Backtesting

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

Getting data for backtesting and hyperopt

To download data (candles / OHLCV) needed for backtesting and hyperoptimization use the freqtrade download-data command.

If no additional parameter is specified, freqtrade will download data for "1m" and "5m" timeframes. Exchange and pairs will come from config.json (if specified using -c/--config). Otherwise --exchange becomes mandatory.

Alternatively, a pairs.json file can be used.

If you are using Binance for example:

  • create a directory user_data/data/binance and copy pairs.json in that directory.
  • 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:

freqtrade download-data --exchange binance

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

  • To use a different directory than the exchange specific default, use --datadir user_data/data/some_directory.
  • To change the exchange used to download the tickers, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
  • To use pairs.json from some other directory, use --pairs-file some_other_dir/pairs.json.
  • To download ticker data for only 10 days, use --days 10 (defaults to 30 days).
  • 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 most other options.

Test your strategy with Backtesting

Now you have good Buy and Sell strategies and some historic data, you want to test it against real data. This is what we call backtesting.

Backtesting will use the crypto-currencies (pairs) from your config file and load ticker data from user_data/data/<exchange> by default. If no data is available for the exchange / pair / ticker interval combination, backtesting will ask you to download them first using freqtrade download-data. For details on downloading, please refer to the relevant section in the documentation.

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)

freqtrade backtesting

With 1 min tickers

freqtrade backtesting --ticker-interval 1m

Using a different on-disk ticker-data source

Assume you downloaded the history data from the Bittrex exchange and kept it in the user_data/data/bittrex-20180101 directory. You can then use this data for backtesting as follows:

freqtrade backtesting --datadir user_data/data/bittrex-20180101

With a (custom) strategy file

freqtrade -s SampleStrategy backtesting

Where -s SampleStrategy refers to the class name within the strategy file sample_strategy.py found in the freqtrade/user_data/strategies directory.

Comparing multiple Strategies

freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --ticker-interval 5m

Where SampleStrategy1 and AwesomeStrategy refer to class names of strategies.

Exporting trades to file

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 directory.

Exporting trades to file specifying a custom filename

freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.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:

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

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 as shown in our data analysis backtesting section.

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 directory.

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

This will save the results to user_data/backtest_results/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