stable/docs/backtesting.md
creslin 9dbe5fdb85 Update back testing document to include example using Posix timestamps
as timerange

e.g
--timerange=1527595200-1527618600
2018-06-02 19:49:23 +03:00

164 lines
6.1 KiB
Markdown

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