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Update some documentation for short trading

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Matthias 2022-03-05 14:53:00 +01:00
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5 changed files with 89 additions and 30 deletions
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2
docs/configuration.md
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@ -86,7 +86,7 @@ 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 | `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) | `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. | `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 (former ticker interval) 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` ...). [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 | `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` | **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 | `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

12
docs/hyperopt.md
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@ -153,8 +153,8 @@ Checklist on all tasks / possibilities in hyperopt
Depending on the space you want to optimize, only some of the below are required: Depending on the space you want to optimize, only some of the below are required:
* define parameters with `space='buy'` - for buy signal optimization * define parameters with `space='buy'` - for entry signal optimization
* define parameters with `space='sell'` - for sell signal optimization * define parameters with `space='sell'` - for exit signal optimization
!!! Note !!! Note
`populate_indicators` needs to create all indicators any of the spaces may use, otherwise hyperopt will not work. `populate_indicators` needs to create all indicators any of the spaces may use, otherwise hyperopt will not work.
@ -204,7 +204,7 @@ There you have two different types of indicators: 1. `guards` and 2. `triggers`.
Hyper-optimization will, for each epoch round, pick one trigger and possibly multiple guards. Hyper-optimization will, for each epoch round, pick one trigger and possibly multiple guards.
#### Sell optimization #### Exit signal optimization
Similar to the entry-signal above, exit-signals can also be optimized. Similar to the entry-signal above, exit-signals can also be optimized.
Place the corresponding settings into the following methods Place the corresponding settings into the following methods
@ -296,7 +296,7 @@ So let's write the buy strategy using these values:
if conditions: if conditions:
dataframe.loc[ dataframe.loc[
reduce(lambda x, y: x & y, conditions), reduce(lambda x, y: x & y, conditions),
'buy'] = 1 'enter_long'] = 1
return dataframe return dataframe
``` ```
@ -376,7 +376,7 @@ class MyAwesomeStrategy(IStrategy):
if conditions: if conditions:
dataframe.loc[ dataframe.loc[
reduce(lambda x, y: x & y, conditions), reduce(lambda x, y: x & y, conditions),
'buy'] = 1 'enter_long'] = 1
return dataframe return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@ -391,7 +391,7 @@ class MyAwesomeStrategy(IStrategy):
if conditions: if conditions:
dataframe.loc[ dataframe.loc[
reduce(lambda x, y: x & y, conditions), reduce(lambda x, y: x & y, conditions),
'sell'] = 1 'exit_long'] = 1
return dataframe return dataframe
``` ```

4
docs/strategy-advanced.md
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@ -89,7 +89,7 @@ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
(dataframe['rsi'] < 35) & (dataframe['rsi'] < 35) &
(dataframe['volume'] > 0) (dataframe['volume'] > 0)
), ),
['buy', 'enter_tag']] = (1, 'buy_signal_rsi') ['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')
return dataframe return dataframe
@ -117,7 +117,7 @@ def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
(dataframe['rsi'] > 70) & (dataframe['rsi'] > 70) &
(dataframe['volume'] > 0) (dataframe['volume'] > 0)
), ),
['sell', 'exit_tag']] = (1, 'exit_rsi') ['exit_long', 'exit_tag']] = (1, 'exit_rsi')
return dataframe return dataframe
``` ```

4
docs/strategy-callbacks.md
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@ -664,7 +664,7 @@ class DigDeeperStrategy(IStrategy):
if last_candle['close'] < previous_candle['close']: if last_candle['close'] < previous_candle['close']:
return None return None
filled_buys = trade.select_filled_orders('buy') filled_entries = trade.select_filled_orders(trade.enter_side)
count_of_entries = trade.nr_of_successful_entries count_of_entries = trade.nr_of_successful_entries
# Allow up to 3 additional increasingly larger buys (4 in total) # Allow up to 3 additional increasingly larger buys (4 in total)
# Initial buy is 1x # Initial buy is 1x
@ -676,7 +676,7 @@ class DigDeeperStrategy(IStrategy):
# Hope you have a deep wallet! # Hope you have a deep wallet!
try: try:
# This returns first order stake size # This returns first order stake size
stake_amount = filled_buys[0].cost stake_amount = filled_entries[0].cost
# This then calculates current safety order size # This then calculates current safety order size
stake_amount = stake_amount * (1 + (count_of_entries * 0.25)) stake_amount = stake_amount * (1 + (count_of_entries * 0.25))
return stake_amount return stake_amount

95
docs/strategy-customization.md
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@ -26,8 +26,8 @@ This will create a new strategy file from a template, which will be located unde
A strategy file contains all the information needed to build a good strategy: A strategy file contains all the information needed to build a good strategy:
- Indicators - Indicators
- Buy strategy rules - Entry strategy rules
- Sell strategy rules - Exit strategy rules
- Minimal ROI recommended - Minimal ROI recommended
- Stoploss strongly recommended - Stoploss strongly recommended
@ -82,7 +82,7 @@ As a dataframe is a table, simple python comparisons like the following will not
``` python ``` python
if dataframe['rsi'] > 30: if dataframe['rsi'] > 30:
dataframe['buy'] = 1 dataframe['enter_long'] = 1
``` ```
The above section will fail with `The truth value of a Series is ambiguous. [...]`. The above section will fail with `The truth value of a Series is ambiguous. [...]`.
@ -92,7 +92,7 @@ This must instead be written in a pandas-compatible way, so the operation is per
``` python ``` python
dataframe.loc[ dataframe.loc[
(dataframe['rsi'] > 30) (dataframe['rsi'] > 30)
, 'buy'] = 1 , 'enter_long'] = 1
``` ```
With this section, you have a new column in your dataframe, which has `1` assigned whenever RSI is above 30. With this section, you have a new column in your dataframe, which has `1` assigned whenever RSI is above 30.
@ -199,13 +199,13 @@ If this data is available, indicators will be calculated with this extended time
!!! Note !!! Note
If data for the startup period is not available, then the timerange will be adjusted to account for this startup period - so Backtesting would start at 2019-01-01 08:30:00. If data for the startup period is not available, then the timerange will be adjusted to account for this startup period - so Backtesting would start at 2019-01-01 08:30:00.
### Buy signal rules ### Entry signal rules
Edit the method `populate_buy_trend()` in your strategy file to update your buy strategy. Edit the method `populate_buy_trend()` in your strategy file to update your entry strategy.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected. It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
This method will also define a new column, `"buy"`, which needs to contain 1 for buys, and 0 for "no action". This method will also define a new column, `"enter_long"`, which needs to contain 1 for entries, and 0 for "no action".
Sample from `user_data/strategies/sample_strategy.py`: Sample from `user_data/strategies/sample_strategy.py`:
@ -224,22 +224,50 @@ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard (dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard
(dataframe['volume'] > 0) # Make sure Volume is not 0 (dataframe['volume'] > 0) # Make sure Volume is not 0
), ),
'buy'] = 1 ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
return dataframe return dataframe
``` ```
??? Note "Enter short trades"
Short-entries can be created by setting `enter_short` (corresponds to `enter_long` for long trades).
The `enter_tag` column remains identical.
Short-trades need to be supported by your exchange and market configuration!
```python
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['enter_long', 'enter_tag']] = (1, 'rsi_cross')
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['rsi'], 70)) & # Signal: RSI crosses below 70
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['enter_short', 'enter_tag']] = (1, 'rsi_cross')
return dataframe
```
!!! Note !!! Note
Buying requires sellers to buy from - therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods. Buying requires sellers to buy from - therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods.
### Sell signal rules ### Exit signal rules
Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy. Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
Please note that the sell-signal is only used if `use_sell_signal` is set to true in the configuration. Please note that the sell-signal is only used if `use_sell_signal` is set to true in the configuration.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected. It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
This method will also define a new column, `"sell"`, which needs to contain 1 for sells, and 0 for "no action". This method will also define a new column, `"exit_long"`, which needs to contain 1 for sells, and 0 for "no action".
Sample from `user_data/strategies/sample_strategy.py`: Sample from `user_data/strategies/sample_strategy.py`:
@ -258,10 +286,36 @@ def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard (dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard
(dataframe['volume'] > 0) # Make sure Volume is not 0 (dataframe['volume'] > 0) # Make sure Volume is not 0
), ),
'sell'] = 1 ['exit_long', 'exit_tag']] = (1, 'rsi_too_high')
return dataframe return dataframe
``` ```
??? Note "Exit short trades"
Short-exits can be created by setting `exit_short` (corresponds to `exit_long`).
The `exit_tag` column remains identical.
Short-trades need to be supported by your exchange and market configuration!
```python
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['exit_long', 'exit_tag']] = (1, 'rsi_too_high')
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['rsi'], 30)) & # Signal: RSI crosses below 30
(dataframe['tema'] < dataframe['bb_middleband']) & # Guard
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['exit_short', 'exit_tag']] = (1, 'rsi_too_low')
return dataframe
```
### Minimal ROI ### Minimal ROI
This dict defines the minimal Return On Investment (ROI) a trade should reach before selling, independent from the sell signal. This dict defines the minimal Return On Investment (ROI) a trade should reach before selling, independent from the sell signal.
@ -325,7 +379,7 @@ stoploss = -0.10
For the full documentation on stoploss features, look at the dedicated [stoploss page](stoploss.md). For the full documentation on stoploss features, look at the dedicated [stoploss page](stoploss.md).
### Timeframe (formerly ticker interval) ### Timeframe
This is the set of candles the bot should download and use for the analysis. This is the set of candles the bot should download and use for the analysis.
Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work. Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
@ -454,7 +508,7 @@ for more information.
# Define BTC/STAKE informative pair. Available in populate_indicators and other methods as # Define BTC/STAKE informative pair. Available in populate_indicators and other methods as
# 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable # 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable
# instead of hardcoding actual stake currency. Available in populate_indicators and other # instead of hard-coding actual stake currency. Available in populate_indicators and other
# methods as 'btc_usdt_rsi_1h' (when stake currency is USDT). # methods as 'btc_usdt_rsi_1h' (when stake currency is USDT).
@informative('1h', 'BTC/{stake}') @informative('1h', 'BTC/{stake}')
def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@ -501,7 +555,7 @@ for more information.
& &
(dataframe['volume'] > 0) (dataframe['volume'] > 0)
), ),
['buy', 'enter_tag']] = (1, 'buy_signal_rsi') ['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')
return dataframe return dataframe
``` ```
@ -716,7 +770,7 @@ class SampleStrategy(IStrategy):
(dataframe['rsi_1d'] < 30) & # Ensure daily RSI is < 30 (dataframe['rsi_1d'] < 30) & # Ensure daily RSI is < 30
(dataframe['volume'] > 0) # Ensure this candle had volume (important for backtesting) (dataframe['volume'] > 0) # Ensure this candle had volume (important for backtesting)
), ),
'buy'] = 1 ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
``` ```
@ -922,7 +976,7 @@ if self.config['runmode'].value in ('live', 'dry_run'):
Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015). Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015).
``` json ``` json
{'pair': "ETH/BTC", 'profit': 0.015, 'count': 5} {"pair": "ETH/BTC", "profit": 0.015, "count": 5}
``` ```
!!! Warning !!! Warning
@ -985,7 +1039,7 @@ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
( (
#>> whatever condition<<< #>> whatever condition<<<
), ),
'buy'] = 1 ['enter_long', 'enter_tag']] = (1, 'somestring')
# Print the Analyzed pair # Print the Analyzed pair
print(f"result for {metadata['pair']}") print(f"result for {metadata['pair']}")
@ -1014,7 +1068,12 @@ The following lists some common patterns which should be avoided to prevent frus
### Colliding signals ### Colliding signals
When buy and sell signals collide (both `'buy'` and `'sell'` are 1), freqtrade will do nothing and ignore the entry (buy) signal. This will avoid trades that buy, and sell immediately. Obviously, this can potentially lead to missed entries. When conflicting signals collide (e.g. both `'enter_long'` and `'exit_long'` are 1), freqtrade will do nothing and ignore the entry signal. This will avoid trades that buy, and sell immediately. Obviously, this can potentially lead to missed entries.
The following rules apply, and entry signals will be ignored if more than one of the 3 signals is set:
- `enter_long` -> `exit_long`, `exit_short`
- `enter_short` -> `exit_short`, `enter_long`
## Further strategy ideas ## Further strategy ideas