164 lines
6.1 KiB
Markdown
164 lines
6.1 KiB
Markdown
# Backtesting
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This page explains how to validate your strategy performance by using
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Backtesting.
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## Table of Contents
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- [Test your strategy with Backtesting](#test-your-strategy-with-backtesting)
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- [Understand the backtesting result](#understand-the-backtesting-result)
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## Test your strategy with Backtesting
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Now you have good Buy and Sell strategies, 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 (pair) from your config file
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and load static tickers located in
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[/freqtrade/tests/testdata](https://github.com/gcarq/freqtrade/tree/develop/freqtrade/tests/testdata).
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If the 5 min and 1 min ticker for the crypto-currencies to test is not
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already in the `testdata` folder, backtesting will download them
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automatically. Testdata files will not be updated until you specify it.
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The result of backtesting will confirm you if your bot as more chance to
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make a profit than a loss.
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The backtesting is very easy with freqtrade.
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### Run a backtesting against the currencies listed in your config file
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**With 5 min tickers (Per default)**
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```bash
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python3 ./freqtrade/main.py backtesting --realistic-simulation
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```
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**With 1 min tickers**
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```bash
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python3 ./freqtrade/main.py backtesting --realistic-simulation --ticker-interval 1m
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```
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**Update cached pairs with the latest data**
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```bash
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python3 ./freqtrade/main.py backtesting --realistic-simulation --refresh-pairs-cached
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```
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**With live data (do not alter your testdata files)**
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```bash
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python3 ./freqtrade/main.py backtesting --realistic-simulation --live
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```
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**Using a different on-disk ticker-data source**
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```bash
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python3 ./freqtrade/main.py backtesting --datadir freqtrade/tests/testdata-20180101
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```
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**With a (custom) strategy file**
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```bash
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python3 ./freqtrade/main.py -s TestStrategy backtesting
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```
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Where `-s TestStrategy` refers to the class name within the strategy file `test_strategy.py` found in the `freqtrade/user_data/strategies` directory
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**Exporting trades to file**
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```bash
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python3 ./freqtrade/main.py backtesting --export trades
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```
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**Running backtest with smaller testset**
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Use the `--timerange` argument to change how much of the testset
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you want to use. The last N ticks/timeframes will be used.
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Example:
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```bash
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python3 ./freqtrade/main.py backtesting --timerange=-200
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```
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***Advanced use of timerange***
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Doing `--timerange=-200` will get the last 200 timeframes
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from your inputdata. You can also specify specific dates,
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or a range span indexed by start and stop.
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The full timerange specification:
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- Use last 123 tickframes of data: `--timerange=-123`
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- Use first 123 tickframes of data: `--timerange=123-`
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- Use tickframes from line 123 through 456: `--timerange=123-456`
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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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**Update testdata directory**
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To update your testdata directory, or download into another testdata directory:
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```bash
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mkdir -p user_data/data/testdata-20180113
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cp freqtrade/tests/testdata/pairs.json user_data/data-20180113
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cd user_data/data-20180113
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```
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Possibly edit pairs.json file to include/exclude pairs
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```bash
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python3 freqtrade/tests/testdata/download_backtest_data.py -p pairs.json
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```
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The script will read your pairs.json file, and download ticker data
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into the current working directory.
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For help about backtesting usage, please refer to
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[Backtesting commands](#backtesting-commands).
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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 buy count avg profit % total profit BTC avg duration
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-------- ----------- -------------- ------------------ --------------
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ETH/BTC 56 -0.67 -0.00075455 62.3
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LTC/BTC 38 -0.48 -0.00036315 57.9
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ETC/BTC 42 -1.15 -0.00096469 67.0
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DASH/BTC 72 -0.62 -0.00089368 39.9
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ZEC/BTC 45 -0.46 -0.00041387 63.2
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XLM/BTC 24 -0.88 -0.00041846 47.7
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NXT/BTC 24 0.68 0.00031833 40.2
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POWR/BTC 35 0.98 0.00064887 45.3
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ADA/BTC 43 -0.39 -0.00032292 55.0
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XMR/BTC 40 -0.40 -0.00032181 47.4
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TOTAL 419 -0.41 -0.00348593 52.9
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```
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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 419 -0.41 -0.00348593 52.9
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```
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We understand the bot has made `419` trades for an average duration of
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`52.9` min, with a performance of `-0.41%` (loss), that means it has
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lost a total of `-0.00348593 BTC`.
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As you will see your strategy performance will be influenced by your buy
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strategy, your sell strategy, and also by the `minimal_roi` and
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`stop_loss` you have set.
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As for an example if your minimal_roi is only `"0": 0.01`. You cannot
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expect the bot to make more profit than 1% (because it will sell every
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time a trade will reach 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 a lot of chance that the bot will never reach this
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profit. Hence, keep in mind that your performance is a mix of your
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strategies, your configuration, and the crypto-currency you have set up.
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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](https://github.com/gcarq/freqtrade/blob/develop/docs/hyperopt.md)
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