adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

fix backtesting.md formatting

Browse Source
This commit is contained in:
xmatthias 2018-06-13 06:52:17 +02:00
parent 0f117d480e
commit 6e68c3b230
1 changed files with 34 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

52
docs/backtesting.md
View File

@ -1,17 +1,19 @@
# 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/freqtrade/freqtrade/tree/develop/freqtrade/tests/testdata).
@ -19,70 +21,80 @@ 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 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)**
#### With 5 min tickers (Per default)
```bash
python3 ./freqtrade/main.py backtesting --realistic-simulation
```
**With 1 min tickers**
#### With 1 min tickers
```bash
python3 ./freqtrade/main.py backtesting --realistic-simulation --ticker-interval 1m
```
**Update cached pairs with the latest data**
#### 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)**
#### 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**
#### Using a different on-disk ticker-data source
```bash
python3 ./freqtrade/main.py backtesting --datadir freqtrade/tests/testdata-20180101
```
**With a (custom) strategy file**
#### 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**
#### Exporting trades to file
```bash
python3 ./freqtrade/main.py backtesting --export trades
```
**Exporting trades to file specifying a custom filename**
#### Exporting trades to file specifying a custom filename
```bash
python3 ./freqtrade/main.py backtesting --export trades --export-filename=backtest_teststrategy.json
```
#### Running backtest with smaller testset
**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***
#### 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`
@ -92,11 +104,12 @@ The full timerange specification:
- Use tickframes between POSIX timestamps 1527595200 1527618600:
`--timerange=1527595200-1527618600`
#### Downloading new set of ticker data
**Downloading new set of ticker data**
To download new set of backtesting ticker data, you can use a download script.
If you are using Binance for example:
- create a folder `user_data/data/binance` and copy `pairs.json` in that folder.
- update the `pairs.json` to contain the currency pairs you are interested in.
@ -119,14 +132,14 @@ This will download ticker data for all the currency pairs you defined in `pairs.
- To download ticker data for only 10 days, use `--days 10`.
- Use `--timeframes` to specify which tickers to download. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute tickers.
For help about backtesting usage, please refer to
[Backtesting commands](#backtesting-commands).
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
@ -146,6 +159,7 @@ 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
```
@ -161,6 +175,7 @@ strategy, your sell strategy, and also by the `minimal_roi` and
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
@ -173,6 +188,7 @@ 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/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)