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Update documentation with indicator_startup_period

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Matthias 2019-10-27 10:17:02 +01:00
parent 2bc74882e9
commit c4cb098d14
2 changed files with 35 additions and 2 deletions
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2
docs/backtesting.md
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@ -72,6 +72,8 @@ The exported trades can be used for [further analysis](#further-backtest-result-
freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json
``` ```
Please also read about the [strategy startup period](strategy-customization.md#strategy-startup-period).
#### Supplying custom fee value #### Supplying custom fee value
Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt. Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.

35
docs/strategy-customization.md
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@ -117,6 +117,37 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
Look into the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/sample_strategy.py). Look into the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/sample_strategy.py).
Then uncomment indicators you need. Then uncomment indicators you need.
### Strategy startup period
Most indicators have an "instable period", in which they are either not available, or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
To account for this, the strategy has an attribute, `startup_candle_count`.
This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
In this example strategy, this should be set to 100 (`startup_candle_count = 100`), since the longest needed history is 100 candles.
``` python
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
```
By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.
!!! Warning:
`startup_candle_count` should be below `ohlcv_candle_limit` (which is 500 for most exchanges) - since only this amount of candles will be available during trading operations.
#### Example
Let's try to backtest 1 month (January 2019) of 5m candles.
``` bash
freqtrade backtesting --timerange 20190101-20190201 --ticker-interval 5m
```
Since backtesting knows it needs 100 candles to generate valid buy-signals, it'll load data from `20190101 - (100 * 5m)` - which is ~2019-12-31 15:30:00.
If this data is available, Indicators will be calculated with this extended timerange. The startup period (Up to 2019-01-01 00:00:00) will then be removed before starting backtesting.
!!! 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.
### Buy signal rules ### Buy 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 buy strategy.
@ -267,10 +298,10 @@ class Awesomestrategy(IStrategy):
``` ```
!!! Warning !!! Warning
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash. The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
!!! Note !!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary. If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
### Additional data (DataProvider) ### Additional data (DataProvider)