freqtrade_origin/docs/backtesting.md

Backtesting

This page explains how to validate your strategy performance by using Backtesting.

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.

Backtesting will use the crypto-currencies (pair) from your config file and load static tickers located in /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)

python3 ./freqtrade/main.py backtesting

With 1 min tickers

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

Update cached pairs with the latest data

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

With live data (do not alter your testdata files)

python3 ./freqtrade/main.py backtesting --live

Using a different on-disk ticker-data source

python3 ./freqtrade/main.py backtesting --datadir freqtrade/tests/testdata-20180101

With a (custom) strategy file

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

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.

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

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:

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.

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

Then run:

python scripts/download_backtest_data.py --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.

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%).

"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.

Backtesting multiple strategies

To backtest multiple strategies, a list of Strategies can be provided.

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 should give a nice runtime boost.

All listed Strategies need to be in the same folder.

freqtrade backtesting --timerange 20180401-20180410 --ticker-interval 5m --strategy-list Strategy001 Strategy002 --export trades

This will save the results to user_data/backtest_data/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.

=================================================== Strategy Summary ====================================================
| Strategy   |   buy count |   avg profit % |   cum profit % |   total profit ETH | avg duration    |   profit |   loss |
|:-----------|------------:|---------------:|---------------:|-------------------:|:----------------|---------:|-------:|
| Strategy1  |          19 |          -0.76 |         -14.39 |        -0.01440287 | 15:48:00        |       15 |      4 |
| Strategy2  |           6 |          -2.73 |         -16.40 |        -0.01641299 | 1 day, 14:12:00 |        3 |      3 |

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