stable/docs/backtesting.md

# 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).  
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
```

#### 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

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`

#### 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.

```bash
mkdir -p user_data/data/binance
cp freqtrade/tests/testdata/pairs.json user_data/data/binance
```

Then run:

```bash
python scripts/download_backtest_data --exchange binance
```

This will download ticker data for all the currency pairs you defined in `pairs.json`.

- To use a different folder than the exchange specific default, use `--export user_data/data/some_directory`.
- To change the exchange used to download the tickers, use `--exchange`. Default is `bittrex`.
- To use `pairs.json` from some other folder, use `--pairs-file some_other_dir/pairs.json`.
- 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).

## 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/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)