233 lines
12 KiB
Markdown
233 lines
12 KiB
Markdown
# Backtesting
|
|
|
|
This page explains how to validate your strategy performance by using Backtesting.
|
|
|
|
Backtesting requires historic data to be available.
|
|
To learn how to get data for the pairs and exchange you're interrested in, head over to the [Data downloading](data-download.md) section of the documentation.
|
|
|
|
## Test your strategy with Backtesting
|
|
|
|
Now you have good Buy and Sell strategies and some historic data, you want to test it against
|
|
real data. This is what we call
|
|
[backtesting](https://en.wikipedia.org/wiki/Backtesting).
|
|
|
|
Backtesting will use the crypto-currencies (pairs) from your config file
|
|
and load ticker data from `user_data/data/<exchange>` by default.
|
|
If no data is available for the exchange / pair / ticker interval combination, backtesting will
|
|
ask you to download them first using `freqtrade download-data`.
|
|
For details on downloading, please refer to the [Downloading data](data-download.md) section in the documentation.
|
|
|
|
The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
|
|
|
|
### Run a backtesting against the currencies listed in your config file
|
|
|
|
#### With 5 min tickers (Per default)
|
|
|
|
```bash
|
|
freqtrade backtesting
|
|
```
|
|
|
|
#### With 1 min tickers
|
|
|
|
```bash
|
|
freqtrade backtesting --ticker-interval 1m
|
|
```
|
|
|
|
#### Using a different on-disk ticker-data source
|
|
|
|
Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory.
|
|
You can then use this data for backtesting as follows:
|
|
|
|
```bash
|
|
freqtrade backtesting --datadir user_data/data/bittrex-20180101
|
|
```
|
|
|
|
#### With a (custom) strategy file
|
|
|
|
```bash
|
|
freqtrade -s SampleStrategy backtesting
|
|
```
|
|
|
|
Where `-s SampleStrategy` refers to the class name within the strategy file `sample_strategy.py` found in the `freqtrade/user_data/strategies` directory.
|
|
|
|
#### Comparing multiple Strategies
|
|
|
|
```bash
|
|
freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --ticker-interval 5m
|
|
```
|
|
|
|
Where `SampleStrategy1` and `AwesomeStrategy` refer to class names of strategies.
|
|
|
|
#### Exporting trades to file
|
|
|
|
```bash
|
|
freqtrade backtesting --export trades
|
|
```
|
|
|
|
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.
|
|
|
|
#### Exporting trades to file specifying a custom filename
|
|
|
|
```bash
|
|
freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json
|
|
```
|
|
|
|
#### Running backtest with smaller testset
|
|
|
|
Use the `--timerange` argument to change how much of the testset you want to use.
|
|
|
|
Example:
|
|
|
|
```bash
|
|
freqtrade backtesting --timerange=20190501-
|
|
```
|
|
|
|
#### Advanced use of timerange
|
|
|
|
Doing `--timerange=20190101-` will all available data starting with January 1st, 2019 from your inputdata.
|
|
You can also specify specific dates, or a range span indexed by start and stop.
|
|
|
|
The full timerange specification:
|
|
|
|
|
|
- Use tickframes till 2018/01/31: `--timerange=-20180131`
|
|
- Use tickframes since 2018/01/31: `--timerange=20180131-`
|
|
- Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
|
|
- Use tickframes between POSIX timestamps 1527595200 1527618600:
|
|
`--timerange=1527595200-1527618600`
|
|
- Use last 123 tickframes of data: `--timerange=-123`
|
|
- Use first 123 tickframes of data: `--timerange=123-`
|
|
- Use tickframes from line 123 through 456: `--timerange=123-456`
|
|
|
|
!!! warning
|
|
Be carefull when using non-date functions - these do not allow you to specify precise dates, so if you updated the test-data it will probably use a different dataset.
|
|
|
|
## Understand the backtesting result
|
|
|
|
The most important in the backtesting is to understand the result.
|
|
|
|
A backtesting result will look like that:
|
|
|
|
```
|
|
========================================================= BACKTESTING REPORT ========================================================
|
|
| pair | buy count | avg profit % | cum profit % | tot profit BTC | tot profit % | avg duration | profit | loss |
|
|
|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
|
|
| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 | 21 |
|
|
| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 | 8 |
|
|
| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 | 14 |
|
|
| DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 | 7 |
|
|
| ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 | 10 |
|
|
| EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 | 20 |
|
|
| ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 | 15 |
|
|
| ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 | 17 |
|
|
| IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 | 18 |
|
|
| LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 | 9 |
|
|
| LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 | 21 |
|
|
| NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 | 7 |
|
|
| NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 | 13 |
|
|
| REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 | 5 |
|
|
| XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 | 9 |
|
|
| XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 | 11 |
|
|
| XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 | 23 |
|
|
| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 | 15 |
|
|
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 |
|
|
========================================================= SELL REASON STATS =========================================================
|
|
| Sell Reason | Count |
|
|
|:-------------------|--------:|
|
|
| trailing_stop_loss | 205 |
|
|
| stop_loss | 166 |
|
|
| sell_signal | 56 |
|
|
| force_sell | 2 |
|
|
====================================================== LEFT OPEN TRADES REPORT ======================================================
|
|
| pair | buy count | avg profit % | cum profit % | tot profit BTC | tot profit % | avg duration | profit | loss |
|
|
|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
|
|
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 | 0 |
|
|
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 | 0 |
|
|
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 | 0 |
|
|
```
|
|
|
|
The 1st table contains all trades the bot made, including "left open trades".
|
|
|
|
The 2nd table contains a recap of sell reasons.
|
|
|
|
The 3rd table contains all trades the bot had to `forcesell` at the end of the backtest period to present a full picture.
|
|
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.
|
|
These trades are also included in the first table, but are extracted separately for clarity.
|
|
|
|
The last line will give you the overall performance of your strategy,
|
|
here:
|
|
|
|
```
|
|
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 |
|
|
```
|
|
|
|
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
|
|
earned a total of `0.00762792 BTC` starting with a capital of 0.01 BTC.
|
|
|
|
The column `avg profit %` shows the average profit for all trades made while the column `cum profit %` sums all the profits/losses.
|
|
The column `tot profit %` shows instead the total profit % in relation to allocated capital (`max_open_trades * stake_amount`).
|
|
In the above results we have `max_open_trades=2 stake_amount=0.005` in config so `(76.20/100) * (0.005 * 2) =~ 0.00762792 BTC`.
|
|
|
|
Your strategy performance is influenced by your buy strategy, your sell strategy, and also by the `minimal_roi` and `stop_loss` you have set.
|
|
|
|
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 will reach 1%).
|
|
|
|
```json
|
|
"minimal_roi": {
|
|
"0": 0.01
|
|
},
|
|
```
|
|
|
|
On the other hand, if you set a too high `minimal_roi` like `"0": 0.55`
|
|
(55%), there is almost no chance that the bot will never reach this profit.
|
|
Hence, keep in mind that your performance is a mix of the different elements of the strategy, your configuration, and the crypto-currency pairs you have set up.
|
|
|
|
### Assumptions made by backtesting
|
|
|
|
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
|
|
|
|
- Buys happen at open-price
|
|
- Low happens before high for stoploss, protecting capital first.
|
|
- ROI sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
|
|
- Stoploss sells happen exactly at stoploss price, even if low was lower
|
|
- Trailing stoploss
|
|
- High happens first - adjusting stoploss
|
|
- Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
|
|
- 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)
|
|
|
|
### Further backtest-result analysis
|
|
|
|
To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
|
|
You can then load the trades to perform further analysis as shown in our [data analysis](data-analysis.md#backtesting) backtesting section.
|
|
|
|
## Backtesting multiple strategies
|
|
|
|
To compare multiple strategies, a list of Strategies can be provided to backtesting.
|
|
|
|
This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple
|
|
strategies you'd like to compare, this will give a nice runtime boost.
|
|
|
|
All listed Strategies need to be in the same directory.
|
|
|
|
``` bash
|
|
freqtrade backtesting --timerange 20180401-20180410 --ticker-interval 5m --strategy-list Strategy001 Strategy002 --export trades
|
|
```
|
|
|
|
This will save the results to `user_data/backtest_results/backtest-result-<strategy>.json`, injecting the strategy-name into the target filename.
|
|
There will be an additional table comparing win/losses of the different strategies (identical to the "Total" row in the first table).
|
|
Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
|
|
|
|
```
|
|
=========================================================== Strategy Summary ===========================================================
|
|
| Strategy | buy count | avg profit % | cum profit % | tot profit BTC | tot profit % | avg duration | profit | loss |
|
|
|:------------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
|
|
| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 |
|
|
| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 825 |
|
|
```
|
|
|
|
## Next step
|
|
|
|
Great, your strategy is profitable. What if the bot can give your the
|
|
optimal parameters to use for your strategy?
|
|
Your next step is to learn [how to find optimal parameters with Hyperopt](hyperopt.md)
|