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Merge remote-tracking branch 'upstream/develop' into feature/fetch-public-trades

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Joe Schr
2024-07-04 11:01:17 +02:00
parent 0e0c585aee 7b6cd0cb9c
commit 05b2d8a2bf
51 changed files with 237 additions and 84 deletions
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4
docs/configuration.md
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@@ -650,9 +650,9 @@ Once you will be happy with your bot performance running in the Dry-run mode, yo
* API-keys may or may not be provided. Only Read-Only operations (i.e. operations that do not alter account state) on the exchange are performed in dry-run mode.
* Wallets (`/balance`) are simulated based on `dry_run_wallet`.
* Orders are simulated, and will not be posted to the exchange.
* Market orders fill based on orderbook volume the moment the order is placed.
* Market orders fill based on orderbook volume the moment the order is placed, with a maximum slippage of 5%.
* Limit orders fill once the price reaches the defined level - or time out based on `unfilledtimeout` settings.
* Limit orders will be converted to market orders if they cross the price by more than 1%.
* Limit orders will be converted to market orders if they cross the price by more than 1%, and will be filled immediately based regular market order rules (see point about Market orders above).
* In combination with `stoploss_on_exchange`, the stop_loss price is assumed to be filled.
* Open orders (not trades, which are stored in the database) are kept open after bot restarts, with the assumption that they were not filled while being offline.

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docs/freqai-running.md
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@@ -73,11 +73,11 @@ Backtesting mode requires [downloading the necessary data](#downloading-data-to-
---
### Saving prediction data
### Saving backtesting prediction data
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.
An additional directory called `backtesting_predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder.
An additional directory called `backtesting_predictions`, which contains all the predictions stored in `feather` format, will be created in the `unique-id` folder.
To change your **features**, you **must** set a new `identifier` in the config to signal to FreqAI to train new models.
@@ -89,7 +89,6 @@ FreqAI allow you to reuse live historic predictions through the backtest paramet
The `--timerange` parameter must not be informed, as it will be automatically calculated through the data in the historic predictions file.
### Downloading data to cover the full backtest period
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.