257 lines
15 KiB
Markdown
257 lines
15 KiB
Markdown
# Backtesting
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This page explains how to validate your strategy performance by using Backtesting.
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Backtesting requires historic data to be available.
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To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
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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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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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For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
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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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!!! 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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Please read the [pairlists documentation](configuration.md#pairlists) for more information.
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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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#### With 5 min candle (OHLCV) data (per default)
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```bash
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freqtrade backtesting
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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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```
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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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```
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#### With a (custom) strategy file
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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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```bash
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freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timeframe 5m
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```
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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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```bash
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freqtrade backtesting --export trades --config config.json --strategy SampleStrategy
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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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```bash
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freqtrade 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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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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This fee must be a ratio, and will be applied twice (once for trade entry, and once for trade exit).
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For example, if the buying and selling commission fee is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
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```bash
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freqtrade backtesting --fee 0.001
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```
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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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Use the `--timerange` argument to change how much of the testset 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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```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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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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## Understand the backtesting result
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The most important in the backtesting is to understand the result.
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A backtesting result will look like that:
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```
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========================================================= BACKTESTING REPORT ========================================================
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| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |
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|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
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| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 | 0 | 21 |
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| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 | 0 | 8 |
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| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 | 0 | 14 |
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| DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 | 0 | 7 |
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| ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 | 0 | 10 |
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| EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 | 0 | 20 |
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| ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 | 0 | 15 |
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| ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 | 0 | 17 |
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| IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 | 0 | 18 |
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| LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 | 0 | 9 |
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| LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 | 0 | 21 |
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| NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 | 0 | 7 |
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| NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 | 0 | 13 |
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| REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 | 0 | 5 |
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| XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 | 0 | 9 |
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| XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 | 0 | 11 |
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| XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 | 0 | 23 |
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| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 | 0 | 15 |
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| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 |
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========================================================= SELL REASON STATS =========================================================
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| Sell Reason | Sells | Wins | Draws | Losses |
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|:-------------------|--------:|------:|-------:|--------:|
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| trailing_stop_loss | 205 | 150 | 0 | 55 |
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| stop_loss | 166 | 0 | 0 | 166 |
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| sell_signal | 56 | 36 | 0 | 20 |
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| force_sell | 2 | 0 | 0 | 2 |
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====================================================== LEFT OPEN TRADES REPORT ======================================================
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| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |
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|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
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| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 | 0 | 0 |
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| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 | 0 | 0 |
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| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 | 0 | 0 |
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```
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The 1st table contains all trades the bot made, including "left open trades".
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The 2nd table contains a recap of sell reasons.
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This table can tell you which area needs some additional work (i.e. all `sell_signal` trades are losses, so we should disable the sell-signal or work on improving that).
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The 3rd table contains all trades the bot had to `forcesell` at the end of the backtest period to present a full picture.
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This is necessary to simulate realistic behaviour, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
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These trades are also included in the first table, but are extracted separately for clarity.
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The last line will give you the overall performance of your strategy,
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here:
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```
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| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 |
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```
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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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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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For example, if your `minimal_roi` is only `"0": 0.01` you cannot expect the bot to make more profit than 1% (because it will sell every time a trade reaches 1%).
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```json
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"minimal_roi": {
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"0": 0.01
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},
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```
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On the other hand, if you set a too high `minimal_roi` like `"0": 0.55`
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(55%), there is almost no chance that the bot will ever reach this profit.
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Hence, keep in mind that your performance is an integral mix of all different elements of the strategy, your configuration, and the crypto-currency pairs you have set up.
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### Assumptions made by backtesting
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Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
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- Buys happen at open-price
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- Sell signal sells happen at open-price of the following candle
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- Low happens before high for stoploss, protecting capital first
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- ROI
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- sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
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- sells are never "below the candle", so a ROI of 2% may result in a sell at 2.4% if low was at 2.4% profit
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- Forcesells caused by `<N>=-1` ROI entries use low as sell value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
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- Stoploss sells happen exactly at stoploss price, even if low was lower
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- Trailing stoploss
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- High happens first - adjusting stoploss
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- Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
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- Sell-reason does not explain if a trade was positive or negative, just what triggered the sell (this can look odd if negative ROI values are used)
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- Stoploss (and trailing stoploss) is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` and/or `trailing_stop` sell reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes.
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Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.
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Also, keep in mind that past results don't guarantee future success.
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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.
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### Further backtest-result analysis
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To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
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You can then load the trades to perform further analysis as shown in our [data analysis](data-analysis.md#backtesting) backtesting section.
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## Backtesting multiple strategies
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To compare multiple strategies, a list of Strategies can be provided to backtesting.
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This is limited to 1 timeframe value per run. However, data is only loaded once from disk so if you have multiple
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strategies you'd like to compare, this will give a nice runtime boost.
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All listed Strategies need to be in the same directory.
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``` bash
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freqtrade backtesting --timerange 20180401-20180410 --timeframe 5m --strategy-list Strategy001 Strategy002 --export trades
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```
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This will save the results to `user_data/backtest_results/backtest-result-<strategy>.json`, injecting the strategy-name into the target filename.
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There will be an additional table comparing win/losses of the different strategies (identical to the "Total" row in the first table).
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Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
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```
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=========================================================== STRATEGY SUMMARY ===========================================================
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| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |
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|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|
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| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 |
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| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 825 |
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```
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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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Your next step is to learn [how to find optimal parameters with Hyperopt](hyperopt.md)
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