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Merge branch 'develop' into feature_keyval_storage

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Update rpc/telegram to use MAX_MESSAGE_LENGTH.
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eSeR1805
2022-08-11 11:26:45 +03:00
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20
docs/backtesting.md
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@@ -514,6 +514,7 @@ You can then load the trades to perform further analysis as shown in the [data a
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
- Exchange [trading limits](#trading-limits-in-backtesting) are respected
- Buys happen at open-price
- All orders are filled at the requested price (no slippage, no unfilled orders)
- Exit-signal exits happen at open-price of the consecutive candle
@@ -543,7 +544,24 @@ Also, keep in mind that past results don't guarantee future success.
In addition to the above assumptions, strategy authors should carefully read the [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies) section, to avoid using data in backtesting which is not available in real market conditions.
### Improved backtest accuracy
### Trading limits in backtesting
Exchanges have certain trading limits, like minimum base currency, or minimum stake (quote) currency.
These limits are usually listed in the exchange documentation as "trading rules" or similar.
Backtesting (as well as live and dry-run) does honor these limits, and will ensure that a stoploss can be placed below this value - so the value will be slightly higher than what the exchange specifies.
Freqtrade has however no information about historic limits.
This can lead to situations where trading-limits are inflated by using a historic price, resulting in minimum amounts > 50$.
For example:
BTC minimum tradable amount is 0.001.
BTC trades at 22.000\$ today (0.001 BTC is related to this) - but the backtesting period includes prices as high as 50.000\$.
Today's minimum would be `0.001 * 22_000` - or 22\$.
However the limit could also be 50$ - based on `0.001 * 50_000` in some historic setting.
## Improved backtest accuracy
One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or viceversa?).
So assuming you run backtesting with a 1h timeframe, there will be 4 prices for that candle (Open, High, Low, Close).

5
docs/bot-basics.md
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@@ -20,7 +20,9 @@ All profit calculations of Freqtrade include fees. For Backtesting / Hyperopt /
## Bot execution logic
Starting freqtrade in dry-run or live mode (using `freqtrade trade`) will start the bot and start the bot iteration loop.
By default, loop runs every few seconds (`internals.process_throttle_secs`) and does roughly the following in the following sequence:
This will also run the `bot_start()` callback.
By default, the bot loop runs every few seconds (`internals.process_throttle_secs`) and performs the following actions:
* Fetch open trades from persistence.
* Calculate current list of tradable pairs.
@@ -54,6 +56,7 @@ This loop will be repeated again and again until the bot is stopped.
[backtesting](backtesting.md) or [hyperopt](hyperopt.md) do only part of the above logic, since most of the trading operations are fully simulated.
* Load historic data for configured pairlist.
* Calls `bot_start()` once.
* Calls `bot_loop_start()` once.
* Calculate indicators (calls `populate_indicators()` once per pair).
* Calculate entry / exit signals (calls `populate_entry_trend()` and `populate_exit_trend()` once per pair).

40
docs/configuration.md
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@@ -105,7 +105,7 @@ This is similar to using multiple `--config` parameters, but simpler in usage as
``` json title="Result"
{
"max_open_trades": 10,
"max_open_trades": 3,
"stake_currency": "USDT",
"stake_amount": "unlimited"
}
@@ -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

32
docs/developer.md
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@@ -68,6 +68,36 @@ def test_method_to_test(caplog):
```
### Debug configuration
To debug freqtrade, we recommend VSCode with the following launch configuration (located in `.vscode/launch.json`).
Details will obviously vary between setups - but this should work to get you started.
``` json
{
"name": "freqtrade trade",
"type": "python",
"request": "launch",
"module": "freqtrade",
"console": "integratedTerminal",
"args": [
"trade",
// Optional:
// "--userdir", "user_data",
"--strategy",
"MyAwesomeStrategy",
]
},
```
Command line arguments can be added in the `"args"` array.
This method can also be used to debug a strategy, by setting the breakpoints within the strategy.
A similar setup can also be taken for Pycharm - using `freqtrade` as module name, and setting the command line arguments as "parameters".
!!! Note "Startup directory"
This assumes that you have the repository checked out, and the editor is started at the repository root level (so setup.py is at the top level of your repository).
## ErrorHandling
Freqtrade Exceptions all inherit from `FreqtradeException`.
@@ -334,7 +364,7 @@ lev_tiers = exchange.fetch_leverage_tiers()
# Assumes this is running in the root of the repository.
file = Path('freqtrade/exchange/binance_leverage_tiers.json')
json.dump(lev_tiers, file.open('w'), indent=2)
json.dump(dict(sorted(lev_tiers.items())), file.open('w'), indent=2)
```

50
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
@@ -271,7 +278,8 @@ The last one we call `trigger` and use it to decide which buy trigger we want to
!!! Note "Parameter space assignment"
Parameters must either be assigned to a variable named `buy_*` or `sell_*` - or contain `space='buy'` | `space='sell'` to be assigned to a space correctly.
If no parameter is available for a space, you'll receive the error that no space was found when running hyperopt.
If no parameter is available for a space, you'll receive the error that no space was found when running hyperopt.
Parameters with unclear space (e.g. `adx_period = IntParameter(4, 24, default=14)` - no explicit nor implicit space) will not be detected and will therefore be ignored.
So let's write the buy strategy using these values:
@@ -334,6 +342,7 @@ There are four parameter types each suited for different purposes.
## Optimizing an indicator parameter
Assuming you have a simple strategy in mind - a EMA cross strategy (2 Moving averages crossing) - and you'd like to find the ideal parameters for this strategy.
By default, we assume a stoploss of 5% - and a take-profit (`minimal_roi`) of 10% - which means freqtrade will sell the trade once 10% profit has been reached.
``` python
from pandas import DataFrame
@@ -348,6 +357,9 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
class MyAwesomeStrategy(IStrategy):
stoploss = -0.05
timeframe = '15m'
minimal_roi = {
"0": 0.10
},
# Define the parameter spaces
buy_ema_short = IntParameter(3, 50, default=5)
buy_ema_long = IntParameter(15, 200, default=50)
@@ -382,7 +394,7 @@ class MyAwesomeStrategy(IStrategy):
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions = []
conditions.append(qtpylib.crossed_above(
dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}']
))
@@ -403,7 +415,7 @@ Using `self.buy_ema_short.range` will return a range object containing all entri
In this case (`IntParameter(3, 50, default=5)`), the loop would run for all numbers between 3 and 50 (`[3, 4, 5, ... 49, 50]`).
By using this in a loop, hyperopt will generate 48 new columns (`['buy_ema_3', 'buy_ema_4', ... , 'buy_ema_50']`).
Hyperopt itself will then use the selected value to create the buy and sell signals
Hyperopt itself will then use the selected value to create the buy and sell signals.
While this strategy is most likely too simple to provide consistent profit, it should serve as an example how optimize indicator parameters.
@@ -862,10 +874,28 @@ 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.
If you see `The objective has been evaluated at this point before.` - then this is a sign that your space has been exhausted, or is close to that.
Basically all points in your space have been hit (or a local minima has been hit) - and hyperopt does no longer find points in the multi-dimensional space it did not try yet.
Freqtrade tries to counter the "local minima" problem by using new, randomized points in this case.
Example:
``` python
buy_ema_short = IntParameter(5, 20, default=10, space="buy", optimize=True)
# This is the only parameter in the buy space
```
The `buy_ema_short` space has 15 possible values (`5, 6, ... 19, 20`). If you now run hyperopt for the buy space, hyperopt will only have 15 values to try before running out of options.
Your epochs should therefore be aligned to the possible values - or you should be ready to interrupt a run if you norice a lot of `The objective has been evaluated at this point before.` warnings.
## Show details of Hyperopt results

3
docs/includes/protections.md
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@@ -50,6 +50,8 @@ This applies across all pairs, unless `only_per_pair` is set to true, which will
Similarly, this protection will by default look at all trades (long and short). For futures bots, setting `only_per_side` will make the bot only consider one side, and will then only lock this one side, allowing for example shorts to continue after a series of long stoplosses.
`required_profit` will determine the required relative profit (or loss) for stoplosses to consider. This should normally not be set and defaults to 0.0 - which means all losing stoplosses will be triggering a block.
The below example stops trading for all pairs for 4 candles after the last trade if the bot hit stoploss 4 times within the last 24 candles.
``` python
@@ -61,6 +63,7 @@ def protections(self):
"lookback_period_candles": 24,
"trade_limit": 4,
"stop_duration_candles": 4,
"required_profit": 0.0,
"only_per_pair": False,
"only_per_side": False
}

7
docs/requirements-docs.txt
View File

@@ -1,5 +1,6 @@
mkdocs==1.3.0
mkdocs-material==8.3.4
mdx_truly_sane_lists==1.2
markdown==3.3.7
mkdocs==1.3.1
mkdocs-material==8.3.9
mdx_truly_sane_lists==1.3
pymdown-extensions==9.5
jinja2==3.1.2

29
docs/stoploss.md
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@@ -130,7 +130,7 @@ In summary: The stoploss will be adjusted to be always be -10% of the highest ob
### Trailing stop loss, custom positive loss
It is also possible to have a default stop loss, when you are in the red with your buy (buy - fee), but once you hit positive result the system will utilize a new stop loss, which can have a different value.
You could also have a default stop loss when you are in the red with your buy (buy - fee), but once you hit a positive result (or an offset you define) the system will utilize a new stop loss, which can have a different value.
For example, your default stop loss is -10%, but once you have more than 0% profit (example 0.1%) a different trailing stoploss will be used.
!!! Note
@@ -142,6 +142,8 @@ Both values require `trailing_stop` to be set to true and `trailing_stop_positiv
stoploss = -0.10
trailing_stop = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.0
trailing_only_offset_is_reached = False # Default - not necessary for this example
```
For example, simplified math:
@@ -156,11 +158,31 @@ For example, simplified math:
The 0.02 would translate to a -2% stop loss.
Before this, `stoploss` is used for the trailing stoploss.
!!! Tip "Use an offset to change your stoploss"
Use `trailing_stop_positive_offset` to ensure that your new trailing stoploss will be in profit by setting `trailing_stop_positive_offset` higher than `trailing_stop_positive`. Your first new stoploss value will then already have locked in profits.
Example with simplified math:
``` python
stoploss = -0.10
trailing_stop = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.03
```
* the bot buys an asset at a price of 100$
* the stop loss is defined at -10%, so the stop loss would get triggered once the asset drops below 90$
* 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 103.5$ = 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
It is also possible to use a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
You can also keep a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
If `"trailing_only_offset_is_reached": true` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`.
If `trailing_only_offset_is_reached = True` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`.
This option can be used with or without `trailing_stop_positive`, but uses `trailing_stop_positive_offset` as offset.
``` python
@@ -203,7 +225,6 @@ If price moves 1% - you've lost 10$ of your own capital - therfore stoploss will
Make sure to be aware of this, and avoid using too tight stoploss (at 10x leverage, 10% risk may be too little to allow the trade to "breath" a little).
## Changing stoploss on open trades
A stoploss on an open trade can be changed by changing the value in the configuration or strategy and use the `/reload_config` command (alternatively, completely stopping and restarting the bot also works).

2
docs/strategy-advanced.md
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@@ -292,3 +292,5 @@ for val in self.buy_ema_short.range:
# Append columns to existing dataframe
merged_frame = pd.concat(frames, axis=1)
```
Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existant.

74
docs/strategy-callbacks.md
View File

@@ -82,8 +82,9 @@ Called before entering a trade, makes it possible to manage your position size w
```python
class AwesomeStrategy(IStrategy):
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
entry_tag: Optional[str], side: str, **kwargs) -> float:
proposed_stake: float, min_stake: Optional[float], max_stake: float,
leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
@@ -622,12 +623,13 @@ class AwesomeStrategy(IStrategy):
!!! Warning
`confirm_trade_exit()` can prevent stoploss exits, causing significant losses as this would ignore stoploss exits.
`confirm_trade_exit()` will not be called for Liquidations - as liquidations are forced by the exchange, and therefore cannot be rejected.
## Adjust trade position
The `position_adjustment_enable` strategy property enables the usage of `adjust_trade_position()` callback in the strategy.
For performance reasons, it's disabled by default and freqtrade will show a warning message on startup if enabled.
`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging).
`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging) or to increase or decrease positions.
`max_entry_position_adjustment` property is used to limit the number of additional buys per trade (on top of the first buy) that the bot can execute. By default, the value is -1 which means the bot have no limit on number of adjustment buys.
@@ -635,10 +637,13 @@ The strategy is expected to return a stake_amount (in stake currency) between `m
If there are not enough funds in the wallet (the return value is above `max_stake`) then the signal will be ignored.
Additional orders also result in additional fees and those orders don't count towards `max_open_trades`.
This callback is **not** called when there is an open order (either buy or sell) waiting for execution, or when you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`.
This callback is **not** called when there is an open order (either buy or sell) waiting for execution.
`adjust_trade_position()` is called very frequently for the duration of a trade, so you must keep your implementation as performant as possible.
Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position, no matter if it's a long or short trade. Modifications to leverage are not possible.
Additional Buys are ignored once you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`, but the callback is called anyway looking for partial exits.
Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position (negative values will decrease your position), no matter if it's a long or short trade. Modifications to leverage are not possible.
!!! Note "About stake size"
Using fixed stake size means it will be the amount used for the first order, just like without position adjustment.
@@ -647,12 +652,12 @@ Position adjustments will always be applied in the direction of the trade, so a
!!! Warning
Stoploss is still calculated from the initial opening price, not averaged price.
Regular stoploss rules still apply (cannot move down).
!!! Warning "/stopbuy"
While `/stopbuy` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades.
!!! Warning "Backtesting"
During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so performance will be affected.
During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
``` python
from freqtrade.persistence import Trade
@@ -675,29 +680,49 @@ class DigDeeperStrategy(IStrategy):
# This is called when placing the initial order (opening trade)
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: Optional[float], max_stake: float,
entry_tag: Optional[str], side: str, **kwargs) -> float:
leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
# We need to leave most of the funds for possible further DCA orders
# This also applies to fixed stakes
return proposed_stake / self.max_dca_multiplier
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, min_stake: Optional[float],
max_stake: float, **kwargs):
current_rate: float, current_profit: float,
min_stake: Optional[float], max_stake: float,
current_entry_rate: float, current_exit_rate: float,
current_entry_profit: float, current_exit_profit: float,
**kwargs) -> Optional[float]:
"""
Custom trade adjustment logic, returning the stake amount that a trade should be increased.
This means extra buy orders with additional fees.
Custom trade adjustment logic, returning the stake amount that a trade should be
increased or decreased.
This means extra buy or sell orders with additional fees.
Only called when `position_adjustment_enable` is set to True.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns None
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param current_rate: Current buy rate.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param min_stake: Minimal stake size allowed by exchange.
:param max_stake: Balance available for trading.
:param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
:param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
:param current_entry_rate: Current rate using entry pricing.
:param current_exit_rate: Current rate using exit pricing.
:param current_entry_profit: Current profit using entry pricing.
:param current_exit_profit: Current profit using exit pricing.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: Stake amount to adjust your trade
:return float: Stake amount to adjust your trade,
Positive values to increase position, Negative values to decrease position.
Return None for no action.
"""
if current_profit > 0.05 and trade.nr_of_successful_exits == 0:
# Take half of the profit at +5%
return -(trade.stake_amount / 2)
if current_profit > -0.05:
return None
@@ -732,6 +757,25 @@ class DigDeeperStrategy(IStrategy):
```
### Position adjust calculations
* Entry rates are calculated using weighted averages.
* Exits will not influence the average entry rate.
* Partial exit relative profit is relative to the average entry price at this point.
* Final exit relative profit is calculated based on the total invested capital. (See example below)
??? example "Calculation example"
*This example assumes 0 fees for simplicity, and a long position on an imaginary coin.*
* Buy 100@8\$
* Buy 100@9\$ -> Avg price: 8.5\$
* Sell 100@10\$ -> Avg price: 8.5\$, realized profit 150\$, 17.65%
* Buy 150@11\$ -> Avg price: 10\$, realized profit 150\$, 17.65%
* Sell 100@12\$ -> Avg price: 10\$, total realized profit 350\$, 20%
* Sell 150@14\$ -> Avg price: 10\$, total realized profit 950\$, 40%
The total profit for this trade was 950$ on a 3350$ investment (`100@8$ + 100@9$ + 150@11$`). As such - the final relative profit is 28.35% (`950 / 3350`).
## Adjust Entry Price
The `adjust_entry_price()` callback may be used by strategy developer to refresh/replace limit orders upon arrival of new candles.

20
docs/strategy-customization.md
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@@ -646,6 +646,9 @@ This is where calling `self.dp.current_whitelist()` comes in handy.
return informative_pairs
```
??? Note "Plotting with current_whitelist"
Current whitelist is not supported for `plot-dataframe`, as this command is usually used by providing an explicit pairlist - and would therefore make the return values of this method misleading.
### *get_pair_dataframe(pair, timeframe)*
``` python
@@ -731,6 +734,23 @@ if self.dp:
!!! Warning "Warning about backtesting"
This method will always return up-to-date values - so usage during backtesting / hyperopt will lead to wrong results.
### Send Notification
The dataprovider `.send_msg()` function allows you to send custom notifications from your strategy.
Identical notifications will only be sent once per candle, unless the 2nd argument (`always_send`) is set to True.
``` python
self.dp.send_msg(f"{metadata['pair']} just got hot!")
# Force send this notification, avoid caching (Please read warning below!)
self.dp.send_msg(f"{metadata['pair']} just got hot!", always_send=True)
```
Notifications will only be sent in trading modes (Live/Dry-run) - so this method can be called without conditions for backtesting.
!!! Warning "Spamming"
You can spam yourself pretty good by setting `always_send=True` in this method. Use this with great care and only in conditions you know will not happen throughout a candle to avoid a message every 5 seconds.
### Complete Data-provider sample
```python

4
docs/strategy_analysis_example.md
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@@ -31,11 +31,13 @@ pair = "BTC/USDT"
```python
# Load data using values set above
from freqtrade.data.history import load_pair_history
from freqtrade.enums import CandleType
candles = load_pair_history(datadir=data_location,
timeframe=config["timeframe"],
pair=pair,
data_format = "hdf5",
candle_type=CandleType.SPOT,
)
# Confirm success
@@ -93,7 +95,7 @@ from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats
# if backtest_dir points to a directory, it'll automatically load the last backtest file.
backtest_dir = config["user_data_dir"] / "backtest_results"
# backtest_dir can also point to a specific file
# backtest_dir can also point to a specific file
# backtest_dir = config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"
```

4
docs/strategy_migration.md
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@@ -18,7 +18,7 @@ Note : `forcesell`, `forcebuy`, `emergencysell` are changed to `force_exit`, `fo
* [`check_buy_timeout()` -> `check_entry_timeout()`](#custom_entry_timeout)
* [`check_sell_timeout()` -> `check_exit_timeout()`](#custom_entry_timeout)
* New `side` argument to callbacks without trade object
* [`custom_stake_amount`](#custom-stake-amount)
* [`custom_stake_amount`](#custom_stake_amount)
* [`confirm_trade_entry`](#confirm_trade_entry)
* [`custom_entry_price`](#custom_entry_price)
* [Changed argument name in `confirm_trade_exit`](#confirm_trade_exit)
@@ -192,7 +192,7 @@ class AwesomeStrategy(IStrategy):
return False
```
### Custom-stake-amount
### `custom_stake_amount`
New string argument `side` - which can be either `"long"` or `"short"`.

7
docs/telegram-usage.md
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@@ -97,7 +97,9 @@ Example configuration showing the different settings:
"entry_fill": "off",
"exit_fill": "off",
"protection_trigger": "off",
"protection_trigger_global": "on"
"protection_trigger_global": "on",
"strategy_msg": "off",
"show_candle": "off"
},
"reload": true,
"balance_dust_level": 0.01
@@ -108,7 +110,8 @@ Example configuration showing the different settings:
`exit` notifications are sent when the order is placed, while `exit_fill` notifications are sent when the order is filled on the exchange.
`*_fill` notifications are off by default and must be explicitly enabled.
`protection_trigger` notifications are sent when a protection triggers and `protection_trigger_global` notifications trigger when global protections are triggered.
`strategy_msg` - Receive notifications from the strategy, sent via `self.dp.send_msg()` from the strategy [more details](strategy-customization.md#send-notification).
`show_candle` - show candle values as part of entry/exit messages. Only possible values are `"ohlc"` or `"off"`.
`balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown.
`reload` allows you to disable reload-buttons on selected messages.