Improve backtesting documentation
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Matthias
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@ -95,8 +95,7 @@ Strategy arguments:
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## Test your strategy with Backtesting
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Now you have good Buy and Sell strategies and some historic data, you want to test it against
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real data. This is what we call
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[backtesting](https://en.wikipedia.org/wiki/Backtesting).
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real data. This is what we call [backtesting](https://en.wikipedia.org/wiki/Backtesting).
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Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
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If no data is available for the exchange / pair / timeframe combination, backtesting will ask you to download them first using `freqtrade download-data`.
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@ -104,6 +103,8 @@ For details on downloading, please refer to the [Data Downloading](data-download
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The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
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All profit calculations include fees, and freqtrade will use the exchange's default fees for the calculation.
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!!! Warning "Using dynamic pairlists for backtesting"
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Using dynamic pairlists is possible, however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
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Also, when using pairlists other than StaticPairlist, reproducability of backtesting-results cannot be guaranteed.
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@ -111,38 +112,46 @@ The result of backtesting will confirm if your bot has better odds of making a p
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To achieve reproducible results, best generate a pairlist via the [`test-pairlist`](utils.md#test-pairlist) command and use that as static pairlist.
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### Run a backtesting against the currencies listed in your config file
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### Example backtesting commands
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#### With 5 min candle (OHLCV) data (per default)
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With 5 min candle (OHLCV) data (per default)
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```bash
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freqtrade backtesting
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freqtrade backtesting --strategy AwesomeStrategy
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```
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#### With 1 min candle (OHLCV) data
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Where `--strategy AwesomeStrategy` / `-s AwesomeStrategy` refers to the class name of the strategy, which is within a python file in the `user_data/strategies` directory.
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---
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With 1 min candle (OHLCV) data
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```bash
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freqtrade backtesting --timeframe 1m
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freqtrade backtesting --strategy AwesomeStrategy --timeframe 1m
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```
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#### Using a different on-disk historical candle (OHLCV) data source
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---
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Providing a custom starting balance of 1000 (in stake currency)
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```bash
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freqtrade backtesting --strategy AwesomeStrategy --dry-run-wallet 1000
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```
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---
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Using a different on-disk historical candle (OHLCV) data source
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Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory.
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You can then use this data for backtesting as follows:
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```bash
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freqtrade --datadir user_data/data/bittrex-20180101 backtesting
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freqtrade backtesting --strategy AwesomeStrategy --datadir user_data/data/bittrex-20180101
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```
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#### With a (custom) strategy file
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---
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```bash
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freqtrade backtesting -s SampleStrategy
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```
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Where `-s SampleStrategy` refers to the class name within the strategy file `sample_strategy.py` found in the `freqtrade/user_data/strategies` directory.
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#### Comparing multiple Strategies
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Comparing multiple Strategies
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```bash
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freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timeframe 5m
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@ -150,23 +159,29 @@ freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timefram
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Where `SampleStrategy1` and `AwesomeStrategy` refer to class names of strategies.
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#### Exporting trades to file
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---
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Exporting trades to file
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```bash
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freqtrade backtesting --export trades --config config.json --strategy SampleStrategy
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freqtrade backtesting --strategy backtesting --export trades --config config.json
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```
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The exported trades can be used for [further analysis](#further-backtest-result-analysis), or can be used by the plotting script `plot_dataframe.py` in the scripts directory.
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#### Exporting trades to file specifying a custom filename
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---
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Exporting trades to file specifying a custom filename
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```bash
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freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json
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freqtrade backtesting --strategy backtesting --export trades --export-filename=backtest_samplestrategy.json
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```
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Please also read about the [strategy startup period](strategy-customization.md#strategy-startup-period).
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#### Supplying custom fee value
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---
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Supplying custom fee value
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Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.
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To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
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@ -181,26 +196,26 @@ freqtrade backtesting --fee 0.001
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!!! Note
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Only supply this option (or the corresponding configuration parameter) if you want to experiment with different fee values. By default, Backtesting fetches the default fee from the exchange pair/market info.
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#### Running backtest with smaller testset by using timerange
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---
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Use the `--timerange` argument to change how much of the testset you want to use.
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Running backtest with smaller test-set by using timerange
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Use the `--timerange` argument to change how much of the test-set you want to use.
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For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your inputdata.
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For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your input data.
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```bash
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freqtrade backtesting --timerange=20190501-
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```
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You can also specify particular dates or a range span indexed by start and stop.
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You can also specify particular date ranges.
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The full timerange specification:
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- Use tickframes till 2018/01/31: `--timerange=-20180131`
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- Use tickframes since 2018/01/31: `--timerange=20180131-`
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- Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
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- Use tickframes between POSIX timestamps 1527595200 1527618600:
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`--timerange=1527595200-1527618600`
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- Use data until 2018/01/31: `--timerange=-20180131`
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- Use data since 2018/01/31: `--timerange=20180131-`
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- Use data since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
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- Use data between POSIX / epoch timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`
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## Understand the backtesting result
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@ -296,9 +311,9 @@ here:
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The bot has made `429` trades for an average duration of `4:12:00`, with a performance of `76.20%` (profit), that means it has
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earned a total of `0.00762792 BTC` starting with a capital of 0.01 BTC.
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The column `avg profit %` shows the average profit for all trades made while the column `cum profit %` sums up all the profits/losses.
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The column `tot profit %` shows instead the total profit % in relation to allocated capital (`max_open_trades * stake_amount`).
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In the above results we have `max_open_trades=2` and `stake_amount=0.005` in config so `tot_profit %` will be `(76.20/100) * (0.005 * 2) =~ 0.00762792 BTC`.
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The column `Avg Profit %` shows the average profit for all trades made while the column `Cum Profit %` sums up all the profits/losses.
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The column `Tot Profit %` shows instead the total profit % in relation to allocated capital (`max_open_trades * stake_amount`).
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In the above results we have `max_open_trades=2` and `stake_amount=0.005` in config so `Tot Profit %` will be `(76.20/100) * (0.005 * 2) =~ 0.00762792 BTC`.
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Your strategy performance is influenced by your buy strategy, your sell strategy, and also by the `minimal_roi` and `stop_loss` you have set.
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@ -452,6 +467,5 @@ Detailed output for all strategies one after the other will be available, so mak
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## Next step
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Great, your strategy is profitable. What if the bot can give your the
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optimal parameters to use for your strategy?
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Great, your strategy is profitable. What if the bot can give your the optimal parameters to use for your strategy?
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Your next step is to learn [how to find optimal parameters with Hyperopt](hyperopt.md)
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