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 has better odds of making 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
```

#### With 1 min tickers

```bash
python3 ./freqtrade/main.py backtesting --ticker-interval 1m
```

#### Update cached pairs with the latest data

```bash
python3 ./freqtrade/main.py backtesting --refresh-pairs-cached
```

#### With live data (do not alter your testdata files)

```bash
python3 ./freqtrade/main.py backtesting --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
```

The exported trades can be read using the following code for manual analysis, or can be used by the plotting script `plot_dataframe.py` in the scripts folder.

``` python
import json
from pathlib import Path
import pandas as pd

filename=Path('user_data/backtest_data/backtest-result.json')

with filename.open() as file:
        data = json.load(file)

columns = ["pair", "profit", "opents", "closets", "index", "duration",
           "open_rate", "close_rate", "open_at_end", "sell_reason"]
df = pd.DataFrame(data, columns=columns)

df['opents'] = pd.to_datetime(df['opents'],
                              unit='s',
                              utc=True,
                              infer_datetime_format=True
                             )
df['closets'] = pd.to_datetime(df['closets'],
                               unit='s',
                               utc=True,
                               infer_datetime_format=True
                              )
```

If you have some ideas for interesting / helpful backtest data analysis, feel free to submit a PR so the community can benefit from it.

#### 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 |   profit |   loss |
|:---------|------------:|---------------:|-------------------:|---------------:|---------:|-------:|
| ETH/BTC  |          44 |           0.18 |         0.00159118 |           50.9 |       44 |      0 |
| LTC/BTC  |          27 |           0.10 |         0.00051931 |          103.1 |       26 |      1 |
| ETC/BTC  |          24 |           0.05 |         0.00022434 |          166.0 |       22 |      2 |
| DASH/BTC |          29 |           0.18 |         0.00103223 |          192.2 |       29 |      0 |
| ZEC/BTC  |          65 |          -0.02 |        -0.00020621 |          202.7 |       62 |      3 |
| XLM/BTC  |          35 |           0.02 |         0.00012877 |          242.4 |       32 |      3 |
| BCH/BTC  |          12 |           0.62 |         0.00149284 |           50.0 |       12 |      0 |
| POWR/BTC |          21 |           0.26 |         0.00108215 |          134.8 |       21 |      0 |
| ADA/BTC  |          54 |          -0.19 |        -0.00205202 |          191.3 |       47 |      7 |
| XMR/BTC  |          24 |          -0.43 |        -0.00206013 |          120.6 |       20 |      4 |
| TOTAL    |         335 |           0.03 |         0.00175246 |          157.9 |      315 |     20 |
2018-06-13 06:57:27,347 - freqtrade.optimize.backtesting - INFO -
====================================== LEFT OPEN TRADES REPORT ======================================
| pair     |   buy count |   avg profit % |   total profit BTC |   avg duration |   profit |   loss |
|:---------|------------:|---------------:|-------------------:|---------------:|---------:|-------:|
| ETH/BTC  |           3 |           0.16 |         0.00009619 |           25.0 |        3 |      0 |
| LTC/BTC  |           1 |          -1.00 |        -0.00020118 |         1085.0 |        0 |      1 |
| ETC/BTC  |           2 |          -1.80 |        -0.00071933 |         1092.5 |        0 |      2 |
| DASH/BTC |           0 |         nan    |         0.00000000 |          nan   |        0 |      0 |
| ZEC/BTC  |           3 |          -4.27 |        -0.00256826 |         1301.7 |        0 |      3 |
| XLM/BTC  |           3 |          -1.11 |        -0.00066744 |          965.0 |        0 |      3 |
| BCH/BTC  |           0 |         nan    |         0.00000000 |          nan   |        0 |      0 |
| POWR/BTC |           0 |         nan    |         0.00000000 |          nan   |        0 |      0 |
| ADA/BTC  |           7 |          -3.58 |        -0.00503604 |          850.0 |        0 |      7 |
| XMR/BTC  |           4 |          -3.79 |        -0.00303456 |          291.2 |        0 |      4 |
| TOTAL    |          23 |          -2.63 |        -0.01213062 |          750.4 |        3 |     20 |

```

The 1st table will contain all trades the bot made.

The 2nd table will contain all trades the bot had to `forcesell` at the end of the backtest period to prsent a full picture.
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             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)