stable/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 backtesting

With 1 min tickers

python3 freqtrade backtesting --ticker-interval 1m

Update cached pairs with the latest data

python3 freqtrade backtesting --refresh-pairs-cached

With live data (do not alter your testdata files)

python3 freqtrade backtesting --live

Using a different on-disk ticker-data source

python3 freqtrade backtesting --datadir freqtrade/tests/testdata-20180101

With a (custom) strategy file

python3 freqtrade -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 backtesting --export trades

The exported trades can be used for further analysis, or can be used by the plotting script plot_dataframe.py in the scripts folder.

Exporting trades to file specifying a custom filename

python3 freqtrade 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 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 --datadir 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.
• To use exchange, timeframe and list of pairs as defined in your configuration file, use the -c/--config option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine -c/--config with other options.

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 % |   cum profit % |   tot profit BTC |   tot profit % | avg duration   |   profit |   loss |
|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
| ADA/BTC  |          35 |          -0.11 |          -3.88 |      -0.00019428 |          -1.94 | 4:35:00        |       14 |     21 |
| ARK/BTC  |          11 |          -0.41 |          -4.52 |      -0.00022647 |          -2.26 | 2:03:00        |        3 |      8 |
| BTS/BTC  |          32 |           0.31 |           9.78 |       0.00048938 |           4.89 | 5:05:00        |       18 |     14 |
| DASH/BTC |          13 |          -0.08 |          -1.07 |      -0.00005343 |          -0.53 | 4:39:00        |        6 |      7 |
| ENG/BTC  |          18 |           1.36 |          24.54 |       0.00122807 |          12.27 | 2:50:00        |        8 |     10 |
| EOS/BTC  |          36 |           0.08 |           3.06 |       0.00015304 |           1.53 | 3:34:00        |       16 |     20 |
| ETC/BTC  |          26 |           0.37 |           9.51 |       0.00047576 |           4.75 | 6:14:00        |       11 |     15 |
| ETH/BTC  |          33 |           0.30 |           9.96 |       0.00049856 |           4.98 | 7:31:00        |       16 |     17 |
| IOTA/BTC |          32 |           0.03 |           1.09 |       0.00005444 |           0.54 | 3:12:00        |       14 |     18 |
| LSK/BTC  |          15 |           1.75 |          26.26 |       0.00131413 |          13.13 | 2:58:00        |        6 |      9 |
| LTC/BTC  |          32 |          -0.04 |          -1.38 |      -0.00006886 |          -0.69 | 4:49:00        |       11 |     21 |
| NANO/BTC |          17 |           1.26 |          21.39 |       0.00107058 |          10.70 | 1:55:00        |       10 |      7 |
| NEO/BTC  |          23 |           0.82 |          18.97 |       0.00094936 |           9.48 | 2:59:00        |       10 |     13 |
| REQ/BTC  |           9 |           1.17 |          10.54 |       0.00052734 |           5.27 | 3:47:00        |        4 |      5 |
| XLM/BTC  |          16 |           1.22 |          19.54 |       0.00097800 |           9.77 | 3:15:00        |        7 |      9 |
| XMR/BTC  |          23 |          -0.18 |          -4.13 |      -0.00020696 |          -2.07 | 5:30:00        |       12 |     11 |
| XRP/BTC  |          35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00        |       12 |     23 |
| ZEC/BTC  |          22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00        |        7 |     15 |
| TOTAL    |         429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |      186 |    243 |
========================================================= SELL REASON STATS =========================================================
| Sell Reason        |   Count |
|:-------------------|--------:|
| trailing_stop_loss |     205 |
| stop_loss          |     166 |
| sell_signal        |      56 |
| force_sell         |       2 |
====================================================== LEFT OPEN TRADES REPORT ======================================================
| pair     |   buy count |   avg profit % |   cum profit % |   tot profit BTC |   tot profit % | avg duration   |   profit |   loss |
|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
| ADA/BTC  |           1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |        1 |      0 |
| LTC/BTC  |           1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |        1 |      0 |
| TOTAL    |           2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |        2 |      0 |

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

The 2nd table will contain a recap of sell reasons.

The 3rd table will contain all trades the bot had to forcesell at the end of the backtest period to present 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    |         429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |      186 |    243 |

We understand the bot has made 429 trades for an average duration of 4:12:00, with a performance of 76.20% (profit), that means it has earned a total of 0.00762792 BTC starting with a capital of 0.01 BTC.

The column avg profit % shows the average profit for all trades made while the column cum profit % sums all the profits/losses. The column tot profit % shows instead the total profit % in relation to allocated capital (max_open_trades * stake_amount). In the above results we have max_open_trades=2 stake_amount=0.005 in config so (76.20/100) * (0.005 * 2) =~ 0.00762792 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.

Further backtest-result analysis

To further analyze your backtest results, you can export the trades. You can then load the trades to perform further analysis.

A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.

Freqtrade provides an easy to load the backtest results, which is load_backtest_data - and takes a path to the backtest-results file.

from freqtrade.data.btanalysis import load_backtest_data
df = load_backtest_data("user_data/backtest-result.json")

# Show value-counts per pair
df.groupby("pair")["sell_reason"].value_counts()

This will allow you to drill deeper into your backtest results, and perform analysis which would make the regular backtest-output unreadable.

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

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 % |   tot profit BTC |   tot profit % | avg duration   |   profit |   loss |
|:------------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
| Strategy1   |         429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |      186 |    243 |
| Strategy2   |        1487 |          -0.13 |        -197.58 |      -0.00988917 |         -98.79 | 4:43:00        |      662 |    825 |

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