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fix example in the docs, increasing startup to 400 on ema100

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Stefano Ariestasia
2023-09-21 11:08:47 +09:00
parent e77b9de89e
commit 32b0098ec1
2 changed files with 7 additions and 5 deletions
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docs/recursive-analysis.md
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# Lookahead analysis # Recursive analysis
This page explains how to validate your strategy in terms of recursive formula issue. This page explains how to validate your strategy in terms of recursive formula issue.
docs/strategy-customization.md
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@@ -168,10 +168,12 @@ Most indicators have an instable startup period, in which they are either not av
To account for this, the strategy can be assigned the `startup_candle_count` attribute. To account for this, the strategy can be assigned the `startup_candle_count` attribute.
This should be set to the maximum number of candles that the strategy requires to calculate stable indicators. In the case where a user includes higher timeframes with informative pairs, the `startup_candle_count` does not necessarily change. The value is the maximum period (in candles) that any of the informatives timeframes need to compute stable indicators. This should be set to the maximum number of candles that the strategy requires to calculate stable indicators. In the case where a user includes higher timeframes with informative pairs, the `startup_candle_count` does not necessarily change. The value is the maximum period (in candles) that any of the informatives timeframes need to compute stable indicators.
In this example strategy, this should be set to 100 (`startup_candle_count = 100`), since the longest needed history is 100 candles. You can use [recursive-analysis](recursive-analysis.md) to check and find the correct `startup_candle_count` to be used.
In this example strategy, this should be set to 400 (`startup_candle_count = 400`), since the minimum needed history for ema100 calculation to make sure the value is correct is 400 candles.
``` python ``` python
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=400)
``` ```
By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt. By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.
@@ -193,11 +195,11 @@ Let's try to backtest 1 month (January 2019) of 5m candles using an example stra
freqtrade backtesting --timerange 20190101-20190201 --timeframe 5m freqtrade backtesting --timerange 20190101-20190201 --timeframe 5m
``` ```
Assuming `startup_candle_count` is set to 100, backtesting knows it needs 100 candles to generate valid buy signals. It will load data from `20190101 - (100 * 5m)` - which is ~2018-12-31 15:30:00. Assuming `startup_candle_count` is set to 400, backtesting knows it needs 400 candles to generate valid buy signals. It will load data from `20190101 - (400 * 5m)` - which is ~2018-12-30 11:40:00.
If this data is available, indicators will be calculated with this extended timerange. The instable startup period (up to 2019-01-01 00:00:00) will then be removed before starting backtesting. If this data is available, indicators will be calculated with this extended timerange. The instable startup period (up to 2019-01-01 00:00:00) will then be removed before starting backtesting.
!!! 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 2019-01-02 09:20:00.
### Entry signal rules ### Entry signal rules