains remain.
|
||||
:param tr_train: the training timerange
|
||||
:param train_it: the train iteration for the current pair (the sliding window progress)
|
||||
:param pair: the current pair
|
||||
:param total_trains: total trains (total number of slides for the sliding window)
|
||||
"""
|
||||
tr_train_startts_str = datetime.fromtimestamp(
|
||||
tr_train.startts,
|
||||
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
|
||||
tr_train_stopts_str = datetime.fromtimestamp(
|
||||
tr_train.stopts,
|
||||
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
|
||||
|
||||
if not self.config.get("freqai_backtest_live_models", False):
|
||||
logger.info(
|
||||
f"Training {pair}, {self.pair_it}/{self.total_pairs} pairs"
|
||||
f" from {tr_train_startts_str} "
|
||||
f"to {tr_train_stopts_str}, {train_it}/{total_trains} "
|
||||
"trains"
|
||||
)
|
||||
# Following methods which are overridden by user made prediction models.
|
||||
# See freqai/prediction_models/CatboostPredictionModel.py for an example.
|
||||
|
||||
|
||||
@ -218,3 +218,19 @@ def record_params(config: Dict[str, Any], full_path: Path) -> None:
|
||||
default=str,
|
||||
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
|
||||
)
|
||||
|
||||
|
||||
def get_timerange_backtest_live_models(config: Config) -> str:
|
||||
"""
|
||||
Returns a formated timerange for backtest live/ready models
|
||||
:param config: Configuration dictionary
|
||||
|
||||
:return: a string timerange (format example: '20220801-20220822')
|
||||
"""
|
||||
dk = FreqaiDataKitchen(config)
|
||||
models_path = dk.get_full_models_path(config)
|
||||
timerange, _ = dk.get_timerange_and_assets_end_dates_from_ready_models(models_path)
|
||||
start_date = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
|
||||
end_date = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
|
||||
tr = f"{start_date.strftime('%Y%m%d')}-{end_date.strftime('%Y%m%d')}"
|
||||
return tr
|
||||
|
||||
@ -134,6 +134,10 @@ class Backtesting:
|
||||
self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
|
||||
self.precision_mode = self.exchange.precisionMode
|
||||
|
||||
if self.config.get('freqai_backtest_live_models', False):
|
||||
from freqtrade.freqai.utils import get_timerange_backtest_live_models
|
||||
self.config['timerange'] = get_timerange_backtest_live_models(self.config)
|
||||
|
||||
self.timerange = TimeRange.parse_timerange(
|
||||
None if self.config.get('timerange') is None else str(self.config.get('timerange')))
|
||||
|
||||
|
||||
@ -667,7 +667,7 @@ class LocalTrade():
|
||||
self.close(order.safe_price)
|
||||
else:
|
||||
self.recalc_trade_from_orders()
|
||||
elif order.ft_order_side == 'stoploss':
|
||||
elif order.ft_order_side == 'stoploss' and order.status not in ('canceled', 'open'):
|
||||
self.stoploss_order_id = None
|
||||
self.close_rate_requested = self.stop_loss
|
||||
self.exit_reason = ExitType.STOPLOSS_ON_EXCHANGE.value
|
||||
|
||||
@ -150,14 +150,20 @@ class Worker:
|
||||
if timeframe:
|
||||
next_tf = timeframe_to_next_date(timeframe)
|
||||
# Maximum throttling should be until new candle arrives
|
||||
# Offset of 0.2s is added to ensure a new candle has been issued.
|
||||
next_tf_with_offset = next_tf.timestamp() - time.time() + timeframe_offset
|
||||
# Offset is added to ensure a new candle has been issued.
|
||||
next_tft = next_tf.timestamp() - time.time()
|
||||
next_tf_with_offset = next_tft + timeframe_offset
|
||||
if next_tft < sleep_duration and sleep_duration < next_tf_with_offset:
|
||||
# Avoid hitting a new loop between the new candle and the candle with offset
|
||||
sleep_duration = next_tf_with_offset
|
||||
sleep_duration = min(sleep_duration, next_tf_with_offset)
|
||||
sleep_duration = max(sleep_duration, 0.0)
|
||||
# next_iter = datetime.now(timezone.utc) + timedelta(seconds=sleep_duration)
|
||||
|
||||
logger.debug(f"Throttling with '{func.__name__}()': sleep for {sleep_duration:.2f} s, "
|
||||
f"last iteration took {time_passed:.2f} s.")
|
||||
f"last iteration took {time_passed:.2f} s."
|
||||
# f"next: {next_iter}"
|
||||
)
|
||||
self._sleep(sleep_duration)
|
||||
return result
|
||||
|
||||
|
||||
@ -10,7 +10,7 @@ flake8==5.0.4
|
||||
flake8-tidy-imports==4.8.0
|
||||
mypy==0.982
|
||||
pre-commit==2.20.0
|
||||
pytest==7.1.3
|
||||
pytest==7.2.0
|
||||
pytest-asyncio==0.20.1
|
||||
pytest-cov==4.0.0
|
||||
pytest-mock==3.10.0
|
||||
|
||||
@ -1,10 +1,11 @@
|
||||
# Include all requirements to run the bot.
|
||||
-r requirements.txt
|
||||
-r requirements-plot.txt
|
||||
|
||||
# Required for freqai
|
||||
scikit-learn==1.1.3
|
||||
joblib==1.2.0
|
||||
catboost==1.1; platform_machine != 'aarch64'
|
||||
catboost==1.1.1; platform_machine != 'aarch64'
|
||||
lightgbm==3.3.3
|
||||
xgboost==1.6.2
|
||||
xgboost==1.7.1
|
||||
tensorboard==2.10.1
|
||||
|
||||
@ -6,4 +6,4 @@ scipy==1.9.3
|
||||
scikit-learn==1.1.3
|
||||
scikit-optimize==0.9.0
|
||||
filelock==3.8.0
|
||||
progressbar2==4.1.1
|
||||
progressbar2==4.2.0
|
||||
|
||||
@ -2,17 +2,17 @@ numpy==1.23.4
|
||||
pandas==1.5.1
|
||||
pandas-ta==0.3.14b
|
||||
|
||||
ccxt==2.0.96
|
||||
ccxt==2.1.54
|
||||
# Pin cryptography for now due to rust build errors with piwheels
|
||||
cryptography==38.0.1
|
||||
aiohttp==3.8.3
|
||||
SQLAlchemy==1.4.42
|
||||
SQLAlchemy==1.4.43
|
||||
python-telegram-bot==13.14
|
||||
arrow==1.2.3
|
||||
cachetools==4.2.2
|
||||
requests==2.28.1
|
||||
urllib3==1.26.12
|
||||
jsonschema==4.16.0
|
||||
jsonschema==4.17.0
|
||||
TA-Lib==0.4.25
|
||||
technical==1.3.0
|
||||
tabulate==0.9.0
|
||||
@ -29,7 +29,7 @@ py_find_1st==1.1.5
|
||||
# Load ticker files 30% faster
|
||||
python-rapidjson==1.9
|
||||
# Properly format api responses
|
||||
orjson==3.8.0
|
||||
orjson==3.8.1
|
||||
|
||||
# Notify systemd
|
||||
sdnotify==0.3.2
|
||||
@ -46,7 +46,7 @@ psutil==5.9.3
|
||||
colorama==0.4.6
|
||||
# Building config files interactively
|
||||
questionary==1.10.0
|
||||
prompt-toolkit==3.0.31
|
||||
prompt-toolkit==3.0.32
|
||||
# Extensions to datetime library
|
||||
python-dateutil==2.8.2
|
||||
|
||||
|
||||
@ -3,8 +3,11 @@ from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from unittest.mock import PropertyMock
|
||||
|
||||
import pytest
|
||||
|
||||
from freqtrade.commands.optimize_commands import setup_optimize_configuration
|
||||
from freqtrade.enums import RunMode
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.optimize.backtesting import Backtesting
|
||||
from tests.conftest import (CURRENT_TEST_STRATEGY, get_args, log_has_re, patch_exchange,
|
||||
patched_configuration_load_config_file)
|
||||
@ -51,3 +54,32 @@ def test_freqai_backtest_load_data(freqai_conf, mocker, caplog):
|
||||
assert log_has_re('Increasing startup_candle_count for freqai to.*', caplog)
|
||||
|
||||
Backtesting.cleanup()
|
||||
|
||||
|
||||
def test_freqai_backtest_live_models_model_not_found(freqai_conf, mocker, testdatadir, caplog):
|
||||
patch_exchange(mocker)
|
||||
|
||||
now = datetime.now(timezone.utc)
|
||||
mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
|
||||
PropertyMock(return_value=['HULUMULU/USDT', 'XRP/USDT']))
|
||||
mocker.patch('freqtrade.optimize.backtesting.history.load_data')
|
||||
mocker.patch('freqtrade.optimize.backtesting.history.get_timerange', return_value=(now, now))
|
||||
freqai_conf["timerange"] = ""
|
||||
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', '5m',
|
||||
'--freqai-backtest-live-models'
|
||||
]
|
||||
args = get_args(args)
|
||||
bt_config = setup_optimize_configuration(args, RunMode.BACKTEST)
|
||||
|
||||
with pytest.raises(OperationalException,
|
||||
match=r".* Saved models are required to run backtest .*"):
|
||||
Backtesting(bt_config)
|
||||
|
||||
Backtesting.cleanup()
|
||||
|
||||
@ -22,6 +22,7 @@ def test_update_historic_data(mocker, freqai_conf):
|
||||
historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
|
||||
dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
|
||||
candle_difference = dp_candles - historic_candles
|
||||
freqai.dk.pair = "ADA/BTC"
|
||||
freqai.dd.update_historic_data(strategy, freqai.dk)
|
||||
|
||||
updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
|
||||
|
||||
@ -1,13 +1,18 @@
|
||||
import shutil
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from pathlib import Path
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
import pytest
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from tests.conftest import log_has_re
|
||||
from tests.freqai.conftest import (get_patched_data_kitchen, make_data_dictionary,
|
||||
make_unfiltered_dataframe)
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.utils import get_timerange_backtest_live_models
|
||||
from tests.conftest import get_patched_exchange, log_has_re
|
||||
from tests.freqai.conftest import (get_patched_data_kitchen, get_patched_freqai_strategy,
|
||||
make_data_dictionary, make_unfiltered_dataframe)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
@ -159,3 +164,98 @@ def test_make_train_test_datasets(mocker, freqai_conf):
|
||||
assert data_dictionary
|
||||
assert len(data_dictionary) == 7
|
||||
assert len(data_dictionary['train_features'].index) == 1916
|
||||
|
||||
|
||||
def test_get_pairs_timestamp_validation(mocker, freqai_conf):
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
freqai_conf['freqai'].update({"identifier": "invalid_id"})
|
||||
model_path = freqai.dk.get_full_models_path(freqai_conf)
|
||||
with pytest.raises(
|
||||
OperationalException,
|
||||
match=r'.*required to run backtest with the freqai-backtest-live-models.*'
|
||||
):
|
||||
freqai.dk.get_assets_timestamps_training_from_ready_models(model_path)
|
||||
|
||||
|
||||
@pytest.mark.parametrize('model', [
|
||||
'LightGBMRegressor'
|
||||
])
|
||||
def test_get_timerange_from_ready_models(mocker, freqai_conf, model):
|
||||
freqai_conf.update({"freqaimodel": model})
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
freqai_conf.update({"strategy": "freqai_test_strat"})
|
||||
|
||||
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("20180101-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
data_load_timerange = TimeRange.parse_timerange("20180101-20180130")
|
||||
|
||||
# 1516233600 (2018-01-18 00:00) - Start Training 1
|
||||
# 1516406400 (2018-01-20 00:00) - End Training 1 (Backtest slice 1)
|
||||
# 1516579200 (2018-01-22 00:00) - End Training 2 (Backtest slice 2)
|
||||
# 1516838400 (2018-01-25 00:00) - End Timerange
|
||||
|
||||
new_timerange = TimeRange("date", "date", 1516233600, 1516406400)
|
||||
freqai.extract_data_and_train_model(
|
||||
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
new_timerange = TimeRange("date", "date", 1516406400, 1516579200)
|
||||
freqai.extract_data_and_train_model(
|
||||
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
model_path = freqai.dk.get_full_models_path(freqai_conf)
|
||||
(backtesting_timerange,
|
||||
pairs_end_dates) = freqai.dk.get_timerange_and_assets_end_dates_from_ready_models(
|
||||
models_path=model_path)
|
||||
|
||||
assert len(pairs_end_dates["ADA"]) == 2
|
||||
assert backtesting_timerange.startts == 1516406400
|
||||
assert backtesting_timerange.stopts == 1516838400
|
||||
|
||||
backtesting_string_timerange = get_timerange_backtest_live_models(freqai_conf)
|
||||
assert backtesting_string_timerange == '20180120-20180125'
|
||||
|
||||
|
||||
@pytest.mark.parametrize('model', [
|
||||
'LightGBMRegressor'
|
||||
])
|
||||
def test_get_full_model_path(mocker, freqai_conf, model):
|
||||
freqai_conf.update({"freqaimodel": model})
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
freqai_conf.update({"strategy": "freqai_test_strat"})
|
||||
|
||||
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)
|
||||
|
||||
model_path = freqai.dk.get_full_models_path(freqai_conf)
|
||||
assert model_path.is_dir() is True
|
||||
|
||||
@ -195,6 +195,7 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
df = freqai.cache_corr_pairlist_dfs(df, freqai.dk)
|
||||
for i in range(5):
|
||||
df[f'%-constant_{i}'] = i
|
||||
# df.loc[:, f'%-constant_{i}'] = i
|
||||
@ -340,6 +341,7 @@ def test_follow_mode(mocker, freqai_conf):
|
||||
|
||||
df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
|
||||
|
||||
freqai.dk.pair = "ADA/BTC"
|
||||
freqai.start_live(df, metadata, strategy, freqai.dk)
|
||||
|
||||
assert len(freqai.dk.return_dataframe.index) == 5702
|
||||
|
||||
@ -764,6 +764,7 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
|
||||
'max_rate': [0.10501, 0.1038888],
|
||||
'is_open': [False, False],
|
||||
'enter_tag': [None, None],
|
||||
"leverage": [1.0, 1.0],
|
||||
"is_short": [False, False],
|
||||
'open_timestamp': [1517251200000, 1517283000000],
|
||||
'close_timestamp': [1517265300000, 1517285400000],
|
||||
|
||||
@ -72,6 +72,7 @@ def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) ->
|
||||
'max_rate': [0.10481985, 0.1038888],
|
||||
'is_open': [False, False],
|
||||
'enter_tag': [None, None],
|
||||
'leverage': [1.0, 1.0],
|
||||
'is_short': [False, False],
|
||||
'open_timestamp': [1517251200000, 1517283000000],
|
||||
'close_timestamp': [1517265300000, 1517285400000],
|
||||
|
||||
@ -1538,3 +1538,85 @@ def test_flat_vars_to_nested_dict(caplog):
|
||||
|
||||
assert log_has("Loading variable 'FREQTRADE__EXCHANGE__SOME_SETTING'", caplog)
|
||||
assert not log_has("Loading variable 'NOT_RELEVANT'", caplog)
|
||||
|
||||
|
||||
def test_setup_hyperopt_freqai(mocker, default_conf, caplog) -> None:
|
||||
patched_configuration_load_config_file(mocker, default_conf)
|
||||
mocker.patch(
|
||||
'freqtrade.configuration.configuration.create_datadir',
|
||||
lambda c, x: x
|
||||
)
|
||||
mocker.patch(
|
||||
'freqtrade.configuration.configuration.create_userdata_dir',
|
||||
lambda x, *args, **kwargs: Path(x)
|
||||
)
|
||||
arglist = [
|
||||
'hyperopt',
|
||||
'--config', 'config.json',
|
||||
'--strategy', CURRENT_TEST_STRATEGY,
|
||||
'--timerange', '20220801-20220805',
|
||||
"--freqaimodel",
|
||||
"LightGBMRegressorMultiTarget",
|
||||
"--analyze-per-epoch"
|
||||
]
|
||||
|
||||
args = Arguments(arglist).get_parsed_arg()
|
||||
|
||||
configuration = Configuration(args)
|
||||
config = configuration.get_config()
|
||||
config['freqai'] = {
|
||||
"enabled": True
|
||||
}
|
||||
with pytest.raises(
|
||||
OperationalException, match=r".*analyze-per-epoch parameter is not supported.*"
|
||||
):
|
||||
validate_config_consistency(config)
|
||||
|
||||
|
||||
def test_setup_freqai_backtesting(mocker, default_conf, caplog) -> None:
|
||||
patched_configuration_load_config_file(mocker, default_conf)
|
||||
mocker.patch(
|
||||
'freqtrade.configuration.configuration.create_datadir',
|
||||
lambda c, x: x
|
||||
)
|
||||
mocker.patch(
|
||||
'freqtrade.configuration.configuration.create_userdata_dir',
|
||||
lambda x, *args, **kwargs: Path(x)
|
||||
)
|
||||
arglist = [
|
||||
'backtesting',
|
||||
'--config', 'config.json',
|
||||
'--strategy', CURRENT_TEST_STRATEGY,
|
||||
'--timerange', '20220801-20220805',
|
||||
"--freqaimodel",
|
||||
"LightGBMRegressorMultiTarget",
|
||||
"--freqai-backtest-live-models"
|
||||
]
|
||||
|
||||
args = Arguments(arglist).get_parsed_arg()
|
||||
|
||||
configuration = Configuration(args)
|
||||
config = configuration.get_config()
|
||||
config['runmode'] = RunMode.BACKTEST
|
||||
|
||||
with pytest.raises(
|
||||
OperationalException, match=r".*--freqai-backtest-live-models parameter is only.*"
|
||||
):
|
||||
validate_config_consistency(config)
|
||||
|
||||
conf = deepcopy(config)
|
||||
conf['freqai'] = {
|
||||
"enabled": True
|
||||
}
|
||||
with pytest.raises(
|
||||
OperationalException, match=r".* timerange parameter is not supported with .*"
|
||||
):
|
||||
validate_config_consistency(conf)
|
||||
|
||||
conf['timerange'] = None
|
||||
conf['freqai_backtest_live_models'] = False
|
||||
|
||||
with pytest.raises(
|
||||
OperationalException, match=r".* pass --timerange if you intend to use FreqAI .*"
|
||||
):
|
||||
validate_config_consistency(conf)
|
||||
|
||||
@ -5305,7 +5305,7 @@ def test_get_valid_price(mocker, default_conf_usdt) -> None:
|
||||
])
|
||||
def test_update_funding_fees_schedule(mocker, default_conf, trading_mode, calls, time_machine,
|
||||
t1, t2):
|
||||
time_machine.move_to(f"{t1} +00:00")
|
||||
time_machine.move_to(f"{t1} +00:00", tick=False)
|
||||
|
||||
patch_RPCManager(mocker)
|
||||
patch_exchange(mocker)
|
||||
@ -5314,7 +5314,7 @@ def test_update_funding_fees_schedule(mocker, default_conf, trading_mode, calls,
|
||||
default_conf['margin_mode'] = 'isolated'
|
||||
freqtrade = get_patched_freqtradebot(mocker, default_conf)
|
||||
|
||||
time_machine.move_to(f"{t2} +00:00")
|
||||
time_machine.move_to(f"{t2} +00:00", tick=False)
|
||||
# Check schedule jobs in debugging with freqtrade._schedule.jobs
|
||||
freqtrade._schedule.run_pending()
|
||||
|
||||
|
||||
@ -113,6 +113,16 @@ def test_throttle_sleep_time(mocker, default_conf, caplog) -> None:
|
||||
# 300 (5m) - 60 (1m - see set time above) - 5 (duration of throttled_func) = 235
|
||||
assert 235.2 < sleep_mock.call_args[0][0] < 235.6
|
||||
|
||||
t.move_to("2022-09-01 05:04:51 +00:00")
|
||||
sleep_mock.reset_mock()
|
||||
# Offset of 5s, so we hit the sweet-spot between "candle" and "candle offset"
|
||||
# Which should not get a throttle iteration to avoid late candle fetching
|
||||
assert worker._throttle(throttled_func, throttle_secs=10, timeframe='5m',
|
||||
timeframe_offset=5, x=1.2) == 42
|
||||
assert sleep_mock.call_count == 1
|
||||
# Time is slightly bigger than throttle secs due to the high timeframe offset.
|
||||
assert 11.1 < sleep_mock.call_args[0][0] < 13.2
|
||||
|
||||
|
||||
def test_throttle_with_assets(mocker, default_conf) -> None:
|
||||
def throttled_func(nb_assets=-1):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user