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2
.gitignore vendored
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@ -84,6 +84,8 @@ instance/
# Sphinx documentation
docs/_build/
# Mkdocs documentation
site/
# PyBuilder
target/

2
.pre-commit-config.yaml
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@ -15,7 +15,7 @@ repos:
additional_dependencies:
- types-cachetools==5.2.1
- types-filelock==3.2.7
- types-requests==2.28.0
- types-requests==2.28.1
- types-tabulate==0.8.11
- types-python-dateutil==2.8.18
# stages: [push]

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docs/configuration.md
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@ -116,6 +116,9 @@ This is similar to using multiple `--config` parameters, but simpler in usage as
The table below will list all configuration parameters available.
Freqtrade can also load many options via command line (CLI) arguments (check out the commands `--help` output for details).
### Configuration option prevalence
The prevalence for all Options is as follows:
- CLI arguments override any other option
@ -123,6 +126,8 @@ The prevalence for all Options is as follows:
- Configuration files are used in sequence (the last file wins) and override Strategy configurations.
- Strategy configurations are only used if they are not set via configuration or command-line arguments. These options are marked with [Strategy Override](#parameters-in-the-strategy) in the below table.
### Parameters table
Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
| Parameter | Description |
@ -135,7 +140,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> **Datatype:** Positive Float as ratio.
| `timeframe` | The timeframe to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `timeframe` | The timeframe to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). Usually missing in configuration, and specified in the strategy. [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
@ -148,13 +153,16 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `fee` | Fee used during backtesting / dry-runs. Should normally not be configured, which has freqtrade fall back to the exchange default fee. Set as ratio (e.g. 0.001 = 0.1%). Fee is applied twice for each trade, once when buying, once when selling. <br> **Datatype:** Float (as ratio)
| `futures_funding_rate` | User-specified funding rate to be used when historical funding rates are not available from the exchange. This does not overwrite real historical rates. It is recommended that this be set to 0 unless you are testing a specific coin and you understand how the funding rate will affect freqtrade's profit calculations. [More information here](leverage.md#unavailable-funding-rates) <br>*Defaults to None.*<br> **Datatype:** Float
| `trading_mode` | Specifies if you want to trade regularly, trade with leverage, or trade contracts whose prices are derived from matching cryptocurrency prices. [leverage documentation](leverage.md). <br>*Defaults to `"spot"`.* <br> **Datatype:** String
| `margin_mode` | When trading with leverage, this determines if the collateral owned by the trader will be shared or isolated to each trading pair [leverage documentation](leverage.md). <br> **Datatype:** String
| `liquidation_buffer` | A ratio specifying how large of a safety net to place between the liquidation price and the stoploss to prevent a position from reaching the liquidation price [leverage documentation](leverage.md). <br>*Defaults to `0.05`.* <br> **Datatype:** Float
| | **Unfilled timeout**
| `unfilledtimeout.entry` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled entry order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `unfilledtimeout.exit` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled exit order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `unfilledtimeout.unit` | Unit to use in unfilledtimeout setting. Note: If you set unfilledtimeout.unit to "seconds", "internals.process_throttle_secs" must be inferior or equal to timeout [Strategy Override](#parameters-in-the-strategy). <br> *Defaults to `minutes`.* <br> **Datatype:** String
| `unfilledtimeout.exit_timeout_count` | How many times can exit orders time out. Once this number of timeouts is reached, an emergency exit is triggered. 0 to disable and allow unlimited order cancels. [Strategy Override](#parameters-in-the-strategy).<br>*Defaults to `0`.* <br> **Datatype:** Integer
| | **Pricing**
| `entry_pricing.price_side` | Select the side of the spread the bot should look at to get the entry rate. [More information below](#buy-price-side).<br> *Defaults to `same`.* <br> **Datatype:** String (either `ask`, `bid`, `same` or `other`).
| `entry_pricing.price_last_balance` | **Required.** Interpolate the bidding price. More information [below](#entry-price-without-orderbook-enabled).
| `entry_pricing.use_order_book` | Enable entering using the rates in [Order Book Entry](#entry-price-with-orderbook-enabled). <br> *Defaults to `True`.*<br> **Datatype:** Boolean
@ -165,6 +173,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `exit_pricing.price_last_balance` | Interpolate the exiting price. More information [below](#exit-price-without-orderbook-enabled).
| `exit_pricing.use_order_book` | Enable exiting of open trades using [Order Book Exit](#exit-price-with-orderbook-enabled). <br> *Defaults to `True`.*<br> **Datatype:** Boolean
| `exit_pricing.order_book_top` | Bot will use the top N rate in Order Book "price_side" to exit. I.e. a value of 2 will allow the bot to pick the 2nd ask rate in [Order Book Exit](#exit-price-with-orderbook-enabled)<br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `custom_price_max_distance_ratio` | Configure maximum distance ratio between current and custom entry or exit price. <br>*Defaults to `0.02` 2%).*<br> **Datatype:** Positive float
| | **TODO**
| `use_exit_signal` | Use exit signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `exit_profit_only` | Wait until the bot reaches `exit_profit_offset` before taking an exit decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `exit_profit_offset` | Exit-signal is only active above this value. Only active in combination with `exit_profit_only=True`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0`.* <br> **Datatype:** Float (as ratio)
@ -172,8 +182,9 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `ignore_buying_expired_candle_after` | Specifies the number of seconds until a buy signal is no longer used. <br> **Datatype:** Integer
| `order_types` | Configure order-types depending on the action (`"entry"`, `"exit"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
| `order_time_in_force` | Configure time in force for entry and exit orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
| `custom_price_max_distance_ratio` | Configure maximum distance ratio between current and custom entry or exit price. <br>*Defaults to `0.02` 2%).*<br> **Datatype:** Positive float
| `recursive_strategy_search` | Set to `true` to recursively search sub-directories inside `user_data/strategies` for a strategy. <br> **Datatype:** Boolean
| `position_adjustment_enable` | Enables the strategy to use position adjustments (additional buys or sells). [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*<br> **Datatype:** Boolean
| `max_entry_position_adjustment` | Maximum additional order(s) for each open trade on top of the first entry Order. Set it to `-1` for unlimited additional orders. [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `-1`.*<br> **Datatype:** Positive Integer or -1
| | **Exchange**
| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> **Datatype:** Boolean
| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
@ -190,14 +201,19 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `exchange.skip_open_order_update` | Skips open order updates on startup should the exchange cause problems. Only relevant in live conditions.<br>*Defaults to `false`<br> **Datatype:** Boolean
| `exchange.unknown_fee_rate` | Fallback value to use when calculating trading fees. This can be useful for exchanges which have fees in non-tradable currencies. The value provided here will be multiplied with the "fee cost".<br>*Defaults to `None`<br> **Datatype:** float
| `exchange.log_responses` | Log relevant exchange responses. For debug mode only - use with care.<br>*Defaults to `false`<br> **Datatype:** Boolean
| `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| | **Plugins**
| `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation of all possible configuration options.
| `pairlists` | Define one or more pairlists to be used. [More information](plugins.md#pairlists-and-pairlist-handlers). <br>*Defaults to `StaticPairList`.* <br> **Datatype:** List of Dicts
| `protections` | Define one or more protections to be used. [More information](plugins.md#protections). <br> **Datatype:** List of Dicts
| | **Telegram**
| `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `telegram.balance_dust_level` | Dust-level (in stake currency) - currencies with a balance below this will not be shown by `/balance`. <br> **Datatype:** float
| `telegram.reload` | Allow "reload" buttons on telegram messages. <br>*Defaults to `True`.<br> **Datatype:** boolean
| `telegram.notification_settings.*` | Detailed notification settings. Refer to the [telegram documentation](telegram-usage.md) for details.<br> **Datatype:** dictionary
| | **Webhook**
| `webhook.enabled` | Enable usage of Webhook notifications <br> **Datatype:** Boolean
| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhookentry` | Payload to send on entry. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
@ -207,6 +223,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `webhook.webhookexitcancel` | Payload to send on exit order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhookexitfill` | Payload to send on exit order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| | **Rest API / FreqUI**
| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4
| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>**Datatype:** Integer between 1024 and 65535
@ -214,23 +231,22 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
| `bot_name` | Name of the bot. Passed via API to a client - can be shown to distinguish / name bots.<br> *Defaults to `freqtrade`*<br> **Datatype:** String
| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
| | **Other**
| `initial_state` | Defines the initial application state. If set to stopped, then the bot has to be explicitly started via `/start` RPC command. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
| `force_entry_enable` | Enables the RPC Commands to force a Trade entry. More information below. <br> **Datatype:** Boolean
| `disable_dataframe_checks` | Disable checking the OHLCV dataframe returned from the strategy methods for correctness. Only use when intentionally changing the dataframe and understand what you are doing. [Strategy Override](#parameters-in-the-strategy).<br> *Defaults to `False`*. <br> **Datatype:** Boolean
| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
| `internals.process_throttle_secs` | Set the process throttle, or minimum loop duration for one bot iteration loop. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
| `recursive_strategy_search` | Set to `true` to recursively search sub-directories inside `user_data/strategies` for a strategy. <br> **Datatype:** Boolean
| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
| `add_config_files` | Additional config files. These files will be loaded and merged with the current config file. The files are resolved relative to the initial file.<br> *Defaults to `[]`*. <br> **Datatype:** List of strings
| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
| `position_adjustment_enable` | Enables the strategy to use position adjustments (additional buys or sells). [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*<br> **Datatype:** Boolean
| `max_entry_position_adjustment` | Maximum additional order(s) for each open trade on top of the first entry Order. Set it to `-1` for unlimited additional orders. [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `-1`.*<br> **Datatype:** Positive Integer or -1
| `futures_funding_rate` | User-specified funding rate to be used when historical funding rates are not available from the exchange. This does not overwrite real historical rates. It is recommended that this be set to 0 unless you are testing a specific coin and you understand how the funding rate will affect freqtrade's profit calculations. [More information here](leverage.md#unavailable-funding-rates) <br>*Defaults to None.*<br> **Datatype:** Float
### Parameters in the strategy

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docs/freqai.md
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@ -183,12 +183,11 @@ various configuration parameters which multiply the feature set such as `include
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
:param coin: the name of the coin which will modify the feature names.
"""
with self.freqai.lock:

23
docs/hyperopt.md
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@ -40,13 +40,15 @@ pip install -r requirements-hyperopt.txt
```
usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[-i TIMEFRAME] [--timerange TIMERANGE]
[--recursive-strategy-search] [-i TIMEFRAME]
[--timerange TIMERANGE]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[-p PAIRS [PAIRS ...]] [--hyperopt-path PATH]
[--eps] [--dmmp] [--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET] [-e INT]
[--dry-run-wallet DRY_RUN_WALLET]
[--timeframe-detail TIMEFRAME_DETAIL] [-e INT]
[--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]]
[--print-all] [--no-color] [--print-json] [-j JOBS]
[--random-state INT] [--min-trades INT]
@ -89,6 +91,9 @@ optional arguments:
--dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
Starting balance, used for backtesting / hyperopt and
dry-runs.
--timeframe-detail TIMEFRAME_DETAIL
Specify detail timeframe for backtesting (`1m`, `5m`,
`30m`, `1h`, `1d`).
-e INT, --epochs INT Specify number of epochs (default: 100).
--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]
Specify which parameters to hyperopt. Space-separated
@ -146,7 +151,9 @@ Strategy arguments:
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
--recursive-strategy-search
Recursively search for a strategy in the strategies
folder.
```
### Hyperopt checklist
@ -867,10 +874,12 @@ You can also enable position stacking in the configuration file by explicitly se
As hyperopt consumes a lot of memory (the complete data needs to be in memory once per parallel backtesting process), it's likely that you run into "out of memory" errors.
To combat these, you have multiple options:
* reduce the amount of pairs
* reduce the timerange used (`--timerange <timerange>`)
* reduce the number of parallel processes (`-j <n>`)
* Increase the memory of your machine
* Reduce the amount of pairs.
* Reduce the timerange used (`--timerange <timerange>`).
* Avoid using `--timeframe-detail` (this loads a lot of additional data into memory).
* Reduce the number of parallel processes (`-j <n>`).
* Increase the memory of your machine.
## The objective has been evaluated at this point before.

4
docs/requirements-docs.txt
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@ -1,6 +1,6 @@
markdown==3.3.7
markdown==3.4.1
mkdocs==1.3.0
mkdocs-material==8.3.9
mdx_truly_sane_lists==1.2
mdx_truly_sane_lists==1.3
pymdown-extensions==9.5
jinja2==3.1.2

4
docs/stoploss.md
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@ -175,8 +175,8 @@ Before this, `stoploss` is used for the trailing stoploss.
* assuming the asset now increases to 102$
* the stoploss will now be at 91.8$ - 10% below the highest observed rate
* assuming the asset now increases to 103.5$ (above the offset configured)
* the stop loss will now be -2% of 103$ = 101.42$
* now the asset drops in value to 102\$, the stop loss will still be 101.42$ and would trigger once price breaks below 101.42$
* the stop loss will now be -2% of 103.5$ = 101.43$
* now the asset drops in value to 102\$, the stop loss will still be 101.43$ and would trigger once price breaks below 101.43$
### Trailing stop loss only once the trade has reached a certain offset

2
freqtrade/commands/arguments.py
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@ -29,7 +29,7 @@ ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_pos
ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
"position_stacking", "use_max_market_positions",
"enable_protections", "dry_run_wallet",
"enable_protections", "dry_run_wallet", "timeframe_detail",
"epochs", "spaces", "print_all",
"print_colorized", "print_json", "hyperopt_jobs",
"hyperopt_random_state", "hyperopt_min_trades",

1
freqtrade/constants.py
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@ -591,3 +591,4 @@ TradeList = List[List]
LongShort = Literal['long', 'short']
EntryExit = Literal['entry', 'exit']
BuySell = Literal['buy', 'sell']
MakerTaker = Literal['maker', 'taker']

36
freqtrade/exchange/exchange.py
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@ -20,7 +20,7 @@ from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, Precise, decimal_to_
from pandas import DataFrame
from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
EntryExit, ListPairsWithTimeframes, PairWithTimeframe)
EntryExit, ListPairsWithTimeframes, MakerTaker, PairWithTimeframe)
from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
@ -88,7 +88,8 @@ class Exchange:
# TradingMode.SPOT always supported and not required in this list
]
def __init__(self, config: Dict[str, Any], validate: bool = True, freqai: bool = False) -> None:
def __init__(self, config: Dict[str, Any], validate: bool = True,
load_leverage_tiers: bool = False) -> None:
"""
Initializes this module with the given config,
it does basic validation whether the specified exchange and pairs are valid.
@ -186,7 +187,7 @@ class Exchange:
self.markets_refresh_interval: int = exchange_config.get(
"markets_refresh_interval", 60) * 60
if self.trading_mode != TradingMode.SPOT and freqai is False:
if self.trading_mode != TradingMode.SPOT and load_leverage_tiers:
self.fill_leverage_tiers()
self.additional_exchange_init()
@ -850,20 +851,27 @@ class Exchange:
'filled': _amount,
'cost': (dry_order['amount'] * average) / leverage
})
dry_order = self.add_dry_order_fee(pair, dry_order)
# market orders will always incurr taker fees
dry_order = self.add_dry_order_fee(pair, dry_order, 'taker')
dry_order = self.check_dry_limit_order_filled(dry_order)
dry_order = self.check_dry_limit_order_filled(dry_order, immediate=True)
self._dry_run_open_orders[dry_order["id"]] = dry_order
# Copy order and close it - so the returned order is open unless it's a market order
return dry_order
def add_dry_order_fee(self, pair: str, dry_order: Dict[str, Any]) -> Dict[str, Any]:
def add_dry_order_fee(
self,
pair: str,
dry_order: Dict[str, Any],
taker_or_maker: MakerTaker,
) -> Dict[str, Any]:
fee = self.get_fee(pair, taker_or_maker=taker_or_maker)
dry_order.update({
'fee': {
'currency': self.get_pair_quote_currency(pair),
'cost': dry_order['cost'] * self.get_fee(pair),
'rate': self.get_fee(pair)
'cost': dry_order['cost'] * fee,
'rate': fee
}
})
return dry_order
@ -929,7 +937,8 @@ class Exchange:
pass
return False
def check_dry_limit_order_filled(self, order: Dict[str, Any]) -> Dict[str, Any]:
def check_dry_limit_order_filled(
self, order: Dict[str, Any], immediate: bool = False) -> Dict[str, Any]:
"""
Check dry-run limit order fill and update fee (if it filled).
"""
@ -943,7 +952,12 @@ class Exchange:
'filled': order['amount'],
'remaining': 0,
})
self.add_dry_order_fee(pair, order)
self.add_dry_order_fee(
pair,
order,
'taker' if immediate else 'maker',
)
return order
@ -1601,7 +1615,7 @@ class Exchange:
@retrier
def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
price: float = 1, taker_or_maker: str = 'maker') -> float:
price: float = 1, taker_or_maker: MakerTaker = 'maker') -> float:
try:
if self._config['dry_run'] and self._config.get('fee', None) is not None:
return self._config['fee']

0
freqtrade/freqai/__init__.py Normal file
View File

24
freqtrade/freqai/data_drawer.py
View File

@ -151,7 +151,7 @@ class FreqaiDataDrawer:
for pair in whitelist_pairs:
self.follower_dict[pair] = {}
with open(self.follow_path, "w") as fp:
with open(self.follower_dict_path, "w") as fp:
json.dump(self.follower_dict, fp, default=self.np_encoder)
def np_encoder(self, object):
@ -163,13 +163,12 @@ class FreqaiDataDrawer:
Locate and load existing model metadata from persistent storage. If not located,
create a new one and append the current pair to it and prepare it for its first
training
:params:
metadata: dict = strategy furnished pair metadata
:returns:
model_filename: str = unique filename used for loading persistent objects from disk
trained_timestamp: int = the last time the coin was trained
coin_first: bool = If the coin is fresh without metadata
return_null_array: bool = Follower could not find pair metadata
:param pair: str: pair to lookup
:return:
model_filename: str = unique filename used for loading persistent objects from disk
trained_timestamp: int = the last time the coin was trained
coin_first: bool = If the coin is fresh without metadata
return_null_array: bool = Follower could not find pair metadata
"""
pair_in_dict = self.pair_dict.get(pair)
data_path_set = self.pair_dict.get(pair, {}).get("data_path", None)
@ -277,13 +276,12 @@ class FreqaiDataDrawer:
)
df = pd.concat([prepend_df, df], axis=0)
def attach_return_values_to_return_dataframe(self, pair: str, dataframe) -> DataFrame:
def attach_return_values_to_return_dataframe(
self, pair: str, dataframe: DataFrame) -> DataFrame:
"""
Attach the return values to the strat dataframe
:params:
dataframe: DataFrame = strat dataframe
:returns:
dataframe: DataFrame = strat dataframe with return values attached
:param dataframe: DataFrame = strategy dataframe
:return: DataFrame = strat dataframe with return values attached
"""
df = self.model_return_values[pair]
to_keep = [col for col in dataframe.columns if not col.startswith("&")]

2
freqtrade/freqai/data_kitchen.py
View File

@ -923,7 +923,7 @@ class FreqaiDataKitchen:
and training the model.
"""
exchange = ExchangeResolver.load_exchange(
self.config["exchange"]["name"], self.config, validate=False, freqai=True
self.config["exchange"]["name"], self.config, validate=False, load_leverage_tiers=False
)
new_pairs_days = int((timerange.stopts - timerange.startts) / SECONDS_IN_DAY)

90
freqtrade/freqai/freqai_interface.py
View File

@ -65,7 +65,6 @@ class IFreqaiModel(ABC):
self.data_split_parameters = config.get("freqai", {}).get("data_split_parameters")
self.model_training_parameters = config.get("freqai", {}).get("model_training_parameters")
self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
self.model = None
self.retrain = False
self.first = True
self.set_full_path()
@ -92,11 +91,11 @@ class IFreqaiModel(ABC):
Entry point to the FreqaiModel from a specific pair, it will train a new model if
necessary before making the prediction.
:params:
:dataframe: Full dataframe coming from strategy - it contains entire
backtesting timerange + additional historical data necessary to train
:param dataframe: Full dataframe coming from strategy - it contains entire
backtesting timerange + additional historical data necessary to train
the model.
:metadata: pair metadata coming from strategy.
:param metadata: pair metadata coming from strategy.
:param strategy: Strategy to train on
"""
self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
@ -129,8 +128,7 @@ class IFreqaiModel(ABC):
Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
to improve model youth. This function is agnostic to data preparation/collection/storage,
it simply trains on what ever data is available in the self.dd.
:params:
strategy: IStrategy = The user defined strategy class
:param strategy: IStrategy = The user defined strategy class
"""
while 1:
time.sleep(1)
@ -164,12 +162,11 @@ class IFreqaiModel(ABC):
following the training window). FreqAI slides the window and sequentially builds
the backtesting results before returning the concatenated results for the full
backtesting period back to the strategy.
:params:
dataframe: DataFrame = strategy passed dataframe
metadata: Dict = pair metadata
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:returns:
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:param dataframe: DataFrame = strategy passed dataframe
:param metadata: Dict = pair metadata
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:return:
FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
self.pair_it += 1
@ -239,13 +236,12 @@ class IFreqaiModel(ABC):
"""
The main broad execution for dry/live. This function will check if a retraining should be
performed, and if so, retrain and reset the model.
:params:
dataframe: DataFrame = strategy passed dataframe
metadata: Dict = pair metadata
strategy: IStrategy = currently employed strategy
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:param dataframe: DataFrame = strategy passed dataframe
:param metadata: Dict = pair metadata
:param strategy: IStrategy = currently employed strategy
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:returns:
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
# update follower
@ -290,7 +286,7 @@ class IFreqaiModel(ABC):
elif self.follow_mode:
dk.set_paths(metadata["pair"], trained_timestamp)
logger.info(
"FreqAI instance set to follow_mode, finding existing pair"
"FreqAI instance set to follow_mode, finding existing pair "
f"using { self.identifier }"
)
@ -353,9 +349,9 @@ class IFreqaiModel(ABC):
"""
Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
to a folder holding existing models.
:params:
dataframe: DataFrame = strategy provided dataframe
dk: FreqaiDataKitchen = non-persistent data container/analyzer for current coin/bot loop
:param dataframe: DataFrame = strategy provided dataframe
:param dk: FreqaiDataKitchen = non-persistent data container/analyzer for
current coin/bot loop
"""
dk.find_features(dataframe)
if "training_features_list_raw" in dk.data:
@ -375,8 +371,8 @@ class IFreqaiModel(ABC):
"""
Base data cleaning method for train
Any function inside this method should drop training data points from the filtered_dataframe
based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example
of how outlier data points are dropped from the dataframe used for training.
based on user decided logic. See FreqaiDataKitchen::use_SVM_to_remove_outliers() for an
example of how outlier data points are dropped from the dataframe used for training.
"""
if self.freqai_info.get("feature_parameters", {}).get(
@ -461,13 +457,14 @@ class IFreqaiModel(ABC):
"""
Retreive data and train model in single threaded mode (only used if model directory is empty
upon startup for dry/live )
:params:
new_trained_timerange: TimeRange = the timerange to train the model on
metadata: dict = strategy provided metadata
strategy: IStrategy = user defined strategy object
dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
data_load_timerange: TimeRange = the amount of data to be loaded for populate_any_indicators
(larger than new_trained_timerange so that new_trained_timerange does not contain any NaNs)
:param new_trained_timerange: TimeRange = the timerange to train the model on
:param metadata: dict = strategy provided metadata
:param strategy: IStrategy = user defined strategy object
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
:param data_load_timerange: TimeRange = the amount of data to be loaded
for populate_any_indicators
(larger than new_trained_timerange so that
new_trained_timerange does not contain any NaNs)
"""
corr_dataframes, base_dataframes = dk.get_base_and_corr_dataframes(
@ -515,11 +512,9 @@ class IFreqaiModel(ABC):
"""
Filter the training data and train a model to it. Train makes heavy use of the datahandler
for storing, saving, loading, and analyzing the data.
:params:
:unfiltered_dataframe: Full dataframe for the current training period
:metadata: pair metadata from strategy.
:returns:
:model: Trained model which can be used to inference (self.predict)
:param unfiltered_dataframe: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:return: Trained model which can be used to inference (self.predict)
"""
@abstractmethod
@ -528,9 +523,8 @@ class IFreqaiModel(ABC):
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
management will be properly handled by Freqai.
:params:
data_dictionary: Dict = the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: Dict = the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
return
@ -541,9 +535,9 @@ class IFreqaiModel(ABC):
) -> Tuple[DataFrame, npt.ArrayLike]:
"""
Filter the prediction features data and predict with it.
:param:
unfiltered_dataframe: Full dataframe for the current backtest period.
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:param unfiltered_dataframe: Full dataframe for the current backtest period.
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:param first: boolean = whether this is the first prediction or not.
:return:
:predictions: np.array of predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
@ -554,12 +548,10 @@ class IFreqaiModel(ABC):
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
"""
User defines the dataframe to be returned to strategy here.
:params:
dataframe: DataFrame = the full dataframe for the current prediction (live)
or --timerange (backtesting)
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:returns:
dataframe: DataFrame = dataframe filled with user defined data
:param dataframe: DataFrame = the full dataframe for the current prediction (live)
or --timerange (backtesting)
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:return: dataframe: DataFrame = dataframe filled with user defined data
"""
return

14
freqtrade/freqai/prediction_models/BaseRegressionModel.py
View File

@ -1,6 +1,7 @@
import logging
from typing import Tuple
from typing import Any, Tuple
import numpy.typing as npt
from pandas import DataFrame
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
@ -28,14 +29,13 @@ class BaseRegressionModel(IFreqaiModel):
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
) -> Tuple[DataFrame, DataFrame]:
) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data.
:params:
:unfiltered_dataframe: Full dataframe for the current training period
:metadata: pair metadata from strategy.
:returns:
:param unfiltered_dataframe: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:return:
:model: Trained model which can be used to inference (self.predict)
"""
@ -84,7 +84,7 @@ class BaseRegressionModel(IFreqaiModel):
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
) -> Tuple[DataFrame, DataFrame]:
) -> Tuple[DataFrame, npt.ArrayLike]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.

5
freqtrade/freqai/prediction_models/BaseTensorFlowModel.py
View File

@ -31,9 +31,8 @@ class BaseTensorFlowModel(IFreqaiModel):
"""
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data.
:params:
:unfiltered_dataframe: Full dataframe for the current training period
:metadata: pair metadata from strategy.
:param unfiltered_dataframe: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:returns:
:model: Trained model which can be used to inference (self.predict)
"""

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

@ -19,9 +19,8 @@ class CatboostPredictionModel(BaseRegressionModel):
def fit(self, data_dictionary: Dict) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
train_data = Pool(

5
freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py
View File

@ -20,9 +20,8 @@ class CatboostPredictionMultiModel(BaseRegressionModel):
def fit(self, data_dictionary: Dict) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
cbr = CatBoostRegressor(

5
freqtrade/freqai/prediction_models/LightGBMPredictionModel.py
View File

@ -21,9 +21,8 @@ class LightGBMPredictionModel(BaseRegressionModel):
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
management will be properly handled by Freqai.
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])

5
freqtrade/freqai/prediction_models/LightGBMPredictionMultiModel.py
View File

@ -20,9 +20,8 @@ class LightGBMPredictionMultiModel(BaseRegressionModel):
def fit(self, data_dictionary: Dict) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
lgb = LGBMRegressor(**self.model_training_parameters)

0
freqtrade/freqai/prediction_models/__init__.py Normal file
View File

3
freqtrade/freqtradebot.py
View File

@ -65,7 +65,8 @@ class FreqtradeBot(LoggingMixin):
# Check config consistency here since strategies can set certain options
validate_config_consistency(config)
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
self.exchange = ExchangeResolver.load_exchange(
self.config['exchange']['name'], self.config, load_leverage_tiers=True)
init_db(self.config['db_url'])

3
freqtrade/optimize/backtesting.py
View File

@ -84,7 +84,8 @@ class Backtesting:
self.processed_dfs: Dict[str, Dict] = {}
self._exchange_name = self.config['exchange']['name']
self.exchange = ExchangeResolver.load_exchange(self._exchange_name, self.config)
self.exchange = ExchangeResolver.load_exchange(
self._exchange_name, self.config, load_leverage_tiers=True)
self.dataprovider = DataProvider(self.config, self.exchange)
if self.config.get('strategy_list'):

22
freqtrade/plot/plotting.py
View File

@ -255,18 +255,18 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
"""
# Trades can be empty
if trades is not None and len(trades) > 0:
# Create description for sell summarizing the trade
# Create description for exit summarizing the trade
trades['desc'] = trades.apply(
lambda row: f"{row['profit_ratio']:.2%}, " +
(f"{row['enter_tag']}, " if row['enter_tag'] is not None else "") +
f"{row['exit_reason']}, " +
f"{row['trade_duration']} min",
axis=1)
trade_buys = go.Scatter(
trade_entries = go.Scatter(
x=trades["open_date"],
y=trades["open_rate"],
mode='markers',
name='Trade buy',
name='Trade entry',
text=trades["desc"],
marker=dict(
symbol='circle-open',
@ -277,12 +277,12 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
)
)
trade_sells = go.Scatter(
trade_exits = go.Scatter(
x=trades.loc[trades['profit_ratio'] > 0, "close_date"],
y=trades.loc[trades['profit_ratio'] > 0, "close_rate"],
text=trades.loc[trades['profit_ratio'] > 0, "desc"],
mode='markers',
name='Sell - Profit',
name='Exit - Profit',
marker=dict(
symbol='square-open',
size=11,
@ -290,12 +290,12 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
color='green'
)
)
trade_sells_loss = go.Scatter(
trade_exits_loss = go.Scatter(
x=trades.loc[trades['profit_ratio'] <= 0, "close_date"],
y=trades.loc[trades['profit_ratio'] <= 0, "close_rate"],
text=trades.loc[trades['profit_ratio'] <= 0, "desc"],
mode='markers',
name='Sell - Loss',
name='Exit - Loss',
marker=dict(
symbol='square-open',
size=11,
@ -303,9 +303,9 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
color='red'
)
)
fig.add_trace(trade_buys, 1, 1)
fig.add_trace(trade_sells, 1, 1)
fig.add_trace(trade_sells_loss, 1, 1)
fig.add_trace(trade_entries, 1, 1)
fig.add_trace(trade_exits, 1, 1)
fig.add_trace(trade_exits_loss, 1, 1)
else:
logger.warning("No trades found.")
return fig
@ -444,7 +444,7 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
Generate the graph from the data generated by Backtesting or from DB
Volume will always be ploted in row2, so Row 1 and 3 are to our disposal for custom indicators
:param pair: Pair to Display on the graph
:param data: OHLCV DataFrame containing indicators and buy/sell signals
:param data: OHLCV DataFrame containing indicators and entry/exit signals
:param trades: All trades created
:param indicators1: List containing Main plot indicators
:param indicators2: List containing Sub plot indicators

13
freqtrade/resolvers/exchange_resolver.py
View File

@ -19,7 +19,7 @@ class ExchangeResolver(IResolver):
@staticmethod
def load_exchange(exchange_name: str, config: dict, validate: bool = True,
freqai: bool = False) -> Exchange:
load_leverage_tiers: bool = False) -> Exchange:
"""
Load the custom class from config parameter
:param exchange_name: name of the Exchange to load
@ -30,10 +30,13 @@ class ExchangeResolver(IResolver):
exchange_name = exchange_name.title()
exchange = None
try:
exchange = ExchangeResolver._load_exchange(exchange_name,
kwargs={'config': config,
'validate': validate,
'freqai': freqai})
exchange = ExchangeResolver._load_exchange(
exchange_name,
kwargs={
'config': config,
'validate': validate,
'load_leverage_tiers': load_leverage_tiers}
)
except ImportError:
logger.info(
f"No {exchange_name} specific subclass found. Using the generic class instead.")

2
freqtrade/rpc/api_server/deps.py
View File

@ -37,7 +37,7 @@ def get_exchange(config=Depends(get_config)):
if not ApiServer._exchange:
from freqtrade.resolvers import ExchangeResolver
ApiServer._exchange = ExchangeResolver.load_exchange(
config['exchange']['name'], config)
config['exchange']['name'], config, load_leverage_tiers=False)
return ApiServer._exchange

11
freqtrade/strategy/interface.py
View File

@ -566,12 +566,11 @@ class IStrategy(ABC, HyperStrategyMixin):
additional features here, but must follow the naming convention.
This method is *only* used in FreqaiDataKitchen class and therefore
it is only called if FreqAI is active.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
:param coin: the name of the coin which will modify the feature names.
"""
return df

11
freqtrade/templates/FreqaiExampleStrategy.py
View File

@ -74,12 +74,11 @@ class FreqaiExampleStrategy(IStrategy):
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
:param coin: the name of the coin which will modify the feature names.
"""
with self.freqai.lock:

4
requirements-dev.txt
View File

@ -11,7 +11,7 @@ flake8-tidy-imports==4.8.0
mypy==0.961
pre-commit==2.20.0
pytest==7.1.2
pytest-asyncio==0.18.3
pytest-asyncio==0.19.0
pytest-cov==3.0.0
pytest-mock==3.8.2
pytest-random-order==1.0.4
@ -25,6 +25,6 @@ nbconvert==6.5.0
# mypy types
types-cachetools==5.2.1
types-filelock==3.2.7
types-requests==2.28.0
types-requests==2.28.1
types-tabulate==0.8.11
types-python-dateutil==2.8.18

6
requirements.txt
View File

@ -2,7 +2,7 @@ numpy==1.23.1
pandas==1.4.3
pandas-ta==0.3.14b
ccxt==1.90.47
ccxt==1.90.89
# Pin cryptography for now due to rust build errors with piwheels
cryptography==37.0.4
aiohttp==3.8.1
@ -12,7 +12,7 @@ arrow==1.2.2
cachetools==4.2.2
requests==2.28.1
urllib3==1.26.10
jsonschema==4.6.2
jsonschema==4.7.2
TA-Lib==0.4.24
technical==1.3.0
tabulate==0.8.10
@ -34,7 +34,7 @@ orjson==3.7.7
sdnotify==0.3.2
# API Server
fastapi==0.78.0
fastapi==0.79.0
uvicorn==0.18.2
pyjwt==2.4.0
aiofiles==0.8.0

21
tests/conftest.py
View File

@ -148,7 +148,7 @@ def get_patched_exchange(mocker, config, api_mock=None, id='binance',
patch_exchange(mocker, api_mock, id, mock_markets, mock_supported_modes)
config['exchange']['name'] = id
try:
exchange = ExchangeResolver.load_exchange(id, config)
exchange = ExchangeResolver.load_exchange(id, config, load_leverage_tiers=True)
except ImportError:
exchange = Exchange(config)
return exchange
@ -2609,7 +2609,7 @@ def open_trade_usdt():
pair='ADA/USDT',
open_rate=2.0,
exchange='binance',
open_order_id='123456789',
open_order_id='123456789_exit',
amount=30.0,
fee_open=0.0,
fee_close=0.0,
@ -2634,6 +2634,23 @@ def open_trade_usdt():
cost=trade.open_rate * trade.amount,
order_date=trade.open_date,
order_filled_date=trade.open_date,
),
Order(
ft_order_side='exit',
ft_pair=trade.pair,
ft_is_open=True,
order_id='123456789_exit',
status="open",
symbol=trade.pair,
order_type="limit",
side="sell",
price=trade.open_rate,
average=trade.open_rate,
filled=trade.amount,
remaining=0,
cost=trade.open_rate * trade.amount,
order_date=trade.open_date,
order_filled_date=trade.open_date,
)
]
return trade

3
tests/exchange/test_ccxt_compat.py
View File

@ -137,7 +137,8 @@ def exchange_futures(request, exchange_conf, class_mocker):
'freqtrade.exchange.binance.Binance.fill_leverage_tiers')
class_mocker.patch('freqtrade.exchange.exchange.Exchange.fetch_trading_fees')
class_mocker.patch('freqtrade.exchange.okx.Okx.additional_exchange_init')
exchange = ExchangeResolver.load_exchange(request.param, exchange_conf, validate=True)
exchange = ExchangeResolver.load_exchange(
request.param, exchange_conf, validate=True, load_leverage_tiers=True)
yield exchange, request.param

51
tests/exchange/test_exchange.py
View File

@ -1138,6 +1138,57 @@ def test_create_dry_run_order(default_conf, mocker, side, exchange_name, leverag
assert order["cost"] == 1 * 200
@pytest.mark.parametrize('side,is_short,order_reason', [
("buy", False, "entry"),
("sell", False, "exit"),
("buy", True, "exit"),
("sell", True, "entry"),
])
@pytest.mark.parametrize("order_type,price_side,fee", [
("limit", "same", 1.0),
("limit", "other", 2.0),
("market", "same", 2.0),
("market", "other", 2.0),
])
def test_create_dry_run_order_fees(
default_conf,
mocker,
side,
order_type,
is_short,
order_reason,
price_side,
fee,
):
mocker.patch(
'freqtrade.exchange.Exchange.get_fee',
side_effect=lambda symbol, taker_or_maker: 2.0 if taker_or_maker == 'taker' else 1.0
)
mocker.patch('freqtrade.exchange.Exchange._is_dry_limit_order_filled',
return_value=price_side == 'other')
exchange = get_patched_exchange(mocker, default_conf)
order = exchange.create_dry_run_order(
pair='LTC/USDT',
ordertype=order_type,
side=side,
amount=10,
rate=2.0,
leverage=1.0
)
if price_side == 'other' or order_type == 'market':
assert order['fee']['rate'] == fee
return
else:
assert order['fee'] is None
mocker.patch('freqtrade.exchange.Exchange._is_dry_limit_order_filled',
return_value=price_side != 'other')
order1 = exchange.fetch_dry_run_order(order['id'])
assert order1['fee']['rate'] == fee
@pytest.mark.parametrize("side,startprice,endprice", [
("buy", 25.563, 25.566),
("sell", 25.566, 25.563)

100
tests/freqai/test_freqai_interface.py
View File

@ -34,26 +34,10 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf):
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert (
Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_model.joblib"))
.resolve()
.exists()
)
assert (
Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_metadata.json"))
.resolve()
.exists()
)
assert (
Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_trained_df.pkl"))
.resolve()
.exists()
)
assert (
Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_svm_model.joblib"))
.resolve()
.exists()
)
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))
@ -161,3 +145,79 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
)
shutil.rmtree(Path(freqai.dk.full_path))
def test_follow_mode(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
metadata = {"pair": "ADA/BTC"}
freqai.dd.set_pair_dict_info(metadata)
# freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").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()
# start the follower and ask it to predict on existing files
freqai_conf.get("freqai", {}).update({"follow_mode": "true"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd, freqai.live)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
freqai.start_live(df, metadata, strategy, freqai.dk)
assert len(freqai.dk.return_dataframe.index) == 5702
shutil.rmtree(Path(freqai.dk.full_path))
def test_principal_component_analysis(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
{"princpial_component_analysis": "true"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_pca_object.pkl")
shutil.rmtree(Path(freqai.dk.full_path))

61
tests/optimize/test_backtesting.py
View File

@ -90,28 +90,6 @@ def load_data_test(what, testdatadir):
fill_missing=True)}
def simple_backtest(config, contour, mocker, testdatadir) -> None:
patch_exchange(mocker)
config['timeframe'] = '1m'
backtesting = Backtesting(config)
backtesting._set_strategy(backtesting.strategylist[0])
data = load_data_test(contour, testdatadir)
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
assert isinstance(processed, dict)
results = backtesting.backtest(
processed=processed,
start_date=min_date,
end_date=max_date,
max_open_trades=1,
position_stacking=False,
enable_protections=config.get('enable_protections', False),
)
# results :: <class 'pandas.core.frame.DataFrame'>
return results
# FIX: fixturize this?
def _make_backtest_conf(mocker, datadir, conf=None, pair='UNITTEST/BTC'):
data = history.load_data(datadir=datadir, timeframe='1m', pairs=[pair])
@ -942,6 +920,7 @@ def test_backtest_dataprovider_analyzed_df(default_conf, fee, mocker, testdatadi
def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatadir) -> None:
# While this test IS a copy of test_backtest_pricecontours, it's needed to ensure
# results do not carry-over to the next run, which is not given by using parametrize.
patch_exchange(mocker)
default_conf['protections'] = [
{
"method": "CooldownPeriod",
@ -949,6 +928,7 @@ def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatad
}]
default_conf['enable_protections'] = True
default_conf['timeframe'] = '1m'
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf'))
@ -959,12 +939,27 @@ def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatad
['sine', 9],
['raise', 10],
]
backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
# While entry-signals are unrealistic, running backtesting
# over and over again should not cause different results
for [contour, numres] in tests:
# Debug output for random test failure
print(f"{contour}, {numres}")
assert len(simple_backtest(default_conf, contour, mocker, testdatadir)['results']) == numres
data = load_data_test(contour, testdatadir)
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
assert isinstance(processed, dict)
results = backtesting.backtest(
processed=processed,
start_date=min_date,
end_date=max_date,
max_open_trades=1,
position_stacking=False,
enable_protections=default_conf.get('enable_protections', False),
)
assert len(results['results']) == numres
@pytest.mark.parametrize('protections,contour,expected', [
@ -990,7 +985,25 @@ def test_backtest_pricecontours(default_conf, fee, mocker, testdatadir,
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
# While entry-signals are unrealistic, running backtesting
# over and over again should not cause different results
assert len(simple_backtest(default_conf, contour, mocker, testdatadir)['results']) == expected
patch_exchange(mocker)
default_conf['timeframe'] = '1m'
backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
data = load_data_test(contour, testdatadir)
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
assert isinstance(processed, dict)
results = backtesting.backtest(
processed=processed,
start_date=min_date,
end_date=max_date,
max_open_trades=1,
position_stacking=False,
enable_protections=default_conf.get('enable_protections', False),
)
assert len(results['results']) == expected
def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir):

31
tests/plugins/test_protections.py
View File

@ -6,6 +6,7 @@ import pytest
from freqtrade import constants
from freqtrade.enums import ExitType
from freqtrade.persistence import PairLocks, Trade
from freqtrade.persistence.trade_model import Order
from freqtrade.plugins.protectionmanager import ProtectionManager
from tests.conftest import get_patched_freqtradebot, log_has_re
@ -30,7 +31,37 @@ def generate_mock_trade(pair: str, fee: float, is_open: bool,
amount=0.01 / open_rate,
exchange='binance',
is_short=is_short,
leverage=1,
)
trade.orders.append(Order(
ft_order_side=trade.entry_side,
order_id=f'{pair}-{trade.entry_side}-{trade.open_date}',
ft_pair=pair,
amount=trade.amount,
filled=trade.amount,
remaining=0,
price=open_rate,
average=open_rate,
status="closed",
order_type="market",
side=trade.entry_side,
))
if not is_open:
trade.orders.append(Order(
ft_order_side=trade.exit_side,
order_id=f'{pair}-{trade.exit_side}-{trade.close_date}',
ft_pair=pair,
amount=trade.amount,
filled=trade.amount,
remaining=0,
price=open_rate * (2 - profit_rate if is_short else profit_rate),
average=open_rate * (2 - profit_rate if is_short else profit_rate),
status="closed",
order_type="market",
side=trade.exit_side,
))
trade.recalc_open_trade_value()
if not is_open:
trade.close(open_rate * (2 - profit_rate if is_short else profit_rate))

2
tests/rpc/test_rpc.py
View File

@ -830,6 +830,8 @@ def test_rpc_force_exit(default_conf, ticker, fee, mocker) -> None:
assert cancel_order_mock.call_count == 2
assert trade.amount == amount
trade = Trade.query.filter(Trade.id == '3').first()
# make an limit-sell open trade
mocker.patch(
'freqtrade.exchange.Exchange.fetch_order',

3
tests/rpc/test_rpc_telegram.py
View File

@ -686,6 +686,7 @@ def test_profit_handle(default_conf_usdt, update, ticker_usdt, ticker_sell_up, f
# Simulate fulfilled LIMIT_SELL order for trade
oobj = Order.parse_from_ccxt_object(
limit_sell_order_usdt, limit_sell_order_usdt['symbol'], 'sell')
trade.orders.append(oobj)
trade.update_trade(oobj)
trade.close_date = datetime.now(timezone.utc)
@ -707,7 +708,7 @@ def test_profit_handle(default_conf_usdt, update, ticker_usdt, ticker_sell_up, f
assert '*Best Performing:* `ETH/USDT: 9.45%`' in msg_mock.call_args_list[-1][0][0]
assert '*Max Drawdown:*' in msg_mock.call_args_list[-1][0][0]
assert '*Profit factor:*' in msg_mock.call_args_list[-1][0][0]
assert '*Trading volume:* `60 USDT`' in msg_mock.call_args_list[-1][0][0]
assert '*Trading volume:* `126 USDT`' in msg_mock.call_args_list[-1][0][0]
@pytest.mark.parametrize('is_short', [True, False])

53
tests/test_freqtradebot.py
View File

@ -2060,8 +2060,9 @@ def test_update_trade_state_orderexception(mocker, default_conf_usdt, caplog) ->
@pytest.mark.parametrize("is_short", [False, True])
def test_update_trade_state_sell(
default_conf_usdt, trades_for_order, limit_order_open, limit_order, is_short, mocker,
default_conf_usdt, trades_for_order, limit_order_open, limit_order, is_short, mocker
):
buy_order = limit_order[entry_side(is_short)]
open_order = limit_order_open[exit_side(is_short)]
l_order = limit_order[exit_side(is_short)]
mocker.patch('freqtrade.exchange.Exchange.get_trades_for_order', return_value=trades_for_order)
@ -2088,6 +2089,9 @@ def test_update_trade_state_sell(
leverage=1,
is_short=is_short,
)
order = Order.parse_from_ccxt_object(buy_order, 'LTC/ETH', entry_side(is_short))
trade.orders.append(order)
order = Order.parse_from_ccxt_object(open_order, 'LTC/ETH', exit_side(is_short))
trade.orders.append(order)
assert order.status == 'open'
@ -2135,8 +2139,6 @@ def test_handle_trade(
assert trade
time.sleep(0.01) # Race condition fix
oobj = Order.parse_from_ccxt_object(enter_order, enter_order['symbol'], entry_side(is_short))
trade.update_trade(oobj)
assert trade.is_open is True
freqtrade.wallets.update()
@ -2146,11 +2148,15 @@ def test_handle_trade(
assert trade.open_order_id == exit_order['id']
# Simulate fulfilled LIMIT_SELL order for trade
oobj = Order.parse_from_ccxt_object(exit_order, exit_order['symbol'], exit_side(is_short))
trade.update_trade(oobj)
trade.orders[-1].ft_is_open = False
trade.orders[-1].status = 'closed'
trade.orders[-1].filled = trade.orders[-1].remaining
trade.orders[-1].remaining = 0.0
assert trade.close_rate == 2.0 if is_short else 2.2
assert trade.close_profit == close_profit
trade.update_trade(trade.orders[-1])
assert trade.close_rate == (2.0 if is_short else 2.2)
assert pytest.approx(trade.close_profit) == close_profit
assert trade.calc_profit(trade.close_rate) == 5.685
assert trade.close_date is not None
assert trade.exit_reason == 'sell_signal1'
@ -2753,6 +2759,8 @@ def test_check_handle_cancelled_exit(
cancel_order_mock = MagicMock()
limit_sell_order_old.update({"status": "canceled", 'filled': 0.0})
limit_sell_order_old['side'] = 'buy' if is_short else 'sell'
limit_sell_order_old['id'] = open_trade_usdt.open_order_id
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
@ -2787,6 +2795,7 @@ def test_manage_open_orders_partial(
rpc_mock = patch_RPCManager(mocker)
open_trade.is_short = is_short
open_trade.leverage = leverage
open_trade.orders[0].ft_order_side = 'sell' if is_short else 'buy'
limit_buy_order_old_partial['id'] = open_trade.open_order_id
limit_buy_order_old_partial['side'] = 'sell' if is_short else 'buy'
limit_buy_canceled = deepcopy(limit_buy_order_old_partial)
@ -2872,6 +2881,7 @@ def test_manage_open_orders_partial_except(
limit_buy_order_old_partial_canceled, mocker
) -> None:
open_trade.is_short = is_short
open_trade.orders[0].ft_order_side = 'sell' if is_short else 'buy'
rpc_mock = patch_RPCManager(mocker)
limit_buy_order_old_partial_canceled['id'] = open_trade.open_order_id
limit_buy_order_old_partial['id'] = open_trade.open_order_id
@ -3090,7 +3100,27 @@ def test_handle_cancel_exit_limit(mocker, default_conf_usdt, fee) -> None:
close_date=arrow.utcnow().datetime,
exit_reason="sell_reason_whatever",
)
order = {'remaining': 1,
trade.orders = [
Order(
ft_order_side='buy',
ft_pair=trade.pair,
ft_is_open=True,
order_id='123456',
status="closed",
symbol=trade.pair,
order_type="market",
side="buy",
price=trade.open_rate,
average=trade.open_rate,
filled=trade.amount,
remaining=0,
cost=trade.open_rate * trade.amount,
order_date=trade.open_date,
order_filled_date=trade.open_date,
),
]
order = {'id': "123456",
'remaining': 1,
'amount': 1,
'status': "open"}
reason = CANCEL_REASON['TIMEOUT']
@ -3626,7 +3656,7 @@ def test_execute_trade_exit_market_order(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker_usdt,
get_fee=fee,
_is_dry_limit_order_filled=MagicMock(return_value=False),
_is_dry_limit_order_filled=MagicMock(return_value=True),
)
patch_whitelist(mocker, default_conf_usdt)
freqtrade = FreqtradeBot(default_conf_usdt)
@ -3642,7 +3672,8 @@ def test_execute_trade_exit_market_order(
# Increase the price and sell it
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker_usdt_sell_up
fetch_ticker=ticker_usdt_sell_up,
_is_dry_limit_order_filled=MagicMock(return_value=False),
)
freqtrade.config['order_types']['exit'] = 'market'
@ -3655,7 +3686,7 @@ def test_execute_trade_exit_market_order(
assert not trade.is_open
assert trade.close_profit == profit_ratio
assert rpc_mock.call_count == 3
assert rpc_mock.call_count == 4
last_msg = rpc_mock.call_args_list[-2][0][0]
assert {
'type': RPCMessageType.EXIT,

39
tests/test_persistence.py
View File

@ -481,6 +481,7 @@ def test_update_limit_order(fee, caplog, limit_buy_order_usdt, limit_sell_order_
trade.open_order_id = 'something'
oobj = Order.parse_from_ccxt_object(enter_order, 'ADA/USDT', entry_side)
trade.orders.append(oobj)
trade.update_trade(oobj)
assert trade.open_order_id is None
assert trade.open_rate == open_rate
@ -496,11 +497,12 @@ def test_update_limit_order(fee, caplog, limit_buy_order_usdt, limit_sell_order_
trade.open_order_id = 'something'
time_machine.move_to("2022-03-31 21:45:05 +00:00")
oobj = Order.parse_from_ccxt_object(exit_order, 'ADA/USDT', exit_side)
trade.orders.append(oobj)
trade.update_trade(oobj)
assert trade.open_order_id is None
assert trade.close_rate == close_rate
assert trade.close_profit == profit
assert pytest.approx(trade.close_profit) == profit
assert trade.close_date is not None
assert log_has_re(f"LIMIT_{exit_side.upper()} has been fulfilled for "
r"Trade\(id=2, pair=ADA/USDT, amount=30.00000000, "
@ -529,6 +531,7 @@ def test_update_market_order(market_buy_order_usdt, market_sell_order_usdt, fee,
trade.open_order_id = 'something'
oobj = Order.parse_from_ccxt_object(market_buy_order_usdt, 'ADA/USDT', 'buy')
trade.orders.append(oobj)
trade.update_trade(oobj)
assert trade.open_order_id is None
assert trade.open_rate == 2.0
@ -543,10 +546,11 @@ def test_update_market_order(market_buy_order_usdt, market_sell_order_usdt, fee,
trade.is_open = True
trade.open_order_id = 'something'
oobj = Order.parse_from_ccxt_object(market_sell_order_usdt, 'ADA/USDT', 'sell')
trade.orders.append(oobj)
trade.update_trade(oobj)
assert trade.open_order_id is None
assert trade.close_rate == 2.2
assert trade.close_profit == round(0.0945137157107232, 8)
assert pytest.approx(trade.close_profit) == 0.094513715710723
assert trade.close_date is not None
assert log_has_re(r"MARKET_SELL has been fulfilled for Trade\(id=1, "
r"pair=ADA/USDT, amount=30.00000000, is_short=False, leverage=1.0, "
@ -624,14 +628,41 @@ def test_trade_close(limit_buy_order_usdt, limit_sell_order_usdt, fee):
open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=10),
interest_rate=0.0005,
exchange='binance',
trading_mode=margin
trading_mode=margin,
leverage=1.0,
)
trade.orders.append(Order(
ft_order_side=trade.entry_side,
order_id=f'{trade.pair}-{trade.entry_side}-{trade.open_date}',
ft_pair=trade.pair,
amount=trade.amount,
filled=trade.amount,
remaining=0,
price=trade.open_rate,
average=trade.open_rate,
status="closed",
order_type="limit",
side=trade.entry_side,
))
trade.orders.append(Order(
ft_order_side=trade.exit_side,
order_id=f'{trade.pair}-{trade.exit_side}-{trade.open_date}',
ft_pair=trade.pair,
amount=trade.amount,
filled=trade.amount,
remaining=0,
price=2.2,
average=2.2,
status="closed",
order_type="limit",
side=trade.exit_side,
))
assert trade.close_profit is None
assert trade.close_date is None
assert trade.is_open is True
trade.close(2.2)
assert trade.is_open is False
assert trade.close_profit == round(0.0945137157107232, 8)
assert pytest.approx(trade.close_profit) == 0.094513715
assert trade.close_date is not None
new_date = arrow.Arrow(2020, 2, 2, 15, 6, 1).datetime,

36
tests/test_plotting.py
View File

@ -72,7 +72,7 @@ def test_add_indicators(default_conf, testdatadir, caplog):
strategy = StrategyResolver.load_strategy(default_conf)
# Generate buy/sell signals and indicators
# Generate entry/exit signals and indicators
data = strategy.analyze_ticker(data, {'pair': pair})
fig = generate_empty_figure()
@ -113,7 +113,7 @@ def test_add_areas(default_conf, testdatadir, caplog):
ind_plain = {"macd": {"fill_to": "macdhist"}}
strategy = StrategyResolver.load_strategy(default_conf)
# Generate buy/sell signals and indicators
# Generate entry/exit signals and indicators
data = strategy.analyze_ticker(data, {'pair': pair})
fig = generate_empty_figure()
@ -165,24 +165,24 @@ def test_plot_trades(testdatadir, caplog):
fig = plot_trades(fig, trades)
figure = fig1.layout.figure
# Check buys - color, should be in first graph, ...
trade_buy = find_trace_in_fig_data(figure.data, 'Trade buy')
assert isinstance(trade_buy, go.Scatter)
assert trade_buy.yaxis == 'y'
assert len(trades) == len(trade_buy.x)
assert trade_buy.marker.color == 'cyan'
assert trade_buy.marker.symbol == 'circle-open'
assert trade_buy.text[0] == '3.99%, buy_tag, roi, 15 min'
# Check entry - color, should be in first graph, ...
trade_entries = find_trace_in_fig_data(figure.data, 'Trade entry')
assert isinstance(trade_entries, go.Scatter)
assert trade_entries.yaxis == 'y'
assert len(trades) == len(trade_entries.x)
assert trade_entries.marker.color == 'cyan'
assert trade_entries.marker.symbol == 'circle-open'
assert trade_entries.text[0] == '3.99%, buy_tag, roi, 15 min'
trade_sell = find_trace_in_fig_data(figure.data, 'Sell - Profit')
assert isinstance(trade_sell, go.Scatter)
assert trade_sell.yaxis == 'y'
assert len(trades.loc[trades['profit_ratio'] > 0]) == len(trade_sell.x)
assert trade_sell.marker.color == 'green'
assert trade_sell.marker.symbol == 'square-open'
assert trade_sell.text[0] == '3.99%, buy_tag, roi, 15 min'
trade_exit = find_trace_in_fig_data(figure.data, 'Exit - Profit')
assert isinstance(trade_exit, go.Scatter)
assert trade_exit.yaxis == 'y'
assert len(trades.loc[trades['profit_ratio'] > 0]) == len(trade_exit.x)
assert trade_exit.marker.color == 'green'
assert trade_exit.marker.symbol == 'square-open'
assert trade_exit.text[0] == '3.99%, buy_tag, roi, 15 min'
trade_sell_loss = find_trace_in_fig_data(figure.data, 'Sell - Loss')
trade_sell_loss = find_trace_in_fig_data(figure.data, 'Exit - Loss')
assert isinstance(trade_sell_loss, go.Scatter)
assert trade_sell_loss.yaxis == 'y'
assert len(trades.loc[trades['profit_ratio'] <= 0]) == len(trade_sell_loss.x)