Backtesting¶
This page explains how to validate your strategy performance by using Backtesting.
Backtesting requires historic data to be available. To learn how to get data for the pairs and exchange you're interested in, head over to the Data Downloading section of the documentation.
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 Data Downloading section in the documentation.
The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
Using dynamic pairlists for backtesting
While using dynamic pairlists during backtesting is not possible, a dynamic pairlist using current data can be generated via the test-pairlist command, and needs to be specified as "pair_whitelist" attribute in the configuration.
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 --datadir user_data/data/bittrex-20180101 backtesting
With a (custom) strategy file¶
freqtrade backtesting -s SampleStrategy
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
Please also read about the strategy startup period.
Supplying custom fee value¶
Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.
To account for this in backtesting, you can use the --fee command line option to supply this value to backtesting.
This fee must be a ratio, and will be applied twice (once for trade entry, and once for trade exit).
For example, if the buying and selling commission fee is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
freqtrade backtesting --fee 0.001
Note
Only supply this option (or the corresponding configuration parameter) if you want to experiment with different fee values. By default, Backtesting fetches the default fee from the exchange pair/market info.
Running backtest with smaller testset by using timerange¶
Use the --timerange argument to change how much of the testset you want to use.
For example, running backtesting with the --timerange=20190501- option will use all available data starting with May 1st, 2019 from your inputdata.
freqtrade backtesting --timerange=20190501-
You can also specify particular dates or a range span indexed by start and stop.
The full timerange specification:
- 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 | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 | 0 | 21 |
| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 | 0 | 8 |
| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 | 0 | 14 |
| DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 | 0 | 7 |
| ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 | 0 | 10 |
| EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 | 0 | 20 |
| ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 | 0 | 15 |
| ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 | 0 | 17 |
| IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 | 0 | 18 |
| LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 | 0 | 9 |
| LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 | 0 | 21 |
| NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 | 0 | 7 |
| NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 | 0 | 13 |
| REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 | 0 | 5 |
| XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 | 0 | 9 |
| XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 | 0 | 11 |
| XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 | 0 | 23 |
| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 | 0 | 15 |
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 |
========================================================= SELL REASON STATS =========================================================
| Sell Reason | Sells | Wins | Draws | Losses |
|:-------------------|--------:|------:|-------:|--------:|
| trailing_stop_loss | 205 | 150 | 0 | 55 |
| stop_loss | 166 | 0 | 0 | 166 |
| sell_signal | 56 | 36 | 0 | 20 |
| force_sell | 2 | 0 | 0 | 2 |
====================================================== LEFT OPEN TRADES REPORT ======================================================
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 | 0 | 0 |
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 | 0 | 0 |
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 | 0 | 0 |
The 1st table contains all trades the bot made, including "left open trades".
The 2nd table contains a recap of sell reasons.
This table can tell you which area needs some additional work (i.e. all sell_signal trades are losses, so we should disable the sell-signal or work on improving that).
The 3rd table contains all trades the bot had to forcesell at the end of the backtest period to present a full picture.
This is necessary to simulate realistic behaviour, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
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 |
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 up 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 and stake_amount=0.005 in config so tot_profit % will be (76.20/100) * (0.005 * 2) =~ 0.00762792 BTC.
Your strategy performance is influenced by your buy strategy, your sell strategy, and also by the minimal_roi and stop_loss you have set.
For 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 reaches 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 almost no chance that the bot will ever reach this profit.
Hence, keep in mind that your performance is an integral mix of all different elements of the strategy, your configuration, and the crypto-currency pairs you have set up.
Assumptions made by backtesting¶
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
- Buys happen at open-price
- Sell signal sells happen at open-price of the following candle
- Low happens before high for stoploss, protecting capital first.
- ROI
- sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
- sells are never "below the candle", so a ROI of 2% may result in a sell at 2.4% if low was at 2.4% profit
- Forcesells caused by
<N>=-1ROI entries use low as sell value, unless N falls on the candle open (e.g.120: -1for 1h candles)
- Stoploss sells happen exactly at stoploss price, even if low was lower
- Trailing stoploss
- High happens first - adjusting stoploss
- Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
- Sell-reason does not explain if a trade was positive or negative, just what triggered the sell (this can look odd if negative ROI values are used)
Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will never replace running a strategy in dry-run mode. Also, keep in mind that past results don't guarantee future success.
In addition to the above assumptions, strategy authors should carefully read the Common Mistakes section, to avoid using data in backtesting which is not available in real market conditions.
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 compare multiple strategies, a list of Strategies can be provided to backtesting.
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 will 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 to see the details per strategy.
=========================================================== STRATEGY SUMMARY ===========================================================
| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |
|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|
| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 |
| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 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